1341results about How to "Avoid instability" patented technology

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention discloses a charging pile dynamic power scheduling method, and relates to the technical field of charging pile power scheduling, and the method specifically comprises the following steps: obtaining node state information of each remaining normal node in real time, and generating a remaining bearing capability index and a load balancing index of each normal node; and constructing a power distribution evaluation model for the generated residual bearing capacity index and the load balancing index of each normal node, generating a power distribution evaluation coefficient of each normal node, analyzing the power distribution evaluation coefficient after generation, and evaluating the power bearing capacity of each normal node. Dividing each normal node into a high-bearing node, a middle-bearing node and a low-bearing node according to an evaluation result; and respectively adopting a corresponding power scheduling strategy according to a division result of each normal node. According to the invention, the problem that the power resource cannot be quickly and efficiently allocated after the fault node exits is solved, and efficient and stable dynamic balanced power scheduling and system operation are realized.

The invention belongs to the technical field of Internet of Vehicles, and particularly relates to an LTE and WiFi switching method using a deep learning network, which comprises the following steps: acquiring the strength, delay and packet loss rate data of LTE and WiFi signals in real time, judging the geographic position and motion state of equipment in combination with a GPS and an IMU, and selecting an optimal network switching strategy according to the current state by using a deep reinforcement learning algorithm. Switching operation is executed, non-inductive switching is guaranteed, the network state and the switching history are stored, and the DQN model is updated periodically. According to the technical scheme, the vehicle-mounted navigation system and the IMU are combined, the system can recognize a specific scene in advance and pre-judge network switching, through the DQN algorithm, the system has the self-learning capability and can adapt to different environments, double-network concurrent connection is adopted to ensure that a user does not perceive the switching process, the WiFi network is preferentially used to save LTE flow, and instability caused by signal fluctuation is avoided.

The invention discloses a power marketing knowledge graph construction method, system and device and a storage medium, and relates to the technical field of power system data management. At present, integration and application of multi-source heterogeneous data are difficult, data anomaly detection and treatment efficiency is low, and data circulation is unstable. The method comprises the following steps: collecting data of a plurality of sources, and carrying out data cleaning and standardization processing; classifying the data according to structuralization, semi-structuralization and non-structuralization, and forming feature vectors through a feature extraction method; extracting knowledge units from the feature vectors by using natural language processing and machine learning technologies, fusing knowledge from different sources through rules or models, and constructing a unified knowledge base; generating a knowledge graph in a graph database by using a knowledge base, and constructing a graph structure of nodes and edges; and carrying out real-time monitoring on data flow in the knowledge graph, and analyzing path efficiency. According to the technical scheme, the data management efficiency is improved, data circulation is monitored in real time, and the business process is optimized.

The invention relates to a load matching optimization method of a multi-channel gate driver and related equipment thereof. The method comprises the following steps of: acquiring grid current, grid voltage and switching frequency of the multi-channel parallel grid driver to obtain driver load operation data; performing region division processing on the operation space to obtain a safe operation region and a transition region; inputting a first adaptive prediction model for feature extraction to obtain a load balancing prediction result; parallel optimization calculation is carried out on the control strategy to obtain each driving channel control parameter, and each driving channel control parameter comprises a grid driving voltage regulating variable and a driving time sequence regulating variable; performing real-time adjustment on each driving channel control parameter to obtain a load balance adjustment instruction; and performing performance index evaluation calculation to obtain a performance optimization parameter, and updating the first adaptive prediction model to obtain a second adaptive prediction model. According to the invention, through cooperative control of voltage regulation and time sequence compensation, dynamic balance of each channel load is realized.

