Tunnel intelligent design method based on similarity

Abstract

The application belongs to the technical field of transportation, and discloses a tunnel intelligent design method based on similarity. The method comprises the following steps: quantifying historical design data to obtain a survey and design database; classifying influence factors of a proposed tunnel project to determine a plurality of target engineering type influence factor sets; inputting the target engineering type influence factor sets and the survey and design database into a similarity calculation model to determine the similarity of each tunnel project and each engineering type in the proposed tunnel project; and determining a target tunnel design scheme according to the similarity of each engineering type. Through the above method, the reusability of successful cases in tunnel projects is improved. With the aid of digital means, various influence parameters in the project are quantified into information that can be operated by machines, similarity matching is performed, and the recommended data help technicians save a lot of time for case reference, thereby improving the efficiency in the tunnel project design process.

Description

Technical Field

[0001] This invention relates to the field of transportation technology, and in particular to a similarity-based intelligent tunnel design method. Background Technology

[0002] With the continuous development of transportation technology, the amount of rail transit engineering has increased explosively, and tunnel engineering is a key and difficult point in rail transit.

[0003] Currently, there are numerous successful tunnel construction cases, all the fruits of the tireless work of engineering technicians. However, given the many factors influencing tunnel design, a large number of historical design drawings have not been fully utilized and have not been transformed into data assets. This means that in different tunnel projects, engineers still need to start the design process from scratch based on experience. This inability to effectively reuse numerous successful cases leads to a waste of technical resources. Currently, tunnel design mainly relies on manual experience and lacks intelligent methods. How to improve the efficiency of the tunnel engineering design process has become an urgent technical problem for engineering technicians to solve.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a similarity-based intelligent tunnel design method, which aims to solve the technical problem of how to improve the efficiency of the tunnel engineering design process in the existing technology.

[0006] To achieve the above objectives, this invention provides a similarity-based intelligent tunnel design method, the method comprising the following steps:

[0007] Obtain historical design data, which includes multiple tunnel engineering design data;

[0008] The historical design data is quantified to obtain a survey and design database;

[0009] Identify the influencing factors of the proposed tunnel project;

[0010] The influencing factors of the proposed tunnel project are classified to determine a set of influencing factors for multiple target project types;

[0011] The set of influencing factors of the target project type and the survey and design database are input into the similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project;

[0012] The target tunnel design scheme is determined based on the similarity of the various engineering types.

[0013] Optionally, the step of inputting the set of influencing factors of the target project type and the survey and design database into the similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project includes:

[0014] The survey and design database is classified to determine the set of influencing factors for each tunnel engineering design data.

[0015] The similarity of multiple project types is determined by matching the set of influencing factors for the target project type with the corresponding set of influencing factors for the project type in the design data of each project.

[0016] Optionally, the step of matching the set of influencing factors for the target project type with the corresponding set of influencing factors for each project in the design data to determine the similarity of multiple project types includes:

[0017] The set of influencing factors for the target project type and each factor in the set of influencing factors for the project type are transformed into influencing factor vector features;

[0018] Based on the cosine similarity calculation formula and Pearson correlation coefficient, the similarity between the set of influencing factors of the target project type and the set of influencing factors of the project type is calculated for the characteristics of the influencing factor vector.

[0019] Optionally, the step of converting the set of influencing factors for the target project type and each factor in the set of influencing factors for the project type into influencing factor vector features includes:

[0020] Based on the survey and design database, weights are designed for each influencing factor, and the weight values ​​of each influencing factor are determined.

[0021] Based on the weight values ​​of the influencing factors, the set of influencing factors for the target project type and each factor in the set of influencing factors for the project type are transformed into influencing factor vector features.

[0022] Optionally, determining the target tunnel design scheme based on the similarity of the various project types includes:

[0023] The similarity of each project type is sorted to determine the sorting result;

[0024] Select the recommended historical engineering design parameters for each engineering type based on the sorting results;

[0025] The target tunnel design scheme is determined based on the historical engineering design parameters.

[0026] Optionally, the recommended historical engineering design parameters include: recommended portal type, recommended portal location, recommended lining type, recommended advanced support measures, and construction method;

[0027] The step of determining the target tunnel design scheme based on the recommended historical engineering design parameters includes:

[0028] The actual tunnel entrance location is obtained by adjusting the recommended entrance location based on the geographical environment and geological factors of the proposed tunnel project.

