CN115983688B - Tower department operation level assessment method based on tower crane monitoring system - Google Patents
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Abstract
The invention belongs to the technical field of big data management, and particularly relates to a tower department operation level assessment method based on a tower crane monitoring system, which comprises the following steps: s1: acquiring tower crane monitoring data and tower department training data; s2: preprocessing tower crane monitoring data and tower department training data; s3: based on the preprocessed tower crane monitoring data and tower department training data, constructing a tower department violation rate calculation model, and respectively calculating the violation rates of all tower departments; s4: based on the violation rate of each tower department, carrying out statistical analysis by combining the monitoring data of the tower crane, evaluating the operation behavior and the level of each tower department according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower department; through evaluating the operation level of the tower department, objective and effective evaluation is carried out on the tower department, and meanwhile, according to key influence factors influencing the violation rate of the tower department, the system can assist management staff in carrying out targeted management on the tower department and a construction site, so that the construction efficiency is improved, and the construction safety is ensured.
Description
Technical Field
The invention belongs to the technical field of big data management, and particularly relates to a tower department operation level assessment method based on a tower crane monitoring system.
Background
Along with the development of science and technology and the rapid increase of the number of driving equipment, drivers face serious challenges in terms of driving safety, and the rule violating actions of drivers of large-scale equipment are one of main reasons for causing major safety accidents on construction sites, so that the rule violating and the rule violating driving actions of the drivers are perceived and stopped in time, and are one of important measures for preventing accident hidden dangers. The existing large-scale equipment such as the tower crane is provided with a monitoring camera in a cab, so that not only can the indoor and outdoor environments in the driving process be monitored in real time, but also videos can be conveniently called after accidents occur to obtain evidence, but the illegal and illegal driving behaviors of the tower crane cannot be timely perceived, and objective and scientific evaluation and assessment of the operation behaviors of the tower crane related to great safety cannot be timely carried out.
Therefore, the monitoring system is arranged to collect the operation data generated by the tower crane in the process of operating the tower crane and upload the operation data to the intelligent construction site management platform, the intelligent management platform receives the mass safety, quality, environment, video and other multi-element data returned by the monitoring terminal, and a manager can monitor each independent part of the tower crane equipment through the monitoring system, but is difficult to integrate and excavate and analyze other mass data, so that the data resource is wasted; meanwhile, the operation behaviors of the tower drivers still depend on personal experiences of management staff to select and make decisions, 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 in an intelligent chemical industry can be reasonably and effectively utilized.
Disclosure of Invention
The invention provides a tower department operation level assessment method based on a tower crane monitoring system, which comprises the steps of constructing a tower department violation rate calculation model by collecting and processing tower crane monitoring data and tower department training data, assessing the operation level of each tower department by carrying out statistical analysis on the tower department violation rate, and simultaneously acquiring key influence factors influencing the tower department violation rate, so as to provide references for construction site management, targeted training of tower department operation behaviors and the like; the intelligent construction site management system can fully, reasonably and effectively utilize tower crane monitoring data in the intelligent construction site, so that basis is provided for construction site management and tower department management, objective, scientific and effective management is achieved, development processes of construction enterprises in intelligent, informationized and digital modes can be promoted, and the power-assisted enterprises are transformed, upgraded and enhanced.
A tower operation level assessment method based on a tower crane monitoring system, comprising:
s1: acquiring tower crane monitoring data and tower department training data;
s2: preprocessing tower crane monitoring data and tower department training data;
S3: based on the preprocessed tower crane monitoring data and tower department training data, constructing a tower department violation rate calculation model, and respectively calculating the violation rates of all tower departments;
s4: based on the violation rate of each tower department, carrying out statistical analysis by combining the monitoring data of the tower crane, and evaluating the operation behavior and the level of each tower department according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower department.
The method comprises the steps of constructing a tower department violation rate calculation model by collecting and processing tower machine monitoring data and tower department training data, evaluating the operation level of each tower department by carrying out statistical analysis on the tower department violation rate, and simultaneously acquiring key influence factors influencing the tower department violation rate, so as to provide references for construction site management, targeted training of tower department operation behaviors and the like; the intelligent construction site management system can fully, reasonably and effectively utilize tower crane monitoring data in the intelligent construction site, so that basis is provided for construction site management and tower department management, objective, scientific and effective management is achieved, development processes of construction enterprises in intelligent, informationized and digital modes can be promoted, and the power-assisted enterprises are transformed, upgraded and enhanced.
