106results about How to "Improve forecast" patented technology

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention provides a security risk assessment method and system for digital assets, and the method comprises the steps: obtaining a unique identifier and an ownership record of the digital assets, and determining key points of risk assessment, the key points comprising encrypted storage, transaction history and a circulation mechanism of the digital assets; for each key point, identifying identity authentication, access control and data encryption security factors in the key point, and performing security compliance verification on privacy protection and compliance requirements of the digital assets by adopting a probability statistical method; generating a plurality of digital asset cross-chain compatibility and value evaluation factor combinations through a Monte Carlo simulation method, simulating security events and consequences thereof in different scenes, and calculating the occurrence probability and loss influence of each scene; and according to the optimized risk assessment model, assessing the overall security risk of the digital assets, identifying the key link with the highest risk, and assessing the potential loss influence of the key link.

The invention discloses a low earth orbit satellite Internet of Things communication method and system based on a dynamic block chain, and the method specifically comprises the steps: taking at least one ground station as a consensus node in each network partition, forming a consensus network, and selecting a main node from a plurality of consensus nodes of each consensus network through employing an RBFT algorithm; a satellite transit algorithm is adopted to predict transit information of each low-orbit satellite, and the low-orbit satellites passing through any consensus network are set as accounting nodes of the consensus network according to the transit information; the method comprises the following steps: acquiring a service request of a satellite communication terminal, forwarding the service request to a main node of a consensus network to which the service request belongs through a low-orbit satellite of a transit satellite communication terminal, performing verification and priority ranking on the service request according to the main node, and if the verification is passed, performing broadcast and consensus verification on all nodes of the current consensus network. According to the invention, the communication efficiency and safety of the low-orbit satellite Internet of Things are improved, and reliable technical support is provided for satellite Internet application.

The invention relates to the technical field of data processing, and discloses a CNN-BiLSTM-MATT time sequence prediction method, which comprises a plurality of feature value inputs and a feature value output. Carrying out data normalization processing; performing normalization processing on the input data, mapping a normalized data set between [-1, 1], and performing reverse normalization processing on the output data after prediction; the hyper-parameters in CNN-BiLSTM-MATT are optimized, the hyper-parameters in CNN-BiLSTM are initialized according to an IHO algorithm, the hyper-parameters comprise the learning rate, the L2 regularization coefficient and the hidden layer number, and the optimal hyper-parameter value is obtained in the iteration process; performing feature extraction: performing convolutional feature extraction by using a convolutional neural network CNN, and taking obtained data features as input of subsequent BiLSTM; and carrying out time sequence analysis. The method overcomes the defects of a current time sequence prediction method. The method can be widely applied to the parameter optimization problem, the convergence degree is improved, and the optimal solution is efficiently found.

The invention discloses a multi-time-sequence three-dimensional visualization expression method for flood routing, and the method comprises the steps: analyzing NetCDF data of a two-dimensional hydrodynamic model, building an element index library and a space library, and storing the indexes of water level, water depth, flow velocity and the like and the space information of an irregular triangular mesh. And generating an association relationship between the grid points and the element indexes, and creating a time sequence cache attribute table and a spatial data set. And reading three-dimensional point space data and element index data, constructing a three-dimensional space grid, and performing range cutting by using a two-dimensional surface data set. And generating a time sequence service conforming to a three-dimensional model tile data format by adopting a thread pool technology. And loading a three-dimensional service and a terrain service at a browser end, carrying out color and height rendering based on the element values, and dynamically displaying the flood process. And a timer is arranged to realize three-dimensional visual playing moment by moment. The method has the advantages that the dynamic display capability of flood routing is effectively improved, and efficient three-dimensional visual rendering and real-time playing are supported.

The invention discloses a shield tunnel risk early warning method and system based on digital twinning and federated learning, and the method comprises the steps: a shield tunnel construction side client serves as an edge calculation terminal, and obtains basic parameters; a first shield digital twinning system, a second shield digital twinning system and a third shield digital twinning system are correspondingly constructed by the shield machine producer client side, the shield tunnel construction side client side and the third-party client side according to the basic parameters; a shield construction risk early warning model is arranged in each shield digital twin system; the shield machine production side client, the shield tunnel construction side client and the third side client are all participants, and each participant sends model parameters of the respective shield construction risk early warning model to the central server; the central server is used for averaging the parameters of the three models and issuing the average value of the parameters to each participant; and each participant updates the model of the participant according to the average value of the parameters, and realizes shield construction risk early warning based on the updated model.

