240results about How to "Reduce delays" patented technology

The invention relates to the technical field of electric line fault detection, and discloses a power transmission line fault early warning system and method based on big data driving, and the system comprises a state prediction module, a risk assessment module, an early warning decision module and a management execution module. The state prediction module constructs a prediction model by using historical operation data, environmental conditions and line parameters, and outputs future state indexes; the risk assessment module comprehensively considers multi-source information such as real-time operation data and historical maintenance records, and assesses a current fault risk index; the early warning decision-making module models the management process into a Markov decision-making process, and carries out early warning decision-making based on the output of the two modules; and the management execution module is responsible for managing and executing the decision result in real time. By comprehensively utilizing the big data technology, the accuracy and the real-time performance of power transmission line fault early warning are improved, the defects of a traditional fault management mode are overcome, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention relates to a cloud side-end integrated collaborative digital-intelligent traffic collaborative management and control method, system and device and a medium, and the method comprises the steps: dynamically adjusting the main line speed limit and ramp control rate through driving main line variable speed limit and traffic induction collaborative management and control based on a hierarchical control architecture, so as to enable the performance of a regional road network to be optimal; a collaborative optimization framework is constructed and utilized under a hierarchical control framework, an optimal control scheme of a single ramp is expanded to each partition of a road network in a random environment, and an optimal joint behavior strategy and a behavior selection strategy of each agent are determined, so that adaptive collaborative control of each agent is realized; and a traffic flow optimal equalization strategy of the whole road network is obtained. Therefore, by integrating a main line variable speed limit and traffic induction collaborative management and control technology, a main line and multi-ramp collaborative optimization framework and intelligent agent configuration, optimization of regional road network performance, optimal balance of overall road network traffic flow and improvement of traffic management and control accuracy and response speed are achieved.

The invention discloses a mine tunnel operation period deformation monitoring method and system, and relates to the technical field of mine tunnel monitoring, and the method comprises the following steps: carrying out the real-time monitoring of the deformation condition in a mine tunnel through a sensor device, and transmitting the collected monitoring data to a data processing center through a wireless transmission module. Real-time monitoring of mine tunnel deformation is achieved through the multiple sensors and the wireless transmission technology, the problem of data lag is solved, and monitoring accuracy and timeliness are improved. And in combination with a multi-objective optimization algorithm, the system automatically adjusts key parameters such as a supporting structure and a mining sequence according to real-time data, so that an optimal safety control scheme is ensured. According to the system, the operation efficiency and safety are improved, manual misjudgment is reduced, and the control strategy can be continuously optimized according to the operation stage.

The invention discloses a multi-warehouse intelligent distribution and scheduling method for raw material inventory. The method comprises the following steps: collecting raw material inventory data of each warehouse, and monitoring the inventory data of the warehouses in real time; based on the real-time inventory data and the historical inventory data, the inventory state of each warehouse is analyzed to determine the inventory balance state of each warehouse, and the warehouses with excess or short inventory are identified; and calculating an optimal inventory distribution scheme by adopting a dynamic adjustment algorithm based on an inventory state analysis result. According to the invention, by collecting the real-time inventory data and the historical inventory data, the inventory state of each warehouse is deeply analyzed, the inventory balance state is determined, and the warehouse with excess or short inventory is identified. Through the real-time data monitoring and analysis, the system can provide accurate inventory early warning, help managers to take measures in time, avoid production interruption and resource waste, and improve the accuracy and predictability of inventory management.

The invention relates to the technical field of production scheduling of manufacturing workshops, in particular to a distributed intelligent production scheduling decision-making system based on cloud computing. The system is composed of a cloud computing platform, a data acquisition module, a data processing module, an intelligent production scheduling engine and a distributed execution control module. The problems of performance bottleneck, data acquisition limitation, lack of flexibility and adaptability of a production scheduling scheme and the like in a traditional production scheduling system are solved, intelligent distribution and scheduling of production tasks can be realized through fusion application of advanced technologies such as cloud computing, big data and artificial intelligence, the production efficiency and the product quality are improved, and the production cost is reduced. And greater economic benefits and social benefits are created for enterprises.

