A steel structure platform and early warning system and method for safety monitoring of a floor

Abstract

The present application belongs to the technical field of steel structure platform monitoring, and specifically relates to a steel structure platform and an early warning system and method for safety monitoring of a floor. The present application can timely discover potential safety hazards by monitoring the safety status of the steel structure platform and the floor in real time, thereby effectively preventing and reducing accidents. By collecting and analyzing real-time data, combining evaluation functions and early warning mechanisms, the present application can accurately evaluate the safety status of the steel structure platform and the floor, and take corresponding early warning measures according to the evaluation results. In addition, by matching the early warning level and the monitoring frequency, the present application can also optimize the allocation of monitoring resources and improve the monitoring efficiency. That is, when the risk is low, the system can reduce the monitoring frequency to save resources; when the risk is high, the system can increase the monitoring frequency to ensure the safety of the steel structure platform and the floor.

Description

Technical Field

[0001] This invention belongs to the field of steel structure platform monitoring technology, specifically relating to an early warning system and method for safety monitoring of steel structure platforms and mezzanines. Background Technology

[0002] With the development of industry and construction, steel structure platforms and mezzanines are being used more and more widely in various projects. However, due to the long-term exposure of steel structures to complex environmental conditions, such as temperature changes, humidity, chemical corrosion, and mechanical stress, their structural safety and durability will gradually decrease. Therefore, it is particularly important to conduct effective safety monitoring and early warning for steel structure platforms and mezzanines to prevent potential safety accidents.

[0003] Traditional monitoring methods often rely on manual inspections, which are not only time-consuming and labor-intensive, but also make it difficult to achieve real-time and comprehensive monitoring of steel structure platforms and mezzanines. In addition, when abnormalities occur, traditional methods cannot issue timely warnings, resulting in potential risks not being addressed in a timely manner. Therefore, this solution provides a method that can accurately assess the safety status of steel structure platforms and mezzanines and issue real-time warning signals to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide an early warning system and method for safety monitoring of steel structure platforms and mezzanines, which can accurately assess the safety status of steel structure platforms and mezzanines and issue early warning signals in a timely manner to improve monitoring efficiency and safety.

[0005] The specific technical solution adopted by this invention is as follows:

[0006] An early warning method for safety monitoring of steel structure platforms and mezzanines includes:

[0007] The real-time data of the steel structure platform and mezzanine are investigated by pre-deployed monitoring points. The real-time data includes structural strain data, tilt data, and vibration data. The real-time data is pre-processed, and the evaluation status of the steel structure platform and mezzanine is evaluated based on the pre-processed real-time data. The evaluation status includes safety status and risk status.

[0008] Under safe conditions, record real-time data to the inspection log and draw trend charts based on the real-time data;

[0009] Under risk conditions, an early warning mechanism is triggered, the risk difference is output synchronously, and the early warning level is matched according to the risk difference to determine the early warning intensity;

[0010] The monitoring points that trigger the risk status are identified as risk locations, and the frequency of risk signals issued at each risk location is counted. The monitoring frequency of the corresponding risk location is then adjusted based on the warning level.

[0011] In a preferred embodiment, the real-time data is acquired based on the structural characteristics of the steel structure platform and the mezzanine, and monitoring points are set up, including the middle of the column, the end of the column, the center of the platform, key positions of the platform, and the main load-bearing components.

[0012] Data acquisition equipment is deployed at the monitoring points. The data acquisition equipment includes surface strain gauges, inclinometers, and vibration sensors. The surface strain gauges are deployed at the monitoring points in the middle of the column and the center of the platform to collect strain data. The inclinometers are deployed at the monitoring points at the ends of the column and key monitoring points on the platform to collect tilt data. The vibration sensors are deployed at the monitoring points at the main load-bearing components of the platform to collect vibration data. When the surface strain gauges, inclinometers, and vibration sensors are collecting data, the real-time data from the data acquisition equipment is synchronized through timestamps.

[0013] After the real-time data is output, it is preprocessed. The preprocessing includes data cleaning, outlier removal, and data normalization to eliminate interference from non-structural vibration signals in the steel structure platform and mezzanine, as well as noise during the tilt data acquisition process.

[0014] In a preferred embodiment, the step of evaluating the assessment status of the steel structure platform and mezzanine based on preprocessed real-time data includes:

[0015] Acquire real-time data of the steel structure platform and mezzanine, and record them as parameters to be evaluated;

[0016] Obtain the evaluation threshold and compare the condition parameter to be evaluated with the evaluation threshold;

[0017] If the condition parameter to be evaluated is greater than or equal to the evaluation threshold, it indicates that the corresponding real-time data is abnormal, and the safety status of the steel platform and the partition is recorded as an abnormal state.

[0018] If the condition parameter to be evaluated is less than the evaluation threshold, it indicates that the corresponding real-time data is normal, and the safety status of the steel platform and the partition is recorded as a safe status.

[0019] In a preferred embodiment, under the safe condition, historical monitoring data of the steel structure platform and mezzanine are collected from the inspection log.

