A safety state determination method for tower crane and steel platform integrated equipment

Abstract

The application discloses a safety state judgment method of tower crane and steel platform integrated equipment, comprising: data acquisition, data preprocessing, monitoring window construction, monitoring window feature extraction, establishing a reference window, calculating single-index risk deviation, calculating single-index risk value, multi-index weighted risk score and safety state grading judgment. The application can avoid false positives or false negatives and identify early risk signs, can identify multi-index coupled risks, can adapt to different working conditions, improve the identification and judgment ability of the safety state of the equipment in the dynamic process under complex working conditions, can distinguish short-term fluctuations and sustained abnormalities, can use stress and strain data of the key weak position of the coupling connection part of the tower crane and the steel platform to participate in the judgment, use a sliding window to fuse and judge six types of core monitoring indexes, and improve the reliability, accuracy and authenticity of the safety state judgment of the equipment.

Description

Technical Field

[0001] This invention relates to the field of construction safety monitoring, and in particular to a method for determining the safety status of an integrated tower crane and steel platform equipment. Background Technology

[0002] In the construction of high-rise and super high-rise buildings, tower cranes are often integrated with attached steel platforms, support steel platforms, or working steel platforms to jointly undertake functions such as hoisting operations, local load-bearing, and work support. During actual operation, this type of integrated equipment is affected by the hoisting load, the dynamic load caused by rotation and luffing, as well as wind loads, platform stress redistribution, and changes in the stiffness of connection parts. Its overall stress state exhibits significant coupling, dynamism, and uncertainty.

[0003] To determine the safety status of integrated equipment, current engineering practices typically employ single-index limit alarm methods for safety monitoring. For example, thresholds are set for indicators such as tilt angle, wind speed, torque, strain, or vibration. An alarm is triggered when a certain indicator reaches the threshold. While this single-index limit alarm method can determine the safety status of the integrated equipment, its method is relatively simple and has the following shortcomings in complex construction environments:

[0004] The normal fluctuation range of the same indicator varies significantly under different operating conditions, which can easily lead to false alarms or missed alarms. For example, under conditions of higher wind speed, larger amplitude, or higher lifting moment, the tower crane tilt angle, stress and strain at the coupling connection between tower crane 11 and steel platform 12, and vibration level are often higher than under normal operating conditions. If a fixed threshold is still used, false alarms are likely to occur. Conversely, if some hidden dangers gradually accumulate, they may be missed because the absolute threshold has not been reached.

[0005] Existing methods only focus on whether a single indicator limit exceeds a set threshold. However, the safety risks of integrated tower crane and steel platform equipment usually stem from the combined anomalies of multiple indicators. For example, when the lifting torque increases, the strain at the coupling connection between tower crane 11 and steel platform 12 increases, the tower crane vibration fluctuations intensify, and the tilt angle slowly rises simultaneously, it often indicates a significant increase in overall safety risk. In such cases, a single indicator limit cannot determine whether the integrated equipment is in an unsafe state.

[0006] Traditional methods often rely on instantaneous sampled values ​​for judgment, making it difficult to effectively distinguish between transient disturbances and sustained anomalies. In engineering sites, there are often situations such as short-term impacts, occasional pulses, and gusts of wind disturbance. Judging solely by instantaneous values ​​can easily lead to false alarms, while relying only on long-term average values ​​may mask early safety signs of short-term anomalies escalating.

[0007] Existing methods do not adequately address the stress and deformation evolution at the coupling connection between the tower crane and the steel platform, a critical and vulnerable location. Summary of the Invention

[0008] The purpose of this invention is to provide a method for determining the safety status of integrated tower crane and steel platform equipment, in order to solve the problems of the existing technology that uses a single index limit method to determine the safety status of integrated equipment, such as poor adaptability under different working conditions, easy to generate false alarms or missed alarms, insufficient identification of risks coupled with multiple indicators, weak ability to distinguish between short-term fluctuations and continuous anomalies, and insufficient attention to the key weak points of the coupling connection between the tower crane and the steel platform.

[0009] To address the aforementioned technical problems, this invention provides a method for determining the safety status of an integrated tower crane and steel platform equipment, comprising:

[0010] Data Acquisition: During equipment operation, continuously collect data on tower crane tilt angle, ambient wind speed, tower crane lifting moment, stress at the coupling connection between the tower crane and the steel platform, strain at the coupling connection between the tower crane and the steel platform, and tower crane vibration acceleration as multi-source indicators.