Further, in the step S1, tower crane monitoring data are obtained through an intelligent management platform; the tower crane monitoring data comprises:
Tower crane equipment number or equipment ID;
the tower department attendance data is obtained through a real-name attendance management system; the tower attendance data comprise a working period and a working time length;
the tower crane operation data comprises time points, amplitude, height, azimuth, crane weight, wind speed, moment, X inclination angle, Y inclination angle, alarm condition and braking state; the amplitude data is acquired by an amplitude sensor; the height data are acquired by a height sensor; the azimuth data are acquired by adopting a rotation angle sensor; the hanging 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 an inclination angle sensor; the alarm condition is obtained through a tower crane black box and an anti-collision module, and the tower department alarm data of the alarm condition comprises five types, namely: the first is the high-level pre-warning data, which includes high-level pre-warning data and low-level pre-warning data; the second is amplitude pre-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 early warning data and Y-angle high-order early warning data; the fourth is the load high-order pre-warning data; the fifth is the current collision pre-alert data;
The hanging time detail data comprises a starting time, an ending time, a maximum hanging weight, a starting angle, a starting amplitude, a starting height, an ending angle, an ending height and an ending amplitude.
Acquiring working time period and working time length of a tower department by acquiring the tower department attendance data; by acquiring the tower operation data and the tower alarm data, the operation behavior habit and the operation proficiency of each tower can be mined according to the data.
Further, in the step S1, the tower training data is derived and acquired through a training system; the tower department training data comprises training education duration of the tower department receiving training education, the culture degree of the tower department and the working life of the tower department.
Further, in the step S2, the process of preprocessing the tower crane monitoring data, the tower crane training data and the tower crane alarm data specifically includes:
s21: integrating the tower department attendance data and the tower department training data to construct a tower department information data set;
s22: adopting R software to correlate the tower department information data set and tower crane monitoring data;
s23: carrying out data cleaning on the correlated tower department information data set and tower crane monitoring data;
s231: screening data;
s232: removing invalid data, redundant data and singular values;
S24: and eliminating the tower alarm data caused by the influence of objective factors.
And eliminating interference caused by system data by associating the tower department information data set with tower crane monitoring data.
Further, in the step S3, in the tower regulation violation rate calculation model, a calculation formula of an average regulation violation rate of the mth tower is as follows:
wherein F is m Average violation rate of mth tower, F mn The average violation rate of the alarm data of the nth tower crane appears for the mth tower department;
the calculation formula of the average violation rate of the alarm data of the nth tower crane in the mth tower department is as follows:
wherein S is mn The working circulation quantity T of the alarm data of the nth tower crane appears for the mth tower department m All work circulation amounts for the mth tower; wherein m is E [1, M],n∈[1,5]M is the total amount of the tassel.
The work circulation amount is a completed process including crane operation and normal stop from the start of lifting one article to the start of lifting the next article according to the specification of crane design GB 3811-2008.
The ratio of the work circulation volume with alarm condition in all the work circulation volumes completed by the tower is defined as the average violation rate of the tower, and a tower violation rate model is constructed based on the ratio, so that the operation level of each tower is reasonably reflected.
Further, in S4, the statistical analysis specifically includes:
s41: adopting SPSS software to analyze and count influence factors which influence the violation rate of the tower department;
s42: based on the statistical influence factors influencing the rate of the tower department violations, a Monte Carlo sampling method is adopted, crystal Ball software is utilized to conduct single factor sensitivity analysis so as to obtain key influence factors influencing the uncertainty of the rate of the tower department violations, quantifiable key influence factors are selected, and the association relation between each key influence factor and the rate of the tower department violations is determined according to the principle of 'first order';
s43: and calculating the average value and standard deviation of the violation rate of each tower, and quantifying by adopting a scatter diagram to evaluate the operation level of each tower.
By adopting single factor sensitivity analysis, key influence factors influencing the rate of the tower department violation are obtained, quantifiable key influence factors are analyzed, the influence category of the key influence factors on the rate of the tower department violation is mastered, and further effective and key management on the tower department on a construction site is realized; meanwhile, the average value and the standard deviation of the violation rate of the tower sauce are calculated, and the method is quantified by adopting a scatter diagram and used for evaluating the operation level of each tower sauce, so that each tower sauce is trained and managed in a targeted manner according to a calculation result, the project progress is improved, and meanwhile, the operation level of the tower sauce is improved.
Further, in S41, the influencing factors that influence the rate of the tower violation include:
an operating period;
the working time length;
training education time;
other influencing factors include whether to add hook visualization, signaling engineering command scheduling level and tower culture level.
Further, in S42, the process of performing the 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 rule breaking rate of the tassel;
s422: establishing a data model conforming to 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 a cumulative distribution curve of the violation rate of each tower;
s424: fixing other influence factors, and quantitatively calculating the relative influence of each single influence factor on the tower department violation rate by combining the grabbing results of the computer so as to represent the influence degree of each single influence factor on the tower department violation rate;
further, in S423, the calculation formula for capturing by using the computer is as follows:
f=p (working period |training education period|other influencing factors)
Wherein F is the rate of tower violation; p is the probability of randomly sampling the work period, work duration, training education duration, other influencing factors.
