Roof pipe truss steel structure hoisting safety early warning method and device under sensing monitoring

Abstract

The invention discloses a roof pipe truss steel structure hoisting safety early warning method and device under sensing monitoring, and relates to the technical field of sensing monitoring. The method comprises the steps that Q lifting points and K steel structure stress points of a target pipe truss steel structure are obtained; the Q lifting points and the K steel structure stress points are continuously monitored, and Q lifting point monitoring data set sequences and K steel structure stress point monitoring set sequences are obtained; traversing Q lifting point monitoring data group sequences and K steel structure stress point monitoring group sequences to perform risk coefficient identification, and determining a primary risk monitoring object and a secondary risk monitoring object; and asynchronous dual-channel safety monitoring is carried out on the first-level risk monitoring object and the second-level risk monitoring object to obtain first safety early warning information. The technical problem that in the prior art, potential safety hazards cannot be timely and effectively warned in the hoisting process of the roof pipe truss steel structure is solved, and the technical effect that hoisting points and stress points are continuously monitored through sensing monitoring, and safety warning of hoisting operation is achieved is achieved.

Description

Technical Field

[0001] The present invention relates to the field of sensor monitoring technology, and in particular to a safety early warning method and device for hoisting a roof tube truss steel structure under sensor monitoring. Background Art

[0002] With the continuous expansion of construction projects and the gradual increase in construction difficulty, hoisting safety has become an important issue that needs to be addressed urgently, especially during the hoisting of large roof tube truss steel structures. Traditional hoisting safety monitoring methods rely heavily on manual inspection and empirical judgment, and often fail to detect potential safety hazards in a timely manner, especially when monitoring hoisting points and stress points, where there are large blind spots. With the development of sensor technology, real-time monitoring using sensor arrays has become an effective way to improve hoisting safety. However, existing sensor monitoring systems mostly focus on monitoring a single data point and lack a comprehensive assessment of multiple points and dimensions during the hoisting process, resulting in insufficient risk identification and early warning capabilities. Summary of the Invention

[0003] The present application provides a safety early warning method and device for the hoisting of a roof tube truss steel structure under sensor monitoring, which solves the technical problem in the prior art that safety hazards cannot be promptly and effectively warned during the hoisting of a roof tube truss steel structure.

[0004] In a first aspect of the present application, a sensor-monitored early warning method for the hoisting safety of a roof tube truss steel structure is provided, the method comprising: Acquire Q lifting points and K stress points of the target tube truss steel structure, where Q and K are positive integers; utilize a sensor monitoring array to continuously monitor the Q lifting points and K stress points of the steel structure, respectively, to obtain Q lifting point monitoring data group sequences and K steel structure stress point monitoring group sequences; traverse the Q lifting point monitoring data group sequences and the K steel structure stress point monitoring group sequences to identify risk factors, and determine primary risk monitoring objects and secondary risk monitoring objects; perform asynchronous dual-channel safety monitoring on the primary risk monitoring objects and the secondary risk monitoring objects to obtain first safety warning information, wherein the asynchronous dual channel includes a first monitoring sub-channel and a second monitoring sub-channel.

[0005] The second aspect of the present application provides a sensor-monitored roof tube truss steel structure hoisting safety warning device, the device comprising: A key point acquisition module is used to obtain Q hanging points and K steel structure stress points of the target tube truss steel structure, where Q and K are positive integers; a data acquisition module is used to use a sensor monitoring array to continuously monitor the Q hanging points and K steel structure stress points respectively, and obtain Q hanging point monitoring data group sequences and K steel structure stress point monitoring group sequences; a risk identification module is used to traverse the Q hanging point monitoring data group sequences and K steel structure stress point monitoring group sequences to identify risk factors and determine the first-level risk monitoring objects and the second-level risk monitoring objects; a safety monitoring module is used to perform asynchronous dual-channel safety monitoring on the first-level risk monitoring objects and the second-level risk monitoring objects to obtain first safety warning information, where the asynchronous dual channel includes a first monitoring sub-channel and a second monitoring sub-channel.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, obtain Q lifting points and K stress points of the target tube truss steel structure, where Q and K are positive integers. Next, use the sensor monitoring array to continuously monitor the Q lifting points and K stress points of the steel structure, respectively, to obtain Q lifting point monitoring data group sequences and K steel structure stress point monitoring group sequences. Then, traverse the Q lifting point monitoring data group sequences and K steel structure stress point monitoring group sequences to identify risk factors and determine the first-level risk monitoring objects and second-level risk monitoring objects. Finally, perform asynchronous dual-channel safety monitoring on the first-level risk monitoring objects and the second-level risk monitoring objects to obtain the first safety warning information, where the asynchronous dual-channel includes a first monitoring sub-channel and a second monitoring sub-channel. This solves the technical problem in the prior art that safety hazards cannot be effectively and timely warned during the hoisting process of the roof tube truss steel structure, and achieves the technical effect of continuously monitoring the lifting points and stress points through sensor monitoring to achieve safety warnings for the hoisting operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic diagram of a process flow for a safety early warning method for hoisting a roof tube truss steel structure under sensor monitoring provided in an embodiment of the present application; Figure 2 Schematic diagram of the structure of the roof tube truss steel structure hoisting safety warning device under sensor monitoring provided in an embodiment of the present application.