[0011] Data preprocessing: unifying the timestamps of the collected multi-source indicators;

[0012] Monitoring window construction: For each type of indicator after a unified timestamp, a sliding window is constructed according to the set window length and window step size to build a monitoring window;

[0013] Monitoring window feature extraction: Calculate the window mean and window standard deviation for each type of indicator within the current monitoring window;

[0014] Establish a benchmark window: Under normal equipment conditions, continuously collect various indicators as normal samples and divide the monitoring window into a sliding window according to the same window length and step size to establish a benchmark window. Calculate the window mean and window standard deviation of various normal sample indicators within each benchmark window as the level benchmark and fluctuation benchmark under normal equipment conditions, respectively.

[0015] Calculate the risk deviation of a single indicator; calculate the mean deviation and standard deviation deviation of each monitoring indicator relative to the baseline window in real time within the monitoring window;

[0016] Calculating the risk value of a single indicator: The risk value of a single indicator is obtained by linearly combining the deviation from the mean and the deviation from the standard deviation.

[0017] Multi-indicator weighted risk scoring: The risk values ​​of each individual indicator are weighted and summed according to preset weights to obtain the overall risk score of the equipment at the current moment;

[0018] Safety status classification: Based on the relationship between the overall risk score and the preset classification threshold, the equipment safety status is divided into multiple levels.

[0019] Furthermore, the safety status determination method for the integrated tower crane and steel platform equipment provided by the present invention also includes:

[0020] Continuous window triggering mechanism: When a certain level of security status appears in multiple consecutive monitoring windows, the judgment result of that level of security status is triggered.

[0021] Furthermore, the safety status determination method for the integrated tower crane and steel platform equipment provided by the present invention includes data preprocessing, which involves unifying the timestamps of the collected multi-source indicators so that each monitoring indicator forms a corresponding sampling sequence under the same time reference; grouping the multi-source monitoring indicators after unifying the timestamps by working condition and time domain; and performing missing value processing, outlier removal, and filtering and noise reduction processing on the multi-source monitoring indicators after unifying the timestamps to obtain multi-source indicators with effective time series for statistical analysis.

[0022] Furthermore, the safety status determination method for the integrated tower crane and steel platform equipment provided by this invention, in the construction of the monitoring window, divides the sliding window according to Formula 1 based on the set window length and window step size:

[0023] (1);

[0024] In the formula, This is a sliding window, where L is the window length. Let be the sampled value of the i-th type of monitoring indicator at time t, where the window step size is set to more than one sampling interval.

[0025] Furthermore, the safety status determination method for the integrated tower crane and steel platform equipment provided by this invention, in the feature acquisition of the monitoring window,

[0026] Calculate the window mean using Formula 2:

[0027] (2);

[0028] Calculate the window standard deviation using formula 3:

[0029] (3).

[0030] In the formula, Let be the window mean of the i-th type of monitoring indicator at time t, used to reflect the overall level of this type of monitoring indicator within the current time period. Let be the standard deviation of the i-th type of monitoring indicator at time t, used to reflect the degree of fluctuation of this type of monitoring indicator within the current time period. Let be the value of the i-th type of monitoring indicator in the k-th sliding window;

[0031] Furthermore, the safety status determination method for the integrated tower crane and steel platform equipment provided by this invention, in establishing a benchmark window,

[0032] Calculate the average value of the window mean sequence under normal conditions for the i-th index under a certain working condition group according to Formula 4:

[0033] (4);

[0034] Calculate the standard deviation of the window mean sequence under normal conditions for the i-th indicator in a certain working condition group according to Formula 5:

[0035] (5);

[0036] Calculate the average value of the window standard deviation sequence under normal conditions for the i-th index under a certain working condition group according to Formula 6:

[0037] (6);

[0038] Calculate the standard deviation of the window standard deviation sequence under normal conditions for the i-th index under a certain working condition group according to Formula 7:

[0039] (7);

[0040] In the formula, Let be the average value of the window mean sequence under normal conditions for a certain working condition group of the i-th index. Let be the standard deviation of the window mean sequence under normal conditions for a certain working condition group of the i-th index. Let be the average of the standard deviation series of the window under normal conditions for a certain working condition group of the i-th index. Let N be the standard deviation of the window standard deviation sequence under normal conditions for a certain working condition group of the i-th index, and N be the number of normal sample windows. Let be the window mean of the i-th indicator in the j-th normal sample window; Let be the window standard deviation of the i-th indicator in the j-th normal sample window.

[0041] Furthermore, the safety status determination method for the integrated tower crane and steel platform equipment provided by this invention, in calculating the risk deviation of a single index,

[0042] Calculate the deviation from the mean using Formula 8:

[0043] (8);

[0044] Calculate the standard deviation deviation according to Formula 9:

[0045] (9);

[0046] In the formula, Let be the deviation of the mean from the i-th indicator. Let be the standard deviation of the i-th indicator. A tiny positive number set to prevent the denominator from being zero.

