618results about How to "Improve control" patented technology

The invention relates to the field of temperature control, in particular to an automatic control method and system for a constant-temperature test room. The method comprises the following steps: acquiring real-time indoor operation monitoring parameters and external environment multi-dimensional data of a constant-temperature test room; performing indoor operation state analysis on the real-time indoor operation monitoring parameters, and constructing a laboratory dynamic state space model; carrying out multi-time-point environment temperature fluctuation calculation on the external environment multi-dimensional data, and carrying out nonlinear correlation learning to generate indoor state-environment nonlinear correlation characteristics; carrying out dynamic temperature evolution simulation on the laboratory dynamic state space model based on the indoor state-environment nonlinear correlation characteristics, and carrying out dynamic temperature evolution prediction to obtain temperature prediction values of a plurality of regions; constant-temperature deviation compensation calculation is carried out on the temperature prediction values of the multiple areas, then intelligent temperature control compensation is carried out, and an intelligent temperature control strategy is constructed; according to the invention, efficient and accurate constant-temperature automatic control is realized.

The invention provides a multi-protocol communication intelligent switch gateway control method and device, and the method comprises the steps: constructing a knowledge graph according to a network topological graph and protocol information of an intelligent switch, and obtaining a switch networking knowledge graph; determining an inference rule according to the switch networking knowledge graph to obtain an inference rule engine; acquiring a historical data packet of the intelligent switch in a preset time period, and analyzing and converting the historical data packet to obtain a data conversion module; integrating the inference rule engine and the data conversion module to obtain a protocol conversion module of the intelligent gateway; dynamically designing the protocol conversion module according to a preset implementation framework to obtain a dynamic protocol adaptation mechanism; evaluation and performance optimization are carried out on the dynamic protocol adaptation mechanism, an optimized protocol conversion system is obtained, and the optimized protocol conversion system is used for assisting the intelligent switch gateway to control the intelligent switch.

The invention relates to the technical field of computer television image communication, in particular to a computer television all-in-one machine control method, system and device. The method comprises the following steps: through multi-view image acquisition and processing, generating a three-dimensional scene grid and carrying out scene element analysis to form a scene element set, then chasing limbs of a user, generating a user limb image set, restoring an environment basic tone, obtaining display content of the all-in-one machine and carrying out content semantic analysis, and finally obtaining a user limb image set. Calculating interaction intensity to obtain content interaction intensity data, speculating a user body language and performing user intention prediction in combination with the interaction intensity data, adjusting display content of the all-in-one machine based on the predicted user operation intention, generating simulation adjustment content, performing composition optimization through a scene element set, and generating updated display content. The method for controlling the computer and television all-in-one machine is more accurate and more efficient.

The invention relates to the technical field of brain-computer interfaces, and discloses a self-adaptive dynamic feedback brain-computer interface training method and system based on virtual reality. The training method comprises the steps that a user performs motor imagery according to a training scene presented by the VR display device; the electroencephalogram acquisition equipment acquires electroencephalogram signals; configuring a data processing module, and operating a motor imagery continuous control model to calculate characteristic parameters by taking the electroencephalogram signals as input; under the condition that the task exists, the characteristic parameters are used as input, and a dynamic self-adaptive characteristic-feedback mapping model is operated to calculate control parameters; sending the control parameters to a functional electrical stimulation device to load electrical stimulation of corresponding intensity to the muscle to obtain tactile feedback; meanwhile, the dynamic adaptive feature-feedback mapping model is dynamically updated according to latest input feature parameters; the control parameters are sent to the VR display device to present visual feedback, and the user adjusts the motor imagery based on the visual feedback and the haptic feedback. The participation sense of the user and the training efficiency can be improved.

The invention provides a voltage out-of-limit control system based on multi-agent deep reinforcement learning, and the system comprises a power grid region division module which is used for dividing a power grid into a plurality of regions, and each region at least comprises one piece of photovoltaic equipment; the photovoltaic equipment control strategy model building module is used for carrying out modeling by adopting a partially observable Markov decision process aiming at photovoltaic equipment in each region of the power grid to obtain a photovoltaic equipment control strategy model; and the photovoltaic equipment control strategy output module is used for inputting the current operation parameters of the power grid in each region into the photovoltaic equipment control strategy model, and solving the photovoltaic equipment control strategy model by using a double-delay depth deterministic strategy gradient algorithm. And obtaining reactive power of each photovoltaic inverter and outputting the reactive power to a power grid of a corresponding area to control the voltage of each node to be within a preset range. According to the method, the problems of voltage fluctuation and out-of-limit caused by distributed photovoltaic access to the power grid are effectively solved, and the control efficiency and the control effect are improved.

