CN116010826B - Construction safety early warning method and system for building engineering - Google Patents
Construction safety early warning method and system for building engineering Download PDFInfo
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- CN116010826B CN116010826B CN202211691547.1A CN202211691547A CN116010826B CN 116010826 B CN116010826 B CN 116010826B CN 202211691547 A CN202211691547 A CN 202211691547A CN 116010826 B CN116010826 B CN 116010826B
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Abstract
The invention discloses a construction safety early warning method and a construction safety early warning system for constructional engineering, which relate to the technical field of constructional engineering, and the method comprises the following steps: constructing a safety early warning information list based on target monitoring construction project information; according to the safety early warning classification information, determining environmental object early warning information and personnel operation early warning information, and carrying out safety early warning parameter characteristic matching from a safety early warning information list; data acquisition is carried out on the target monitoring construction project, and a monitoring data source is obtained; performing feature analysis to determine a feature set of the acquired data; performing traversal comparison on the acquired data feature set by utilizing the environmental object safety early warning parameter features and the personnel operation safety early warning parameter features to determine traversal comparison results; and combining the safety early warning information list to determine construction safety early warning information. The invention solves the technical problems of long feedback time and poor early warning quality of construction safety early warning in the prior art, and achieves the technical effects of improving the safety early warning efficiency and guaranteeing the construction safety.
Description
Technical Field
The invention relates to the technical field of constructional engineering, in particular to a construction safety early warning method and system for constructional engineering.
Background
Along with the rapid development of economy, the requirements of people on life quality are continuously improved, and the development of the building industry is also a daily and monthly variation. The construction safety of constructional engineering is also receiving more and more attention.
At present, a supervision mechanism is established in the construction process of the building engineering, and a third party supervision personnel is used for inspecting site construction, so that potential safety hazards are inspected for construction equipment, construction progress and construction safety guarantee measures of each project, potential hazards which possibly cause safety accidents are warned, and the construction safety is guaranteed.
However, as the complexity of construction projects is continuously improved, the potential safety hazards are only warned by the supervision personnel, and the potential safety hazards are not only limited by the capacity of the supervision personnel, but also cannot be patrolled on the construction site in real time, so that the potential safety hazards cannot be identified in time, and safety accidents are caused. In the prior art, the technical problems of long construction safety early warning feedback time and poor early warning quality exist.
Disclosure of Invention
The application provides a construction safety early warning method and system for constructional engineering, which are used for solving the technical problems of long construction safety early warning feedback time and poor early warning quality in the prior art.
In view of the above problems, the present application provides a construction safety early warning method and system for construction engineering.
In a first aspect of the present application, a construction safety pre-warning method for a building engineering is provided, and the method includes:
constructing a safety early warning information list based on target monitoring construction project information, wherein the safety early warning information list comprises safety early warning classification information and safety early warning parameter characteristics;
according to the safety early warning classification information, determining environmental object early warning information and personnel operation early warning information, and based on the environmental object early warning information and the personnel operation early warning information, carrying out safety early warning parameter characteristic matching from the safety early warning information list according to the safety early warning classification information to obtain environmental object safety early warning parameter characteristics and personnel operation safety early warning parameter characteristics;
the method comprises the steps that data acquisition is carried out on a target monitoring construction project through an Internet of things sensor and image acquisition equipment, and a monitoring data source is obtained;
performing feature analysis on the monitoring data source to determine a collected data feature set;
performing traversal comparison on the acquired data feature set by utilizing the environmental object safety early warning parameter features and the personnel operation safety early warning parameter features to determine traversal comparison results;
And determining construction safety early warning information according to the traversal comparison result and the safety early warning information list.
In a second aspect of the present application, there is provided a construction safety pre-warning system for a construction project, the system comprising:
the early warning information list construction module is used for constructing a safety early warning information list based on target monitoring construction project information, and the safety early warning information list comprises safety early warning classification information and safety early warning parameter characteristics;
the early warning parameter characteristic obtaining module is used for determining environmental object early warning information and personnel operation early warning information according to the safety early warning classification information, carrying out safety early warning parameter characteristic matching from the safety early warning information list according to the safety early warning classification information based on the environmental object early warning information and the personnel operation early warning information, and obtaining environmental object safety early warning parameter characteristics and personnel operation safety early warning parameter characteristics;
the monitoring data source acquisition module is used for acquiring data of a target monitoring construction project through the sensor of the Internet of things and the image acquisition equipment to acquire a monitoring data source;
The data feature set determining module is used for carrying out feature analysis on the monitoring data source and determining an acquired data feature set;
the traversal comparison result determining module is used for carrying out traversal comparison on the acquired data feature set by utilizing the safety early warning parameter features of the environmental object and the safety early warning parameter features of personnel operation so as to determine traversal comparison results;
and the safety early warning information determining module is used for determining construction safety early warning information according to the traversal comparison result and the safety early warning information list.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the construction method, a safety early warning information list is constructed based on target monitoring construction project information, the safety early warning information list comprises safety early warning classification information and safety early warning parameter characteristics, then environment object early warning information and personnel operation early warning information are determined according to the safety early warning classification information, safety early warning parameter characteristic matching is conducted from the safety early warning information list according to the environment object early warning information and the personnel operation early warning information, environment object safety early warning parameter characteristics and personnel operation safety early warning parameter characteristics are obtained, further data acquisition is conducted on target monitoring construction projects through an Internet of things sensor and image acquisition equipment, a monitoring data source is obtained, then feature analysis is conducted on the monitoring data source, an acquisition data feature set is determined, then traversing comparison is conducted on the acquisition data feature set through the environment object safety early warning parameter characteristics and the personnel operation safety early warning parameter characteristics, traversing comparison results are determined, and then construction safety early warning information is determined according to the traversing comparison results and the safety early warning information list. The efficiency of construction safety precaution has been reached and construction safety has been guaranteed, the technical effect of in time discerning the potential safety hazard.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a construction safety early warning method of a building engineering according to an embodiment of the present application;
fig. 2 is a schematic flow chart of constructing a safety warning information list in a construction safety warning method of a building engineering according to an embodiment of the present application;
fig. 3 is a schematic flow chart of determining a feature set of collected data in a construction safety pre-warning method of a building engineering according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a construction safety early warning system for a building engineering according to an embodiment of the present application.
