Method and system for monitoring safety of underground pipe gallery structure based on groundwater level data

Abstract

The application discloses a comprehensive pipe gallery structure safety monitoring method and system based on underground water level data, relates to the technical field of safety monitoring, and comprises the following steps: arranging P pressure water level gauges in a target comprehensive pipe gallery; performing structure response node positioning to obtain a plurality of spatial structure response nodes; deploying a plurality of multi-modal sensing device groups at the plurality of spatial structure response nodes; integrating P real-time underground water levels returned by the P pressure water level gauges and a plurality of multi-modal structure sensing data returned by the plurality of multi-modal sensing device groups on a safety monitoring platform; performing pipe gallery local risk dynamic quantification to obtain a pipe gallery local risk distribution; and converting the pipe gallery local risk distribution into a pipe gallery risk heat map for output. The application solves the technical problem that the influence of underground water level changes on a comprehensive pipe gallery structure is difficult to realize multi-source dynamic sensing and risk quantification in the prior art, and achieves the technical effect of realizing real-time dynamic monitoring and visual output of comprehensive pipe gallery structure risks.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology, specifically to a method and system for safety monitoring of integrated utility tunnel structures based on groundwater level data. Background Technology

[0002] As a crucial component of urban underground infrastructure, integrated utility tunnels exist in a complex underground environment and are significantly affected by changes in groundwater levels. With the continuous development and utilization of urban underground space, fluctuations in groundwater levels can cause the tunnel structure to float, deform, or experience stress concentration, thereby impacting its overall stability and service safety. Although a certain number of water level monitoring devices and structural sensors have been deployed in engineering practice, the limited dimensions of the sensing data, uneven spatial distribution, and lack of effective correlation modeling between structural response and water level changes make it difficult to dynamically identify and quantitatively assess structural risks caused by groundwater levels. This poses significant hidden dangers to the safe operation and maintenance of integrated utility tunnels throughout their entire lifecycle. Summary of the Invention

[0003] This application provides a method and system for safety monitoring of integrated utility tunnel structures based on groundwater level data, which is used to address the technical problem that it is difficult to achieve multi-source dynamic perception and risk quantification of the impact of groundwater level changes on integrated utility tunnel structures in the prior art.

[0004] In view of the above problems, this application provides a method and system for safety monitoring of integrated utility tunnel structures based on groundwater level data.

[0005] The first aspect of this application provides a method for safety monitoring of integrated utility tunnel structures based on groundwater level data, the method comprising:

[0006] The system interactively obtains structural design information and construction data of the target integrated utility tunnel; based on the construction data, it deploys P pressure level gauges in the target integrated utility tunnel; based on the structural design information, it locates structural response nodes to obtain multiple spatial structural response nodes; according to the risk attributes of the tunnel nodes, it deploys multiple multimodal sensing device groups at the multiple spatial structural response nodes; the safety monitoring platform integrates P real-time groundwater levels returned by the P pressure level gauges and multiple multimodal structural sensing data returned by the multiple multimodal sensing device groups; the safety monitoring platform dynamically quantifies the local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data to obtain the local risk distribution of the utility tunnel; and converts the local risk distribution of the utility tunnel into a heat map of utility tunnel risks for output.

[0007] A second aspect of this application provides a comprehensive utility tunnel structure safety monitoring system based on groundwater level data, the system comprising:

[0008] The system comprises the following modules: an interaction module for obtaining structural design information and construction data of the target integrated utility tunnel; a deployment module for deploying P pressure level gauges in the target integrated utility tunnel based on the construction data; a positioning module for locating structural response nodes based on the structural design information to obtain multiple spatial structural response nodes; a deployment module for deploying multiple multimodal sensing device groups at the multiple spatial structural response nodes according to the risk attributes of the tunnel nodes; a sensing module for integrating P real-time groundwater levels returned by the P pressure level gauges and multiple multimodal structural sensing data returned by the multiple multimodal sensing device groups into a safety monitoring platform; a quantification module for dynamically quantifying the local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data to obtain the local risk distribution of the utility tunnel; and an output module for converting the local risk distribution of the utility tunnel into a heat map of the utility tunnel risk.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application interactively obtains structural design information and construction data of the target integrated utility tunnel; deploys P pressure level gauges in the target integrated utility tunnel according to the construction data; locates structural response nodes based on the structural design information to obtain multiple spatial structural response nodes; deploys multiple multimodal sensing device groups at the multiple spatial structural response nodes according to the risk attributes of the tunnel nodes; the safety monitoring platform integrates P real-time groundwater levels returned by the P pressure level gauges and multiple multimodal structural sensing data returned by the multiple multimodal sensing device groups; the safety monitoring platform performs dynamic quantification of local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data to obtain the local risk distribution of the utility tunnel; and converts the local risk distribution of the utility tunnel into a heat map of utility tunnel risks for output. This invention solves the technical problem in the prior art where it is difficult to achieve multi-source dynamic sensing and risk quantification of the impact of groundwater level changes on the structure of integrated utility tunnels. By fusing multimodal sensing data of groundwater level and structural response to perform dynamic quantification of local risks, it achieves the technical effect of real-time dynamic monitoring and visualization output of the structural risks of integrated utility tunnels. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1A schematic diagram of the process for a comprehensive utility tunnel structure safety monitoring method based on groundwater level data provided in this application embodiment;