The invention relates to the technical field of charging load prediction, in particular to an electric vehicle charging load prediction method and system combining sequence decomposition and feature extraction and a storage medium. Comprising the steps of performing variational mode decomposition on time sequence load data to obtain a plurality of intrinsic mode functions; performing zero padding expansion on each intrinsic mode function, and then converting the intrinsic mode function to a frequency domain; enhancing the frequency domain data through a multi-layer perceptron based on complex domain calculation; constructing a prediction model based on a complex field attention module and a residual module, inputting the enhanced spectrum data into the prediction model, extracting a data characteristic frequency combination through the complex field attention module and the residual module, and carrying out weighted summation on all intrinsic mode functions as the output of the prediction model; and calculating a difference value between real data and prediction data according to a mean square error of a time sequence and an average absolute value error of a frequency spectrum to obtain time domain and frequency domain mixed loss, and updating prediction model parameters. According to the invention, the problem of large prediction error under the influence of random load fluctuation is solved.

The invention discloses a laser engraving machine temperature compensation control system based on multi-source temperature data fusion, and the system comprises a temperature collection module which comprises a plurality of temperature sensors disposed at key parts of a laser engraving machine and is used for obtaining the overall temperature field distribution data; the algorithm module comprises an improved Kalman filtering algorithm used for eliminating temperature data noise; the multi-point weighted fusion algorithm is used for carrying out spatial distribution fusion calculation on the multi-point temperature data; the thermal deformation prediction algorithm integrates heat conduction, natural convection and radiation heat exchange models and is used for predicting the temperature change of the whole system; a self-adaptive compensation algorithm is adopted, and PID parameters are dynamically adjusted according to real-time error feedback; and the compensation execution module is used for performing real-time position compensation on the laser engraving machine according to the compensation amount output by the algorithm module.

The invention discloses a mechanism-data cooperative driven carbon dioxide mineralization process inversion method and system, and the method comprises the steps: determining a mineralization storage target region, and taking a mineralization process real-time monitoring data signal as input data based on an ultrasonic phased array monitoring system, an acoustic emission monitoring system and an optical fiber micro-nano multi-parameter co-acquisition monitoring system; and mineralization process physical field models such as a mineralization process fracture field, a seepage field and a temperature field are used as output data. Using simulation software to obtain a mineralization process physical field model to further supplement input and output data; a mineralization process fusion inversion model is constructed by using an LSTM neural network, and physical fields of a fracture field, a seepage field and a temperature field in the mineralization process are obtained through fusion inversion output by inputting real-time mineralization process reaction actual measurement monitoring data; according to the method, physical field model construction and prediction are carried out on the complex mineralization and storage process through mechanism and data dual drive, and the fusion inversion capability of the mineralization and storage process can be improved.

The invention belongs to the technical field of building energy conservation and carbon reduction, discloses a building energy conservation and carbon reduction method, device and equipment and a storage medium, and constructs a high-precision building BIM model. A digital twinborn model of a Xian mansion is constructed by applying a BIM technology, and the model not only depicts the physical structure of the building in detail, but also simulates key operation details such as personnel flow and equipment operation. By strategically simplifying the model, it is ensured that the model is efficient and accurate. The comparative analysis of the corrected model and actually measured data shows that the prediction error of the model is only 0.10%, the extremely high consistency of the prediction result and the actual situation is highlighted, the high accuracy of model correction is verified, and the high standard requirement of in-depth research is met; according to the invention, balance optimization of carbon emission minimization and comfort maximization is realized. By intelligently adjusting the operation strategy of the air conditioning equipment, the method obviously reduces the carbon emission of the building while ensuring the living comfort.

