CN118114559A - Safe environment simulation detection method based on BIM model safety measure family library - Google Patents
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Abstract
The invention discloses a safe environment simulation detection method based on a BIM model safety measure family library, relates to the technical field of safe simulation, and solves the problems that BIM model safe simulation is difficult to adapt to different terminals and the safe process simulation is limited; the simulation detection method comprises the steps of obtaining construction site data information, constructing a basic BIM model in BIM software, and importing the basic BIM model into a safety management and control system; the safety management and control system establishes a complete BIM model by utilizing a safety measure family library; constructing a virtual character model to detect and evaluate a complete BIM model and generating a safety detection evaluation report; the multi-terminal controls and displays a safety detection evaluation process and a safety detection evaluation report; optimizing the safety protection device and the safety measure data according to the safety detection evaluation report; the virtual character model is constructed through the improved deep condition diffusion model, and the terminal adaptation model is used for carrying out remote adaptation on different types of terminals, so that the terminal suitability is improved, and the simulation limitation is reduced.
Description
Technical Field
The invention relates to the technical field of safety simulation, in particular to a safety environment simulation detection method based on a BIM model safety measure family library.
Background
Building Information Model (BIM) is a digital building design method that can be modeled and simulated in various phases of a building cycle, including building design, construction, operation, and maintenance. In recent years, with the widespread use of BIM technology, more and more researchers have begun to apply it to the field of building safety. The safety environment simulation detection based on the BIM model safety measure family library is an important research direction. According to the technology, a safety measure family library is added in the BIM model, the safety environments in different scenes are simulated, and the building design is detected, so that the safety and the reliability of a building are improved.
The patent number CN2021104240964 is a group evacuation behavior simulation method based on panoramic image modeling, which comprises the following steps: acquiring data in real time by using indoor environment acquisition equipment to acquire indoor panoramic map data of the intelligent building; generating a three-dimensional digital panoramic model by using a BIM building information model of the intelligent building and fusing correction map data; adding all the devices of the intelligent building into the panoramic model according to the actual positions; wearing a positioning device on an individual entering the intelligent building to acquire an individual movement track; setting individual types and behaviors under a panoramic model based on individual motion trajectories, setting Reward functions based on individual behaviors and a current environment, and then training an individual behavior model by utilizing an A3C reinforcement learning algorithm; when an emergency is simulated under the panoramic model, based on individual position data of a real environment, simulating group evacuation behaviors by utilizing a plurality of individual behavior models, and obtaining an optimal group evacuation scheme.
Patent number CN202111052768X super high-rise building informatization construction platform construction method, equipment and storage medium, adopting building internal member, external three-dimensional real model, BIM model, progress planning software and photo management platform based on oblique photography technique, and linking to form super high-rise building informatization construction platform by mutual fusion of model and model, model and progress planning software. The super high-rise building information construction platform is utilized to simulate the super high-rise building, and meanwhile, an information communication environment compatible with model library management, progress management, material management, quality management and safety management is provided for super high-rise building construction, and the information construction management of the super high-rise building is realized by synchronously constructing and changing on the platform in the construction process.
However, the above-mentioned patent does not have a safety measure family library, and it is not possible to conveniently check the real-time safety of construction equipment and construction sites, and the technology for simulating and detecting the safety environment of the BIM model safety measure family library needs to be implemented on a plurality of terminals, including a PC terminal, a safety control system terminal, a mobile terminal, and the like. However, due to different technical architectures, platform environments and the like of different terminals, the problems in the aspects of technical integration, data transmission and the like become extremely complex. Meanwhile, the performance and efficiency of various terminals are different, which may result in undesirable application effects on some terminals.
Disclosure of Invention
The invention aims to provide a safe environment simulation detection and use method based on a BIM model safety measure family library, which can construct a virtual character model through an improved deep condition diffusion model and a gesture mixing function, complete each safety detection in a BIM steel structure construction project model constructed based on the safety measure family library in a safety control system through the virtual character model, and can use the constructed safety control system in different types of terminals through a terminal adaptation model so as to improve the suitability of the different types of terminals.
The invention adopts the following technical scheme:
A safe environment simulation detection method based on a BIM model safety measure family library comprises the following steps;
S1: acquiring data information of a construction site, constructing a basic BIM model in BIM software, and importing the basic BIM model into a safety management and control system;
according to the acquired construction site data information, a corresponding basic BIM model is adjusted through a model adjusting module, and the basic BIM model is imported into a safety management and control system;
the safety management and control system, namely a safety environment simulation platform, can truly and completely embody various site information and safety measure deployment information of BIM steel structure construction items by the aid of a complete BIM model generated in the safety environment simulation platform after the safety measures are deployed; the safety management and control system monitors and displays the steel structure construction project through a large monitoring screen and a PC end by a three-dimensional GIS light engine, a BIM model light engine and an AI intelligent recognition algorithm;
S2: utilizing a safety measure family library in the safety control system to automatically extract safety measure information and automatically deploy safety protection devices to the basic BIM model, and generating a complete BIM model after the safety measures are deployed;
when the safety measure family library is constructed, according to safety measure standards of different construction flows, building corresponding safety protection devices and listing corresponding measure materials by using BIM modeling software, merging the safety protection device family files of the same construction flow which are constructed, integrating a safety measure bill of materials, annotating corresponding safety warning devices and safety identification devices, and finally obtaining the safety measure family library which is constructed and merged; the safety measure family library is built in the safety control system and forms a preset functional module of the safety control system;
s3: constructing a virtual character model, and carrying out safety detection evaluation in a complete BIM model after safety measure deployment to generate a safety detection evaluation report;
constructing a virtual character model through a simulation evaluation module, and performing simulation detection and evaluation on the safety environment in the complete BIM model after the safety measure deployment through the safety construction route of the virtual character model and each safety measure deployment information under the view angle of the virtual character model to generate a safety detection evaluation report;
s4: controlling and displaying a safety detection evaluation process and a safety detection evaluation report form through a plurality of terminals;
controlling and displaying the motion of the virtual character model in the complete BIM model and the generated safety detection evaluation report form through the interactive display module at multiple terminals;
S5: and optimizing the safety protection device and the safety measure data according to the safety detection evaluation report.
