Online monitoring and early warning system and method for high-altitude operations based on wireless sensors

Abstract

The present invention relates to the field of risk prediction technology, and in particular to an online monitoring and early warning system and method for aerial work based on wireless sensors. The method comprises the following steps: deploying a multi-type wireless sensor array on the aerial work platform to collect structural stress data, environmental temperature and humidity, and personnel status data, and constructing a multi-source standard data set; performing spatiotemporal alignment on the multi-source standard data set to generate a synchronized aerial data set; using the synchronized aerial data set to construct a three-dimensional feature space and generate a fusion feature matrix for aerial work; and generating an aerial work risk prediction model based on the fusion feature matrix for aerial work. Therefore, the present invention improves the risk prediction accuracy and real-time response capability in aerial work through the fusion of multi-source data, alignment of spatiotemporal features, dynamic simulation of risk evolution, and construction of a multi-level response mechanism, ultimately forming a comprehensive, systematic, and efficient safety management and control system.

Description

Technical Field

[0001] The present invention relates to the technical field of risk prediction, and in particular to an online monitoring and early warning system and method for aerial work based on wireless sensors. Background Art

[0002] Traditional height-based work safety monitoring systems primarily rely on single data sources or static sensors, such as temperature and humidity sensors and structural stress sensors. These approaches fail to fully consider the real-time integration and synchronization of multi-source data, limiting monitoring accuracy and real-time performance in dynamic and complex work environments. Existing monitoring systems often rely on localized data collection, lacking a holistic perspective and failing to fully reflect the global risks of height-based work. Even when incorporating multiple sensors, many systems fail to effectively address the spatiotemporal alignment and fusion of multidimensional data, resulting in temporal disarray and spatial errors between data, compromising the accuracy of risk assessments. Although some height-based work safety monitoring systems have incorporated model-based risk prediction methods, existing technologies often rely on simple threshold judgments or static models, failing to account for the dynamic impact of the work environment and workers, resulting in an inability to accurately and accurately reflect the evolving risk process in real time. Existing early warning mechanisms often utilize fixed thresholds for grading, lacking the flexibility to adapt to dynamic variations in variables such as wind speed, structural stress, and platform attitude angle in real-world work environments. This makes accurate early warnings difficult, especially in high wind speeds or under challenging environmental conditions, where emergency response capabilities are limited. Most existing high-altitude work monitoring systems lack the ability to integrate multi-dimensional and multimodal data and deep learning models. This hinders comprehensive, multi-layered risk analysis of high-altitude work environments. While some systems can perform data noise reduction and baseline drift compensation, they still struggle with data processing accuracy and efficiency. This can be especially problematic when real-time performance is critical, where system processing latency becomes a critical bottleneck. Summary of the Invention

[0003] Based on this, it is necessary to provide an online monitoring and early warning system and method for high-altitude operations based on wireless sensors to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a method for online monitoring and early warning of high-altitude operations based on wireless sensors is provided, the method comprising the following steps:

[0005] Step S1: deploying a multi-type wireless sensor array on the aerial work platform to collect structural stress data, environmental temperature and humidity, and personnel status data to construct a multi-source standard data set;

[0006] Step S2: Perform spatiotemporal alignment on the multi-source standard datasets to generate a synchronized high-altitude dataset; construct a three-dimensional feature space using the synchronized high-altitude dataset and generate a high-altitude work fusion feature matrix; generate a high-altitude work risk prediction model based on the high-altitude work fusion feature matrix;

[0007] Step S3: Using the high-altitude work risk prediction model to output the high-altitude risk prediction level, and perform risk control classification processing to obtain high-altitude risk classification transmission data; using the high-altitude risk classification transmission data to perform stress-wind speed-attitude joint simulation to obtain high-altitude risk evolution prediction results; using the high-altitude risk evolution prediction results to construct compensation parameters, and using the compensation parameters to optimize the high-altitude work risk prediction model to obtain a high-altitude work risk optimization model;

[0008] Step S4: Based on the high-altitude operation risk optimization model, intelligent deduction of the risk situation is performed, and hierarchical coding is performed to obtain a high-altitude multi-level prediction instruction set; a response rule library is constructed based on the high-altitude multi-level prediction instruction set, and early warning full-process monitoring data is generated; based on the early warning full-process monitoring data, three-dimensional visualization reconstruction is performed to obtain a high-altitude operation safety situation map.

[0009] The beneficial effect of the present invention is that, through multi-level data collection, processing and modeling, it helps to effectively improve the safety and risk management level of high-altitude operations. First, the deployment of multi-type wireless sensor arrays can simultaneously collect multi-source data such as structural stress, ambient temperature and humidity, and personnel status. The diversity of this data enables the system to comprehensively monitor the working environment and personnel's working status, laying the foundation for subsequent data analysis and modeling. After data collection, through spatiotemporal alignment and the construction of a three-dimensional feature space, a more accurate synchronized high-altitude data set can be generated, and a fusion feature matrix can be generated by combining this data set, further improving the correlation and accuracy of the data in the spatial and temporal dimensions. This process enables the high-altitude operation risk prediction model to comprehensively analyze the risk situation from multiple angles and dimensions when evaluating multiple risk factors, and improve the accuracy of risk prediction based on the fusion feature matrix. The risk level output by the risk prediction model can be graded, so that the response measures of different risk levels can be accurately triggered according to preset rules. At the same time, through the risk classification transmission data, stress-wind speed-attitude joint simulation can be performed, which can analyze the risk evolution process in high-altitude operations in real time, and further refine the model parameters through error differential comparison, thereby optimizing the risk prediction capability. The optimized risk prediction model for high-altitude operations provides more accurate risk level predictions and precisely codes the predicted results, generating a multi-level prediction instruction set. These instruction sets, through the establishment of a response rule base, provide clear operational guidelines for the subsequent early warning system, ensuring timely and efficient risk response measures. Through the application of visualization technology, the full-process early warning monitoring data can be converted into a three-dimensional safety situation map, providing an intuitive and easy-to-understand visualization. This helps managers understand the safety status of high-altitude operations in real time, make timely decisions, and effectively prevent safety accidents.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: deploying temperature and humidity sensors at the supporting points of the hanging basket with a sampling frequency of 1 Hz to collect temperature and humidity sensor data;

[0012] Step S12: a three-axis accelerometer and a gyroscope are set on the operator, with an attitude angle accuracy of 0.1 degrees and a sampling frequency of 50 Hz to collect operator status data;

[0013] Step S13: deploying a piezoelectric structural stress sensor at the seat belt anchorage point with a range of 0-50 kN, a sampling frequency of 10 Hz, and an accuracy of 0.5% to collect structural stress data;

[0014] Step S14: Using an adaptive Kalman filter to perform noise reduction processing on the temperature and humidity sensor data, personnel status data, and structural stress data, and adjusting the filter parameters according to the 0-100 Hz ambient noise spectrum to generate high-altitude work noise reduction data with a signal-to-noise ratio (SNR) greater than 25 dB;

[0015] Step S15: Baseline drift compensation is performed on the high-altitude work noise reduction data, where the long-term drift rate is <0.05% / month, to obtain a multi-source standard data set.

[0016] By deploying temperature and humidity sensors at the basket support points, triaxial accelerometers and gyroscopes on the workers, and piezoelectric structural stress sensors at the safety belt anchor points, the system comprehensively collects critical environmental, personnel, and structural data during high-altitude operations. This multi-source data provides a comprehensive foundation for subsequent risk prediction and safety monitoring. Due to the presence of noise interference from both equipment and the outside world, the use of an adaptive Kalman filter to reduce noise from multiple sensor data is particularly important. The Kalman filter optimizes filter parameters based on the signal-to-noise ratio (SNR), effectively denoising data within different signal frequency ranges and ensuring clearer and more accurate signals. Furthermore, adjusting filter parameters based on the 0-100Hz ambient noise spectrum further adapts to the influence of noise in different frequency bands in actual high-altitude operation environments, further improving the signal-to-noise ratio and reliability of the data. Next, baseline drift compensation is performed on the high-altitude operation data to eliminate the effects of long-term sensor drift, maintain long-term data stability, and enhance the reliability of the data obtained during long-term monitoring. The long-term drift rate is controlled to <0.05% / month, ensuring data accuracy and consistency and preventing drift from affecting prediction results. Ultimately, through this series of data processing steps, the resulting multi-source standardized dataset provides high-quality data support for monitoring and risk prediction during aerial work. This data can provide precise information for subsequent risk assessment, situation analysis, and decision support systems, helping to effectively reduce safety risks during aerial work. Therefore, from a data perspective, this process ensures the reliability and accuracy of monitoring data during aerial work through precise acquisition, noise suppression, signal optimization, and drift compensation, providing a solid foundation for subsequent safety warning and risk prediction models.

[0017] Preferably, step S2 includes the following steps:

[0018] Step S21: Using 50 Hz cubic spline interpolation to perform spatiotemporal alignment on the multi-source standard datasets to obtain a synchronized high-altitude dataset;

[0019] Step S22: Extract timestamps from the temperature and humidity sensor data and map them into a high-altitude work time axis; perform physical distribution analysis on the structural stress data and map them into spatial topological relationships, thereby completing the construction of the high-altitude work spatial axis; integrate feature data based on the synchronized high-altitude data set and construct a feature tensor to obtain the high-altitude work feature axis;

[0020] Step S23: performing spatiotemporal feature cross processing on the aerial work time axis, the aerial work space axis, and the aerial work feature axis, and constructing a three-dimensional feature space to obtain a aerial work fusion feature matrix;

[0021] Step S24: Generate a high-altitude work risk prediction model based on the high-altitude work fusion feature matrix.

