Underground comprehensive pipe gallery intelligent operation and maintenance management system based on BIM

Abstract

The invention provides an underground comprehensive pipe gallery intelligent operation and maintenance management system based on BIM, and relates to the technical field of building information, and the system comprises a three-dimensional integration module which is used for constructing a three-dimensional digital integration platform of an underground comprehensive pipe gallery at a cloud end, uploading full life cycle data, ownership unit information and real-time operation parameters of the communication pipeline to a cloud central database, distributing a unique code for each pipeline unit, and establishing a corresponding cloud index relationship; and the space correction module is used for presetting three positioning monitoring nodes at the main inlet of the pipe gallery, the ventilation key node and the pipeline intersection based on the three-dimensional digital integration platform, constructing a stable triangular positioning monitoring architecture, calculating the geometric center and side length relation of the monitoring architecture through the three-dimensional coordinates of the three positioning monitoring nodes, and correcting the three-dimensional coordinates of the three positioning monitoring nodes according to the three-dimensional coordinates of the three positioning monitoring nodes. And generating a space topology correction value. According to the invention, the safety of the operation and maintenance of the underground comprehensive pipe gallery is realized by integrating the whole life cycle data of the pipe gallery, spatial positioning, intelligent risk early warning and multi-department cooperative linkage.

Description

Technical Field

[0001] The present invention relates to the field of building information technology, and in particular to a BIM-based intelligent operation and maintenance management system for underground integrated pipe corridors. Background Art

[0002] The limitations of traditional technologies are becoming increasingly apparent in the daily operation and maintenance of underground utility corridors. Many O&M projects still rely on paper drawings and Excel spreadsheets to record pipeline information, a management approach that presents a host of issues. For example, during an inspection, staff discovered a minor leak in a section of power pipeline. However, the paper drawings did not clearly indicate the pipeline's route and material, and the Excel spreadsheets had not been updated with recent renovations in the area. This made it impossible to quickly assess the leak's potential impact on surrounding communication and drainage pipelines, significantly delaying troubleshooting and repairs.

[0003] Furthermore, the shortcomings of traditional technology are particularly evident when faced with sudden failures. For example, during a winter cold snap, a heating pipe in the tunnel ruptured due to a sudden drop in temperature, gushing out a large amount of hot water. Traditional manual inspections and telephone reporting methods are inefficient, and there is a lack of effective collaborative communication between departments. Upon arrival, maintenance personnel, unable to visually visualize the tunnel's three-dimensional structure, struggled to accurately assess the impact of maintenance work on nearby gas pipelines. Repeated communication and coordination during the development of the repair plan further delayed repairs, ultimately resulting in a 12-hour heating outage in several surrounding communities. This caused inconvenience to residents and exposed the shortcomings of traditional operation and maintenance management technologies. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent operation and maintenance management system for underground comprehensive pipeline corridors based on BIM, which integrates the entire life cycle data of the pipeline corridor and realizes dynamic management of pipeline information and rapid fault location.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] First, a BIM-based intelligent operation and maintenance management system for underground integrated pipe corridors, including:

[0007] The 3D integration module is used to build a 3D digital integration platform for underground integrated pipeline corridors in the cloud. It uploads the full life cycle data, ownership information, and real-time operating parameters of communication pipelines to the cloud central database, assigns a unique code to each pipeline unit, and establishes a corresponding cloud index relationship.

[0008] The spatial correction module is used to preset three positioning monitoring nodes at the main entrance of the tunnel, key ventilation nodes, and pipeline intersections based on a three-dimensional digital integration platform to build a stable triangulated positioning monitoring architecture. The geometric center and side length relationship of the monitoring architecture are calculated using the three-dimensional coordinates of the three positioning monitoring nodes to generate spatial topology correction values.

[0009] Build an early warning module to construct a dynamic network based on spatial topology correction values, update pipeline layout data in real time, and trigger collision risk warnings;

[0010] The positioning decision module integrates the real-time pipeline corridor environment data collected by IoT sensors with the 3D digital integration platform. When leaks, temperature, and pressure anomalies are detected, the module automatically locates the anomaly based on the correction values ​​of the monitoring area and the 3D coordinates of the pipeline using the triangulated positioning monitoring architecture, generating a decision dataset that includes emergency operation instructions.

[0011] The closed-loop response module is used to simulate the distribution of obstacles on the emergency repair path based on the decision data set through spatial topology correction values, generate the final cloud-based detour plan in combination with real-time equipment status data, and simultaneously issue linkage instructions to fire-fighting equipment, ventilation equipment, and remote valve control terminals.

[0012] Furthermore, a three-dimensional digital integration platform for underground integrated pipeline corridors is built on the cloud. The full life cycle data, ownership information, and real-time operating parameters of communication pipelines are uploaded to the cloud central database. A unique code is assigned to each pipeline unit, and a corresponding cloud index relationship is established, including:

[0013] In the cloud-based central database, independent data storage blocks are divided according to ownership units, pipeline types, and operation and maintenance stages. Design parameters, construction acceptance data, and past operation and maintenance records of communication pipelines are classified and stored in corresponding blocks.

[0014] Based on the data block division rules, a cloud-based coding service generates a code for each pipeline unit, including the ownership unit identifier, pipeline type code, and three-dimensional coordinate feature values. A bidirectional index relationship table with time series labels is established between the code and the design parameters and construction acceptance data of the corresponding block.

[0015] According to the timing tags of the bidirectional index relationship table, the operating parameters uploaded by the pipeline sensor are received in real time through the cloud data channel, and the parameter values ​​are matched with the corresponding pipeline codes and entered into the dynamic fields of the index relationship table, and the status identification and attribute parameters of the pipeline in the three-dimensional digital integration platform are synchronously updated.

[0016] Furthermore, based on the 3D digital integration platform, three positioning monitoring nodes are preset at the main entrance of the tunnel, key ventilation nodes, and pipeline intersections to build a stable triangulated positioning monitoring architecture. The geometric center and side length relationship of the monitoring architecture are calculated using the 3D coordinates of the three positioning monitoring nodes to generate spatial topology correction values, including:

[0017] Based on the pipeline layout data and ownership information stored in the 3D digital integration platform, a first positioning monitoring node is deployed at the main entrance of the tunnel to monitor the flow rate and structural stability of the in-and-out pipelines. A second positioning monitoring node is deployed at key ventilation nodes to collect air quality, temperature, humidity, and wind speed data. A third positioning monitoring node is deployed at the intersection of power, water, and gas pipelines to monitor pressure and displacement changes in the multi-pipeline interaction area.

[0018] Based on the three-dimensional coordinate data of the three positioning monitoring nodes, the geometric center coordinates of the triangulated positioning monitoring architecture area are calculated, and based on the difference between the maximum and minimum values ​​of the distance between adjacent nodes, the initial topological parameters representing the pipeline distribution density are generated;

[0019] Combined with the actual pipeline burial depth data and pipeline type distribution characteristics stored in the central database, the initial topology parameters are weightedly corrected to generate spatial topology correction values ​​that reflect the pipeline crossing depth, safety spacing and risk level of hidden areas.

