CN113537729B - Wisdom construction safety control system - Google Patents

Wisdom construction safety control system Download PDF

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CN113537729B
CN113537729B CN202110706622.6A CN202110706622A CN113537729B CN 113537729 B CN113537729 B CN 113537729B CN 202110706622 A CN202110706622 A CN 202110706622A CN 113537729 B CN113537729 B CN 113537729B
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discrete point
empirical
safety
construction safety
scoring
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CN113537729A (en
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刘淼
唐明明
王浩
吕培印
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BEIJING AGILETECH ENGINEERING CONSULTANTS CO LTD
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BEIJING AGILETECH ENGINEERING CONSULTANTS CO LTD
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/16—Real estate
    • G06Q50/165—Land development
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G06Q50/265—Personal security, identity or safety
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The invention discloses an intelligent construction safety control system, which is characterized in that: the construction safety data intelligent evaluation layer comprises a comprehensive safety target setting module, an experience discrete point conversion target function module, a weighted summation scoring module and a safety alarm module; the comprehensive safety target setting module is used for carrying out safety intelligent evaluation on the four aspects of people, machines, rings and pipes as a whole; the experience discrete point conversion target function module is used for converting expert experience discrete points of people, machines, rings and pipes into continuous functions, so that the multi-element heterogeneous data of the people, the machines, the rings and the pipes are unified to the same dimension; the weighted summation scoring module realizes the overall safe scoring of successive recursion of the tree structure by people, machines, rings and pipes through a weighted summation method; the invention adopts the tree structure of converting empirical discrete points into a target function and grading work points to realize the quantitative evaluation of the overall safety state of the construction site.

Description

Wisdom construction safety control system
Technical Field
The invention belongs to the technical field of building construction, and particularly relates to an intelligent construction safety control system.
Background
Along with the popularization and application of the internet of things technology, building construction gradually enters a digital stage, and various intelligent construction site safety supervision platforms are developed vigorously. However, the current platform is only limited to monitoring and early warning of single monitoring data, lacks deeper data analysis and utilization, and has no quantitative evaluation standard and method for the overall safety state of the construction site.
The current platform is only limited to monitoring and early warning of single monitoring data, and the problems exist: the analysis and determination of the safety state is very one-sided. Because the factors influencing the construction safety are multi-layer, multi-branch and multi-node, not only the first layer, but also the second layer and the third layer are provided; not only the multilayer, but also the inside of each layer can have a plurality of branches, and a plurality of nodes can be arranged under each branch, so that a plurality of factors of the safety problem are like a tree structure. A single data item can compare with a luxurious tree? Obviously, single monitoring and early warning and multiple monitoring and early warning based on a tree structure are completely negligible.
The current platform lacks deeper data analysis and utilization, and has the following problems: the expert experience gives an early warning value instead of a safety score, the safety score is description of the current situation, including the current management situation, the current equipment situation and the current environment situation, the safety score can play a role in preventing, correcting and punishing, and the early warning value cannot describe the current safety situation.
The current platform can not carry out comprehensive monitoring and early warning to the factors influencing safety of a construction site and carry out deeper data analysis and utilization, and the difficulty lies in that: the factors influencing the safety are multi-dimensional, and due to the fact that the dimensions are not uniform, weighting summation cannot be carried out, and comprehensive monitoring and early warning cannot be carried out, and deeper data analysis and utilization cannot be carried out. Only one evaluation method can be adopted for each dimension, and the process is relatively chaotic. For example, a safety score is calculated from the age of the operating worker, which is a dimension; calculating a safety score according to the concentration of harmful gases in the working environment, which is a dimension; the safety score is calculated according to the wind speed of a construction site, which is a dimension, and because things of different dimensions cannot be quantitatively described together, the quantitative description that the prior art can only be limited to monitoring and early warning of single monitoring data and cannot integrate various factors together for overall construction safety is formed.
Disclosure of Invention
The invention provides an intelligent construction safety control system for solving the problems in the prior art, and aims to solve the problems that the existing platform is only limited to monitoring and early warning of single monitoring data, deeper data analysis and utilization are lacked, and quantitative evaluation standards and methods for the overall safety state of a construction site are not available.
