Method for building safety boundary dynamic convergence and related equipment

Abstract

The invention discloses a method for dynamic convergence of a building safety boundary and related equipment, which can effectively realize real-time dynamic updating of the safety boundary. The method comprises the following steps: acquiring real-time state data of a building construction site, wherein the real-time state data comprises construction equipment operation data, personnel position data and environment sensing data; based on the real-time state data, a dynamic risk field model is constructed in the digital twin platform, the dynamic risk field model is used for simulating risk areas corresponding to one or more risk sources, and the risk sources comprise construction equipment positions and personnel positions; obtaining safety boundary convergence parameters of the building construction site according to the dynamic risk field model and the constraint conditions, wherein the safety boundary convergence parameters are used for defining a safety boundary adjustment mode of the building construction site; generating a control instruction based on the security boundary convergence parameter; and driving one or more safety warning devices according to the control instruction so as to dynamically update the safety boundary of the building construction site.

Description

Technical Field

[0001] This application relates to the field of building safety technology, and in particular to a method and related equipment for dynamic convergence of building safety boundaries. Background Technology

[0002] In the construction industry, safety is always of paramount importance. Construction site environments are complex and changeable, with numerous potential safety risks. These risks are not only diverse but also interconnected and dynamically changing, making safety management extremely difficult.

[0003] On construction sites, safety boundaries are crucial for on-site safety. Traditionally, boundary demarcation is done before construction based on drawings and experience. After demarcation, the boundary remains static and almost unchanged throughout the construction process. However, on a construction site, the movement of construction equipment, personnel, and the progress of the project all alter the location and nature of risk sources. For example, changes in the crane's operating position alter the swing range of the boom, potentially turning a previously safe area into a hazardous one; changes in personnel working positions also change safety distance requirements.

[0004] Static safety boundaries are obviously unable to adapt to dynamic changes, which can easily lead to a mismatch between the safety boundaries and actual risks, thus failing to effectively play a safety protection role and increasing the probability of safety accidents. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method and related equipment for dynamic convergence of building safety boundaries.

[0006] The technical solution provided in this application is described below: A first aspect of this application provides a method for dynamic convergence of building safety boundaries, the method comprising: Acquire real-time status data of the construction site, including construction equipment operation data, personnel location data, and environmental perception data; Based on the real-time status data, a dynamic risk field model is constructed in the digital twin platform. The dynamic risk field model is used to simulate the risk area corresponding to one or more risk sources, including the location of construction equipment and the location of personnel. The safety boundary convergence parameters of the construction site are obtained based on the dynamic risk field model and constraints. These safety boundary convergence parameters are used to define the safety boundary adjustment method of the construction site. Control commands are generated based on the aforementioned safety boundary convergence parameters; The control command drives one or more safety warning devices to dynamically update the safety boundaries of the construction site.

[0007] Optionally, based on the real-time status data, a dynamic risk field model is constructed in the digital twin platform. This dynamic risk field model simulates risk areas corresponding to one or more risk sources, including the locations of construction equipment and personnel. Based on the real-time status data, one or more risk sources are identified and registered in the digital twin platform, and the physical entity of each risk source is bound to the corresponding virtual model; In the digital twin platform, the intensity of the risk field generated by each risk source in the surrounding space is simulated and calculated based on real-time data of each risk source. The risk field intensities calculated from one or more risk sources are superimposed and synthesized to simulate and generate a comprehensive risk field covering the construction site. The comprehensive risk field is corrected based on the environmental perception data to obtain the corrected comprehensive risk field; Based on a preset risk level threshold, the modified comprehensive risk field is divided to obtain the division result; Based on the classification results, dynamic risk zones of different levels are simulated and generated in the digital twin platform.

[0008] Optionally, the safety boundary convergence parameters of the construction site are obtained based on the dynamic risk field model and constraints. These safety boundary convergence parameters define the safety boundary adjustment method for the construction site, including: Define predefined security rules; The predefined security rules are encoded into risk thresholds corresponding to the security level of the comprehensive risk field; Transform the risk threshold into a constraint condition; Based on the constraints, the boundary of the comprehensive risk field is extracted and optimized to solve for the target safety boundary profile. The target safety boundary profile is transformed into convergence parameters that define the dynamic adjustment method of the integrated risk field.

[0009] Optionally, one or more safety warning devices may be driven according to the control command to dynamically update the safety boundaries of the construction site, including: Receive the control command; Parse the control commands to obtain the parsing results; The analysis results are sent to one or more safety warning devices associated with the safety boundary of the construction site to drive the one or more safety warning devices to change their working status and generate updated physical warnings; The updated physical warning effect enables dynamic updating of the safety boundaries of the construction site.

[0010] Optionally, after generating control commands based on the convergence parameters, the method further includes: On the digital twin platform, one or more safety warning devices are simulated and driven based on the control commands to determine whether there are control conflicts between the safety warning devices; If so, the convergence parameters are adjusted based on the control conflict to obtain the target convergence parameters; Control commands are generated based on the target convergence parameters.

[0011] Optionally, the risk field intensities calculated from one or more risk sources are superimposed and synthesized to simulate and generate a comprehensive risk field covering the construction site, including: Establish a unified spatial benchmark; The risk field intensity data of one or more risk sources are standardized and mapped to the spatial reference to form a risk data set; The space of the construction site is discretized into a grid to construct a spatial unit system adapted for risk calculation. Based on the risk data set, the risk field intensity within each unit of the spatial unit system is aggregated and calculated to obtain the aggregation result; The aggregation results are processed continuously to generate a comprehensive risk field covering the construction site.

[0012] Optionally, the comprehensive risk field is subjected to boundary extraction and optimization calculation based on the constraints to solve for the target safety boundary profile, including: The constraints and the integrated risk field are standardized. The initial safety boundary is generated based on the standardized constraints. The initial safety boundary is modified to resolve constraints and physical environment conflicts in order to obtain a modified safety boundary. The modified safety boundary is geometrically optimized, and the constraint satisfaction of the optimized modified safety boundary is verified. Once the verification is confirmed to be successful, the target safety boundary profile will be output.

[0013] A second aspect of this application provides an apparatus for dynamic convergence of building safety boundaries, the apparatus comprising: The first acquisition unit is used to acquire real-time status data of the construction site, including construction equipment operation data, personnel location data, and environmental perception data. The construction unit, based on the real-time status data, constructs a dynamic risk field model in the digital twin platform. The dynamic risk field model is used to simulate the risk area corresponding to one or more risk sources, including the location of construction equipment and the location of personnel. The second acquisition unit is used to acquire the safety boundary convergence parameters of the construction site according to the dynamic risk field model and constraints. The safety boundary convergence parameters are used to define the safety boundary adjustment method of the construction site. The generation unit generates control commands based on the security boundary convergence parameters; The drive unit is used to drive one or more safety warning devices according to the control command to dynamically update the safety boundary of the construction site.

[0014] A third aspect of this application provides an apparatus for dynamic convergence of building safety boundaries, the apparatus comprising: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to perform the method as described in the first aspect and any one of the first aspects.

[0015] A fourth aspect of this application provides a computer-readable storage medium on which a program is stored, which, when executed on a computer, performs the method as described in the first aspect and any one of the first aspects.

