A multi-dimensional quality and safety intelligent analysis and early warning method for wind power construction

Abstract

The application discloses a kind of wind power construction multidimensional quality safety intelligent analysis and early warning method, belong to wind power construction intelligent monitoring and safety early warning field.Through the multi-source sensor network collection environment, equipment, personnel and process parameters, the digital twin model of construction scene is constructed, and the combined risk analysis is carried out using multi-modal deep learning, the three-dimensional real-time risk index of personnel, equipment, environment is output, the differentiated early warning and response strategy is generated, and the closed-loop optimization is realized through continuous learning.The application can significantly improve the accuracy of wind power construction risk identification and the timeliness of response, realize the change from passive response to active prevention and control.

Description

Technical Field

[0001] This invention relates to intelligent monitoring and safety early warning in wind power construction, and more particularly to a multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction. Background Technology

[0002] With the large-scale development of the wind power industry, construction scenarios are becoming increasingly complex, involving multiple risks such as high-altitude operations, heavy lifting, and extreme weather. Traditional management relies on manual inspections and experience-based judgment, resulting in problems such as delayed response, insufficient coverage, and data gaps. Existing technologies mostly focus on single-dimensional monitoring (such as weather warnings and video surveillance), lacking joint analysis and coordinated early warning of multi-dimensional data, making it difficult to achieve accurate risk identification and proactive intervention in complex and coupled scenarios. Furthermore, the discrete storage and post-event traceability of quality and safety data cannot support dynamic optimization and closed-loop risk management of the construction process. Therefore, there is an urgent need for a method that integrates multi-dimensional data and has real-time analysis and adaptive early warning capabilities to improve the quality and safety management of wind power construction. Summary of the Invention

[0003] Purpose of the Invention: The purpose of this invention is to provide a multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction. By constructing a multi-source sensing network and a digital twin model, combined with multimodal deep learning and continuous learning mechanisms, it achieves real-time analysis, dynamic early warning, and closed-loop optimization of construction quality and safety. This aims to solve the problems of insufficient multi-dimensional data fusion, delayed risk identification and response, and lack of closed-loop optimization in quality control in existing technologies.

[0004] Technical Solution: A multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction, comprising the following steps:

[0005] S1. Data Acquisition: Multi-dimensional real-time monitoring data is acquired through a multi-source sensor network deployed at the wind power construction site. This data includes: environmental parameters, equipment status parameters, personnel behavior data, and key parameters of the construction process. Environmental parameters include wind speed, wind direction, temperature, humidity, air pressure, precipitation, visibility, and ultraviolet radiation. Equipment status parameters include the torque, angle, outrigger stress, vibration acceleration, and operating temperature of the hoisting machinery. Personnel behavior data is acquired through video recognition and positioning terminals, including personnel location, posture, whether they are wearing safety equipment, and their work trajectory. Key parameters of the construction process include the concrete temperature and setting time of the foundation pouring, the verticality of the tower, bolt tightening torque, weld temperature, and cable laying path.

[0006] S2. Multi-dimensional feature fusion: The above data is synchronized in time and aligned in space to construct a unified digital twin model of the construction scene. The correlation between the time dimension and the spatial dimension is integrated in this model to obtain a multi-dimensional spatiotemporal feature sequence.

[0007] S3. Risk Assessment: A multimodal deep learning model is used to jointly analyze multidimensional spatiotemporal feature sequences and output real-time risk quantification indicators in at least three dimensions, including: personnel operation safety risk index, equipment operation stability risk index, and environment and process coupling risk index.

[0008] S4. Early Warning Decision: Based on various risk indices and their changing trends, combined with preset thresholds and historical risk evolution patterns, generate differentiated early warning levels and response strategies, including at least one of the following: immediate work stoppage, adjustment of work processes, increased protection, enhanced inspection, and normal monitoring.

[0009] S5. Closed-loop optimization: Continuously collect construction process data and handling results after early warning to form a risk-handling-effect closed-loop sample, and update the risk assessment model and early warning decision parameters online.