In order to solve the technical problem, the invention adopts the following technical scheme:
an intelligent construction safety control system comprises a construction safety data acquisition layer, a construction safety data intelligent evaluation layer and a construction safety data application layer; the construction safety data acquisition layer uploads the acquired data to the construction safety data intelligent evaluation layer through a sensor or a computer terminal; the construction safety data intelligent evaluation layer transmits an intelligent evaluation result to the construction safety data application layer; the construction safety data application layer sends the scoring results of the construction safety data intelligent evaluation layer to managers at all levels through a computer terminal; the managers at all levels perform related improvement on the construction safety according to the grading result, and upload the acquired data to the construction safety data intelligent evaluation layer again through a sensor or a computer terminal of the construction safety data acquisition layer according to the related improvement result on the construction safety, so that closed-loop feedback is formed;
the method is characterized in that: the intelligent evaluation layer for the construction safety data comprises a comprehensive safety target setting module, an experience discrete point conversion target function module, a weighted summation scoring module and a safety alarm module; the comprehensive safety target setting module is used for carrying out safety intelligent evaluation on the four aspects of people, machines, rings and pipes as a whole; the experience discrete point conversion target function module is used for converting expert experience discrete points of people, machines, rings and pipes into continuous functions, so that the multi-element heterogeneous data of the people, the machines, the rings and the pipes are unified to the same dimension; the weighted summation scoring module realizes the overall safe scoring of successive recursion of the tree structure by people, machines, rings and pipes through a weighted summation method;
the comprehensive safety target setting module of the construction safety data intelligent evaluation layer divides four aspects of human, machine, ring and pipe into multiple stages, multiple branches and multiple nodes for management; the multiple stages comprise a first stage, a second stage and a third stage; the multi-branch is that four branches of personnel management and control, operation environment, mechanical equipment and safety management are divided in the first stage; the said multinode, it is a plurality of nodes of subdividing again in the second level, every node is regarded as the father node of the next level of child node, including: basic item nodes and unsafe behavior nodes of personnel management and control branches; an environment monitoring node and a fire node of the operation environment branch; the mechanical equipment is divided into an excavator, a battery truck, a shield machine, a bridge crane, a tower crane and a muck truck node; an information processing node of the security management branch.
The experience discrete point conversion module comprises experience discrete point fitting functions in four aspects of personnel management and control, working environment, mechanical equipment and safety management; the experience discrete point fitting function in the aspect of personnel management and control comprises experience discrete point fitting function modules of continuous operation, education training, hidden danger association scores, basic items and unsafe behaviors respectively; the empirical discrete point fitting function in the aspect of the operating environment respectively comprises empirical discrete point fitting function modules of environment monitoring, engineering monitoring and fire monitoring; the empirical discrete point fitting functions in the aspect of mechanical equipment respectively comprise empirical discrete point fitting function modules of an excavator, a battery car, a shield machine, a bridge/tower crane and a muck truck; the empirical discrete point fitting functions in the aspect of safety management respectively comprise login frequency, manager proportion, information processing and unit hidden danger empirical discrete point fitting functions.
The empirical discrete point fitting functions of the basic item nodes respectively comprise empirical discrete point fitting functions of work age, cultural degree, work risk and certificate effectiveness; the empirical discrete point fitting functions of the unsafe behavior nodes respectively comprise empirical discrete point fitting functions of safety helmets, reflective clothes, smoking, dangerous area staying and quasi-entering area staying; the empirical discrete point fitting functions of the environmental monitoring respectively comprise empirical discrete point fitting functions of wind speed, dust, harmful gas, temperature, humidity and noise; the fire experience discrete point fitting functions respectively comprise the experience discrete point fitting functions of electrical fire, smoke detection and fire control inspection; the empirical discrete point fitting functions of the excavator respectively comprise empirical discrete point fitting functions of maintenance and delivery time; the empirical discrete point fitting functions of the battery car respectively comprise empirical discrete point fitting functions of overspeed and sliding; the empirical discrete point fitting functions of the shield tunneling machine respectively comprise empirical discrete point fitting functions of tunneling speed, soil bin pressure, tunneling attitude and slag discharge amount; the empirical discrete point fitting function of the bridge crane comprises empirical discrete point fitting functions of non-driver operation, dangerous operation and maintenance; the empirical discrete point fitting functions of the information processing respectively comprise the empirical discrete point fitting functions of response timeliness and closed-loop management.
