Intelligent design method for overall scheme of tunnel portal

Abstract

The application discloses an intelligent design method for a tunnel portal overall scheme, relates to the technical field of tunnel construction design, and comprises the following steps: obtaining target tunnel data of a tunnel portal to be designed, wherein the target tunnel data comprises topographic point cloud data, tunnel line data and tunnel structure size; inputting the target tunnel data into a light-dark boundary mileage prediction model to obtain a target light-dark boundary mileage design value; inputting the target tunnel data and the target light-dark boundary mileage design value into a portal mileage prediction model to obtain a target portal mileage design value; determining a target portal length according to the target light-dark boundary mileage design value and the target portal mileage design value, determining a target portal type of the target portal length based on a potential portal length table; and determining a target portal subdivision type according to the target portal mileage design value, the target portal type and the target tunnel data. The application can improve design efficiency and design precision.

Description

Technical Field

[0001] This application relates to the field of tunnel construction design technology, and in particular to an intelligent design method for the overall scheme of tunnel entrance. Background Technology

[0002] Currently, tunnel portal design relies on manual design within two-dimensional cross-sectional and longitudinal sections. The main process is as follows: 1. Select a potential boundary between open and closed sections and a potential portal mileage within the design mileage range; 2. Draw cross-sectional and longitudinal sections and observe the relative position of the terrain and the tunnel to determine if the potential boundary between open and closed sections and portal mileage are suitable; 3. Repeat steps 1 and 2 until suitable boundary between open and closed sections and portal mileage are found; 4. Select potential portal length and portal type, and determine their suitability based on the cross-sectional and longitudinal sections; 5. Repeat step 4 until suitable portal length and portal type are found. It is evident that designing tunnel portals using traditional methods requires continuous iterative attempts. The entire design process is not only time-consuming and labor-intensive but also highly dependent on the experience of the designers.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide an intelligent design method for the overall scheme of tunnel entrances, which aims to solve the technical problems that designing tunnel entrances in the traditional way is not only time-consuming and labor-intensive, but also highly dependent on the experience of designers.

[0005] To achieve the above objectives, this application proposes an intelligent design method for the overall scheme of a tunnel entrance, the method comprising:

[0006] Obtain target tunnel data for the entrance of the tunnel to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data, and tunnel structural dimensions;

[0007] The target tunnel data is input into the light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value;

[0008] The target tunnel data and the target light-dark boundary mileage design value are input into the tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value.

[0009] The target portal length is determined based on the target light-dark boundary mileage design value and the target portal mileage design value, and the target portal type is determined based on the potential portal length table.

[0010] Based on the target portal mileage design value, the target portal type, and the target tunnel data, the target portal sub-type is determined.

[0011] In one embodiment, the step of determining the target portal length based on the target light-dark boundary mileage design value and the target portal mileage design value, and determining the target portal type based on the potential portal length table, includes:

[0012] Determine the preferred order of portal types and determine the potential portal length range for each portal type;

[0013] Based on the preferred order and the potential portal length range, a potential portal length table is constructed;

[0014] The potential portal length range in the potential portal length table is filtered to include the target portal length, and the target portal type with the target portal length is determined from the filtered portal types according to the preferred order.

[0015] In one embodiment, the step of determining the potential portal length range for each portal type includes:

[0016] Statistics were compiled on the range of tunnel entrance length and the range of open tunnel length for each type of tunnel entrance.

[0017] The potential portal length range for each portal type is obtained by merging and deduplicating the portal length range and the open portal length range.

[0018] In one embodiment, the step of determining the sub-type of the portal based on the target portal mileage design value, the target portal type, and the target tunnel data includes:

[0019] When the portal type is a cut portal, the tunnel length of the tunnel to be designed is determined based on the target tunnel data;

[0020] Based on the comparison results between the tunnel length and the critical portal length parameter, the number of openings is determined;

[0021] The sub-type of the tunnel is determined based on the target tunnel type and the number of openings.

[0022] In one embodiment, the step of determining the sub-type of the portal based on the target portal mileage design value, the target portal type, and the target tunnel data includes:

[0023] When the portal type is a wall-type portal, a first feature, a second feature, a third feature, and a fourth feature are determined based on the target portal mileage design value and the target tunnel data. The first feature is the first elevation difference between a topographic point on the left side of the tunnel and a first reference line on the cross-section at the target portal mileage design value. The second feature is the second elevation difference between a topographic point on the right side of the tunnel and the first reference line on the cross-section at the target portal mileage design value. The third feature is the third elevation difference between a topographic point on the left side of the tunnel and a second reference line on the cross-section at the target portal mileage design value. The fourth feature is the fourth elevation difference between a topographic point on the right side of the tunnel and the second reference line on the cross-section at the target portal mileage design value. The first reference line is a straight line on the cross-section at the target portal mileage design value that passes through the top of the tunnel and is parallel to the bottom of the tunnel. The second reference line is a straight line on the cross-section at the target portal mileage design value that is parallel to the first reference line and has an elevation difference of a preset distance from the center point of the tunnel. The second reference line is higher than the top of the tunnel.

[0024] Based on the wall-type doorway sub-category discrimination table, the target doorway sub-type is determined according to the first feature, the second feature, the third feature, and the fourth feature.

[0025] In one embodiment, the step of obtaining the target tunnel data at the entrance of the tunnel to be designed further includes:

[0026] Acquire tunnel data at the entrance of a constructed tunnel, wherein the tunnel data includes topographic point cloud data, route data, tunnel structural dimensions, and design parameters;

[0027] A mileage prediction model for the light-dark boundary is established based on the tunnel data, and a mileage prediction model for the tunnel entrance is also established based on the tunnel data.

[0028] In one embodiment, the step of establishing the light-dark boundary mileage prediction model based on the tunnel data includes:

[0029] The design start mileage, design end mileage, light-dark boundary mileage, and design axis are determined based on the tunnel data.

[0030] Multiple mileages are uniformly selected along the design axis within the range of the design start mileage and the design end mileage. The first boundary mileage feature and the second boundary mileage feature of each mileage on the cross section are calculated in sequence. The first sequence feature is determined based on the first boundary mileage feature and the second boundary mileage feature. The first boundary mileage feature is the distance between the topographic line on the cross section and the top of the tunnel, and the second boundary mileage feature is the topographic slope on the cross section.

[0031] Based on the design start mileage, the design end mileage, and the light-dark boundary mileage, determine the relative position of the light-dark boundary mileage of the constructed tunnel entrance;

[0032] Based on the first sequence features and the relative positions of the light-dark boundary mileage, a first dataset is constructed, and based on the first dataset, a light-dark boundary mileage prediction model is established.

