Intelligent operation and maintenance management system and method for submarine tunnel

Abstract

The present invention belongs to the technical field of intelligent operation and maintenance management of tunnels, and relates to an intelligent operation and maintenance management system and method for submarine tunnels. The system includes: a GIS+BIM system, which combines BIM converted into a GIS compatible format with GIS map data to obtain BIM equipment component information, and converts BIM equipment component spatial coordinate information into GIS coordinate information; an Internet of Things system, which binds the collected real-time data of various types of equipment in the submarine tunnel with the BIM equipment component information, and generates and sends an early warning signal when the real-time data is judged to be abnormal; a tunnel operation and maintenance system, which manages equipment ledger information, faults and inspections; a big data analysis system, which generates an auxiliary management plan based on the prediction results of tunnel traffic flow, on-site environment and equipment failures; and a main control system, which receives, forwards and displays data sent by the four systems, and controls the execution of the auxiliary management plan. The present invention realizes multi-dimensional and full-coverage intelligent operation and maintenance management of submarine tunnels, and has high management efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent operation and maintenance management of tunnels, and relates to an intelligent operation and maintenance management system and method for submarine tunnels. Background Art

[0002] With the rapid development of global infrastructure construction, the number and scale of marine tunnels, as important transportation hubs connecting land and sea, are increasing. The construction technology of marine tunnels is relatively mature, but due to their special marine environment, tunnels face a series of complex problems during operation and maintenance. Common challenges include corrosion of tunnel structures, seawater infiltration, equipment aging, tunnel ventilation and drainage system failures, etc. These problems not only increase the maintenance cost of tunnels, but may also affect the safety and service life of tunnels. Therefore, how to efficiently and accurately operate and maintain marine tunnels has become a technical problem that the engineering community needs to solve urgently.

[0003] Traditional tunnel operation and maintenance management mainly relies on manual inspections and regular maintenance. Although this method can identify structural damage or equipment failures in the tunnel to a certain extent, manual inspections often have delays and limitations, and cannot obtain the operating status of the tunnel in real time. Especially for infrastructure such as marine tunnels that are complex and difficult to access, manual inspections are inefficient and pose certain safety risks. At the same time, traditional maintenance methods are mostly based on fixed-cycle inspections rather than the actual operating status of the tunnel, which may lead to problems of insufficient or excessive maintenance, further increasing operating costs.

[0004] With the continuous development of the Internet of Things, sensor technology and big data analysis technology, the intelligent trend of tunnel operation and maintenance management is gradually emerging. Modern intelligent operation and maintenance systems can monitor the key operating parameters of tunnels in real time by installing various sensors in the tunnel (such as structural health monitoring sensors, temperature and humidity sensors, stress sensors, etc.), and transmit the data to the operation and maintenance management platform for analysis and processing. This monitoring method based on the Internet of Things can greatly improve the accuracy and real-time performance of operation and maintenance, help to promptly discover potential problems and avoid major accidents.

[0005] However, the existing intelligent operation and maintenance system for marine tunnels still has certain deficiencies in information integration and management efficiency. For example, the existing system can usually only monitor a single or partial parameter of the tunnel, and cannot comprehensively monitor and predict the entire life cycle of the tunnel. In addition, how to organically combine BIM (Building Information Model), GIS (Geographic Information System) and Internet of Things technology to build a multi-dimensional, full-coverage intelligent operation and maintenance management system is still a difficulty in current technology. Especially for marine tunnels, which are affected by the marine environment such as humidity and corrosion, how to use intelligent technology for comprehensive and efficient operation and maintenance management still requires further research and innovation.

[0006] Therefore, the existing technology urgently needs a more complete intelligent operation and maintenance management system for marine tunnels. The system can integrate advanced technologies such as BIM, GIS, and the Internet of Things to conduct comprehensive real-time monitoring of the structural health, equipment status, and environmental parameters of the tunnel. Through big data analysis and prediction models, it can provide more accurate and efficient operation and maintenance management solutions to ensure the long-term safety and efficient operation of the tunnel. Summary of the invention

[0007] In view of the above-mentioned problems such as low management efficiency existing in the existing marine tunnel management process, the present invention provides an intelligent operation and maintenance management system and method for submarine tunnels. With the submarine tunnel BIM (Building Information Model) as the data basis, GIS (Geographic Information System) is organically combined with the Internet of Things technology and big data analysis technology to realize multi-dimensional and full-coverage intelligent operation and maintenance management of submarine tunnels, improve management efficiency, and ensure the long-term safe and efficient operation of submarine tunnels.