[0010] Furthermore, the multi-source sensor network includes: meteorological monitoring stations, laser wind radar, airborne multispectral and thermal imaging cameras, ground-based lidar, UAV aerial survey systems, torque and angle sensors for hoisting machinery, outrigger pressure sensors, vibration and temperature sensors, personnel positioning tags, smart safety helmets, camera arrays, concrete temperature sensors, tilt sensors, bolt torque sensors, weld temperature sensors, and cable laying trajectory recording devices.

[0011] Furthermore, the multi-dimensional feature fusion step specifically includes: aligning the timestamps of each data source to a unified clock, performing spatial registration based on BeiDou positioning and point cloud registration, mapping the coordinates of four types of elements—personnel, equipment, environment, and processes—in the digital twin model, and constructing a four-dimensional tensor representation with time steps as the sequence, elements as the channel, and space as the grid, and inputting it into a multimodal deep learning model.

[0012] Furthermore, the multimodal deep learning model employs an attention mechanism to fuse features from different modalities and captures the dynamic correlation between personnel movement trajectories, equipment operating status, and environmental parameters through a spatiotemporal graph convolutional network. The model takes historical time windows as input and outputs predictions of multiple risk indices within a preset future time period.

[0013] Furthermore, the early warning decision-making step adopts a risk-response matrix, generates different levels of early warning based on the risk index and rate of change, and links the on-site alarm terminal and control interface to realize the restriction or shutdown of hoisting equipment, vibration reminders for personnel positioning equipment, and dynamic expansion of electronic fences.

[0014] Furthermore, the closed-loop optimization step also includes: constructing a knowledge graph of risk-disposal-effect based on the correlation analysis between risk disposal results and subsequent construction quality data, continuously optimizing the early warning threshold and strategy parameters, and realizing the adaptive evolution of the model.

[0015] Furthermore, the method also includes: generating a risk pre-assessment report before key construction nodes, including the probability of extreme weather, equipment load distribution, personnel fatigue index and process conflict points within the prediction window, and dynamically updating the early warning level based on real-time data during construction.

[0016] Furthermore, the digital twin model supports real-time visualization and keeps synchronized with multi-source data from the site, enabling it to trace back historical states and predict future scenarios.

[0017] Furthermore, the method also includes: adopting stage-specific risk indicators and early warning strategies for different construction stages (foundation construction, tower hoisting, blade installation, electrical connection), and performing conflict detection and scheduling optimization between processes based on a digital twin model.

[0018] Beneficial effects:

[0019] (1) This invention achieves comprehensive sensing of all elements of the wind power construction site by deploying four types of sensing devices: environment, equipment, personnel, and processes. Compared with traditional single-dimensional monitoring methods, this invention is the first to uniformly collect and integrate environmental parameters (wind speed, wind direction, temperature, humidity, air pressure, precipitation, visibility, ultraviolet radiation), equipment status parameters (torque, angle, outrigger force, vibration acceleration, and operating temperature of hoisting machinery), personnel behavior data (personnel location, posture, whether they are wearing safety equipment, and work trajectory), and key node parameters of construction processes (concrete temperature, setting time, tower verticality, bolt tightening torque, weld temperature, and cable laying path). This multi-dimensional data fusion method can capture complex risk coupling relationships that traditional methods cannot identify.

[0020] (2) This invention innovatively combines digital twin technology with a multimodal deep learning model to construct a unified digital twin model of a wind power construction site. It captures the dynamic correlation between personnel movement trajectories, equipment operating status, and environmental parameters through a spatiotemporal graph convolutional network. The digital twin model supports real-time visualization, historical status retrospection, and future scenario projection, providing a complete four-dimensional spatial-temporal-element analysis framework for risk assessment. The multimodal deep learning model employs an attention mechanism to fuse features from different modalities, enabling it to automatically learn and identify complex nonlinear risk coupling patterns.

[0021] (3) This invention constructs a complete closed-loop system from data perception to risk identification and then to linkage response. The early warning decision-making step adopts a risk-response matrix, generates differentiated early warning levels (normal, attention, warning, danger) based on the risk index and rate of change, and links the on-site alarm terminal and control interface to achieve automated response. Specific linkage functions include: limiting or stopping the hoisting equipment, vibration reminders for personnel positioning equipment, dynamic expansion of electronic fences, and audible and visual prompts for on-site alarm terminals.

