Clean room electromechanical installation construction safety visual monitoring system

Abstract

The invention relates to the technical field of electromechanical construction, in particular to a clean room electromechanical installation construction safety visual monitoring system. Comprising a multi-modal data acquisition unit, an efficient transmission unit, an edge computing node, a feature extraction module, a feature fusion module, a processor module, a security risk analysis engine, an intelligent decision module, a dynamic weighting module, an intelligent security helmet and a visual terminal. The efficient transmission unit is connected with the multi-modal data acquisition unit, the edge computing node is connected with the efficient transmission unit, the feature extraction module is connected with the edge computing node, and the feature fusion module is connected with the feature extraction module. The technical problems of limited monitoring range, untimely data acquisition, insufficient analysis and processing capability and difficulty in realizing real-time and accurate monitoring of the whole construction process in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical construction, and in particular to a clean room electromechanical installation construction safety visualization monitoring system. Background Art

[0002] Cleanrooms, essential infrastructure in modern industrial production, scientific research, and healthcare, are fundamentally about maintaining a highly clean, dust-free environment to meet the stringent requirements of specific processes or experiments for parameters such as air quality, temperature, humidity, and pressure. With the continuous advancement of technology and industrial upgrading, the application of cleanrooms is expanding, and increasingly stringent standards are being placed on the quality and safety of cleanroom electromechanical installation and construction. Cleanroom electromechanical installation and construction encompasses multiple specialized areas, including electrical, plumbing, air conditioning, ventilation, and automation control, making it a complex and sophisticated system. During construction, not only must the coordinated operation of various systems be ensured, but also the specific cleanroom construction specifications and standards must be strictly adhered to to avoid contamination risks caused by improper construction. Therefore, comprehensive and efficient safety monitoring of the cleanroom electromechanical installation and construction process is crucial to ensuring smooth construction, preventing safety incidents, and maintaining the long-term stable operation of the cleanroom.

[0003] However, traditional clean room electromechanical installation construction safety monitoring methods rely on manual inspections and regular testing, resulting in limited monitoring scope, untimely data acquisition, and insufficient analysis and processing capabilities, making it difficult to achieve real-time and accurate monitoring of the entire construction process. Summary of the Invention

[0004] The purpose of the present invention is to provide a clean room electromechanical installation construction safety visualization monitoring system, aiming to solve the technical problems in the existing technology that the safety monitoring method relies on manual inspections and regular inspections, resulting in limited monitoring range, untimely data acquisition, insufficient analysis and processing capabilities, and difficulty in achieving real-time and accurate monitoring of the entire construction process.

[0005] To achieve the above objectives, the present invention adopts a clean room electromechanical installation construction safety visualization monitoring system, which includes a multimodal data acquisition unit, an efficient transmission unit, an edge computing node, a feature extraction module, a feature fusion module, a processor module, a safety risk analysis engine, an intelligent decision-making module, a dynamic weighting module, an intelligent safety helmet and a visualization terminal;

[0006] The efficient transmission unit is connected to the multimodal data acquisition unit, the edge computing node is connected to the efficient transmission unit, the feature extraction module is connected to the edge computing node, the feature fusion module is connected to the feature extraction module, the processor module is connected to the feature fusion module, the processor is embedded with the security risk analysis engine, the intelligent decision module is connected to the processor module, the dynamic weighting module is connected to the feature fusion module, the visualization terminal is connected to the processor module, and the visualization terminal is connected to the smart safety helmet;

[0007] The multimodal data acquisition module is used to collect various data of the installation construction site and transmit them to the edge computing node through the efficient transmission unit;

[0008] The edge computing node is used to pre-process the received data;

[0009] The feature extraction module extracts key features from the preprocessed data;

[0010] The feature fusion module fuses the extracted key features and transmits the fused data to the processor module;

[0011] The dynamic weighting module is used to assign weights to different data during feature fusion;

[0012] The processor module performs risk analysis based on the security risk assessment model in the security risk analysis engine and the fused data to identify potential security risks;

[0013] The intelligent decision-making module makes intelligent decisions based on the risk analysis results;

[0014] The visual terminal is used to direct the evacuation of staff wearing the smart safety helmets.

[0015] Among them, the multimodal data acquisition unit includes an environmental parameter acquisition module, a personnel positioning and tracking module, an equipment status monitoring module, a video data acquisition module and a gas monitoring module; the environmental parameter acquisition module, the personnel positioning and tracking module, the equipment status monitoring module, the video data acquisition module and the gas monitoring module are all connected to the edge computing node.

