Wind power large piece hoisting monitoring system based on digital twinning

Abstract

The invention discloses a wind power large piece hoisting monitoring system based on digital twinning, and belongs to the technical field of wind power construction. Comprising a ground meteorological environment acquisition module; a hoisting data monitoring and acquisition system; and the hoisting digital twin system comprises digital hoisting equipment, an integrated climate early warning system, a hoisting parameter display system and a knowledge base system. According to the method, meteorological information near a hoisting site can be obtained in time, pre-alarm is carried out on the whole hoisting process through the knowledge base of the twin system, countermeasures for hoisting under various adverse conditions are guided, and risk management and control in the wind power large piece hoisting process are met.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power construction, and in particular to a wind power large-scale hoisting monitoring system based on digital twins. Background Art

[0002] The hoisting of large wind power equipment is a key part of wind power construction. During the hoisting process, multiple equipment are often required to be hoisted together; and because wind power sites are mostly located on flat land or mountain tops with high wind speeds, the local wind speed is greatly affected by the geographical environment, and the hoisting process is greatly affected by wind and weather conditions.

[0003] Therefore, it is particularly important to control lifting in combination with various external factors.

[0004] At present, in the relevant technologies in the field of hoisting, many emphasis is placed on real-time monitoring and early warning of tower cranes. For example, the patent with application number "2020109912145" discloses a method for monitoring and early warning of tower cranes for construction, including the following steps: GPRS and GPS information acquisition, MEMS sensor setting, wireless signal acquisition, monitoring coordinate confirmation, construction range mapping, construction range center point confirmation, drone flight shooting monitoring and real-time reception and monitoring of data results; through MEMS sensors, the vibration conditions of multiple standard sections of multiple tower cranes at the construction site are monitored in real time, so as to conduct complete monitoring and early warning of the tower cranes at the construction site, so as to monitor the body of the tower crane when it is working and guide the operation and use of the tower crane; the patent targets tower cranes dedicated to the construction industry. The patent with application number "2019111608390" discloses a tower crane monitoring and real-time alarm system based on BIM model, including a tower crane monitoring end and a BIM model end. The tower crane monitoring end includes a boom monitoring end and a tower body monitoring end. The boom monitoring end includes a length monitoring module, a boom inclination monitoring module, and an extract size fuzzy estimation module; the tower body monitoring end includes a tower body inclination monitoring module and a height monitoring module; the BIM model end includes a data integration module for integrating the data monitored by the tower crane monitoring end, a simulation module for simulating according to the information extracted by the data integration module, and a workload arrangement module for estimating working hours; and also includes an alarm end for issuing an alarm when an abnormal situation occurs on site or abnormal data is generated by the BIM model end. And so on.

[0005] Disadvantages of existing technology:

[0006] (1) This refers to tower crane lifting, excluding crawler cranes, all-terrain cranes, truck cranes and other lifting methods;

[0007] (2) During the lifting process, only wind external factors are generally monitored, and extreme weather conditions such as rainfall and thunderstorms are not monitored;

[0008] (3) The hoisting process simulation only involves the collision simulation between tower cranes, and does not involve other simulation types such as force;

[0009] (4) There is no knowledge base support, and there are only early warnings during the monitoring of the lifting process, but no knowledge support on how to deal with adverse conditions;

[0010] (5) The MEMS chip is only used to sense the horizontal posture of the tower crane and does not involve the spatial posture of the boom.

[0011] Therefore, the applicant conducted technical research and improvements based on actual engineering experience. Summary of the invention

[0012] The purpose of the present invention is to propose a wind power large-scale lifting monitoring system based on digital twins; to conduct real-time digital monitoring of the entire lifting process, and to push relevant lifting knowledge in a timely manner according to changes in meteorological data, and to adopt lifting control strategies in a timely manner, so as to provide services for monitoring and early warning of wind power large-scale lifting; and, it can be applied to various lifting methods such as tower cranes, crawler cranes and truck cranes.

[0013] In order to achieve the above object, the present invention adopts the following technical solutions:

[0014] A wind power large-scale hoisting monitoring system based on digital twins, including:

[0015] Ground meteorological environment acquisition module;

[0016] Hoisting data monitoring and collection system;

[0017] Hoisting digital twin system: includes digital hoisting equipment, integrated climate warning system, hoisting parameter display system, and knowledge base system.