[0022] This invention uses 50Hz cubic spline interpolation to perform spatiotemporal alignment of multi-source standard datasets, ensuring temporal and spatial consistency of data from different sources. This operation helps eliminate temporal and spatial biases during data acquisition, ensuring consistency and comparability of data from different sensors at the same time point, providing a unified foundation for subsequent analysis. Through this spatiotemporal alignment, the system obtains a synchronized high-altitude dataset, laying a solid foundation for subsequent data analysis and feature extraction. Secondly, by extracting timestamps from the temperature and humidity sensor data and mapping them onto the high-altitude work time axis, combined with physical distribution analysis of the structural stress data, a spatial axis for high-altitude work is further constructed. This process converts environmental and structural data into a spatiotemporal coordinate system that directly reflects the state of high-altitude work and environmental changes, enabling comparison and analysis of data from different dimensions within the same framework. Next, feature data integration and feature tensor construction are performed based on the synchronized high-altitude dataset, successfully mapping the multidimensional data onto the high-altitude work feature axis, thereby providing multidimensional, three-dimensional input data for the subsequent high-altitude work risk prediction model. This operation not only preserves the independence of each data source but also supports cross-analysis of data. On this basis, the time, space, and feature axes are cross-processed to construct a three-dimensional feature space and generate a fused feature matrix for high-altitude work. This matrix enables the system to comprehensively consider multiple influencing factors across time, space, and feature dimensions, effectively improving the comprehensiveness and accuracy of high-altitude work risk prediction. Ultimately, a high-altitude work risk prediction model is generated based on this fused feature matrix. From a data perspective, all these steps together form a highly integrated and accurate data processing framework, providing reliable and dynamic decision-making support for real-time high-altitude work risk prediction.

[0023] Preferably, step S24 includes the following steps:

[0024] Step S241: performing time series long-term dependency analysis on the aerial work fusion feature matrix and performing vectorization to obtain a time step feature vector;

[0025] Step S242: constructing a spatial topological structure for the aerial work fusion feature matrix and performing physical distance analysis on edge nodes to obtain spatial dimension feature data;

[0026] Step S243: performing a product of the time step feature vector and the spatial dimension feature data to generate interactive features, thereby obtaining cross-modal cross-feature data of aerial work;

[0027] Step S244: performing risk level classification main task analysis based on the cross-modal cross-feature data of aerial work to obtain cross-entropy loss data of the aerial work main task; performing stress prediction auxiliary task analysis based on the cross-modal cross-feature data of aerial work to obtain cross-entropy loss data of the aerial work auxiliary task;

[0028] Step S245: Hyperparameter tuning is performed on the cross entropy loss data of the main task of high-altitude work and the cross entropy loss data of the auxiliary task of high-altitude work, and the model is deployed in a lightweight manner to obtain a high-altitude work risk prediction model.

[0029] The present invention performs time series long-term dependency analysis on the aerial work fusion feature matrix, so that the system can identify and capture long-term temporal dependencies, which is crucial for processing data with significant time series characteristics. By vectorizing these long-term dependencies and generating time step feature vectors, the characteristics of each time point can accurately reflect its historical information and potential future changes. In this process, the processing method of time series data provides input data with strong timeliness and memory characteristics for the prediction of aerial work risks. Then, by constructing a spatial topological structure for the aerial work fusion feature matrix and performing physical distance analysis on the edge nodes, meaningful feature data is extracted from the spatial dimension. This operation clarifies the distribution relationship of spatial data, ensures that structural information and spatial influencing factors can be properly reflected in the analysis, and thus enhances the sensitivity of the risk prediction model to spatial factors. Subsequently, by multiplying the time step feature vector and the spatial dimension feature data to generate interactive features, the system can capture the interaction between the two dimensions of time and space. This cross-modal feature combination is crucial for accurately predicting potential risks in the aerial work process. By analyzing the obtained cross-modal cross-feature data for aerial work, further analysis of the main task of risk level classification and the auxiliary task of stress prediction is performed. This enables simultaneous risk level prediction and stress state monitoring within a multi-task framework, thus ensuring the model's stable operation in multi-task scenarios. By calculating the cross-entropy loss and tuning the model's hyperparameters, the model's accuracy and stability in predicting aerial work risks are ensured. Finally, after lightweight deployment, the resulting aerial work risk prediction model is capable of efficient and real-time risk prediction in practical applications. This series of data processing steps not only enhances the model's predictive capabilities but also ensures its wide applicability and efficiency in aerial work environments.

[0030] Preferably, step S3 of outputting the high-altitude risk prediction level using the high-altitude work risk prediction model and performing risk management and control grading processing includes:

[0031] Obtain real-time multi-source high-altitude data;

[0032] Use real-time multi-source high-altitude data to conduct prediction analysis on the high-altitude operation risk prediction model, and classify and transmit the analysis results to obtain the high-altitude risk prediction level. The classification and transmission based on the analysis results include:

[0033] When the classification result is risk level 0-1, it is classified as normal mode, sampling at 10Hz and transmitting at a data compression ratio of 5:1;

[0034] When the classification result is risk level 2-3, it is classified as early warning mode and transmitted at 50Hz sampling and a compression ratio of 2:1;

[0035] When the classification result is risk level 4, it is classified as emergency mode and sampled at 100 Hz for lossless transmission.

[0036] This invention collects real-time, multi-source high-altitude data, providing multi-dimensional, comprehensive information input for the risk prediction model. This data, encompassing diverse sources such as the environment, structure, and personnel status, is timely and diverse, providing essential support for subsequent analysis. By integrating this multi-source data, the system can accurately monitor multiple risk factors during high-altitude operations. Furthermore, based on this real-time, multi-source data, the risk prediction model analyzes and outputs a risk level, providing a basis for subsequent classified transmission strategies. This classified transmission mechanism, by quantitatively and qualitatively categorizing different risk levels, enables the adoption of different sampling frequencies and data compression strategies for different risk scenarios, effectively balancing data transmission bandwidth and system resource requirements. For example, when the risk level is low (0-1), conventional transmission is used. A lower sampling frequency and high compression ratio help reduce data volume and improve transmission efficiency. When the risk level rises to early warning mode (2-3), the sampling frequency is increased and the compression ratio is appropriately reduced, enabling more real-time data support while maintaining system response speed and data transmission reliability. In emergency mode (risk level 4), the sampling frequency is further increased, and lossless transmission is employed to ensure the accuracy and real-time nature of risk warning information. This classified transmission strategy ensures that the system can transmit data at appropriate rates and accuracy in different risk scenarios, providing timely and accurate support for decision-making during aerial work. Efficient data transmission and processing not only improves real-time performance but also reduces resource consumption in high-risk situations, ensuring the system's efficient response capabilities in emergency situations. This intelligent data transmission and risk response mechanism optimizes data communication and system performance through flexible adjustment of sampling frequency and compression strategies, providing effective technical support for the safe monitoring of aerial work.

[0037] Preferably, step S3 of using high-altitude risk graded transmission data to perform stress-wind speed-attitude joint simulation includes:

[0038] Mark the surface force distribution according to the high-altitude risk classification transmission data, and perform wind pressure analysis to obtain high-altitude wind pressure simulation data;

[0039] Based on the high-altitude wind pressure simulation data, flow field boundary analysis is performed and structural deformation stress is solved to obtain high-altitude stress-wind field coupling simulation data;

[0040] The platform attitude angle is positioned according to the high-altitude stress-wind field coupling simulation data to obtain the high-altitude reference point. The high-altitude reference point is used to calculate the additional bending moment offset of the center of gravity offset of the high-altitude stress-wind field coupling simulation data, and the simulation step size is iterated with 0.1s to obtain the high-altitude stress-wind speed-attitude coupling simulation data.

[0041] Based on the high-altitude stress-wind speed-attitude coupling simulation data, the platform attitude change curve is constructed, and the risk evolution path analysis is performed to obtain the high-altitude risk evolution prediction results.

[0042] Through step-by-step simulation analysis and data iteration, the present invention fully demonstrates the comprehensive application of multi-dimensional, interactive data processing in high-altitude operations, and ultimately achieves the prediction and accurate assessment of high-altitude risk evolution. From the data level, first, surface force distribution marking and wind pressure analysis are performed through high-altitude risk classification transmission data, providing basic data for further simulation calculations. These data reflect the wind pressure distribution in the high-altitude working environment at the physical level, and provide preliminary input for flow field boundary analysis, ensuring the accuracy and reliability of the simulation results. On this basis, through further processing of the wind pressure simulation data, a flow field boundary model of high-altitude wind pressure is constructed, and high-altitude stress-wind field coupling simulation data is obtained by solving the structural deformation stress. This link is crucial for accurately predicting the stress conditions of the working platform under different wind field conditions. Combined with the high-altitude stress-wind field coupling simulation data, the high-altitude reference point is determined by positioning the platform attitude angle, providing a reference framework for subsequent calculations. Further using this reference point, the center of gravity offset calculation and additional bending moment offset solution are performed on the stress-wind field coupling data, and the risk assessment model is iteratively optimized using a simulation step size of 0.1 seconds. This process refines the complex coupling relationship between wind pressure, stress, and platform posture through iterative calculations, enabling more accurate predictions of the evolution of risks during aerial work and timely reflection of existing structural crises. In this way, the system can dynamically adjust risk assessments based on real-time wind field changes and generate more accurate risk prediction data. This series of data analysis and simulation steps, combined with other steps, ensures the efficiency and accuracy of aerial work risk prediction, providing important technical support for safety monitoring and decision-making during aerial work, reducing the probability of potential accidents and improving operational safety.