[0016] Among them, the efficient transmission unit includes a compression module, a transmission module and a decompression module. The transmission modules are connected to the multimodal data acquisition unit and the edge computing node, and the compression module and the decompression module are connected to the multimodal data acquisition unit and the edge computing node respectively.

[0017] The clean room electromechanical installation construction safety visualization monitoring system further comprises a diffusion simulation module and a route optimization module, and the route optimization module is connected to the visualization terminal and the diffusion simulation module.

[0018] Wherein, the clean room electromechanical installation construction safety visual monitoring system further includes a data analysis module and a report generation module, the data analysis module is connected to the processing module, and the report generation module is connected to the data analysis module;

[0019] The data analysis module is used to perform in-depth analysis on the data received by the processor module, and to generate a detailed construction safety report and a quality assessment report through the report generation module.

[0020] The clean room electromechanical installation construction safety visualization monitoring system further includes a time-space alignment verification module, which is connected to the edge computing node;

[0021] The time-space alignment verification module adopts the NTP-PTP hybrid clock synchronization protocol to eliminate the delay difference of multi-source data.

[0022] The clean room electromechanical installation construction safety visualization monitoring system further includes an adaptive optimization module, which is connected to the safety risk analysis engine.

[0023] The clean room electromechanical installation construction safety visualization monitoring system further comprises a linkage interaction module, which is connected to the fire protection system of the building where the clean room is located.

[0024] The present invention provides a clean room electromechanical installation construction safety visualization monitoring system. When used, first, the multimodal data acquisition unit collects various data of the installation construction site, including environmental parameters, equipment operating status, personnel activity trajectory, etc., and transmits them to the edge computing node quickly and stably through the efficient transmission unit; the edge computing node performs preliminary cleaning, screening and format conversion on the received data, the feature extraction module accurately extracts key features from the pre-processed data, the feature fusion module deeply fuses the extracted key features, and assigns reasonable weights to different data through the dynamic weighting module to highlight key information, weaken interference factors, and form more representative fusion data. According to the data; the processor module conducts a comprehensive and in-depth risk analysis based on the built-in safety risk assessment model in the safety risk analysis engine and the fused data, and accurately identifies potential safety risks; the intelligent decision-making module automatically generates corresponding response strategies and suggestions according to the risk analysis results, and displays them to on-site managers in real time through the visual terminal, and at the same time directs the staff wearing the smart safety helmets to evacuate or take other necessary measures. In this way, the technical problem that the safety monitoring method in the existing technology relies on manual inspections and regular testing, has limited monitoring scope, untimely data acquisition, insufficient analysis and processing capabilities, and is difficult to achieve real-time and accurate monitoring of the entire construction process is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 This is a principle block diagram of the first embodiment of the present invention.

[0027] Figure 2 This is a principle block diagram of the second embodiment of the present invention.

[0028] Figure 3 This is a principle block diagram of the third embodiment of the present invention.

[0029] 101-Multimodal data acquisition unit, 102-Efficient transmission unit, 103-Edge computing node, 104-Feature extraction module, 105-Feature fusion module, 106-Processor module, 107-Safety risk analysis engine, 108-Intelligent decision-making module, 109-Dynamic weighting module, 110-Intelligent safety helmet, 111-Visual terminal, 112-Diffusion simulation module, 113-Route optimization module, 114-Environmental parameter acquisition module, 115-Personnel positioning and tracking module, 116-Equipment status monitoring module, 117-Video data acquisition module, 118-Gas monitoring module, 119-Compression module, 120-Transmission module, 121-Decompression module, 201-Data analysis module, 202-Report generation module, 203-Spatiotemporal alignment verification module, 204-Adaptive optimization module, 205-Linkage interaction module, 301-Safety training module, 302-Effect evaluation module, 303-Encryption module. DETAILED DESCRIPTION

[0030] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0031] The first embodiment of this application is:

[0032] See also Figure 1 , Figure 1 This is a principle block diagram of the first embodiment of the present invention.