[0020] Obtain a first evaluation function, input the historical monitoring data into the first evaluation function, and record the output of the first evaluation function as a trend loss parameter;

[0021] The safe execution time of the steel structure platform and mezzanine is determined based on the trend loss parameters, and the current node is offset based on the safe execution time to obtain the predicted risk node;

[0022] If the predicted risk node coincides with the preset monitoring node, the predicted risk node is offset in the opposite direction to obtain the early warning node.

[0023] If the predicted risk node does not coincide with the preset monitoring node, then the monitoring node that is adjacent to the predicted risk node and located before the predicted risk node will be recorded as the warning node.

[0024] In a preferred embodiment, the step of determining the safe operating time of the steel structure platform and mezzanine based on the trend loss parameter includes:

[0025] Obtain the trend loss parameter and the evaluation threshold corresponding to the trend loss parameter;

[0026] Obtain the second evaluation function;

[0027] The trend loss parameter and the evaluation threshold are input into the second evaluation function, and the output of the second evaluation function is recorded as the safe execution time.

[0028] In a preferred embodiment, the step of outputting the risk difference includes:

[0029] Obtain the risk category of the steel structure and mezzanine under the aforementioned risk status, wherein the risk category includes fixed risks and persistent risks;

[0030] If the risk category is a fixed risk, it indicates that the risk of the steel structure and the mezzanine has been determined, and the difference between the real-time data and the assessment threshold is recorded as the risk difference.

[0031] If the risk category is a persistent risk, then a monitoring period is established, and multiple sampling nodes are set up within the monitoring period;

[0032] Real-time data is collected at each of the sampling nodes, and when the real-time data at the sampling node is continuously greater than the evaluation threshold, the difference between the real-time data at the last risk node and the evaluation threshold is recorded as the risk difference.

[0033] In a preferred embodiment, the step of matching early warning levels based on risk differences includes:

[0034] Obtain the risk difference between the steel structure platform and the mezzanine;

[0035] Obtain classification intervals, wherein multiple classification intervals are set, and each classification interval corresponds to a warning level;

[0036] The risk difference is matched with the classification interval to determine the warning level corresponding to the steel structure platform and mezzanine under the risk difference.

[0037] The higher the warning level, the stronger the warning signal.

[0038] In a preferred embodiment, the step of statistically analyzing the frequency of risk signals issued at each risk location and adjusting the monitoring frequency of the corresponding risk location based on the warning level includes:

[0039] Obtain the risk frequency and risk difference at each risk location of the steel structure platform;

[0040] The number of risks at the risk location is recorded as the first condition parameter, and the risk difference at the risk location is recorded as the second condition parameter.

[0041] Obtain the comprehensive evaluation function, and input the first condition parameter and the second condition parameter into the comprehensive evaluation function together, and record the output result of the comprehensive evaluation function as the comprehensive evaluation score;

[0042] An assessment interval is obtained, and the comprehensive assessment score is compared with the assessment interval to match the monitoring frequency of the risk location, wherein each assessment interval corresponds to a monitoring frequency.

[0043] The present invention also provides an early warning system for safety monitoring of steel structure platforms and mezzanines, and the early warning method for safety monitoring of steel structure platforms and mezzanines includes:

[0044] The monitoring module is used to investigate the real-time data of the steel structure platform and mezzanine through pre-deployed monitoring points. The real-time data includes structural strain data, tilt data, and vibration data. The real-time data is pre-processed, and the evaluation status of the steel structure platform and mezzanine is evaluated based on the pre-processed real-time data. The evaluation status includes a safe status and a risk status.

[0045] The inspection record module is used to record real-time data to the inspection log under safe conditions and to draw trend charts based on the real-time data.

[0046] The risk warning module is used to trigger the warning mechanism under risk conditions, output the risk difference synchronously, and match the warning level according to the risk difference to determine the warning intensity.

[0047] The monitoring optimization module is used to identify the monitoring points that trigger risk states as risk locations, count the frequency of risk signals issued at each risk location, and adjust the monitoring frequency of the corresponding risk location based on the warning level.

[0048] And, an electronic device, the electronic device comprising:

[0049] At least one processor;

[0050] and a memory communicatively connected to the at least one processor;

[0051] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the aforementioned early warning method for safety monitoring of the steel structure platform and mezzanine.

