Construction site intelligent safety management system and method based on BIM technology

Abstract

The invention discloses a construction site intelligent safety management system and method based on a BIM technology, and relates to the technical field of constructional engineering, and the method comprises the following steps: S100, building a three-dimensional digital model of a construction site by using BIM software, and collecting construction site data by deploying a video camera, personnel positioning equipment and an environment sensor; s200, preprocessing the acquired data, and performing potential safety hazard identification and environment detection on the construction site; s300, fusing and displaying the generated BIM three-dimensional digital model and the processed data through spatial alignment, data mapping and dynamic updating; s400, calculating the probability of occurrence of potential safety hazards on the construction site and a comprehensive decision score; and S500, carrying out quantitative assessment on the potential safety hazards of the construction site by constructing a risk assessment model, sorting the calculated risk scores of the potential safety hazards, and determining the priority processing sequence of the potential safety hazards of the construction site, so that intelligent safety management of the construction site is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of building engineering, and in particular to an intelligent safety management system and method for a construction site based on BIM technology. Background Art

[0002] With the continuous expansion of the scale of construction projects, the safety management of construction sites has become more and more complex and important. Traditional safety management methods have problems such as information dispersion, poor real-time performance and slow response, and are difficult to adapt to the needs of modern construction sites. BIM technology, as a digital tool, can provide rich data information throughout the life cycle of construction projects, providing new technical means for the intelligent safety management of construction sites.

[0003] However, the current intelligent safety management of construction sites has the following problems: First, at the construction site, the investigation of safety hazards and emergency response often rely on manual inspection, especially when there are many construction workers and the construction area is wide. Manual inspection is not only time-consuming, but also prone to omissions. Especially in high-risk periods or complex construction scenarios, manual management often cannot detect potential safety hazards in a timely manner; secondly, traditional safety management methods mainly rely on manual experience for judgment and manual recording, and cannot quickly integrate and analyze the multi-dimensional data of construction site video surveillance data, personnel positioning data and environmental monitoring data; finally, the lack of comprehensive visualization and real-time analysis of data makes it difficult for construction management personnel to understand the overall safety status of the construction site in a timely manner. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent safety management system and method for construction sites based on BIM technology to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a construction site intelligent safety management method based on BIM technology, the method comprising the following steps: S100, use BIM software to create a three-dimensional digital model of the construction site, and collect construction site data by deploying video cameras, personnel positioning equipment and environmental sensors, including video surveillance data, personnel movement trajectory and environmental data; S200, preprocessing the data obtained in step S100, using a convolutional neural network (CNN) to identify safety hazards at the construction site from the preprocessed video surveillance data, and applying an environmental data analysis algorithm to process and analyze the preprocessed environmental detection data; S300, fusing the BIM three-dimensional digital model generated in step S100 and step S200 with the processed data through spatial alignment, data mapping and dynamic updating, and displaying the generated data fusion model in three-dimensional form using GIS technology; S400, using Bayesian decision theory to calculate the probability of occurrence of safety hazards at the construction site, and generating a comprehensive decision score based on the Bayesian calculation results combined with the degree of danger of the safety hazard event; S500. By constructing a risk assessment model, a quantitative assessment is made on the severity, impact scope, probability of occurrence and comprehensive decision-making score of hidden dangers, the calculated risk scores of hidden dangers are ranked, and the priority order of handling safety hazards at the construction site is determined.

[0006] In step S100, the specific steps include: S101. Use BIM software to build a three-dimensional model of the construction site, including building structure, equipment layout and construction path; install cameras at the construction entrance, construction platform and high-altitude work area of ​​the construction site to obtain video surveillance data of the construction site; equip construction workers at the construction site with RFID tags or GPS positioning equipment to obtain the location and activity trajectory of construction workers; arrange temperature, humidity, noise and dust monitoring sensors at the construction site to cover the entire construction area, and continuously collect environmental data of the construction site; S102: Transmit the data acquired in step S101 via a wireless network.

