Underground pipe gallery asset digital management platform based on BIM

Abstract

The invention relates to the technical field of underground pipe gallery digital management, and discloses a BIM-based underground pipe gallery asset digital management platform, which comprises a data acquisition module, a dynamic digital twinborn body module, an event processing engine, a self-iteration data bus module, a spatial topology driving module and a self-healing logic triggering module. For the industry pain point of insufficient data real-time performance under the long-distance and multi-node scene of the underground pipe gallery, geometric attributes of a BIM model and sensor data are deeply bound through a dynamic digital twinborn body module, twinborn nodes synchronously bearing space coordinates, physical attributes and real-time state labels are constructed, the limitation of traditional BIM static visualization is broken through, and the real-time performance of the underground pipe gallery is improved. Second-level state synchronization and full-link data traceability of high-risk events such as leakage and equipment faults are achieved, collaborative design of lightweight architecture and edge computing is achieved, the low-cost modification requirement of the old pipe gallery is met, and the real-time performance and reliability of asset management are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital management of underground pipe corridors, and in particular to a digital management platform for underground pipe corridor assets based on BIM. Background Art

[0002] As a lifeline project of a city, an underground pipe corridor typically has the characteristics of long distance (usually several kilometers to dozens of kilometers) and dense nodes (including hundreds to thousands of sensors and devices). In this scenario, the traditional method relies on the combination of static display of BIM models and manual inspections, facing two core contradictions:

[0003] 1. Data synchronization delay caused by long distance: Sensor data needs to be transmitted across regions to the central server and updated asynchronously with the BIM model, resulting in serious insufficient response timeliness for high-risk events (such as leakage and equipment failure);

[0004] 2. Processing blockage caused by high concurrency of multiple nodes: When processing a large number of sensor alarms, video monitoring, and work order tasks in parallel, the traditional timestamp sequence mechanism cannot distinguish the relevance and influence range of events, and key alarms are easily blocked by low-priority tasks.

[0005] In the prior art, typical solutions achieve visual monitoring by integrating sensor data into the BIM model, but they have the following essential defects. For example, traditional BIM only serves as a carrier of geometric information, and real-time data needs to be imported asynchronously through manual or independent systems, resulting in the separation of the model and data. For example, the alarm of cross-regional events (such as pipeline leakage) in a long-distance pipe corridor needs to wait for the data alignment of multiple systems, and full-link state synchronization cannot be achieved; the processing of multi-source events depends on a fixed time sequence, lacking dynamic priority determination based on spatial topology and data relevance. For example, in a multi-node scenario, a leakage at one location may affect dozens of associated pipelines, but the traditional system still queues and processes them in time sequence due to its inability to identify topological relevance, delaying emergency response. Summary of the Invention

[0006] The technical problem solved by the present invention is to provide a digital management platform for underground pipe corridor assets based on BIM to solve the problems of data synchronization delay in long-distance pipe corridors, event processing blockage in multi-node high-concurrency scenarios, inaccurate positioning of hidden faults, and contradictions between transformation costs and compatibility as mentioned in the above background art in view of the defects existing in the above prior art.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is as follows: A digital management platform for underground pipe corridor assets based on BIM, comprising:

[0008] A data acquisition module, configured to obtain real-time operation data of the underground pipe corridor, where the real-time operation data includes sensor data, video monitoring data, and manual work order data;

[0009] A dynamic digital twin module, connected to the data acquisition module, is used to bind the geometric attributes in the BIM model with the real-time operation data to generate dynamic twin nodes. Each dynamic twin node includes spatial coordinates, physical attribute tags, and real-time status tags;

[0010] An event processing engine, connected to the dynamic digital twin module, is used to receive multi-source event inputs and attach lineage tags to each event. The lineage tags include event trigger source identifiers, associated data node lists, and impact scope identifiers based on the BIM topology;

[0011] A self-iterative data bus module is used to generate lightweight association fingerprints according to the lineage tags. The lightweight association fingerprints match event priority weights through a predefined association rule library. The priority weights are calculated by the following formula:

[0012] W = α·T + β·S,

[0013] where W is the event priority weight, T is the normalized value of the event timestamp, S is the number of pipes covered by the impact scope identifier in the lineage tag, and α and β are preset coefficients and satisfy α + β = 1;

[0014] A spatial topology driving module, integrated into the BIM model, is used to store the topological data of the connection relationships of the pipe gallery assets and preload the topological data into the predefined association rule library as the core basis for event association determination;

[0015] A self-healing logic trigger module, connected to the self-iterative data bus module, is used to start an exception handling mechanism when the lightweight association fingerprint matches a preset fault mode. The exception handling mechanism includes sensor calibration instructions or alternative data source switching instructions.