[0018] Preferably, the ground meteorological environment acquisition module includes: a small meteorological station, and a wind force measurement component installed on a hoisting device and a wind tube;

[0019] The small meteorological station is installed near the construction site; the wind measurement components are respectively arranged on wind tubes at different heights to measure the wind forces at different heights; a plurality of wind measurement components are arranged on the boom and hook ends of the lifting equipment to monitor and collect the wind conditions at different positions and heights of the lifting equipment in real time.

[0020] Preferably, the ground meteorological environment acquisition module also includes: a wind speed and meteorological acquisition module installed in the constructed wind turbine generator set.

[0021] Preferably, the hoisting equipment data monitoring and collection system comprises: hoisting equipment, and a hoisting parameter monitoring component;

[0022] The hoisting parameter monitoring component is installed on the hoisting equipment to collect and monitor: hoisting weight, hoisting output, cable tension, lifting height, hydraulic parameters;

[0023] The hoisting parameter monitoring component also includes: a MEMS chip;

[0024] Use MEMS chips to determine the spatial position information of each component in the lifting equipment;

[0025] Use GPS to obtain the position of the cab (x0, y0, z0), the installation position length L2 of the MEMS chip on the boom, and calculate the position of the hook (x1, y1, z1); where s l Indicates the length from the hook to the highest point of the long arm;

[0026] x1=L2×cosγ×cosθ+x0;

[0027] y1=L2×cosγ×sinθ+y0;

[0028] z1=L2×sinγ+z0-s l ;

[0029] If it is a tower crane, γ is equal to 0 degrees.

[0030] Preferably, when working:

[0031] The hoisting digital twin system is pre-installed with a hoisting equipment model;

[0032] Calculate the true shape of the lifting equipment through the spatial posture information of each mechanism of the lifting equipment;

[0033] The climate data transmitted by the ground meteorological environment acquisition module and the hoisting parameters transmitted by the hoisting data monitoring and acquisition system are automatically matched in the knowledge base;

[0034] If the alarm information is matched, an alarm will be automatically sounded and linked to the corresponding knowledge base according to the threshold; the disposal measures obtained in advance through various simulations and expert opinions will prompt the lifting operator how to proceed with the next operation.

[0035] Preferably, the position transformation and knowledge base matching of the hoisting digital twin system includes the following steps:

[0036] a11) Establish a digital model of the lifting equipment;

[0037] a12) According to the spatial posture information obtained from the MEMS chip, the digital model is transformed to form a digital mapping from real lifting to virtual lifting;

[0038] The transformation of the tower crane's spatial position is expressed by formula 1

[0039] [x0 y0 z0 1]=[x1 y1 z1 1]*W;

[0040] Among them, [x0 y0 z0 1] represents the original coordinates, [x1 y1 z1 1] represents the changed coordinates, and the transformation matrix W is expressed by formula 2

[0041]

[0042] Among them, a ij (i=1,2,3,j=1,2,3) represents the rotation component of the model about the xyz axis, a x 、a y 、a z Indicates the offset of the model on the xyz axis;

[0043] a13) Early warning, alarm and disposal;

[0044] During the lifting operation, the on-site parameters are compared with the knowledge base parameters; if they are close to the threshold conditions, a warning is issued; if they reach the threshold conditions, an alarm is issued, and response measures are popped up for the operator's reference.

[0045] Preferably, in step a13: boundary conditions are added, numerical calculations and simulations are performed, and a corresponding hoisting knowledge base is formed; numerical analysis is used to simulate the hoisting conditions under various adverse working conditions, and the thresholds and countermeasures under various adverse conditions are indicated, and these data and operating measures are stored in the system, and displayed accordingly according to the external environment and real-time operations.

[0046] Preferably, the formation of the hoisting knowledge base includes the following steps:

[0047] c11) Establish a wind field model;

[0048] Based on the collected meteorological data, the wind field model is constructed using computational fluid dynamics algorithms. The air flow around the wind tube and on the lifting path is visualized and simulated to accurately display the wind flow characteristics at different locations.