[0007] In step S200, the specific steps include: S201, preprocessing the collected video surveillance data, personnel positioning data and environmental data by weighted averaging method and Kalman filter; S202, performing a convolution operation on the preprocessed video surveillance data to extract local features in the video frame. The convolution operation calculation formula is as follows: ; Among them, x(i,j) represents the pixel value of the input image, y(i,j) represents the pixel value of the output feature map, that is, the feature value after convolution, (i,j) represents the coordinates of the pixel, k(p,q) represents the pixel value of the convolution kernel, (p,q) represents the coordinates of the elements in the convolution kernel, P and Q represent the height and width of the convolution kernel respectively, and traverse all positions of the input image to generate a complete output feature map; then the main features are highlighted through the pooling operation, and the image features related to safety in the video frame are extracted through convolution and pooling operations; finally, the extracted feature image is integrated through the fully connected layer, and the potential safety hazards at the construction site are identified and output using the Softmax classifier; S203. Calculate the mean and standard deviation of the environmental detection data after preprocessing, and set the upper and lower environmental detection thresholds according to the calculated mean and standard deviation. The calculation formula for setting the lower environmental detection threshold is as follows: L=μ-k1*σ; wherein L represents the set lower environmental detection threshold, μ represents the mean of the environmental detection data, k1 represents the adjustment parameter, and σ represents the standard deviation of the environmental detection data. The calculation formula for setting the upper environmental detection threshold is as follows: U=μ+k2*σ; wherein U represents the set upper environmental detection threshold, k2 represents the adjustment parameter, and the environmental detection data x is compared with the set upper and lower environmental detection thresholds. When the environmental detection data x is less than the set lower environmental detection threshold L or the environmental detection data x is greater than the set upper environmental detection threshold U, it is marked as data abnormality and the alarm mechanism is triggered.

[0008] In step S300, the specific steps include: S301, spatially align the BIM three-dimensional model and the processed data for data fusion, obtain the three-dimensional coordinates obtained by video monitoring or personnel positioning in step S100 and the three-dimensional coordinates of the BIM three-dimensional model, and convert the three-dimensional coordinate data obtained by video monitoring or personnel positioning into a unified BIM model coordinate system by applying a coordinate transformation formula, the formula is as follows: ; Among them, (X1, Y1, Z1) represents the three-dimensional coordinates obtained by video monitoring or personnel positioning, s represents the scaling factor, (X2, Y2, Z2) represents the three-dimensional coordinates of the BIM three-dimensional model, (T x ,T y ,T z ) represents the translation vector; S302, identifying the foreground object by comparing the difference between the current frame and the background image, the specific formula is as follows: t (x,y)=|I t (x,y)-B(x,y)|; where D t (x, y) represents the difference value at each pixel position, that is, the difference image, I t (x, y) represents the pixel value of the current frame image, B(x, y) represents the pixel value of the background image, and the pixel point in the difference image is judged whether it belongs to the foreground by the set foreground judgment threshold. When the difference image exceeds the set foreground judgment threshold, it is judged as the foreground; S303, mapping the processed data to the BIM 3D model through a linear interpolation algorithm, the linear difference formula is as follows: y=y1+(x-x1)*(y2-y1) / (x2-x1); wherein (x1, y1) and (x2, y2) are known data points, x represents the point to be interpolated, and y represents the interpolation result; S304, by acquiring the processed data in real time, continuously performing the spatial alignment and data mapping process to update the data fusion model; S305. Use GIS technology to display the generated data fusion model in three-dimensional form.

[0009] In step S400, the specific steps include: S401. Based on the Bayesian decision formula, the data fusion model generated in step S304 is used to calculate the probability of occurrence of potential safety hazards at the construction site. The formula is as follows: P(H|E)=P(E|H)*P(H) / P(E); wherein P(H|E) represents the probability of potential safety hazard H occurring when event E occurs, P(E|H) represents the probability of event E occurring when potential safety hazard H occurs, P(H) represents the prior probability of potential safety hazard H occurring, and P(E) represents the prior probability of event E occurring; S402: Calculate the comprehensive decision score. The formula is as follows: ; Where D represents the comprehensive decision score, P(H i |E i ) represents the conditional probability of the occurrence of the i-th hidden danger, C i represents the weight of the i-th hidden danger, and n represents the total number of hidden dangers.

[0010] In step S500, the specific steps include: S501. By constructing a risk assessment model, a quantitative assessment of the safety hazards at the construction site is conducted in combination with the severity, impact range, probability of occurrence and comprehensive decision-making score of the hidden dangers. The calculation formula is as follows: ; Among them, R i represents the risk score of the ith hidden danger, S i Indicates the severity of the i-th hidden danger, I i represents the impact range of the i-th hidden danger, and α represents the adjustment parameter; S502, the calculated R i Sort and determine the priority order of the safety hazards at the construction site, and prioritize the highest R i As a priority hidden danger.