Reference numerals illustrate: the system comprises an early warning information list construction module 11, an early warning parameter characteristic acquisition module 12, a monitoring data source acquisition module 13, a data characteristic set determination module 14, a traversal comparison result determination module 15 and a safety early warning information determination module 16.
Detailed Description
The application provides a construction safety early warning method for construction engineering, which is used for solving the technical problems of long construction safety early warning feedback time and poor early warning quality in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a construction safety pre-warning method for a building engineering, where the method includes:
step S100: constructing a safety early warning information list based on target monitoring construction project information, wherein the safety early warning information list comprises safety early warning classification information and safety early warning parameter characteristics;
further, as shown in fig. 2, the step S100 of the embodiment of the present application further includes:
step S110: determining construction project attribute information according to target monitoring construction project information;
step S120: obtaining construction project difficulty information and construction flow information according to the construction project attribute information;
step S130: based on the construction flow information, carrying out safety analysis on construction environments and construction tools of each flow, and determining safety influence factors of each construction flow information;
step S140: according to the construction project difficulty information, carrying out construction difficulty risk factor analysis and determining a difficulty risk factor;
step S150: fusing the safety influence factors of the construction flow information and the difficulty risk factors to construct a risk factor set;
Step S160: based on the risk factor set, matching is carried out through a historical security management database, and security event information is determined;
step S170: and determining safety early warning classification information according to the safety event information, and constructing the safety early warning information list by utilizing the safety early warning classification information and the risk factor set.
Specifically, the target monitoring construction project information is information describing details of any construction project that needs to be monitored for construction safety, and includes project names, project uses, construction units, establishment units, construction addresses, and the like. The safety early warning information list is determined according to the target monitoring construction project information, and is matched with the monitoring construction project, and the risk information list possibly influencing construction safety in the construction process is provided. The safety early warning information list comprises safety early warning classification information and safety early warning parameter information. The safety early warning classification information is early warning information obtained by classifying the early warning information according to different categories which affect construction safety. The safety early warning parameter features are features for describing the conditions which are shown when various risk factors in the safety early warning information easily cause potential safety hazards, such as in the construction process, operators should wear safety helmets on construction sites in real time, and the safety early warning parameter features are that the constructors acquired through the image acquisition device do not wear the safety helmets, and production safety accidents are easily caused at the moment. The method includes the steps of taking a monitoring target as a classification basis, classifying safety event information, such as monitoring equipment and an operation environment in a construction process, classifying events which easily generate safety accidents in the monitoring process into one type, monitoring personnel safety and construction operation in the construction process, and classifying the events which easily generate construction safety accidents in the monitoring process into another type.
Specifically, the project use, the project address and the like are used as search factors, and the target monitoring construction project information is searched and extracted to obtain the construction project attribute information. The construction project attribute information is information describing different characteristics of a construction project and comprises attribute information such as project use, construction scheme, construction flow, construction personnel safety and the like. The construction project difficulty information is obtained by analyzing description contents in the multidimensional description of the construction project in the construction project attribute information, determining the construction project difficulty according to project purposes such as municipal construction, subways, highways, overpass construction, residential projects, building construction and the like, and analyzing implementation difficulty of the construction project according to technical characteristics of a construction scheme. And analyzing the construction progress safety and the construction flow information in the construction project attribute information to obtain the construction flow information of the construction project. The construction project difficulty information reflects the construction difficulty degree of the building engineering in the construction process, including the process complexity, the construction feasibility degree and the like. The construction flow information is information describing the construction sequence in the construction process of the building engineering, and comprises construction key nodes, construction process flows, construction supervision nodes and the like.