The invention provides a sparse space wind vector field abnormity early warning method based on multi-source heterogeneous spatio-temporal data. Comprising the following steps: S1, obtaining multi-source heterogeneous wind vector data and carrying out space-time standardization; s2, constructing a multi-scale geographic spatio-temporal weighted autoregression model and updating the weight; s3, decomposing a vector field and constructing a regional early warning pilot feature set; s4, boundary flux calculation and PINN-GPR fitting of a sparse space connected domain are carried out; s5, four-dimensional spatio-temporal data variational assimilation and sparse space boundary flux updating are carried out; and S6, abnormal wind field early warning based on regional early warning pilot feature matching. According to the method, the abnormal early warning of the sparse space wind vector field and the adaptive updating of the early warning library can be realized.

The invention relates to the field of legal service management, and discloses a legal service management system based on a block chain, and the system comprises the following modules: a block chain network, an intelligent contract module, a data storage module, a user interface module, a feedback and monitoring module and a user management module, the non-tampering property and the transparency are ensured; the intelligent contract module automatically executes contract terms and supports intelligent management; the data storage module adopts a hierarchical structure, and on-chain data and off-chain data are stored in a distinguished manner, so that the storage and access efficiency is optimized; and the user interface module provides user-friendly operation experience through a visual tool. By using the block chain technology, all contract execution and compliance audit data are encrypted and stored in a distributed mode, it is ensured that tampering is avoided and whole-course traceability is achieved, and a user and related mechanisms can verify the authenticity and integrity of the contract data at any time.

The invention discloses a multi-agent cooperative control system based on multi-mode brain-computer interface-visual tracking, and the system comprises the following modules: a signal collection and processing module which employs an 8-lead electroencephalogram cap to collect electroencephalogram original data, selects occipital region parts (PO7, PO8, P3, P4, Pz, O1, O2 and Oz) as an electroencephalogram data collection region, and carries out the EEG data collection; the feature extraction and classification module is used for extracting SSVEP signal features by using a space-time convolutional network, optimizing classification precision in combination with an integrated learning strategy, extracting EOG signals based on a Transform model, integrating an LMS adaptive filter and denoising the EEG signals in combination with wavelet transform, expanding the frequency tracking range of the adaptive filter, and reducing signal loss to the greatest extent; the cooperative control strategy design module is used for mapping the electroencephalogram signals into control instructions of different intelligent agents, and cooperative control over the unmanned aerial vehicle and the trolley is achieved; and the target detection and tracking module is used for optimizing an unmanned aerial vehicle visual tracking system based on a PP-YOLOE model and realizing accurate tracking and automatic adjustment of the trolley.

The invention, which relates to the technical field of artificial intelligence, discloses a warehouse-in and warehouse-out path planning method and system for intelligent warehousing, and the method comprises the steps: generating an optimal operation path of a to-be-operated carrier through a path planning model; the method comprises the following steps: predicting whether a current carrier has a collision risk with all carriers in a warehouse in a time dimension or not through a time block, predicting whether the current carrier has a collision risk with all carriers in a warehouse in a space dimension or not through a space block, and when the time block and the space block simultaneously detect that the current carrier has the collision risk at a certain moment, judging whether the current carrier has the collision risk; the decision block judges that the current carrier has a collision conflict, and determines an optimal avoidance strategy through an action value function; according to the optimal avoidance strategy, the avoidance waiting carrier is determined, the total waiting time and the waiting space position of the avoidance waiting carrier are calculated, and the avoidance strategy is executed. According to the technical scheme, the problem that in-out garage path planning and collision processing cannot be intelligently and accurately carried out in the prior art can be solved.

The invention relates to the technical field of freight scheduling, in particular to a freight scheduling information management method and system and a storage medium. The method comprises the following steps that uncompleted order collection is carried out on a freight platform to obtain an uncompleted order set, and the uncompleted order set comprises a plurality of uncompleted orders; each uncompleted order at least comprises an order ID, order release time, target cargo attribute data, publisher identity data, a departure place, a destination, an order state identifier, an order transportation task schedule, cargo stacking attributes, predicted delivery time and cargo expected arrival time; and performing order state classification and marking on the uncompleted order set to obtain a classified order set. By optimizing the loading space utilization rate of the vehicle, the unloaded and half-loaded conditions of the vehicle can be reduced, so that the fuel consumption and the transportation cost are reduced.