[0047] Furthermore, the safety status determination method for the integrated tower crane and steel platform equipment provided by this invention, in calculating the single-index risk value,

[0048] Calculate the risk value of a single indicator according to Formula 10:

[0049] (10);

[0050] In the formula, The single-indicator risk value of the i-th indicator. and Let be the internal weights of the i-th indicator in terms of mean deviation and standard deviation, respectively, and satisfy . .

[0051] Furthermore, the safety status determination method for integrated tower crane and steel platform equipment provided by this invention, in constructing a multi-index weighted risk score,

[0052] Calculate the overall risk score of the equipment at the current moment according to Formula 11:

[0053] (11);

[0054] In the formula, The overall risk score for the equipment at the current moment, where n is the number of indicators included in the judgment. Let be the external comprehensive weight corresponding to the i-th indicator, and satisfy: .

[0055] Furthermore, the safety status determination method for the integrated tower crane and steel platform equipment provided by this invention, in the safety status classification determination,

[0056] when When the time comes, it is determined to be a safe state;

[0057] when When this time, it is determined to be in a state of attention;

[0058] when When this occurs, it is determined to be a warning state;

[0059] when When this occurs, it is determined to be a dangerous situation;

[0060] in, , , This is a preset scoring threshold.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] The safety status determination method for integrated tower crane and steel platform equipment provided by this invention uses data on tower crane tilt angle, ambient wind speed, tower crane lifting moment, stress at the coupling connection between the tower crane and steel platform, strain at the coupling connection between the tower crane and steel platform, and tower crane vibration acceleration as core monitoring indicators. This method can simultaneously reflect the overall attitude of the tower crane, wind disturbance, working load, stress and deformation of key connecting components between the tower crane and steel platform, and the dynamic response of the tower crane. Employing a sliding window statistical analysis method, the method transforms the original instantaneous monitoring values ​​into the window mean and window standard deviation, which is beneficial for simultaneously characterizing two types of abnormal states: sustained offset and enhanced fluctuation, thereby effectively distinguishing between instantaneous disturbances and sustained... This method avoids the false alarms caused by relying solely on instantaneous values ​​to determine the safety status of equipment in complex operating conditions. It improves the ability to identify and judge the safety status of equipment during dynamic processes. The method uses a single-index risk deviation calculation method in the form of mean and standard deviation, which not only focuses on whether the monitored index continuously deviates from the normal level, but also on whether its fluctuations significantly increase, thereby improving the ability to identify early risk signs of equipment. The method weighted and integrated the risk values ​​of multiple key indicators to form an overall risk score, which, compared with the traditional single-index limit warning method, can better reflect the comprehensive risk status of equipment under multi-source coupling effects, reducing the problems of missed and false alarms. Attached Figure Description

[0063] Figure 1 This is a flowchart of the safety status determination method for integrated tower crane and steel platform equipment;

[0064] Figure 2 This is a schematic diagram of the elevation structure of the integrated tower crane and steel platform equipment with sensors installed.

[0065] As shown in the figure:

[0066] 1. Integrated tower crane and steel platform equipment; 11. Tower crane; 12. Steel platform;

[0067] 2. Tilt sensor, 3. Vibration acceleration sensor, 4. Wind speed sensor, 5. Stress and strain sensor, 6. Torque sensor. Detailed Implementation

[0068] The present invention will now be described in detail with reference to the accompanying drawings. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0069] Please refer to Figures 1 to 2 This invention provides a method for determining the safety status of an integrated tower crane and steel platform equipment (hereinafter referred to as "equipment") 1 based on a sliding window and multi-index weighted risk scoring, comprising the following steps:

[0070] Step S1, Sensor Deployment: Deploy tilt sensor 2, vibration acceleration sensor 3, and wind speed sensor 4 on the integrated tower crane and steel platform equipment 1. Deploy stress and strain sensor 5 at the coupling connection between tower crane 11 and steel platform 12. The tilt sensor 2, vibration acceleration sensor 3, and wind speed sensor 4 are deployed on tower crane 11, while the stress and strain sensor 5 is deployed at the coupling connection between tower crane 11 and steel platform 12. The sampling frequency of each sensor is set to 1Hz to 10Hz as needed. A PLC or other controller connected to the various sensors and the control system of tower crane 11 can be deployed on tower crane 11 or steel platform 12. Alternatively, the control system of tower crane 11 can be connected to the various sensors. In this case, the controller, the device with the controller, or the control system of tower crane 11 will be the main entity executing the following steps.

[0071] Step S2, Data Acquisition: While the equipment 1 is in operation, continuously collect data on the tower crane tilt angle, ambient wind speed, tower crane lifting torque, stress at the coupling connection between the tower crane and the steel platform, strain at the coupling connection between the tower crane and the steel platform, and tower crane vibration acceleration as multi-source indicators.