The invention discloses a multi-modal traffic flow prediction method based on multi-source data feature fusion. The method comprises the following steps: acquiring traffic data; dividing a training set and a test set according to the traffic data; constructing a traffic prediction model, and training the traffic prediction model by adopting the training set; verifying the traffic prediction model by using the test set; the traffic prediction model comprises a cloud image encoder used for carrying out feature extraction on cloud image data to obtain cloud image features; the space-time encoder is used for performing feature extraction on the space-time data to obtain space-time features; the fusion module is used for fusing the cloud image features and the spatial-temporal features to obtain fusion features; and carrying out addition operation on the cloud picture features, the spatial-temporal features and the fusion features, and inputting into a spatial-temporal decoder for prediction to obtain a final prediction result. According to the method, multi-azimuth and multi-dimensional information is fused, redundant information in the spatial-temporal feature extraction process is simplified, efficient and high-precision prediction is achieved, and the method can be widely applied to the technical field of traffic flow prediction.

The invention provides a traditional Chinese medicine abnormal state recognition method, system and device and a storage medium, and relates to the field of abnormal recognition. The method comprises the following steps: acquiring product information of a to-be-detected traditional Chinese medicine, and determining a data acquisition standard of the to-be-detected traditional Chinese medicine based on the product information of the to-be-detected traditional Chinese medicine and a preset detection rule; based on a data acquisition standard of the traditional Chinese medicine to be detected, inputting the image data into a preset image anomaly detection model to obtain a preliminary image anomaly region; performing feature fusion on the visual feature data and the spectral feature data to obtain an abnormal feature vector; and inputting the abnormal feature vector into a preset abnormal state judgment model, outputting abnormal state data, and performing abnormal state judgment based on the abnormal state data and a preset abnormal state judgment threshold to obtain an abnormal state detection result of the to-be-detected traditional Chinese medicine. Through the scheme, the abnormal state of the traditional Chinese medicine can be identified timely and accurately.

The invention relates to the technical field of infrared target detection, in particular to a light-weight real-time infrared target detection method and system for an unmanned ship, and the method comprises the steps: collecting infrared data through an infrared thermal imager carried by the unmanned ship, and carrying out the preprocessing, marking and dividing. And a lightweight GLAF-MobileNetV4 network is adopted as a backbone of the detection method, so that the real-time performance is improved, and model parameters are greatly reduced. Wherein the lightweight global and local adaptive fusion attention can ingeniously integrate global and local information, and local information fusion is adjusted and optimized through dynamic weight. Meanwhile, the designed dynamic adaptive multi-scale feature fusion encoder adaptively adjusts the fusion proportion according to the importance of the multi-scale features by using an attention guidance mechanism. The method is simple and convenient to deploy, improves the target detection precision and real-time performance of the unmanned ship under the low-light condition, and is remarkable in detection efficiency.

The invention belongs to the technical field of magnetic suspension centrifugal compressor control, and particularly relates to a magnetic suspension centrifugal compressor constant flow and constant pressure control method and system.The method comprises the steps that multi-parameter data of a compressor are obtained, and a working condition analysis model is constructed through the multi-parameter data and historical operation data; training and updating the constructed working condition analysis model; adjusting a working condition demand weight according to a judgment result of the working condition analysis model, introducing a prediction control algorithm according to historical data and working condition characteristics, and performing loss compensation through a dynamic response model established by predicting a current change trend; dynamic adjustment and optimization are carried out based on the real-time working condition and the change rate of pressure and flow; and accurately controlling the output power of the compressor through a dynamic optimization result. Dynamic adjustment and optimization are carried out based on the real-time working condition and the change rate of the pressure and the flow, the precision of pressure and flow control is further improved, and adaptability is improved.

