Construction visualization intelligent management system and method based on Internet of Things

Abstract

The invention discloses a construction visualization intelligent management system and method based on the Internet of Things, and relates to the technical field of construction visualization, and the system comprises a data collection module, a construction risk analysis module, a construction visualization module, and a risk management feedback unit. The data acquisition module is used for monitoring laser scanning data and image data of a construction site in real time; the construction risk analysis module is used for calculating and analyzing a construction environment risk factor, an equipment operation risk factor and a high-altitude operation risk factor of each point in the construction site, and performing weighted analysis on the comprehensive construction risk of each point; the construction visualization module performs color fusion on the image acquisition data, constructs a three-dimensional model of the construction site, and is also used for drawing a three-dimensional construction risk map according to the construction risk factors of each point of the construction site and the comprehensive construction risk; and the risk management feedback unit is used for sending regional risk feedback information to the supervisor.

Description

Technical Field

[0001] The present invention relates to the technical field of construction visualization, and in particular to an intelligent construction visualization management system and method based on the Internet of Things. Background Art

[0002] With the popularization and continuous updating of modern construction methods, the supervision requirements for various responsibilities in construction sites have become more detailed and standardized, and the supervision requirements for safety risks in construction sites are particularly important. Among the existing safety supervision methods in construction sites, manual supervision and video supervision are usually adopted; among them, manual supervision specifically sets up safety officer posts and assigns fixed personnel to conduct construction safety supervision and inspections. In actual scenes, the judgment of safety risks depends on the experience of safety officers, and simultaneous construction at multiple points puts great pressure on human resources; in the video supervision method, image acquisition has noise interference, and the actual scene color cannot be restored. Images from different angles cannot achieve image unification, and there is a lack of more scientific and reasonable methods in the assessment of construction safety risks, which still relies more on the experience of video supervisors and image judgment results. Summary of the invention

[0003] The purpose of the present invention is to provide a construction visualization intelligent management system and method based on the Internet of Things to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: A construction visualization intelligent management method based on the Internet of Things includes the following analysis steps: Step S100: Use multiple sensors to perform laser scanning on the construction site and collect image data of the construction site, divide the construction site according to the location of the construction equipment, and mark the construction status of each area in the site; Step S200: constructing a three-dimensional model of the site according to the laser scanning results, weighted fusion of the site image data acquired by each sensor, coloring the three-dimensional model of the site, and displaying the scene through real-time rendering; Step S300: Conduct construction scene safety detection on the painted site three-dimensional model, calculate and analyze the construction risk factors of each point in the site, and perform weighted analysis on the comprehensive construction risk of each point; wherein the construction risk factors include construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors; Step S400: scaling the construction risk factors of each point in the construction site to obtain the RGB values ​​of each point in the construction risk map, and then drawing a three-dimensional construction risk map based on the comprehensive construction risks of each point in the construction site; Step S500: Set a comprehensive construction risk threshold. When personnel enter the construction site, a comprehensive construction risk threshold is judged for each point on the construction site, and regional risk feedback information is sent to supervisors based on the judgment results.

[0005] In the above technical solution, step S100 includes the following contents: Use multiple sensors to perform laser scanning on the construction site and collect image data of the construction site, set the equipment construction activity radius, take the running construction equipment as the center point, and set the equipment construction activity radius as the construction range of the construction equipment; if any point in the construction site is in the construction range of the running construction equipment, set the construction status mark of the point to 1; if the point is not included in the construction range of all running construction equipment, set the construction status mark of the point to 0; by performing image acquisition and laser scanning on the construction site, accurately identify whether each point in the construction site is in the construction range of each construction equipment, and provide an accurate data basis for the subsequent evaluation of the construction risk factors of each point in the construction site.