[0072] The tower crane tilt angle data is acquired by tilt sensor 2, which reflects the overall attitude deviation and stability of the tower crane; the ambient wind speed data is acquired by wind speed sensor 4, which characterizes the intensity of external environmental disturbances; the lifting moment data is acquired by reading the tower crane control system, which characterizes the working load level of the tower crane; the stress and strain data of the coupling connection between the tower crane and the steel platform are acquired by stress and strain sensor 5, which respectively reflect the stress level when the tower crane load is transferred to the steel platform system and reflect the deformation response of the key connection area; the tower crane vibration acceleration data is acquired by vibration acceleration sensor 3, which reflects the dynamic response of the tower crane structure and the level of abnormal vibration.

[0073] When necessary, auxiliary operating condition identification data such as wind direction, load, amplitude, slewing angular velocity, and torque can also be collected, but the core input for safety status determination is preferably based on the above six types of key data. For example, a torque sensor 6 is installed on the tower crane 11 to detect the torque data of the tower crane 11 in the equipment 11.

[0074] Step S3, Data Preprocessing: The collected multi-source indicators are timestamped. This involves timestamping the collected multi-source indicators to ensure that each monitoring indicator forms a corresponding sampling sequence under the same time reference, i.e., synchronization is achieved through a unified clock. The timestamped multi-source monitoring indicators can be grouped by operating condition and time domain, with each group possessing typical characteristics for subsequent analysis and matching. To remove noise from the multi-source indicator data, missing value processing, outlier removal, and filtering denoising can be performed on the timestamped multi-source monitoring indicators to obtain effective time series multi-source indicators for statistical analysis. Missing values ​​can be processed using adjacent interpolation, mean interpolation, linear interpolation, or time-series compensation methods; outliers can be identified and removed using the three-standard-deviation criterion, box plot method, or median deviation method; and vibration acceleration and environmental wind speed, which are highly fluctuating data, can be denoised using moving average filtering, median filtering, or low-pass filtering.

[0075] Step S4, Monitoring Window Construction: For each type of indicator after a unified timestamp, a sliding window is constructed according to the set window length and window step size to create a monitoring window. To achieve the purpose of constructing the monitoring window, the sliding window can be divided according to Formula 1 based on the set window length and window step size:

[0076] (1);

[0077] In the formula, This is a sliding window, where L is the window length. Let be the sampled value of the i-th type of monitoring indicator at time t, where the window step size is set to one or more sampling intervals, including the number itself. The window length can be determined according to the on-site sampling frequency and actual needs; for example, the number of data points corresponding to 30 seconds, 60 seconds, or 120 seconds can be selected. By introducing a sliding window, a single instantaneous sampled value can be expanded into statistical characteristics over a period of time, thereby improving the reliability of the determination of the safety status of equipment 1 and retaining short-term abnormal information.

[0078] Step S5, Monitoring Window Feature Extraction: Calculate the window mean and window standard deviation for each type of indicator within the current monitoring window. Specifically:

[0079] Calculate the window mean using Formula 2:

[0080] (2);

[0081] Calculate the window standard deviation using formula 3:

[0082] (3);

[0083] In the formula, Let be the window mean of the i-th type of monitoring indicator at time t, used to reflect the overall level of this type of monitoring indicator within the current time period. Let be the standard deviation of the i-th type of monitoring indicator at time t, used to reflect the degree of fluctuation of this type of monitoring indicator within the current time period. Let be the value of the i-th type of monitoring indicator in the k-th sliding window.

[0084] For indicators such as tower crane tilt angle, stress at the coupling connection between tower crane 11 and steel platform 12, strain at the coupling connection between tower crane 11 and steel platform 12, and lifting moment, the window mean value better reflects continuous offset or continuous high load conditions; for indicators such as vibration acceleration and wind speed, the window standard deviation better reflects the enhanced characteristics of abnormal fluctuations. By analyzing the stress and strain data at the coupling connection between tower crane 11 and steel platform 12, abnormal states during the load transfer process from tower crane 11 to steel platform 12 can be better reflected, serving as an important basis for determining the safety of the integrated equipment 1 of tower crane 11 and steel platform 12. This overcomes the situation in existing technologies where insufficient attention or neglect is paid to the stress and strain data at the coupling connection between tower crane 11 and steel platform 12.

[0085] For example, the sliding window length is set to 60 sampling points, and the window step size is 1 sampling point. For each indicator, the window mean and standard deviation are calculated within each window. The mean of the tower crane tilt angle within the current window reflects whether the tower crane has experienced continuous displacement in the recent period. The standard deviation of the vibration acceleration within the current window reflects whether there is a significant amplification of structural vibration. The mean and standard deviation of the stress and strain at the coupling connection between tower crane 11 and steel platform 12 within the current window are used together to reflect whether there is increased stress and enhanced fluctuation at key connection locations.

