60 results about "Closed loop response" patented technology

The invention discloses a public institution kitchen energy data management system based on cloud data, particularly relates to the field of energy management, and comprises a multi-source heterogeneous data acquisition module, an edge intelligent preprocessing module, a cloud data lake storage module, a multi-dimensional analysis engine module, an intelligent decision center module and a visual interaction module. According to the method, space-time alignment of multi-source data is realized through protocol adaptive conversion and millisecond time synchronization technologies, a high-consistency analysis basis is constructed, real-time index calculation and adaptive model optimization are implemented by edge calculation and cloud dynamic modeling collaborative architecture, hidden energy loss is accurately identified, and the real-time performance of the system is improved. The intelligent decision center is combined with digital twinborn verification to generate a control strategy, a minute-level closed-loop response mechanism is formed, automatic adjustment and optimization of equipment parameters and intelligent distribution of maintenance work orders are achieved, manual intervention delay is eliminated, timeliness and execution accuracy of an energy strategy are guaranteed, and multi-system collaborative management efficiency is remarkably improved.

The invention discloses an automatic production line control system and method for stamping and welding vehicle body parts, and relates to the technical field of vehicle body part production. The system comprises a welding robot module, an integrated visual detection module, an image processing server, a control PLC, a welding parameter regulator, a defective workpiece processing module, a data tracing cloud platform, a multispectral illumination module, a deep learning acceleration card and a welding spot temperature infrared sensor. On-line full inspection of the welding spot quality is achieved, the omission ratio is extremely low, and the integrated vision module maintains the imaging definition in a splashing environment through a nanometer coated lens and a piezoelectric self-cleaning technology; the multi-spectral lighting module solves the problem of reflective interference of an aluminum vehicle body, the defect recognition accuracy is high, the real-time closed-loop response is fast, the pseudo soldering recurrence rate is low through dynamic current compensation, the pneumatic marking positioning precision is high, accurate coordinates are provided for repair, the system supports high-speed detection of welding spots, and the high-beat requirement of a vehicle body part punching and welding automatic production line is met.

The invention discloses an intelligent self-adaptive vehicle suspension system based on deep learning and a control method, and belongs to the technical field of vehicle suspensions.The system comprises a quantum state collaborative sensing module, a Lie group road condition prediction module, a topology invariant optimization module and a dual closed-loop control module; the quantum state collaborative perception module realizes multi-sensor deep fusion based on a quantum state entanglement theory, the fusion precision is improved by more than 40%, the Lie group road condition prediction module establishes a road surface dynamic evolution model by using a special Euclidean group SE (3), the prediction precision is improved by 45%, and the topology invariant optimization module extracts road surface topology invariants through continuous coherence, so that the road surface dynamic evolution model is optimized. The suspension parameters are optimized on the octyl manifold, the convergence speed is increased by 60%, the dual closed-loop control module constructs a forward prediction and backward correction double closed loop, the response time is shortened to 15 milliseconds, and the comfort and controllability of the vehicle are remarkably improved.

The invention discloses a detection system of an absorption spectrum water quality multi-parameter analyzer, and relates to the field of water quality detection. Comprising a water sample acquisition and self-adaptive preprocessing module, a full-spectrum dynamic acquisition and waveband optimization module, a multi-dimensional data fusion processing module, a dynamic evolutionary model training and optimization module, an intelligent pollution traceability and parameter inversion module and a real-time decision and early warning response module. According to the detection system of the absorption spectrum water quality multi-parameter analyzer, through multi-dimensional data fusion, a dynamic evolution model and a spectrum fingerprint traceability technology, the pollutant concentration can be accurately inverted, the pollution source can be rapidly positioned, and the diffusion trend is predicted in combination with hydrological-meteorological data; graded early warning and customized disposal suggestions are synchronously generated, and the environmental protection supervision platform and the treatment terminal are linked through the Internet of Things, so that closed-loop response of detection, source tracing, early warning and disposal is realized.

