1158results about How to "Effective regulation" patented technology

The invention discloses an AI spraying system control method capable of achieving autonomous 3D scanning, and the method comprises the steps: obtaining three-dimensional surface data of a to-be-coated workpiece through high-precision 3D scanning equipment, constructing a surface model of the workpiece according to the data, and extracting surface feature parameters. Coating parameters set by a user are received, the coating parameters comprise the viscosity, the chemical property and the film thickness requirement of paint, the surface characteristic parameters and the coating parameters are input into the spraying optimization model, and an initial spraying track and technological parameters are generated through calculation in combination with historical data. Environment parameters are collected and associated with the initial process parameters, and the adjusted spraying track and the adjusted process parameters are generated through dynamic environment compensation. And through a robot control system, coating operation is executed according to the adjusted parameters, the spraying effect is optimized in real time, and it is ensured that the surface of the workpiece is evenly coated. Efficient automation of the spraying process is achieved, the coating quality and consistency are remarkably improved, and the automatic spraying device is particularly suitable for coating of workpieces with complex curved surfaces or changeable shapes.

The invention provides an in-vehicle air conditioner adjusting control method, device and equipment and a storage medium, and the method comprises the steps that in the vehicle running process, in-vehicle environment parameters and in-vehicle environment parameters are collected in real time, and an environment parameter set is generated; and calculating a human body heat balance model of the passenger according to the parameter set, and generating a multi-region thermal comfort index set. And based on the environment parameter set and the thermal comfort index set, the operation state of the air conditioner system is evaluated, and an air conditioner state evaluation result is generated. And multi-strategy parallel reasoning is carried out through a preset deep reinforcement learning model, and an optimal control strategy set of the air conditioning system is generated. And according to the strategy set, distributed cooperative control is conducted on an air conditioner executing mechanism, the adjusting effect is fed back in real time, a dynamic adjusting parameter set is generated, and fine adjustment is conducted on the control strategy. According to the method, through multi-dimensional environment parameter collection and model reasoning, the air conditioner control strategy is dynamically adjusted according to the real-time environment and the thermal comfort degree, more efficient air conditioner system adjustment is achieved, and the comfort of passengers is improved.

The invention relates to the technical field of stacking, in particular to a stacking-based distributed scheduling system and a path planning method thereof, comprising a distributed scheduling architecture, an autonomous task allocation module, a cooperative work module and a load balancing module, the distributed scheduling architecture comprises a task management layer capable of dynamically adjusting a task allocation strategy according to real-time order data and a scheduling layer composed of a plurality of scheduling nodes, and each scheduling node can monitor the state and task progress of the stacker in real time and dynamically adjust task allocation and path planning according to a monitoring result. The stackers execute according to task allocation and path planning instructions of a scheduling layer, the autonomous task allocation module configured to each stacker can independently decide task paths and execution sequences according to task priorities, stacker positions and current states, so that the stackers can quickly respond to task changes, and dependence on a central scheduling system is reduced.

The invention provides a production business mode construction method based on an industrial internet platform, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source data, and carrying out the preprocessing of the multi-source data; calculating the adaptation degree of the equipment and the task; dynamic influence parameters are extracted, and operation parameters in the equipment data are adjusted in combination with the adaptation result; constructing a weighted multi-objective function in combination with a completion time limit, a resource demand and a priority factor in the task data; generating a task priority sequence by solving the multi-objective function, and generating a task scheduling strategy according to the task priority sequence; in combination with the real-time state parameters in the equipment data, calculating the utilization efficiency of the equipment resources; performing priority ranking on the equipment according to the equipment resource use efficiency, and generating a resource allocation scheme through a segmented dynamic optimization algorithm; generating a production business mode according to the task scheduling strategy and the resource allocation scheme; according to the invention, the autonomy and accuracy of the production business mode are improved.