The invention discloses an emergency vehicle road section priority passing control method considering a network-connected automatic driving special road, and the method comprises the steps: collecting the vehicle state information of an emergency vehicle and all vehicles of a target road section, and guiding the emergency vehicle to enter the network-connected automatic driving special road of the target road section if the emergency vehicle enters the target road section, calculating a dynamic emptying area in real time; a networked automatic driving vehicle in a dynamic emptying area is used as an optimized vehicle, and a multi-target optimization model which is established by taking minimum emergency vehicle delay and minimum interference to surrounding vehicles as targets is used for optimizing a speed change value and a lane changing decision of the optimized vehicle so as to control the optimized vehicle. Therefore, the networked automatic driving vehicle which affects passing in front of the emergency vehicle is emptied. According to the method, the longitudinal following and transverse lane changing decisions of the networked automatic driving vehicles in the social vehicles in the mixed traffic flow environment are optimized, and the influence on the social vehicles is reduced while the emergency vehicles are guaranteed to pass preferentially.

The invention discloses a transparent logistics supply chain tracing management system fused with a block chain technology. The transparent logistics supply chain tracing management system comprises a data acquisition module, a block chain storage module, an intelligent contract module, a user interface module and a data analysis module, the data acquisition module is responsible for collecting real-time data of each link of a supply chain and transmitting the data to the block chain storage module; the block chain storage module uses SHA-256 and ECDSA algorithms to ensure the security and authenticity of the data; according to the method, the blockchain technology is utilized, data of all links of the supply chain is subjected to chaining storage, the data are open, transparent and non-tampering, and the credibility of the data is enhanced; through an SHA-256 Hash algorithm and an ECDSA signature algorithm, the security and integrity of the data in the transmission and storage process are ensured, and the data are prevented from being tampered or forged; and transactions and operations in the supply chain are automatically executed through the intelligent contract module according to a preset rule, so that human intervention is reduced, and the automation level and the operation efficiency of the system are improved.

The invention provides a collaborative two-stage multi-runway departure flight ordering method, which is specially designed for relieving the congestion problem of a multi-runway airport in a peak period, ensures that the departure process of the flight is more efficient, and accords with scientific planning. The method comprises the following steps: constructing a multi-runway dynamic push-out rate control model to determine an optimal queuing threshold value of a taxiway; constructing a flight ordering and assigning model with the goal of minimizing flight delay and operation cost; and proposing a continuous Markov chain-based interactive whale algorithm, and carrying out flight push-out sequence and runway resource allocation. According to the method, the departure flight taxiing amount of the airport scene can be accurately identified, taxiway waiting time caused by runway occupation after the flight is launched is reduced, the actual significance is achieved for improving the flight punctuality rate and improving the overall airport operation efficiency, and the current green aviation development concept is met.

The invention discloses a pre-accepted work order closed-loop management system, which comprises a sensitive first-aid repair work order automatic research and judgment module, a non-sensitive work order handling module, a pre-accepted work order accurate closed-loop grading management and control module, a closed-loop process management module, a comprehensive auditing module and a visualization module, an intelligent grading management and control rule, a responsibility investigation mechanism, a timestamp technology and the like are embedded into the corresponding module to enhance the disposal effect of the pre-accepted work order; according to the invention, by establishing an online pre-acceptance work order closed-loop management system, sensitive emergency pre-acceptance work orders can be found in time, and the core problems of untimely work order reporting, information blocking, large workload, difficult supervision, difficult management and control, difficult statistical analysis, difficult employee assessment and the like in the existing pre-acceptance work order processing process are solved; and workers are assisted to realize whole-process closed-loop management of processing time node management and control of each link of a pre-accepted work order, accurate order making of sensitive problems, time-limited grading, automatic circulation and the like, and the quality and efficiency of power supply service are improved.

The invention relates to the technical field of data processing, and discloses a BIM-driven steel structure collaborative design method, device and equipment. The method comprises the steps of performing parameterization processing on building information data to obtain intelligent BIM data; performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data; performing intelligent structure analysis processing on structure information in the collaborative design data to obtain optimized structure data; performing adaptive component design processing on the optimized structure data to obtain customized component data; performing virtual construction simulation processing on the customized component data to obtain construction optimization data; intelligent operation and maintenance processing is carried out on the construction optimization data and the actual construction data, full-life-cycle management data are obtained, and the full-life-cycle management data comprise real-time monitoring data, performance evaluation results and predictive maintenance suggestions. The efficiency and accuracy of steel structure collaborative design based on BIM driving are improved.

