Whole-process engineering consultation intelligent monitoring method and system based on internet of things

Abstract

The application discloses a whole-process engineering consultation intelligent monitoring method and system based on Internet of Things, and relates to the technical field of intelligent monitoring.The method comprises the following steps: obtaining a dangerous area and corresponding area parameters according to a BIM progress model; obtaining position data of a worker in real time, and monitoring the position data; when the position data is located in the dangerous area, obtaining a working point wind speed and a working point inclination; calculating an engineering risk value according to the area parameters, the position data, the working point wind speed and the working point inclination; and giving a safety warning to the worker according to the engineering risk value.The application obtains a safety distance and a protection state of a dangerous area as a space risk according to a BIM progress model, monitors a wind speed and an inclination of a working position of a worker in real time as an environmental risk, and performs safety evaluation on a construction site according to the space risk and the environmental risk, so that a whole-cycle coverage of safety detection of engineering construction is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, specifically to an intelligent monitoring method and system for the entire process of engineering consulting based on the Internet of Things. Background Technology

[0002] Full-process engineering consulting integrates the management of a construction project throughout its entire lifecycle, from planning and design to construction and operation, which helps improve project efficiency and optimize resource allocation. Currently, full-process engineering consulting has relatively mature processes and technologies in areas such as preliminary surveying and research, design evaluation, cost consulting, and engineering supervision, and it manages project construction by integrating building information.

[0003] BIM (Building Information Modeling) is a core technology in full-process engineering consulting, spanning the design and construction phases. By leveraging BIM technology, full-process engineering consulting can achieve data sharing and enable scientific management of the construction process through collaborative decision-making. However, BIM-based engineering construction management consulting has a long model update cycle and focuses on overall project management, such as progress monitoring (determining if the project is ahead of schedule), cost control (assessing and adjusting for cost overruns), and path conflict detection—a macro-level focus. It lacks micro-level monitoring of project progress and safety risks at the construction site. Furthermore, on-site safety risk monitoring categorizes workers based on their status, granting them high-level operational authority and often involving direct operations in hazardous areas, yet assigning them low monitoring weight, resulting in inherent risks in their work. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring method and system for the entire process of engineering consulting based on the Internet of Things. This invention obtains the dangerous areas and safe distances of the construction site according to the BIM progress model, and judges the protection status of the dangerous areas as spatial risks based on the construction progress; it monitors the working positions of workers in real time and obtains the wind speed and tilt angle of the working positions as environmental risks; and it conducts a safety assessment of the construction site based on spatial and environmental risks, so as to achieve full-cycle safety monitoring coverage of engineering construction.

[0005] The objective of this invention is achieved through the following technical means:

[0006] In a first aspect, the present invention provides an intelligent monitoring method for the entire process of engineering consulting based on the Internet of Things, comprising the following steps:

[0007] Based on the BIM progress model, obtain the hazardous areas and their corresponding area parameters;

[0008] The location data of the workers is acquired in real time and monitored.

[0009] When the location data is located in the danger zone, obtain the wind speed and tilt angle of the work site;

[0010] Calculate the engineering risk value based on the regional parameters, the location data, the wind speed at the work site, and the tilt angle at the work site;

[0011] Based on the aforementioned project risk values, safety warnings will be issued to the workers.

[0012] The area parameters include: safety distance and protection status.

[0013] Preferably, before obtaining the hazardous area and its corresponding parameters based on the BIM progress model, the following steps are further included:

[0014] Acquire 3D point cloud data of the construction site;

[0015] Based on the 3D point cloud data and the BIM progress model, the BIM progress model is synchronized.

[0016] Based on the BIM progress model after data synchronization, the hazardous areas and their corresponding parameters are updated.

[0017] Preferably, the step of synchronizing the BIM progress model based on the 3D point cloud data and the BIM progress model includes the following steps:

[0018] The coordinate system of the three-dimensional point cloud data is matched with that of the BIM progress model, and the three-dimensional point cloud data is superimposed and fused to obtain a superimposed model.

[0019] Based on the superposition model, the number of completed components is obtained;

[0020] Calculate the schedule deviation rate based on the number of completed components and the planned number of components;

[0021] The BIM schedule model is adjusted based on the schedule deviation rate.

[0022] Preferably, obtaining the number of completed components based on the superposition model includes the following steps:

[0023] Construct a component recognition model and train the component recognition model;

[0024] The superimposed model is input into the component recognition model, and the component type of the superimposed model is output.