The invention belongs to the technical field of metal processing and forming, and particularly relates to seamless metal composite pipe rolling equipment with a multi-fulcrum guide plate adjusting device and a method. The multi-fulcrum guide plate adjusting device comprises an adjusting hydraulic cylinder, a base end cover, a sliding block seat, a sliding block seat pressing plate, a push rod, a Y-shaped connecting frame, a guide plate, a pin shaft, a pin shaft baffle, a sliding block seat end cover, a locking seat, a locking hydraulic cylinder, a universal coupling and an adjusting rod. The guide plate is accurately controlled through the adjusting hydraulic cylinder and the locking hydraulic cylinder to achieve up-down, autorotation, swinging and other multi-freedom-degree position adjustment, the device can be suitable for rolling processes with different working condition requirements, the guide plate does not need to be replaced many times, rolling can be conducted only by adjusting the guide plate, and after the guide plate works for a long time and is abraded, the service life is prolonged. Abrasion loss can be compensated by adjusting the position of the guide plate, and production efficiency is improved; and meanwhile, the push rod and the adjusting rod are clamped and locked through the locking hydraulic cylinder, and it is guaranteed that the multi-fulcrum guide plate adjusting device has enough strength in the rolling compounding process.

The invention provides a multi-modal knowledge graph completion method based on modal hierarchy fusion, a multi-modal knowledge graph completion model MHF is used to introduce visual features and text features, the model comprises an independent learning layer, a knowledge graph independent learning structure embedding # imgabs0 #, a visual embedding # imgabs1 # and a text embedding # imgabs2 # are embedded from a data set segmented by multiple modals, and the independent learning layer is used to carry out multi-modal hierarchical fusion on the knowledge graph independent learning structure embedding # imgabs0 #, the visual embedding # imgabs1 #, the text embedding # imgabs2 # and the text embedding # imgabs1 # and the text embedding # imgabs2 #; meanwhile, the scale of the external features is adjusted by adopting a scaling factor; the modal fusion layer is used for fusing the structural features with the visual features and the text features respectively to obtain two fusion embedding modes, and the two fusion embedding modes are included in final loss calculation; the semantic constraint layer is used for further processing the five kinds of embedding through a factor interaction regularizer, and five additional regularization loss items generated in the processing process are calculated in final loss; according to the method, the structure data and the external data are integrated, so that the visual data and the text data are more reasonably applied, the expression ability of the model is effectively improved, and a new thought is provided for comprehensiveness and accuracy expression of entities in the knowledge graph.

The invention discloses a wireless flow regulating valve control method and system based on the Internet of Things, and relates to the technical field of wireless flow regulating valves, and the method comprises the following steps: 1, collecting the flow, pressure, temperature and vibration data of a fluid in real time based on a wireless sensor node, and calculating the flow, pressure, temperature and vibration data of the fluid based on a data processing unit and a machine learning reasoning unit; 2, sensor data, local data summarization, preprocessing and partial control decision data are received through a gateway; 3, uploading the data to a cloud server based on a distributed gateway network module; step 4, based on the cloud server cluster module, adopting a deep reinforcement learning algorithm to carry out adaptive learning and optimization, generating a flow regulation instruction and sending the flow regulation instruction to a flow regulation valve; 5, based on the intelligent flow regulating valve module, the control model is adjusted in real time through a multi-dimensional feedback system; and step 6, performing global optimization and maintenance on the overall control strategy based on a multi-dimensional user interface module.

The invention provides a face sketch synthesis method and system based on perceptual loss, and relates to the technical field of image processing, the quality of face sketch synthesis is improved by introducing a strategy based on perceptual loss, high and low frequency features of a face image are extracted by adopting a VGG19 network, and weighted fusion of cross-resolution features is realized through an NSRM module. Therefore, the details and overall perception of the facial features are effectively captured and reserved on different scales, the fineness of the image is improved on the local details, the consistency and the natural sense of the image are enhanced on the global features, the distance between the generated image and the real image in the feature space can be better matched by using the perception loss, and the image quality is improved. And comprehensive optimization is carried out in combination with various losses such as adversarial loss and spectral consistency loss, so that the parameters of the generator and the discriminator are adjusted more efficiently in the training process.

