Safety construction management system and method based on electric power construction project

Abstract

The invention belongs to the field of safety construction based on electric power construction engineering, and particularly relates to a safety construction management system and method based on electric power construction engineering, and the system comprises a construction site real-time monitoring camera and a sensor network. The intelligent decision-making layer is divided into a risk assessment module, a construction scheme optimization module and an emergency response module; the construction execution layer comprises personnel scheduling, equipment management, material transportation and on-site construction; the data collaboration center comprises a deep learning model, a fault early warning mechanism and a cost benefit analysis module; the deep learning model can process multi-source data, input variables comprise construction progress, safety risk level, weather forecast and cost budget, and output instructions are construction scheme adjustment suggestions and resource allocation optimization schemes. According to the data-driven decision-making mechanism, the system can dynamically adjust the construction scheme according to real-time data, and the construction efficiency and safety are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of safe construction based on electric power construction engineering, and in particular to a safe construction management system and method based on electric power construction engineering. BACKGROUND

[0002] Electric power construction engineering, as an important part of national infrastructure construction, has the characteristics of large scale, long cycle, complex technology, and variable construction environment. In recent years, with the increasing number of electric power construction projects, the importance of construction safety management has become increasingly prominent. According to relevant statistics, electric power construction safety accidents occur from time to time, posing a serious threat to people's life and property safety. Electric power construction safety management involves multiple links, including safety supervision of construction sites, safety training of personnel, safe operation of equipment, and improvement of emergency plans. However, the current safety construction management of electric power construction engineering still faces many challenges, such as chaotic construction site safety management, incomplete safety management system, inadequate safety supervision, and imperfect emergency plans. The existence of these problems not only affects the smooth progress of electric power construction engineering, but also poses a threat to the safety of construction personnel. With the rapid development of information technology, digital and intelligent technologies are increasingly applied in electric power construction engineering. For example, the State Grid Xiangshan 1# Offshore Wind Farm (Phase II) Project has built a smart management system by using satellite and 5G communication technologies, significantly improving the level of construction management. This shows that with the help of advanced technical means, the problems existing in traditional safety management can be effectively solved, and the level of safety construction management of electric power construction engineering can be improved.

[0003] The prior art still has the following problems:

[0004] Traditional safety construction management methods often rely on manual inspection and post-processing, making it difficult to achieve real-time, comprehensive monitoring and management. Therefore, it is of great significance to develop a safety construction management system based on electric power construction for improving the level of electric power construction safety management.

[0005] Therefore, a safety construction management system and method based on electric power construction engineering are proposed to solve the above problems. SUMMARY

[0006] The technical solution adopted by the present application to solve its technical problems is that traditional safety construction management methods often rely on manual inspection and post-processing, making it difficult to achieve real-time, comprehensive monitoring and management. Therefore, a safety construction management system based on electric power construction engineering is developed, which includes a safety risk monitoring layer, an intelligent decision-making layer, a construction execution layer, and a data coordination hub.

[0007] The safety risk monitoring layer includes real-time monitoring cameras and sensor networks at the construction site; the intelligent decision-making layer is divided into a risk assessment module, a construction plan optimization module, and an emergency response module; the construction execution layer includes personnel scheduling, equipment management, material transportation, and on-site construction; and the data collaboration hub includes a deep learning model, a fault early warning mechanism, and a cost-benefit analysis module.

[0008] Preferably, the real-time monitoring camera at the construction site has a 360-degree rotation function and integrates night vision.

[0009] Preferably, the sensor network is capable of real-time monitoring of temperature, humidity, dust concentration, and harmful gas concentration at the construction site.

[0010] Preferably, the risk assessment module of the intelligent decision-making layer uses the Analytic Hierarchy Process (AHP) combined with fuzzy logic algorithm to dynamically assess the safety risks at the construction site.

[0011] Preferably, the construction plan optimization module of the intelligent decision-making layer optimizes the construction schedule based on a genetic algorithm to ensure a balance between construction efficiency and safety.

[0012] Preferably, the emergency response module of the intelligent decision-making layer adopts an event-driven multi-threaded processing mechanism to quickly respond to sudden safety incidents at the construction site.

[0013] Preferably, the personnel scheduling strategy of the construction execution layer is to dynamically allocate work tasks based on the priority of construction tasks and the matching degree of personnel skills.

[0014] Preferably, the equipment management strategy of the construction execution layer is to use Internet of Things (IoT) technology to monitor the equipment operating status in real time and provide early warnings of equipment failures.

[0015] Preferably, the deep learning model of the data collaboration hub takes construction progress, safety risk level, weather forecast and cost budget as input variables, and outputs construction plan adjustment suggestions and resource allocation optimization schemes as output instructions.