[0006] In the above technical solution, step S200 includes the following contents: Construct a three-dimensional model of the construction site, obtain the laser scanning data of each sensor, extract the azimuth coordinates and color information of each point on the model surface, classify the model color data with the same points, calculate the fused color data of each point through weighted fusion, and for any point in the model, calculate the color distance weight of the model color data of each sensor according to the formula: ; Among them, w i is the color distance weight value of the color data of sensor i model, f i is the visual mark of sensor i for the current point, k w is the distance weight coefficient, R i , G i , B i is the RGB value of the current point in the model color data obtained by sensor i, It is the average value of the RGB value of the current point in the model color data obtained by each sensor; if the current point is visible to the sensor, the sensor sets the value of the current point visible flag to 1; if the current point is not visible to the sensor, the sensor sets the value of the current point visible flag to 0; the color data fusion weight is set according to the color mean distance for the color data obtained at the same point to reduce the interference of environmental occlusion and noise of the image data obtained by a single sensor; The color distance weights of the color data of each sensor model are further processed according to the formula: ; Among them, W iis the color data fusion weight of sensor i model, ε is a constant parameter used to avoid the denominator being 0, n is the sensor number, N s is the number of sensors, w n is the color distance weight value of the model color data n; the color distance weight of the color data is further processed to ensure that the final calculated fused color data value is within the range of [0,255], which meets the actual data requirements; Then calculate the fused color data of the current point in the construction site model according to the formula: ; in, is the fused color data of the current point, using the RGB value of the current point to participate in the calculation, i is the sensor number, N s is the number of sensors, W i is the color data fusion weight of sensor i model, C i The current point model color data obtained by sensor i; Calculate and annotate the fused color data of each point in the model, render the model in real time, and display the three-dimensional real-time image of the construction site on the supervision terminal; render the construction site image in real time through weighted fusion of image color data, provide supervisors with a more credible real-time image of the construction site, and provide reasonable and credible image data for unified supervision of multi-point construction on the site.

[0007] In the above technical solution, step S300 includes the following contents: Obtain historical construction site image data, annotate risk types for areas with construction risks, use annotated images to build an artificial intelligence intelligent risk detection model, perform risk detection and identification on the current construction site, and evaluate various types of construction risk factors in various areas of the construction site; among them, construction risk factors include construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors; For construction environment risk factors, analyze the environmental parameters that cause safety risks on the site in historical construction data, and use the formula Calculate the construction environment risk factor R at each point in the construction site v ; where v is the environmental parameter number, N v is the number of environmental parameters, k v is the risk assessment weight of environmental parameter v, x v is the quantized value of the environmental parameter x; For the equipment operation risk factor, the construction status of each point in the construction site and the distance between each point in the construction site and the running construction equipment are obtained through images. According to the formula: Calculate the risk factor R of equipment operation at each point in the construction site e ; Among them, S eis the construction status mark of the area where the current point is located, N in is the number of construction equipment that includes the current point in the construction scope, e is the number of the construction equipment in operation, N e is the number of construction equipment in operation, d e is the distance between the current point and the center point of the equipment area, d o The length of the line between the current point and the center point of the equipment's footprint intercepted by the equipment's footprint; For the risk factor of high-altitude work, the height of each point in the construction site from the ground is detected according to the formula: Calculate the risk factor R of high-altitude work at each point in the construction site l ; where k l is the safety factor of the high-altitude safety facility at the current point, h l is the height of the current point from the ground, h total The height from the highest point of the construction site to the ground; Then according to the formula: The comprehensive construction risk of each point in the construction site is calculated by weighting each construction risk factor; v , k e , k l They are the assessment weights of construction environment risk factor, equipment operation risk factor, and height operation risk factor; Analyze various types of construction safety risks in the construction site in detail, conduct risk assessment on each point in the construction site, and provide a more accurate data basis for supervisors to conduct unified supervision within the construction site.