Further, in S424, a calculation formula for quantitatively calculating the relative influence of the single influencing factor on the tower violation rate is as follows:
in the formula, the minimum value of the rule breaking rate of the tassel is the minimum value of the rule breaking rate of the tassel in all grabbing results of the computer; the middle value of the tower rule-breaking rate is the middle value of the tower rule-breaking rate in all grabbing results of the computer; the maximum value of the tower rule breaking rate is the maximum value of the tower rule breaking rate in all grabbing results of the computer.
The relative influence of the single influence factor on the tower department violation rate is calculated and used for evaluating the influence degree of the single factor on the tower department violation rate, so that effective management is achieved, and powerful and effective guidance is provided for on-site tower department management and the prevention of potential safety hazards.
The beneficial effects of the invention are as follows:
according to the invention, the tower crane monitoring data and the tower department training data are collected and processed, a tower department violation rate calculation model is constructed, the operation level of each tower department is evaluated through statistical analysis on the tower department violation rate, and meanwhile, key influence factors influencing the tower department violation rate are obtained, so that references are provided for construction site management, targeted training of tower department operation behaviors and the like; the intelligent construction site management system can fully, reasonably and effectively utilize tower crane monitoring data in the intelligent construction site, so that basis is provided for construction site management and tower department management, objective, scientific and effective management is achieved, development processes of construction enterprises in intelligent, informationized and digital modes can be promoted, and the power-assisted enterprises are transformed, upgraded and enhanced; by adopting single factor sensitivity analysis, key influence factors influencing the rate of the tower department violation are obtained, quantifiable key influence factors are analyzed, the influence category of the key influence factors on the rate of the tower department violation is mastered, and further effective and key management on the tower department on a construction site is realized; meanwhile, the average value and the standard deviation of the violation rate of the tower sauce are calculated, and the method is quantified by adopting a scatter diagram and used for evaluating the operation level of each tower sauce, so that each tower sauce is trained and managed in a targeted manner according to a calculation result, the project progress is improved, and meanwhile, the operation level of the tower sauce is improved.
Drawings
FIG. 1 is a schematic diagram of a tower crane monitoring system according to the present invention;
FIG. 2 is a flow chart of the present invention;
FIG. 3 is a schematic interface diagram of the tower monitoring data of example 2;
FIG. 4 is a schematic interface diagram of the tower attendance data of example 2;
FIG. 5 is a schematic diagram of an interface of the tower alarm data in example 2;
FIG. 6 is a schematic interface diagram of training education duration of the tutor receiving training education in example 2;
FIG. 7 is a bar graph of average daily violation rates over a selected period of 2022, 9, 1 to 2022, 9, 15 of example 2;
FIG. 8 is a flow chart of the idea process of performing single factor sensitivity analysis in example 2;
FIG. 9a is a schematic diagram of the result of performing five iterations of random sampling and 50 grabs calculation using the Monte Carlo sampling method in example 2;
FIG. 9b is a schematic diagram showing the results of performing random sampling and 50 grabbing calculations in combination with five iterations of Latin hypercube sampling in example 2;
FIG. 10 is a bar graph of the relative effect of individual influencing factors on the rate of tower violations in example 2;
FIG. 11 is a bar graph of operating period versus Tast violation rate for example 2;
FIG. 12a is a pie chart showing five alarm conditions when the working period is a shift in example 2;
FIG. 12b is a pie chart showing five alarm conditions when the working hours are late shifts in example 2;
FIG. 12c is a pie chart showing five alarm conditions when the working period is night shift in example 2;
FIG. 13 is a bar graph of the relationship between the operating time period and the rate of tower violations in example 2;
FIG. 14 is a bar graph of training education duration versus tower violation rate for example 2;
fig. 15 is a scatter plot of the relationship between the mean and standard deviation of the tower violation rate in example 2.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the experimental methods described in the following embodiments, unless otherwise specified, are all conventional methods, and the reagents and materials, unless otherwise specified, are all commercially available; in the description of the present invention, the terms "transverse", "longitudinal", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus are not to be construed as limiting the present invention.
Furthermore, the terms "horizontal," "vertical," "overhang," and the like do not denote a requirement that the component be absolutely horizontal or overhang, but rather may be slightly inclined. As "horizontal" merely means that its 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 should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Fig. 1 shows a tower crane monitoring system, which comprises a monitoring system and an intelligent management platform, wherein the monitoring system collects data related to the 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 collecting monitoring data of the tower crane; 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 overload of the tower crane, management of special operators and anti-collision during tower operation of a tower crane group, so that safety accidents are reduced, and casualties are avoided to the greatest extent.
Wherein, monitoring devices includes:
the height sensor is used for collecting tower crane height data;
the inclination angle sensor is used for collecting X inclination angle data and Y inclination angle data of the tower crane;
the weight sensor is used for acquiring crane weight data of the tower crane;
the rotation angle sensor is used for collecting tower crane azimuth data;
a wind speed sensor for acquiring wind speed data;
and the amplitude sensor is used for collecting amplitude data of the tower crane.