[0020] Furthermore, a dynamic network is constructed based on the spatial topology correction value, which updates pipeline layout data in real time and triggers collision risk warnings, including:

[0021] Based on the spatial topology correction value, pipeline crossing depth, safety spacing and hidden area risk level data are extracted. The corrected spatial topology parameters are combined with the pipeline geometry and connection relationship data in the 3D digital integration platform to construct a dynamic network structure.

[0022] Based on the pipeline geometry, material properties, and real-time operating parameters in the dynamic network structure, the pipeline layout is dynamically rendered in the 3D digital integration platform, dynamically displaying the intersection depth, spacing distribution, and hidden area details;

[0023] Preset pipeline intersection angle safety thresholds and adjacent pipeline spacing safety thresholds. When the real-time rendered pipeline intersection angle is less than the threshold, a collision risk warning signal is triggered.

[0024] The risk level is divided according to the degree of deviation from the threshold, and early warning information is generated according to the risk level, including abnormal location coordinates, pipeline code, risk description and recommended operations.

[0025] Furthermore, the real-time data collected by IoT sensors on the pipeline corridor environment is integrated with the 3D digital integration platform. When leaks, temperature, and pressure anomalies are detected, the specific location of the anomaly is automatically calibrated based on the correction value of the monitoring area and the 3D coordinates of the pipeline in the triangulated positioning monitoring architecture. This generates a decision-making data set with emergency operation instructions, including:

[0026] Receive the real-time data streams of leakage concentration, temperature change rate, and pressure gradient uploaded by IoT sensors within the monitoring area of ​​the triangulated positioning monitoring architecture. Align the sensor data with the three-dimensional coordinates of the pipeline on the three-dimensional digital integration platform through the cloud data engine to generate a spatially correlated abnormal data set.

[0027] Based on the pipeline intersection depth parameter in the spatial topology correction value, spatial compensation calculation is performed on the coordinates in the abnormal data set to obtain the corrected three-dimensional coordinates of the abnormal point and the influence radius;

[0028] Based on the corrected outlier coordinates, a reverse search is performed on a bidirectional index relationship table with time series labels to extract the encrypted identifier of the corresponding pipeline unit, pipeline material parameters from the construction acceptance data, and the timeliness of similar fault handling from past operation and maintenance records. This constructs an outlier feature dataset that includes pipeline failure levels and emergency response timeliness thresholds.

[0029] Based on the abnormal point feature dataset, the preset association rules between pipeline material and leakage diffusion rate, and the comparison relationship between temperature threshold and ventilation efficiency in the cloud central database are called to generate a decision dataset for emergency operation instructions.

[0030] Furthermore, based on the corrected outlier coordinates, a reverse search is performed on the bidirectional index relationship table with time series labels to extract the encrypted identifier of the corresponding pipeline unit, the pipeline material parameters in the construction acceptance data, and the timeliness of similar fault handling in past operation and maintenance records. This constructs an outlier feature dataset that includes pipeline failure levels and emergency response timeliness thresholds, including:

[0031] Based on the compressive strength, corrosion resistance and past leakage records of the pipeline material parameters, the preset pipeline material pressure-corrosion grading rules are used to determine the classification of the leakage diffusion rate of the current abnormal point and generate the corresponding pipeline failure level;

[0032] Based on the pipeline failure level, the shortest handling time, average handling time, and the committed response time defined in the service level agreement of the ownership unit are extracted. Based on the proportional relationship between the shortest handling time and the committed response time, combined with the distribution range of the average handling time, an emergency response time threshold associated with the current abnormal point location is generated;

[0033] Based on the pipeline failure level and emergency response time threshold, combined with the emergency resource distribution density corresponding to the encrypted identification of the ownership unit and the cross-department collaborative response rules, an abnormal point feature dataset is generated, including abnormal point disposal, multi-department collaborative path constraints and dynamic operation instructions matching the time threshold.

[0034] Furthermore, based on the decision dataset, the spatial topology correction value is used to simulate the distribution of obstacles on the emergency repair path. Combined with real-time equipment status data, the final cloud-based detour plan is generated, and linkage instructions are simultaneously issued to the fire protection equipment, ventilation equipment, and remote valve control terminals, including:

[0035] Based on the pipeline intersection depth parameter in the spatial topology correction value, the 3D coordinate set of pipeline intersections, valve nodes, and ventilation shafts on the emergency repair path is dynamically rendered in the 3D digital integrated platform to construct a 3D spatial obstacle topology network including topological constraints.

[0036] Based on a three-dimensional obstacle topology network, the cloud-based path planning engine integrates real-time equipment status data, including valve opening and closing angles, ventilation device speeds, and fire tank water levels, to analyze obstacle distribution feasibility and generate an initial detour solution set that includes alternative route safety factors and equipment operation sequences.

[0037] Based on the initial detour plan set, combined with pipeline distribution density parameters and collision risk level data, the spatial topological relationship between each path and the high-risk hidden area is calculated to generate a final detour plan with avoidance coordinate points, emergency resource deployment density, and equipment coordination parameters.

[0038] The equipment operation instructions in the final detour plan are disassembled according to the authority of the owning unit, and the real-time task status of multi-department resource scheduling is updated at the same time.

[0039] Furthermore, the equipment operation instructions include sending the sprinkler area coordinates and water volume control parameters to the fire-fighting device, sending directional boost instructions and abnormal point coordinate radius to the ventilation device, and transmitting opening and closing commands with timing locks to the valve control terminal.

[0040] In a second aspect, a computing device includes:

[0041] one or more processors;

[0042] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the system.

[0043] According to a third aspect, a computer-readable storage medium stores a program, which implements the system when executed by a processor.

[0044] The above solution of the present invention includes at least the following beneficial effects:

[0045] This system integrates various data from the entire lifecycle of underground utility corridors, presenting information on power, communications, and drainage pipelines. Operations and maintenance personnel can intuitively access detailed information on pipeline layout, materials, and specifications, providing accurate data support for daily inspections and management. Should a fault occur within the corridor, such as a pipe rupture or equipment malfunction, the fault point is quickly located based on the integrated data. Combined with the status of surrounding pipelines and equipment, the system intelligently analyzes the impact area and generates a final repair plan, shortening troubleshooting and repair time and minimizing the impact on the surrounding area. This system breaks down the communication barriers between departments in traditional operations and maintenance, enabling real-time information sharing and collaboration across operations, maintenance, and management. In emergencies, all departments can respond quickly and efficiently using a unified information platform, improving overall operations and management efficiency. Real-time monitoring and analysis of corridor operational data enables early prediction of potential risks, such as pipeline aging and abnormal environmental parameters, and timely warnings. Based on these warnings, operations and maintenance personnel can implement preventive maintenance measures, reducing the probability of accidents and ensuring the safe and stable operation of the corridor. Accurate fault location and efficient operation and maintenance management reduce the waste of manpower and material resources, extend the service life of the pipeline corridor and pipeline equipment, avoid major economic losses caused by faults, and reduce the operation and maintenance costs of underground integrated pipeline corridors from multiple aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a schematic diagram of a BIM-based intelligent operation and maintenance management system for an underground integrated pipeline corridor provided by an embodiment of the present invention.

