Tunnel support system collaborative design method and system based on artificial intelligence

Abstract

The invention provides a tunnel supporting system collaborative design method and system based on artificial intelligence, and relates to the technical field of tunnel supporting, and the method comprises the steps: obtaining geological survey data of a target tunnel and a historical tunnel supporting system design scheme; performing feature extraction according to a historical tunnel support system design scheme to obtain a historical data vector set; performing machine learning modeling according to the historical data vector set, and performing prediction processing based on geological survey data of the target tunnel to obtain a support design demand report; topological design of the supporting structure is carried out according to the supporting design demand report, and a topological model of the supporting structure is obtained; and performing optimization processing according to the topological model to obtain a tunnel support system design scheme. According to the method, the topological relation of the supporting structure is optimized through the force steering algorithm, coordinated layout between the supporting units is achieved, the construction risk and cost are effectively reduced, and a more intelligent and efficient solution is provided for design of a tunnel supporting system.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel support, and in particular to a collaborative design method and system for a tunnel support system based on artificial intelligence. Background Art

[0002] At present, tunnel support design occupies a vital position in tunnel engineering, especially in complex geological environments and changeable construction conditions. Existing tunnel support design methods usually rely on standardized design formulas and empirical formulas. Although these methods can solve common design problems to a certain extent, they still face many challenges. First, existing technologies often find it difficult to fully consider the complexity of geological conditions, especially the changes in dynamic factors such as soil type, groundwater level, and stress state, which directly affect the stability and safety of the support structure. Secondly, the synergy and interdependence between support units are often ignored, resulting in the design of the support structure being not sophisticated enough and unable to give full play to the role of each support unit. In addition, the existing design methods rely on manual experience for adjustment, and cannot efficiently design complex environments in a short time. They often require multiple iterations and corrections, resulting in a long design cycle and low efficiency. These problems not only limit the adaptability of existing technologies, but also increase the risks and costs during tunnel construction.

[0003] Based on the above-mentioned shortcomings of the prior art, there is an urgent need for a collaborative design method and system for a tunnel support system based on artificial intelligence. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for collaborative design of tunnel support systems based on artificial intelligence to improve the above problems. In order to achieve the above purpose, the technical solutions adopted by the present invention are as follows: In a first aspect, the present application provides a collaborative design method for a tunnel support system based on artificial intelligence, comprising: Obtain geological survey data of the target tunnel and historical tunnel support system design plan; Perform feature extraction according to the historical tunnel support system design scheme to obtain a historical data vector set; Performing machine learning modeling based on the historical data vector set and performing prediction processing based on the geological survey data of the target tunnel to obtain a support design demand report; Performing topological design of the support structure according to the support design requirement report, by creating a topological framework of the support structure, clarifying the mutual relationship and spatial layout between the support units, and obtaining a topological model of the support structure; An optimization process is performed based on the topological model to obtain a design scheme for the tunnel support system.

[0005] In the second aspect, the present application also provides a tunnel support system collaborative design system based on artificial intelligence, including: An acquisition module is used to obtain geological survey data of the target tunnel and the design scheme of the historical tunnel support system; An extraction module, used for extracting features according to the historical tunnel support system design scheme to obtain a historical data vector set; A prediction module, used for performing machine learning modeling based on the historical data vector set and performing prediction processing based on the geological survey data of the target tunnel to obtain a support design demand report; A construction module is used to perform topological design of the support structure according to the support design requirement report, and obtain a topological model of the support structure by creating a topological framework of the support structure, clarifying the mutual relationship and spatial layout between the support units; The optimization module is used to perform optimization processing according to the topological model to obtain a design scheme for the tunnel support system.

[0006] The beneficial effects of the present invention are: The present invention conducts in-depth analysis of historical tunnel support design schemes and geological survey data of target tunnels, and uses machine learning models, graph theory and other algorithms to accurately quantify the dependencies and synergies between support units, thereby overcoming the problem that existing design methods cannot effectively handle complex geological conditions and changeable construction environments. By introducing advanced technologies such as graph convolutional networks and multi-objective optimization, the spatial layout of support units can be optimized, and while ensuring safety, stability and construction efficiency, manual intervention and corrections in the design process can be reduced, greatly improving design efficiency and accuracy. In addition, the present invention optimizes the topological relationship of the support structure through a force-directed algorithm, achieves a coordinated layout between support units, effectively reduces construction risks and costs, and provides a more intelligent and efficient solution for the design of tunnel support systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic diagram of a collaborative design method for a tunnel support system based on artificial intelligence according to an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a collaborative design system for a tunnel support system based on artificial intelligence described in an embodiment of the present invention; Figure 3It is a schematic diagram of the structure of a collaborative design device for a tunnel support system based on artificial intelligence described in an embodiment of the present invention.

[0009] Markings in the figure: 800, a collaborative design device for a tunnel support system based on artificial intelligence; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, extraction module; 903, prediction module; 904, construction module; 905, optimization module. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0011] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0012] Embodiment 1: This embodiment provides a collaborative design method for a tunnel support system based on artificial intelligence.