The invention provides an ionosphere TEC prediction model integrating double mechanisms of self-attention and self-correlation, and belongs to the technical field of ionosphere delay prediction, and the model comprises a trend prediction module, a correlation analysis module and a sequence fusion module. Wherein the trend prediction module predicts the overall change trends of an ionized layer TEC sequence and space environment parameters by means of an autocorrelation operation mechanism; the correlation analysis module analyzes the mutual relation among the ionized layer TEC sequence, the space environment parameters and the longitude and latitude positions by using a self-attention mechanism; and the sequence fusion module performs further integration and optimization on the overall trend of the ionized layer TEC sequence by using the correlation information obtained by the correlation analysis module, and finally obtains an ionized layer TEC prediction result fusing the space environment and the latitude and longitude position information. According to the method, the spatial environment data and the longitude and latitude position information are fully utilized, and the accuracy of model prediction is greatly improved.

The invention discloses an AI identification method and system for a millimeter wave image overlapping target based on a three-branch network, and a model training method, and the method comprises the steps: carrying out the cooperative work of three independent branch networks: an analysis branch network, an aggregation branch network, and a prediction boundary branch network; the problem of security inspection target detection in millimeter wave images with low resolution, mutual interference or mutual overlapping is solved. The analysis branch network stores detail information in the high-resolution feature map; the aggregation branch network realizes feature position offset alignment of high-resolution and low-resolution feature maps through a recursion fusion module; and the prediction boundary branch network extracts high-frequency features, and enhances perception and prediction of the target edge. The multi-branch network structure effectively realizes image segmentation and identification through fusion among branches. While the segmentation performance of the model is improved, the model significantly reduces the calculation complexity, reduces the detection error and missed detection probability in the millimeter wave image, and finally achieves the precise detection of overlapped objects.

The invention relates to an unknown forged image detection method based on stack generalization ensemble learning, a medium and a terminal, and in the primary learning stage of stack generalization ensemble learning, a data set is input into two classification networks for a traditional forged image and a deep forged image. A Convnextv2-based multi-source feature enhancement model is provided for a traditional forged image to carry out forging detection; a space and channel reconstruction convolution and Transform hybrid network model is proposed for a deep counterfeit image to carry out counterfeit detection. The two classification networks are trained through a k-fold cross validation method to obtain a plurality of primary learners, and prediction results of the primary learners are input into a meta-learner fractional order Kolmogorov-Arnold network in a stacking generalization integrated learning meta-learning stage to be trained. According to the method, the defect that single model prediction is easy to fit to single distribution is overcome, and the detection precision for unknown forged images in a real environment is improved.

The present invention discloses an intelligent control method for an electric control valve actuator for a ship. The present invention pre-processes and comprehensively evaluates the collected fluid pressure data at a ship control center, first calculates the pressure evaluation value within a set time period, then matches it with a preset range interval to obtain a pressure index, and combines it with an ideal pressure index corresponding to the operating condition, so that the valve opening adjustment can better adapt to the host's requirements for changes in fuel pressure under different working conditions. At the same time, for a gradually rising or falling pressure state, the pressure change rate is calculated and the state interval is divided, and the pressure change index is calculated in combination with weight coefficients of different intervals. The opening optimization value and the adjustment direction are determined according to the pressure change index and a preset index value range, thereby achieving precise control, being able to respond in a timely manner to changes in the operating conditions of the ship's main engine and fluctuations in the pressure of the fuel system, effectively ensuring a stable supply of fuel flow and pressure, and improving the operating efficiency and stability of the ship's main engine.

The invention belongs to the technical field of bridge disease diagnosis, and particularly discloses and provides a bridge disease diagnosis and evaluation method based on a monitoring and sensing technology, which comprises the following steps: monitoring structure diseases, material diseases and traffic diseases of a bridge; respectively confirming a structure disease influence index, a material disease influence index and a traffic load disease influence index of the bridge, further confirming a disease influence grade of the bridge, and performing corresponding feedback; according to the method, the structural disease influence index of the bridge is determined according to the structural information of each bridge part, so that the attention on bridge deformation is improved, the assessment accuracy of the bridge structural disease is guaranteed, the reliability of bridge durability assessment is improved, the prediction timeliness of the development trend of the bridge disease is guaranteed, and the prediction efficiency is improved. And meanwhile, comprehensive bridge disease diagnosis is carried out in multiple dimensions according to the structure disease, the material disease and the traffic load disease of the bridge, so that the effectiveness of bridge disease assessment is guaranteed.