[0052] The technical effects achieved by this invention are as follows:

[0053] This invention monitors the safety status of the steel structure platform and mezzanine in real time, enabling timely detection of potential safety hazards and effectively preventing and reducing accidents. By collecting and analyzing real-time data, combined with evaluation functions and early warning mechanisms, this invention can accurately assess the safety status of the steel structure platform and mezzanine, and take corresponding early warning measures based on the assessment results. Furthermore, by matching early warning levels and monitoring frequencies, this invention can optimize the allocation of monitoring resources and improve monitoring efficiency. That is, when the risk is low, the system can reduce the monitoring frequency to save resources; while when the risk is high, the system increases the monitoring frequency to ensure the safety of the steel structure platform and mezzanine. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0055] Figure 2 This is a schematic diagram of the system modules of the present invention;

[0056] Figure 3 This is a schematic diagram of the electronic device structure of the present invention;

[0057] Figure 4 This is a schematic diagram of the steel structure platform monitoring provided by the present invention. Detailed Implementation

[0058] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0061] Please see Figure 1 As shown, the present invention provides an early warning method for safety monitoring of steel structure platforms and mezzanines, comprising:

[0062] S1. Investigate the real-time data of the steel structure platform and mezzanine through pre-deployed monitoring points. The real-time data includes structural strain data, tilt data, and vibration data. Preprocess the real-time data and evaluate the assessment status of the steel structure platform and mezzanine based on the preprocessed real-time data. The assessment status includes safety status and risk status.

[0063] S2. In a safe state, record real-time data to the inspection log and draw a trend chart based on the real-time data;

[0064] S3. Under risk conditions, trigger the early warning mechanism, output the risk difference simultaneously, and match the early warning level according to the risk difference to determine the early warning intensity;

[0065] S4. Identify the monitoring points that trigger the risk status as risk locations, and count the frequency of risk signals issued at each risk location. Adjust the monitoring frequency of the corresponding risk location based on the warning level.

[0066] As described in steps S1-S6 above, the material and equipment platform structure is subject to various uncertainties at each stage of its planning, design, manufacturing, installation, and use. Any failure to meet standards in any of these areas could damage the platform, causing serious safety hazards. Platform deformation and collapse would have a significant impact on the company's normal operations, potentially leading to equipment and cargo loss, and even personal injury or death. Therefore, prevention is crucial. In this embodiment, it is first necessary to examine the real-time data of the steel structure platform and its mezzanine. Real-time data includes structural strain data, tilt data, and vibration data. Structural strain data reflects the stress state of the steel, and tilt data is used to determine... The overall stability of the platform is assessed, and vibration data helps analyze the platform's response under dynamic loads. Based on real-time data, the safety status of the steel structure platform and mezzanine is evaluated. The evaluation results output the assessment status of the steel structure platform and mezzanine, which is divided into two types: safe status and risk status. A safe status indicates that the steel structure platform and mezzanine currently have no obvious safety hazards, while a risk status indicates potential safety issues requiring further attention and handling. In a safe status, the image and vibration data of the steel structure platform and mezzanine are recorded in the inspection log as historical records for subsequent analysis and comparison. Because steel structure platforms may experience material fatigue and environmental corrosion during long-term use, continuous monitoring of their condition is necessary even under safe conditions. Therefore, trend charts are generated based on inspection logs under safe conditions, allowing managers to visually observe platform status changes and promptly identify any potential trends. However, if the assessment indicates the steel structure platform and mezzanine are in a risky state, an early warning mechanism will be immediately triggered. This mechanism will simultaneously issue an early warning signal to notify relevant personnel to take appropriate intervention measures. It will also output a risk difference, the gap between the current state and the safety threshold, to more accurately understand the degree of risk. Based on the risk difference, an early warning level will be assigned to the steel structure platform and mezzanine, determining the intensity of the warning signal. This ensures managers can take appropriate countermeasures based on the severity of the risk. Finally, the risk locations of the steel structure platform and mezzanine will be identified, and the number of risk signals issued at each location will be counted. Based on the number of risk signals and the warning intensity, a monitoring frequency will be assigned to the corresponding risk location. For locations with frequent risk signals, the monitoring frequency will be increased to track their safety status and ensure timely detection and handling of potential safety hazards.

[0067] In a preferred embodiment, real-time data acquisition is based on the structural characteristics of the steel structure platform and mezzanine, and monitoring points are set up, including the middle of the column, the end of the column, the center of the platform, key positions of the platform, and the main load-bearing components.

[0068] Data acquisition equipment is deployed at the monitoring points, including surface strain gauges, inclinometers, and vibration sensors. Surface strain gauges are deployed at the monitoring points in the middle of the column and the center of the platform to collect strain data. Inclinometers are deployed at the monitoring points at the ends of the column and key monitoring points on the platform to collect tilt data. Vibration sensors are deployed at the monitoring points on the main load-bearing components of the platform to collect vibration data. When collecting data, the surface strain gauges, inclinometers, and vibration sensors synchronize the real-time data from the data acquisition equipment through timestamps.