The invention aims to provide a hotel price prediction system combining reinforcement learning and a long short-term memory network, and relates to the technical field of neural networks, and the system comprises a data acquisition multi-dimensional time sequence feature extraction unit which is used for acquiring historical operation data of a hotel, carrying out the multi-scale decomposition of a multi-dimensional time sequence through wavelet transform, and obtaining a multi-dimensional time sequence feature extraction unit; obtaining a wavelet coefficient as a multi-dimensional time sequence feature; the long and short-term memory network processing unit is used for inputting the multi-dimensional time sequence features into a long and short-term memory network as input and outputting a hidden state; and the price distribution prediction unit is used for using the price prediction model according to the hidden state, generating predicted price distribution, defining a Markov decision process, using the predicted price distribution as a reinforcement learning model, and generating a final price prediction result according to the predicted price distribution. According to the invention, the precision of price prediction is improved, and the coping capability of the system for market fluctuation is greatly enhanced.

The invention discloses a tracking processing method for a baseband signal of a receiver, which comprises the following steps of: firstly, performing frequency correction through a frequency-locked loop, and performing phase correction through a phase-locked loop to realize accurate synchronization of carrier frequency and phase; then bit synchronization processing and incoherent integration are carried out, and the output signal-to-noise ratio of the loop and the measurement precision of the code phase are improved; and finally, the local pseudo-random code generator generates a local pseudo-random code, then the local pseudo-random code generator outputs three paths of pseudo-random codes which are mutually delayed by half a chip, namely a lead code, a real-time code and a lagging code, correlation values of the three paths of pseudo-random codes are obtained, a code phase error is obtained by calculating the correlation value difference of the lead code and the lagging code, and the local pseudo-random code is obtained. And after the code phase error is processed by a loop filter, an adjustment amount of a digital control oscillator is generated, so that the phase of the local pseudo-random code is adjusted. According to the invention, continuous adjustment of the digital control oscillator of the local carrier and the pseudo-random code is realized, so that correct navigation data information is demodulated for positioning calculation.

The invention belongs to the field of road vehicle control, and relates to a method and a system for judging a braking decision error of a tank truck driver. The system comprises an information acquisition module, a danger perception calculation module, a braking intention extraction module, a braking starting point prediction module, a braking curve prediction module, a liquid oscillation force calculation module and a decision error risk calculation module, and the decision error risk calculation module is used for calculating the risk of the liquid oscillation force based on data provided by the braking curve prediction module and the liquid oscillation force calculation module. Predicting the future dynamic behavior of the vehicle, and calculating the error risk of the next decision of the driver according to the predicted speed and position; according to the system, liquid shaking, driver behaviors and vehicle movement are comprehensively considered, and the problems that in the prior art, dynamic changes of liquid are not fully captured and driver braking decision prediction accuracy is insufficient in the field of tank truck driving decision can be solved by constructing a multi-layer liquid shaking model, a driver braking decision model and a long-time prediction method.

The invention relates to a method for controlling the stability of a transport vehicle. Comprising the following steps that dynamic parameters of the speed, the acceleration, the steering angle and the sideslip angle of a vehicle are collected through a vehicle sensor comprising an accelerometer, a gyroscope and a wheel speed sensor; in combination with various sensors such as a vehicle-mounted radar, a laser radar and a camera, conditions of wet, slippery, rugged and uneven road surfaces are monitored in real time, and road surface friction coefficient parameters are generated through a data fusion algorithm; according to the dynamic state of the vehicle and the real-time road surface state, the stability of the vehicle is evaluated through a mathematical model, and the potential risk of current instability is calculated; a plurality of stability control modes are preset according to different driving scenes including expressways, mountain roads and wet and slippery roads, and different control parameters and algorithms are adopted in each mode; a sensor including a driver state monitoring system inside a vehicle and external environment data including meteorological data and traffic information are utilized to predict operation behaviors including sudden acceleration and sudden braking of a driver.

The invention relates to an adaptive control method, system and device for a switching power supply and a storage medium, and the method comprises the steps: obtaining power supply parameter data in real time, and carrying out the preprocessing of the power supply parameter data, and obtaining standard state data; performing load analysis on the standard state data to obtain real-time load data, and determining a corresponding power supply modulation mode according to the real-time load data; obtaining standard state data in a preset time period, and inputting the standard state data into a pre-trained load prediction model to obtain a load prediction result; and determining a corresponding prediction modulation mode based on the load prediction result, and pre-inputting the prediction modulation mode by adopting a smooth switching algorithm so as to improve the response speed and stability of the system. The invention has the effect of improving the stability of the switching power supply.