Specifically, the safety influence factors of the construction flow information are obtained by analyzing the construction environment of each construction node and the safety degree of the construction tool facing the construction task of each node based on the construction flow information. For example, in the process of leveling the outer facade of the building, a tripod needs to be built, and at the moment, the stability of the tripod and the firmness of the guardrails outside the tripod have great influence on the construction safety of leveling operation. The safety factor at this time is the firmness of the hand and foot rest and the firmness of the guardrail outside the hand and foot rest. And carrying out multidimensional construction difficulty risk analysis on construction project difficulty information, and determining risk factors which obstruct construction progress in the construction process according to whether the construction project can be successfully completed. The difficulty risk factors are factors describing risk factors which are difficult to execute in the construction process, and include entrance coordination time of construction equipment, whether a construction environment can meet requirements, whether power supply can meet field use and the like. And fusing the safety influence factors of the construction flow information and the difficulty risk factors in the same type, and juxtaposing the different types to obtain the risk factor set. The risk factor set is a factor set describing risk factors in the construction process of the construction engineering from two aspects of construction safety and construction difficulty.
Specifically, the risk factors in the risk factor set are used as indexes, matching is performed in the historical safety management database, the safety accident situation caused by the risk factors in the historical safety event is determined, and information describing the safety accident situation is summarized to obtain the safety event information. The historical safety management database is used for collecting information of safety accident management conditions of the building engineering in historical time and managing the information uniformly. The safety event information is information describing safety accidents and comprises information such as accident reasons, accident time, personnel involved, accident scale and the like.
Specifically, the safety event information is classified based on the safety event information and the cause of the safety accident as the classification basis, for example, whether the safety event information is caused by improper operation of personnel or non-human factors such as equipment faults, so as to obtain the safety early warning classification information. The safety early warning classification information reflects the type of safety risk event in the construction process of the building engineering. And then, matching the risk factor group with the safety early warning classification information, and summarizing risk factors conforming to various safety early warning information in the safety early warning classification information, so as to obtain the safety early warning information list. And carrying out feature analysis for the subsequent data acquired, and carrying out feature comparison with various safety early warning information in a safety early warning information list, so as to determine the safety risk, and carrying out early warning on whether an object abnormality or a personnel operation abnormality occurs.
Step S200: according to the safety early warning classification information, determining environmental object early warning information and personnel operation early warning information, and based on the environmental object early warning information and the personnel operation early warning information, carrying out safety early warning parameter characteristic matching from the safety early warning information list according to the safety early warning classification information to obtain environmental object safety early warning parameter characteristics and personnel operation safety early warning parameter characteristics;
specifically, the environmental object early warning information is the safety accident early warning condition which is determined according to the safety early warning classification information and is caused by factors such as environment, construction equipment and the like. The personnel operation early warning information is the safety accident early warning condition which is determined according to the safety early warning classification information and is caused by artificial factors such as improper operation of constructors. And taking the environment object early warning information and the personnel operation early warning information as matching indexes, and performing safety early warning parameter characteristic matching in the safety early warning information list to obtain the environment object safety early warning parameter characteristic and the personnel operation safety early warning parameter characteristic. The environmental object safety pre-warning parameter features are features for describing the situation corresponding to the safety accident caused by the environmental object and comprise a line connection mode, an equipment use state and the like. The personnel operation safety early warning parameter characteristics are characteristics for describing the conditions corresponding to safety accidents caused by personnel operation problems, and comprise whether personnel operate according to operation regulations, whether safety helmets are worn or not, and the like. And a comparison basis is provided for subsequent construction safety monitoring through the determined parameter characteristics.
Step S300: the method comprises the steps that data acquisition is carried out on a target monitoring construction project through an Internet of things sensor and image acquisition equipment, and a monitoring data source is obtained;
specifically, in the construction safety monitoring process, firstly, construction data are acquired on a construction site, and the monitoring data source is obtained by utilizing an Internet of things sensor and image acquisition equipment to acquire the construction data. The monitoring data source is data for carrying out multidimensional description on a construction site of the building engineering, and comprises constructor gestures, construction environment temperature, construction power supply data and the like. Illustratively, the construction site is monitored by an internet of things sensor, such as a force monitoring sensor, a vibration sensor, a temperature sensor and the like, and the image is acquired on the site by using an image acquisition device, such as a video camera, a CCD (charge coupled device) camera, an infrared camera and the like.
Step S400: performing feature analysis on the monitoring data source to determine a collected data feature set;
further, as shown in fig. 3, the feature analysis is performed on the monitored data source to determine the collected data feature set, and step S400 in this embodiment of the present application further includes:
step S410: acquiring monitoring data information acquired by the sensor of the Internet of things, wherein the monitoring data information comprises monitoring position information, monitoring numerical value information and monitoring parameter types;
Step S420: positioning a monitoring target according to the monitoring position information to obtain target object information;
step S430: performing target object correlation analysis according to the target object information and the monitoring position information, and determining the position relationship and the target influence relationship between target objects;
step S440: based on the position relation and the target influence relation between the target objects, carrying out influence analysis on the monitoring parameter types and the corresponding monitoring numerical information to determine influence prediction information;
step S450: and respectively carrying out target object characteristic analysis according to the monitoring data information and the influence prediction information, and determining the acquired data characteristics.