[0016] As a further solution of the present invention, the construction of the lineage tag includes: determining an initial associated node based on the device identifier of the event trigger source; expanding the associated data node list according to the predefined spatial topology relationship in the BIM model, where the spatial topology relationship includes pipe connection directions and valve position information; generating an impact scope identifier based on the correlation analysis of historical maintenance records and real-time operation data, and the impact scope identifier is a set of pipe numbers covered in the lineage tag.

[0017] As a further solution of the present invention, the generation of the lightweight association fingerprint includes: extracting high-frequency association patterns from the predefined association rule library, where the high-frequency association patterns include the binding rule of leakage events with humidity sensors in the same area and the mapping rule of equipment failures with historical maintenance records; compressing the key features of the lineage tag using a hash algorithm to generate a unique fingerprint identifier, and the output of the hash algorithm satisfies the following formula:

[0018] H = Hash(K1 || K2 || K3),

[0019] where H is the fingerprint identifier, K1 is the event type code, K2 is the associated sensor identifier, and K3 is the timestamp of the most recent maintenance; the fingerprint identifier is dynamically matched with the weight value in the predefined association rule library.

[0020] As a further aspect of the present invention, in the spatial topology driving module, the connection relationship topology data is parsed from the BIM model through the IFC standard interface, and the topology data includes the pipeline connection direction, valve position information, and area isolation boundary.

[0021] As a further aspect of the present invention, the real-time status label update method of the dynamic digital twin module is as follows: according to the synchronization order of the self-iterative data bus module, the dynamic twin nodes are incrementally updated according to the event priority; for events with a priority weight higher than the preset threshold, the status labels of the associated nodes are updated in real time and a visual alarm is triggered; for events with a priority weight lower than the preset threshold, a batch caching mechanism is used for delayed update.

[0022] As a further aspect of the present invention, the data acquisition module further includes: edge computing nodes deployed at the pipe gallery site for preprocessing the sensor data, and the preprocessing includes noise filtering, data normalization, and timestamp alignment; a data compression unit that encodes the video surveillance data using a lossless compression algorithm and transmits the compressed data to the dynamic digital twin module.

[0023] As a further aspect of the present invention, the visual alarm of the dynamic digital twin module includes: rendering the alarm area in red highlight in the BIM model; generating an alarm impact path animation, and the animation simulates the fault diffusion direction and impact range based on the spatial topology data.

[0024] As a further aspect of the present invention, the logic for dynamically adjusting the synchronization order of the self-iterative data bus module includes: when there is an overlap in the influence range of the blood relationship labels of multiple events, the event that affects the largest number of pipelines is processed first; when the event priority weights are the same, they are processed in timestamp order, and the processing results are used to reverse-optimize the weight values in the predefined association rule library.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows: Aiming at the problem of insufficient data real-time performance in the scenarios of long-distance and multi-node of underground pipe corridors, the geometric attributes of the BIM model are deeply bound with sensor data and work order information through the dynamic digital twin module, so that each twin node synchronously bears spatial coordinates, physical attributes and real-time status tags, breaking through the limitation of traditional BIM only for static visualization, realizing the second-level status synchronization and full-link data traceability capabilities of high-risk events such as leakage and equipment failures, as well as the dynamic expansion based on event lineage tags, such as recording the trigger source, associated data nodes and topological influence range and the hash compression technology of lightweight associated fingerprints, upgrading the data priority determination from time-sequence dependence to data relevance drive, combining with the pre-defined pipeline connection relationship in BIM, realizing zero-latency response to key alarm events such as cross-regional leakage and intelligent asynchronous caching of non-emergency tasks such as inspection work orders, eliminating the processing blockage caused by traditional manual rule setting from the root, and enabling it to better cope with the usage characteristics of long-distance and multi-data of underground pipe corridors. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a schematic diagram of the functional module structure of the present invention.

