CN115983688A - Tower crane monitoring system-based tower crane operation level assessment method - Google Patents
Tower crane monitoring system-based tower crane operation level assessment method Download PDFInfo
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Abstract
The invention belongs to the technical field of big data management, and particularly relates to a tower crane operation level evaluation method based on a tower crane monitoring system, which comprises the following steps: s1: acquiring tower crane monitoring data and tower crane training data; s2: preprocessing tower crane monitoring data and tower crane training data; s3: constructing a tower crane violation rate calculation model based on the preprocessed tower crane monitoring data and the preprocessed tower crane training data, and calculating the violation rates of the towers respectively; s4: based on the violation rate of each tower company, performing statistical analysis by combining tower crane monitoring data, evaluating the operation behaviors and levels of each tower company according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower company; the operating level of the tower department is evaluated, the tower department is objectively and effectively evaluated, and meanwhile, according to key influence factors influencing the violation rate of the tower department, management personnel can be helped to carry out targeted management on the tower department and a construction site, so that the construction efficiency is improved, and the construction safety is guaranteed.
Description
Technical Field
The invention belongs to the technical field of big data management, and particularly relates to a tower crane monitoring system-based tower crane operation level evaluation method.
Background
With the development of science and technology and the rapid increase of the number of driving equipment, drivers face a serious challenge in the aspect of driving safety, violation and regulation behaviors of drivers of large-scale equipment are one of main reasons of major safety accidents of construction sites, and the drivers can timely sense and stop the violation and regulation driving behaviors of the drivers, so that the drivers are one of important measures for preventing accident hidden dangers. The existing cab of a lot of large-scale equipment such as tower cranes is provided with a monitoring camera, so that the indoor and outdoor environments in the driving process can be monitored in real time, and the video can be conveniently called to obtain evidence after an accident happens, but the method cannot timely sense violation and violation driving behaviors of the tower crane and further cannot timely perform objective and scientific evaluation and examination on the operation behaviors of the tower crane related to great safety.
Therefore, operating data generated by the tower crane in the tower crane operation process are collected through the monitoring system and uploaded to the intelligent construction site management platform, the intelligent management platform receives massive safety, quality, environment, video and other metadata returned by the monitoring terminal, and managers can monitor each independent part of the tower crane equipment through the monitoring system, but are difficult to integrate, mine and analyze other massive data, so that data resource waste is caused; meanwhile, the operation behavior of the tower driver still depends on personal experience of management personnel to make selection and decision, and overall objective, scientific and effective management is difficult to achieve.
Therefore, a method for evaluating the operation level of an operator is needed, and mass data returned from an intelligent construction site can be reasonably and effectively utilized.
Disclosure of Invention
The invention provides a tower crane monitoring system-based tower crane operation level evaluation method, which comprises the steps of constructing a tower crane violation rate calculation model by acquiring and processing tower crane monitoring data and tower crane training data, evaluating the operation level of each tower crane by statistically analyzing the tower crane violation rate, and simultaneously obtaining key influence factors influencing the tower crane violation rate, so as to provide references for construction site management, tower crane operation behavior targeted training and the like; the intelligent tower crane monitoring system can fully, reasonably and effectively utilize the tower crane monitoring data in the intelligent construction site, provides basis for construction site management and tower crane management, achieves overall objective, scientific and effective management, can promote the development process of construction enterprises in intellectualization, informatization and digitization, and assists the enterprises in transformation, upgrading and synergy.
A tower crane monitoring system-based tower crane operation level assessment method comprises the following steps:
s1: acquiring tower crane monitoring data and tower crane training data;
s2: preprocessing tower crane monitoring data and tower crane training data;
s3: constructing a tower company violation rate calculation model based on the preprocessed tower crane monitoring data and the preprocessed tower company training data, and calculating the violation rates of each tower company respectively;
s4: and based on the violation rate of each tower company, performing statistical analysis by combining tower crane monitoring data, evaluating the operation behaviors and levels of each tower company according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower company.
The method comprises the steps of constructing a tower crane violation rate calculation model by collecting and processing tower crane monitoring data and tower crane training data, evaluating the operation level of each tower crane by statistically analyzing the tower crane violation rate, and simultaneously acquiring key influence factors influencing the tower crane violation rate, so as to provide references for construction site management, tower crane operation behavior targeted training and the like; the intelligent tower crane monitoring system can fully, reasonably and effectively utilize the tower crane monitoring data in the intelligent construction site, provides basis for construction site management and tower crane management, achieves overall objective, scientific and effective management, can promote the development process of construction enterprises in intellectualization, informatization and digitization, and assists the enterprises in transformation, upgrading and synergy.