And through a plurality of sensors, the operation data of the tower crane are collected and used for providing data support for subsequent data integration and data processing.
The tower crane black box and the anti-collision module are respectively used for collecting the alarm data of the tower crane, and the alarm data comprise high-level pre-alarm data, high-level low-level pre-alarm data, amplitude high-level pre-alarm data, amplitude low-level pre-alarm data, X-angle high-level pre-alarm data, Y-angle high-level pre-alarm data, load high-level pre-alarm data and current collision pre-alarm data; the alarm data of the tower crane are acquired, so that data support is provided for subsequent excavation analysis of tower operation habits and proficiency.
The display device comprises a host and a display and is used for monitoring the operation conditions of the monitoring device, the tower crane black box and the anti-collision module.
Specifically, the intelligent management platform receives the tower crane monitoring data uploaded by the monitoring system, performs visual display and management on the tower crane monitoring data, inquires basic information, installation positions, authorized operators, current running states, early warning alarm information historical data, running data analysis and the like of the tower crane, can remotely retrieve and view the basic information, the installation positions, the authorized operators, the current running states, the early warning alarm information historical data, the running data analysis and the like, and stores construction project historical production data.
According to the tower department operation level assessment method based on the tower crane monitoring system shown in fig. 2, a tower department violation rate calculation model is built by collecting and processing tower crane monitoring data and tower department training data, the operation level of each tower department is assessed by carrying out statistical analysis on the tower department violation rate, and meanwhile key influence factors influencing the tower department violation rate are obtained, so that references are provided for construction site management, targeted training of tower department operation behaviors and the like; the intelligent construction site management system can fully, reasonably and effectively utilize tower crane monitoring data in the intelligent construction site, so that basis is provided for construction site management and tower department management, objective, scientific and effective management is achieved, development processes of construction enterprises in intelligent, informationized and digital modes can be promoted, and the power-assisted enterprises are transformed, upgraded and enhanced. The method specifically comprises the following steps:
S1: acquiring tower crane monitoring data and tower department training data;
the tower crane monitoring data are acquired through an intelligent management platform; the tower crane monitoring data comprises:
tower crane equipment number or equipment ID;
the tower department attendance data is obtained through a real-name attendance management system; the tower attendance data comprise a working period and a working time length;
the tower crane operation data comprises time points, amplitude, height, azimuth, crane weight, wind speed, moment, X inclination angle, Y inclination angle, alarm condition and braking state; amplitude data are acquired by an amplitude sensor; the height data are acquired by a height sensor; the azimuth data are acquired by a rotation angle sensor; the hanging weight is obtained by a weight sensor; the wind speed data is obtained by a wind speed sensor; the X dip angle data and the Y dip angle data are obtained by adopting a dip angle sensor; the alarm condition is obtained through a tower crane black box and an anti-collision module, and the tower department alarm data of the alarm condition comprises five types, namely: the first is the high-level pre-warning data, which includes high-level pre-warning data and low-level pre-warning data; the second is amplitude pre-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 early warning data and Y-angle high-order early warning data; the fourth is the load high-order pre-warning data; the fifth is the current collision pre-alert data;
The hanging time detail data comprises a starting time, an ending time, a maximum hanging weight, a starting angle, a starting amplitude, a starting height, an ending angle, an ending height and an ending amplitude.
Acquiring working time period and working time length of a tower department by acquiring the tower department attendance data; by acquiring the tower operation data and the tower alarm data, the operation behavior habit and the operation proficiency of each tower can be mined according to the data.
The tower training data are derived and acquired through a training system; the tower department training data comprises training education duration of the tower department receiving training education, the culture degree of the tower department and the working life of the tower department.
S2: preprocessing tower crane monitoring data and tower department training data;
the process for preprocessing the tower crane monitoring data, the tower crane training data and the tower crane alarm data specifically comprises the following steps:
s21: integrating the tower department attendance data and the tower department training data to construct a tower department information data set;
s22: adopting R software to correlate the tower department information data set and tower crane monitoring data;
s23: carrying out data cleaning on the correlated tower department information data set and tower crane monitoring data;
s231: screening data;
s232: removing invalid data, redundant data and singular values;
S24: and eliminating the tower alarm data caused by the influence of objective factors.
And eliminating interference caused by system data by associating the tower department information data set with tower crane monitoring data.
S3: based on the preprocessed tower crane monitoring data and tower department training data, constructing a tower department violation rate calculation model, and respectively calculating the violation rates of all tower departments;
in the calculating model of the violation rate of the tower, the calculating formula of the average violation rate of the mth tower is as follows:
wherein F is m Average violation rate of mth tower, F mn The average violation rate of the alarm data of the nth tower crane appears for the mth tower department;
the calculation formula of the average violation rate of the alarm data of the nth tower crane in the mth tower department is as follows:
wherein S is mn The working circulation quantity T of the alarm data of the nth tower crane appears for the mth tower department m All work circulation amounts for the mth tower; wherein m is E [1, M],n∈[1,5]M is the total amount of the tassel.