[0008] In a first aspect, the present invention provides an intelligent operation and maintenance management system for a submarine tunnel, comprising:

[0009] GIS+BIM system, which converts the undersea tunnel BIM into a GIS-compatible format through lightweight processing, combines the BIM in the GIS-compatible format with GIS map data to obtain BIM equipment component information, and converts the BIM equipment component spatial coordinate information into GIS coordinate information to obtain the actual geographic information of the BIM equipment components;

[0010] The IoT system is used to collect real-time data from various types of equipment in the submarine tunnel, clean, convert, store and analyze the real-time data of the equipment, bind the real-time data of the equipment with the BIM equipment component information, and generate and send early warning signals when the real-time data is judged to be abnormal;

[0011] Tunnel operation and maintenance system, used for subsea tunnel equipment ledger information management, fault management and inspection management;

[0012] Big data analysis system, used to predict tunnel traffic volume, on-site environment and equipment failures, and generate auxiliary management plans based on the prediction results;

[0013] The main control system is used to receive, forward, and display data sent by the GIS+BIM system, Internet of Things system, tunnel operation and maintenance system, and big data analysis system, and to control the execution of auxiliary management solutions.

[0014] In some embodiments, the GIS+BIM system includes:

[0015] The lightweight processing unit compresses, simplifies geometry, optimizes textures, controls level details, and converts the format of the submarine tunnel BIM to obtain a GIS-compatible BIM format;

[0016] GIS, for loading map data;

[0017] The fusion module aligns the BIM in GIS compatible format on the GIS map and combines it with the loaded map data to obtain BIM equipment component information;

[0018] The coordinate conversion module converts the spatial coordinate information of BIM equipment components into GIS coordinate information.

[0019] In some embodiments, the lightweight processing unit includes:

[0020] Compression module, which uses data compression algorithm to compress BIM equipment component information, remove redundant data, optimize data structure, and separate BIM equipment component information;

[0021] The optimization processing module simplifies BIM geometry, optimizes textures, and controls level details to reduce BIM complexity and rendering burden;

[0022] The format conversion module converts BIM into GIS compatible format through model conversion software.

[0023] In some embodiments, the IoT system includes:

[0024] Data acquisition module, used to collect real-time data from various devices in the submarine tunnel;

[0025] Data storage module, which cleans, converts and stores the collected real-time data of the equipment;

[0026] The data analysis module performs multi-dimensional calculation and analysis on the stored real-time data of the equipment, binds the real-time data of the equipment with the BIM equipment component information, and generates an early warning signal when the real-time data of the equipment is judged to be abnormal;

[0027] Data alarm module sends early warning signals to other related systems.

[0028] In some embodiments, the data storage module includes:

[0029] Data cleaning module, used to remove duplicate data, correct erroneous data, and fill in missing data values;

[0030] The data conversion module is used to convert data from the original format to a format suitable for storage in the HBase database.

[0031] In some embodiments, the data analysis module includes:

[0032] Time series analysis module, used to detect the trend of data over time;

[0033] Anomaly detection module, which compares historical data with real-time data, identifies abnormal values ​​or abnormal patterns, and generates alarm signals;

[0034] The association module is used to bind the real-time data of the equipment with the BIM equipment component information.

[0035] In some embodiments, the tunnel operation and maintenance system includes:

[0036] Equipment management module, used to manage the equipment ledger information of the submarine tunnel;

[0037] Fault management module, used for equipment fault reporting, fault repair and repair result acceptance;

[0038] The inspection management module is used to formulate and execute inspection tasks and report inspection progress and results.

[0039] In some embodiments, the big data analysis system includes:

[0040] The traffic flow prediction module predicts the traffic flow based on historical traffic flow data and generates a traffic flow auxiliary management plan based on the traffic flow prediction results;

[0041] The on-site environment prediction module predicts the on-site environment through the environmental prediction model based on the on-site environmental data, and generates an on-site environmental auxiliary management plan based on the on-site environmental prediction results;

[0042] The fault prediction module predicts the fault cycle based on the fault information through the fault prediction model, and generates a maintenance plan based on the fault cycle prediction results.

[0043] In some embodiments, the specific method by which the traffic flow prediction module predicts traffic flow based on historical traffic flow data is: cleaning, organizing and classifying historical data, and combining date and time tags, using statistical methods and machine learning methods to predict traffic flow.

[0044] In some embodiments, the main control system includes:

[0045] The main control module is used to receive and forward data sent by the GIS+BIM system, the Internet of Things system, the tunnel operation and maintenance system, and the big data analysis system, and to control the execution of auxiliary management solutions;

[0046] The display module is used to display data sent by the GIS+BIM system, the Internet of Things system, the tunnel operation and maintenance system, and the big data analysis system.

[0047] In a second aspect, the present invention provides an intelligent operation and maintenance management method for a submarine tunnel, which adopts the intelligent operation and maintenance management system for a submarine tunnel described in the first aspect of the present invention, and comprises the following steps:

[0048] Lightweight processing steps: lightweight processing of the undersea tunnel BIM into a GIS compatible format;

[0049] Fusion step: align the BIM in GIS compatible format on the GIS map and combine it with the map data to obtain BIM equipment component information;

[0050] Coordinate conversion step: convert the BIM equipment component space coordinate information into GIS coordinate information;

[0051] Data collection steps: collect real-time data from each device in the submarine tunnel;

[0052] Data storage steps: clean, convert and store the collected real-time data of the equipment;

[0053] Data analysis steps: Perform multi-dimensional calculation and analysis on the stored real-time data of the equipment, bind the real-time data of the equipment with the BIM equipment component information, and generate and send an early warning signal when the real-time data is judged to be abnormal;

[0054] Prediction step: predict tunnel traffic volume, on-site environment and equipment failures based on environmental data and fault information, and generate auxiliary management plans based on the prediction results;

[0055] Device management steps: Execute auxiliary management solutions to assist in managing tunnel devices.