The invention belongs to the technical field of underground gas storage, and particularly relates to a long-term efficient hydrogen storage composite lining structure for an underground rock cavern and a preparation method of the long-term efficient hydrogen storage composite lining structure. The long-term efficient hydrogen storage composite lining structure for the underground rock cavern comprises surrounding rock and an internal composite structure of the surrounding rock. The surrounding rock reinforcing layer is formed by pressure pre-grouting in surrounding rock and can resist tensile stress transmitted to the surrounding rock by internal air pressure and enhance air tightness; the permeable layer is arranged on the inner side of the surrounding rock reinforcing layer and can discharge underground water to prevent hydrostatic pressure from causing extrusion deformation damage to the concrete bearing layer during construction and causing instability of the sealing steel lining; the reinforced concrete bearing layer is arranged on the inner side of the permeable layer and the outer side of the slip layer, and micro self-healing capsules capable of repairing cracks and enhancing sealing performance are evenly added in the injection molding process; the anti-drag sliding layer is arranged between the steel lining and the bearing layer and inhibits shear damage of the sealing steel lining to the bearing layer under high internal pressure; the steel lining sealing layer is arranged on the inner side of the sliding layer to bear part of gas load and seal gas.

The invention relates to a fault tracing method and device based on causal analysis, and electronic equipment, and the method comprises the steps: obtaining a to-be-processed data set which comprises a plurality of related variables corresponding to a target fault in a target system when the target fault occurs in the target system, and a data sequence of each related variable in a preset time; performing causal analysis on the to-be-processed data set, and determining a dependent variable having a causal relationship with the target fault in the plurality of related variables; based on a Bayesian network model, an intervention probability corresponding to each dependent variable is determined, and the intervention probability corresponding to each dependent variable is used for representing that the dependent variable is a probability value of a fault cause; and determining a fault reason corresponding to the target fault according to the intervention probability corresponding to each dependent variable. According to the embodiment of the invention, the influence of a hybrid factor and an intermediary variable can be eliminated, the accuracy of a fault tracing result is improved, and the instability of the fault tracing result caused by noise is avoided.

The invention relates to the technical field of intelligent water supply scheduling optimization, and particularly discloses a water supply scheduling method and system based on a multi-objective optimization model, and the method comprises the steps: calculating a water supply suitability score through the comprehensive analysis of water quality, water supply amount and water supply energy consumption data, and dividing the water supply scheduling into suitable scheduling and non-suitable scheduling according to the score; for proper scheduling, continuing to execute the current water supply scheme and reducing the monitoring frequency; for non-suitable scheduling, determining an adjustment interval by analyzing a water quality deviation index, a water supply amount fluctuation index and a water supply energy consumption index in a plurality of monitoring periods, and adjusting water supply data according to the adjustment interval so as to improve a water supply suitability degree score; the reliability and stability of the water supply system can be improved, energy conservation, emission reduction and resource optimization configuration are achieved, and efficient and stable operation of the water supply system is ensured.

The invention relates to the technical field of hydraulic engineering operation prediction, in particular to a hydraulic engineering operation trend prediction method and system. The method comprises the following steps: acquiring multi-source hydraulic engineering data, and generating a hydraulic engineering three-dimensional model through multi-source space fusion processing and three-dimensional model simulation; performing geometric complexity identification and stress path analysis on the three-dimensional model to determine key nodes in the water conservancy project; historical water conservancy data are obtained, time sequence conversion and potential mode mining are carried out, and potential modes related to engineering are obtained; performing stress superposition comparison on the key nodes of the water conservancy project and the potential mode to generate stress superposition comparison data, and iteratively adjusting the key nodes according to a result to obtain adjusted stress nodes; constructing a prediction model based on the adjusted stress node and the potential mode, performing operation trend prediction, and generating an operation trend prediction result of the water conservancy project; the high-precision and high-adaptability water conservancy project operation trend prediction method is realized.