[0069] In this implementation, during the pre-deployment of monitoring points, the locations of the monitoring points are determined based on the specific structural characteristics of the steel structure platform and mezzanine, such as the height of the columns, the area of ​​the platform, and the layout of the load-bearing components. This ensures that various data of the steel structure platform and mezzanine can be monitored comprehensively and accurately. Setting monitoring points in the middle of the columns allows for monitoring of the strain of the columns under stress, thereby determining the strength and stability of the columns. Setting monitoring points at the ends of the columns allows for monitoring of the platform's tilt. The placement of monitoring points at the platform center and key locations helps to capture the strain and deformation of the platform under different stress states. Key locations can be... The platform's connection nodes, edge areas, and areas bearing concentrated loads are designed to facilitate timely feedback on areas prone to stress concentration or abnormal deformation. Monitoring points are installed at major load-bearing components to monitor the platform's vibration under dynamic loads. Data acquisition devices are then installed at each monitoring point to facilitate real-time data collection and transmission. These devices can include surface strain gauges, inclinometers, and vibration sensors. Surface strain gauges measure strain changes on the steel structure surface, inclinometers monitor changes in the platform's or columns' tilt angle, and vibration sensors collect vibration response data under external loads. Data acquisition equipment can be added or adjusted according to actual needs. For example, temperature sensors can be added to monitor the impact of ambient temperature changes on the performance of the steel structure, or displacement sensors can be added to capture the overall displacement response of the platform under wind loads or other dynamic forces. Then, time stamp synchronization technology is used to align the data from each monitoring point in time to ensure the temporal consistency of multi-source heterogeneous data in subsequent analysis. Of course, different types of data acquisition equipment may have different acquisition frequencies. Time alignment can be performed based on a sliding window during time stamp synchronization to ensure that data acquired at different frequencies can be fused under a unified time reference. For example, the sampling frequency of the surface strain gauge is 10Hz, the sampling frequency of the inclinometer is 5Hz, and the sampling frequency of the vibration sensor is 100Hz. There are significant differences in the sampling frequencies of the three devices. Therefore, a sliding time window method is used for data alignment. The data collected in each time window is interpolated to ensure that the data collected by different devices are consistent on the time axis. In addition, after the real-time data is output, the real-time data is preprocessed, including data cleaning, outlier removal, and data normalization, to eliminate interference from non-structural vibration signals in the steel structure platform and mezzanine, as well as noise during the tilt data acquisition process.

[0070] In a preferred embodiment, the step of evaluating the assessment status of the steel structure platform and mezzanine based on preprocessed real-time data includes:

[0071] S101. Obtain real-time data of the steel structure platform and mezzanine, and record it as the condition parameters to be evaluated;

[0072] S102. Obtain the evaluation threshold and compare the condition parameter to be evaluated with the evaluation threshold;

[0073] If the condition parameter to be evaluated is greater than or equal to the evaluation threshold, it indicates that the corresponding real-time data is abnormal, and the safety status of the steel platform and the mezzanine is recorded as abnormal.

[0074] If the parameter to be evaluated is less than the evaluation threshold, it indicates that the corresponding real-time data is normal, and the safety status of the steel platform and the mezzanine is recorded as safe.

[0075] As described in steps S101-S102 above, to ensure the safe operation of the steel structure platform and its mezzanine, it is first necessary to acquire real-time data of the steel structure platform and its mezzanine as parameters to be evaluated, including but not limited to key indicators such as stress, strain, displacement, and vibration frequency. This allows for a comprehensive understanding of the current operating status of the steel structure platform and its mezzanine. Next, a pre-set evaluation threshold is acquired. This threshold is set based on relevant safety standards and historical data and is used to determine whether the real-time data is within a safe range. The parameters to be evaluated are compared in detail with the evaluation threshold to determine if there are any potential safety hazards. If the parameters to be evaluated are found to be greater than or equal to the evaluation threshold during the comparison, it means that there is an abnormality in the real-time data. In this case, the safety status of the steel structure platform and its mezzanine will be recorded as abnormal, and a corresponding alarm mechanism will be triggered to promptly remind relevant personnel to take necessary safety measures to prevent potential safety accidents. Conversely, if the parameters to be evaluated are less than the evaluation threshold, it indicates that the real-time data is within the normal range and the safety status of the steel structure platform and its mezzanine is good. In this case, the safety status will be recorded as safe, but continuous monitoring of the real-time data is still required to ensure that it remains within the evaluation threshold.

[0076] In a preferred embodiment, under safe conditions, historical monitoring data of the steel structure platform and mezzanine are collected from the inspection log.

[0077] Obtain the first evaluation function, input historical monitoring data into the first evaluation function, and record the output of the first evaluation function as the trend loss parameter;

[0078] The safe execution time of the steel structure platform and mezzanine is determined based on the trend loss parameters, and the current node is offset based on the safe execution time to obtain the predicted risk node;

[0079] If the predicted risk node coincides with the preset monitoring node, the predicted risk node is offset in the opposite direction to obtain the warning node.

[0080] If the predicted risk node does not coincide with the preset monitoring node, the monitoring node that is adjacent to the predicted risk node and located before the predicted risk node will be recorded as the warning node.