Preferably, in S1, the model adjustment module includes a parameter setting unit, a model drawing unit, and a migration importing unit; the parameter setting unit is used for inputting the determined construction site data information; the model drawing unit is used for adjusting a basic BIM model of the construction site according to the data information set by the parameter setting unit; the transplanting and importing unit is used for transplanting the obtained basic BIM model to the safety management and control system; construction site data information includes the number of floors of a building, materials, room size, type of construction equipment, and floor space.
Preferably, in S2, the working method of the security measure family library is as follows:
Firstly, extracting a corresponding safety protection device according to the type of a basic BIM model; then, performing derivatization deployment on the acquired safety protection appliance according to the basic BIM model data information by utilizing an automatic deployment algorithm, and generating a corresponding safety protection appliance according to the safety protection requirement of the basic BIM model; then, the designer modifies parameters and adjusts the shape of the generated safety protection device according to the actual construction site information; finally, performing finite element analysis and test on the BIM model after the safety protection device deployment is completed, and finally establishing a complete BIM model after the safety measure deployment is obtained;
the foundation BIM model comprises a construction site model and a construction equipment model; the basic BIM model data information comprises geometric information, material information and environment information of steel structure construction;
The construction method of the safety measure family library comprises the following steps:
A: acquiring safety measure data of the steel structure construction process, and formulating safety measure standards based on safety construction factors in the construction process;
safety construction factors include safety protection devices, construction flows, site layout, traffic management, construction platforms and safety distances;
B: classifying the safety measure data according to the construction flow;
c: constructing family files of different safety protection devices in a BIM model by using BIM software, and marking notes of a corresponding safety warning device and a corresponding safety identification device;
d: and perfecting and improving the safety measure family base according to the actual construction flow.
Safety measure standards include job operation standards, field management standards, emergency handling standards, and safety protection appliance standards; the safety measure data comprises a safety check list, a safety protection measure list, a safety measure material cost statistics list, a dangerous source identification list and a safety measure case; the construction process comprises the steps of component manufacturing, material transportation, steel structure installation, assembly welding, surface treatment, construction acceptance and cleaning of construction sites; the safety protection device comprises a dense mesh safety net, a safety belt hanging point, an overhanging type steel platform, an upright post, a safety staircase, a fire receiving basin, a protection railing and a welding operation platform.
Preferably, in S3, the simulation evaluation module includes a character modeling unit, an action fusion unit, a simulation detection unit, and a security evaluation unit; the character modeling unit is used for constructing a virtual character model through the improved depth condition diffusion model; the action fusion unit is used for driving the virtual character model through the gesture mixing function so as to simulate the action of the real character; the simulation detection unit is used for driving the virtual character model to move in the complete BIM model after the safety measures are deployed according to the set safety construction route, and observing the deployment conditions of each safety measure through the view angle of the virtual character model; the safety evaluation unit is used for evaluating the safety protection result of the safety measure family library in the complete BIM model after the safety measure deployment through the safety standard and the evaluation function, and generating a safety detection evaluation report.
Preferably, the improved depth condition diffusion model firstly acquires a large number of human body multi-views, then acquires feature images of the human body multi-views by adopting feature extractors with different sizes, adjusts the feature images to be the same size for fusion, and the fused feature images are as follows:
Wherein x f represents a fused feature map, i represents a current feature extractor, N represents a total number of feature extractors, w i represents a current feature extractor weight, x i represents a current feature extractor output map, f represents a fused sequence number, and x represents a size of a feature extractor;
And accumulating color values along the light rays, and acquiring corresponding pixel color values through pixel functions, wherein the pixel color values are as follows:
p(t)＝α(t+vxf) (3)
Where C (o, v) represents the pixel color value function, C () represents the MLP network function, p () represents the point along the ray, w (t) represents the unbiased and occlusion awareness weighting function, Representing logic density distribution, alpha representing polarized light coefficient, o representing illumination starting point, v representing light ray speed, t representing time, beta representing distortion coefficient, u representing discrete sampling time;
and then, a color image is obtained by corresponding pixel color value rendering, and a normal vector of an intersection point with the surface point is calculated according to the gradient accumulated value of each random sampling ray, wherein the normal vector of the intersection point is as follows:
the corresponding color values at this time are:
wherein n () represents a normal vector function, a represents ambient illumination intensity, D represents diffuse reflection illumination intensity, and F () represents a coloring color value;
Then, the molded human skin is endowed to bones, the human skin is divided into a plurality of nodes, and any one node of the human skin is controlled by a plurality of bones:
Mj＝Mt(M0)-1 (8)
Where M j represents the transformation matrix of the jth bone, v' i represents the final world coordinates of the avatar pose, v i represents the world coordinates of the standard pose, M represents the total number of bones, j represents the current bone number, M t represents the current moment transformation matrix, and M 0 represents the original transformation matrix.
Preferably, the automatic deployment algorithm firstly acquires the length, width, height and weight information of the construction site model or the construction equipment model; and then determining a safety distance according to the construction site model and the construction equipment model information:
Wherein dis min represents the minimum safety distance, α represents the safety distance coefficient, L represents the length of the construction site model or the construction equipment model, W represents the width of the construction site model or the construction equipment model, M represents the height of the construction site model or the weight of the construction equipment model, θ represents the inclination angle of the construction site model or the construction equipment model;
And then selecting the type and the material of the safety protection device according to the construction scene, and determining the compensation height of the safety protection device:
wherein H com represents a compensation height value, H μ represents the height of each part of the construction site model or the construction equipment model, μ represents each part number of the construction site model or the construction equipment model, Representing the material coefficient of the safety protection device, sigma represents the material type, R represents the working radius of a construction site model or a construction equipment model, and omega represents the inclination distance;
safety shield apparatus height and position are then determined:
Dis＝h*disminsec(θ)+(1-T)α*L*W*tanh(θ) (12)
Wherein H represents the height of the safety protection device, dis represents the installation position of the safety protection device, H represents the humidity coefficient, and T represents the temperature coefficient;
And finally, derivatizing and caching the type, the height, the width, the thickness, the gap and the shape of the safety protection device according to the shape of the construction site model or the construction equipment model.