The invention belongs to the technical field of mobile communication, and relates to an unmanned aerial vehicle adjustment method in a forest fire scene, which comprises the following steps: constructing a system model; the system model comprises an unmanned aerial vehicle model and a forest fire environment model; according to the unmanned aerial vehicle model and the forest fire environment model, constructing an optimization problem by taking minimization of energy consumption of the unmanned aerial vehicle as a target; the optimization problem is converted into a Markov decision process, the optimization problem is solved by adopting a deep reinforcement learning algorithm based on the Markov decision process, and the attitude of the unmanned aerial vehicle is obtained; according to the method, the unmanned aerial vehicle model and the forest fire environment model are constructed by comprehensively considering stroke, smoke and flame spreading in the forest fire scene, the optimization problem is constructed according to the unmanned aerial vehicle model and the forest fire environment model, and the target function of the optimization problem considers the total energy consumption of flight, hovering, data collection and transmission of the unmanned aerial vehicle. Therefore, the method adapts to the actual environment condition of forest fire, and the attitude of the unmanned aerial vehicle can be adjusted more effectively.

The invention relates to a heat treatment method for additive manufacturing of a nickel-based superalloy, and relates to the technical field of additive manufacturing. The heat treatment method comprises the following steps that the additive manufacturing nickel-based superalloy is placed at the first temperature, and first-step stress relief annealing treatment is carried out; and then the temperature is increased to a second temperature for second-step stress relief annealing treatment, and after cooling, the additive manufacturing nickel-based high-temperature alloy subjected to stress relief annealing treatment is obtained. The first temperature is 850-890 DEG C, and the second temperature is 900-950 DEG C; carrying out solution treatment on the additive manufacturing nickel-based superalloy subjected to the stress relief annealing treatment to obtain the additive manufacturing nickel-based superalloy subjected to the solution treatment; and aging treatment is conducted on the additive manufacturing nickel-based superalloy obtained after solution treatment, and the additive manufacturing nickel-based superalloy obtained after heat treatment is obtained. The method is mainly used for improving the volume fraction of the gamma'phase of the additive manufacturing nickel-based superalloy, obtaining the gamma 'phase with high cubic degree and proper size, and eliminating cracking and recrystallization caused by residual stress.

The invention discloses a grinding device for internal tooth forming, and particularly relates to the technical field of grinding, the grinding device comprises a sliding rail frame, a positioning plate is slidably mounted at the top of the sliding rail frame, a grinding mechanism is slidably mounted on one side of the positioning plate, and a telescopic cylinder for driving the positioning plate to move is fixedly mounted on the side, away from the grinding mechanism, of the positioning plate; a positioning mechanism is fixedly installed at the top of the sliding rail frame, and a driving mechanism is rotationally installed on the outer wall of the positioning mechanism. A plurality of limiting assemblies which are annularly and sequentially arranged around the outer wall of the gear at equal intervals are arranged, the multiple limiting assemblies are adjusted through repulsive magnetic strips so as to limit and abut against the outer wall of the gear, and suction cups which are fixedly attracted to the two sides of the gear are arranged in a first clamping plate and a second clamping plate; the limiting work of the outer wall of the gear is simpler and more convenient, stress is uniform, deformation and damage are not prone to occurring, and actual use is more convenient.

The invention relates to the field of microbial culture management, in particular to a microbial culture management system and method. The method comprises the following steps: calculating growth factors based on the number of microorganisms in a microorganism growth process and microorganism culture environment parameters which are collected in real time, and calculating a predicted value of the number of microorganisms at a future moment; detecting the growth state of the microorganisms based on the predicted value of the number of the microorganisms in the future; the initial adjustment amount of the microbial culture environment parameters is obtained through a multi-parameter nonlinear optimization algorithm, the final adjustment amount of the microbial culture environment parameters is obtained by introducing a synergistic effect mechanism, and the microbial culture environment parameters are adjusted. The problems that traditional microbial culture environment parameters depend on manual monitoring and experience adjustment, so that response is lagged, and adjustment is not accurate enough are solved; the future growth trend of microorganisms is difficult to accurately predict, so that the culture management reaction is delayed; the problems that the microorganism culture environment is imbalanced and optimal management cannot be realized due to the fact that microorganism culture environment parameters cannot be automatically optimized are solved.