The present invention relates to the technical field of information system integration, and in particular to an ERP system launch method. The method comprises the following steps: obtaining ERP demand data; performing ERP related function module analysis according to the ERP demand data to generate ERP related function modules; performing ERP system architecture analysis based on the ERP related function modules to establish the ERP system architecture; obtaining historical enterprise source data; performing historical enterprise decision feature analysis based on the historical enterprise source data to generate historical enterprise decision feature data; performing intelligent business practice engine design based on the historical enterprise decision feature data to generate an intelligent business practice engine; performing intelligent business practice function configuration processing of the system architecture on the ERP system architecture based on the intelligent business practice engine to generate an ERP system, and transmitting the ERP system to a cloud server to execute the ERP system launch operation. The present invention realizes intelligent business management of the ERP system.

The invention is suitable for the technical field of foundation pit gas film monitoring, and provides a foundation pit gas film environment monitoring system which can monitor the normal use performance and the stress characteristic of a current gas film building, that is to say, the comprehensive performance monitoring of the performance of the gas film building can be realized by utilizing the characteristics of monitoring systems of all branches; according to the invention, the system is simple in structure, facilitates the mastering and control of the building performance, solves the problem that an air film building lacks an effective monitoring means at present, effectively prevents the film collapse accident caused when the large-space air-filled film building deals with the snowy weather, can give an early warning in advance to inform related personnel to remove the danger, and avoids the occurrence of safety accidents. The technical problem that an existing adopted constant-pressure control strategy cannot effectively prevent the film top from collapsing during heavy snow load is solved, long-term monitoring of data is facilitated, automatic collection and transmission of monitoring data are achieved through the field monitoring data collection subsystem, and early warning and real-time issuing of monitoring information are achieved through the early warning and information issuing subsystem.

The invention relates to an Internet of Things equipment deployment and control management method, in particular to a building Internet of Things equipment deployment and control management method. Comprising the following steps: deploying various sensors including an infrared sensor, a camera and an air quality sensor in a building, and collecting personnel flow and environment change data in real time; through data analysis and scene classification, the data are converted into scenes including office peaks, night modes and emergency evacuation of the building; training a scene recognition model by using a random forest classifier; a time sequence prediction model is adopted to predict scene change in a future short period, and a corresponding equipment control instruction is generated; the method comprises the following steps: establishing an incidence matrix of use frequency, spatial position and function interaction between devices by using sensor data, and identifying a device cooperation mode; dynamically generating an equipment deployment scheme through a graph theory algorithm based on the incidence matrix; issuing the generated deployment scheme to each equipment control node; and performing fine adjustment according to real-time data feedback.

The invention belongs to but not limited to the technical field of seismic facies intelligent identification, and particularly relates to a three-dimensional seismic facies intelligent identification method and system based on V-Net, and the method comprises the steps: S1, obtaining three-dimensional seismic data and a corresponding seismic facies interpretation result; s2, establishing a learning sample for three-dimensional seismic phase identification; s3, building a seismic facies intelligent identification model based on V-Net; and S4, identifying the three-dimensional seismic facies of unknown data. According to the method, seismic facies recognition is taken as a three-dimensional image segmentation problem, and meanwhile, residual errors are added in modeling, so that the rationality of a model stretching hypothesis space is optimized, and the precision of seismic facies intelligent recognition can be effectively improved. According to the method, the residual layer is added on the basis of three-dimensional U-Net modeling, the structure of the intelligent model is optimized, the reasonability of the model expanding hypothesis space is guaranteed, and the overall feature learning ability of the network is improved.

The invention discloses an aircraft adaptive interval management and control method and system, and relates to the technical field of air traffic management. An aircraft adaptive interval management and control system comprises a flight conflict basic unit module, a flight conflict scene generation module, a flight conflict resolution module and a flight conflict learning module. According to the method, the aircraft flight conflict scene training set can be constructed by constructing the basic flight unit of the aircraft and generating the flight trajectory vector set and combining the flight conflict generation model; the training set is utilized to train an aircraft autonomous interval management and control model through an improved Markov decision model, so that effective strategy output is realized when a flight conflict occurs; the model is updated through analysis of the test result, the adaptability and accuracy of the autonomous interval management and control model of the aircraft are improved, the flight safety of the aircraft in a complex environment is improved in the process, and the prediction and processing capacity of the model for flight conflicts is enhanced.