[0029] The design specifications were verified based on the actual opening location, recommended opening form, recommended lining type, recommended advanced support measures, and construction methods, and the verification results were obtained.

[0030] The target tunnel design scheme is obtained by optimizing the verification results.

[0031] Optionally, after determining the target tunnel design scheme based on the similarity of the various project types, the method further includes:

[0032] Based on the target tunnel design scheme, determine the influencing factors and engineering design parameters for the target tunnel;

[0033] The influencing factors and engineering design parameters of the target tunnel are entered into the survey and design database to update the survey and design database.

[0034] Furthermore, to achieve the above objectives, the present invention also proposes a similarity-based intelligent tunnel design device, which includes:

[0035] The acquisition module is used to acquire historical design data, which includes multiple tunnel engineering design data.

[0036] The processing module is used to quantify the historical design data to obtain a survey and design database;

[0037] The acquisition module is also used to acquire influencing factors of the proposed tunnel project;

[0038] The processing module is also used to classify the influencing factors of the proposed tunnel project and determine a set of influencing factors for multiple target project types;

[0039] The processing module is also used to input the set of influencing factors of the target project type and the survey and design database into the similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project;

[0040] The processing module is also used to determine the target tunnel design scheme based on the similarity of the various engineering types.

[0041] Furthermore, to achieve the above objectives, the present invention also proposes a similarity-based intelligent tunnel design device, which includes: a memory, a processor, and a similarity-based intelligent tunnel design program stored in the memory and executable on the processor. The similarity-based intelligent tunnel design program is configured to implement the steps of the similarity-based intelligent tunnel design method described above.

[0042] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a similarity-based intelligent tunnel design program, which, when executed by a processor, implements the steps of the similarity-based intelligent tunnel design method described above.

[0043] This invention acquires historical design data, including multiple tunnel engineering design documents; quantifies this historical design data to obtain a survey and design database; identifies influencing factors for proposed tunnel projects; classifies these influencing factors to determine multiple target project type influencing factor sets; inputs these target project type influencing factor sets and the survey and design database into a similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project; and determines the target tunnel design scheme based on the similarity of each project type. Through this method, the reusability of successful cases in tunnel engineering is improved. By using digital means, various influencing parameters in the project are quantified into information that can be processed by machines, performing similarity matching and data recommendation. This saves technicians a significant amount of time spent on case references, improving efficiency in the tunnel engineering design process. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the structure of a similarity-based intelligent tunnel design device in the hardware operating environment involved in the embodiments of the present invention;

[0045] Figure 2 This is a flowchart illustrating the first embodiment of the similarity-based intelligent tunnel design method of the present invention.

[0046] Figure 3 This is a schematic diagram illustrating the types of influencing factors in an embodiment of the similarity-based intelligent tunnel design method of the present invention;

[0047] Figure 4 This is a schematic diagram of design parameters for an embodiment of the similarity-based intelligent tunnel design method of the present invention;

[0048] Figure 5 This is a schematic diagram of the technical route of an embodiment of the tunnel intelligent design method based on similarity of the present invention;

[0049] Figure 6This is a flowchart illustrating the second embodiment of the similarity-based intelligent tunnel design method of the present invention;

[0050] Figure 7 This is a structural block diagram of the first embodiment of the tunnel intelligent design device based on similarity according to the present invention.

[0051] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0053] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a similarity-based intelligent tunnel design device for the hardware operating environment involved in the embodiments of the present invention.

[0054] like Figure 1 As shown, the similarity-based intelligent tunnel design device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0055] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on similarity-based intelligent tunnel design devices and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0056] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a similarity-based intelligent tunnel design program.

[0057] exist Figure 1 In the similarity-based intelligent tunnel design device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the similarity-based intelligent tunnel design device of the present invention can be set in the similarity-based intelligent tunnel design device. The similarity-based intelligent tunnel design device calls the similarity-based intelligent tunnel design program stored in the memory 1005 through the processor 1001 and executes the similarity-based intelligent tunnel design method provided in the embodiment of the present invention.

[0058] This invention provides a similarity-based intelligent tunnel design method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a similarity-based intelligent tunnel design method of the present invention.