[0049] c12) Force analysis model of hoisted objects;

[0050] Combined with the wind field model, a force analysis model of the hoisted object during the hoisting process is constructed; using the principles of aerodynamics, taking into account factors such as the shape, size, material, and weight of the hoisted object, the lift, drag, torque and other forces and moments of the hoisted object under different wind forces and wind directions are calculated; using the finite element analysis method, the hoisted object is discretized into multiple tiny units, and the force of each unit under the action of wind flow is accurately calculated; then, the overall force state of the hoisted object is obtained by integration;

[0051] c13) Simulate adverse working conditions;

[0052] Simulate the impact of adverse working conditions on the hoisting operation of hoisted objects;

[0053] c14) Setting of impact assessment indicators;

[0054] Define indicators to measure the impact of wind flow on lifting operations;

[0055] c15) Forming thresholds and response measures;

[0056] Based on various evaluation indicators, the warning and alarm thresholds are calculated comprehensively; based on the simulation analysis results, corresponding response measures are formed.

[0057] Preferably, the hoisting digital twin system further comprises: a digitized hoisting object;

[0058] The lifting equipment data monitoring and collection system further includes: a camera mounted on the lifting equipment and facing the lifting object, and a gyroscope mounted on the lifting object;

[0059] When the hoisted object is a wind tube, the gyroscope is placed at both ends of the wind tube;

[0060] When the hoisted object is a wind blade, the gyroscope is placed at the tip of the three wind blades;

[0061] It also includes: a wind force measurement component installed at the tip of the wind blade.

[0062] Preferably, the hoisting digital twin system further includes: an optimal hoisting time and operation control time calculation model;

[0063] Utilize historical wind data and weather forecast information to predict wind speed changes at different time periods in the future. Combined with the lifting impact assessment indicators, set the "lifting operation threshold range". For each optimal lifting time window, consider the time required for the standard process of wind blade lifting, equipment preparation time, and reserved emergency processing time to calculate the corresponding lifting operation control time.

[0064] Compared with the prior art, the present invention provides a wind power large-scale hoisting monitoring system based on digital twin, which has the following beneficial effects.

[0065] 1. The present invention can timely obtain meteorological information near the hoisting site, and use the knowledge base of the twin system to pre-alarm the entire hoisting process, as well as guide the response measures for hoisting under various adverse conditions, to meet the risk control of the hoisting process of large wind power units.

[0066] 2. The present invention can be applied to various lifting modes such as tower crane, crawler crane and truck crane.

[0067] 3. In the present invention, the ground meteorological environment acquisition module collects a variety of meteorological data and introduces multi-source data to improve the accuracy and reliability of meteorological monitoring; the hoisting data monitoring and acquisition system provides detailed data support for accurate hoisting; it automatically matches climate data and hoisting parameters, and can automatically alarm when an alarm message appears, and associate with the corresponding knowledge base to prompt the operator to take countermeasures; establish a variety of models to assist hoisting, and provide a basis for hoisting operation impact analysis; have an optimal hoisting time and operation control time calculation model to ensure that hoisting is carried out under suitable wind conditions.

[0068] Other advantages, objectives and features of the present invention will be described in part in the following description; and in part, will be apparent to those skilled in the art based on an examination of the following; or, may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a structural schematic diagram of the present invention.

[0070] Figure 2 Schematic diagram of a small weather station.

[0071] Figure 3 This is a schematic diagram of the structure of the lifting equipment.

[0072] Figure 4 Schematic diagram of the deflection angle.

[0073] Figure 5 Schematic diagram of the pitch angle.

[0074] Figure 6 Schematic diagram of digital lifting equipment.

[0075] Figure 7 Illustration of digital lifting Figure 1 .

[0076] Figure 8 Illustration of digital lifting Figure 2 . DETAILED DESCRIPTION

[0077] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0078] Reference Figure 1 , a wind power large-scale hoisting monitoring system based on digital twin, including:

[0079] Ground meteorological environment acquisition module;

[0080] Hoisting data monitoring and collection system;

[0081] Hoisting digital twin system: includes digital hoisting equipment, integrated climate warning system, hoisting parameter display system, and knowledge base system.

[0082] The ground meteorological environment acquisition module includes: a small meteorological station, and wind measurement components installed on hoisting equipment and wind pipes.

[0083] Among them, a small weather station is installed near the construction site to collect meteorological data near the construction site, including: wind conditions, temperature, humidity, precipitation probability and probability forecast of other extreme weather.