The invention relates to the technical field of industrial data analysis, and discloses an artificial intelligence-based industrial equipment operation data analysis method and system, equipment and a medium, and the method comprises the steps: carrying out the dynamic noise reduction processing of the multi-source sensor data of the industrial equipment, and dynamically adjusting the size of a filtering window; performing cross-modal feature alignment on the multi-sensor data after noise reduction, and respectively extracting time-frequency domain features; constructing a lightweight state evaluation model based on the equipment maintenance knowledge base, performing equipment state evaluation on the fused features, and outputting a confidence index; performing multi-stage verification on the evaluation result, and dynamically correcting the confidence index according to the verification result; and triggering a control instruction in a grading manner according to the corrected confidence index, and completing closed-loop response from data analysis to equipment control. The method breaks through the technical bottleneck of a traditional method in a dynamic industrial scene on the premise of ensuring privacy security (data are not delivered out of a factory), and provides a standardized solution for predictive maintenance and intelligent control.

The invention provides a method and a system for accurately regulating and controlling waste gas denitration air quantity of a glass melting furnace. According to the method, an ultrasonic Doppler sensor group is arranged around a glass melting furnace flue, ultrasonic beams are synchronously emitted to collect flow velocity pulsation data required by exhaust gas denitration air volume, time sequence waveforms are decomposed to generate multi-dimensional frequency characteristics, and the multi-dimensional frequency characteristics are aggregated into a multi-dimensional flow velocity vector representing a flow field state; vector deviation is synchronously corrected through space coordinate alignment and a time sequence, and difference variable quantity is extracted and fused to generate a flow field thermodynamic diagram; establishing a time-space correlation model of the multi-dimensional flow velocity vector and the thermodynamic diagram, predicting an air volume adjusting threshold value meeting the denitration requirement, and converting the air volume adjusting threshold value into a fan rotating speed control signal to drive an execution mechanism; and according to waste gas pulsation response data caused by fan rotating speed change and thermodynamic diagram updating frequency relevance, multi-dimensional flow velocity vectors are dynamically adjusted to form closed-loop regulation and control. According to the application, the dynamic demand of the denitration air volume and the millisecond-level closed-loop response executed by the fan are realized, the flue gas purification efficiency is improved, and the energy consumption is reduced.

The invention provides a method and a system for optimizing power closed-loop response time of a wind and light storage system. The method comprises the following steps: collecting multi-source historical operation data of the wind and light storage system within continuous preset duration; carrying out global optimization on key parameters of the echo state network by adopting a sethead whale migration algorithm; based on the collected data, outputting an advanced prediction result of the output of the wind turbine unit, the output of the photovoltaic unit and the change trend of the energy storage SOC in a future time period through the optimized echo state network; taking the advanced prediction result as input, and based on a weighted objective function taking power tracking error minimization and power closed-loop response time minimization as targets, adopting an adaptive snake vulture optimization algorithm to solve the objective function to obtain optimal control parameters of the wind-solar storage system; the optimal control parameters are analyzed into real-time control instructions through the FPGA controller so as to drive the energy storage unit, the wind generating set and the photovoltaic generating set to execute power regulation and control, and collaborative optimization of power closed-loop response time shortening and tracking precision improvement is achieved.

The invention discloses a multi-source information driven self-adaptive interface test script generation method. The method comprises the steps that S1, multi-source input fusion is carried out, one or more kinds of test information are obtained from multiple data sources, cross validation and intention alignment are carried out on the basis of semantic understanding, and multi-source test information is generated; s2, unified protocol conversion: converting the multi-source test information into framework-independent standardized intermediate representation through a general protocol conversion layer, and dynamically generating executable scripts of different test frameworks; s3, intelligent scheduling and execution: executing in a local or distributed cluster according to the task scale intelligent scheduling script, and collecting an execution data set; and S4, performing exception attribution and feedback, analyzing an execution result, performing three-dimensional analysis through a multi-source fusion exception attribution engine if the execution result fails, accurately judging that a failure reason is interface update or system Bug, and triggering a corresponding closed-loop response. According to the invention, intelligent generation, cross-framework multiplexing and adaptive maintenance of the test script are realized.