[0043] Preferably, step S4 of intelligently deducing risk situations based on the high-altitude work risk optimization model and performing hierarchical coding includes:

[0044] Risk level prediction is performed based on the height operation risk optimization model to obtain real-time risk prediction data. The hierarchical trigger conditions for risk level prediction include:

[0045] If the risk level is greater than or equal to 1 and the risk probability is greater than 0.6 or the single-point stress is greater than 150 MPa, a level 1 warning is triggered, a level 1 control instruction is generated, and a local sound and light alarm is issued at the hanging basket support point based on the level 1 control instruction;

[0046] If the risk level is greater than or equal to 2 and the wind speed is greater than 12m / s or the platform attitude angle is greater than 5 degrees for more than 10 seconds, a level 2 warning is triggered and a level 2 control instruction is generated. Based on the level 2 control instruction, the warning reminder is pushed to the staff's mobile device using the remote platform;

[0047] If the risk level is 3 and the risk probability is greater than 0.9 or the stress of the key structure exceeds the material yield strength of 0.8, a level 3 warning is triggered, a level 3 control instruction is generated, and emergency braking is applied to the safety belt anchor point and the basket support point based on the level 3 control instruction;

[0048] The third-level control instructions include the second-level control instructions and the first-level control instructions, and the second-level control instructions include the first-level control instructions;

[0049] The real-time risk prediction data is prioritized and a binary instruction structure is constructed to obtain a high-altitude multi-level prediction instruction set.

[0050] The present invention utilizes a risk optimization model for high-altitude operations to perform real-time risk predictions. This allows the system to assess the safety status of high-altitude operations in real time based on a variety of triggering conditions (such as risk level, risk probability, single-point stress, wind speed, and platform attitude angle), and to generate corresponding warning information and control instructions based on the prediction results. This process not only enables the system to make real-time decisions based on risk level and other key parameters, but also enables timely responses by triggering specific conditions (such as changes in wind speed or platform attitude angle) to prevent potential safety hazards. By prioritizing risk level data, the system ensures timely responses to high-risk work environments, allowing for more stringent control measures. Furthermore, the construction of a binary instruction structure enables efficient processing and precise transmission of risk prediction data. With a clear and well-defined set of control instructions, the system can provide early warning and emergency response at multiple levels, ensuring the safety of both workers and platforms. The precise risk prediction model and priority allocation used during data transmission and control instruction generation not only enhances the real-time monitoring capabilities of high-altitude operations but also optimizes the risk management process, effectively integrating early warning and control measures at each level and avoiding blind spots in risk management. Ultimately, through this refined data analysis and prediction mechanism, safety hazards and accidents can be minimized, the smooth progress of high-altitude operations can be ensured, and the safety management level of high-altitude operations can be effectively improved.

[0051] Preferably, step S4 of constructing a response rule base based on the high-altitude multi-level prediction instruction set and generating early warning full-process monitoring data includes:

[0052] Perform AND / OR logic nesting based on high-altitude multi-level prediction instructions and build a rule base logic tree;

[0053] Mark important nodes in the entire prediction process according to the rule base logic tree to obtain important nodes for high-altitude operation prediction;

[0054] The whole process is traced based on the important nodes predicted for high-altitude operations to obtain the full process path for risk prediction; the whole process is warned based on the full process path for risk prediction to obtain the full process monitoring data for warning.

[0055] By nesting AND / OR logic based on multi-level high-altitude prediction instructions, the present invention enables the system to flexibly and effectively combine and judge different conditions and scenarios, thereby more accurately simulating and predicting risks at different risk levels. This logical nesting enables the system to automatically adjust judgment rules based on real-time data changes and accurately respond to different risk scenarios, improving the system's adaptability and fault tolerance to complex scenarios. Furthermore, the construction of a rule-based logic tree further strengthens the system's decision-making support capabilities. Through structured data processing, it ensures that the system's analysis of high-altitude work risks can be systematically and traceably processed according to rules. Next, based on the rule-based logic tree's marking and tracing of important nodes throughout the entire prediction process, the system can clearly identify and annotate risk points at every stage of the entire process, providing clear guidance for subsequent risk prediction paths. This node marking and path tracing method allows risk prediction to be not limited to a single time point but to cover the entire operation process horizontally, thereby achieving comprehensive monitoring of potential safety hazards. Finally, through full-process early warning based on the full risk prediction path, the system can provide real-time early warnings at every key node in the process and generate complete monitoring data. The generation of this monitoring data not only provides a comprehensive risk assessment but also provides a reliable basis for real-time emergency response and decision-making, significantly improving the efficiency and accuracy of high-altitude work safety management. Overall, this process, by strengthening data processing and analysis capabilities, makes high-altitude work risk management more intelligent, systematic, and dynamic, effectively ensuring operational safety.

[0056] Preferably, the three-dimensional visualization reconstruction based on the early warning full-process monitoring data in step S4 includes:

[0057] The monitoring data of the entire early warning process is analyzed for iso-stress values, and the iso-stress surface of high-altitude operations is constructed; HSL color space mapping is performed based on the iso-stress surface of high-altitude operations to obtain the risk warning stress cloud map;

[0058] The monitoring data of the entire early warning process is updated with delayed location, and personnel are dynamically marked based on the 30-second history of the operation trajectory to generate a risk warning personnel movement trajectory map;

[0059] The full-process monitoring data of the early warning is used to generate a semi-transparent surface according to the real-time risk prediction data to obtain a risk level thermal layer. Warning particles are superimposed on the three-level warning areas of the risk level thermal layer, where the density of the warning particles is proportional to the risk probability.

[0060] The risk warning stress cloud map, risk warning personnel movement trajectory map and risk level thermal layer are reconstructed into three-dimensional visualization to obtain the high-altitude operation safety situation map.

[0061] By analyzing the stress values ​​of the entire early warning process and constructing an isostress surface for high-altitude operations, the system can provide a comprehensive risk assessment based on precise stress data, ensuring the stability and structural safety of the work platform. This process accurately identifies structural risks present during high-altitude operations and generates a risk warning stress cloud map through HSL color space mapping, converting the structural stress data into intuitive visualizations. This allows managers to intuitively understand the risk distribution and quickly identify potential hazardous areas. Furthermore, by performing delayed location updates on the full early warning process monitoring data and dynamically tagging personnel based on historical work trajectory data, a risk warning personnel movement trajectory map is generated, enabling real-time monitoring of personnel movements and their respective risk areas. This process strengthens the correlation between personnel location and risk, further enhancing early warning capabilities for personnel safety. Furthermore, a risk level thermal map generated by a semi-transparent surface analyzes real-time risk prediction data to visually display the risk levels of different areas, providing timely risk alerts for personnel actions. On this basis, warning particles are superimposed on the three-level warning areas of the risk level thermal layer, where the density of the particles is positively correlated with the risk probability. This method enhances the significance of the warning area by quantifying the risk level, effectively improving the accuracy and response speed of the emergency response. Finally, the risk warning stress cloud map, the risk warning personnel movement trajectory map and the risk level thermal layer are three-dimensionally reconstructed to generate a high-altitude operation safety situation map. Combined with multi-dimensional monitoring data, a comprehensive risk display is provided, enabling safety managers to fully grasp the safety status of the operation in a complex operating environment. The application of this multi-level, multi-dimensional data processing and visualization technology has greatly improved the intelligence level and decision-making efficiency of high-altitude operation safety management, and provided more intuitive and accurate support for safety management. Therefore, the present invention improves the risk prediction accuracy and real-time response capability in high-altitude operations through the fusion of multi-source data, the alignment of spatiotemporal features, the dynamic simulation of risk evolution and the construction of a multi-level response mechanism, and ultimately forms a comprehensive, systematic and efficient safety management and control system.

[0062] The beneficial effects of the present invention lie in the fact that the data acquisition and construction module deploys structural stress sensors, environmental sensing devices, and human monitoring units on aerial work platforms to achieve real-time collection of multidimensional data such as structural status, work environment, and personnel behavior. Through format conversion, encoding unification, and timestamp standardization, this module constructs a timely and interoperable multi-source standard dataset, providing fundamental data support for subsequent model training and inference. Subsequently, the feature fusion and modeling module synchronizes this standard dataset through time alignment and spatial mapping, forming a unified spatiotemporal data representation of the entire aerial work process. On this basis, the feature dimensions of different data modalities are fused to establish a multivariate three-dimensional feature space structure. This fusion feature matrix, which incorporates the synergistic properties of multi-source information, is then generated. Based on this matrix, an initial risk prediction model is constructed, ensuring that the model fully incorporates correlation information between variables such as stress, wind speed, temperature and humidity, posture, and personnel status. In the risk evolution simulation and model optimization module, the system uses the fusion model to predict the level of aerial risk and, combined with the classification results, performs adaptive data transmission, effectively matching communication resources with risk levels and avoiding data redundancy or loss of important information. Furthermore, for high-risk samples, a joint simulation of stress, wind speed, and attitude parameters is performed to capture the coupled evolution of multidimensional physical variables and derive a dynamic risk evolution path. The simulation results are compared with the original model predictions, and compensation parameters, including absolute error, gradient error, and physical consistency error, are constructed. This allows for adaptive optimization of the model structure or weights, improving prediction accuracy and stability. Finally, in the visualization prediction and response construction module, the system hierarchically encodes high-dimensional risk situation data based on the optimized model output and generates a multi-level instruction set containing logical response rules. Through logical deduction from the rule base and linked control with monitoring data, a full-process simulation of the risk situation and a closed-loop response path are achieved. Furthermore, a 3D visualization engine is used to spatially integrate and dynamically present data such as risk level, stress distribution, and personnel movement, reconstructing a panoramic safety situation map of the aerial work area. This provides a multi-scale, interactive, and intuitive representation for risk assessment and decision-making. The entire process ensures the integrity and robustness of the monitoring-prediction-response chain at the data structure level, achieving a data-driven closed-loop intelligent safety management system for aerial work. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of the steps of an online monitoring and early warning method for high-altitude operations based on wireless sensors;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0065] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0066] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0067] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0068] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0069] To achieve this, please refer to Figures 1 to 2 , an online monitoring and early warning method for high-altitude operations based on wireless sensors, the method comprising the following steps:

[0070] Step S1: deploying a multi-type wireless sensor array on the aerial work platform to collect structural stress data, environmental temperature and humidity, and personnel status data to construct a multi-source standard data set;

[0071] Step S2: Perform spatiotemporal alignment on the multi-source standard datasets to generate a synchronized high-altitude dataset; construct a three-dimensional feature space using the synchronized high-altitude dataset and generate a high-altitude work fusion feature matrix; generate a high-altitude work risk prediction model based on the high-altitude work fusion feature matrix;

[0072] Step S3: Using the high-altitude work risk prediction model to output the high-altitude risk prediction level, and perform risk control classification processing to obtain high-altitude risk classification transmission data; using the high-altitude risk classification transmission data to perform stress-wind speed-attitude joint simulation to obtain high-altitude risk evolution prediction results; using the high-altitude risk evolution prediction results to construct compensation parameters, and using the compensation parameters to optimize the high-altitude work risk prediction model to obtain a high-altitude work risk optimization model;

[0073] Step S4: Based on the high-altitude operation risk optimization model, intelligent deduction of the risk situation is performed, and hierarchical coding is performed to obtain a high-altitude multi-level prediction instruction set; a response rule library is constructed based on the high-altitude multi-level prediction instruction set, and early warning full-process monitoring data is generated; based on the early warning full-process monitoring data, three-dimensional visualization reconstruction is performed to obtain a high-altitude operation safety situation map.