The method is used for reasonably reflecting the operation level of each tower department by defining and constructing a tower department violation rate model.
S4: based on the violation rate of each tower department, carrying out statistical analysis by combining the monitoring data of the tower crane, and evaluating the operation behavior and the level of each tower department according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower department.
The statistical analysis process specifically comprises the following steps:
s41: adopting SPSS software to analyze and count influence factors which influence the violation rate of the tower department;
wherein, the influence factors that influence the rate of violating regulations of tower include:
an operating period;
the working time length;
training education time;
other influencing factors, including whether to additionally install hook visualization, signal engineering command scheduling level and tower culture degree; the signal engineering command dispatching level is obtained through scoring by an operator, wherein the scoring comprises 1 score to 10 scores.
S42: based on the statistical influence factors influencing the rate of the tower department violations, a Monte Carlo sampling method is adopted, crystal Ball software is utilized to conduct single factor sensitivity analysis so as to obtain key influence factors influencing the uncertainty of the rate of the tower department violations, quantifiable key influence factors are selected, and the association relation between each key influence factor and the rate of the tower department violations is determined according to the principle of 'first order';
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 results are combined and post-processed to obtain the target value. When the random sampling times are enough, the sample capacity is enough to obtain the probability distribution of the target value, and the probability distribution is used for reflecting the distribution characteristics of the target problem.
In this embodiment, the process of performing the 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 rule breaking rate of the tassel;
s422: establishing a data model conforming to 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 a cumulative distribution curve of the violation rate of each tower;
the Latin hypercube sampling method is introduced and used for accurately representing the distribution characteristics of input parameters, the parameter distribution interval is divided into a plurality of intervals which are complementarily overlapped, so that each interval has the same probability, and the random sampling points in each interval are used for representing the parameter characteristics in each interval, so that the randomness of the sampling result and the independence among multiple variables can be ensured; meanwhile, the computer grabbing speed is high, and the method is suitable for random sampling under the condition of low probability, so that uncertainty analysis is conveniently carried out on influence factors.
The calculation formula for grabbing by adopting a computer is as follows:
f=p (working period |training education period|other influencing factors)
Wherein F is the rate of tower violation; p is the probability of randomly sampling the work period, work duration, training education duration, other influencing factors.
S424: fixing other influence factors, and quantitatively calculating the relative influence of each single influence factor on the tower department violation rate by combining the grabbing results of the computer so as to represent the influence degree of each single influence factor on the tower department violation rate;
the calculation formula for quantitatively calculating the relative influence of a single influence factor on the tower regulation violation rate is as follows:
in the formula, the minimum value of the rule breaking rate of the tassel is the minimum value of the rule breaking rate of the tassel in all grabbing results of the computer; the middle value of the tower rule-breaking rate is the middle value of the tower rule-breaking rate in all grabbing results of the computer; the maximum value of the tower rule breaking rate is the maximum value of the tower rule breaking rate in all grabbing results of the computer.
Based on an uncertainty analysis result, the key influence factors of the tiger hills on the tower violation rate can be combined with the relative influence result of the single influence factors on the tower violation rate, and quantifiable key influence factors are analyzed to master the influence category of the key influence factors on the tower violation rate, so that the effective and key management of the tower on the construction site is realized;
S43: and calculating the average value and standard deviation of the violation rate of each tower, and quantifying by adopting a scatter diagram to evaluate the operation level of each tower.
The method comprises the steps of calculating the mean value and standard deviation of the violation rate of the tower, quantifying by adopting a scatter diagram, evaluating the operation level of each tower, and further carrying out targeted training and management on each tower according to the calculation result so as to improve project progress and improve the operation level of the tower.
Example 2
The embodiment provides another tower 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 department training data;
in the embodiment, 50.53 ten thousand data of 3 tower cranes which are monitored and returned in 2022, 9, 1, 11, 30 and 2022 are acquired through a data warehouse technology ELT, and tower crane monitoring data are constructed. The data return pulse interval is that data is returned every 40s when idle, and data is returned every 6s when running.
In the embodiment, 1366 pieces of attendance data formed by 10-bit tassels in 2022, 9-1-2022, 11-30 are acquired and acquired through a real-name attendance management system, the tassels attendance data are constructed, different working periods of a white shift, a night shift and a night shift are divided, the white shift is 6:00-18:00, and the corresponding working time is 12 hours; the night shift is 18:00-24:00, and the corresponding working time is 6 hours; the night shift is 24:00-6:00, and the corresponding working time is 6h.