The invention relates to the technical field of robots, in particular to a linear driving device and a robot, the linear driving device comprises a device body, a reduction gearbox and a multi-stage linear transmission section are arranged on the device body, and a transmission mechanism and a synchronous belt mechanism are arranged in the device body; the reduction gearbox can drive the transmission mechanism to rotate; the transmission mechanism can drive the synchronous belt mechanism to advance; the synchronous belt mechanisms comprise the first synchronous belt mechanism and the second synchronous belt mechanism, and the first synchronous belt mechanism and the second synchronous belt mechanism can stretch into the multi-stage linear transmission section, are connected with the multi-stage linear transmission section and can drive the multi-stage linear transmission section to stretch out and draw back. A supporting structure is arranged in the multi-stage linear transmission section, so that sectional type multi-stage arrangement is adopted in a linear part, and stretching and retracting can be realized during working so as to be stored in a very small space; by arranging the first synchronous belt mechanism and the second synchronous belt mechanism which are oppositely arranged, transmission failure caused by unilateral instability can be effectively avoided.

The invention relates to the field of medical equipment, and discloses a medical intelligent multi-layer protective gas conveying system, method and device and electronic equipment, and the system comprises a data collection module which is used for collecting gas flow, pressure, temperature and leakage state information in real time; the path optimization module is used for dynamically optimizing a gas conveying path; the control execution module adjusts the opening degree of the valve and the pumping power according to the optimization result; the feedback updating module feeds back the execution effect after path switching to the optimization model, and dynamically adjusts optimization parameters; the monitoring platform module visually displays the operation state of the system and provides alarming and fault positioning functions under abnormal conditions. Through closed-loop optimization and intelligent regulation and control, the energy consumption and leakage risk are reduced, the gas conveying efficiency and stability are improved, meanwhile, the real-time requirement in a complex medical scene is met, and the system is suitable for conveying scenes of medical gas such as oxygen and anesthetic gas.

The invention discloses an intelligent online detector for a 10kV lightning arrester, and the detector comprises a data collection module which is used for obtaining a DC volt-ampere characteristic curve of the lightning arrester under different test conditions; the data preprocessing module is used for eliminating the linear or nonlinear trend of the volt-ampere characteristic curve and eliminating abnormal values; the piecewise linear fitting module is used for segmenting the volt-ampere characteristic curve and performing linear fitting on data of each segment to obtain linear parameters of each segment; the parameter optimization and separation module is used for optimizing the piecewise linear fitting result, separating the surface leakage current and the valve plate current data correction and calculation module, and performing correction calculation based on the optimized parameters to obtain key electrical parameters; and the state evaluation and early warning module is used for evaluating the operation state of the lightning arrester according to the corrected electrical parameters and providing early warning information. According to the method, instability caused by high-order derivative calculation can be avoided, and the accuracy and stability of an evaluation result are ensured.

The invention provides an ionic liquid additive for a solid-state lithium metal battery electrolyte and a method for preparing the solid-state lithium metal battery electrolyte by using the ionic liquid additive. The bistrifluoromethanesulfonyl imide salt containing terphenyl and lithium carboxylate obtained through Michael addition reaction is captured by open metal sites of the MOF material UiO-66-NH2, the structural stability of the material can be improved, more transmission channels are provided for lithium ions, and therefore the transference number of the lithium ions is increased, the performance of the lithium metal battery is improved, and the service life of the lithium metal battery is prolonged. Especially in the aspect of charge and discharge rate. The electrolyte material disclosed by the invention has good stability with lithium metal, so that electrolyte decomposition can be prevented, and an unstable phenomenon, such as lithium dendrite growth, on the surface of an electrode can be avoided, so that a lithium metal battery can show excellent electrochemical performance, including better cycling stability, high coulombic efficiency and longer battery life.