[0074] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a method for online monitoring and early warning of high-altitude operations based on wireless sensors according to the present invention. In this example, the method for online monitoring and early warning of high-altitude operations based on wireless sensors includes the following steps:

[0075] Step S1: deploying a multi-type wireless sensor array on the aerial work platform to collect structural stress data, environmental temperature and humidity, and personnel status data to construct a multi-source standard data set;

[0076] In one embodiment of the present invention, a wireless sensor array with heterogeneous sensing capabilities is deployed. This array includes strain gauges for detecting local stress and overall load changes in steel structures, temperature and humidity sensor modules for detecting changes in ambient temperature and humidity, and inertial measurement units (IMUs) and vital sign sensors for real-time monitoring of workers' movement behavior and physiological status. Sensor deployment should be partitioned based on the platform's key load-bearing components, environmentally exposed areas, and worker movement paths to ensure the spatial resolution of data acquisition and comprehensive coverage of key indicators. During the data collection phase, distributed information aggregation between nodes is achieved through low-power wireless transmission protocols (such as ZigBee or LoRa). Raw signals from various sensor types are synchronously transmitted to edge computing terminals for preprocessing. At the data level, this preprocessing process includes standardizing the sampling rate of stress data, timestamp calibration and outlier removal for temperature and humidity data, and posture resolution and noise filtering for worker status data. All data types are initially synchronized using a time alignment algorithm and then converted into a structured data format, forming standardized records containing time tags, spatial locations, numerical content, and sensor source identifiers. Ultimately, a multi-source standard dataset with a unified format, extensible fields, and multidimensional semantic identifiers is formed.

[0077] Step S2: Perform spatiotemporal alignment on the multi-source standard datasets to generate a synchronized high-altitude dataset; construct a three-dimensional feature space using the synchronized high-altitude dataset and generate a high-altitude work fusion feature matrix; generate a high-altitude work risk prediction model based on the high-altitude work fusion feature matrix;

[0078] In this embodiment of the present invention, a fusion algorithm based on global timestamp registration and spatial coordinate mapping is employed to interpolate and correct for the time deviations generated by asynchronous sampling between different sensor types. The data from various sensor nodes is then mapped into a unified spatial reference frame using the geometric modeling information of the platform structure. This process uses a sliding window mechanism and dynamic time warping (DTW) to match continuous time series to ensure synchronization within a certain time scale, thereby generating a synchronized high-altitude dataset with spatiotemporal consistency. Furthermore, a data mining algorithm is used to normalize, transform, and perform local cluster analysis on the multidimensional data of structural stress, environmental variables, and personnel status, thereby establishing corresponding mapping relationships in a three-dimensional feature space. This three-dimensional space is defined as the structural physical state dimension (e.g., strain, load), the environmental state dimension (e.g., temperature, wind speed), and the personnel dynamic dimension (e.g., acceleration, attitude angle) to achieve semantic integration of cross-dimensional information. Based on this spatial structure, a high-altitude work fusion feature matrix is ​​constructed. The matrix elements are composed of multiple indicators representing various data sources within specific time segments and spatial regions. Its representational power is enhanced through tensor reconstruction or convolutional coding. Subsequently, a time series modeling method based on probabilistic graphical models or Bayesian networks is used to construct a predictive mapping relationship from the fusion feature matrix to the risk level of high-altitude work status, completing the generation of the high-altitude work risk prediction model.

[0079] Step S3: Using the high-altitude work risk prediction model to output the high-altitude risk prediction level, and perform risk control classification processing to obtain high-altitude risk classification transmission data; using the high-altitude risk classification transmission data to perform stress-wind speed-attitude joint simulation to obtain high-altitude risk evolution prediction results; using the high-altitude risk evolution prediction results to construct compensation parameters, and using the compensation parameters to optimize the high-altitude work risk prediction model to obtain a high-altitude work risk optimization model;

[0080] In this embodiment of the present invention, after obtaining a fused feature matrix for aerial work and constructing an initial risk prediction model, the system first quantitatively assesses the aerial work status based on the model output, generating a risk prediction level with clear interval boundaries. This process discretizes the prediction results using a multi-class classifier and sets risk level thresholds based on the statistical distribution of historical risk events and expert experience, thereby forming a hierarchical risk management system. Subsequently, a high-altitude risk classification transmission data package containing risk level labels, spatial location coordinates, time stamps, and corresponding operational parameters is constructed as input to drive a coupled stress-wind speed-attitude simulation. This simulation module uses the finite element method (FEM) to model the dynamic evolution of structural stress responses, incorporates CFD (computational fluid dynamics) data to fit wind speed effects, and incorporates an attitude trajectory reconstruction algorithm to simulate and quantify the real-time posture changes of personnel on the aerial work platform. The simulation integrates the interactive evolutionary relationships of the three variables to generate a time-evolving aerial risk situation sequence, from which risk evolution prediction results are extracted. To verify the generalization ability of the prediction model and its consistency with the actual dynamic process, the evolutionary results are compared with the output of the original risk model through error differential analysis. Residual distribution analysis and covariance estimation techniques are used to extract key deviation characteristics. Based on this, the original prediction model parameters are retrospectively fine-tuned using genetic algorithm optimization or particle swarm optimization methods to adjust the model's weight distribution and response sensitivity in the multidimensional feature space. This ultimately results in a dynamically updated optimization model for high-altitude work risk.

[0081] Step S4: Based on the high-altitude operation risk optimization model, intelligent deduction of the risk situation is performed, and hierarchical coding is performed to obtain a high-altitude multi-level prediction instruction set; a response rule library is constructed based on the high-altitude multi-level prediction instruction set, and early warning full-process monitoring data is generated; based on the early warning full-process monitoring data, three-dimensional visualization reconstruction is performed to obtain a high-altitude operation safety situation map.

[0082] In an embodiment of the present invention, after constructing a risk optimization model for high-altitude operations, the system uses the model to predict risk levels based on the input fused feature data and, based on the prediction results, standardizes and categorizes risk states for each level. This process maps the risk prediction output into an operational, multi-level prediction instruction set by establishing a mapping relationship between risk levels and response strategies. Each instruction item includes a risk level identifier, response priority, warning trigger threshold, spatial location information, and a recommended response time window. This instruction set forms the core driving data source for the subsequent response mechanism. At the data level, the system further constructs a response rule base based on historical emergency response records, an expert rule base, and simulation feedback. This structure is represented by a multidimensional matching table that defines response strategies, control actions, and communication paths for different risk levels in different spatiotemporal scenarios. A state transition matrix is ​​also introduced to describe the dynamic evolution of different responses. Subsequently, the high-altitude multi-level prediction instruction set is logically mapped to the response rule base to generate early warning monitoring data covering the entire process, including the entire chain of data flow from initial identification, response dispatch, execution feedback, and state closure. To achieve interpretable and intuitive risk perception, the system reconstructs a 3D visualization based on the aforementioned full-process monitoring data. Using geometric rendering techniques based on point cloud or mesh modeling, it integrates spatial topological information of structural components, the distribution of risk thermal values, and response status nodes to construct a safety situation map for aerial work. This situation map, through time-series overlay, hierarchical mapping, and interactive graphic coding techniques, represents the dynamic risk evolution process, response behavior, and structural changes within a unified visual framework, achieving the integrated expression and semantic presentation of multi-source dynamic data.

[0083] Preferably, step S1 includes the following steps:

[0084] Step S11: deploying temperature and humidity sensors at the supporting points of the hanging basket with a sampling frequency of 1 Hz to collect temperature and humidity sensor data;

[0085] Step S12: a three-axis accelerometer and a gyroscope are set on the operator, with an attitude angle accuracy of 0.1 degrees and a sampling frequency of 50 Hz to collect operator status data;

[0086] Step S13: deploying a piezoelectric structural stress sensor at the seat belt anchorage point with a range of 0-50 kN, a sampling frequency of 10 Hz, and an accuracy of 0.5% to collect structural stress data;

[0087] Step S14: Using an adaptive Kalman filter to perform noise reduction processing on the temperature and humidity sensor data, personnel status data, and structural stress data, and adjusting the filter parameters according to the 0-100 Hz ambient noise spectrum to generate high-altitude work noise reduction data with a signal-to-noise ratio (SNR) greater than 25 dB;

[0088] Step S15: Baseline drift compensation is performed on the high-altitude work noise reduction data, where the long-term drift rate is <0.05% / month, to obtain a multi-source standard data set.