The weighted summation scoring module comprises a same-layer weight calculation module, a fitting function score module and a work point score calculation module; the same-layer weight calculation comprises weight calculation of similar child nodes at the same level, weight calculation of similar nodes at the same level and weight calculation of similar branches at the same level; the same class at the same level is each child node, or each branch with the same parent item at the same level; the calculation of the fitting function score comprises the calculation of a fitting function of the same-level subnodes, the calculation of a fitting function of the same-level nodes and the calculation of a fitting function of the same-level branches; the work point scoring calculation comprises weighted summation of the same-level same-class child nodes, weighted summation of the same-level same-class nodes and weighted summation of the same-level same-class branches.
The same-layer weight calculation formula is as follows:
Figure BDA0003131544850000041
equation 6-1 is equivalent to the following equations
Figure BDA0003131544850000051
Figure BDA0003131544850000052
…
Figure BDA0003131544850000053
The work point scoring calculation formula is
G＝QiYi (6-2)
Equation 6-2 is equivalent to this equation
G＝Q1Y1+Q2Y2+…+QnYn
Y1、Y2、YNFitting function scores corresponding to similar child nodes, nodes and branches at the same level;
the construction safety data acquisition layer comprises personnel management and control data acquisition, operation environment data acquisition, mechanical equipment data acquisition and safety management data acquisition; the personnel management and control data acquisition comprises continuous operation time, education training scores, hidden danger association scores, basic item data and unsafe behavior data which are input and acquired through a computer terminal; the working environment data acquisition comprises the steps of acquiring wind speed, dust, harmful gas, temperature, humidity and noise data, engineering detection and fire information through a sensor; the mechanical equipment data acquisition comprises excavator maintenance and delivery time information, battery car overspeed and sliding information, shield tunneling machine tunneling speed, soil cabin pressure, tunneling attitude and slag discharge amount information and non-driver operation, dangerous operation and maintenance information of the bridge/tower crane, which are input through a sensor and a computer terminal; the safety management data acquisition comprises login frequency, manager proportion, information processing response timeliness, closed-loop management information and unit hidden danger information.
The grading results of the construction safety data intelligent evaluation layer comprise personnel management and control evaluation grading results, working environment evaluation grading results, mechanical equipment evaluation grading results and safety management evaluation grading results which are sent to managers at all levels; the personnel management and control evaluation scoring results comprise continuous operation time, education training, potential hazard association, basic items and safety scoring of unsafe behaviors; the operation environment evaluation scoring results comprise environment monitoring, engineering monitoring and fire safety scoring; the mechanical equipment evaluation scoring results comprise safety scoring of an excavator, a battery truck, a shield machine, a bridge crane, a tower crane and a muck truck; the safety management evaluation scoring result comprises login frequency, manager proportion, information processing and safety scoring of unit hidden danger.
Advantageous effects of the invention
1. The invention adopts a method of converting empirical discrete points into a target function, solves the problem of unified dimension of multi-element heterogeneous data such as personnel, equipment, environment, management and the like, solves the bottleneck problem of multi-element heterogeneous normalization by integrating expert scoring results and drawing a scatter diagram, selecting a proper fitting function according to the configuration of the scatter diagram, fitting by using a mathematical tool to obtain a function coefficient, and weighing summation and data test and inspection, and lays a solid foundation for quantitative analysis of the overall safety state of a construction site.
2. The invention adopts a tree structure based on work point scoring, and realizes the quantitative evaluation of the overall safety state of the construction site: by adopting a weighted summation-based work point scoring method, the grade-by-grade score recursion from a tip to a node, from the node to a branch and from the branch to a root is realized; making the scores of the tip and the node support each other, the scores of the node and the branch support each other, and the scores of the branch and the root support each other; after combination, a new effect of comprehensive safety scoring is achieved, compared with the effect of single scoring in the prior art, the effect is much more accurate and superior, and the method has prominent substantive characteristics and remarkable progress.
