CN117172542A - Big data-based construction site inspection management system - Google Patents
Big data-based construction site inspection management system Download PDFInfo
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Abstract
The application discloses a construction site inspection management system based on big data, which relates to the technical field of construction inspection, and comprises an inspection area dividing module, an inspection data acquisition module, an inspection data analysis module, an inspection analysis scheduling module and an inspection record management module; the method comprises the steps that a construction site electronic map is built in a delimited construction area, the construction site electronic map is divided into areas by a patrol area division module, a plurality of patrol areas are obtained, and each patrol area is displayed on a visual platform; the technical key points are as follows: the partition design is adopted, so that the acquired data are more accurate, the accuracy of the construction site danger assessment value Das is improved, and the danger grade G and the construction site danger assessment value Das in each inspection area can be intuitively displayed on a visual platform; and completing the dispatching of maintenance personnel according to the size of the dispatching priority coefficient Fit, and reasonably distributing the dispatching of the personnel.
Description
Technical Field
The application relates to the technical field of construction inspection, in particular to a construction site inspection management system based on big data.
Background
The inspection of the safety construction refers to checking hidden danger, harmful and dangerous factors, defects and the like possibly existing in the production process and safety management so as to determine the existence state of the hidden danger, harmful and dangerous factors, defects and the like and convert the hidden danger, harmful and dangerous factors, defects into accident conditions, so that corrective measures can be formulated, hidden danger, harmful and dangerous factors are eliminated, and the production safety is ensured.
In the Chinese application of the application with the application publication number of CN104700161A, a construction site inspection optimization method and a construction site inspection optimization system based on risk factor identification are disclosed, wherein the method specifically comprises the following steps: setting and storing risk factors existing in a construction site and risk grade information corresponding to the risk factors in an intelligent pipe network inspection system in advance; collecting construction site information in real time, and matching the collected construction site information with risk factors preset by a system and the corresponding risk grade information of the risk factors; if the information is successfully matched, the intelligent pipe network inspection system automatically generates an inspection period of the construction site according to a preset rule, and distributes relevant site management personnel to carry out inspection management work of the construction site according to the inspection period.
In the above application, although the risk factors and the risk level matching process is provided, the risk factors are collected in the whole construction area, and a large error exists in the subsequent risk level matching process, meanwhile, in the actual inspection work, the situation of insufficient personnel often occurs, and synchronous dispatch cannot be performed for the areas with the same risk level, so that the rationality of personnel dispatch needs to be improved.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides a construction site inspection management system based on big data, which adopts a partition design, so that the acquired data is more accurate, the accuracy of a construction site danger evaluation value Das is improved, and the danger grade G and the construction site danger evaluation value Das in each inspection area can be intuitively displayed on a visual platform; the dispatching of maintenance personnel is completed according to the size of the dispatching priority coefficient Fit, and when the situation that management personnel are insufficient is dealt with, the dispatching of the personnel can be reasonably distributed, so that the problem in the background technology is solved.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme:
the construction site patrol management system based on big data comprises a patrol area dividing module, a patrol data acquisition module, a patrol data analysis module, a patrol analysis scheduling module and a patrol record management module;
the method comprises the steps that a construction site electronic map is built in a delimited construction area, the construction site electronic map is divided into areas by a patrol area division module, a plurality of patrol areas are obtained, and each patrol area is displayed on a visual platform;
collecting environment data, equipment data and personnel data in each inspection area through an inspection data collecting module, and completing data preprocessing after summarizing the data in each inspection area; wherein, the mean/median/mode filling method is a method for filling missing values, and when some missing values exist in the data set, the missing values can be filled by using the method; mean filling method: adding all the data without the missing values, dividing the data by the total amount of the data to obtain a mean value, and filling the missing values by the mean value; median filling method: sorting all non-missing values, finding the numerical value of the middle position as a median, and filling the missing values by using the median; mode filling method: taking the numerical value with the highest occurrence frequency of all non-missing values as a mode, and filling the missing values with the mode; the method is a missing value filling mode required by the application, and the specific method can be determined according to the data characteristics and application scenes;
the preprocessed data are analyzed by a patrol data analysis module, extraction and classification of the data are completed, a corresponding environment data set, a corresponding equipment data set and a corresponding personnel data set are built, and a construction site danger assessment value Das is generated;
matching the obtained construction site hazard assessment value Das to a corresponding hazard level G in a patrol analysis scheduling module, synchronously recording the occurrence frequency R of the corresponding hazard level G in a selected time interval, and generating a dispatch priority coefficient Fit, wherein the dispatch priority coefficient Fit and the sequence of dispatch personnel in a corresponding patrol area are positively correlated, and specifically means that: when the dispatching priority coefficient Fit in the patrol area is higher, the dispatching sequence of the corresponding dispatching personnel is more forward;
for example, in a delimited construction area, if the dispatching priority coefficient Fit is highest in a certain inspection area, the inspection area will obtain a first batch of overhaulers, after the part of overhaulers arrive at the inspection area, overhauling and recording operations will be performed, and for a certain inspection area with the dispatching priority coefficient Fit being highest, a second batch of overhaulers will be obtained.