[0022] (4) This invention establishes a closed-loop optimization mechanism, continuously collecting construction process data and handling results after early warning to form a risk-handling-effect closed-loop sample. The risk assessment model and early warning decision parameters are updated through online learning. Simultaneously, based on the correlation analysis between risk handling results and subsequent construction quality data, the system constructs a risk-handling-effect knowledge graph, accumulating historical experience and optimizing early warning thresholds and strategy parameters. This continuous learning mechanism enables the system to adapt to the risk characteristics of different projects, regions, and climates, achieving continuous optimization of the model and strategies.

[0023] (5) This invention possesses excellent environmental adaptability and scenario universality through adaptive threshold adjustment and stage-specific strategy configuration. The system can automatically adapt to different geographical environments such as mountains, plains, deserts, and seas, and automatically adjust early warning strategies for different climatic conditions such as high temperature, low temperature, strong wind, rainstorm, and lightning. At the same time, the system adopts stage-specific risk indicators and early warning strategies for different construction stages such as foundation construction, tower hoisting, blade installation, and electrical connection, and performs conflict detection and scheduling optimization between processes based on a digital twin model. This scenario adaptability enables this invention to maintain efficient risk management capabilities in different projects, different stages, and different environments. Attached Figure Description

[0024] Figure 1 This is an overall flowchart of the method of the present invention;

[0025] Figure 2 A schematic diagram of a multi-source sensor network deployment;

[0026] Figure 3 A schematic diagram of the structure of a digital twin model and a risk assessment model;

[0027] Figure 4 This is a schematic diagram of the early warning decision-making and closed-loop optimization process. Detailed Implementation

[0028] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] Example 1

[0030] This embodiment uses the tower hoisting stage of a mountain wind farm as an example for illustration.

[0031] The following multi-source sensor network will be deployed on-site:

[0032] Meteorological monitoring station: Real-time collection of environmental parameters such as wind speed, wind direction, temperature, humidity, air pressure, precipitation, visibility, and ultraviolet radiation;

[0033] Laser wind radar: provides high-precision three-dimensional wind field distribution information;

[0034] Airborne multispectral and thermal imaging camera: mounted on a drone to monitor a wide area of ​​the construction site;

[0035] Ground-based lidar: Constructing high-precision 3D point cloud models of construction sites;

[0036] Unmanned aerial survey system: Regularly conducts patrols and takes pictures to acquire panoramic images;

[0037] Lifting machinery sensors: torque sensors, angle sensors, outrigger pressure sensors, vibration and temperature sensors;

[0038] Personnel positioning tag: Beidou / GPS dual-mode positioning, accuracy better than 0.5 meters;

[0039] Smart safety helmet: integrates camera, accelerometer and microphone, can recognize wearing status and behavior;

[0040] Camera array: Covers key work areas, enabling comprehensive monitoring without blind spots;

[0041] Concrete temperature sensor: monitors changes in concrete temperature during foundation pouring;

[0042] Tilt sensor: monitors the verticality of the tower;

[0043] Bolt torque sensor: records bolt tightening torque in real time;

[0044] Weld temperature sensor: monitors the temperature during the welding process;

[0045] Cable laying trajectory recording device: records the cable laying path and depth.

[0046] The specific implementation process is as follows:

[0047] (1) Data collection

[0048] The system collects multi-dimensional data in real time through the aforementioned multi-source sensor network. The data sampling frequency is set according to the parameter characteristics: meteorological data is collected once per minute, equipment status data once per second, personnel behavior data once per second, and key process node parameters are collected in real time.

[0049] (2) Multi-dimensional feature fusion

[0050] Perform time synchronization and spatial alignment on the collected data:

[0051] Time synchronization: Align the timestamps of all data sources to a unified clock, with the error controlled within 100 milliseconds;

[0052] Spatial registration: Based on BeiDou positioning and point cloud registration technology, all elements are mapped to a unified coordinate system;

[0053] Digital twin construction: In the digital twin model, four types of elements, namely personnel, equipment, environment and process, are mapped by coordinates to construct a four-dimensional tensor representation (time × space × element × feature).

