A BIM-based underground comprehensive pipe gallery intelligent operation and maintenance management system

Abstract

The application provides a BIM-based underground comprehensive pipe gallery intelligent operation and maintenance management system, and relates to the field of building information technology, comprising: a three-dimensional integration module, which is used for constructing a three-dimensional digital integration platform of the underground comprehensive pipe gallery in the cloud, uploading the whole life cycle data, ownership unit information and real-time operation parameters of the communication pipeline to the cloud central database, and assigning a unique code to each pipeline unit and establishing a corresponding cloud index relationship; a space correction module, which is used for presetting three positioning monitoring nodes at the main entrance of the pipe gallery, the key nodes of ventilation and the pipeline intersection based on the three-dimensional digital integration platform, constructing a stable triangular positioning monitoring framework, and calculating the geometric center and side length relationship of the monitoring framework through the three-dimensional coordinates of the three positioning monitoring nodes to generate a space topology correction value. The application realizes the safety of the underground comprehensive pipe gallery operation and maintenance through the integration of the whole life cycle data of the pipe gallery, space positioning, intelligent risk early warning and multi-department collaborative linkage.

Description

Technical Field

[0001] This 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 utility tunnels. Background Technology

[0002] In the daily operation and maintenance of underground utility tunnels, the limitations of traditional technologies are becoming increasingly apparent. Many maintenance projects still rely on paper drawings and Excel spreadsheets to record pipeline information, which presents a series of problems. For example, during a routine inspection, staff discovered a minor leak in a section of electrical cable. However, the pipeline route and material information marked on the paper drawings were unclear, and the Excel spreadsheet was not updated with recent renovations in the area. This made it impossible to quickly assess the potential impact of the leak on surrounding communication and drainage pipelines, severely delaying troubleshooting and repair.

[0003] Furthermore, the shortcomings of traditional technologies become apparent when faced with sudden malfunctions. During a winter cold snap, a heating pipe in the utility tunnel ruptured due to a sudden drop in temperature, causing a large amount of hot water to gush out. Traditional methods of manual inspection and telephone reporting were inefficient, and the lack of effective communication and coordination between departments meant that maintenance personnel, unable to visually assess the three-dimensional structure of the utility tunnel, struggled to accurately determine the impact of repairs on nearby gas pipelines. Repeated communication and coordination during the development of a repair plan further delayed the emergency repair, ultimately resulting in heating outages for up to 12 hours in several surrounding residential areas, causing inconvenience to residents and exposing the deficiencies 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 a BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels, which integrates data of the entire life cycle of the utility tunnels to realize dynamic management of pipeline information and rapid fault location.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels includes:

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

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

[0009] A warning module is built 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 and decision module is used to integrate the real-time collection of the pipe gallery environment by IoT sensors with the three-dimensional digital integration platform. When leakage, temperature and pressure abnormalities are detected, the module combines the correction value of the monitoring area of ​​the triangular positioning monitoring architecture and the three-dimensional coordinates of the pipeline to automatically mark the specific location of the abnormal point and generate a decision dataset including 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 dataset and spatial topology correction value, and generate the final cloud detour plan by combining real-time equipment status data, and synchronously issue linkage commands to fire-fighting equipment, ventilation equipment and remote valve control terminals.

[0012] Furthermore, a three-dimensional digital integration platform for underground utility tunnels is constructed in the cloud. This platform uploads the entire lifecycle data of communication pipelines, ownership information, and real-time operating parameters to a central cloud database. A unique code is assigned to each pipeline unit, establishing a corresponding cloud-based index relationship, including:

[0013] The cloud-based central database is divided into independent data storage blocks according to ownership unit, pipeline type and operation and maintenance stage. The design parameters, construction and acceptance data and past operation and maintenance records of communication pipelines are classified and stored in the corresponding blocks.

[0014] Based on the data block division rules, a code including ownership unit identifier, pipeline type code and three-dimensional coordinate feature value is generated for each pipeline unit through cloud coding service. The code is then linked with the design parameters and construction acceptance data of the corresponding block to establish a two-way index relationship table with time-series labels.

[0015] Based on the time-series labels of the bidirectional index relationship table, the system receives the operating parameters uploaded by pipeline sensors in real time through the cloud data channel, matches the parameter values ​​with the corresponding pipeline codes, and inputs them into the dynamic fields of the index relationship table, thus synchronously updating the status identifiers and attribute parameters of the pipelines in the 3D digital integration platform.

[0016] Furthermore, based on a 3D digital integration platform, three positioning monitoring nodes are pre-set at the main entrance of the utility tunnel, key ventilation nodes, and pipeline intersections to construct a stable triangular positioning monitoring architecture. The geometric center and side length relationships 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 set up at the main entrance of the utility tunnel to monitor the flow rate and structural stability of the incoming and outgoing pipelines; a second positioning monitoring node is set up at the key ventilation node to collect air quality, temperature, humidity and wind speed data; and a third positioning monitoring node is set up 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 and monitoring nodes, the geometric center coordinates of the triangular positioning and monitoring architecture area are calculated, and based on the difference between the maximum and minimum distances between adjacent nodes, initial topology parameters characterizing pipeline distribution density are generated.

[0019] By combining the actual burial depth data of pipelines and the distribution characteristics of pipeline types stored in the central database, the initial topology parameters are weighted and corrected to generate spatial topology correction values ​​that reflect the pipeline crossing depth, safety distance, and risk level of concealed areas.

[0020] Furthermore, a dynamic network is constructed based on spatial topology correction values ​​to update pipeline layout data in real time and trigger collision risk warnings, including:

[0021] Based on the spatial topology correction value, the pipeline crossing depth, safety distance and risk level of the hidden area are extracted, and the corrected spatial topology parameters are combined with the pipeline geometric dimensions and connection relationship data in the three-dimensional 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 details of hidden areas.

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

[0024] Risk levels are categorized based on the degree of deviation from the threshold, and early warning information is generated according to the risk level, including the coordinates of the abnormal location, pipeline code, risk description, and recommended actions.

[0025] Furthermore, by integrating real-time data collected by IoT sensors on the pipe gallery environment with a 3D digital integration platform, when leaks, temperature, or pressure anomalies are detected, the system automatically pinpoints the exact location of the anomaly by combining the correction values ​​of the monitoring area from the triangulation monitoring architecture with the 3D coordinates of the pipeline. This generates a decision dataset including emergency operation instructions, which includes:

[0026] The system receives real-time data streams of leakage concentration, temperature change rate, and pressure gradient from IoT sensors within the monitoring area of ​​the triangulation monitoring architecture. Through a cloud data engine, the sensor data is aligned with the pipeline's three-dimensional coordinates on the three-dimensional digital integration platform to generate a set of anomalous data with spatial correlation.