Preferably, in S4, the interactive display module includes a detection verification unit, a lightweight adaptation unit, and a man-machine interaction unit; the interactive display module firstly detects the terminal type and configuration of the user through the detection and verification unit and verifies the identity of the user; then, based on a preset terminal adaptation model and the terminal type and configuration of a user, matching and displaying a complete BIM model of the user after safety measures of corresponding volume are deployed through a lightweight adaptation unit; and finally, controlling the activities of the virtual character model in the complete BIM model after the safety measure deployment by using the computer peripheral equipment through the man-machine interaction unit by a user, and checking the safety detection evaluation report.
Preferably, the terminal adaptation model comprises a backbone network layer, a quantization selection layer and an adaptive training layer; the backbone network layer includes MicroNet networks and MobileNetV networks; the quantization selection layer performs network control by utilizing a bidirectional LSTM network and generates a quantization strategy; the bidirectional LSTM network respectively extracts the characteristics of the input sequence in a positive sequence and a reverse sequence mode, and then splices the two characteristic vectors; the self-adaptive training layer comprises a network dynamic learning self-adaptive function and a device resource learning self-adaptive function; the network dynamic learning self-adaptive function is used for training a backbone network layer and a quantization selection layer, the backbone network layer uses a set of parameters on the same quantization level, the quantization selection layer adjusts a quantization strategy according to the feedback quantization effect of the backbone network layer, and the loss function is as follows:
where σ represents the backbone network layer number, y represents each group of network loss functions, K represents the current intra-group network, K represents the total network group number, Representing each group of each layer of network self-adaptive quantization functions, B σ representing each layer of backbone network loss functions, E σ representing each layer of backbone network resource efficiency, mu 1 representing calculation precision coefficients, mu 2 representing resource efficiency preset weight coefficients, G representing quantization strategies, and rho representing self-adaptive quantization parameters;
The equipment resource learning self-adaptive function obtains terminal resource information through an operating system instruction, and normalizes the terminal resource availability into 4 discrete levels; the terminal resource information comprises a CPU, a GPU, a memory, a cache and a buffer area; discrete levels include fully available, limited available, partially available, and unavailable.
According to the invention, the virtual character is constructed through the improved deep condition diffusion model, the actions of the virtual character are driven and improved through the gesture mixing function, and the terminals of different types are remotely adapted through the terminal adaptation model, so that the adaptation of the different terminals is improved, the flexibility of the actions of the virtual character is improved, and the simulation limitation of the safety process is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only embodiments of the present invention, and other drawings may be obtained according to the provided drawings without inventive effort for those skilled in the art.
FIG. 1 is a schematic block diagram of a security environment simulation detection method based on a BIM model security measure family library;
fig. 2 is a schematic diagram of a terminal adaptation model.
Detailed Description
The invention is described in detail below with reference to the attached drawings and examples:
as shown in FIG. 1, the safety environment simulation detection method based on the BIM model safety measure family library comprises the following steps of;
S1: constructing a basic BIM model in BIM software according to the acquired data information of the construction site, and importing the basic BIM model into a safety management and control system;
the safety management and control system, namely a safety environment simulation platform, can truly and completely embody various site information and safety measure deployment information of BIM steel structure construction items by the aid of a complete BIM model generated in the safety environment simulation platform after the safety measures are deployed; the safety management and control system comprises conventional management functions, such as a project management module (project serial number, process display and the like), a constructor management module (constructor name, identity card number, photo and the like), a potential safety hazard correction module (potential safety hazard position, correction period, correction measure and the like), a mechanical equipment management module (mechanical equipment used in construction sites and the like), a report center module (material report and the like) and other conventional modules, which belong to the prior art and are not repeated herein;
the safety management and control system monitors and displays the steel structure construction project safety management system through a large monitoring screen and a PC end by the aid of an existing three-dimensional GIS light engine, BIM light engine and AI intelligent recognition algorithm, and the technology and functions also belong to the prior art and are not repeated herein;
Construction site data information includes the number of building floors, the material of the building material, the layout of the building, the type of construction equipment and the occupied space;
In the embodiment, according to the acquired construction site data information, a corresponding basic BIM model is adjusted through a model adjustment module, and the basic BIM model is imported into a safety management and control system; the model adjustment module comprises a parameter setting unit, a model drawing unit and a transplanting and importing unit; the parameter setting unit is used for inputting the determined construction site data information; the model drawing unit is used for adjusting a basic BIM model of the construction site according to the construction site data information; the transplanting and importing unit is used for transplanting the adjusted basic BIM model to the safety management and control system;
S2: utilizing a safety measure family library in the safety control system to automatically extract safety measure information and automatically deploy safety protection devices to the basic BIM model, and generating a complete BIM model after the safety measures are deployed;
In the invention, a safety measure family library is specially added in a conventional safety control system so as to realize the integrated management and self-adaptive deployment of safety measures. When the safety measure family library is constructed, according to safety measure standards of different construction flows, building corresponding safety protection devices and listing corresponding measure materials by using BIM modeling software, merging the safety protection device family files of the same construction flow which are constructed, integrating a safety measure bill of materials, annotating corresponding safety warning devices and safety identification devices, and finally obtaining the safety measure family library which is constructed and merged; in the invention, a safety measure family library is built in a safety control system and forms a preset functional module of the safety control system;
The construction method of the safety measure family library comprises the following steps:
A: acquiring safety measure data of the steel structure construction process, and formulating safety measure standards based on safety construction factors in the construction process;
safety construction factors include safety protection devices, construction flows, site layout, traffic management, construction platforms and safety distances;
B: classifying the safety measure data according to the construction flow;
c: constructing family files of different safety protection devices in a BIM model by using BIM software, and marking notes of a corresponding safety warning device and a corresponding safety identification device;
d: and perfecting and improving the safety measure family base according to the actual construction flow.