[0009] Explanation of the accompanying symbols: key point acquisition module 11, data acquisition module 12, risk identification module 13, safety monitoring module 14. DETAILED DESCRIPTION

[0010] This application solves the technical problem in the prior art that safety hazards cannot be promptly and effectively warned during the hoisting of roof tube truss steel structures by providing a safety warning method and device for the hoisting of roof tube truss steel structures under sensor monitoring.

[0011] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0012] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0013] Example 1, as Figure 1 As shown, the present application provides a safety early warning method for hoisting a roof tube truss steel structure under sensor monitoring, wherein the method includes: Obtain Q hanging points and K stress points of the target tube truss steel structure, where Q and K are positive integers.

[0014] Based on the target tube truss steel structure's hoisting plan, identify Q lifting points and K stress points on the target roof tube truss steel structure, where Q and K are positive integers. A lifting point is the connection point between the lifting equipment (such as a crane or hoist) and the steel structure during the hoisting operation; a stress point is the key location on the steel structure that bears external loads or forces.

[0015] The Q lifting points and K steel structure stress points are continuously monitored by using a sensor monitoring array to obtain Q lifting point monitoring data group sequences and K steel structure stress point monitoring group sequences.

[0016] Furthermore, the sensor monitoring array includes strain sensors, displacement sensors, load sensors and accelerometers.

[0017] A sensor monitoring array is used to continuously monitor Q lifting points and K stress points on the steel structure, obtaining real-time data on these points and stress points for dynamic structural assessment during the lifting process. Specifically, appropriate sensors are installed for each lifting point and stress point. Lifting points typically require strain sensors, displacement sensors, and load sensors to monitor mechanical changes during the lifting process, such as their displacement, stress, and load changes. Stress points on the steel structure require sensors such as strain sensors and accelerometers to detect the stress state and deformation of the structure in real time. Once the sensor array is installed, it continuously monitors the Q lifting points and K stress points on the steel structure in real time. The sensors at each lifting point and stress point periodically collect data and send it to the monitoring system, which organizes this data into a data set sequence. For each lifting point, Q lifting point monitoring data sets are obtained, while for each stress point, K steel structure monitoring data sets are obtained. These monitoring data sets contain real-time information on the changes in lifting points and stress points during the lifting process, such as stress, displacement, vibration, load, and other important data. This data will provide a foundation for subsequent risk assessment, anomaly monitoring, and early warning. Through this continuous monitoring data, the dynamic changes of the structure during the lifting process can be accurately grasped, allowing potential safety risks and hidden dangers to be promptly identified, ensuring the smooth progress of the lifting operation.

[0018] The Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences are traversed to identify risk factors and determine the first-level risk monitoring objects and the second-level risk monitoring objects.

[0019] By traversing the Q lifting point monitoring data set sequences and the K steel structure stress point monitoring data set sequences, the system will analyze and process the monitoring data of each lifting point and stress point. Based on this data, the risk coefficient of each lifting point and stress point is calculated. The calculation method of the risk coefficient includes a comprehensive assessment of multiple factors, such as the volatility of the monitoring data, the amplitude of the change, and the degree of deviation from the safety threshold. Specifically, for each lifting point and stress point, the fluctuation factor and correlation factor of its data sequence can be calculated, and the risk coefficient of the lifting point or stress point is obtained through weighted calculation. The risk coefficient reflects the degree of safety hazards that may exist at that point during the lifting process.

[0020] After completing the risk factor calculation, the system will classify and identify the Q lifting points and K steel structure stress points based on the preset risk factor threshold. Based on the level of the risk factor, the monitoring objects are divided into primary risk monitoring objects and secondary risk monitoring objects. Primary risk monitoring objects are those points with higher risk factors, indicating that they have greater safety hazards during the lifting process, which may lead to structural instability, overloading of lifting equipment and other problems; secondary risk monitoring objects are those points with lower risk factors, but still have certain hidden dangers. Usually, the risk factors of these points do not reach the primary risk threshold, but they also need attention and monitoring.

[0021] Furthermore, the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences are traversed to identify risk factors and determine the first-level risk monitoring objects and the second-level risk monitoring objects, including: The Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences are respectively subjected to fluctuation factor identification to obtain Q hanging point monitoring fluctuation factors and K steel structure stress point monitoring fluctuation factors; the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences are traversed to perform data feature iterative association identification to obtain Q hanging point monitoring correlation factors and K steel structure stress point monitoring correlation factors; weighted calculation is performed on the Q hanging point monitoring fluctuation factors and the Q hanging point monitoring correlation factors, as well as the K steel structure stress point monitoring fluctuation factors and the K steel structure stress point monitoring correlation factors to obtain Q hanging point risk coefficients and K steel structure stress point risk coefficients; the Q hanging point risk coefficients and the K steel structure stress point risk coefficients are identified according to a preset risk factor threshold to obtain a first-level risk monitoring object and a second-level risk monitoring object.

