Highway construction safety risk management system and management method thereof

Abstract

The invention discloses a highway construction safety risk management method, and relates to the technical field of safety risk management, and the method comprises the following steps: constructing a virtual construction site model based on a digital twin technology, and reflecting the real dynamic change of a construction site in real time through the model; taking the data mark of the high-risk area as input, and inputting the data mark into a path optimization algorithm; determining whether the path has hidden dangers or not through a path optimization algorithm, if yes, providing adjustment suggestions by the model, and updating the path again through the path optimization algorithm; and pushing the finally optimized safety path to intelligent equipment and automatic mechanical equipment of construction personnel. The method combines real-time data and historical information through a path optimization algorithm, can dynamically adjust the construction path, avoids a high-risk area, and guarantees the safety of construction personnel and equipment. The path optimization not only considers the safety, but also can improve the construction efficiency and reduce the shutdown time caused by sudden accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of safety risk management technology, and in particular to a highway construction safety risk management system and a management method thereof. Background Art

[0002] Highway construction typically involves a large number of machines, workers, and complex environments, all of which pose potential safety risks. Dangerous areas on the construction site (such as landslides, congested areas, and areas near hazardous equipment) can threaten the safety of workers and equipment, necessitating real-time monitoring and route optimization to ensure smooth construction. However, traditional construction site safety management methods rely heavily on manual monitoring and static safety planning, lacking mechanisms for dynamic monitoring and route optimization. This can lead to delayed responses to unexpected risk events and can even lead to inadequate risk assessments or route planning errors, increasing the probability of accidents.

[0003] With the development of drones, sensors, the Internet of Things, and digital twin technologies, more and more modern technologies are being applied to safety risk management in highway construction. Using drones and sensors to collect real-time environmental data from the construction site, combined with digital twin technology to build a virtual construction site model, can reflect dynamic changes in real time. This enables more accurate and dynamic risk assessment and route planning. However, current technologies still have some shortcomings, particularly in real-time risk assessment and route optimization. Accurately assessing route risks based on real-time data and intelligently optimizing them have become key technical challenges in construction safety management. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the above problems in the prior art, the present invention is proposed.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a highway construction safety risk management method, the method comprising the following steps:

[0007] Step 1: Collect construction site data through drones, monitoring equipment, and sensors. Utilize this data to build a virtual construction site model based on digital twin technology. This model will reflect the actual dynamic changes of the construction site in real time.

[0008] Step 2: In the virtual construction site model, high-risk areas are identified by combining sensor and historical data. These areas are then labeled with real-time data. The labeled data of high-risk areas are then fed into the path optimization algorithm.

[0009] Step 3: An initial construction path is determined based on the basic conditions, constraints, and pre-set plans of the construction site. This path is fed back into the virtual construction site model for simulation. A path optimization algorithm is then used to determine if there are any potential hazards along the path. If so, the model provides adjustment suggestions, and the path optimization algorithm then updates the path.

[0010] Step 4: The final optimized safe path is pushed to the construction workers' smart devices and automated mechanical equipment, so that every person or equipment on site can follow the path guidance to ensure that risk areas are avoided and construction efficiency is improved.

[0011] As a preferred solution of the highway construction safety risk management method of the present invention, in the step 2, the real-time data marking of these areas includes: risk data marking R i (t), risk density marker ψ i And the deviation penalty mark Φ of the path passing through the high-risk area i .

[0012] As a preferred solution of the highway construction safety risk management method of the present invention, wherein: the risk data mark R i The expression of (t) is:

[0013] R i (t) = w1F s (t)+w2F h (t)+w3F o (t)

[0014] Among them, w1, w2, and w3 are weight coefficients, reflecting the importance of different data sources. s (t) represents the danger index collected by the sensor in real time, F h (t) represents the risk level in historical data, F o (t) represents weather factors;

[0015] The risk density marker ψ i The expression is:

[0016] ψ i =exp(-γ((xx i ) 2 )+(yy i ) 2 +(zz i )2 )

[0017] Among them, (x i ,y i ,z i ) represents the center coordinates of the high-risk area, (x, y, z) represents the center coordinates of the required construction point, and γ represents the spatial attenuation coefficient, which controls the influence of the risk range;

[0018] The offset penalty mark Φ i The expression is:

[0019]

[0020] Among them, represents the distance between the path point (x, y) and the center of the high-risk area. The smaller the distance, the greater the penalty.