And recording the data set in the system through the patrol record management module, and connecting the system with the operation end.
Further, the construction site electronic map is built by the following steps: using an unmanned aerial vehicle carrying a machine vision system to image a delimited construction area, and completing 3D modeling according to a depth camera to obtain a construction electronic map;
in the patrol area dividing module, each patrol area is displayed on a visual platform in the form of an electronic map, and the information displayed by the visual platform further comprises: the corresponding hazard class G under the patrol area, the frequency R of occurrence of the corresponding hazard class and the dispatching priority coefficient Fit.
Further, the patrol data acquisition module comprises an environment monitoring unit, an equipment monitoring unit and a personnel monitoring unit;
the environment monitoring unit monitors environment data in a corresponding patrol area to obtain an environment index I, and the environment data comprises: average temperature T, average humidity H, and average wind speed V; the environment monitoring unit 21 includes a temperature sensor for acquiring temperature data, a humidity collector for acquiring humidity data, and a wind speed sensor for acquiring wind speed;
after dimensionless treatment is carried out on the average temperature T, the average humidity H and the average wind speed V, an environment index I is generated according to the following mode:
the meaning and the value of the parameters are as follows: gamma is more than or equal to 0.05 and less than or equal to 1.05, the specific value of gamma is adjusted and set by a user, C 1 Is a constant correction coefficient;
the equipment monitoring unit monitors equipment data in a corresponding inspection area, acquires equipment failure rate E, and generates the equipment failure rate E by the following steps:
equipment failure rate e=the number of failed equipment in the corresponding patrol area/the total number of equipment in the corresponding patrol area;
the personnel monitoring unit monitors personnel data in the corresponding patrol area to acquire the number N of illegal personnel, and the mode of generating the number N of illegal personnel is as follows: the personnel in the corresponding patrol area wears the safety helmet with the built-in GPS locator, the position of the personnel is positioned in real time through the GPS locator, and the personnel monitoring unit monitors the position of the personnel wearing the safety helmet, and if the personnel is positioned in the corresponding patrol area, the personnel is not illegal; if the number of the illegal persons exceeds the corresponding inspection area, the number of the illegal persons N is obtained through statistics; specifically, in order to ensure that personnel in the inspection area wear safety helmets with built-in GPS positioners, an identification camera is required to be installed at the entrance position of the construction site and used for identifying whether the personnel wear the safety helmets, and if the personnel are identified not to wear the safety helmets, an early warning is sent out; if the person is identified as wearing the safety helmet, the safety helmet does not respond.
Further, the personnel monitoring unit comprises a judging subunit and a counting subunit;
the judging subunit is used for judging whether personnel wearing safety helmets in the corresponding inspection area violate regulations or not;
the statistics subunit is used for counting the number N of offenders in the corresponding patrol area.
Further, in the inspection data acquisition module, the data preprocessing mode is data cleaning, and the data cleaning at least includes: and (5) removing repeated data, and completing filling of missing values, identification and abnormal value removal by using a mean/median/mode filling method.
Further, in the inspection data analysis module, the environment index I, the equipment failure rate E and the number of offenders N are obtained, and after dimensionless processing, a construction site danger assessment value Das is generated according to the following mode:
wherein alpha and beta are variable constant parameters, alpha is more than or equal to 0.57 and less than or equal to 1.38,0.43 and beta is more than or equal to 2.62, the specific values are adjusted and set by a user, and C 2 Is a constant correction coefficient.