Further, in the S1, tower crane monitoring data is acquired through an intelligent management platform; the tower crane monitoring data comprises:
tower crane equipment number or equipment ID;
the tower driver attendance data is acquired through a real-name system attendance management system; the tower driver attendance data comprises working time periods and working duration;
the operation data of the tower crane comprises time points, amplitude, height, azimuth, hoisting weight, wind speed, moment, X inclination angle, Y inclination angle, alarm condition and brake state; the amplitude data is acquired by adopting an amplitude sensor; the height data is acquired by adopting a height sensor; the azimuth data is acquired by adopting a rotary angle sensor; the hoisting weight is acquired by a weight sensor; the wind speed data is acquired by a wind speed sensor; the X inclination angle data and the Y inclination angle data are acquired by adopting inclination sensors; the alarm condition is obtained through a tower crane black box and an anti-collision module, and the tower crane alarm data of the alarm condition comprises five types, namely: the first is height early warning data which comprises height high-order pre-warning data and height low-order pre-warning data; the second type is amplitude early warning data which comprises amplitude high-order pre-warning data and amplitude low-order pre-warning data; the third is inclination angle early warning data which comprises X angle high-order pre-warning data and Y angle high-order pre-warning data; the fourth is the high-order pre-warning data of the load; the fifth is the current collision pre-alarm data;
hanging number detail data comprising a start time, an end time, a maximum hanging weight, a start angle, a start amplitude, a start height, an end angle, an end height, an end amplitude.
Acquiring the working time period and working duration of the tower driver by acquiring the tower driver attendance data; by acquiring the operating data of the towers and the warning data of the towers, the operation behavior habits and the operation proficiency of the towers can be mined according to the data.
Further, in the step S1, the tower driver training data are obtained by being exported through a training system; the tassel training data includes training education duration, tassel cultural degree, and tassel working duration for the tassel to receive the training education.
Further, in S2, the process of preprocessing the tower crane monitoring data, the tower crane training data and the tower crane warning data specifically includes:
s21: integrating the tower driver attendance data and the tower driver training data to construct a tower driver information data set;
s22: using R software to associate the tower crane information data set with the tower crane monitoring data;
s23: performing data cleaning on the correlated tower crane information data set and tower crane monitoring data;
s231: screening data;
s232: eliminating invalid data, redundant data and singular values;
s24: and eliminating the tower alarm data caused by the influence of objective factors.
Interference caused by system data is eliminated by associating the tower crane information data set with the tower crane monitoring data.
Further, in S3, in the tower violation rate calculation model, the calculation formula of the average violation rate of the mth tower is as follows:
in the formula, F m Average violation rate of mth tower, F mn The average violation rate of the nth type of tower crane alarm data appears for the mth tower crane;
the calculation formula of the average violation rate of the nth tower crane alarm data of the mth tower crane is as follows:
in the formula, S mn Working circulation volume, T, of nth type tower crane alarm data for mth tower crane m All the work circulation quantities of the mth tower department; wherein m is [1,M ]],n∈[1,5]And M is total amount of the tassel.
The work circulation amount is a process which is finished from the beginning of hoisting one article to the beginning of hoisting the next article, including the operation and normal stop of the crane, according to the regulations of crane design specification GB 3811-2008.
The ratio of the working circulation volume with the alarm condition in all the working circulation volumes finished by the tower department is defined as the average violation rate of the tower department, and a tower department violation rate model is constructed based on the ratio, so that the operation level of each tower department can be reasonably reflected.
Further, in S4, the process of statistical analysis specifically includes:
s41: analyzing and counting influence factors influencing the violation rate of the tower department by adopting SPSS software;
s42: based on the influence factors influencing the violation rate of the tower company, a Monte Carlo sampling method and Crystal Ball software are adopted to carry out single factor sensitivity analysis so as to obtain the key influence factors influencing the uncertainty of the violation rate of the tower company, quantifiable key influence factors are selected, and the incidence relation between each key influence factor and the violation rate of the tower company is determined according to a 'decide one' principle;
s43: and calculating the mean value and the standard deviation of the violation rate of each tower department, and quantifying by adopting a scatter diagram to evaluate the operation level of each tower department.
By adopting single factor sensitivity analysis, key influence factors influencing the violation rate of the tower department are obtained, quantifiable key influence factors are analyzed, the influence scope of the key influence factors on the violation rate of the tower department is mastered, and further effective and important management on the tower department on a construction site is realized; meanwhile, the mean value and the standard deviation of the violation rate of the tower company are calculated, and the scatter diagram is quantized to evaluate the operation level of each tower company, so that the targeted training and management are performed on each tower company according to the calculation result, the project progress is improved, and the operation level of the tower company is improved.
Further, in S41, the influencing factors influencing the tower violation rate include:
a working period;
the working time length;
training education duration;
other influencing factors include whether to install a hook visualization, the signal worker commands the dispatch level and the culture degree of the tower department.
Further, in S42, the process of performing single-factor sensitivity analysis on the tower violation rate by using the monte carlo sampling method specifically includes:
s421: carrying out mathematical statistics on influence factors influencing the violation rate of the tower department;
s422: establishing a data model according with the parameter distribution rule of each influence factor;
s423: introducing a latin hypercube sampling method, and carrying out random sampling and grabbing calculation by using a pseudo random number generated by a computer to obtain an accumulative distribution curve of the violation rate of each tower department;
s424: fixing other influence factors, combining the computer grasping result, and quantitatively calculating the relative influence of each single influence factor on the violation rate of the tower driver so as to represent the influence degree of each single influence factor on the violation rate of the tower driver;
further, in S423, a calculation formula for capturing by using a computer is as follows:
f = P (working time | training education time | other influencing factors)
Wherein F is the rate of the tower violation; p is the probability of randomly sampling the work period, the work duration, the training education duration, other influencing factors.