The invention discloses an intelligent automobile path tracking system for urgent danger avoidance, and particularly relates to the technical field of intelligent automobiles, the system comprises an environment sensing module, the output end of the environment sensing module is connected with a danger assessment module, and the output end of the danger assessment module is connected with a path planning module. The output end of the path planning module is connected with a decision planning module, the output end of the decision planning module is connected with a vehicle control module, and the output end of the vehicle control module is connected with a communication and interaction module. Through cooperative work of all the modules, perception of the surrounding environment of the vehicle, danger assessment, path planning, decision making, vehicle control and communication interaction with the outside are achieved, under the dangerous condition, the system can make a response rapidly, effective danger avoiding measures are taken, the driving safety of the vehicle and the accuracy of path tracking are ensured, and the driving safety of the vehicle is improved. And a comprehensive and reliable guarantee is provided for safe driving of the intelligent automobile.

The invention discloses a traffic meteorological data fusion method based on inverse distance weight interpolation, and relates to the technical field of traffic meteorological data fusion. The traffic meteorological data fusion method based on inverse distance weight interpolation comprises the following steps: acquiring longitude values, latitude values and visibility measurement values of a plurality of time points of each traffic meteorological station in a set area; each traffic weather station in the set area is screened and analyzed based on the visibility measurement values of the plurality of time points to obtain a plurality of effective traffic weather stations, and the data from different time points and places are integrated and analyzed, so that the data utilization rate and accuracy are remarkably improved; the time sequence analysis of the visibility helps to understand the behavior pattern of each station changing with time, the spatial analysis ensures the continuity and consistency of data in geography, and the integration method optimizes the data processing flow, so that the visibility prediction is more accurate, and the complex traffic and meteorological condition analysis is effectively supported.

According to the order production control method and device, the electronic equipment and the storage medium provided by the invention, the order information of the order, the process plan information of the order and the working center information of the working center are acquired, and the working center is used for producing products of the order; decomposing the order into a plurality of processes according to the process plan information, and determining the time required by each process of each order; calculating the earliest starting time of each process of each order according to the order information and the required time; and generating a production scheduling scheme based on the earliest start time and the work center information, and controlling the work center to work based on the production scheduling scheme to produce the order, so that the automation degree of order processing and production scheduling can be improved, and human errors and delay are reduced.

The invention discloses a building construction site arrangement optimization method and system and a storage medium. According to the main scheme, the method comprises the steps that topographic data, building layout data and equipment layout data of a building construction site are acquired; drawing a construction site rough arrangement diagram according to the collected building layout data and equipment layout data by using CAD software, and importing the rough arrangement diagram into BIM modeling software to generate a construction site basic model; analyzing the construction site basic model of the BIM technology, and marking a problem area of the basic model; an optimization model is formed by changing the layout of a problem area of the basic model; and analyzing the optimization model to judge whether the optimization model meets the standard or not. According to the technical scheme, intelligent optimization and real-time monitoring of construction site arrangement can be realized, the construction efficiency is improved, the cost is reduced, and the construction safety is enhanced.

The invention provides an automatic management system and method for a steel plate warehouse area, and relates to the technical field of industrial automation and intelligent warehousing, and the system comprises a steel plate recognition warehousing module, a warehouse area division module, an informatization warehousing module, an automatic crane and a warehouse management system. Through an efficient automation technology and an intelligent management strategy, the operation efficiency and the storage utilization rate of a steel plate storage area are remarkably improved, the deflection problem caused by mixed storage of steel plates of multiple specifications is effectively avoided, accurate carrying is achieved, manual intervention is reduced, and the system is widely applied to the fields such as shipyards and steel mills with complex steel plate management requirements and has high popularization value. And the production efficiency and the operation safety are comprehensively improved.