[0008] In the above technical solution, step S400 includes the following contents: The construction risk factors at each point in the construction site are scaled to the range of [0,255]. The values ​​of the equipment operation risk factor and the height operation risk factor are in the range of [0,1], so the calculated values ​​are multiplied by 255 for numerical scaling; For the construction environment risk factor, set the construction environment risk reference value and the corresponding construction environment risk normalized reference value, scale the construction environment risk factor value to the range of [0,255], according to the formula: ; in, is the scaled construction environment risk factor value, is the value of the construction environment risk factor before scaling, is the normalized reference value of construction environment risk, is the construction environment risk reference value; when the scaled construction environment risk factor value is greater than 255, the value is corrected to 255; The scaled values ​​of the construction environment risk factor, equipment operation risk factor, and high-altitude operation risk factor of each point in the construction environment are used as the red, blue, and green color values ​​of the point, and the comprehensive construction risk of the point is used as the z-axis value of the point. With due east and due north as the positive directions of the x and y axes of the image, any point on the construction site boundary is selected as the origin to draw a three-dimensional construction risk map; By scaling each type of construction safety risk to the range of [0,255] and setting it as the RGB value of each point in the construction site, and setting the comprehensive construction risk as the z-axis data of the three-dimensional construction risk map, the construction risks at different locations in the construction site can be reflected intuitively and in detail. Compared with video supervision, it is more convenient and reduces the supervisor's reliance on experience, providing a more stable and practical visual supervision data foundation for multi-point construction in large-scale construction sites.

[0009] In the above technical solution, step S500 includes the following contents: Set comprehensive construction risk threshold C 0 In the three-dimensional construction risk map, plane z=C 0 The image is intercepted and the part above the plane is determined as a high-risk construction area; Obtain the RGB values ​​of high-risk construction areas in the three-dimensional construction risk map, sort the red, green and blue values, and send regional risk feedback information to supervisors; the regional risk feedback information includes calculation parameters of various construction risk factors in high-risk areas, rendering images of construction site models in high-risk areas, and three-dimensional construction risk maps of high-risk areas.

[0010] A construction visualization intelligent management system based on the Internet of Things using the construction visualization intelligent management method based on the Internet of Things described in the above technical solution, the system includes a data acquisition module, a construction risk analysis module, a construction visualization module, and a risk management feedback unit; The data acquisition module is used to monitor the laser scanning data and image data of the construction site in real time; the construction risk analysis module is used to calculate and analyze the construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors of each point in the construction site, and to weightedly analyze the comprehensive construction risks of each point; the construction visualization module performs color fusion on the image acquisition data, constructs a three-dimensional model of the construction site, and is also used to draw a three-dimensional construction risk map based on the construction risk factors of each point in the construction site and the comprehensive construction risks; the risk management feedback unit is used to send regional risk feedback information to supervisors.

[0011] In the above technical solution, the data acquisition module includes an image acquisition unit and a site scanning unit: The image acquisition unit uses an image acquisition sensor to acquire image data of a given area in the construction site at multiple angles; the site scanning unit is used to acquire laser scanning data of the construction site.

[0012] In the above technical solution, the construction risk analysis module includes a construction environment risk factor analysis unit, an equipment operation risk factor analysis unit, a high-altitude operation risk factor analysis unit, and a comprehensive construction risk analysis unit; The construction environment risk factor analysis unit is used to analyze the environmental parameters that cause safety risks in historical construction data and calculate the construction environment risk factors of each point in the construction site; the equipment operation risk factor analysis unit calculates the equipment operation risk factor of each point in the construction site based on the construction status of each point in the construction site and the distance between each point and each construction equipment; the high-altitude operation risk factor analysis unit is used to calculate the high-altitude operation risk factors of various places in the construction site; the comprehensive construction risk analysis unit is used to weighted and comprehensive calculate the comprehensive construction risks of each point in the construction site.

[0013] In the above technical solution, the construction visualization module includes an image fusion unit, a construction site model building unit, an image rendering unit, a three-dimensional construction risk map drawing unit, and a visualization management unit; The image fusion unit is used to fuse the construction site images obtained from different angles by each image acquisition sensor; the construction site model construction unit constructs a construction site model based on the laser scanning data of the construction site; the image rendering unit renders the construction site model based on the construction site image fusion data; the three-dimensional construction risk map drawing unit draws a three-dimensional construction risk map based on the construction risk factors of each point at the construction site; the visualization management unit sends the construction site model and the three-dimensional construction risk map to the supervisor terminal and provides graphic zooming and switching functions.