[0016] As can be seen from the above technical solutions, this application has the following beneficial effects: 1. This application provides a comprehensive and dynamic data source for risk identification by acquiring three key types of data in real time: construction equipment operation, personnel location, and environmental perception. This avoids misjudgment of risks due to information lag or omission and allows for accurate capture of risk status at the construction site.

[0017] 2. This application relies on a digital twin platform to construct a dynamic risk field model, which intuitively simulates and presents the risk areas corresponding to risk sources such as construction equipment and personnel. This breaks through the limitations of traditional "static prediction" and makes the risk distribution and diffusion trend clearly visible, making it easier to identify potential safety hazards in advance.

[0018] 3. This application generates safety boundary convergence parameters based on a dynamic risk field model and constraints. It can flexibly adjust the definition and adjustment method of the safety boundary according to real-time risk changes on site, replacing the fixed traditional safety boundary and making safety protection more in line with the actual working conditions on site.

[0019] 4. This application generates control commands by converging parameters and drives safety warning devices to achieve real-time dynamic updates of safety boundaries. This ensures that when risks occur or change, warning measures can be quickly and synchronously adjusted, effectively reducing the probability of safety accidents such as personnel accidentally entering dangerous areas and equipment collisions during cross-operation. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of an embodiment of the method for dynamic convergence of building safety boundaries in this application; Figure 2 This is a schematic diagram of another embodiment of the method for dynamic convergence of building safety boundaries according to this application; Figure 3 This is a schematic diagram of another embodiment of the method for dynamic convergence of building safety boundaries according to this application; Figure 4 This is a schematic diagram of another embodiment of the method for dynamic convergence of building safety boundaries according to this application; Figure 5 This is a schematic diagram of another embodiment of the method for dynamic convergence of building safety boundaries according to this application; Figure 6 This is a schematic diagram of another embodiment of the method for dynamic convergence of building safety boundaries according to this application; Figure 7 This is a schematic diagram of one embodiment of the dynamic convergence device for building safety boundaries in this application; Figure 8 This is a schematic diagram of another embodiment of the dynamic convergence device for building safety boundaries in this application. Detailed Implementation

[0022] In this embodiment, the execution entity of the method is not limited to a specific type of computing device, server, or control system. The method can be executed by any hardware, software, or hardware / software combination system capable of providing the necessary computing and data processing capabilities, such as a general-purpose computer, a distributed server cluster, a networked control unit, a virtualization processing platform, or a cloud computing environment.

[0023] The job event flow path construction method described in this embodiment can also be implemented through embedded programs, application software modules, or by controlling remote execution units through network communication interfaces. Regardless of whether the specific execution entity is a single device, multiple parallel collaborative processing units, or a scalable distributed processing system, this method can be operated according to the step sequence and logic described in the following embodiments.

[0024] The method of this application is applicable to any system capable of performing necessary computation and data processing operations, and the system is not limited to a specific type of computing device, server cluster, virtualization platform, network control unit, or cloud computing environment. Regardless of whether the executing entity is a single device, multiple collaborative processing units, or a distributed computing platform, the method of this application can be operated according to the step sequence and logic described in the embodiments.

[0025] On construction sites, safety boundaries are crucial for on-site safety. Traditional boundary demarcation methods rely on experience and drawings before construction begins, and these boundaries remain static and almost unchanged throughout the construction process. However, on a construction site, the movement of construction equipment, personnel, and the progress of the project all alter the location and nature of risk sources. For example, changes in the crane's operating position alter the boom's swing range, potentially turning a previously safe area into a hazardous one. Changes in personnel working positions also change safety distance requirements. Static safety boundaries are clearly unable to adapt to dynamic changes, easily leading to a mismatch between the safety boundary and the actual risk, failing to effectively provide safety protection, and increasing the probability of accidents.

[0026] Based on this, this application provides a method and related equipment for dynamic convergence of building safety boundaries. It can flexibly adjust the definition and adjustment method of safety boundaries according to real-time risk changes on site, replacing the fixed traditional safety boundaries. This makes safety protection more in line with the actual working conditions on site, effectively realizes real-time dynamic updates of safety boundaries, and ensures that warning measures can be quickly and synchronously adjusted when risks occur or change. This effectively reduces the probability of safety accidents such as personnel accidentally entering dangerous areas and equipment collisions during cross-operation.

[0027] Please see Figure 1 This application discloses a method for dynamic convergence of building safety boundaries, the method comprising: 101. Obtain real-time status data of the construction site, including construction equipment operation data, personnel location data, and environmental perception data; 102. Based on the real-time status data, a dynamic risk field model is constructed in the digital twin platform. The dynamic risk field model is used to simulate the risk area corresponding to one or more risk sources. The risk sources include the location of construction equipment and the location of personnel. 103. Obtain the safety boundary convergence parameters of the construction site based on the dynamic risk field model and constraints. The safety boundary convergence parameters are used to define the safety boundary adjustment method of the construction site. 104. Generate control commands based on the aforementioned safety boundary convergence parameters; 105. Drive one or more safety warning devices according to the control command to dynamically update the safety boundary of the construction site.

[0028] In this embodiment, real-time status data of the construction site is first acquired, including construction equipment operation data, personnel location data, and environmental perception data. Then, based on the real-time status data, a dynamic risk field model is constructed in a digital twin platform. The dynamic risk field model is used to simulate the risk areas corresponding to one or more risk sources, including the locations of construction equipment and personnel. Next, safety boundary convergence parameters of the construction site are obtained according to the dynamic risk field model and constraints. The safety boundary convergence parameters are used to define the adjustment method of the safety boundary of the construction site. Then, control commands are generated based on the safety boundary convergence parameters. Finally, one or more safety warning devices are driven according to the control commands to dynamically update the safety boundary of the construction site.

[0029] In step 101, real-time status data of the construction site is first acquired. This acquisition of construction equipment operation data relies on sensors installed on various devices, such as torque sensors for tower cranes, speed sensors for elevators, and engine speed sensors for excavators. These sensors collect real-time operating parameters, such as the current lifting capacity, slewing angle, and luffing length of the tower crane; the operating floors and load conditions of the elevator; and whether the equipment is in normal operating condition and whether there are any fault warning signals. They also record the real-time location information of the equipment to ensure accurate positioning of each piece of equipment on site. Personnel location data is obtained through smart safety helmets or positioning wristbands equipped with construction workers. These devices have built-in GPS positioning modules that can transmit the specific coordinates of personnel on the construction site in real time, facilitating subsequent risk analysis. Acquiring environmental perception data requires deploying environmental monitoring equipment in different areas of the construction site. For example, temperature and humidity sensors, noise sensors, dust concentration sensors, and harmful gas detection devices are installed in key locations such as around the foundation pit, material storage area, and work surface. The equipment collects on-site temperature, humidity, PM2.5 concentration, noise decibel value, and the presence of harmful gases such as carbon monoxide and hydrogen sulfide in real time. This multi-dimensional data is transmitted to the data processing center in real time through the Internet of Things gateway, providing a complete and accurate data source for the next step of model building.

[0030] In step 102, after acquiring real-time status data, a dynamic risk field model is constructed in the digital twin platform based on this data. The digital twin platform first builds a virtual scene that maps 1:1 to the physical site based on the actual terrain, building structure, and construction layout of the construction site. Then, it integrates the acquired data on the location of construction equipment, personnel, equipment operating parameters, and environmental data into the virtual scene in real time. When constructing the dynamic risk field model, the risk sources are first identified, namely the locations of construction equipment and personnel. This is because construction equipment poses a mechanical injury risk during operation; for example, the swing radius of a tower crane and the operating radius of an excavator are considered high-risk areas. Furthermore, personnel entering these areas or overcrowding leading to evacuation difficulties also constitutes a risk source.