Further, based on the position relationship and the target influence relationship between the targets, the influence analysis is performed on the monitoring parameter category and the corresponding monitoring numerical information, and the influence prediction information is determined, where step S440 in this embodiment of the present application further includes:
step S441: constructing a monitoring target relation network according to the position relation and the target influence relation among all the monitoring targets;
step S442: determining a monitoring target to be predicted and a monitoring parameter type from all monitoring targets, and determining a relation chain to be predicted according to the monitoring target to be predicted, the monitoring parameter type and the monitoring target relation network;
Step S443: performing influence analysis based on the position relation and the target influence relation in the relation chain to be predicted to obtain an influence coefficient;
step S444: and obtaining influence prediction information according to the influence coefficient and the monitoring numerical information.
Specifically, the characteristic analysis is performed on the monitoring data source, so that the data characteristics of the data source in the construction process are obtained, all the data characteristics are summarized, and the acquired data characteristic set is obtained. The characteristic value of the acquired data is obtained by performing deep analysis on the monitoring data source and analyzing from two angles of monitoring data and predicting influence, and comprises a parameter type, a parameter value and the like. And acquiring monitoring data transmitted from the Internet of things sensor, and extracting the acquired monitoring data from three dimensions of a monitoring position, a monitoring numerical value and a monitoring parameter category to acquire the monitoring data information. The monitoring data information reflects relevant information of a detection object of the sensor of the Internet of things, and comprises monitoring position information, monitoring numerical value information and monitoring parameter types. The monitoring position information is a monitoring position set by the sensor of the Internet of things, and can reflect the position of the monitored object in the construction process of the building engineering. The monitoring numerical value information is obtained by reading the numerical value acquired by the sensor of the Internet of things. The monitoring parameter class is obtained by analyzing the monitoring data class of the sensor of the Internet of things, and reflects the purpose of monitoring and the acquired data type.
Specifically, the position of the monitoring target of the internet of things sensor in the construction site is positioned according to the position information reflected in the monitoring position information, so that the monitoring target of the internet of things sensor, namely the target information, is determined according to the construction site drawing of the building construction project. And carrying out target object correlation analysis according to the target object information and the monitoring position information. In other words, the correlation degree between the targets is analyzed according to the target information and the monitoring position information, so as to obtain the position relationship and the target influence relationship between the targets. The position relation between the targets is the distance between the targets and the azimuth information between the targets. The target influence relationship is an association relationship between target objects, such as that the power of the air exchange machine is closely related to the environmental temperature of equipment around the air exchange machine.
Specifically, according to the position relation among the monitoring targets, the target influence relation is used for constructing a monitoring target relation network, namely, on the basis of the position relation, relation network nodes of the monitoring target relation network are established by taking each monitoring target as an object, and furthermore, the association relation among all nodes is constructed by the target influence relation, so that the monitoring target relation network is constructed. The monitoring target relation network reflects the association relation among monitoring objects of the Internet of things sensor. And determining the monitoring target to be predicted and the type of the monitoring parameter from all the monitoring targets. The monitoring target to be predicted is an object which needs to predict the monitoring parameters of the monitoring target according to the association relation. The monitoring data type is the data type of the monitoring target to be predicted, such as temperature, power, pressure and the like. And determining nodes related to the monitoring target by taking the monitoring target to be predicted as a monitoring object based on the monitoring target relation network, and determining the relation chain to be predicted according to the association relation in the monitoring target relation network. The relation chain to be predicted is a main relation chain for analysis in prediction and comprises a monitoring target related to the monitoring target to be predicted and an association relation between the monitoring targets. And performing influence analysis according to the position relation and the target influence relation in the relation chain to be predicted, namely determining the influence degree of the target to be predicted on the relation chain to be predicted, and obtaining the influence coefficient. And further, based on the monitoring numerical information, the influence coefficient is used as the influence degree of the monitoring numerical information to obtain the numerical information predicted after the monitoring numerical information is influenced, and the numerical information predicted after the monitoring numerical information is influenced is used as influence prediction information. The influence prediction information reflects the influence of the monitoring object to be predicted. And carrying out feature analysis on the target object according to the monitoring data information and the influence prediction information respectively, and carrying out feature extraction to obtain the acquired data features. Wherein, the liquid crystal display device comprises a liquid crystal display device,
Further, performing feature analysis on the monitored data source to determine an acquired data feature set, and step S400 in the embodiment of the present application further includes:
step S460: obtaining image information acquired by the image acquisition equipment;
step S470: inputting the image information into an identification model to determine an image identification main body;
step S480: dividing the image information based on the image recognition subject to determine subject-related image information;
step S490: and determining a target monitoring main body according to the image recognition main body, and extracting features of main body associated image information corresponding to the target monitoring main body to obtain acquired data features.