The invention discloses a fumigation treatment multi-parameter comprehensive monitoring system which comprises a multi-parameter sensor monitoring module, a multi-modal data processing and analyzing module, a system intelligent monitoring and adjusting module, a fumigation environment control module, a remote maintenance and management module and a system interaction and feedback module. The multi-parameter sensor monitoring module is used for collecting parameter data. The multi-modal data processing and analyzing module is used for fusing and analyzing data. The system intelligent monitoring and adjusting module is used for intelligently monitoring and adjusting fumigation strategies. The fumigation environment control module is used for adjusting environment parameters. The remote maintenance and management module is used for system maintenance and management. And the system interaction and feedback module is used for display interaction of data and a control state. According to the fumigation treatment multi-parameter comprehensive monitoring system, a multi-modal data fusion algorithm based on information channel decomposition is provided for multi-parameter data fusion and analysis; and a multi-parameter intelligent monitoring algorithm based on a multi-layer feedforward neural network is provided to intelligently monitor the fumigation environment.

The invention discloses a lateral bending spine model rapid restoration method based on semantic feature optimization, and relates to the technical field of medical image analysis and biomedical engineering, and the method comprises the steps: carrying out the segmentation recognition operation of a pre-collected medical image through the morphological feature and geometric analysis technology, obtaining the three-dimensional semantic information of the spine structure based on the segmentation recognition result; extracting morphological parameters on the basis of the three-dimensional semantic information of the spine structure, and exploring an association relationship among the morphological parameters by using a correlation analysis technology; and generating a spinal semantic parameter combination based on the association relationship and the morphological parameters, and searching an optimal spinal semantic parameter combination by using an optimization algorithm to repair the scoliosis spinal model. According to the method, based on a global optimization strategy of an optimization algorithm, a reasonable evaluation function is designed to quantify the performance of each parameter combination on a scoliosis model repair task, and the optimal semantic parameter combination weight is automatically found, so that effective adjustment of scoliosis spine model parameters is guided.

The invention discloses a metasurface optical element and a design method, device and system thereof. The method comprises the following steps: acquiring sample data; processing the sample data based on the metasurface optical element under the current configuration parameters to obtain optical modulation data; inputting the optical modulation data into a prediction model to obtain a first prediction result; determining a loss function according to the first prediction result and the labeling information of the sample data; updating configuration parameters of the metasurface optical element according to the loss function, or updating both the configuration parameters of the metasurface optical element and model parameters of the prediction model until the loss function converges; wherein updating of the configuration parameters of the metasurface optical element comprises the step of shifting the position coordinates of the at least one nanostructure unit. According to the method provided by the invention, the nano-structure units can be freely distributed on the metasurface optical element and are not limited by the center position of a fixed grid region, so that the precision and flexibility of metasurface phase modulation are improved.

The invention discloses an improved sliding-mode active-disturbance-rejection control method for a two-stage energy storage converter, and the method comprises the steps: firstly, building a mathematical model of the energy storage converter in a three-phase static coordinate system, converting the mathematical model into a d-q rotating coordinate system, converting the mathematical model into a second-order active-disturbance-rejection normal form on the basis, and then carrying out the design of a super-spiral expansion state observer. In order to reduce system buffeting, a discontinuous switching item sign function in the super-spiral expansion state observer is replaced by a hyperbola function; secondly, a variable gain function which can change in real time along with observation errors is designed to replace the linear gain of ST-ESO, so that the disturbance observation capability is improved; and then, a superhelix integral terminal sliding mode is introduced as a feedback controller, so that the control precision and the convergence speed of the system are improved, and the robustness of the system is better improved.