[0013] Figure 2 A schematic diagram of the integrated utility tunnel structure safety monitoring system based on groundwater level data provided in this application embodiment.

[0014] Explanation of reference numerals in the attached diagram: Interaction module 11, Deployment module 12, Positioning module 13, Deployment module 14, Sensing module 15, Quantization module 16, Output module 17. Detailed Implementation

[0015] This application provides a method and system for safety monitoring of integrated utility tunnel structures based on groundwater level data. It addresses the technical problem in existing technologies where it is difficult to achieve multi-source dynamic perception and risk quantification of the impact of groundwater level changes on integrated utility tunnel structures. By integrating multimodal perception data of groundwater level and structural response, local risk dynamic quantification is achieved, thereby realizing the technical effect of real-time dynamic monitoring and visualization output of integrated utility tunnel structural risks.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a method for safety monitoring of integrated utility tunnel structures based on groundwater level data, the method comprising:

[0019] Step S100: Interact to obtain the structural design information and construction data of the target integrated utility tunnel.

[0020] In this embodiment, the system first accesses a Building Information Modeling (BIM) platform to retrieve structural design drawings and parameter documents related to the target integrated utility tunnel, extracting structural design information including cross-sectional dimensions, longitudinal layout, structural component material parameters, and soil cover depth distribution. Subsequently, it accesses a construction process database to read construction logs, geological survey data, and construction phase monitoring reports, obtaining actual construction parameters, monitoring well layout records, and construction progress markers, among other construction data. Through this data retrieval and parsing process, the system achieves interactive acquisition of the target integrated utility tunnel's structural design information and construction data.

[0021] Step S200: Install P pressure level gauges in the target integrated utility tunnel according to the construction data.

[0022] In this embodiment, based on construction data, the axis of the target integrated utility tunnel is first spatially located, and P standard groundwater monitoring wells are planned and deployed along this axis, constrained by a preset monitoring interval. Subsequently, a corresponding pressure level gauge is deployed in each monitoring well, thereby completing the deployment of P groundwater monitoring locations.

[0023] Furthermore, in the method provided in the application embodiment, the method of deploying P groundwater monitoring wells in the target integrated utility tunnel according to the construction data further includes:

[0024] Based on the construction data, the axis of the utility tunnel is spatially located; with a preset monitoring interval as a constraint, standard monitoring wells are laid out along the axis of the utility tunnel in the target integrated utility tunnel to obtain P groundwater monitoring wells; and P pressure level gauges are deployed on the P groundwater monitoring wells.

[0025] In this embodiment, the plan layout and longitudinal section drawings in the building construction data are first analyzed using CAD drawing software. Coordinate control points are extracted by the axis markings in the drawings, and then the centerline coordinate sequence of the target integrated utility tunnel is generated to complete the spatial positioning of the tunnel axis.

[0026] Along the already located pipeline corridor axis, the monitoring points are planned using an equidistant division method. Based on the preset monitoring interval, the length of the pipeline corridor axis is divided into several segments at fixed intervals, determining P monitoring well placement points. At each determined placement point, rotary drilling is used to drill holes extending to the main aquifer below the pipeline corridor floor. After drilling, filter pipes are installed, quartz sand filter media is backfilled, and clay sealing is applied sequentially to construct standard groundwater monitoring wells with long-term stable sampling capabilities, ultimately forming P groundwater monitoring wells.

[0027] After the construction of the above-mentioned well structure is completed, P pressure level gauges are installed in each of the P groundwater monitoring wells. The level gauges are constructed based on strain gauge pressure sensors, calculate the groundwater level by sensing the hydrostatic pressure in real time, and support remote data transmission, so as to realize high-precision sensing and continuous acquisition of water level data at the P monitoring points.

[0028] Furthermore, the method provided in the application embodiments also includes:

[0029] Constrained by a preset monitoring spatial scale, the axial building structure sequence is retrieved from the structural design information along the axis of the utility tunnel; structural deformation risk dispersion prediction is performed on the axial building structure sequence to locate multiple axial risk areas; the monitoring interval scale is updated according to multiple local failure risk coefficients of the multiple axial risk areas to obtain multiple compensation monitoring scales; constrained by the monitoring interval scale, multiple sets of compensation monitoring wells are deployed along the axis of the utility tunnel in the multiple axial risk areas; the multiple sets of compensation monitoring wells are used to monitor and compensate the P groundwater monitoring wells.