The invention relates to the field of flight simulation, and discloses a multi-degree-of-freedom motion control method for flight driving rocker and accelerator simulation, which comprises the following steps: receiving attitude data from flight simulation software in real time, constructing a rotation matrix based on the attitude data, and calculating displacement data of platform angular points; the target stroke of the linear electric cylinders is calculated in combination with the displacement data, and the target stroke is optimized, so that movement of the multiple electric cylinders is coordinated, and the mechanical stroke range of the electric cylinders is met; meanwhile, dynamic feedback data including vibration feedback and damping feedback are generated based on the flight environment parameters, the dynamic feedback data are superposed into the control instruction, and an electric cylinder is driven to adjust the attitude of the platform; the platform motion state is detected and updated in real time through a closed-loop control system, and it is ensured that platform motion and a flight simulation software scene are highly synchronized. The system has the advantages of high-precision attitude control, real-time dynamic response and good system stability, can adapt to various flight simulation scenes, and provides real flight simulation experience.

The invention provides a method for regulating and controlling technical parameters in a continuous preparation process of a filling material, and relates to the technical field of mine filling automatic control. According to the method, aiming at the condition that the material level of the stirrer is in a safety interval, pumping pressure-stirrer material level double-rate interval control is adopted, that is, the material level of the stirrer is maintained in a safety range required by a process, and meanwhile, the pumping pressure for conveying a filling material required by underground filling is ensured; after the pumping pressure and the material level of the stirrer are both stable, aiming at the condition that the concentration of the slurry pump is not in a set concentration range, adopting concentration fuzzy PID control, and adjusting the concentration of the slurry pump until the concentration is stable in the set range; aiming at the conditions that the concentration of a slurry pump is in a set concentration range and the frequency of a stirrer is in a normal operation range, torque fuzzy PID control is adopted, the frequency of the stirrer is adjusted, and energy conservation and emission reduction are achieved on the premise that the shear stress of a filling material is stabilized.

The invention discloses a vertical lathe and a using method for furnace pipe machining, and relates to lathes, the vertical lathe comprises a machining table, a turning head is arranged on the machining table through a longitudinal moving assembly, two machining stations are arranged on the machining table, and the longitudinal moving assembly can drive the turning head to move between the two machining stations; the two machining stations are each provided with a machining assembly, and each machining assembly comprises a clamping table, a fixing unit, an overturning unit and a rotating unit. Through overturning of the clamping tables, chippings on the clamping tables fall and cannot be accumulated on the clamping tables, through the two clamping tables and the movable turning head, the machining efficiency of the turning head cannot be reduced when the chippings are cleaned by the clamping tables, and when the turning head conducts machining on one clamping table, the supporting assembly supports the clamping tables, so that the machining efficiency of the turning head is improved. The clamping table capable of being overturned is prevented from being unstable, the stability is improved, meanwhile, the overturned clamping table is cleaned through the cleaning brush, the cleaning effect on the clamping table is better, and chippings are prevented from affecting part machining.

The invention relates to the technical field of data processing, in particular to a coating equipment fault real-time early warning method, which comprises the following steps of: acquiring pressure data and vibration data, acquiring instability of each pressure data according to surrounding data of each pressure data, and determining whether each pressure data is unstable or not according to the instability of each pressure data. Obtaining a characteristic value of each piece of pressure data, obtaining the optimal degree of each clustering result according to the characteristic value of each piece of pressure data and the correlation of the abnormal degree of each piece of pressure data in each clustering result, obtaining the optimal number of midpoints, obtaining the optimal clustering result, and obtaining the optimal clustering result according to the optimal clustering result. The final anomaly degree of each piece of pressure data is obtained, then the fault of the coating equipment is judged, and the anomaly detection accuracy is improved.