[0059] In this embodiment, the similarity-based intelligent tunnel design method includes the following steps:

[0060] Step S10: Obtain historical design data, which includes multiple tunnel engineering design data.

[0061] It should be noted that the execution subject in this embodiment is a smart terminal. The smart terminal can be a server, a computer, or other devices with the same or similar functions as a server. This embodiment does not limit this, but only uses a server as an example for illustration.

[0062] It should be noted that this embodiment is applied to the process of tunnel engineering design. By matching the influencing factors of the current proposed tunnel project with the influencing factors in historical engineering data, the design parameters that best match the influencing factors of the current proposed tunnel project are found.

[0063] It is understood that historical design data refers to information from previous tunnel engineering designs, such as digital models stored in computers, exploration data, surveying data, and paper materials. This historical design data includes multiple tunnel engineering design documents.

[0064] Step S20: Quantify the historical design data to obtain a survey and design database.

[0065] It should be noted that historical design data may contain a large amount of textual content or information not expressed in data form. Therefore, it is necessary to quantify this data and transform it into data content that can be stored in a database and processed by a model. For example, analyzing data in tunnel 2D design drawings mainly includes geographical and topographical data, geological data, and engineering design data. Combining the characteristics of various data types with design specifications, design experience, and expert opinions, unstructured data is quantified, and structured data is categorized. This categorized data is then entered into the tunnel survey and design database.

[0066] Step S30: Obtain the influencing factors of the proposed tunnel project.

[0067] It should be noted that the influencing factors of tunnel engineering are those factors that need to be considered during the tunnel design process and will affect the tunnel engineering design, such as... Figure 3 As shown, the influencing factors of tunnel engineering include information from geographical environment and geological factors. The influencing factors of the proposed tunnel engineering are the influencing factors of the tunnel engineering at the location where the tunnel will be built. The influencing factors of tunnel engineering are generally obtained through preliminary surveying and geological exploration. Currently, they can be directly obtained from surveying information platforms and exploration information platforms.

[0068] Step S40: Classify the influencing factors of the proposed tunnel project and determine the set of influencing factors for multiple target project types.

[0069] It should be noted that the set of influencing factors for the project type analyzes various influencing factors of the proposed tunnel project. Combining the classification characteristics of tunnel influencing factors, geographical environment, geological factors, and engineering design factors are quantified and processed to form the set of influencing factors for the target project type. The classification of influencing factors for the proposed tunnel project is necessary because, in the actual design process, such as… Figure 3 As shown, there are many different influencing factors. However, different influencing factors may affect different aspects of the design process. Therefore, the influencing factors should be classified according to the project type to separate the related influencing factors from different project types for calculation. Project types include portal type, portal location, lining type, advanced support measures, and construction methods, etc. This embodiment does not limit these factors but only considers... Figure 4 Please refer to the various sections in Engineering Design I3, for example: Figure 3In engineering design, tunnel burial (A16) is directly affected by tunnel depth (A2) in the geographical environment, but its correlation with water inflow (A7) may not be significant. Therefore, using all factors for similarity calculation may lead to highly inaccurate similarity calculations for tunnel depth parameters in engineering design. Thus, it is necessary to categorize these factors and select those with higher correlation as similarity calculation indicators for the corresponding engineering types. Different engineering types correspond to different sets of influencing factors for the target engineering type.

[0070] Step S50: Input the set of influencing factors of the target project type and the survey and design database into the similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project.

[0071] It should be noted that the process of inputting the set of influencing factors of the target project type and the survey and design database into the similarity calculation model is to match the set of influencing factors of the target project type with the corresponding influencing factors in the survey and design database, and calculate the similarity between multiple influencing factors corresponding to each historical project in the database and the set of influencing factors of the target project type. This will identify schemes with similar geographical environment and / or geological factors in this project type as a reference.

[0072] Step S60: Determine the target tunnel design scheme based on the similarity of the various engineering types.

[0073] Understandably, after obtaining the similarity of different project types, we can know the degree of conformity between different historical projects and the current project.

[0074] In this embodiment, the similarity of each project type is sorted to determine the sorting result; recommended historical project design parameters corresponding to each project type are selected based on the sorting result; and the target tunnel design scheme is determined based on the historical project design parameters.