[0086] Step S6, Establishing a baseline window: Under the normal state of Equipment 1 (i.e., without abnormal faults), various indicators are continuously collected as normal samples, and a baseline window is established by dividing the window into sliding windows with the same window length and step size as the monitoring window. Different data groups can be made according to operating conditions and environmental changes. Within each baseline window, the window mean and window standard deviation of various normal sample indicators are calculated as the level baseline and fluctuation baseline of Equipment 1 under normal state, respectively. Specifically:

[0087] Calculate the average value of the window mean sequence under normal conditions for the i-th index under a certain working condition group according to Formula 4:

[0088] (4);

[0089] Calculate the standard deviation of the window mean sequence under normal conditions for the i-th indicator in a certain working condition group according to Formula 5:

[0090] (5);

[0091] Calculate the average value of the window standard deviation sequence under normal conditions for the i-th index under a certain working condition group according to Formula 6:

[0092] (6);

[0093] Calculate the standard deviation of the window standard deviation sequence under normal conditions for the i-th index under a certain working condition group according to Formula 7:

[0094] (7);

[0095] In the formula, Let be the average value of the window mean sequence under normal conditions for a certain working condition group of the i-th index. Let be the standard deviation of the window mean sequence under normal conditions for a certain working condition group of the i-th index. Let be the average of the standard deviation series of the window under normal conditions for a certain working condition group of the i-th index. Let N be the standard deviation of the window standard deviation sequence under normal conditions for a certain working condition group of the i-th index, and N be the number of normal sample windows. Let be the window mean of the i-th indicator in the j-th normal sample window; Let be the window standard deviation of the i-th indicator in the j-th normal sample window.

[0096] This step allows us to statistically analyze the central and discrete parameters of the window mean and standard deviation of each indicator under normal operating conditions. The purpose of this step is to establish a "level benchmark" and "fluctuation benchmark" for each key indicator under normal equipment operating conditions, providing a comparative basis for subsequent deviation calculations.

[0097] For example, during the normal operation of equipment, several days of normal monitoring data are continuously collected. A normal sample window set is formed using the same sliding window parameters. The obtained data is appropriately grouped and analyzed according to the working conditions and environmental conditions. The mean and standard deviation of the mean sequence and standard deviation sequence of each indicator window are calculated for each group to obtain the benchmark indicators under different working conditions. The statistical benchmark parameters of each indicator under normal conditions are formed, that is, the benchmark window is constructed.

[0098] It should be noted that the baseline window construction example only illustrates execution after the monitoring window construction, but it is not limited to this. The monitoring window and the baseline window can be executed in parallel.

[0099] Step S7: Calculate the risk deviation of each individual indicator; within the monitoring window, calculate the mean deviation and standard deviation deviation of each monitoring indicator relative to the baseline window in real time. Specifically:

[0100] Calculate the deviation from the mean using Formula 8:

[0101] (8);

[0102] Calculate the standard deviation deviation according to Formula 9:

[0103] (9);

[0104] In the formula, Let be the deviation of the mean from the i-th indicator. Let be the standard deviation of the i-th indicator. A tiny positive number set to prevent the denominator from being zero.

[0105] The single-indicator risk deviation is used to evaluate the risk level of the i-th indicator in the real-time monitoring window from two aspects: "mean deviation" and "standard deviation".

[0106] Step S8, Calculate the risk value of a single indicator: The mean deviation and standard deviation are linearly combined to obtain the risk value of the single indicator. Specifically:

[0107] Calculate the risk value of a single indicator according to Formula 10:

[0108] (10);

[0109] In the formula, The single-indicator risk value of the i-th indicator. and Let be the internal weights of the i-th indicator in terms of mean deviation and standard deviation, respectively, and satisfy . .

[0110] Different internal weights can be set for different types of indicators based on their engineering significance. For example, for indicators that are more "horizontal risk" such as tower crane tilt angle, lifting moment, stress at the coupling connection between tower crane 11 and steel platform 12, and strain at the coupling connection between tower crane 11 and steel platform 12, the weight of the mean deviation term can be appropriately increased; for indicators that are more "fluctuation risk" such as vibration acceleration and wind speed, the weight of the standard deviation term can be appropriately increased. This can more accurately reflect the abnormal behavior of different monitoring indicators.