Specifically, the database of the image acquisition equipment is extracted, so that the acquired image information of the construction site of the construction project is obtained, wherein the image information comprises information such as multi-angle site photos, acquisition time, acquisition equipment and the like. The recognition model is a functional model which is constructed on the basis of a convolutional neural network and used for recognizing the image main body. The method comprises the steps of obtaining a sample image and a sample image recognition main body from big data, using the sample image and the sample image recognition main body as a training set, marking the sample image recognition main body, training a model constructed by taking a convolutional neural network as a framework by using the training set, and supervising the training process by using the marked sample image recognition main body until the model is trained to be converged, so as to obtain the recognition model. Further, the image information is inputted as input information into the recognition model, and the image recognition subject is outputted. Wherein the image recognition subject is a main subject of shooting in the image. Dividing the image information based on the image recognition subject, in other words, taking the image recognition subject as a key factor, determining the image information related to the key factor in the image, and taking the image information as the subject-related image information. Wherein the subject-associated image information reflects image information related to an image recognition subject. And taking the image recognition main body as a target monitoring main body, wherein the target monitoring main body is the main object for monitoring. And extracting the characteristics of the main body associated image information corresponding to the target monitoring main body, and extracting main body action characteristics which can be reflected in the image, thereby being used as the acquired data characteristics. Thus, the action features are accurately identified and extracted.
Step S500: performing traversal comparison on the acquired data feature set by utilizing the environmental object safety early warning parameter features and the personnel operation safety early warning parameter features to determine traversal comparison results;
specifically, the traversing comparison result is obtained by comparing the safety pre-warning parameter characteristics of the environmental object, the safety pre-warning parameter characteristics of personnel operation and the collected data characteristic set one by one, and extracting the characteristics successfully matched with the safety pre-warning parameter characteristics of the environmental object and the safety pre-warning parameter characteristics of personnel operation in the collected data characteristic set. Therefore, the risk condition in the collected data feature set is identified, and a basis is provided for subsequent early warning analysis.
Step S600: and determining construction safety early warning information according to the traversal comparison result and the safety early warning information list.
Further, step S600 in the embodiment of the present application further includes:
step S610: acquiring construction flow information, and determining construction danger coefficients, construction environment information and construction attitude information according to the construction flow information;
step S620: determining a construction safety range according to the construction danger coefficient and the construction environment information;
Step S630: determining construction tolerance attitude information according to the construction risk coefficient and the construction attitude information;
step S640: determining a safety monitoring frame by using the construction safety range and the construction tolerance posture information, and embedding the safety monitoring frame into the image acquisition equipment;
step S650: and carrying out gesture safety frame recognition on the personnel by using image acquisition equipment, and sending early warning information when the gesture safety frame is exceeded.
Specifically, the construction safety early warning information is the risk condition of the construction engineering in the construction process, which is obtained after the analysis of the traversal comparison result and the safety early warning information list. And obtaining construction danger coefficient, construction environment information and construction posture information in the construction process according to the construction flow information. The construction risk coefficient reflects the risk degree of each construction project in the construction process, and the construction environment information reflects the site environment, power supply and personnel guarantee conditions of the construction project in the construction process. The construction posture information is construction operation standard information of the building engineering in the construction process. And determining a construction safety range according to the construction danger coefficient and the construction environment information, in other words, determining the construction safety in accordance with the construction process according to the construction danger coefficient and the construction environment information, and ensuring the construction operation range in which the construction is smoothly carried out, namely the construction safety range. And determining construction tolerance posture information according to the construction risk coefficient and the construction posture information, namely determining the operation range in construction according to the construction risk coefficient and the construction posture information within a safety allowable range. The construction tolerance posture information is a safety allowable range of each operation in construction operation, an exemplary voltage range used in construction process, a standing range of personnel in construction process and the like. The construction safety range and the range in the construction tolerance posture information are used as safety monitoring frames, namely the safety range is operated in the construction process, the safety monitoring frames are embedded into the image acquisition equipment, namely the safety operation range is input into the image acquisition equipment as a range frame for identifying actions, further, the posture of a person is acquired through the image acquisition equipment, further, the person is compared with the posture in the safety monitoring frames, and when the comparison result exceeds the safety monitoring frames, early warning information is sent. The early warning information is used for reminding workers that the behaviors of the workers exceed the safety range and are required to be corrected in time.
Further, step S600 in the embodiment of the present application further includes:
step S660: according to the construction environment information and the construction posture information, carrying out construction flow environment influence analysis, determining environment influence information, and obtaining an environment information threshold value based on the environment influence information;
step S670: acquiring weather information in real time, and constructing a weather timing sequence chain;
step S680: performing environmental impact analysis of each time node based on the meteorological time sequence chain, and determining environmental impact prediction information;
step S690: and when the environmental impact prediction information reaches the environmental information threshold value, sending early warning information.