[0013] See also Figure 1 , the figure shows that the method includes steps S100 to S500.

[0014] Step S100, obtaining geological survey data of the target tunnel and historical tunnel support system design scheme; It is understandable that geological survey data include information such as soil type, groundwater level, soil bearing capacity, rock structure, groundwater permeability, and seismic activity, which can fully reflect the geological characteristics of the tunnel construction area. Specifically, soil type and soil characteristics directly affect the selection of support design schemes, groundwater level and permeability are related to tunnel waterproofing design, rock structure and crack development determine the stability requirements of the support structure, and seismic activity and soil vibration characteristics require that the design must consider seismic performance. The accuracy of geological survey data determines the reliability of support structure design. Therefore, modern exploration technologies such as drilling, geological radar and remote sensing are used to ensure the comprehensiveness and accuracy of data. In addition, the historical tunnel support system design provides a reference for the design, which includes the support structure types, construction processes, material selection, challenges and solutions used in past tunnel projects.

[0015] Step S200, extracting features according to historical tunnel support system design schemes to obtain a historical data vector set; It should be noted that the design schemes of historical tunnel support systems include multiple key factors, such as support structure type, construction method, material use, support unit configuration, geological condition adaptation, etc. This information directly affects the design effect and construction efficiency of the support structure. When performing feature extraction, it is first necessary to identify the specific features of these schemes, such as the selection of support structures (such as shotcrete, steel support, etc.), material strength, construction technology used, construction sequence, etc. By converting these features into numerical data, they provide input for subsequent machine learning models. Secondly, in order to deal with the diversity between different design schemes, the design schemes need to be standardized or normalized during the feature extraction process to make them suitable for machine learning algorithms. In addition, there may be success and failure cases under different design schemes in the historical data. When extracting these features, it is also necessary to consider their impact on the tunnel support design to ensure that this information can reflect the support effect under different geological conditions. By extracting the features of the historical tunnel support design schemes, a historical data vector set is finally constructed. Each data vector represents the design scheme of a historical case and contains various features related to the effect of the support structure.

[0016] Step S300: Perform machine learning modeling based on the historical data vector set, and perform prediction processing based on the geological survey data of the target tunnel to obtain a support design requirement report; It is understandable that by performing machine learning modeling on the historical data vector set, the model can identify the potential relationship between the support structure configuration and design requirements under different geological conditions based on the experience of past tunnel support design schemes. By training the model, a system with intelligent learning capabilities can be obtained, which can automatically identify the most appropriate support scheme based on specific geological characteristics. The geological survey data of the target tunnel will be used as the input of the model and combined with the design cases in the historical data vector set to further improve the accuracy of the prediction. In practical applications, these geological data not only affect the type of support structure selection, but also affect the size, strength requirements and construction technology of the structure. By making predictions based on the geological data of the target tunnel, the machine learning model can intelligently adjust the prediction results to ensure that the design requirements are consistent with the specific construction environment. Step S400: Perform topological design of the support structure according to the support design requirement report, and obtain a topological model of the support structure by creating a topological framework of the support structure, clarifying the mutual relationship and spatial layout between the support units; It should be noted that this step can transform complex support design requirements into clear structural layouts through topological design, so that the relationship between support units can be effectively managed and optimized. Through precise topological framework and optimized spatial layout, the support structure can not only meet geological and design requirements, but also improve construction efficiency, reduce conflicts and errors that may occur during construction, and enhance the overall stability and safety of the support structure.

[0017] Step S500: Optimize the topological model to obtain a design solution for the tunnel support system.

[0018] It is understandable that the optimization process involves matching the functional requirements and engineering constraints between support units. For example, the configuration of support units must not only take into account geological conditions such as soil bearing capacity and groundwater level, but also comprehensively consider factors such as construction period, cost, and material selection. Through multi-objective optimization algorithms (such as particle swarm optimization, genetic algorithms, etc.), these goals can be balanced to ensure the rationality of support unit configuration, avoid over-design or under-design, and thus maximize the performance and benefits of the support system.

[0019] Further, step S200 includes step S210 to step S230.

