Method and System for Early Warning of Deformation Risk of Underground Pipe Gallery Structure

Abstract

The present invention discloses a method and system for early warning of deformation risks of an underground pipe gallery structure, which relates to the technical field of early warning of underground pipe gallery structures. The method includes: obtaining the deformation information of the underground pipe gallery structure; digitally modeling the underground pipe gallery structure to establish a digital model; performing deformation marking in the digital model to obtain a deformation type identifier and a deformation level identifier; performing deformation simulation using the digital model to obtain deformation limit-exceeding nodes and obtaining the structural state data of the deformation limit-exceeding nodes; constructing an association rule model according to the deformation type identifier, the deformation level identifier, and the structural state data; and obtaining the deformation risk early warning information of the underground pipe gallery structure according to the association rule model. It solves the technical problem in the prior art that it is difficult to accurately early warn of the deformation risks of the underground pipe gallery structure, resulting in the difficulty in timely discovering and handling potential safety hazards, and achieves the technical effect of accurately early warning of the deformation risks of the underground pipe gallery structure and improving the safety and reliability of the underground pipe gallery structure.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning for the structure of an underground utility tunnel, and particularly to a method and system for early warning of deformation risks of the underground utility tunnel structure. Background Art

[0002] With the acceleration of the urbanization process and the continuous improvement of infrastructure construction, the underground utility tunnel, as an important part of the urban lifeline, undertakes multiple key functions such as power, communication, water supply, and drainage. However, due to the influence of various factors such as geological conditions, construction quality, and service life, during the construction and operation of the underground utility tunnel, problems such as abnormal structural deformation and water leakage gradually emerge, posing a great challenge to the safe operation of the underground utility tunnel. These problems may not only lead to facility damage and function failure, but also trigger serious safety accidents, threatening the lives and property safety of citizens. Traditional deformation early warning methods often rely on manual inspections and simple monitoring equipment, making it difficult to accurately predict and warn of deformation risks and difficult to detect and handle potential safety hazards in a timely manner. Summary of the Invention

[0003] The embodiments of the present application provide a method and system for early warning of deformation risks of the underground utility tunnel structure, which solve the technical problem in the prior art that it is difficult to accurately warn of the deformation risks of the underground utility tunnel structure, resulting in potential safety hazards being difficult to detect and handle in a timely manner.

[0004] In view of the above problems, the embodiments of the present application provide a method and system for early warning of deformation risks of the underground utility tunnel structure.

[0005] In the first aspect of the embodiments of the present application, a method for early warning of deformation risks of the underground utility tunnel structure is provided. The method includes:

[0006] Obtaining the deformation information of the underground utility tunnel structure, where the deformation information includes the deformation position; digitally modeling the underground utility tunnel structure to establish a digital model; based on the deformation position, performing deformation marking in the digital model to obtain a deformation type identifier and a deformation level identifier; using the digital model to perform deformation simulation to obtain deformation limit-exceeding nodes, and obtaining the structural state data of the deformation limit-exceeding nodes; constructing an association rule model according to the deformation type identifier, the deformation level identifier, and the structural state data; and obtaining the deformation risk early warning information of the underground utility tunnel structure according to the association rule model.

[0007] In the second aspect of the embodiments of the present application, a system for early warning of deformation risks of the underground utility tunnel structure is provided. The system includes:

[0008] An information acquisition module, which is used to acquire the deformation information of the underground pipe gallery structure, and the deformation information includes the deformation position; a modeling module, which is used to perform digital modeling on the underground pipe gallery structure to establish a digital model; a deformation marking module, which is used to perform deformation marking in the digital model based on the deformation position to obtain a deformation type identifier and a deformation level identifier; a deformation simulation module, which is used to perform deformation simulation using the digital model to obtain deformation exceeding nodes and obtain the structural state data of the deformation exceeding nodes; a model construction module, which is used to construct an association rule model according to the deformation type identifier, the deformation level identifier, and the structural state data; an early warning module, which is used to obtain the deformation risk early warning information of the underground pipe gallery structure according to the association rule model.