[0033] In one embodiment, the step of establishing a tunnel entrance mileage prediction model based on the tunnel data includes:

[0034] The tunnel boundary design mileage, the light-dark boundary mileage, and the design axis are determined based on the tunnel data.

[0035] Within the design mileage of the tunnel boundary and the light-dark boundary mileage, multiple mileages are uniformly selected along the design axis. The first tunnel entrance mileage feature and the second tunnel entrance mileage feature of each mileage are calculated sequentially on the cross section. The second sequence feature is determined based on the first tunnel entrance mileage feature and the second tunnel entrance mileage feature. The first tunnel entrance mileage feature is the distance between the left topographic line and the retaining wall on the cross section, and the second tunnel entrance mileage feature is the distance between the right topographic line and the retaining wall on the cross section.

[0036] The second sequence features are subjected to feature masking processing to obtain the second target sequence features;

[0037] The tunnel entrance mileage is determined based on the tunnel data, and the relative position of the tunnel entrance mileage of the constructed tunnel is determined based on the tunnel entrance mileage.

[0038] Based on the features of the second target sequence and the relative position of the tunnel entrance mileage, a second dataset is constructed, and based on the second dataset, a tunnel entrance mileage prediction model is established.

[0039] In one embodiment, the step of inputting the target tunnel data into the light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value includes:

[0040] The target tunnel data is input into the tunnel entrance light-dark boundary mileage prediction model to obtain the relative position of the target light-dark boundary mileage.

[0041] Based on the light-dark boundary mileage conversion formula, the relative position of the target light-dark boundary mileage is converted into the target light-dark boundary mileage design value.

[0042] In one embodiment, the step of inputting the target tunnel data and the target light-dark boundary mileage design value into the tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value includes:

[0043] The target tunnel data and the target light-dark boundary mileage design value are input into the tunnel entrance mileage prediction model to obtain the relative position of the tunnel entrance mileage.

[0044] Based on the tunnel entrance mileage conversion formula, the relative position of the tunnel entrance mileage is converted into the tunnel entrance mileage design value.

[0045] Furthermore, to achieve the above objectives, this application also proposes an intelligent design device for the overall scheme of a tunnel entrance, the intelligent design device for the overall scheme of a tunnel entrance comprising:

[0046] The acquisition module is used to acquire target tunnel data at the entrance of the tunnel to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data, and tunnel structure dimensions;

[0047] The input module is used to input the target tunnel data into the light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value;

[0048] The input module is also used to input the target tunnel data and the target light-dark boundary mileage design value into the tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value.

[0049] The determination module is used to determine the target portal length based on the target light-dark boundary mileage design value and the target portal mileage design value, and to determine the target portal type based on the potential portal length table;

[0050] The determining module is further configured to determine the sub-type of the target portal based on the target portal mileage design value, the target portal type, and the target tunnel data.

[0051] In addition, to achieve the above objectives, this application also proposes an intelligent design device for the overall scheme of a tunnel entrance, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the intelligent design method for the overall scheme of a tunnel entrance as described above.

[0052] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the intelligent design method for the overall tunnel portal scheme as described above.

[0053] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the intelligent design method for the overall tunnel portal scheme as described above.

[0054] One or more technical solutions proposed in this application have at least the following technical effects:

[0055] The intelligent design method for the overall tunnel portal scheme proposed in this application involves: acquiring target tunnel data for the tunnel portal to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data, and tunnel structural dimensions; inputting the target tunnel data into a light-dark boundary mileage prediction model to obtain a target light-dark boundary mileage design value; inputting the target tunnel data and the target light-dark boundary mileage design value into a portal mileage prediction model to obtain a target portal mileage design value; determining the target portal length based on the target light-dark boundary mileage design value and the target portal mileage design value, and determining the target portal type based on a potential portal length table; and determining the target portal sub-type based on the target portal mileage design value, the target portal type, and the target tunnel data. This invention solves the technical problem that traditional methods of designing tunnel entrances are not only time-consuming and labor-intensive, but also highly dependent on the experience of designers. Compared with existing technologies, this application can predict the light-dark boundary mileage and entrance mileage of the tunnel entrance to be designed by using established light-dark boundary mileage prediction models and entrance mileage prediction models. The design of the tunnel entrance can be achieved without repeated attempts by designers, thus achieving efficient design. Furthermore, this application provides a unified method for determining the type of tunnel entrance and its sub-types. The design results are not affected by the subjectivity of designers, making the design results of the tunnel entrance to be designed more stable and reliable. Attached Figure Description

[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating an embodiment of the intelligent design method for the overall tunnel portal scheme of this application;

[0059] Figure 2 This is a sequence feature extraction diagram of the light and dark boundary mileage provided in Embodiment 1 of the intelligent design method for the overall tunnel portal scheme of this application;

[0060] Figure 3An LSTM network model diagram provided for the first embodiment of the intelligent design method for the overall tunnel portal scheme of this application;

[0061] Figure 4 This is a sequence feature extraction diagram of the tunnel portal mileage provided in Embodiment 1 of the intelligent design method for the overall tunnel portal scheme of this application.

[0062] Figure 5 A technical roadmap provided for an embodiment of the intelligent design method for the overall tunnel portal scheme of this application;

[0063] Figure 6 A diagram illustrating the significance of wall-type portal features provided in Embodiment 1 of the intelligent design method for the overall tunnel portal scheme of this application;

[0064] Figure 7 A flowchart illustrating the second embodiment of the intelligent design method for the overall tunnel portal scheme of this application;

[0065] Figure 8 A process diagram of the potential portal length provided in Embodiment 2 of the intelligent design method for the overall tunnel portal scheme of this application;

[0066] Figure 9 The topographic point cloud data map of the designed tunnel portal is provided in Embodiment 2 of the intelligent design method for the overall tunnel portal scheme of this application.

[0067] Figure 10 The topographic point cloud data map of the tunnel portal to be designed is provided in Embodiment 2 of the intelligent design method for the overall tunnel portal scheme of this application.

[0068] Figure 11 A trend diagram of the loss value variation of the light-dark boundary mileage prediction model provided in Embodiment 2 of the intelligent design method for the overall tunnel portal scheme of this application;

[0069] Figure 12 A trend diagram of the loss value change of the tunnel portal mileage prediction model provided in Embodiment 2 of the intelligent design method for the overall tunnel portal scheme of this application;

[0070] Figure 13 This is a schematic diagram of the modular structure of the intelligent design device for the overall tunnel entrance scheme in an embodiment of this application;

[0071] Figure 14 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the intelligent design method of the overall tunnel entrance scheme in the embodiments of this application.