[0025] Calculate the number of completed components based on the component type.

[0026] Preferably, the step of calculating the engineering risk value based on the regional parameters, the location data, the wind speed at the work site, and the inclination angle at the work site includes the following steps:

[0027] Calculate spatial risk based on the region parameters and the location data;

[0028] Calculate the environmental risk based on the wind speed and tilt angle at the work site;

[0029] The engineering risk value is calculated based on the spatial risk and the environmental risk.

[0030] Preferably, the step of calculating spatial risk based on the region parameters and the location data includes the following steps:

[0031] Based on the location data, the distance between the worker and the hazardous area is calculated and recorded as the actual distance;

[0032] Calculate the protection coefficient based on the protection status;

[0033] The spatial risk is calculated based on the safe distance, the actual distance, and the protection coefficient.

[0034] Preferably, the calculation of environmental risk based on the wind speed and tilt angle at the work site includes the following steps:

[0035] Obtain wind speed threshold and tilt angle threshold;

[0036] Calculate the wind speed weight and tilt angle weight based on the schedule deviation rate;

[0037] The environmental risk is calculated based on the wind speed threshold, the wind speed at the work site, the wind speed weight, the tilt angle threshold, the tilt angle at the work site, and the tilt angle weight.

[0038] Secondly, the present invention provides an intelligent monitoring system for whole-process engineering consulting based on the Internet of Things, which applies the above-mentioned intelligent monitoring method for whole-process engineering consulting based on the Internet of Things, including: a hazardous area analysis module, a location data monitoring module, an environmental parameter acquisition module, an engineering risk assessment module, and a construction safety early warning module;

[0039] The hazardous area analysis module is used to obtain hazardous areas and their corresponding area parameters based on the BIM progress model.

[0040] The location data monitoring module is used to acquire the location data of the workers in real time and monitor the location data;

[0041] The environmental parameter acquisition module is used to acquire the wind speed and tilt angle of the work site when the location data is located in the dangerous area.

[0042] The engineering risk assessment module is used to calculate the engineering risk value based on the regional parameters, the location data, the wind speed at the work site, and the tilt angle at the work site.

[0043] The construction safety early warning module is used to provide safety warnings to workers based on the project risk value.

[0044] The area parameters include: safety distance and protection status.

[0045] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device executes the above-described intelligent monitoring method for whole-process engineering consulting based on the Internet of Things.

[0046] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor of an electronic device, cause the processor to execute the aforementioned intelligent monitoring method for whole-process engineering consulting based on the Internet of Things.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] This invention obtains hazardous areas and safe distances to hazardous areas at the construction site based on the BIM progress model, and determines the protection status of hazardous areas as spatial risks based on the construction progress; it monitors the work positions of workers in real time and obtains the wind speed and tilt angle at the work positions as environmental risks; and it conducts safety assessments of the construction site based on spatial and environmental risks, achieving full-cycle safety monitoring coverage for engineering construction. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0051] Figure 1A flowchart illustrating an intelligent monitoring method for the entire process of engineering consulting based on the Internet of Things (IoT) provided in this embodiment;

[0052] Figure 2 This embodiment provides a flowchart illustrating step S4, which calculates the engineering risk value based on regional parameters, location data, wind speed at the work site, and inclination angle at the work site.

[0053] Figure 3 This embodiment provides a flowchart illustrating step S41, which calculates spatial risk based on regional parameters and location data.

[0054] Figure 4 This embodiment provides a flowchart illustrating step S42, which calculates environmental risk based on wind speed and tilt angle at the work site.

[0055] Figure 5 A schematic diagram of the structure of an IoT-based intelligent monitoring system for the entire process of engineering consulting provided in this embodiment;

[0056] Figure 6 This is a schematic diagram of the structure of an electronic device provided in this embodiment. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0059] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" and "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0060] Example 1

[0061] This embodiment provides a method for intelligent monitoring of the entire engineering consulting process based on the Internet of Things, such as... Figure 1 As shown, it includes the following steps:

[0062] S1. Based on the BIM progress model, obtain the hazardous areas and their corresponding area parameters;

[0063] S2, acquires real-time location data of workers and monitors the location data;

[0064] S3, when the location data is in a hazardous area, obtain the wind speed and tilt angle of the work site;

[0065] S4. Calculate the project risk value based on regional parameters, location data, wind speed at the work site, and tilt angle at the work site.