[0054] (3) Risk assessment

[0055] A multimodal deep learning model is used to jointly analyze multidimensional spatiotemporal feature sequences:

[0056] Model architecture: Based on the Transformer attention mechanism, fusing features from different modalities;

[0057] Spatiotemporal modeling: Spatiotemporal graph convolutional network (ST-GCN) is used to capture the dynamic relationship between personnel movement trajectory, equipment operating status and environmental parameters;

[0058] Output: Using historical 60-minute data as input, output the risk index in three dimensions for the next 15 minutes.

[0059] (4) Early warning decision

[0060] Based on various risk indices and their changing trends, differentiated early warnings are generated:

[0061] Risk classification: Normal (green), Attention (yellow), Warning (orange), Danger (red);

[0062] Risk-Response Matrix: Matching corresponding response strategies based on risk level and rate of change;

[0063] Linkage control: When the risk reaches the red level, the hoisting equipment will be immediately restricted or stopped, the personnel positioning terminal will be triggered to vibrate and the range of the electronic fence will be expanded.

[0064] (5) Closed-loop optimization

[0065] Continuously collect construction process data and handling results after early warning:

[0066] Form a closed-loop model of risk-control-effect;

[0067] Update risk assessment model parameters online;

[0068] Construct a risk-response-effect knowledge graph to accumulate response experience;

[0069] Adaptive optimization of early warning thresholds and strategy parameters.

[0070] Example 2

[0071] During the concrete foundation pouring stage, the following specific control measures are implemented:

[0072] Quality monitoring

[0073] Concrete temperature monitoring: Real-time monitoring of the internal temperature distribution of concrete using pre-embedded temperature sensors;

[0074] Setting time prediction: Based on historical temperature data, predict the setting time of concrete;

[0075] Flatness monitoring: Real-time monitoring of the flatness of the base surface using ground-based lidar.

[0076] Intelligent regulation

[0077] When the concrete temperature is below the preset threshold (15℃), the system automatically sends a heating command to the curing equipment to ensure the quality of concrete curing; when the flatness deviation exceeds the set range (±2mm), the system triggers a rework warning and process adjustment suggestions.

[0078] Digital twin applications

[0079] Data throughout the entire process is updated synchronously in the digital twin model, supporting the following functions:

[0080] Historical review: Query the construction status at any point in time;

[0081] Future projection: Based on predictive models, simulate the effects of different construction schemes;

[0082] Process traceability: When quality problems occur, the responsible link can be quickly located.

[0083] Example 3

[0084] In view of the special environment of offshore wind power construction, this method is adapted as follows:

[0085] Marine environmental monitoring

[0086] Increase marine meteorological parameters such as wave height, tides, ocean currents, and visibility;

[0087] Deploy AIS (Automatic Identification System) to monitor the location of construction vessels in real time;

[0088] Deploy marine weather radar to provide early warnings of typhoons and other extreme weather events.

[0089] Personnel safety management

[0090] Construction workers are equipped with smart life jackets that integrate BeiDou positioning, water fall detection, and one-button alarm functions;

[0091] Install electronic fences to prevent people from accidentally entering dangerous areas;

[0092] Real-time monitoring of personnel fatigue status to prevent fatigue-related work.

[0093] Equipment Collaborative Management

[0094] Based on AIS data, construct a probability ellipse for the future position of ships;

[0095] Dynamically calculate the collision risk index between ships and wind turbine pile foundations;

[0096] The vessel operation plan is automatically adjusted based on the risk level.