The application provides a PI controller parameter setting method and device, relates to the technical field of automation control, is applied to a PI control system, the PI control system comprises a PI controller and a controlled object, and the method comprises the following steps: acquiring a characteristic response curve corresponding to the controlled object; acquiring a first-order inertia delay model by using a square difference integral minimum processing method according to the characteristic response curve; and determining target parameters corresponding to the PI controller according to a first formula acquired in advance through the first-order inertia delay model, so that the dynamic performance of a closed-loop response curve reaches an optimal state. The application finds optimal parameters by using the distribution of root loci on the real axis, realizes the optimal dynamic performance of the closed-loop response curve, and endows controller parameter setting and design with clear physical meanings.

The invention relates to a big data prediction method and device based on machine learning, equipment and a medium, and the method comprises the steps: converting millisecond-level power grid real-time monitoring data into frequency domain features through wavelet packet transformation, and solving the timeliness conflict between high-frequency data and low-frequency commercial data; a tensor fusion technology is utilized to align multi-source heterogeneous information such as power grid topology, enterprise carbon emission and financial data, and unified space-time representation is constructed; structured constraint features are generated in combination with semantic analysis of policies and regulations, and dynamic association between environmental policies and commercial activities is quantified through cross-modal embedding; modeling a risk conduction path based on a dynamic graph neural network, and optimizing a prediction model according to an asymmetric loss mechanism; and finally, outputting a supply chain risk control strategy, and realizing closed-loop response from power grid fluctuation monitoring to supply chain decision. According to the method, the limitation of a traditional model in the aspects of cross-scale data fusion and policy conduction quantification is broken through, and the prediction precision and the response timeliness in an emergency risk scene are remarkably improved.

The invention relates to the technical field of power system intelligent management, in particular to an intelligent abnormal load behavior recognition and response method and system. Collecting multi-modal data, and carrying out cleaning, normalization and noise reduction processing on the power data, the time sequence features and the environment data; by establishing a multi-level model based on deep learning, load behavior characteristics are extracted, and high-precision classification of abnormal behaviors is realized; generating a simulation sample in combination with the generative adversarial network to enhance the generalization ability of the model; and a closed-loop response mechanism is designed, and rapid response and management of abnormal load behaviors are realized through a dynamic optimization strategy. The method can improve the recognition accuracy and response speed of the abnormal load behavior, improves the operation safety and stability of a power system, and has the characteristics of flexibility, high efficiency and intelligence.

To make a control amount of a controlled object follow a target value even when an unknown disturbance exists, while effectively utilizing an existing controller.SOLUTION: A control device according to one aspect is a control device disposed between a higher-level control device that outputs a first target value for a control amount of a controlled object and a lower-level control device that controls the controlled object in accordance with a given second target value, and includes: a model parameter estimation unit that estimates parameters of a closed-loop response model that models the response of closed-loop control consisting of the lower-level control device and the controlled object based on the control amount and the second target value; a target value conversion unit that calculates a third target value by converting the first target value so that the control amount asymptotically approaches the first control amount based on the first target value, the second target value, and the parameters; and a switching unit that outputs either the first target value or the third target value to the lower-level control device as the next second target value.SELECTED DRAWING: Figure 2

The present application relates to the field of power system control and power electronic energy storage technology, in particular to a generator set transient power compensation system and method thereof, the method comprising: collecting stator voltage, stator current, rotor angular velocity, power angle and DC bus voltage, and mapping into a generalized state vector; constructing a port-controlled Hamilton system model containing an initial interconnection matrix and an initial damping matrix based on preset physical parameters and obtaining a Hamilton energy function; taking partial derivative of the Hamilton energy function with respect to the generalized state vector, calculating an energy gradient vector, dynamically reconstructing a virtual interconnection matrix and a virtual damping matrix, analyzing control input and converting into a duty cycle command to send to an energy storage converter to perform transient power compensation; the system comprises a data acquisition module, a Hamilton energy controller and an energy storage converter; faster transient recovery, lower mechanical stress and more stable closed-loop response can be achieved.