[0030] In this embodiment, a pre-defined monitoring space scale is used as a constraint. The axial building structure sequence is retrieved from the structural design information along the utility tunnel axis. This sequence includes load-bearing components such as longitudinal beams, floor slabs, and walls, arranged in a spatial layout order. By reading the spatial coordinates, component dimensions, and material strength parameters from the structural drawings, a structural unit chain corresponding to the utility tunnel axis is constructed.

[0031] Next, structural deformation risk dispersion prediction is performed on the axial building structure sequence. A topological modeling approach is used to divide the structural unit chain into multiple longitudinal beam unit branches. Based on structural weakness factors such as abrupt changes in cross-section, abrupt changes in support structure, and spacing between connection nodes, each branch is traversed to identify areas with significant stiffness changes or concentrated deformation. The strain amplitude and deformation rate under water level changes are then assessed to obtain the local failure risk coefficient for each branch, thereby locating multiple axial risk areas.

[0032] Then, based on the local failure risk coefficients of multiple axis risk areas, the originally set monitoring interval scale is updated. The update method is as follows: when the local failure risk coefficient of a certain risk area is high (e.g., above the set risk threshold), the original monitoring interval scale is reduced, for example, from 30 meters to 15 meters, to increase monitoring density; conversely, for areas with low risk coefficients (e.g., below the risk threshold), the monitoring interval scale can be appropriately relaxed, for example, from 30 meters to 45 meters, to optimize deployment resources. This dynamic adjustment strategy based on risk level forms multiple compensatory monitoring scales, ensuring that key areas obtain higher monitoring resolution.

[0033] Subsequently, based on the updated monitoring interval scale, supplementary monitoring points were designed along the pipeline corridor axis in multiple identified axial risk areas. Multiple sets of compensation monitoring wells were constructed using rotary drilling. After drilling was completed, filter pipes were installed, quartz sand filter material was backfilled, and sealing layers were set up in sequence to ensure that the compensation monitoring wells had the same functions and performance as the original monitoring wells.

[0034] Finally, multiple sets of compensation monitoring wells are used to monitor and compensate for the P groundwater monitoring wells. That is, by integrating and analyzing the groundwater level data obtained by the compensation monitoring wells with the water level data of the original P groundwater monitoring wells, the water level changes in key risk areas of the utility tunnel can be accurately captured.

[0035] Furthermore, the method provided in the application embodiment, which predicts the dispersion of structural deformation risk of the axial building structure sequence and locates multiple axial risk areas, also includes:

[0036] By performing topological modeling on the axial building structure sequence, a longitudinal beam unit chain is obtained; the longitudinal beam unit chain is divided into M longitudinal beam unit branches; based on the set of structural weak factors, the M longitudinal beam unit branches are traversed to obtain M sets of weak factor nodes; the local failure impact of the M longitudinal beam unit branches is quantified according to the mapping of the M sets of weak factor nodes to obtain M local failure risk coefficients; based on a preset risk scale, the M local failure risk coefficients are traversed to filter out the multiple axial risk regions from the M longitudinal beam unit branches.

[0037] In this embodiment, structural layout data extraction tools such as Excel or AutoCAD are first used to number and organize the components such as longitudinal beams, walls, and base plates in the structural design information. Based on the connection relationships of the components, a list representing the actual layout sequence is formed, establishing a longitudinal beam unit chain. This longitudinal beam unit chain reflects the spatial continuity of structural components and serves as the basis for subsequent structural area division and analysis.

[0038] Secondly, the longitudinal beam unit chain is divided equally according to the number of components, using a fixed-number average segmentation method to divide it into M longitudinal beam unit branches. Each branch contains a continuous set of components, representing a local structural area on the pipe gallery axis, which facilitates subsequent unit risk identification.

[0039] Next, each branch is traversed based on the set of structural weak factors. Weak factors include indicators such as abrupt changes in cross-sectional dimensions, excessive span length, and lack of support. By directly comparing the rate of change of cross-sectional height, span length, and support structure of each component, the locations of components that meet the weakness criteria are identified as weak factor nodes. For example, when the cross-sectional height change of a component exceeds 15%, or the span is greater than the standard value of 6 meters, it is determined to be a weak node. Through this process, M sets of weak factor nodes are obtained.

[0040] Then, based on the M groups of weak factor nodes, the local failure impact on the M longitudinal beam unit branches is quantified. The local failure risk coefficient of each branch segment is calculated using the node proportion method. Taking the total number of branches as the base (e.g., 5), if 2 of them are weak nodes, the risk coefficient is set to 2 / 5 = 0.4. Through this process, M local failure risk coefficients are obtained.