[0084] Preferably, Figure 2 As shown; the small weather station consists of poles, solar panels, wind power generation components, and meteorological collection components; it monitors various meteorological data such as wind speed, wind direction, temperature, humidity, air pressure, total radiation, rainfall, evaporation, etc. in real time; it adopts two energy modes, solar energy and wind power, adopts embedded technology, uses 4G / 5G, WiFi, GPRS, etc. to transmit data, and GPS for location positioning.

[0085] The wind measurement component measures wind speed and direction.

[0086] Wind force measurement components are installed on wind tubes at different heights to measure wind forces at different heights. Zigbee communication modules are used to send data with millisecond-level timestamps, so as to accurately analyze the comprehensive impact of wind forces at different heights on wind blade installation at the same time.

[0087] In addition, multiple wind measurement components are installed at the boom and hook ends of the lifting equipment to monitor and collect wind conditions at different positions and heights of the lifting equipment in real time, including at least the wind at the high end of the boom and the end of the rope.

[0088] In addition, the wind measurement component uses a microcomputer chip to transmit data through a communication module (4G / 5G, WiFi, GPRS, Zigbee, Lora, etc.).

[0089] Furthermore, the ground meteorological environment collection module also includes: a wind speed and meteorological collection module installed in the constructed wind turbine generator set.

[0090] It is understandable that the wind turbine has its own wind speed and meteorological data collection module; connecting it and introducing more data sources will further improve the accuracy of meteorological monitoring and forecasting.

[0091] The lifting equipment data monitoring and collection system includes: lifting equipment, and lifting parameter monitoring components.

[0092] The lifting equipment consists of: one or more large-tonnage lifting equipment; it can be a single lifting equipment, or a cluster of multiple lifting equipment, including a main crane and an auxiliary crane.

[0093] like Figure 3 As shown; the hoisting parameter monitoring component is installed on the hoisting equipment to collect and monitor: hoisting weight, hoisting output, cable tension, lifting height, and hydraulic parameters.

[0094] In addition, the lifting parameter monitoring component also includes: MEMS chip; using the MEMS chip to determine the spatial posture (pitch angle yaw, yaw angle pitch) information of each component in the lifting equipment.

[0095] Among them, Figure 4 , 5 As shown; the pitch angle is represented by γ and the deflection angle is represented by θ.

[0096] Use GPS to obtain the position of the cab (x0, y0, z0), the installation position length L2 of the MEMS chip on the boom, and calculate the position of the hook (x1, y1, z1).

[0097] where s l Indicates the length from the hook to the highest point of the long arm;

[0098] x1=L2×cosγ×cosθ+x0 ;

[0099] y1=L2×cosγ×sinθ+y0 ;

[0100] z1=L2×sinγ+z0-s l ;

[0101] The forearm posture can also be solved using the above method.

[0102] If it is a tower crane, γ is equal to 0 degrees.

[0103] like Figure 6 , 7 , as shown in 8; During operation:

[0104] The hoisting digital twin system is pre-installed with a hoisting equipment model;

[0105] The real form of the lifting equipment is calculated through the spatial posture information of each mechanism of the lifting equipment, and the position change is displayed to achieve the purpose of interaction between the physical equipment and the digital model;

[0106] The climate data transmitted by the ground meteorological environment acquisition module and the hoisting parameters transmitted by the hoisting data monitoring and acquisition system are automatically matched in the knowledge base;

[0107] If the alarm information is matched, an alarm will be automatically sounded and linked to the corresponding knowledge base according to the threshold; the disposal measures obtained in advance through various simulations and expert opinions will prompt the lifting operator how to proceed with the next operation.

[0108] The position transformation and knowledge base matching of the hoisting digital twin system include the following steps.

[0109] a11) Establish a digital model of the lifting equipment.

[0110] a12) According to the spatial posture information obtained from the MEMS chip, the digital model is transformed to form a digital mapping from real lifting to virtual lifting.

[0111] The transformation of the tower crane's spatial position is expressed by formula 1

[0112] [x0 y0 z0 1]=[x1 y1 z1 1]*W;

[0113] Among them, [x0 y0 z0 1] represents the original coordinates, [x1 y1 z1 1] represents the changed coordinates, and the transformation matrix W is expressed by formula 2

[0114]

[0115] Among them, a ij (i=1,2,3,j=1,2,3) represents the rotation component of the model about the xyz axis, a x 、a y 、a z Indicates the offset of the model on the xyz axis.