[0047] Figure 2 This is a flowchart provided by an embodiment of the present invention, which simulates the distribution of obstacles in the emergency repair path based on a decision data set through spatial topology correction values, generates a final cloud-based detour plan in combination with real-time equipment status data, and simultaneously issues linkage instructions to fire-fighting equipment, ventilation equipment and remote valve control terminals. DETAILED DESCRIPTION

[0048] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0049] like Figure 1 As shown, an embodiment of the present invention proposes a BIM-based intelligent operation and maintenance management system for an underground integrated pipe gallery, comprising:

[0050] The 3D integration module is used to build a 3D digital integration platform for underground integrated pipeline corridors in the cloud. It uploads the full life cycle data, ownership information, and real-time operating parameters of communication pipelines to the cloud central database, assigns a unique code to each pipeline unit, and establishes a corresponding cloud index relationship.

[0051] The spatial correction module is used to preset three positioning monitoring nodes at the main entrance of the tunnel, key ventilation nodes, and pipeline intersections based on a three-dimensional digital integration platform to build a stable triangulated positioning monitoring architecture. The geometric center and side length relationship of the monitoring architecture are calculated using the three-dimensional coordinates of the three positioning monitoring nodes to generate spatial topology correction values.

[0052] Build an early warning module to construct a dynamic network based on spatial topology correction values, update pipeline layout data in real time, and trigger collision risk warnings;

[0053] The positioning decision module integrates the real-time pipeline corridor environment data collected by IoT sensors with the 3D digital integration platform. When leaks, temperature, and pressure anomalies are detected, the module automatically locates the anomaly based on the correction values ​​of the monitoring area and the 3D coordinates of the pipeline using the triangulated positioning monitoring architecture, generating a decision dataset that includes emergency operation instructions.

[0054] The closed-loop response module is used to simulate the distribution of obstacles on the emergency repair path based on the decision data set through spatial topology correction values, generate the final cloud-based detour plan in combination with real-time equipment status data, and simultaneously issue linkage instructions to fire-fighting equipment, ventilation equipment, and remote valve control terminals.

[0055] In an embodiment of the present invention, a three-dimensional digital integration platform is constructed on the cloud to achieve centralized management of communication pipeline full life cycle data, ownership unit information and real-time operating parameters. A unique code is assigned to each pipeline unit and a cloud index is established, allowing operation and maintenance personnel to quickly and accurately query and call various types of information, saying goodbye to the traditional problem of information dispersion and difficulty in finding information. Positioning monitoring nodes are preset at key locations in the corridor to build a monitoring area of ​​a stable triangulated positioning monitoring architecture. Spatial topology correction values ​​are generated by calculating geometric relationships, effectively improving the accuracy of corridor spatial positioning, solving the error problem existing in traditional positioning, and ensuring more accurate control of the spatial position of pipelines and equipment in the corridor. A dynamic network is constructed based on the spatial topology correction value, and pipeline layout data is updated in real time. Potential pipeline collision risks can be discovered in a timely manner and early warnings can be triggered, helping operation and maintenance personnel take measures in advance to avoid failures caused by pipeline collisions, ensuring the safe operation of various pipelines in the corridor, and reducing the probability of accidents. By integrating environmental data collected by IoT sensors with a three-dimensional digital integration platform, the system can quickly and accurately locate the anomaly when leaks, temperature, and pressure anomalies are detected, combined with spatial correction values ​​and the pipeline's three-dimensional coordinates. This system also generates a decision-making dataset containing emergency operation instructions, providing scientific and effective guidance for troubleshooting. Based on the decision-making dataset, spatial topology correction values ​​are used to simulate the distribution of obstacles along the repair path, generate cloud-based detour plans, and simultaneously issue linkage instructions to achieve dynamic scheduling of resources across multiple departments. This process effectively integrates resources from all parties involved in the repair process, avoiding repair delays caused by poor information flow and improper resource allocation, ensuring a rapid, efficient, and orderly emergency response and minimizing losses caused by failures.

[0056] In a preferred embodiment of the present invention, a three-dimensional digital integrated platform for underground integrated pipeline corridors is constructed in the cloud. The full life cycle data, ownership information, and real-time operating parameters of communication pipelines are uploaded to a central database in the cloud. A unique code is assigned to each pipeline unit, and a corresponding cloud index relationship is established, which may include:

[0057] In the cloud-based central database, independent data storage blocks are divided according to ownership units, pipeline types, and operation and maintenance stages. Design parameters, construction acceptance data, and past operation and maintenance records of communication pipelines are classified and stored in corresponding blocks.

[0058] Based on the data block division rules, a cloud-based coding service generates a code for each pipeline unit, including the ownership unit identifier, pipeline type code, and three-dimensional coordinate feature values. A bidirectional index relationship table with time series labels is established between the code and the design parameters and construction acceptance data of the corresponding block.

[0059] According to the timing tags of the bidirectional index relationship table, the operating parameters uploaded by the pipeline sensor are received in real time through the cloud data channel, and the parameter values ​​are matched with the corresponding pipeline codes and entered into the dynamic fields of the index relationship table, and the status identification and attribute parameters of the pipeline in the three-dimensional digital integration platform are synchronously updated.

[0060] In this embodiment of the present invention, operations personnel will partition independent data storage blocks within the cloud-based central database based on the actual conditions of the communication pipelines within the tunnel, according to ownership units (e.g., different telecommunications operators), pipeline types (fiber, cable, etc.), and operation and maintenance phases (design, construction, operation, etc.). For example, data for all fiber pipelines of a telecommunications operator, from the design to the operation phase, will be stored in the fiber pipeline data block corresponding to that operator. This ensures that data from different sources and types has its own distinct "storage space," preventing data clutter and accumulation. Based on these partitioned data blocks, the cloud-based coding service generates a unique code for each pipeline unit. The code includes an ownership unit identifier (to distinguish between different ownership units), a pipeline type code (to identify the specific pipeline type), and three-dimensional coordinate feature values ​​(to locate the pipeline in the tunnel). For example, for a specific fiber pipeline, its code is generated by combining the operator identifier, the fiber type code, and its three-dimensional coordinate information within the tunnel. A bidirectional index relationship table is also established between this code and the design parameters and construction acceptance data in the corresponding data block, and time series tags are added. This is like establishing a detailed "archive index" for each pipeline. The corresponding data can be quickly found through the coding, and the corresponding pipeline can be reversed from the data. The time series label records the order in which the data is generated, making it easy to trace data changes. The pipeline sensors in the pipeline corridor will collect operating parameters in real time, such as pipeline temperature, pressure and other data. These data are transmitted through the cloud data channel, and the operating parameters are matched with the corresponding pipeline codes according to the time series labels in the bidirectional index relationship table. Once the match is successful, the parameter value is entered into the dynamic field of the index relationship table, and the status identification and attribute parameters of the pipeline in the three-dimensional digital integration platform are updated synchronously. For example, when the temperature of a pipeline rises, the sensor collects the data and transmits it, finds the corresponding pipeline code, updates the temperature data to the index table, and intuitively displays the status identification of the abnormal pipeline temperature on the platform, so that operation and maintenance personnel can grasp the pipeline operation status in a timely manner.