The invention discloses an intelligent precise feeding method and system based on a pig farm unit, corollary equipment and a monitoring and business use terminal, and belongs to the field of intelligent precise animal feeding. According to key factors such as weight, environment and health of pigs in each pig house unit every day, different nutritional requirements of each pig house unit are precisely calculated, and the feeding efficiency is improved. A feed formula and a feeding execution strategy matched with the nutrition requirement amount are dynamically generated according to the actual conditions such as the feed type, the pig house distribution distance, the feeding time and the number of times in the production of the user, and finally the system monitors the feeding strategy executed by the mixing equipment and indexes such as excess feed of each unit in real time; therefore, closed-loop response is made to the whole-process closed-loop management of pig feeding from nutrient requirement amount calculation, feeding strategy making and execution result monitoring, and precise and fine feeding with daily dynamic response and difference adaptation is achieved. Finally, the adaptation degree of the feeding amount and the required amount of the individual growth nutrition of the pigs can be greatly improved, and the feeding cost is saved.

The application discloses a kind of real-time pre-support steel structure for foundation pit construction and construction technology, and relates to the technical field of foundation pit support construction, comprising first steel support mechanism, second steel support mechanism, H-shaped steel vertical connecting rod, the first steel support mechanism is rotatably connected with second steel support mechanism by two groups of H-shaped steel vertical connecting rod.The first steel support mechanism and the second steel support mechanism are formed into a linkage whole by the hinged mechanism and the H-shaped steel vertical connecting rod, the ground is assembled once, without secondary lifting, avoid additional load on the edge of pit, save high-altitude work platform erection, process is more simple, construction is safer, with the help of support articulated joint and jack, the length of support is accurately adjusted and prestress is applied, cooperate with servo system to monitor and dynamically compensate axial force and position in real time, form force control and position control double closed loop, response is fast, precision is high, completely solve the problem of traditional support axial force adjustment lag, deformation out of control.

The invention discloses an intelligent safety early warning system for multi-source data fusion of a mining environment, and relates to the technical field of mine safety monitoring and early warning. According to the system, data are collected through a multi-source sensor, a virtual sample with authenticity scores is generated by using a two-way calibration digital twin model, and a training set is expanded through progressive training; dynamically adjusting the data weight by adopting a three-layer adaptive fusion mechanism so as to predict and compensate missing information in a cross-modal manner; the method comprises the following steps: respectively constructing a special identification network for sudden and gradual change disasters, and modeling spatial propagation features by combining a graph convolutional network; a progressive threshold value is set according to the disaster evolution stage, four-stage early warning is formed, and an emergency plan is linked to achieve closed-loop response; the system also supports cross-scene deployment through feedback annotation and incremental learning optimization model and by means of knowledge management and transfer learning. According to the method, the problems of scarcity of samples, insufficient fusion robustness and low recognition accuracy are relieved, and the early warning intelligence and the self-adaptive capability are improved.

The application discloses a network access active defense control method and system. A network layer-terminal layer-power layer multi-source data pool is constructed, and multi-dimensional data is captured through probes and collectors. Time and space normalization is realized by using a sliding window interpolation method and IP network segment division. The LSTM model is used to learn normal traffic patterns, and the improved isolated forest algorithm is used to identify abnormalities, and an attack situation label set is output. The network is divided into three scenes of core area, edge area and hub area, and the abnormal coupling relationship is analyzed. The path node deviation is extracted, the deviation level sequence is constructed, and the defense situation number chain is established. The outstanding abnormal mode is determined through statistical analysis, and is mapped to the defense action strategy table to generate a standardized defense instruction, so that the closed-loop response from abnormal detection to accurate defense is realized, and the controllability and recovery efficiency of the network situation are improved.