[0020] Step S210: Modeling the dependency relationship between structures according to the historical tunnel support system design scheme, quantifying the spatial and functional dependencies of different support units, and calculating the weight relationship between the support units to obtain a weighted graph; Specifically, first, the modeling of inter-structural dependencies is to identify the interactions and dependencies between support units by analyzing the design schemes of historical tunnel support systems. The functional roles of support units in the support structure are usually interconnected, and the performance of a support unit may be affected by other units. For example, shotcrete support needs to work together with steel support to improve structural stability, or the spatial position of a support unit may be restricted by the layout of adjacent units. Therefore, when modeling, it is necessary to describe the functional requirements, construction sequence, geological environment adaptability and other information of each support unit in detail. Next, the spatial and functional dependencies of different support units are quantified, and these relationships can be converted into numerical values ​​by mathematical methods. For example, spatial dependency can be measured by the distance and relative position between support units, while functional dependency may involve parameters such as the bearing capacity and support strength of support units in the geological environment. By quantifying these parameters, the relationship between support units can be accurately described. Commonly used quantification methods include expressing the degree of coordination between different support units as a numerical value and reflecting the difference in relative importance between units by weighting. Calculating the weight relationship between support units is to further convert these quantified dependencies into weighted graphs. In a weighted graph, support units are considered as nodes of the graph, and the edges between nodes represent the dependencies between support units. The weights of the edges represent the degree of mutual influence or synergy between support units. The weight calculation method can be based on factors such as the distance between support units, functional dependency, and construction difficulty. For example, the weight between two support units may be inversely proportional to the physical distance between them, and directly proportional to their functional closeness. Through this process, the resulting weighted graph can accurately reflect the dependencies and interactions between support units.

[0021] Step S220: Based on the graph convolutional network, the weighted graph is embedded with nodes of each support unit and its relationship, the collaborative characteristics of the support units under different geological conditions are learned, and the embedding features of the support units are obtained; It can be understood that the graph convolution network can effectively capture the dependency between nodes by performing convolution operations on the local neighborhood of the nodes in the graph. Unlike traditional neural networks, the graph convolution network can directly process graph structure data, learn the high-order features of nodes by aggregating the information of neighboring nodes, so that the representation of the node in the entire graph can reflect the relationship with the surrounding nodes. In the process of node embedding, the graph convolution network updates the embedding vector of each node through iterative calculation. Each layer of convolution operation passes the information of the adjacent nodes to the current node, thereby gradually learning the high-order features of the node. In the context of support design, node embedding is regarded as the "representation" of the support unit in the geological environment. It not only contains the characteristic information of a single support unit, but also can reflect the synergy and interdependence between support units by aggregating the information of neighboring nodes. The node embedding features obtained by training the graph convolution network can deeply reveal the dynamic synergy characteristics between support units, such as how the interaction of support units affects the stability and safety of the overall design under different soil types, groundwater levels, rock structures and other conditions. This step uses the node embedding ability of the graph convolution network to convert the complex dependency between support units into feature vectors that can be processed by the machine learning model. These embedded features not only accurately capture the local properties of each support unit, but also integrate the mutual influence and synergy between support units, so that the model can better adapt to the support design requirements under different geological conditions.

[0022] Step S230: Generate a historical data vector set by integrating the historical support design and the corresponding geological environment data according to the embedding characteristics of the support unit.

[0023] It can be understood that this fusion process forms a more comprehensive feature vector by connecting or weighting the numerical characteristics of the historical design scheme with the numerical characteristics of the geological data. This process ensures that the historical information of the design scheme and the actual situation of the corresponding geological environment are taken into account in the same vector set, thereby more realistically reflecting the relationship between the support design requirements and the geological conditions.

[0024] Furthermore, step S300 includes step S310 to step S330.

[0025] Step S310: Based on the historical data vector set, clustering is performed on the historical design schemes to identify support design patterns under different geological conditions and obtain support design requirement pattern groups; In this step, by clustering the historical data vector set, historical cases with similar design requirements and geological conditions can be grouped together to form support design pattern groups. Each group represents the common characteristics of the schemes and methods used in support design under a specific geological environment.

[0026] Step S320: predicting the support design demand according to the support design demand pattern group and combining the geological survey data of the target tunnel to obtain a prediction result; Specifically, considering that tunnel support design is not just a simple match based on historical design data, but also needs to consider the complexity and diversity of geological characteristics, a weighted similarity matching algorithm is introduced. This algorithm first calculates the similarity between the geological survey data of the target tunnel and each group in the historical data vector set, and assigns different weights according to the similarity, so that the group with more similar geological conditions to the target tunnel occupies a larger proportion in the prediction. In order to further improve the prediction accuracy, a multi-level feature extraction model based on deep learning is adopted, such as a hybrid architecture combining convolutional neural network and long short-term memory network, extracting local features of geological data through convolutional layer, and using long short-term memory network layer to capture the temporal dynamic information of geological changes. The model can not only learn the static geological-design relationship in historical data, but also learn the dynamic trend of geological condition changes, so as to better adapt to the changes in complex geological conditions in tunnel design. Through this improvement, the prediction method combines the advantages of weighted similarity matching and deep learning model, so that the model can understand the complex relationship between the geological data of the target tunnel and the historical design case in a higher dimension. When generating a support design demand forecast, the geological survey data of the target tunnel, the historical design pattern group and the similarity weight derived from the algorithm are integrated to intelligently predict the support design scheme that best suits the target tunnel, including technical parameters such as support structure type, material selection, and strength requirements.

[0027] Step S330: perform fuzzy reasoning processing according to the prediction results, and obtain a support design requirement report by adaptively adjusting the weights of the fuzzy rules.