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

[0010] First, obtain the deformation information of the underground pipe gallery structure, and the deformation information includes the deformation position. Then, perform digital modeling on the underground pipe gallery structure to establish a digital model. Based on the deformation position, perform deformation marking in the digital model to obtain a deformation type identifier and a deformation level identifier. Then, perform deformation simulation using the digital model to obtain deformation exceeding nodes and obtain the structural state data of the deformation exceeding nodes. Construct an association rule model according to the deformation type identifier, the deformation level identifier, and the structural state data. Finally, obtain the deformation risk early warning information of the underground pipe gallery structure according to the association rule model. It solves the technical problem in the prior art that it is difficult to accurately warn of the deformation risk of the underground pipe gallery structure, resulting in the difficulty in timely discovering and handling potential safety hazards, and achieves the technical effect of accurately warning of the deformation risk of the underground pipe gallery structure and improving the safety and reliability of the underground pipe gallery structure. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0012] Figure 1 It is a schematic flowchart of a method for early warning of deformation risk of an underground pipe gallery structure provided by an embodiment of this application;

[0013] Figure 2 It is a schematic structural diagram of a system for early warning of deformation risk of an underground pipe gallery structure provided by an embodiment of this application.

[0014] Explanation of the reference numerals: information acquisition module 11 , modeling module 12 , deformation marking module 13 , deformation simulation module 14 , model building module 15 , early warning module 16 . DETAILED DESCRIPTION

[0015] The embodiments of the present application provide a method and system for warning of deformation risks of underground utility corridor structures, thereby solving the technical problem in the prior art that it is difficult to accurately warn of deformation risks of underground utility corridor structures, resulting in difficulty in timely discovery and handling of safety hazards.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the embodiment of the present application provides a method for early warning of deformation risk of underground pipe gallery structure, wherein the method comprises:

[0020] Deformation information of the underground pipe gallery structure is obtained, where the deformation information includes a deformation position.

[0021] Deformation sensors (such as strain gauges, displacement meters, etc.) are installed at key locations of underground pipe corridors. These sensors can monitor and record the deformation information of the structure in real time. The deformation information usually includes the deformation location, which is used to assess the health status and potential risks of underground pipe corridors. High-definition cameras or other detection equipment carried by drones are used to inspect the exterior or accessible parts of underground pipe corridors. By taking photos or videos, possible signs of deformation, such as cracks and dislocations, can be identified.

[0022] Furthermore, the deformation information of the underground pipe gallery structure is obtained, including:

[0023] The underground pipe gallery structure is scanned as a whole to obtain structural status data; abnormal features are extracted from the structural status data, and the deformation information is generated according to the abnormal feature extraction results.

[0024] Preferably, use advanced scanning technologies (such as lidar scanning, 3D laser scanning, photogrammetry, etc.) to conduct an overall scan of the underground utility tunnel structure. These technologies can generate high-precision 3D models, reflecting the geometric shape and details of the utility tunnel structure. Through the overall scan, structural state data of the underground utility tunnel structure can be obtained. The structural state data includes geometric dimensions such as pipe diameter, wall thickness, length, etc., surface states such as abnormal features like cracks, spalling, bulging, etc., and material information such as pipe material, coating condition, etc. After obtaining the structural state data, the next step is to extract abnormal features. Abnormal features usually refer to obvious changes or deviations in the structure compared with the normal state. Specifically, for the scanned image data, image processing technologies (such as edge detection, texture analysis, image segmentation, etc.) can be used to identify abnormal features such as cracks and bulges; for numerical data (such as geometric dimensions, material information, etc.), methods such as statistical analysis and machine learning can be used to detect outliers. According to the results of abnormal feature extraction, deformation information of the underground utility tunnel structure is generated. The deformation information usually includes deformation position, deformation amount, deformation type (such as elastic deformation, plastic deformation, etc.).

[0025] Perform digital modeling on the underground utility tunnel structure to establish a digital model.

[0026] The digital model can provide a detailed view of the structure. Digital modeling of the underground utility tunnel structure is carried out to ensure accurate monitoring. According to the detailed data of the underground utility tunnel structure, use modeling software to conduct digital modeling to obtain the digital model of the underground utility tunnel structure.

[0027] Based on the deformation position, perform deformation marking in the digital model to obtain deformation type identification and deformation level identification.