[0061] The partitioning of data storage blocks improves the standardization and organization of data management. Data for different ownership units and types of pipelines is stored independently, making it easier for operations and maintenance personnel to quickly find the data they need. The establishment of a unique code and bidirectional index relationship table ensures precise association between pipelines and data, making information queries fast and accurate. With the pipeline code, operations and maintenance personnel can quickly access all relevant data from design to operation, including design parameters and construction acceptance status. Conversely, the data can also be used to quickly locate the corresponding pipeline unit. This precise association also facilitates full lifecycle management of pipelines. Time-series tags clearly illustrate pipeline status changes at different stages, providing comprehensive data support for pipeline maintenance and renovation. Real-time data updates enable operations and maintenance personnel to maintain a timely and accurate understanding of pipeline operating status. Sensor data is synchronized to the platform in real time for display. If a pipeline anomaly occurs, such as temperature or pressure exceeding normal ranges, operations and maintenance personnel can immediately detect and take action. This effectively shortens fault detection time and improves fault warning capabilities, providing strong support for the safe and stable operation of the pipeline corridor and avoiding serious accidents and losses caused by undetected problems.

[0062] In a preferred embodiment of the present invention, based on a three-dimensional digital integrated platform, three positioning monitoring nodes are preset at the main entrance of the tunnel, key ventilation nodes, and pipeline intersections to build a stable triangulated positioning monitoring framework. The geometric center and side length relationship of the monitoring framework are calculated using the three-dimensional coordinates of the three positioning monitoring nodes to generate a spatial topology correction value, which may include:

[0063] Based on the pipeline layout data and ownership information stored in the 3D digital integration platform, a first positioning monitoring node is deployed at the main entrance of the tunnel to monitor the flow rate and structural stability of the in-and-out pipelines. A second positioning monitoring node is deployed at key ventilation nodes to collect air quality, temperature, humidity, and wind speed data. A third positioning monitoring node is deployed at the intersection of power, water, and gas pipelines to monitor pressure and displacement changes in the multi-pipeline interaction area.

[0064] Based on the three-dimensional coordinate data of the three positioning monitoring nodes, the geometric center coordinates of the triangulated positioning monitoring architecture area are calculated, and based on the difference between the maximum and minimum values ​​of the distance between adjacent nodes, the initial topological parameters representing the pipeline distribution density are generated;

[0065] Combined with the actual pipeline burial depth data and pipeline type distribution characteristics stored in the central database, the initial topology parameters are weightedly corrected to generate spatial topology correction values ​​that reflect the pipeline crossing depth, safety spacing and risk level of hidden areas.

[0066] In an embodiment of the present invention, operations and maintenance personnel plan and deploy positioning monitoring nodes based on the pipeline layout data and ownership information within the corridor recorded in the three-dimensional digital integration platform. At the main entrance of the corridor, as this is the key passage for pipelines to enter and exit the corridor, a first positioning monitoring node is deployed. This node is equipped with corresponding monitoring equipment to continuously monitor the flow data of the pipelines in and out. At the same time, a sensor device is used to detect the structural stability of the main entrance of the corridor and observe whether there are cracks or abnormal settlement. At the key ventilation node, considering that this location is crucial for regulating the air quality, temperature, humidity, and wind speed in the corridor, a second positioning monitoring node is deployed and equipped with equipment such as air quality detectors, temperature and humidity sensors, and anemometers to collect relevant data in real time. At the intersection of power, water, and gas pipelines, because multiple pipelines are concentrated and the risk of mutual influence is high, a third positioning monitoring node is deployed. Using pressure sensors and displacement monitoring equipment, it closely monitors the pressure changes and displacement of each pipeline in this area to promptly detect potential hazards.

[0067] After the three positioning monitoring nodes are deployed and their three-dimensional coordinate data are obtained, the key step of calculating the regional geometric center coordinates and initial topological parameters of the triangulated positioning monitoring architecture is entered. The first step is to calculate the geometric center coordinates of the monitoring architecture area, that is, the center of gravity position. Assume that the three positioning monitoring nodes are 、 、 , and their corresponding three-dimensional coordinates are 、 ( ), ( ). In actual calculation, first place the three nodes in The coordinate values ​​in the axis direction are added, that is ( + + ), and then divided by the number of nodes 3, we get the center of gravity Coordinate values ​​in the axis direction ; Similarly, in In the axis direction, the three nodes Add the coordinates and divide by 3 to get ;exist The same operation is performed in the axis direction to obtain Through this series of calculations, the coordinates of the center of gravity of the area of ​​the triangulated positioning monitoring architecture are finally determined as ( , , ), this coordinate is the geometric center coordinate. Next, measure the distance between adjacent nodes. According to the distance formula between two points in three-dimensional space, calculate the node and The distance between , similarly calculate the node and The distance between and nodes and The distance between After obtaining the lengths of the three sides, compare them and find the maximum value. and minimum value , and then calculate the difference between the two Finally, the initial topology parameters are generated based on this difference relationship. A large value means that the distribution of pipelines in the area surrounded by the three positioning monitoring nodes is extremely uneven. For example, a large difference may indicate that pipelines are densely distributed within a certain distance, while they are sparsely distributed within another distance. This situation can easily lead to collision risks during construction or maintenance, and is not conducive to the rational allocation of resources. On the contrary, if Small values ​​indicate relatively even distribution of pipelines within the area. This means pipeline layout complexity and risk are relatively low when planning, constructing, or maintaining the corridor. Through these calculations and analyses, we ultimately generate initial topological parameters that provide a preliminary understanding of pipeline distribution density.

[0068] After obtaining initial topological parameters reflecting pipeline density, they are weighted and corrected based on the rich pipeline information in the central database to generate a spatial topology correction value that better reflects actual conditions. Two key pieces of information are extracted from the central database. First, actual pipeline depth data is used. The depths of all pipelines within the corridor are categorized into three levels: shallow (0-2 meters), medium (2-5 meters), and deep (over 5 meters). Second, the distribution characteristics of pipeline types are analyzed. These clearly distinguish between power, gas, telecommunication, and drainage pipelines, and the corresponding safety spacing standards and potential risk levels are compiled for each pipeline type. For example, a gas pipeline is classified as high-risk because a leak could cause an explosion. However, even if a failure occurs in a standard telecommunication pipeline, the impact on other pipelines and the surrounding environment is minimal, making it a low-risk category. Subsequently, appropriate weights are assigned to each influencing factor. Taking pipeline type as an example, high-risk gas pipelines are extremely dangerous in the event of an accident, so the initial topology parameter impact factor related to the gas pipeline is given a high weight of 0.8; medium-risk power pipelines are given a weight of 0.6 considering the risks of leakage, short circuit, etc.; low-risk ordinary communication pipelines are given a low weight of 0.2. In terms of pipeline burial depth, pipelines in shallow buried areas are greatly affected by ground construction and environmental changes, so the parameter weight corresponding to shallow buried pipelines is set to 0.7; medium buried areas are given a weight of 0.5; and deep buried areas are given a weight of 0.3. After completing the weight setting, the weighted calculation begins. Assuming that the initial topology parameters are , the parameter impact value related to the gas pipeline type is , corresponding weight =0.8; the parameter impact value related to the power pipeline type is , corresponding weight =0.6; the parameter impact value related to the communication pipeline type is , corresponding weight =0.2; the parameter influence value of shallow buried pipeline is , corresponding weight =0.7, and so on. Multiply each influencing factor by the corresponding weight and sum it up, then multiply it by the initial topological parameter to obtain the corrected parameter value, and finally generate the spatial topology correction value.