[0056] Compared with the related art, the intelligent operation and maintenance management system and method for undersea tunnels provided by the present invention take the BIM (Building Information Model) of undersea tunnels as the data basis, combine BIM in GIS-compatible format with GIS map data to obtain BIM equipment component information, bind the real-time data of equipment collected by the Internet of Things with the BIM equipment component information, and endow the virtual building entity with a large amount of building information through the combination of BIM and GIS. Combined with the Internet of Things technology and big data analysis technology, the tunnel condition is predicted to realize multi-dimensional and full-coverage intelligent operation and maintenance management of undersea tunnels, improve management efficiency, and ensure the long-term safe and efficient operation of undersea tunnels. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0058] Figure 1 This is a structural block diagram of the intelligent operation and maintenance management system for a submarine tunnel according to an embodiment of the present invention;

[0059] Figure 2 This is a structural block diagram of the GIS+BIM system described in an embodiment of the present invention;

[0060] Figure 3 This is a structural block diagram of a lightweight processing unit according to an embodiment of the present invention;

[0061] Figure 4 This is a structural block diagram of the Internet of Things system according to an embodiment of the present invention;

[0062] Figure 5 This is a structural block diagram of the data storage module according to an embodiment of the present invention;

[0063] Figure 6 This is a structural block diagram of the data analysis module according to an embodiment of the present invention;

[0064] Figure 7 This is a structural block diagram of the tunnel operation and maintenance system according to an embodiment of the present invention;

[0065] Figure 8 This is a structural block diagram of the big data analysis system according to an embodiment of the present invention;

[0066] Fig. 9 is a structural block diagram of the main control system according to an embodiment of the present invention;

[0067] Fig.10 This is a flow chart of the intelligent operation and maintenance management method for an undersea tunnel according to an embodiment of the present invention.

[0068] In the figure: 1. GIS+BIM system, 11. Lightweight processing unit, 111. Compression module, 112. Optimization processing module, 113. Format conversion module, 12. GIS, 13. Fusion module, 14. Coordinate conversion module, 2. Internet of Things system, 21. Data acquisition module, 22. Data storage module, 221. Data cleaning module, 222. Data conversion module, 23. Data analysis module, 231. Time series analysis module, 232. Anomaly detection module, 233. Association module, 24. Data alarm module, 3. Tunnel operation and maintenance system, 31. Equipment management module, 32. Fault management module, 33. Inspection management module, 4. Big data analysis system, 41. Traffic flow prediction module, 42. On-site environment prediction module, 43. Fault prediction module, 5. Main control system, 51. Main control module, 52. Display module. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0070] Obviously, the drawings described below are only some examples or embodiments of the present invention. For ordinary technicians in this field, the present invention can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed by the present invention, some changes in design, manufacturing or production based on the technical content disclosed by the present invention are just conventional technical means, and should not be understood as insufficient content disclosed by the present invention.

[0071] Reference to "embodiments" in the present invention means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in the present invention may be combined with other embodiments without conflict.

[0072] Unless otherwise defined, the technical terms or scientific terms involved in the present invention shall have the usual meanings understood by persons with ordinary skills in the technical field to which the present invention belongs. The words "one", "a", "a", "the" and the like involved in the present invention do not indicate a quantity limitation and may indicate the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in the present invention are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device including a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in the present invention are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in the present invention refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships. For example, "A and / or B" may indicate: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects in the front and back relation are in an "or" relationship. The terms "first", "second", "third" and the like involved in the present invention are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0073] An embodiment of the present invention provides an intelligent operation and maintenance management system for a submarine tunnel. Figure 1 : is a structural block diagram of an intelligent operation and maintenance management system for a submarine tunnel according to an embodiment of the present invention. Figure 1As shown, the intelligent operation and maintenance management system of the undersea tunnel includes a GIS+BIM system 1, an Internet of Things system 2, a tunnel operation and maintenance system 3, a big data analysis system 4 and a main control system 5.

[0074] The GIS+BIM system 1 is used to convert the submarine tunnel BIM into a GIS compatible format through lightweight processing, combine the BIM in the GIS compatible format with the GIS map data to obtain the BIM equipment component information, and convert the BIM equipment component spatial coordinate information into GIS coordinate information to obtain the actual geographic information of the BIM equipment component. Figure 2 The GIS+BIM system 1 includes a lightweight processing unit 11, a GIS 12, a fusion module 13 and a coordinate conversion module 14.