Further, the patrol analysis scheduling module comprises a matching unit and a scheduling unit;
the matching unit is used for matching the construction site hazard assessment value Das in the corresponding inspection area with the hazard class G, recording the occurrence frequency R of the corresponding hazard class G in a selected time interval through a recording subunit built in the matching unit, and generating a dispatching priority coefficient Fit according to the following mode:
Fit＝G n *R
the meaning and the value of the parameters are as follows: n is more than or equal to 1 and less than or equal to 3, n is weight, and the specific value is adjusted and set by a user.
Further, in the matching unit, a mapping relationship is established between the construction site hazard assessment value Das in the corresponding inspection area and the hazard class G, and the hazard class G is determined by a definition subunit built in the matching unit, where the hazard class G includes: the first, second and third levels are synchronized to define the threshold range of the corresponding construction site hazard assessment value Das under the hazard level G so as to determine the hazard level G of the construction site hazard assessment value Das;
it should be noted that: the risk level is lowest when the risk level G is level I, and the risk level starts to be high when the risk level G is level III or above.
Further, the scheduling unit performs first personnel dispatch on the patrol area with the highest dispatching priority coefficient Fit in the defined construction area based on the positive correlation between the priority dispatching coefficient Fit and the sequence of dispatched personnel in the corresponding patrol area.
(III) beneficial effects
The application provides a construction site inspection management system based on big data, which has the following beneficial effects:
1. in the system, by dividing each inspection area for the construction area, environmental factors, equipment factors and personnel factors are considered, the construction site hazard assessment value Das can be obtained, and due to the adoption of the partition design, the acquired data are more accurate, the accuracy of the construction site hazard assessment value Das is improved, the hazard class G and the construction site hazard assessment value Das in each inspection area can be intuitively displayed on a visual platform, the effective supervision of workers is facilitated, the labor cost is reduced, and meanwhile the supervision efficiency of the workers is greatly improved;
2. by designing the matching unit and the scheduling unit in the patrol analysis scheduling module, the judgment of the danger level G of each patrol area can be efficiently completed, the danger level G and the frequency R appearing in the corresponding danger level G are comprehensively considered, the dispatching priority coefficient Fit can be obtained, the scheduling of maintenance personnel is completed according to the size of the dispatching priority coefficient Fit, the problem that unmanned dispatching can occur in areas with high danger degree can be effectively avoided, the system can make accurate and efficient judgment under the condition of not being interfered by external personnel, and reasonable distribution can be made for dispatching personnel when the condition of insufficient management personnel is met.
Drawings
FIG. 1 is a block diagram of the overall system architecture of the big data based construction site inspection management system of the present application;
FIG. 2 is an overall flow chart of a construction site inspection management method based on big data;
in the figure: 10. a patrol area dividing module; 20. a patrol data acquisition module; 21. an environment monitoring unit; 22. an equipment monitoring unit; 23. a personnel monitoring unit; 231. judging a subunit; 232. a statistics subunit; 30. a patrol data analysis module; 40. a patrol analysis scheduling module; 41. a matching unit; 411. a recording subunit; 412. defining a subunit; 42. a scheduling unit; 50. and a patrol record management module.
Detailed Description
The following description of the embodiments of the present application 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 application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1:
referring to fig. 2, the application provides a construction site inspection management method based on big data, which comprises the following steps:
step one, building a construction site electronic map in a delimited construction area, carrying out area division on the construction site electronic map by a patrol area division module 10 to obtain a plurality of patrol areas, and displaying each patrol area on a visual platform; the boundaries among the patrol areas can be visually observed on a visual platform;
the first step comprises the following steps:
the construction site electronic map is built by the following steps: using an unmanned aerial vehicle carrying a machine vision system to image a delimited construction area, and completing 3D modeling according to a depth camera to obtain a construction electronic map;
in the patrol area dividing module 10, each patrol area is displayed on a visualization platform in the form of an electronic map, and the information that the visualization platform is further used for displaying includes: the method comprises the steps of corresponding to a danger level G in a patrol area, frequency R of occurrence of the corresponding danger level and dispatch priority coefficient Fit, wherein a tool used by a visual platform is a liquid crystal display screen.