[0069] Positioning monitoring nodes are deployed according to the functions and risk characteristics of different locations in the corridor, enabling targeted monitoring of key corridor areas. Monitoring pipeline flow and structural stability at the main entrance helps promptly detect abnormal pipeline connections or structural damage, ensuring the safety of the corridor's "portal." Collecting environmental data at key ventilation nodes effectively monitors air quality and ventilation conditions within the corridor, ensuring a suitable environment and avoiding safety incidents caused by poor ventilation. Monitoring pressure and displacement changes at pipeline intersections can proactively identify potential risks in areas where multiple pipelines intersect, preventing accidents caused by pipeline compression and leakage. By calculating the geometric center coordinates of the triangulated monitoring architecture area and generating initial topological parameters based on the difference in node spacing, quantitative metrics are provided for corridor spatial analysis. Operations and maintenance personnel can intuitively understand the general distribution of pipelines and determine which areas are densely populated and which are more sparsely populated. This helps plan new pipeline laying and maintenance operations, avoiding densely populated areas, minimizing the impact of construction on existing pipelines, and improving construction safety and efficiency. By combining multiple factors to weightedly modify the initial topology parameters and generate a spatial topology correction value, the assessment of the tunnel space is more scientific and accurate. This correction value fully considers key factors such as pipeline depth and type, accurately reflecting the actual risk conditions in different areas. Based on this value, operations and maintenance personnel can more clearly identify high-risk and hidden areas within the tunnel, strengthen inspections and maintenance in a targeted manner, and take preventive measures in advance to reduce the probability of accidents. This also enables more reasonable decisions based on accurate risk assessments during spatial planning and resource allocation, ensuring the safe and efficient operation of the tunnel.

[0070] In a preferred embodiment of the present invention, a dynamic network is constructed based on the spatial topology correction value, pipeline layout data is updated in real time, and collision risk warning is triggered, which may include:

[0071] Based on the spatial topology correction value, pipeline crossing depth, safety spacing and hidden area risk level data are extracted. The corrected spatial topology parameters are combined with the pipeline geometry and connection relationship data in the 3D digital integration platform to construct a dynamic network structure.

[0072] Based on the pipeline geometry, material properties, and real-time operating parameters in the dynamic network structure, the pipeline layout is dynamically rendered in the 3D digital integration platform, dynamically displaying the intersection depth, spacing distribution, and hidden area details;

[0073] Preset pipeline intersection angle safety thresholds and adjacent pipeline spacing safety thresholds. When the real-time rendered pipeline intersection angle is less than the threshold, a collision risk warning signal is triggered.

[0074] The risk level is divided according to the degree of deviation from the threshold, and early warning information is generated according to the risk level, including abnormal location coordinates, pipeline code, risk description and recommended operations.

[0075] In this embodiment of the present invention, key data, including pipeline intersection depth, safe spacing, and hidden area risk level, is first extracted from the generated spatial topology correction values. For example, for a particular pipeline intersection, the intersection depth value, whether the safe spacing meets the standard, and the risk level assessment result are obtained. These corrected spatial topology parameters are then integrated with pipeline geometry (e.g., pipeline diameter and length) and connectivity data (e.g., how pipelines connect to equipment and nodes) stored in the 3D digital integration platform. This integration process is like building a virtual "pipeline corridor network," mapping the specific information of each pipeline to its actual layout, creating a dynamic network structure that reflects the actual status of the pipelines within the corridor in real time. Based on this constructed dynamic network structure, the pipeline layout is dynamically displayed on the 3D digital integration platform using pipeline geometry, material properties (e.g., metal, plastic, etc.), and real-time operating parameters (e.g., temperature and pressure data). During the rendering process, pipelines are differentiated based on their actual conditions, and detailed information on pipeline intersection depth, spacing distribution, and hidden areas is intuitively presented. Preset safety thresholds for pipeline intersection angles and adjacent pipeline spacing are determined based on industry standards and practical experience. For example, a pipeline intersection angle less than 30 degrees may pose a collision risk, and a distance between adjacent gas pipelines less than 1 meter is considered a dangerous distance. During real-time pipeline layout rendering, these data are continuously monitored. If a pipeline intersection angle in a certain area falls below the preset safety threshold, or if the distance between adjacent pipelines falls below the safety threshold, a collision risk warning signal is immediately triggered, alerting operations and maintenance personnel to a potentially dangerous situation, much like an alarm. Once a collision risk warning is triggered, the risk level is determined based on the degree of deviation from the threshold. For example, if the pipeline intersection angle deviates slightly from the safety threshold, the risk level is low; if the deviation is greater, the risk level is high. Detailed warning information is then generated based on the risk level. The early warning information includes the coordinates of the abnormal location (accurate to the specific three-dimensional location within the corridor), the code of the pipeline involved (the code can be used to quickly query detailed information about the pipeline), the risk description (specifying whether it is an intersection angle problem or a spacing problem, and the severity of the problem) and recommended actions (such as strengthening monitoring for low risks and immediately arranging personnel for rectification and maintenance for high risks), providing clear and unambiguous processing instructions for operation and maintenance personnel.

[0076] By integrating multi-dimensional data to build a dynamic network structure, comprehensive pipeline information within the tunnel is integrated and updated in real time. Operations and maintenance personnel can quickly access key pipeline information on a unified platform, eliminating the tedious process of searching for data across multiple files. This comprehensive and real-time information display improves the efficiency and accuracy of operations and maintenance management. Dynamic rendering intuitively presents pipeline layout and risk details, simplifying the understanding of complex pipeline information. Potential risk areas can be quickly identified. This intuitive display helps operators plan inspection routes in advance and conduct targeted inspections of key areas, avoiding risks that could be missed due to lack of intuitive information and improving the effectiveness of risk prevention and control. Preset thresholds and real-time monitoring trigger early warnings ensure timely alerts before pipeline collision risks occur. This early warning mechanism shortens risk discovery time, freeing up valuable response time for operators, effectively reducing the probability of equipment damage and leaks caused by pipeline collisions, and ensuring the safe and stable operation of all pipelines within the tunnel. Scientific risk grading and detailed early warning information enable operators to prioritize and allocate resources based on risk severity. For situations with high risk levels, professional personnel can be immediately organized to carry out emergency repairs; for situations with low risk levels, reasonable monitoring and maintenance plans can be formulated.

[0077] In a preferred embodiment of the present invention, the real-time data collected by IoT sensors on the pipeline corridor environment is integrated with a three-dimensional digital integration platform. When leaks, temperature, and pressure anomalies are detected, the specific location of the anomaly is automatically calibrated by combining the correction value of the monitoring area and the three-dimensional coordinates of the pipeline using the triangulated positioning monitoring architecture. A decision-making data set including emergency operation instructions is generated, which may include:

[0078] Receive the real-time data streams of leakage concentration, temperature change rate, and pressure gradient uploaded by IoT sensors within the monitoring area of ​​the triangulated positioning monitoring architecture. Align the sensor data with the three-dimensional coordinates of the pipeline on the three-dimensional digital integration platform through the cloud data engine to generate a spatially correlated abnormal data set.