[0072] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0073] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0074] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0075] The main solution of this application embodiment is as follows: First, acquire target tunnel data for the tunnel entrance to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data, and tunnel structural dimensions. Second, input the target tunnel data into a light-dark boundary mileage prediction model to obtain a target light-dark boundary mileage design value. Third, input the target tunnel data and the target light-dark boundary mileage design value into a tunnel entrance mileage prediction model to obtain a target tunnel entrance mileage design value. Fourth, determine the target tunnel entrance length based on the target light-dark boundary mileage design value and the target tunnel entrance mileage design value, and determine the target tunnel entrance type based on a potential tunnel entrance length table. Fifth, determine the target tunnel entrance sub-type based on the target tunnel entrance mileage design value, the target tunnel entrance type, and the target tunnel data.

[0076] In this embodiment, for ease of description, the following description will focus on the intelligent design equipment that identifies the overall scheme of the tunnel entrance.

[0077] Because designing tunnel entrances using traditional methods requires continuous iteration and experimentation, the entire design process is not only time-consuming and labor-intensive, but also highly dependent on the experience of the designers.

[0078] This application provides a solution that can predict the light-dark boundary mileage and the tunnel portal mileage of the tunnel to be designed by using established light-dark boundary mileage prediction models and portal mileage prediction models. This eliminates the need for designers to repeatedly attempt to design the tunnel portal, thus achieving efficient design. Furthermore, this application provides a unified method for determining the portal type and its sub-types, ensuring that the design results are not affected by the designer's subjectivity, making the design results of the tunnel portal more stable and reliable.

[0079] As can be seen from the above embodiments, this application obtains target tunnel data for the tunnel entrance to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data, and tunnel structural dimensions; inputs the target tunnel data into a light-dark boundary mileage prediction model to obtain a target light-dark boundary mileage design value; inputs the target tunnel data and the target light-dark boundary mileage design value into a tunnel entrance mileage prediction model to obtain a target tunnel entrance mileage design value; determines the target tunnel entrance length based on the target light-dark boundary mileage design value and the target tunnel entrance mileage design value, and determines the target tunnel entrance type based on a potential tunnel entrance length table; and determines the target tunnel entrance sub-type based on the target tunnel entrance mileage design value, the target tunnel entrance type, and the target tunnel data. This invention solves the technical problem that traditional methods of designing tunnel entrances are not only time-consuming and labor-intensive, but also highly dependent on the experience of designers. Compared with existing technologies, this application can predict the light-dark boundary mileage and entrance mileage of the tunnel entrance to be designed by using established light-dark boundary mileage prediction models and entrance mileage prediction models. The design of the tunnel entrance can be achieved without repeated attempts by designers, thus achieving efficient design. Furthermore, this application provides a unified method for determining the type of tunnel entrance and its sub-types. The design results are not affected by the subjectivity of designers, making the design results of the tunnel entrance to be designed more stable and reliable.

[0080] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of realizing the above functions, or an intelligent design device for the overall tunnel portal scheme, etc. The following description uses an intelligent design device for the overall tunnel portal scheme as an example to illustrate this embodiment and the subsequent embodiments.

[0081] Based on this, embodiments of this application provide an intelligent design method for the overall scheme of a tunnel entrance, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the intelligent design method for the overall tunnel entrance scheme of this application.

[0082] In this embodiment, the intelligent design method for the overall tunnel entrance scheme includes steps S10 to S40:

[0083] Step S10: Obtain target tunnel data for the entrance of the tunnel to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data, and tunnel structure dimensions.

[0084] In one feasible implementation, the step of obtaining the target tunnel data of the tunnel entrance to be designed further includes: obtaining tunnel data of the tunnel entrance of the constructed tunnel, wherein the tunnel data includes terrain point cloud data, route data, tunnel structure dimensions, and design parameters; establishing the light-dark boundary mileage prediction model based on the tunnel data, and establishing the entrance mileage prediction model based on the tunnel data.

[0085] It should be noted that when establishing the prediction model for the boundary mileage between light and dark areas, the sequential features reflecting the relative position of the terrain and the tunnel, as well as the terrain slope, can be extracted from the tunnel data at the entrance of the constructed tunnel within the design starting and ending mileage range. Then, the relative position of the boundary mileage between light and dark areas is calculated based on the design parameters. Finally, a prediction model for the boundary mileage between light and dark areas is established using a deep learning algorithm.

[0086] It should be noted that when establishing the tunnel entrance mileage prediction model, the sequential features reflecting the relative position of the terrain and the tunnel can be extracted from the tunnel data of the tunnel entrances of the constructed tunnels within the range of the light and dark boundary mileage and the design boundary mileage. The sequential features are then masked using the non-potential tunnel entrance mileage. The relative position of the tunnel entrance mileage is calculated based on the design parameters. Finally, a prediction model for the tunnel entrance mileage is established using a deep learning algorithm.

[0087] In this embodiment, the tunnel data of the tunnel entrance of the constructed tunnel is used to construct features and a prediction model of the light-dark boundary mileage and the tunnel entrance mileage is established by using deep learning technology. This realizes the automated calculation of the light-dark boundary mileage and the tunnel entrance mileage, thereby improving the design efficiency.

[0088] In one feasible implementation, the step of establishing the light-dark boundary mileage prediction model based on the tunnel data includes: determining the design start mileage, design end mileage, light-dark boundary mileage, and design axis according to the tunnel data; uniformly selecting multiple mileages along the design axis within the range of the design start mileage and the design end mileage, sequentially calculating the first boundary mileage feature and the second boundary mileage feature of each mileage on the cross section, and determining a first sequence feature based on the first boundary mileage feature and the second boundary mileage feature, wherein the first boundary mileage feature is the distance between the topographic line on the cross section and the tunnel top, and the second boundary mileage feature is the topographic slope on the cross section; determining the relative position of the light-dark boundary mileage at the entrance of the constructed tunnel according to the design start mileage, the design end mileage, and the light-dark boundary mileage; constructing a first dataset based on the first sequence feature and the relative position of the light-dark boundary mileage, and establishing the light-dark boundary mileage prediction model based on the first dataset.

[0089] In the specific implementation, N mileages are uniformly selected along the design axis within the design start mileage and design end mileage range. The first and second boundary mileage features on the cross-sections corresponding to the N mileages are calculated sequentially. Based on these two boundary mileage features, a first sequence feature is formed. The extraction process of the first sequence feature is as follows: Figure 2 As shown, Feature 1 (i.e., the first boundary mileage feature) is the distance between the topographic line on the cross section and the top of the tunnel, and Feature 2 (i.e., the second boundary mileage feature) is the slope of the topographic line on the cross section. The first sequence of features represents the overall relative position of the topographic line and the tunnel and the topographic slope within the design start mileage and end mileage range.