[0116] a13) Early warning, alarm and disposal;

[0117] During the lifting operation, the on-site parameters are compared with the knowledge base parameters; if they are close to the threshold conditions, a warning is issued; if they reach the threshold conditions, an alarm is issued, and response measures are popped up for the operator's reference.

[0118] In step a13:

[0119] Add boundary conditions, perform numerical calculations and simulations, and form a corresponding lifting knowledge base;

[0120] Numerical analysis is used to simulate the lifting conditions under various adverse working conditions, and the thresholds and countermeasures under various adverse conditions are marked. These data and operating measures are stored in the system and displayed accordingly according to the external environment and real-time operations.

[0121] For example, add the safe load data and safe operation range data of the lifting equipment to form a lifting knowledge base corresponding to this operation; integrate into the current scene to analyze the impact of external environmental conditions (such as strong winds, rain and snow) on lifting; calculate the impact range of different adverse conditions, form pre-warning thresholds, and generate response measures (such as reducing the lifting speed in windy weather, adjusting the traction direction of the auxiliary hoist on the hoisted object, etc.); with the real-time changes of the external environment and lifting operations, dynamic calculation and analysis are performed to compare the on-site parameters with the knowledge base parameters; when the threshold is triggered, the pre-warning and response measures are displayed.

[0122] The formation of the lifting knowledge base includes the following steps.

[0123] c11) Establish a wind field model;

[0124] Based on the collected meteorological data, a wind field model is constructed using the computational fluid dynamics (CFD) algorithm. The air flow around the wind duct and on the lifting path is visualized and simulated to accurately display the velocity vector, vortex distribution and other characteristics of the wind flow at different positions. Through continuous optimization of the wind field model, it can accurately reflect the actual wind conditions at the construction site, providing a basis for the subsequent analysis of the impact of wind blade lifting. Among them, appropriate boundary conditions are set in the model, taking into account the terrain (such as the impact of different terrains such as mountains and plains on wind) and the disturbance of the wind field caused by surrounding obstacles, so that the simulated wind field is as close to the real scene as possible.

[0125] c12) Force analysis model of hoisted objects;

[0126] Combined with the wind field model, a force analysis model of the hoisted object during the hoisting process is constructed; using the principles of aerodynamics, taking into account factors such as the shape, size, material, and weight of the hoisted object, the lift, drag, torque and other forces and moments of the hoisted object under different wind forces and wind directions are calculated;

[0127] Finite element analysis is used to discretize the hoisted object into multiple tiny units, and the stress condition of each unit under the action of wind flow is accurately calculated; then, the overall stress state of the hoisted object is obtained by integration; for example, for a 50-meter-long, 3-ton wind blade, based on its airfoil parameters and existing wind data, the force magnitude and direction changes of each part of the hoisted object under different wind speed and wind direction combinations can be calculated.

[0128] c13) Simulate adverse working conditions;

[0129] Simulate the impact of adverse working conditions on the lifting operations of hoisted objects.

[0130] c14) Setting of impact assessment indicators;

[0131] Define the index used to measure the effect of wind flow on lifting operations.

[0132] For example: the swing amplitude of the hoisted object: the real-time swing angle of the hoisted object during the hoisting process is measured by a gyroscope, and the relationship between different wind forces and the swing amplitude of the hoisted object is analyzed; when the swing amplitude exceeds a certain threshold (such as ±10 degrees), it affects the precise docking of the hoisted object and the tower, and corresponding adjustment measures need to be taken at this time.

[0133] For example, the hoisted objects are subjected to stress; appropriate stress ranges are set according to different hoisted objects. When the stress on the hoisted objects under the action of wind flow exceeds the range, the hoisted objects will be damaged, and corresponding adjustment measures need to be taken at this time.

[0134] For example, the lifting equipment bears tension; the lifting equipment is set to a tension that it can safely withstand; when the tension of wind flow and hoisted objects on the lifting equipment exceeds the safe range, it is easy to cause dangers such as equipment overturning; at this time, corresponding adjustment measures need to be taken.

[0135] c15) Forming thresholds and response measures;

[0136] Based on various evaluation indicators, the warning and alarm thresholds are calculated comprehensively; based on the simulation analysis results, corresponding response measures are formed.