[0075] It should be noted that similarity highlights the degree of compatibility between two schemes in terms of geographical environment and geological factors. Therefore, by ranking the similarity of each engineering type, the project with the highest similarity of influencing factors can be obtained. Specifically, this embodiment proposes an optimal scheme for the target tunnel design. For example, a certain threshold is set for the similarity. Proposed tunnels that do not meet the threshold are recalculated after correcting or reducing the target conditions. If the conditions are still not met after adjusting the target conditions, a separate tunnel design is performed. The design cases that meet the threshold are ranked by similarity. The engineering characteristics of the proposed tunnel are analyzed, combined with design specifications and design experience, and referenced from actual engineering cases with high similarity rankings, to recommend design parameters for the proposed tunnel project. Based on the recommended parameters, the engineering design scheme for the corresponding engineering type can be determined.

[0076] It should be noted that the method for obtaining historical engineering design parameters is to find historical projects with high similarity to various engineering types in the proposed tunnel project, and then use the engineering design scheme corresponding to the same engineering type used in those historical projects with high similarity. For example, if there is an engineering type a1 in the proposed tunnel, and the influencing factors for engineering type a1 are A, B, and C, then by matching engineering type a2 with influencing factors that have high similarity to A, B, and C in the survey and design database, the design parameters used in a2 at that time can be obtained as the recommended parameters.

[0077] In this implementation, the actual tunnel entrance location is obtained by adjusting the recommended entrance location in conjunction with the geographical environment and geological factors of the proposed tunnel project; the design specifications are verified based on the actual tunnel entrance location, recommended portal type, recommended lining type, recommended advanced support measures, and construction methods to obtain the verification results; and the target tunnel design scheme is obtained by optimizing the process based on the verification results.

[0078] It should be noted that the tunnel design parameters in this embodiment are obtained through... Figure 4 It should be noted that since the location of the tunnel entrance often needs to be determined based on the overall route plan, it is difficult for two sets of tunnel entrance locations to be completely similar. The actual tunnel entrance location needs to be adjusted according to the actual geological factors. Then, the design specifications are verified based on the recommended tunnel entrance form, recommended lining type, recommended advanced support measures and construction methods in the project with the highest similarity to ensure that the recommended parameters will not cause a lot of problems in use.

[0079] In this embodiment, the design influencing factors and engineering design parameters of the target tunnel are determined according to the target tunnel design scheme; the design influencing factors and engineering design parameters of the target tunnel are entered into the survey and design database to complete the update of the survey and design database.

[0080] It should be noted that, in order to ensure the sustainability of the system and keep pace with the times, the design influencing factors and design parameters of the proposed tunnel can be entered into the tunnel survey and design database after the final design scheme of the proposed tunnel is determined, so as to continuously expand the cases in the database.

[0081] In practical implementation, the overall process can be as follows: First, identify the influencing factors of tunnel design and establish a tunnel survey and design database. Second, comprehensively analyze the characteristics of the survey and design database and classify the influencing factors. Third, calculate the weight of each feature to obtain its influence weight. Fourth, extract data from the proposed tunnel project based on the feature classification and set it as the target condition. Fifth, use both the tunnel survey and design database and the target condition as input conditions and perform similarity matching calculations for each case feature. Sixth, combine the feature weights to perform case matching for the entire design scheme. Seventh, set a certain threshold for similarity; for proposed tunnels that do not meet the threshold, revise the target conditions and perform calculations again. Sixth, rank the design cases that meet the threshold based on similarity, and manually optimize the scheme based on the specific engineering characteristics, design specifications, and design experience of the target tunnel. Finally, determine the design scheme of the target tunnel and enter the design case into the tunnel survey and design database, continuously expanding the database of cases. The specific implementation process of this embodiment can be as follows: Figure 5 As shown. When the recommended parameters have certain shortcomings, engineering designers can optimize the scheme based on the characteristics of the actual project and their design experience. Even so, efficiency is still significantly improved. Furthermore, after calculating the similarity for each project type, the overall scheme similarity between different projects can be calculated. By using the similarity of features of each case, the similarity of the design schemes for the entire proposed tunnel project can be comprehensively calculated.