[0028] Figure 2 It is a schematic diagram of the functional structure of the dynamic digital twin module in the present invention.

[0029] Figure 3 It is a schematic diagram of the event processing priority determination process in the present invention.

[0030] Figure 4 It is a schematic diagram of the dynamic twin node generation and transmission process in the present invention.

[0031] Figure 5 It is a schematic diagram of the abnormal processing process of the self-healing logic trigger module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0033] In the present invention, a BIM-based digital management platform for underground utility tunnels includes: a data acquisition module for obtaining real-time operation data of the underground utility tunnels, where the real-time operation data includes sensor data, video surveillance data, and manual work order data;

[0034] A dynamic digital twin module, connected to the data acquisition module, for binding geometric attributes in the BIM model with the real-time operation data to generate dynamic twin nodes, and each dynamic twin node includes spatial coordinates, physical attribute tags, and real-time status tags;

[0035] An event processing engine, connected to the dynamic digital twin module, for receiving multi-source event inputs and attaching lineage tags to each event, where the lineage tags include event trigger source identifiers, associated data node lists, and impact scope identifiers based on BIM topology;

[0036] A self-iterative data bus module for generating lightweight association fingerprints according to the lineage tags, where the lightweight association fingerprints match event priority weights through a predefined association rule library, and the priority weights are calculated by the following formula:

[0037] W = α·T + β·S,

[0038] where W is the event priority weight, T is the normalized value of the event timestamp, S is the number of pipes covered by the impact scope identifier in the lineage tag, and α and β are preset coefficients and satisfy α + β = 1;

[0039] A space topology driving module, integrated in the BIM model, for storing topological data of the connection relationships of utility tunnel assets and preloading the topological data into the predefined association rule library as the core basis for event association determination;

[0040] A self-healing logic trigger module, connected to the self-iterative data bus module, for starting an exception handling mechanism when the lightweight association fingerprint matches a preset fault mode, and the exception handling mechanism includes sensor calibration instructions or alternative data source switching instructions.

[0041] As a further solution of the present invention, the construction of the lineage tag includes: determining an initial associated node based on the device identifier of the event trigger source; expanding the associated data node list according to the predefined space topology relationship in the BIM model, where the space topology relationship includes pipe connection directions and valve position information; generating an impact scope identifier based on the correlation analysis of historical maintenance records and real-time operation data, and the impact scope identifier is a set of pipe numbers covered in the lineage tag.

[0042] As a further solution of the present invention, the generation of the lightweight associated fingerprint includes: extracting high-frequency association patterns from the predefined association rule library, where the high-frequency association patterns include the binding rule of leakage events and humidity sensors in the same area, and the mapping rule of equipment failures and historical maintenance records; compressing the key features of the lineage tags using a hash algorithm to generate a unique fingerprint identifier, and the output of the hash algorithm satisfies the following formula:

[0043] H = Hash(K1‖K2‖K3),

[0044] where H is the fingerprint identifier, K1 is the event type code, K2 is the associated sensor identifier, and K3 is the timestamp of the last maintenance; dynamically matching the fingerprint identifier with the weight value in the predefined association rule library.

[0045] As a further solution of the present invention, in the spatial topology driving module, the connection relationship topology data is parsed from the BIM model through the IFC standard interface, and the topology data includes the pipeline connection direction, valve position information, and regional isolation boundary.

[0046] As a further solution of the present invention, the real-time status tag update method of the dynamic digital twin module is: according to the synchronization sequence of the self-iterative data bus module, incrementally update the dynamic twin nodes according to the event priority; for events with a priority weight higher than the preset threshold, update the status tags of the associated nodes in real time and trigger a visual alarm; for events with a priority weight lower than the preset threshold, use a batch caching mechanism to delay the update.

[0047] As a further solution of the present invention, the data acquisition module further includes: edge computing nodes deployed at the pipe gallery site for preprocessing the sensor data, where the preprocessing includes noise filtering, data normalization, and timestamp alignment; a data compression unit that encodes the video surveillance data using a lossless compression algorithm and transmits the compressed data to the dynamic digital twin module.

[0048] As a further solution of the present invention, the visual alarm of the dynamic digital twin module includes: rendering the alarm area in red highlight in the BIM model; generating an alarm impact path animation, where the animation simulates the fault diffusion direction and impact range based on the spatial topology data.