[0079] Based on the pipeline intersection depth parameter in the spatial topology correction value, spatial compensation calculation is performed on the coordinates in the abnormal data set to obtain the corrected three-dimensional coordinates of the abnormal point and the influence radius;

[0080] Based on the corrected outlier coordinates, a reverse search is performed on a bidirectional index relationship table with time series labels to extract the encrypted ownership identifier of the corresponding pipeline unit, pipeline material parameters from the construction acceptance data, and similar fault handling time limits from past operation and maintenance records. This constructs an outlier feature dataset including pipeline failure levels and emergency response time limit thresholds. Specifically, based on the compressive strength and corrosion resistance of the pipeline material parameters and past leakage records, the default pipeline material pressure-corrosion grading rules are used to determine the current outlier leakage diffusion rate classification and generate the corresponding pipeline failure level. Based on the pipeline failure level, the shortest handling time, average handling time, and the promised response time defined in the ownership unit's service level agreement are extracted. Based on the proportional relationship between the shortest handling time and the promised response time, combined with the distribution range of the average handling time, an emergency response time limit threshold associated with the current outlier location is generated. Based on the pipeline failure level and the emergency response time limit threshold, combined with the emergency resource distribution density corresponding to the ownership unit's encrypted identifier and the cross-departmental collaborative response rules, an outlier feature dataset is generated including constraints for outlier handling, multi-department collaborative paths, and dynamic operation instructions matching the time threshold.

[0081] Based on the abnormal point feature dataset, the preset association rules between pipeline material and leakage diffusion rate, and the comparison relationship between temperature threshold and ventilation efficiency in the cloud central database are called to generate a decision dataset for emergency operation instructions.

[0082] In an embodiment of the present invention, various data streams are received in real time from IoT sensors in the monitoring area of ​​the triangulation positioning monitoring architecture. These data streams include leakage concentration, temperature change rate, and pressure gradient environmental data. After receiving these data, the cloud data engine will align and match the sensor data with the three-dimensional coordinates of the pipeline in the three-dimensional digital integration platform. Specifically, the data detected by each sensor is mapped to a specific pipeline position in three-dimensional space. For example, if a sensor located at a coordinate detects an abnormal leakage concentration, this concentration data will be associated with the three-dimensional coordinates of the pipeline at that location. In this way, an abnormal data set including spatial correlation is generated, which clearly shows which environmental parameters are abnormal in which pipelines.

[0083] In the actual pipe corridor environment, the cross layout of pipelines will interfere with the accurate positioning of the sensor for abnormal points. Therefore, it is necessary to process the coordinates in the abnormal data set based on the pipeline cross depth parameter in the spatial topology correction value, and retrieve the pipeline cross depth data of the target area from the spatial topology correction value. For example, in the intersection area of ​​the power and water supply and drainage pipelines in a pipe corridor, the correction value shows that the power pipeline is located 0.8 meters above and the drainage pipeline is below. When the sensor in this area detects a leakage anomaly in the water supply and drainage pipeline, the initial positioning coordinates are ( ), we begin to analyze the impact of the intersection depth on the abnormal propagation path. Since the leaking water will penetrate downward due to gravity and spread to both sides due to the obstruction of the crossing pipeline, the actual leakage point may not be the vertical position detected by the sensor. Assuming that past experience shows that in this type of crossing structure, the lateral offset distance of the leakage point is proportional to the intersection depth, and the offset coefficient is 0.5, then the initial horizontal coordinate is set to Adjust 0.4 meters to the left (0.8 meters × 0.5) to get the corrected coordinates ( ); At the same time, considering the water flow penetration characteristics, the vertical coordinate Adjust downward by 0.3 meters to obtain the final corrected coordinates ( ). After determining the coordinates of the abnormal point, the impact range is estimated in combination with real-time detection data. If the leakage concentration rises from 5% to 20% within 10 minutes, the leakage is judged to be in a rapid diffusion state according to the preset concentration-diffusion rate comparison table; at the same time, referring to the soil permeability data of the intersection area, the impact radius is expanded from the initial 2 meters to 5 meters. If the temperature of the power pipeline is detected to be abnormal, the impact radius is reduced from 3 meters to 1.5 meters based on the insulation material properties of the cross pipeline (such as the thermal conductivity of the ceramic insulation layer) and the temperature change rate. Through this multi-parameter fusion analysis, the precise impact range of the abnormal situation is finally determined.

[0084] Based on the corrected outlier coordinates, a reverse search is performed on a bidirectional index table with time series labels. This index table records the associations between pipeline units and various data types, allowing the corresponding pipeline units to be quickly found using the outlier coordinates. The encrypted identifier of the pipeline unit's ownership is then extracted from the index table to clarify the responsible party. Pipeline material parameters from construction acceptance data are extracted to understand the pipeline's basic characteristics. The timeliness of handling similar failures from past operation and maintenance records is also extracted. Combining this information, an outlier feature dataset is constructed, including pipeline failure levels and emergency response timeliness thresholds. Based on this constructed outlier feature dataset, various association rules and correlations pre-set in the cloud-based central database are invoked. For example, the association rule between pipeline material and leak diffusion rate is invoked to predict the potential leak spread rate and range based on the current pipeline material and leak situation. The correlation between temperature threshold and ventilation efficiency is used to determine the ventilation volume required to effectively reduce risk under the current temperature anomaly. Combining these rules and correlations generates a decision dataset containing emergency operation instructions. These instructions are specific and unambiguous, such as recommending closing a valve or activating a specific ventilation device, providing direct operational guidance to operation and maintenance personnel.

[0085] By aligning sensor data with the three-dimensional coordinates of pipelines, a precise match between environmental data and spatial location is achieved. This allows operations and maintenance personnel to quickly locate the specific pipeline and location where an anomaly has occurred, shortening troubleshooting time. The coordinates of anomaly points are corrected to account for pipeline intersection depth, improving the accuracy of anomaly location. For example, when arranging repair personnel and equipment, the required resources can be determined based on the impact radius, avoiding resource waste or shortages. Pipeline-related information is extracted from multiple dimensions to construct a feature dataset. The encrypted identification of ownership units clarifies the responsible parties and facilitates rapid coordination of relevant units. Pipeline material parameters and past operations and maintenance records facilitate the development of more scientific and reasonable emergency plans. Emergency operation instructions are generated based on preset rules, making the decision-making process more scientific and standardized. These instructions are highly targeted and operational, guiding operations and maintenance personnel to quickly take effective countermeasures and reduce losses caused by delayed decisions or improper operations.

[0086] like Figure 2 As shown, in a preferred embodiment of the present invention, based on the decision data set, the obstacle distribution of the emergency repair path is simulated by using the spatial topology correction value, and the final cloud-based detour plan is generated in combination with the real-time equipment status data. The linkage instructions are then synchronously issued to the fire protection equipment, ventilation equipment, and remote valve control terminal, which may include:

[0087] Based on the pipeline intersection depth parameter in the spatial topology correction value, the 3D coordinate set of pipeline intersections, valve nodes, and ventilation shafts on the emergency repair path is dynamically rendered in the 3D digital integrated platform to construct a 3D spatial obstacle topology network including topological constraints.

[0088] Based on a three-dimensional obstacle topology network, the cloud-based path planning engine integrates real-time equipment status data, including valve opening and closing angles, ventilation device speeds, and fire tank water levels, to analyze obstacle distribution feasibility and generate an initial detour solution set that includes alternative route safety factors and equipment operation sequences.

[0089] Based on the initial detour plan set, combined with pipeline distribution density parameters and collision risk level data, the spatial topological relationship between each path and the high-risk hidden area is calculated to generate a final detour plan with avoidance coordinate points, emergency resource deployment density, and equipment coordination parameters.