The invention provides an electric heating gas energy storage collaborative charge state disturbance suppression optimization method, and relates to the technical field of information, and the method comprises the steps: carrying out the filtering processing of a second-level disturbance signal in the real-time operation data of a power grid through an adaptive Kalman filtering algorithm, so as to improve the signal-to-noise ratio; performing time-frequency domain feature extraction on the filtered disturbance signal by using a wavelet analysis method, and judging the type and severity of the disturbance signal in combination with a preset disturbance mode library; and according to the type and characteristics of the disturbance signal. According to the method, an electric energy storage collaborative optimization control model is constructed by adopting a robust optimization theory, the uncertainty of disturbance signals and electric energy storage characteristics is considered, an optimal power instruction is solved, in the control process, the state of an electric energy storage unit is dynamically evaluated, a strategy is adjusted, online optimization is carried out according to power grid index changes, the control effect is continuously improved, and the control efficiency is improved. Through cooperative optimization control of electric energy storage, second-level disturbance of the power grid is effectively suppressed, and safe and stable operation of the power grid is guaranteed.

The invention belongs to the field of intelligent control systems, and particularly relates to a shield attitude real-time intelligent control method based on machine learning, according to the method, a data acquisition module can more comprehensively and accurately reflect the actual condition of shield construction, and in the control process, key parameters such as the working state of a jack and the pressure of a hydraulic system are monitored in real time, so that the control accuracy is improved. Abnormal conditions can be found in time, an emergency plan can be started, and the safety of shield construction is guaranteed. According to the real-time monitoring and feedback optimization mechanism, the safety and reliability of shield construction are effectively improved, and the construction risk is reduced.

The invention belongs to the technical field of computer vision, and provides a point cloud completion method and system based on a self-supervised conditional diffusion model, and the method comprises the steps: obtaining a target image; segmenting the obtained target image, and mapping segmentation information of the target image to a point cloud space to obtain a preliminary incomplete point cloud; performing point cloud denoising processing on the obtained preliminary incomplete point cloud; and according to the denoised preliminary incomplete point cloud and a self-supervised conditional diffusion model adopting a multi-scale feature fusion mechanism, capturing point cloud space geometric features, speculating and complementing missing point cloud, generating completed point cloud data, and completing point cloud complementing based on the self-supervised conditional diffusion model.

The invention discloses a primary frequency modulation control method and system based on a collaborative optimization strategy, and the method comprises the steps: obtaining a system frequency deviation value of a power system, and triggering frequency modulation control when the system frequency deviation value exceeds a preset frequency deviation threshold value; obtaining and classifying various frequency modulation resources, and determining frequency modulation priorities of the various frequency modulation resources; based on the classified frequency modulation resources, a multi-target optimization model is constructed and solved, and the optimal output adjustment amount of each type of frequency modulation resources is obtained; converting the optimal output adjustment amount of each frequency modulation resource into a corresponding frequency modulation control instruction, and sending the frequency modulation control instruction to a corresponding control device; the system frequency is monitored in real time, and when the system frequency deviation value obtained in real time is within a preset frequency deviation threshold value, frequency modulation control is completed. The frequency of the power system is intelligently and accurately regulated and controlled, the frequency modulation response speed and the control effect are improved, the frequency modulation cost is reduced, and the stability and the reliability of the system are enhanced.