Safety measure standards include job operation standards, field management standards, emergency handling standards, and safety protection appliance standards; the safety measure data comprises a safety check list, a safety protection measure list, a safety measure material cost statistics list, a dangerous source identification list and a safety measure case; the construction process comprises component manufacturing, material transportation, steel structure installation, assembly welding, surface treatment, construction acceptance and construction site cleaning; the safety protection device comprises a dense mesh safety net, a safety belt hanging point, an overhanging type steel platform, a stand column, a safety staircase, a fire receiving basin, a protection railing, a welding operation platform and the like;
The operation standard refers to a series of normative requirements and operation procedures formulated for ensuring the safety of operators, engineering quality, environmental protection and normal operation of equipment and facilities when various operations are carried out on a construction site, and generally comprises the contents of operation preparation, operation process, operation ending, emergency response and the like; the field management standard comprises various construction field management requirement files issued by the country; the emergency processing standard refers to a series of normative requirements and operation procedures formulated for rapidly and effectively coping and disposing when emergencies or emergency situations occur in the construction site of the building in the country, guaranteeing personnel safety and reducing property loss and environmental pollution; the safety protection device standard refers to the specific specifications and requirements of various protection devices used on construction sites for guaranteeing the safety of operators and preventing accidents; the dangerous source identification table comprises relevant safety standards issued by the country and all necessary information required in the regulations so as to ensure that the dangerous source is properly evaluated and controlled; the safety check list is formulated by enterprises according to national related laws and regulations, industry standards and the enterprise's own safety production regulation system, and comprises a series of check items, so as to systematically identify and evaluate potential safety hazards in construction sites and ensure the safety of construction activities; the safety protection measure list comprises a series of protection measures, and aims to systematically identify and evaluate potential safety hazards in construction sites and take corresponding protection measures to ensure the safety of construction activities; a safety measure material cost statistical table, namely various material costs of the safety protection device required by different construction flows;
The working method of the safety measure family library comprises the following steps:
Firstly, extracting a corresponding safety protection device according to the type of a basic BIM model; then, performing derivatization deployment on the acquired safety protection appliance according to the basic BIM model data information by utilizing an automatic deployment algorithm, and generating a corresponding safety protection appliance according to the safety protection requirement of the basic BIM model; then, the designer modifies parameters and adjusts the shape of the generated safety protection device according to the actual construction site information; finally, performing finite element analysis and test on the BIM model after the safety protection device deployment is completed, and finally establishing a complete BIM model after the safety measure deployment is obtained;
the foundation BIM model comprises a construction site model and a construction equipment model; the basic BIM model data information comprises geometric information, material information, environment information and the like of steel structure construction;
s3: constructing a virtual character model, and carrying out safety detection evaluation in a complete BIM model after safety measure deployment to generate a safety detection evaluation report;
In the embodiment, a virtual character model is constructed through a simulation evaluation module, and a safety environment in a complete BIM model after safety measure deployment is simulated, detected and evaluated through a safety construction route of the virtual character model and various safety measure deployment information under a virtual character model view angle, so as to generate a safety detection evaluation report;
The simulation evaluation module comprises a character modeling unit, an action fusion unit, a simulation detection unit and a safety evaluation unit; the character modeling unit is used for constructing a virtual character model through the improved depth condition diffusion model; the action fusion unit is used for driving the virtual character model through the gesture mixing function so as to simulate the action of the real character; the simulation detection unit is used for driving the virtual character model to move in the complete BIM model after the safety measures are deployed according to the set safety construction route, and observing the deployment conditions of each safety measure through the view angle of the virtual character model; the safety evaluation unit is used for evaluating the safety protection result of the safety measure family library in the complete BIM model after the safety measure deployment through the safety standard and the evaluation function, and generating a safety detection evaluation report;
s4: controlling and displaying a safety detection evaluation process and a safety detection evaluation report form through a plurality of terminals;
In the embodiment, through an interactive display module, the motion of the virtual character model in the complete BIM model and the generated safety detection evaluation report are controlled and displayed at multiple terminals; the interactive display module comprises a detection and verification unit, a light-weight adaptation unit and a man-machine interaction unit; the interactive display module firstly detects the terminal type and configuration of the user through the detection and verification unit and verifies the identity of the user; then, based on a preset terminal adaptation model and the terminal type and configuration of a user, matching and displaying a complete BIM model of the user after safety measures of corresponding volume are deployed through a lightweight adaptation unit; finally, a user controls the activities of the virtual character model in the complete BIM model after safety measure deployment and views a safety detection evaluation report by utilizing computer peripheral equipment (touch pad, mouse, keyboard and the like) through a human-computer interaction unit;
S5: and optimizing the safety protection device and the safety measure data according to the safety detection evaluation report.