[0049] As a further solution of the present invention, the logic of the self-iterative data bus module for dynamically adjusting the synchronization sequence includes: when the influence ranges of the lineage tags of multiple events overlap, give priority to processing the event that affects the largest number of pipelines; when the event priority weights are the same, process them in timestamp order, and reverse-optimize the weight value of the predefined association rule library with the processing results.

[0050] As a further solution of the present invention, when the self-healing logic trigger module executes the exception handling mechanism, it further includes: when it is detected that the sensor data is abnormal and the lightweight associated fingerprint matches the leakage event + normal humidity mode, a sensor calibration instruction is generated; when it is detected that the same alarm event is triggered more than three times continuously in the same area, the system automatically switches to the backup data source and marks the area as a high-risk state, and the high-risk state is updated to a red alarm identifier through the real-time status label of the digital twin node.

[0051] As a further solution of the present invention, the construction of the predefined association rule library includes: extracting the event association pattern from the historical operation and maintenance data, and establishing a mapping relationship table of event type - associated sensor - affected pipeline; calculating the association threshold between the pipeline pressure mutation and the vibration frequency based on the hydrodynamic equation, and embedding the threshold into the rule determination logic.

[0052] Example 1: In the data collection stage, the system obtains the original data from various sensors, video surveillance, and work order systems through the on-site edge computing nodes. For the sensor data, a noise filtering algorithm (such as moving average or median filtering) is used for preliminary processing, and then the original value is normalized to a predetermined range (such as 0 to 1). The normalization process uses the following method: the minimum and maximum values within a certain time window are denoted as T min and T max , then the normalized value of a single collected data T raw is:

[0053]

[0054] Ensure that this value is between 0 and 1; the video data is processed by a lossless compression algorithm and then transmitted. This step ensures that the input data for subsequent modules is consistent and has a high signal-to-noise ratio. Next, the system deeply binds the geometric information in the BIM model with the above processed real-time data to construct a digital twin node corresponding to each physical asset in the underground pipe gallery. Each node includes the spatial coordinates from the BIM, labels describing physical attributes (such as pipe diameter, material, installation location, etc.), and status labels updated according to the real-time data. The status update adopts an event trigger mechanism. When an abnormality (such as leakage, equipment failure) is detected, the digital twin node timely updates the display status to provide a basis for subsequent processing.

[0055] When the system detects an abnormal event, the event processing module immediately collects the event information and constructs a lineage tag containing the event source identifier, the list of associated data nodes, and the impact scope identifier obtained based on the BIM spatial topology. Among them, the list of associated data nodes is obtained by parsing the predefined pipeline connection directions, valve position information, and area isolation boundaries in the BIM model, and the impact scope identifier reflects the number of pipelines associated with the abnormal event. The number of pipelines here is the basis for the value of the variable S in the subsequent calculation, and its value is the total number of relevant pipe segments involved in the BIM topology. To sort the events by priority, the system uses the formula:

[0056] W = α·T + β·S,

[0057] to assign a weight to each event. Here, T is the normalized value of the event timestamp (as mentioned before, its value is between 0 and 1), and S is the number of pipelines within the impact scope; the preset coefficients α and β satisfy α + β = 1, and their specific values are preset according to the actual on-site requirements. For example, in some scenarios, the timeliness of the event can be emphasized (increasing the proportion of α), while in other scenarios, more attention is paid to the physical impact scope (increasing the proportion of β). This embodiment details the normalization method and the basis for coefficient selection to ensure that each step of the calculation has a clear parameter source and technical logic support. On this basis, the self-iterative data bus module uses the predefined association rule library to generate lightweight association fingerprints for the lineage tags of the events. The fingerprint generation uses the formula:

[0058] H = Hash(K1‖K2‖K3),

[0059] where K1 is the event type code, used to represent the event category; K2 is the associated sensor identifier, identifying the specific sensor that triggered the event; K3 is the timestamp of the last maintenance, reflecting the historical state of the device. The concatenated data is processed using a standard hashing algorithm (such as SHA-256) to ensure that the generated fingerprints are unique and comparable, thus supporting subsequent rule matching and event priority adjustment. When the spatial topology driving module parses the BIM model, it obtains the connection relationship data of each asset in the pipe gallery through the IFC standard interface, including the connection directions of the pipelines, valve positions, and area isolation boundaries. The parsing results are pre-loaded into the predefined association rule library as the basis for event impact scope determination and dynamic adjustment to ensure that the system can accurately reflect the actual physical connection situation when processing multi-node events.