[0089] In an embodiment of the present invention, the system deploys multiple sensor nodes at key locations on the aerial work platform to achieve multi-source, simultaneous perception of the structure, environment, and personnel status. Specifically, temperature and humidity sensors are deployed at the support points of the hanging basket, with a sampling frequency set to 1 Hz, to continuously record air temperature and relative humidity data in the work environment. A triaxial accelerometer and gyroscope module is embedded in the wearable device worn by the operator. By setting a high-frequency sampling rate of 50 Hz and an attitude angle accuracy of 0.1 degrees, it enables real-time capture of the operator's dynamic posture in three-dimensional space and tracking of subtle fluctuations in displacement behavior. Furthermore, piezoelectric structural stress sensors are deployed at the anchor points of the safety belts, with a sampling frequency of 10 Hz, a range of 0 to 50 kN, and a measurement accuracy of within ±0.5%. These sensors are used to obtain stress fluctuation information at the load-bearing points of the aerial work platform and identify potential structural anomalies. For the three types of raw data mentioned above, noise reduction is required because the data collection process is susceptible to factors such as mechanical vibration, electromagnetic interference, and meteorological disturbances. The system uses an adaptive Kalman filter to process raw sensor data, dynamically adjusting the filter gain and observation covariance matrix parameters based on a preset ambient noise spectrum range (0–100Hz) to ensure that the output signal-to-noise ratio (SNR) remains above 25dB. The filter incorporates state prior estimation and observation residual correction mechanisms in the recursive process, performing state fusion and anomaly suppression on temperature and humidity series, acceleration vectors, and stress signals, respectively. After filtering, the system still needs to address drift caused by long-term sensor operation. A time-weighted sliding regression method is introduced to compensate for baseline drift, maintaining a drift rate below 0.05% per month during long-term observations. This creates a multi-source standard dataset for aerial work with high data stability and good temporal consistency, providing a unified data foundation for subsequent modeling and simulation.

[0090] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0091] Step S21: Using 50 Hz cubic spline interpolation to perform spatiotemporal alignment on the multi-source standard datasets to obtain a synchronized high-altitude dataset;

[0092] Step S22: Extract timestamps from the temperature and humidity sensor data and map them into a high-altitude work time axis; perform physical distribution analysis on the structural stress data and map them into spatial topological relationships, thereby completing the construction of the high-altitude work spatial axis; integrate feature data based on the synchronized high-altitude data set and construct a feature tensor to obtain the high-altitude work feature axis;

[0093] Step S23: performing spatiotemporal feature cross processing on the aerial work time axis, the aerial work space axis, and the aerial work feature axis, and constructing a three-dimensional feature space to obtain a aerial work fusion feature matrix;

[0094] Step S24: Generate a high-altitude work risk prediction model based on the high-altitude work fusion feature matrix.

[0095] In an embodiment of the present invention, a 50Hz cubic spline interpolation algorithm is applied to the constructed multi-source standard data set to achieve unified alignment of the sensor asynchronous sampling data in the time dimension. This interpolation method is based on the timestamps of the sample points. By constructing a continuously differentiable cubic polynomial function in the segmented interval, it estimates missing or non-equally spaced data points, ensuring that various data sources have corresponding valid observation values ​​at the same time step, thereby forming a synchronized high-altitude data set with strict time consistency. Based on this data set, the original timestamps of the temperature and humidity sensor sequence are first extracted, and a standardized time coordinate axis is constructed based on this as the "time axis" to describe the dynamic evolution of the high-altitude operation process; secondly, the observation data of the structural stress sensor is distributed physically mapped in combination with its deployment position, and the spatial relative relationship between the structural components and the component topology mapping function are established, thereby constructing the high-altitude operation space axis to express the spatial distribution characteristics of the structural state. At the same time, the state variables from different sensors in the synchronized data are standardized and integrated. A multidimensional feature tensor is constructed through variable encoding, principal component analysis, or spatiotemporal covariance analysis. The tensor axes represent different categories of physical quantities, sampling time steps, and sensor deployment locations, respectively, thereby establishing a high-altitude work feature axis that reflects the state evolution law. After the three axes are constructed, the system uses spatiotemporal cross-processing as the core operation to perform tensor fusion of the time, space, and feature axes. A unified three-dimensional feature space is constructed using tensor products, attention mechanisms, or multi-scale resampling strategies. Each tensor unit simultaneously carries the composite information of the current time, spatial position, and corresponding physical state variables, ultimately forming a fused feature matrix for high-altitude work. This matrix serves as the input data foundation for the risk prediction and analysis unit. Combined with supervised learning mechanisms (such as time series prediction networks, graph neural networks, or multi-channel LSTM structures), it is trained to generate a high-altitude work risk prediction model with spatiotemporal state perception capabilities.

[0096] It is particularly important that step S22 includes:

[0097] Step S221: extracting timestamps from the temperature and humidity sensor data, performing global clock synchronization, and mapping adjacent timestamps to the aerial work time axis at 20ms intervals;

[0098] Step S222: Perform tetrahedral unit meshing on the structural stress data and construct a stress distribution field to obtain physical distribution state data; perform graph structure topological relationship modeling on the physical distribution state data and perform adjacency matrix mapping to obtain a high-altitude work space axis construction;

[0099] Step S223: Perform numerical feature analysis on the synchronized high-altitude data set to obtain numerical feature data of the high-altitude data; perform categorical feature analysis on the synchronized high-altitude data set to obtain categorical feature data of the high-altitude data; construct a feature tensor of the numerical feature data of the high-altitude data and the categorical feature data of the high-altitude data to obtain a feature axis of the high-altitude operation.

[0100] In this embodiment of the present invention, for continuous time series data collected by temperature and humidity sensors, the timestamp of each record is extracted. Using a global clock synchronization mechanism, the local time bases of all sensors are aligned to a unified global clock standard, avoiding data alignment errors caused by time drift or sensor clock errors. Furthermore, a linear time index mapping is performed with a fixed 20ms interval between adjacent timestamps to form a time axis sequence for high-altitude operations that can be used for modeling. This provides high-resolution temporal continuity support for subsequent time series modeling. For structural stress data, the traditional two-dimensional discrete point sampling method is replaced by a three-dimensional meshing method using tetrahedral elements to construct a fine-grained stress distribution field. This meshing method maintains the accuracy of the original structural stress while providing a foundation for subsequent continuity modeling of the physical field. Based on this mesh, the connectivity between unit nodes is extracted, and a graph structure is constructed based on the spatial correlation between the nodes. The topological connectivity between the nodes is expressed by constructing an adjacency matrix, thereby forming a spatial axis for high-altitude operations that can be used in graph neural networks or spatial relationship reasoning, enabling the modeling and representation of structural stress in spatial dimensions. Feature analysis and processing are performed on the overall data content of the synchronized high-altitude dataset from both numerical and categorical perspectives. Numerical features such as temperature, humidity, and stress values ​​are extracted using standardization, normalization, or statistical extraction to extract their representative features. Categorical features such as sensor location, equipment number, and operation type are converted to categorical features through methods such as one-hot encoding and embedding vectors. The feature data of both dimensions is uniformly constructed into a multidimensional feature tensor, achieving high-dimensional organization and semantic integration of the data structure. This is then used to construct a high-altitude operation feature axis that represents the comprehensive characteristics of the operation status.

[0101] Preferably, step S24 includes the following steps:

[0102] Step S241: performing time series long-term dependency analysis on the aerial work fusion feature matrix and performing vectorization to obtain a time step feature vector;

[0103] Step S242: constructing a spatial topological structure for the aerial work fusion feature matrix and performing physical distance analysis on edge nodes to obtain spatial dimension feature data;

[0104] Step S243: performing a product of the time step feature vector and the spatial dimension feature data to generate interactive features, thereby obtaining cross-modal cross-feature data of aerial work;

[0105] Step S244: performing risk level classification main task analysis based on the cross-modal cross-feature data of aerial work to obtain cross-entropy loss data of the aerial work main task; performing stress prediction auxiliary task analysis based on the cross-modal cross-feature data of aerial work to obtain cross-entropy loss data of the aerial work auxiliary task;

[0106] Step S245: Hyperparameter tuning is performed on the cross entropy loss data of the main task of high-altitude work and the cross entropy loss data of the auxiliary task of high-altitude work, and the model is deployed in a lightweight manner to obtain a high-altitude work risk prediction model.

[0107] In this embodiment of the present invention, a long-term dependency analysis along the time series dimension is conducted based on the fused feature matrix of aerial work. Specifically, a sliding window mechanism is used to extract feature segments at different time steps. An attention mechanism or gated recurrent units (such as LSTM or GRU) is introduced to identify implicit coupling relationships between key time nodes, thereby capturing the potential evolution of the work status along the time dimension. This process converts the original tensor sequence into a set of time-step feature vectors, each of which embeds an aggregated representation of the structural stress, environmental variables, and personnel status at the corresponding time point. Simultaneously, spatial-level topological modeling is performed on the original fused feature matrix. Based on the sensor deployment structure, a spatial graph model consisting of nodes (sensor locations) and edges (physical or structural connection relationships) is constructed. Edge weights are then assigned based on the Euclidean distance between nodes, component connection constraints, or the construction structure diagram, extracting spatial-dimensional feature data reflecting the spatial dependencies of the sensors. After constructing the data structures in both time and space, a modal cross-fusion operation is performed. Each time-step feature vector is tensor-producted or nested-fused with the corresponding spatial dimension feature. This generates cross-modal cross-feature data for aerial work, reflecting the deep coupling between time, space, and physical states. Subsequently, primary and auxiliary task analysis modules are constructed for this cross-feature set. The primary task performs aerial risk classification using a multi-class cross-entropy loss function, while the auxiliary task performs stress prediction based on structural stress data, also optimized using a cross-entropy or mean squared error loss function. The dual-task loss results are jointly tuned using a weighted strategy or attention control mechanism. Hyperparameters (such as learning rate, regularization coefficient, and task weights) are automatically searched and adjusted within the hyperparameter space to achieve an optimal balance between tasks. Finally, while ensuring predictive performance, model compression is further implemented through model pruning, parameter quantization, or distillation, enabling lightweight deployment of the aerial work risk prediction model.