The invention discloses a target model interaction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a reasoning instruction; performing data reasoning based on the reasoning instruction through the target model to obtain reasoning process data and a reasoning result; wherein the reasoning process data comprises a plurality of branch nodes and a plurality of conclusion nodes which are cascaded in a data reasoning process; and displaying the reasoning process data and the reasoning result. Through the mode, the transparency and the interpretability of the reasoning path of the target model can be improved, and the efficiency of generating the model output result meeting the user expectation by the target model is improved.

The invention relates to the technical field of intelligent building construction path planning methods, in particular to an AI-based intelligent building construction path planning method and system, and the method comprises the following steps: mapping site construction data into a virtual scene graph which comprises construction machinery information and environment information; on the basis of an AI algorithm and the virtual scene graph, a reasonable construction sequence and path are automatically planned for all machines in construction, and a construction scheme is generated; simulating the construction scheme to verify feasibility and risk points of the construction scheme; the construction scheme is optimized according to the risk point, and an optimal construction scheme is generated, so that the dynamic change of the construction site can be captured in real time, and intelligent path planning and resource allocation can be carried out based on the information; the method provided by the invention has self-learning and optimization capabilities, can continuously accumulate experience from practice, and improves the accuracy and efficiency of planning.

The invention relates to the technical field of path planning, and discloses an underwater desilting robot path planning method based on a digital twin model, which corrects an initial weight value by comprehensively considering a historical success rate factor, a meteorological influence factor, an underwater biological activity influence factor and a water flow influence factor based on an initial desilting path. And finally, selecting the initial dredging path with the maximum correction weight value as an optimized dredging path. By introducing a historical success rate factor, the method can learn and optimize from past dredging tasks, ensures that current path planning is based on static topographic map and historical task experience teaching and training, and improves the reliability of path planning. And according to the historical task success rate factors, which areas easily have problems in the past are identified, and the high-risk areas are automatically avoided when a new path is planned, so that the intelligence and adaptability of path planning are further enhanced. The underwater biological activity influence factors are considered, so that the robot can autonomously recognize and avoid the sensitive ecological area.

The invention relates to the technical field of vehicle control, and discloses a vehicle-mounted semantic standardization method, device and equipment, a medium and a product, and the method comprises the steps: obtaining a pre-trained large language model; obtaining a target original vehicle control command; obtaining a format template used for representing the target standard vehicle control command; forming a cue word according to the target original vehicle control command and the format template; and inputting the target original vehicle control command and the cue word into the large language model, and outputting the converted target standard vehicle control command through the large language model. Based on the ability of the large language model to directly understand the text meaning, the original vehicle control command and the standard vehicle control command are directly paired to generate the training sample of the text style, so that the training sample is input into the large language model for training, the threshold of model training is reduced, and the training efficiency and the training accuracy are improved; and high accuracy and high automation degree of vehicle-mounted semantic standardization are considered.

The present application discloses a load balancing control method and system for an energy storage device, and relates to the technical field of energy storage load regulation. The method first obtains the historical operating data of the target energy storage device, and pre-processes it through the first Gaussian processing method to generate the first operating data. Subsequently, the first modal decomposition method is used to further process these data to decompose the multi-scale time series components, thereby generating the second operating data. These data are further input into a hybrid neural network, which includes two parts: the first sub-network extracts the spatial features of the data, and the second sub-network performs time dependency analysis. Based on the first load control information generated by the network, a control strategy for the target energy storage device is formulated. The system can also collect environmental data in real time, and adjust the noise parameters based on these data to optimize data processing and prediction accuracy.