[0090] In practical implementation, the relative position of the light-dark boundary mileage can be calculated based on the light-dark boundary mileage, the design start mileage, and the design end mileage in the tunnel data (the relative position of the light-dark boundary mileage includes the relative position of the light-dark boundary mileage at the entrance and the exit). The specific calculation formula is as follows:

[0091]

[0092] In the formula, S b S represents the distance between light and dark boundaries. s S represents the design starting mileage. e Indicates the design termination mileage, y jb Indicates the relative position of the mileage separating the light and dark areas at the import point, y cb Indicates the relative position of the mileage dividing the light and dark areas at the exit.

[0093] In the specific implementation, when establishing the light-dark boundary mileage prediction model, the extracted first sequence features are standardized and combined with the relative positions of the light-dark boundary mileages to form the first dataset. The first dataset is further divided into a training set and a test set, where the training set is used to build the model and the test set is used to evaluate the model. Figure 3 As shown, a prediction model for the light-dark boundary mileage can be established based on the existing LSTM algorithm. The input feature shape of the model is N×2, and the output is a single neuron (representing the relative position of the light-dark boundary mileage).

[0094] In one feasible implementation, the step of establishing a tunnel entrance mileage prediction model based on the tunnel data includes: determining the tunnel boundary design mileage, the light-dark boundary mileage, and the design axis based on the tunnel data; uniformly selecting multiple mileages along the design axis within the range of the tunnel boundary design mileage and the light-dark boundary mileage; sequentially calculating the first tunnel entrance mileage feature and the second tunnel entrance mileage feature of each mileage on the cross section; determining a second sequence feature based on the first tunnel entrance mileage feature and the second tunnel entrance mileage feature, wherein the first tunnel entrance mileage feature is the distance between the left topographic line and the retaining wall on the cross section, and the second tunnel entrance mileage feature is the distance between the right topographic line and the retaining wall on the cross section; performing feature masking processing on the second sequence feature to obtain a second target sequence feature; determining the tunnel entrance mileage based on the tunnel data; determining the relative position of the tunnel entrance mileage of the constructed tunnel based on the tunnel entrance mileage; constructing a second dataset based on the second target sequence feature and the relative position of the tunnel entrance mileage; and establishing the tunnel entrance mileage prediction model based on the second dataset.

[0095] In the specific implementation, M mileages are uniformly selected along the design axis within the range of the light-dark boundary mileage and the design boundary mileage. The first and second tunnel entrance mileage features on the corresponding cross-sections of these M mileages are then calculated sequentially to form a second sequence of features. The extraction process of the second sequence of features is as follows: Figure 4 As shown, the first tunnel entrance mileage feature (feature 1) is the distance between the topographic line on the left side of the cross section and the retaining wall, and the second tunnel entrance mileage feature (feature 2) is the distance between the topographic line on the right side of the cross section and the retaining wall. The second sequence of features can well reflect the overall relative position of the topographic line and the tunnel within the range of the light and dark boundary mileage and the design boundary mileage.

[0096] In the specific implementation, the feature masking process involves setting the features corresponding to non-potential tunnel entrance mileages in the second sequence features to 0. Specifically, based on the tunnel data of the designed tunnel entrances, the potential tunnel entrance lengths corresponding to all tunnel entrance types are calculated. For the entrance, the potential tunnel entrance mileage is the difference between the light / dark boundary mileage and the potential tunnel entrance length. For the exit, the potential tunnel entrance mileage is the sum of the light / dark boundary mileage and the potential tunnel entrance length. The potential and non-potential tunnel entrance mileages are further mapped to the second sequence features. After setting the features corresponding to non-potential tunnel entrance mileages to 0, the second target sequence features are obtained.

[0097] In practical implementation, the relative position of the tunnel entrance mileage can be calculated based on the tunnel data, including the boundary mileage between open and closed sections, the design start mileage, the design end mileage, and the entrance mileage (the relative position of the entrance mileage includes the relative position of the inlet entrance mileage and the relative position of the outlet entrance mileage). The specific calculation formula is as follows:

[0098]

[0099] In the formula, S b S represents the distance between light and dark boundaries. s S represents the design starting mileage. e S represents the design termination mileage. p Indicates the distance to the tunnel entrance, y jp Indicates the relative position of the tunnel entrance mileage, y cp This indicates the relative position of the tunnel exit mileage.

[0100] In the specific implementation, when establishing the tunnel entrance mileage prediction model, the extracted second target sequence features are standardized and combined with the relative position of the tunnel entrance mileage to form a second dataset. This second dataset is further divided into a training set and a test set. The training set is used to build the model, and the test set is used to evaluate the model. Figure 3 As shown, a prediction model for the mileage of the tunnel entrance can be established based on the existing LSTM algorithm. The input feature shape of the model is N×2, and the output is a neuron (representing the relative position of the tunnel entrance mileage).

[0101] Step S20: Input the target tunnel data into the light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value;

[0102] In one feasible implementation, the step of inputting the target tunnel data into the light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value includes: inputting the target tunnel data into the tunnel entrance light-dark boundary mileage prediction model to obtain the relative position of the target light-dark boundary mileage; and converting the relative position of the target light-dark boundary mileage into the target light-dark boundary mileage design value based on the light-dark boundary mileage conversion formula.

[0103] It should be noted that by inputting the topographic point cloud data, tunnel alignment data, and tunnel structural dimensions (i.e., target tunnel data) of the tunnel to be designed into the light-dark boundary mileage prediction model, the relative position of the target light-dark boundary mileage can be obtained. Then, the relative position of the target light-dark boundary mileage is converted into the target light-dark boundary mileage design value (the target light-dark boundary mileage design value includes the target entrance light-dark boundary mileage design value and the target exit light-dark boundary mileage design value). The specific conversion formula is as follows:

[0104]

[0105] In the formula, S b S represents the distance between light and dark boundaries. s S represents the design starting mileage. e Indicates the design termination mileage, y jb Indicates the relative position of the mileage separating the light and dark areas at the import point, ycb Indicates the relative position of the mileage separating the light and dark areas at the exit. This indicates the design value of the mileage separating the light and dark areas at the target import point. This indicates the design value for the mileage dividing the target exit from light to dark.

[0106] Step S30: Input the target tunnel data and the target light-dark boundary mileage design value into the tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value.

[0107] In one feasible implementation, the step of inputting the target tunnel data and the target light-dark boundary mileage design value into the tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value includes: inputting the target tunnel data and the target light-dark boundary mileage design value into the tunnel entrance mileage prediction model to obtain the relative position of the tunnel entrance mileage; and converting the relative position of the tunnel entrance mileage into the tunnel entrance mileage design value based on the tunnel entrance mileage conversion formula.