[0031] The dynamic risk field model determines the scope of risk impact based on the type and operating status of the equipment. For example, when a tower crane is lifting a heavy load, the risk area is larger than when it is unloaded, and it changes in real time with the rotation angle. Simultaneously, it incorporates environmental data; for instance, strong winds expand the risk impact area of ​​the tower crane, while low visibility reduces the safety perception range for personnel, thus adjusting the boundaries and risk levels of the risk area. The dynamic risk field model visually presents the risk area in a virtual scene, using gradient areas of different colors to represent risk levels: red for extremely high risk, yellow for medium risk, and blue for low risk. Furthermore, as real-time data is updated, the location of the risk source, the extent of the risk area, and the risk level in the dynamic risk field model are dynamically adjusted synchronously, ensuring that the risk state in the virtual scene is completely consistent with the physical site. This provides an intuitive and accurate risk basis for subsequently determining safety boundaries.

[0032] In step 103, after obtaining the dynamic risk field model, the safety boundary convergence parameters are obtained based on the dynamic risk field model and constraints. The constraints need to be determined in conjunction with the safety specifications for building construction, the actual working conditions on site, and management requirements. For example, the safety distance within the tower crane slewing radius that prohibits non-working personnel from entering, the standard for setting up guardrails around deep foundation pits, the minimum safety interval between different work areas, the width requirements of evacuation passages when personnel are dense during peak construction periods, and the standard for adjusting safety distances under severe weather conditions.

[0033] When obtaining the convergence parameters of the safety boundary, the distribution and risk level of each risk area in the dynamic risk field model are first analyzed. For example, in the model, if there are people active in the operating risk area of ​​a certain excavator, it is necessary to calculate the actual distance between the current position of the personnel and the boundary of the excavator's risk area according to the constraint condition that "the minimum safe distance between the personnel and the operating radius of the excavator shall not be less than 5 meters". If the actual distance is less than 5 meters, it is necessary to determine the adjustment direction of the safety boundary, either by converging the safety boundary of the personnel activity area away from the excavator, or by converging the boundary of the excavator's operating risk area inward, to ensure that the two reach a safe distance of 5 meters.

[0034] The specific parameters for safety boundary convergence include the direction of adjustment, such as left, right, inward contraction, and outward expansion; the magnitude of adjustment, such as contraction of 2 meters and expansion of 1.5 meters; the rate of adjustment, such as completing contraction within 1 minute to avoid chaos on site due to excessively rapid adjustment; and the triggering conditions for adjustment, such as triggering convergence when the distance between personnel and the risk area is less than the value specified by the constraint conditions, and triggering parameter updates when the location of the risk source changes. The determination of these parameters needs to be based on the real-time risk status in the dynamic risk field model, while also meeting the constraint conditions, to ensure that the adjustment of the safety boundary is both scientific and compliant, and can accurately respond to changes in on-site risks.

[0035] In step 104, after obtaining the safety boundary convergence parameters, control commands are generated based on these parameters. First, the safety boundary convergence parameters are parsed, transforming abstract data such as "adjustment direction, adjustment range, and adjustment rate" into specific command logic executable by the safety warning equipment. For example, if the parameter "shrink the safety boundary around the foundation pit inward by 1 meter at an adjustment rate of 0.5 meters per minute" corresponds to a command that specifies which area's safety warning equipment needs to act, the method of action, and the timing of the action. Simultaneously, the type and control protocol of the construction site's safety warning equipment must be considered. For instance, audible and visual alarms use the RS485 communication protocol, while electronic fences use LoRa wireless communication. The commands are encapsulated according to the corresponding protocol format to ensure accurate device identification. Furthermore, the generated control commands also include priority judgments. For example, when multiple risk sources simultaneously trigger safety boundary adjustments, the control commands for high-risk areas, such as the risk area when a tower crane is lifting heavy objects, are executed first. To avoid equipment malfunctions caused by command conflicts, pre-verification should be performed after the command is generated. The effect of command execution should be simulated through a digital twin platform. For example, whether the simulated electronic fence boundary shrinks by 1 meter and fully matches the safe area in the dynamic risk field model should be verified to ensure that the safe boundary can be accurately adjusted after the command is executed, thus avoiding security vulnerabilities or false alarms.

[0036] Finally, in step 105, the safety warning devices are driven according to control commands to achieve dynamic updates of the safety boundary. The control commands are transmitted to the corresponding safety warning devices via the on-site communication network. Different types of devices will execute corresponding actions based on the commands: for example, after receiving the command "retract the boundary by 1 meter," the electronic fence device will drive the fence's telescopic mechanism or adjust the electronic signal range of the virtual fence, causing the actual physical fence boundary or the electronic fence's sensing boundary to simultaneously retract by 1 meter. Upon receiving the command, the audible and visual alarm will adjust the coverage of the warning sound and the area of ​​the flashing lights to ensure that personnel within the retracted safety boundary can clearly receive the warning signal. The LED warning signs will update their display content, for example, changing from "safe distance 5 meters" to "safe distance 4 meters," and will use color changes, such as from yellow to orange, to remind personnel of the boundary change. During the device's operation, on-site sensors will collect the device's execution status in real time, such as whether the electronic fence has completed retraction and whether the audible and visual alarm is working properly, and feed this status data back to the data processing center for comparison with the generated control commands to ensure that the device accurately executes the commands. Simultaneously, the digital twin platform updates the safety boundary display in the virtual scene, ensuring consistency between the virtual scene and the physical site's safety boundaries. Managers can monitor these updates in real time through the platform. This process creates a closed loop from control commands to equipment execution, ensuring that the safety boundaries at the construction site are dynamically adjusted based on real-time risk conditions, effectively preventing safety accidents.

[0037] Please refer to Figure 2 According to some embodiments of the present invention, in step 102, a dynamic risk field model is constructed in the digital twin platform based on the real-time status data. The dynamic risk field model is used to simulate the risk area corresponding to one or more risk sources. The risk sources include the location of construction equipment and the location of personnel, and may specifically include, but are not limited to, the following: 201. Based on the real-time status data, identify and register one or more risk sources in the digital twin platform, and bind the physical entity of each risk source to the corresponding virtual model; 202. In the digital twin platform, the intensity of the risk field generated by each risk source in the surrounding space is simulated and calculated based on the real-time data of each risk source. 203. The risk field intensities calculated from one or more risk sources are superimposed and synthesized to simulate and generate a comprehensive risk field covering the construction site; 204. Based on the environmental perception data, correct the comprehensive risk field to obtain the corrected comprehensive risk field; 205. Based on a preset risk level threshold, the modified comprehensive risk field is divided to obtain the division result; 206. Based on the division results, dynamic risk areas of different levels are simulated and generated in the digital twin platform.

[0038] In this embodiment, the identification, registration, and virtual-physical binding of risk sources are completed in the digital twin platform. First, the digital twin platform classifies and analyzes real-time status data, identifying equipment with mechanical operation risks such as tower cranes, excavators, and elevators from the construction equipment operation data, and locating construction personnel within the work area from the personnel location data. These devices and personnel are the core risk sources at the construction site; improper operation of equipment may cause accidents such as mechanical collisions and falling objects, while personnel entering high-risk areas may face threats to their personal safety.