Step two, collecting environmental data, equipment data and personnel data in each inspection area through the inspection data collecting module 20, and completing data preprocessing after summarizing the data in each inspection area, wherein the data preprocessing mode is data cleaning, and the data cleaning at least comprises: removing repeated data, and completing filling of missing values, identification and abnormal value removal by using a mean/median/mode filling method;
wherein, the mean/median/mode filling method is a method for filling missing values, and when some missing values exist in the data set, the missing values can be filled by using the method; mean filling method: adding all the data without the missing values, dividing the data by the total amount of the data to obtain a mean value, and filling the missing values by the mean value; median filling method: sorting all non-missing values, finding the numerical value of the middle position as a median, and filling the missing values by using the median; mode filling method: taking the numerical value with the highest occurrence frequency of all non-missing values as a mode, and filling the missing values with the mode; the method is a missing value filling mode required in the application, and the specific method can be determined according to the data characteristics and application scenes.
The second step comprises the following contents:
the inspection data acquisition module 20 comprises an environment monitoring unit 21, an equipment monitoring unit 22 and a personnel monitoring unit 23;
the environmental monitoring unit 21 monitors environmental data in the corresponding patrol area, obtains an environmental index I, and the environmental data includes: average temperature T, average humidity H, and average wind speed V; the environment monitoring unit 21 includes a temperature sensor for acquiring temperature data, a humidity collector for acquiring humidity data, and a wind speed sensor for acquiring wind speed;
after dimensionless treatment is carried out on the average temperature T, the average humidity H and the average wind speed V, an environment index I is generated according to the following mode:
the meaning and the value of the parameters are as follows: gamma is more than or equal to 0.05 and less than or equal to 1.05, the specific value of gamma is adjusted and set by a user, C 1 Is a constant correction coefficient;
the device monitoring unit 22 monitors device data in the corresponding inspection area, obtains a device failure rate E, and generates the device failure rate E in the following manner:
equipment failure rate e=the number of failed equipment in the corresponding patrol area/the total number of equipment in the corresponding patrol area;
the personnel monitoring unit 23 monitors personnel data in the corresponding patrol area to obtain the number N of illegal personnel, and the mode of generating the number N of illegal personnel is as follows: the personnel in the corresponding patrol area wears the safety helmet with the built-in GPS locator, the position of the personnel is positioned in real time through the GPS locator, and the personnel monitoring unit 23 monitors the position of the personnel wearing the safety helmet, if the personnel is positioned in the corresponding patrol area, the personnel is not illegal; if the number of the illegal persons exceeds the corresponding inspection area, the number of the illegal persons N is obtained through statistics;
in an actual application scene, in order to ensure that personnel in a patrol area wear safety helmets with built-in GPS positioners, an identification camera is required to be installed at an entrance position of a construction site and used for identifying whether the personnel wear the safety helmets, and if the personnel are identified not to wear the safety helmets, an early warning is sent; if the person is identified as wearing the safety helmet, the safety helmet does not respond.
The personnel monitoring unit 23 includes a judging subunit 231 and a statistics subunit 232;
the judging subunit 231 is configured to judge whether a person wearing the safety helmet in the corresponding inspection area violates rules;
the statistics subunit 232 is configured to count the number N of offenders in the corresponding patrol area.
Thirdly, analyzing the preprocessed data by a patrol data analysis module 30, completing extraction and classification of the data, constructing a corresponding environment data set, a corresponding equipment data set and a corresponding personnel data set, and generating a construction site danger assessment value Das;
the third step comprises the following contents:
in the inspection data analysis module 30, the environmental index I, the equipment failure rate E, and the number of offenders N are obtained, and after dimensionless processing, a construction site hazard assessment value Das is generated as follows:
wherein alpha and beta are variable constant parameters, alpha is more than or equal to 0.57 and less than or equal to 1.38,0.43 and beta is more than or equal to 2.62, the specific values are adjusted and set by a user, and C 2 Is a constant correction coefficient.
Combining step one to step three can draw the following conclusion:
by dividing each inspection area for the construction area, environmental factors, equipment factors and personnel factors are considered, the construction site danger assessment value Das can be obtained, and due to the adoption of the partition design, the collected data are more accurate, the accuracy of the construction site danger assessment value Das is improved, the danger grade G and the construction site danger assessment value Das in each inspection area can be intuitively displayed on a visual platform, the effective supervision of workers is facilitated, the supervision efficiency of the workers is greatly improved while the labor cost is reduced.