[0022] Preferably, first, a fluctuation analysis is performed on the monitoring data set sequence of Q hanging points to identify the fluctuation of each hanging point data and obtain Q hanging point monitoring fluctuation factors; similarly, a fluctuation analysis is performed on the monitoring data set sequence of K steel structure stress points to identify the fluctuation of stress point data and obtain K steel structure stress point monitoring fluctuation factors; the fluctuation factor reflects the amplitude and instability of the data changes of the hanging points and stress points during the lifting process. Then, data feature iterative correlation identification is further performed; by gradually traversing each hanging point monitoring data set sequence and stress point monitoring data set sequence, the key features in the data are extracted, and through the iterative correlation analysis method, Q hanging point monitoring correlation factors and K steel structure stress point monitoring correlation factors are identified, wherein the hanging point monitoring correlation factors reveal the interaction relationship between the hanging points, and the steel structure stress point monitoring correlation factors reveal the interaction relationship between the steel structure stress points. Then, a weighted calculation is performed based on the Q lifting point monitoring fluctuation factors and the Q lifting point monitoring correlation factors according to the preset weight distribution to obtain the Q lifting point risk coefficients. The weight distribution can be set based on factors such as the importance of the lifting point in the structure and historical data performance. Similarly, a weighted calculation is performed based on the K steel structure stress point monitoring fluctuation factors and the K steel structure stress point monitoring correlation factors to obtain the K steel structure stress point risk coefficients. Finally, the Q lifting point risk coefficients and the K steel structure stress point risk coefficients are identified and classified according to the preset risk coefficient threshold. If the risk coefficient of a lifting point or stress point exceeds the preset first-level risk threshold, the point is marked as a first-level risk monitoring object, indicating that it has a high safety hazard and requires priority monitoring and treatment. If the risk coefficient of a lifting point or stress point is between the first and second-level risks, the point is marked as a second-level risk monitoring object, indicating that it still has certain safety hazards, but is less dangerous than the first-level risk object and still requires monitoring, but with a relatively lower priority.

[0023] Furthermore, the Q hanging point monitoring data set sequences and the K steel structure stress point monitoring data set sequences are respectively subjected to fluctuation factor identification to obtain the Q hanging point monitoring fluctuation factors and the K steel structure stress point monitoring fluctuation factors, including: The Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences are traversed to perform fluctuation variance calculation to obtain Q first hanging point sub-monitoring fluctuation factors and K first steel structure stress point monitoring fluctuation factors; the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences are traversed to perform maximum amplitude comparison analysis to obtain Q second hanging point sub-monitoring fluctuation factors and K second steel structure stress point monitoring fluctuation factors; weighted calculation is performed on the Q first hanging point sub-monitoring fluctuation factors and the Q second hanging point monitoring fluctuation factors, as well as the K first steel structure stress point monitoring fluctuation factors and the K second steel structure stress point monitoring fluctuation factors, to obtain Q hanging point monitoring fluctuation factors and K steel structure stress point monitoring fluctuation factors.

[0024] Specifically, the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences are traversed, and the fluctuation variance of the monitoring data of each hanging point and steel structure stress point is calculated respectively. The fluctuation variance is an indicator to measure the fluctuation amplitude of the data, which can reflect the change amplitude and stability of the monitoring data; for each hanging point and each stress point, the variance calculation of its monitoring data sequence will be performed to obtain the fluctuation variance of each hanging point and stress point, thereby obtaining Q first hanging point sub-monitoring fluctuation factors and K first steel structure stress point monitoring fluctuation factors. Next, a maximum amplitude comparison analysis is performed on the monitoring data series of the Q hanging points and the K stress points of the steel structure. Specifically, for each hanging point and stress point, the maximum and minimum values of the monitoring data series are identified, and the difference between the two is calculated to obtain the maximum amplitude of the point. The sum of the maximum amplitudes of all Q hanging points and K stress points of the steel structure is calculated. For each hanging point and stress point, the ratio of its maximum amplitude to the sum of all maximum amplitudes is calculated, thereby obtaining Q second hanging point sub-monitoring fluctuation factors and K second steel structure stress point sub-monitoring fluctuation factors. Then, a weighted calculation is performed on the first and second sub-monitoring fluctuation factors of the Q hanging points, as well as the first and second sub-monitoring fluctuation factors of the K steel structure stress points. Different weights are assigned according to the importance of fluctuation variance and maximum amplitude in the assessment, and the Q hanging point monitoring fluctuation factors and K steel structure stress point monitoring fluctuation factors are calculated.

[0025] Furthermore, the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences are traversed to perform iterative correlation identification of data features, and the Q hanging point monitoring correlation factors and the K steel structure stress point monitoring correlation factors are obtained, including: Extract a first hanging point monitoring data group sequence from the Q hanging point monitoring data group sequences; traverse the first hanging point monitoring data group sequence to perform data feature extraction to obtain a first hanging point monitoring feature sequence; perform data feature iterative association identification on two adjacent first hanging point monitoring features of the first hanging point monitoring feature sequence in order from front to back to obtain a first hanging point monitoring feature association factor sequence; calculate the mean of the first hanging point monitoring feature association factor sequence to obtain a first hanging point monitoring association factor; perform data feature iterative association identification on the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences respectively to obtain the Q hanging point monitoring association factors and the K steel structure stress point monitoring association factors.