[0081] In this implementation, under the premise of ensuring safety, detailed monitoring data collection is required for the steel structure platform and its mezzanine. This involves extracting historical monitoring data from the inspection log to gain a comprehensive understanding of the condition of the steel structure and mezzanine. Then, a preset first evaluation function is introduced, and the historical monitoring data is input into this function. The trend loss parameter can then be calculated. The expression for the first evaluation function is as follows: In the formula, Indicates the trend loss parameter. This indicates the time period spanned by the inspection log. This indicates the number of historical monitoring data extracted. and This parameter represents the historical monitoring data under adjacent recorded nodes. It helps us assess the health status of the structure. Then, based on the calculated trend loss parameter, we can determine the safe operating time of the steel structure platform and its mezzanine under the current conditions. The safe operating time refers to the time that the steel structure and mezzanine can continue to operate without safety issues. After the safe operating time is output, the current node will be offset to output the predicted risk nodes that may have risks. If the predicted risk node happens to coincide with the preset monitoring node, then the predicted risk node needs to be offset in the opposite direction to determine an early warning node. The early warning node is the time point for taking measures in advance to avoid potential safety risks. Conversely, if the predicted risk node does not coincide with the preset monitoring node, we need to find the monitoring node that is adjacent to and before the predicted risk node and record it as an early warning node. After the early warning node is output, the steel structure and mezzanine need to be maintained accordingly to ensure the safe operation of the structure.

[0082] In a preferred embodiment, the step of determining the safe operating time of the steel structure platform and mezzanine based on trend loss parameters includes:

[0083] Obtain the trend loss parameters and the corresponding evaluation thresholds;

[0084] Obtain the second evaluation function;

[0085] The trend loss parameter and the evaluation threshold are input into the second evaluation function, and the output of the second evaluation function is recorded as the safe execution time.

[0086] In this embodiment, to determine the safe execution time of the steel structure platform and mezzanine, it is first necessary to obtain the trend loss parameter and the corresponding evaluation threshold. Secondly, a preset second evaluation function needs to be introduced, the expression of which is: In the formula, Indicates the duration of safe execution. Indicates the evaluation threshold. This indicates the inspection data of the last recorded steel structure platform and mezzanine in the inspection log. Through the calculation of the second evaluation function, the required safe execution time of the steel structure platform and mezzanine can be obtained, so as to serve as a reference in actual operation and ensure that the steel structure platform and mezzanine are used within a safe time range.

[0087] In a preferred embodiment, the step of outputting the risk difference includes:

[0088] S301. Obtain the risk category of the steel structure and mezzanine under the risk status, whereby the risk category includes fixed risk and continuous risk;

[0089] S302. If the risk category is fixed risk, it indicates that the risk of the steel structure and mezzanine has been determined, and the difference between the real-time data and the assessment threshold is recorded as the risk difference.

[0090] S303. If the risk category is a persistent risk, then a monitoring period shall be established, and multiple sampling nodes shall be set up within the monitoring period;

[0091] S304. Collect real-time data at each sampling node, and when the real-time data at the sampling node is continuously greater than the evaluation threshold, record the difference between the real-time data at the last risk node and the evaluation threshold as the risk difference.

[0092] As described in steps S301-S304 above, when determining the risk difference, it is first necessary to obtain the risk category of the steel structure and mezzanine under the risk state. This risk category can be divided into two main categories: fixed risk and persistent risk. Fixed risk is a clear and unchangeable risk, such as cracks or bending in the steel structure, while persistent risk is a continuous risk that cannot be determined at once, such as vibration of the steel structure or vibration caused by loose threads. If the risk category is determined to be fixed risk, it means that the risk faced by the steel structure and mezzanine is clear and fixed. At this time, it is necessary to simultaneously record the difference between the real-time data and the evaluation threshold. This will be recorded as a risk difference for subsequent risk assessment and management. However, if the risk category is determined to be a persistent risk, different measures are required. This involves establishing a monitoring period and setting up multiple sampling nodes within that period. The purpose of these sampling nodes is to monitor risk changes in more detail. During the monitoring period, real-time data needs to be collected from each sampling node. When the real-time data at a sampling node continuously exceeds the assessment threshold, the difference between the real-time data at the last risk node and the assessment threshold needs to be recorded. This difference will be recorded as a risk difference to facilitate the quantification and management of persistent risks.

[0093] In a preferred embodiment, the step of matching the warning level based on the risk difference includes:

[0094] S305. Obtain the risk difference between the steel structure platform and the mezzanine.

[0095] S306. Obtain the classification interval, wherein there are multiple classification intervals, and each classification interval corresponds to a warning level;

[0096] S307. Match the risk difference with the classification interval to determine the warning level corresponding to the steel structure platform and mezzanine under the risk difference;

[0097] The higher the warning level, the stronger the warning signal.