[0033] The present invention provides a clean room electromechanical installation construction safety visualization monitoring system, including a multimodal data acquisition unit 101, an efficient transmission unit 102, an edge computing node 103, a feature extraction module 104, a feature fusion module 105, a processor module 106, a safety risk analysis engine 107, an intelligent decision-making module 108, a dynamic weighting module 109, an intelligent safety helmet 110, a visualization terminal 111, a diffusion simulation module 112 and a route optimization module 113. The multimodal data acquisition unit 101 includes an environmental parameter acquisition module 114, a personnel positioning and tracking module 115, an equipment status monitoring module 116, a video data acquisition module 117 and a gas monitoring module 118. The efficient transmission unit 102 includes a compression module 119, a transmission module 120 and a decompression module 121. The above solution solves the problem that the safety monitoring method in the prior art relies on manual inspections and regular inspections, has a limited monitoring range, untimely data acquisition, insufficient analysis and processing capabilities, and is difficult to achieve real-time and accurate monitoring of the entire construction process.

[0034] In this specific embodiment, the multimodal data acquisition module comprehensively and meticulously collects various data on the installation construction site, including but not limited to environmental parameters (such as temperature, humidity, cleanliness, gas), personnel activity trajectories (such as location, movement), etc. These data are then quickly and stably transmitted to the edge computing node 103 via the efficient transmission unit 102;

[0035] The edge computing node 103 performs preliminary cleaning, screening and format conversion on the received data based on the data cleaning and compression algorithm to remove noise and redundant information;

[0036] The feature extraction module 104 accurately extracts key features from the pre-processed data based on the convolutional neural network (CNN) or recurrent neural network (RNN) algorithm in deep learning;

[0037] The feature fusion module 105 performs deep fusion of the extracted key features based on a multi-source information fusion algorithm, and assigns reasonable weights to different data through the dynamic weighting module 109. The weight assignment is based on a feature importance assessment algorithm to highlight key information and weaken interference factors, thereby forming more representative fused data;

[0038] The processor module 106 is based on the security risk assessment model in the security risk analysis engine 107. The model uses an ensemble learning algorithm (such as random forest, gradient boosting tree, etc.) to combine the fused data for risk analysis, and accurately identifies potential security risks through pattern recognition and anomaly detection technology;

[0039] The intelligent decision-making module 108 automatically generates optimal response strategies and suggestions based on the risk analysis results based on the reinforcement learning algorithm, and displays them to on-site managers in real time through the visualization terminal 111;

[0040] The visual terminal 111 is used to direct the staff wearing the smart safety helmet 110 to evacuate.

[0041] Among them, the efficient transmission unit 102 is connected to the multimodal data acquisition unit 101, the edge computing node 103 is connected to the efficient transmission unit 102, the feature extraction module 104 is connected to the edge computing node 103, the feature fusion module 105 is connected to the feature extraction module 104, the processor module 106 is connected to the feature fusion module 105, the processor is embedded with the security risk analysis engine 107, the intelligent decision module 108 is connected to the processor module 106, the dynamic weighting module 109 is connected to the feature fusion module 105, the visualization terminal 111 is connected to the processor module 106, and the visualization terminal 111 is connected to the smart safety helmet 110. In specific use, first, the multimodal data acquisition unit 101 collects various data of the installation construction site, including environmental parameters, equipment operation status, personnel activity trajectory, etc., and transmits them to the edge computing node 103 quickly and stably through the efficient transmission unit 102; the edge computing node 103 performs After preliminary cleaning, screening and format conversion, the feature extraction module 104 accurately extracts key features from the preprocessed data, and the feature fusion module 105 deeply fuses the extracted key features. The dynamic weighting module 109 assigns reasonable weights to different data to highlight key information, weaken interference factors, and form more representative fused data. The processor module 106, based on the built-in safety risk assessment model in the safety risk analysis engine 107, combines the fused data to conduct a comprehensive and in-depth risk analysis and accurately identify potential safety risks. The intelligent decision-making module 108 automatically generates corresponding response strategies and suggestions based on the risk analysis results, and displays them to the on-site management personnel in real time through the visualization terminal 111. At the same time, it directs the staff wearing the smart safety helmet 110 to evacuate or take other necessary measures. In this way, the safety monitoring method in the existing technology relies on manual inspections and regular testing, and has the problems of limited monitoring range, untimely data acquisition, insufficient analysis and processing capabilities, and difficulty in achieving real-time and accurate monitoring of the entire construction process.