The tower crane alarm data are recorded by a tower crane black box from 2022, 9, 1, 11, 30, and 2022; the high-order pre-alarm data is correspondingly encoded into 8-1, the low-order pre-alarm data is correspondingly encoded into 8-3, the amplitude high-order pre-alarm data is correspondingly encoded into 9-1, the amplitude low-order pre-alarm data is correspondingly encoded into 9-2, the X-angle high-order pre-alarm data is correspondingly encoded into 10-1, the Y-angle high-order pre-alarm data is correspondingly encoded into 10-5, the load high-order pre-alarm data is correspondingly encoded into 11-1, and the current collision pre-alarm data is correspondingly encoded into 14-1.
FIG. 3 is a schematic diagram showing an interface of tower monitoring data; FIG. 4 is a schematic diagram showing an interface of the tower attendance data; FIG. 5 is a schematic diagram of an interface for tower alarm data.
In the embodiment, the tower training data is data of on-line answering, education short video and platform assessment when 10-bit tower is connected with a network through WIFI.
FIG. 6 is an interface diagram of a training education duration of a tower receiving training education.
T2: preprocessing tower crane monitoring data and tower department training data;
t21: integrating the tower department attendance data and the tower department training data to construct a tower department information data set;
T22: adopting R software to correlate the tower department information data set and tower crane monitoring data;
t23: carrying out data cleaning on the correlated tower department information data set and tower crane monitoring data;
t231: screening data;
t232: removing invalid data, redundant data and singular values;
t24: and eliminating the tower alarm data caused by the influence of objective factors.
T3: based on the preprocessed tower crane monitoring data and tower department training data, constructing a tower department violation rate calculation model, and respectively calculating the violation rates of all tower departments;
in this example, a histogram of average daily violations over a selected period of 2022, 9, 1, to 2022, 9, 15 is shown in fig. 7.
T4: based on the violation rate of each tower department, carrying out statistical analysis by combining the monitoring data of the tower crane, and evaluating the operation behavior and the level of each tower department according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower department.
T41: adopting SPSS software to analyze and count influence factors which influence the violation rate of the tower department;
in this embodiment, the influencing factors that influence the rate of the tower violation include:
an operational period, comprising: white shift, night shift and night shift;
the working time length is 1-12 h;
Training education time length, which comprises 1-6 h;
other influencing factors, including whether to additionally install hook visualization, signal engineering command scheduling level and tower culture degree; whether the lifting hook is additionally arranged or not is visualized, including yes or no; the signal command scheduling level comprises 1 minute to 10 minutes; the culture degree of the tas comprises primary school, junior middle school and senior middle school.
T42: based on the statistical influence factors influencing the rate of the tower department violations, performing single factor sensitivity analysis by using Crystal Ball software to obtain key influence factors influencing the uncertainty of the rate of the tower department violations, selecting quantifiable key influence factors, and determining the association relation between each key influence factor and the rate of the tower department violations according to the principle of' first order;
t421: carrying out single factor sensitivity analysis on the tessellation rate by adopting a Monte Carlo sampling method;
t4211: carrying out mathematical statistics on influence factors influencing the rule breaking rate of the tassel;
t4212: establishing a data model conforming to the parameter distribution rule of each influence factor;
the tower culture degree, training education time, working time, whether the lifting hook is additionally arranged for visualization, and the signal engineering command dispatching level accords with the discrete distribution characteristics, and a discrete distribution data model is respectively constructed.
T4213: 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 a cumulative distribution curve of the violation rate of each tower;
in this embodiment, fig. 8 is a flow chart of a thought process for performing single factor sensitivity analysis, which is convenient for random sampling and grabbing by a computer.
Fig. 9a is a schematic diagram of the result of performing random sampling and grabbing 50 computations by adopting five iterations of 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, when the distribution range of influencing factors is input, sample points can fall on any position, so that samples are easily concentrated on positions with higher rule breaking rate of the tower, and according to the result of the embodiment, the problem of sample aggregation is generated due to fewer sampling times, and the representativeness of the sampling result is influenced.
Fig. 9b is a schematic diagram showing the result of performing random sampling and grabbing 50 calculations in combination with five iterations of the latin hypercube sampling method. After the Latin hypercube sampling method is introduced, the samples are uniformly distributed, and the sampling result is more representative.
T4214: fixing other influence factors, and quantitatively calculating the relative influence of each single influence factor on the tower department violation rate by combining the grabbing results of the computer so as to represent the influence degree of each single influence factor on the tower department violation rate;
in this example, a bar graph of the relative impact of individual influencing factors on the rate of tower violations is shown in FIG. 10. In the figure, whether the hanging hook is installed is visualized, the working period, the working time length, the training education time length, the signal command scheduling level, the relative influence of the culture degree of the tower department on the violation rate of the tower department is 95.45 percent, 60.52 percent,
59.27%, 48.98%, 42.23%, 41.06%, and the influence degree of each single influence factor on the rule-breaking rate of the tower sequentially comprises, from large to small: visualization of whether to install the lifting hook or not > working period > working time > training education time
The signal command scheduling level is more than the tower culture level. Whether the hook visualization is additionally arranged has great influence on the rate of the tower department violation, and the management layer can be prompted to install the adaptive hook visualization for the tower crane within the limited condition range so as to reduce the rate of the tower department violation; the signal engineering command scheduling level has a certain influence on the rate of the tower department violation, and prompts the management layer to strengthen the selection, education and cooperation with the tower department of signal engineering personnel; the tower culture degree has limited influence on the tower violation rate, is influenced by the service life of the tower, and can improve the reference when the management layer adopts the tower.