[0098] As described in steps S305-S307 above, to ensure the safe operation of the steel structure platform and mezzanine, it is first necessary to obtain the risk difference of the steel structure platform and mezzanine. The risk difference refers to the difference between the actual state and the expected state of the steel structure platform and mezzanine within a certain period of time. Secondly, it is necessary to obtain the preset classification intervals. The classification intervals refer to the ranges in which the risk difference is divided into different levels. In order to conduct risk assessment more accurately, multiple classification intervals are set, and each classification interval corresponds to a warning level. The specific classification intervals need to be set according to the actual project requirements and historical data. Next, the obtained risk difference is matched with the classification intervals. By comparing the value of the risk difference with the range of each classification interval, the warning level corresponding to the steel structure platform and mezzanine under the risk difference can be determined. According to the matching results, the warning level of the steel structure platform and mezzanine can be determined. The higher the warning level, the higher the warning intensity of the corresponding warning signal. This means that when the risk difference of the steel structure platform and mezzanine is large, a stronger warning signal needs to be issued to remind relevant personnel to take corresponding safety measures.

[0099] In a preferred embodiment, the step of statistically analyzing the frequency of risk signals issued at each risk location and adjusting the monitoring frequency of the corresponding risk location based on the warning level includes:

[0100] Obtain the risk frequency and risk difference at each risk location of the steel structure platform;

[0101] Record the number of risks at a risk location as the first condition parameter, and record the risk difference at a risk location as the second condition parameter;

[0102] Obtain the comprehensive evaluation function, input the first condition parameter and the second condition parameter into the comprehensive evaluation function, and record the output of the comprehensive evaluation function as the comprehensive evaluation score;

[0103] Obtain the assessment intervals and compare the comprehensive assessment scores with the assessment intervals to match the monitoring frequency of the risk locations. Each assessment interval corresponds to a monitoring frequency.

[0104] Among them, after the warning signal is issued, the monitoring backtracking period is constructed based on the node where the warning signal was issued;

[0105] Collect load data at risk locations during the monitoring and retrospective period;

[0106] If the load data does not change during the monitoring and retrospective period, it indicates that the structural stability at the risk location is abnormal. The warning level and the monitoring frequency of real-time data at the risk location will be directly increased to the maximum, and work activities on the steel structure platform and mezzanine will be stopped.

[0107] If the load data changes during the monitoring retrospective period, the monitoring frequency will be maintained consistent with the warning level to continue real-time monitoring of the risk location;

[0108] If the load data corresponding to the risk location returns to the state before the warning, but the real-time data at the risk location still does not return to the state before the warning, the warning level and the monitoring frequency of the real-time data at the risk location will be directly increased to the maximum, and the work activities on the steel structure platform and mezzanine will be stopped. If the real-time data at the risk location returns to the state before the warning, the warning status will be canceled and the normal monitoring frequency will be restored.

[0109] In this implementation, when determining the warning level corresponding to the risk status, historical risk records for each risk location on the steel structure platform are first obtained to extract the number of risks and the corresponding risk difference for each risk location. Then, the number of risks for each risk location is used as the first condition parameter, and the risk difference is used as the second condition parameter. A preset comprehensive evaluation function is introduced, and the first and second condition parameters are input into the comprehensive evaluation function. A comprehensive evaluation score is calculated to achieve a comprehensive quantitative assessment of the risk status of the risk location. The expression of the comprehensive evaluation function is as follows: In the formula, This indicates the overall evaluation score. and These represent the weighting factors for the first and second conditional parameters, respectively. and These represent the first and second conditional parameters, respectively. Next, a preset evaluation interval is obtained. This interval, set based on historical data and relevant experience, is used to divide the comprehensive evaluation score into different levels, each corresponding to a specific monitoring frequency. By comparing the comprehensive evaluation score with the evaluation interval, the monitoring frequency corresponding to the risk location can be determined, facilitating more frequent collection of real-time data at the risk location. It should be noted that after the warning signal is issued, to ensure accurate assessment of the structural stability of the risk location, a reverse backtracking is performed based on the warning signal's issuance node, constructing a monitoring backtracking period. The duration of this backtracking period is dynamically set according to the risk level, with an optimal range of 15 to 60 minutes. Then, load data at the risk location is collected. This load data reflects the stress on the steel structure platform during actual operation. By comparing whether the load data changes within the monitoring backtracking period, the structural stability of the risk location can be further determined. If the load data remains unchanged, but the real-time data at the risk location shows an anomaly, it indicates a potential structural instability at that location. In this case, the warning level and the monitoring frequency of the real-time data at the risk location should be increased to the maximum, and all work activities on the steel structure platform and mezzanine should be immediately stopped to ensure safety. Conversely, if the load data changes, and the changes in the real-time data at the risk location are consistent with the changes in the load data, it indicates that the changes at the risk location may be caused by normal load changes, such as load fluctuations caused by unloading. In this case, the monitoring frequency can be maintained at the same level as the warning level to continue real-time monitoring of the risk location until the load data corresponding to the risk location returns to normal. Then, it should be further determined whether the real-time data at the risk location has returned to the pre-warning state. If it has not yet returned, the warning level and monitoring frequency should also be increased to the maximum, and all work activities should be stopped. If it has returned, the warning status can be canceled, and the normal monitoring frequency can be restored.