Drawings
FIG. 1 is a three-layer architecture diagram of the intelligent construction safety control system of the present invention;
FIG. 2 is a structural diagram of an intelligent evaluation layer of construction safety data according to the present invention;
FIG. 3 is a schematic diagram of the overall security target setting of the present invention;
FIG. 4 is a block diagram of a weighted sum scoring module according to the present invention;
FIG. 5-1 is a shield tunneling machine tunneling speed function configuration according to the present invention;
FIG. 5-2 is a shield tunneling machine earth reservoir pressure function configuration of the present invention;
5-3 are the tunneling attitude function configuration of the shield tunneling machine of the present invention;
5-4 are the configuration of the function of the slag output of the shield machine of the invention;
FIG. 6 is a construction safety data acquisition layer structure view according to the present invention;
fig. 7 is a construction safety data application layer structure diagram according to the present invention.
Detailed Description
Design principle of the invention
The intelligent engineering safety control system is used for performing deep integration analysis on various monitoring data of the intelligent construction site platform, converting expert practical experience into an evaluation standard system which can be identified by a computer, and forming the intelligent engineering safety control system which can be applied to the ground. The innovation is mainly represented in the following two aspects:
1. and solving the multi-element heterogeneous problem of the security score by adopting a normalization technology. The invention adopts a normalization method, which is divided into two steps, wherein in the first step, data of different dimensions of personnel, equipment, environment and management are uniformly expressed by experience discrete points, and the data of different dimensions are uniformly expressed to the same dimension form of the experience discrete points by the mode of the experience discrete points; and secondly, converting the expert experience into a mathematical formula. Aiming at each acquired monitoring data, the empirical discrete points given by expert investigation are only the scoring values of individual points, have discreteness, are not beneficial to direct use of a computer, and discrete expert investigation results must be converted into continuous mathematical functions through a mathematical method. The invention mainly adopts a least square fitting method, which comprises the steps of integrating expert scoring results and drawing a scatter diagram, selecting a proper fitting function according to the configuration of the scatter diagram, fitting by using a mathematical tool to obtain a function coefficient, weighting summation, and data test and inspection.
2. And solving the overall quantitative evaluation problem by adopting a tree structure based on work point grading. The invention adopts a weighted summation method to recur scores layer by layer from the tip to the root, and obtains a new effect after combination: recursion is carried out from the tip to a node, and the score of the node adopts a weighted summation method; recursion is carried out from the node to a branch, and the fraction of the branch adopts a weighted summation method; the branch is pushed to the root, and the fraction of the root adopts a weighted sum method; the method comprises the following steps of mutual support of a tip and a node, mutual support of a node and a branch, and mutual support of a branch and a root; the root is the intelligent construction safety total score of the construction unit; the branches are four branches of personnel management and control, working environment, mechanical equipment and safety management; the nodes are two nodes of a personnel management and control branch, two nodes of a working environment branch, five nodes of a mechanical equipment branch and one node of a safety management branch, and for the nodes without ends, the nodes are the ends of the branches of the upper layer. The invention applies the weighted summation technology to the layer-by-layer recursive scoring of the tree structure, and solves the problem that the conventional platform has no quantitative evaluation standard and method for the overall safety state of the construction site.
Based on the principle of the invention, the invention designs an intelligent construction safety control system,
an intelligent construction safety control system is shown in figure 1 and comprises a construction safety data acquisition layer, a construction safety data intelligent evaluation layer and a construction safety data application layer; the construction safety data acquisition layer uploads the acquired data to the construction safety data intelligent evaluation layer through a sensor or a computer terminal; the construction safety data intelligent evaluation layer transmits an intelligent evaluation result to the construction safety data application layer; the construction safety data application layer sends the scoring results of the construction safety data intelligent evaluation layer to managers at all levels through a computer terminal; the managers at all levels perform construction safety related improvement according to the grading result, and upload the acquired data to the construction safety data intelligent evaluation layer again through a sensor or a computer terminal of the construction safety data acquisition layer according to the construction safety related improvement result, so that closed-loop feedback is formed;
the intelligent evaluation layer of the construction safety data is shown in FIG. 2 and comprises a comprehensive safety target setting module, an experience discrete point conversion target function module, a weighted summation scoring module and a safety alarm module; the comprehensive safety target setting module is used for performing safety intelligent evaluation on four aspects of people, machines, rings and pipes as a whole; the experience discrete point conversion target function module is used for converting expert experience discrete points of people, machines, rings and pipes into continuous functions, so that the multi-element heterogeneous data of the people, the machines, the rings and the pipes are unified to the same dimension; the weighted summation scoring module realizes the overall safe scoring of successive recursion of the tree structure by people, machines, rings and pipes through a weighted summation method;
the comprehensive security target setting module, as shown in fig. 3, includes that four aspects of people, machines, rings and pipes are divided into multiple levels, multiple branches and multiple nodes for management; the multiple stages comprise a first stage, a second stage and a third stage; the multi-branch is that four branches of personnel management and control, operation environment, mechanical equipment and safety management are divided in the first stage; the said multinode, it is a plurality of nodes of subdividing again in the second level, every node is regarded as the father node of the next level of child node, including: basic item nodes and unsafe behavior nodes of personnel management and control branches; an environment monitoring node and a fire node of the operation environment branch; the mechanical equipment is divided into an excavator, a battery truck, a shield machine, a bridge crane, a tower crane and a muck truck node; an information processing node of the security management branch.