[0066] S5, based on the project risk value, provides safety warnings to workers;

[0067] Area parameters include: safety distance and protection status.

[0068] It should be noted that the BIM schedule model integrates the construction schedule plan on the basis of the Building Information Model (BIM), forming a dynamic and visual building model with time attributes. It includes a 3D visualization model and a large amount of data embedded in the model, enabling the digitization of building entities into calculable objects and managing project progress. By analyzing the BIM schedule model, hazardous areas on the construction site, such as edges and openings, can be identified, and their geometric parameters, including safety distances and protection status, can be obtained. The safety distance is the minimum permissible distance between the hazardous area and workers, and the protection status indicates whether protective measures have been implemented for that hazardous area.

[0069] IoT devices are installed at the construction site to monitor the real-time work of personnel, acquiring data such as their location, wind speed, and tilt angle. Specifically, the IoT sensing layer includes UWB base stations, wind speed sensors, and tilt sensors installed at the construction site. UWB tags are attached to workers, and UWB base stations locate their positions, acquiring their location data. Additionally, wind speed and tilt sensors are installed on workers to obtain wind speed and tilt angle data during their work.

[0070] By combining the area parameters of hazardous areas obtained from the BIM progress model with real-time construction data acquired by IoT devices, the engineering risk value of the construction process is calculated, and safety warnings are issued to workers. In some embodiments, when the engineering risk value is less than a first threshold, monitoring of workers continues and the data is recorded in a log; when the engineering risk value is greater than or equal to the first threshold, an alarm is issued to both workers and managers, indicating the existence of operational risks.

[0071] In this embodiment, the dangerous areas and safe distances of the construction site are obtained based on the BIM progress model, and the protection status of the dangerous areas is judged as spatial risk based on the construction progress; the working positions of the workers are monitored in real time, and the wind speed and tilt angle of the working positions are obtained as environmental risk; a safety assessment of the construction site is carried out based on spatial risk and environmental risk, so as to achieve full-cycle coverage of safety inspection of the project construction.

[0072] In some embodiments, before step S1, which involves obtaining the hazardous area and its corresponding parameters based on the BIM progress model, the following steps are also included:

[0073] Acquire 3D point cloud data of the construction site;

[0074] Based on the 3D point cloud data and the BIM progress model, the BIM progress model is synchronized.

[0075] Based on the BIM progress model after data synchronization, the hazardous areas and their corresponding area parameters are updated.

[0076] It should be noted that the BIM progress model needs to be updated according to the actual construction progress at the construction site, such as changes in hazardous areas and their protection status. Therefore, it is necessary to obtain the actual progress of the construction site. In some embodiments, the synchronization of BIM progress model data is performed before the start of each day's construction by acquiring 3D point cloud data of the construction site and synchronizing the data with the BIM progress model. Specifically, synchronizing the BIM progress model based on the 3D point cloud data and the BIM progress model includes the following steps:

[0077] The coordinate system of the 3D point cloud data is matched with that of the BIM progress model, and the 3D point cloud data is overlaid and fused to obtain the overlaid model.

[0078] Based on the overlay model, obtain the number of completed components;

[0079] Calculate the schedule deviation rate based on the number of completed components and the planned number of components;

[0080] Adjust the BIM schedule model based on the schedule deviation rate.

[0081] It should be noted that cameras are installed at different locations on the construction site to acquire images and reconstruct a 3D point cloud model, resulting in multiple 3D point cloud datasets. To compare the BIM progress model with the 3D point cloud datasets and identify completed works (i.e., the number of completed components), thereby determining the difference between the actual and planned completion percentages, the multiple 3D point cloud datasets are registered with the 3D model of the BIM progress model to obtain an overlay model. The number of completed components is then identified on the overlay model, and the schedule deviation rate is calculated based on the planned number of components in the BIM progress model, adjusting the BIM progress model accordingly. The registration of the 3D point cloud data with the BIM progress model is existing technology and will not be elaborated upon here; the schedule deviation rate is calculated based on the number of completed components and the planned number of components.

[0082] Specifically, the formula for calculating the schedule deviation rate is as follows:

[0083] ,

[0084] in, Schedule deviation rate To complete the required number of components, The number of planned components.

[0085] In this embodiment, three-dimensional point cloud data is acquired from multiple locations on the construction site, and the three-dimensional point cloud data is registered with the BIM progress model to obtain an overlay model. The overlay model is then analyzed to obtain the progress deviation value of the completed components and the number of completed components, thereby improving the accuracy of progress monitoring.