The invention relates to the technical field of logistics vehicle scheduling, in particular to a logistics vehicle scheduling method based on a visual perception industrial vehicle ADAS system. The method comprises the following steps: acquiring the speed of a logistics vehicle, and further acquiring a reference time length for a target vehicle to reach a specified position at a previous moment; predicting a target position of the non-idle logistics vehicle according to the speed of the non-idle logistics vehicle and the path length from the position of the non-idle logistics vehicle in the planned path corresponding to the reference duration to the specified position; and based on the planned advancing direction and position of the target vehicle at the current moment and the target position and speed of the non-idle logistics vehicle, obtaining a target prediction duration, correcting a cost function of the target vehicle arriving at the designated position in the A-star algorithm, and obtaining a corrected cost function to schedule the logistics vehicle. According to the method, the cost function in the A-star algorithm is corrected, so that congestion and path conflicts of the logistics vehicles are reduced, and the accuracy and efficiency of logistics vehicle scheduling are improved.

The invention discloses an intelligent control method applied to a ship electric control valve actuator, and the method comprises the steps: carrying out the preprocessing and comprehensive evaluation of collected fluid pressure data at a ship control center, firstly calculating a pressure evaluation value in a set time period, and then carrying out the matching with a preset range interval to obtain a pressure index, the method comprises the following steps of: calculating a pressure change rate and dividing a state interval for a gradually rising or falling pressure state, calculating a pressure change index by combining weight coefficients of different intervals, and calculating a pressure change index by combining an ideal pressure index corresponding to an operation working condition, so that the valve opening degree adjustment can better adapt to the change requirement of a main engine on fuel pressure under different working conditions; and the opening degree optimization value and the adjusting direction are determined according to the pressure change index and the preset index value range, accurate control is achieved, changes of the operation working condition of the ship main engine and fluctuation of the pressure of a fuel system can be responded in time, stable supply of fuel flow and pressure is effectively guaranteed, and the operation efficiency and stability of the ship main engine are improved.

The invention provides a general bypass reconciliation and early warning system based on big data, which belongs to the technical field of information, adopts a layered architecture design and comprises a data acquisition layer, a data processing layer, an intelligent reconciliation layer, an early warning decision-making layer and a user interaction layer. All the layers carry out efficient data exchange and instruction transmission through standardized interfaces, it is ensured that all function modules of the system can work cooperatively, and automatic account checking and abnormal early warning of cross-system and cross-platform transaction data are achieved.

The invention discloses a power load prediction method and device based on a hybrid ELM algorithm, electronic equipment and a storage medium, and the method achieves the prediction of a power load through an ELM prediction model (MT-ELM) mixed with MINRES and hidden layer traversal, can achieve the estimation of the number of optimal hidden layer nodes, and eliminates the overfitting. Therefore, the prediction level of the power load is effectively improved. The invention discloses a power load prediction method based on a hybrid ELM algorithm. The method comprises the following steps: S1, performing variation on input and output training sets to construct a training set suitable for an ELM prediction model; s2, calculating a hidden layer output matrix H of the ELM neural network through a calculation mode of a classic ELM model; and S3, traversing and estimating the number of optimal hidden points by using the input and output test sets, eliminating ELM over-fitting through a mixed MINRES method, learning through data over the years and generating an ELM mixed model, and predicting the future short-term power load condition through the generated model.

The invention discloses a project risk dynamic management method, system and equipment based on big data, and the method comprises the steps: obtaining all material data generated in a project whole-process management process based on a constructed project management system, carrying out the data processing of original data through an information processing module, and constructing a project risk database; performing project risk prediction by applying a long short-term memory (LSTM) network based on the project risk database, and judging whether the project has a risk at the moment and a corresponding risk level; if the project has the risk, starting a project risk type identification algorithm, performing risk category identification based on project data by applying a gradient boosting decision tree (GBDT) algorithm, and determining a project risk category; and based on the risk level and type of the project, matching a project risk database to determine risk countermeasures, and pushing the risk countermeasures to related managers in real time. According to the method, the prediction model and the identification model are combined, so that the risk category can be identified more accurately, and the prediction and identification rate of the project risk is improved.