[0041] After obtaining M local failure risk coefficients, risk assessment criteria are established, and the risk coefficients are systematically screened. For example, branches with a risk coefficient of not less than 0.6 are identified as high-risk areas, branches with a risk coefficient between 0.3 and 0.6 are identified as medium-risk areas, and those below 0.3 are identified as low-risk areas. Finally, areas with high risk levels are selected from the M longitudinal beam unit branches as axial risk areas. These areas will serve as priority targets for subsequent adjustments to the compensation monitoring scale and the deployment of compensation monitoring wells.

[0042] Step S300: Based on the structural design information, locate the structural response nodes to obtain multiple spatial structural response nodes.

[0043] In this embodiment, when locating structural response nodes based on structural design information, the cross-sectional dimension set and the soil cover depth distribution map of the utility tunnel are first extracted as the basic data for structural layout and load conditions. According to a predefined geometric abrupt change criterion, the cross-sectional dimension set is traversed segment by segment to identify locations with significant stiffness changes, forming a stiffness variation node set. Simultaneously, according to a load sensitivity criterion, the soil cover depth distribution map is analyzed to mark areas of abrupt changes in soil cover thickness, forming a soil cover gradient node set. Finally, the stiffness variation node set and the soil cover gradient node set are spatially fused, and combined with the node coordinates and component positional relationships, multiple spatial structural response nodes are comprehensively located.

[0044] Furthermore, in the method provided in the application embodiments, the structural response node is located based on the structural design information to obtain multiple spatial structural response nodes, and the method further includes:

[0045] The structural design information is analyzed to obtain the cross-sectional dimensions of the utility tunnel and the soil cover depth distribution map; a geometric mutation criterion and a load sensitivity criterion are predefined; the geometric mutation criterion is used to traverse the cross-sectional dimensions of the utility tunnel to locate the stiffness variation node set; the load sensitivity criterion is used to traverse the soil cover depth distribution map to locate the soil cover gradient node set; the stiffness variation node set and the soil cover gradient node set are spatially fused to obtain the multiple spatial structural response nodes.

[0046] In this embodiment, AutoCAD software is first used to analyze the structural construction drawings and geological profiles to extract cross-sectional structural data and corresponding soil cover depth information along the integrated utility tunnel. A cross-sectional dimension set and a soil cover depth distribution map are then constructed. The cross-sectional dimension set includes parameters such as top slab thickness, bottom slab thickness, sidewall thickness, structural clear height, and clear width. The soil cover depth distribution map records the soil layer thickness above each longitudinal measuring point.

[0047] Next, we predefine the geometric mutation criterion and the load sensitivity criterion. The geometric mutation criterion identifies points where the structural stiffness changes significantly by calculating the relative rate of change of critical dimensions between adjacent cross sections. For example, when the thickness of any cross section member (such as the thickness of the base plate) changes by more than 15% compared to the previous cross section, it is judged as a point of structural stiffness mutation. The load sensitivity criterion judges based on the rate of change of soil cover thickness between adjacent longitudinal measuring points. When the difference exceeds 1.0 meter or the relative change exceeds 20%, it is regarded as a point of soil load variation.

[0048] On the cross-sectional dimension set, the thickness variation values ​​of the top slab, bottom slab, and sidewalls of each measuring point relative to the previous measuring point are calculated one by one. The rate of change is statistically analyzed using the relative difference method. Measuring points with a rate of change exceeding a set threshold are marked as stiffness variation nodes, and a set of stiffness variation nodes is formed. At the same time, on the soil cover depth distribution map, depth comparison is performed along the longitudinal path. The change in soil cover depth for each segment is obtained using the first-order difference analysis method. Combining absolute value judgment and proportional judgment, measuring points that meet the load sensitivity criterion are marked as soil cover gradient nodes, and a set of soil cover gradient nodes is formed.

[0049] Finally, based on the three-dimensional structural coordinates, the stiffness variation node set and the soil gradient node set are spatially matched. A spatial adjacency fusion method is used, setting a longitudinal tolerance threshold (e.g., 1 meter). When the longitudinal position difference between two nodes is within the threshold range, they are determined to be in the same structurally sensitive section, and the nodes are merged. After spatial fusion processing, multiple spatial structural response nodes are obtained.

[0050] Step S400: Based on the risk attributes of the utility tunnel nodes, deploy multiple multimodal sensing device groups at the multiple spatial structure response nodes.

[0051] In this embodiment of the application, when deploying multiple multimodal sensing device groups at multiple spatial structure response nodes, the risk type of each node is first determined based on its structural attributes and geological conditions. For example, if there is a significant change in the structural cross-section at a node and a sudden change in the soil cover depth, the node is identified as a high-risk node; if there is only a slight change in soil cover or abnormal local structural features, it is identified as a medium-risk node; if the structural continuity is good and the change in soil cover is gradual, it is identified as a low-risk node.