[0014] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, laser scanning and multi-angle image acquisition are performed on the construction site, and the constructed construction site model is rendered in real time through weighted fusion of image color data, thereby avoiding the problems of viewing angle shielding and image noise in camera supervision, and providing supervisors with more accurate construction site images; the present invention also conducts detailed analysis of various types of construction risks in the construction site, calculates the comprehensive construction risks, and draws a three-dimensional construction risk map according to the calculation results. In addition, each individual construction risk factor is used as the RGB value of the three-dimensional construction risk map for image coloring, which more intuitively reflects the construction risks of various regional locations in the construction site, provides supervisors with accurate and intuitive image data for unified supervision of large construction sites, and reduces the human resource pressure of multi-point construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a construction visualization intelligent management method based on the Internet of Things of the present invention; Figure 2 This is an organizational structure chart of a construction visualization intelligent management system based on the Internet of Things in the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] Example: See Figure 1-Figure 2 , the present invention provides a technical solution, like Figure 1 As shown, the present invention provides a construction visualization intelligent management method based on the Internet of Things, comprising the following analysis steps: Step S100: Use multiple sensors to perform laser scanning on the construction site and collect image data of the construction site, divide the construction site according to the location of the construction equipment, and mark the construction status of each area in the site; Step S200: constructing a three-dimensional model of the site according to the laser scanning results, weighted fusion of the site image data acquired by each sensor, coloring the three-dimensional model of the site, and displaying the scene through real-time rendering; Step S300: Conduct construction scene safety detection on the painted site three-dimensional model, calculate and analyze the construction risk factors of each point in the site, and perform weighted analysis on the comprehensive construction risk of each point; wherein the construction risk factors include construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors; Step S400: scaling the construction risk factors of each point in the construction site to obtain the RGB values ​​of each point in the construction risk map, and then drawing a three-dimensional construction risk map based on the comprehensive construction risks of each point in the construction site; Step S500: Set a comprehensive construction risk threshold. When personnel enter the construction site, a comprehensive construction risk threshold is judged for each point on the construction site, and regional risk feedback information is sent to supervisors based on the judgment results.

[0018] Step S100 includes the following contents: Use multiple sensors to perform laser scanning on the construction site and collect image data of the construction site, set the equipment construction activity radius, and set the equipment construction activity radius as the construction range of the construction equipment with the construction equipment in operation as the center point; if any point in the construction site is within the construction range of the construction equipment in operation, set the construction status flag of the point to 1; if the point is not included in the construction range of all construction equipment in operation, set the construction status flag of the point to 0.

[0019] Step S200 includes the following contents: Construct a three-dimensional model of the construction site, obtain the laser scanning data of each sensor, extract the azimuth coordinates and color information of each point on the model surface, classify the model color data with the same points, calculate the fused color data of each point through weighted fusion, and for any point in the model, calculate the color distance weight of the model color data of each sensor according to the formula: ; Among them, w i is the color distance weight value of the color data of sensor i model, f i is the visual mark of sensor i for the current point, k w is the distance weight coefficient, R i , G i , B i is the RGB value of the current point in the model color data obtained by sensor i, It is the average value of the RGB value of the current point in the model color data obtained by each sensor; if the current point is visible to the sensor, the sensor takes the value of the visible flag of the current point as 1; if the current point is not visible to the sensor, the sensor takes the value of the visible flag of the current point as 0; The color distance weights of the color data of each sensor model are further processed according to the formula: ; Among them, W i is the color data fusion weight of sensor i model, ε is a constant parameter used to avoid the denominator being 0, n is the sensor number, N s is the number of sensors, w n is the color distance weight value of the model color data n; Then calculate the fused color data of the current point in the construction site model according to the formula: ; in, is the fused color data of the current point, using the RGB value of the current point to participate in the calculation, i is the sensor number, N s is the number of sensors, W i is the color data fusion weight of sensor i model, C iThe current point model color data obtained by sensor i; Calculate and annotate the fused color data of each point in the model, render the model in real time, and display the three-dimensional real-time image of the construction site on the supervision terminal; For the above calculation formula, the following calculation example is given: Set the distance weight coefficient to 1, the parameter parameter to 0.0001, assume that there are 3 sensors for the same point x and all of them are visible to point x, and the RGB values ​​are as follows: sensor1: R=120, G=130, B=140, sensor2: R=110, G=120, B=130, sensor3: R=100, G=110, B=120; calculate the RGB mean values ​​respectively: , the image data fusion weights obtained by each sensor are further calculated as follows: sensor1: 0.0491, sensor2: 0.9009, sensor3: 0.0491, and then the fusion color data RGB value of point x is calculated as follows: R=109.9, G=119.9, B=129.9.