[0039] After identifying the risk sources, the digital twin platform will assign a unique identifier to each risk source for registration. For example, it will label "Tower Crane A" with information such as equipment number, work group, and operation type, and associate "Construction Worker B" with identity information, job type, and authorized work area, so as to ensure that each risk source is traceable and manageable.

[0040] Next, the physical entities of the risk source are bound to the virtual models in the digital twin platform: the platform calls a pre-built virtual model that is 1:1 replicated from the physical equipment, such as a 3D model containing details such as the tower crane's mechanical structure, moving joints, and operating radius. Through information such as position coordinates and equipment parameters in the real-time status data, the real-time status of the physical equipment is synchronously mapped onto the virtual model. For example, changes in the lifting height of the physical tower crane will synchronously drive the height adjustment of the virtual tower crane model, and the movement of construction personnel will update the position of the personnel icon in the virtual model in real time, realizing the virtual-physical linkage of "when the physical entity moves, the virtual model responds synchronously", providing an accurate virtual carrier for subsequent risk field calculation.

[0041] After completing the virtual-to-real binding of risk sources, the risk field intensity is calculated based on the real-time data of each risk source. It's important to note that risk field intensity here refers to the degree of safety threat posed by the risk source to the surrounding space. The calculation process must consider the type of risk source, its real-time operating status, and the patterns of its safety impact. Taking a tower crane as an example, the platform extracts parameters such as lifting weight, slewing angle, luffing length, and lifting height from its real-time operating data. If the tower crane is currently lifting a 5-ton load at a slewing speed of 0.8 revolutions per minute, its risk impact range will be larger, and its risk field intensity will be higher. The calculation incorporates a preset risk field intensity algorithm. For example, using the tower crane's slewing center as the origin, the maximum radius of the risk impact is determined based on the lifting weight. For instance, the maximum radius is 20 meters when lifting 5 tons and 15 meters when lifting 3 tons. This is combined with the linear or non-linear attenuation of the distance from the origin, while also incorporating the stability parameters of the equipment's operation.

[0042] For construction workers as a risk source, the intensity is calculated based on real-time data such as the hazard coefficient of their area (e.g., the hazard coefficient at the edge of the excavation pit is higher than that on flat ground), whether they are wearing protective equipment, and whether they are within the operating radius of equipment. If a worker is not wearing a safety helmet and is within the operating radius of the tower crane, the intensity of the surrounding risk field will be significantly higher than that of a worker wearing full protective equipment and in a safe area. Through this calculation, each risk source forms an independent risk field in virtual space, centered on itself, with intensity varying with spatial location.

[0043] After the independent risk field of each risk source is calculated, these risk fields need to be superimposed and synthesized to generate a comprehensive risk field covering the entire construction site. At the construction site, multiple risk sources often coexist and influence each other: for example, the operating radius of tower crane A overlaps with the operating area of ​​excavator B, and construction worker C is also in this overlapping area. In this case, the risk field of a single risk source cannot fully reflect the actual risk of the area.

[0044] During the overlay and synthesis process, the platform divides the virtual scene into several small spatial grids, such as 1m x 1m x 1m cube grids. For each grid cell, it extracts the intensity values ​​of all independent risk fields covering that cell and calculates the comprehensive risk intensity of that cell according to a preset maximum value rule. If a grid cell is simultaneously covered by the risk field of tower crane A (intensity 60), the risk field of excavator B (intensity 40), and the risk field of personnel C (intensity 30), the comprehensive intensity of that cell is 130 when using the summation rule, and 60 when using the maximum value rule. The specific rule will be determined according to the risk type of the construction site. By calculating the comprehensive intensity of all grid cells, a continuous comprehensive risk field covering the entire virtual space of the construction site is finally formed, intuitively presenting the risk superposition situation in different areas. For example, areas with dense equipment and a large number of personnel will form a "high-value area" of comprehensive risk intensity, while office areas far away from all risk sources will be "low-value areas".

[0045] After generating the comprehensive risk field, it is corrected based on the environmental perception data obtained in step 101 to obtain the corrected comprehensive risk field. Among them, factors such as temperature and humidity, noise, dust concentration, harmful gas content, wind force, and visibility in the environmental perception data directly affect the actual degree of risk. If these factors are ignored, the accuracy of the comprehensive risk field will be greatly reduced.

[0046] For example, when environmental sensing data shows that the wind force at the construction site reaches level 6, the stability of the tower crane will decrease, and the originally calculated risk field intensity of the tower crane needs to be adjusted upward. At the same time, the risk impact radius will also expand because strong winds may cause the swaying amplitude of the suspended object to increase, threatening areas at a greater distance. If the dust concentration exceeds the standard, it will reduce the visibility of construction personnel. At this time, the risk field intensity around the personnel needs to be adjusted upward because it is difficult for personnel to detect the movement of surrounding equipment in time, increasing the probability of accidents. In high-temperature environments, equipment is prone to overheating failures, and the risk field intensity of the equipment also needs to be adjusted accordingly.

[0047] During the correction process, the digital twin platform will establish a correlation model between environmental factors and risk intensity, and substitute real-time environmental data into the correlation model to dynamically adjust the intensity value of each grid unit in the comprehensive risk field, so as to ensure that the corrected comprehensive risk field can truly reflect the on-site risk status under the superposition of environmental factors.

[0048] Having established the revised comprehensive risk field, it is further divided according to preset risk level thresholds to obtain the classification results. These preset risk level thresholds are formulated based on construction safety regulations, enterprise safety management standards, and the actual risk tolerance of the construction site. They are typically divided into multiple levels, such as dividing risk intensity into five levels: "extremely high risk (≥150), high risk (100-149), medium risk (50-99), low risk (1-49), and no risk (0)." Each level corresponds to specific safety control requirements. During the classification, the platform traverses all grid units in the revised comprehensive risk field, comparing the comprehensive risk intensity of each unit with the preset thresholds: if a unit's intensity is 160, exceeding the "extremely high risk" threshold of 150, then the unit is classified as extremely high risk; if the intensity is 80, falling within the "medium risk" threshold range of 50-99, then it is classified as medium risk. Through this unit-by-unit classification, the final risk level classification result for the entire construction site is formed. This result clearly marks the risk level of different areas, providing a basis for subsequent risk area visualization.

[0049] Finally, based on the classification results, dynamic risk zones of different levels are simulated and generated in the digital twin platform. The platform will use visualization rendering technology to assign exclusive visual identifiers to different risk levels. For example, dark red represents extremely high-risk areas, bright red represents high-risk areas, yellow represents medium-risk areas, blue represents low-risk areas, and green represents no-risk areas. At the same time, a semi-transparent gradient effect is used to present the transition of risk intensity. For example, the center of the extremely high-risk area is the darkest color, gradually fading towards the edge, naturally connecting with the high-risk areas.