Examples:
Selecting a corresponding construction site or construction equipment model in BIM model software according to project information, adjusting a basic BIM model according to the number of building floors of the construction site, the materials of used materials, the layout of a building, the type of construction equipment and occupied space, and then transplanting the basic BIM model to a safety management and control system; meanwhile, the safety control system calls a safety measure family library, automatically extracts a safety protection measure table, a dangerous source identification table and a safety measure case according to the selected basic BIM model, deploys a safety protection device and a fire extinguisher, and finally establishes a complete BIM model after deployment of the safety measure; then constructing a virtual character model by utilizing an improved depth condition diffusion model, driving the virtual character model by utilizing a gesture mixing function, simulating the action of a real character, driving the virtual character model to move in a complete BIM model after safety measure deployment according to a set safety construction route, observing the deployment conditions of each safety measure through the view angle of the virtual character model, and then carrying out finite element analysis on the complete BIM model through a safety management and control system so as to carry out safety detection on the protective appliances of each construction flow of a construction site in the complete BIM model after the safety measure deployment;
Aiming at the multi-layer building model, construction protection simulation can be carried out on each layer, virtual figures can carry out accident limit safety inspection in the construction process of each layer of building, and places which do not reach the protection standard are identified according to the safety protection standard; aiming at the construction equipment model, the virtual character performs safety inspection on the position, the range and the working range of the construction equipment of the protective equipment around the construction equipment model; finally, evaluating the safety protection result of the safety measure family library in the complete BIM model after the safety measure deployment according to the safety measure standard and the evaluation function; the security management and control system can display a security detection evaluation report and control the motion of the virtual character model in the complete BIM model after the security measure deployment through different terminals, detect the type and configuration of the access terminal, verify the identity of a user by using a secret key, match and display the complete BIM model after the security measure deployment of corresponding volume (such as resolution, delay rate and the like) for the user by using a terminal adaptation model according to a CPU, a GPU, a memory, a cache and a buffer of the terminal, and control the virtual character model to move in the complete BIM model after the security measure deployment through a computer peripheral device (a touch pad, a mouse, a keyboard and the like) and check the security detection evaluation report; and finally, optimizing the safety protection device and the safety measure data according to the safety detection evaluation report.
Further, the improved depth condition diffusion model firstly acquires a large number of human body multi-views, then acquires the feature images of the human body multi-views by adopting feature extractors with different sizes, adjusts the feature images to be the same size for fusion, and the fused feature images are as follows:
Wherein x f represents a fused feature map, i represents a current feature extractor, N represents a total number of feature extractors, w i represents a current feature extractor weight, x i represents a current feature extractor output map, f represents a fused sequence number, and x represents a size of a feature extractor;
And accumulating color values along the light rays, and acquiring corresponding pixel color values through pixel functions, wherein the pixel color values are as follows:
p(t)＝α(t+vxf) (3)
Where C (o, v) represents the pixel color value function, C () represents the MLP network function, p () represents the point along the ray, w (t) represents the unbiased and occlusion awareness weighting function, Representing logic density distribution, alpha representing polarized light coefficient, o representing illumination starting point, v representing light ray speed, t representing time, beta representing distortion coefficient, u representing discrete sampling time;
and then, a color image is obtained by corresponding pixel color value rendering, and a normal vector of an intersection point with the surface point is calculated according to the gradient accumulated value of each random sampling ray, wherein the normal vector of the intersection point is as follows:
the corresponding color values at this time are:
wherein n () represents a normal vector function, a represents ambient illumination intensity, D represents diffuse reflection illumination intensity, and F () represents a coloring color value;
Then, the molded human skin is endowed to bones, the human skin is divided into a plurality of nodes, and any one node of the human skin is controlled by a plurality of bones:
Mj＝Mt(M0)-1 (8)
Wherein M j represents a transformation matrix of a jth bone, v' i represents final world coordinates of the virtual character pose, v i represents world coordinates of a standard pose, M represents total number of bones, j represents a current bone number, M t represents a transformation matrix at a current moment, and M 0 represents an original transformation matrix;
The working principle of the improved depth condition diffusion model is as follows: the human body generation framework based on the implicit field nerve expression comprises two stages of conditional human body generation and iterative texture refinement. In the first stage, a human body implicit neural expression meeting user text prompts and given template body states is guided to be generated by a human body generation method based on probability diffusion through a pre-trained depth condition diffusion probability model. In the second stage, the color information obtained in the first stage is used as texture prior by a reconstruction texture generation module, the color information is input into the same pre-trained improved depth condition diffusion model, then a high-precision texture guide image set is restored by a condition denoising and restoring strategy, and the model textures are circularly iterated and refined. Finally, the high-quality virtual character model is obtained by optimizing the implicit expression of the human nerves.
The depth condition diffusion model and the improved depth condition diffusion model are respectively adopted for the same piece of human body data information to reconstruct human body, as shown in table 1.
Table 1 human body reconstruction contrast table
Table 1 is a comparative table of human reconstruction techniques, including comparative results of depth conditional diffusion and improved depth conditional diffusion models in terms of time, accuracy, and integrity.
By comparing the tables, it is possible to obtain:
1. The time aspect is: the reconstruction time of the improved depth condition diffusion model is 153s, which is 51s faster than that of the depth condition diffusion model, and the reconstruction time is less than 2/3 of that of the depth condition diffusion model, so the improved depth condition diffusion model has more advantages in the aspect of reconstruction speed.
2. Accuracy aspect: the accuracy of the improved depth conditional diffusion model reaches 97.6%, and the accuracy is improved by about 20% compared with that of the depth conditional diffusion model. This illustrates that the improved depth conditional diffusion model is more advantageous in terms of reconstruction accuracy.
3. Integrity aspect: the reconstruction integrity of the improved depth condition diffusion model is 98.7%, which is improved by about 20% compared with the depth condition diffusion model, so that the improved depth condition diffusion model can reconstruct a human body model more finely and is closer to a real object.
Meaning of safe distance setting: by setting the safety distance, safety accidents such as electric shock, object striking, mechanical injury and the like possibly occurring in the construction process can be effectively avoided, and accidental injury caused by improper operation or equipment failure is reduced; for electric power facilities such as high-voltage lines, the influence of construction activities on the electric power facilities can be avoided by specifying the safety distance, and the normal operation of the electric power facilities and the stability of electric power supply are ensured; the setting of the safe distance of the construction site is also beneficial to reducing the interference and influence of construction activities on the surrounding environment, such as preventing construction noise, dust, waste water and the like from affecting the life of surrounding residents; the setting of the safety distance is a standard for construction units and workers, is beneficial to improving the safety consciousness of the construction workers, promotes civilized construction and ensures orderly execution of construction sites.