[0011] An intelligent safety management system for construction sites based on BIM technology, the system includes: a data acquisition module, a data processing module and a safety management module, the data acquisition module is used to create a three-dimensional digital model of the construction site using BIM software, and collect construction site data by deploying video cameras, personnel positioning equipment and environmental sensors; the data processing module is used to pre-process, image recognize, detect the environment and build a three-dimensional model for the data obtained from the data acquisition module; the safety management module is used to calculate the probability of occurrence of safety hazards at the construction site using Bayesian decision theory, generate a comprehensive decision score based on the Bayesian calculation results combined with the danger level of the safety hazard event, and quantitatively evaluate the safety hazards at the construction site by building a risk assessment model.

[0012] The data acquisition module includes a BIM model building unit, a centralized video monitoring unit, a construction personnel positioning unit and a construction environment detection unit. The BIM model building unit is used to create a three-dimensional digital model of the construction site through BIM software. The centralized video monitoring unit is used to collect video monitoring data of the construction site by deploying video cameras. The construction personnel positioning unit is used to obtain the location and activity trajectory of the construction personnel by equipping the construction personnel at the construction site with RFID tags or GPS positioning devices. The construction environment detection unit is used to arrange temperature, humidity, noise and dust monitoring sensors at the construction site to cover the entire construction area and continuously collect environmental data of the construction site. The output end of the data acquisition module is connected to the input end of the data processing module.

[0013] The data processing module includes a data preprocessing unit, an image recognition unit, an environment detection unit and a three-dimensional model construction unit. The data preprocessing unit is used to preprocess the data through a Kalman filter algorithm. The image recognition unit is used to use a convolutional neural network (CNN) image processing technology to process and analyze the video surveillance data and detect safety hazards. The environment detection unit is used to calculate the mean and standard deviation of the preprocessed environment detection data, set the upper and lower limit environment detection thresholds through the calculated mean and standard deviation, and compare them with the environment detection data to identify data anomalies. The three-dimensional model construction unit is used to fuse the generated BIM three-dimensional digital model with the processed data through spatial alignment, data mapping and dynamic updating, and use GIS technology to display the generated data fusion model in three-dimensional form. The output end of the data processing module is connected to the input end of the security management module.

[0014] The safety management module includes a risk probability calculation unit, a comprehensive decision score calculation unit and an alarm unit. The risk probability calculation unit is used to calculate the probability of occurrence of safety hazard events at the construction site based on the Bayesian decision formula and the generated data fusion model. The comprehensive decision score calculation unit is used to calculate the comprehensive decision score. The alarm unit constructs a risk assessment model to quantitatively evaluate the severity, impact range, occurrence probability and comprehensive decision score of the hidden danger, sort the calculated risk scores of the hidden danger, determine the priority order of the safety hazards at the construction site, issue an early warning and notify the management personnel to take measures.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The three-dimensional construction model constructed through BIM technology combines multiple data sources such as on-site video surveillance, environmental data, and personnel positioning to collect and integrate safety data from the construction site in real time. By building a risk assessment model, the severity, impact range, probability of occurrence, and comprehensive decision-making score of safety hazards at the construction site are quantitatively evaluated. The calculated risk scores of the hazards are ranked to determine the priority order of safety hazards at the construction site.

[0016] 2. BIM three-dimensional model and data visualization technology are used to display the safety status of the construction site in three-dimensional form, supporting management personnel to conduct global and dynamic monitoring of the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the intelligent safety management method for construction sites based on BIM technology of the present invention; Figure 2 It is a structural schematic diagram of the intelligent safety management system for construction sites based on BIM technology of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1-Figure 2 As shown, the present invention provides a technical solution, a construction site intelligent safety management method based on BIM technology, the method comprising the following steps: S100, use BIM software to create a three-dimensional digital model of the construction site, and collect construction site data by deploying video cameras, personnel positioning equipment and environmental sensors, including video surveillance data, personnel movement trajectory and environmental data; S200, preprocessing the data obtained in step S100, using a convolutional neural network (CNN) to identify safety hazards at the construction site from the preprocessed video surveillance data, and applying an environmental data analysis algorithm to process and analyze the preprocessed environmental detection data; S300, fusing the BIM three-dimensional digital model generated in step S100 and step S200 with the processed data through spatial alignment, data mapping and dynamic updating, and displaying the generated data fusion model in three-dimensional form using GIS technology; S400, using Bayesian decision theory to calculate the probability of occurrence of safety hazards at the construction site, and generating a comprehensive decision score based on the Bayesian calculation results combined with the degree of danger of the safety hazard event; S500. By constructing a risk assessment model, a quantitative assessment is made on the severity, impact scope, probability of occurrence and comprehensive decision-making score of hidden dangers, the calculated risk scores of hidden dangers are ranked, and the priority order of handling safety hazards at the construction site is determined.