[0111] Step S9, Multi-indicator Weighted Risk Scoring: The risk values ​​of each individual indicator are weighted and summed according to preset weights to obtain the overall risk score of Equipment 1 at the current moment. Specifically:

[0112] Calculate the overall risk score of Equipment 1 at the current moment according to Formula 11:

[0113] (11);

[0114] In the formula, Let n be the overall risk score for Equipment 1 at the current moment, and n be the number of indicators included in the judgment. Let be the external comprehensive weight corresponding to the i-th indicator, and satisfy: .

[0115] In this embodiment of the invention, n is preferably one of the six key indicators mentioned above, corresponding to the tower crane tilt angle, wind speed, lifting moment, stress at the coupling connection between tower crane 11 and steel platform 12, strain at the coupling connection between tower crane 11 and steel platform 12, and tower crane vibration acceleration. The weights can be determined based on structural safety sensitivity, historical accident experience, expert scoring, analytic hierarchy process (AHP), or entropy weighting. Preferably, the weights of lifting moment, stress at the coupling connection between tower crane 11 and steel platform 12, strain at the coupling connection between tower crane 11 and steel platform 12, and tower crane tilt angle are slightly higher than those of wind speed and vibration acceleration, to reflect the more direct impact of load level, stress on key connections, and overall stability on the safety status.

[0116] Step S10, Safety Status Classification Determination: Based on the relationship between the overall risk score and the preset classification threshold, the safety status of Equipment 1 is divided into multiple levels. Preferably, it is divided into four levels: safe status, concern status, warning status, and dangerous status.

[0117] when When the time is right, it is determined to be a safe state.

[0118] when When this happens, it is considered to be in a state of being watched.

[0119] when When the warning state is reached, equipment 1 can be inspected to clear the warning state.

[0120] when When the condition is deemed dangerous, a shutdown order can be issued for equipment 1 and reinforcement measures can be taken.

[0121] After the warning is cleared or reinforcement measures are taken, the work can continue.

[0122] in, , , The preset scoring threshold can be determined by combining historical normal samples, historical abnormal samples, finite element simulation results, field test results, and expert experience.

[0123] To avoid misjudgments caused by instantaneous changes, the safety status determination method for the integrated tower crane and steel platform equipment 1 provided in this embodiment of the invention may further include the following in the safety status classification determination:

[0124] Continuous window triggering mechanism: When a certain level of safety status occurs consecutively in multiple monitoring windows, the judgment result for that level of safety status is triggered. That is, the judgment result for that level is only officially output when a certain level of safety status occurs consecutively for a preset number of windows K. For example, a warning is output when three consecutive windows reach the warning state, and a danger alarm is output when two consecutive windows reach the danger state. This mechanism can effectively suppress the influence of single-pulse interference on the judgment result.

[0125] For example: In the real-time judgment phase, the real-time mean deviation of the i-th indicator is calculated. Deviation from standard deviation For example, the tilt angle of the tower crane can be taken as... , This indicates a greater focus on the overall increase in tilt angle; however, vibration acceleration can be considered... , This indicates a greater focus on enhanced vibration fluctuations; the stress and strain at the coupling connection between tower crane 11 and steel platform 12 can be considered... , It takes into account both sustained high stress and local response fluctuations.

[0126] In a preferred embodiment, the combined weights of the six core indicators can be set as follows: tower crane tilt angle 0.18, wind speed 0.10, lifting moment 0.20, stress at the coupling connection between tower crane 11 and steel platform 12 0.20, strain at the coupling connection between tower crane 11 and steel platform 12 0.20, and tower crane vibration acceleration 0.12. The above weights are merely examples and can be adjusted according to the characteristics of the project.

[0127] Using the aforementioned single-indicator risk values ​​and comprehensive weights, the overall risk score (R(t)) is calculated. Assume the preset thresholds are as follows: , , If the overall risk score is less than 1.0, it is considered safe; if the score is between 1.0 and 2.0, it is considered a concern; if the score is between 2.0 and 3.0, it is considered a warning; and if the score is not less than 3.0, it is considered dangerous. Furthermore, if three consecutive warning windows appear, a formal warning is output; if two consecutive dangerous windows appear, a danger alarm is output and a restricted operation or shutdown signal is sent to the control system.

[0128] To achieve platform monitoring, the safety status determination method for the integrated tower crane and steel platform equipment 1 provided in this embodiment of the invention may further include:

[0129] Step S11, Result Output and Early Warning Processing: The overall risk score, risk values ​​of each individual indicator, main deviation indicators, and corresponding safety status levels at the current moment are transmitted to the monitoring platform. When the safety status reaches the early warning or danger level, the monitoring platform automatically triggers early warning and response measures. Data processing after acquisition can also be performed by the monitoring platform, replacing the controller, equipment with a controller, or the control system of tower crane 11 as the execution entity. When the safety status reaches the early warning or danger level, the monitoring platform can automatically trigger audible and visual alarms, remote information push, work prompts, load reduction control, restricted operation, or shutdown interlocking, among other response measures.