[0028] It is understandable that the core of this process is to fine-tune the support design requirements through the fuzzy reasoning system so that the prediction results can better adapt to the actual tunnel engineering needs. First of all, fuzzy reasoning is a method of dealing with uncertainty and ambiguity through fuzzy logic, which is particularly suitable for situations where there is an uncertain or fuzzy relationship between geological conditions and support design requirements. In the prediction of support design requirements, the relationship between geological data and design requirements is often not clear, but there is a certain degree of ambiguity. For example, the relationship between soil type and support structure type may vary due to geological changes.

[0029] Therefore, in this step, preliminary fuzzy rules are first generated based on the prediction results. These rules describe the relationship between the support design requirements under different geological conditions. For example, "if the soil type is soft soil and the groundwater level is high, use shotcrete support". These rules are generated based on experience or historical data in the initial stage, but their weights may need to be adjusted according to specific circumstances in actual applications. In order to ensure the accuracy and adaptability of the rules, the method of adaptively adjusting the weights of fuzzy rules is adopted. This process dynamically changes the influence of each fuzzy rule by learning and adjusting the feedback of actual geological conditions and design requirements, so as to more accurately reflect the design requirements of the target tunnel. Preferably, the adaptive adjustment can be achieved through a reinforcement learning algorithm. At each reasoning process, the weights of the fuzzy rules are adjusted according to the feedback results of the design requirements (such as the stability and economy of the support structure), so that the system can gradually optimize the reasoning process and finally obtain more reasonable support design requirements. In this way, the fuzzy reasoning system can adapt to the changes of different tunnel designs in real time and improve the accuracy and operability of the prediction results.

[0030] Further, step S400 includes step S410 to step S430.

[0031] Step S410: According to the support design requirement report, a preliminary relationship diagram between support units is constructed through a graph theory model guided by preset constraints; It should be noted that, unlike the previous weighted graph, the relationship graph here focuses more on the functional dependency based on design requirements and the constraints in engineering practice, rather than directly quantifying the mutual influence intensity between support units. First, the support design requirement report provides the design requirements and required functions of each support unit, such as the type, configuration, and adaptability of the support unit to geological conditions. This information provides the basis for establishing the relationship graph. In the graph theory model, the node represents each support unit, and the edge represents the relationship between the support units, but the edge here does not focus on the quantification of the dependency intensity, but is defined according to specific engineering constraints. For example, the spatial layout of some support units must meet specific spacing requirements, or need to be installed in a specific order to ensure the stability of the support. In this case, the connection of the edge and the relative position between the support units will be set according to these actual constraints. The preset constraints play a guiding role in constructing the preliminary relationship graph. These conditions include construction sequence, physical isolation requirements between support units, relative position relationship, etc. Through these constraints, it can be ensured that the layout between the support units meets the restrictions in actual construction, avoiding the situation that the design plan cannot be implemented in practice. For example, if some support units need to be close to groundwater sources for watertightness control, then these units need to be connected in the relationship diagram to represent their spatial dependence. In other cases, some support units may need to be away from unstable geological areas, and such constraints also need to be reflected in the relationship diagram.

[0032] Step S420: quantitatively evaluate the priorities and interactions of the support units according to the preliminary relationship diagram, calculate the importance of each support unit in the overall support structure by establishing a weight relationship matrix, and obtain the weight relationship between the support units; It can be understood that by quantifying the priority and interaction of support units, the role and status of each support unit in the overall design can be clarified. The establishment of the weight relationship matrix makes the optimization of support design more scientific and accurate, which not only ensures the synergy between support units, but also improves the efficiency and feasibility of the design.

[0033] Step S430: According to the weight relationship, a spatial distribution model of the support units is constructed, and the positions and synergies of the support units are determined based on the mutual relationships and weight distribution to obtain a topological model.

[0034] It should be noted that by constructing a spatial distribution model of support units, designers can determine the optimal position of each unit in the support structure based on the functional dependencies and interactions between the support units. Support units with high weight values ​​need to be arranged in positions with strong interdependence to enhance the stability of the structure. To achieve this goal, commonly used optimization algorithms such as force-directed algorithms or particle swarm optimization algorithms can simulate the mutual attraction and repulsion between support units, adjust their positions, and ensure that the various parts of the structure can work in coordination. In this process, synergy not only includes the physical proximity of support units, but also involves the need for collaborative work during construction. Finally, the optimized layout describes the position and interdependence of support units in detail by generating a topological model. The model provides clear guidance for actual construction, ensures the overall stability, construction efficiency and safety of the support structure, reduces potential construction conflicts, and improves the accuracy and flexibility of the design.

[0035] Further, step S420 includes step S421 to step S423.