[0042] Secondly, the environmental parameter acquisition module 114, the personnel location tracking module 115, the equipment status monitoring module 116, the video data acquisition module 117 and the gas monitoring module 118 are all connected to the edge computing node 103;

[0043] The environmental parameter acquisition module 114 is based on high-precision sensors and intelligent calibration algorithms to collect environmental parameters of the installation construction site in real time. The personnel positioning and tracking module 115 uses UWB (ultra-wideband) positioning technology, combined with a particle filter algorithm, to perform high-precision positioning and trajectory tracking of construction personnel, and to grasp personnel dynamics in real time. The equipment status monitoring module 116 is based on vibration analysis and spectrum analysis algorithms to monitor the equipment operation status in real time. The video data acquisition module 117 uses high-definition cameras and image recognition algorithms to conduct all-round video monitoring of the construction site, capturing personnel behavior and equipment operation details. The gas monitoring module 118 is based on gas sensors and multi-component gas analysis algorithms to monitor harmful gases and smoke at the construction site in real time to ensure the safety of the construction environment.

[0044] At the same time, the transmission module 120 is connected to the multimodal data acquisition unit 101 and the edge computing node 103, and the compression module 119 and the decompression module 121 are connected to the multimodal data acquisition unit 101 and the edge computing node 103 respectively. The compression module 119 adopts a lossless compression algorithm (such as JPEG2000, H.265, etc.) to efficiently compress the collected data and reduce the transmission bandwidth occupancy. The transmission module 120 is based on adaptive modulation coding (AMC) and dynamic bandwidth allocation algorithm to ensure stable transmission of data in a complex network environment. The decompression module 121 adopts a decompression algorithm corresponding to the compression module 119 to ensure the complete recovery of the data at the edge computing node 103.

[0045] In addition, the route optimization module 113 is connected to the visualization terminal 111 and the diffusion simulation module 112. The diffusion simulation module 112 simulates and predicts emergency situations such as gas leakage and fire smoke diffusion at the construction site based on fluid mechanics models and numerical simulation algorithms, providing a scientific basis for emergency response.

[0046] The route optimization module 113 is based on graph theory and path planning algorithms, and provides optimal route suggestions for personnel evacuation according to the diffusion simulation results, which are displayed on the visualization terminal 111 to facilitate management personnel to command safe evacuation.

[0047] When using a clean room electromechanical installation construction safety visualization monitoring system of this embodiment, first, the multimodal data acquisition unit 101 collects various data of the installation construction site, including environmental parameters, equipment operating status, personnel activity trajectory, etc., and transmits them to the edge computing node 103 quickly and stably through the efficient transmission unit 102; the edge computing node 103 performs preliminary cleaning, screening and format conversion on the received data, the feature extraction module 104 accurately extracts key features from the pre-processed data, the feature fusion module 105 deeply fuses the extracted key features, and assigns reasonable weights to different data through the dynamic weighting module 109 to highlight key information, weaken interference factors, and form a more representative The processor module 106 performs a comprehensive and in-depth risk analysis based on the built-in safety risk assessment model in the safety risk analysis engine 107 and the fused data, and accurately identifies potential safety risks; the intelligent decision-making module 108 automatically generates corresponding response strategies and suggestions according to the risk analysis results, and displays them to the on-site management personnel in real time through the visual terminal 111, and at the same time directs the staff wearing the intelligent safety helmet 110 to evacuate or take other necessary measures. In this way, the technical problem that the safety monitoring method in the prior art relies on manual inspections and regular inspections, has limited monitoring scope, untimely data acquisition, insufficient analysis and processing capabilities, and is difficult to achieve real-time and accurate monitoring of the entire construction process is solved.

[0048] The second embodiment of the present application is:

[0049] Based on the first embodiment, please refer to Figure 2 , Figure 2 This is a principle block diagram of the second embodiment of the present invention.

[0050] The present invention provides a clean room electromechanical installation construction safety visualization monitoring system, which also includes a data analysis module 201, a report generation module 202, a time-space alignment verification module 203, an adaptive optimization module 204 and a linkage interaction module 205.