The invention relates to the technical field of lithium iron phosphate grinding, in particular to a grinding device for lithium iron phosphate production and a grinding method.The device comprises a tank body, a driving assembly and a first grinding assembly, a tank cover is installed at the top of the tank body, the driving assembly comprises a driving motor installed at the top of the tank cover, and a rotating shaft is fixedly arranged on an output shaft of the driving motor; the rotating shaft vertically extends into the tank body, the first grinding assembly is installed on the rotating shaft and comprises a first grinding seat, the first grinding seat is rotationally connected to the rotating shaft in a sleeving mode, the peripheral wall of the first grinding seat is fixedly arranged on the inner wall of the tank body, a conical groove is formed in the top face of the first grinding seat, and the rotating shaft is fixedly sleeved with a conical block; and a scraper is fixedly arranged on the peripheral wall of the conical block through a connecting block. According to the invention, the control capability on the granularity of the lithium iron phosphate material is improved, the uniform distribution of the granularity of the material is ensured, the problem of excessive grinding or insufficient grinding is avoided, the risk of material caking caused by the grinding wheel in the grinding process is reduced, and the grinding efficiency is improved.

The invention discloses an efficient regulation and control system and method for plateau low-oxygen environment damage, and relates to the technical field of human body low-oxygen damage analysis, detection and regulation and control, and the system comprises a data monitoring module, a signal screening module, a decision oxygen distribution module and a monitoring interaction module; the data monitoring module is used for acquiring a physiological state related to the plateau hypoxia adaptive capacity in real time; acquiring physiological data with high modeling value through an optimization algorithm by using a signal screening module based on test data, and constructing a discrete state space model of a physiological state; an oxygen distribution concentration decision is determined through a predictive control algorithm by utilizing a decision oxygen distribution module, so that oxygen supply regulation and control are realized; and the monitoring interaction module is used for storing, recording and displaying data, issuing an alarm and predicting dangerous conditions. Therefore, by means of the method, effective oxygen supply, blood oxygen dynamic monitoring and real-time alarming can be achieved, and health and safety of individuals in oxygen-deficient environments such as high-altitude areas and the like are guaranteed.

The invention provides a multi-path main road signal cooperative control method considering speed fluctuation, and the method comprises the steps: firstly collecting traffic data, including road network geometric data, traffic flow data and signal control data, and determining a plurality of main paths according to the traffic flow data; then, based on the running speed of each vehicle on each main path, the vehicle running average speed and the speed fluctuation quantity on each main path are calculated; and finally, constructing a signal control optimization model: constructing a target function by taking weighted green wave bandwidth maximization of all paths as a target, adding a loop integer constraint, an interference constraint, a speed fluctuation space-time constraint, a highest speed fluctuation constraint and a lowest speed fluctuation constraint, solving the signal control optimization model, and obtaining an optimized signal period and green wave bandwidth. And outputting the optimized signal timing strategy. The signal timing generated by the method has a good prospect in the aspect of reducing travel delay and parking times, and a better multi-path collaborative effect can be realized.

The invention provides a motor stator vibration suppression method and system based on an electromagnetic pad and deep learning, and relates to the field of electrical technology, and the method comprises the steps: collecting a vibration signal of a motor stator, and carrying out the preprocessing of the vibration signal; inputting the preprocessed signal into a deep learning model, and training and optimizing the deep learning model; fusing the features through a full connection layer to obtain a comprehensive feature vector; predicting the future vibration state of the motor stator by using the comprehensive feature vector; establishing an electromagnetic force model, and designing an adaptive control law based on the predicted future vibration state and the electromagnetic force model; electromagnetic force required by vibration suppression is calculated by using a self-adaptive control law and is converted into a control signal to adjust the electromagnetic pad; a multi-objective optimization algorithm is used to consider the vibration suppression effect and energy consumption at the same time, an adaptive control law is adjusted, and closed-loop feedback control is formed to continuously optimize the vibration suppression effect.

The embodiment of the invention provides a sound effect control method and device and computing equipment. The sound effect control method comprises the steps that a scene picture to be controlled is divided into at least two scene areas; in response to a sound effect control event of a target scene region, determining region content information of the target scene region, and determining a target sound effect parameter of the target scene region according to the region content information, the target scene region being any one of the at least two scene regions; and controlling the sound effect of the target scene area according to the target sound effect parameter of the target scene area. Regional division is carried out on a scene picture to be controlled, sound effects of different scene regions are independently regulated and controlled, different sound effects are allowed to be played in different scene regions, the layering sense and dynamic expressive force of the sound effects are improved, dynamic sound effect control is achieved based on region content information of scene dynamic change, and the user experience is improved. The sound effect in a complex scene is prevented from being disjointed with an actual scene, and the control effect of the sound effect is improved.