[0026] First, the monitoring data of a single hanging point is selected from Q hanging point monitoring data group sequences as the first hanging point monitoring data group sequence. The monitoring data group sequence of each hanging point contains the monitoring data of the hanging point at multiple moments during the lifting process. Next, the first hanging point monitoring data group sequence is traversed to extract data features, that is, to extract representative features, such as data volatility, trend changes, and periodicity. These features can reflect the specific performance of the hanging point in terms of force and deformation during the lifting process; the extracted feature data will constitute the first hanging point monitoring feature sequence. Then, the first hanging point monitoring feature sequence is subjected to iterative data feature association identification, that is, in order from front to back, the two adjacent first hanging point monitoring features are gradually associated and identified to obtain the first hanging point monitoring feature association factor sequence; the first hanging point monitoring feature association factor sequence is averaged to obtain the first hanging point monitoring association factor, which reflects the average degree of association between adjacent features of the hanging point during the monitoring period. Repeat the above steps for the Q hanging point monitoring data group sequences, calculate the monitoring correlation factor of each hanging point respectively, and finally obtain the Q hanging point monitoring correlation factors; the data processing process of the K steel structure load-bearing point monitoring group sequence is similar to that of the hanging point monitoring data group sequence. Extract the monitoring data group sequence of each load-bearing point, perform feature extraction, iterative association identification and mean calculation, and finally obtain the K steel structure load-bearing point monitoring correlation factors.

[0027] Furthermore, the first lifting point monitoring feature sequence is subjected to iterative data feature correlation identification on two adjacent first lifting point monitoring features in order from front to back to obtain a first lifting point monitoring feature correlation factor sequence, including: Extract any two adjacent first hanging point monitoring features in the first hanging point monitoring feature sequence; perform feature similarity identification on the two first hanging point monitoring features to obtain a first hanging point monitoring feature similarity set; normalize the first hanging point monitoring feature similarity set and fill it into an initially empty matrix to obtain a first correlation matrix; identify correlation factors based on the first correlation matrix and the two first hanging point monitoring features to obtain first hanging point monitoring feature correlation factors; perform iterative correlation identification on the data features of two adjacent first hanging point monitoring features in the first hanging point monitoring feature sequence to obtain a first hanging point monitoring feature correlation factor sequence.

[0028] From the first hanging point monitoring feature sequence, any two adjacent first hanging point monitoring features are extracted in descending order. These two adjacent monitoring features represent the performance of the hanging point at different time points. By comparing their similarities, we can determine whether these two features are correlated. Feature similarity is then identified for these two adjacent first hanging point monitoring features to measure the degree of similarity between them. The similarity can be quantified by calculating the Euclidean distance, cosine similarity, or Pearson correlation coefficient between the two features. Based on these similarity metrics, a first hanging point monitoring feature similarity set is obtained, which contains the similarity values between all pairs of adjacent features.

[0029] The similarity set of the first hanging point monitoring features is normalized using the Softmax formula. Softmax can transform a set of values into a probability distribution so that the output of each value is between 0 and 1, and the sum of all values is 1. The Softmax formula is as follows: ;in, represents the similarity value of the i-th monitoring feature, Yes Perform exponential operations to increase the weight of larger values. It is the exponential sum of the similarity values of all monitoring features, and is used to normalize the similarity values of all features. For each pair of adjacent monitoring features in the first hanging point monitoring feature similarity set, the Softmax formula is used to calculate the normalized similarity value. Specifically, for each item in the set, the Softmax formula will calculate the relative size between the item and other items in the set so that the output result can meet the normalization requirements. After the Softmax normalized values are calculated, these normalized values will be filled into an initially empty matrix to form the first correlation matrix. Each element of the matrix represents the normalized similarity value between different monitoring features. After the Softmax normalized similarity values are filled into the matrix, each element in the matrix represents the degree of correlation between the two monitoring features.

[0030] Based on the obtained first correlation matrix and the two extracted first hanging point monitoring features, correlation factors are identified. This involves analyzing the relationships between the monitoring features and quantifying their correlation. For example, the correlation factor can be identified by calculating the weighted average of the correlation values in the matrix or using other correlation evaluation methods. Ultimately, the first hanging point monitoring feature correlation factor is obtained, which quantifies the strength of the correlation between the two monitoring features.

[0031] The above data feature iterative correlation identification is performed on all adjacent monitoring features in the first lifting point monitoring feature sequence. After this series of steps, a first lifting point monitoring feature correlation factor sequence is obtained. This sequence reflects the feature correlation between the lifting points at each moment in the lifting process, thereby revealing the changing patterns of the lifting points in the time series and potential risk associations.

[0032] Furthermore, identifying correlation factors based on the first correlation matrix and the two first hanging point monitoring features to obtain first hanging point monitoring feature correlation factors includes: The first correlation matrix is convolved with the two first hanging point monitoring features to obtain two first hanging point monitoring enhanced features. The similarity between the two first hanging point monitoring enhanced features and the corresponding first hanging point monitoring features is calculated again, and the average of the calculation results is obtained to obtain the first hanging point monitoring feature correlation factor.

[0033] First, the first correlation matrix is convolved with the two first-hanging-point monitoring features. Convolution involves using the correlation matrix as a convolution kernel and performing a sliding calculation on the monitoring feature data. During this process, each element in the first correlation matrix represents the similarity between features, while the monitoring features contain data such as the force and displacement of the hanging points at different time points. The convolution operation effectively captures the local correlation between the monitoring features and the correlation matrix, thereby generating a higher-dimensional feature representation, namely the two first-hanging-point monitoring enhanced features. These enhanced features fuse information from the original features and the correlation matrix during the convolution process, providing more expressive features and helping to identify more nuanced patterns of change between hanging-point data.