[0016] Preferably, it includes the following steps:

[0017] S1. Real-time acquisition and fusion of multi-source data (data layer);

[0018] S2, Feature Extraction and Security Risk Assessment (Analysis Layer);

[0019] S3, Intelligent Decision-Making and Construction Scheme Optimization (Decision-Making Level);

[0020] S4. Execution Feedback and Closed-Loop Optimization (Execution Layer).

[0021] The advantages of this invention are:

[0022] 1. A comprehensive, real-time monitoring system for the construction site is achieved through real-time surveillance cameras and a sensor network. This multi-dimensional monitoring approach can promptly detect potential safety risks, such as environmental changes, personnel violations, and equipment malfunctions. In particular, the 360-degree rotation and night vision capabilities of the cameras, along with the real-time monitoring of temperature, humidity, dust concentration, and harmful gas concentrations by the sensors, greatly enhance the system's monitoring capabilities. The system is divided into risk assessment, construction plan optimization, and emergency response modules, forming a complete decision support system. The risk assessment module uses the Analytic Hierarchy Process (AHP) combined with fuzzy logic algorithms to dynamically assess safety risks at the construction site. The construction plan optimization module uses a genetic algorithm to optimize the construction schedule, ensuring a balance between construction efficiency and safety. The emergency response module employs an event-driven, multi-threaded processing mechanism to quickly respond to sudden safety incidents. This hierarchical decision-making mechanism allows the system to flexibly adjust strategies according to different situations, covering key aspects such as personnel scheduling, equipment management, material transportation, and on-site construction. The personnel scheduling strategy dynamically allocates tasks based on the priority of construction tasks and the matching degree of personnel skills, ensuring the efficient use of human resources. The equipment management strategy uses IoT technology to monitor equipment operating status in real time, providing early warnings of equipment failures and reducing their impact on construction safety. As the system's "brain," it integrates a deep learning model, a fault warning mechanism, and a cost-benefit analysis module. The deep learning model can process multi-source data, with input variables including construction progress, safety risk level, weather forecast, and cost budget, and outputting suggestions for adjusting construction plans and optimizing resource allocation. This data-driven decision-making mechanism enables the system to dynamically adjust construction plans based on real-time data, improving construction efficiency and safety.

[0023] 2. This system achieves end-to-end management from data acquisition to decision execution through a tightly coordinated multi-layered architecture. The safety risk monitoring layer provides real-time data support to the intelligent decision-making layer, which generates optimized solutions and issues instructions based on the data. The construction execution layer is responsible for the specific implementation, while the data collaboration center monitors and optimizes the entire process. This collaborative working mechanism ensures efficient cooperation between the various parts of the system and improves overall management efficiency.

[0024] 3. The system employs multi-source data real-time acquisition and fusion technology, integrating various types of data from the construction site, including video surveillance data, sensor data, equipment operation data, and personnel positioning data. Through data association algorithms and a distributed computing architecture, data from different sources is standardized and integrated into a unified data model. This multi-source data fusion technology provides more comprehensive and accurate information support, laying a solid foundation for intelligent decision-making. The risk assessment module uses the Analytic Hierarchy Process (AHP) combined with fuzzy logic algorithms to dynamically assess safety risks at the construction site. This method comprehensively considers the influence of multiple factors, improving the accuracy and reliability of risk assessment. The construction plan optimization module optimizes the construction schedule based on genetic algorithms, finding the optimal construction plan in complex construction environments and ensuring a balance between construction efficiency and safety. The emergency response module adopts an event-driven multi-threaded processing mechanism, enabling rapid response to sudden safety events and reducing accident losses. The deep learning model in the data collaboration hub can handle complex multi-source data, with input variables including construction progress, safety risk level, weather forecast, and cost budget, and output instructions including construction plan adjustment suggestions and resource allocation optimization schemes. This deep learning-based decision support system can dynamically adjust construction plans based on real-time data, improving construction efficiency and safety.

[0025] 4. The system utilizes multi-source data real-time acquisition and fusion technology to comprehensively and in real-time monitor various conditions at the construction site. Whether it's environmental changes, personnel behavior, or equipment operating status, it can promptly capture and feed back to the decision-making level, ensuring construction safety. The system employs intelligent decision-making algorithms and deep learning models to dynamically adjust construction plans and resource allocation based on real-time data. This intelligent decision-making mechanism effectively addresses the complex and ever-changing situations at the construction site, improving construction efficiency and safety. Through close collaboration across multiple layers, the system achieves end-to-end management from data acquisition to decision execution. Efficient coordination between different parts improves overall management efficiency and reduces management costs. The system not only prioritizes construction safety but also optimizes resource allocation through a cost-benefit analysis module to maximize construction benefits. This design philosophy, balancing safety and efficiency, allows the system to improve the economic benefits of construction projects while ensuring construction safety. The system's emergency response module uses an event-driven, multi-threaded processing mechanism to quickly respond to sudden safety incidents. This rapid response capability enables timely measures to be taken when accidents occur, reducing losses and protecting the lives of construction personnel. The system's architecture design has good scalability and can be flexibly adjusted according to different construction projects and needs. Whether it's a small-scale power system upgrade project or a large-scale power construction project, this system can adapt to and provide effective safety management support. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the framework structure of a safety construction management system for power construction projects according to the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1

[0030] The system comprises a safety risk monitoring layer, an intelligent decision-making layer, a construction execution layer, and a data collaboration hub. The safety risk monitoring layer includes real-time monitoring cameras and a sensor network at the construction site.