[0020] Step S300 includes the following contents: Obtain historical construction site image data, annotate risk types for areas with construction risks, use annotated images to build an artificial intelligence intelligent risk detection model, perform risk detection and identification on the current construction site, and evaluate various types of construction risk factors in various areas of the construction site; among them, construction risk factors include construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors; For construction environment risk factors, analyze the environmental parameters that cause safety risks on the site in historical construction data, and use the formula Calculate the construction environment risk factor R at each point in the construction site v ; where v is the environmental parameter number, N v is the number of environmental parameters, k v is the risk assessment weight of environmental parameter v, x v is the quantized value of the environmental parameter x; In the specific implementation, for environmental risk factors, select environmental parameters that may cause construction risks in the construction environment, such as water accumulation, dust accumulation, and air dust concentration, for quantitative analysis; For the equipment operation risk factor, the construction status of each point in the construction site and the distance between each point in the construction site and the running construction equipment are obtained through images. According to the formula: Calculate the risk factor R of equipment operation at each point in the construction site e ; Among them, S e is the construction status mark of the area where the current point is located, N inis the number of construction equipment that includes the current point in the construction scope, e is the number of the construction equipment in operation, N e is the number of construction equipment in operation, d e is the distance between the current point and the center point of the equipment area, d o The length of the line between the current point and the center point of the equipment's footprint intercepted by the equipment's footprint; In the specific implementation, for the equipment operation risk factor, the geometric center point of the construction equipment area is obtained, and the construction radius of each equipment can be selected to calculate the equipment construction risk factor. The irregular construction range can also be selected according to the actual range of the construction equipment, and then the intersection of the line connecting the measurement point and the center point of the equipment area and the boundary of the selected range is obtained. The length of the line connecting the intersection and the center point of the equipment area is d. o The value of For the risk factor of high-altitude work, the height of each point in the construction site from the ground is detected according to the formula: Calculate the risk factor R of high-altitude work at each point in the construction site l ; where k l is the safety factor of the high-altitude safety facility at the current point, h l is the height of the current point from the ground, h total The height from the highest point of the construction site to the ground; Then according to the formula: The comprehensive construction risk of each point in the construction site is calculated by weighting each construction risk factor; v , k e , k l They are the assessment weights of construction environment risk factor, equipment operation risk factor and high-altitude operation risk factor respectively.

[0021] Step S400 includes the following contents: The construction risk factors at each point in the construction site are scaled to the range of [0,255]. The values ​​of the equipment operation risk factor and the height operation risk factor are in the range of [0,1], so the calculated values ​​are multiplied by 255 for numerical scaling; For the construction environment risk factor, set the construction environment risk reference value and the corresponding construction environment risk normalized reference value, scale the construction environment risk factor value to the range of [0,255], according to the formula: ; in, is the scaled construction environment risk factor value, is the value of the construction environment risk factor before scaling, is the normalized reference value of construction environment risk, is the construction environment risk reference value; when the scaled construction environment risk factor value is greater than 255, the value is corrected to 255; The scaled values ​​of the construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors of each point in the construction environment are used as the red, blue, and green color values ​​of the point, and the comprehensive construction risk of the point is used as the z-axis value of the point. With due east and due north as the positive directions of the x and y axes of the image, any point on the boundary of the construction site is selected as the origin to draw a three-dimensional construction risk map.