Specifically, the degree to which the construction operation in the construction process is affected by the environment is analyzed based on the construction environment information and the construction posture information, and for example, if the construction site is rainy, outdoor construction cannot be performed at this time. The environmental impact information reflects the degree of impact of the environment on the construction operation, the environmental information threshold is the lowest value of impact of the environment on the construction, and when the environmental impact information exceeds the environmental information threshold, the construction operation on site is affected at this time. In the case of light rain, the construction on site is not affected, but the rainfall is too large, so that water accumulation is caused, and the construction is affected at the moment. And acquiring weather information in real time, so as to construct the weather timing sequence chain. The weather timing chain is a chain reflecting the weather of the construction site, which is obtained according to weather and the corresponding time sequence. And further, carrying out environmental impact analysis on each time node by using a meteorological time sequence chain to obtain the degree of environmental impact of each time node in site construction, and carrying out impact analysis on time nodes which do not occur according to the occurred weather to obtain the environmental impact prediction information. And further, when the environmental impact prediction information reaches the environmental information threshold, early warning information is sent.
In summary, the embodiments of the present application have at least the following technical effects:
according to the embodiment of the application, through carrying out deep analysis on information of a target monitoring construction project, risk factors in the construction process are obtained, then feature conditions of safety influence caused by the risk factors are obtained, so that a safety early warning information list is obtained, a target for obtaining the safety early warning conditions in the project is achieved, safety early warning parameter features of an environmental object and personnel operation safety early warning parameter features are obtained through carrying out safety early warning parameter feature matching from the safety early warning information list according to safety early warning classification information, then data acquisition is carried out on the target monitoring construction project through an Internet of things sensor and image acquisition equipment, a target for real-time monitoring is achieved, then feature analysis is carried out on data obtained through monitoring, an acquired data feature set is determined, then traversing comparison is carried out on the acquired data feature set and the safety early warning parameter features of the environmental object and the personnel operation safety early warning parameter features, and the construction safety early warning information is determined through combining with the safety early warning information list. The technical effects of improving the accuracy of safety early warning and shortening the feedback time are achieved.
Example two
Based on the same inventive concept as the construction safety pre-warning method of a building engineering in the foregoing embodiments, as shown in fig. 4, the present application provides a construction safety pre-warning system of a building engineering, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the early warning information list construction module 11 is used for constructing a safety early warning information list based on target monitoring construction project information, wherein the safety early warning information list comprises safety early warning classification information and safety early warning parameter characteristics;
the early warning parameter characteristic obtaining module 12 is configured to determine environmental object early warning information and personnel operation early warning information according to the safety early warning classification information, and perform safety early warning parameter characteristic matching from the safety early warning information list according to the safety early warning classification information based on the environmental object early warning information and the personnel operation early warning information, so as to obtain environmental object safety early warning parameter characteristics and personnel operation safety early warning parameter characteristics;
the monitoring data source obtaining module 13 is used for obtaining a monitoring data source by carrying out data acquisition on a target monitoring construction project through an internet of things sensor and image acquisition equipment;
The data feature set determining module 14, where the data feature set determining module 14 is configured to perform feature analysis on the monitored data source to determine an acquired data feature set;
the traversal comparison result determining module 15 is configured to perform traversal comparison on the collected data feature set by using the environmental object safety early warning parameter feature and the personnel operation safety early warning parameter feature, so as to determine a traversal comparison result;
the safety early warning information determining module 16, wherein the safety early warning information determining module 16 is configured to determine construction safety early warning information according to the traversal comparison result and the safety early warning information list.
Further, the system further comprises:
the project attribute information obtaining unit is used for determining construction project attribute information according to target monitoring construction project information;
the construction information obtaining unit is used for obtaining construction project difficulty information and construction flow information according to the construction project attribute information;
the safety influence factor determining unit is used for carrying out safety analysis on each flow construction environment and each construction tool based on the construction flow information and determining the safety influence factor of each construction flow information;
The difficulty risk factor determining unit is used for analyzing construction difficulty risk factors according to the construction project difficulty information and determining difficulty risk factors;
the risk factor set construction unit is used for fusing the safety influence factors of the construction flow information and the difficulty risk factors to construct a risk factor set;
the security event information determining unit is used for determining security event information based on the risk factor set through matching of a historical security management database;
and the early warning information list construction unit is used for determining safety early warning classification information according to the safety event information and constructing the safety early warning information list by utilizing the safety early warning classification information and the risk factor set.
Further, the system further comprises:
the monitoring data information acquisition unit is used for acquiring monitoring data information acquired by the sensor of the Internet of things, wherein the monitoring data information comprises monitoring position information, monitoring numerical value information and monitoring parameter types;
The target object information obtaining unit is used for positioning a monitoring target according to the monitoring position information to obtain target object information;
the target object correlation analysis unit is used for carrying out target object correlation analysis according to the target object information and the monitoring position information to determine the position relationship and the target influence relationship between the target objects;
the influence prediction information determining unit is used for performing influence analysis on the monitoring parameter types and the corresponding monitoring numerical information based on the position relation and the target influence relation between the target objects to determine influence prediction information;
and the characteristic analysis unit is used for respectively carrying out characteristic analysis on the target object according to the monitoring data information and the influence prediction information and determining the characteristic of the acquired data.