The invention discloses a power distribution network unknown measurement information completion method based on data flow mapping, and relates to the technical field of power distribution network monitoring and control. The invention discloses a method for complementing unknown measurement information of a power distribution network based on data power flow mapping. The method comprises the following steps: step 1, constructing a data power flow mapping model; step 2, speculating and complementing missing data; 3, real-time updating and intelligent optimization are carried out; step 4, system integration and application; wherein the step 1 comprises the following sub-steps; step 1.1, data acquisition and preprocessing; step 1.2, feature extraction and analysis; 1.3, constructing a mapping model; wherein the step 2 comprises the following sub-steps; step 2.1, identification and speculation of missing data; step 2.2, constructing a speculative model; and step 2.3, data completion.

The invention discloses a torque system and suspension system decoupling method of a single-winding magnetic suspension permanent magnet synchronous motor, the motor adopts a suspension parallel single-winding structure, the torque system decouples suspension current, permanent magnets adopt consequent poles, and the suspension system decouples a rotor position angle; aiming at a current loop of the suspension system, regarding a coupling item of a torque system to the suspension system as disturbance, designing a sliding mode disturbance observer based on an improved superhelix algorithm, observing and compensating the disturbance, and realizing decoupling of the suspension system and torque control; aiming at the displacement / suspension force ring of the suspension system, designing a sliding-mode active-disturbance-rejection controller, observing the disturbance of the rotor displacement and compensating the suspension force to realize decoupling among different suspension degrees of freedom; the residual coupling factors of the torque system are regarded as system disturbance, and the disturbance is observed and compensated in real time by using a sliding mode-active disturbance rejection composite control method, so that decoupling of the residual coupling factors is realized. According to the invention, comprehensive decoupling of the torque system and the suspension system is realized.

The invention provides a vehicle transverse motion control method and a vehicle transverse motion control system. The vehicle transverse motion control method comprises the steps that a feedback rotation angle is calculated based on an LQR algorithm; calculating a feed-forward rotation angle based on preview tracking control; and adding the feedback rotation angle and the feedforward rotation angle to obtain a front wheel rotation angle original value which is used for participating in vehicle transverse motion control. Through the configuration, on one hand, the advantages of simple calculation and less calculation resource occupation of the LQR algorithm and the preview tracking control algorithm can be played, and on the other hand, the control effect can be effectively improved by combining the calculation results of the LQR algorithm and the preview tracking control algorithm. The vehicle transverse motion control system is hardware for operating the control method. Both of the two can solve the problem that in the prior art, a vehicle transverse movement online control method cannot meet the requirements of actual application scenes.

The invention discloses an anti-interference control method for a longitudinal model in a ground effect area of a ground effect aircraft, and the method comprises the steps: firstly arranging a transition process for system expected input and system actual output through a tracking differentiator, and enabling the transition process to reduce the initial value deviation as much as possible; secondly, a new nonlinear function is introduced to improve the extended observer, so that the anti-interference capability of the system is greatly enhanced, and the robustness of the system is enhanced; finally, a new nonlinear function is introduced, a nonlinear feedback law is improved, a system control law is obtained, and the control precision of the system is greatly improved.

The invention relates to the technical field of sound console, and discloses a balance adjustment method, system and device for a sound console volume controller, and a medium. The method comprises the following steps: processing an audio signal of an input end of the sound console to obtain an initial system parameter matrix; performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise feature data matrix; performing first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix; performing second-order frequency equalization calculation according to the volume control data matrix to obtain a frequency equalization control data matrix; performing iterative optimization calculation on the control parameters to obtain an optimal control parameter matrix; and performing dual-channel parallel calculation on the transfer function of the volume control channel and the transfer function of the frequency equalization channel to obtain an audio signal of the output end of the sound console, thereby realizing independent adjustment of volume control and frequency equalization, and ensuring high quality of audio output.