[0020] In step S100, the specific steps include: S101. Use BIM software to build a three-dimensional model of the construction site, including building structure, equipment layout and construction path; install cameras at the construction entrance, construction platform and high-altitude work area of ​​the construction site to obtain video surveillance data of the construction site; equip construction workers at the construction site with RFID tags or GPS positioning equipment to obtain the location and activity trajectory of construction workers; arrange temperature, humidity, noise and dust monitoring sensors at the construction site to cover the entire construction area, and continuously collect environmental data of the construction site; S102: Transmit the data acquired in step S101 via a wireless network.

[0021] In step S200, the specific steps include: S201, preprocessing the collected video surveillance data, personnel positioning data and environmental data by weighted averaging method and Kalman filter; S202, performing a convolution operation on the preprocessed video surveillance data to extract local features in the video frame. The convolution operation calculation formula is as follows: ; Among them, x(i,j) represents the pixel value of the input image, y(i,j) represents the pixel value of the output feature map, that is, the feature value after convolution, (i,j) represents the coordinates of the pixel, k(p,q) represents the pixel value of the convolution kernel, (p,q) represents the coordinates of the elements in the convolution kernel, P and Q represent the height and width of the convolution kernel respectively, and traverse all positions of the input image to generate a complete output feature map; then the main features are highlighted through the pooling operation, and the image features related to safety in the video frame are extracted through convolution and pooling operations; finally, the extracted feature image is integrated through the fully connected layer, and the potential safety hazards at the construction site are identified and output using the Softmax classifier; S203. Calculate the mean and standard deviation of the environmental detection data after preprocessing, and set the upper and lower environmental detection thresholds according to the calculated mean and standard deviation. The calculation formula for setting the lower environmental detection threshold is as follows: L=μ-k1*σ; wherein L represents the set lower environmental detection threshold, μ represents the mean of the environmental detection data, k1 represents the adjustment parameter, and σ represents the standard deviation of the environmental detection data. The calculation formula for setting the upper environmental detection threshold is as follows: U=μ+k2*σ; wherein U represents the set upper environmental detection threshold, k2 represents the adjustment parameter, and the environmental detection data x is compared with the set upper and lower environmental detection thresholds. When the environmental detection data x is less than the set lower environmental detection threshold L or the environmental detection data x is greater than the set upper environmental detection threshold U, it is marked as data abnormality and the alarm mechanism is triggered.

[0022] In step S300, the specific steps include: S301, spatially align the BIM three-dimensional model and the processed data for data fusion, obtain the three-dimensional coordinates obtained by video monitoring or personnel positioning in step S100 and the three-dimensional coordinates of the BIM three-dimensional model, and convert the three-dimensional coordinate data obtained by video monitoring or personnel positioning into a unified BIM model coordinate system by applying a coordinate transformation formula, the formula is as follows: ; Among them, (X1, Y1, Z1) represents the three-dimensional coordinates obtained by video monitoring or personnel positioning, s represents the scaling factor, (X2, Y2, Z2) represents the three-dimensional coordinates of the BIM three-dimensional model, (T x ,T y ,T z ) represents the translation vector; S302, identifying the foreground object by comparing the difference between the current frame and the background image, the specific formula is as follows: t (x,y)=|I t (x,y)-B(x,y)|; where D t (x, y) represents the difference value at each pixel position, that is, the difference image, It (x, y) represents the pixel value of the current frame image, B(x, y) represents the pixel value of the background image, and the pixel point in the difference image is judged whether it belongs to the foreground by the set foreground judgment threshold. When the difference image exceeds the set foreground judgment threshold, it is judged as the foreground; S303, mapping the processed data to the BIM 3D model through a linear interpolation algorithm, the linear difference formula is as follows: y=y1+(x-x1)*(y2-y1) / (x2-x1); wherein (x1, y1) and (x2, y2) are known data points, x represents the point to be interpolated, and y represents the interpolation result; S304, by acquiring the processed data in real time, continuously performing the spatial alignment and data mapping process to update the data fusion model; S305. Use GIS technology to display the generated data fusion model in three-dimensional form.