Further, the system further comprises:
the target relation network construction unit is used for constructing a monitoring target relation network according to the position relation and the target influence relation among all the monitoring targets;
the relation chain to be predicted determining unit is used for determining a monitoring target to be predicted and a monitoring parameter type from all monitoring targets, and determining a relation chain to be predicted according to the monitoring target to be predicted, the monitoring parameter type and the monitoring target relation network;
The influence coefficient obtaining unit is used for carrying out influence analysis based on the position relation and the target influence relation in the relation chain to be predicted to obtain an influence coefficient;
and the influence prediction information obtaining unit is used for obtaining influence prediction information according to the influence coefficient and the monitoring numerical value information.
Further, the system further comprises:
the image information acquisition unit is used for acquiring the image information acquired by the image acquisition equipment;
the identification subject determining unit is used for inputting the image information into an identification model and determining an image identification subject;
the related image information determining unit is used for dividing the image information based on the image recognition main body and determining main body related image information;
and the feature extraction unit is used for determining a target monitoring main body according to the image recognition main body, and carrying out feature extraction on main body associated image information corresponding to the target monitoring main body to obtain acquired data features.
Further, the system further comprises:
The risk coefficient determining unit is used for obtaining construction flow information and determining construction risk coefficient, construction environment information and construction posture information according to the construction flow information;
the construction safety range determining unit is used for determining a construction safety range according to the construction danger coefficient and the construction environment information;
the construction tolerance posture determining unit is used for determining construction tolerance posture information according to the construction risk coefficient and the construction posture information;
the safety monitoring frame determining unit is used for determining a safety monitoring frame by utilizing the construction safety range and the construction tolerance posture information, and embedding the safety monitoring frame into the image acquisition equipment;
and the early warning information sending unit is used for identifying the gesture safety frame of the personnel by using the image acquisition equipment, and sending early warning information when the gesture safety frame exceeds the safety monitoring frame.
Further, the system further comprises:
the environment information threshold obtaining unit is used for carrying out construction flow environment influence analysis according to the construction environment information and the construction posture information, determining environment influence information and obtaining an environment information threshold based on the environment influence information;
The weather timing chain construction unit is used for obtaining weather information in real time and constructing a weather timing chain;
the influence prediction information determining unit is used for carrying out environmental impact analysis of each time node based on the meteorological time sequence chain and determining environmental impact prediction information;
and the early warning unit is used for sending early warning information when the environmental impact prediction information reaches the environmental information threshold value.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (6)
1. The construction safety early warning method for the constructional engineering is characterized by comprising the following steps of:
constructing a safety early warning information list based on target monitoring construction project information, wherein the safety early warning information list comprises safety early warning classification information and safety early warning parameter characteristics;
according to the safety early warning classification information, determining environmental object early warning information and personnel operation early warning information, and based on the environmental object early warning information and the personnel operation early warning information, carrying out safety early warning parameter characteristic matching from the safety early warning information list according to the safety early warning classification information to obtain environmental object safety early warning parameter characteristics and personnel operation safety early warning parameter characteristics;
The method comprises the steps that data acquisition is carried out on a target monitoring construction project through an Internet of things sensor and image acquisition equipment, and a monitoring data source is obtained;
performing feature analysis on the monitoring data source to determine a collected data feature set;
performing traversal comparison on the acquired data feature set by utilizing the environmental object safety early warning parameter features and the personnel operation safety early warning parameter features to determine traversal comparison results;
determining construction safety early warning information according to the traversal comparison result and the safety early warning information list;
the feature analysis is performed on the monitoring data source, and the determination of the collected data feature set comprises the following steps:
acquiring monitoring data information acquired by the sensor of the Internet of things, wherein the monitoring data information comprises monitoring position information, monitoring numerical value information and monitoring parameter types;
positioning a monitoring target according to the monitoring position information to obtain target object information;
performing target object correlation analysis according to the target object information and the monitoring position information, and determining the position relationship and the target influence relationship between target objects;
based on the position relation and the target influence relation between the target objects, carrying out influence analysis on the monitoring parameter types and the corresponding monitoring numerical information to determine influence prediction information;
Respectively carrying out target object characteristic analysis according to the monitoring data information and the influence prediction information, and determining collected data characteristics;
based on the position relation and the target influence relation between the targets, the influence analysis is performed on the monitoring parameter category and the corresponding monitoring numerical information, and the influence prediction information is determined, including:
constructing a monitoring target relation network according to the position relation and the target influence relation among all the monitoring targets;
determining a monitoring target to be predicted and a monitoring parameter type from all monitoring targets, and determining a relation chain to be predicted according to the monitoring target to be predicted, the monitoring parameter type and the monitoring target relation network;
performing influence analysis based on the position relation and the target influence relation in the relation chain to be predicted to obtain an influence coefficient;
and obtaining influence prediction information according to the influence coefficient and the monitoring numerical information.