The invention provides a digital tee joint extrusion production method and equipment, and belongs to the technical field of tee joint extrusion production control. The method comprises the following steps: determining a corresponding extrusion operation parameter group sequence in real time; and inputting the extrusion operation parameter group sequence into the three-way production simulation model to determine defect prediction development information. Determining defect correction development information based on the defect prediction development information and preset standard operation regulation and control parameter information; and sending the defect correction development information and the extrusion operation parameter group sequence of the correction time period to a management terminal as defect correction information, so as to store the defect correction information and the evaluation label to a preset database based on the feedback evaluation information. And when a second extrusion material instruction is received, determining a defect correction deviation based on a preset database and the extrusion operation parameter group sequence of the second extrusion material, and updating preset standard operation regulation and control parameter information and / or sending the defect correction deviation to the user terminal based on the defect correction deviation so as to control the three-way extrusion device to produce.

The invention discloses a multi-modal trajectory prediction and planning integrated method based on candidate lanes and speed embedding, and the method comprises the steps: sampling candidate lanes based on the position of a target obstacle and a high-precision map, and carrying out the clustering and filtering of the candidate lanes; inputting the information of the target obstacle and the information of the candidate lane into a feature fusion module and a candidate lane scoring module to obtain scores of the candidate lane; speed embedding is carried out on the candidate lanes to obtain candidate lane information containing different longitudinal behaviors, and the candidate lane information is sent to a trajectory regression module to obtain a predicted trajectory; sending the predicted trajectory into a trajectory scoring module to obtain the confidence coefficient of the multi-modal trajectory; the steps of self-vehicle trajectory planning are roughly the same as those of trajectory prediction; and the predicted trajectory of the target obstacle and the planned trajectory of the vehicle are sent to a Cost scoring module for comprehensive scoring. According to the method, the predicted and planned trajectory is obtained based on candidate lanes and speed embedding, so that the model has the characteristics of low delay and high interpretability while the prediction and planning precision of the model is higher.

The invention discloses a medical data processing method and system combining big data and smart medical treatment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a historical anatomical record based on the big data, and carrying out the analysis to obtain an anatomical data set; calling a preset data processing mechanism for processing and analyzing to establish an anatomical medical database; calling a predetermined association analysis strategy to perform association analysis to obtain an association list; performing multi-dimensional feature collection on a target user of smart medical treatment to obtain target feature data; collaboratively forming target input data; and activating the intelligent prediction model for analysis to obtain a target output result and performing early warning intervention processing. The technical problems of low medical data processing efficiency, insufficient association between anatomical knowledge and patient individual features and limited intelligent early warning ability in the prior art are solved, and the technical effects of efficiently mining anatomical data values, accurately associating patient multi-dimensional features and improving intelligent medical prediction and intervention ability are achieved.

According to the signal processing method, the transmission method, the electronic equipment and the angle detection device provided by the invention, the rotation angle data is generated and processed in the installation process of the borehole stressmeter, so that the rotation angle of the borehole stressmeter can be accurately monitored in real time, and the rotation angle is converted into the signal adaptive to the sectional dimension of the connecting device for transmission. By modulating the frequency band of the signal, the problem of signal attenuation in a deep hole environment can be effectively solved, the signal can still be stably transmitted in a complex environment, and the data precision and the transmission reliability in the installation process of the borehole stressometer are improved, so that the geological disaster prediction and prevention efficiency is improved.

The present invention relates to the technical field of electric power system, specifically, a low-frequency off-grid automatic control method and device for a new energy electric power system, a fixed platform, and an automatic control device further comprising: an adjustment component is installed on the upper surface of the fixed platform, for actually removing the load; the adjustment component comprises a primary rotating member, a secondary rotating member and a tertiary rotating member, the primary rotating member is completely identical to the tertiary rotating member, the primary rotating member contacts one of the secondary rotating members, and the other secondary rotating member contacts the tertiary rotating member; a crossover connector is fixedly arranged at the bottom of the tertiary rotating member. It can realize the simultaneous opening and closing of multiple load switches, and can also realize the simultaneous opening of some load switches according to the actual situation of the power grid, while the rest are in a closed state. Through the setting of the crossover connector, two non-adjacent load switches can be controlled to be connected, and two non-adjacent load switches can be disconnected.