Further, in S424, the formula for quantitatively calculating the relative influence of the single influence factor on the tower violation rate is:
in the formula, the minimum value of the violation rate of the tower department is the minimum value of the violation rate of the tower department in all the grabbing results of the computer; the median of the rate of violation of the company department is the median of the rate of violation of the company department in all the grasping results of the computer; the maximum value of the tarsi violation rate is the maximum value of the tarsi violation rate in all the grabbing results of the computer.
The method is used for evaluating the influence degree of the single factor on the violation rate of the tower department by calculating the relative influence of the single influence factor on the violation rate of the tower department, so that effective management is achieved, and powerful and effective guidance is provided for field tower department management and potential safety hazard avoidance.
The invention has the beneficial effects that:
according to the method, a tower crane violation rate calculation model is constructed by collecting and processing tower crane monitoring data and tower crane training data, the operating level of each tower crane is evaluated by statistically analyzing the tower crane violation rate, key influence factors influencing the tower crane violation rate are obtained at the same time, and references are provided for construction site management, tower crane operating behavior targeted training and the like; the tower crane monitoring data in the intelligent construction site can be fully, reasonably and effectively utilized, so that a basis is provided for construction site management and tower crane management, overall objective, scientific and effective management is achieved, the development process of construction enterprises in intellectualization, informatization and digitization can be promoted, and the enterprises are assisted in transformation, upgrading and synergy; by adopting single factor sensitivity analysis, key influence factors influencing the violation rate of the tower department are obtained, quantifiable key influence factors are analyzed, the influence scope of the key influence factors on the violation rate of the tower department is mastered, and further effective and important management on the tower department on a construction site is realized; meanwhile, the mean value and the standard deviation of the violation rate of the tower company are calculated, a scatter diagram is quantized and used for evaluating the operation level of each tower company, and then the tower company is trained and managed in a targeted mode according to the calculation result, so that the project progress is improved, and the operation level of the tower company is improved.
Drawings
FIG. 1 is a schematic structural diagram of a tower crane monitoring system in the invention;
FIG. 2 is a flow chart of the present invention;
FIG. 3 is a schematic interface diagram of the data of the tower monitor in example 2;
FIG. 4 is a schematic interface diagram of the tower driver attendance data in example 2;
FIG. 5 is a schematic interface diagram of the tower alarm data in example 2;
FIG. 6 is a schematic view of an interface of a training education duration in which the tower driver receives the training education in example 2;
FIG. 7 is a bar graph of the average violation rates for each day in the selected time period of 1/9/2022 to 15/9/2022 in example 2;
FIG. 8 is a flowchart of the idea process of performing single factor sensitivity analysis in example 2;
FIG. 9a is a diagram showing the results of five iterations of random sampling and 50 calculation grabbing in example 2 by the Monte Carlo sampling method;
FIG. 9b is a diagram showing the results of five iterations of random sampling and 50 calculation grabbing in combination with the Latin hypercube sampling method in example 2;
FIG. 10 is a bar graph of the relative impact of each single influencing factor on the rate of tower violation for example 2;
FIG. 11 is a bar graph of the relationship between the period of operation and the rate of violation of the tower in example 2;
FIG. 12a is a pie chart showing five alarm situations occurring during the working hours of the white shift in example 2;
FIG. 12b is a pie chart of five alarm situations occurring during the working hours of the night shift in example 2;
FIG. 12c is a pie chart of five alarm situations occurring when the working period is night shift in example 2;
FIG. 13 is a bar graph of the relationship between operating hours and rate of tower violation for example 2;
FIG. 14 is a bar graph showing the relationship between the training education duration and the rate of violation of the company in example 2;
FIG. 15 is a scatter plot of the relationship between the mean and standard deviation of the rate of tarsi violations in example 2.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is to be noted that the experimental methods described in the following embodiments are all conventional methods unless otherwise specified, and the reagents and materials, if not otherwise specified, are commercially available; in the description of the present invention, the terms "lateral", "longitudinal", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not imply that the components are required to be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present application, it is further noted that, unless expressly stated or limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in this application will be understood to be a specific case for those of ordinary skill in the art.
Example 1
Fig. 1 shows a tower crane monitoring system, which includes a monitoring system and an intelligent management platform, wherein the monitoring system collects data related to a tower crane and uploads the data to the intelligent management platform.
Specifically, the monitoring system comprises a monitoring device, an anti-collision module, a tower crane black box and a display device, wherein the monitoring device, the anti-collision module and the tower crane black box are respectively connected with the display device and are used for acquiring tower crane monitoring data; the monitoring system is applied to the tower crane and is independent of a safety monitoring system on the tower crane, and is used for preventing the tower crane from being overloaded, managing special operating personnel and preventing the tower crane from being collided during tower group operation, so that the safety accidents are reduced, and casualties are avoided to the maximum extent.
Wherein, monitoring devices includes:
the height sensor is used for acquiring height data of the tower crane;
the inclination angle sensor is used for acquiring X inclination angle data and Y inclination angle data of the tower crane;
the weight sensor is used for acquiring the hoisting data of the tower crane;
the rotating angle sensor is used for collecting azimuth data of the tower crane;
the wind speed sensor is used for acquiring wind speed data;
and the amplitude sensor is used for acquiring tower crane amplitude data.
The operation data of the tower crane is collected through a plurality of sensors and is used for providing data support for subsequent data integration and data processing.