[0031] Intelligent Decision-Making Layer: Divided into risk assessment module, construction plan optimization module, and emergency response module. Construction Execution Layer: Includes personnel scheduling, equipment management, material transportation, and on-site construction.

[0032] Data collaboration hub: includes deep learning models, fault early warning mechanisms, and cost-benefit analysis modules.

[0033] Step 1: Real-time acquisition and fusion of multi-source data (data layer)

[0034] 1. Safety monitoring at the construction site

[0035] (1) A real-time monitoring camera at the construction site, with 360-degree rotation function and integrated night vision function, is used to monitor the activities of personnel, the operating status of equipment and whether there are any violations at the construction site in real time.

[0036] (2) Sensor network to monitor environmental parameters such as temperature, humidity, dust concentration, harmful gas concentration, and noise intensity at the construction site in real time, as well as operating status data such as vibration, pressure, and current of equipment.

[0037] (3) Personnel positioning system: Real-time monitoring of the location and activity trajectory of construction personnel through RFID or UWB technology to ensure personnel safety.

[0038] 2. External data access

[0039] (1) The meteorological station collects weather data around the construction site, including temperature, humidity, wind speed, rainfall, and weather warning information for the next 72 hours (such as rainstorm, strong wind, high temperature, etc.).

[0040] (2) Wearable devices such as smart safety helmets and smart bracelets can upload the physiological data (such as heart rate and body temperature) and safety status (such as whether they are wearing safety helmets and safety belts) of construction workers in real time.

[0041] (3) Equipment management system, which monitors the operating status of construction equipment in real time, including equipment operating time, fault alarm information and maintenance records.

[0042] 3. Data Fusion Hub

[0043] (1) Construct a spatiotemporal database, align timestamps with spatial coordinates, integrate multi-source data such as camera images, sensor data, personnel positioning information, and meteorological data to form a unified data model, and map it to the three-dimensional model of the construction site to facilitate real-time monitoring and analysis of the database, aligning timestamps with spatial coordinates (such as mapping geothermal data to the GIS three-dimensional mine model).

[0044] Step Two: Feature Extraction and Security Risk Assessment (Analysis Layer)

[0045] 1. Key Feature Extraction

[0046] (1) Calculate the rate of change of environmental parameters at the construction site, such as the rate of change of temperature (>5℃ / h) or the rate of change of concentration of harmful gases (>10%), and identify potential safety risk areas.

[0047] (2) Analyze the behavior patterns of construction workers and use image recognition technology to determine whether there are any violations (such as not wearing a safety helmet or safety belt, or entering a dangerous area).

[0048] (3) Predict equipment failure trends. Based on equipment operation data (such as vibration and current) and historical failure records, use machine learning algorithms (such as LSTM) to predict the failure probability and remaining service life of the equipment.

[0049] 2. Safety Risk Assessment

[0050] (1) The risk assessment module adopts the analytic hierarchy process (AHP) combined with fuzzy logic algorithm to comprehensively consider environmental risks, personnel behavior risks and equipment failure risks, and dynamically assess the overall safety risk level of the construction site.

[0051] (2) Abnormal operating condition diagnosis: If a sudden increase in the concentration of harmful gases (>50%) or abnormal equipment vibration (increase in vibration amplitude >20%) is detected, an alarm will be triggered immediately and the source of risk will be located.

[0052] Step 3: Intelligent Decision-Making and Construction Scheme Optimization (Decision-Making Level)

[0053] 1. Risk assessment and initial decision-making

[0054] The intelligent decision-making system is initialized with the objective function of maximizing construction safety and efficiency while minimizing construction costs.

[0055] 2. Exploration Phase

[0056] In the early stages of construction, the environmental response at the construction site is explored by randomly adjusting the construction plan (such as personnel allocation and equipment scheduling), and data on safety risks and construction efficiency under different plans are collected.

[0057] 3. Utilization stage

[0058] Based on risk assessment results and historical data, the optimal construction plan is output using deep learning models (such as reinforcement learning algorithms). For example, when severe weather warnings are issued, the priority of construction tasks is adjusted to prioritize indoor operations; and equipment maintenance is arranged in advance when the risk of equipment failure is high.