[0090] The equipment operation instructions in the final detour plan are disassembled according to the authority of the owning unit, and the real-time task status of multi-department resource scheduling is updated at the same time. Specifically, the equipment operation instructions include sending the sprinkler area coordinates and water volume control parameters to the fire-fighting equipment, sending directional boost instructions and abnormal point coordinate radius to the ventilation device, and transmitting opening and closing commands with timing locks to the valve control terminal.

[0091] In this embodiment of the present invention, pipeline intersection depth parameters are extracted from spatial topology correction values. Simultaneously, the 3D coordinate information for all pipeline intersections, valve nodes, and ventilation shafts along the repair route is retrieved within a 3D digital integration platform. For example, along a particular repair route, the specific coordinates of five pipeline intersections, three valve nodes, and two ventilation shafts are determined. This coordinate information is then integrated and dynamically rendered in 3D based on their actual spatial relationships, constructing a 3D spatial obstacle topology network with topological constraints. This network clearly displays the locations and relationships of all obstacles along the repair route that could potentially impact access and operations. Based on this constructed 3D spatial obstacle topology network, real-time equipment status data is further integrated, including parameters such as valve opening and closing angles, ventilation device speed, and fire water tank water level. For example, if the current opening and closing angle of a valve is 45 degrees, the ventilation device speed is 1200 rpm, and the fire water tank water level is 60% remaining, the cloud-based path planning engine then performs a traversal feasibility analysis based on the obstacle distribution. This analysis considers the impact of the obstacle's spatial location and equipment operating status on accessibility. For example, if a valve is half-open, the engine assesses whether it will obstruct the passage of repair personnel and equipment; and whether the low speed of a ventilation device will affect the air quality and safety of the repair area. Based on the analysis results, the engine generates multiple initial detour plans, each including the safety factor of the alternative route (e.g., Route D has a safety factor of 0.8, Route E has a safety factor of 0.7) and the equipment operation sequence (e.g., closing a certain valve first, then activating a specific ventilation device). Based on this initial set of detour plans, combined with pipeline density parameters and collision risk level data, the plan is further optimized. The spatial topological relationship between each alternative route and high-risk hidden areas (such as those where shallow gas pipelines intersect) is used to determine whether a particular route is close to a high-risk area. The closer the distance, the higher the risk. The initial plan is then adjusted and refined to generate the final detour plan. The final plan clearly identifies coordinate points that need to be avoided, plans the density of emergency resources (e.g., doubling the number of firefighting equipment deployment near high-risk areas), and determines equipment coordination parameters (e.g., the time interval between the activation of firefighting and ventilation devices), ensuring both safety and efficiency. At the same time, the task status of multi-department resource scheduling is updated in real time, including information such as personnel availability and equipment transportation progress. Each department cooperates to carry out emergency work and monitor the execution process based on the received instructions and task status. Once problems arise, instructions are adjusted in a timely manner to form a complete cloud-driven closed-loop emergency response process until the emergency task is completed.

[0092] By constructing a detailed obstacle topology network, operations and maintenance personnel and emergency response personnel can clearly understand the complexities of the repair route before formulating a repair plan. This prevents route planning errors and equipment collisions caused by unfamiliarity with obstacle locations, laying the foundation for efficient and safe repair work and saving on-site survey and planning time. Real-time equipment status data is integrated into the initial detour plan, fully considering the impact of actual equipment operating conditions on repairs. This generated plan is more tailored to the actual scenario, ensuring that repair personnel are not hindered by abnormal equipment operating conditions during execution. Furthermore, multiple alternative plans provide flexible options and can be quickly adjusted based on actual site conditions, improving the adaptability and reliability of emergency response. The final detour plan, generated by combining multiple data points, enables scientific planning of repair routes and precise risk control. Marking avoidance coordinate points and planning resource density effectively reduces the possibility of collisions with high-risk pipelines during repairs, ensuring the safety of repair personnel and equipment. Clarifying equipment coordination parameters enables efficient coordination between devices, improving the overall effectiveness of emergency response and minimizing losses caused by accidents. By breaking down instructions according to the authority of the respective units and updating task status in real time, we achieve efficient collaboration and rational resource allocation across multiple departments. Each department clearly defines its responsibilities and task progress, avoiding inefficient emergency response efforts caused by poor communication and unclear responsibilities. Real-time monitoring and dynamic adjustment capabilities ensure that the emergency response process can be optimized based on actual conditions, forming a complete and efficient emergency management system to safeguard the safe and stable operation of the underground utility corridor.

[0093] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0094] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0095] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A BIM-based intelligent operation and maintenance management system for underground integrated pipe corridors, characterized in that: include: The 3D integration module is used to build a 3D digital integration platform for underground integrated pipeline corridors in the cloud. It uploads the full life cycle data, ownership information, and real-time operating parameters of communication pipelines to the cloud central database, assigns a unique code to each pipeline unit, and establishes a corresponding cloud index relationship. The spatial correction module is used to preset three positioning monitoring nodes at the main entrance of the tunnel, key ventilation nodes, and pipeline intersections based on a three-dimensional digital integration platform to build a stable triangulated positioning monitoring architecture. The geometric center and side length relationship of the monitoring architecture are calculated using the three-dimensional coordinates of the three positioning monitoring nodes to generate spatial topology correction values. Build an early warning module to construct a dynamic network based on spatial topology correction values, update pipeline layout data in real time, and trigger collision risk warnings; The positioning decision module integrates the real-time pipeline corridor environment data collected by IoT sensors with the 3D digital integration platform. When leaks, temperature, and pressure anomalies are detected, the module automatically locates the anomaly based on the correction values ​​of the monitoring area and the 3D coordinates of the pipeline using the triangulated positioning monitoring architecture, generating a decision dataset that includes emergency operation instructions. The closed-loop response module is used to simulate the distribution of obstacles on the emergency repair path based on the decision data set through spatial topology correction values, generate the final cloud-based detour plan in combination with real-time equipment status data, and simultaneously issue linkage instructions to fire-fighting equipment, ventilation equipment, and remote valve control terminals.

2. The BIM-based intelligent operation and maintenance management system for underground integrated pipe gallery according to claim 1 is characterized in that: A 3D digital integrated platform for underground utility corridors is built in the cloud. The full lifecycle data, ownership information, and real-time operating parameters of communication pipelines are uploaded to the cloud-based central database. A unique code is assigned to each pipeline unit, and a corresponding cloud-based index relationship is established, including: In the cloud-based central database, independent data storage blocks are divided according to ownership units, pipeline types, and operation and maintenance stages. Design parameters, construction acceptance data, and past operation and maintenance records of communication pipelines are classified and stored in corresponding blocks. Based on the data block division rules, a cloud-based coding service generates a code for each pipeline unit, including the ownership unit identifier, pipeline type code, and three-dimensional coordinate feature values. A bidirectional index relationship table with time series labels is established between the code and the design parameters and construction acceptance data of the corresponding block. According to the timing tags of the bidirectional index relationship table, the operating parameters uploaded by the pipeline sensor are received in real time through the cloud data channel, and the parameter values ​​are matched with the corresponding pipeline codes and entered into the dynamic fields of the index relationship table, and the status identification and attribute parameters of the pipeline in the three-dimensional digital integration platform are synchronously updated.