The invention provides an interaction method based on a language interaction interface and used for end-cloud model combination and computing equipment. The method comprises the following steps: calling an end-side intelligent assistant in response to a user request; judging a user intention through an end side model; according to the user intention, different interaction results are generated according to the intention recognition result; wherein if it is judged that the user intention is the first type of intention, the end side model directly processes the user intention and returns a result to complete interaction; and if it is judged that the user intention is a second type intention, entering a cloud side large model for processing, and returning a result to an end side to complete interaction. According to the technical scheme, the user interaction boundary can be weakened, the operation process is more flexible, the user interaction cost is reduced, and the interaction delay time of a large model is shortened.

The invention relates to an ion regulation inhibitor for preventing slurry from covering, application and a lepidolite ore beneficiation method. The invention discloses an ion regulation inhibitor for preventing slime from covering. The ion regulation inhibitor comprises a starch inhibitor, a sodium salt inhibitor and a metal ion regulator. The invention provides application of the ion regulation inhibitor capable of preventing the slime from covering in ore dressing of lepidolite ore. The invention provides a beneficiation method of lepidolite ore. The beneficiation method comprises the following steps: adding water into ore powder of the lepidolite ore to prepare ore pulp; a pH regulator, a collecting agent and the ion regulation inhibitor are added into the ore pulp for roughing, and roughing concentrate and roughing tailings are obtained; the ion regulation inhibitor is added into the roughing concentrate for concentration, and lepidolite concentrate is obtained; and a collecting agent is added into the roughing tailings for scavenging, and flotation tailings are obtained. The problems that an existing lepidolite ore beneficiation method is large in lithium ore loss, low in beneficiation efficiency and complex in process are solved.

The invention belongs to the field of biotechnology and medical configuration products, and provides a mucoprotein-tannic acid packaged probiotic preparation as well as a preparation method and application thereof. According to the preparation method disclosed by the invention, tannic acid and mucoprotein are encapsulated in escherichia coli Nissle 1917 and lactobacillus plantarum NC8, so that the mucoprotein-tannic acid encapsulated probiotic preparation is obtained. The probiotic microbial agent prepared by the invention has excellent resistance to severe environment of gastrointestinal tracts, and the adhesiveness of the probiotic microbial agent in intestinal tracts is enhanced, so that the colonization and growth of the probiotics in a mucous layer are enhanced. Meanwhile, bacterial enteritis, especially enteritis caused by enterotoxigenic escherichia coli (ETEC), can be effectively treated.

The invention provides a temperature and humidity optimization control method and device based on sensor adaptive calibration. The method comprises the steps that reading sets corresponding to all temperature and humidity sensors in an air conditioning system are obtained in real time; a to-be-calibrated reading in the reading set is determined through a preset calibration strategy, and the temperature and humidity sensor corresponding to the to-be-calibrated reading is calibrated; judging whether the temperature and humidity of the current environment of the target area indicated by the calibrated reading set are within a preset temperature and humidity allowable deviation range or not; if yes, the preset temperature and humidity are in a change following state, and variable reset of the proportional-integral-differential controller is triggered; if not, a corresponding temperature and humidity adjusting strategy is executed according to the deviation between the actual temperature and humidity and the preset temperature and humidity. The air conditioning system can accurately sense the environment temperature and humidity change, accurate calibration and effective adjustment are achieved, and a stable and appropriate temperature and humidity environment is provided for a target area.

The centrifugal pump comprises an impeller and a volute, the impeller comprises a front cover plate, a rear cover plate, main blades, first guide blades and second guide blades, and the multiple first guide blades and the multiple second guide blades are installed on the inner side face of the rear cover plate and located on the upstream of the main blades; the device is characterized in that the first guide vane is arranged on the first adjusting ring, the second guide vane is arranged on the second adjusting ring, and the first adjusting ring and / or the second adjusting ring can rotate in the circumferential direction so as to adjust the circumferential position and the liquid flow angle of the first guide vane relative to the second guide vane. And / or the circumferential position and the liquid flow angle of the second guide vane relative to the main vane. According to the centrifugal pump, the pressure difference between the pressure surface and the negative pressure surface of the main blade can be adjusted, and the liquid flow state between the impeller runners can be adjusted, so that the anti-cavitation performance of the centrifugal pump can be effectively improved, the hydraulic loss of the centrifugal pump is reduced, and the hydraulic efficiency of the centrifugal pump is improved.