The invention discloses a split type back door control method, device and equipment and a storage medium, and relates to the technical field of vehicle back door control, the split type back door control method comprises the steps that an actuation instruction of a split type back door is responded, and a first back door or a second back door is controlled to actuate according to the actuation instruction; when the split type back door stops acting, a target back door which stops acting is determined; detecting whether the position where the target back door stops acting is located in a collision area or not; and when the position where the target back door stops acting is located in the collision area, the first back door and / or the second back door are / is controlled to move based on the emergency position. By arranging the emergency position, when collision between the first back door and the second back door is about to happen, the corresponding back door is controlled to quickly return to the emergency position, the split type back door can be quickly controlled, the waiting time is shortened, and the control effect is improved.

The invention discloses a method for conserving predatory natural enemies in a wheat field by using a functional plant combination, and belongs to the technical field of ecological prevention and control of agricultural pests. According to the method, before wheat is sown, functional plant zones for intercropping cnidium monnieri and broad beans are arranged on ridges and roadsides of a wheat field. The cnidium monnieri and the broad beans are planted in the wheat field system, a nutrient source and a sheltering place can be provided for natural enemy insects, predatory natural enemies such as hoverflies, ladybirds and spiders are attracted to migrate in advance, and the population number of the predatory natural enemies is increased; the predatory natural enemy with conservation of the functional plant formed by combining the cnidium monnieri and the broad beans can enter a wheat field to play a role when the wheat pests occur, and the survivability and the pest control ability of the predatory natural enemy are enhanced, so that the pest population density is controlled, the ecological control effect on the wheat pests is realized, and the pesticide consumption is reduced.

The present invention relates to the field of smart grid technology, specifically a distribution terminal communication network system based on WAPI, the system includes a distribution data collection module, a distribution data compression module, a data encryption processing module, a network parameter adjustment module, a distribution anomaly identification module and a distribution scheduling adjustment module. In the present invention, by introducing real-time data processing and encryption technology based on WAPI, the security of data and the efficiency of the network are improved, the data encryption processing ensures the security of information during transmission, enhances the system's control ability over power network operation and monitoring, and adjusts the sending frequency of data packets according to the network status, dynamically optimizes network parameters, improves the network's adaptability and stability, and implements anomaly identification and fault response to ensure the continuity and safety of power grid operation. By optimizing the data processing process and strengthening safety measures, the overall performance and reliability of the power system are significantly improved.

The invention relates to a PMSM control method based on a BP neural network and a superspiral sliding mode. Comprising the following steps: (1) constructing a mathematical model of a permanent magnet synchronous motor (PMSM); (2) on the basis of the mathematical model in the step (1), constructing a BP neural network PI controller which takes an expected value omega *, an actual value omega and an error e as inputs, takes a proportionality coefficient Kp and an integral coefficient Ki of the PI controller as outputs, and contains seven nodes in a hidden layer; the rotating speed ring continuously updates the weight value through the BP neural network so as to adjust the values of Kp and Ki in real time; (3) based on the mathematical model in the step (1), constructing an STA current loop controller for super-spiral sliding mode and feedforward decoupling control; and (4) PMSM control is realized based on the BP neural network PI controller in the step (2) and the STA current loop controller established in the step (3). The method has smaller overshoot and anti-interference capability, and has very positive significance for improving the control performance of the permanent magnet synchronous motor.

The invention discloses a foot tub parameter intelligent adjustment method and system based on machine learning. The method comprises the steps of data collection, data screening, foot tub parameter prediction model establishment, global parameter optimization and foot tub parameter adjustment. The invention relates to the technical field of smart homes, in particular to a foot tub parameter intelligent adjusting method and system based on machine learning, according to the scheme, data screening is carried out by dividing data neighborhoods and defining data complexity and local density, and the accuracy and reliability of abnormal value recognition are improved; a model is established by designing an excitation function, a front double transformation layer, a residual adjustment layer and a loss function, so that the expression ability, prediction accuracy and prediction error control ability of the model are improved; parameters are optimized by designing a parameter search function and a search rule, and the search direction and step length are dynamically adjusted, so that the search process is more efficient, the anti-interference capability of the search process is enhanced, and local optimum can be jumped out.