[0027] Based on the pipeline crossing 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 and influence radius of the abnormal points.

[0028] Based on the corrected coordinates of the outliers, a bidirectional index table with time-series labels is retrieved in reverse. The encrypted identifier of the ownership unit of the corresponding pipeline unit, the pipeline material parameters in the construction and acceptance data, and the timeliness of handling similar faults in past operation and maintenance records are extracted to construct an outlier feature dataset including pipeline failure level and emergency response timeliness threshold.

[0029] Based on the anomaly feature dataset, the system calls the preset rules for the correlation between pipeline material and leakage diffusion rate, and the relationship between temperature threshold and ventilation efficiency in the central cloud database to generate a decision dataset for emergency operation instructions.

[0030] Furthermore, based on the corrected anomaly coordinates, a reverse retrieval of the bidirectional indexed relation table with time-series labels is performed to extract the encrypted identifier of the ownership unit of the corresponding pipeline unit, pipeline material parameters from the construction and acceptance data, and the timeliness of handling similar faults from past operation and maintenance records. This constructs an anomaly feature dataset including pipeline failure levels and emergency response timeliness thresholds, comprising:

[0031] Based on the compressive strength, corrosion resistance and past leakage records in the pipeline material parameters, the level classification of the leakage diffusion rate at the current abnormal point is determined by the preset pipeline material compressive strength-corrosion classification rules, and the corresponding pipeline failure level is generated.

[0032] Based on the pipeline failure level, the shortest response time, average response time, and promised response time defined in the service level agreement of the owner are extracted. Based on the ratio between the shortest response time and the promised response time, and combined with the distribution range of the average response time, an emergency response time threshold associated with the current anomaly location is generated.

[0033] Based on the pipeline failure level and the emergency response time threshold, combined with the encrypted identification of the corresponding emergency resource distribution density of the property rights unit and the cross-departmental collaborative response rules, an abnormal point feature data set is generated, including the disposal of abnormal points, the constraint conditions of the multi-departmental collaborative path, and the dynamic operation instructions matching the time threshold.

[0034] Further, based on the decision data set, the distribution of obstacles on the repair path is simulated through the spatial topology correction value, and the final cloud detour plan is generated by combining the real-time device status data, and the linkage instructions are synchronously sent to the fire-fighting device, the ventilation device, and the remote valve control terminal, including:

[0035] Based on the pipeline crossing depth parameter in the spatial topology correction value, the three-dimensional coordinate sets of the pipeline crossing points, valve nodes, and ventilation shafts on the repair path are dynamically rendered in the three-dimensional digital integration platform, and a three-dimensional space obstacle topology network including topological constraints is constructed;

[0036] Based on the three-dimensional space obstacle topology network, integrating the valve opening and closing angle, ventilation device rotation speed, and fire water tank water level parameters in the real-time device status data, the cloud path planning engine performs a feasibility analysis on the distribution of obstacles to generate an initial detour plan set including the safety factor of alternative routes and the device operation timing sequence;

[0037] According to the initial detour plan set, combined with the pipeline distribution density parameter and the collision risk level data, by calculating the spatial topology relationship between each path and the high-risk hidden area, a final detour plan is generated, marking the avoidance coordinate points, the emergency resource delivery density, and the device collaboration parameters;

[0038] The device operation instructions in the final detour plan are disassembled according to the property rights unit permissions, and at the same time, the real-time task status of multi-departmental resource scheduling is updated.

[0039] Further, the device operation instructions include sending the sprinkler area coordinates and water volume control parameters to the fire-fighting device, sending the directional pressurization instruction and the abnormal point coordinate radius to the ventilation device, and transmitting the opening and closing command with a time sequence lock to the valve control terminal.

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

[0041] One or more processors;

[0042] A storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the system.

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

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

[0045] Integrating various data throughout the entire lifecycle of underground utility tunnels, the system presents information on power, communication, and drainage pipelines. Maintenance personnel can intuitively access detailed information on pipeline layout, materials, and specifications, providing precise data support for daily inspections and management. In the event of a fault within the tunnel, such as a pipe rupture or equipment malfunction, the integrated data allows for rapid fault location. Combined with information on surrounding pipelines and equipment, the system intelligently analyzes the impact range of the fault, generating a final emergency repair plan, shortening troubleshooting and repair time, and minimizing the impact on surrounding areas. It breaks down communication barriers between departments in traditional maintenance, enabling real-time information sharing and collaborative operations among maintenance, repair, and management departments. In emergencies, departments can respond quickly and cooperate efficiently based on a unified information platform, improving overall maintenance management efficiency. Real-time monitoring and analysis of tunnel operation data allows for the early prediction of potential risks, such as pipeline aging and abnormal environmental parameters, and timely issuance of warnings. Maintenance personnel can then take preventative maintenance measures based on these warnings, reducing the probability of accidents and ensuring the safe and stable operation of the utility tunnel. Accurate fault location and efficient operation and maintenance management reduce the waste of manpower and material resources, extend the service life of utility tunnels and pipeline equipment, and avoid major economic losses caused by faults, thereby reducing the operation and maintenance costs of underground utility tunnels in many ways. Attached Figure Description

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

[0047] Figure 2 This is a flowchart illustrating the process provided by an embodiment of the present invention, which uses a decision dataset to simulate the distribution of obstacles along the emergency repair path through spatial topology correction values, combines real-time equipment status data to generate a final cloud-based detour plan, and synchronously sends linkage commands to fire-fighting equipment, ventilation equipment, and remote valve control terminals. Detailed Implementation

[0048] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the 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 underground integrated utility tunnels, comprising:

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

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

[0052] A warning module is built 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 and decision module is used to integrate the real-time collection of the pipe gallery environment by IoT sensors with the three-dimensional digital integration platform. When leakage, temperature and pressure abnormalities are detected, the module combines the correction value of the monitoring area of ​​the triangular positioning monitoring architecture and the three-dimensional coordinates of the pipeline to automatically mark the specific location of the abnormal point and generate a decision dataset including 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 dataset and spatial topology correction value, and generate the final cloud detour plan by combining real-time equipment status data, and synchronously issue linkage commands 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 in the cloud to achieve centralized management of the whole life cycle data of communication pipelines, the information of the ownership units, and the real-time operation parameters. A unique code is assigned to each pipeline unit and a cloud index is established, enabling operation and maintenance personnel to quickly and accurately query and call various types of information, thus bidding farewell to the problems of traditional scattered information and difficult search. Positioning and monitoring nodes are preset at key positions in the utility tunnel to construct a monitoring area with a stable triangular positioning monitoring architecture. By calculating geometric relationships, a spatial topology correction value is generated, effectively improving the accuracy of the spatial positioning of the utility tunnel, solving the error problem existing in traditional positioning, and ensuring more accurate control of the spatial positions of pipelines and equipment in the utility tunnel. A dynamic network is constructed based on the spatial topology correction value to update the pipeline layout data in real time, enabling timely discovery of potential pipeline collision risks and triggering warnings, helping operation and maintenance personnel to take measures in advance to avoid failures caused by pipeline collisions, ensuring the safe operation of various pipelines in the utility tunnel, and reducing the probability of accidents. The environmental data collected by the Internet of Things sensors is integrated with the three-dimensional digital integration platform. When leaks, abnormal temperatures, and pressures are detected, combined with the spatial correction value and the three-dimensional coordinates of the pipelines, the positions of the abnormal points can be quickly and accurately marked, and a decision-making data set including emergency operation instructions can be generated, providing scientific and effective guidance for fault handling. Based on the decision-making data set, the distribution of obstacles on the repair path is simulated using the spatial topology correction value, a cloud bypass plan is generated, and linkage instructions are synchronously issued to achieve dynamic scheduling of resources from multiple departments. This process can effectively integrate resources from all parties and the repair process, avoiding repair delays caused by poor information flow and improper resource allocation, ensuring rapid, efficient, and orderly emergency response, and reducing losses caused by faults.

[0056] In a preferred embodiment of the present invention, a three-dimensional digital integration platform for an underground utility tunnel is constructed in the cloud. The whole life cycle data of communication pipelines, the information of the ownership units, and the real-time operation parameters are uploaded to the cloud central database, and a unique code is assigned to each pipeline unit to establish a corresponding cloud index relationship, which may include:

[0057] Independent data storage blocks are divided in the cloud central database according to the ownership units, pipeline types, and operation and maintenance stages, and the design parameters, construction acceptance data, and past operation and maintenance records of the communication pipelines are classified and stored in the corresponding blocks;

[0058] Based on the division rules of the data blocks, a code including the ownership unit identifier, pipeline type code, and three-dimensional coordinate characteristic value is generated for each pipeline unit through the cloud coding service, and a two-way index relationship table with time sequence tags is established between the code and the design parameters and construction acceptance data of the corresponding block;

[0059] Based on the time-series labels of the bidirectional index relationship table, the system receives the operating parameters uploaded by pipeline sensors in real time through the cloud data channel, matches the parameter values ​​with the corresponding pipeline codes, and inputs them into the dynamic fields of the index relationship table, thus synchronously updating the status identifiers and attribute parameters of the pipelines in the 3D digital integration platform.

[0060] In this embodiment of the invention, maintenance personnel will divide the communication pipelines within the utility tunnel into independent data storage blocks in the central cloud database according to the actual situation of the pipelines, based on ownership (e.g., different communication operators), pipeline type (fiber optic, cable, etc.), and maintenance stage (design, construction, operation, etc.). For example, all data on a communication operator's fiber optic pipelines from the design stage to the operation stage will be uniformly stored in the fiber optic pipeline data block corresponding to that operator. In this way, data from different sources and of different types will have their own clear "storage space," avoiding data chaos and accumulation. Based on the pre-divided data blocks, the cloud coding service will generate a specific code for each pipeline unit. The code includes an ownership identifier (used to distinguish different ownership units), a pipeline type code (clearly indicating the specific type of pipeline), and three-dimensional coordinate feature values ​​(locating the pipeline's position in the utility tunnel). For example, for a certain fiber optic pipeline, its code will be generated by combining the identifier of the operator, the fiber type code, and its three-dimensional coordinate information in the utility tunnel. At the same time, a two-way index relationship table will be established between this code and the design parameters and construction acceptance data in the corresponding data block, and a time sequence tag will be added. This is akin to creating a detailed "archive index" for each pipeline. The coding allows for quick retrieval of corresponding data and enables reverse lookup of the pipeline from the data. Furthermore, the time-series tags record the order in which the data was generated, facilitating the tracking of data changes. Pipeline sensors within the utility tunnel collect operational parameters in real time, such as pipeline temperature and pressure. This data is transmitted via a cloud data channel. Based on the time-series tags in the bidirectional index table, the operational parameters are matched with the corresponding pipeline codes. Once a match is successful, the parameter value is input into the dynamic field of the index table, and the status identifier and attribute parameters of the pipeline in the 3D digital integration platform are updated simultaneously. For example, when the temperature of a pipeline rises, the sensor collects and transmits the data, finds the corresponding pipeline code, updates the temperature data in the index table, and displays the abnormal temperature status of the pipeline on the platform, allowing maintenance personnel to promptly grasp the pipeline's operational status.

[0061] The data storage block division enhances the standardization and organization of data management. Data from different ownership units and different types of pipelines is stored independently, facilitating quick retrieval of required data by maintenance personnel. The establishment of unique codes and bidirectional indexed relationship tables enables precise correlation between pipelines and data, making information retrieval fast and accurate. Maintenance personnel only need to know the pipeline code to quickly obtain all relevant data from design to operation, including design parameters and construction acceptance status; conversely, they can quickly locate the corresponding pipeline unit through the data. This precise correlation also facilitates full lifecycle management of pipelines. Time-series tags clearly show the status changes of pipelines at different stages, providing comprehensive data support for pipeline maintenance and renovation. The real-time data update function allows maintenance personnel to grasp the operational status of pipelines promptly and accurately. Data collected by sensors is synchronized to the platform display in real time. Once an anomaly occurs in the pipeline, such as temperature or pressure exceeding normal ranges, maintenance personnel can detect it immediately and take measures, effectively shortening the fault detection time, improving fault early warning capabilities, and providing strong support for ensuring the safe and stable operation of the pipeline corridor, avoiding serious accidents and losses caused by failure to detect problems in a timely manner.