[0021] As a preferred solution of the highway construction safety risk management method of the present invention, the steps of the path optimization algorithm are as follows:

[0022] S301: Calculate the risk value R of hidden dangers during the entire construction process based on the initial construction path path This value is used to identify potential problems in the path and is used as the basis for the path. The calculation formula is:

[0023]

[0024] Among them, R path It is the quantitative value of risk on the path, indicating that the path passes through each high-risk area A i The comprehensive risk of N is the total number of high-risk areas;

[0025] S302: If the calculated result is greater than the safety threshold, indicating that the hidden danger of the path is too high, the path is readjusted through the path optimization objective function to select an alternative path with lower risk and higher construction efficiency.

[0026] As a preferred solution of the highway construction safety risk management method of the present invention, the formula of the path optimization objective function is:

[0027]

[0028] The optimization goal of the objective function is to maximize the path score P, α represents the risk weight coefficient, which is used to adjust the relative importance of risk, β represents the path efficiency penalty coefficient, which reflects the length or efficiency constraint of the path, and [T1, T2] represents the entire construction period.

[0029] As a preferred solution of the highway construction safety risk management method of the present invention, if the path score P→∞ is maximized, it means that the path completely avoids all high-risk areas and the path efficiency is extremely high;

[0030] If the maximum path score P→0, it means that the path always passes through high-risk areas, or the path is too long, resulting in extremely low efficiency.

[0031] As a preferred solution of the highway construction safety risk management method of the present invention, wherein: the adjusted path P in the virtual construction site model updated Perform a second simulation. If there are no hidden dangers, confirm the path as the final path and output it to construction personnel and equipment.

[0032] The risk management system of the highway construction safety risk management method as described above includes:

[0033] The data acquisition module is responsible for collecting various data from the construction site in real time, including environmental data, construction personnel activity data, the operating status of construction machinery and equipment, and visual and infrared data from surveillance cameras, sensors, drones, etc.;

[0034] The digital twin modeling and virtual construction site module builds a virtual digital twin model based on the data obtained from the data acquisition module, reflecting the dynamic changes of the construction site in real time;

[0035] Risk assessment and hidden danger detection module, which uses advanced algorithm models to conduct risk assessment based on real-time and historical data and identify potential high-risk areas;

[0036] Path planning and optimization module, which uses path optimization algorithms to optimize the routes of construction personnel and machinery to avoid passing through high-risk areas;

[0037] The simulation and feedback adjustment module is responsible for simulating the initially planned path in the virtual construction site model and verifying the safety of the path;

[0038] As well as the safety monitoring and alarm module, which is responsible for real-time monitoring of the safety status of the construction site and detecting abnormal situations in real time based on data sources such as sensors and monitoring equipment.

[0039] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned method for highway construction safety risk assessment are implemented.

[0040] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned highway construction safety risk management method.

[0041] Beneficial effects of the present invention:

[0042] 1. This invention uses a path optimization algorithm that combines real-time data with historical information to dynamically adjust construction routes, avoiding high-risk areas and ensuring the safety of construction personnel and equipment. Path optimization not only considers safety but also improves construction efficiency and reduces downtime caused by unexpected accidents.

[0043] 2. The present invention accurately assesses the overall risk of a route by calculating the hidden danger risk value, and provides adjustment suggestions when safety hazards are found on the route, thereby reducing the probability of accidents.

[0044] 3. This invention combines real-time feedback and simulation to continuously adjust and optimize the path optimization algorithm, providing an automated path planning and risk management solution. Construction personnel can operate according to the pushed optimized path, reducing errors caused by manual judgment and improving construction safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0046] Figure 1 This is a flow chart of a highway construction safety risk management method proposed by the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0050] Reference Figure 1 , as an embodiment of the present invention, provides a highway construction safety risk management method, the method comprising the following steps:

[0051] Step 1: Collect construction site data through drones, monitoring equipment, and sensors. Utilize this data to build a virtual construction site model based on digital twin technology. This model will reflect the actual dynamic changes of the construction site in real time.

[0052] Step 2: In the virtual construction site model, combine sensors and historical data to set high-risk areas and mark these areas with real-time data, including: risk data marking R i (t), risk density marker ψ i And the deviation penalty mark Φ of the path passing through the high-risk area i .