[0108] Preferably, step S3 of outputting the high-altitude risk prediction level using the high-altitude work risk prediction model and performing risk management and control grading processing includes:

[0109] Obtain real-time multi-source high-altitude data;

[0110] Use real-time multi-source high-altitude data to conduct prediction analysis on the high-altitude operation risk prediction model, and classify and transmit the analysis results to obtain the high-altitude risk prediction level. The classification and transmission based on the analysis results include:

[0111] When the classification result is risk level 0-1, it is classified as normal mode, sampling at 10Hz and transmitting at a data compression ratio of 5:1;

[0112] When the classification result is risk level 2-3, it is classified as early warning mode and transmitted at 50Hz sampling and a compression ratio of 2:1;

[0113] When the classification result is risk level 4, it is classified as emergency mode and sampled at 100 Hz for lossless transmission.

[0114] In this embodiment of the present invention, real-time multi-source aerial work data, encompassing data sources such as structural stress sensors, attitude monitoring units, and environmental perception modules, is used to ensure continuous sampling of multidimensional information at high frequency. After data acquisition, it is fed into a high-altitude work risk prediction model through a low-latency channel for dynamic inference processing. During the predictive analysis phase, the system rapidly classifies and determines the current state based on the mapping relationship between the input spatiotemporal continuous data and the parameters in the previously constructed aerial work risk prediction model. It then outputs a precise risk level indicator, categorized into five levels, from 0 to 4, reflecting the evolving safety status of the operation, from routine to emergency. Subsequently, the data transmission strategy is adaptively switched based on the determined risk level, enabling dynamic scheduling of bandwidth resources and optimizing transmission efficiency. Specifically, when the system outputs a risk level of 0 or 1, indicating a stable and routine aerial work state, data is acquired at a sampling frequency of 10 Hz. A lossy compression strategy with a five-fold compression ratio is implemented based on temporal redundancy and feature sparsity, preserving key feature points and reducing redundant information, thereby improving bandwidth utilization while ensuring efficiency. When the risk level rises to 2 or 3, the system enters early warning mode, the sampling frequency is increased to 50Hz to capture finer-grained data changes, and the compression ratio is reduced to 2:1 to retain more contextual information for downstream risk assessment, taking into account both real-time and accuracy. When the risk level reaches the highest level 4, the system switches to emergency transmission mode. At this time, it is necessary to ensure data integrity and accuracy, so the full amount of data is sampled at a high frequency of 100Hz, all compression algorithms are canceled, and a lossless transmission strategy is implemented to ensure that each sensor sampling point reaches the analysis end or control unit directly at the network level without any information loss. In addition, to support the dynamic switching of the above-mentioned adaptive transmission strategy, the system needs to be configured with a set of event-driven data scheduling mechanisms and level-triggered network management modules to achieve instant matching and fine control between risk levels and transmission strategies, thereby completing efficient information flow management under multi-level risk response at the data level.

[0115] Preferably, step S3 of using high-altitude risk graded transmission data to perform stress-wind speed-attitude joint simulation includes:

[0116] Mark the surface force distribution according to the high-altitude risk classification transmission data, and perform wind pressure analysis to obtain high-altitude wind pressure simulation data;

[0117] Based on the high-altitude wind pressure simulation data, flow field boundary analysis is performed and structural deformation stress is solved to obtain high-altitude stress-wind field coupling simulation data;

[0118] The platform attitude angle is positioned according to the high-altitude stress-wind field coupling simulation data to obtain the high-altitude reference point. The high-altitude reference point is used to calculate the additional bending moment offset of the center of gravity offset of the high-altitude stress-wind field coupling simulation data, and the simulation step size is iterated with 0.1s to obtain the high-altitude stress-wind speed-attitude coupling simulation data.

[0119] Based on the high-altitude stress-wind speed-attitude coupling simulation data, the platform attitude change curve is constructed, and the risk evolution path analysis is performed to obtain the high-altitude risk evolution prediction results.

[0120] In this embodiment of the present invention, risk-graded transmission data acquired from the aerial work site is used to perform preliminary marking of the platform's load-bearing surfaces. A surface force density mapping matrix is ​​constructed based on the spatial distribution of the platform's structural nodes and wind direction and speed parameters, and a wind pressure profile analysis is performed on this matrix. During the wind pressure analysis, the actual wind load time series signal is fitted to the boundary conditions of the platform's load-bearing region to extract the local maximum wind pressure areas and their changing trends, generating high-altitude wind pressure simulation data. This wind pressure simulation data is then input into a fluid-structure interaction (FSI) framework for flow field boundary condition identification and zoning modeling, thereby facilitating the numerical solution of the structural deformation and stress response. During this stage, a finite element mesh is used to establish the connection between wind pressure application points and nodes on the platform structure. Combined with material mechanical properties, stress concentration areas are resolved and initial conditions are corrected, ultimately generating high-altitude stress-wind field coupled simulation data. Next, based on the simulation data, the system extracts information on the platform's attitude angle changes under wind field disturbances. Using the attitude solution model, the system determines the absolute reference direction corresponding to the attitude angle. The structural coordinate system is then corrected using this direction, thereby locating the aerial work platform's reference point coordinate system. This reference point is used for subsequent center of gravity offset analysis, which is to quantify the offset of the platform's mass center under the action of wind pressure interference, and by constructing an additional bending moment offset model, the overall stress condition of the platform is corrected, and a simulation time step of 0.1 seconds is set, and an explicit dynamics method is used for time-series iterative solution. The simulation results at each moment will feedback stress redistribution, structural displacement changes and wind field re-action patterns, thereby generating a set of high-altitude risk evolution prediction data including time-series structural response, wind pressure disturbance distribution and attitude evolution, providing detailed data support for subsequent error analysis, model backtracking adjustment and other links. This process has extremely high requirements for spatiotemporal accuracy, data consistency and coupling condition setting, and involves deep fusion processing of multi-source heterogeneous data such as wind field analysis, structural mechanics simulation and attitude positioning.

[0121] Preferably, step S4 of intelligently deducing risk situations based on the high-altitude work risk optimization model and performing hierarchical coding includes:

[0122] Risk level prediction is performed based on the height operation risk optimization model to obtain real-time risk prediction data. The hierarchical trigger conditions for risk level prediction include:

[0123] If the risk level is greater than or equal to 1 and the risk probability is greater than 0.6 or the single-point stress is greater than 150 MPa, a level 1 warning is triggered, a level 1 control instruction is generated, and a local sound and light alarm is issued at the hanging basket support point based on the level 1 control instruction;

[0124] If the risk level is greater than or equal to 2 and the wind speed is greater than 12m / s or the platform attitude angle is greater than 5 degrees for more than 10 seconds, a level 2 warning is triggered and a level 2 control instruction is generated. Based on the level 2 control instruction, the warning reminder is pushed to the staff's mobile device using the remote platform;

[0125] If the risk level is 3 and the risk probability is greater than 0.9 or the stress of the key structure exceeds the material yield strength of 0.8, a level 3 warning is triggered, a level 3 control instruction is generated, and emergency braking is applied to the safety belt anchor point and the basket support point based on the level 3 control instruction;

[0126] The third-level control instructions include the second-level control instructions and the first-level control instructions, and the second-level control instructions include the first-level control instructions;

[0127] The real-time risk prediction data is prioritized and a binary instruction structure is constructed to obtain a high-altitude multi-level prediction instruction set.

[0128] In an embodiment of the present invention, a risk optimization model based on real-time sensor data calculates the risk level of an aerial work platform and related risk prediction data. This data includes multiple risk indicators, such as risk level, risk probability, single-point stress, wind speed, platform attitude angle, and other key parameters. The model dynamically calculates and outputs the risk level by analyzing real-time data. It also combines various environmental and structural data to trigger different levels of warnings and control commands, such as emergency response when wind speeds exceed 12 m / s or when the platform attitude angle exceeds 5 degrees for more than 10 seconds. To ensure the operability and responsiveness of each level of warning, the system sets trigger conditions based on specific thresholds. For example, when the risk level is ≥1 and the risk probability is ≥0.6, or when the single-point stress exceeds 150 MPa, the system automatically triggers a level 1 warning and generates corresponding control commands. These control commands prompt the local audible and visual alarm system to issue warning alerts. When the risk level is ≥2 and the wind speed or platform attitude exceeds a set threshold, a level 2 warning is triggered, generating level 2 control commands that are pushed to the remote platform and alert relevant personnel. If the risk level is 3 and meets more serious conditions, such as the risk probability is greater than 0.9 or the stress of the critical structure exceeds 80% of the material yield strength, the system triggers a three-level warning and generates a three-level control instruction to perform emergency braking. The three-level control instruction includes not only the second-level control instruction but also the first-level control instruction during its execution, thereby ensuring the comprehensiveness and hierarchy of the response. The system will finally assign priorities to the instructions at each level based on the real-time prediction data, and construct a binary instruction structure to encode different warning information into binary signals, thereby forming a multi-level prediction instruction set for high-altitude operations. This process involves high-speed processing of real-time data, dynamic judgment of complex decision-making rules, and the design of a multi-level response mechanism, aiming to ensure the safety of high-altitude operations and generate precise control instructions based on changes in the actual working environment.

[0129] It is particularly important to construct compensation parameters using the high-altitude risk evolution prediction results, including:

[0130] The absolute error comparison is performed using the high-altitude risk evolution prediction results, and the discrete level difference is calculated to obtain the high-altitude risk absolute error data;

[0131] The high-altitude risk evolution prediction results are used to compare the gradient errors and calculate the difference of the first-order derivatives to obtain the high-altitude risk gradient error data;

[0132] The high-altitude risk evolution prediction results are used to compare physical consistency errors and conduct mechanical law compliance analysis to obtain high-altitude risk consistency error data;

[0133] The high-altitude risk absolute error data, high-altitude risk gradient error data and high-altitude risk consistency error data are subjected to feature contribution analysis, and bias compensation parameters are generated to obtain compensation parameters.