[0050] When generating dynamic risk zones, the platform combines the division results with geographical information in the virtual scene, such as construction roads, foundation pits, scaffolding, and material storage yards, to ensure that the risk zones accurately match the actual site layout. For example, extremely high-risk zones will precisely cover the overlapping area between the tower crane's operating radius and the edge of the foundation pit, while medium-risk zones will correspond to personnel access areas around the material storage yards. Simultaneously, since real-time status data is continuously transmitted to the platform, when the status of the risk source or environmental conditions change, the platform will repeat steps 202-205 of the calculation and division, synchronously updating the range and color of the dynamic risk zones: for example, after the tower crane moves, the extremely high-risk zone will shift synchronously with the tower crane's position; after the wind weakens, the risk zone range will shrink accordingly, truly achieving dynamic control where "risk changes are reflected in zone updates." Managers can intuitively grasp the real-time risk distribution at the construction site through the platform, providing intuitive and accurate visualization support for safety decisions and on-site management.

[0051] Please refer to Figure 3 According to some embodiments of the present invention, in step 103, the safety boundary convergence parameters of the construction site are obtained based on the dynamic risk field model and constraints. These safety boundary convergence parameters are used to define the safety boundary adjustment method of the construction site, and may specifically include, but are not limited to, the following: 301. Define predefined security rules; 302. Encode the predefined security rules into risk thresholds corresponding to the security level of the comprehensive risk field; 303. Convert the risk threshold into a constraint condition; 304. Based on the constraints, perform boundary extraction and optimization calculations on the comprehensive risk field to solve for the target safety boundary profile; 305. Transform the target safety boundary profile into convergence parameters that define the dynamic adjustment method of the integrated risk field.

[0052] In this embodiment, determining predefined safety rules requires combining construction industry safety standards, actual working conditions at the construction site, equipment characteristics, and personnel management requirements to clarify safety judgment standards for various scenarios. These rules must cover the core risk points of the entire construction process. These predefined safety rules need to be specific and clear, including both mandatory requirements of national and industry standards and supplementary details based on the project's own site layout, equipment models, personnel configuration, and other personalized circumstances, to ensure that the rules can directly guide subsequent risk assessment and boundary calculation. Specific limitations are not made for the specific predefined safety rules here.

[0053] After defining the predefined safety rules, these rules are encoded into risk thresholds corresponding to the safety levels of the comprehensive risk field. The comprehensive risk field, based on a dynamic risk field model, integrates risks from multiple dimensions, including construction equipment, personnel, and the environment, resulting in a holistic risk presentation. Its safety levels are typically divided into four tiers: extremely high risk, high risk, medium risk, and low risk, with different risk response strategies corresponding to different tiers. The encoding process transforms the qualitative predefined safety rules into quantitative numerical standards, making risk assessment measurable.

[0054] For example, regarding the rule "No non-operating personnel are allowed to stay within the slewing radius of the tower crane," if the safety level of the "tower crane operating area" in the comprehensive risk field is "extremely high risk," it can be coded as "Extremely high risk threshold: When the distance between personnel and the center of the tower crane's slewing is ≤15 meters, the risk value reaches an extremely high risk level." Regarding the rule "No loading is allowed within 1.2 meters of the perimeter of the foundation pit," if the safety level of the "perimeter area of ​​the foundation pit" is "high risk," it can be coded as "High risk threshold: When the distance between the loading area and the edge of the foundation pit is ≤1.2 meters, and the loading weight is ≥5kN / m², the risk value reaches a high risk level." For environmental risk rules, "PM2.5 concentration exceeding 150μg / m³" can be coded as "Medium risk threshold: PM2.5 concentration ≥150μg / m³ and <250μg / m³ is medium risk, ≥250μg / m³ is extremely high risk." Through this encoding, each predefined security rule corresponds to a specific numerical threshold for a particular security level in the comprehensive risk field, providing a clear quantitative basis for subsequent risk assessment.

[0055] Next, the risk threshold obtained in step 302 is transformed into a constraint condition that must be followed when extracting and optimizing the boundary of the comprehensive risk field. The expression of the constraint condition conforms to the logic of mathematical calculation and model operation, ensuring that it can be recognized and applied by the algorithm of the digital twin platform.

[0056] For example, for the "extremely high risk threshold: distance between personnel and the tower crane's slewing center ≤ 15 meters", the transformed constraint can be "in the comprehensive risk field, the boundary of the risk area related to tower crane operation must meet the requirement that the distance from the tower crane's slewing center is ≥ 15 meters, and the boundary must not contain personnel location data"; for the "high risk threshold: distance between the loading area and the edge of the foundation pit ≤ 1.2 meters, and the loading weight ≥ 5kN / m²", the constraint can be set as "the boundary of the risk area around the foundation pit must ensure that the distance between the boundary and the edge of the foundation pit is ≥ 1.2 meters, and the weight of all loading points within the boundary is ≤ 5kN / m²"; for the "medium risk threshold: PM2.5 concentration ≥ 150μg / m³" of environmental risk, the constraint is "in the comprehensive risk field, the safety boundary corresponding to the environmental risk must include all areas with PM2.5 concentration ≥ 150μg / m³ within the control scope, and the corresponding protective measure command generation conditions must be triggered within the boundary". These constraints define the "insurmountable standards" for the integrated risk field when calculating the safety boundary, ensuring that the subsequently calculated boundary meets the requirements of the predefined safety rules.

[0057] After the constraints are determined, the boundary extraction and optimization calculations of the comprehensive risk field are performed based on the constraints to solve for the target safety boundary contour. This step relies on the algorithmic capabilities of the digital twin platform, using the constraints of step 303 as input to analyze, filter, and optimize the risk distribution of the comprehensive risk field, ultimately obtaining a clear and accurate target safety boundary contour. Specifically, based on real-time risk data from the comprehensive risk field, such as equipment location, personnel coordinates, and environmental parameters, combined with the numerical standards in the constraints, preliminary boundary areas satisfying the condition that "risk values ​​do not exceed the corresponding thresholds" are extracted. For example, based on the constraint that "the distance between the risk area boundary of the tower crane operation and the slewing center is ≥15 meters," areas more than 15 meters away from the tower crane's slewing center and without personnel are selected from the comprehensive risk field as preliminary safety boundaries.

[0058] Subsequently, the algorithm optimizes the initial boundary calculations, such as handling irregular inflection points, eliminating overlapping areas, and adjusting the smoothness of the boundary. For example, when the initial boundary of the tower crane operation area partially overlaps with the safety boundary around the foundation pit, the algorithm prioritizes retaining the 15-meter boundary for the tower crane operation based on their respective risk levels, while fine-tuning the boundary around the foundation pit to ensure it does not conflict with the extremely high-risk constraints of the tower crane. The final target safety boundary profile is a visualized and executable boundary range that satisfies all constraints and adapts to the actual layout of the construction site, and can be clearly presented in the digital twin platform.

[0059] Finally, the target safety boundary profile is transformed into convergence parameters that define the dynamic adjustment method of the comprehensive risk field. This step converts the target safety boundary profile obtained in step 304 into "parameter instructions" that can subsequently drive the safety warning equipment. The transformation process requires breaking down the spatial characteristics of the target safety boundary profile into specific convergence parameters, including adjustment direction, adjustment range, and adjustment priority. For example, if the target safety boundary profile indicates that "the safety boundary of the tower crane operation area needs to be expanded from the current 12 meters to 15 meters," then the transformed convergence parameters are: "Adjustment direction: The tower crane operation safety boundary expands away from the center of rotation; Adjustment range: 3 meters; Adjustment priority: Very high; Adjustment trigger condition: Adjustment is initiated immediately when personnel are detected entering the 12-15 meter range." These convergence parameters clarify the dynamic adjustment method of the comprehensive risk field. Based on these parameters, specific instructions can then be generated to drive the actions of equipment such as audible and visual alarms and electronic fences, ultimately achieving dynamic updates to the safety boundary at the construction site and forming a complete closed loop from rule definition to equipment execution.