The system comprises a tower crane black box, an anti-collision module, a data acquisition module and a data processing module, wherein the tower crane black box and the anti-collision module are respectively used for acquiring tower crane alarm data, including high-order pre-alarm data, low-order pre-alarm data, high-order pre-alarm data with amplitude, low-order pre-alarm data with amplitude, high-order pre-alarm data with X angle, high-order pre-alarm data with Y angle, high-order pre-alarm data with load weight and current collision pre-alarm data; the tower crane alarm data is acquired through collection, and data support is provided for subsequent mining and analyzing the operation habits and proficiency of the tower crane.
The display device comprises a host and a display and is used for monitoring the running conditions of the monitoring device, the tower crane black box and the anti-collision module.
Specifically, the intelligent management platform receives tower crane monitoring data uploaded by the monitoring system, visually displays and manages the tower crane monitoring data, queries basic information, installation positions, authorized operators, current running states, early warning information historical data, running data analysis and the like of the tower crane, can remotely retrieve and view the tower crane monitoring data, and stores historical data generated by construction projects.
In the tower crane operation level evaluation method based on the tower crane monitoring system shown in fig. 2, a tower crane violation rate calculation model is constructed by collecting and processing tower crane monitoring data and tower crane training data, the tower crane violation rate is statistically analyzed, the operation level of each tower crane is evaluated, key influence factors influencing the tower crane violation rate are obtained, and references are provided for construction site management, tower crane operation behavior targeted training and the like; the tower crane monitoring data under the intelligent construction site can be fully, reasonably and effectively utilized, so that a basis is provided for construction site management and tower crane management, objective, scientific and effective management is achieved overall, the development process of construction enterprises in intellectualization, informatization and digitization can be promoted, and the transformation, upgrading and synergizing of power-assisted enterprises can be promoted. The method specifically comprises the following steps:
s1: acquiring tower crane monitoring data and tower crane training data;
the monitoring data of the tower crane is acquired through an intelligent management platform; the tower crane monitoring data comprises:
tower crane equipment number or equipment ID;
the tower driver attendance data is acquired through a real-name system attendance management system; the tower driver attendance data comprises working time periods and working duration;
the operation data of the tower crane comprises time points, amplitude, height, azimuth, hoisting weight, wind speed, moment, X inclination angle, Y inclination angle, alarm condition and brake state; amplitude data is acquired by an amplitude sensor; acquiring height data by adopting a height sensor; azimuth data is obtained by adopting a rotary angle sensor; the hoisting weight is obtained by a weight sensor; acquiring wind speed data by adopting a wind speed sensor; acquiring X inclination angle data and Y inclination angle data by adopting an inclination angle sensor; the warning condition is obtained through the tower crane black box and the anti-collision module, and the tower crane warning data of the warning condition comprises five types, namely: the first is height early warning data which comprises height high-order pre-warning data and height low-order pre-warning data; the second type is amplitude early warning data which comprises amplitude high-order pre-warning data and amplitude low-order pre-warning data; the third is inclination angle early warning data which comprises X-angle high-order pre-warning data and Y-angle high-order pre-warning data; the fourth is the high-order pre-warning data of the load; the fifth is the current collision pre-alarm data;
hoisting number detail data comprising a start time, an end time, a maximum hoisting weight, a start angle, a start amplitude, a start height, an end angle, an end height, an end amplitude.
Acquiring the working time period and working duration of the tower driver by acquiring the tower driver attendance data; by acquiring the operating data of the towers and the warning data of the towers, the operation behavior habits and the operation proficiency of the towers can be mined according to the data.
The method comprises the following steps that (1) the tower driver training data are obtained through the derivation of a training system; the tassel training data includes training education duration, tassel cultural degree, and tassel working duration for the tassel to receive the training education.
S2: preprocessing tower crane monitoring data and tower crane training data;
the process for preprocessing the tower crane monitoring data, the tower crane driver training data and the tower crane warning data specifically comprises the following steps:
s21: integrating the tower driver test attendance data and the tower driver training data to construct a tower driver information data set;
s22: using R software to associate the tower crane information data set with the tower crane monitoring data;
s23: performing data cleaning on the correlated tower crane information data set and tower crane monitoring data;
s231: screening data;
s232: eliminating invalid data, redundant data and singular values;
s24: and eliminating the tower alarm data caused by the influence of objective factors.
Interference caused by system data is eliminated by associating the tower crane information data set with the tower crane monitoring data.
S3: constructing a tower crane violation rate calculation model based on the preprocessed tower crane monitoring data and the preprocessed tower crane training data, and calculating the violation rates of the towers respectively;
in the tower department violation rate calculation model, the calculation formula of the average violation rate of the mth tower department is as follows:
in the formula, F m Average violation rate of mth tower, F mn The average violation rate of the nth type of tower crane alarm data appears for the mth tower crane;
the calculation formula of the average violation rate of the nth type of tower crane alarm data of the mth tower crane is as follows:
in the formula, S mn Working circulation volume, T, of nth type tower crane alarm data for mth tower crane m All the work circulation quantities of the mth tower department; wherein m is [1,M ]],n∈[1,5]And M is total amount of the tassel.
And a tower violation rate model is defined and constructed for reasonably reflecting the operation level of each tower.
S4: and based on the violation rate of each tower company, performing statistical analysis by combining tower crane monitoring data, evaluating the operation behaviors and levels of each tower company according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower company.