[0130] In practical applications, when equipment 1 is operating under conditions of high wind speed and large lifting moment, even if a single instantaneous data point has not yet reached the absolute alarm threshold, if the average tower crane tilt angle continues to rise, the average stress and strain at the coupling connection between tower crane 11 and steel platform 12 are higher than the normal benchmark, and the standard deviation of vibration acceleration increases significantly, the overall risk score will continue to rise, thus prompting a timely warning. This judgment method can identify risk accumulation trends earlier than traditional single-threshold alarms and is more suitable for continuous safety monitoring of integrated tower crane and steel platform equipment.

[0131] The safety status determination method for the integrated tower crane and steel platform equipment 1 based on sliding window and multi-index weighted risk scoring provided in this invention embodiment uses data on tower crane tilt angle, ambient wind speed, tower crane lifting moment, stress at the coupling connection between tower crane 11 and steel platform 12, strain at the coupling connection between tower crane 11 and steel platform 12, and tower crane vibration acceleration as core monitoring indicators. This method can simultaneously reflect the overall attitude of the tower crane, wind disturbance, working load, stress and deformation of key connecting components between the tower crane and steel platform, and the dynamic response of the tower crane. The sliding window statistical analysis method transforms the original instantaneous monitoring values ​​into the window mean and window standard deviation, which is beneficial for simultaneously characterizing two types of abnormal states: continuous offset and enhanced fluctuation. This effectively distinguishes between instantaneous disturbances and continuous anomalies, avoiding the false alarms caused by judging the safety status of equipment 1 solely based on instantaneous values ​​in existing technologies. It improves the ability to identify and judge the safety status of equipment in dynamic processes under complex operating conditions. The single-index risk deviation calculation method using the mean and standard deviation not only focuses on whether the monitored index continuously deviates from the normal level, but also on whether its fluctuations significantly increase, thereby improving the ability to identify early risk signs of equipment 1. The risk values ​​of multiple key indicators are weighted and integrated to form an overall risk score, which, compared with the traditional single-index limit warning method, can better reflect the comprehensive risk status of equipment 1 under the combined effect of multiple sources, reducing the problems of missed and false alarms.

[0132] The safety status determination method for integrated tower crane and steel platform equipment 1 based on sliding window and multi-index weighted risk scoring provided in this invention embodiment combines sliding window statistics, mean and standard deviation deviation analysis, and multi-index weighted risk scoring to achieve graded determination and early warning output of the overall safety status of the integrated tower crane and steel platform equipment, with the advantage of reliable judgment results. Its algorithm structure is simple, its physical meaning is clear, and its parameters are highly interpretable, facilitating both on-site engineering implementation and subsequent promotion and application in patents, standards, and monitoring platforms.

[0133] The safety status determination method for integrated tower crane and steel platform equipment 1 based on sliding window and multi-index weighted risk scoring provided in this invention can avoid false alarms or missed alarms and identify early risk signs. It can identify risks coupled with multiple indicators, adapt to different working conditions, improve the ability to identify and judge the safety status of equipment in dynamic processes under complex working conditions, distinguish between short-term fluctuations and continuous anomalies, use stress and strain data of key weak points in the coupling connection between the tower crane and the steel platform to participate in the determination, and use the sliding window to integrate and judge six types of core monitoring indicators, thereby improving the reliability, accuracy and authenticity of equipment safety status judgment.

[0134] This invention is not limited to the specific embodiments described above. Obviously, the embodiments described above are only a part of the embodiments of this invention, not all of them. All other embodiments obtained by those skilled in the art based on the described embodiments of this invention are within the scope of protection of this invention. Those skilled in the art can make other modifications and variations to this invention. Therefore, if these modifications and variations of this invention fall within the scope of the claims of this invention, then this invention also intends to include these modifications and variations.

Claims

1. A method for determining the safety status of an integrated tower crane and steel platform equipment, characterized in that, include: Data Acquisition: During equipment operation, continuously collect data on tower crane tilt angle, ambient wind speed, tower crane lifting moment, stress at the coupling connection between the tower crane and the steel platform, strain at the coupling connection between the tower crane and the steel platform, and tower crane vibration acceleration as multi-source indicators. Data preprocessing: unifying the timestamps of the collected multi-source indicators; Monitoring window construction: For each type of indicator after a unified timestamp, a sliding window is constructed according to the set window length and window step size to build a monitoring window; Monitoring window feature extraction: Calculate the window mean and window standard deviation for each type of indicator within the current monitoring window; Establish a benchmark window: Under normal equipment conditions, continuously collect various indicators as normal samples and divide the monitoring window into a sliding window according to the same window length and step size to establish a benchmark window. Calculate the window mean and window standard deviation of various normal sample indicators within each benchmark window as the level benchmark and fluctuation benchmark under normal equipment conditions, respectively. Calculate the risk deviation of a single indicator; Within the monitoring window, the deviation of each monitoring indicator from the mean and standard deviation relative to the baseline window is calculated in real time. Calculating the risk value of a single indicator: The risk value of a single indicator is obtained by linearly combining the deviation from the mean and the deviation from the standard deviation. Multi-indicator weighted risk scoring: The risk values ​​of each individual indicator are weighted and summed according to preset weights to obtain the overall risk score of the equipment at the current moment; Safety status classification: Based on the relationship between the overall risk score and the preset classification threshold, the equipment safety status is divided into multiple levels.

2. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 1, characterized in that, Also includes: Continuous window triggering mechanism: When a certain level of security status appears in multiple consecutive monitoring windows, the judgment result of that level of security status is triggered.

3. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 1, characterized in that, The data preprocessing includes unifying the timestamps of the collected multi-source indicators, so that each monitoring indicator forms a corresponding sampling sequence under the same time reference; grouping the multi-source monitoring indicators after unifying the timestamps according to working conditions and time domains; and performing missing value processing, outlier removal, and filtering and noise reduction on the multi-source monitoring indicators after unifying the timestamps to obtain multi-source indicators with effective time series for statistical analysis.

4. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 3, characterized in that, In the construction of the monitoring window, the sliding window is divided according to the set window length and window step size, based on Formula 1: （1）； In the formula, This is a sliding window, where L is the window length. Let be the sampled value of the i-th type of monitoring indicator at time t, where the window step size is set to one or more sampling intervals.

5. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 4, characterized in that, In the feature capture of the monitoring window, Calculate the window mean using Formula 2: （2）； Calculate the window standard deviation using formula 3: （3）； In the formula, Let be the window mean of the i-th type of monitoring indicator at time t, used to reflect the overall level of this type of monitoring indicator within the current time period. Let be the standard deviation of the i-th type of monitoring indicator at time t, used to reflect the degree of fluctuation of this type of monitoring indicator within the current time period. Let be the value of the i-th type of monitoring indicator in the k-th sliding window.

6. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 5, characterized in that, In the baseline establishment window, Calculate the average value of the window mean sequence under normal conditions for the i-th index under a certain working condition group according to Formula 4: （4）； Calculate the standard deviation of the window mean sequence under normal conditions for the i-th indicator in a certain working condition group according to Formula 5: （5）； Calculate the average value of the window standard deviation sequence under normal conditions for the i-th index under a certain working condition group according to Formula 6: （6）； Calculate the standard deviation of the window standard deviation sequence under normal conditions for the i-th index under a certain working condition group according to Formula 7: （7）； In the formula, Let be the average value of the window mean sequence under normal conditions for a certain working condition group of the i-th index. Let be the standard deviation of the window mean sequence under normal conditions for a certain working condition group of the i-th index. Let be the average of the standard deviation series of the window under normal conditions for a certain working condition group of the i-th index. Let N be the standard deviation of the window standard deviation sequence under normal conditions for a certain working condition group of the i-th indicator, and N be the number of normal sample windows. Let be the window mean of the i-th indicator in the j-th normal sample window; Let be the window standard deviation of the i-th indicator in the j-th normal sample window.

7. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 6, characterized in that, In calculating the risk deviation of a single indicator Calculate the deviation from the mean using Formula 8: （8）； Calculate the standard deviation deviation according to formula 9: （9）； In the formula, Let be the deviation of the mean from the i-th indicator. Let be the standard deviation of the i-th indicator. A tiny positive number set to prevent the denominator from being zero.

8. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 7, characterized in that, In calculating the risk value of a single indicator Calculate the risk value of a single indicator according to Formula 10: （10）； In the formula, The single-indicator risk value of the i-th indicator. and Let be the internal weights of the i-th indicator in terms of mean deviation and standard deviation, respectively, and satisfy . .

9. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 8, characterized in that, In constructing a multi-indicator weighted risk score Calculate the overall risk score of the equipment at the current moment according to Formula 11: （11）； In the formula, The overall risk score for the equipment at the current moment, where n is the number of indicators included in the judgment. Let be the external comprehensive weight corresponding to the i-th indicator, and satisfy: .

10. The method for determining the safety status of the integrated tower crane and steel platform equipment according to claim 9, characterized in that, In the determination of safety status classification when When the time comes, it is determined to be a safe state; when When this time, it is determined to be in a state of attention; when When this occurs, it is determined to be a warning state; when When this occurs, it is determined to be a dangerous situation; in, , , This is a preset scoring threshold.

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