[0036] Step S421: Based on the preliminary relationship diagram, an adjacency matrix is ​​used to quantify the dependencies and interactions between the support units, and a functional dependency matrix is ​​established; Specifically, in graph theory, the adjacency matrix is ​​a square matrix, and the elements in the matrix represent the strength of the relationship between support units. The numerical values ​​of the elements in the adjacency matrix represent the degree of interaction between support units. The larger the value, the stronger the dependence or interaction between support units. This matrix provides quantitative and actionable data support for each pair of support units, which can help designers understand the relationship between support units more clearly. On the basis of the adjacency matrix, a functional dependency matrix is ​​further constructed to convert the functional requirements and interaction relationships of support units into numerical features. The functional dependency matrix reflects the synergy and functional dependency between support units. For example, some support units have strong functional dependencies and may need to be arranged in the same location or at a specific construction stage. The functional dependency matrix explicitly expresses these relationships to support the optimal design of the support structure and subsequent decision-making. Therefore, the functional dependency matrix is ​​defined. ,in: ; In the formula, Indicates support unit Support unit Functional dependency ratio; Indicates support unit Support unit The intensity of dependence; and Indicates the serial number of the support unit; for summing variables; Indicates the total number of support units.

[0037] Step S422: According to the functional dependency matrix, the priorities between the support units are quantified by the hierarchical analysis method, the relative importance and priority of each support unit are set based on expert knowledge, and the weight of each support unit is calculated in combination with the dependency matrix to obtain an importance score; It can be understood that the functional dependency matrix has quantified the mutual relationship and functional dependency between the support units. The next step is to rank the importance of the support units according to these relationships. The AHP is a classic multi-criteria decision-making method that converts complex decision-making problems into simple ratio comparisons by constructing a judgment matrix. Here, the AHP is used to quantify the priority between support units. The relative importance of each support unit in the overall design is determined by expert evaluation of the functional requirements, spatial layout, construction sequence, etc. of the support units. Expert knowledge is usually based on experience and historical data to evaluate the criticality of each support unit. For example, under certain complex geological conditions, some support units may be critical to stability, so their weight is large. Then, the dependency matrix and expert knowledge are combined through the AHP to calculate the weight of each support unit. The judgment matrix in the AHP calculates the weight of each support unit by comparing the relative importance of the support units pairwise, and obtains a comprehensive importance score. This score reflects the importance and priority of the support unit in the overall support structure. This approach allows the relative position and role of each support unit in the design to be quantified, ensuring that those units of higher importance are given priority when optimizing the layout.

[0038] Step S423: Calculate the weight relationship of each support unit in the overall support structure according to the importance score to obtain a weight relationship matrix.

[0039] It should be noted that the weight relationship matrix not only considers the relative importance of a single support unit, but also integrates the interdependence between support units. Through this matrix, the weight distribution and interaction between support units can be clearly understood, and the structure can be optimized and adjusted accordingly to ensure that the functional and spatial requirements of each unit can be met.

[0040] Further, step S430 includes step S431 to step S433.

[0041] Step S431, segmenting the spatial distribution of the support units according to the weight relationship to obtain a spatial distribution model; It should be noted that the weight relationship matrix provides a quantitative weight value for each pair of support units. These weight values ​​not only characterize the relative importance of the units, but also reflect their mutual dependence. When performing spatial distribution segmentation, the relative position of the support units in space needs to be determined based on these weight values. For support units with larger weight values, they may need to be arranged in relatively close positions to reduce functional conflicts and increase the synergy of support. Conversely, units with smaller weights can be relatively dispersed or arranged in uncritical positions.

[0042] The construction of the spatial distribution model can be achieved through a variety of optimization algorithms, such as the optimization method based on the force-oriented algorithm. In this method, the relationship between the support units is regarded as a "force field", and the units with larger weights have stronger attraction, while the units with smaller weights have weaker attraction or repulsion. By simulating this force field effect, the position of the support unit can be adjusted so that each unit is automatically adjusted to the optimal position according to its relative importance and dependence on other units. This optimization not only takes into account the spatial distance between the support units, but also takes into account the contribution of each unit to the entire structure, ensuring the stability of the support structure and the smooth progress of construction.

[0043] Step S432: According to the spatial distribution model, the spatial layout of the support units is optimized by using a force-directed algorithm, and the conflicts in the layout are reduced by simulating the mutual repulsive force and attractive force between the support units to obtain an optimized spatial layout result; The repulsive force formula is: ; The attraction formula is: ; in, Indicates support unit and support units The repulsive force between A constant representing the repulsive force; Indicates support unit and support units The distance between Indicates support unit and support units The attraction between A constant representing the force of attraction.

[0044] Step S433: Optimize according to the optimization spatial layout result, adjust the position based on the interaction force between the support units, and obtain a topological model.