[0023] In step S400, the specific steps include: S401. Based on the Bayesian decision formula, the data fusion model generated in step S304 is used to calculate the probability of occurrence of potential safety hazards at the construction site. The formula is as follows: P(H|E)=P(E|H)*P(H) / P(E); wherein P(H|E) represents the probability of potential safety hazard H occurring when event E occurs, P(E|H) represents the probability of event E occurring when potential safety hazard H occurs, P(H) represents the prior probability of potential safety hazard H occurring, and P(E) represents the prior probability of event E occurring; S402: Calculate the comprehensive decision score. The formula is as follows: ; Where D represents the comprehensive decision score, P(H i |E i ) represents the conditional probability of the occurrence of the i-th hidden danger, C i represents the weight of the i-th hidden danger, and n represents the total number of hidden dangers.

[0024] In step S500, the specific steps include: S501. By constructing a risk assessment model, a quantitative assessment of the safety hazards at the construction site is conducted in combination with the severity, impact range, probability of occurrence and comprehensive decision-making score of the hidden dangers. The calculation formula is as follows: ; Among them, R i represents the risk score of the ith hidden danger, S i Indicates the severity of the i-th hidden danger, I i represents the impact range of the i-th hidden danger, and α represents the adjustment parameter; S502, the calculated Ri Sort and determine the priority order of the safety hazards at the construction site, and prioritize the highest R i As a priority hidden danger.

[0025] An intelligent safety management system for construction sites based on BIM technology, the system includes: a data acquisition module, a data processing module and a safety management module, the data acquisition module is used to create a three-dimensional digital model of the construction site using BIM software, and collect construction site data by deploying video cameras, personnel positioning equipment and environmental sensors; the data processing module is used to pre-process, image recognize, detect the environment and build a three-dimensional model for the data obtained from the data acquisition module; the safety management module is used to calculate the probability of occurrence of safety hazards at the construction site using Bayesian decision theory, generate a comprehensive decision score based on the Bayesian calculation results combined with the danger level of the safety hazard event, and quantitatively evaluate the safety hazards at the construction site by building a risk assessment model.

[0026] The data acquisition module includes a BIM model building unit, a centralized video monitoring unit, a construction personnel positioning unit and a construction environment detection unit. The BIM model building unit is used to create a three-dimensional digital model of the construction site through BIM software. The centralized video monitoring unit is used to collect video monitoring data of the construction site by deploying video cameras. The construction personnel positioning unit is used to obtain the location and activity trajectory of the construction personnel by equipping the construction personnel at the construction site with RFID tags or GPS positioning devices. The construction environment detection unit is used to arrange temperature, humidity, noise and dust monitoring sensors at the construction site to cover the entire construction area and continuously collect environmental data of the construction site. The output end of the data acquisition module is connected to the input end of the data processing module.

[0027] The data processing module includes a data preprocessing unit, an image recognition unit, an environment detection unit and a three-dimensional model construction unit. The data preprocessing unit is used to preprocess the data through a Kalman filter algorithm. The image recognition unit is used to use a convolutional neural network (CNN) image processing technology to process and analyze the video surveillance data and detect safety hazards. The environment detection unit is used to calculate the mean and standard deviation of the preprocessed environment detection data, set the upper and lower limit environment detection thresholds through the calculated mean and standard deviation, and compare them with the environment detection data to identify data anomalies. The three-dimensional model construction unit is used to fuse the generated BIM three-dimensional digital model with the processed data through spatial alignment, data mapping and dynamic updating, and use GIS technology to display the generated data fusion model in three-dimensional form. The output end of the data processing module is connected to the input end of the security management module.

[0028] The safety management module includes a risk probability calculation unit, a comprehensive decision score calculation unit and an alarm unit. The risk probability calculation unit is used to calculate the probability of occurrence of safety hazard events at the construction site based on the Bayesian decision formula and the generated data fusion model. The comprehensive decision score calculation unit is used to calculate the comprehensive decision score. The alarm unit constructs a risk assessment model to quantitatively evaluate the severity, impact range, occurrence probability and comprehensive decision score of the hidden danger, sort the calculated risk scores of the hidden dangers, determine the priority order of the safety hazards at the construction site, issue an early warning and notify the management personnel to take measures.

