CN117236688A - Building safety accident risk prevention and early warning system based on big data technology - Google Patents
Building safety accident risk prevention and early warning system based on big data technology Download PDFInfo
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Abstract
The invention relates to the technical field of building safety accident early warning, in particular to a building safety accident risk prevention and early warning system based on a big data technology, which comprises a big data acquisition building accident risk unit, a risk mining data characteristic extraction processing unit, an accident risk assessment and prevention unit, an engineering safety calculation and early warning unit and a risk data model building unit, wherein the big data acquisition building accident risk unit is used for acquiring the data of building safety information and building a database; the risk mining data characteristic extraction processing unit is used for performing risk mining on the data acquired by the big data acquisition building accident risk unit; the accident risk assessment and prevention unit is used for comprehensively analyzing the risk factors mined by the risk mining data characteristic extraction processing unit to obtain construction engineering safety risk assessment data; the safety risk level of the site is intuitively known, so that closed-loop management is realized by adopting a corresponding risk classification management and control method, and risk accident loss is reduced.
Description
Technical Field
The invention relates to the technical field of building safety accident early warning, in particular to a building safety accident risk prevention and early warning system based on a big data technology.
Background
The safety problem in the building construction process is always the most concerned problem of engineers and building practitioners, is one of the topics of public general concern, and is still a very serious social problem and one of important factors for preventing urban development although the occurrence rate of various building safety accidents is reduced, so that an efficient and accurate building safety prediction and early warning system is urgently needed; the traditional building safety management method is based on management experience and specifications, for example, the current situation in a place is mastered by monitoring a safety place in real time through intelligent monitoring, and risk analysis and evaluation are carried out according to hidden dangers existing in the place, so that early warning and emergency response are realized on the existing hidden dangers, but various factors influencing the safety of the building site exist, including external factors such as climate change, logistics management, field equipment, personnel behaviors, process and equipment states, and the like, and the situations of risk hidden dangers exist in the factors.
The traditional method is mainly used for data acquisition aiming at each professional project, such as template projects, foundation pit projects and the like, and is excessively heavy and professional, so that when projects are increasingly complex, the limitation of knowledge and experience of practitioners and management staff is amplified, correct knowledge and utilization can not be carried out aiming at the checked results, and especially in the background of project construction guided by using game theory, system theory and control theory ideas, the game balance points among quality, safety and progress are deviated, so that construction engineering safety accidents are caused.
Therefore, the patent provides a construction safety accident risk prevention and early warning system scheme based on the big data technology, so as to solve the defects of the technology.
Disclosure of Invention
The invention aims to provide a building safety accident risk prevention and early warning system based on a big data technology so as to solve the problems in the background technology.
In order to achieve the above purpose, the invention provides a building safety accident risk prevention and early warning system based on big data technology, which comprises a big data acquisition building accident risk unit, a risk mining data characteristic extraction processing unit, an accident risk assessment prevention unit, an engineering safety calculation and early warning unit and a risk data model building unit, wherein the big data acquisition building accident risk unit is used for acquiring the data of building safety information and building a database, and the big data acquisition building accident risk unit comprises a project full environment information data acquisition module and an AI hidden danger problem identification module;
the risk mining data characteristic extraction processing unit is used for performing risk mining on the data acquired by the big data acquisition building accident risk unit, and comprises a data preprocessing module and a characteristic extraction module;
the accident risk assessment and prevention unit is used for comprehensively analyzing the risk factors mined by the risk mining data characteristic extraction processing unit to obtain construction engineering safety risk assessment data, and comprises a data comparison module and a risk prediction module;
the engineering safety calculation and early warning unit is used for reminding and early warning when the engineering safety risk reaches an early warning threshold value, and comprises an early warning threshold value calculation module and an early warning prompt module;
the risk data model building unit is used for tracing the history of occurrence of the risk event, and comprises a time sequence module which is used for analyzing and classifying the history risk data, building a risk model and predicting future engineering safety risk in construction.
As a further improvement of the technical scheme, the project full environment information data acquisition module acquires and collects the real environment data of the construction site through a project full environment information acquisition technology;
the AI hidden trouble problem identification module is used for automatically identifying hidden trouble problems existing in a construction project site, so that data are comprehensively and accurately acquired.
As a further improvement of the technical scheme, the project full environment information data acquisition module comprises a project full environment information acquisition data acquisition module and a satellite data receiving module;
the project full-scope information acquisition data acquisition module is used for acquiring project full-scope of a construction project in real time and generating a project full-scope photo capable of reflecting real conditions; the satellite data receiving module is used for being connected with the Internet through satellite signals in the field.
