2353results about How to "Improve controllability" patented technology

The invention relates to the technical field of monitoring and early warning of power equipment, in particular to a monitoring and early warning method and system for substation power equipment. Comprising a data acquisition module, an intelligent diagnosis module, a risk assessment module, a collaborative early warning module and an interaction control module. The data acquisition module acquires equipment operation data including current, voltage, temperature and humidity and vibration signals. And the intelligent diagnosis module identifies equipment abnormity through the generative adversarial network and the graph neural network. The risk assessment module generates a risk assessment curve based on a fractional order differential model and a reinforcement learning algorithm. And the collaborative early warning module generates a graded early warning scheme by utilizing edge calculation. And the interaction control module displays a state and optimizes a control strategy through a human-computer interface. The intelligent monitoring and management level of the power equipment of the transformer substation is improved, the service life of the equipment is prolonged, and the operation and maintenance cost is reduced.

The invention discloses a data quality monitoring method and system based on a knowledge graph. According to the method, data tables and fields in multi-source heterogeneous data are constructed into graph nodes, graph edges are constructed through main and foreign key constraint relationships and field value overlapping analysis, and a data blood relationship knowledge graph is formed; business rules are coded into the atlas to execute data constraint check, and an abnormal data influence range is traversed and tracked through a directed graph; and dynamic monitoring is realized based on the time change of the abnormal score sequence. The invention further provides system implementation including data acquisition and preprocessing, knowledge graph construction, quality monitoring, problem diagnosis and quality evaluation. The method solves the problems that a traditional method is difficult to track the influence range of abnormal data and lacks systematic evaluation, and improves the automation level and accuracy of data quality management.

The invention discloses a link reliability tracking and optimizing method combined with real-time monitoring, and relates to the technical field of data transmission. Comprising the following steps: constructing a topological graph of a full link based on component information and a data flow relationship of a data medium station, and deploying a data acquisition probe to acquire link data in real time; a potential fault or an abnormal link is automatically identified; based on the data map, sorting the data dependency relationship of the upstream and downstream of the link, recording the relevance and dependency path of each component, and forming an accurate link blood relationship map; link abnormal conditions are captured and backtracked, log information is summarized in a unified mode, and centralized analysis and problem positioning are carried out; and on the basis of operation historical data of the link, according to the time sequence prediction model, identifying a bottleneck and a high-risk area in the link, dynamically adjusting a transmission strategy of the link, and optimizing load balance and a link priority of a station in the data in real time. According to the invention, the influence on the service when the fault occurs is reduced, and the abnormal problem positioning efficiency is improved.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention discloses a rock tunnel intelligent supporting system and method adaptive to geological conditions, and relates to the technical field of tunnel construction automation and intellectualization. The system comprises an intelligent advanced geological forecast module, an intelligent surrounding rock quality evaluation system, a self-adaptive support design optimization module, an intelligent construction equipment monitoring system, an environment monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system and an integrated control system. Through real-time monitoring and analysis of the intelligent advanced geological forecast module and the safety early warning system, potential geological risks and safety threats in the tunnel construction process can be found in time. And through an adaptive support design optimization algorithm, support parameters and a construction scheme can be dynamically adjusted according to real-time geological data and a surrounding rock quality evaluation result. The integrated control system serves as the core of the whole intelligent supporting system, and unified management and coordination of all the sub-modules are achieved.

The invention relates to the technical field of process data optimization, and discloses a traditional Chinese medicine extraction process optimization method and system based on dynamic countercurrent extraction and a medium. The method comprises the following steps: carrying out online monitoring data processing on a traditional Chinese medicinal material digital feature data set and a dynamic countercurrent extraction parameter initial data set to obtain a countercurrent extraction process real-time monitoring data stream and parameter associated data, and carrying out multi-dimensional parameter optimization processing; a dynamic countercurrent extraction process optimization parameter group is obtained and is analyzed and processed in real time, and an automatic parameter regulation and control instruction sequence and process abnormity early warning data are obtained; and performing quality characteristic data processing on the extract after execution of the automatic parameter regulation and control instruction sequence to obtain a process quality evaluation data set and a process tracing data set, and performing association analysis processing to obtain a dynamic countercurrent extraction process parameter optimization rule base. According to the invention, intelligent monitoring and automatic parameter optimization of the extraction process are realized, so that the accuracy and controllability of the traditional Chinese medicine extraction process are improved.