[0086] In some embodiments, obtaining the number of completed components based on the overlay model includes the following steps:

[0087] Construct a component recognition model and train the component recognition model;

[0088] Input the superimposed model into the component recognition model, and output the component type of the superimposed model;

[0089] Calculate the number of components based on the component type.

[0090] It should be noted that the 3D point cloud data of the overlay model, for each point, consists of 3D coordinates and other attributes. This can be represented as a matrix of 3 times the number of points in space, or a matrix of 3 times the sum of the points and other attributes multiplied by the number of points in space. Other attributes include RGB color values ​​and normal vectors. By dividing the 3D point cloud data of the overlay model into several regions, segmenting data points with the same and different attributes, and defining label values ​​for the segmented regions, the semantic category of each 3D point cloud data point can be obtained.

[0091] The component recognition model is a pre-trained multilayer perceptron. It extracts and encodes features from the 3D point cloud data of the overlay model, learns the extracted key features, and assigns values ​​to the 3D point cloud data of the overlay model, achieving rapid and accurate identification and classification of components within the overlay model. Specifically, the component recognition model consists of three hidden layers and an output layer. Both the hidden and output layers are fully connected layers. They perform feature extraction and position encoding on the input data. After passing through the three fully connected layers, the probability of each 3D point cloud data point belonging to each component type is calculated, and the component type with the highest probability is output.

[0092] The construction of the component recognition model training set involves acquiring a point cloud model and manually annotating it. Manual annotation includes point cloud cropping, region selection, type labeling, and data merging. Component types include walls, columns, and pipes. The component recognition model is then trained using the training set, and its weights are adjusted. Specifically, the loss function of the component recognition model is expressed as follows:

[0093] ,

[0094] in, For loss function, The number of feature vectors, For the number of component types, For the first The true probability of each feature vector For the first Each feature vector belongs to type The predicted probability.

[0095] In some embodiments, step S4 involves calculating the engineering risk value based on regional parameters, location data, wind speed at the work site, and inclination angle at the work site. Figure 2 As shown, it includes the following steps:

[0096] S41, Calculate spatial risk based on regional parameters and location data;

[0097] S42, calculate the environmental risk based on the wind speed and tilt angle at the work site;

[0098] S43, Calculate the engineering risk value based on spatial risk and environmental risk.

[0099] It should be noted that the engineering risk value includes spatial risk and environmental risk around the workers. Spatial risk is calculated using the safe distance and protection status of the hazardous area and the location data of the workers; environmental risk is calculated using the wind speed and tilt angle at the workers' work point.

[0100] Specifically, the formula for calculating the engineering risk value is as follows:

[0101] ,

[0102] in, This represents the engineering risk value. For space risks, Environmental risks.

[0103] Step S41: Calculate spatial risk based on regional parameters and location data, such as... Figure 3 As shown, it includes the following steps:

[0104] S411, Calculate the distance between the worker and the hazardous area based on the location data, and record it as the actual distance;

[0105] S412, Calculate the protection factor based on the protection status;

[0106] S413 calculates spatial risk based on safe distance, actual distance, and protection factor.

[0107] Step S42: Calculate the environmental risk based on the wind speed and tilt angle at the work site, such as... Figure 4 As shown, it includes the following steps:

[0108] S421, obtain the wind speed threshold and tilt angle threshold;

[0109] S422, calculate the wind speed weight and tilt angle weight based on the schedule deviation rate;

[0110] S423 calculates environmental risk based on wind speed threshold, wind speed at the work site, wind speed weight, tilt angle threshold, tilt angle at the work site, and tilt angle weight.

[0111] It should be noted that the protection coefficient is calculated based on the protection status and schedule deviation rate. When protective measures are set up in a dangerous area, the protection status is recorded as 1; when no protective measures are set up in a dangerous area, the protection status is recorded as 0. When the schedule is ahead of schedule or behind schedule, the reliability of the protection status will decrease, such as when schedule differences lead to the simplification of protective measures or when workers violate regulations. Therefore, by obtaining data from historical accident records, constructing a regression model for analysis, the relationship between the protection coefficient and the schedule deviation rate can be obtained.

[0112] Specifically, the formula for calculating the protection factor is as follows:

[0113] ,

[0114] in, For the protection factor, This represents the schedule deviation rate.