[0028] In the digital model of the underground utility tunnel structure, based on the obtained deformation position information, perform deformation type identification and deformation level identification. Specifically, according to the deformation position information, determine the specific positions where deformation occurs in the digital model of the underground utility tunnel structure, such as key parts like pipes, brackets, connection points, etc.; according to the deformation characteristics, determine the deformation type identification, and further evaluate the severity or level of the deformation. By comparing the deformation amount with a preset threshold and analyzing the impact of the deformation on the structural performance, the deformation level is then determined. Use different markings to perform deformation type identification and deformation level identification at the deformation position.

[0029] Use the digital model to conduct deformation simulation to obtain deformation limit nodes, and obtain the structural state data of the deformation limit nodes.

[0030] Furthermore, the structural state data includes but is not limited to displacement data, stress data, deformation data, environmental data, and material property data.

[0031] Perform deformation simulation using the digital model of the underground pipe gallery structure to simulate the deformation of the underground pipe gallery structure under different conditions, including different types of simulations such as static analysis, dynamic analysis, and temperature field analysis. According to the simulation results, analyze the deformation distribution and trend of the structure to find the nodes where the deformation value exceeds the preset threshold, that is, the nodes with excessive deformation. For each node with excessive deformation, extract the structural state data of the node with excessive deformation, including but not limited to displacement data, stress data, deformation data, environmental data, and material property data. Among them, the displacement data describes the position change of the structure under the action of external loads or internal stresses; the stress data reflects the magnitude and direction of the forces acting on each point inside the structure; the deformation data describes the shape change of the structure under the action of external loads or internal stresses, including different types of deformations such as bending, twisting, and expansion; the environmental data involves external environmental factors that affect the structural performance, such as temperature, humidity, soil pressure, and groundwater level; the material property data describes the basic physical and mechanical properties of the materials that make up the structure, such as elastic modulus, yield strength, and fracture toughness.

[0032] Construct an association rule model based on the deformation type identifier, the deformation level identifier, and the structural state data.

[0033] Based on the deformation type identifier, the deformation level identifier, and the structural state data, an association rule model can be constructed, and the association rule model can reveal the potential associations and patterns between these identifiers and the structural state data.

[0034] Furthermore, constructing an association rule model based on the deformation type identifier, the deformation level identifier, and the structural state data includes:

[0035] Obtain multiple deformation levels, divide the structural state data based on the multiple deformation levels to obtain a deformation limit information table; analyze the deformation limit information table through an association rule mining algorithm to obtain the association rule model.

[0036] In the monitoring of the underground pipe gallery structure, after obtaining multiple deformation levels, the structural state data can be classified according to these deformation levels, and further a deformation limit exceeded information table can be generated. Then, the association rule mining algorithm is used to analyze this information table to construct an association rule model. Specifically, multiple deformation levels are obtained, such as primary deformation, secondary deformation, tertiary deformation, etc. Each level corresponds to a different severity of deformation. For example, in the case of bending, the primary level only shows a slight trend, the secondary level results in bending, and the tertiary level leads to direct fracture. Based on historical data and expert experience, the structural state data (such as displacement, stress, deformation, etc.) of each monitoring point is matched with the corresponding deformation level. According to the matching results, the structural state data is classified into different deformation level categories. The deformation limit exceeded information table includes monitoring point information, deformation level, structural state data (such as displacement value, stress value, etc.). According to the classified structural state data, the corresponding information is filled into the deformation limit exceeded information table. The data of each monitoring point at different time points should be recorded. According to the characteristics and requirements of the data, a suitable association rule mining algorithm, such as the Apriori algorithm, the FP-Growth algorithm, etc., is selected to perform necessary preprocessing on the deformation limit exceeded information table, such as data cleaning, transformation, etc., to meet the requirements of the selected algorithm. For the selected association rule mining algorithm, necessary parameters are set, such as minimum support, minimum confidence, etc. The deformation limit exceeded information table is used as input data to run the association rule mining algorithm. The algorithm will analyze the patterns in the data and generate a series of association rules, which will constitute the association rule model.

[0037] According to the association rule model, deformation risk warning information of the underground pipe gallery structure is obtained.