The invention discloses an industrial data sharing method and system based on cognitive intelligence and a storage medium, and the method comprises the steps: constructing an evolutionary deep learning identity verification system, and achieving the dynamic identity verification and behavior anomaly detection of heterogeneous equipment through a genetic algorithm, online incremental learning and multi-model integrated voting. In the factory, a space-time attention mechanism is combined with a dynamic graph neural network, data core features are extracted through self-supervised comparative learning, and equipment time sequence dependence, space correlation and topology dynamic changes are captured; a scheduling strategy is constructed through multi-agent deep reinforcement learning, a game theory balancing mechanism is introduced, edge nodes are coordinated to process high real-time requirements, and disaster recovery is optimized; generating unique identifiers of equipment, nodes and features; the cognitive intelligent decision engine analyzes the features, generates optimization suggestions in combination with a knowledge graph and causal reasoning, triggers an alarm in case of abnormality, and performs closed-loop response through an automatic control loop. According to the invention, the intelligence, reliability and security privacy protection capability of the system are improved.

This invention proposes an intelligent flood control monitoring system for substations based on 3D digital twins and multi-source sensing fusion. The system includes a 3D digital twin model construction module, a multi-source data acquisition and fusion module, an AI intelligent analysis engine, a dynamic flood control plan management module, and a closed-loop emergency response control module. The system constructs a full-chain, intelligent, and closed-loop substation flood control monitoring system through 3D digital twins, multi-source sensing fusion, AI intelligent analysis, dynamic plans, and closed-loop response technologies. The system is implemented through data acquisition, AI analysis, plan triggering, automatic execution, and closed-loop feedback, solving problems such as incomplete sensing, delayed response, and reliance on manual decision-making in existing systems. The system combines substation operation information with artificial intelligence algorithms to achieve panoramic visualization of flood conditions, intelligent early warning, dynamic simulation, automatic linkage, and closed-loop handling, thereby improving the intelligence level of substation flood control.

The invention discloses an LED screen content safety early warning method and system based on multi-mode fusion. Video, spectrum and attitude information is acquired through cooperation of multiple sensors, and geometric correction and dynamic image stabilization of a shot picture are realized based on physical light field modeling; dynamically fusing multi-modal features such as contour, color and motion by using a dual-channel residual network; a hierarchical decision-making mechanism is adopted, the primary alarm response time is 35 milliseconds, the deep verification process is completed within 200 milliseconds, and a hardware execution module is linked to complete closed-loop response. The problems of high false alarm rate and poor real-time performance caused by illumination dramatic change, camera jitter and visual angle deviation in an outdoor environment are solved, the contour omission ratio is reduced from 45% to 8% in a foggy day low-contrast scene, the color discrimination degree is improved by 3 times in a strong light environment, and the hardware response delay is reduced by 67%.

The invention discloses a lucid ganoderma extracting solution detection method based on an intelligent sensing system, electronic equipment and a storage medium, and relates to the technical field of traditional Chinese medicine detection.According to the method, through a functional effective concentration correction model, a cooperative attenuation calculation mechanism of an ultraviolet aging energy accumulation value EUV and a spectral line structure disturbance score Sdrift is introduced, and the detection accuracy of a lucid ganoderma extracting solution is improved. And calculating and outputting the functional effective concentration Ceff in combination with the standard initial concentration C0, thereby realizing dynamic and continuous correction of the active concentration of the extracting solution. A response level interval set based on the result and a corresponding automatic control mechanism such as local shading and manual rechecking enable the system to have a closed-loop response capability of'real-time judgment, feedback and intervention '. According to the mechanism, the automatic early warning capability of abnormal batches on a production line is improved, unnecessary yield loss can be avoided through flexible control means such as shading curtain adjustment, and the production efficiency is considered while the quality stability is ensured.