3. The BIM-based intelligent operation and maintenance management system for underground integrated pipe gallery according to claim 2 is characterized in that: Based on the 3D digital integration platform, three positioning monitoring nodes are preset at the main entrance of the tunnel, key ventilation nodes, and pipeline intersections to build a stable triangulated positioning monitoring architecture. The geometric center and side length relationship of the monitoring architecture are calculated using the 3D coordinates of the three positioning monitoring nodes to generate spatial topology correction values, including: Based on the pipeline layout data and ownership information stored in the 3D digital integration platform, a first positioning monitoring node is deployed at the main entrance of the tunnel to monitor the flow rate and structural stability of the in-and-out pipelines. A second positioning monitoring node is deployed at key ventilation nodes to collect air quality, temperature, humidity, and wind speed data. A third positioning monitoring node is deployed at the intersection of power, water, and gas pipelines to monitor pressure and displacement changes in the multi-pipeline interaction area. Based on the three-dimensional coordinate data of the three positioning monitoring nodes, the geometric center coordinates of the triangulated positioning monitoring architecture area are calculated, and based on the difference between the maximum and minimum values ​​of the distance between adjacent nodes, the initial topological parameters representing the pipeline distribution density are generated; Combined with the actual pipeline burial depth data and pipeline type distribution characteristics stored in the central database, the initial topology parameters are weightedly corrected to generate spatial topology correction values ​​that reflect the pipeline crossing depth, safety spacing and risk level of hidden areas.

4. The BIM-based intelligent operation and maintenance management system for underground integrated pipe gallery according to claim 3 is characterized in that: Build a dynamic network based on spatial topology correction values, update pipeline layout data in real time, and trigger collision risk warnings, including: Based on the spatial topology correction value, pipeline crossing depth, safety spacing and hidden area risk level data are extracted. The corrected spatial topology parameters are combined with the pipeline geometry and connection relationship data in the 3D digital integration platform to construct a dynamic network structure. Based on the pipeline geometry, material properties, and real-time operating parameters in the dynamic network structure, the pipeline layout is dynamically rendered in the 3D digital integration platform, dynamically displaying the intersection depth, spacing distribution, and hidden area details; Preset pipeline intersection angle safety thresholds and adjacent pipeline spacing safety thresholds. When the real-time rendered pipeline intersection angle is less than the threshold, a collision risk warning signal is triggered. The risk level is divided according to the degree of deviation from the threshold, and early warning information is generated according to the risk level, including abnormal location coordinates, pipeline code, risk description and recommended operations.

5. The BIM-based intelligent operation and maintenance management system for underground integrated pipe gallery according to claim 4 is characterized in that: The real-time data collected by IoT sensors on the pipeline corridor environment is integrated with the 3D digital integration platform. When leaks, temperature, and pressure anomalies are detected, the specific location of the anomaly is automatically calibrated based on the correction value of the monitoring area and the 3D coordinates of the pipeline in the triangulated positioning monitoring architecture. This generates a decision-making data set with emergency operation instructions, including: Receive the real-time data streams of leakage concentration, temperature change rate, and pressure gradient uploaded by IoT sensors within the monitoring area of ​​the triangulated positioning monitoring architecture. Align the sensor data with the three-dimensional coordinates of the pipeline on the three-dimensional digital integration platform through the cloud data engine to generate a spatially correlated abnormal data set. Based on the pipeline intersection depth parameter in the spatial topology correction value, spatial compensation calculation is performed on the coordinates in the abnormal data set to obtain the corrected three-dimensional coordinates of the abnormal point and the influence radius; Based on the corrected outlier coordinates, a reverse search is performed on a bidirectional index relationship table with time series labels to extract the encrypted identifier of the corresponding pipeline unit, pipeline material parameters from the construction acceptance data, and the timeliness of similar fault handling from past operation and maintenance records. This constructs an outlier feature dataset that includes pipeline failure levels and emergency response timeliness thresholds. Based on the abnormal point feature dataset, the preset association rules between pipeline material and leakage diffusion rate, and the comparison relationship between temperature threshold and ventilation efficiency in the cloud central database are called to generate a decision dataset for emergency operation instructions.

6. The BIM-based intelligent operation and maintenance management system for underground integrated pipe gallery according to claim 5 is characterized in that: Based on the corrected outlier coordinates, a reverse search is performed on the bidirectional index relationship table with time series labels to extract the encrypted identifier of the corresponding pipeline unit, the pipeline material parameters from the construction acceptance data, and the timeliness of similar fault handling from past operation and maintenance records. This constructs an outlier feature dataset that includes pipeline failure levels and emergency response timeliness thresholds, including: Based on the compressive strength, corrosion resistance and past leakage records of the pipeline material parameters, the preset pipeline material pressure-corrosion grading rules are used to determine the classification of the leakage diffusion rate of the current abnormal point and generate the corresponding pipeline failure level; Based on the pipeline failure level, the shortest handling time, average handling time, and the committed response time defined in the service level agreement of the ownership unit are extracted. Based on the proportional relationship between the shortest handling time and the committed response time, combined with the distribution range of the average handling time, an emergency response time threshold associated with the current abnormal point location is generated; Based on the pipeline failure level and emergency response time threshold, combined with the emergency resource distribution density corresponding to the encrypted identification of the ownership unit and the cross-department collaborative response rules, an abnormal point feature dataset is generated, including abnormal point disposal, multi-department collaborative path constraints and dynamic operation instructions matching the time threshold.

7. The BIM-based intelligent operation and maintenance management system for underground integrated pipe gallery according to claim 6 is characterized in that: Based on the decision dataset, the obstacle distribution along the repair path is simulated using spatial topology correction values. The final cloud-based detour plan is generated in combination with real-time equipment status data. Linked commands are then simultaneously issued to firefighting equipment, ventilation equipment, and remote valve control terminals, including: Based on the pipeline intersection depth parameter in the spatial topology correction value, the 3D coordinate set of pipeline intersections, valve nodes, and ventilation shafts on the emergency repair path is dynamically rendered in the 3D digital integrated platform to construct a 3D spatial obstacle topology network including topological constraints. Based on a three-dimensional obstacle topology network, the cloud-based path planning engine integrates real-time equipment status data, including valve opening and closing angles, ventilation device speeds, and fire tank water levels, to analyze obstacle distribution feasibility and generate an initial detour solution set that includes alternative route safety factors and equipment operation sequences. Based on the initial detour plan set, combined with pipeline distribution density parameters and collision risk level data, the spatial topological relationship between each path and the high-risk hidden area is calculated to generate a final detour plan with avoidance coordinate points, emergency resource deployment density, and equipment coordination parameters. The equipment operation instructions in the final detour plan are disassembled according to the authority of the owning unit, and the real-time task status of multi-department resource scheduling is updated at the same time.

8. The BIM-based intelligent operation and maintenance management system for underground integrated pipe gallery according to claim 7 is characterized in that: The equipment operation instructions include sending the sprinkler area coordinates and water volume control parameters to the fire-fighting device, sending the directional boost instruction and abnormal point coordinate radius to the ventilation device, and transmitting the opening and closing commands with timing locks to the valve control terminal.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the system according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed by a processor, implements the system according to any one of claims 1 to 8.

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