[0060] Finally, when the self-iterative data bus module detects that the lightweight associated fingerprint matches the fault mode in the rule base, the self-healing logic trigger module starts the exception handling mechanism. Exception handling includes two cases: one is that when abnormal sensor data is detected and the fingerprint matching result indicates a leakage event, the system automatically generates a sensor calibration instruction; the other is that when the same exception continuously appears in the same area exceeding the set number of times, the system automatically switches to the backup data source and updates the status label of the digital twin node to a red alarm for timely on-site handling measures, which are all extended implementation methods known to those of ordinary skill in the art.

[0061] Embodiment 2: In a typical embodiment of the present invention, the BIM-based digital management platform for underground utility tunnel assets realizes the dynamic digital management of underground utility tunnel assets through the collaborative work of each functional module. Figure 1 It is a schematic diagram of the functional module structure of the BIM-based digital management platform for underground utility tunnel assets of the present invention. As shown in the figure, the platform includes a data acquisition module, a dynamic digital twin module, an event processing engine, a self-iterative data bus module, a self-healing logic trigger module, and a space topology driving module integrated into the BIM model. The data acquisition module is used to obtain the real-time operation data of the underground utility tunnel, including sensor data, video surveillance data, and manual work order data, and transmit the collected data to the dynamic digital twin module. The dynamic digital twin module generates dynamic twin nodes including spatial coordinates, physical attribute labels, and real-time status labels based on the geometric attributes of the BIM model and in combination with the real-time operation data provided by the data acquisition module, and at the same time transmits the synchronization sequence information to the self-iterative data bus module. The event processing engine is used to receive the event information transmitted by the dynamic digital twin module, analyze the multi-source event input, and attach lineage labels. The self-iterative data bus module receives the data of the dynamic digital twin module according to the synchronization sequence, combines the event information provided by the event processing engine, generates a lightweight associated fingerprint using the topology data, and transmits the data to the self-healing logic trigger module. The self-healing logic trigger module is used to start the exception handling mechanism when the lightweight associated fingerprint matches the preset fault mode, including a sensor calibration instruction or a backup data source switching instruction. In addition, the space topology driving module is integrated into the BIM model, pre-stores the topology data of the connection relationship of the utility tunnel assets, and provides topology data support to the self-iterative data bus module to ensure the accuracy of the spatial topology association determination process of data synchronization and event processing.

[0062] Figure 2This is a schematic diagram of the functional structure of the dynamic digital twin module in the BIM-based digital management platform for underground utility tunnel assets of the present invention. As shown in the figure, the dynamic digital twin module includes three functions: receiving real-time operation data, binding BIM geometric attributes, and generating dynamic twin nodes. The function of receiving real-time operation data is used to obtain the real-time operation data of the underground utility tunnel from the data acquisition module, including sensor data, video surveillance data, and manual work order data, and receive this operation data in real time. The function of binding BIM geometric attributes is used to associate the received real-time operation data with the geometric attributes in the BIM model to ensure that the geometric information of the digital twin model is synchronized and updated with the real-time data. After the data binding is completed, the dynamic digital twin module further generates dynamic twin nodes, which include spatial coordinates, physical attribute tags, and real-time status tags. Among them, the spatial coordinates are used to identify the specific location of the utility tunnel assets in the BIM model, the physical attribute tags are used to describe the physical characteristic information of the utility tunnel assets, and the real-time status tags are used to dynamically identify the operation status of the utility tunnel assets. Through this module, the real-time synchronization and dynamic visualization display of the spatial information and operation data of the underground utility tunnel assets are realized.

[0063] Figure 3 This is a schematic diagram of the event processing priority determination process in the BIM-based digital management platform for underground utility tunnel assets of the present invention. As shown in the figure, this process first receives the lineage tags of multiple events, and the system determines whether there is an overlap in the influence range. If there is an overlap in the influence range, then compare the number of affected pipelines, and the one with more pipelines is given priority. If there is no overlap in the influence range, then compare the event priority weights, and the one with a higher weight is given priority. When the weights are the same, process them in timestamp order. The system processes the events according to the above comparison results, and after the event processing is completed, reversely optimizes the weight values of the predefined association rule library to achieve dynamic adjustment of the event processing rules. Through this process, it can be ensured that the event priority determination is accurate and timely in the scenario of high concurrency of multiple events, and the response efficiency and reliability of the underground utility tunnel asset management platform are improved.