[0029] In the embodiment: a three-dimensional model is constructed using BIM software to establish the building structure, equipment layout and construction path information of the construction site. Every 10 seconds, a real-time video stream of the construction site is collected through a video surveillance camera. RFID tags or GPS positioning devices are equipped for 100 construction workers on site to obtain the location and activity trajectory of the construction workers in real time. Temperature, humidity, noise and dust monitoring sensors are arranged at the construction site to collect environmental data of the construction site. The collected video surveillance data, personnel positioning data and environmental data are pre-processed by weighted averaging and Kalman filter. A convolutional neural network CNN is used to identify safety hazards in the video surveillance data to detect whether there are safety violations by personnel, and an adjustment parameter k is set. 1 is 1.5, and the parameter k2 is adjusted to 1.5. According to the ambient temperature data x={22,24,23,25,21,27,30,35,29,26,28,32,33,31,29,34}, the mean is calculated to be 27.25 and the standard deviation is 4.04. The upper and lower limits of the environmental detection thresholds are calculated to obtain the lower limit of the environmental detection threshold L is 21.19, and the upper limit of the environmental detection threshold U is 33.31. It is judged that the ambient temperature data at the next moment is 22℃, and the ambient temperature data at the next moment is normal. The environmental detection data is continued to be detected. The location of the construction personnel, the status of the equipment and the environmental data are updated to the BIM three-dimensional model in real time through spatial alignment, data mapping and dynamic update to generate a dynamic three-dimensional model. According to the monitoring data, the hidden dangers of the construction site include: hidden danger H1: construction workers do not wear safety helmets, hidden danger H2: construction workers enter the dangerous area of ​​the high-altitude operation area, hidden danger H3: tower crane equipment failure, events include: event E1: ambient temperature exceeds 35℃, event E2: construction workers do not wear safety belts and safety protection equipment, event E3: construction site noise exceeds the specified noise level of 105dB, according to the Bayesian decision formula, the prior probability of construction workers not wearing safety helmets is calculated to be P(H1)=0.05, the prior probability of construction workers entering the dangerous area is P(H2)=0.03, the prior probability of equipment failure is P(H3)=0.02, and the conditional probability of temperature exceeding the standard when construction workers do not wear safety helmets is P(H1)=0.05. The probability of the construction workers entering the dangerous area without wearing safety equipment is P(E2|H2)=0.7. The conditional probability of high noise exposure when the equipment fails is P(E3|H3)=0.8. The prior probability of the temperature exceeding the standard event is P(E1)=0.4. The prior probability of the event of not wearing safety protection equipment is P(E2)=0.5. The prior probability of the event of high noise exposure is P(E3)=0.3. The probability of each hidden danger event is calculated using the Bayesian formula. The conditional probability of the hidden danger H1 construction workers not wearing safety helmets is P(H1|E1)=0.075. The conditional probability of the hidden danger H2 construction workers entering the dangerous area is P(H2|E2)=0.042, the conditional probability of equipment failure of hidden danger H3 P(H3|E3) = 0.0533, set the weight of hidden danger H1 C1 = 0.8, the weight of hidden danger H2 C2 = 0.6, the weight of hidden danger H3 C3 = 0.5, calculate the comprehensive decision score D = 0.11185 according to the comprehensive decision score calculation formula, set the severity of construction workers not wearing safety helmets S1 = 0.8, the severity of construction workers entering dangerous areas S2 = 0.9, the severity of equipment failure S3 = 0.7, and the severity of construction workers not wearing safety helmets S4 = 0.8. The influence range of wearing a helmet is I1=0.6, the influence range of construction personnel entering the dangerous area is I2=0.8, the influence range of equipment failure is I3=0.7, and the adjustment parameter α=2. According to the comprehensive risk score calculation formula, the risk score of hidden danger H1 is R1=0.1595, the risk score of hidden danger H2 is R2=0.0565, and the risk score of hidden danger H3 is R3=0.0727. The calculated risk scores are sorted to determine the priority order of safety hazards at the construction site, and hidden danger H1 will be dealt with first.

[0030] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. The intelligent safety management method for construction sites based on BIM technology is characterized by: The method comprises the following steps: S100, use BIM software to create a three-dimensional digital model of the construction site, and collect construction site data by deploying video cameras, personnel positioning equipment and environmental sensors, including video surveillance data, personnel movement trajectory and environmental data; S200, preprocessing the data obtained in step S100, using a convolutional neural network (CNN) to identify safety hazards at the construction site from the preprocessed video surveillance data, and applying an environmental data analysis algorithm to process and analyze the preprocessed environmental detection data; S300, fusing the BIM three-dimensional digital model generated in step S100 and step S200 with the processed data through spatial alignment, data mapping and dynamic updating, and displaying the generated data fusion model in three-dimensional form using GIS technology; S400, using Bayesian decision theory to calculate the probability of occurrence of safety hazards at the construction site, and generating a comprehensive decision score based on the Bayesian calculation results combined with the degree of danger of the safety hazard event; S500. By constructing a risk assessment model, a quantitative assessment of the safety hazards at the construction site is conducted in combination with the severity, impact scope, probability of occurrence and comprehensive decision-making score of the hidden dangers, the risk scores of the calculated hidden dangers are ranked, and the priority order of handling the safety hazards at the construction site is determined.