[0022] Step S500 includes the following contents: Set comprehensive construction risk threshold C 0 In the three-dimensional construction risk map, plane z=C 0 The image is intercepted and the part above the plane is determined as a high-risk construction area; Obtain the RGB values ​​of high-risk construction areas in the three-dimensional construction risk map, sort the red, green and blue values, and send regional risk feedback information to supervisors; the regional risk feedback information includes calculation parameters of various construction risk factors in high-risk areas, rendering images of construction site models in high-risk areas, and three-dimensional construction risk maps of high-risk areas.

[0023] like Figure 2 As shown, the present invention also provides a construction visualization intelligent management system based on the Internet of Things, the system includes a data acquisition module, a construction risk analysis module, a construction visualization module, and a risk management feedback unit; The data acquisition module is used to monitor the laser scanning data and image data of the construction site in real time; the construction risk analysis module is used to calculate and analyze the construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors of each point in the construction site, and to weightedly analyze the comprehensive construction risks of each point; the construction visualization module performs color fusion on the image acquisition data, constructs a three-dimensional model of the construction site, and is also used to draw a three-dimensional construction risk map based on the construction risk factors of each point in the construction site and the comprehensive construction risks; the risk management feedback unit is used to send regional risk feedback information to supervisors.

[0024] The data acquisition module includes an image acquisition unit and a site scanning unit: The image acquisition unit uses an image acquisition sensor to acquire image data of a given area in the construction site at multiple angles; the site scanning unit is used to acquire laser scanning data of the construction site.

[0025] The construction risk analysis module includes a construction environment risk factor analysis unit, an equipment operation risk factor analysis unit, a high-altitude operation risk factor analysis unit, and a comprehensive construction risk analysis unit; The construction environment risk factor analysis unit is used to analyze the environmental parameters that cause safety risks in historical construction data and calculate the construction environment risk factors of each point in the construction site; the equipment operation risk factor analysis unit calculates the equipment operation risk factor of each point in the construction site based on the construction status of each point in the construction site and the distance between each point and each construction equipment; the high-altitude operation risk factor analysis unit is used to calculate the high-altitude operation risk factors of various places in the construction site; the comprehensive construction risk analysis unit is used to weighted and comprehensive calculate the comprehensive construction risks of each point in the construction site.

[0026] The construction visualization module includes: an image fusion unit, a construction site model building unit, an image rendering unit, a three-dimensional construction risk map drawing unit, and a visualization management unit; The image fusion unit is used to fuse the construction site images obtained from different angles by each image acquisition sensor; the construction site model construction unit constructs a construction site model based on the laser scanning data of the construction site; the image rendering unit renders the construction site model based on the construction site image fusion data; the three-dimensional construction risk map drawing unit draws a three-dimensional construction risk map based on the construction risk factors of each point at the construction site; the visualization management unit sends the construction site model and the three-dimensional construction risk map to the supervisor terminal and provides graphic zooming and switching functions.

[0027] 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. A construction visualization intelligent management method based on the Internet of Things, characterized by , the method comprises the following analysis steps: Step S100: Use multiple sensors to perform laser scanning on the construction site and collect image data of the construction site, divide the construction site according to the location of the construction equipment, and mark the construction status of each area in the site; Step S200: constructing a three-dimensional model of the site according to the laser scanning results, weighted fusion of the site image data acquired by each sensor, coloring the three-dimensional model of the site, and displaying the scene through real-time rendering; Step S300: Conduct construction scene safety detection on the painted site three-dimensional model, calculate and analyze the construction risk factors of each point in the site, and perform weighted analysis on the comprehensive construction risk of each point; wherein the construction risk factors include construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors; Step S400: scaling the construction risk factors of each point in the construction site to obtain the RGB values ​​of each point in the construction risk map, and then drawing a three-dimensional construction risk map based on the comprehensive construction risks of each point in the construction site; Step S500: Set a comprehensive construction risk threshold. When personnel enter the construction site, a comprehensive construction risk threshold is judged for each point on the construction site, and regional risk feedback information is sent to supervisors based on the judgment results.