Wherein, the statistical analysis process specifically comprises:
s41: analyzing and counting influence factors influencing the violation rate of the tower driver by adopting SPSS software;
wherein, the influencing factors influencing the violation rate of the tower department include:
a working period;
the working time length;
training education duration;
other influencing factors comprise whether a hook is additionally arranged for visualization, the command and dispatching level of a signaler and the culture degree of a tower department; the signal worker commands the dispatching level to be obtained by scoring by an operator, wherein the scoring comprises 1-10 points.
S42: based on the influence factors influencing the violation rate of the tower company, a Monte Carlo sampling method and Crystal Ball software are adopted to carry out single factor sensitivity analysis so as to obtain the key influence factors influencing the uncertainty of the violation rate of the tower company, quantifiable key influence factors are selected, and the incidence relation between each key influence factor and the violation rate of the tower company is determined according to a 'decide one' principle;
a Monte Carlo sampling method is adopted for reflecting the distribution characteristics of the target problem, each random variable is randomly sampled according to the corresponding distribution probability, and then the sampling result is combined for post-processing, so that the target value is obtained. When the random sampling times are enough, the sample capacity is large enough, and then the probability distribution of the target value can be obtained for reflecting the distribution characteristics of the target problem.
In this embodiment, the process of performing single-factor sensitivity analysis on the tower violation rate by using the monte carlo sampling method specifically includes:
s421: carrying out mathematical statistics on influence factors influencing the violation rate of the tower department;
s422: establishing a data model according with the parameter distribution rule of each influence factor;
s423: introducing a latin hypercube sampling method, and carrying out random sampling and grabbing calculation by using a pseudo random number generated by a computer to obtain an accumulative distribution curve of the violation rate of each tower department;
introducing a Latin hypercube sampling method for accurately representing the distribution characteristics of input parameters, dividing a parameter distribution interval into a plurality of complementary overlapped intervals to ensure that each interval has the same probability, and randomly extracting sample points in each interval to represent the parameter characteristics in each interval so as to ensure the randomness of sampling results and the independence among multiple variables; meanwhile, the computer is high in grabbing speed and suitable for random sampling under the condition of low probability, so that uncertainty analysis is conducted on the influence factors.
Wherein, the calculation formula for grabbing by the computer is as follows:
f = P (working time | training education time | other influencing factors)
Wherein F is the rate of tarsi violation; p is the probability of randomly sampling the work period, the work duration, the training education duration, other influencing factors.
S424: fixing other influence factors, combining the computer grasping result, and quantitatively calculating the relative influence of each single influence factor on the violation rate of the tower department so as to represent the influence degree of each single influence factor on the violation rate of the tower department;
the formula for quantitatively calculating the relative influence of the single influence factor on the violation rate of the tower department is as follows:
in the formula, the minimum value of the tarsi violation rate is the minimum value of the tarsi violation rate in all the grabbing results of the computer; the median of the rate of violation of the company department is the median of the rate of violation of the company department in all the grasping results of the computer; the maximum value of the violation rate of the company is the maximum value of the violation rate of the company in all the grabbing results of the computer.
Based on an uncertainty analysis result, by combining a relative influence result of a single influence factor on the tarsi violation rate, key influence factors of Huqiu influencing the tarsi violation rate can be analyzed, quantifiable key influence factors can be analyzed, the influence scope of the key influence factors on the tarsi violation rate can be mastered, and effective and important management on the construction site tarsi can be further realized;
s43: and calculating the mean value and the standard deviation of the violation rate of each tower department, and quantifying by adopting a scatter diagram to evaluate the operation level of each tower department.
The mean value and the standard deviation of the violation rate of the tower company are calculated, and the scatter diagram is quantized to evaluate the operation level of each tower company, so that the targeted training and management are performed on each tower company according to the calculation result, the project progress is improved, and the operation level of the tower company is improved.
Example 2
The embodiment provides another tower crane operation level evaluation method based on a tower crane monitoring system, which specifically comprises the following steps:
t1: acquiring tower crane monitoring data and tower crane training data;
in this embodiment, 50.53 million data monitored and returned by 3 tower cranes within 9 months 1 day of 2022 and 11 months 30 days of 2022 is acquired through a data warehouse technology ELT, and tower crane monitoring data is constructed. The data return pulse interval is that data is returned once every 40s when the data return pulse interval is idle and data is returned once every 6s when the data return pulse interval is running.
In the embodiment, 1366 pieces of attendance data formed by 10-bit tower department in 9-1-2022-11-30 days in 2022 are acquired by a real-name system attendance management system, tower department attendance data are constructed, different working periods of a white class, a night class and a night class are divided, the white class is 6-00, and the corresponding working period is 12h; the night shift is 18; the night shift is 24.
The tower crane alarm data is recorded by a tower crane black box from 9 months 1 day in 2022 to 11 months 30 days in 2022; the corresponding code of the high-high pre-alarm data is 8-1, the corresponding code of the high-low pre-alarm data is 8-3, the corresponding code of the amplitude high pre-alarm data is 9-1, the corresponding code of the amplitude low pre-alarm data is 9-2, the corresponding code of the X-angle high pre-alarm data is 10-1, the corresponding code of the Y-angle high pre-alarm data is 10-5, the corresponding code of the load high pre-alarm data is 11-1, and the corresponding code of the current collision pre-alarm data is 14-1.