[0137] For example, if the swing amplitude of the hoisted object is too large, add a guide rope; if the hoisted object is subjected to too much force, temporarily adjust the windward direction of the hoisted object; if the load-bearing capacity of the hoisting equipment is exceeded, stop the operation and add more hoisting equipment.

[0138] Furthermore, the hoisting digital twin system also includes: digitized hoisting objects.

[0139] The data monitoring and collection system for hoisting equipment also includes: a camera installed on the hoisting equipment and facing the hoisted object, and a gyroscope placed on the hoisted object. When the hoisted object is a wind tube, the gyroscopes are placed at both ends of the wind tube to monitor the vertical shape of the wind tube; when the hoisted object is a fan blade, the gyroscopes are placed at the tips of the three fan blades to monitor the swing angle of the fan blade.

[0140] It also includes: a wind force measurement component installed at the tip of the wind blade.

[0141] In the early stage of the lifting operation, if the wind is strong, try to make the overall vertical surface of the wind blade consistent with the wind direction to reduce the impact of wind on the lifting; in the later stage of the lifting operation, adjust the vertical shape of the docking surface of the wind blade through the gyroscope data.

[0142] Furthermore, the hoisting digital twin system also includes:

[0143] Optimal lifting time and operation control time calculation model.

[0144] Using historical wind data and weather forecast information, predict the wind changes in different periods in the future (such as the next 24 hours). Combined with the lifting impact assessment indicators, set the "lifting operation threshold range"; for example, wind speeds of 3-8m / s and wind direction change rates within ±10° per hour are more suitable wind conditions for lifting; in the predicted wind time series, select the time periods that meet the threshold range as potential optimal lifting time windows. For each optimal lifting time window, consider the time required for the standard process of wind blade lifting, equipment preparation time, and reserved emergency handling time, and calculate the corresponding lifting operation control time; for example, the wind blade lifting process takes 30 minutes from lifting to installation, 10 minutes for equipment preparation, and 10 minutes for emergency handling. If a certain optimal lifting time window is 9:00-10:30 am, then the lifting operation control time is 8:30-10:30 am, ensuring that the entire lifting operation can be successfully completed under suitable wind conditions.

[0145] The following steps are included in the prediction of wind change conditions.

[0146] Multi-source meteorological data fusion: Satellite meteorological data, surrounding meteorological station data, and numerical weather prediction (NWP) model data are introduced, and multi-source data are fused through Kalman filtering and Bayesian estimation methods to improve the accuracy and reliability of meteorological data; in addition, autoencoders are used to reduce noise, and generative adversarial networks (GANs) are used to enhance the data set to fill possible data gaps.

[0147] Kalman filter formula in, is the state estimate, K k is the Kalman gain, z k is the observed value, H is the observation matrix. Bayesian estimation is done through the posterior probability To update the model parameters θ, where P(x|θ) is the likelihood function and P(θ) is the prior distribution.

[0148] Wind prediction model establishment: Analyze historical wind data, including stationarity analysis, model identification, parameter estimation and diagnostic test: In addition to the traditional ARIMA model, long short-term memory variant GRU (Gated Recurrent Unit) and LSTM network are used for time series analysis; the LSTM memory unit update rule is Among them, ft 、i t , They represent the forget gate, input gate and candidate memory respectively; at the same time, the XGBoost algorithm is used for feature selection and model optimization to improve the prediction accuracy; the long short-term memory network (LSTM) is used to train the historical meteorological data to predict the key meteorological parameters such as wind speed and wind direction in the future; at the same time, the support vector machine (SVM) is used to classify and regress the meteorological data to warn of extreme weather events; for the support vector machine (SVM), the objective function is minimized At the same time, the constraints y i (ω·x i +b)≥1-ξ i ,ξ i ≥0; where ω is the weight vector, C is the penalty coefficient, ξ i It is a slack variable; combined with the RBF kernel function for classification tasks; the Transformer architecture is introduced to capture complex time dependencies through the self-attention mechanism.

[0149] Anomaly detection and early warning: Use K-means clustering algorithm to monitor abnormal changes in meteorological data in real time and issue alarms in time; use Bayesian network to model the uncertainty of meteorological data and evaluate the risk level of meteorological conditions in real time;

[0150] Visualization of meteorological data: The real-time meteorological data of the construction site is displayed through 3D visualization technology, combined with virtual reality (VR) technology, so that operators can intuitively understand the meteorological conditions; at the same time, combined with geographic information system (GIS) technology, the meteorological data is combined with the geographic information of the construction site to achieve geographic visualization of meteorological data.