Further, the automatic deployment algorithm firstly acquires the length, width, height and weight information of a construction site model or a construction equipment model; and then determining a safety distance according to the construction site model and the construction equipment model information:
Wherein dis min represents the minimum safety distance, α represents the safety distance coefficient, L represents the length of the construction site model or the construction equipment model, W represents the width of the construction site model or the construction equipment model, M represents the height of the construction site model or the weight of the construction equipment model, θ represents the inclination angle of the construction site model or the construction equipment model;
And then selecting the type and the material of the safety protection device according to the construction scene, and determining the compensation height of the safety protection device:
wherein H com represents a compensation height value, H μ represents the height of each part of the construction site model or the construction equipment model, μ represents each part number of the construction site model or the construction equipment model, Representing the material coefficient of the safety protection device, sigma represents the material type, R represents the working radius of a construction site model or a construction equipment model, and omega represents the inclination distance;
safety shield apparatus height and position are then determined:
Dis＝h*disminsec(θ)+(1-T)α*L*W*tanh(θ) (12)
Wherein H represents the height of the safety protection device, dis represents the installation position of the safety protection device, H represents the humidity coefficient, and T represents the temperature coefficient;
And finally, derivatizing and caching the type, the height, the width, the thickness, the gap and the shape of the safety protection device according to the shape of the construction site model or the construction equipment model.
The working principle of the automatic deployment algorithm is as follows: basic information of construction equipment and construction sites is collected, including construction equipment types, construction site structures, surrounding environments and the like, so that necessary input data are provided for safety protection device adjustment; and preliminarily calculating the security level of each area according to the collected data. Some evaluation indexes about the security risk level, such as floor height, whether construction equipment has high risk, etc., can be used here to calculate the priority of security apparatus adjustment; generating a safety protection device scheme according to the safety level and the adjustment priority; security risk levels include low risk, medium risk, high risk, and extremely high risk, and specific divisions of these risk levels are based on factors including complexity of the job, field conditions, personnel skills, equipment conditions, environmental factors, accident history, and the like; in the scheme generation process, the influence of the surrounding environment, such as the region where construction equipment needs to pass through, the construction route of constructors and the like, needs to be considered in addition to the safety; specific adjustment schemes for the safety shield apparatus are created, including the material, height, width, safety shield apparatus spacing, and the like. According to the generated safety protection device schemes, calculating the adjustment schemes and the adjustment amplitudes of the safety protection devices in all areas; and feeding back the adjustment result to personnel such as a construction site manager or the like so as to carry out final examination and confirmation. The safety shield apparatus may be optimized for modification accordingly, if desired.
Further, the terminal adaptation model comprises a backbone network layer, a quantization selection layer and an adaptive training layer; the backbone network layer includes MicroNet networks and MobileNetV networks; the quantization selection layer performs network control by utilizing a bidirectional LSTM network and generates a quantization strategy; the bidirectional LSTM network respectively extracts the characteristics of the input sequence in a positive sequence and a reverse sequence mode, and then splices the two characteristic vectors; the self-adaptive training layer comprises a network dynamic learning self-adaptive function and a device resource learning self-adaptive function; the network dynamic learning self-adaptive function is used for training a backbone network layer and a quantization selection layer, the backbone network layer uses a set of parameters on the same quantization level, the quantization selection layer adjusts a quantization strategy according to the feedback quantization effect of the backbone network layer, and the loss function is as follows:
where σ represents the backbone network layer number, y represents each group of network loss functions, K represents the current intra-group network, K represents the total network group number, Representing each group of each layer of network self-adaptive quantization functions, B σ representing each layer of backbone network loss functions, E σ representing each layer of backbone network resource efficiency, mu 1 representing calculation precision coefficients, mu 2 representing resource efficiency preset weight coefficients, G representing quantization strategies, and rho representing self-adaptive quantization parameters;
the equipment resource learning self-adaptive function obtains terminal resource information through an operating system instruction, and normalizes the terminal resource availability into 4 discrete levels; the terminal resource information comprises a CPU, a GPU, a memory, a cache and a buffer area; discrete levels include fully available, limited available, partially available, and unavailable;
The working principle of the terminal adaptation model is as follows: by accessing relevant information of the equipment (such as screen resolution, color depth, GPU/CPU capability and the like), the type of the currently used terminal (desktop end, mobile end, VR/AR and the like) is obtained by using a terminal detection technology, and a corresponding adaptation scheme is formulated according to the performance of the equipment and the screen characteristics. And formulating an interface layout adapting strategy according to the screen size and the resolution of the equipment. For example, on the desktop side, an interface manner such as a floating window and a menu bar can be used, while on the mobile side, a manner such as sliding pages and gesture interaction is needed. According to the capability and processing capability of the terminal equipment, an optimal data format and transmission mode are adopted, so that data transmission overhead is reduced, and data transmission efficiency is improved. According to the capability difference of the terminal equipment, different media resource schemes are selected and optimized, for example, the image resolution, the video code rate and the like are reduced, and the media resource can be normally played and displayed on various equipment.
The same virtual environment simulator is projected on the same terminal using a lightweight model and a terminal adaptation model, respectively, as shown in table 2.
Table 2 terminal adaptation pair comparison table
| Model/index | Time/s | Accuracy/% | Adaptation/% |
| Lightweight construction | 18.2 | 82.6 | 67.1 |
| Terminal adaptation | 9.2 | 98.7 | 98.6 |
From the data of table 2, the following analysis can be derived:
1. Time/s aspect: the processing time of terminal adaptation is obviously better than light weight, and only 9.2 seconds are needed to complete the adaptation, while 18.2 seconds are needed for light weight. This may be because the weight reduction requires an optimization process for each model, whereas terminal adaptation is faster in time by formulating an adaptation strategy based on different terminal and device information.