As a further improvement of the technical scheme, the data preprocessing module is used for removing abnormal data and noise, and preprocessing and normalizing the data;
the characteristic extraction module is used for extracting characteristic parameters of various accident hidden dangers.
As a further improvement of the technical scheme, the data comparison module is used for comprehensively evaluating the acquired hidden danger data and the safety threshold value to obtain the engineering construction safety risk level; the risk prediction module is used for predicting the safety risk of the project under construction through the safety risk level obtained by the data comparison module.
As a further improvement of the technical scheme, the risk prediction module adopts a multiple linear regression model for predicting the safety risk of the in-building engineering, and the expression is as follows:
Y＝β0+β1*X1+β2*X2+…+βn*Xn+ε
wherein the safety index Y is a dependent variable, X1, X2, … and Xn are independent variables, beta 0, beta 1, beta 2, … and beta n are coefficients of a model, and epsilon is an error term;
in the prediction of the safety risk of the building engineering, the number of hidden dangers related to the safety risk of the building engineering is selected as independent variable input, and a multiple linear regression model is used for analysis modeling to obtain a prediction result of the safety risk of the building engineering, wherein the prediction result comprises the following states:
state one: when the value of the security measure is lower than the risk control value S 1 When the construction project is in a construction project site, a great risk exists, and the construction project should be immediately subjected to comprehensive shutdown for modification;
state two: when the value of the security measure is lower than the risk control value S 2 When the construction project is in a construction project site, a large risk exists, and the construction project should be immediately rectified by adopting local shutdown;
state three: when the value of the security measure is lower than the risk control value S 3 When the construction project is constructed, the construction project has general risks, and the construction project should take corrective measures in a limited period;
state four: when the value of the security measure is lower than the risk control value S 4 At this point there is a slight risk on the construction project site and the construction project should continue to remain.
The relation of the values is as follows: s is S 1 <S 2 <S 3 <S 4 ，S n Is an early warning threshold.
As a further improvement of the technical scheme, the early warning threshold calculation module is used for calculating an early warning threshold of the safety risk of the construction project; and the early warning prompt module is used for sending risk early warning information to the user.
As a further improvement of the technical scheme, the early warning threshold calculation module adopts the following formula to calculate the early warning threshold:
early warning threshold = a×r+b×s+c×t
Wherein R, S, T respectively represents characteristic values of project accident potential balance points, the difficulty level of potential correction, capability evaluation indexes of field management teams and the like, and a, b and c are coefficients of various factors.
Compared with the prior art, the invention has the beneficial effects that:
in the building safety accident risk prevention and early warning system based on the big data technology, a multiple linear regression model is fitted by big data algorithm through accumulating mass data in construction engineering safety and risk inspection, accident statistical analysis and the like for more than ten years in the past; the system automatically divides the potential safety hazard level, automatically reduces the potential safety hazard according to different accident types, enables management personnel of the building site risk accident to know risk information and possibly occurring risk of various accidents, so as to guide the participating parties of the project to adopt differentiated key risk management and control, avoid larger and more accidents, reduce loss caused by the risk accident, assist the project to build reasonable game balance points under the guiding of ideas such as game theory, control theory and information theory, and the like, and further avoid the blind project from reasonably managing and controlling the safety hazard.
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Fig. 1 is a schematic block diagram showing the overall structure of embodiment 1 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a first embodiment of the present invention is shown, and the present embodiment provides a building safety accident risk prevention and early warning system based on big data technology, which includes a big data collection building accident risk unit, a risk mining data feature extraction processing unit, an accident risk assessment prevention unit, an engineering safety calculation and early warning unit, and a risk data model establishment unit;
the big data acquisition building accident risk unit is used for acquiring the data of building safety information and establishing a database;
the big data acquisition building accident risk unit comprises a whole-environment information data acquisition module and a building data acquisition module;
the system comprises a project full environment information acquisition technology, a full environment information data acquisition module, a sampling system and a sampling system, wherein the project full environment information acquisition technology is used for acquiring and acquiring project full environment information on site real environment data; the AI hidden trouble problem identification module is used for automatically identifying hidden trouble problems existing in a construction project site, so that data are comprehensively and accurately obtained, and subjective prejudice sampling is avoided.