The module for converting the experience discrete points into the target function is shown in fig. 3 and respectively comprises an experience discrete point fitting function in four aspects of personnel management and control, an operating environment, mechanical equipment and safety management; the experience discrete point fitting function in the aspect of personnel management and control comprises experience discrete point fitting function modules of continuous operation, education training, hidden danger association scores, basic items and unsafe behaviors respectively; the empirical discrete point fitting function in the aspect of the operating environment respectively comprises empirical discrete point fitting function modules of environment monitoring, engineering monitoring and fire monitoring; the empirical discrete point fitting functions in the aspect of mechanical equipment respectively comprise empirical discrete point fitting function modules of an excavator, a battery car, a shield machine, a bridge/tower crane and a muck truck; the empirical discrete point fitting functions in the aspect of safety management respectively comprise login frequency, manager proportion, information processing and unit hidden danger empirical discrete point fitting functions.
As shown in fig. 3, the empirical discrete point fitting functions of the basic term nodes respectively include empirical discrete point fitting functions of work age, cultural degree, work risk, and certificate validity; the empirical discrete point fitting functions of the unsafe behavior nodes respectively comprise empirical discrete point fitting functions of safety helmets, reflective clothes, smoking, dangerous area staying and quasi-entering area staying; the empirical discrete point fitting functions of the environmental monitoring respectively comprise empirical discrete point fitting functions of wind speed, dust, harmful gas, temperature, humidity and noise; the fire experience discrete point fitting functions respectively comprise the experience discrete point fitting functions of electrical fire, smoke detection and fire control inspection; the empirical discrete point fitting functions of the excavator respectively comprise empirical discrete point fitting functions of maintenance and delivery time; the empirical discrete point fitting functions of the battery car respectively comprise empirical discrete point fitting functions of overspeed and sliding; the empirical discrete point fitting functions of the shield tunneling machine respectively comprise empirical discrete point fitting functions of tunneling speed, soil bin pressure, tunneling attitude and slag discharge amount; the empirical discrete point fitting function of the bridge crane comprises empirical discrete point fitting functions of non-driver operation, dangerous operation and maintenance; the empirical discrete point fitting functions of the information processing respectively comprise the empirical discrete point fitting functions of response timeliness and closed-loop management.
The weighted summation scoring module, as shown in fig. 4, includes a same-layer weight calculation module, a fitting function score module, and a work point score calculation module; the same-layer weight calculation comprises weight calculation of similar child nodes at the same level, weight calculation of similar nodes at the same level and weight calculation of similar branches at the same level; the same class at the same level is each child node, or each branch with the same parent item at the same level; the calculation of the fitting function score comprises the calculation of a fitting function of the same-level subnodes, the calculation of a fitting function of the same-level nodes and the calculation of a fitting function of the same-level branches; the work point scoring calculation comprises weighted summation of the same-level same-class child nodes, weighted summation of the same-level same-class nodes and weighted summation of the same-level same-class branches.