FIG. 3 is a schematic interface diagram of the tower monitor data; FIG. 4 is a schematic illustration of an interface for tower driver attendance data; FIG. 5 is a schematic diagram of an interface for the tower alarm data.
In this embodiment, the tower driver training data is data for 10 tower drivers to participate in on-line answering, short education video and platform assessment when connected to a network through WIFI.
Fig. 6 is a schematic view of an interface of a training education session in which the tower receives training education.
T2: preprocessing tower crane monitoring data and tower crane training data;
t21: integrating the tower driver attendance data and the tower driver training data to construct a tower driver information data set;
t22: using R software to associate the tower crane information data set with the tower crane monitoring data;
t23: performing data cleaning on the correlated tower crane information data set and tower crane monitoring data;
t231: screening data;
t232: eliminating invalid data, redundant data and singular values;
t24: and eliminating the tower alarm data caused by the influence of objective factors.
T3: constructing a tower company violation rate calculation model based on the preprocessed tower crane monitoring data and the preprocessed tower company training data, and calculating the violation rates of each tower company respectively;
in this embodiment, fig. 7 is a bar graph showing the average violation rate per day for a selected time period of 2022, 9/month 1 to 2022, 9/month 15.
T4: and based on the violation rate of each tower company, performing statistical analysis by combining tower crane monitoring data, evaluating the operation behaviors and levels of each tower company according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower company.
T41: analyzing and counting influence factors influencing the violation rate of the tower driver by adopting SPSS software;
in this embodiment, the influencing factors influencing the tower violation rate include:
an operating session, comprising: white class, night class and night class;
the working time is 1-12 h;
training education duration which comprises 1 h-6 h;
other influencing factors comprise whether a hook is additionally arranged for visualization, the command and dispatching level of a signaler and the culture degree of a tower driver; whether the lifting hook is additionally arranged can be visualized, including yes or no; the signal worker commands the dispatching level to include 1 minute to 10 minutes; the culture degree of tassel includes primary school, high school, and higher school.
T42: based on the influence factors influencing the violation rate of the tower driver, single factor sensitivity analysis is carried out by adopting Crystal Ball software to obtain the key influence factors influencing the uncertainty of the violation rate of the tower driver, quantifiable key influence factors are selected, and the incidence relation between each key influence factor and the violation rate of the tower driver is determined according to a 'one-out-one' principle;
t421: carrying out single factor sensitivity analysis on the rate of the tower violation by adopting a Monte Carlo sampling method;
t4211: carrying out mathematical statistics on influence factors influencing the violation rate of the tower department;
t4212: establishing a data model according with the parameter distribution rule of each influence factor;
and (3) respectively constructing a discrete distribution data model by using the culture degree of the tower department, the training education time, the working time period, the working time, the visualization of whether a lifting hook is additionally arranged and the signal worker command scheduling level to accord with the discrete distribution characteristics.
T4213: introducing a Latin hypercube sampling method, and performing random sampling and grabbing calculation by using pseudo random numbers generated by a computer to obtain a cumulative distribution curve of the violation rate of each tower department;
in this embodiment, fig. 8 is a flowchart illustrating an idea process for performing single-factor sensitive analysis, which is convenient for random sampling and capturing by using a computer.
Fig. 9a is a schematic diagram showing the results of five iterations of random sampling and 50 calculation grabbing by the monte carlo sampling method, wherein A1, A2, A3, A4 and A5 are random numbers generated randomly. The Monte Carlo sampling method is completely random sampling, so that when the distribution range of influencing factors is input, sample points can fall at any positions, the samples are more concentrated at positions with higher Tesla rate, and according to the result of the embodiment, the problem of sample aggregation is caused due to less sampling times, and the representativeness of the sampling result is further influenced.
Fig. 9b is a diagram showing the results of five iterations of random sampling and 50 calculation grabbing in combination with the latin hypercube sampling method. After the Latin hypercube sampling method is introduced, samples are distributed uniformly, and sampling results are more representative.
T4214: fixing other influence factors, combining the computer grasping result, and quantitatively calculating the relative influence of each single influence factor on the violation rate of the tower driver so as to represent the influence degree of each single influence factor on the violation rate of the tower driver;
in this embodiment, a bar graph of the relative impact of each single influencing factor on the rate of tower violation is shown in FIG. 10. In the drawing, the relative influences of the installation of the lifting hook visualization, the working time period, the working time length, the training education time length, the signal worker command dispatching level and the tower driver culture degree on the tower driver violation rate are respectively 95.45 percent, 60.52 percent,
59.27%, 48.98%, 42.23% and 41.06%, and further the influence degree of each single influence factor on the rate of the tower driver violation is as follows from large to small: whether the lifting hook is additionally arranged for visualization > working period > working duration > training education duration
The command and dispatching level of the signal worker is more than the culture degree of the tower department. Whether the hook visualization is additionally arranged has great influence on the violation rate of the tower crane, and the management layer can be prompted to install the adaptive hook visualization for the tower crane in a limited condition range so as to reduce the violation rate of the tower crane; the signal worker commands the scheduling level to have certain influence on the violation rate of the tower department, and prompts a management layer that the selection, education and the matching with the tower department of the signal worker need to be strengthened; the tame culture degree has limited influence on the rate of the tame violation, and the tame culture degree is influenced by the operating life of the tame, so that the reference can be improved when the management layer engages the tame.