The invention belongs to the technical field of path planning, and particularly relates to a robot path planning method based on an RRT improved algorithm, and the method comprises the steps: obtaining environment space information needing path planning, defining parameters, and obtaining the minimum distance between a coordinate point and an obstacle; a bidirectional random tree is constructed and initialized, and sampling points are obtained through a sampling function; finding a nearest neighbor point, selecting a new node, carrying out safety distance judgment, and if not, carrying out sampling again; generating a discretization path; setting a cost function during local node expansion; performing loop iteration to generate discrete point cost, taking a minimum cost value, searching an optimal node and inserting the optimal node into the current tree; an overall path Path after local optimization is obtained; and discretizing the whole Path, setting a cost function of nodes during global planning, selecting the node with the minimum cost for reconnection, and finally obtaining a smooth and effective optimal path. According to the method, the optimality and smoothness of the path are effectively improved.

The dynamic hot runner temperature control method suitable for the mold comprises the steps that S1, a temperature sensor is installed in a mold hot runner to collect flow characteristics, heat distribution and temperature original data in the material solidification process, and the temperature original data are transmitted to a temperature control system; s2, the required power adjustment amount Pc is calculated through a PID controller in the temperature control system according to the deviation value delta T between the actual temperature Ta and the set target temperature Ts, and the power adjustment amount Pc is sent to a heater for adjustment; s3, constructing a temperature prediction model M, and predicting the temperature change trend of each section of the mold hot runner through the temperature prediction model M; and S4, a multi-objective optimization function Ft is created, an optimal combination solution St is searched through a particle swarm optimization algorithm PSO, and the optimal set value combination of the hot runner temperature is maintained according to an optimization objective. According to the invention, the energy consumption is reduced while uniform temperature distribution is ensured, the temperature control precision and the response speed are obviously improved, and the requirement of human intervention is reduced.

The invention discloses an image generation method based on a mixed quantum classical condition generative adversarial network. The method comprises the following steps: firstly, converting a random condition label and a sample real condition label into one-hot codes, processing input noise and the random condition label by using a quantum circuit, performing measurement to convert a characteristic quantum state into a classical characteristic vector, and obtaining generated image data from the characteristic vector through a classical single-layer neural network. And transmitting the sample real image data and the real condition label into a discriminator to obtain a real image discrimination result, and transmitting the generated image data and the random condition label into the discriminator to obtain a generated image data discrimination result. And calculating discrimination loss based on the two discrimination results, training a discriminator and a quantum condition generator, obtaining a trained mixed quantum classical condition generative adversarial network, and generating a condition image. Based on the above scheme, the problems of unstable classical GAN training and many training parameters are solved, the limitation of the quantum GAN in a condition generation task is broken through, and the application of the quantum generative adversarial network in scenes such as image generation and pattern recognition in the field of real artificial intelligence is further promoted.

The invention relates to the technical field of wireless communication testing, and particularly discloses a Bluetooth headset anti-interference performance testing system which comprises an interference signal generation module, an interference signal dynamic simulation module, a signal stability monitoring and evaluation module and an anti-interference capability identification module. An interference signal comprising a modulation signal, a cross-band noise signal and a dynamic radio frequency signal is generated and injected into a test environment, and a real interference scene is simulated by using dynamic conversion of signal strength and frequency; the monitoring module obtains the intensity and frequency characteristics of the interference signal in real time, and comprehensively evaluates the signal stability based on an intensity abnormal coefficient and a frequency abnormal coefficient; and the anti-interference capability identification module calculates an anti-interference capability coefficient by using a support vector machine in combination with performance data of the Bluetooth earphone under different interference levels, and realizes graded evaluation of earphone performance.

The invention discloses a TCBL wind field high-resolution LES simulation method facing gray area parameterization, a medium and a program product. According to the method, a TCBL idealized initial gradient wind field is constructed based on a modified Rankine vortex model, controllability and stability of initial conditions are ensured by specifying key kinetic parameters, and thermodynamic profiles under different wind speed conditions are obtained based on synthetic analysis of tropical cyclone sounding data. And correcting the bottom of the boundary layer by adopting a quadratic and logarithmic function fitting method. Through high-resolution grid configuration and dynamic boundary conditions, secondary grid turbulence is processed in combination with an SGS-TKE closed model, and adaptability of the model to strong rotation and high wind shear environments is optimized by adopting a dynamic correction mechanism. Besides, the stability of thermodynamic variables is maintained through large-scale tendency correction, and high-resolution three-dimensional wind field and turbulence statistical reference data are output and used for verifying and improving a turbulence parameterization scheme of the TCBL under gray region resolution.