[0151] The wind power large-scale hoisting monitoring system based on digital twin proposed in the present invention is mainly aimed at the hoisting of large wind power equipment. It can timely obtain meteorological information near the hoisting site, and use the knowledge base of the twin system to pre-alarm the entire hoisting process, as well as guide the response measures for hoisting under various adverse conditions, to meet the risk control of the wind power large-scale hoisting process.

[0152] In the present invention, the ground meteorological environment acquisition module covers small meteorological stations, wind measurement components at different locations, and wind speed and meteorological acquisition modules of constructed wind turbines, which can collect a variety of meteorological data; and introduce multi-source data to improve the accuracy and reliability of meteorological monitoring; wind measurement components are widely distributed, can measure wind forces at different heights and locations, and are accompanied by millisecond-level timestamps, which is convenient for accurately analyzing the comprehensive impact of wind forces at different heights at the same time on hoisting; the hoisting data monitoring and acquisition system collects hoisting weight, output, cable tension, lifting height, hydraulic parameters, etc., and uses MEMS chips to determine the spatial posture of each component of the hoisting equipment, providing detailed data support for precise hoisting; the digital twin model is based on Based on the collected data, the physical equipment and digital models can interact and the position changes of the lifting equipment can be displayed in real time. By automatically matching climate data and lifting parameters, it can automatically alarm when an alarm message appears, and associate the corresponding knowledge base to prompt the operator to take countermeasures, covering the thresholds and response strategies under various adverse working conditions. A variety of models are established to assist lifting, such as wind field models to visualize and simulate air flow, and hoisting object force analysis models to accurately calculate the force, providing a basis for the analysis of the impact of lifting operations. It has an optimal lifting time and operation control time calculation model, which integrates historical wind data, weather forecasts and other information, selects suitable time periods for lifting, calculates the corresponding operation control time, and ensures that lifting is carried out under suitable wind conditions.

[0153] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0154] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A wind power large-scale hoisting monitoring system based on digital twin, characterized in that: include: Ground meteorological environment acquisition module; Hoisting data monitoring and collection system; Hoisting digital twin system: includes digital hoisting equipment, integrated climate warning system, hoisting parameter display system, and knowledge base system.

2. The wind power large-scale hoisting monitoring system based on digital twin according to claim 1 is characterized in that: The ground meteorological environment acquisition module includes: a small meteorological station, and a wind force measurement component installed on a hoisting device and a wind tube; The small meteorological station is installed near the construction site; the wind measurement components are respectively arranged on wind tubes at different heights to measure the wind forces at different heights; a plurality of wind measurement components are arranged on the boom and hook ends of the lifting equipment to monitor and collect the wind conditions at different positions and heights of the lifting equipment in real time.

3. The wind power large-scale hoisting monitoring system based on digital twin according to claim 1 is characterized in that: The ground meteorological environment acquisition module also includes: a wind speed and meteorological acquisition module arranged on the constructed wind turbine generator set.

4. The wind power large-scale hoisting monitoring system based on digital twin according to claim 1 is characterized in that: The hoisting equipment data monitoring and collection system comprises: hoisting equipment, and hoisting parameter monitoring components; The hoisting parameter monitoring component is installed on the hoisting equipment to collect and monitor: hoisting weight, hoisting output, cable tension, lifting height, hydraulic parameters; The hoisting parameter monitoring component also includes: a MEMS chip; Use MEMS chips to determine the spatial position information of each component in the lifting equipment; Use GPS to obtain the position of the cab (x0, y0, z0), the installation position length L2 of the MEMS chip on the boom, and calculate the position of the hook (x1, y1, z1); where s l Indicates the length from the hook to the highest point of the long arm; x1=L2×cosγ×cosθ+x0; y1=L2×cosγ×sinθ+y0; z1=L2×sinγ+z0-s l ; If it is a tower crane, γ is equal to 0 degrees.