[0082] This embodiment acquires historical design data, which includes multiple tunnel engineering design documents. The historical design data is quantified to obtain a survey and design database. Influencing factors for the proposed tunnel project are acquired. These influencing factors are categorized to determine multiple sets of influencing factors for target project types. The sets of influencing factors for target project types and the survey and design database are input into a similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project. Based on the similarity of each project type, a target tunnel design scheme is determined. This approach improves the reusability of successful cases in tunnel engineering. By using digital means, various influencing parameters in the project are quantified into information that can be processed by machines. Similarity matching and data recommendation save technicians a significant amount of time spent on case references, thus improving efficiency in the tunnel engineering design process.

[0083] refer to Figure 6 , Figure 6 This is a flowchart illustrating a second embodiment of a similarity-based intelligent tunnel design method of the present invention.

[0084] Based on the first embodiment described above, the tunnel intelligent design method based on similarity in this embodiment further includes, in step S50:

[0085] Step S51: Classify the survey and design database to determine the set of influencing factors for engineering types in the engineering design data of each tunnel project.

[0086] It should be noted that the set of influencing factors includes all influencing factors involved in the current project type. The classification process here is consistent with that for the proposed tunnel project, which involves selecting the influencing factors required for the specific project type. The corresponding relationship is that the survey and design database contains a large number of project instances, each containing several project types, and each project type can be further categorized to identify multiple corresponding influencing factors.

[0087] Step S52: Match the target project type influencing factor set with the corresponding project type influencing factor set in the design data of each project to determine the similarity of multiple project types.

[0088] It should be noted that the purpose of calculating similarity is to find historical projects that have similar influencing factors to each type of project in the proposed tunnel project, and then identify the corresponding project types from these historical projects. Since a project may have multiple different types, each project type needs to be matched one by one with the project types in each historical project to find the project types that exist in historical projects with high similarity in influencing factors.

[0089] In this embodiment, the set of influencing factors for the target project type and each factor in the set of influencing factors for the project type are transformed into influencing factor vector features. The influencing factor vector features are calculated according to the cosine similarity calculation formula and the Pearson correlation coefficient to obtain the project type similarity between the set of influencing factors for the target project type and the set of influencing factors for the project type in each project type.

[0090] Specifically, the similarity calculation process can be as follows: for each case feature, perform similarity calculation based on Euclidean distance, similarity calculation based on cosine formula, and similarity calculation based on Pearson correlation coefficient to obtain the similarity of each case feature of the proposed tunnel project.

[0091] In this embodiment, the weights of each influencing factor are designed based on the survey and design database to determine the weight value of each influencing factor; based on the weight values ​​of the influencing factors, the set of influencing factors for the target project type and each factor in the set of influencing factors for the project type are transformed into influencing factor vector features.

[0092] In practical implementation, since the degree of influence or relevance of each influencing factor on the solution varies, it is necessary to unify the influence of influencing factors on similarity calculation by setting weights for different data. For example, the XGBoost method is used to analyze various feature factors and determine the importance ranking of each type of feature; the normalization algorithm is used to calculate the influence weight of each feature.

[0093] This embodiment categorizes the survey and design database to determine the set of influencing factors for each tunnel engineering design document. It then matches the target engineering type influencing factor set with the corresponding engineering type influencing factor sets in the design documents of each engineering project to determine the similarity of multiple engineering types. This approach allows for the separate calculation of similarity for influencing factors of different engineering types, avoiding the difficulty of finding similar cases within a limited pool when calculating overall similarity, thus improving the flexibility of the recommended solution.

[0094] Furthermore, this embodiment of the invention also proposes a storage medium storing a similarity-based intelligent tunnel design program, which, when executed by a processor, implements the steps of the similarity-based intelligent tunnel design method described above.

[0095] Reference Figure 7 , Figure 7 This is a structural block diagram of the first embodiment of the tunnel intelligent design device based on similarity according to the present invention.

[0096] like Figure 7 As shown, the tunnel intelligent design device based on similarity proposed in this embodiment of the invention includes:

[0097] The acquisition module 10 is used to acquire historical design data, which includes multiple tunnel engineering design data.

[0098] Processing module 20 is used to quantify the historical design data to obtain a survey and design database.

[0099] The acquisition module 10 is also used to acquire the influencing factors of the proposed tunnel project.

[0100] The processing module 20 is also used to classify the influencing factors of the proposed tunnel project and determine a set of influencing factors for multiple target project types.

[0101] The processing module 20 is also used to input the set of influencing factors of the target project type and the survey and design database into the similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project.