The invention discloses an advertisement budget distribution method and system based on machine learning and user behaviors, and the method comprises the following steps: collecting the historical behavior data of a user, and extracting the feature data of the historical behavior data through feature engineering; constructing a user probability prediction model, and training the model by using the feature data; in an advertisement putting process, collecting real-time behavior data of a user, inputting the real-time behavior data into the trained model, and predicting an advertisement clicking probability of the user and an advertisement commodity purchasing probability of the user; a fusion model is constructed, the fusion model calculates the conversion rate of the user according to the advertisement clicking probability of the user and the advertisement commodity purchasing probability of the user, and calculates the channel conversion rate according to the putting channel; distributing a preliminary advertisement budget according to the historical data of the putting channel; and dynamically adjusting the preliminary advertisement budget of the putting channel based on the channel conversion rate. According to the invention, precise distribution of the advertisement budget can be realized, and the overall conversion effect of the advertisement is improved.

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

The invention relates to the technical field of plant extraction, in particular to an intelligent plant extraction control method and system, and the method comprises the following steps: based on historic plant extraction data, including temperature, pressure and pH value during extraction, analyzing the long-term trend of parameters, predicting the requirements of plant extraction conditions by simulating parameter reactions under various conditions, and obtaining a plant extraction result; and obtaining a parameter prediction result. According to the method, long-term trend prediction of parameters such as temperature, pressure and pH value is realized through analysis of historically recorded plant extraction data, so that the parameters of extraction equipment are adjusted in advance, changes of different plant materials and environmental conditions are effectively adapted, the extraction efficiency is optimized, and by dynamically simulating the expression of the extraction efficiency in various time periods, the extraction efficiency is improved. According to the innovative scheme, an optimal extraction time window is mined, resource consumption and extraction time in the extraction process are reduced, and meanwhile the quality and consistency of extracts are kept.

The invention relates to the technical field of medium and small span bridge construction and control, in particular to a precast beam size matching method and system based on a three-dimensional laser scanner, and the method comprises the steps: obtaining precast beam point cloud data, carrying out the preprocessing of the precast beam point cloud data, carrying out the registration of the preprocessed precast beam point cloud data with a BIM model, and obtaining the size of a precast beam. The automatic detection of the structure size is realized; then, by obtaining interface angular points of the precast beams, conducting virtual splicing by aligning the cross section angular points of the adjacent precast beams, simulating the field splicing process and obtaining key construction details, accurate and reliable data support is provided for follow-up construction, it is ensured that each beam section can conform to design specifications during splicing, and therefore potential safety hazards are reduced; the controllability and scientificity of the construction process are improved, the construction efficiency is improved, the construction period is shortened, the service life of the bridge structure is prolonged, and the structural reliability is improved. The problems of low precast beam parameter detection efficiency, high operation difficulty and low accuracy in the prior art are solved.

The invention relates to the technical field of tourist route recommendation, in particular to a tourist route recommendation system and method based on big data. The method comprises the following steps: collecting travel demand data and basic sign information of a user, and performing hierarchical analysis on a demand to obtain master / slave demand data; then, through demand behavior intersection analysis, muscle soreness intensity is simulated in combination with user physical sign data, and the physical degeneration rate is further estimated; based on the physical decline rate, making a rest strategy, and optimizing distribution and adjustment of tourist routes; and finally, transmitting the adjusted tourist route data to a user terminal. According to the invention, the tourist route recommendation technology is improved, so that the tourist route recommendation technology is more perfect.

The invention provides a semiconductor equipment data acquisition method and a semiconductor equipment data acquisition system supporting standardized communication transformation, which are used for realizing data acquisition and process control on different types of semiconductor equipment, formulating corresponding communication rules for equipment conforming to a communication standard, and converting an equipment communication protocol by a cluster system so as to match a standard interface. For equipment which does not completely meet the standard but can realize communication through programming, the trunking system directly acquires required data and converts the required data to meet the standard interface specification. For equipment without communication capability, data is acquired through an equipment interface by adopting an image recognition technology, data input and function operation are realized through simulating keyboard and mouse operation, and finally, protocol conversion from data to a standard interface is completed by a cluster equipment control system. And the equipment data is converted into a message data packet conforming to a standard semiconductor equipment communication interface, so that docking with a semiconductor equipment data analysis and process control system is realized, and the production efficiency and quality of the semiconductor equipment are improved.

The invention discloses a nail gun and a control method thereof, and the nail gun comprises a shell; the motor is arranged in the shell; the percussion assembly is arranged in a nailing period, can move from an initial position to a percussion position so as to knock out a nail to a workpiece, and can move from the percussion position to the initial position; the parameter detection unit is at least connected with the motor and at least detects the number of rotation turns of the motor; the controller is at least connected with the parameter detection unit; the controller is configured to control the motor to operate at a constant rotating speed when the number of rotation turns is larger than or equal to a first turn number threshold value and smaller than a second turn number threshold value.