[0052] After the risk attributes are determined, corresponding multimodal sensing device groups are configured for different types of nodes. High-risk nodes are equipped with a complete device group including strain sensors, displacement sensors, tilt sensors, water level sensors, and temperature and humidity sensors to simultaneously monitor structural response and environmental changes; medium-risk nodes are equipped with strain sensors, water level sensors, and temperature and humidity sensors, mainly to monitor local deformation and groundwater changes; low-risk nodes are equipped with only water level sensors or temperature and humidity sensors to provide basic environmental data.

[0053] Step S500: The safety monitoring platform integrates the P real-time groundwater levels transmitted by the P pressure level gauges and the multiple multimodal structural sensing data transmitted by the multiple multimodal sensing device groups.

[0054] Furthermore, the method provided in the application embodiments also includes:

[0055] The safety monitoring platform connects the P pressure level gauges and multiple multimodal sensing device groups based on the Industrial Internet of Things. The safety monitoring platform receives and integrates the P real-time groundwater level and multiple multimodal structural sensing data based on the MQTT protocol.

[0056] In this embodiment, the safety monitoring platform integrates P real-time groundwater levels transmitted from P pressure level gauges and multiple multimodal structural sensing data transmitted from multiple multimodal sensing device groups. Specifically, device access is first achieved through an industrial IoT communication link. All pressure level gauges and multimodal sensing device groups connect to an edge gateway via wired or wireless means. The edge gateway performs time synchronization processing on the data collected by each device and packages it into a standard message format. Subsequently, the edge gateway publishes the data corresponding to each device to a designated topic channel of the safety monitoring platform via the MQTT protocol. The platform, acting as a unified MQTT subscriber, subscribes to the topics of the P pressure level gauges and multiple sensing devices in real time and receives data streams from each channel at a set frequency. After receiving the P real-time groundwater level data, the platform categorizes and aligns the data according to the device number, and simultaneously receives and organizes the multimodal structural sensing data, including structural response information from strain sensors, displacement sensors, tilt sensors, etc. Through this access process, the safety monitoring platform completes the real-time reception and integration of all sensor data.

[0057] Step S600: The safety monitoring platform performs dynamic quantification of local risks in the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data to obtain the local risk distribution of the utility tunnel.

[0058] In this embodiment, when the safety monitoring platform dynamically quantifies the local risks of the utility tunnel based on P real-time groundwater levels and multiple multimodal structural sensing data, it calculates the buoyancy of P pipe segment units and combines this with the soil cover depth to obtain the soil cover gravity of P well locations, thereby outputting P anti-buoyancy safety factors. Multiple structural sensing data are divided into P groups according to groundwater monitoring wells, and a dynamic coupling analysis of water level and structural response is performed to obtain P axial risk distributions of the utility tunnel. Finally, the risk results of each segment are spatially stitched together to form the local risk distribution of the utility tunnel.

[0059] Furthermore, in the method provided in the application embodiment, the safety monitoring platform dynamically quantifies the local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data to obtain the local risk distribution of the utility tunnel, and further includes:

[0060] The buoyancy of P pipe segment units is calculated based on the P real-time groundwater levels; the soil cover weight of P well locations is retrieved from the soil cover depth distribution map based on the P groundwater monitoring wells; P anti-buoyancy safety factors are calculated and output based on the buoyancy of the P pipe segment units and the soil cover weight of the P well locations; the multiple multimodal structural sensing data are divided into P groups of multimodal structural sensing data based on the P axial monitoring intervals of the P real-time groundwater levels; water level-structure dynamic coupling analysis is performed on the P anti-buoyancy safety factors and the P groups of multimodal structural sensing data to obtain P axial risk distributions of the pipe gallery; the P axial risk distributions of the pipe gallery are spatially spliced ​​to output the local risk distribution of the pipe gallery.

[0061] In this embodiment, the current groundwater level of each groundwater monitoring well is first obtained by transmitting data from a pressure level gauge. Combining the burial depth and cross-sectional dimensions of the pipe gallery floor slab recorded in the structural design information, a static buoyancy calculation method is used. This method calculates the buoyancy of each of the P corresponding pipe segment units by multiplying the buoyancy by the water column height by the unit weight of the water by the area of ​​the floor slab.

[0062] Subsequently, based on the P groundwater monitoring wells, the overburden depth data corresponding to each well location was extracted from the overburden depth distribution map. Combined with the unit weight value of backfill or undisturbed soil provided in the geological survey data, the overburden weight above each of the P well locations was calculated by multiplying the unit weight by the overburden thickness and then by the structural area.

