Line-Dalin engineering collaborative site selection optimization method, readable storage medium and device

Abstract

The present invention relates to the technical field of railway construction line selection, and specifically to a line-large-temporary engineering collaborative site selection optimization method, a readable storage medium and a device. The line-large-temporary engineering collaborative site selection optimization method disclosed in the present invention specifically includes: first discretizing the line selection area into a voxel set, obtaining the line traffic suitability evaluation index of the voxels in the voxel set and the large-temporary engineering layout suitability evaluation index of the voxels, and obtaining the distribution field and the distribution field of all voxels in the line selection area; then obtaining the first-level gravitational field of line traffic suitability and the second-level gravitational field of large-temporary engineering layout suitability; finally, integrating the obtained first-level gravitational field of line traffic suitability and the second-level gravitational field of large-temporary engineering layout suitability into the search process of the particle swarm algorithm, guiding the particle search to focus on the line selection area suitable for the line-temporary layout scheme, so that it can quickly converge to the line-temporary collaborative optimization scheme, and meet the needs of line-temporary collaborative optimization in complex line selection environments.

Description

Technical Field

[0001] The present invention relates to the technical field of railway construction line selection, and in particular to a line-large temporary engineering collaborative site selection optimization method, a readable storage medium and a device. Background Art

[0002] Route selection and design is the forerunner and foundation of railway construction, and is also an overall work related to the overall situation of the entire railway construction. Its basic tasks are to plan the basic direction of the route, decide the main technical standards, determine the spatial position of the route, and coordinate the layout of various structures according to the functional requirements of the project and the natural, social and economic environment of the area through which it passes. Ideal route selection and design also need to ensure the timely delivery of the railway project. For this, large-scale temporary projects such as vertical and inclined shafts and construction access roads (hereinafter referred to as large temporary projects) are the key to ensuring their smooth construction. In a complex route selection environment, the "first line and then temporary" method designed according to the natural construction sequence is prone to the situation that the route plan is better, but it cannot produce a feasible large temporary project plan, which leads to the situation that the better route plan is not feasible. Therefore, the route-large temporary project (line-temporary) collaborative optimization method should be adopted in a complex route selection environment. The traditional existing route-temporary collaborative optimization method guided by the "trial and error method" is very easy to dissipate a lot of computing time and resources in the search space that is not suitable for the layout of the route-temporary plan in complex and dangerous mountainous areas due to the blindness of its solution search, and it is even difficult to generate a feasible route-temporary plan.

[0003] Therefore, there is an urgent need for a line-to-line guided optimization method to improve the efficiency and solution quality of line-to-line collaborative optimization in complex line selection environments. Summary of the invention

[0004] The purpose of the present invention is to provide a line-Dalin project collaborative site selection optimization method, and the specific technical scheme is as follows:

[0005] A line-Dalin project collaborative site selection optimization method comprises the following steps:

[0006] Step S1, discretize the line selection area into a voxel set; obtain the line traffic suitability evaluation index of the voxels in the voxel set and the large temporary engineering layout suitability evaluation index of the voxels; obtain the The distribution field and The distribution field of ; among which: is the route suitability value, Set suitability values ​​for large temporary projects;

[0007] Step S2: based on all voxels in the line selection area obtained in step S1 The distribution field of the voxels is divided into high-suitability voxel clusters and low-suitability voxel clusters. The attraction of high suitability on the line is abstracted as the first attraction, and the repulsion of low suitability on the line is abstracted as the first repulsion. The first attraction and the first repulsion are integrated to form the first-level gravitational field of line suitability.

[0008] The tunnel start and end sections are regarded as the layout range of temporary large-scale projects. According to the maximum spacing requirements and minimum spacing requirements for temporary large-scale projects, all voxels in the line selection area are divided into voxel clusters where temporary large-scale projects can be laid out and voxel clusters where temporary large-scale projects cannot be laid out. The attraction of voxels where temporary large-scale projects can be laid out on temporary large-scale projects is abstracted as the second attraction, and the attraction of voxels where temporary large-scale projects cannot be laid out on temporary large-scale projects is abstracted as the second repulsion. The second attraction and the second repulsion are integrated to form a secondary gravitational field for the suitability of temporary large-scale projects.