The whole-scene information acquisition module comprises a whole-scene information acquisition data acquisition module and a satellite data receiving module;
the full-environment information acquisition data acquisition module is used for acquiring field information and data without dead angles by utilizing existing intelligent construction site equipment and sensors on the field and a flow type data acquisition system; the satellite data receiving module is used for carrying out data communication through a satellite; the risk mining data characteristic extraction processing unit is used for performing risk mining on the data acquired by the big data acquisition building accident risk unit;
the risk mining data feature extraction processing unit comprises a data preprocessing module and a feature extraction module;
the data preprocessing module is used for removing abnormal data and noise, and preprocessing and normalizing the data; the feature extraction module is used for extracting feature parameters of building safety;
the data preprocessing module can divide sample points into core points, boundary points and noise points through a clustering algorithm, noise points and abnormal data in data are automatically filtered in the clustering process, and the principle of the clustering algorithm is as follows: randomly selecting k objects, wherein the instance is more likely to be in which type from which point, taking the central point of each type as a new object, and iterating until classification is completed;
when the characteristic extraction module extracts the characteristic parameters of hidden danger, the characteristic extraction module can extract the characteristic parameters of surrounding environment conditions, whether the operation personnel are involved, whether the operation personnel are dangerous or super-dangerous, the accident types (such as electric shock accidents, fire/explosion accidents, high falling/object striking accidents, mechanical injury accidents, collapse accidents, hoisting injury accidents and other accidents) and the like, so that the related information of the construction site can be more perfect and appear in a database, the follow-up data acquired on the site can be conveniently and comprehensively compared, a risk prediction report can be obtained more accurately, and powerful support is provided for the follow-up construction engineering safety risk prediction and recognition.
The accident risk assessment and prevention unit is used for comprehensively analyzing the risk factors mined by the risk mining data characteristic extraction processing unit to obtain construction engineering safety risk assessment data;
the accident risk assessment and prevention unit comprises a data comparison module and a risk prediction module;
the data comparison module is used for comprehensively evaluating the collected field data, projects and sub-project characteristics to obtain the safety risk level of the construction project; and the risk prediction module predicts the safety risk of the building according to the safety risk grade obtained by the data comparison module.
The data comparison module comprehensively evaluates collected site data, projects and sub-project characteristics, such as unsafe behavior of site personnel, unsafe state of objects, unsafe factors in the environment and other direct reasons, and administrative defects and other indirect reasons, to obtain a safety index, a machine learning algorithm can be adopted to input data acquired by various sensors on site and cleaned into a model, so that the model can automatically output the safety risk level of the on-site project, and the on-site project can be pertinently adjusted and improved in technical management, material purchase, special inspection and acceptance, fund use, power equipment management, education and training and other aspects according to the obtained safety risk level, for example, for high-risk on-site projects, project lead team can be stopped (locally) to be modified, project lead team can be adjusted, and the management department can be notified and punishment advice can be submitted.
The machine learning algorithm is an algorithm for automatically finding rules and statistical relationships from data by learning and analyzing the data and generating a prediction model or a decision model.
The engineering safety calculation and early warning unit is used for reminding and early warning when the engineering safety risk reaches an early warning threshold;
the engineering safety calculation and early warning unit comprises an early warning threshold calculation module and an early warning prompt module;
the early warning threshold calculation module is used for calculating an early warning threshold of the safety risk of the construction project; the early warning prompt module is used for sending risk early warning information to the user.
The early warning threshold calculation module calculates the early warning threshold by adopting the following formula:
early warning threshold = a×r+b×s+c×t
Wherein R, S, T respectively represents characteristic values of project accident potential balance points, the difficulty level of potential correction, capability evaluation indexes of field management teams and the like, and a, b and c are coefficients of various factors.
If the early warning threshold exceeds the set early warning threshold, the early warning prompt module automatically gives an alarm.
For example, we can set the hidden danger balance point eigenvalues of significant risk to be constant 60, a=1, b=1.5, c= -1, now assuming that the following data is collected in a particular project: after the data of a certain project is acquired and identified, the major accident potential exists through feature extraction, the correction difficulty is 9, and the evaluation value of the management and control capability of a construction project site is 4. Then, based on the above parameters, we can calculate the early warning threshold: the early warning threshold value=1x60+1.5x9+ (-1) x5=68.5, if the safety index score obtained by the regression model does not exceed the value, the system can send out early warning, meanwhile, the fuzzy clustering method in fuzzy mathematics adopted by the system can not send out alarm by taking a certain score as a limit, the situation that the qualification of the total contractor unit, the construction in different places, the regional situation, the hidden danger relate to the current supervision important point and the like can be comprehensively considered, the comprehensive judgment is carried out by adopting the methods of fuzzy logic, fuzzy clustering and the like, and even if the safety index is higher than the threshold value, the early warning can still be sent out, the countermeasure proposal is proposed, and the like.