[0062] In a preferred embodiment of the present invention, based on a three-dimensional digital integration platform, three positioning and monitoring nodes are preset at the main entrance of the utility tunnel, key ventilation nodes, and pipeline intersections to construct a stable triangular positioning and monitoring architecture. The geometric center and side length relationship of the monitoring architecture are calculated using the three-dimensional coordinates of the three positioning and monitoring nodes to generate spatial topology correction values, 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 set up at the main entrance of the utility tunnel to monitor the flow rate and structural stability of the incoming and outgoing pipelines; a second positioning monitoring node is set up at the key ventilation node to collect air quality, temperature, humidity and wind speed data; and a third positioning monitoring node is set up 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 and monitoring nodes, the geometric center coordinates of the triangular positioning and monitoring architecture area are calculated, and based on the difference between the maximum and minimum distances between adjacent nodes, initial topology parameters characterizing pipeline distribution density are generated.

[0065] By combining the actual burial depth data of pipelines and the distribution characteristics of pipeline types stored in the central database, the initial topology parameters are weighted and corrected to generate spatial topology correction values ​​that reflect the pipeline crossing depth, safety distance, and risk level of concealed areas.

[0066] In this embodiment of the invention, maintenance personnel plan and deploy location monitoring nodes based on the pipeline layout data and ownership information recorded in the 3D digital integration platform. At the main entrance of the utility tunnel, a first location monitoring node is deployed as it is a critical passage for pipelines entering and exiting the tunnel. This node is equipped with corresponding monitoring equipment to continuously monitor the flow data of the entering and exiting pipelines. Simultaneously, sensors are used to detect the structural stability of the main entrance, observing for cracks or abnormal settlement. At a critical ventilation node, considering its crucial role in regulating air quality, temperature, humidity, and wind speed within the tunnel, a second location monitoring node is deployed, equipped with 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, where multiple pipelines are concentrated and the risk of mutual interference is high, a third location monitoring node is deployed. Pressure sensors and displacement monitoring equipment are used to closely monitor pressure changes and displacement of each pipeline in this area, promptly identifying potential hazards.

[0067] After the three positioning and monitoring nodes are deployed and their 3D coordinate data is acquired, the crucial step of calculating the geometric center coordinates and initial topological parameters of the triangulation positioning and monitoring architecture area begins. The first step is to calculate the geometric center coordinates of the monitoring architecture area, i.e., the centroid position. Assume the three positioning and monitoring nodes are... , , Their corresponding three-dimensional coordinates are respectively , ( ), ( In actual calculations, the three nodes are first placed in... The coordinate values ​​along the axis are added together, that is ( + + Divide by the number of nodes (3) to get the centroid at... Coordinate values ​​in the axial direction Similarly, in Along the axis, the three nodes Add the coordinates together and divide by 3 to get ;exist Perform the same operation along the axis to obtain... Through this series of calculations, the centroid coordinates of the area of ​​the triangulation monitoring architecture were finally determined to be ( , , This coordinate system represents the geometric center coordinates. Next, the distance between adjacent nodes is measured. The distance between two points in three-dimensional space is calculated using the formula for the distance between the nodes. and Distance between Similarly, the nodes are calculated. and Distance between and nodes and Distance between After obtaining the lengths of these three sides, compare their sizes and find the maximum value. and minimum value Then calculate the difference between the two. Finally, initial topology parameters are generated based on this difference relationship. If... A large value indicates that the pipeline distribution within the area enclosed by the three monitoring nodes is extremely uneven. For example, a large difference may indicate that the pipelines are densely packed in one section and sparsely distributed in another. This situation can easily lead to collision risks during construction or maintenance and is not conducive to the rational allocation of resources; conversely, if... A smaller value indicates that the pipeline distribution in the area is relatively uniform, resulting in lower pipeline layout complexity and risks during pipeline corridor planning, construction, or maintenance. Through such calculations and analysis, initial topology parameters that can preliminarily reflect the pipeline distribution density are generated.

[0068] After obtaining the initial topology parameters reflecting the pipeline distribution density, a weighted correction is needed, incorporating the rich pipeline information in the central database, to generate spatial topology correction values ​​that better reflect the actual situation. Two key types of information are extracted from the central database. First, actual pipeline burial depth data, classifying the burial depth of all pipelines within the utility tunnel into three levels: shallow (0-2 meters), medium (2-5 meters), and deep (over 5 meters). Second, pipeline type distribution characteristics, clearly distinguishing between different types such as power lines, gas lines, communication lines, and drainage lines, and compiling the corresponding safety distance standards and potential risk levels for each type. For example, a gas pipeline leak could potentially cause an explosion and is therefore classified as a high-risk type; a common communication line, even if it malfunctions, has a relatively small impact on other pipelines and the surrounding environment, thus belonging to a low-risk type. Next, appropriate weights are assigned to different influencing factors. Taking pipeline type as an example, high-risk gas pipelines, due to the extremely serious hazards posed by accidents, are assigned a high weight of 0.8 to the initial topology parameter influence factor related to gas pipelines; medium-risk power pipelines, considering the risks of leakage and short circuits, are assigned a weight of 0.6; and low-risk ordinary communication pipelines are assigned a low weight of 0.2. Regarding pipeline burial depth, pipelines in shallow burial areas are greatly affected by surface construction and environmental changes, so the parameter weights corresponding to shallow burial level pipelines are set to 0.7; medium burial areas are assigned a weight of 0.5; and deep burial areas are assigned a weight of 0.3. After setting the weights, the weighted calculation begins. Assume the initial topology parameters are... The parameter impact value related to the gas pipeline type is Corresponding weight =0.8; the influence value of parameters related to power line type is... Corresponding weight =0.6; the parameter influence value related to the communication pipeline type is... Corresponding weight =0.2; the parameter influence value of shallow buried pipelines is Corresponding weight =0.7, and so on. Multiply all the influencing factors by their corresponding weights and sum them up. Then multiply the sum by the initial topology parameters to obtain the corrected parameter values, and finally generate the spatial topology correction values.