[0009] Step S3, integrate the first-level gravity field of line traffic suitability and the second-level gravity field of large temporary project layout suitability obtained in step S2 into the search process of the particle swarm algorithm, guide the particle swarm to the search area suitable for the line-temporary scheme layout, and iterate and optimize to obtain the optimal line-temporary collaborative optimization scheme.

[0010] Preferably, before obtaining the route traffic suitability evaluation index of the voxels in the voxel set and the large temporary engineering layout suitability evaluation index of the voxels in step S1, the method further includes:

[0011] According to the difference between the center elevation of each voxel in the voxel set and the surface elevation, which corresponds to the threshold of the set bridge, tunnel, and roadbed, the voxels are divided into different structural layers. The structural layer includes bridges, roadbeds, and tunnels, as follows:

[0012] ;

[0013] in, is the difference between the voxel center elevation and the surface elevation; is the maximum bridge height; is the bridge-fill boundary height; It is the depth of the excavation-tunnel boundary.

[0014] Preferably, in step S1: the evaluation index of route suitability includes the cost , structural stability And short straight direction deviation Three indicators; the evaluation indicators for the suitability of the layout of the large temporary project include the length of the vertical / inclined shaft , Construction access road transportation efficiency And the construction period of the temporary project Three indicators.

[0015] Preferably, cost The calculation formula is as follows:

[0016] ;

[0017] in: is the unit price of the bridge; It is a single piece of roadbed; is the unit price of the tunnel; is the width of the voxel; is the volume of the voxel;

[0018] Structural stability The calculation formula is as follows:

[0019] ;

[0020] in: The slope of the fill / cut for the roadbed; The overlying rock mass of the tunnel is heavy; is the lateral pressure coefficient of surrounding rock; is the friction angle on both sides of the tunnel roof soil column; is the tunnel portal width;

[0021] Short straight direction deviation The calculation formula is as follows:

[0022] ;

[0023] in: It is the voxel level deviation from the spatial connection line value of the starting and ending points; It is the vertical deviation of the voxel from the spatial connection line of the starting and ending points;

[0024] Construction access road transportation efficiency The calculation formula is as follows:

[0025] ;

[0026] in: It is the straight-line distance between the starting and ending points of the construction access road; is the construction access road extension coefficient; It is the height difference between the starting point and the end point of the construction access road; is the transport vehicle speed;

[0027] Construction period of temporary project The calculation formula is as follows:

[0028] ;

[0029] in: The construction period of the vertical / inclined shaft; is the excavation speed of the vertical / inclined shaft; The construction period for the construction access road.

[0030] Preferably, the first attraction has a high fitness for the voxel Gravity The calculation expression is as follows:

[0031] ;

[0032] in: For voxels located in high-suitability environments of value; is the number of voxels in the high suitability environment; Voxel To Voxel distance; Voxel Point to voxel The unit vector of Voxel The distance to the end of the line; Voxel The distance to the end of the line;

[0033] The first repulsive force has low fitness for voxels The repulsive force The calculation expression is as follows:

[0034] ;

[0035] in: For voxels located in low-suitability environments of value; is the number of voxels in the low suitability environment; Voxel To Voxel distance; Voxel Point to voxel The unit vector of Voxel The distance to the end of the line;

[0036] In the second attraction, large temporary engineering voxels can be arranged on voxels Gravity The calculation expression is as follows:

[0037] ;

[0038] in: For voxels located in a large temporary engineering environment of value; is the number of voxels that can be deployed in a large temporary engineering environment; Voxel To Voxel distance; Voxel Point to voxel The unit vector of

[0039] The second repulsive force cannot be placed on large temporary engineering voxels. The repulsive force The calculation expression is as follows:

[0040] ;

[0041] in: The number of voxels that cannot be arranged is large temporary engineering; Voxel To Voxel distance; Voxel Point to voxel The unit vector of .