T422: the key influencing factors which can be quantified are selected, determining the association relation between each key influence factor and the rule violation rate of the tower according to the rule of' definite first proposal;
in the embodiment, three key influencing factors including quantifiable working time period, working time period and training education time period are selected; changing one key influence factor, fixing other influence factors to be the same or constant, and observing the change of the rule-breaking rate of the tower; by grasping the influence of key influence factors on the violation rate of the tower, the construction site can be improved in a targeted manner, and then the occurrence of potential safety hazards is avoided.
Wherein, FIG. 11 shows the relationship between the working period and the rate of the tower department violation, which shows the rate of the tower department violation of the 10-bit tower department in three different working periods of the shift, the shift and the night shift. Wherein the average violation rate of night shifts > the average violation rate of white shifts > the average violation rate of night shifts. As shown in fig. 12a, the data generated by the alarm condition occurring in the shift mainly includes front collision pre-alarm data, load high-order pre-alarm data and amplitude pre-alarm data; as shown in fig. 12b, the data generated by the alarm condition occurring in the late shift mainly includes load high-order pre-alarm data, amplitude pre-alarm data and height pre-alarm data; as shown in fig. 12c, the data generated by the alarm conditions in the night shift mainly comprises load high-level pre-alarm data, amplitude pre-alarm data and height pre-alarm data, and can be subjected to key supervision and management by a manager according to the distribution of the rule breaking rates of the towelettes in different time periods so as to reduce the average rule breaking rate of the towelettes.
Fig. 13 shows a relationship between a working time period and a tower violation rate, and shows the tower violation rate under different working time periods when a certain tower working time period is a white shift. The tower department violation rate and the working time length of each hour of the tower department under the working time period of the white shift are in a unitary quadratic function correlation relation, namely, the tower department violation rate is firstly reduced and then is increased along with the increase of the working time length, so that a manager is required to strengthen the field management force when the tower department starts to work and works in the near future, and the working state of the tower department is prompted, so that the average violation rate of the tower department is reduced.
Wherein, FIG. 14 shows the relationship between training education duration and the rate of tower violations, which shows the rate of tower violations at different training education durations received by the tower. The longer the time length of the tower department receives training education, the lower the rate of the tower department violation is, so that a manager is prompted to frequently train the tower department, the cognitive level of the tower department is improved, and the rate of the tower department violation is reduced.
T43: and calculating the average value and standard deviation of the violation rate of each tower, and quantifying by adopting a scatter diagram to evaluate the operation level of each tower.
In this example, as shown in fig. 15, the relationship between the mean value and the standard deviation of the tower violation rate is shown. Wherein, the larger the average value of the rule breaking rate of the tassel is, the lower the operation level of the tassel is; the smaller the average value of the violation rate of the tassel is, the higher the operation level of the tassel is; the larger the standard deviation of the violation rate of the tassel is, the worse the operation stability of the tassel is; the smaller the average value of the violation rate of the tassel is, the better the operation stability of the tassel is. The management layer can be prompted to carry out special training education on the tower department with larger average value of the violation rate of the tower department and larger standard deviation of the violation rate of the tower department, so that bad operation habits of the tower department are improved, and the operation level of the tower department is improved, so that the operation level of the tower department on a construction site is improved.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (8)
1. A method for evaluating the operational level of a tower crane based on a tower crane monitoring system, comprising:
s1: acquiring tower crane monitoring data and tower department training data;
s2: preprocessing tower crane monitoring data and tower department training data;
S3: based on the preprocessed tower crane monitoring data and tower department training data, constructing a tower department violation rate calculation model, and respectively calculating the violation rates of all tower departments;
s4: based on the violation rate of each tower department, carrying out statistical analysis by combining the monitoring data of the tower crane, evaluating the operation behavior and the level of each tower department according to the statistical analysis result, and simultaneously obtaining key influence factors influencing the violation rate of the tower department;
in the step S1, tower crane monitoring data are acquired through an intelligent management platform; the tower crane monitoring data comprises:
tower crane equipment number or equipment ID;
the tower department attendance data is obtained through a real-name attendance management system; the tower attendance data comprise a working period and a working time length;
the tower crane operation data comprises time points, amplitude, height, azimuth, crane weight, wind speed, moment, X inclination angle, Y inclination angle, alarm condition and braking state; the amplitude data is acquired by an amplitude sensor; the height data are acquired by a height sensor; the azimuth data are acquired by a rotation angle sensor; the hanging 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 an inclination angle sensor; the alarm condition is obtained through a tower crane black box and an anti-collision module, and the tower department alarm data of the alarm condition comprises five types, namely: the first is the high-level pre-warning data, which includes high-level pre-warning data and low-level pre-warning data; the second is amplitude pre-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 early warning data and Y-angle high-order early warning data; the fourth is the load high-order pre-warning data; the fifth is the current collision pre-alert data;
The hanging time detail data comprises a starting time, an ending time, a maximum hanging weight, a starting angle, a starting amplitude, a starting height, an ending angle, an ending height and an ending amplitude;
in the step S3, in the tower regulation violation rate calculation model, the calculation formula of the average regulation violation rate of the mth tower is as follows:
wherein F is m Average violation rate of mth tower, F mn The average violation rate of the alarm data of the nth tower crane appears for the mth tower department;
the calculation formula of the average violation rate of the alarm data of the nth tower crane in the mth tower department is as follows:
wherein S is mn The working circulation quantity T of the alarm data of the nth tower crane appears for the mth tower department m All work circulation amounts for the mth tower; wherein m is E [1, M],n∈[1,5]M is the total amount of the tassel.