[0069] Based on the functional and risk characteristics of different locations within the utility tunnel, positioning monitoring nodes are deployed to achieve targeted monitoring of key areas. Monitoring pipeline flow and structural stability at the main entrance helps to promptly detect abnormal pipeline connections or structural damage, ensuring the safety of the tunnel's "gateway." Collecting environmental data at key ventilation nodes effectively monitors air quality and ventilation within the tunnel, ensuring a suitable environment and preventing safety accidents caused by poor ventilation. Monitoring pressure and displacement changes at pipeline intersections can detect potential risks in areas with multiple pipeline interactions in advance, preventing accidents caused by pipeline compression or leakage. By calculating the geometric center coordinates of the triangulated monitoring architecture area and generating initial topological parameters based on the node spacing differences, quantitative indicators are provided for the spatial analysis of the utility tunnel. Maintenance personnel can intuitively understand the approximate pipeline distribution, determining which areas have dense pipeline distribution and which are sparse. This helps in rationally arranging construction plans when planning new pipeline laying and maintenance work, avoiding densely populated pipeline areas, reducing the impact of construction on existing pipelines, and improving construction safety and efficiency. By combining multiple factors to weightedly adjust the initial topology parameters and generate a spatial topology correction value, the assessment of utility tunnel space becomes more scientific and accurate. This correction value fully considers key factors such as pipeline burial depth and type, accurately reflecting the actual risk status of different areas. Based on this value, maintenance personnel can more clearly identify high-risk hidden areas within the utility tunnel, enabling them to strengthen targeted inspections and maintenance, take preventative measures in advance, and reduce the probability of accidents. Furthermore, when conducting spatial planning and resource allocation, more rational decisions can be made based on accurate risk assessments, ensuring the safe and efficient operation of the utility tunnel.

[0070] In a preferred embodiment of the present invention, constructing a dynamic network based on spatial topology correction values, updating pipeline layout data in real time, and triggering collision risk warnings may include:

[0071] Based on the spatial topology correction value, the pipeline crossing depth, safety distance and risk level of the hidden area are extracted, and the corrected spatial topology parameters are combined with the pipeline geometric dimensions and connection relationship data in the three-dimensional 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 details of hidden areas.

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

[0074] Risk levels are categorized based on the degree of deviation from the threshold, and early warning information is generated according to the risk level, including the coordinates of the abnormal location, pipeline code, risk description, and recommended actions.

[0075] In this embodiment of the invention, key data such as pipeline crossing depth, safety spacing, and risk level of concealed areas are first extracted from the generated spatial topology correction values. For example, for a certain pipeline crossing area, its crossing depth value, whether the safety spacing meets the standard, and the risk level assessment result are obtained. Then, these corrected spatial topology parameters are integrated with the pipeline geometric dimensions (such as pipeline diameter and length) and connection relationship data (such as the connection method between pipelines and equipment and nodes) stored in the 3D digital integration platform. In the integration process, it is like building a virtual "pipeline corridor network", matching the specific information of each pipeline according to the actual layout relationship, and constructing a dynamic network structure that can reflect the actual status of pipelines in the pipeline corridor in real time. Based on the constructed dynamic network structure, the pipeline geometric dimensions, material properties (such as metal material, plastic material, etc.) and real-time operating parameters (such as temperature and pressure data) are used to dynamically display the pipeline layout on the 3D digital integration platform. During the rendering process, various types of pipelines are distinguished according to the actual situation of the pipelines, and the pipeline crossing depth, spacing distribution, and concealed area details are presented intuitively. Preset safety thresholds for pipeline crossing angles and adjacent pipeline spacing are established. These thresholds are determined based on industry standards and practical experience. For example, a pipeline crossing angle <30 degrees may pose a collision risk, and a distance of <1 meter between adjacent gas pipelines is considered a dangerous distance. During real-time pipeline layout rendering, these data are continuously monitored. Once a pipeline crossing angle <the preset safety threshold or an adjacent pipeline spacing <the safety threshold is detected in a certain area, a collision risk warning signal is immediately triggered, alerting maintenance personnel to a potential hazard. After a collision risk warning is triggered, the risk level is determined based on the degree of deviation from the threshold. For example, a small deviation of the pipeline crossing angle from the safety threshold is classified as low risk, while a larger deviation is classified as high risk. Detailed warning information is then generated based on the different risk levels. The early warning information includes the coordinates of the abnormal location (accurate to the specific three-dimensional location within the utility tunnel), the code of the pipeline involved (the code allows for quick lookup of detailed pipeline information), a risk description (specifically, whether it is an intersection angle issue or a spacing issue, and the severity of the problem), and recommended actions (e.g., for low-risk issues, it is recommended to strengthen monitoring; for high-risk issues, it is recommended to immediately arrange personnel for rectification and maintenance), providing clear and specific handling guidelines for operation and maintenance personnel.

[0076] By integrating multi-dimensional data to construct a dynamic network structure, comprehensive integration and real-time updates of pipeline information within the utility tunnel are achieved. Maintenance personnel can quickly access various key pipeline information on a unified platform, eliminating the tedious process of searching for data in multiple files. This comprehensive and real-time information display improves the efficiency and accuracy of maintenance management. Dynamic rendering presents pipeline layout and risk details intuitively, reducing the difficulty for maintenance personnel to understand complex pipeline information. Potential risk areas are quickly identified. This intuitive display helps maintenance personnel plan inspection routes in advance, conduct targeted inspections of key areas, avoid risk omissions due to unclear information, and improve the effectiveness of risk prevention and control. Preset thresholds and real-time monitoring trigger early warnings, issuing alerts before pipeline collision risks occur. This early warning mechanism shortens risk discovery time, buys valuable processing time for maintenance personnel, effectively reduces the probability of equipment damage, leaks, and other accidents caused by pipeline collisions, and ensures the safe and stable operation of various pipelines within the utility tunnel. Scientific risk level classification and detailed early warning information enable maintenance personnel to rationally arrange the processing sequence and resource allocation according to the severity of risks. For high-risk situations, professional personnel can be immediately organized for emergency repairs; for low-risk situations, a reasonable monitoring and maintenance plan should be developed.

[0077] In a preferred embodiment of the present invention, the real-time acquisition of the pipe gallery environment by IoT sensors is integrated with a three-dimensional digital integration platform. When leakage, temperature, or pressure anomalies are detected, the specific location of the anomaly point is automatically determined by combining the correction value of the monitoring area of ​​the triangulation monitoring architecture and the three-dimensional coordinates of the pipeline. A decision dataset including emergency operation instructions is generated, which may include:

[0078] The system receives real-time data streams of leakage concentration, temperature change rate, and pressure gradient from IoT sensors within the monitoring area of ​​the triangulation monitoring architecture. Through a cloud data engine, the sensor data is aligned with the pipeline's three-dimensional coordinates on the three-dimensional digital integration platform to generate a set of anomalous data with spatial correlation.

[0079] Based on the pipeline crossing 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 and influence radius of the abnormal points.