The invention discloses a ship navigation risk assessment and decision-making method and system, a medium and a product. The method comprises the following steps: acquiring ship navigation parameters, and meteorological data and ocean data within a future preset duration; determining a meteorological influence value and an ocean influence value according to the meteorological data and the ocean data; inputting the sailing speed value, the load tonnage value, the meteorological influence value and the ocean influence value into a preset sailing risk assessment function to obtain a sailing risk value in a future preset duration; when the navigation risk value exceeds a preset risk threshold value, searching a reachable port; determining harbor information of reachable harbors according to a preset harbor database; determining an optimal reachable port based on the wind sheltering condition, the berth state and the geographic position; calculating an optimal wind-sheltering path between the ship and the optimal reachable port and the sailing duration and sailing fuel consumption of the optimal wind-sheltering path; and visually displaying the optimal wind sheltering path, the sailing duration and the sailing fuel consumption. By implementing the method, the ship navigation decision making efficiency is improved.

The invention discloses a middle and low voltage distribution network cooperative regulation and control method and device based on reliability evaluation, and belongs to the technical field of power system optimization. According to the method, a zone area in the low-voltage power distribution network is projected into a feasible space to obtain a feasible region, flexible resources such as distributed photovoltaic, electric heating loads and distributed energy storage of the zone area of the low-voltage power distribution network are projected and aggregated into constraint conditions of virtual load nodes in the medium-voltage power distribution network, and a medium-voltage power distribution network model and a corresponding objective function are constructed based on the constraint conditions. And obtaining a switching action sequence corresponding to the medium-voltage power distribution network after solving the objective function. According to the method, a medium and low voltage power distribution network cooperative reliability improvement strategy considering the virtual load nodes is provided, all flexible resources of a low voltage power distribution network zone area are represented through the virtual load nodes, and the variable dimension and the calculation complexity of medium and low voltage power distribution network reliability evaluation are reduced; the linkage between the flexible resources of the transformer area and the medium-voltage power distribution network switch is realized, and the reliability of the medium-low voltage power distribution network is effectively improved.

The invention discloses a precise advertisement putting optimization method and system, and relates to the technical field of advertisement putting, and the method comprises the following steps: collecting and recording key parameter information of different data sources in a merging process in real time, providing basic data for the subsequent anomaly detection, and providing an optimization result for guaranteeing the comprehensiveness of the data; recording each step of operation in all merging processes; and carrying out anomaly analysis on the key parameter information subjected to the standardized processing, and identifying abnormal data generated in the merging process by analyzing multi-dimensional information. According to the invention, by monitoring the cross-session persistence information and the platform situation consistency information in real time, the system can effectively prevent the problem of data merging errors or segmentation, improve the accuracy of user portraits, and accurately push advertisement contents, thereby improving the click rate and the conversion rate. A multi-level anomaly detection and grading processing mechanism ensures that risks are timely handled, the safety of data integration and the stability of advertisement putting are improved, and the data integrity is guaranteed.

The numerical control machine tool base casting machining equipment comprises a forming mold and a casting ladle mechanism, and the forming mold comprises a mold body, a buckling mechanism, an exhaust mechanism and an air extraction mechanism; the casting ladle mechanism comprises a hoisting mechanism, a casting ladle body and a casting ladle driving mechanism. On the basis that casting machining is not affected, gas generated by thermal decomposition of organic matter in coated sand can be effectively and efficiently exhausted, and therefore the defects of air holes, pits and the like of castings are prevented; and the controllability of casting construction can be improved, so that the safety risk of casting construction is reduced.

The invention provides a music generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a text prompt word transmitted by a user, and enabling the text prompt word to be used for determining a music element of to-be-generated music; processing the text cue word by adopting a music score generation model to generate an initial music score; modifying the initial music score according to the modification opinion transmitted by the user until no modification opinion is generated, and obtaining a target music score; and processing the target music score by adopting a music generation model to generate target music. According to the invention, the controllability of the generated music is improved.

The invention discloses a blue-green algae growth prediction method and an intelligent regulation and control device, and relates to the field of blue-green algae growth prediction and regulation and control, and the method comprises the steps: carrying out the collection of blue-green algae growth related data, data preprocessing, random forest model construction, SVM model construction, and model weight fusion of a final random forest prediction model and a final SVM prediction model to obtain a fusion model. And dynamically optimizing the fusion model, finally obtaining the dynamically optimized fusion model, obtaining a real-time prediction result according to the dynamically optimized fusion model by using the blue-green algae growth related data collected by the sensor module in real time, and adjusting the blue-green algae growth culture conditions in real time. According to the method, the advantages of the random forest model and the SVM model are combined, a fusion model with dynamic optimization and real-time adjustment functions is developed, and the accuracy and adaptability of blue-green algae growth prediction are remarkably improved. And meanwhile, data acquisition, processing and prediction modules are integrated, so that the accuracy of blue-green algae growth prediction and the automation degree of the system are remarkably improved.