[0134] In an embodiment of the present invention, in the first stage of error assessment, the absolute error is calculated by comparing the point-to-point difference between the high-altitude risk evolution prediction results and the actual observation results, and then different discrete level differences are divided according to the error distribution. This process not only obtains the numerical scale of the risk prediction error, but also divides the error into classifiable level information, thereby facilitating the subsequent model to identify and respond to errors of different severity. Subsequently, in the gradient error assessment link, by performing first-order derivative calculations on the predicted results and the actual results respectively, the difference in their trend rate of change over time is obtained, and based on the derivative difference, the error amplitude of the risk prediction in the dynamic change trend is quantified, that is, the high-altitude risk gradient error data. This method is not limited to static error comparison, but further examines the accuracy of the model's portrayal of the risk change trend over time. In the physical consistency error analysis part, based on the principles of structural mechanics, the prediction results are compared with the physical laws that should be followed under actual working conditions. For example, the prediction data is analyzed to determine whether the changes in the structural force direction are consistent with the known wind pressure direction, whether the stress response satisfies equilibrium conditions, and whether the structural deformation conforms to linear or nonlinear constitutive relations. Physical consistency error data is then extracted to quantify the degree to which the model output adheres to physical logic. These three types of error data are used to evaluate model performance from multiple perspectives, including numerical accuracy, variation trends, and physical rationality. For these three types of error data, feature contribution analysis techniques, such as those based on Shapley values ​​or information gain, are used to identify the primary influencing factors of each feature on error generation, thereby quantifying the contribution of each input dimension or structural module to the error. Based on this, and combined with the error contribution results, a bias compensation parameter generation strategy is implemented. This includes introducing correction factors, adjusting model output weights, and constructing error countermeasure channels. This results in a data-driven set of error correction parameters that improves the accuracy and stability of subsequent model prediction outputs.

[0135] Preferably, step S4 of constructing a response rule base based on the high-altitude multi-level prediction instruction set and generating early warning full-process monitoring data includes:

[0136] Perform AND / OR logic nesting based on high-altitude multi-level prediction instructions and build a rule base logic tree;

[0137] Mark important nodes in the entire prediction process according to the rule base logic tree to obtain important nodes for high-altitude operation prediction;

[0138] The whole process is traced based on the important nodes predicted for high-altitude operations to obtain the full process path for risk prediction; the whole process is warned based on the full process path for risk prediction to obtain the full process monitoring data for warning.

[0139] In this embodiment of the present invention, based on the multi-level prediction instructions for high-altitude operations, prediction instructions at different levels are combined and linked through AND / OR logic nesting to construct a logic tree containing various conditional judgments and response rules. This logic tree not only determines the triggering conditions for different risk levels but also provides a framework for subsequent decision-making. By establishing a rule base logic tree, the system can dynamically determine the control instructions that need to be triggered based on actual monitoring data in different warning scenarios and effectively match them with the prediction instructions. Furthermore, the rule base is responsible for maintaining the order of rule execution under different conditions, ensuring the logical coherence of the data flow and the timeliness of the response. Next, based on the rule base logic tree, important nodes in the entire high-altitude operation process are marked. These nodes represent data points that require special attention in the early warning system, including risk level, key environmental parameters, and operator status. By marking these nodes, the system can comprehensively identify key moments in the risk process and accurately track them. The system then traces the entire process based on these marked important nodes, tracking the cause and effect of each key node, thereby forming a complete risk prediction path. This process not only helps to understand the occurrence mechanism of each potential risk point, but also provides decision makers with more detailed background information to help determine whether preventive or emergency measures are needed. Ultimately, based on the full-process path of risk prediction, the system generates and executes full-process early warnings. This link generates early warning full-process monitoring data by collecting all prediction data, logical rules, and marked nodes. These monitoring data will reflect the safety situation during the operation in real time and provide data support for subsequent risk prevention and control measures. Overall, the system ensures accurate prediction and dynamic adjustment of high-altitude operation risks through data-level spatiotemporal analysis, logical decision-making, and path tracing, thereby providing strong data support for safety management.

[0140] Preferably, the three-dimensional visualization reconstruction based on the early warning full-process monitoring data in step S4 includes:

[0141] The monitoring data of the entire early warning process is analyzed for iso-stress values, and the iso-stress surface of high-altitude operations is constructed; HSL color space mapping is performed based on the iso-stress surface of high-altitude operations to obtain the risk warning stress cloud map;

[0142] The full-process monitoring data of the early warning is updated with delayed location, and personnel are dynamically marked based on the 30-second history of the operation trajectory to generate a risk warning personnel movement trajectory map;

[0143] The full-process monitoring data of the early warning is used to generate a semi-transparent surface according to the real-time risk prediction data to obtain a risk level thermal layer. Warning particles are superimposed on the three-level warning areas of the risk level thermal layer, where the density of the warning particles is proportional to the risk probability.

[0144] The risk warning stress cloud map, risk warning personnel movement trajectory map and risk level thermal layer are reconstructed into three-dimensional visualization to obtain the high-altitude operation safety situation map.

[0145] In an embodiment of the present invention, dynamic data visualization supports efficient risk warning and operational safety management. First, based on monitoring data from the entire early warning process, iso-stress surfaces for high-altitude operations are constructed through iso-stress value analysis. This process processes the monitored structural stress information at the data level and refines the spatial distribution of stress, enabling the stress values ​​to accurately reflect the structural safety status of each area on the work platform. Next, using HSL color space mapping technology, the stress distribution of high-altitude operations is mapped into a visually appealing stress cloud map. In this cloud map, color changes represent varying stress levels, providing intuitive risk assessment for operators and managers. Furthermore, based on real-time monitoring data and operator movement trajectories, the system performs delayed position updates and, combined with 30 seconds of historical data, dynamically tags operators. This generates a risk warning operator trajectory map reflecting their dynamic location and trajectory. This graphical display helps track operator behavior patterns in real time and identify potential risk areas or deviations from the normal path. Subsequently, based on real-time risk prediction data, the system converts this data into a semi-transparent surface to generate a risk level thermal map, effectively displaying the spatial distribution of areas with different risk levels. The three-level warning areas of the thermal layer are processed using particle overlay processing, where the particle density is adjusted according to the risk probability. This technology highlights high-risk areas on the thermal map, reminding managers to pay attention to important safety risk points. Finally, the generated risk warning stress cloud map, personnel movement trajectory map, and risk level thermal layer are reconstructed using 3D visualization technology and integrated into a comprehensive high-altitude work safety situation map. This 3D visualization intuitively displays various safety indicators and their dynamic changes during high-altitude work, helping relevant personnel quickly obtain comprehensive information on the working environment, personnel location, and risk level, thereby achieving more accurate and efficient safety management.

[0146] In this specification, a wireless sensor-based online monitoring and early warning system for high-altitude operations is provided, which is used to execute the above-mentioned wireless sensor-based online monitoring and early warning method for high-altitude operations. The wireless sensor-based online monitoring and early warning system for high-altitude operations includes:

[0147] The data acquisition and construction module is used to deploy multiple types of wireless sensor arrays on aerial work platforms to collect structural stress data, environmental temperature and humidity, and personnel status data, and construct a multi-source standard data set;

[0148] The feature fusion and modeling module is used to perform spatiotemporal alignment of multi-source standard datasets to generate a synchronized high-altitude dataset; construct a three-dimensional feature space using the synchronized high-altitude dataset and generate a high-altitude operation fusion feature matrix; and generate a high-altitude operation risk prediction model based on the high-altitude operation fusion feature matrix;

[0149] The risk evolution simulation and model optimization module is used to use the high-altitude work risk prediction model to output the high-altitude risk prediction level, and perform risk management and control classification processing to obtain high-altitude risk classification transmission data; use the high-altitude risk classification transmission data to perform stress-wind speed-attitude joint simulation to obtain high-altitude risk evolution prediction results; use the high-altitude risk evolution prediction results to construct compensation parameters, and use the compensation parameters to optimize the high-altitude work risk prediction model to obtain a high-altitude work risk optimization model;

[0150] The visual prediction and response construction module is used to intelligently deduce the risk situation based on the high-altitude operation risk optimization model, and perform hierarchical coding to obtain a high-altitude multi-level prediction instruction set; build a response rule library based on the high-altitude multi-level prediction instruction set, and generate early warning full-process monitoring data; perform three-dimensional visual reconstruction based on the early warning full-process monitoring data to obtain a high-altitude operation safety situation map.