[0034] Next, a similarity calculation is performed between the two convolved first-point monitoring enhancement features and their respective corresponding first-point monitoring features. This similarity calculation can be based on various methods, such as Euclidean distance and cosine similarity, which can reflect the degree of numerical similarity between the enhanced features and the original features. The calculated similarity values are then averaged to obtain a value representing the overall strength of the relationship, namely the first-point monitoring feature correlation factor. The first-point monitoring feature correlation factor reflects the overall strength of the correlation between the two sets of monitoring features. A high correlation factor indicates that the change trends between the two sets of monitoring features are similar, and there may be a strong common change pattern, which has a significant impact on the safety of the lifting operation. A low correlation factor indicates that the changes between these features are relatively independent and may have a low correlation.

[0035] Asynchronous dual-channel security monitoring is performed on the first-level risk monitoring object and the second-level risk monitoring object to obtain first security warning information, wherein the asynchronous dual-channel includes a first monitoring sub-channel and a second monitoring sub-channel.

[0036] Identified Level 1 and Level 2 risk monitoring targets are monitored in real time through asynchronous dual-channel safety monitoring, generating first-level safety warnings. Asynchronous dual-channel safety monitoring divides monitoring tasks into two channels—the first and second monitoring sub-channels—and employs different monitoring strategies for targets of different risk levels. Specifically, Level 1 risk monitoring targets are assigned to the first sub-channel, which monitors at a higher frequency to obtain real-time data on these high-risk areas and promptly respond to any anomalies. Level 2 risk monitoring targets are assigned to the second sub-channel, which monitors at a lower frequency but still ensures effective monitoring of potential risks. Specifically, different monitoring frequencies are assigned to Level 1 and Level 2 risk monitoring targets based on historical lifting data and risk assessment results. Through this asynchronous approach, the system can intensively monitor Level 1 risk targets in the first sub-channel while monitoring Level 2 risk targets at a lower frequency in the second sub-channel. This effectively allocates resources and ensures that high-risk areas are prioritized without increasing computational overhead.

[0037] In each monitoring sub-channel, the collected monitoring data is analyzed in real time to detect any anomalies or potential safety risks; if an abnormality is detected in the first monitoring sub-channel or the second monitoring sub-channel, the system should immediately generate a first safety warning message, which may include the specific type, location, severity and recommended response measures of the abnormality.

[0038] Furthermore, performing asynchronous dual-channel security monitoring on the first-level risk monitoring object and the second-level risk monitoring object to obtain first security warning information includes: Obtain a historical hoisting log set; traverse the historical hoisting log set to perform risk classification, and obtain a first-level historical hoisting log set and a second-level historical hoisting log set; traverse the first-level historical hoisting log set and the second-level historical hoisting log set to perform abnormal time window identification, and obtain a first-level abnormal time window set and a second-level abnormal time window set; based on the size of the first-level abnormal time window set and the second-level abnormal time window set, perform monitoring frequency configuration, and obtain a first-level monitoring frequency and a second-level monitoring frequency; construct a first monitoring sub-channel and a second monitoring sub-channel of the asynchronous dual-channel according to the first-level monitoring frequency and the second-level monitoring frequency.

[0039] Specifically, all relevant historical lifting logs are extracted or collected from the database. These logs should contain the time, location, equipment information, operation process, monitoring data and any safety or fault-related event records of the lifting operation; the historical lifting log collection is traversed, and the logs are divided into a first-level historical lifting log collection (high risk) and a second-level historical lifting log collection (low risk) according to the risk factors recorded in the log (such as lifting weight, height, environmental conditions, equipment status, etc.) and event consequences (such as whether it causes accidents, equipment damage or casualties); the first-level and second-level historical lifting log collections are traversed, and the time series data in each log is analyzed to identify abnormal time windows in the lifting operation. The abnormal time window can be defined as the time period in which the monitoring data exceeds the normal range, the operation process deviates or a safety incident occurs in the lifting operation; according to the analysis results, the first-level abnormal time window collection (high-risk time window) and the second-level abnormal time window collection are obtained respectively. (low-risk time window); based on the size of the first-level abnormal time window set and the second-level abnormal time window set (that is, the number and duration of abnormal time windows), and the ratio of these abnormal time windows to the overall lifting operation time, configure the first-level monitoring frequency and the second-level monitoring frequency. Generally speaking, the more abnormal time windows there are, the longer the duration, or the higher the ratio to the overall operation time, the higher the corresponding monitoring frequency should be; the monitoring frequency can be expressed in time intervals, such as monitoring once every minute or every hour, or it can be event-triggered, such as monitoring at the beginning and end of each lifting operation or when a specific event occurs; according to the configured first-level monitoring frequency and second-level monitoring frequency, the first monitoring sub-channel and the second monitoring sub-channel of the asynchronous dual-channel monitoring system are constructed. The first monitoring sub-channel is used to monitor the first-level risk monitoring object at the first-level monitoring frequency, and the second monitoring sub-channel is used to monitor the second-level risk monitoring object at the second-level monitoring frequency.

[0040] Furthermore, a warning feedback window is preset, and abnormality investigation is performed within the warning feedback window to obtain first warning feedback information.

[0041] After building an effective asynchronous dual-channel security monitoring system and monitoring the first-level risk monitoring objects and second-level risk monitoring objects to obtain the first security warning information, in order to ensure the effectiveness and timeliness of the warning information, it is necessary to preset a warning feedback window and perform abnormality investigation within the window to obtain the first warning feedback information.