2. The method of claim 1, wherein constructing a safety precaution information list based on the target monitoring construction project information comprises:
determining construction project attribute information according to target monitoring construction project information;
obtaining construction project difficulty information and construction flow information according to the construction project attribute information;
Based on the construction flow information, carrying out safety analysis on construction environments and construction tools of each flow, and determining safety influence factors of each construction flow information;
according to the construction project difficulty information, carrying out construction difficulty risk factor analysis and determining a difficulty risk factor;
fusing the safety influence factors of the construction flow information and the difficulty risk factors to construct a risk factor set;
based on the risk factor set, matching is carried out through a historical security management database, and security event information is determined;
and determining safety early warning classification information according to the safety event information, and constructing the safety early warning information list by utilizing the safety early warning classification information and the risk factor set.
3. The method of claim 1, wherein performing a feature analysis on the monitored data source to determine a set of collected data features comprises:
obtaining image information acquired by the image acquisition equipment;
inputting the image information into an identification model to determine an image identification main body;
dividing the image information based on the image recognition subject to determine subject-related image information;
and determining a target monitoring main body according to the image recognition main body, and extracting features of main body associated image information corresponding to the target monitoring main body to obtain acquired data features.
4. The method of claim 1, wherein the method further comprises:
acquiring construction flow information, and determining construction danger coefficients, construction environment information and construction attitude information according to the construction flow information;
determining a construction safety range according to the construction danger coefficient and the construction environment information;
determining construction tolerance attitude information according to the construction risk coefficient and the construction attitude information;
determining a safety monitoring frame by using the construction safety range and the construction tolerance posture information, and embedding the safety monitoring frame into the image acquisition equipment;
and carrying out gesture safety frame recognition on the personnel by using image acquisition equipment, and sending early warning information when the gesture safety frame is exceeded.
5. The method of claim 4, wherein the method comprises:
according to the construction environment information and the construction posture information, carrying out construction flow environment influence analysis, determining environment influence information, and obtaining an environment information threshold value based on the environment influence information;
acquiring weather information in real time, and constructing a weather timing sequence chain;
performing environmental impact analysis of each time node based on the meteorological time sequence chain, and determining environmental impact prediction information;
And when the environmental impact prediction information reaches the environmental information threshold value, sending early warning information.
6. A construction safety precaution system for a construction project, the system comprising:
the early warning information list construction module is used for constructing a safety early warning information list based on target monitoring construction project information, and the safety early warning information list comprises safety early warning classification information and safety early warning parameter characteristics;
the early warning parameter characteristic obtaining module is used for determining environmental object early warning information and personnel operation early warning information according to the safety early warning classification information, carrying out safety early warning parameter characteristic matching from the safety early warning information list according to the safety early warning classification information based on the environmental object early warning information and the personnel operation early warning information, and obtaining environmental object safety early warning parameter characteristics and personnel operation safety early warning parameter characteristics;
the monitoring data source acquisition module is used for acquiring data of a target monitoring construction project through the sensor of the Internet of things and the image acquisition equipment to acquire a monitoring data source;
The data feature set determining module is used for carrying out feature analysis on the monitoring data source and determining an acquired data feature set;
the traversal comparison result determining module is used for carrying out traversal comparison on the acquired data feature set by utilizing the safety early warning parameter features of the environmental object and the safety early warning parameter features of personnel operation so as to determine traversal comparison results;
the safety early warning information determining module is used for determining construction safety early warning information according to the traversal comparison result and the safety early warning information list;
the monitoring data information acquisition unit is used for acquiring monitoring data information acquired by the sensor of the Internet of things, wherein the monitoring data information comprises monitoring position information, monitoring numerical value information and monitoring parameter types;
the target object information obtaining unit is used for positioning a monitoring target according to the monitoring position information to obtain target object information;
the target object correlation analysis unit is used for carrying out target object correlation analysis according to the target object information and the monitoring position information to determine the position relationship and the target influence relationship between the target objects;
The influence prediction information determining unit is used for performing influence analysis on the monitoring parameter types and the corresponding monitoring numerical information based on the position relation and the target influence relation between the target objects to determine influence prediction information;
the characteristic analysis unit is used for respectively carrying out characteristic analysis on the target object according to the monitoring data information and the influence prediction information and determining the characteristic of the acquired data;
the target relation network construction unit is used for constructing a monitoring target relation network according to the position relation and the target influence relation among all the monitoring targets;
the relation chain to be predicted determining unit is used for determining a monitoring target to be predicted and a monitoring parameter type from all monitoring targets, and determining a relation chain to be predicted according to the monitoring target to be predicted, the monitoring parameter type and the monitoring target relation network;
the influence coefficient obtaining unit is used for carrying out influence analysis based on the position relation and the target influence relation in the relation chain to be predicted to obtain an influence coefficient;
And the influence prediction information obtaining unit is used for obtaining influence prediction information according to the influence coefficient and the monitoring numerical value information.
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-
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- 2022-12-28 CN CN202211691547.1A patent/CN116010826B/en active Active
Non-Patent Citations (2)
| Title |
|---|
| 基于BIM与物联网的地下工程施工安全风险预警与实时控制研究;陈永高 等;科技通报(07);全文 * |
| 基于卷积神经网络的建筑工程施工安全预警研究;赵静;中国优秀硕士学位论文全文数据库;全文 * |
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