The invention discloses an intelligent scheduling method based on port bulk cargo transfer. The method comprises the steps of S1, obtaining related information of bulk cargos to be transferred and related information of transport vehicles; s2, matching transport vehicles of corresponding types; s3, calculating a transfer train number; s4, calculating the number of transport vehicles; when the number of the transport vehicles is one, executing the step S5; when the number of the transport vehicles is two or more than two, executing the step S6; s5, generating transfer scheduling planning information according to the loading position of the bulk cargo to be transferred and the departure position of the corresponding transport vehicle; s6, determining the departure sequence of the transport vehicles: for the first departure transport vehicle, calculating corresponding transfer scheduling planning information based on the step S5; and for the second transport vehicle and the subsequent departure transport vehicle, corresponding transfer scheduling planning information is generated according to the transfer scheduling planning information of the previous transport vehicle. According to the invention, the accuracy, orderliness and collaboration of bulk cargo transfer scheduling can be improved.

The invention discloses a hospital outpatient emergency service system based on a cloud server, and relates to the technical field of outpatient emergency services. The cloud data center is in communication connection with a medical data acquisition module, an abnormity identification module, an abnormity analysis module, an emergency early warning evaluation module, a medical care resource allocation module, a medical material management module and a mobile application module. According to the hospital outpatient service emergency service system based on the cloud server, potential emergency requirements or risk points are rapidly identified by monitoring medical material inventory, patient flow and medical staff working state data of all departments in real time, the emergency early warning module sends out an early warning signal, and the emergency early warning module sends out an early warning signal. According to the method and the system, the emergency response process of triggering replenishment reminding and deploying medical staff or medical supplies can be immediately started, so that the processing efficiency of hospital outpatient service in dealing with emergencies is remarkably improved, and delay and loss caused by information lag or untimely processing are reduced.

The invention relates to a digital twinning-based rail transit star chain system and a corresponding communication scheme design optimization method. The system comprises a sensing network, a station center digital twin server cluster system, a station center supervision system and corresponding intelligent service businesses, wherein the sensing network covers rail transit star chain systems of trains, trackside infrastructures, station centers and the like in an omnibearing manner. According to the system and the method, all parameters of an urban rail transit system based on an orbital star chain in an actual physical domain are mapped to a virtual digital domain system, so that all-directional supervision of a full-connection system, effective utilization of multi-source data characteristics and short-time accurate modeling of a channel environment with variable position environment parameters are realized; a communication link selection strategy model is constructed in combination with a model-based reinforcement learning method, and loop optimization is realized and an optimal communication link decision scheme is finally obtained by continuously training the model in a digital domain and collecting samples and verifying the model in a physical domain.

The invention discloses an ellipsoid curved glass curtain wall installation method based on BIM cooperative positioning. The ellipsoid curved glass curtain wall installation method comprises the five steps of building a BIM model, determining control points, conducting field measurement and positioning, installing curtain wall units and conducting cooperative positioning adjustment. Ellipsoid parameters and glass unit sizes are determined through a BI model, key control points are selected, field measurement is carried out, actual and theoretical coordinates are compared, installation is started from the center of the bottom, the position is monitored and adjusted in real time, and precision is ensured. According to the method, efficient and accurate installation of the ellipsoid curved glass curtain wall can be achieved, and the construction efficiency and the engineering quality are improved.

The invention discloses a goods yard container positioning and identification system based on Beidou RTK positioning, the management level of a container goods yard is remarkably improved by applying Beidou RTK positioning, container number identification and visual analysis modules, the accurate position information of a gantry crane or a reach stacker is obtained in real time through the Beidou RTK technology, the positioning accuracy reaches the sub-meter level, and the positioning accuracy reaches the sub-meter level. The container number identification module uses a camera and a machine learning technology to automatically identify container numbers and replace manual input, the accuracy and efficiency of data entry are improved, informatization management of containers is realized, and the visual analysis module draws a goods allocation electronic map, so that the management efficiency of the containers is improved. Information such as the position, the container number, the size and the weight of the container is visually displayed, the state of the container position is monitored in real time, managers are assisted to optimize operation plans, and problems are found and solved in time.

The invention discloses a cruise path planning system and method for an intelligent shifting vehicle, and the method comprises the steps: collecting static and dynamic obstacle information in real time through a laser sensor, a monocular camera and a depth camera, and carrying out the processing through Kalman filtering, thereby obtaining precise obstacle information; a static path planning algorithm is adopted on a known map, and an initial path is found; historical pedestrian flow information in different time periods and seasons is collected, and the predicted pedestrian density on a specific route in a future period of time is analyzed by using a convolutional neural model; and optimizing path planning according to the predicted pedestrian density and the accurate obstacle information. Static and dynamic obstacle information in the environment is collected in real time through multiple sensors, Kalman filtering is used for data fusion and processing, historical pedestrian flow information is collected, deep learning analysis is carried out through a convolutional neural network, path planning is made to be based on current obstacle information, the pedestrian high density is predicted, and path planning precision is improved. Congested road sections are avoided in advance, and the perspectiveness and rationality of path planning are improved.