Step four, matching the obtained construction site hazard assessment value Das to a corresponding hazard class G in a patrol analysis scheduling module 40, and synchronously recording the occurrence frequency R of the corresponding hazard class G in a selected time interval to generate a dispatch priority coefficient Fit which is positively correlated with the sequence of dispatch personnel in a corresponding patrol area;
the fourth step comprises the following contents:
the patrol analysis scheduling module 40 includes a matching unit 41 and a scheduling unit 42;
the matching unit 41 is configured to match the construction site risk assessment value Das in the corresponding inspection area with the risk level G, record, by using a recording subunit 411 in the matching unit 41, the frequency R of occurrence of the corresponding risk level G in the selected time interval, and generate the dispatch priority coefficient Fit as follows:
Fit＝G n *R
the meaning and the value of the parameters are as follows: n is more than or equal to 1 and less than or equal to 3, n is weight, and the specific value is adjusted and set by a user.
In the matching unit 41, a mapping relationship is established between the construction site risk assessment value Das in the corresponding inspection area and a risk level G, and the risk level G is determined by a definition subunit 412 built in the matching unit 41, where the risk level G includes: the first, second and third levels are synchronized to define the threshold range of the corresponding construction site hazard assessment value Das under the hazard level G so as to determine the hazard level G of the construction site hazard assessment value Das; the scheduling unit 42 performs first-batch personnel dispatch on the patrol area with the highest dispatching priority coefficient Fit in the delimited construction area based on the positive correlation between the priority dispatching coefficient Fit and the sequence of dispatching personnel in the corresponding patrol area;
the danger level is lowest when the danger level G is the level I, and the danger level starts to be higher when the danger level G is the level III or above;
in an actual scene, the priority of the dispatch operation can be determined not only by considering the hazard level G in the corresponding patrol area, but also by considering the frequency R of the corresponding hazard level G, if the frequency R is too high, the dispatch priority coefficient Fit will be increased.
The following can be concluded in combination with the above-described contents of step four:
the matching unit 41 and the scheduling unit 42 are designed in the patrol analysis scheduling module 40, so that the judgment of the danger level G of each patrol area can be efficiently completed, the frequency R of the danger level G and the frequency R of the corresponding danger level G are comprehensively considered, the dispatching priority coefficient Fit can be obtained, the scheduling of maintenance personnel can be completed according to the size of the dispatching priority coefficient Fit, the problem that no personnel can be dispatched in the area with high danger degree can be effectively avoided, the system can make accurate and efficient judgment under the condition of not being interfered by external personnel, and reasonable distribution can be made for dispatching personnel when the condition that management personnel are insufficient is met.
And fifthly, recording the data set in the system through the patrol record management module 50, and connecting the system with an operation end, so that the data in the system can be deleted, backed up and checked.
Example 2:
referring to fig. 1, the present application provides a construction site inspection management system based on big data, comprising:
the patrol area dividing module 10 establishes a construction electronic map in the defined construction area, and the construction electronic map is established by the following steps: using an unmanned aerial vehicle carrying a machine vision system to image a delimited construction area, completing 3D modeling according to a depth camera to obtain a construction electronic map, carrying out area division on the construction electronic map by a patrol area division module 10 to obtain a plurality of patrol areas, and displaying each patrol area on a visual platform, wherein the visual platform is further used for displaying information comprising: the corresponding dangerous grade G under the patrol area, the frequency R of occurrence of the corresponding dangerous grade and the dispatching priority coefficient Fit;
the patrol data acquisition module 20 acquires environment data, equipment data and personnel data in each patrol area, and performs data preprocessing after summarizing the data in each patrol area;
the inspection data acquisition module 20 comprises an environment monitoring unit 21, an equipment monitoring unit 22 and a personnel monitoring unit 23; the environmental monitoring unit 21 monitors environmental data in the corresponding patrol area, obtains an environmental index I, and the environmental data includes: average temperature T, average humidity H, and average wind speed V; the equipment monitoring unit 22 monitors the equipment data in the corresponding inspection area to acquire an equipment failure rate E; the personnel monitoring unit 23 monitors personnel data in the corresponding patrol area to obtain the number N of illegal personnel, and the mode of generating the number N of illegal personnel is as follows: the personnel in the corresponding patrol area wears the safety helmet with the built-in GPS locator, the position of the personnel is positioned in real time through the GPS locator, and the personnel monitoring unit 23 monitors the position of the personnel wearing the safety helmet, if the personnel is positioned in the corresponding patrol area, the personnel is not illegal; if the number of the illegal persons exceeds the corresponding inspection area, the number of the illegal persons is counted; the personnel monitoring unit 23 includes a judging subunit 231 and a statistics subunit 232; the judging subunit 231 is configured to judge whether a person wearing the safety helmet in the corresponding inspection area violates rules; the statistics subunit 232 is used for counting the number N of offenders in the corresponding patrol area;