[0045] It is understandable that the basic idea of ​​the force-directed algorithm is to adjust the position of the nodes by simulating the mutual repulsion and attraction between nodes (support units). The repulsion force is usually stronger the closer the distance between the support units, the purpose is to avoid the support units being too close; the attraction force is stronger the dependence between the support units, the greater the attraction between them, the purpose is to ensure that the relative position of the support units in space can reflect their functional coordination requirements. In practical applications, the position update of the support units is completed through an iterative process. At each iteration, the nodes are adjusted according to the interaction forces until a balance state is reached, that is, the interaction forces between all support units are balanced.

[0046] In the specific optimization process, the strength of mutual repulsion and attraction will be affected by the weight relationship matrix. Support units with larger weights will have stronger attraction, prompting them to move closer, while units with smaller weights may be dispersed. Through this optimization process, the layout of support units can effectively reduce spatial conflicts, avoid unreasonable overlap or overly compact layout between support units, and ensure that the functional dependencies between support units are met.

[0047] Further, step S500 includes step S510 to step S530.

[0048] Step S510: matching the functional requirements of the support units with the engineering constraints according to the topological model, and obtaining the support unit configuration result by analyzing the fit between the functional requirements of each support unit during construction and the geological conditions; It can be understood that the topological model has been constructed through the previous steps, providing the dependencies, spatial layout and functional synergy between the support units. On this basis, the functional requirements of the support unit refer to the actual role of each unit in the support structure, such as bearing capacity, stability, support, etc. These functional requirements are usually affected by geological conditions, construction environment and support structure design objectives. For example, in areas with low soil bearing capacity, some support units may require higher strength or denser configuration, while in geological areas with harder rocks, different types of support units may be required. Through the analysis of the fit between functional requirements and geological conditions, it is possible to identify which support units can best adapt to specific geological conditions and ensure that they can meet stability and safety requirements during construction. Secondly, engineering constraints include factors such as construction technology, construction time, and budget, which usually inevitably affect the configuration of support units in actual design. For example, some support units may not be widely used in certain areas due to the difficulty of construction and high material costs, and need to be adjusted according to the actual construction situation. In this process, the configuration of the support units is adjusted according to the geological conditions, construction requirements and resource availability to ensure that the design solution can not only meet the functional requirements but also be efficiently implemented in actual construction.

[0049] Step S520: Perform finite element analysis according to the support unit configuration result, simulate the mechanical properties of the support unit, verify the adaptability of the support structure in the geological environment, and obtain the strength verification result; Specifically, the support unit configuration results have been obtained in the previous step. These configuration results provide detailed guidance for the layout of the support structure, material selection, and the relationship between each unit. Based on these configuration results, the next step is to convert the design of the support unit into a finite element model. In this model, the support unit is regarded as a finite number of small units (or elements) that are connected to each other through nodes and boundary conditions to simulate the mechanical behavior of the entire support structure. Through the finite element method, each unit can be analyzed in detail, considering its deformation, stress, and strain under different loading conditions.

[0050] When simulating mechanical properties, in addition to considering the impact of geological conditions (such as soil type, groundwater level, rock formation characteristics, etc.) on the support unit, it is also necessary to simulate the effects of external loads, such as soil pressure, seismic force, temporary loads during construction, etc. These factors will affect the overall stability of the support structure. Therefore, finite element analysis can provide the response of each support unit under specific working conditions and identify potential weak links or dangerous areas. Through finite element analysis, the final strength verification results will indicate the adaptability and safety of the support structure in the geological environment. These results will include key data such as strength distribution, stress concentration areas, and deformation of each support unit when subjected to mechanical loads.

[0051] Step S530: Optimize the support design scheme according to the strength verification result, and obtain the tunnel support system design scheme by optimizing and adjusting the configuration and mutual relationship of the support units.

[0052] It should be noted that the first step in the optimization process is to adjust the configuration of the support units to ensure that the interdependent support units are reasonably arranged in space. This not only involves adjusting the position of the support units, but also includes the improvement of the stress concentration areas found in the strength verification. For example, by increasing the number of support units, adjusting their arrangement, or adjusting the distance between units, the stress concentration in the local area can be reduced, thereby improving the stability of the overall structure.

[0053] Next, the relationship between the support units is optimized, taking into account the synergy between the support units. For example, if some support units are highly dependent on other units in terms of function, these units need to be configured more closely or coordinated in other ways. During the optimization process, designers may also need to adjust the size, material or structure type of the support units to meet the requirements of the strength verification. For example, some support units may need to be strengthened or use more suitable materials to ensure their stability and safety under specific geological conditions.

[0054] Finally, after optimization and adjustment, the tunnel support system design will be a verified and optimized support structure that can operate stably in the geological environment and meet the technical requirements of the construction process. This design not only enhances the safety and stability of the support structure, but also improves the operability and economy of the construction, ensuring the practical feasibility of the design.