[0080] Based on the corrected anomaly coordinates, a bidirectional index table with time-series labels is retrieved in reverse. The encrypted identifier of the corresponding pipeline unit's ownership, pipeline material parameters from construction and acceptance data, and the timeliness of handling similar faults from past maintenance records are extracted to construct an anomaly feature dataset including pipeline failure levels and emergency response timeliness thresholds. Specifically, this includes: determining the current anomaly point's leakage diffusion rate level based on pipeline material parameters such as compressive strength, corrosion resistance, and past leakage records, using pre-defined pipeline material compressive strength-corrosion grading rules; generating the corresponding pipeline failure level; extracting the shortest handling time, average handling time, and the promised response time defined in the ownership unit's service level agreement based on the pipeline failure level; generating an emergency response timeliness threshold associated with the current anomaly point location based on the ratio of the shortest handling time to the promised response time, combined with the distribution range of the average handling time; and generating an anomaly point feature dataset including anomaly point handling, constraints on multi-department collaborative paths, and dynamic operation instructions matching time thresholds based on the pipeline failure level and emergency response timeliness thresholds, combined with the emergency resource distribution density corresponding to the ownership unit's encrypted identifier and cross-departmental collaborative response rules.

[0081] Based on the anomaly feature dataset, the system calls the preset rules for the correlation between pipeline material and leakage diffusion rate, and the relationship between temperature threshold and ventilation efficiency in the central cloud database to generate a decision dataset for emergency operation instructions.

[0082] In this embodiment of the invention, various data streams uploaded in real time from IoT sensors within the monitoring area of ​​the triangulation monitoring architecture are received. These data streams include environmental data such as leakage concentration, temperature change rate, and pressure gradient. After receiving this data, the cloud data engine aligns and matches the sensor data with the three-dimensional coordinates of the pipeline in the three-dimensional digital integration platform. Specifically, this involves mapping the data detected by each sensor to a specific pipeline location in three-dimensional space. For example, if a sensor at a certain 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 pipeline locations have experienced anomalies in which environmental parameters.

[0083] In actual utility tunnel environments, the intersecting layout of pipelines can interfere with the accurate location of anomalies by sensors. Therefore, it is necessary to process the coordinates in the anomaly dataset based on the pipeline intersection depth parameter in the spatial topology correction value, and retrieve the pipeline intersection depth data of the target area from the spatial topology correction value. For example, in an area where power and water supply / drainage pipelines intersect in a utility tunnel, 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 leak in the water supply / drainage pipeline, the initial location coordinates are ( When analyzing the impact of the intersection depth on the abnormal propagation path, since leaking water will seep downwards due to gravity and diffuse laterally due to the obstruction of the intersecting pipelines, the actual leak point may not be the vertical location detected by the sensor. Assuming past experience shows that in such intersection structures, the lateral offset distance of the leak point is proportional to the intersection depth, with an offset coefficient of 0.5, the initial horizontal coordinate is... Adjust 0.4 meters to the left (0.8 meters × 0.5 meters) to obtain the corrected coordinates. ); Simultaneously considering the water flow seepage characteristics, the vertical axis... Adjust downwards by 0.3 meters to obtain the final corrected coordinates. After determining the coordinates of the anomaly point, the impact range is estimated based on real-time monitoring data. If the leakage concentration rises from 5% to 20% within 10 minutes, a pre-set concentration-diffusion rate comparison table indicates that the leak is in a rapid diffusion state; simultaneously, referring to soil permeability data in the intersection area, the impact radius is expanded from the initial 2 meters to 5 meters. If an abnormal temperature is detected in the power line, the impact radius is reduced from 3 meters to 1.5 meters based on the insulation material properties of the intersection lines (such as the thermal conductivity of ceramic insulation layers) and the rate of temperature change. Through this multi-parameter fusion analysis, the precise impact range of the anomaly is ultimately determined.

[0084] Based on the corrected anomaly coordinates, a bidirectional index table with time-series labels is retrieved in reverse. This index table records the association information between pipeline units and various types of data, allowing for quick location of the corresponding pipeline unit using the anomaly coordinates. Then, the encrypted identifier of the ownership unit for each pipeline unit is extracted from the index table to clarify the responsible party; pipeline material parameters are extracted from construction and acceptance data to understand the basic characteristics of the pipeline; and the timeliness of handling similar faults in past maintenance records is extracted. Combining this information, an anomaly feature dataset is constructed, including pipeline failure levels and emergency response timeliness thresholds. Based on this constructed anomaly feature dataset, various preset association rules and correlation relationships in the cloud-based central database are invoked. For example, the association rule between pipeline material and leakage diffusion rate is invoked to predict the possible diffusion speed and range of the leak based on the current pipeline material and leakage situation; the correlation relationship between temperature threshold and ventilation efficiency is invoked to determine the required ventilation volume to effectively reduce risk under current abnormal temperature conditions. Combining these rules and relationships, a decision dataset including emergency operation instructions is generated. These instructions are specific and clear, such as suggesting the closure of a valve or the activation of specific ventilation equipment, providing direct operational guidance for maintenance personnel.

[0085] By aligning sensor data with the three-dimensional coordinates of pipelines, precise matching of environmental data and spatial location is achieved. This allows maintenance personnel to quickly pinpoint the specific pipeline and location where the anomaly occurred, shortening troubleshooting time. Correcting the anomaly point coordinates by considering pipeline intersection depth improves the accuracy of anomaly location. For example, when deploying repair personnel and equipment, the required resources can be determined based on the radius of influence, avoiding resource waste or inadequacy. A feature dataset is constructed by extracting pipeline-related information from multiple dimensions, with encrypted identification of the ownership unit clearly defining the responsible party, facilitating rapid coordination with relevant units. Pipeline material parameters and past maintenance records help in developing 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 operable, guiding maintenance personnel to quickly take effective countermeasures and reduce losses caused by decision-making delays or improper operation.

[0086] like Figure 2 As shown, in a preferred embodiment of the present invention, based on a decision dataset, the distribution of obstacles along the repair path is simulated using spatial topology correction values. This is combined with real-time equipment status data to generate a final cloud-based detour plan, and then synchronized with fire-fighting equipment, ventilation systems, and remote valve control terminals, issuing linkage commands. This may include:

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

[0088] Based on a three-dimensional spatial obstacle topology network, and integrating parameters such as valve opening and closing angles, ventilation device rotation speeds, and fire water tank levels from real-time equipment status data, a cloud-based path planning engine is used to perform a feasibility analysis of obstacle distribution and generate an initial detour scheme 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 final detour plan is generated by calculating the spatial topological relationship between each path and high-risk concealed areas, and marking avoidance coordinate points, emergency resource deployment density and equipment coordination parameters.