[0059] 4. Real-time updates

[0060] New monitoring data is received every 10 seconds, and construction plans and safety measures are fine-tuned in real time to ensure the safety and efficiency of the construction process.

[0061] Step 4: Execution Feedback and Closed-Loop Optimization (Execution Layer)

[0062] 1. Multi-system linkage control

[0063] (1) Personnel scheduling system: Dynamically allocate work tasks based on the priority of construction tasks and the matching degree of personnel skills to ensure personnel safety and construction efficiency. For example, increase safety supervision personnel in high-risk areas.

[0064] (2) Equipment Management System: The system monitors the equipment operating status in real time through Internet of Things (IoT) technology, provides early warnings of equipment failures, and dynamically adjusts equipment operating parameters (such as adjusting the lifting speed of the crane) according to the construction plan.

[0065] 2. Implementation of safety measures

[0066] (1) Construction site safety protection system: Based on the risk assessment results, the status of safety protection facilities is automatically adjusted, such as automatically activating alarms, shutting down dangerous equipment, and sealing off dangerous areas in high-risk areas.

[0067] (2) Personnel safety reminder system: Send safety reminder information to construction personnel in real time through smart safety helmets or smart wristbands, such as "The concentration of harmful gas in the area ahead exceeds the standard, please evacuate".

[0068] 3. Fault self-healing mechanism

[0069] (1) Equipment Failure Response: When an equipment failure is detected, the backup equipment is automatically activated and maintenance personnel are notified to carry out emergency repairs. For example, when a crane fails, the system automatically switches to the backup crane.

[0070] (2) Emergency response for personnel safety: When personnel are detected to be in a dangerous state (such as abnormal heart rate or high body temperature), the emergency plan will be automatically activated, such as calling for medical rescue and guiding personnel to evacuate from the dangerous area.

[0071] 4. Closed-loop optimization

[0072] Based on the feedback information, the parameters of the intelligent decision-making model are adjusted to optimize the construction plan and safety measures. For example, if a construction plan is found to have a high safety risk during actual implementation, the risk weight of the plan is adjusted, and the construction plan is re-optimized.

[0073] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A safety construction management system for power construction projects, characterized in that: The system includes a safety risk monitoring layer, an intelligent decision-making layer, a construction execution layer, and a data collaboration center; The safety risk monitoring layer includes real-time monitoring cameras and sensor networks at the construction site; the intelligent decision-making layer is divided into a risk assessment module, a construction plan optimization module, and an emergency response module; the construction execution layer includes personnel scheduling, equipment management, material transportation, and on-site construction; and the data collaboration hub includes a deep learning model, a fault early warning mechanism, and a cost-benefit analysis module.

2. The safety construction management system for power construction projects according to claim 1, characterized in that: The construction site real-time monitoring camera has a 360-degree rotation function and integrates night vision.

3. The safety construction management system for power construction projects according to claim 1, characterized in that: The sensor network is capable of monitoring the temperature, humidity, dust concentration, and harmful gas concentration at the construction site in real time.

4. The safety construction management system for power construction projects according to claim 1, characterized in that: The risk assessment module of the intelligent decision-making layer uses the Analytic Hierarchy Process (AHP) combined with fuzzy logic algorithms to dynamically assess the safety risks at the construction site.

5. A safety construction management system for power construction projects according to claim 1, characterized in that: The intelligent decision-making layer's construction plan optimization module optimizes the construction schedule based on a genetic algorithm, ensuring a balance between construction efficiency and safety.

6. A safety construction management system for power construction projects according to claim 1, characterized in that: The emergency response module of the intelligent decision-making layer adopts an event-driven multi-threaded processing mechanism to quickly respond to sudden safety incidents at the construction site.

7. A safety construction management system for power construction projects according to claim 1, characterized in that: The personnel scheduling strategy of the construction execution layer is as follows: work tasks are dynamically allocated based on the priority of construction tasks and the matching degree of personnel skills.

8. A safety construction management system for power construction projects according to claim 1, characterized in that: The equipment management strategy of the construction execution layer is to use Internet of Things (IoT) technology to monitor the equipment operating status in real time and provide early warnings of equipment failures.

9. A safety construction management system for power construction projects according to claim 1, characterized in that: The deep learning model of the data collaboration hub takes construction progress, safety risk level, weather forecast and cost budget as input variables, and outputs suggestions for adjusting construction plans and optimization of resource allocation.

10. A multi-source data collaborative control method for a safety construction management system based on power construction projects according to claim 1, characterized in that: Includes the following steps: S1. Real-time acquisition and fusion of multi-source data (data layer); S2, Feature Extraction and Security Risk Assessment (Analysis Layer); S3, Intelligent Decision-Making and Construction Scheme Optimization (Decision-Making Level); S4. Execution Feedback and Closed-Loop Optimization (Execution Layer).

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