2. The method for evaluating the operation level of a tower crane based on the monitoring system of claim 1, wherein in S1, the training data of the tower crane is obtained through the derivation of a training system; the tower department training data comprises training education duration of the tower department receiving training education, the culture degree of the tower department and the working life of the tower department.
3. The method for evaluating the operation level of a tower crane based on the tower crane monitoring system according to claim 2, wherein in S2, the process of preprocessing the tower crane monitoring data, the tower crane training data and the tower crane alarm data specifically comprises:
S21: integrating the tower department attendance data and the tower department training data to construct a tower department information data set;
s22: adopting R software to correlate the tower department information data set and tower crane monitoring data;
s23: carrying out data cleaning on the correlated tower department information data set and tower crane monitoring data;
s231: screening data;
s232: removing invalid data, redundant data and singular values;
s24: and eliminating the tower alarm data caused by the influence of objective factors.
4. The method for evaluating the operation level of a tower crane based on the monitoring system of claim 1, wherein in S4, the statistical analysis process specifically includes:
s41: adopting SPSS software to analyze and count influence factors which influence the violation rate of the tower department;
s42: based on the statistical influence factors influencing the rate of the tower department violations, a Monte Carlo sampling method is adopted, crystal Ball software is utilized to conduct single factor sensitivity analysis so as to obtain key influence factors influencing the uncertainty of the rate of the tower department violations, quantifiable key influence factors are selected, and the association relation between each key influence factor and the rate of the tower department violations is determined according to the principle of 'first order';
s43: and calculating the average value and standard deviation of the violation rate of each tower, and quantifying by adopting a scatter diagram to evaluate the operation level of each tower.
5. The method for evaluating the operation level of a tower crane based on the monitoring system of claim 4, wherein in S41, the influencing factors influencing the rate of violations of the tower crane include:
an operating period;
the working time length;
training education time;
other influencing factors include whether to add hook visualization, signaling engineering command scheduling level and tower culture level.
6. The method for evaluating the operation level of a tower crane based on the monitoring system of claim 5, wherein in S42, the process of performing the single factor sensitivity analysis on the rate of violations of the tower crane by using the monte carlo sampling method specifically comprises:
s421: carrying out mathematical statistics on influence factors influencing the rule breaking rate of the tassel;
s422: establishing a data model conforming to 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 a cumulative distribution curve of the violation rate of each tower;
s424: and fixing other influence factors, and quantitatively calculating the relative influence of each single influence factor on the tower violation rate by combining the grabbing results of the computer so as to characterize the influence degree of each single influence factor on the tower violation rate.
7. The method for evaluating the operation level of a tower crane based on the monitoring system of claim 6, wherein in S423, the calculation formula for grabbing by a computer is as follows:
f=p (working period |working duration|training education duration|other influencing factors)
Wherein F is the rate of tower violation; p is the probability of randomly sampling the work period, work duration, training education duration, other influencing factors.
8. The method for evaluating the operation level of a tower crane based on the monitoring system of claim 6, wherein in S424, a calculation formula for quantitatively calculating the relative influence of a single influencing factor on the rate of violations of the tower crane is:
in the formula, the minimum value of the rule breaking rate of the tassel is the minimum value of the rule breaking rate of the tassel in all grabbing results of the computer; the middle value of the tower rule-breaking rate is the middle value of the tower rule-breaking rate in all grabbing results of the computer; the maximum value of the tower rule breaking rate is the maximum value of the tower rule breaking rate in all grabbing results of the computer.
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| 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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| 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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