[0064] Figure 4This is a schematic diagram of the dynamic twin node generation and transmission process in the BIM-based digital management platform for underground utility tunnel assets of the present invention. As shown in the figure, this process first obtains geometric attributes from the BIM model, extracts the spatial coordinates and physical information of the utility tunnel assets in the BIM model. Subsequently, the system receives real-time sensor data, including the real-time operation data collected by various sensors deployed on-site. The system binds the obtained geometric attributes with the sensor data to achieve the corresponding association of geometric attributes and real-time data, ensuring the synchronous update of the geometric information and real-time operation status of the utility tunnel assets. After the binding is completed, the system generates dynamic twin nodes, and each node contains spatial coordinates, physical attribute tags, and real-time status tags. The three together constitute a complete dynamic twin data structure, accurately reflecting the real-time operation status of the underground utility tunnel assets. Finally, the system transmits the generated dynamic twin nodes to the event processing engine for subsequent event recognition, correlation analysis, and priority determination processing, ensuring the data synchronization and logical connection between the dynamic digital twin module of the underground utility tunnel asset management platform and the event processing engine.

[0065] Figure 5 This is a schematic diagram of the exception handling process of the self-healing logic trigger module in the BIM-based digital management platform for underground utility tunnel assets of the present invention. As shown in the figure, this process starts with receiving the lightweight association fingerprint from the self-iterative data bus module. The start of the process is marked by the solid circle at the upper part of the figure, indicating the start of the exception handling process. When the lightweight association fingerprint is received, the system first determines whether the fingerprint matches a preset fault mode. If it does not match, it enters the state of waiting for a new association fingerprint. If the fingerprint matches a preset fault mode, it further branches according to the type of the fault mode. When the system determines that the fault mode is sensor data anomaly, it generates a sensor calibration instruction. If the fault mode is to switch the data source, it generates a standby data source switching instruction and executes the data source switching. If the fault mode does not belong to the above two cases, it executes other exception handling. When all the exception handling processes are completed, the process termination is marked by the solid circle at the lower part of the figure, indicating the end of this exception handling process. Through this process, the system can automatically identify and classify different types of fault modes according to the lightweight association fingerprint from the self-iterative data bus module, including generating a sensor calibration instruction, generating a standby data source switching instruction, and executing other exception handling, effectively improving the self-healing ability and operation stability of the digital management platform for underground utility tunnel assets.

[0066] Example 3: During the event handling process, the system uses a weight calculation method to determine the priority of multi-source abnormal events. The calculation formula for the weight value is:

[0067] W = α × T + β × S,

[0068] Among them, the variable T represents the normalized value of the event timestamp, and its value range is from 0 to 1. The specific value is determined according to the following formula after counting the minimum and maximum values of the timestamps of all collected events in the recent period (such as 24 hours):

[0069]

[0070] Among them, T raw is the original timestamp of the current abnormal event, T min and T max are the minimum and maximum values of all event timestamps in this period respectively, ensuring the effectiveness and dynamic adjustability of the normalization calculation result. The variable S is the number of pipelines involved within the influence range of the current abnormal event, and its value comes from the spatial topological relationship obtained by parsing the BIM model. The system pre-parses the connection relationship of the underground pipe gallery, extracts the set of pipe segment numbers associated with the abnormal event in real time, and counts its quantity as the S value input; the coefficients α and β are preset weight parameters, satisfying α + β = 1, and their values are flexibly set according to the application scenario requirements. For example, in a scenario where real-time response needs to be prioritized, the value of α can be appropriately increased; if more attention is paid to the physical influence range of the event, the proportion of β can be increased. The parameter setting principle is to ensure that the system has flexible priority determination ability in long-distance and multi-node scenarios. In addition, during the event processing process, the self-iterative data bus module realizes event pattern comparison and historical data association by generating lightweight association fingerprints. The fingerprint generation formula is:

[0071] H = Hash(K1||K2||K3),

[0072] Among them, K1 is the event type code, and the system presets a unique code for each type of abnormal event; K2 is the associated sensor identifier, taken from the unique number of the on-site sensor device; K3 is the timestamp of the last maintenance, sourced from the system maintenance record database. The fingerprint generation process includes three steps: feature extraction, data format standardization, and hash calculation, ensuring that the generated fingerprint is unique, comparable, and has fast retrieval ability. During the operation of the system, the fingerprint is not only used for matching with the predefined association rule library, but also for continuously optimizing the event determination rules to improve data synchronization efficiency and event processing accuracy.