5. The wind power large-scale hoisting monitoring system based on digital twin according to claim 1 is characterized in that: While working: The hoisting digital twin system is pre-installed with a hoisting equipment model; Calculate the true shape of the lifting equipment through the spatial posture information of each mechanism of the lifting equipment; The climate data transmitted by the ground meteorological environment acquisition module and the hoisting parameters transmitted by the hoisting data monitoring and acquisition system are automatically matched in the knowledge base; If the alarm information is matched, an alarm will be automatically sounded and linked to the corresponding knowledge base according to the threshold; the disposal measures obtained in advance through various simulations and expert opinions will prompt the lifting operator how to proceed with the next operation.

6. The wind power large-scale hoisting monitoring system based on digital twin according to claim 5 is characterized in that: The position transformation and knowledge base matching of the hoisting digital twin system includes the following steps: a11) Establish a digital model of the lifting equipment; a12) According to the spatial posture information obtained from the MEMS chip, the digital model is transformed to form a digital mapping from real lifting to virtual lifting; The transformation of the tower crane's spatial position is expressed by formula 1 [x0 y0 z0 1]=[x1 y1 z1 1]*W; Among them, [x0 y0 z0 1] represents the original coordinates, [x1 y1 z1 1] represents the changed coordinates, and the transformation matrix W is expressed by formula 2 Among them, a ij (i=1,2,3,j=1,2,3) represents the rotation component of the model about the xyz axis, a x 、a y 、a z Indicates the offset of the model on the xyz axis; a13) Early warning, alarm and disposal; During the lifting operation, the on-site parameters are compared with the knowledge base parameters; if they are close to the threshold conditions, a warning is issued; if they reach the threshold conditions, an alarm is issued, and response measures are popped up for the operator's reference.

7. The wind power large-scale hoisting monitoring system based on digital twin according to claim 6 is characterized in that: In step a13: add boundary conditions, perform numerical calculations and simulations, and form a corresponding hoisting knowledge base; use numerical analysis to simulate the hoisting conditions under various adverse conditions, and indicate the thresholds and countermeasures under various adverse conditions, store these data and operating measures in the system, and display them accordingly according to the external environment and real-time operations.

8. The wind power large-scale hoisting monitoring system based on digital twin according to claim 7 is characterized in that: The formation of the lifting knowledge base includes the following steps: c11) Establish a wind field model; Based on the collected meteorological data, the wind field model is constructed using computational fluid dynamics algorithms. The air flow around the wind tube and on the lifting path is visualized and simulated to accurately display the wind flow characteristics at different locations. c12) Force analysis model of hoisted objects; Combined with the wind field model, a force analysis model of the hoisted object during the hoisting process is constructed; using the principles of aerodynamics, taking into account factors such as the shape, size, material, and weight of the hoisted object, the lift, drag, torque and other forces and moments of the hoisted object under different wind forces and wind directions are calculated; using the finite element analysis method, the hoisted object is discretized into multiple tiny units, and the force of each unit under the action of wind flow is accurately calculated; then, the overall force state of the hoisted object is obtained by integration; c13) Simulate adverse working conditions; Simulate the impact of adverse working conditions on the hoisting operation of hoisted objects; c14) Setting of impact assessment indicators; Define indicators to measure the impact of wind flow on lifting operations; c15) Forming thresholds and response measures; Based on various evaluation indicators, the warning and alarm thresholds are calculated comprehensively; based on the simulation analysis results, corresponding response measures are formed.

9. The wind power large-scale hoisting monitoring system based on digital twin according to claim 1 is characterized in that: The hoisting digital twin system also includes: a digitized hoisting object; The lifting equipment data monitoring and acquisition system further includes: a camera mounted on the lifting equipment and facing the lifting object, and a gyroscope mounted on the lifting object; When the hoisted object is a wind tube, the gyroscope is placed at both ends of the wind tube; When the hoisted object is a wind blade, the gyroscope is placed at the tip of the three wind blades; It also includes: a wind force measurement component installed at the tip of the wind blade.

10. The wind power large-scale hoisting monitoring system based on digital twin according to claim 1 is characterized in that: The hoisting digital twin system also includes: an optimal hoisting time and operation control time calculation model; Utilize historical wind data and weather forecast information to predict wind force changes at different times in the future. Combined with the lifting impact assessment indicators, set the "lifting operation threshold range". For each optimal lifting time window, consider the time required for the standard process of wind blade lifting, equipment preparation time, and reserved emergency handling time to calculate the corresponding lifting operation control time.

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