[0075] The lightweight processing unit 1 compresses, simplifies geometry, optimizes texture, controls hierarchical details, and converts the format of the submarine tunnel BIM to obtain a BIM in a GIS-compatible format. The data lightweight processing unit 11 uses data compression, optimization processing, and format conversion to achieve lightweight models and seamless integration with GIS.

[0076] In some embodiments, see Figure 3 The lightweight processing unit 11 includes a compression module 111, an optimization processing module 112 and a format conversion module 113.

[0077] The compression module 111 compresses the BIM equipment component information by using a data compression algorithm, removes redundant data, optimizes the data structure, separates the BIM equipment component information, and reduces the size of the BIM file.

[0078] The optimization processing module 112 performs geometric simplification, texture optimization, and hierarchical detail control on BIM to reduce the complexity and rendering burden of BIM. Specifically, the optimization processing module 112 simplifies the BIM geometric structure. For example, for complex three-dimensional components, some details that have little impact on the final presentation are deleted or merged to reduce the number of unnecessary polygons and vertices. Commonly used geometric simplification algorithms include polygon mesh simplification (such as Quadric Decimation) or LOD (level of detail) generation methods. The geometric simplification operation ensures that the calculation amount and storage requirements of BIM are reduced without losing key information, and the loading speed and rendering efficiency of BIM are improved. The optimization processing module 112 optimizes the texture information of BIM. For example, by reducing the use of high-resolution textures, compression or resolution reduction methods are used to reduce the size of the texture, thereby reducing the memory and computing resources required for rendering. At the same time, for small or unimportant geometric elements, simple colors or low-resolution textures can be selected to reduce the rendering burden. The optimization processing module 112 controls the different levels of details of BIM. LOD technology is used to automatically adjust the rendering complexity of BIM according to the viewing distance and usage scenario: a high-detail model is displayed when the user is close, and a simplified model is used when the user is far away or the viewing angle is not important. Through the LOD mechanism, the optimization processing module 112 can dynamically adjust the rendering level of the model to balance performance and visual effects, which is particularly suitable for complex BIM of large buildings or infrastructure.

[0079] The format conversion module 113 converts the BIM into a GIS compatible format through a model conversion software. Specifically, the format conversion module 113 converts the undersea tunnel BIM model from rvt format to gltf format through the professional model conversion software Autodesk Revit. The format conversion module 113 can use different conversion tools or software, depending on the specific needs and application scenarios. For example, in addition to Autodesk Revit, you can also use FME, Blender or a custom conversion script to achieve the conversion from rvt format to gltf or other GIS compatible formats (such as CityGML, KML). In addition, the conversion process can also support the export of the model to other lightweight formats such as OBJ or Collada according to the requirements of different GIS to meet the data display and processing requirements in specific applications.

[0080] GIS 12 is used to load map data. Specifically, GIS 12 causes map data to be loaded through local GIS. The map data may be an offline electronic map or a geographic information data package, which needs to be obtained from a third-party geographic information supplier.

[0081] The fusion module 13 aligns the BIM in GIS compatible format on the GIS map, and combines it with the loaded map data to obtain BIM equipment component information, so that users can intuitively view and manage BIM equipment component information on the GIS map.

[0082] The coordinate conversion module 14 converts the BIM equipment component spatial coordinate information into GIS coordinate information. The BIM equipment component spatial coordinates are converted into GIS coordinates to obtain the actual geographical location information of each device in the submarine tunnel.

[0083] The IoT system 2 is used to collect real-time data from various devices in the submarine tunnel, clean, convert, store and analyze the real-time data of the devices, bind the real-time data of the devices with the BIM device component information, and generate and send early warning signals when the real-time data is judged to be abnormal. Figure 4 The Internet of Things system 2 includes a data acquisition module 21, a data storage module 22, a data analysis module 23 and a data alarm module 24.

[0084] The data acquisition module 21 is used to collect real-time data of various devices in the submarine tunnel. Specifically, the data acquisition module collects real-time data of various devices in the submarine tunnel in real time through the modbus protocol, including real-time data of key devices such as fans, traffic lights, information boards, wind speed and direction sensors, flame detectors, temperature detectors, smoke detectors, CO / VI sensors, and light intensity sensors.

[0085] The data storage module 22 cleans, converts and stores the collected real-time data of the equipment.

[0086] In some embodiments, see Figure 5 The data storage module 22 includes a data cleaning module 221 and a data conversion module 222. The data cleaning module 221 is used to remove duplicate data, correct erroneous data, and fill in missing data values ​​to ensure the accuracy and integrity of the data. The data conversion module 222 is used to convert data from the original format to a format suitable for storage in the HBase database, for example, to structure and standardize the data for subsequent data analysis and processing.

[0087] The data analysis module 23 performs multi-dimensional calculation analysis on the stored real-time equipment data, binds the real-time equipment data with the BIM equipment component information, and generates an early warning signal when it is determined that the real-time equipment data is abnormal.