[0115] The formula for calculating space risk is as follows:

[0116] ,

[0117] in, For space risks, For a safe distance, This is the actual distance. In protective mode, This is the protection factor.

[0118] Wind speed threshold and tilt angle threshold are the maximum permissible wind speed and maximum tilt angle during construction. When the schedule is ahead of schedule, it means high construction intensity, long construction time, and high worker fatigue, requiring an increase in the impact of wind speed. When the schedule is behind schedule, it means increased equipment usage intensity and increased platform stacking load, requiring an increase in the impact of tilt angle. Therefore, the weights of wind speed and tilt angle are calculated based on the schedule deviation rate to calculate environmental risk.

[0119] Specifically, the formula for calculating the wind speed weight is as follows:

[0120] ,

[0121] in, Assuming wind speed as the weighting, Schedule deviation rate;

[0122] The formula for calculating environmental risk is as follows:

[0123] ,

[0124] in, For environmental risks, Assuming wind speed as the weighting, Wind speed at the work site The wind speed threshold, As the tilt angle weight, The angle of inclination of the work point. The tilt angle threshold, .

[0125] Example 2

[0126] This embodiment provides an intelligent monitoring system for the entire process of engineering consulting based on the Internet of Things (IoT). It applies the aforementioned intelligent monitoring method for the entire process of engineering consulting based on the IoT, such as... Figure 5 As shown, it includes: a hazardous area analysis module, a location data monitoring module, an environmental parameter acquisition module, an engineering risk assessment module, and a construction safety early warning module;

[0127] The hazardous area analysis module is used to obtain hazardous areas and their corresponding area parameters based on the BIM progress model.

[0128] The location data monitoring module is used to acquire and monitor the location data of workers in real time.

[0129] The environmental parameter acquisition module is used to acquire the wind speed and tilt angle of the work site when the location data is located in a hazardous area.

[0130] The engineering risk assessment module is used to calculate the engineering risk value based on regional parameters, location data, wind speed at the work site, and tilt angle at the work site.

[0131] The construction safety early warning module is used to provide safety warnings to workers based on the project risk value.

[0132] Area parameters include: safety distance and protection status.

[0133] In this embodiment, the dangerous areas and safe distances of the construction site are obtained based on the BIM progress model, and the protection status of the dangerous areas is judged as spatial risk based on the construction progress; the working positions of the workers are monitored in real time, and the wind speed and tilt angle of the working positions are obtained as environmental risk; a safety assessment of the construction site is carried out based on spatial risk and environmental risk, so as to achieve full-cycle coverage of safety inspection of the project construction.

[0134] It should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the module division described above is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, each functional module can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0135] Example 3

[0136] This embodiment provides an electronic device 2, such as... Figure 6 As shown, there is a processor 21 and a memory 22. The memory 22 is used to store computer program code, which includes computer instructions. When the processor 21 executes the computer instructions, the electronic device executes the above-mentioned intelligent monitoring method for whole-process engineering consulting based on the Internet of Things.

[0137] The electronic device 2 includes a processor 21, a memory 22, an output device 23, and an input device 24. The processor 21, memory 22, output device 23, and input device 24 are coupled together via connectors, which may include various interfaces, transmission lines, or buses, etc., and are not limited in this embodiment of the invention. It should be understood that in various embodiments of the invention, coupling refers to mutual connection through a specific method, including direct connection or indirect connection through other devices, such as through various interfaces, transmission lines, buses, etc.

[0138] The processor 21 can be one or more graphics processing units (GPUs). If the processor 21 is a GPU, the GPU can be a single-core GPU or a multi-core GPU. Optionally, the processor 21 can be a processor group composed of multiple GPUs, with the multiple processors coupled to each other via one or more buses. Optionally, the processor 21 can also be other types of processors, etc., and this embodiment of the invention is not limited thereto.

[0139] The memory 22 can be used to store computer program instructions, as well as various types of computer program code, including program code for executing the present invention. Optionally, the memory 22 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), and the memory 22 is used for related instructions and data.

[0140] Input device 24 is used to input data and / or signals, and output device 23 is used to output data and / or signals. Output device 23 and input device 24 can be independent devices or an integrated device.

[0141] This embodiment provides a computer-readable storage medium storing a computer program, which includes program instructions. When executed by the processor of an electronic device, the program instructions cause the processor to execute the aforementioned intelligent monitoring method for whole-process engineering consulting based on the Internet of Things.