The invention discloses a capacity expansion management and control method for predictable easily-congested road sections, and relates to the technical field of traffic management and control, historical data of each ETC portal in a near period of time on an expressway are collected, and historical traffic operation states of each road section in the monitoring range of each ETC portal are calculated; according to the historical traffic operation state of each road section, easily-congested road sections are screened, easily-congested routes are formed, and the daily average traffic volume of the easily-congested routes is calculated; and formulating a capacity expansion control strategy according to the daily average traffic volume of the easily-congested route. The method is used for solving the problems of large traffic flow and low traffic efficiency of predictable easily-congested road sections, capacity expansion can be carried out in a way of adding lanes and the like for regular and long-term easily-congested road sections so as to improve the traffic capacity, the method is a mesoscopic periodic strategy, conventional microcosmic management and control are improved to mesoscopic management and control, and the traffic efficiency is improved. Optimized configuration and efficient utilization of traffic resources can be realized, and the operation efficiency and the service level of the whole traffic system can be improved.

The invention discloses an intelligent networked vehicle following optimization and cooperative lane changing method for improving a safety potential field, and the method comprises the steps: enabling a vehicle to obtain the information of a target lane and the information of a current lane, and achieving the safety following control of a traffic flow; the vehicle generates a lane changing intention at first, the vehicle calculates and identifies all possible lane changing gaps and safety gaps needed for evaluating lane changing and judges whether the safe lane changing gaps exist or not, if yes, the optimal lane changing gap is selected from the safety gaps, and the vehicle starts to execute lane changing operation; if not, calculating the number of previous lane changing cooperative vehicles of the selectable lane changing gap, selecting the lane changing gap needing the minimum number m of cooperative acceleration vehicles, and establishing an m threshold condition based on the actual situation of the traffic flow; the invention emphasizes safety and cooperation, provides a new improved safety potential field lane changing model based on a traditional safety potential field theory, improves a traditional lane changing control mode, and further realizes safe and efficient car following and lane changing.

The invention discloses an airborne multi-source integrity monitoring method and device for an aviation navigation operation network, and belongs to the field of satellite navigation, and the method comprises the steps: constructing an overall design architecture of an airborne multi-source high-performance navigation receiver; an ionosphere gradient monitoring algorithm based on carrier phase difference is provided, IGM detection statistical error characteristics of the carrier phase are researched, an ionosphere gradient monitoring threshold is established, and a switching mechanism from an airborne DFree mode to an IFree mode is realized; an aviation navigation multipath mitigation suppression algorithm is researched, multivariate feature extraction of observation data is combined, an observation data feature matrix is established, a parallel time convolutional neural network architecture is designed, and the weight is updated through a stochastic gradient descent method. According to the airborne multi-source integrity monitoring method and device for the aviation navigation operation network provided by the invention, accurate monitoring of the ionosphere gradient is realized, a switching mechanism between a DFree mode and an IFree mode is optimized, and the integrity monitoring capability and the system availability of the aviation navigation operation network are improved.

The invention discloses a vibration control method for lifting construction of a target object, and the method comprises the steps: measuring the height of the target object at any moment, determining the mass of a damping box body, and determining the optimal frequency ratio and the optimal damping ratio of a TMD and a bearing structure based on the mass of the damping box body, the TMD comprises the damping box body and a lifting device used for lifting the damping box body, the bearing structure comprises a lifting target object and a distance measuring device for measuring the height of the target object, the suspension length of the damping box body is determined based on the height of the target object under the condition that the optimal frequency ratio and the optimal damping ratio serve as constraint conditions, and the damping box body is lifted according to the suspension length, so that the damping box body is lifted at the moment t; the suspension length of the damping box body is matched with the height of the target object so as to eliminate or weaken the vibration influence. In the construction process of lifting the target object, vibration, caused by wind loads, of the target object can be eliminated or weakened, and the construction safety is improved.