[0108] It should be noted that the basic data (i.e., target tunnel data), such as the topographic point cloud data, tunnel alignment data, and tunnel structural dimensions, as well as the target light-dark boundary mileage design value, can be input into the light-dark boundary mileage prediction model to obtain the relative position of the target tunnel entrance mileage. Then, the relative position of the target tunnel entrance mileage can be converted into the target tunnel entrance mileage design value (the target tunnel entrance mileage design value includes the target inlet tunnel entrance mileage design value and the target outlet tunnel entrance mileage design value). The specific conversion formula is as follows:

[0109]

[0110] In the formula, S b S represents the distance between light and dark boundaries. s S represents the design starting mileage. e Indicates the design termination mileage, y jp Indicates the relative position of the tunnel entrance mileage, y cp Indicates the relative position of the tunnel exit mileage. This indicates the design value of the mileage separating the light and dark areas at the target import point. This indicates the design value for the mileage dividing the target exit from light to dark.

[0111] Step S40: Determine the target portal length based on the target light-dark boundary mileage design value and the target portal mileage design value, and determine the target portal type based on the potential portal length table.

[0112] It should be noted that the potential portal length table is designed in advance. The potential portal length table can be used to represent the mapping relationship between potential portal length and portal type. The potential portal length table mainly includes two portal types: wall-type portal and cut-out portal.

[0113] It is understandable that the length of the target tunnel entrance is the difference between the design value of the target light-dark boundary mileage and the design value of the target tunnel entrance mileage.

[0114] In practice, the target portal type can be determined from the potential portal length table based on the target portal length.

[0115] Step S50: Determine the sub-type of the target portal based on the target portal mileage design value, the target portal type, and the target tunnel data.

[0116] In specific implementations, such as Figure 5 The technical roadmap for intelligent design of tunnel entrances shown can first establish a tunnel entrance light-dark boundary mileage prediction model and an entrance mileage prediction model based on the tunnel data of the designed tunnel entrances, and set the determination method for the entrance type, entrance length, and entrance sub-type. Then, the tunnel data of the tunnel entrance to be designed is used to obtain the light-dark boundary mileage and entrance mileage of the tunnel entrance to be designed according to the light-dark boundary mileage prediction model and the entrance mileage prediction model. Finally, the entrance type, entrance length, and entrance sub-type of the tunnel entrance to be designed are determined according to the determination method of the entrance type, entrance length, and entrance sub-type.

[0117] In one feasible implementation, the step of determining the sub-type of the portal based on the target portal mileage design value, the target portal type, and the target tunnel data includes: when the portal type is a cut portal, determining the tunnel length of the tunnel to be designed based on the target tunnel data; determining the number of openings based on the comparison result between the tunnel length and the critical portal length parameter; and determining the sub-type of the portal based on the target portal type and the number of openings.

[0118] It should be noted that the critical portal length parameter is preset.

[0119] In practical implementation, inverted-cut portals, oblique-cut portals, and straight-cut portals are all types of cut portals. The number of openings for cut portals is further determined based on the tunnel length, thus forming a sub-type of portals with an attached number of openings. Specifically, the formula for calculating the number of openings based on the tunnel length is as follows:

[0120]

[0121] In the formula, T is the tunnel length, and n k denoted as the number of openings, and a, b, c, and d as the critical portal length parameters.

[0122] In one feasible implementation, the step of determining the sub-type of the tunnel portal based on the target portal mileage design value, the target portal type, and the target tunnel data includes: when the portal type is a wall-type portal, determining a first feature, a second feature, a third feature, and a fourth feature based on the target portal mileage design value and the target tunnel data, wherein the first feature is the first elevation difference between a topographic point on the left side of the tunnel and a first reference line on the cross-section at the target portal mileage design value; the second feature is the second elevation difference between a topographic point on the right side of the tunnel and the first reference line on the cross-section at the target portal mileage design value; and the third feature is the second elevation difference between a topographic point on the right side of the tunnel and the first reference line on the cross-section at the target portal mileage design value. The third elevation difference between the topographic point on the left side of the tunnel and the second reference line on the cross-section; the fourth feature is the fourth elevation difference between the topographic point on the right side of the tunnel and the second reference line on the cross-section at the target tunnel entrance mileage design value; the first reference line is a straight line on the cross-section at the target tunnel entrance mileage design value that passes through the top of the tunnel and is parallel to the bottom of the tunnel; the second reference line is a straight line on the cross-section at the target tunnel entrance mileage design value that has an elevation difference of a preset distance from the center point of the tunnel and is parallel to the first reference line; the second reference line is higher than the top of the tunnel; based on the wall-type tunnel portal sub-category discrimination table, the target tunnel portal sub-type is determined according to the first feature, the second feature, the third feature, and the fourth feature.

[0123] In practice, wall-type portals and open-cut wall-type portals are generally wall-type portals. When the target portal type of the tunnel to be designed is a wall-type portal, it is also necessary to calculate the tunnel slope support type at the portal mileage to form a wall-type portal with slope support type.

[0124] Understandably, the left and right terrain points can be preset. The left terrain point refers to the point on the left side of the cross-sectional terrain line, and the right terrain point refers to the point on the right side of the cross-sectional terrain line.

[0125] It should be noted that the sub-types of wall-type portals formed by the slope support types on the left and right sides of the tunnel entrance can be determined based on the first, second, third, and fourth characteristics.

[0126] In specific implementations, such as Figure 6 As shown, Feature 1 (i.e., the first feature) and Feature 2 (i.e., the second feature) are the elevation differences between the topographic point A on the left side of the tunnel and the topographic point B on the right side of the tunnel on the cross section and the reference line 1 (i.e., the first reference line), respectively. Feature 3 (i.e., the third feature) and Feature 4 (i.e., the fourth feature) are the elevation differences between the topographic point A on the left side of the tunnel and the topographic point B on the right side of the tunnel on the cross section and the reference line 2 (i.e., the second reference line), respectively. The specific calculation method for each feature is as follows:

[0127] f1 = z A -z o -R

[0128] f2=z B -z o -R

[0129] f3 = z A -z o -h1

[0130] f4 = z A -z o -h1

[0131] In the formula, f1 represents the first feature, f2 represents the second feature, f3 represents the third feature, f4 represents the third feature, and z o Here, z is the z-coordinate of the tunnel center point on the cross-section, R is the tunnel radius, h1 is the elevation difference between reference line 2 (i.e., the second reference line) and the tunnel center point, and z... A This represents the z-coordinate value of terrain point A (i.e., the terrain point on the left). B This represents the z-coordinate value of terrain point B (i.e., the terrain point on the right).

[0132] It should be noted that the sub-category discrimination table for wall-type openings is pre-built and can determine the sub-type of wall-type openings based on the first feature, the second feature, the third feature, and the fourth feature.