[0097] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction, characterized in that, Includes the following steps: S1. Data Acquisition: Multi-dimensional real-time monitoring data is acquired through a multi-source sensor network deployed at the wind power construction site. This data includes: environmental parameters, equipment status parameters, personnel behavior data, and key parameters of the construction process. Environmental parameters include wind speed, wind direction, temperature, humidity, air pressure, precipitation, visibility, and ultraviolet radiation. Equipment status parameters include the torque, angle, outrigger stress, vibration acceleration, and operating temperature of the hoisting machinery. Personnel behavior data is acquired through video recognition and positioning terminals, including personnel location, posture, whether they are wearing safety equipment, and their work trajectory. Key parameters of the construction process include the concrete temperature and setting time of the foundation pouring, the verticality of the tower, bolt tightening torque, weld temperature, and cable laying path. S2. Multi-dimensional feature fusion: The above data is synchronized in time and aligned in space to construct a unified digital twin model of the construction scene. The correlation between the time dimension and the spatial dimension is integrated in this model to obtain a multi-dimensional spatiotemporal feature sequence. S3. Risk Assessment: A multimodal deep learning model is used to jointly analyze multidimensional spatiotemporal feature sequences and output real-time risk quantification indicators in at least three dimensions, including: personnel operation safety risk index, equipment operation stability risk index, and environment and process coupling risk index. S4. Early Warning Decision: Based on various risk indices and their changing trends, combined with preset thresholds and historical risk evolution patterns, generate differentiated early warning levels and response strategies, including at least one of the following: immediate work stoppage, adjustment of work processes, increased protection, enhanced inspection, and normal monitoring. S5. Closed-loop optimization: Continuously collect construction process data and handling results after early warning to form a risk-handling-effect closed-loop sample, and update the risk assessment model and early warning decision parameters online.

2. The multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction according to claim 1, characterized in that, The multi-source sensor network includes: meteorological monitoring stations, laser wind radar, airborne multispectral and thermal imaging cameras, ground-based lidar, UAV aerial survey systems, torque and angle sensors for hoisting machinery, outrigger pressure sensors, vibration and temperature sensors, personnel positioning tags, smart safety helmets, camera arrays, concrete temperature sensors, tilt sensors, bolt torque sensors, weld temperature sensors, and cable laying trajectory recording devices.

3. The multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction according to claim 1, characterized in that, The multi-dimensional feature fusion steps specifically include: aligning the timestamps of each data source to a unified clock, performing spatial registration based on BeiDou positioning and point cloud registration, mapping the coordinates of four types of elements—personnel, equipment, environment, and processes—in the digital twin model, and constructing a four-dimensional tensor representation with time steps as the sequence, elements as the channel, and space as the grid, which is then input into a multimodal deep learning model.

4. The multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction according to claim 1, characterized in that, The multimodal deep learning model uses an attention mechanism to fuse features from different modalities and captures the dynamic correlation between personnel movement trajectories, equipment operating status and environmental parameters through a spatiotemporal graph convolutional network. The model takes historical time windows as input and outputs predictions of multiple risk indices within a preset future time period.

5. The multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction according to claim 1, characterized in that, The early warning decision-making process adopts a risk-response matrix, generates different levels of early warning based on the risk index and rate of change, and links the on-site alarm terminal and control interface to realize the restriction or shutdown of hoisting equipment, vibration reminders for personnel positioning equipment, and dynamic expansion of electronic fences.

6. The multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction according to claim 1, characterized in that, The closed-loop optimization steps also include: constructing a knowledge graph of risk-disposal-effect based on the correlation analysis between risk disposal results and subsequent construction quality data, continuously optimizing the early warning threshold and strategy parameters, and realizing the adaptive evolution of the model.

7. The multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction according to claim 1, characterized in that, The method also includes: generating a risk pre-assessment report before key construction nodes, including the probability of extreme weather, equipment load distribution, personnel fatigue index and process conflict points within the prediction window, and dynamically updating the early warning level based on real-time data during construction.

8. The multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction according to claim 1, characterized in that, The digital twin model supports real-time visualization and keeps synchronized with multi-source data from the site, enabling it to trace back historical states and predict future scenarios.

9. The multi-dimensional intelligent analysis and early warning method for quality and safety in wind power construction according to claim 1, characterized in that, The method also includes: adopting stage-specific risk indicators and early warning strategies for different construction stages, and performing conflict detection and scheduling optimization between processes based on a digital twin model.

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