[0051] For this specific implementation, the data analysis module 201 is connected to the processing module, and the report generation module 202 is connected to the data analysis module 201;

[0052] The data analysis module 201 is used to perform in-depth analysis on the data received by the processor module 106 and generate a detailed construction safety report and quality assessment report through the report generation module 202;

[0053] The data analysis module 201 uses data mining algorithms (such as association rule mining, cluster analysis, decision trees, etc.) and machine learning algorithms (such as regression analysis, support vector machines, neural networks, etc.) to conduct in-depth analysis of the data and mine potential patterns, trends and associations in the data. Through the template-based report generation algorithm of the report generation module 202, the analysis results can be presented to management personnel in the form of detailed and intuitive construction safety reports and quality assessment reports, so that they can grasp the construction situation in a timely and accurate manner.

[0054] Wherein, the spatiotemporal alignment verification module 203 is connected to the edge computing node 103;

[0055] The spatiotemporal alignment verification module 203 uses the NTP-PTP hybrid clock synchronization protocol. This protocol combines the advantages of the Network Time Protocol (NTP) and the Precision Time Protocol (PTP) to achieve high-precision clock synchronization. By establishing a clock synchronization relationship between the edge computing node 103 and the data acquisition device, the collected data is timestamped and corrected to eliminate the time delay difference between multi-source data.

[0056] Secondly, the adaptive optimization module 204 is connected to the safety risk analysis engine 107. The adaptive optimization module 204 adopts the online learning algorithm or adaptive filtering algorithm in machine learning, and can automatically adjust the parameters and configuration of the safety risk analysis model according to the ever-changing construction environment and conditions, so that the model always maintains the best performance and adapts to new risk characteristics and changing trends, thereby more accurately identifying potential safety risks.

[0057] Thirdly, the linkage interaction module 205 is connected to the fire protection system of the building where the clean room is located, and the linkage interaction module 205 is linked with the fire protection system of the building where the clean room is located to realize functions such as automatic fire alarm and automatic start of fire protection equipment.

[0058] A clean room electromechanical installation construction safety visualization monitoring system using the present embodiment, when specifically used, the time-space alignment verification module 203 adopts the NTP-PTP hybrid clock synchronization protocol. This protocol combines the advantages of the Network Time Protocol (NTP) and the Precision Time Protocol (PTP), and can achieve high-precision clock synchronization. By establishing a clock synchronization relationship between the edge computing node 103 and the data acquisition device, the collected data is timestamped and corrected to eliminate the time delay difference between multi-source data. The adaptive optimization module 204 adopts the online learning algorithm or adaptive filtering algorithm in machine learning, which can automatically adjust the parameters and configuration of the safety risk analysis model according to the changing construction environment and conditions, so that the model always maintains the best performance and adapts to new risk characteristics and changing trends, thereby more accurately identifying potential safety risks. The linkage interaction module 205 is linked with the fire protection system of the building where the clean room is located to realize functions such as automatic fire alarm and automatic start of fire protection equipment.

[0059] The third embodiment of the present application is:

[0060] Based on the second embodiment, please refer to Figure 3 , Figure 3 This is a principle block diagram of the third embodiment of the present invention.

[0061] The present invention provides a clean room electromechanical installation construction safety visualization monitoring system, which also includes a safety training module 301, an effect evaluation module 302 and an encryption module 303.

[0062] According to this specific embodiment, the safety training module 301 is connected to the processor module 106, and the effect evaluation module 302 is connected to the safety training module 301. The safety training module 301 is used to conduct pre-job training for construction personnel, and training can be conducted through VR technology. The effect evaluation module 302 is used to evaluate the results of the safety training, and construction can be carried out if the effect is achieved.

[0063] The encryption module 303 is connected to the efficient transmission unit 102, and the encryption module 303 uses a symmetric encryption algorithm (such as AES) or an asymmetric encryption algorithm (such as RSA) to encrypt the data to encrypt the efficient transmission unit 102 to avoid data leakage.

[0064] Using a cleanroom electromechanical installation construction safety visualization monitoring system of this embodiment, the safety training module 301 is used to provide pre-job training for construction personnel, which can be carried out through VR technology. The effect evaluation module 302 is used to evaluate the results of the safety training, and construction can be carried out if the results are achieved. The encryption module 303 uses a symmetric encryption algorithm (such as AES) or an asymmetric encryption algorithm (such as RSA) to encrypt data for the efficient transmission unit 102 to prevent data leakage.

[0065] The present invention realizes 24-hour uninterrupted monitoring of the construction site through multimodal data collection and efficient transmission. Any abnormality can be quickly captured and warned, greatly improving the timeliness and accuracy of monitoring.