[0055] Embodiment 2: like Figure 2 As shown, this embodiment provides a tunnel support system collaborative design system based on artificial intelligence, and the system includes: An acquisition module 901 is used to acquire geological survey data of a target tunnel and a historical tunnel support system design scheme; Extraction module 902, used to extract features according to historical tunnel support system design schemes to obtain a historical data vector set; Prediction module 903, used to perform machine learning modeling based on the historical data vector set, and perform prediction processing based on the geological survey data of the target tunnel to obtain a support design demand report; The construction module 904 is used to perform topological design of the support structure according to the support design requirement report, and obtain a topological model of the support structure by creating a topological framework of the support structure, clarifying the mutual relationship and spatial layout between the support units; The optimization module 905 is used to perform optimization processing according to the topological model to obtain a design scheme for the tunnel support system.

[0056] Furthermore, in a specific embodiment disclosed in the present invention, the extraction module 902 includes: The first extraction unit is used to model the inter-structural dependency relationship according to the historical tunnel support system design scheme, and obtain the weighted graph by quantifying the spatial and functional dependencies of different support units and calculating the weight relationship between the support units; The second extraction unit, based on the graph convolutional network, embeds nodes of each support unit and its relationship into the weighted graph, learns the collaborative characteristics of the support units under different geological conditions, and obtains the embedding features of the support units; The third extraction unit is used to generate a historical data vector set by fusing the historical support design with the corresponding geological environment data according to the embedding characteristics of the support unit.

[0057] Furthermore, in a specific implementation of the present invention, the prediction module 903 includes: The first prediction unit is used to identify the support design modes under different geological conditions by clustering the historical design schemes according to the historical data vector set, and obtain the support design demand mode group; The second prediction unit is used to predict the support design demand according to the support design demand pattern group and the geological survey data of the target tunnel to obtain a prediction result; The third prediction unit is used to perform fuzzy reasoning processing according to the prediction results, and obtain a support design demand report by adaptively adjusting the weights of the fuzzy rules.

[0058] Embodiment 3: Corresponding to the above method embodiment, this embodiment also provides a tunnel support system collaborative design device based on artificial intelligence. The tunnel support system collaborative design device based on artificial intelligence described below and the tunnel support system collaborative design method based on artificial intelligence described above can refer to each other.

[0059] Figure 3 FIG. 8 is a block diagram of an artificial intelligence-based tunnel support system collaborative design device 800 according to an exemplary embodiment. Figure 3 As shown, the tunnel support system collaborative design device 800 based on artificial intelligence may include: a processor 801, a memory 802. The tunnel support system collaborative design device 800 based on artificial intelligence may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0060] The processor 801 is used to control the overall operation of the artificial intelligence-based tunnel support system collaborative design device 800 to complete all or part of the steps in the artificial intelligence-based tunnel support system collaborative design method. The memory 802 is used to store various types of data to support the operation of the artificial intelligence-based tunnel support system collaborative design device 800, which may include, for example, instructions for any application or method operating on the artificial intelligence-based tunnel support system collaborative design device 800, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the artificial intelligence-based tunnel support system collaborative design device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0061] In an exemplary embodiment, an artificial intelligence-based tunnel support system collaborative design device 800 can be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned artificial intelligence-based tunnel support system collaborative design method.

[0062] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for collaborative design of a tunnel support system based on artificial intelligence are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by a processor 801 of a device 800 for collaborative design of a tunnel support system based on artificial intelligence to complete the above-mentioned method for collaborative design of a tunnel support system based on artificial intelligence.

[0063] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A collaborative design method for tunnel support system based on artificial intelligence, characterized in that: include: Obtain geological survey data of the target tunnel and historical tunnel support system design plan; Perform feature extraction according to the historical tunnel support system design scheme to obtain a historical data vector set; Performing machine learning modeling based on the historical data vector set and performing prediction processing based on the geological survey data of the target tunnel to obtain a support design demand report; Performing topological design of the support structure according to the support design requirement report, by creating a topological framework of the support structure, clarifying the mutual relationship and spatial layout between the support units, and obtaining a topological model of the support structure; An optimization process is performed based on the topological model to obtain a design scheme for the tunnel support system.

2. The method for collaborative design of tunnel support system based on artificial intelligence according to claim 1, characterized in that: According to the historical tunnel support system design scheme, feature extraction is performed to obtain a historical data vector set, including: According to the historical tunnel support system design scheme, the inter-structural dependency relationship is modeled, and a weighted graph is obtained by quantifying the spatial and functional dependencies of different support units and calculating the weight relationship between the support units; Based on the graph convolutional network, the weighted graph is embedded with nodes of each support unit and its relationship, the collaborative characteristics of the support units under different geological conditions are learned, and the embedding features of the support units are obtained; According to the embedding characteristics of the support unit, a historical data vector set is generated by fusing the historical support design with the corresponding geological environment data.

3. The collaborative design method of tunnel support system based on artificial intelligence according to claim 1 is characterized in that: Machine learning modeling is performed based on the historical data vector set, and prediction processing is performed based on the geological survey data of the target tunnel to obtain a support design demand report, including: According to the historical data vector set, by clustering the historical design schemes, the support design patterns under different geological conditions are identified to obtain the support design demand pattern groups; According to the support design demand pattern group, combined with the geological survey data of the target tunnel, support design demand prediction is performed to obtain a prediction result; Fuzzy reasoning is performed according to the prediction results, and the weight of the fuzzy rules is adaptively adjusted to obtain a support design requirement report.