[0053] Risk Data Marker R i The expression of (t) is:

[0054] R i (t) = w1F s (t)+w2F h (t)+w3F o (t)

[0055] Among them, w1, w2, and w3 are weight coefficients, reflecting the importance of different data sources. s (t) represents the danger index collected by the sensor in real time, F h (t) represents the risk level in historical data, F o (t) represents weather factors;

[0056] Risk density marker ψ i The expression is:

[0057] ψ i =exp(-γ((xx i ) 2 )+(yy i ) 2 +(zz i ) 2 )

[0058] Among them, (x i ,yi ,z i ) represents the center coordinates of the high-risk area, (x, y, z) represents the center coordinates of the required construction point, and γ represents the spatial attenuation coefficient, which controls the influence of the risk range;

[0059] Offset penalty marker Φ i The expression is:

[0060]

[0061] Here, represents the distance between the path point (x, y) and the center of the high-risk area. The smaller the distance, the greater the penalty. Then, the data mark of the high-risk area is used as input and input into the path optimization algorithm. This ensures a comprehensive and dynamic understanding of the risks at the construction site and provides a basis for the next decision. The focus is on marking and inputting data of high-risk areas to ensure that the input of the path optimization algorithm is comprehensive and accurate.

[0062] Step 3: Determine an initial construction path based on the basic conditions, restrictions, and preset plans of the construction site. Feed it back into the virtual construction site model for simulation, and use the path optimization algorithm to determine whether there are hidden dangers along the path. If there are hidden dangers, the model will provide adjustment suggestions, and the path optimization algorithm will update the path again.

[0063] Specifically, the steps of the path optimization algorithm are as follows:

[0064] S301: Calculate the risk value R of hidden dangers during the entire construction process based on the initial construction path path This value is used to identify potential problems in the path and is used as the basis for the path. The calculation formula is:

[0065]

[0066] Among them, R path It is the quantitative value of risk on the path, indicating that the path passes through each high-risk area A i The comprehensive risk of a route is represented by N, where N represents the total number of high-risk areas. By calculating the hidden danger risk value, the overall risk of the route is accurately assessed, and adjustment suggestions are provided when safety hazards are found on the route, thereby reducing the probability of accidents.

[0067] S302: If the calculated result is greater than the safety threshold, indicating that the hidden danger of the path is too high, the path is readjusted through the path optimization objective function to select an alternative path with lower risk and higher construction efficiency.

[0068] The formula of the path optimization objective function is:

[0069]

[0070] The optimization goal of the objective function is to maximize the path score P, α represents the risk weight coefficient, which is used to adjust the relative importance of risk, β represents the path efficiency penalty coefficient, which reflects the length or efficiency constraint of the path, and [T1, T2] represents the entire construction period.

[0071] If the maximized path score P→∞, it means that the path completely avoids all high-risk areas and the path efficiency is extremely high; if the maximized path score P→0, it means that the path always passes through high-risk areas, or the path is too long, resulting in extremely low efficiency.

[0072] Step 4: Adjust the path P in the virtual construction site model updated A second simulation is conducted. If there are no hidden dangers, the path is confirmed as the final path. This final optimized safe path is pushed to the construction workers' smart devices and automated mechanical equipment, so that every person or equipment on site can act according to the path guidance, ensuring that risk areas are avoided and construction efficiency is improved.

[0073] To verify the effectiveness of the solution of the present invention, the technical effects of the solution are demonstrated through data from actual construction scenarios, including how to assess path risks in real time, optimize path selection, and dynamically adjust paths to ensure safety.

[0074] Table 1 Data comparison of different path planning

[0075]

[0076] Through the above data analysis, the risk value R of the preliminary path is obtained. path When the risk is high (such as paths P1 and P3), the risk value of the optimized path is significantly reduced. By adjusting the path, high-risk areas on the construction site are avoided, significantly improving safety.

[0077] Path P1 initially had a high risk score (0.65), but after adjustment, the risk score dropped to 0.40, and the length was reduced by 50 meters. This demonstrates that while risk was reduced, construction efficiency was also significantly improved. The promotion of safe routes has resulted in high worker satisfaction, particularly on Paths P1 and P5, where the optimized routes made workers feel safer.

[0078] The path optimization in this embodiment not only takes into account the safety of construction workers, but also dynamically adapts to real-time changing environmental factors at the construction site (such as weather changes, real-time location of mechanical equipment, etc.), ensuring real-time updating and optimization of the construction path.

[0079] This embodiment also provides a computer device suitable for a highway construction safety risk management method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a highway construction safety risk management method proposed in the above embodiment.