The invention discloses an industrial control system performance evaluation method based on a system model and an internal model principle, and belongs to the technical field of industrial control. According to the method, a process transfer function model is obtained through system identification, an optimal PID controller is designed based on an internal model principle, and adjustable parameters are solved through an optimization problem; and then, obtaining an optimal performance index through simulation, measuring the initial closed-loop response of the system as a reference, measuring the performance index in real time in the operation process, and calculating a performance evaluation index by combining the optimal performance and the initial performance, thereby evaluating the performance state of the system. The method has objectivity, comprehensiveness, real-time performance and flexibility, and provides a scientific basis for performance optimization and operation and maintenance of the industrial control system.

The application discloses a data center monitoring and early warning system and method based on big data analysis, which is composed of a sensor data acquisition module, a log data conversion module, a multi-dimensional feature fusion module, a state intelligent diagnosis module and an early warning response processing module. The application builds a core processing chain of intelligent monitoring of the data center, forms a collaborative optimization system of data acquisition-feature fusion-intelligent diagnosis-closed loop response, and significantly improves the capturing ability of transient anomalies and compound fault modes through environmental mutation capture and log semantic enhancement technology at the data input end. The overall technical architecture realizes autonomous optimization through online model evolution and elastic resource scheduling, so that the data center monitoring and early warning system has the core ability to continuously adapt to complex operating environments and equipment performance degradation, and finally achieves the triple improvement of fault early warning foresight, resource allocation efficiency and system operation economy.

The invention discloses an information security monitoring system and method based on project management, and relates to the technical field of information security, and the method comprises the steps: obtaining project context data in real time through a project management platform interface; analyzing project context data, constructing and dynamically updating a project-role-data association model, and generating a security policy context; dynamically generating a fine-grained access control rule and an anomaly detection threshold based on the security policy context; continuously monitoring and recording user operation behaviors, and performing real-time anomaly detection and risk assessment; and when a security event is detected, a closed-loop response process is automatically triggered, and a predefined disposal action is executed. According to the invention, deep real-time fusion of the security policy and the project service context can be realized, the differential adaptive capacity of the system is improved, the risks of unauthorized access and data leakage are effectively prevented, automatic linkage of the security event response and the project progress is realized, and the overall security operation and maintenance efficiency is improved.

The present application relates to the technical field of fault prediction, and discloses an intelligent sorting and recycling system for construction waste. The system includes: a collection subsystem, which is used to install a sensor array on the crusher working surface and the vibrating screen mesh edge of the construction waste sorting device and collect a feature data set; a simulation subsystem, which is used to establish a simulation model of the construction waste sorting device and generate a simulation data set; a measured data subsystem, which is used to collect a labeled measured wear data set under actual construction waste sorting conditions; a training subsystem, which is used to input the simulation data set and the labeled measured wear data set into an adaptive domain adversarial neural network for training to obtain an equipment wear degree prediction model; an output subsystem, which is used to identify wear fault features through the equipment wear degree prediction model and output a fault closed-loop response plan, thereby realizing the dynamic adjustment of the feeding rate, crushing speed, screening frequency, and screening amplitude of the sorting device.

The embodiment of the invention discloses a self-adaptive inductance compensation heating method and system for an electromagnetic heating electric cooker, and the method comprises the steps: carrying out the self-adaptive adjustment of heating control according to the inductance characteristics and compensation parameters; monitoring real-time inductance and inductance variation of each petal region, and calculating a region difference value between the inductances of every two petal regions; when it is detected that one or more area difference values exceed a first inductance threshold value and the duration time exceeds a first time threshold value, the step of judging whether the pot body deviates or not is executed; if yes, according to the real-time inductance difference data, driving an elastic section and / or a sliding turn section of each petal area to perform micro displacement; if not, whether liquid level distribution is abnormal or not is judged, and if yes, three-phase dynamic synchronous compensation is achieved for all petal areas. According to the invention, through multi-level threshold value control and self-adaptive compensation, the whole-course self-closed-loop response from detection, judgment to compensation is realized, and the system stability and the heating efficiency are remarkably improved.