The utility model relates to a multi-beam laser interference photoetching system which comprises a high-power laser, five high-reflectivity mirrors, two semi-reflecting and semi-transmitting mirrors, three groups of 1 / 2 wave plates and polaroids, thirteen lens supports and a sample table, the high-power laser emits a beam of light, the beam of light is divided into three coherent light beams by the five high-reflectivity mirrors and the two semi-reflecting and semi-transmitting mirrors, and the three coherent light beams are connected with the sample table. The relative positions of the five high-reflectivity mirrors and the two semi-reflecting and semi-transmitting mirrors have a certain corresponding relation with the incident angle and the space angle of the three coherent light beams, the light intensity energy density and the polarization state of the coherent light beams are changed by adjusting the angles of the three groups of 1 / 2 wave plates and the polaroids, and the light intensity energy density and the polarization state of the coherent light beams are changed through the incident angle, the space angle and the polarization state. The period and the morphology of the patterned micro-nano structure are changed, and the characteristic size of the patterned micro-nano structure is changed by changing the light intensity energy density and the polarization state, so that the process is simplified, and the control capability of the patterned micro-nano structure is improved.

The invention relates to the technical field of non-road mobile mechanical dynamics models, in particular to a method for establishing a digital model of a vibration system of a road roller. The method comprises the following steps: acquiring an initial pavement material vibration characteristic data set; performing multi-physical field coupling analysis on the initial pavement material vibration characteristic data set to obtain pavement material response characteristic data; performing dynamic system modeling on the vibration system of the road roller based on the pavement material response characteristic data to obtain an initial digital model of the vibration system; performing dynamic feature extraction on the initial digital model of the vibration system to obtain dynamic parameter mapping data of the vibration system; and performing parameter calibration on the initial digital model of the vibration system based on the dynamic parameter mapping data of the vibration system to obtain a digital model of the vibration system. The intelligent vibration system model can accurately capture the dynamic characteristics of soil, and the optimal compaction effect is achieved.

The invention discloses a method for calculating the influence sensitivity of multiple stations and multiple control parameters on the short-circuit current of an alternating current system, and the method comprises the steps: building a calculation data model of the short-circuit current of a power grid, and enabling the model to comprise at least one new energy or energy storage device equivalent to a controlled current source; based on the calculation data model and the independent variable selected by each station, utilizing an iteration method to calculate the short-circuit current increment of each station; calculating the short-circuit current of each station fault point according to the short-circuit current increment; and calculating a sensitivity index of the independent variable relative to the short-circuit current of the specified fault point according to the short-circuit current.

The invention relates to an unmanned ship cluster cooperative tracking control method based on model predictive control and control law learning, and belongs to the field of unmanned ship formation trajectory tracking control. The method aims at solving the problems that an existing centralized control algorithm is high in calculation complexity, and a distributed control algorithm is poor in control effect. Firstly, kinematics and dynamics models of the unmanned ship are established, then a distributed dual-mode control algorithm and a centralized model predictive control algorithm are designed, a long short-term memory (LSTM) network is constructed to learn a deep mapping relation between a global control law and a distributed control law, and then, the deep mapping relation between the global control law and the distributed control law is learned by aiming at the problem that an offline learning method is poor in expandability. A self-adaptive model switching strategy is designed, the LSTM model is switched in a self-adaptive mode according to the characteristics of the tracking trajectory, the cooperative control effect is improved while the calculation time is shortened, and it is ensured that the multiple unmanned ship systems can track the time-varying target trajectory in real time according to various expected formations.

The invention discloses a sliding mode-extended state observer vibration control method based on time delay compensation in the field of vibration control. The sliding mode-extended state observer vibration control method comprises the following steps: step 1) carrying out novel modeling on a four-side fixed support plate system; 2) designing an extended state observer; 3) designing a sliding mode controller; 4) designing a time delay compensator; and step 5) calculating the control quantity by using the NI-PCIe acquisition card and the Simulink. According to the invention, based on NI-PCIe hardware-in-the-loop platform development, the problems of system time lag, model uncertainty, influence of an inertial actuator and external excitation existing in a four-side fixed support plate are solved, and the total disturbance of the system including internal and external disturbance is estimated through an extended state observer. A content system is simplified into an integral series connection type for control by eliminating total disturbance through feedforward, and a delay compensator based on an enhanced differentiator is introduced to carry out non-delay design on the system, so that the vibration suppression performance of the sliding mode controller is improved.