[0073] To further improve the stability and reliability of the system, after the self-healing logic trigger module detects an abnormal event, it will automatically execute the abnormal handling process based on the fingerprint matching result. Specifically, when the fingerprint matching result shows that the abnormal event is consistent with the abnormal pattern of sensor data, the system will automatically send a calibration instruction to the corresponding sensor. The instruction includes calibration parameters (such as sampling frequency, sensitivity correction value, etc.), and is sent through the edge computing nodes deployed on-site to ensure that the instruction takes effect in real time and restores the accuracy of sensor data acquisition. When the same alarm event is continuously triggered more than three times in the same area, the system determines that there is a potential fault risk in the acquisition channel of this area and automatically switches the data input to the backup data source. The backup data source is preset by the platform at the on-site of the pipe gallery, and the acquisition channel is physically isolated from the main data source to ensure the independence and stability of the data acquisition link after the switch. At the same time, the system marks this area as a high-risk state in the real-time status label of the dynamic twin node and synchronously presents it on the visualization interface for the convenience of operation and maintenance personnel to respond in a timely manner. These are all extended implementation methods known to those of ordinary skill in the art.

[0074] Example 4: In the actual application process, to ensure the effectiveness and adaptability of the calculation of event priority weights, the value ranges of the preset weight coefficients α and β need to be reasonably set according to the pipe gallery operation and maintenance requirements, the scale of the pipeline network, and the historical data statistics results. In this example, the system completes the setting and dynamic adjustment of the weight coefficients through the following steps:

[0075] Step 1: Establish the parameter value range. In the initialization stage of the platform, based on the statistical analysis of the historical event data provided by the pipe gallery operation and maintenance department, determine the initial recommended value ranges of the weight coefficients α and β. For example, when the average patrol response time limit requirement of the pipe gallery is relatively high, it is recommended that the value range of α be 0.6 to 0.8, and the value range of β be 0.2 to 0.4, reflecting the emphasis on the real-time nature of the event. When the pipe gallery network structure is complex and the topological correlation is strong, it is recommended that the value range of α be 0.4 to 0.6, and the value range of β be 0.4 to 0.6, taking into account both the real-time nature and the influence range of the event.

[0076] Step 2: Define the parameter setting basis. The specific values of the weight coefficients α and β are set flexibly according to the platform operation and maintenance scenario. The parameter setting process includes: counting the average response time of the pipe gallery alarm events in the past six months; analyzing the average number of pipelines affected by different alarm type events; determining the demand weights for real-time nature and influence range based on the statistical results.

[0077] Step 3: Dynamic adjustment mechanism. During the operation of the platform, continuously monitor the processing results of alarm events, and dynamically adjust the weight coefficients α and β based on two indicators: event processing delay and misjudgment rate. The specific adjustment logic is as follows: When the platform detects that the average processing delay of events exceeds a preset threshold (e.g., 30 seconds), the system automatically increases the value of ɑ; when the platform detects that the misjudgment rate of events (e.g., the error rate of impact range recognition) exceeds a preset threshold (e.g., 10%), the system automatically increases the value of β; during the adjustment process, the system maintains the identity relationship of α + β = 1 to avoid the impact of parameter drift on system stability. This dynamic adjustment process is automatically executed by the preset rule engine of the system to ensure that the platform can maintain the optimal event priority determination effect at different operation stages. All of these are extension implementation methods known to those of ordinary skill in the art.

[0078] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes within the meaning and scope of the equivalent elements of the claims in the present invention.