The risk data model building unit is used for tracing the history of occurrence of the risk event;
the risk data model building unit comprises a time sequence module;
the time sequence module is used for analyzing and classifying the historical risk data, establishing a risk model, predicting future engineering safety risk, for example, when a construction site is at different time points on a construction stage time axis, collecting and analyzing the historical data of similar projects through a big data technology so as to judge the type and the sequence of accidents which possibly occur in the time period, thereby deploying corresponding safety measures and equipment in advance, strengthening management work in a certain aspect and the like.
In summary, the invention collects and acquires the information of the potential accident hazards of the construction engineering through the big data collection construction accident risk unit, and uniformly uploads the data into the database, so as to store the risk traceability data for the later risk data model building unit;
specifically, the risk prediction module adopts a multiple linear regression model for predicting the safety risk of the in-building engineering, and the expression is:
Y＝β0+β1*X1+β2*X2+…+βn*Xn+ε
wherein Y is a dependent variable, X1, X2, …, xn are independent variables, β0, β1, β2, …, βn are coefficients of the model, ε is an error term;
in the prediction of the construction safety risk, independent variables related to the construction safety risk, such as the number of accident hidden dangers of various levels, and the like, can be selected as input, and analysis modeling is performed by using a multiple linear regression model, so that a prediction result of the construction safety risk is obtained.
For example, we can collect a series of buildings with accidents, record each construction engineering attribute and corresponding accident data thereof, such as accident type, direct cause, indirect cause, casualties, property loss, etc., after determining the level of hidden danger in various accident types by big data recommendation algorithm, etc., build a multiple linear regression model, the hidden danger number of various levels is taken as independent variables X1, X2 …, xn, and the construction engineering safety risk with statistical regularity is taken as dependent variable Y, wherein β0, β1, β2, β3, …, βn are the intercept and coefficients of four independent variables respectively, and the error term epsilon is the unavoidable random error caused by other unaccounted factors;
we can train the model using historical data and get the following coefficient values: β0= 81.28, β1= -2.66, β2= -0.71, β3= -0.364, β4= -0.006; after field recognition and determination by the feature module, the number of accident hidden dangers at each level is respectively X 1 ＝3、X 2 ＝6、X 3 ＝7、X 4 =10, then by bringing these data into the model described above, we can get the predicted security risk for the building: construction safety risk y= 81.28-2.66×3-0.71×6-0.364×7-0.006×10=66.43, the early warning threshold value of the major risk is 68.6 in the previous case, and the prediction result means that the major accident risk exists in the construction engineering, countermeasures corresponding to the major risk need to be taken, and the comprehensive shutdown should be immediately taken for modification, so that the major accident hidden danger is eliminated, and the safety risk of the construction engineering is reduced.
The predicted outcome includes the following states:
state one: when the value of the safety measure is lower than the risk control value S1, at the moment, a major risk exists on the construction project site, the construction project should be immediately rectified by adopting comprehensive shutdown, and major accident potential is eliminated so as to reduce the safety risk of the construction project;
state two: when the value of the safety measure is lower than the risk control value S2, a larger risk exists on the construction project site, and the construction project should be immediately rectified by adopting local shutdown, so that the hidden danger of major accidents is eliminated or weakened to reduce the safety risk of the construction project;
state three: when the value of the safety measure is lower than the risk control value S3, the general risk exists at the site of the project under construction, and the limit correction measures should be adopted in the project under construction;
state four: when the value of the safety measure is lower than the risk control value S4, there is a slight risk at the site of the construction project, and the construction project should be kept.
The relation of the values is as follows: s1< S2< S3< S4, sn is an early warning threshold.