[0038] The structural state data of the underground pipe gallery structure is input into the association rule model. The association rule model will, according to the association rules, identify the deformation level or risk level corresponding to the current structural state data. According to the deformation level or risk level output by the association rule model and in combination with a preset risk threshold, deformation risk warning information is generated. The warning information may include warning levels (such as primary warning, secondary warning, etc.), warning areas (specific to which section of the pipe gallery or which monitoring point), warning reasons (such as displacement exceeding the standard, stress concentration, etc.), and recommended countermeasures.

[0039] Furthermore, the method further includes:

[0040] Taking the standard underground pipe gallery structure as the target, repair prediction is carried out on the underground pipe gallery structure, including:

[0041] Conducting a process flow inspection on the underground pipe gallery structure to determine the process flow to be repaired;

[0042] Based on the process flow to be repaired, predict the repair difficulty, repair cost, and repair effect, and obtain the predicted repair difficulty coefficient, predicted repair cost coefficient, and predicted repair effect coefficient as the repair prediction results;

[0043] Perform repair adjustment based on the repair prediction results.

[0044] Taking the standard underground utility tunnel structure as the target, conduct repair prediction and perform repair adjustment accordingly. Specifically, conduct a comprehensive inspection of the process flow of the underground utility tunnel structure, including but not limited to inspections of pipelines, equipment, connectors, support structures, etc., to determine the process flow to be repaired; based on the specific situation and historical data of the process flow to be repaired, use a prediction model to predict the repair difficulty, repair cost, and repair effect, and obtain the predicted repair difficulty coefficient, predicted repair cost coefficient, and predicted repair effect coefficient as the repair prediction results; perform repair adjustment according to the repair prediction results.

[0045] Furthermore, performing repair adjustment based on the repair prediction results includes:

[0046] Input the predicted repair difficulty coefficient, predicted repair cost coefficient, and predicted repair effect coefficient into the repair adjustment evaluation model, and output the repair adjustment evaluation coefficient;

[0047] If the repair adjustment evaluation coefficient meets the repair adjustment constraints, perform repair adjustment based on the process flow to be repaired;

[0048] Otherwise, abandon the repair and retrieve the structure replacement plan.

[0049] The repair adjustment evaluation model is constructed based on the repair adjustment evaluation function and can comprehensively evaluate the feasibility, economy, and effect of the repair strategy. Input the repair difficulty coefficient, repair cost coefficient, and repair effect coefficient as input data into the repair adjustment evaluation model. The repair adjustment evaluation model calculates and evaluates based on the input predicted coefficients and outputs a repair adjustment evaluation coefficient. According to the preset repair adjustment constraint conditions, determine whether the repair adjustment evaluation coefficient meets the requirements. Among them, the repair adjustment constraint conditions may include the upper limit of the repair difficulty coefficient, the budget limit of the repair cost coefficient, the minimum requirement of the repair effect coefficient, etc. If the repair adjustment evaluation coefficient meets the repair adjustment constraint conditions, perform repair adjustment according to the process flow to be repaired. If it does not meet the repair adjustment constraint conditions, abandon the repair of this process flow and retrieve the structure replacement plan.

[0050] Furthermore, the repair adjustment evaluation model includes a repair adjustment evaluation function, and the repair adjustment evaluation function is as follows:

[0051]

[0052] Among them, R re is the repair adjustment evaluation coefficient, w eff , w dif , w co are the weight values, and their sum is 1. R eff is the predicted repair effect coefficient, R dif is the predicted repair difficulty coefficient, R co is the predicted repair cost coefficient.

[0053] The repair adjustment evaluation model includes a repair adjustment evaluation function. Among them, R re is the repair adjustment evaluation coefficient, w eff , w dif , w co are the weight values, and their sum is 1. R eff is the predicted repair effect coefficient, which is a value between 0 and 1. The larger the value, the better the repair effect; R dif is the predicted repair difficulty coefficient, which is a value between 0 and 1. The larger the value, the greater the repair difficulty; R co is the predicted repair cost coefficient, which is a value between 0 and 1. The larger the value, the higher the repair cost. The repair adjustment evaluation coefficient R re will comprehensively consider the repair effect, repair difficulty, and repair cost. If the repair effect is good and the repair difficulty and cost are low, then R re is larger, indicating that this is a relatively ideal repair adjustment plan. On the contrary, if the repair effect is poor, the repair difficulty is high, or the repair cost is high, then R re is smaller, indicating that this repair adjustment plan may not be the best choice.