the patrol data analysis module 30 analyzes the preprocessed data by the patrol data analysis module 30, extracts and classifies the data, builds a corresponding environment data set, a corresponding equipment data set and a corresponding personnel data set, and generates a construction site danger assessment value Das; acquiring an environment index I, an equipment failure rate E and the number N of offenders, and acquiring a construction site danger evaluation value Das after dimensionless processing;
the patrol analysis scheduling module 40 is used for matching the acquired construction site hazard assessment value Das to the corresponding hazard level G, synchronously recording the occurrence frequency R of the corresponding hazard level G in a selected time interval, and generating a dispatching priority coefficient Fit which is positively correlated with the sequence of dispatching personnel in the corresponding patrol area;
the patrol analysis scheduling module 40 includes a matching unit 41 and a scheduling unit 42;
the matching unit 41 is configured to match the construction site risk assessment value Das in the corresponding inspection area with the risk level G, and record the occurrence frequency R of the corresponding risk level G in the selected time interval through the recording subunit 411 in the matching unit 41; in the matching unit 41, a mapping relationship is established between the construction site risk assessment value Das in the corresponding inspection area and a risk level G, and the risk level G is determined by a definition subunit 412 built in the matching unit 41, where the risk level G includes: the first, second and third levels are synchronized to define the threshold range of the corresponding construction site hazard assessment value Das under the hazard level G so as to determine the hazard level G of the construction site hazard assessment value Das; the scheduling unit 42 performs first-batch personnel dispatch on the patrol area with the highest dispatching priority coefficient Fit in the delimited construction area based on the positive correlation between the priority dispatching coefficient Fit and the sequence of dispatching personnel in the corresponding patrol area;
specifically, the matching unit 41 and the scheduling unit 42 are designed in the inspection analysis scheduling module 40, so that the judgment of the danger level G of each inspection area can be efficiently completed, the danger level G and the frequency R appearing in the corresponding danger level G are comprehensively considered, the dispatching priority coefficient Fit can be obtained, the scheduling of maintenance personnel can be completed according to the size of the dispatching priority coefficient Fit, the problem that unmanned dispatching can occur in areas with high danger degree can be effectively avoided, the system can make accurate and efficient judgment under the condition of not being interfered by external personnel, and reasonable distribution can be made for dispatching personnel when the condition that management personnel are insufficient is met.
The patrol record management module 50 records the data set in the system, and connects the system with the operation end, so that the data in the system can be deleted, backed up and checked.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (10)
1. The utility model provides a job site inspection management system based on big data which characterized in that: the system comprises a patrol area dividing module (10), a patrol data acquisition module (20), a patrol data analysis module (30), a patrol analysis scheduling module (40) and a patrol record management module (50);
the method comprises the steps that a construction site electronic map is built in a delimited construction area, the construction site electronic map is divided into areas by a patrol area dividing module (10), a plurality of patrol areas are obtained, and each patrol area is displayed on a visual platform;
the environment data, the equipment data and the personnel data in each patrol area are collected through the patrol data collection module (20), and the data in each patrol area are summarized to complete data preprocessing;
the preprocessed data is analyzed by a patrol data analysis module (30), extraction and classification of the data are completed, a corresponding environment data set, a corresponding equipment data set and a corresponding personnel data set are built, and a construction site danger assessment value Das is generated;
in the patrol analysis scheduling module (40), the acquired construction site hazard assessment value Das is matched to the corresponding hazard class G, the occurrence frequency R of the corresponding hazard class G is synchronously recorded in a selected time interval, a dispatching priority coefficient Fit is generated, and the dispatching priority coefficient Fit and the sequence of dispatching personnel in the corresponding patrol area are positively correlated;
and recording the data set in the system through a patrol record management module (50), and connecting the system with an operation end.
2. A big data based job site patrol management system according to claim 1, wherein: the construction site electronic map is built by the following steps: and imaging the delimited construction area by using an unmanned aerial vehicle carrying a machine vision system, and completing 3D modeling according to a depth camera to obtain a construction electronic map.