2. According to the method of construction visualization intelligent management based on the Internet of Things in claim 1, it is characterized in that: The step S100 includes the following contents: Use multiple sensors to perform laser scanning on the construction site and collect image data of the construction site, set the equipment construction activity radius, and set the equipment construction activity radius as the construction range of the construction equipment with the construction equipment in operation as the center point; if any point in the construction site is within the construction range of the construction equipment in operation, set the construction status flag of the point to 1; if the point is not included in the construction range of all construction equipment in operation, set the construction status flag of the point to 0.

3. According to the method of construction visualization intelligent management based on the Internet of Things in claim 1, it is characterized in that: The step S200 includes the following contents: Construct a three-dimensional model of the construction site, obtain the laser scanning data of each sensor, extract the azimuth coordinates and color information of each point on the model surface, classify the model color data with the same points, calculate the fused color data of each point through weighted fusion, and for any point in the model, calculate the color distance weight of the model color data of each sensor according to the formula: ； Among them, w i is the color distance weight value of the color data of sensor i model, f i is the visual mark of sensor i for the current point, k w is the distance weight coefficient, R i , G i , B i is the RGB value of the current point in the model color data obtained by sensor i, It is the average value of the RGB value of the current point in the model color data obtained by each sensor; if the current point is visible to the sensor, the sensor takes the value of the visible flag of the current point as 1; if the current point is not visible to the sensor, the sensor takes the value of the visible flag of the current point as 0; The color distance weights of the color data of each sensor model are further processed according to the formula: ； Among them, W i is the color data fusion weight of sensor i model, ε is a constant parameter used to avoid the denominator being 0, n is the sensor number, N s is the number of sensors, w n is the color distance weight value of the model color data n; Then calculate the fused color data of the current point in the construction site model according to the formula: ； in, is the fused color data of the current point, using the RGB value of the current point to participate in the calculation, i is the sensor number, N s is the number of sensors, W i is the color data fusion weight of sensor i model, C i The current point model color data obtained by sensor i; Calculate and annotate the fused color data of each point in the model, render the model in real time, and display the three-dimensional real-time image of the construction site on the supervision terminal.

4. According to the method of construction visualization intelligent management based on the Internet of Things in claim 1, it is characterized in that: The step S300 includes the following contents: Obtain historical construction site image data, annotate risk types for areas with construction risks, use annotated images to build an artificial intelligence intelligent risk detection model, perform risk detection and identification on the current construction site, and evaluate various types of construction risk factors in various areas of the construction site; among them, construction risk factors include construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors; For construction environment risk factors, analyze the environmental parameters that cause safety risks in the site in historical construction data, and use the formula Calculate the construction environment risk factor R at each point in the construction site v ; where v is the environmental parameter number, N v is the number of environmental parameters, k v is the risk assessment weight of environmental parameter v, x v is the quantized value of the environmental parameter x; For the equipment operation risk factor, the construction status of each point in the construction site and the distance between each point in the construction site and the running construction equipment are obtained through images. According to the formula: Calculate the risk factor R of equipment operation at each point in the construction site e ; Among them, S e is the construction status mark of the area where the current point is located, N in is the number of construction equipment that includes the current point in the construction scope, e is the number of the construction equipment in operation, N e is the number of construction equipment in operation, d e is the distance between the current point and the center point of the equipment area, d o The length of the line between the current point and the center point of the equipment's footprint intercepted by the equipment's footprint; For the risk factor of high-altitude work, the height of each point in the construction site from the ground is detected according to the formula: Calculate the risk factor R of high-altitude work at each point in the construction site l ; where k l is the safety factor of the high-altitude safety facility at the current point, h l is the height of the current point from the ground, h total The height from the highest point of the construction site to the ground; Then according to the formula: The comprehensive construction risk of each point in the construction site is calculated by weighting each construction risk factor; v , k e , k l They are the assessment weights of construction environment risk factor, equipment operation risk factor and high-altitude operation risk factor respectively.