[0088] In some embodiments, see Figure 6The data analysis module 23 includes a time series analysis module 231, an anomaly detection module 232 and an association module 233. The time series analysis module 231 is used to detect the change trend of data over time. The anomaly detection module 232 is used to compare historical data and real-time data, identify abnormal values ​​or abnormal patterns, and generate alarm signals. The association module 233 is used to bind the real-time data of the equipment with the BIM equipment component information.

[0089] Specifically, the time series analysis module 231 records the timestamp of the device data and processes the data using mathematical algorithms (such as moving average, exponential smoothing, etc.) according to the data change trend within the time interval to identify the change trend of the data over time. Through time series analysis, users can better understand the history and trend of device operation and facilitate the prediction of future device operation status.

[0090] Specifically, the anomaly detection module 232 compares historical data with real-time data and uses statistical methods or machine learning models (such as Z-score detection, isolated forest, etc.) to find possible anomalies, helping to identify abnormal data that deviates from normal data and its distribution. After identifying the anomaly, the module generates an alarm signal so that the user can take timely measures. The anomaly detection module 232 can effectively identify potential equipment failures or abnormal operations, ensure the safe operation of the equipment, and reduce the failure rate. By generating an alarm signal, the user can be notified in time, enabling him to take maintenance measures before the equipment problem expands.

[0091] Specifically, the association module 233 binds the real-time data of the equipment with the equipment component information in the BIM through the pre-set equipment information or sensor ID, realizing a two-way data flow between the BIM and the actual equipment, so that the operation status of the equipment can be intuitively reflected in the BIM. By binding the real-time data of the equipment with the BIM, the association module 233 enhances the visualization and management efficiency of the data, enabling users to monitor the operation status of the equipment in real time in the BIM, thereby optimizing the equipment management and decision-making process.

[0092] The data alarm module 24 sends the warning signal to other related systems (e.g., equipment monitoring system, emergency management system, tunnel operation and maintenance system, and main control system). For example, when the data analysis module determines that the fan is abnormal, the data alarm module will send the warning information to the main control module and the tunnel operation and maintenance system so that relevant personnel can quickly troubleshoot and deal with the emergency, and display the warning information so that the management can understand the situation in real time and make decisions.

[0093] The tunnel operation and maintenance system 3 is used for the management of equipment records, faults and inspections in submarine tunnels, and mainly handles the daily operation and maintenance inspections of submarine tunnel equipment. Figure 7The tunnel operation and maintenance system 3 includes a device management module 31, a fault management module 32 and an inspection management module 33.

[0094] The equipment management module 31 is used to manage the equipment ledger information of the submarine tunnel, including equipment registration, detailed information recording, operation turntable tracking and maintenance history update, etc., to facilitate query and statistics, so that users can grasp the detailed information and overall situation of the equipment at any time.

[0095] The fault management module 32 is used for equipment fault reporting, fault repair and repair result acceptance. Once a device fails, the on-site personnel or inspection personnel can report the fault information to the main control system 5 through the fault management module 32, and the main control system 5 will send the fault information to the maintenance personnel or maintenance department to ensure that the fault can be handled in time. After the maintenance is completed, the maintenance personnel will send the maintenance results to the main control system 5 through the fault management module 32, and the main control system 5 will send it to the acceptance personnel for acceptance to ensure that the equipment resumes normal operation.

[0096] The inspection management module 33 is used to formulate and execute inspection tasks, and report the inspection progress and inspection results. Specifically, users can flexibly set inspection tasks through the inspection management module 33 according to the actual situation of the submarine tunnel project, including the equipment, content, time and personnel of the inspection. The inspection personnel conduct on-site inspections according to the task requirements and report the inspection results to the relevant managers or management departments in a timely manner through the inspection management module 33, so that the managers can understand the progress and results of the inspection at any time, providing strong support for the daily operation and maintenance of the tunnel.

[0097] The big data analysis system 4 is used to predict tunnel traffic volume, on-site environment and equipment failures, and generate auxiliary management plans based on the prediction results, providing comprehensive data support for undersea tunnel management, effectively reducing resource waste, optimizing the scheduling of personnel and equipment, and thus reducing operating costs. Figure 8 The big data analysis system 4 includes a traffic flow prediction module 41, a site environment prediction module 42 and a fault prediction module 43.

[0098] The traffic flow prediction module 41 predicts the traffic flow according to the historical traffic flow data, and generates a traffic flow auxiliary management plan according to the traffic flow prediction results. Specifically, the traffic flow prediction module 41 conducts in-depth analysis of the tunnel's historical traffic flow data, including data cleaning (removing outliers and noise data), sorting (arranging in chronological order) and classification (classifying according to different date and time tags, such as working days, holidays, bad weather, etc.), and combines date and time tags, such as working days, holidays, bad weather, rush hour information, etc., to use statistical methods (time series analysis, regression analysis) and machine learning methods (such as support vector machines, random forests, LSTM, etc.) to accurately predict the tunnel traffic flow and assist in tunnel management decisions. Time series analysis can capture the periodic changes in traffic flow, regression analysis can find the relationship between traffic flow and external factors (such as weather, holidays), and machine learning methods can learn prediction rules from complex historical data. By analyzing the influence of different time periods (such as rush hour) and external environment, more accurate traffic flow prediction results can be generated. Based on the traffic flow prediction results, a traffic flow auxiliary management plan is generated to assist tunnel managers in making intelligent management decisions, helping managers to better plan traffic flow, reduce congestion and improve traffic efficiency, effectively reduce resource waste and reduce operating costs. For example, when it is predicted that the traffic flow during peak hours will be large, more traffic diversion personnel can be arranged in advance or the signal light timing can be adjusted to effectively alleviate traffic pressure.