The embodiment of the invention provides a role playing model training method, and the method comprises the steps: obtaining a dialogue data set and a role setting set of a plurality of roles, and constructing a plurality of groups of training data based on the dialogue data set and the role setting set of the plurality of roles; on the basis of the multiple groups of training data, performing supervision and fine tuning on a pre-trained base model to obtain an initial role playing model; and obtaining a preference data set, and performing preference optimization on the initial role playing model based on the preference data set to obtain a target role playing model. The training method provided by the embodiment of the invention is carried out in stages, and different training samples are constructed in each stage to optimize the base model, so that the model can be aligned with role playing tasks, human preferences and value views, and finally a role playing model with excellent performance in the aspects of dialogue ability, role consistency, role playing attraction and the like is obtained.

The invention relates to a preparation method of a three-dimensional ferrocobalt metal oxide oxygen evolution catalyst. According to the method, foamed nickel is selected as a carrier, the foamed nickel is pretreated, a metal ion and ethanol mixed solution is evenly adsorbed to the surface of the foamed nickel, then open fire ignition is carried out, and a second-level self-limiting combustion process is carried out, so that metal ions form a ferrocobalt metal oxide material with a three-dimensional network structure in situ on the surface of the foamed nickel. The method is simple and easy to implement, a target product can be generated within second-level time, and the area of a foamed nickel matrix is adjusted according to needs, so that the three-dimensional ferrocobalt metal oxide catalyst is prepared on a large scale. The material obtained by the invention is used as an oxygen evolution anode catalyst, and the three-dimensional network-shaped ferrocobalt metal oxide oxygen evolution catalyst shows excellent electrochemical performance.

The invention discloses a mask generation and image decoupling diffusion three-dimensional medical image generation method, relates to the technical field of image processing and analysis, and solves the problems of poor generalization ability, low calculation efficiency and high dependence on training data during image conversion in the prior art. The method comprises the following steps: acquiring a mask image, and processing the mask image by using a multi-condition diffusion probability model to obtain a multi-label mask sequence; screening the three-dimensional medical image data according to a screening condition, determining an informed slice set, and inputting the multi-label mask sequence and the informed slice set into a volume diffusion generation model to obtain a three-dimensional composite image; random noise is added to the three-dimensional composite image, a diffusion semantic refinement generation model is utilized, the three-dimensional composite image added with the random noise is refined, and a refined three-dimensional image is obtained; according to the invention, data decoupling and model generation volume consistency are realized, and a three-dimensional medical image with higher quality can be generated.

The invention relates to the technical field of artificial intelligence, and discloses a multi-modal model training fine-tuning optimization method based on artificial intelligence, which comprises the following specific steps: preparing and preprocessing data, collecting a large-scale multi-modal data set, constructing an image text data set containing one million high-quality images and text description accurately corresponding to the images, and performing fine-tuning optimization on the image text data set. According to the method, data are subjected to comprehensive and meticulous cleaning operation, noise data and mistakenly labeled data in the data are strictly removed, so that the accuracy and reliability of the data are ensured, and the effective utilization rate of the data can be increased by at least 20% through data cleaning; wherein the image data is screened, blurred, low-resolution and unclear-content images are removed, it is ensured that each image can clearly convey specific visual information, and text description, grammar error checking, spelling error and semantic ambiguity parts are corrected or deleted.

The invention relates to the technical field of annealing furnaces, in particular to a swirl injection type energy-saving annealing furnace. The device comprises outer shell plates and an air duct, the outer shell plates are spliced to form a hearth, the air duct is arranged in the outer shell plates to form an air path for convective circulation, the air duct is connected with a circulating fan, and air in the air duct and an inner cavity of the hearth is driven by the circulating fan to actively convectively circulate. The air direction of air convection circulation of the air channel faces the side face and the lower portion of the workpiece needing annealing, and a heat preservation layer is attached to the inner surface of the outer shell plate. The shell plate is used for supporting a main body structure of the furnace body, the air duct is used for achieving circulation convection of air flow, the annealing efficiency and the annealing quality of the metal coiled materials are improved through circulation convection of high-temperature air flow, the heat preservation layer is used for storing internal heat, reducing energy leakage and effectively reducing loss, and the circulation fan is used for providing power for circulation convection of the air flow.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.