[0060] Please refer to Figure 4 According to some embodiments of the present invention, in step 105, one or more safety warning devices are driven according to the control command to dynamically update the safety boundary of the construction site. Specifically, this may include, but is not limited to, the following: 401. Receive the control command; 402. Parse the control command to obtain the parsing result; 403. Send the parsing results to one or more safety warning devices associated with the safety boundary of the construction site to drive one or more safety warning devices to change their working status and generate updated physical warnings; 404. The safety boundary of the construction site is dynamically updated through the updated physical warning effect.

[0061] In this embodiment of the application, after the instruction is received, the control instruction is parsed to obtain the parsing result. Since the generated control instruction is usually encapsulated in encrypted binary code or standardized protocol format for easy transmission, the security warning device cannot directly recognize it, so it needs to be parsed.

[0062] The built-in parsing module of the command receiving terminal first breaks down the command content according to preset command format rules, extracting key information related to the actions of the safety warning equipment. This information is the core component of the parsing result. Specifically, the parsing result will clarify the following: First, the target equipment, that is, which one or more safety warning devices need to perform actions, such as "No. 3 audible and visual alarm on the east side of the foundation pit" or "electronic fence device within the slewing radius of the tower crane", which is accurately located by the equipment number or unique identifier; Second, the action command, that is, the specific operation that the equipment needs to perform, such as "the audible and visual alarm switches to high-frequency flashing + high-decibel alarm mode", "the electronic fence boundary shrinks inward by 1.2 meters and activates the touch alarm", "the LED warning screen updates to display the words 'No Entry Within the Safety Boundary'", etc.; Third, the execution parameters, including the time requirements of the action (such as "activate the alarm within 3 seconds" or "complete the boundary adjustment within 5 minutes") and the intensity standards of the action (such as "the alarm sound level is not less than 110 decibels" or "the brightness of the warning light is not less than 500 cd / m²"). The quality of this analysis step directly determines the accuracy of subsequent equipment actions. If there is a deviation in the analysis, it may lead to equipment malfunction or failure to operate. Therefore, a verification mechanism is added during the analysis process to compare whether the analyzed equipment identification and action instructions conform to the equipment ledger and safety regulations at the construction site, ensuring the correctness of the analysis results.

[0063] After obtaining accurate analysis results, these results are sent to one or more safety warning devices associated with the safety boundary of the construction site. This drives these devices to change their operating status and generate updated physical warnings. Specifically, the correlation between the target devices in the analysis results and the safety boundary needs to be confirmed first. These devices are pre-deployed at key locations along the safety boundary based on the safety layout of the construction site. For example, electronic fences are laid out along the original safety boundary, audible and visual alarms are installed at the entrances and exits of the safety boundary or in areas easily accessible to personnel, and LED warning screens are placed in conspicuous locations around the safety boundary to ensure that the device actions directly affect safety boundary control. The command receiving terminal sends the analysis results to the target devices in the form of control signals that the devices can recognize. Different types of safety warning devices will change their operating status accordingly after receiving the signal: for example, after receiving the "boundary contraction" signal, the electronic fence will contract and adjust the signal range of the sensing electrodes to complete the synchronous update of the physical boundary and the sensing boundary. After receiving the "high-frequency alarm" signal, the buzzer of the audible and visual alarm will switch to a high-frequency sound mode. The LED lights will change from constant light to high-frequency flashing, creating a stronger audible and visual warning. The LED warning screen updates its display based on the text instructions in the analysis results, clearly indicating the current safety boundary scope and control requirements. These changes in the operating status of the equipment ultimately translate into physical warnings that on-site personnel can directly observe and perceive, making the abstract adjustment of safety boundaries concrete.

[0064] Finally, the safety boundaries of the construction site are dynamically updated. Specifically, the updated physical warnings are no longer the original fixed warning states, but new warning forms that match the dynamic risk field model and conform to the safety boundary convergence parameters. Their core function is to redefine the safety boundary range of the construction site through the "change" in physical warnings, guiding the behavior of personnel and equipment. For example, when the electronic fence retracts, its physical structure forms a new physical barrier, clearly delineating a prohibited area; the high-frequency warnings of the audible and visual alarms create a "warning zone" around the new safety boundary, reminding personnel not to approach; and the updated text on the LED warning screen further clarifies the specific location and control rules of the new safety boundary.

[0065] Please refer to Figure 5 According to some embodiments of the present invention, in step 203, the risk field intensities calculated from one or more risk sources are superimposed and synthesized to simulate and generate a comprehensive risk field covering the construction site. Specifically, this may include, but is not limited to, the following: 501. Establish a unified spatial benchmark; 502. Standardize the risk field intensity data of one or more risk sources and map them to the spatial reference to form a risk data set; 503. The space of the construction site is discretized into a grid to construct a spatial unit system adapted for risk calculation; 504. Based on the aforementioned risk data set, aggregate calculations are performed on the risk field intensity within each unit of the spatial unit system to obtain aggregate results; 505. The aggregation results are processed continuously to generate a comprehensive risk field covering the construction site.

[0066] In this embodiment, the first step is to establish a unified spatial benchmark, which is a prerequisite for ensuring the accurate fusion of all subsequent risk data. Since the acquired risk source data, such as the locations of construction equipment and personnel, may come from different positioning systems, the coordinate benchmarks and coordinate system types may differ. Direct use of these systems could lead to spatial misalignment when the risk fields are superimposed. Therefore, it is necessary to first determine a unified spatial benchmark adapted to the construction site. This is typically based on the construction site's coordinate system, and all spatial locations of risk sources and environmental perception data are converted to this unified coordinate system. For example, a three-dimensional local coordinate system (X, Y, Z) can be established using a fixed benchmark point on the construction site as the origin, ensuring that the spatial location of each risk source has a unique and accurate coordinate identifier under this unified benchmark.

[0067] After establishing a unified spatial benchmark, the risk field intensity data is standardized and mapped to the spatial benchmark. Specifically, due to differences in the calculation logic and numerical range of risk field intensities for different risk sources—for example, the risk field intensity of construction equipment might be calculated based on the reciprocal of the distance from the equipment's center, with a numerical range of 0-10—while the risk field intensity of densely populated areas might be based on the number of people per unit area, with a numerical range of 0-5—directly superimposing these risk field intensity data of different magnitudes and dimensions would lead to distorted results and fail to accurately reflect the overall risk. Therefore, it is necessary to first standardize the risk field intensity data of each risk source. The Z-score standardization algorithm is used to uniformly transform the risk field intensity values ​​of different risk sources into the same interval of 0-1, eliminating magnitude differences. Subsequently, based on the coordinate position of each risk source under a unified spatial reference, the standardized risk field intensity data is precisely mapped to the corresponding spatial coordinate point. For example, if the coordinates of a tower crane under a unified coordinate system are (X1, Y1, Z1), and its standardized risk field intensity is 0.8, then the data pair "(X1, Y1, Z1) - 0.8" is included in the risk data set to ensure that each spatial coordinate point corresponds to a clear risk field intensity data, forming a structured risk data set that can be used for subsequent calculations.