The invention relates to an aqueous alkaline zinc-iodine battery and a preparation method thereof, and belongs to the technical field of aqueous zinc-iodine batteries. The battery includes: a positive electrode; a negative electrode; and an aqueous alkaline electrolyte; wherein the positive electrode comprises a current collector, a positive electrode active material, a conductive agent and an adhesive, and the positive electrode active material is iodide; the aqueous alkaline electrolyte comprises a chlorine-containing additive, a zinc salt electrolyte, an alkaline inorganic salt and water. The aqueous alkaline zinc-iodine battery provided by the invention is low in cost and simple to manufacture. According to the aqueous alkaline zinc-iodine battery provided by the invention, the iodide is adopted as the positive electrode active material, so that instability and potential safety hazards caused by taking an iodine elementary substance as the positive electrode at present are avoided. The battery disclosed by the invention releases a high-potential discharge platform at 1.65-1.68 V, the discharge capacity reaches 372-385 mAh.g <-1 >, and the battery shows rapid oxidation-reduction kinetics and excellent long-acting cycle stability.

The invention discloses a batch task optimization method based on dynamic priority scheduling. The method comprises the steps of creating tasks, setting priority information of the tasks, adding the tasks into a scheduling queue of a task execution system, sorting the tasks according to an initial priority, scanning all the tasks in the scheduling queue at a set first time interval so as to obtain the priority of each task, performing resource data acquisition on the task execution system at a set second time interval to obtain a resource utilization rate of the task execution system, judging whether the resource utilization rate is lower than a corresponding safety threshold or not, if the resource utilization rate is lower than the corresponding safety threshold, controlling tasks with high initial priorities to be executed preferentially, and otherwise, controlling tasks with high initial priorities to be executed preferentially. And adjusting the initial priority of the task. The task sequence is dynamically adjusted according to the priorities and resource conditions of the tasks, so that the tasks with high priorities are processed preferentially, and the overall task processing efficiency and response speed are improved.

The invention provides a pressure test control method and system for a universal pressure testing machine, and relates to the technical field of material testing machines, and the method comprises the steps: obtaining hydraulic cylinder parameters and servo valve parameters, building a pressure control mathematical model, calculating a transfer function, initializing a feedback controller, determining the number of hidden layer nodes, and setting an activation function and a network weight. A self-adaptive feedback controller is obtained; generating a closed-loop control system, constructing an objective function based on an integral absolute error and adjustment time, determining a Pareto optimal solution set in combination with a multi-objective genetic algorithm, selecting a neural network training sample, generating a training sample set, performing offline pre-training, and updating a network weight to obtain a pre-trained neural network; and the feedback controller is embedded into a pressure control system, outputs feedback controller parameters, sets an anti-saturation strategy of the feedback controller, judges whether the feedback controller is saturated or not, limits the amplitude of an integral item according to the relation between an error signal and a saturation direction, determines whether to stop integration or not, and generates a press control strategy under the current working condition.

The utility model relates to the technical field of robot magnetic bases, and discloses a magnetic attraction base for fixing a collaborative robot, which comprises a table top, a plurality of positioning plates are fixedly connected to the periphery of the bottom of the table top, magnets are fixedly connected to the outer sides of the positioning plates, and second bevel gears are rotatably connected to the tops of the magnets. And the periphery of the outer wall of the table top is rotationally connected with first bevel gears, the first bevel gears are connected with the second bevel gears in a meshed mode, the top of the outer wall of the table top is rotationally connected with a bevel gear ring, and the bevel gear ring is connected with the first bevel gears in a meshed mode. According to the robot fixing device, through cooperation of the conical gear ring, the first conical gear and the second conical gear, instability during fixing and dismounting at inclined positions such as a wall surface can be prevented, and through cooperation of the worm, the turbine and the U-shaped block, robots of different specifications and sizes can be fixed.