[0080] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0081] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a highway construction safety risk management method as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A highway construction safety risk management method, characterized in that: The method comprises the following steps: Step 1: Collect construction site data through drones, monitoring equipment, and sensors. Utilize this data to build a virtual construction site model based on digital twin technology. This model will reflect the actual dynamic changes of the construction site in real time. Step 2: In the virtual construction site model, high-risk areas are identified by combining sensor and historical data. These areas are then labeled with real-time data. The labeled data of high-risk areas are then fed into the path optimization algorithm. Step 3: An initial construction path is determined based on the basic conditions, constraints, and pre-set plans of the construction site. This path is fed back into the virtual construction site model for simulation. A path optimization algorithm is then used to determine if there are any potential hazards along the path. If so, the model provides adjustment suggestions, and the path optimization algorithm then updates the path. Step 4: The final optimized safe path is pushed to the construction workers' smart devices and automated mechanical equipment, so that every person or equipment on site can follow the path guidance to ensure that risk areas are avoided and construction efficiency is improved.

2. A highway construction safety risk management method according to claim 1, characterized in that: In the step 2, the real-time data marking of these areas includes: risk data marking R i (t), risk density marker ψ i And the deviation penalty mark Φ of the path passing through the high-risk area i .

3. A highway construction safety risk management method according to claim 2, characterized in that: The risk data is marked R i The expression of (t) is: R i (t)=w1F s (t)+w2F h (t)+w3F o (t) Among them, w1, w2, and w3 are weight coefficients, reflecting the importance of different data sources. s (t) represents the danger index collected by the sensor in real time, F h (t) represents the risk level in historical data, F o (t) represents weather factors; The risk density marker ψ i The expression is: ψ i =exp(-γ((x-x i ) 2 )+(y-y i ) 2 +(z-z i ) 2 ) Among them, (x i ,y i ,z i ) represents the center coordinates of the high-risk area, (x, y, z) represents the center coordinates of the required construction point, and γ represents the spatial attenuation coefficient, which controls the influence of the risk range; The offset penalty mark Φ i The expression is: Among them, represents the distance between the path point (x, y) and the center of the high-risk area. The smaller the distance, the greater the penalty.

4. A highway construction safety risk management method according to claim 3, characterized in that: The steps of the path optimization algorithm are as follows: S301: Calculate the risk value R of hidden dangers during the entire construction process based on the initial construction path path This value is used to identify potential problems in the path and is used as the basis for the path. The calculation formula is: Among them, R path It is the quantitative value of risk on the path, indicating that the path passes through each high-risk area A i The comprehensive risk of N is the total number of high-risk areas; S302: If the calculated result is greater than the safety threshold, indicating that the hidden danger of the path is too high, the path is readjusted through the path optimization objective function to select an alternative path with lower risk and higher construction efficiency.

5. A highway construction safety risk management method according to claim 4, characterized in that: The formula of the path optimization objective function is: The optimization goal of the objective function is to maximize the path score P, α represents the risk weight coefficient, which is used to adjust the relative importance of risk, β represents the path efficiency penalty coefficient, which reflects the length or efficiency constraint of the path, and [T1, T2] represents the entire construction period.

6. A highway construction safety risk management method according to claim 5, characterized in that: If the path score P→∞ is maximized, it means that the path completely avoids all high-risk areas and the path efficiency is extremely high; If the maximum path score P→0, it means that the path always passes through high-risk areas, or the path is too long, resulting in extremely low efficiency.

7. A highway construction safety risk management method according to claim 6, characterized in that: The adjusted path P in the virtual construction site model updated Perform a second simulation. If there are no hidden dangers, confirm the path as the final path and output it to construction personnel and equipment.

8. A risk management system applied to a highway construction safety risk management method according to claim 7, characterized in that: The system includes: The data acquisition module is responsible for collecting various data from the construction site in real time, including environmental data, construction personnel activity data, the operating status of construction machinery and equipment, and visual and infrared data from surveillance cameras, sensors, drones, etc.; The digital twin modeling and virtual construction site module builds a virtual digital twin model based on the data obtained from the data acquisition module, reflecting the dynamic changes of the construction site in real time; Risk assessment and hidden danger detection module, which uses advanced algorithm models to conduct risk assessment based on real-time and historical data and identify potential high-risk areas; Path planning and optimization module, which uses path optimization algorithms to optimize the routes of construction personnel and machinery to avoid passing through high-risk areas; The simulation and feedback adjustment module is responsible for simulating the initially planned path in the virtual construction site model and verifying the safety of the path; As well as the safety monitoring and alarm module, which is responsible for real-time monitoring of the safety status of the construction site and detecting abnormal situations in real time based on data sources such as sensors and monitoring equipment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the highway construction safety risk management method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a highway construction safety risk management method according to any one of claims 1 to 7 are implemented.

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