The same-layer weight calculation formula is as follows:
Figure BDA0003131544850000101
equation 6-1 is equivalent to the following equations
Figure BDA0003131544850000111
Figure BDA0003131544850000112
…
Figure BDA0003131544850000113
The work point scoring calculation formula is
G＝QiYi (6-2)
Equation 6-2 is equivalent to this equation
G＝Q1Y1+Q2Y2+…+QnYn
The fitting function scores of the same-level child nodes, nodes and branches corresponding to Y1, Y2 and YN are obtained;
the construction safety data acquisition layer comprises personnel management and control data acquisition, operation environment data acquisition, mechanical equipment data acquisition and safety management data acquisition as shown in fig. 6; the personnel management and control data acquisition comprises continuous operation time, education and training scores, hidden danger association scores, basic item data and unsafe behavior data which are input and acquired through a computer terminal; the working environment data acquisition comprises the steps of acquiring wind speed, dust, harmful gas, temperature, humidity and noise data, engineering detection and fire information through a sensor; the mechanical equipment data acquisition comprises excavator maintenance and delivery time information, battery car overspeed and sliding information, shield tunneling machine tunneling speed, soil cabin pressure, tunneling attitude and slag discharge amount information and non-driver operation, dangerous operation and maintenance information of the bridge/tower crane, which are input through a sensor and a computer terminal; the safety management data acquisition comprises login frequency, manager proportion, information processing response timeliness, closed-loop management information and unit hidden danger information.
The scoring results of the construction safety data intelligent evaluation layer are shown in fig. 7 and comprise personnel management and control evaluation scoring results, operation environment evaluation scoring results, mechanical equipment evaluation scoring results and safety management evaluation scoring results which are sent to managers at all levels; the personnel management and control evaluation scoring results comprise continuous operation time, education training, potential hazard association, basic items and safety scoring of unsafe behaviors; the operation environment evaluation scoring results comprise environment monitoring, engineering monitoring and fire safety scoring; the mechanical equipment evaluation scoring results comprise safety scoring of an excavator, a battery truck, a shield machine, a bridge crane, a tower crane and a muck truck; the safety management evaluation scoring results comprise login frequency, manager proportion, information processing and safety scoring of unit hidden dangers.
The first embodiment is as follows: work point scoring of shield machine
1) Respectively calculating empirical discrete point fitting functions of tunneling speed, soil bin pressure, tunneling attitude and slag discharge quantity of the shield tunneling machine Number of
Tunneling speed
Calculated as the ratio of the tunnelling speed to the allowable speed, the y value is calculated as follows:
Figure BDA0003131544850000121
wherein y0 is 107.01412; -6.96685; t-0.50877.
x is calculated as follows:
Figure BDA0003131544850000122
1) wherein v represents a tunneling speed (mm/min), and vs represents an allowable maximum tunneling speed (mm/min).
2) The function configuration is as shown in FIG. 5-1
Pressure of the soil bin
Calculated as the ratio of the average soil pressure (8 azimuth soil pressure average, unit bar) to the horizontal ground stress, the y value is calculated as follows:
y2＝Ax2+Bx+C
wherein, a ═ -390.20979; b-785.32867; c-297.91608.
x can be roughly estimated as follows:
Figure BDA0003131544850000131
wherein the content of the first and second substances,
Figure BDA0003131544850000132
the average earth pressure (bar) is shown, and h is the shield axis burial depth (m).
The functional configuration is shown in FIG. 5-2;
tunneling attitude
Calculating by the ratio of the attitude deviation and the tolerance deviation of the front end of the shield, and calculating the y value as follows:
Figure BDA0003131544850000133
wherein y0 is 107.82225; -7.90273; t-0.55547.
The values of x are as follows:
Figure BDA0003131544850000134
U＝u+5ut
V＝v+5vt
wherein u and v represent a front end horizontal deviation and a vertical deviation (mm), respectively; ut, vt represent horizontal and vertical trends (mm/m), respectively.
The functional configuration 5-3;
amount of slag discharged
And calculating according to the ratio of the actual slag output amount to the theoretical slag output amount, wherein the y value is calculated as follows:
y4＝Ax2+Bx+C
wherein, a ═ -588.09524; b-1176.19048; c-488.66667.
x can be roughly estimated as follows:
Figure BDA0003131544850000135
where V denotes an actual slag discharge amount (m3), and L denotes a corresponding excavation length (m).
The functional configuration is shown in FIGS. 5-4;
2) respectively calculating the tunneling speed, the soil bin pressure, the tunneling attitude and the weight of the slag discharge of the shield tunneling machine
As shown in fig. 3, the tunneling speed, the soil bin pressure, the tunneling attitude and the slag discharge weight are respectively 5, 9, 3 and 6; weight Q of tunneling speed15/23; weight Q of pressure slag discharge of soil bin29/23; weight Q of slag discharge in tunneling attitude33/23; weight Q of slag discharge4＝6/23；
3)Work point scoring calculation of shield machine
G＝Q1Y1+ Q2 Y2+ Q3 Y3+ Q4 Y4
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.