[0099] The on-site environment prediction module 42 predicts the on-site environment through the environmental prediction model according to the on-site environmental data, and generates an on-site environmental auxiliary management plan according to the on-site environmental prediction results. The on-site environment prediction module 42 collects data such as CO / VI sensors, wind speed and volume, vehicle flow, and tunnel fan operation, and performs preprocessing (including data cleaning, denoising, and normalization) to ensure the accuracy and consistency of the data. After preprocessing, a multivariate regression analysis is used to establish an environmental prediction model based on a neural network. The multivariate regression analysis is used to model the relationship between multiple variables, such as the impact of CO concentration, wind speed and volume, and vehicle flow on the air quality in the tunnel. By analyzing the synergistic effect of these variables, a preliminary environmental prediction model is constructed to describe the impact of each variable on the tunnel environment. The neural network is used for machine learning. By continuously updating and training the environmental prediction model model, the future environmental conditions in the tunnel (such as air quality, visibility changes, etc.) can be predicted in real time to help tunnel managers control the environment and make decisions. The environmental prediction model can monitor the tunnel environment in real time and adjust the operation strategy of the tunnel fan according to the model prediction results, such as adjusting the wind speed, wind direction, or switching the machine on and off, to ensure that the air environment in the tunnel remains in a good state.

[0100] The fault prediction module 43 predicts the fault cycle according to the fault information through the fault prediction model, and generates a maintenance plan according to the fault cycle prediction result. Specifically, the fault prediction module 43 conducts an in-depth analysis of the fault information reported by the operation and maintenance management system, including the type of fault, occurrence time, repair time, impact range, etc., and uses machine learning algorithms such as random forests and neural networks to accurately predict the equipment's fault cycle, providing strong support for the operation and maintenance inspection of massive equipment in the tunnel. For example, when it is predicted that a certain type of equipment is about to enter a high-incidence period of faults, preventive maintenance or replacement of this type of equipment can be arranged in advance, thereby effectively avoiding the occurrence of faults and ensuring the normal operation of the tunnel.

[0101] The main control system 5 is used to receive, forward, and display data sent by the GIS+BIM system, the Internet of Things system, the tunnel operation and maintenance system, and the big data analysis system, and to control the execution of auxiliary management solutions. Overall, the use of the main control system 5 can effectively reduce the workload of tunnel management and improve operational efficiency and safety. Fig. 9 The main control system 5 includes a main control module 51 and a display module 52 .

[0102] The main control module 51 is used to receive and forward data sent by the GIS+BIM system, the Internet of Things system, the tunnel operation and maintenance system, and the big data analysis system, and to control the execution of auxiliary management plans. The main control module 51 is the control center of the entire undersea tunnel intelligent operation and maintenance management system, and is responsible for receiving various types of data from the GIS+BIM system, the Internet of Things system, the tunnel operation and maintenance system, and the big data analysis system. These data may include real-time equipment status, geographic location information, tunnel maintenance records, and analyzed and processed traffic and environmental data. The main control module 51 processes and forwards the received data according to preset rules and strategies, such as distributing key data to various subsystems, or triggering automated management measures based on data changes. When certain conditions are met, the main control module 51 can also execute corresponding auxiliary management plans, such as optimizing the ventilation system in the tunnel, adjusting traffic flow, triggering the alarm system, or notifying maintenance personnel. By integrating the data and management functions of multiple systems, the main control module 51 ensures the intelligence and efficiency of tunnel operation and maintenance management.

[0103] The display module 52 is used to display the data sent by the GIS+BIM system, the Internet of Things system, the tunnel operation and maintenance system, and the big data analysis system. The display module 52 works in conjunction with the main control module 51 and is responsible for displaying the various types of data received on the visual interface. The display module 52 will display the equipment component information in the GIS+BIM system, the real-time data of the sensors in the Internet of Things system, the management status of the tunnel operation and maintenance system, and the traffic flow prediction or environmental prediction results generated by the big data analysis system. Through intuitive graphical display, managers can monitor the operating status and environmental parameters of various equipment in the tunnel in real time to assist decision-making and management.

[0104] The above-mentioned intelligent operation and maintenance management system for undersea tunnels in the embodiment of the present invention combines BIM and GIS to give virtual building entities a large amount of building information, combines Internet of Things technology and big data analysis technology to predict tunnel conditions, and realizes multi-dimensional and full-coverage intelligent operation and maintenance management of undersea tunnels, improves management efficiency, and ensures long-term safe and efficient operation of undersea tunnels.