[0151] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0152] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for online monitoring and early warning of high-altitude operations based on wireless sensors, characterized in that: The following steps are involved: Step S1: deploying a multi-type wireless sensor array on the aerial work platform to collect structural stress data, environmental temperature and humidity, and personnel status data to construct a multi-source standard data set; Step S2: Perform spatiotemporal alignment on the multi-source standard datasets to generate a synchronized upper-air dataset; Use synchronized high-altitude data sets to construct a three-dimensional feature space and generate a high-altitude operation fusion feature matrix; Generate a high-altitude operation risk prediction model based on the high-altitude operation fusion feature matrix; wherein step S2 includes: Step S21: Using 50 Hz cubic spline interpolation to perform spatiotemporal alignment on the multi-source standard datasets to obtain a synchronized high-altitude dataset; Step S22: Extract timestamps from the temperature and humidity sensor data and map them into a high-altitude work time axis; perform physical distribution analysis on the structural stress data and map them into spatial topological relationships, thereby completing the construction of the high-altitude work spatial axis; integrate feature data based on the synchronized high-altitude data set and construct a feature tensor to obtain the high-altitude work feature axis; Step S23: performing spatiotemporal feature cross processing on the aerial work time axis, the aerial work space axis, and the aerial work feature axis, and constructing a three-dimensional feature space to obtain a aerial work fusion feature matrix; Step S24: generating a high-altitude operation risk prediction model based on the high-altitude operation fusion feature matrix; Step S3: using the high-altitude operation risk prediction model to output the high-altitude risk prediction level, and perform risk control classification processing to obtain high-altitude risk classification transmission data; using the high-altitude risk classification transmission data to perform stress-wind speed-attitude joint simulation to obtain high-altitude risk evolution prediction results; using the high-altitude risk evolution prediction results to construct compensation parameters, and using the compensation parameters to optimize the high-altitude operation risk prediction model to obtain a high-altitude operation risk optimization model; wherein, using the high-altitude risk classification transmission data to perform stress-wind speed-attitude joint simulation includes: Mark the surface force distribution according to the high-altitude risk classification transmission data, and perform wind pressure analysis to obtain high-altitude wind pressure simulation data; Based on the high-altitude wind pressure simulation data, flow field boundary analysis is performed and structural deformation stress is solved to obtain high-altitude stress-wind field coupling simulation data; The platform attitude angle is positioned according to the high-altitude stress-wind field coupling simulation data to obtain the high-altitude reference point. The high-altitude reference point is used to calculate the additional bending moment offset of the center of gravity offset of the high-altitude stress-wind field coupling simulation data, and the simulation step size is iterated with 0.1s to obtain the high-altitude stress-wind speed-attitude coupling simulation data. Based on the high-altitude stress-wind speed-attitude coupling simulation data, the platform attitude change curve is constructed, and the risk evolution path analysis is performed to obtain the high-altitude risk evolution prediction results; Step S4: Based on the high-altitude operation risk optimization model, intelligent deduction of the risk situation is performed, and hierarchical coding is performed to obtain a high-altitude multi-level prediction instruction set; a response rule library is constructed based on the high-altitude multi-level prediction instruction set, and early warning full-process monitoring data is generated; based on the early warning full-process monitoring data, three-dimensional visualization reconstruction is performed to obtain a high-altitude operation safety situation map.

2. The online monitoring and early warning method for high-altitude operations based on wireless sensors according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: deploying temperature and humidity sensors at the supporting points of the hanging basket with a sampling frequency of 1 Hz to collect ambient temperature and humidity data; Step S12: a three-axis accelerometer and a gyroscope are set on the operator, with an attitude angle accuracy of 0.1 degrees and a sampling frequency of 50 Hz to collect operator status data; Step S13: deploying a piezoelectric structural stress sensor at the seat belt anchorage point with a range of 0-50 kN, a sampling frequency of 10 Hz, and an accuracy of 0.5% to collect structural stress data; Step S14: Using an adaptive Kalman filter to perform noise reduction processing on the temperature and humidity sensor data, personnel status data, and structural stress data, and adjusting the filter parameters according to the 0-100 Hz ambient noise spectrum to generate high-altitude work noise reduction data with a signal-to-noise ratio (SNR) greater than 25 dB; Step S15: Baseline drift compensation is performed on the high-altitude work noise reduction data, where the long-term drift rate is <0.05% / month, to obtain a multi-source standard data set.

3. The online monitoring and early warning method for high-altitude operations based on wireless sensors according to claim 1 is characterized in that: Step S24 includes the following steps: Step S241: performing time series long-term dependency analysis on the aerial work fusion feature matrix and performing vectorization to obtain a time step feature vector; Step S242: constructing a spatial topological structure for the aerial work fusion feature matrix and performing physical distance analysis on edge nodes to obtain spatial dimension feature data; Step S243: performing a product of the time step feature vector and the spatial dimension feature data to generate interactive features, thereby obtaining cross-modal cross-feature data of aerial work; Step S244: performing risk level classification main task analysis based on the cross-modal cross-feature data of aerial work to obtain cross-entropy loss data of the aerial work main task; performing stress prediction auxiliary task analysis based on the cross-modal cross-feature data of aerial work to obtain cross-entropy loss data of the aerial work auxiliary task; Step S245: Hyperparameter tuning is performed on the cross entropy loss data of the main task of high-altitude work and the cross entropy loss data of the auxiliary task of high-altitude work, and the model is deployed in a lightweight manner to obtain a high-altitude work risk prediction model.

4. The online monitoring and early warning method for high-altitude operations based on wireless sensors according to claim 1 is characterized in that: Step S3 of outputting the high-altitude risk prediction level using the high-altitude work risk prediction model and performing risk management and control grading processing includes: Obtain real-time multi-source high-altitude data; Use real-time multi-source high-altitude data to conduct prediction analysis on the high-altitude operation risk prediction model, and classify and transmit the analysis results to obtain the high-altitude risk prediction level. The classification and transmission based on the analysis results include: When the classification result is risk level 0-1, it is classified as normal mode, sampling at 10Hz and transmitting at a data compression ratio of 5:1; When the classification result is risk level 2-3, it is classified as early warning mode and transmitted at 50Hz sampling and a compression ratio of 2:1; When the classification result is risk level 4, it is classified as emergency mode and sampled at 100 Hz for lossless transmission.

5. The online monitoring and early warning method for high-altitude operations based on wireless sensors according to claim 1 is characterized in that: Step S4 of intelligently deducing risk situations based on the high-altitude work risk optimization model and performing hierarchical coding includes: Based on the risk optimization model for high-altitude operations, intelligent risk situation deduction is performed to obtain real-time risk prediction data. The hierarchical trigger conditions for risk level prediction include: If the risk level is greater than or equal to 1 and the risk probability is greater than 0.6 or the single-point stress is greater than 150 MPa, a level 1 warning is triggered, a level 1 control instruction is generated, and a local sound and light alarm is issued at the hanging basket support point based on the level 1 control instruction; If the risk level is greater than or equal to 2 and the wind speed is greater than 12m / s or the platform attitude angle is greater than 5 degrees for more than 10 seconds, a level 2 warning is triggered and a level 2 control instruction is generated. Based on the level 2 control instruction, the warning reminder is pushed to the staff's mobile device using the remote platform; If the risk level is 3 and the risk probability is greater than 0.9 or the stress of the key structure exceeds the material yield strength of 0.8, a level 3 warning is triggered, a level 3 control instruction is generated, and emergency braking is applied to the safety belt anchor point and the basket support point based on the level 3 control instruction; The third-level control instructions include the second-level control instructions and the first-level control instructions, and the second-level control instructions include the first-level control instructions; The real-time risk prediction data is prioritized and a binary instruction structure is constructed to obtain a high-altitude multi-level prediction instruction set.

6. The online monitoring and early warning method for high-altitude operations based on wireless sensors according to claim 1 is characterized in that: Step S4 builds a response rule base based on the high-altitude multi-level prediction instruction set and generates early warning full-process monitoring data including: Perform AND / OR logic nesting based on high-altitude multi-level prediction instructions and build a rule base logic tree; Mark important nodes in the entire prediction process according to the rule base logic tree to obtain important nodes for high-altitude operation prediction; The whole process is traced based on the important nodes predicted for high-altitude operations to obtain the full process path for risk prediction; the whole process is warned based on the full process path for risk prediction to obtain the full process monitoring data for warning.

7. The online monitoring and early warning method for high-altitude operations based on wireless sensors according to claim 1 is characterized in that: Step S4 of performing three-dimensional visualization reconstruction based on the early warning full-process monitoring data includes: The monitoring data of the entire early warning process is analyzed for iso-stress values, and the iso-stress surface of high-altitude operations is constructed; HSL color space mapping is performed based on the iso-stress surface of high-altitude operations to obtain the risk warning stress cloud map; The monitoring data of the entire early warning process is updated with delayed location, and personnel are dynamically marked based on the 30-second history of the operation trajectory to generate a risk warning personnel movement trajectory map; The full-process monitoring data of the early warning is used to generate a semi-transparent surface according to the real-time risk prediction data to obtain a risk level thermal layer. Warning particles are superimposed on the three-level warning areas of the risk level thermal layer, where the density of the warning particles is proportional to the risk probability. The risk warning stress cloud map, risk warning personnel movement trajectory map and risk level thermal layer are reconstructed into three-dimensional visualization to obtain the high-altitude operation safety situation map.

8. An online monitoring and early warning system for high-altitude operations based on wireless sensors, characterized in that: The method for online monitoring and early warning of aerial work based on wireless sensors according to claim 1 is used to implement the method. The online monitoring and early warning system for aerial work based on wireless sensors comprises: The data acquisition and construction module is used to deploy multiple types of wireless sensor arrays on aerial work platforms to collect structural stress data, environmental temperature and humidity, and personnel status data, and construct a multi-source standard data set; The feature fusion and modeling module is used to perform spatiotemporal alignment of multi-source standard datasets to generate a synchronized high-altitude dataset; construct a three-dimensional feature space using the synchronized high-altitude dataset and generate a high-altitude operation fusion feature matrix; and generate a high-altitude operation risk prediction model based on the high-altitude operation fusion feature matrix; The risk evolution simulation and model optimization module is used to use the high-altitude work risk prediction model to output the high-altitude risk prediction level, and perform risk management and control classification processing to obtain high-altitude risk classification transmission data; use the high-altitude risk classification transmission data to perform stress-wind speed-attitude joint simulation to obtain high-altitude risk evolution prediction results; use the high-altitude risk evolution prediction results to construct compensation parameters, and use the compensation parameters to optimize the high-altitude work risk prediction model to obtain a high-altitude work risk optimization model; The visual prediction and response construction module is used to intelligently deduce the risk situation based on the high-altitude operation risk optimization model, and perform hierarchical coding to obtain a high-altitude multi-level prediction instruction set; build a response rule library based on the high-altitude multi-level prediction instruction set, and generate early warning full-process monitoring data; perform three-dimensional visual reconstruction based on the early warning full-process monitoring data to obtain a high-altitude operation safety situation map.

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