Claims (6)

1. An intelligent construction safety control system comprises a construction safety data acquisition layer, a construction safety data intelligent evaluation layer and a construction safety data application layer; the construction safety data acquisition layer uploads the acquired data to the construction safety data intelligent evaluation layer through a sensor or a computer terminal; the construction safety data intelligent evaluation layer transmits an intelligent evaluation result to the construction safety data application layer; the construction safety data application layer sends the scoring results of the construction safety data intelligent evaluation layer to managers at all levels through a computer terminal; the managers at all levels perform construction safety related improvement according to the grading result, and upload the acquired data to the construction safety data intelligent evaluation layer again through a sensor or a computer terminal of the construction safety data acquisition layer according to the construction safety related improvement result, so that closed-loop feedback is formed;
the method is characterized in that: the intelligent evaluation layer for the construction safety data comprises a comprehensive safety target setting module, an experience discrete point conversion target function module, a weighted summation scoring module and a safety alarm module; the comprehensive safety target setting module is used for carrying out safety intelligent evaluation on the four aspects of people, machines, rings and pipes as a whole;
the comprehensive safety target setting module of the construction safety data intelligent evaluation layer divides four aspects of people, machines, rings and pipes into multiple stages, multiple branches and multiple nodes for management; the multiple stages comprise a first stage, a second stage and a third stage; the multi-branch is that four branches of personnel management and control, operation environment, mechanical equipment and safety management are divided in the first stage; the said multinode, it is a plurality of nodes of subdividing again in the second level, every node is regarded as the father node of the next level of child node, including: basic item nodes and unsafe behavior nodes of personnel management and control branches; an environment monitoring node and a fire node of the operation environment branch; the mechanical equipment is divided into an excavator, a battery truck, a shield machine, a bridge crane, a tower crane and a muck truck node; an information processing node of a security management branch;
the system comprises an expert discrete point conversion module, a target function conversion module, a function parameter analysis module and a function parameter analysis module, wherein the experience discrete point conversion module is used for converting expert experience discrete points of people, machines, rings and pipes into continuous functions respectively, so that multiple heterogeneous data of the people, the machines, the rings and the pipes are unified under the same dimension, the expert experience is converted into a mathematical formula, a discrete expert research result is converted into a continuous mathematical function through a mathematical method, and a least square fitting method is adopted, and the process comprises the steps of integrating expert scoring results and drawing a scatter diagram, selecting a proper fitting function according to the configuration of the scatter diagram, and fitting by adopting a mathematical tool to obtain a function coefficient; the weighted summation scoring module realizes the overall safe scoring of successive recursion of the tree structure by the scoring results of people, machines, rings and pipes through a weighted summation method;
the experience discrete point is converted into a target function module which comprises an experience discrete point fitting function in four aspects of personnel management and control, working environment, mechanical equipment and safety management; the experience discrete point fitting function in the personnel management and control aspect comprises an experience discrete point fitting function module for continuous operation, education training, hidden danger association scores, basic items and unsafe behaviors; the empirical discrete point fitting function in the aspect of the operating environment comprises empirical discrete point fitting function modules of environment monitoring, engineering monitoring and fire monitoring; the empirical discrete point fitting function of the mechanical equipment aspect comprisesDigging machineA battery car,Shield machineAn empirical discrete point fitting function module of the bridge type/tower type crane and the slag car; the empirical discrete point fitting function in the aspect of safety management comprises a log-in frequency, a manager proportion, information processing and a unit hidden danger empirical discrete point fitting function;
the empirical discrete point fitting function of the environment monitoring, the fire monitoring, the storage battery car and the shield machine comprises the following steps: wind speed, dust, harmful gas, temperature, humidity, noise, electrical fire, smoke sensation, overspeed, vehicle sliding, tunneling speed, soil bin pressure, tunneling attitude and slag discharge quantity fitting function.