[0042] The early warning feedback window is a predefined time period, typically adjusted based on the characteristics of the lifting operation and historical data. During lifting operations, the early warning feedback window is designed to centrally assess detected risks, ensuring timely detection and action. The length and triggering conditions of the early warning feedback window are flexibly adjusted based on factors such as the actual operating environment, the risk assessment of the lifting point, and the load points.

[0043] When the system identifies potential safety risks during the monitoring process, it will input the monitoring data into the preset feedback window for further investigation. During this process, the system conducts a detailed analysis of the data in the early warning feedback window to check for abnormal fluctuations or violations of safety regulations. These anomalies may include overload of the lifting point, excessive deformation of the load point, displacement beyond the safe range, etc. Through continuous monitoring and comparison of data, the system can identify possible risks and abnormal events. After completing the abnormal investigation, the system will generate the first early warning feedback information based on the analysis results. This information will provide a series of safety tips and early warning measures based on the abnormal conditions identified. For example, if the monitoring data finds that the load of the lifting point exceeds the preset safety threshold, the system may issue an alarm, requiring the lifting operation to be suspended and the lifting point to be inspected.

[0044] In summary, the embodiments of the present application have at least the following technical effects: First, obtain Q lifting points and K stress points of the target tube truss steel structure, where Q and K are positive integers. Next, use the sensor monitoring array to continuously monitor the Q lifting points and K stress points of the steel structure, respectively, to obtain Q lifting point monitoring data group sequences and K steel structure stress point monitoring group sequences. Then, traverse the Q lifting point monitoring data group sequences and K steel structure stress point monitoring group sequences to identify risk factors and determine the first-level risk monitoring objects and second-level risk monitoring objects. Finally, perform asynchronous dual-channel safety monitoring on the first-level risk monitoring objects and the second-level risk monitoring objects to obtain the first safety warning information, where the asynchronous dual-channel includes a first monitoring sub-channel and a second monitoring sub-channel. This solves the technical problem in the prior art that safety hazards cannot be effectively and timely warned during the hoisting process of the roof tube truss steel structure, and achieves the technical effect of continuously monitoring the lifting points and stress points through sensor monitoring to achieve safety warnings for the hoisting operation.

[0045] Example 2, based on the same inventive concept as the safety warning method for hoisting the roof tube truss steel structure under sensor monitoring in the above embodiment, Figure 2 As shown, the present application provides a safety warning device for hoisting a roof tube truss steel structure under sensor monitoring, wherein the device includes: The key point acquisition module 11 is used to obtain Q hanging points and K steel structure stress points of the target tube truss steel structure, where Q and K are positive integers; the data acquisition module 12 is used to use the sensor monitoring array to continuously monitor the Q hanging points and K steel structure stress points respectively, and obtain Q hanging point monitoring data group sequences and K steel structure stress point monitoring group sequences; the risk identification module 13 is used to traverse the Q hanging point monitoring data group sequences and K steel structure stress point monitoring group sequences to identify risk factors and determine the first-level risk monitoring objects and the second-level risk monitoring objects; the safety monitoring module 14 is used to perform asynchronous dual-channel safety monitoring on the first-level risk monitoring objects and the second-level risk monitoring objects to obtain first safety warning information, where the asynchronous dual channel includes a first monitoring sub-channel and a second monitoring sub-channel.

[0046] Furthermore, the risk identification module 13 is configured to execute the following method: The Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences are respectively subjected to fluctuation factor identification to obtain Q hanging point monitoring fluctuation factors and K steel structure stress point monitoring fluctuation factors; the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences are traversed to perform data feature iterative association identification to obtain Q hanging point monitoring correlation factors and K steel structure stress point monitoring correlation factors; weighted calculation is performed on the Q hanging point monitoring fluctuation factors and the Q hanging point monitoring correlation factors, as well as the K steel structure stress point monitoring fluctuation factors and the K steel structure stress point monitoring correlation factors to obtain Q hanging point risk coefficients and K steel structure stress point risk coefficients; the Q hanging point risk coefficients and the K steel structure stress point risk coefficients are identified according to a preset risk factor threshold to obtain a first-level risk monitoring object and a second-level risk monitoring object.

[0047] Furthermore, the risk identification module 13 is configured to execute the following method: The Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences are traversed to perform fluctuation variance calculation to obtain Q first hanging point sub-monitoring fluctuation factors and K first steel structure stress point monitoring fluctuation factors; the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences are traversed to perform maximum amplitude comparison analysis to obtain Q second hanging point sub-monitoring fluctuation factors and K second steel structure stress point monitoring fluctuation factors; weighted calculation is performed on the Q first hanging point sub-monitoring fluctuation factors and the Q second hanging point monitoring fluctuation factors, as well as the K first steel structure stress point monitoring fluctuation factors and the K second steel structure stress point monitoring fluctuation factors, to obtain Q hanging point monitoring fluctuation factors and K steel structure stress point monitoring fluctuation factors.