[0133] In practical implementation, the classification table for wall-type doorways is shown in Table 1:

[0134] Table 1. Detailed Classification of Wall-Type Doorways

[0135]

[0136]

[0137] This embodiment acquires target tunnel data for the tunnel entrance to be designed, including terrain point cloud data, tunnel route data, and tunnel structural dimensions. The target tunnel data is then input into a light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value. The target tunnel data and the target light-dark boundary mileage design value are then input into a tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value. Based on the target light-dark boundary mileage design value and the target tunnel entrance mileage design value, the target tunnel entrance length is determined, and the target tunnel entrance type is determined based on a potential tunnel entrance length table. Finally, based on the target tunnel entrance mileage design value, the target tunnel entrance type, and the target tunnel data, a sub-type of the target tunnel entrance is determined. This invention solves the technical problem that traditional methods of designing tunnel entrances are not only time-consuming and labor-intensive, but also highly dependent on the experience of designers. Compared with existing technologies, this application can predict the light-dark boundary mileage and entrance mileage of the tunnel entrance to be designed by using established light-dark boundary mileage prediction models and entrance mileage prediction models. The design of the tunnel entrance can be achieved without repeated attempts by designers, thus achieving efficient design. Furthermore, this application provides a unified method for determining the type of tunnel entrance and its sub-types. The design results are not affected by the subjectivity of designers, making the design results of the tunnel entrance to be designed more stable and reliable.

[0138] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 7 Step S40 includes steps S401 to S403:

[0139] Step S401: Determine the preferred order of portal types and determine the potential portal length range for each portal type;

[0140] It should be noted that the preferred order of portal types can be determined based on different tunnel working conditions. The preferred order is as follows: inverted portal, oblique portal, wall portal, etc.

[0141] In one feasible implementation, the step of determining the potential portal length range for each portal type includes: statistically analyzing the portal length range and open portal length range corresponding to each portal type; and performing fusion and deduplication based on the portal length range and the open portal length range to obtain the potential portal length range for each portal type.

[0142] In specific implementations, such as Figure 8As shown, the potential length of each type of tunnel entrance is determined by the range of tunnel entrance length and the range of open tunnel length. Specifically, the range of tunnel entrance length and the range of open tunnel length under different tunnel entrance types can be statistically analyzed, and the potential length range of the tunnel entrance type can be obtained by fusing and deduplicating the range of tunnel entrance length and the range of open tunnel length.

[0143] Step S402: Based on the preferred order and the potential portal length range, construct a potential portal length table;

[0144] It should be noted that the potential portal length table is designed according to the preferred order of portal types. For example, the portal types in the potential portal length table are sorted from top to bottom according to the preferred order.

[0145] Step S403: Filter out the potential portal length ranges in the potential portal length table that include the target portal length, and determine the target portal type with the target portal length from the filtered portal types according to the preferred order.

[0146] It should be noted that the screening of portal types refers to all portal types in the potential portal length table whose potential portal length range includes the target portal length. After the screening portal types are determined, the portal type that appears first in the screening portal types is selected according to the preferred order and then determined as the target portal type.

[0147] In a specific implementation, the potential portal length range of the portal type in the potential portal length table can be determined sequentially according to the preferred order. If it includes the target portal length, the portal type can be determined as the target portal type for the target portal length.

[0148] The first and second embodiments are described in detail below using specific tunnel entrance design datasets:

[0149] 1. Collect datasets of designed and constructed tunnel portals and datasets of tunnel portals to be designed. The topographic point cloud data for a specific designed and constructed tunnel portal is as follows: Figure 9 As shown, the topographic point cloud data of the entrance to a tunnel to be designed is as follows: Figure 10 As shown in the figure, the other relevant parameters are shown in Table 2.

[0150] Table 2. Relevant parameters for tunnel entrance

[0151]

[0152]

[0153] 2. Based on the collected dataset of designed and constructed tunnel entrances, features are extracted and the relative positions of the light and dark boundary mileage are calculated. A prediction model for the light and dark boundary mileage is then established. The specific process is illustrated below using the designed and constructed tunnel dataset from the case study as an example:

[0154] (2.1) Extract sequence features. The feature values ​​of the light and dark boundary mileage sequence extracted from the designed and constructed tunnel in the case are shown in Table 3. Here, the sequence length is set to 100.

[0155] Table 3. Feature Values ​​of the Light-Dark Boundary Mileage Sequence

[0156]

[0157]

[0158] (2.2) Calculate the relative position of the light-dark boundary mileage. In the case, the relative position of the light-dark boundary mileage at the tunnel entrance that has been designed and constructed is 0.592.

[0159] (2.3) Establish a prediction model for the light-dark boundary mileage. The collected dataset is divided into a training set and a test set of 80% and 20%, respectively. The trend of the loss value of the light-dark boundary mileage prediction model based on the LSTM network model is shown below. Figure 11 As shown, the model's loss value on both the training and test sets decreased rapidly to a small value, and the difference between the two was not significant, indicating that the established model was good.

[0160] 3. Based on the collected dataset of designed and constructed tunnel portals, features are extracted and the relative positions of the portal mileage are calculated. A prediction model for the portal mileage is then established. The specific process is illustrated below using the dataset of designed and constructed tunnels in the case study as an example:

[0161] (3.1) Extracting the sequence features of the tunnel entrance mileage. The sequence feature values ​​of the tunnel entrance mileage extracted in the case study are shown in Table 4. Here, the sequence length is set to 50.

[0162] Table 4. Characteristic Values ​​of the Tunnel Entrance Mileage Sequence

[0163]

[0164]

[0165] (3.2) Feature masking. The potential tunnel entrance mileage is 236627-236675m and the non-potential tunnel entrance mileage is 236675-236685m. The potential and non-potential tunnel entrance mileages are mapped to the sequence features in (3.1). The masked features are shown in Table 5.

[0166] Table 5. Characteristic values ​​of the tunnel entrance mileage concealment sequence.

[0167]

[0168] (3.3) Calculate the relative position of the tunnel entrance mileage. In this case, the relative position of the tunnel entrance mileage is 0.414.

[0169] (3.4) Establish a prediction model for tunnel mileage. The collected dataset is divided into a training set and a test set of 80% and 20%, respectively. The trend of the loss value of the tunnel mileage prediction model based on the LSTM network model is shown below. Figure 12 As shown, the model's loss value on both the training and test sets decreased rapidly to a small value, and the difference between the two was not significant, indicating that the established model was good.

[0170] 4. The specific process for determining the portal type and portal length for the tunnel in the case is as follows.

[0171] (4.1) Determine the potential portal length. The priority order of portal types is: oblique-cut portal, oblique-cut + open portal, inverted-cut portal, inverted-cut + open portal, and open portal. Their respective potential portal lengths are shown in Table 6.