Claims

1. A BIM-based digital management platform for underground pipe corridors, characterized in that, Including: A data acquisition module for obtaining real-time operation data of the underground utility tunnel, where the real-time operation data includes sensor data, video surveillance data, and manual work order data; A dynamic digital twin module, connected to the data acquisition module, for binding the geometric attributes in the BIM model with the real-time operation data to generate dynamic twin nodes, and each dynamic twin node includes spatial coordinates, physical attribute tags, and real-time status tags; An event processing engine, connected to the dynamic digital twin module, for receiving multi-source event inputs and attaching lineage tags to each event, where the lineage tags include event trigger source identifiers, associated data node lists, and impact scope identifiers based on BIM topology; A self-iterative data bus module for generating lightweight association fingerprints according to the lineage tags, and the lightweight association fingerprints match event priority weights through a predefined association rule library, and the priority weights are calculated by the following formula: W = α·T + β·S, where W is the event priority weight, T is the normalized value of the event timestamp, S is the number of pipes covered by the impact scope identifier in the lineage tag, and α and β are preset coefficients and satisfy α + β = 1; A space topology driving module, integrated in the BIM model, for storing the connection relationship topology data of the utility tunnel assets and preloading the topology data into the predefined association rule library as the core basis for event association determination; A self-healing logic trigger module, connected to the self-iterative data bus module, for starting an exception handling mechanism when the lightweight association fingerprint matches a preset fault mode, and the exception handling mechanism includes sensor calibration instructions or alternative data source switching instructions.

2. The digital management platform for underground pipe gallery assets based on BIM according to claim 1, wherein The construction of the lineage tag includes: determining an initial associated node based on the device identifier of the event trigger source; expanding the associated data node list according to the predefined space topology relationship in the BIM model, where the space topology relationship includes pipe connection directions and valve position information; generating an impact scope identifier based on the correlation analysis of historical maintenance records and real-time operation data, and the impact scope identifier is a set of pipe numbers covered in the lineage tag.

3. The digital management platform for underground pipe gallery assets based on BIM according to claim 2, characterized in that, The generation of the lightweight association fingerprint includes: extracting high-frequency association patterns from the predefined association rule library, where the high-frequency association patterns include the binding rule of leakage events with humidity sensors in the same area and the mapping rule of equipment failures with historical maintenance records; compressing the key features of the lineage tag using a hash algorithm to generate a unique fingerprint identifier, and the output of the hash algorithm satisfies the following formula: H = Hash(K1‖K2‖K3), where H is the fingerprint identifier, K1 is the event type code, K2 is the associated sensor identifier, and K3 is the timestamp of the most recent maintenance; dynamically matching the fingerprint identifier with the weight value in the predefined association rule library.

4. The digital management platform for underground pipe gallery assets based on BIM according to claim 1, characterized in that, In the space topology driving module, the connection relationship topology data is parsed from the BIM model through an IFC standard interface, and the topology data includes pipe connection directions, valve position information, and regional isolation boundaries.

5. The digital management platform for underground pipe gallery assets based on BIM according to claim 4, characterized in that, The real-time status label update method of the dynamic digital twin module is as follows: according to the synchronization order of the self-iterative data bus module, incrementally update the dynamic twin nodes according to the event priorities; for events with a priority weight higher than the preset threshold, update the status labels of the associated nodes in real time and trigger visual alarms; for events with a priority weight lower than the preset threshold, use a batch caching mechanism for delayed update.

6. The BIM-based digital management platform for underground pipe gallery assets according to claim 5, characterized in that, The data acquisition module further includes: edge computing nodes deployed at the on-site of the pipe gallery for preprocessing the sensor data, and the preprocessing includes noise filtering, data normalization and timestamp alignment; a data compression unit that encodes the video surveillance data using a lossless compression algorithm and transmits the compressed data to the dynamic digital twin module.

7. The digital management platform for underground pipe gallery assets based on BIM according to claim 1, characterized in that, The visual alarm of the dynamic digital twin module includes: rendering the alarm area in red highlight in the BIM model; generating an alarm impact path animation, and the animation simulates the fault diffusion direction and impact range based on the spatial topology data.

8. The digital management platform for underground pipe gallery assets based on BIM according to claim 1, characterized in that The logic for dynamically adjusting the synchronization order of the self-iterative data bus module includes: when there is an overlap in the influence range of the lineage labels of multiple events, give priority to processing the event that affects the largest number of pipelines; when the event priority weights are the same, process them in timestamp order, and reverse-optimize the weight values of the predefined association rule library with the processing results.

Images