[0105] The second embodiment of the present invention provides a method for intelligent operation and maintenance management of an undersea tunnel, which adopts the intelligent operation and maintenance management system of an undersea tunnel described in the first embodiment of the present invention. Fig.10 : is a flow chart of an intelligent operation and maintenance management method for a submarine tunnel according to an embodiment of the present invention, and the steps of the intelligent operation and maintenance management method for a submarine tunnel include:

[0106] S1. Lightweight processing steps: convert the submarine tunnel BIM into a GIS compatible format through lightweight processing. Through lightweight processing, the file size is reduced, the loading and operation efficiency of BIM in GIS is improved, and the equipment component information in BIM is fully preserved.

[0107] S2, Fusion step: Align the BIM in GIS compatible format on the GIS map and combine it with the map data to obtain BIM equipment component information. During the alignment process, the accurate fusion of BIM and GIS maps is ensured based on geographic coordinates or the geographic features of the tunnel. After combining with the map data, the BIM equipment component information can be directly accessed from GIS, including the specific location, attributes and status of the equipment.

[0108] S3, coordinate conversion step: convert the spatial coordinate information of BIM equipment components into GIS coordinate information. BIM usually uses a local coordinate system, while GIS uses a global or regional coordinate system (such as WGS84), so coordinate system conversion is required. This step is implemented through the coordinate conversion module to ensure the accurate positioning of BIM equipment components on the GIS map.

[0109] S4, data collection step: collect real-time data of each device in the submarine tunnel.

[0110] S5, data storage step: clean, convert and store the collected real-time data of the equipment.

[0111] S6. Data analysis step: Perform multi-dimensional calculation and analysis on the stored real-time data of the equipment, bind the real-time data of the equipment with the BIM equipment component information, and generate and send an early warning signal when the real-time data is judged to be abnormal, so as to intervene in time.

[0112] S7, prediction step: predict tunnel traffic volume, on-site environment and equipment failure based on environmental data and fault information, and generate auxiliary management plans based on the prediction results.

[0113] S8, equipment management step: executing the auxiliary management plan to assist in the management of tunnel equipment.

[0114] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0115] The above-mentioned intelligent operation and maintenance management method for undersea tunnels in the embodiment of the present invention combines BIM and GIS to give virtual building entities a large amount of building information, combines Internet of Things technology and big data analysis technology to predict tunnel conditions, and realizes multi-dimensional and full-coverage intelligent operation and maintenance management of undersea tunnels, improves management efficiency, and ensures long-term safe and efficient operation of undersea tunnels.

[0116] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An intelligent operation and maintenance management system for a submarine tunnel, characterized in that: include: GIS+BIM system, which converts the submarine tunnel BIM into a GIS-compatible format through lightweight processing, combines the BIM in a GIS-compatible format with GIS map data based on geographic coordinates or tunnel geographic features to obtain BIM equipment component information, and converts the BIM equipment component spatial coordinate information into GIS coordinate information to obtain the actual geographic information of the BIM equipment components; The IoT system is used to collect real-time data of various devices in the submarine tunnel through the Modbus protocol, including fans, traffic lights, information boards, wind speed and direction sensors, flame detectors, temperature detectors, smoke detectors, CO / VI sensors, and light intensity sensors; clean, convert, store and analyze the real-time data of the devices, bind the real-time data of the devices with the BIM device component information through pre-set device information or sensor ID, form a two-way data flow between BIM and actual devices, and make the operating status of the devices intuitively reflected in BIM; Generate and send warning signals when real-time data is judged to be abnormal; record the timestamp of device data, and use mathematical algorithms to process device data according to the device data change trend within the time interval to identify the device data change trend over time; Tunnel operation and maintenance system, which is used for equipment ledger information management, fault management and inspection management of submarine tunnels. Equipment ledger information management includes equipment registration, detailed information recording, operation status tracking and maintenance history update. Fault management includes equipment fault reporting, fault repair and maintenance result acceptance. Inspection management includes specifying and executing inspection tasks, reporting inspection progress and inspection results. The big data analysis system is used to predict tunnel traffic volume, on-site environment and equipment failures, and generate auxiliary management plans based on the prediction results. When predicting traffic volume, the historical traffic volume data of the tunnel is cleaned to remove outliers and noise data, arranged in chronological order and classified according to different date tags. In combination with date and time tags, the time series analysis method is used to capture the periodic changes in traffic volume, the regression analysis method is used to find out the relationship between traffic volume and external factors, and the machine learning method is used to learn the prediction rules from historical traffic volume data. When predicting the on-site environment, after the collected on-site environmental data is cleaned, denoised and normalized, the environmental prediction model is established based on the neural network using multivariate regression analysis. The environmental conditions in the tunnel are monitored in real time through the trained environmental prediction model. When building the model, multivariate regression analysis is used to model the relationship between multiple environmental variables. The preliminary environmental prediction model is constructed through the synergistic effect of various environmental variables to describe the impact of various variables on the tunnel environment. The environmental prediction model is continuously updated and trained using neural networks for machine learning. The main control system is used to receive, forward, and display data sent by the GIS+BIM system, Internet of Things system, tunnel operation and maintenance system, and big data analysis system, and to control the execution of auxiliary management solutions.