[0068] Next, the construction site space is discretized into a grid to construct a spatial unit system adapted for risk calculation. Since the construction site space is large and the risk field intensity changes continuously within the space, directly performing risk aggregation calculations on the entire continuous space would result in excessive computational load and low efficiency, while also making it difficult to accurately pinpoint risk differences in different areas. Therefore, it is necessary to divide the construction site space under a unified spatial benchmark into several uniformly sized or adjustable three-dimensional grid units, such as 1m×1m×0.5m cube units, each of which is an independent spatial calculation unit, based on the actual layout of the construction site and the accuracy requirements of risk calculation. This discretization reduces the complexity of subsequent aggregation calculations and allows for precise capture of risk details in different areas through unitization. For example, grid units around deep foundation pits can be denser to more accurately reflect the high-risk gradient changes at the pit edge, while grid units in open material storage areas can be appropriately enlarged, improving efficiency while ensuring calculation accuracy. Ultimately, this constructs a spatial unit system adapted to the actual construction site scenario and meeting the needs of risk calculation.

[0069] After establishing the risk dataset and spatial unit system, the next step is to aggregate and calculate the risk field intensity within each unit based on the risk dataset. Each spatial unit may contain risk data points mapped from multiple risk sources. For example, a grid unit may contain both the risk field from tower cranes and the risk field from personnel activities. Aggregation calculations are needed to integrate these scattered risk data points into a unique risk field intensity value for that unit. The aggregation calculation method needs to be determined based on the risk characteristics. For instance, when multiple risk sources within the same unit have a cumulative effect (e.g., equipment risk and personnel risk coexist, resulting in a higher overall risk), a weighted summation method can be used. This involves weighting the standardized intensity values ​​of each risk data point within the unit according to the hazard level of different risk sources (e.g., setting the tower crane risk weight to 0.6 and the personnel density risk weight to 0.4). If there are mutual influences between risk sources, a coupling coefficient can be introduced for correction calculations. Through this aggregation method, each spatial unit can obtain a unique aggregation result reflecting the combined impact of all risk sources within that unit, achieving the transformation from discrete risk data to unitized risk values.

[0070] Finally, the aggregation results are processed to create a continuous risk field. After the aggregation calculation in step 504, the discrete risk value of each grid cell is obtained. If presented directly, this would result in a "blocky" risk distribution, failing to accurately reflect the continuous and gradual change of risk in space and hindering managers' intuitive perception of the overall risk situation. Therefore, the discrete cell aggregation results need to be processed to create a continuous risk field. Inverse distance weighted interpolation is used to calculate the risk field intensity in the blank areas between cells based on the aggregated risk values ​​of adjacent grid cells, filling the "gaps" between discrete cells and transforming the risk distribution of the entire construction site from a "blocky and discrete" field to a "continuous and smooth" field. The final generated comprehensive risk field will be presented in a visual form, with different colors representing different comprehensive risk levels.

[0071] Please refer to Figure 6 According to some embodiments of the present invention, in step 304, boundary extraction and optimization calculations are performed on the comprehensive risk field based on the constraints to solve for the target safety boundary profile. Specifically, this may include, but is not limited to, the following: 601. Standardize the constraints and the integrated risk field; 602. Generate the initial safety boundary based on the standardized constraints; 603. Correct the initial safety boundary for constraints and physical environment conflicts to obtain a corrected safety boundary; 604. Perform geometric optimization on the modified safety boundary and verify the constraint satisfaction of the optimized modified safety boundary; 605. Once the verification is confirmed to be successful, output the target safety boundary profile.

[0072] In this embodiment, the risk field intensity is presented as a standardized value in the 0-1 range within the comprehensive risk field, and spatial coordinate consistency is achieved by relying on a unified spatial benchmark. The constraints, however, contain various types of heterogeneous information, such as safety regulations, physical environment constraints, and equipment operation constraints. These constraints may differ in their representation and numerical units; some are presented as distance values, some as spatial area ranges, and some as functional requirements. Directly combining these heterogeneous constraints with the comprehensive risk field for calculation will lead to boundary extraction bias due to inconsistent data dimensions and measurement standards.

[0073] Therefore, both need to be standardized first. On the one hand, the various requirements in the constraints are transformed into coordinate parameters and quantitative thresholds consistent with the spatial benchmark of the comprehensive risk field. For example, the constraint "must not cross the edge of the foundation pit" is transformed into the specific coordinate range of the foundation pit edge in a unified coordinate system, and the constraint "reserve a 1.2-meter passage" is transformed into the coordinate difference corresponding to the passage width. On the other hand, the priority of the constraints is transformed into calculable weight coefficients, while ensuring that the quantitative indicators of the constraints are logically compatible with the risk intensity values ​​of the comprehensive risk field. Through standardization, the constraints and the comprehensive risk field form a unified data foundation, providing a reliable basis for the subsequent generation of the initial safety boundary.

[0074] After standardization, the next step is to generate the initial safety boundary based on the standardized constraints. In this process, the initial range of the safety boundary is determined based on the risk distribution of the comprehensive risk field and the standardized constraints. Specifically, high-risk areas are first identified in the comprehensive risk field. Then, according to the principle that "the safety boundary must isolate high-risk areas from personnel and equipment activity areas," the approximate direction of the boundary is initially delineated. Afterward, the standardized constraints are incorporated into the calculation as hard rules for boundary generation. During this process, spatial geometric calculations combine the risk gradient changes of the comprehensive risk field with the quantitative indicators of the constraints to generate a continuous initial safety boundary that can initially isolate risks and satisfy basic constraints. This initial safety boundary is presented as a set of polyline or curve coordinate points in a unified coordinate system.

[0075] After generating the initial safety boundary, it is necessary to correct for constraint conflicts and physical environment conflicts to obtain a revised safety boundary. Since the initial safety boundary is initially generated based on risk distribution and constraints, two types of conflicts may exist: one is constraint conflict, where the initial boundary violates multiple constraints simultaneously or violates high-priority constraints; the other is physical environment conflict, where the initial boundary overlaps with or conflicts with physical entities on the construction site. Therefore, conflict detection and correction of the initial boundary are required: first, by comparing spatial coordinates, each coordinate point of the initial boundary is checked to see if it conforms to all standardized constraints. If constraint conflicts are found, adjustments are made according to constraint priority. For physical environment conflicts, the boundary is locally offset or adjusted using a polygonal line, taking into account the actual terrain and coordinates of physical facilities on the construction site, to ensure that the corrected safety boundary neither violates constraints nor fails to adapt to the physical environment of the site, forming a logically compliant and physically feasible revised safety boundary.

[0076] After conflict correction, the corrected safety boundary undergoes geometric optimization and constraint satisfaction verification. While the corrected safety boundary resolves the conflict issue, it may still have geometrically unreasonable aspects, such as too many sharp, zigzag corners, excessively tortuous local boundary segments, or insufficient overall boundary continuity. Therefore, geometric optimization is necessary. Sharp corners are smoothed using curve smoothing algorithms to make the boundary more gentle; excessively tortuous boundary segments are simplified to reduce the number of inflection points without altering the core isolation function; and the optimized boundary is ensured to remain continuously closed or completely continuous, facilitating the uniform deployment of subsequent safety warning devices.