[0066] The intelligent decision-making module 108 can automatically generate the optimal response strategy based on the risk analysis results, and provide real-time guidance to on-site staff through the visual terminal 111, effectively improving the emergency response speed and handling efficiency.

[0067] The present invention can continuously optimize the security risk assessment model through data analysis based on the adaptive optimization module 204, thereby improving the intelligence level and prediction capability of the monitoring system.

[0068] The combination of the visualization terminal 111 and the smart safety helmet 110 realizes the seamless connection between remote command and on-site operation, ensures the timeliness and accuracy of information transmission, and improves the overall construction efficiency and safety.

[0069] The above disclosure is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A clean room electromechanical installation construction safety visualization monitoring system, characterized in that: It includes a multimodal data acquisition unit, an efficient transmission unit, an edge computing node, a feature extraction module, a feature fusion module, a processor module, a security risk analysis engine, an intelligent decision-making module, a dynamic weighting module, an intelligent safety helmet, and a visualization terminal; The efficient transmission unit is connected to the multimodal data acquisition unit, the edge computing node is connected to the efficient transmission unit, the feature extraction module is connected to the edge computing node, the feature fusion module is connected to the feature extraction module, the processor module is connected to the feature fusion module, the processor is embedded with the security risk analysis engine, the intelligent decision module is connected to the processor module, the dynamic weighting module is connected to the feature fusion module, the visualization terminal is connected to the processor module, and the visualization terminal is connected to the smart safety helmet; The multimodal data acquisition module is used to collect various data of the installation construction site and transmit them to the edge computing node through the efficient transmission unit; The edge computing node is used to pre-process the received data; The feature extraction module extracts key features from the preprocessed data; The feature fusion module fuses the extracted key features and transmits the fused data to the processor module; The dynamic weighting module is used to assign weights to different data during feature fusion; The processor module performs risk analysis based on the security risk assessment model in the security risk analysis engine and the fused data to identify potential security risks; The intelligent decision-making module makes intelligent decisions based on the risk analysis results; The visual terminal is used to direct the evacuation of staff wearing the smart safety helmets.

2. The clean room electromechanical installation construction safety visualization monitoring system according to claim 1, characterized in that: The multimodal data acquisition unit includes an environmental parameter acquisition module, a personnel positioning and tracking module, an equipment status monitoring module, a video data acquisition module and a gas monitoring module; the environmental parameter acquisition module, the personnel positioning and tracking module, the equipment status monitoring module, the video data acquisition module and the gas monitoring module are all connected to the edge computing node.

3. The clean room electromechanical installation construction safety visualization monitoring system according to claim 2, characterized in that: The efficient transmission unit includes a compression module, a transmission module and a decompression module. The transmission modules are connected to the multimodal data acquisition unit and the edge computing node. The compression module and the decompression module are connected to the multimodal data acquisition unit and the edge computing node respectively.

4. The clean room electromechanical installation construction safety visualization monitoring system according to claim 3, characterized in that: The clean room electromechanical installation construction safety visualization monitoring system further includes a diffusion simulation module and a route optimization module, and the route optimization module is connected to the visualization terminal and the diffusion simulation module.

5. The clean room electromechanical installation construction safety visualization monitoring system according to claim 4, characterized in that: The clean room electromechanical installation construction safety visualization monitoring system further includes a data analysis module and a report generation module, wherein the data analysis module is connected to the processing module, and the report generation module is connected to the data analysis module; The data analysis module is used to perform in-depth analysis on the data received by the processor module, and to generate a detailed construction safety report and a quality assessment report through the report generation module.

6. The clean room electromechanical installation construction safety visualization monitoring system according to claim 5, characterized in that: The clean room electromechanical installation construction safety visualization monitoring system further includes a time-space alignment verification module, which is connected to the edge computing node; The time-space alignment verification module adopts the NTP-PTP hybrid clock synchronization protocol to eliminate the delay difference of multi-source data.

7. The clean room electromechanical installation construction safety visualization monitoring system according to claim 6, characterized in that: The clean room electromechanical installation construction safety visualization monitoring system further includes an adaptive optimization module, which is connected to the safety risk analysis engine.

8. The clean room electromechanical installation construction safety visualization monitoring system according to claim 7, characterized in that: The clean room electromechanical installation construction safety visualization monitoring system further comprises a linkage interaction module, and the linkage interaction module is connected to the fire protection system of the building where the clean room is located.

Images