[0102] The processing module 20 is also used to determine the target tunnel design scheme based on the similarity of the various engineering types.

[0103] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0104] In this embodiment, the acquisition module 10 acquires historical design data, which includes multiple tunnel engineering design data. The processing module 20 quantifies the historical design data to obtain a survey and design database. The acquisition module 10 acquires the influencing factors of the proposed tunnel project. The processing module 20 classifies the influencing factors of the proposed tunnel project to determine multiple target project type influencing factor sets. The processing module 20 inputs the target project type influencing factor sets and the survey and design database into a similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project. The processing module 20 determines the target tunnel design scheme based on the similarity of each project type. Through the above method, the reusability of successful cases in tunnel engineering is improved. By using digital means, various influencing parameters in the project are quantified into information that can be processed by machines. Similarity matching and data recommendation help technicians save a lot of time on case references and improve the efficiency of the tunnel engineering design process.

[0105] In one embodiment, the processing module 20 is further configured to classify the survey and design database and determine the set of engineering type influencing factors in each tunnel engineering design data;

[0106] The similarity of multiple project types is determined by matching the set of influencing factors for the target project type with the corresponding set of influencing factors for the project type in the design data of each project.

[0107] In one embodiment, the processing module 20 is further configured to convert the target project type influencing factor set and each factor in the project type influencing factor set into influencing factor vector features;

[0108] Based on the cosine similarity calculation formula and Pearson correlation coefficient, the similarity between the set of influencing factors of the target project type and the set of influencing factors of the project type is calculated for the characteristics of the influencing factor vector.

[0109] In one embodiment, the processing module 20 is further configured to perform weight design on each influencing factor according to the survey and design database, and determine the weight value of each influencing factor;

[0110] Based on the weight values ​​of the influencing factors, the set of influencing factors for the target project type and each factor in the set of influencing factors for the project type are transformed into influencing factor vector features.

[0111] In one embodiment, the processing module 20 is further configured to sort the similarity of the various project types and determine the sorting result;

[0112] Select the recommended historical engineering design parameters for each engineering type based on the sorting results;

[0113] The target tunnel design scheme is determined based on the historical engineering design parameters.

[0114] In one embodiment, the processing module 20 is further configured to determine the target tunnel design scheme based on the recommended historical engineering design parameters, including:

[0115] The actual tunnel entrance location is obtained by adjusting the recommended entrance location based on the geographical environment and geological factors of the proposed tunnel project.

[0116] The design specifications were verified based on the actual opening location, recommended opening form, recommended lining type, recommended advanced support measures, and construction methods, and the verification results were obtained.

[0117] The target tunnel design scheme is obtained by optimizing the verification results.

[0118] In one embodiment, the processing module 20 is further configured to determine the design influencing factors and engineering design parameters of the target tunnel based on the target tunnel design scheme;

[0119] The influencing factors and engineering design parameters of the target tunnel are entered into the survey and design database to update the survey and design database.

[0120] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0121] In addition, for technical details not described in detail in this embodiment, please refer to the similarity-based intelligent tunnel design method provided in any embodiment of the present invention, which will not be repeated here.

[0122] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0123] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0125] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A similarity-based intelligent tunnel design method, characterized in that, The similarity-based intelligent tunnel design method includes: Obtain historical design data, which includes multiple tunnel engineering design data; The historical design data is quantified to obtain a survey and design database; The influencing factors of the proposed tunnel project are identified. These factors are those that need to be considered during the tunnel design process and will affect the tunnel design. These influencing factors include geographical environment, geological factors, and engineering design. The influencing factors of the proposed tunnel project are classified to determine a set of influencing factors for multiple target project types. Among them, the project types include portal type, portal location, lining type, advanced support measures, and construction methods. The set of influencing factors of the target project type and the survey and design database are input into the similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project; The target tunnel design scheme is determined based on the similarity of the various engineering types. The step of inputting the set of influencing factors of the target project type and the survey and design database into the similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project includes: The survey and design database is classified to determine the set of influencing factors for each tunnel engineering design data. The similarity of multiple project types is determined by matching the set of influencing factors for the target project type with the set of influencing factors for the corresponding project type in the design data of each project. The process of matching the set of influencing factors for the target project type with the corresponding set of influencing factors for each project in the design data to determine the similarity of multiple project types includes: The set of influencing factors for the target project type and each factor in the set of influencing factors for the project type are transformed into influencing factor vector features; Based on the cosine similarity calculation formula and Pearson correlation coefficient, the characteristics of the influencing factor vector are calculated to obtain the engineering type similarity between the target engineering type influencing factor set and the engineering type influencing factor set in each engineering type. The step of converting the set of influencing factors for the target project type and each factor in the set of influencing factors for the project type into influencing factor vector features includes: Based on the survey and design database, weights are designed for each influencing factor, and the weight values ​​of each influencing factor are determined. Based on the weight values ​​of the influencing factors, the set of influencing factors for the target project type and each factor in the set of influencing factors for the project type are transformed into influencing factor vector features.