[0063] Then, based on the buoyancy of P pipe segment units and the weight of the soil covering P well locations, the anti-buoyancy safety factor is calculated for each well location using the safety factor calculation method. It is defined as the ratio of the weight of the soil covering to the buoyancy, thus obtaining P anti-buoyancy safety factors.

[0064] Next, based on the axial deployment sequence of P real-time groundwater levels, the collected multimodal structural sensing data are divided into P groups. Using a spatial location matching method, the data collected by strain sensors, displacement sensors, and tilt sensors are assigned to corresponding monitoring intervals according to their coordinates, using the axial position of each water level monitoring well as the dividing line. For example, if the distance between water level monitoring wells A and B is 20 meters, then all sensing data located within this segment are grouped into the AB segment, ultimately resulting in P groups of structural sensing data.

[0065] Subsequently, a dynamic coupling analysis of water level and structure was performed on P anti-buoyancy safety factors and P sets of multimodal structural sensing data. In this process, a rule-matching-based coupling judgment method was used to determine the comprehensive risk status of each monitoring segment. For example, if a segment has an anti-buoyancy safety factor of 0.867, its inclination angle increases from 0.3° to 0.9°, its strain increment reaches 70με, and it is accompanied by an upward trend in water level, then this segment is judged as a high-coupling-risk segment and assigned a risk status value of 1.00; if another segment has an anti-buoyancy safety factor of 1.25, its strain fluctuation is not significant, and its water level remains stable, then this segment is assigned a value of 0.10; if a segment has an anti-buoyancy safety factor of 1.10, but its strain increases slightly and its inclination angle changes by 0.2°, then it is assigned a medium-risk value of 0.50; and if a segment has an anti-buoyancy safety factor of 1.05, its inclination angle changes slightly, and its strain reaches 50με, then it is assigned a value of 0.60. Finally, after analyzing all P segments, P numerical axial risk distributions of the pipe gallery are obtained.

[0066] Finally, the spatial splicing method is used to merge the axial risk distributions of the P utility tunnels according to the structural layout order, and output the local risk distribution of the utility tunnels covering the entire monitoring area.

[0067] Step S700: Convert the local risk distribution of the utility tunnel into a heat map of utility tunnel risk and output it.

[0068] In this embodiment, the risk status value of each section is mapped to a preset color correspondence based on the numerical values ​​of the local risk distribution of the utility tunnel. Using predefined numerical-color mapping rules, such as assigning red to high-risk values ​​and green to low-risk values, the numerical results of the local risk distribution of the utility tunnel are converted into corresponding color intensities, generating a heat map of the utility tunnel risk.

[0069] In summary, the embodiments of this application have at least the following technical effects:

[0070] This application interactively obtains the structural design information and construction data of the target integrated utility tunnel; deploys P pressure level gauges in the target integrated utility tunnel according to the construction data; locates structural response nodes based on the structural design information to obtain multiple spatial structural response nodes; deploys multiple multimodal sensing device groups at the multiple spatial structural response nodes according to the risk attributes of the tunnel nodes; the safety monitoring platform integrates P real-time groundwater levels returned by the P pressure level gauges and multiple multimodal structural sensing data returned by the multiple multimodal sensing device groups; the safety monitoring platform performs dynamic quantification of local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data to obtain the local risk distribution of the utility tunnel; and converts the local risk distribution of the utility tunnel into a risk heat map for output. This invention solves the technical problem in the prior art where it is difficult to achieve multi-source dynamic sensing and risk quantification of the impact of groundwater level changes on the structure of integrated utility tunnels. By fusing multimodal sensing data of groundwater level and structural response to perform dynamic quantification of local risks, it achieves the technical effect of real-time dynamic monitoring and visualization output of the structural risks of integrated utility tunnels.

[0071] Example 2 is based on the same inventive concept as the integrated utility tunnel structure safety monitoring method based on groundwater level data in the previous examples, such as... Figure 2 As shown, this application provides a comprehensive utility tunnel structure safety monitoring system based on groundwater level data. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0072] The system comprises the following modules: an interaction module 11 for interactively obtaining structural design information and construction data of the target integrated utility tunnel; a deployment module 12 for deploying P pressure level gauges in the target integrated utility tunnel according to the construction data; a positioning module 13 for locating structural response nodes based on the structural design information to obtain multiple spatial structural response nodes; a deployment module 14 for deploying multiple multimodal sensing device groups in the multiple spatial structural response nodes according to the risk attributes of the utility tunnel nodes; a sensing module 15 for integrating P real-time groundwater levels returned by the P pressure level gauges and multiple multimodal structural sensing data returned by the multiple multimodal sensing device groups into the safety monitoring platform; a quantification module 16 for the safety monitoring platform to dynamically quantify the local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data to obtain the local risk distribution of the utility tunnel; and an output module 17 for converting the local risk distribution of the utility tunnel into a utility tunnel risk heat map for output.