[0048] Furthermore, the risk identification module 13 is configured to execute the following method: Extract a first hanging point monitoring data group sequence from the Q hanging point monitoring data group sequences; traverse the first hanging point monitoring data group sequence to perform data feature extraction to obtain a first hanging point monitoring feature sequence; perform data feature iterative association identification on two adjacent first hanging point monitoring features of the first hanging point monitoring feature sequence in order from front to back to obtain a first hanging point monitoring feature association factor sequence; calculate the mean of the first hanging point monitoring feature association factor sequence to obtain a first hanging point monitoring association factor; perform data feature iterative association identification on the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring group sequences respectively to obtain the Q hanging point monitoring association factors and the K steel structure stress point monitoring association factors.

[0049] Furthermore, the risk identification module 13 is configured to execute the following method: Extract any two adjacent first hanging point monitoring features in the first hanging point monitoring feature sequence; perform feature similarity identification on the two first hanging point monitoring features to obtain a first hanging point monitoring feature similarity set; normalize the first hanging point monitoring feature similarity set and fill it into an initially empty matrix to obtain a first correlation matrix; identify correlation factors based on the first correlation matrix and the two first hanging point monitoring features to obtain first hanging point monitoring feature correlation factors; perform iterative correlation identification on the data features of two adjacent first hanging point monitoring features in the first hanging point monitoring feature sequence to obtain a first hanging point monitoring feature correlation factor sequence.

[0050] Furthermore, the risk identification module 13 is configured to execute the following method: The first correlation matrix is convolved with the two first hanging point monitoring features to obtain two first hanging point monitoring enhanced features. The similarity between the two first hanging point monitoring enhanced features and the corresponding first hanging point monitoring features is calculated again, and the average of the calculation results is obtained to obtain the first hanging point monitoring feature correlation factor.

[0051] Furthermore, the security monitoring module 14 is configured to execute the following method: Obtain a historical hoisting log set; traverse the historical hoisting log set to perform risk classification, and obtain a first-level historical hoisting log set and a second-level historical hoisting log set; traverse the first-level historical hoisting log set and the second-level historical hoisting log set to perform abnormal time window identification, and obtain a first-level abnormal time window set and a second-level abnormal time window set; based on the size of the first-level abnormal time window set and the second-level abnormal time window set, perform monitoring frequency configuration, and obtain a first-level monitoring frequency and a second-level monitoring frequency; construct a first monitoring sub-channel and a second monitoring sub-channel of the asynchronous dual-channel according to the first-level monitoring frequency and the second-level monitoring frequency.

[0052] Furthermore, the security monitoring module 14 is configured to execute the following method: A warning feedback window is preset, and abnormality investigation is performed within the warning feedback window to obtain first warning feedback information.

[0053] Furthermore, the data acquisition module 12 is used to perform the following method: The sensor monitoring array includes a strain sensor, a displacement sensor, a load sensor and an accelerometer.

[0054] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0055] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0056] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A safety early warning method for hoisting a roof tube truss steel structure under sensor monitoring, characterized in that: The method comprises: Obtain Q hanging points and K stress points of the target tube truss steel structure, where Q and K are positive integers; Utilizing a sensor monitoring array to continuously monitor the Q lifting points and the K stress-bearing points of the steel structure, respectively, to obtain a sequence of Q lifting point monitoring data groups and a sequence of K steel structure stress-bearing point monitoring data groups; Traversing the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences to identify risk factors and determine the first-level risk monitoring objects and the second-level risk monitoring objects; Asynchronous dual-channel security monitoring is performed on the first-level risk monitoring object and the second-level risk monitoring object to obtain first security warning information, wherein the asynchronous dual-channel includes a first monitoring sub-channel and a second monitoring sub-channel.

2. The sensor-monitored roof tube truss steel structure hoisting safety early warning method according to claim 1, characterized in that: Traversing the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences to identify risk factors, and determining the first-level risk monitoring objects and the second-level risk monitoring objects, including: Performing fluctuation factor identification on the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences respectively to obtain Q hanging point monitoring fluctuation factors and K steel structure stress point monitoring fluctuation factors; Traversing the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences to perform iterative correlation identification of data features, and obtaining Q hanging point monitoring correlation factors and K steel structure stress point monitoring correlation factors; Performing weighted calculations on the Q lifting point monitoring fluctuation factors and the Q lifting point monitoring correlation factors, as well as the K steel structure stress point monitoring fluctuation factors and the K steel structure stress point monitoring correlation factors, respectively, to obtain the Q lifting point risk coefficients and the K steel structure stress point risk coefficients; The risk coefficients of the Q hanging points and the risk coefficients of the K steel structure stress points are identified according to a preset risk coefficient threshold value to obtain a first-level risk monitoring object and a second-level risk monitoring object.

3. The sensor-monitored roof tube truss steel structure hoisting safety early warning method according to claim 2, characterized in that: Performing fluctuation factor identification on the Q hanging point monitoring data group sequence and the K steel structure stress point monitoring data group sequence respectively to obtain the Q hanging point monitoring fluctuation factors and the K steel structure stress point monitoring fluctuation factors, including: Traversing the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences to perform fluctuation variance calculation, and obtaining Q first hanging point sub-monitoring fluctuation factors and K first steel structure stress point monitoring fluctuation factors; Traversing the Q lifting point monitoring data group sequences and the K steel structure stress point monitoring data group sequences to perform maximum amplitude comparison analysis, and obtaining Q second lifting point sub-monitoring fluctuation factors and K second steel structure stress point monitoring fluctuation factors; Weighted calculations are performed on the Q first lifting point monitoring fluctuation factors and the Q second lifting point monitoring fluctuation factors, as well as the K first steel structure stress-bearing point monitoring fluctuation factors and the K second steel structure stress-bearing point monitoring fluctuation factors to obtain Q lifting point monitoring fluctuation factors and K steel structure stress-bearing point monitoring fluctuation factors.