2. The intelligent operation and maintenance management system for a submarine tunnel as claimed in claim 1, characterized in that: The GIS+BIM system includes: The lightweight processing unit compresses, simplifies geometry, optimizes textures, controls level details, and converts the format of the submarine tunnel BIM to obtain a GIS-compatible BIM format; GIS, for loading map data; The fusion module aligns the BIM in GIS compatible format on the GIS map and combines it with the loaded map data to obtain BIM equipment component information; The coordinate conversion module converts the spatial coordinate information of BIM equipment components into GIS coordinate information.

3. The intelligent operation and maintenance management system for a submarine tunnel as claimed in claim 2, characterized in that: The lightweight processing unit comprises: Compression module, which uses data compression algorithm to compress BIM equipment component information, remove redundant data, optimize data structure, and separate BIM equipment component information; The optimization processing module simplifies BIM geometry, optimizes textures, and controls level details to reduce BIM complexity and rendering burden; The format conversion module converts BIM into GIS compatible format through model conversion software.

4. The intelligent operation and maintenance management system for a submarine tunnel as claimed in claim 1, characterized in that: The Internet of Things system includes: Data acquisition module, used to collect real-time data from various devices in the submarine tunnel; Data storage module, which cleans, converts and stores the collected real-time data of the equipment; The data analysis module performs multi-dimensional calculation and analysis on the stored real-time data of the equipment, binds the real-time data of the equipment with the BIM equipment component information, and generates an early warning signal when the real-time data of the equipment is judged to be abnormal; Data alarm module sends early warning signals to other related systems.

5. The intelligent operation and maintenance management system for a submarine tunnel as claimed in claim 4, characterized in that: The data storage module comprises: Data cleaning module, used to remove duplicate data, correct erroneous data, and fill in missing data values; The data conversion module is used to convert data from the original format to a format suitable for storage in the HBase database; The data analysis module includes: Time series analysis module, used to detect the trend of data over time; Anomaly detection module, which compares historical data with real-time data, identifies abnormal values ​​or abnormal patterns, and generates alarm signals; The association module is used to bind the real-time data of the equipment with the BIM equipment component information.

6. The intelligent operation and maintenance management system for a submarine tunnel as claimed in claim 1, characterized in that: The tunnel operation and maintenance system comprises: Equipment management module, used to manage the equipment ledger information of the submarine tunnel; Fault management module, used for equipment fault reporting, fault repair and repair result acceptance; The inspection management module is used to formulate and execute inspection tasks and report inspection progress and results.

7. The intelligent operation and maintenance management system for a submarine tunnel as claimed in claim 1, characterized in that: The big data analysis system comprises: The traffic flow prediction module predicts the traffic flow based on historical traffic flow data and generates a traffic flow auxiliary management plan based on the traffic flow prediction results; The on-site environment prediction module predicts the on-site environment through the environmental prediction model based on the on-site environmental data, and generates an on-site environmental auxiliary management plan based on the on-site environmental prediction results; The fault prediction module predicts the fault cycle based on the fault information through the fault prediction model, and generates a maintenance plan based on the fault cycle prediction results.

8. The intelligent operation and maintenance management system for a submarine tunnel as claimed in claim 1, characterized in that: The main control system comprises: The main control module is used to receive and forward data sent by the GIS+BIM system, the Internet of Things system, the tunnel operation and maintenance system, and the big data analysis system, and to control the execution of auxiliary management solutions; The display module is used to display data sent by the GIS+BIM system, the Internet of Things system, the tunnel operation and maintenance system, and the big data analysis system.

9. An intelligent operation and maintenance management method for a submarine tunnel, using the intelligent operation and maintenance management system for a submarine tunnel as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: Lightweight processing steps: lightweight processing of the undersea tunnel BIM into a GIS compatible format; Fusion step: align the BIM in GIS compatible format on the GIS map and combine it with the map data to obtain BIM equipment component information; Coordinate conversion step: convert the BIM equipment component space coordinate information into GIS coordinate information; Data collection steps: collect real-time data from each device in the submarine tunnel; Data storage steps: clean, convert and store the collected real-time data of the equipment; Data analysis steps: Perform multi-dimensional calculation and analysis on the stored real-time data of the equipment, bind the real-time data of the equipment with the BIM equipment component information, and generate and send an early warning signal when the real-time data is judged to be abnormal; Prediction step: predict tunnel traffic volume, on-site environment and equipment failures based on environmental data and fault information, and generate auxiliary management plans based on the prediction results; Device management steps: Execute auxiliary management solutions to assist in managing tunnel devices.

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