[0077] After completing the geometric optimization, the constraint satisfaction needs to be checked again. If the check finds that the optimized boundary has constraint deviation or functional defects, it is necessary to return to the geometric optimization stage for secondary adjustment until the boundary simultaneously satisfies geometric rationality and constraint compliance.

[0078] Once the verification is successful, the target safety boundary profile is output. After geometric optimization and constraint satisfaction verification, if the boundary fully meets all constraints, has a reasonable geometric shape, and effectively achieves risk isolation, the verification is considered successful. At this point, the optimized modified safety boundary is converted into a standardized output format.

[0079] Please see Figure 7 A second aspect of this application provides an apparatus for dynamic convergence of building safety boundaries, the apparatus comprising: The first acquisition unit 701 is used to acquire real-time status data of the construction site, including construction equipment operation data, personnel location data, and environmental perception data. Construction unit 702, based on the real-time status data, constructs a dynamic risk field model in the digital twin platform. The dynamic risk field model is used to simulate the risk area corresponding to one or more risk sources. The risk sources include the location of construction equipment and the location of personnel. The second acquisition unit 703 is used to acquire the safety boundary convergence parameters of the construction site according to the dynamic risk field model and constraints. The safety boundary convergence parameters are used to define the safety boundary adjustment method of the construction site. Generation unit 704 generates control commands based on the safety boundary convergence parameters; The drive unit 705 is used to drive one or more safety warning devices according to the control command to dynamically update the safety boundary of the construction site.

[0080] Please see Figure 8 This application also provides an apparatus for dynamic convergence of building safety boundaries, the apparatus comprising: Processor 801, memory 802, input / output unit 803, bus 804; The processor 801 is connected to the memory 802, the input / output unit 803, and the bus 804; The memory 802 stores a program, and the processor 801 calls the program to execute any of the methods described above.

[0081] This application also relates to a computer-readable storage medium on which a program is stored, which, when run on a computer, causes the computer to perform any of the methods described above.

[0082] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0084] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0085] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0086] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for dynamic convergence of building safety boundaries, characterized in that, The method includes: Acquire real-time status data of the construction site, including construction equipment operation data, personnel location data, and environmental perception data; Based on the real-time status data, a dynamic risk field model is constructed in the digital twin platform. The dynamic risk field model is used to simulate the risk area corresponding to one or more risk sources, including the location of construction equipment and the location of personnel. The safety boundary convergence parameters of the construction site are obtained based on the dynamic risk field model and constraints. These safety boundary convergence parameters are used to define the safety boundary adjustment method of the construction site. Control commands are generated based on the aforementioned safety boundary convergence parameters; The control command drives one or more safety warning devices to dynamically update the safety boundaries of the construction site.

2. The method for dynamic convergence of building safety boundaries according to claim 1, characterized in that, Based on the real-time status data, a dynamic risk field model is constructed in the digital twin platform. This model simulates risk areas corresponding to one or more risk sources, including the locations of construction equipment and personnel. Based on the real-time status data, one or more risk sources are identified and registered in the digital twin platform, and the physical entity of each risk source is bound to the corresponding virtual model; In the digital twin platform, the intensity of the risk field generated by each risk source in the surrounding space is simulated and calculated based on real-time data of each risk source. The risk field intensities calculated from one or more risk sources are superimposed and synthesized to simulate and generate a comprehensive risk field covering the construction site. The comprehensive risk field is corrected based on the environmental perception data to obtain the corrected comprehensive risk field; Based on a preset risk level threshold, the modified comprehensive risk field is divided to obtain the division result; Based on the classification results, dynamic risk zones of different levels are simulated and generated in the digital twin platform.

3. The method for dynamic convergence of building safety boundaries according to claim 2, characterized in that, The safety boundary convergence parameters of the construction site are obtained based on the dynamic risk field model and constraints. These parameters define the adjustment method of the safety boundary at the construction site, including: Define predefined security rules; The predefined security rules are encoded into risk thresholds corresponding to the security level of the comprehensive risk field; Transform the risk threshold into a constraint condition; Based on the constraints, the boundary of the comprehensive risk field is extracted and optimized to solve for the target safety boundary profile. The target safety boundary profile is transformed into convergence parameters that define the dynamic adjustment method of the integrated risk field.

4. The method for dynamic convergence of building safety boundaries according to claim 1, characterized in that, Drive one or more safety warning devices according to the control command to dynamically update the safety boundaries of the construction site, including: Receive the control command; Parse the control commands to obtain the parsing results; The analysis results are sent to one or more safety warning devices associated with the safety boundary of the construction site to drive the one or more safety warning devices to change their working status and generate updated physical warnings; The updated physical warning effect enables dynamic updating of the safety boundaries of the construction site.

5. The method for dynamic convergence of building safety boundaries according to claim 1, characterized in that, After generating control commands based on the convergence parameters, the method further includes: On the digital twin platform, one or more safety warning devices are simulated and driven based on the control commands to determine whether there are control conflicts between the safety warning devices; If so, the convergence parameters are adjusted based on the control conflict to obtain the target convergence parameters; Control commands are generated based on the target convergence parameters.

6. The method for dynamic convergence of building safety boundaries according to claim 2, characterized in that, The calculated risk field intensities from one or more risk sources are superimposed and synthesized to simulate and generate a comprehensive risk field covering the construction site, including: Establish a unified spatial benchmark; The risk field intensity data of one or more risk sources are standardized and mapped to the spatial reference to form a risk data set; The space of the construction site is discretized into a grid to construct a spatial unit system adapted for risk calculation. Based on the risk data set, the risk field intensity within each unit of the spatial unit system is aggregated and calculated to obtain the aggregation result; The aggregation results are processed continuously to generate a comprehensive risk field covering the construction site.

7. The method for dynamic convergence of building safety boundaries according to claim 3, characterized in that, Based on the constraints, the comprehensive risk field is subjected to boundary extraction and optimization calculations to solve for the target safety boundary profile, including: The constraints and the integrated risk field are standardized. The initial safety boundary is generated based on the standardized constraints. The initial safety boundary is modified to resolve constraints and physical environment conflicts in order to obtain a modified safety boundary. The modified safety boundary is geometrically optimized, and the constraint satisfaction of the optimized modified safety boundary is verified. Once the verification is confirmed to be successful, the target safety boundary profile will be output.

8. A device for dynamic convergence of building safety boundaries, characterized in that, The device includes: The first acquisition unit is used to acquire real-time status data of the construction site, including construction equipment operation data, personnel location data, and environmental perception data. The construction unit, based on the real-time status data, constructs a dynamic risk field model in the digital twin platform. The dynamic risk field model is used to simulate the risk area corresponding to one or more risk sources, including the location of construction equipment and the location of personnel. The second acquisition unit is used to acquire the safety boundary convergence parameters of the construction site according to the dynamic risk field model and constraints. The safety boundary convergence parameters are used to define the safety boundary adjustment method of the construction site. The generation unit generates control commands based on the security boundary convergence parameters; The drive unit is used to drive one or more safety warning devices according to the control command to dynamically update the safety boundary of the construction site.

9. A device for dynamic convergence of building safety boundaries, characterized in that, The device includes: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a program that, when executed on a computer, performs the method as described in any one of claims 1 to 7.

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