[0172] Table 6 Potential Portal Lengths

[0173] Archway type Potential portal length oblique cut 24m Angled + Open Cave 29～58m Reverse cut 15m Inverted cut + open channel 20～58m Myeongdong 10～58m

[0174] (4.2) Determine the type and length of the tunnel entrance. The difference between the distance of the boundary between light and dark in step 3 and the distance of the tunnel entrance in step 2 is 24m. It can be concluded that the oblique-cut tunnel entrance includes this distance. Therefore, the major category of the tunnel entrance is oblique-cut and the length of the tunnel entrance is 24m.

[0175] 5. Since the tunnel portal is classified as a cut-type portal, the sub-type of the portal can be determined based on the tunnel length. Given parameters a = 2000m, b = 4000m, c = 6000m, d = 8000m, the sub-type of the portal is a diagonally cut two-hole portal.

[0176] 6. Repeat steps 2 to 5 above to obtain the design values ​​for the tunnel to be designed. The specific process is as follows.

[0177] (6.1) Determine the boundary mileage between light and dark areas. Input the basic data of the tunnel to be designed into the boundary mileage prediction model in step 2. The predicted value of the boundary mileage between light and dark areas is 0.720. This value is further converted into an absolute distance, i.e., the design value of the boundary mileage between light and dark areas is 385471m.

[0178] (6.2) Determine the tunnel entrance mileage. Input the basic data of the tunnel to be designed and the design value of the light and dark boundary mileage in step 2 into the tunnel entrance mileage prediction model in step 3. The predicted value of the tunnel entrance mileage is 0.428, which is further converted into an absolute distance, i.e., the design value of the tunnel entrance mileage is 385450m.

[0179] (6.3) Determine the type and length of the tunnel portal. Based on the design values ​​of the boundary between open and closed sections in step (6.1) and the design values ​​of the tunnel entrance in step (6.2), according to the rules in step four, the major categories of the tunnel portal are oblique cut type + open tunnel portal, and the tunnel portal length is 14m.

[0180] (6.4) Determine the sub-type of the tunnel portal. Input the basic data, the tunnel portal mileage design value in step (6.2), and the tunnel portal category in step (6.3) into the rules in step five. Then the sub-type of the tunnel portal is the oblique cut tunnel portal without opening.

[0181] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the intelligent design method of the overall tunnel portal scheme of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0182] This application also provides an intelligent design device for the overall tunnel portal design; please refer to [reference needed]. Figure 13 The intelligent design device for the overall tunnel entrance scheme includes:

[0183] The acquisition module 10 is used to acquire target tunnel data of the tunnel entrance to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data and tunnel structure dimensions;

[0184] Input module 20 is used to input the target tunnel data into the light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value;

[0185] The input module 20 is also used to input the target tunnel data and the target light-dark boundary mileage design value into the tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value.

[0186] The determination module 30 is used to determine the target portal length based on the target light-dark boundary mileage design value and the target portal mileage design value, and to determine the target portal type based on the potential portal length table;

[0187] The determining module 30 is further configured to determine the target portal sub-type based on the target portal mileage design value, the target portal type, and the target tunnel data.

[0188] The intelligent design device for the overall tunnel portal scheme provided in this application adopts the intelligent design method for the overall tunnel portal scheme in the above embodiments, which can solve the technical problems that the traditional method of designing tunnel portals is not only time-consuming and labor-intensive, but also highly dependent on the experience of designers. Compared with the prior art, the beneficial effects of the intelligent design device for the overall tunnel portal scheme provided in this application are the same as the beneficial effects of the intelligent design method for the overall tunnel portal scheme provided in the above embodiments, and other technical features in the intelligent design device for the overall tunnel portal scheme are the same as those disclosed in the method of the above embodiments, and will not be repeated here.

[0189] This application provides an intelligent design device for an overall tunnel entrance scheme. The intelligent design device for an overall tunnel entrance scheme includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent design method for the overall tunnel entrance scheme in the above embodiment 1.

[0190] The following is for reference. Figure 14 The diagram illustrates a structural schematic of an intelligent design device suitable for implementing the overall tunnel portal design of the embodiments of this application. The intelligent design device for the overall tunnel portal design in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 14 The intelligent design equipment for the overall tunnel entrance scheme shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0191] like Figure 14As shown, the intelligent design device for the overall tunnel entrance scheme may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the intelligent design device for the overall tunnel entrance scheme. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the intelligent design equipment for the overall tunnel portal scheme to exchange data wirelessly or via wired communication with other devices. Although the figure shows an intelligent design equipment for the overall tunnel portal scheme with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0192] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0193] The intelligent design equipment for the overall tunnel portal scheme provided in this application, employing the intelligent design method for the overall tunnel portal scheme in the above embodiments, can solve the technical problems that traditional methods of designing tunnel portals are not only time-consuming and labor-intensive, but also highly dependent on the experience of designers. Compared with the prior art, the beneficial effects of the intelligent design equipment for the overall tunnel portal scheme provided in this application are the same as those of the intelligent design method for the overall tunnel portal scheme provided in the above embodiments, and other technical features in this intelligent design equipment for the overall tunnel portal scheme are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0194] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0195] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0196] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the intelligent design method of the overall tunnel portal scheme in the above embodiments.

[0197] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0198] The aforementioned computer-readable storage medium may be included in the intelligent design equipment for the overall tunnel portal scheme; or it may exist independently and not be assembled into the intelligent design equipment for the overall tunnel portal scheme.

[0199] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by an intelligent design device for the overall tunnel portal design, the intelligent design device for the overall tunnel portal design performs the following: acquires target tunnel data for the tunnel portal to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data, and tunnel structural dimensions; inputs the target tunnel data into a light-dark boundary mileage prediction model to obtain a target light-dark boundary mileage design value; inputs the target tunnel data and the target light-dark boundary mileage design value into a portal mileage prediction model to obtain a target portal mileage design value; determines the target portal length based on the target light-dark boundary mileage design value and the target portal mileage design value, and determines the target portal type based on a potential portal length table; and determines the target portal sub-type based on the target portal mileage design value, the target portal type, and the target tunnel data.

[0200] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0201] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0202] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0203] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the intelligent design method for the overall tunnel portal scheme described above. This solves the technical problem that traditional methods of designing tunnel portals are not only time-consuming and labor-intensive but also highly dependent on the experience of designers. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the intelligent design method for the overall tunnel portal scheme provided in the above embodiments, and will not be elaborated upon here.

[0204] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the intelligent design method for the overall tunnel entrance scheme as described above.

[0205] The computer program product provided in this application can solve the technical problem that designing tunnel entrances using traditional methods is not only time-consuming and labor-intensive, but also highly dependent on the experience of designers. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the intelligent design method for the overall tunnel entrance scheme provided in the above embodiments, and will not be repeated here.