5. The construction visualization intelligent management method based on the Internet of Things according to claim 1 is characterized in that: The step S400 includes the following contents: The construction risk factors at each point in the construction site are scaled to the range of [0,255]. The values ​​of the equipment operation risk factor and the height operation risk factor are in the range of [0,1], so the calculated values ​​are multiplied by 255 for numerical scaling; For the construction environment risk factor, set the construction environment risk reference value and the corresponding construction environment risk normalized reference value, scale the construction environment risk factor value to the range of [0,255], according to the formula: ； in, is the scaled construction environment risk factor value, is the value of the construction environment risk factor before scaling, is the normalized reference value of construction environment risk, is the construction environment risk reference value; when the scaled construction environment risk factor value is greater than 255, the value is corrected to 255; The scaled values ​​of the construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors of each point in the construction environment are used as the red, blue, and green color values ​​of the point, and the comprehensive construction risk of the point is used as the z-axis value of the point. With due east and due north as the positive directions of the x and y axes of the image, any point on the boundary of the construction site is selected as the origin to draw a three-dimensional construction risk map.

6. The construction visualization intelligent management method based on the Internet of Things according to claim 1 is characterized in that: The step S500 includes the following contents: Set the comprehensive construction risk threshold C0, intercept the image at plane z=C0 in the 3D construction risk map, and judge the part above the plane as the high-risk construction area; Obtain the RGB values ​​of high-risk construction areas in the three-dimensional construction risk map, sort the red, green and blue values, and send regional risk feedback information to supervisors; the regional risk feedback information includes calculation parameters of various construction risk factors in high-risk areas, rendering images of construction site models in high-risk areas, and three-dimensional construction risk maps of high-risk areas.

7. A construction visualization intelligent management system based on the Internet of Things using a construction visualization intelligent management method based on the Internet of Things as claimed in any one of claims 1 to 6, characterized in that: The system includes a data acquisition module, a construction risk analysis module, a construction visualization module, and a risk management feedback unit; The data acquisition module is used to monitor the laser scanning data and image data of the construction site in real time; the construction risk analysis module is used to calculate and analyze the construction environment risk factors, equipment operation risk factors, and high-altitude operation risk factors of each point in the construction site, and to weightedly analyze the comprehensive construction risks of each point; the construction visualization module performs color fusion on the image acquisition data, constructs a three-dimensional model of the construction site, and is also used to draw a three-dimensional construction risk map based on the construction risk factors of each point in the construction site and the comprehensive construction risks; the risk management feedback unit is used to send regional risk feedback information to supervisors.

8. The construction visualization intelligent management system based on the Internet of Things according to claim 7 is characterized in that: The data acquisition module includes an image acquisition unit and a site scanning unit: The image acquisition unit uses an image acquisition sensor to acquire image data of a given area in the construction site at multiple angles; the site scanning unit is used to acquire laser scanning data of the construction site.

9. The construction visualization intelligent management system based on the Internet of Things according to claim 7 is characterized in that: The construction risk analysis module includes a construction environment risk factor analysis unit, an equipment operation risk factor analysis unit, a high-altitude operation risk factor analysis unit, and a comprehensive construction risk analysis unit; The construction environment risk factor analysis unit is used to analyze the environmental parameters that cause safety risks in the historical construction data and calculate the construction environment risk factors of each point in the construction site; the equipment operation risk factor analysis unit calculates the equipment operation risk factor of each point according to the construction status of each point in the construction site and the distance between each point and each construction equipment; the high-altitude operation risk factor analysis unit is used to calculate the high-altitude operation risk factors of various places in the construction site; The comprehensive construction risk analysis unit is used to perform weighted comprehensive calculation of the comprehensive construction risk of each point in the construction site.

10. The construction visualization intelligent management system based on the Internet of Things according to claim 7 is characterized in that: The construction visualization module includes an image fusion unit, a construction site model building unit, an image rendering unit, a three-dimensional construction risk map drawing unit, and a visualization management unit; The image fusion unit is used to fuse the construction site images obtained from different angles by each image acquisition sensor; the construction site model construction unit constructs a construction site model based on the laser scanning data of the construction site; the image rendering unit renders the construction site model based on the construction site image fusion data; the three-dimensional construction risk map drawing unit draws a three-dimensional construction risk map based on the construction risk factors of each point at the construction site; the visualization management unit sends the construction site model and the three-dimensional construction risk map to the supervisor terminal and provides graphic zooming and switching functions.

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