[0142] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for intelligent monitoring of the entire process of engineering consulting based on the Internet of Things, characterized in that, Includes the following steps: Based on the BIM progress model, obtain the hazardous areas and their corresponding area parameters; The location data of the workers is acquired in real time and monitored. When the location data is located in the danger zone, obtain the wind speed and tilt angle of the work site; Calculate the engineering risk value based on the regional parameters, the location data, the wind speed at the work site, and the tilt angle at the work site; Based on the aforementioned project risk values, safety warnings will be issued to the workers. The area parameters include: safety distance and protection status.

2. The intelligent monitoring method for the entire process of engineering consulting based on the Internet of Things according to claim 1, characterized in that, Before obtaining the hazardous areas and their corresponding parameters based on the BIM progress model, the following steps are also included: Acquire 3D point cloud data of the construction site; Based on the 3D point cloud data and the BIM progress model, the BIM progress model is synchronized. Based on the BIM progress model after data synchronization, the hazardous areas and their corresponding parameters are updated.

3. The intelligent monitoring method for the entire process of engineering consulting based on the Internet of Things according to claim 2, characterized in that, The step of synchronizing the BIM progress model based on the 3D point cloud data and the BIM progress model includes the following steps: The coordinate system of the three-dimensional point cloud data is matched with that of the BIM progress model, and the three-dimensional point cloud data is superimposed and fused to obtain a superimposed model. Based on the superposition model, the number of completed components is obtained; Calculate the schedule deviation rate based on the number of completed components and the planned number of components; The BIM schedule model is adjusted based on the schedule deviation rate.

4. The intelligent monitoring method for the entire process of engineering consulting based on the Internet of Things according to claim 3, characterized in that, The process of obtaining the number of completed components based on the superposition model includes the following steps: Construct a component recognition model and train the component recognition model; The superimposed model is input into the component recognition model, and the component type of the superimposed model is output. Calculate the number of completed components based on the component type.

5. The intelligent monitoring method for the entire process of engineering consulting based on the Internet of Things according to claim 1, characterized in that, The calculation of the engineering risk value based on the regional parameters, the location data, the wind speed at the work site, and the inclination angle at the work site includes the following steps: Calculate spatial risk based on the region parameters and the location data; Calculate the environmental risk based on the wind speed and tilt angle at the work site; The engineering risk value is calculated based on the spatial risk and the environmental risk.

6. The intelligent monitoring method for the entire process of engineering consulting based on the Internet of Things according to claim 5, characterized in that, The calculation of spatial risk based on the region parameters and the location data includes the following steps: Based on the location data, the distance between the worker and the hazardous area is calculated and recorded as the actual distance; Calculate the protection coefficient based on the protection status; The spatial risk is calculated based on the safe distance, the actual distance, and the protection coefficient.

7. The intelligent monitoring method for the entire process of engineering consulting based on the Internet of Things according to claim 5, characterized in that, The calculation of environmental risk based on the wind speed and tilt angle at the work site includes the following steps: Obtain wind speed threshold and tilt angle threshold; Calculate the wind speed weight and tilt angle weight based on the schedule deviation rate; The environmental risk is calculated based on the wind speed threshold, the wind speed at the work site, the wind speed weight, the tilt angle threshold, the tilt angle at the work site, and the tilt angle weight.

8. An intelligent monitoring system for the entire process of engineering consulting based on the Internet of Things (IoT), employing the intelligent monitoring method for the entire process of engineering consulting based on the IoT as described in any one of claims 1 to 7, characterized in that, include: Hazardous area analysis module, location data monitoring module, environmental parameter acquisition module, engineering risk assessment module, and construction safety early warning module; The hazardous area analysis module is used to obtain hazardous areas and their corresponding area parameters based on the BIM progress model. The location data monitoring module is used to acquire the location data of the workers in real time and monitor the location data; The environmental parameter acquisition module is used to acquire the wind speed and tilt angle of the work site when the location data is located in the dangerous area. The engineering risk assessment module is used to calculate the engineering risk value based on the regional parameters, the location data, the wind speed at the work site, and the tilt angle at the work site. The construction safety early warning module is used to provide safety warnings to workers based on the project risk value. The area parameters include: safety distance and protection status.

9. An electronic device, characterized in that, The device includes a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device performs a smart monitoring method for whole-process engineering consulting based on the Internet of Things as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor of an electronic device, cause the processor to perform the intelligent monitoring method for whole-process engineering consulting based on the Internet of Things as described in any one of claims 1 to 7.

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