[0054] In summary, the embodiments of the present application have at least the following technical effects:

[0055] First, obtain the deformation information of the underground pipe gallery structure, and the deformation information includes the deformation position. Then, perform digital modeling on the underground pipe gallery structure to establish a digital model. Based on the deformation position, perform deformation marking in the digital model to obtain the deformation type identifier and deformation level identifier. Next, use the digital model to perform deformation simulation to obtain the deformation limit nodes and obtain the structural state data of the deformation limit nodes. According to the deformation type identifier, deformation level identifier, and structural state data, construct an association rule model. Finally, according to the association rule model, obtain the deformation risk warning information of the underground pipe gallery structure. It solves the technical problem in the prior art that it is difficult to accurately warn of the deformation risk of the underground pipe gallery structure, resulting in the difficulty of timely discovery and handling of potential safety hazards, and achieves the technical effect of accurately warning of the deformation risk of the underground pipe gallery structure and improving the safety and reliability of the underground pipe gallery structure.

[0056] Embodiment 2

[0057] Based on the same inventive concept as the method for early warning of deformation risks in the underground pipe gallery structure in the foregoing embodiments, as Figure 2 shown, this application provides a system for early warning of deformation risks in the underground pipe gallery structure. The system in the embodiments of this application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0058] An information acquisition module 11, which is used to acquire the deformation information of the underground pipe gallery structure, and the deformation information includes the deformation position; a modeling module 12, which is used to perform digital modeling on the underground pipe gallery structure to establish a digital model; a deformation marking module 13, which is used to perform deformation marking in the digital model based on the deformation position to obtain a deformation type identifier and a deformation level identifier; a deformation simulation module 14, which is used to perform deformation simulation using the digital model to obtain deformation limit nodes, and obtain the structural state data of the deformation limit nodes; a model construction module 15, which is used to construct an association rule model according to the deformation type identifier, the deformation level identifier, and the structural state data; an early warning module 16, which is used to obtain the deformation risk early warning information of the underground pipe gallery structure according to the association rule model.

[0059] Further, the information acquisition module 11 is used to execute the following method:

[0060] Perform an overall scan on the underground pipe gallery structure to obtain structural state data; extract abnormal features from the structural state data, and generate the deformation information according to the abnormal feature extraction result.

[0061] Further, the deformation simulation module 14 is used to execute the following method:

[0062] The structural state data includes but is not limited to displacement data, stress data, deformation data, environmental data, and material property data.

[0063] Further, the model construction module 15 is used to execute the following method:

[0064] Obtain multiple deformation levels, divide the structural state data based on the multiple deformation levels to obtain a deformation limit information table; analyze the deformation limit information table through an association rule mining algorithm to obtain the association rule model.

[0065] Further, the early warning module 16 is used to execute the following method:

[0066] Taking the standard underground utility tunnel structure as the target, the repair prediction of the underground utility tunnel structure is carried out, including: inspecting the technological process of the underground utility tunnel structure to determine the technological process to be repaired; based on the technological process to be repaired, predicting the repair difficulty, repair cost, and repair effect, obtaining the predicted repair difficulty coefficient, predicted repair cost coefficient, and predicted repair effect coefficient as the repair prediction result; and performing repair adjustment based on the repair prediction result.

[0067] Further, the warning module 16 is used to execute the following method:

[0068] Input the predicted repair difficulty coefficient, predicted repair cost coefficient, and predicted repair effect coefficient into the repair adjustment evaluation model, and output the repair adjustment evaluation coefficient; if the repair adjustment evaluation coefficient meets the repair adjustment constraint, perform repair adjustment based on the technological process to be repaired; otherwise, abandon the repair and retrieve the structure replacement plan.