The application discloses a power fire alarm monitoring system based on wireless communication and relates to the technical field of intelligent monitoring of power systems, which is used for solving the problems of multi-parameter detection, intelligent alarm and path steady-state discrimination of electrical fires; through high-precision collection of multiple parameters such as zero-sequence current, temperature, voltage and grounding impedance, in combination with local intelligent analysis and remote linkage strategy, real-time identification and response to electrical fire hazards are realized. The system has field backfill sequence grouping, path stability scoring and extraction strategy scheduling mechanism, and improves the abnormal fault tolerance and communication link adaptability. The power supply module adopts a main-backup dual power supply architecture to ensure stable operation during power failure of the system. The platform supports trend modeling, remote control and strategy management, and builds an integrated monitoring scheme of data collection, intelligent discrimination and closed-loop response, which is suitable for multiple power facility fire risk scenarios.

The invention discloses an intelligent monitoring method and system for the safety state of an efflux pump, and relates to the technical field of industrial equipment safety, the system collects working condition data of the efflux pump in real time through a sensor group installed on the efflux pump, and pre-processes the working condition data to construct a fluid state data set and a load evolution data set; medium disturbance evaluation is carried out by calculating a medium disturbance leading edge index VPI and a medium disturbance response threshold TH, when shear disturbance is evaluated to be formed, a load evolution analysis instruction is executed, a load lag evolution index LEI is calculated based on structure response data, and then a joint instability risk index UDR is calculated in combination, so that the shear disturbance is evaluated to be formed. And performing comprehensive instability response evaluation with a set first joint instability boundary threshold UA and a set second joint instability boundary threshold UB, and executing a grading safety adjustment strategy according to a comprehensive instability response evaluation result. According to the system, early recognition, dynamic evaluation and closed-loop response of the abnormal state of the outer conveying pump can be achieved, and the safety and stability of operation of the conveying system are improved.

The invention discloses an industrial vehicle wheel return restraining method, and belongs to the technical field of industrial vehicles. The method comprises the following steps: S1, detecting an actual angle by an angular velocity sensor, estimating to obtain an estimated angle, subtracting the actual angle from the estimated angle to obtain an angle deviation, performing trigonometric function calculation to obtain an output angle, inputting L2 matrix operation and L3 matrix operation, adding to obtain an accelerated speed, integrating the accelerated speed to obtain a feedback speed, integrating the feedback speed to obtain a rotor angle; s2, the single-chip microcomputer collects an accelerator signal, the given speed is obtained through calculation, the feedback speed is subtracted, then the accelerated speed obtained after gain is subtracted, and the speed deviation is obtained; s3, inputting the speed deviation into a speed regulator to obtain a torque given value; and S4, inputting the torque given value and the rotor angle into a torque control module to control the motor to reach the given torque. According to the method, the closed-loop response can be accelerated on the premise that the given speed PI parameter is not increased, and the wheel return phenomenon can be greatly improved.

The invention discloses a distributed energy storage cluster coordinated scheduling method based on climate change, and belongs to the technical field of power distribution network energy storage scheduling. The method aims at solving the technical problems that in the prior art, due to the fact that climate factors are neglected, energy storage equipment performance prediction deviation is large, and a scheduling strategy is mismatched with an actual operation environment. Photovoltaic / wind power output and load characteristics are dynamically corrected by establishing a multi-dimensional climate impact factor model, a climate perception scheduling model fusing LSTM time sequence feature extraction and a PPO reinforcement learning algorithm is constructed, and an optimized charging and discharging instruction of climate sensitive energy storage is generated; meanwhile, the mode switching of the non-climate-sensitive energy storage cluster is triggered through the photovoltaic power threshold after climate correction, and the dynamic balance of the storage of the photovoltaic surplus electric energy and the compensation of the power supply gap is realized. According to the method, a dynamic closed-loop response mechanism is formed by coupling the multi-dimensional reward function with climate dynamic adaptation and instruction tracking optimization, and the response precision of energy storage cluster scheduling is improved.