4. The method for collaborative design of tunnel support system based on artificial intelligence according to claim 1, characterized in that: The topological design of the support structure is carried out according to the support design requirement report, including: According to the support design requirement report, a preliminary relationship diagram between support units is constructed through a graph theory model guided by preset constraints; According to the preliminary relationship diagram, the priority and interaction of the support units are quantitatively evaluated, and the importance of each support unit in the overall support structure is calculated by establishing a weight relationship matrix to obtain the weight relationship between the support units; According to the weight relationship, a spatial distribution model of the support units is constructed, and the positions and synergies of the support units are determined based on the mutual relationships and weight distribution, so as to obtain a topological model.

5. The method for collaborative design of tunnel support system based on artificial intelligence according to claim 4, characterized in that: A quantitative assessment of the priorities and interactions of the support elements is conducted based on the preliminary relationship diagram, including: Based on the preliminary relationship diagram, an adjacency matrix is ​​used to quantify the dependencies and interactions between the support units, and a functional dependency matrix is ​​established; According to the functional dependency matrix, the priorities between the support units are quantified by the hierarchical analysis method, the relative importance and priority of each support unit are set based on expert knowledge, and the weight of each support unit is calculated in combination with the dependency matrix to obtain an importance score; The weight relationship of each support unit in the overall support structure is calculated according to the importance score to obtain a weight relationship matrix.

6. The method for collaborative design of tunnel support system based on artificial intelligence according to claim 5, characterized in that: According to the weight relationship, by constructing a spatial distribution model of the support unit, and determining the position and synergy of the support unit based on the mutual relationship and weight distribution, a topological model is obtained, including: Segmenting the spatial distribution of the support units according to the weight relationship to obtain a spatial distribution model; According to the spatial distribution model, the spatial layout of the support units is optimized by using a force-directed algorithm, and the conflicts in the layout are reduced by simulating the mutual repulsive force and attractive force between the support units to obtain an optimized spatial layout result; The optimization is performed according to the optimized spatial layout result, and the position is adjusted based on the interaction force between the support units to obtain a topological model.

7. The method for collaborative design of tunnel support system based on artificial intelligence according to claim 1, characterized in that: The optimization process is performed according to the topology model, including: Matching the functional requirements of the support units with the engineering constraints according to the topological model, and obtaining the support unit configuration results by analyzing the fit between the functional requirements of each support unit during construction and the geological conditions; Conducting finite element analysis based on the support unit configuration results, simulating the mechanical properties of the support units, verifying the adaptability of the support structure in the geological environment, and obtaining strength verification results; The support design scheme is optimized according to the strength verification results, and the tunnel support system design scheme is obtained by optimizing and adjusting the configuration and mutual relationship of the support units.

8. A tunnel support system collaborative design system based on artificial intelligence, characterized in that: include: An acquisition module is used to obtain geological survey data of the target tunnel and the design scheme of the historical tunnel support system; An extraction module, used for extracting features according to the historical tunnel support system design scheme to obtain a historical data vector set; A prediction module, used for performing machine learning modeling based on the historical data vector set and performing prediction processing based on the geological survey data of the target tunnel to obtain a support design demand report; A construction module is used to perform topological design of the support structure according to the support design requirement report, and obtain a topological model of the support structure by creating a topological framework of the support structure, clarifying the mutual relationship and spatial layout between the support units; The optimization module is used to perform optimization processing according to the topological model to obtain a design scheme for the tunnel support system.

9. The artificial intelligence-based collaborative design system for tunnel support system according to claim 8, characterized in that: The extraction module comprises: A first extraction unit is used to model the inter-structural dependency relationship according to the historical tunnel support system design scheme, quantify the spatial and functional dependencies of different support units, and calculate the weight relationship between the support units to obtain a weighted graph; The second extraction unit embeds nodes of each support unit and its relationship on the weighted graph based on a graph convolutional network, learns the collaborative characteristics of the support units under different geological conditions, and obtains the embedding features of the support units; The third extraction unit is used to generate a historical data vector set by fusing the historical support design with the corresponding geological environment data according to the embedding characteristics of the support unit.

10. The collaborative design system for tunnel support system based on artificial intelligence according to claim 8, characterized in that: The prediction module comprises: A first prediction unit is used to identify support design modes under different geological conditions by clustering historical design schemes according to the historical data vector set, and obtain a support design demand mode group; A second prediction unit is used to predict the support design demand according to the support design demand pattern group and in combination with the geological survey data of the target tunnel to obtain a prediction result; The third prediction unit is used to perform fuzzy reasoning processing according to the prediction result, and obtain a support design requirement report by adaptively adjusting the weight of the fuzzy rules.

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