2. The intelligent safety management method for construction sites based on BIM technology according to claim 1 is characterized in that: In step S100, the specific steps include: S101. Use BIM software to build a three-dimensional model of the construction site, including building structure, equipment layout and construction path; install cameras at the construction entrance, construction platform and high-altitude work area of ​​the construction site to obtain video surveillance data of the construction site; equip construction workers at the construction site with RFID tags or GPS positioning equipment to obtain the location and activity trajectory of construction workers; arrange temperature, humidity, noise and dust monitoring sensors at the construction site to cover the entire construction area, and continuously collect environmental data of the construction site; S102: Transmit the data acquired in step S101 via a wireless network.

3. The intelligent safety management method for construction sites based on BIM technology according to claim 1 is characterized in that: In step S200, the specific steps include: S201, preprocessing the collected video surveillance data, personnel positioning data and environmental data by weighted averaging method and Kalman filter; S202, performing a convolution operation on the preprocessed video surveillance data to extract local features in the video frame. The convolution operation calculation formula is as follows: ； Among them, x(i,j) represents the pixel value of the input image, y(i,j) represents the pixel value of the output feature map, that is, the feature value after convolution, (i,j) represents the coordinates of the pixel, k(p,q) represents the pixel value of the convolution kernel, (p,q) represents the coordinates of the elements in the convolution kernel, P and Q represent the height and width of the convolution kernel respectively, and traverse all positions of the input image to generate a complete output feature map; then the main features are highlighted through the pooling operation, and the image features related to safety in the video frame are extracted through convolution and pooling operations; finally, the extracted feature image is integrated through the fully connected layer, and the potential safety hazards at the construction site are identified and output using the Softmax classifier; S203. Calculate the mean and standard deviation of the environmental detection data after preprocessing, and set the upper and lower environmental detection thresholds according to the calculated mean and standard deviation. The calculation formula for setting the lower environmental detection threshold is as follows: L=μ-k1*σ; wherein L represents the set lower environmental detection threshold, μ represents the mean of the environmental detection data, k1 represents the adjustment parameter, and σ represents the standard deviation of the environmental detection data. The calculation formula for setting the upper environmental detection threshold is as follows: U=μ+k2*σ; wherein U represents the set upper environmental detection threshold, k2 represents the adjustment parameter, and the environmental detection data x is compared with the set upper and lower environmental detection thresholds. When the environmental detection data x is less than the set lower environmental detection threshold L or the environmental detection data x is greater than the set upper environmental detection threshold U, it is marked as data abnormality and the alarm mechanism is triggered.

4. The intelligent safety management method for construction sites based on BIM technology according to claim 1 is characterized in that: In step S300, the specific steps include: S301, spatially align the BIM three-dimensional model and the processed data for data fusion, obtain the three-dimensional coordinates obtained by video monitoring or personnel positioning in step S100 and the three-dimensional coordinates of the BIM three-dimensional model, and convert the three-dimensional coordinate data obtained by video monitoring or personnel positioning into a unified BIM model coordinate system by applying a coordinate transformation formula, the formula is as follows: ； Among them, (X1, Y1, Z1) represents the three-dimensional coordinates obtained by video monitoring or personnel positioning, s represents the scaling factor, (X2, Y2, Z2) represents the three-dimensional coordinates of the BIM three-dimensional model, (T x ,T y ,T z ) represents the translation vector; S302, identifying the foreground object by comparing the difference between the current frame and the background image, the specific formula is as follows: t (x,y)=|I t (x,y)-B(x,y)|; where D t (x, y) represents the difference value at each pixel position, that is, the difference image, I t (x, y) represents the pixel value of the current frame image, B(x, y) represents the pixel value of the background image, and the pixel point in the difference image is judged whether it belongs to the foreground by the set foreground judgment threshold. When the difference image exceeds the set foreground judgment threshold, it is judged as the foreground; S303, mapping the processed data to the BIM 3D model through a linear interpolation algorithm, the linear difference formula is as follows: y=y1+(x-x1)*(y2-y1) / (x2-x1); wherein (x1, y1) and (x2, y2) are known data points, x represents the point to be interpolated, and y represents the interpolation result; S304, by acquiring the processed data in real time, continuously performing the spatial alignment and data mapping process to update the data fusion model; S305. Use GIS technology to display the generated data fusion model in three-dimensional form.