[0110] Please see Figure 2 An early warning system for safety monitoring of steel structure platforms and mezzanines, using the aforementioned early warning method for safety monitoring of steel structure platforms and mezzanines, includes:

[0111] The monitoring module is used to investigate the real-time data of the steel structure platform and mezzanine through pre-deployed monitoring points. The real-time data includes structural strain data, tilt data, and vibration data. The real-time data is pre-processed, and the assessment status of the steel structure platform and mezzanine is evaluated based on the pre-processed real-time data. The assessment status includes safety status and risk status.

[0112] The inspection record module is used to record real-time data to the inspection log under safe conditions and to draw trend charts based on the real-time data.

[0113] The risk warning module is used to trigger the warning mechanism under risk conditions, output the risk difference synchronously, and match the warning level according to the risk difference to determine the warning intensity.

[0114] The monitoring optimization module is used to identify the monitoring points that trigger risk states as risk locations, count the frequency of risk signals issued at each risk location, and adjust the monitoring frequency of the corresponding risk location based on the warning level.

[0115] The execution process of the aforementioned early warning system corresponds to the execution process of the early warning method used for safety monitoring of steel structure platforms and mezzanines, and will not be repeated here.

[0116] Please see Figure 3 An electronic device, comprising:

[0117] At least one processor;

[0118] and memory that is communicatively connected to at least one processor;

[0119] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to execute the aforementioned early warning method for safety monitoring of the steel structure platform and mezzanine.

[0120] The processor of the aforementioned electronic device can be one or more central processing units (CPUs) or graphics processing units (GPUs) for processing large amounts of data and performing complex computational tasks. The memory can be random access memory (RAM), read-only memory (ROM), flash memory, or other types of storage media for storing computer programs and temporary data. By executing computer programs, the electronic device can achieve real-time monitoring and early warning of the safety status of the steel structure platform and mezzanine. The electronic device may also include an arithmetic logic unit (ALU), input devices, and output devices. The ALU is used to perform mathematical and logical operations in the computer program; the input devices allow users to interact with the electronic device, such as a keyboard, mouse, or touchscreen; and the output devices are used to display calculation results and early warning information, such as a monitor or printer.

[0121] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0122] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. An early warning method for safety monitoring of steel structure platforms and mezzanines, characterized in that: include: The real-time data of the steel structure platform and mezzanine are investigated by pre-deployed monitoring points. The real-time data includes structural strain data, tilt data, and vibration data. The real-time data is pre-processed, and the evaluation status of the steel structure platform and mezzanine is evaluated based on the pre-processed real-time data. The evaluation status includes safety status and risk status. Under safe conditions, record real-time data to the inspection log and draw trend charts based on the real-time data; Under risk conditions, an early warning mechanism is triggered, the risk difference is output synchronously, and the early warning level is matched according to the risk difference to determine the early warning intensity; The monitoring points that trigger the risk status are identified as risk locations, and the frequency of risk signals issued at each risk location is counted. The monitoring frequency of the corresponding risk location is adjusted in combination with the warning level. The step of matching early warning levels based on risk differences includes: Obtain the risk difference between the steel structure platform and the mezzanine; Obtain classification intervals, wherein multiple classification intervals are set, and each classification interval corresponds to a warning level; The risk difference is matched with the classification interval to determine the warning level corresponding to the steel structure platform and mezzanine under the risk difference. The higher the warning level, the stronger the warning signal. The step of statistically analyzing the frequency of risk signals issued at each risk location and adjusting the monitoring frequency of the corresponding risk location based on the warning level includes: Obtain the risk frequency and risk difference at each risk location of the steel structure platform; The number of risks at the risk location is recorded as the first condition parameter, and the risk difference at the risk location is recorded as the second condition parameter. Obtain the comprehensive evaluation function, and input the first condition parameter and the second condition parameter into the comprehensive evaluation function together, and record the output result of the comprehensive evaluation function as the comprehensive evaluation score; An assessment interval is obtained, and the comprehensive assessment score is compared with the assessment interval to match the monitoring frequency of the risk location, wherein each assessment interval corresponds to a monitoring frequency; Wherein, after the warning signal is issued, a reverse backtracking is performed based on the node where the warning signal was issued to construct a monitoring backtracking period; Collect load data at risk locations during the monitoring and retrospective period; If the load data does not change during the monitoring and retrospective period, it indicates that the structural stability at the risk location is abnormal. The warning level and the monitoring frequency of real-time data at the risk location will be directly increased to the maximum, and work activities on the steel structure platform and mezzanine will be stopped. If the load data changes during the monitoring retrospective period, the monitoring frequency will be maintained consistent with the warning level to continue real-time monitoring of the risk location; If the load data corresponding to the risk location returns to the state before the warning, but the real-time data at the risk location still does not return to the state before the warning, the warning level and the monitoring frequency of the real-time data at the risk location will be directly increased to the maximum, and the work activities on the steel structure platform and mezzanine will be stopped. If the real-time data at the risk location returns to the state before the warning, the warning status will be canceled and the normal monitoring frequency will be restored.