2. The method of claim 1, wherein, The process of determining the target tunnel design scheme based on the similarity of the various engineering types includes: The similarity of each project type is sorted to determine the sorting result; Select the recommended historical engineering design parameters for each engineering type based on the sorting results; The target tunnel design scheme is determined based on the historical engineering design parameters.

3. The method of claim 2, wherein, The recommended historical engineering design parameters include: recommended portal type, recommended portal location, recommended lining type, recommended advanced support measures, and construction methods; The step of determining the target tunnel design scheme based on the recommended historical engineering design parameters includes: The actual tunnel entrance location is obtained by adjusting the recommended entrance location based on the geographical environment and geological factors of the proposed tunnel project. The design specifications were verified based on the actual opening location, recommended opening form, recommended lining type, recommended advanced support measures, and construction methods, and the verification results were obtained. The target tunnel design scheme is obtained by optimizing the verification results.

4. The method according to any one of claims 1 to 3, wherein After determining the target tunnel design scheme based on the similarity of the various engineering types, the process further includes: Based on the target tunnel design scheme, determine the influencing factors and engineering design parameters for the target tunnel; The influencing factors and engineering design parameters of the target tunnel are entered into the survey and design database to update the survey and design database.

5. A similarity-based tunnel intelligent design apparatus, characterized by comprising: The similarity-based intelligent tunnel design device includes: The acquisition module is used to acquire historical design data, which includes multiple tunnel engineering design data. The processing module is used to quantify the historical design data to obtain a survey and design database; The acquisition module is also used to acquire the influencing factors of the proposed tunnel project. The influencing factors of the tunnel project are the factors that need to be considered in the tunnel design process and will affect the tunnel project design. The influencing factors include geographical environment, geological factors and engineering design. The processing module is also used to classify the influencing factors of the proposed tunnel project and determine a set of influencing factors for multiple target project types. Among them, the project types include portal type, portal location, lining type, advanced support measures and construction methods. The processing module is also used to input the set of influencing factors of the target project type and the survey and design database into the similarity calculation model to determine the similarity between each tunnel project and each project type in the proposed tunnel project; The processing module is also used to determine the target tunnel design scheme based on the similarity of the various engineering types; The processing module is also used to classify the survey and design database and determine the set of influencing factors of engineering type in the engineering design data of each tunnel project; The similarity of multiple project types is determined by matching the set of influencing factors for the target project type with the set of influencing factors for the corresponding project type in the design data of each project. The processing module is further configured to convert the target project type influencing factor set and each factor in the project type influencing factor set into influencing factor vector features; Based on the cosine similarity calculation formula and Pearson correlation coefficient, the characteristics of the influencing factor vector are calculated to obtain the engineering type similarity between the target engineering type influencing factor set and the engineering type influencing factor set in each engineering type. The processing module is also used to design the weights of each influencing factor based on the survey and design database, and determine the weight values ​​of each influencing factor. Based on the weight values ​​of the influencing factors, the set of influencing factors for the target project type and each factor in the set of influencing factors for the project type are transformed into influencing factor vector features.

6. A similarity-based tunnel intelligent design device, characterized by comprising: The device comprises a memory, a processor, and a similarity-based tunnel intelligent design program stored on the memory and executable on the processor, and the similarity-based tunnel intelligent design program is configured to implement the steps of the similarity-based tunnel intelligent design method according to any one of claims 1 to 4.

7. A storage medium, characterized by The storage medium stores a similarity-based tunnel intelligent design program, and the similarity-based tunnel intelligent design program, when executed by a processor, implements the steps of the similarity-based tunnel intelligent design method according to any one of claims 1 to 4.

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