5. The intelligent safety management method for construction sites based on BIM technology according to claim 1 is characterized in that: In step S400, the specific steps include: S401. Based on the Bayesian decision formula, the data fusion model generated in step S304 is used to calculate the probability of occurrence of potential safety hazards at the construction site. The formula is as follows: P(H|E)=P(E|H)*P(H) / P(E); wherein P(H|E) represents the probability of potential safety hazard H occurring when event E occurs, P(E|H) represents the probability of event E occurring when potential safety hazard H occurs, P(H) represents the prior probability of potential safety hazard H occurring, and P(E) represents the prior probability of event E occurring; S402: Calculate the comprehensive decision score. The formula is as follows: ； Where D represents the comprehensive decision score, P(H i |E i ) represents the conditional probability of the occurrence of the i-th hidden danger, C i represents the weight of the i-th hidden danger, and n represents the total number of hidden dangers.

6. The intelligent safety management method for construction sites based on BIM technology according to claim 1 is characterized in that: In step S500, the specific steps include: S501. By constructing a risk assessment model, a quantitative assessment of the safety hazards at the construction site is conducted in combination with the severity, impact range, probability of occurrence and comprehensive decision-making score of the hidden dangers. The calculation formula is as follows: ； Among them, R i represents the risk score of the ith hidden danger, S i Indicates the severity of the i-th hidden danger, I i represents the impact range of the i-th hidden danger, and α represents the adjustment parameter; S502, the calculated R i Sort and determine the priority order of the safety hazards at the construction site, and prioritize the highest R i As a priority hidden danger.

7. Intelligent safety management system for construction sites based on BIM technology, characterized by: The system includes: a data acquisition module, a data processing module and a safety management module. The data acquisition module is used to create a three-dimensional digital model of the construction site using BIM software, and collect construction site data by deploying video cameras, personnel positioning equipment and environmental sensors; the data processing module is used to preprocess, image recognize, detect the environment and build a three-dimensional model for the data obtained from the data acquisition module; the safety management module is used to use Bayesian decision theory to calculate the probability of occurrence of safety hazards at the construction site, generate a comprehensive decision score based on the Bayesian calculation results combined with the danger level of the safety hazard event, and quantitatively evaluate the safety hazards at the construction site by building a risk assessment model.

8. The intelligent safety management system for construction sites based on BIM technology according to claim 6 is characterized by: The data acquisition module includes a BIM model building unit, a centralized video monitoring unit, a construction personnel positioning unit and a construction environment detection unit. The BIM model building unit is used to create a three-dimensional digital model of the construction site through BIM software. The centralized video monitoring unit is used to collect video monitoring data of the construction site by deploying video cameras. The construction personnel positioning unit is used to obtain the location and activity trajectory of the construction personnel by equipping the construction personnel at the construction site with RFID tags or GPS positioning devices. The construction environment detection unit is used to arrange temperature, humidity, noise and dust monitoring sensors at the construction site to cover the entire construction area and continuously collect environmental data of the construction site. The output end of the data acquisition module is connected to the input end of the data processing module.

9. The intelligent safety management system for construction sites based on BIM technology according to claim 6 is characterized by: The data processing module includes a data preprocessing unit, an image recognition unit, an environment detection unit and a three-dimensional model construction unit. The data preprocessing unit is used to preprocess the data through a Kalman filter algorithm. The image recognition unit is used to use a convolutional neural network (CNN) image processing technology to process and analyze the video surveillance data and detect safety hazards. The environment detection unit is used to calculate the mean and standard deviation of the preprocessed environment detection data, set the upper and lower limit environment detection thresholds through the calculated mean and standard deviation, and compare them with the environment detection data to identify data anomalies. The three-dimensional model construction unit is used to fuse the generated BIM three-dimensional digital model with the processed data through spatial alignment, data mapping and dynamic updating, and use GIS technology to display the generated data fusion model in three-dimensional form. The output end of the data processing module is connected to the input end of the security management module.

10. The intelligent safety management system for construction sites based on BIM technology according to claim 6 is characterized by: The safety management module includes a risk probability calculation unit, a comprehensive decision score calculation unit and an alarm unit. The risk probability calculation unit is used to calculate the probability of occurrence of safety hazard events at the construction site based on the Bayesian decision formula and the generated data fusion model. The comprehensive decision score calculation unit is used to calculate the comprehensive decision score. The alarm unit constructs a risk assessment model to quantitatively evaluate the severity, impact range, occurrence probability and comprehensive decision score of the hidden danger, sort the calculated risk scores of the hidden danger, determine the priority order of the safety hazards at the construction site, issue an early warning and notify the management personnel to take measures.

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