[0090] The equipment operation instructions in the final detour plan are broken down according to the authority of the responsible unit, and the real-time task status of multi-department resource scheduling is updated. Specifically, the equipment operation instructions include sending the coordinates of the sprinkler area and water volume control parameters to the fire-fighting device, sending the directional pressurization instruction and the coordinate radius of the abnormal point to the ventilation device, and transmitting the opening and closing command with time lock to the valve control terminal.

[0091] In this embodiment of the invention, pipeline intersection depth parameters are extracted from spatial topology correction values. Simultaneously, the 3D coordinates of all pipeline intersections, valve nodes, and ventilation shafts along the repair path are located in a 3D digital integration platform. For example, on a certain repair path, the specific coordinates of 5 pipeline intersections, 3 valve nodes, and 2 ventilation shafts are determined. Then, this coordinate information is integrated, and their 3D shapes are dynamically rendered according to their actual spatial relationships, constructing a 3D spatial obstacle topology network including topological constraints. This network clearly shows the positions and interrelationships of all obstacles along the repair path that may affect passage and operations. Based on the 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 rotation speed, and fire water tank level. For example, it is obtained that the current opening and closing angle of a valve is 45 degrees, the ventilation device rotation speed is 1200 revolutions per minute, and the fire water tank level is 60%. Next, the cloud-based path planning engine performs a feasibility analysis of the obstacle distribution. During the analysis, the engine considers the spatial location of obstacles and the impact of equipment operating status on passage. For example, if a valve is half-open, it will be assessed whether it will obstruct the passage of repair personnel and equipment; whether a low-speed ventilation system 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 a safety factor for the alternative routes (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 valve first, then starting a specific ventilation system). Based on the initial detour plan set, combined with pipeline distribution density parameters and collision risk level data, the plans are further optimized. By analyzing the spatial topology relationship between each alternative path and high-risk concealed areas (e.g., areas where shallowly buried gas pipelines intersect), it is determined whether a particular alternative route is close to a high-risk area; the closer the distance, the higher the risk. Then, the initial plans are adjusted and improved to generate the final detour plan. In the final plan, the coordinates of points that need to be avoided are clearly marked, the density of emergency resource deployment is planned (e.g., doubling the deployment of fire-fighting equipment near high-risk areas), and equipment coordination parameters are determined (e.g., the time interval between the linkage activation of fire-fighting equipment and ventilation systems) to ensure that the plan is both safe and efficient. Meanwhile, the task status of resource scheduling across multiple departments is updated in real time, including information such as personnel availability and equipment transportation progress. Based on the received instructions and task status, each department coordinates emergency work, monitors the execution process, and adjusts instructions promptly in case of problems, forming a complete cloud-driven closed-loop emergency response process until the emergency task is completed.

[0092] By constructing a detailed obstacle topology network, maintenance and emergency response personnel can clearly understand the complexities of the repair path before formulating a repair plan. This avoids route planning errors and equipment collisions during repairs due to unfamiliarity with obstacle locations, laying the foundation for efficient and safe repair work and saving time on site surveys and planning. The initial detour plan set is generated by integrating real-time equipment status data, fully considering the impact of actual equipment operation on repairs. This generated plan is more closely aligned with the actual scenario, ensuring that repair personnel are not hindered by abnormal equipment operation during execution. Simultaneously, multiple alternative plans provide flexible selection space, allowing for rapid adjustments based on actual site conditions, improving the adaptability and reliability of emergency response. The final detour plan is generated by combining various data sources, achieving scientific planning of the repair path and precise risk control. Marking avoidance coordinates and planning resource deployment 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 cooperation between devices, improving the overall effectiveness of emergency response and reducing losses caused by accidents. Instructions are broken down according to the authority of the responsible unit, and task status is updated in real time, enabling efficient collaboration and rational resource allocation among multiple departments. Each department clearly understands its own responsibilities and task progress, avoiding inefficiencies in emergency response caused by poor communication and unclear responsibilities. Real-time monitoring and dynamic adjustment functions ensure that the emergency response process can be optimized in a timely manner according to the actual situation, forming a complete and efficient emergency management system and ensuring the safe and stable operation of the underground utility tunnel.

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

[0094] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0095] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels, characterized in that, include: The 3D integration module is used to build a 3D digital integration platform for underground utility tunnels in the cloud. It uploads the full life cycle data of communication pipelines, ownership information and real-time operating parameters to the central database in the cloud, and assigns a unique code to each pipeline unit to establish a corresponding cloud index relationship. The spatial correction module, based on a 3D digital integration platform, pre-sets three positioning monitoring nodes at the main entrance of the utility tunnel, key ventilation nodes, and pipeline intersections to construct a stable triangular positioning monitoring architecture. It then calculates the geometric center and side length relationships of the monitoring architecture using the 3D coordinates of the three monitoring nodes to generate spatial topology correction values. Specifically, this includes: deploying a first positioning monitoring node at the main entrance of the utility tunnel, based on pipeline layout data and ownership information stored in the 3D digital integration platform, to monitor the flow rate and structural stability of incoming and outgoing pipelines; deploying a second positioning monitoring node at key ventilation nodes to collect air quality, temperature, humidity, and wind speed data; and deploying a third positioning monitoring node at the intersection of power, water, and gas pipelines to monitor pressure and displacement changes in the multi-pipeline interaction area. Based on the 3D coordinate data of the three monitoring nodes, it calculates the geometric center coordinates of the triangular positioning monitoring architecture area and generates initial topology parameters characterizing pipeline distribution density based on the difference between the maximum and minimum distances between adjacent nodes. By combining the actual burial depth data of pipelines and the distribution characteristics of pipeline types stored in the central database, the initial topology parameters are weighted and corrected to generate spatial topology correction values ​​that reflect the pipeline crossing depth, safety distance and risk level of concealed areas. A warning module is built 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 and decision module is used to integrate the real-time collection of the pipe gallery environment by IoT sensors with the three-dimensional digital integration platform. When leakage, temperature and pressure abnormalities are detected, the module combines the correction value of the monitoring area of ​​the triangular positioning monitoring architecture and the three-dimensional coordinates of the pipeline to automatically mark the specific location of the abnormal point and generate a decision dataset including 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 dataset and spatial topology correction value, and generate the final cloud detour plan by combining real-time equipment status data, and synchronously issue linkage commands to fire-fighting equipment, ventilation equipment and remote valve control terminals.