[0073] Furthermore, the system is also used to implement the following functions:

[0074] Based on the construction data, the axis of the utility tunnel is spatially located; with a preset monitoring interval as a constraint, standard monitoring wells are laid out along the axis of the utility tunnel in the target integrated utility tunnel to obtain P groundwater monitoring wells; and P pressure level gauges are deployed on the P groundwater monitoring wells.

[0075] Furthermore, the system is also used to implement the following functions:

[0076] Constrained by a preset monitoring spatial scale, the axial building structure sequence is retrieved from the structural design information along the axis of the utility tunnel; structural deformation risk dispersion prediction is performed on the axial building structure sequence to locate multiple axial risk areas; the monitoring interval scale is updated according to multiple local failure risk coefficients of the multiple axial risk areas to obtain multiple compensation monitoring scales; constrained by the monitoring interval scale, multiple sets of compensation monitoring wells are deployed along the axis of the utility tunnel in the multiple axial risk areas; the multiple sets of compensation monitoring wells are used to monitor and compensate the P groundwater monitoring wells.

[0077] Furthermore, the system is also used to implement the following functions:

[0078] By performing topological modeling on the axial building structure sequence, a longitudinal beam unit chain is obtained; the longitudinal beam unit chain is divided into M longitudinal beam unit branches; based on the set of structural weak factors, the M longitudinal beam unit branches are traversed to obtain M sets of weak factor nodes; the local failure impact of the M longitudinal beam unit branches is quantified according to the mapping of the M sets of weak factor nodes to obtain M local failure risk coefficients; based on a preset risk scale, the M local failure risk coefficients are traversed to filter out the multiple axial risk regions from the M longitudinal beam unit branches.

[0079] Furthermore, the system is also used to implement the following functions:

[0080] The structural design information is analyzed to obtain the cross-sectional dimensions of the utility tunnel and the soil cover depth distribution map; a geometric mutation criterion and a load sensitivity criterion are predefined; the geometric mutation criterion is used to traverse the cross-sectional dimensions of the utility tunnel to locate the stiffness variation node set; the load sensitivity criterion is used to traverse the soil cover depth distribution map to locate the soil cover gradient node set; the stiffness variation node set and the soil cover gradient node set are spatially fused to obtain the multiple spatial structural response nodes.

[0081] Furthermore, the system is also used to implement the following functions:

[0082] The safety monitoring platform connects the P pressure level gauges and multiple multimodal sensing device groups based on the Industrial Internet of Things. The safety monitoring platform receives and integrates the P real-time groundwater level and multiple multimodal structural sensing data based on the MQTT protocol.

[0083] Furthermore, the system is also used to implement the following functions:

[0084] The buoyancy of P pipe segment units is calculated based on the P real-time groundwater levels; the soil cover weight of P well locations is retrieved from the soil cover depth distribution map based on the P groundwater monitoring wells; P anti-buoyancy safety factors are calculated and output based on the buoyancy of the P pipe segment units and the soil cover weight of the P well locations; the multiple multimodal structural sensing data are divided into P groups of multimodal structural sensing data based on the P axial monitoring intervals of the P real-time groundwater levels; water level-structure dynamic coupling analysis is performed on the P anti-buoyancy safety factors and the P groups of multimodal structural sensing data to obtain P axial risk distributions of the pipe gallery; the P axial risk distributions of the pipe gallery are spatially spliced ​​to output the local risk distribution of the pipe gallery.

[0085] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0086] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0087] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for safety monitoring of integrated utility tunnel structures based on groundwater level data, characterized in that, The method includes: Interactively obtain structural design information and construction data of the target integrated utility tunnel; Based on the construction data, P pressure level gauges are installed in the target integrated utility tunnel; Based on the structural design information, structural response nodes are located to obtain multiple spatial structural response nodes; Based on the risk attributes of the utility tunnel nodes, multiple multimodal sensing device groups are deployed at the multiple spatial structure response nodes; The safety monitoring platform integrates the P real-time groundwater levels transmitted back by the P pressure level gauges and the multiple multimodal structural sensing data transmitted back by the multiple multimodal sensing device groups. The safety monitoring platform dynamically quantifies the local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data, thereby obtaining the local risk distribution of the utility tunnel. The local risk distribution of the utility tunnel is converted into a heat map of utility tunnel risks and output. Based on the structural design information, structural response nodes are located, resulting in multiple spatial structural response nodes, including: The structural design information was analyzed to obtain the set of cross-sectional dimensions of the utility tunnel and the distribution map of the soil cover depth. Predefined geometric mutation criteria and load sensitivity criteria; The set of cross-sectional dimensions of the utility tunnel is traversed using the geometric mutation criterion to locate the set of stiffness variation nodes. The load sensitivity criterion is used to traverse the soil cover depth distribution map and locate the soil cover gradient node set; The stiffness variation node set and the soil gradient node set are spatially fused to obtain the multiple spatial structure response nodes; The safety monitoring platform dynamically quantifies the local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data, obtaining the local risk distribution of the utility tunnel, including: Calculate the buoyancy of P pipe segment units based on the P real-time groundwater levels; Based on the P groundwater monitoring wells, retrieve the soil cover gravity of the P well locations from the soil cover depth distribution map; Based on the buoyancy of the P pipe segment units and the weight of the soil covering the P well locations, calculate and output P anti-buoyancy safety factors; The multiple multimodal structure sensing data are divided into P groups of multimodal structure sensing data based on the P axial monitoring intervals of the P real-time groundwater levels. A water level-structure dynamic coupling analysis was performed on the P anti-buoyancy safety factors and P sets of multimodal structural sensing data to obtain the P axial risk distributions of the pipe gallery; The axial risk distributions of the P utility tunnels are spatially spliced ​​to output the local risk distributions of the utility tunnels.