2. The intelligent construction safety control system of claim 1, wherein: the empirical discrete point fitting functions of the basic item nodes respectively comprise empirical discrete point fitting functions of work age, cultural degree, work risk and certificate effectiveness; the empirical discrete point fitting functions of the unsafe behavior nodes respectively comprise empirical discrete point fitting functions of safety helmets, reflective clothes, smoking, dangerous area staying and quasi-entering area staying; the empirical discrete point fitting functions of the environmental monitoring respectively comprise empirical discrete point fitting functions of wind speed, dust, harmful gas, temperature, humidity and noise; the empirical discrete point fitting functions of fire monitoring respectively comprise empirical discrete point fitting functions of electrical fire, smoke detection and fire inspection; the empirical discrete point fitting functions of the excavator respectively comprise empirical discrete point fitting functions of maintenance and delivery time; the empirical discrete point fitting functions of the battery car respectively comprise empirical discrete point fitting functions of overspeed and sliding; the empirical discrete point fitting functions of the shield tunneling machine respectively comprise empirical discrete point fitting functions of tunneling speed, soil bin pressure, tunneling attitude and slag discharge amount; the empirical discrete point fitting function of the bridge crane comprises empirical discrete point fitting functions of non-driver operation, dangerous operation and maintenance; the empirical discrete point fitting functions of the information processing respectively comprise the empirical discrete point fitting functions of response timeliness and closed-loop management.
3. The intelligent construction safety control system of claim 1, wherein: the weighted summation scoring module comprises a same-layer weight calculation module, a fitting function score module and a work point score calculation module; the same-layer weight calculation comprises weight calculation of similar child nodes at the same level, weight calculation of similar nodes at the same level and weight calculation of similar branches at the same level; the calculation of the fitting function score comprises the calculation of a fitting function of the same-level subnodes, the calculation of a fitting function of the same-level nodes and the calculation of a fitting function of the same-level branches; the work point scoring calculation comprises weighted summation of the same-level same-class child nodes, weighted summation of the same-level same-class nodes and weighted summation of the same-level same-class branches.
4. The intelligent construction safety control system of claim 3, wherein:
the same-layer weight calculation formula is as follows:
Figure 362881DEST_PATH_IMAGE001
（6-1）
equation 6-1 is equivalent to the following equations
Figure 325338DEST_PATH_IMAGE002
Figure 720547DEST_PATH_IMAGE003
Figure 534919DEST_PATH_IMAGE004
The work point scoring calculation formula is
Figure DEST_PATH_IMAGE002
（6-2）
Equation 6-2 is equivalent to this equation
Figure 354474DEST_PATH_IMAGE006
Y1、Y2、YnFitting function scores corresponding to similar child nodes, nodes and branches in the same level.
5. The intelligent construction safety control system of claim 1, wherein: the construction safety data acquisition layer comprises personnel management and control data acquisition, operation environment data acquisition, mechanical equipment data acquisition and safety management data acquisition; the personnel management and control data acquisition comprises continuous operation time, education training scores, hidden danger association scores, basic item data and unsafe behavior data which are input and acquired through a computer terminal; the working environment data acquisition comprises the steps of acquiring wind speed, dust, harmful gas, temperature, humidity and noise data, engineering detection and fire information through a sensor; the mechanical equipment data acquisition comprises excavator maintenance and delivery time information, battery car overspeed and sliding information, tunneling speed, soil bin pressure, tunneling attitude and slag discharge amount information of the shield tunneling machine, and non-driver operation, dangerous operation and maintenance information of the bridge/tower crane, which are acquired by a sensor or input by a computer terminal; the safety management data acquisition comprises login frequency, manager proportion, information processing response timeliness, closed-loop management information and unit hidden danger information.
6. The intelligent construction safety control system of claim 1, wherein: the scoring results of the construction safety data intelligent evaluation layer comprise personnel management and control evaluation scoring results, operation environment evaluation scoring results, mechanical equipment evaluation scoring results and safety management evaluation scoring results, and the scoring results are sent to managers at all levels through computer terminals; the personnel management and control evaluation scoring results comprise continuous operation time, education training, potential hazard association, basic items and safety scoring of unsafe behaviors; the operation environment evaluation scoring results comprise safety scoring of environment monitoring, engineering monitoring and fire monitoring; the mechanical equipment evaluation scoring results comprise safety scoring of an excavator, a battery truck, a shield machine, a bridge crane, a tower crane and a muck truck; the safety management evaluation scoring result comprises login frequency, manager proportion, information processing and safety scoring of unit hidden danger.
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