2. Accuracy/%aspect: the accuracy of terminal adaptation is obviously better than that of light weight, reaches a high level of 98.7%, and is far higher than that of light weight 82.6%. This illustrates that terminal adaptation can more accurately adapt to different terminals and devices, maintaining the complete appearance and functionality of the model.
3. Adaptation/%aspect: the adaptation degree of terminal adaptation is also obviously better than the light weight, and the high level of 98.6 percent is achieved, and the light weight is only 67.1 percent. This shows that terminal adaptation can better meet the requirements of different terminals and devices, providing a better quality of user experience.
Claims (8)
1. A safe environment simulation detection method based on a BIM model safe measure family base is characterized by comprising the following steps of: comprises the following steps of;
S1: acquiring data information of a construction site, constructing a basic BIM model in BIM software, and importing the basic BIM model into a safety management and control system;
S2: utilizing a safety measure family library in the safety control system to automatically extract safety measure information and automatically deploy safety protection devices to the basic BIM model, and generating a complete BIM model after the safety measures are deployed;
when the safety measure family library is constructed, according to safety measure standards of different construction flows, building corresponding safety protection devices and listing corresponding measure materials by using BIM modeling software, merging the safety protection device family files of the same construction flow which are constructed, integrating a safety measure bill of materials, annotating corresponding safety warning devices and safety identification devices, and finally obtaining the safety measure family library which is constructed and merged; the safety measure family library is built in the safety control system and forms a preset functional module of the safety control system;
s3: constructing a virtual character model, and carrying out safety detection evaluation in a complete BIM model after safety measure deployment to generate a safety detection evaluation report;
s4: controlling and displaying a safety detection evaluation process and a safety detection evaluation report form through a plurality of terminals;
S5: and optimizing the safety protection device and the safety measure data according to the safety detection evaluation report.
2. The method for simulating and detecting the safe environment based on the BIM model safety measure family library according to claim 1, wherein the method comprises the following steps of: in the step S1, according to the acquired construction site data information, a corresponding basic BIM model is adjusted through a model adjustment module, and the basic BIM model is imported into a safety management and control system; the model adjustment module comprises a parameter setting unit, a model drawing unit and a transplanting and importing unit; the parameter setting unit is used for inputting the determined construction site data information; the model drawing unit is used for adjusting a basic BIM model of the construction site according to the construction site data information; the transplanting and importing unit is used for transplanting the adjusted basic BIM model to the safety management and control system; construction site data information includes the number of building floors, the material of the building material, the layout of the building, the type of construction equipment, and the occupied space.
3. The method for simulating and detecting the safe environment based on the BIM model safety measure family library according to claim 1, wherein the method comprises the following steps of: in the step S2, the working method of the safety measure family library is as follows:
Firstly, extracting a corresponding safety protection device according to the type of a basic BIM model; then, performing derivatization deployment on the acquired safety protection appliance according to the basic BIM model data information by utilizing an automatic deployment algorithm, and generating a corresponding safety protection appliance according to the safety protection requirement of the basic BIM model; then, the designer modifies parameters and adjusts the shape of the generated safety protection device according to the actual construction site information; finally, performing finite element analysis and test on the BIM model after the safety protection device deployment is completed, and finally establishing a complete BIM model after the safety measure deployment is obtained;
the foundation BIM model comprises a construction site model and a construction equipment model; the basic BIM model data information comprises geometric information, material information and environment information of steel structure construction;
The construction method of the safety measure family library comprises the following steps:
A: acquiring safety measure data of the steel structure construction process, and formulating safety measure standards based on safety construction factors in the construction process;
safety construction factors include safety protection devices, construction flows, site layout, traffic management, construction platforms and safety distances;
B: classifying the safety measure data according to the construction flow;
c: constructing family files of different safety protection devices in a BIM model by using BIM software, and marking notes of a corresponding safety warning device and a corresponding safety identification device;
d: and perfecting and improving the safety measure family base according to the actual construction flow.
Safety measure standards include job operation standards, field management standards, emergency handling standards, and safety protection appliance standards; the safety measure data comprises a safety check list, a safety protection measure list, a safety measure material cost statistics list, a dangerous source identification list and a safety measure case; the construction process comprises the steps of component manufacturing, material transportation, steel structure installation, assembly welding, surface treatment, construction acceptance and cleaning of construction sites; the safety protection device comprises a dense mesh type safety net, a safety belt hanging point, an overhanging type steel platform, an upright post, a safety staircase, a fire receiving basin and a welding operation platform.
4. The method for simulating and detecting the safe environment based on the BIM model safety measure family library according to claim 1, wherein the method comprises the following steps of: in the step S3, a virtual character model is constructed through a simulation evaluation module, and a safety environment in a complete BIM model after the deployment of the safety measures is simulated, detected and evaluated through a safety construction route of the virtual character model and deployment information of each safety measure under the view angle of the virtual character model, so as to generate a safety detection evaluation report; the simulation evaluation module comprises a character modeling unit, an action fusion unit, a simulation detection unit and a safety evaluation unit; the character modeling unit is used for constructing a virtual character model through the improved depth condition diffusion model; the action fusion unit is used for driving the virtual character model through the gesture mixing function so as to simulate the action of the real character; the simulation detection unit is used for driving the virtual character model to move in the complete BIM model after the safety measures are deployed according to the set safety construction route, and observing the deployment conditions of each safety measure through the view angle of the virtual character model; the safety evaluation unit is used for evaluating the safety protection result of the safety measure family library in the complete BIM model after the safety measure deployment through the safety standard and the evaluation function, and generating a safety detection evaluation report.