T422: selecting quantifiable key influence factors, and determining the incidence relation between each key influence factor and the violation rate of the tower crane according to a' deciding a rule and a rule;
in the embodiment, three key influence factors of quantifiable working period, working duration and training education duration are selected; changing one key influence factor, fixing other influence factors to be the same or keep constant, and observing the change of the violation rate of the tower department; by mastering the influence of key influence factors on the violation rate of the tower crane, the construction site can be improved in a targeted manner, and further the potential safety hazard is avoided.
Fig. 11 shows the relationship between the operating hours and the tower violation rate, which shows the tower violation rates of 10 towers in three different operating hours, namely, white shift, night shift and night shift. Wherein the average violation rate of night class > the average violation rate of white class > the average violation rate of night class. As shown in fig. 12a, data generated by the alarm condition occurring in the white class mainly includes pre-alarm data for front collision, pre-alarm data for high-order load and amplitude pre-alarm data; as shown in fig. 12b, the data generated by the alarm condition occurring in the night shift mainly includes load high-order pre-alarm data, amplitude pre-alarm data and altitude pre-alarm data; as shown in fig. 12c, data generated by the alarm condition occurring in the night shift mainly includes load high-order pre-alarm data, amplitude pre-alarm data and height pre-alarm data, and managers can perform key supervision and management on the alarm condition occurring more according to the distribution of the tower driver violation rates in different time periods, so as to reduce the average tower driver violation rate.
Fig. 13 shows a relationship between the operating time and the tower violation rate, which shows the tower violation rates at different operating times when a certain tower operating time is white class. The violation rate of the tower driver per hour and the working time length of the tower driver in the working time period of the white class are in a unitary quadratic function correlation relationship, namely the violation rate of the tower driver is firstly reduced and then increased along with the increase of the working time length, so that the management personnel are required to strengthen the field management intensity and prompt the working state of the tower driver when the tower driver starts working and is just going off the work, and the average violation rate of the tower driver is reduced.
Wherein fig. 14 shows the relationship between the training education duration and the rate of violation of the tower department, which shows the rate of violation of the tower department for different training education durations received by the tower department. The longer the period of time for receiving the training education by the tower department is, the lower the violation rate of the tower department is, the management staff is prompted to frequently train and educate the tower department, the cognitive level of the tower department is improved, and the violation rate of the tower department is reduced.
T43: and calculating the mean value and the standard deviation of the violation rate of each tower department, and quantifying by adopting a scatter diagram to evaluate the operation level of each tower department.
In this embodiment, as shown in fig. 15, the relationship between the mean and standard deviation of the tower violation rate is shown. Wherein, the larger the average value of the violation rate of the tower company is, the lower the operation level of the tower company is; the smaller the average value of the rate of violation of the cheese is, the higher the operating level of the cheese is; the larger the standard deviation of the violation rate of the tower company is, the worse the operation stability of the tower company is; the smaller the average value of the rate of tower department violation, the better the operation stability of the tower department. The management layer can be prompted to conduct special training education aiming at the tower department with the large average value of the tower department violation rate and the large standard deviation of the tower department violation rate, bad operation habits of the tower department are further improved, the operation level of the tower department is improved, and therefore the operation level of the tower department on a construction site is improved.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A tower crane monitoring system-based tower crane operation level assessment method is characterized by comprising the following steps:
s1: acquiring tower crane monitoring data and tower crane training data;
s2: preprocessing tower crane monitoring data and tower crane training data;
s3: constructing a tower crane violation rate calculation model based on the preprocessed tower crane monitoring data and the preprocessed tower crane training data, and calculating the violation rates of the towers respectively;
s4: and based on the violation rate of each tower company, performing statistical analysis by combining tower crane monitoring data, evaluating the operation behaviors and levels of each tower company according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower company.
2. The tower crane operation level evaluation method based on the tower crane monitoring system as claimed in claim 1, wherein in S1, tower crane monitoring data is obtained through an intelligent management platform; the tower crane monitoring data comprises:
tower crane equipment number or equipment ID;
the tower driver attendance data is acquired through a real-name system attendance management system; the tower driver attendance data comprises working time periods and working duration;
the operation data of the tower crane comprises time points, amplitude, height, azimuth, hoisting weight, wind speed, moment, X inclination angle, Y inclination angle, alarm condition and brake state; the amplitude data is acquired by adopting an amplitude sensor; the height data is obtained by adopting a height sensor; the azimuth data is acquired by adopting a rotary angle sensor; the hoisting weight is obtained by a weight sensor; the wind speed data is acquired by a wind speed sensor; the X inclination angle data and the Y inclination angle data are acquired by adopting inclination sensors; the alarm condition is obtained through a tower crane black box and an anti-collision module, and the tower crane alarm data of the alarm condition comprises five types, namely: the first is height early warning data which comprises height high-order pre-warning data and height low-order pre-warning data; the second type is amplitude early warning data which comprises amplitude high-order pre-warning data and amplitude low-order pre-warning data; the third is inclination angle early warning data which comprises X-angle high-order pre-warning data and Y-angle high-order pre-warning data; the fourth is the high-order pre-warning data of the load; the fifth is the current collision pre-alarm data;
hanging number detail data comprising a start time, an end time, a maximum hanging weight, a start angle, a start amplitude, a start height, an end angle, an end height, an end amplitude.