The invention relates to the technical field of regulation and control, in particular to a glass fiber geogrid production process regulation and control method based on machine learning. The method comprises the following steps: acquiring a yield stage variation characterization value and a strengthening stage variation characterization value of a test sample; according to the yield stage variation characterization value and the strengthening stage variation characterization value of the test sample, a stress-strain curve mutation characterization value corresponding to the test sample is obtained, and according to the stress-strain curve mutation characterization value, the direction for regulating and controlling the production process of the glass fiber geogrid is obtained. And the production process of the glass fiber geogrid is regulated and controlled according to the regulation and control direction. According to the method, the regulation and control direction can be accurately and reliably determined, and the production process of the glass fiber geogrid can be reliably and efficiently regulated and controlled subsequently according to the determined regulation and control direction.

The invention relates to the field of power systems, and discloses a DMMoEQN-based power system parameter identification method, which comprises the following steps: acquiring an initial observation value # imgabs0 # and a target observation value # imgabs1 # of a power system, and selecting an optimal parameter adjustment action # imgabs3 # by an intelligent agent according to a current observation value # imgabs2 # through an online network; an action # imgabs4 # is executed, the electric power system updates a parameter state # imgabs6 # of the electric power system according to the action # imgabs5 #, and a new observation value # imgabs7 # is obtained; calculating a step reward according to an error between the new observation value # imgabs8 # and the target observation value # imgabs9 #, and obtaining a total reward according to the step reward; judging whether the observation value # imgabs10 # of the electric power system is within the error range of the target observation value # imgabs11 #, if so, performing initialization, and continuing training; when the epoch number is larger than the preset maximum epoch number, training is ended, a power system parameter set # imgabs12 # is obtained through the trained power system parameter identification model, and the power system is adjusted according to the parameter set # imgabs13 #. According to the invention, efficient and accurate parameter identification and dynamic adjustment of the power system can be realized.

The invention relates to the field of artificial intelligence, in particular to a process event response method based on a large model, and the method comprises the steps: detecting a text quality parameter and a video quality parameter of business information obtained at each time based on a pre-training model, and determining an information quality parameter according to the text quality parameter and the video quality parameter; determining the information category of the corresponding service information according to the information quality parameter, and determining an analysis mode of a flow processing coefficient according to the information category of the service information obtained each time, including determining the flow processing coefficient according to a reference matching coefficient and an effective information proportion; whether flow scheduling is conducted on the to-be-processed service information or not is periodically determined according to the processing coefficient difference value, the to-be-scheduled service information and the target scheduling processing end are determined based on the historical correlation coefficient and the node processing coefficient, the balance degree of task load distribution is improved, and then the overall processing efficiency of the service information is improved.

The invention relates to the technical field of aircraft control, in particular to an airfoil gust retarding feedforward control method based on a circular rector control technology, which comprises the following steps: modifying the shape of the rear edge of an NACA0012 airfoil, and arranging jet holes in the rear edge to form a circular rector control airfoil; performing a fluid simulation experiment on the circular rector control airfoil based on the gust speed, and constructing a model of the airfoil lift coefficient disturbed by the gust speed; performing a fluid simulation experiment on the circular rector control airfoil based on the jet flow momentum coefficient, and constructing a model of which the airfoil lift coefficient is controlled by the jet flow momentum coefficient; based on the model, obtaining a gust slowing-down feedforward control model of which the input quantity is the vertical gust speed and the output quantity is the jet flow momentum coefficient; controlling jet flow of a trailing edge by adopting the gust slowing-down feedforward control model to complete gust slowing-down control of the circular rector control airfoil; the method can improve the ability of the aircraft to resist the gust disturbance environment.