4. The sensor-monitored roof tube truss steel structure hoisting safety early warning method according to claim 1, characterized in that: Traversing the Q hanging point monitoring data group sequence and the K steel structure stress point monitoring data group sequence to perform iterative correlation identification of data features, and obtaining the Q hanging point monitoring correlation factors and the K steel structure stress point monitoring correlation factors, including: Extracting a first hanging point monitoring data group sequence from the Q hanging point monitoring data group sequences; Traversing the first lifting point monitoring data group sequence to perform data feature extraction to obtain a first lifting point monitoring feature sequence; performing data feature iterative correlation identification on two adjacent first lifting point monitoring features of the first lifting point monitoring feature sequence from front to back to obtain a first lifting point monitoring feature correlation factor sequence; Calculating the mean of the first hanging point monitoring feature correlation factor sequence to obtain a first hanging point monitoring correlation factor; Iterative correlation identification of data features is performed on the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences respectively to obtain the Q hanging point monitoring correlation factors and the K steel structure stress point monitoring correlation factors.

5. The sensor-monitored roof tube truss steel structure hoisting safety early warning method according to claim 4, characterized in that: The first lifting point monitoring feature sequence is sequentially ordered from front to back, and data feature iterative correlation identification is performed on two adjacent first lifting point monitoring features to obtain a first lifting point monitoring feature correlation factor sequence, including: extracting any two adjacent first hanging point monitoring features in the first hanging point monitoring feature sequence; Performing feature similarity recognition on the two first hanging point monitoring features to obtain a first hanging point monitoring feature similarity set; Normalizing the first hanging point monitoring feature similarity set and filling it into an initially empty matrix to obtain a first correlation matrix; Identifying correlation factors based on the first correlation matrix and the two first lifting point monitoring features to obtain first lifting point monitoring feature correlation factors; Iterative correlation identification of data features of two adjacent first hanging point monitoring features is performed on the first hanging point monitoring feature sequence to obtain a first hanging point monitoring feature correlation factor sequence.

6. The sensor-monitored roof tube truss steel structure hoisting safety early warning method according to claim 5, characterized in that: Identifying a correlation factor based on the first correlation matrix and the two first lifting point monitoring features to obtain a first lifting point monitoring feature correlation factor includes: Performing convolution operations on the first correlation matrix and the two first hanging point monitoring features respectively to obtain two first hanging point monitoring enhanced features; The similarities between the two first lifting point monitoring enhancement features and the corresponding first lifting point monitoring features are calculated again, and the average of the calculation results is obtained to obtain the first lifting point monitoring feature correlation factor.

7. The sensor-monitored roof tube truss steel structure hoisting safety early warning method according to claim 1, characterized in that: Performing asynchronous dual-channel security monitoring on the first-level risk monitoring object and the second-level risk monitoring object to obtain first security warning information includes: Get the historical lifting log collection; Traversing the historical hoisting log set to perform risk classification, and obtaining a first-level historical hoisting log set and a second-level historical hoisting log set; Traversing the first-level historical hoisting log set and the second-level historical hoisting log set to identify abnormal time windows, and obtaining a first-level abnormal time window set and a second-level abnormal time window set; Based on the sizes of the first-level abnormal time window set and the second-level abnormal time window set, a monitoring frequency is configured to obtain a first-level monitoring frequency and a second-level monitoring frequency; The first monitoring sub-channel and the second monitoring sub-channel of the asynchronous dual-channel are constructed according to the first-level monitoring frequency and the second-level monitoring frequency.

8. The sensor-monitored roof tube truss steel structure hoisting safety early warning method according to claim 1, characterized in that: A warning feedback window is preset, and abnormality investigation is performed within the warning feedback window to obtain first warning feedback information.

9. The sensor-monitored roof tube truss steel structure hoisting safety early warning method according to claim 1, characterized in that: The sensor monitoring array includes a strain sensor, a displacement sensor, a load sensor and an accelerometer.

10. The sensor-monitored roof tube truss steel structure hoisting safety warning device is characterized by: A device for implementing the safety early warning method for hoisting a roof tube truss steel structure under sensor monitoring according to any one of claims 1 to 9, comprising: A key point acquisition module is used to obtain Q hanging points and K stress points of the target tube truss steel structure, where Q and K are positive integers; a data acquisition module, configured to continuously monitor the Q lifting points and the K stress-bearing points of the steel structure using a sensor monitoring array, and obtain a sequence of monitoring data groups of the Q lifting points and a sequence of monitoring data groups of the K stress-bearing points of the steel structure; A risk identification module is used to traverse the Q hanging point monitoring data group sequences and the K steel structure stress point monitoring data group sequences to identify risk factors and determine the first-level risk monitoring objects and the second-level risk monitoring objects; The safety monitoring module is used to perform asynchronous dual-channel safety monitoring on the first-level risk monitoring object and the second-level risk monitoring object to obtain first safety warning information, wherein the asynchronous dual-channel includes a first monitoring sub-channel and a second monitoring sub-channel.

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