[0069] Further, the warning module 16 is used to execute the following method:

[0070] The repair adjustment evaluation model includes a repair adjustment evaluation function, and the repair adjustment evaluation function is as follows: Wherein, R re is the repair adjustment evaluation coefficient, w eff , w dif , w co are weight values, and their sum is 1, R eff is the predicted repair effect coefficient, R dif is the predicted repair difficulty coefficient, and R co is the predicted repair cost coefficient.

[0071] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above description of specific embodiments of this specification is made. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0073] This specification and the drawings are merely illustrative of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications therein.

Claims

1. A method for early warning of deformation risk of underground pipe gallery structure, characterized in that: The method comprises: Obtaining deformation information of the underground pipe gallery structure, wherein the deformation information includes a deformation position; Performing digital modeling on the underground pipe gallery structure to establish a digital model; Based on the deformation position, deformation marking is performed in the digital model to obtain a deformation type identifier and a deformation level identifier; The digital model is used to perform deformation simulation to obtain a deformation exceeding limit node and obtain structural state data of the deformation exceeding limit node; constructing an association rule model according to the deformation type identifier, the deformation level identifier, and the structural state data; According to the association rule model, deformation risk warning information of the underground pipe gallery structure is obtained; Also includes: Taking the standard underground pipe gallery structure as the target, the repair prediction of the underground pipe gallery structure is carried out, including: Conducting a process inspection on the underground pipe gallery structure to determine the process to be repaired; Based on the process flow to be repaired, the repair difficulty, repair cost and repair effect are predicted to obtain a predicted repair difficulty coefficient, a predicted repair cost coefficient and a predicted repair effect coefficient as a repair prediction result; Performing repair adjustment based on the repair prediction result; Wherein, performing repair adjustment based on the repair prediction result includes: Inputting the predicted repair difficulty coefficient, the predicted repair cost coefficient, and the predicted repair effect coefficient into a repair adjustment evaluation model, and outputting a repair adjustment evaluation coefficient; If the repair adjustment evaluation coefficient satisfies the repair adjustment constraint, repair adjustment is performed based on the process flow to be repaired; Otherwise, abandon the repair and retrieve the structural replacement solution; The repair adjustment evaluation model includes a repair adjustment evaluation function, and the repair adjustment evaluation function is as follows: Among them, R re To repair the adjustment evaluation coefficient, w eff 、w dif 、w co is the weight value, and the sum is 1, R eff is the predicted repair effect coefficient, R dif To predict the repair difficulty coefficient, R co is the coefficient for predicting the repair cost.

2. The method according to claim 1, characterized in that: Obtain deformation information of underground pipe gallery structure, including: Performing an overall scan on the underground pipe gallery structure to obtain structural status data; Abnormal features are extracted from the structural state data, and the deformation information is generated according to the abnormal feature extraction result.

3. The method according to claim 1, characterized in that: The structural state data includes but is not limited to displacement data, stress data, deformation data, environmental data, and material performance data.

4. The method according to claim 1, characterized in that: Constructing an association rule model according to the deformation type identifier, the deformation level identifier, and the structural state data, including: Acquire multiple deformation levels, divide the structural state data based on the multiple deformation levels, and obtain a deformation limit-crossing information table; The deformation exceeding limit information table is analyzed by an association rule mining algorithm to obtain the association rule model.

5. Used in underground pipe gallery structure deformation risk early warning system, characterized in that: The system is used to implement the method for early warning of deformation risk of underground pipe gallery structure according to any one of claims 1 to 4, and comprises: An information acquisition module, the information acquisition module is used to acquire deformation information of the underground pipe gallery structure, the deformation information includes a deformation position; A modeling module, wherein the modeling module is used to digitally model the underground pipe gallery structure and establish a digital model; A deformation marking module, the deformation marking module is used to perform deformation marking in the digital model based on the deformation position to obtain a deformation type identifier and a deformation level identifier; A deformation simulation module, wherein the deformation simulation module is used to perform deformation simulation using the digital model, obtain deformation exceeding limit nodes, and obtain structural state data of the deformation exceeding limit nodes; A model building module, the model building module is used to build an association rule model according to the deformation type identifier, the deformation level identifier, and the structural state data; An early warning module is used to obtain deformation risk early warning information of the underground pipe gallery structure according to the association rule model.

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