3. A big data based job site patrol management system according to claim 1, wherein: in the patrol area dividing module (10), each patrol area is displayed on a visualization platform in the form of an electronic map, and the information displayed by the visualization platform further comprises: the corresponding hazard class G under the patrol area, the frequency R of occurrence of the corresponding hazard class and the dispatching priority coefficient Fit.
4. A big data based job site patrol management system according to claim 1, wherein: the patrol data acquisition module (20) comprises an environment monitoring unit (21), an equipment monitoring unit (22) and a personnel monitoring unit (23);
the environment monitoring unit (21) monitors environment data in a corresponding patrol area to acquire an environment index I, and the environment data comprises: average temperature T, average humidity H, and average wind speed V;
after dimensionless treatment is carried out on the average temperature T, the average humidity H and the average wind speed V, an environment index I is generated according to the following mode:
the meaning and the value of the parameters are as follows: gamma is more than or equal to 0.05 and less than or equal to 1.05, the specific value of gamma is adjusted and set by a user, C 1 Is a constant correction coefficient;
the equipment monitoring unit (22) monitors equipment data in the corresponding inspection area, acquires equipment failure rate E, and generates the equipment failure rate E by the following modes:
equipment failure rate e=the number of failed equipment in the corresponding patrol area/the total number of equipment in the corresponding patrol area;
the personnel monitoring unit (23) monitors personnel data in the corresponding inspection area to acquire the number N of illegal personnel, and the mode for generating the number N of illegal personnel is as follows: the personnel in the corresponding patrol area wears the safety helmet with the built-in GPS locator, the position of the personnel is positioned in real time through the GPS locator, and the personnel monitoring unit (23) monitors the position of the personnel wearing the safety helmet, and if the personnel is positioned in the corresponding patrol area, the personnel is not illegal; if the number of the offenders exceeds the corresponding inspection area, the offenders are violated, and the number N of the offenders is obtained through statistics.
5. The big data based job site inspection management system of claim 4, wherein: the personnel monitoring unit (23) comprises a judging subunit (231) and a statistics subunit (232);
the judging subunit (231) is used for judging whether personnel wearing safety helmets in the corresponding patrol areas violate regulations or not;
the statistics subunit (232) is used for counting the number N of offenders in the corresponding patrol area.
6. The big data based job site inspection management system of claim 5, wherein: in the inspection data acquisition module (20), the data preprocessing mode is data cleaning, and the data cleaning at least comprises: and (5) removing repeated data, and completing filling of missing values, identification and abnormal value removal by using a mean/median/mode filling method.
7. The big data based job site inspection management system of claim 5, wherein: in the inspection data analysis module (30), the environment index I, the equipment failure rate E and the number of offenders N are obtained, dimensionless processing is carried out, and then a construction site danger assessment value Das is generated according to the following mode:
wherein alpha and beta are variable constant parameters, alpha is more than or equal to 0.57 and less than or equal to 1.38,0.43 and beta is more than or equal to 2.62, the specific values are adjusted and set by a user, and C 2 Is a constant correction coefficient.
8. A big data based job site patrol management system according to claim 1, wherein: the patrol analysis scheduling module (40) comprises a matching unit (41) and a scheduling unit (42);
the matching unit (41) is configured to match the construction site hazard assessment value Das in the corresponding inspection area with the hazard class G, record, through a recording subunit (411) built in the matching unit (41), the frequency R of occurrence of the corresponding hazard class G in a selected time interval, and generate the dispatch priority coefficient Fit according to the following manner:
Fit＝G n *R
the meaning and the value of the parameters are as follows: n is more than or equal to 1 and less than or equal to 3, n is weight, and the specific value is adjusted and set by a user.
9. The big data based job site patrol management system of claim 8, wherein: in the matching unit (41), a mapping relationship is established between the construction site hazard assessment value Das in the corresponding inspection area and the hazard class G, and the hazard class G is determined by a definition subunit (412) built in the matching unit (41), wherein the hazard class G comprises: and synchronously defining the threshold range of the corresponding construction site hazard assessment value Das under the hazard level G to determine the hazard level G of the construction site hazard assessment value Das.
10. The big data based job site patrol management system of claim 8, wherein: the scheduling unit (42) performs first personnel dispatch on the patrol area with the highest dispatching priority coefficient Fit in the delimited construction area based on the positive correlation between the priority dispatching coefficient Fit and the sequence of dispatched personnel in the corresponding patrol area.
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