2. The early warning method for safety monitoring of steel structure platforms and mezzanines according to claim 1, characterized in that: The real-time data is acquired by setting up monitoring points based on the structural characteristics of the steel structure platform and the mezzanine. The monitoring points include key locations on the platform and major load-bearing components. The key locations on the platform include the center of the platform and the major load-bearing components include the middle part of the column and the end of the column. Data acquisition equipment is deployed at the monitoring points. The data acquisition equipment includes surface strain gauges, inclinometers, and vibration sensors. The surface strain gauges are deployed at the monitoring points in the middle of the column and the center of the platform to collect strain data. The inclinometers are deployed at the monitoring points at the ends of the column and key monitoring points on the platform to collect tilt data. The vibration sensors are deployed at the monitoring points at the main load-bearing components of the platform to collect vibration data. When the surface strain gauges, inclinometers, and vibration sensors are collecting data, the real-time data from the data acquisition equipment is synchronized through timestamps. After the real-time data is output, it is preprocessed. The preprocessing includes data cleaning, outlier removal, and data normalization to eliminate interference from non-structural vibration signals in the steel structure platform and mezzanine, as well as noise during the tilt data acquisition process.

3. The early warning method for safety monitoring of steel structure platforms and mezzanines according to claim 1, characterized in that: The step of evaluating the assessment status of the steel structure platform and mezzanine based on preprocessed real-time data includes: Acquire the pre-processed real-time data from the steel structure platform and mezzanine, and record it as the condition parameters to be evaluated; Obtain the evaluation threshold and compare the condition parameter to be evaluated with the evaluation threshold; If the condition parameter to be evaluated is greater than or equal to the evaluation threshold, it indicates that the corresponding real-time data is abnormal, and the safety status of the steel platform and the partition is recorded as an abnormal state. If the condition parameter to be evaluated is less than the evaluation threshold, it indicates that the corresponding real-time data is normal, and the safety status of the steel platform and the partition is recorded as a safe status.

4. The early warning method for safety monitoring of steel structure platforms and mezzanines according to claim 1, characterized in that: Under the aforementioned safe condition, historical monitoring data of the steel structure platform and mezzanine are collected from the inspection log. Obtain a first evaluation function, input the historical monitoring data into the first evaluation function, and record the output of the first evaluation function as a trend loss parameter; The safe execution time of the steel structure platform and mezzanine is determined based on the trend loss parameters, and the current node is offset based on the safe execution time to obtain the predicted risk node; If the predicted risk node coincides with the preset monitoring node, the predicted risk node is offset in the opposite direction to obtain the early warning node. If the predicted risk node does not coincide with the preset monitoring node, then the monitoring node that is adjacent to the predicted risk node and located before the predicted risk node will be recorded as the warning node.

5. The early warning method for safety monitoring of a steel structure platform and mezzanine as described in claim 4, characterized in that: The step of determining the safe execution time of the steel structure platform and mezzanine based on the trend loss parameters includes: Obtain the trend loss parameter and the evaluation threshold corresponding to the trend loss parameter; Obtain the second evaluation function; The trend loss parameter and the evaluation threshold are input into the second evaluation function, and the output of the second evaluation function is recorded as the safe execution time.

6. The early warning method for safety monitoring of a steel structure platform and mezzanine as described in claim 1, characterized in that: The step of outputting the risk difference includes: Obtain the risk category of the steel structure and mezzanine under the aforementioned risk status, wherein the risk category includes fixed risks and persistent risks; If the risk category is a fixed risk, it indicates that the risk of the steel structure and the mezzanine has been determined, and the difference between the real-time data and the assessment threshold is recorded as the risk difference. If the risk category is a persistent risk, then a monitoring period is established, and multiple sampling nodes are set up within the monitoring period; Real-time data is collected at each of the sampling nodes, and when the real-time data at the sampling node is continuously greater than the evaluation threshold, the difference between the real-time data at the last risk node and the evaluation threshold is recorded as the risk difference.

7. An early warning system for safety monitoring of steel structure platforms and mezzanines, characterized in that: The early warning method for safety monitoring of steel structure platforms and mezzanines according to any one of claims 1 to 6 includes: The monitoring module is used to investigate the real-time data of the steel structure platform and mezzanine through pre-deployed monitoring points. The real-time data includes structural strain data, tilt data, and vibration data. The real-time data is pre-processed, and the evaluation status of the steel structure platform and mezzanine is evaluated based on the pre-processed real-time data. The safety status includes a safe status and a risk status. The inspection record module is used to record real-time data to the inspection log under safe conditions and to draw trend charts based on the real-time data. The risk warning module is used to trigger the warning mechanism under risk conditions, output the risk difference synchronously, and match the warning level according to the risk difference to determine the warning intensity. The monitoring optimization module is used to identify the monitoring points that trigger risk states as risk locations, count the frequency of risk signals issued at each risk location, and adjust the monitoring frequency of the corresponding risk location based on the warning level.

8. An electronic device, characterized in that: The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the early warning method for safety monitoring of the steel structure platform and mezzanine as described in any one of claims 1 to 6.

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