And the risk information is sent to a risk accident responsible person on the construction site through the engineering safety calculation and early warning unit, so that the risk accident is prevented, and the occurrence of the risk accident is reduced.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. The building safety accident risk prevention and early warning system based on the big data technology comprises a big data acquisition building accident risk unit, a risk mining data characteristic extraction processing unit, an accident risk assessment prevention unit, an engineering safety calculation and early warning unit and a risk data model building unit, and is characterized in that:
the large data acquisition building accident risk unit is used for acquiring the data of building safety information and establishing a database, and comprises a project full environment information data acquisition module and an AI hidden trouble problem identification module;
the risk mining data characteristic extraction processing unit is used for performing risk mining on the data acquired by the big data acquisition building accident risk unit, and comprises a data preprocessing module and a characteristic extraction module;
the accident risk assessment and prevention unit is used for comprehensively analyzing the risk factors mined by the risk mining data characteristic extraction processing unit to obtain construction engineering safety risk assessment data, and comprises a data comparison module and a risk prediction module;
the engineering safety calculation and early warning unit is used for reminding and early warning when the engineering safety risk reaches an early warning threshold value, and comprises an early warning threshold value calculation module and an early warning prompt module;
the risk data model building unit is used for tracing the history of occurrence of the risk event, and comprises a time sequence module which is used for analyzing and classifying the history risk data, building a risk model and predicting future engineering safety risk in construction.
2. The big data technology-based building safety accident risk prevention and early warning system according to claim 1, wherein: the project full-scope information data acquisition module is used for acquiring and collecting the real-world data of the construction site through a project full-scope information acquisition technology;
the AI hidden trouble problem identification module is used for automatically identifying hidden trouble problems existing in a construction project site, so that data are comprehensively and accurately acquired.
3. The big data technology-based building safety accident risk prevention and early warning system according to claim 2, wherein: the project full environment information data acquisition module comprises a project full environment information acquisition data acquisition module and a satellite data receiving module;
the project full-scope information acquisition data acquisition module is used for acquiring project full-scope of a construction project in real time and generating a project full-scope photo capable of reflecting real conditions; the satellite data receiving module is used for being connected with the Internet through satellite signals in the field.
4. The big data technology-based building safety accident risk prevention and early warning system according to claim 1, wherein: the data preprocessing module is used for removing abnormal data and noise, and preprocessing and normalizing the data;
the characteristic extraction module is used for extracting characteristic parameters of various accident hidden dangers.
5. The big data technology-based building safety accident risk prevention and early warning system according to claim 1, wherein: the data comparison module is used for comprehensively evaluating the acquired hidden danger data and the safety threshold value to obtain the engineering construction safety risk level; the risk prediction module is used for predicting the safety risk of the project under construction through the safety risk level obtained by the data comparison module.
6. The big data technology-based building safety accident risk prevention and early warning system according to claim 5, wherein: the risk prediction module adopts a multiple linear regression model and is used for predicting the safety risk of the in-building engineering, and the expression is as follows:
Y＝β0+β1*X1+β2*X2+…+βn*Xn+ε
wherein the safety index Y is a dependent variable, X1, X2, … and Xn are independent variables, beta 0, beta 1, beta 2, … and beta n are coefficients of a model, and epsilon is an error term;
in the prediction of the safety risk of the building engineering, the number of hidden dangers related to the safety risk of the building engineering is selected as independent variable input, and a multiple linear regression model is used for analysis modeling to obtain a prediction result of the safety risk of the building engineering, wherein the prediction result comprises the following states:
state one: when the value of the security measure is lower than the risk control value S 1 When the construction project is in a construction project site, a great risk exists, and the construction project should be immediately subjected to comprehensive shutdown for modification;
state two: when the value of the security measure is lower than the risk control value S 2 When the construction project is in a construction project site, a large risk exists, and the construction project should be immediately rectified by adopting local shutdown;
state three: when the value of the security measure is lower than the risk control value S 3 When the construction project is constructed, the construction project has general risks, and the construction project should take corrective measures in a limited period;
state four: when the value of the security measure is lower than the risk control value S 4 At this point there is a slight risk on the construction project site and the construction project should continue to remain.
The relation of the values is as follows: s is S 1 <S 2 <S 3 <S 4 ，S n Is an early warning threshold.
7. The big data technology-based building safety accident risk prevention and early warning system according to claim 1, wherein: the early warning threshold calculation module is used for calculating an early warning threshold of the safety risk of the construction project; and the early warning prompt module is used for sending risk early warning information to the user.
8. The big data technology-based construction safety accident risk prevention and early warning system according to claim 7, wherein: the early warning threshold calculation module calculates the early warning threshold by adopting the following formula:
early warning threshold = a×r+b×s+c×t
Wherein R, S, T respectively represents characteristic values of project accident potential balance points, the difficulty level of potential correction, capability evaluation indexes of field management teams and the like, and a, b and c are coefficients of various factors.
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