[0206] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An intelligent design method for the overall scheme of a tunnel entrance, characterized in that, The method includes: Obtain target tunnel data for the entrance of the tunnel to be designed, wherein the target tunnel data includes terrain point cloud data, tunnel route data, and tunnel structural dimensions; The target tunnel data is input into the light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value; The target tunnel data and the target light-dark boundary mileage design value are input into the tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value. The target portal length is determined based on the target light-dark boundary mileage design value and the target portal mileage design value, and the target portal type is determined based on the potential portal length table. Based on the target portal mileage design value, the target portal type, and the target tunnel data, the target portal sub-type is determined.

2. The method as described in claim 1, characterized in that, The steps of determining the target portal length based on the target light-dark boundary mileage design value and the target portal mileage design value, and determining the target portal type based on the potential portal length table, include: Determine the preferred order of portal types and determine the potential portal length range for each portal type; Based on the preferred order and the potential portal length range, a potential portal length table is constructed; The potential portal length range in the potential portal length table is filtered to include the target portal length, and the target portal type with the target portal length is determined from the filtered portal types according to the preferred order.

3. The method as described in claim 2, characterized in that, The steps to determine the potential portal length range for each portal type include: Statistics were compiled on the range of tunnel entrance length and the range of open tunnel length for each type of tunnel entrance. The potential portal length range for each portal type is obtained by merging and deduplicating the portal length range and the open portal length range.

4. The method as described in claim 1, characterized in that, The step of determining the sub-type of the target portal based on the target portal mileage design value, the target portal type, and the target tunnel data includes: When the portal type is a cut portal, the tunnel length of the tunnel to be designed is determined based on the target tunnel data; Based on the comparison results between the tunnel length and the critical portal length parameter, the number of openings is determined; The target portal sub-type is determined based on the target portal type and the number of openings.

5. The method as described in claim 1, characterized in that, The step of determining the sub-type of the target portal based on the target portal mileage design value, the target portal type, and the target tunnel data includes: When the portal type is a wall-type portal, a first feature, a second feature, a third feature, and a fourth feature are determined based on the target portal mileage design value and the target tunnel data. The first feature is the first elevation difference between a topographic point on the left side of the tunnel and a first reference line on the cross-section at the target portal mileage design value. The second feature is the second elevation difference between a topographic point on the right side of the tunnel and the first reference line on the cross-section at the target portal mileage design value. The third feature is the third elevation difference between a topographic point on the left side of the tunnel and a second reference line on the cross-section at the target portal mileage design value. The fourth feature is the fourth elevation difference between a topographic point on the right side of the tunnel and the second reference line on the cross-section at the target portal mileage design value. The first reference line is a straight line on the cross-section at the target portal mileage design value that passes through the top of the tunnel and is parallel to the bottom of the tunnel. The second reference line is a straight line on the cross-section at the target portal mileage design value that is parallel to the first reference line and has an elevation difference of a preset distance from the center point of the tunnel. The second reference line is higher than the top of the tunnel. Based on the wall-type doorway sub-category discrimination table, the target doorway sub-type is determined according to the first feature, the second feature, the third feature, and the fourth feature.

6. The method as described in claim 1, characterized in that, The step of obtaining the target tunnel data at the entrance of the tunnel to be designed also includes: Acquire tunnel data at the entrance of a constructed tunnel, wherein the tunnel data includes topographic point cloud data, route data, tunnel structural dimensions, and design parameters; A mileage prediction model for the light-dark boundary is established based on the tunnel data, and a mileage prediction model for the tunnel entrance is also established based on the tunnel data.

7. The method as described in claim 6, characterized in that, The steps for establishing the light-dark boundary mileage prediction model based on the tunnel data include: The design start mileage, design end mileage, light-dark boundary mileage, and design axis are determined based on the tunnel data. Multiple mileages are uniformly selected along the design axis within the range of the design start mileage and the design end mileage. The first boundary mileage feature and the second boundary mileage feature of each mileage on the cross section are calculated in sequence. The first sequence feature is determined based on the first boundary mileage feature and the second boundary mileage feature. The first boundary mileage feature is the distance between the topographic line on the cross section and the top of the tunnel, and the second boundary mileage feature is the topographic slope on the cross section. Based on the design start mileage, the design end mileage, and the light-dark boundary mileage, determine the relative position of the light-dark boundary mileage of the constructed tunnel entrance; Based on the first sequence features and the relative positions of the light-dark boundary mileage, a first dataset is constructed, and based on the first dataset, a light-dark boundary mileage prediction model is established.

8. The method as described in claim 7, characterized in that, The steps for establishing a tunnel entrance mileage prediction model based on the tunnel data include: The tunnel boundary design mileage, the light-dark boundary mileage, and the design axis are determined based on the tunnel data. Within the design mileage of the tunnel boundary and the light-dark boundary mileage, multiple mileages are uniformly selected along the design axis. The first tunnel entrance mileage feature and the second tunnel entrance mileage feature of each mileage are calculated sequentially on the cross section. The second sequence feature is determined based on the first tunnel entrance mileage feature and the second tunnel entrance mileage feature. The first tunnel entrance mileage feature is the distance between the left topographic line and the retaining wall on the cross section, and the second tunnel entrance mileage feature is the distance between the right topographic line and the retaining wall on the cross section. The second sequence features are subjected to feature masking processing to obtain the second target sequence features; The tunnel entrance mileage is determined based on the tunnel data, and the relative position of the tunnel entrance mileage of the constructed tunnel is determined based on the tunnel entrance mileage. Based on the features of the second target sequence and the relative position of the tunnel entrance mileage, a second dataset is constructed, and based on the second dataset, a tunnel entrance mileage prediction model is established.

9. The method as described in claim 1, characterized in that, The step of inputting the target tunnel data into the light-dark boundary mileage prediction model to obtain the target light-dark boundary mileage design value includes: The target tunnel data is input into the tunnel entrance light-dark boundary mileage prediction model to obtain the relative position of the target light-dark boundary mileage. Based on the light-dark boundary mileage conversion formula, the relative position of the target light-dark boundary mileage is converted into the target light-dark boundary mileage design value.

10. The method as described in claim 1, characterized in that, The step of inputting the target tunnel data and the target light-dark boundary mileage design value into the tunnel entrance mileage prediction model to obtain the target tunnel entrance mileage design value includes: The target tunnel data and the target light-dark boundary mileage design value are input into the tunnel entrance mileage prediction model to obtain the relative position of the tunnel entrance mileage. Based on the tunnel entrance mileage conversion formula, the relative position of the tunnel entrance mileage is converted into the tunnel entrance mileage design value.

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