3. The tower crane monitoring system-based tower crane operation level assessment method according to claim 2, wherein in S1, the tower crane training data is obtained by a training system; the tassel training data includes training education duration, tassel cultural degree, and tassel working duration for the tassel to receive the training education.
4. The tower crane monitoring system-based tower crane operation level assessment method according to claim 3, wherein in S2, the process of preprocessing the tower crane monitoring data, the tower crane training data and the tower crane warning data specifically comprises:
s21: integrating the tower driver test attendance data and the tower driver training data to construct a tower driver information data set;
s22: using R software to associate the tower crane information data set with the tower crane monitoring data;
s23: performing data cleaning on the correlated tower crane information data set and tower crane monitoring data;
s231: screening data;
s232: eliminating invalid data, redundant data and singular values;
s24: and eliminating the tower alarm data caused by the influence of objective factors.
5. The tower crane monitoring system-based tower crane operation level evaluation method as claimed in claim 4, wherein in S3, in the tower crane violation rate calculation model, the calculation formula of the average violation rate of the mth tower crane is as follows:
in the formula, F m Average violation rate of mth tower, F mn The average violation rate of the nth type of tower crane alarm data appears for the mth tower crane;
the calculation formula of the average violation rate of the nth tower crane alarm data of the mth tower crane is as follows:
in the formula, S mn Working circulation volume, T, of nth type tower crane alarm data for mth tower crane m All the work circulation quantities of the mth tower department; wherein m is [1,M ]],n∈[1,5]And M is total amount of the tassel.
6. The tower crane operation level evaluation method based on the tower crane monitoring system according to claim 1, wherein in the step S4, the statistical analysis process specifically comprises:
s41: analyzing and counting influence factors influencing the violation rate of the tower driver by adopting SPSS software;
s42: based on the influence factors influencing the violation rate of the tower company, a Monte Carlo sampling method and Crystal Ball software are adopted to carry out single factor sensitivity analysis so as to obtain the key influence factors influencing the uncertainty of the violation rate of the tower company, quantifiable key influence factors are selected, and the incidence relation between each key influence factor and the violation rate of the tower company is determined according to a 'decide one' principle;
s43: and calculating the mean value and the standard deviation of the violation rate of each tower department, and quantifying by adopting a scatter diagram to evaluate the operation level of each tower department.
7. The tower crane operation level evaluation method based on the tower crane monitoring system as claimed in claim 6, wherein in the S41, the influencing factors influencing the violation rate of the tower crane comprise:
a working period;
the working time length;
training education duration;
other influencing factors include whether to install a hook visualization, the signal worker commands the dispatch level and the culture degree of the tower department.
8. The tower crane operation level evaluation method based on the tower crane monitoring system as claimed in claim 7, wherein in S42, the process of performing single factor sensitivity analysis on the tower crane violation rate by using the monte carlo sampling method specifically comprises:
s421: carrying out mathematical statistics on influence factors influencing the violation rate of the tower department;
s422: establishing a data model according with the parameter distribution rule of each influence factor;
s423: introducing a latin hypercube sampling method, and carrying out random sampling and grabbing calculation by using a pseudo random number generated by a computer to obtain an accumulative distribution curve of the violation rate of each tower department;
s424: fixing other influence factors, combining the computer grasping result, and quantitatively calculating the relative influence of each single influence factor on the violation rate of the tower driver so as to represent the influence degree of each single influence factor on the violation rate of the tower driver.
9. The tower crane operation level evaluation method based on the tower crane monitoring system according to claim 8, wherein in S423, a calculation formula for capturing by using a computer is as follows:
f = P (working time | training education time | other influencing factors)
Wherein F is the rate of tarsi violation; p is the probability of random sampling of work hours, training education hours, other influencing factors.
10. The tower crane monitoring system-based tower crane operation level evaluation method according to claim 8, wherein in S424, the calculation formula for quantitatively calculating the relative influence of a single influence factor on the tower crane violation rate is as follows:
in the formula, the minimum value of the violation rate of the tower department is the minimum value of the violation rate of the tower department in all the grabbing results of the computer; the median of the rate of violation of the company department is the median of the rate of violation of the company department in all the grasping results of the computer; the maximum value of the tarsi violation rate is the maximum value of the tarsi violation rate in all the grabbing results of the computer.
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| US20210089979A1 (en) * | 2019-09-24 | 2021-03-25 | cg42 LLC | Analytics system and method for a competitive vulnerability and customer and employee retention |
| CN113538841A (en) * | 2020-04-14 | 2021-10-22 | 广东博智林机器人有限公司 | Tower crane operation monitoring method, monitoring device, storage medium and processor |
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| CN110222918A (en) * | 2019-04-18 | 2019-09-10 | 重庆恢恢信息技术有限公司 | Wisdom building site management system, server and storage medium based on cloud platform |
| US20210089979A1 (en) * | 2019-09-24 | 2021-03-25 | cg42 LLC | Analytics system and method for a competitive vulnerability and customer and employee retention |
| CN113538841A (en) * | 2020-04-14 | 2021-10-22 | 广东博智林机器人有限公司 | Tower crane operation monitoring method, monitoring device, storage medium and processor |
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