2. The integrated utility tunnel structure safety monitoring method based on groundwater level data as described in claim 1, characterized in that, Based on the construction data, P groundwater monitoring wells are installed in the target integrated utility tunnel. The method includes: Based on the aforementioned construction data, the spatial positioning axis of the utility tunnel is determined. With a preset monitoring interval as a constraint, standard monitoring wells are laid out along the axis of the utility tunnel in the target integrated utility tunnel to obtain P groundwater monitoring wells; The P pressure level gauges are deployed in the mapping of the P groundwater monitoring wells.

3. The integrated utility tunnel structure safety monitoring method based on groundwater level data as described in claim 2, characterized in that, The method further includes: Constrained by a preset monitoring space scale, the axial building structure sequence is retrieved from the structural design information along the axis of the utility tunnel. Structural deformation risk dispersion prediction is performed on the axial building structure sequence to locate multiple axial risk areas; The monitoring interval scale is updated based on the multiple local failure risk coefficients of the multiple axis risk areas to obtain multiple compensated monitoring scales; Constrained by the monitoring interval scale, multiple sets of compensation monitoring wells are deployed along the axis of the pipe gallery in the risk areas of the multiple axes; The multiple sets of compensation monitoring wells are used to monitor and compensate the P groundwater monitoring wells.

4. The integrated utility tunnel structure safety monitoring method based on groundwater level data as described in claim 3, characterized in that, The method includes: predicting the dispersion of structural deformation risk for the axial building structure sequence and locating multiple axial risk areas. By performing topological modeling on the aforementioned axial building structure sequence, a longitudinal beam element chain is obtained; The longitudinal beam unit chain is divided into M longitudinal beam unit branches; Based on the set of structural weak factors, the branches of the M longitudinal beam elements are traversed to obtain M sets of weak factor nodes; Based on the mapping of the M weak factor nodes, the local failure impact of the M longitudinal beam unit branches is quantified to obtain M local failure risk coefficients. Based on a preset risk scale, the M local failure risk coefficients are traversed to select the multiple axial risk regions from the M longitudinal beam unit branches.

5. The integrated utility tunnel structure safety monitoring method based on groundwater level data as described in claim 1, characterized in that, The safety monitoring platform connects the P pressure level gauges and multiple multimodal sensing device groups based on the Industrial Internet of Things. The safety monitoring platform receives and integrates the P real-time groundwater level and multiple multimodal structural sensing data based on the MQTT protocol.

6. A comprehensive utility tunnel structure safety monitoring system based on groundwater level data, characterized in that, The system is used to execute the integrated utility tunnel structure safety monitoring method based on groundwater level data as described in any one of claims 1-5, and the system includes: The interactive module is used to interactively obtain structural design information and construction data of the target integrated utility tunnel; A deployment module is used to deploy P pressure level gauges in the target integrated utility tunnel according to the construction data. The positioning module is used to locate the structural response nodes based on the structural design information, thereby obtaining multiple spatial structural response nodes. The deployment module is used to deploy multiple multimodal sensing device groups at the multiple spatial structure response nodes based on the risk attributes of the utility tunnel nodes; The sensing module is used by the safety monitoring platform to integrate the P real-time groundwater levels returned by the P pressure level gauges and the multiple multimodal structural sensing data returned by the multiple multimodal sensing device groups. The quantification module is used by the safety monitoring platform to dynamically quantify the local risks of the utility tunnel based on the P real-time groundwater levels and multiple multimodal structural sensing data, so as to obtain the local risk distribution of the utility tunnel. The output module is used to convert the local risk distribution of the utility tunnel into a heat map of the utility tunnel risk.

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