2. The BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels according to claim 1, characterized in that, A three-dimensional digital integration platform for underground utility tunnels is built in the cloud. This platform uploads the entire lifecycle data of communication pipelines, ownership information, and real-time operating parameters to a central cloud database. Each pipeline unit is assigned a unique code, and a corresponding cloud index relationship is established, including: The cloud-based central database is divided into independent data storage blocks according to ownership unit, pipeline type and operation and maintenance stage. The design parameters, construction and acceptance data and past operation and maintenance records of communication pipelines are classified and stored in the corresponding blocks. Based on the data block division rules, a code including ownership unit identifier, pipeline type code and three-dimensional coordinate feature value is generated for each pipeline unit through cloud coding service. The code is then linked with the design parameters and construction acceptance data of the corresponding block to establish a two-way index relationship table with time-series labels. Based on the time-series labels of the bidirectional index relationship table, the system receives the operating parameters uploaded by pipeline sensors in real time through the cloud data channel, matches the parameter values ​​with the corresponding pipeline codes, and inputs them into the dynamic fields of the index relationship table, thus synchronously updating the status identifiers and attribute parameters of the pipelines in the 3D digital integration platform.

3. The BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels according to claim 2, characterized in that, A dynamic network is constructed based on spatial topology correction values, and pipeline layout data is updated in real time to trigger collision risk warnings, including: Based on the spatial topology correction value, the pipeline crossing depth, safety distance and risk level of the hidden area are extracted, and the corrected spatial topology parameters are combined with the pipeline geometric dimensions and connection relationship data in the three-dimensional 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 details of hidden areas. Preset safety thresholds for pipeline intersection angles and adjacent pipeline spacing. When the real-time rendered pipeline intersection angle is less than the threshold, a collision risk warning signal is triggered. Risk levels are categorized based on the degree of deviation from the threshold, and early warning information is generated according to the risk level, including the coordinates of the abnormal location, pipeline code, risk description, and recommended actions.

4. The BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels according to claim 3, characterized in that, By integrating real-time data collected by IoT sensors on the pipe gallery environment with a 3D digital integration platform, when leaks, temperature, and pressure anomalies are detected, the system automatically pinpoints the exact location of the anomaly by combining the correction values ​​of the monitoring area from the triangulation monitoring architecture with the 3D coordinates of the pipeline. This generates a decision dataset that includes emergency operation instructions, including: The system receives real-time data streams of leakage concentration, temperature change rate, and pressure gradient from IoT sensors within the monitoring area of ​​the triangulation monitoring architecture. Through a cloud data engine, the sensor data is aligned with the pipeline's three-dimensional coordinates on the three-dimensional digital integration platform to generate a set of anomalous data with spatial correlation. Based on the pipeline crossing 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 and influence radius of the abnormal points. Based on the corrected coordinates of the outliers, a bidirectional index table with time-series labels is retrieved in reverse. The encrypted identifier of the ownership unit of the corresponding pipeline unit, the pipeline material parameters in the construction and acceptance data, and the timeliness of handling similar faults in past operation and maintenance records are extracted to construct an outlier feature dataset including pipeline failure level and emergency response timeliness threshold. Based on the anomaly feature dataset, the system calls the preset rules for the correlation between pipeline material and leakage diffusion rate, and the relationship between temperature threshold and ventilation efficiency in the central cloud database to generate a decision dataset for emergency operation instructions.

5. The BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels according to claim 4, characterized in that, Based on the corrected outlier coordinates, a reverse retrieval of the bidirectional indexed relation table with time-series labels is performed. The encrypted identifier of the ownership unit for the corresponding pipeline unit, pipeline material parameters from the construction and acceptance data, and the timeliness of handling similar faults from past maintenance records are extracted to construct an outlier feature dataset including pipeline failure levels and emergency response timeliness thresholds. Based on the compressive strength, corrosion resistance and past leakage records in the pipeline material parameters, the level classification of the leakage diffusion rate at the current abnormal point is determined by the preset pipeline material compressive strength-corrosion classification rules, and the corresponding pipeline failure level is generated. Based on the pipeline failure level, the shortest response time, average response time, and promised response time defined in the service level agreement of the owner are extracted. Based on the ratio between the shortest response time and the promised response time, and combined with the distribution range of the average response time, an emergency response time threshold associated with the current anomaly location is generated. Based on pipeline failure levels and emergency response time thresholds, combined with the emergency resource distribution density corresponding to the encrypted identifiers of the ownership units and cross-departmental collaborative response rules, an anomaly feature dataset is generated, which includes constraints on anomaly handling, multi-departmental collaborative paths, and dynamic operation instructions matched with time thresholds.

6. The BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels according to claim 5, characterized in that, Based on the decision dataset, the distribution of obstacles along the repair path is simulated using spatial topology correction values. Combined with real-time equipment status data, a final cloud-based detour plan is generated, and coordinated commands are simultaneously issued to fire-fighting equipment, ventilation systems, and remote valve control terminals, including: Based on the pipeline intersection depth parameter in the spatial topology correction value, the three-dimensional coordinate set of pipeline intersections, valve nodes and ventilation shafts on the emergency repair path is dynamically rendered in the three-dimensional digital integration platform to construct a three-dimensional spatial obstacle topology network including topological constraints. Based on a three-dimensional spatial obstacle topology network, and integrating parameters such as valve opening and closing angles, ventilation device rotation speeds, and fire water tank levels from real-time equipment status data, a cloud-based path planning engine is used to perform a feasibility analysis of obstacle distribution and generate an initial detour scheme 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 final detour plan is generated by calculating the spatial topological relationship between each path and high-risk concealed areas, and marking avoidance coordinate points, emergency resource deployment density and equipment coordination parameters. The equipment operation instructions in the final detour plan are broken down according to the authority of the responsible unit, and the real-time task status of resource scheduling of multiple departments is updated at the same time.

7. The BIM-based intelligent operation and maintenance management system for underground integrated utility tunnels according to claim 6, characterized in that, The equipment operation instructions include sending the coordinates of the sprinkler area and water volume control parameters to the fire-fighting device, sending directional pressurization instructions and the coordinate radius of abnormal points to the ventilation device, and transmitting opening and closing commands with time locks to the valve control terminal.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the system as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the system as described in any one of claims 1 to 7.

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