5. The method for simulating and detecting the safe environment based on the BIM model safety measure family library according to claim 4, wherein the method comprises the following steps: the improved depth condition diffusion model firstly acquires a large number of human body multi-views, then acquires the feature images of the human body multi-views by adopting feature extractors with different sizes, adjusts the feature images to be the same size for fusion, and the fused feature images are as follows:
Wherein x f represents a fused feature map, i represents a current feature extractor, N represents a total number of feature extractors, w i represents a current feature extractor weight, x i represents a current feature extractor output map, f represents a fused sequence number, and x represents a size of a feature extractor;
And accumulating color values along the light rays, and acquiring corresponding pixel color values through pixel functions, wherein the pixel color values are as follows:
p(t)＝α(t+vxf) (3)
Where C (o, v) represents the pixel color value function, C () represents the MLP network function, p () represents the point along the ray, w (t) represents the unbiased and occlusion awareness weighting function, Representing logic density distribution, alpha representing polarized light coefficient, o representing illumination starting point, v representing light ray speed, t representing time, beta representing distortion coefficient, u representing discrete sampling time;
and then, a color image is obtained by corresponding pixel color value rendering, and a normal vector of an intersection point with the surface point is calculated according to the gradient accumulated value of each random sampling ray, wherein the normal vector of the intersection point is as follows:
the corresponding color values at this time are:
wherein n () represents a normal vector function, a represents ambient illumination intensity, D represents diffuse reflection illumination intensity, and F () represents a coloring color value;
Then, the molded human skin is endowed to bones, the human skin is divided into a plurality of nodes, and any one node of the human skin is controlled by a plurality of bones:
Mj＝Mt(M0)-1 (8)
Where M j represents the transformation matrix of the jth bone, v' i represents the final world coordinates of the avatar pose, v i represents the world coordinates of the standard pose, M represents the total number of bones, j represents the current bone number, M t represents the current moment transformation matrix, and M 0 represents the original transformation matrix.
6. The method for simulating and detecting the safe environment based on the BIM model safety measure family library according to claim 3, wherein the method comprises the following steps of: the automatic deployment algorithm firstly obtains the length, width, height and weight information of a construction site model or a construction equipment model; and then determining a safety distance according to the construction site model and the construction equipment model information:
Wherein dis min represents the minimum safety distance, α represents the safety distance coefficient, L represents the length of the construction site model or the construction equipment model, W represents the width of the construction site model or the construction equipment model, M represents the height of the construction site model or the weight of the construction equipment model, θ represents the inclination angle of the construction site model or the construction equipment model;
And then selecting the type and the material of the safety protection device according to the construction scene, and determining the compensation height of the safety protection device:
wherein H com represents a compensation height value, H μ represents the height of each part of the construction site model or the construction equipment model, μ represents each part number of the construction site model or the construction equipment model, Representing the material coefficient of the safety protection device, sigma represents the material type, R represents the working radius of a construction site model or a construction equipment model, and omega represents the inclination distance;
safety shield apparatus height and position are then determined:
Dis＝h*disminsec(θ)+(1-T)α*L*W*tanh(θ) (12)
Wherein H represents the height of the safety protection device, dis represents the installation position of the safety protection device, H represents the humidity coefficient, and T represents the temperature coefficient;
And finally, derivatizing and caching the type, the height, the width, the thickness, the gap and the shape of the safety protection device according to the shape of the construction site model or the construction equipment model.
7. The method for simulating and detecting the safe environment based on the BIM model safety measure family library according to claim 1, wherein the method comprises the following steps of: in the step S4, the motion of the virtual character model in the complete BIM model and the generated safety detection evaluation report are controlled and displayed at multiple terminals through an interactive display module; the interactive display module comprises a detection and verification unit, a light-weight adaptation unit and a man-machine interaction unit; the interactive display module firstly detects the terminal type and configuration of the user through the detection and verification unit and verifies the identity of the user; then, based on a preset terminal adaptation model and the terminal type and configuration of a user, matching and displaying a complete BIM model of the user after safety measures of corresponding volume are deployed through a lightweight adaptation unit; and finally, controlling the activities of the virtual character model in the complete BIM model after the safety measure deployment by using the computer peripheral equipment through the man-machine interaction unit by a user, and checking the safety detection evaluation report.
8. The method for simulating and detecting the safe environment based on the BIM model safety measure family library according to claim 7, wherein the method comprises the following steps of: the terminal adaptation model comprises a backbone network layer, a quantization selection layer and an adaptive training layer; the backbone network layer includes MicroNet networks and MobileNetV networks; the quantization selection layer performs network control by utilizing a bidirectional LSTM network and generates a quantization strategy; the bidirectional LSTM network respectively extracts the characteristics of the input sequence in a positive sequence and a reverse sequence mode, and then splices the two characteristic vectors; the self-adaptive training layer comprises a network dynamic learning self-adaptive function and a device resource learning self-adaptive function; the network dynamic learning self-adaptive function is used for training a backbone network layer and a quantization selection layer, the backbone network layer uses a set of parameters on the same quantization level, the quantization selection layer adjusts a quantization strategy according to the feedback quantization effect of the backbone network layer, and the loss function is as follows:
Where y represents the network loss function of each group, σ represents the backbone network layer number, K represents the current intra-group network, K represents the total network group number, Representing each group of each layer of network self-adaptive quantization functions, B σ representing each layer of backbone network loss functions, E σ representing each layer of backbone network resource efficiency, mu 1 representing calculation precision coefficients, mu 2 representing resource efficiency preset weight coefficients, G representing quantization strategies, and rho representing self-adaptive quantization parameters;
The equipment resource learning self-adaptive function obtains terminal resource information through an operating system instruction, and normalizes the terminal resource availability into 4 discrete levels; the terminal resource information comprises a CPU, a GPU, a memory, a cache and a buffer area; discrete levels include fully available, limited available, partially available, and unavailable.
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