[0042] Preferably, the step S3 specifically includes:

[0043] Step S3-1, randomly generate a route plan, the geometric shape of the route plan is determined by the position of its spatial control point; integrate multiple line-adjacent plans, regard a line-adjacent plan as a particle, and obtain an initial particle group of the line-adjacent plan;

[0044] Step S3-2, line-to-line scheme particle swarm iterative optimization, to obtain the optimal line-to-line collaborative optimization scheme.

[0045] Preferably, the step S3-2 specifically includes:

[0046] Step S3-2-1, updating the movement speed of each spatial control point of the particle line of the line-adjacent scheme according to the line passing attraction and repulsion stored in the voxel where the spatial control point of the line-adjacent scheme is located;

[0047] Step S3-2-2, updating the position of each spatial control point of the line-protocol particle;

[0048] Step S3-2-3, calculating the cumulative fitness value of the current particle passing through the voxel;

[0049] Step S3-2-4: judge the accumulated fitness value obtained in step S3-2-3. If the fitness value of the current particle is higher than , set the current particle to If its suitability value is higher than , then the current particle is set to , proceed to step S3-2-6; wherein: For particles The individual optimal position of is the global optimal position of all particles;

[0050] Step S3-2-5, return to step S3-2-1 for the next particle until all particles are processed;

[0051] Step S3-2-6: Determine whether the number of iterations meets the requirement. If so, It is output as the optimal line-to-line collaborative optimization solution, otherwise it returns to step S3-2-1.

[0052] Preferably, in step S3-2-1: the motion speed of each spatial control point of the line-proximate scheme particle line is updated by the following formula:

[0053] ;

[0054] in: is the inertia weight; For the The particle Linear spatial control points are The speed of generation; For the The particle Linear spatial control points are The speed of generation; For the population The particle Linear spatial control points are The position of the generation; is the individual learning factor; is the social learning factor; is the influence factor of gravity; is the influencing factor of repulsion; , , as well as are all random numbers distributed in the range [0, 1]; For the The particle The individual optimal position of the control point in the linear space; For all particles The global optimal position of the line space control point; For the The particle The gravitational effect of the line passage stored in the voxel where the line space control point is located; For the The particle The repulsive force of the line passage stored in the voxel where the line space control point is located;

[0055] Update the movement speed of each spatial control point of the line-protocol particle project by the following formula:

[0056] ;

[0057] in: For the The particle A large temporary engineering space control point is The speed of generation; For the The particle A large temporary engineering space control point is The speed of generation; For the population The particle A large temporary engineering space control point is The position of the generation; For the The particle The individual optimal positions of the control points in the large temporary engineering space; For all particles The global optimal position of the control points in the large temporary engineering space; For the The particle The gravitational effect of the large temporary engineering layout stored in the voxel where the large temporary engineering space control point is located; For the The particle The repulsive effect of the large temporary engineering layout stored in the voxel where the large temporary engineering space control point is located;

[0058] In step S3-2-2, the position of each spatial control point of the line-protocol particle is updated by the following formula:

[0059] ;

[0060] in: For the population Particles in The position of the generation; For the population Particles in The position of the generation; For the population Particles in The speed of generation.

[0061] Compared with the prior art, the advantages of the present invention are:

[0062] The present invention discloses a line-large temporary project collaborative site selection optimization method, which specifically includes: first discretizing the line selection area into a voxel set, obtaining the line traffic suitability evaluation index of the voxels in the voxel set and the large temporary project layout suitability evaluation index of the voxels, and obtaining the The distribution field and distribution field; then obtain the first-level gravity field of line traffic suitability and the second-level gravity field of large temporary project layout suitability; finally, integrate the obtained first-level gravity field of line traffic suitability and the second-level gravity field of large temporary project layout suitability into the search process of the particle swarm algorithm, guide the particle search to focus on the line selection area suitable for the line-temporary layout scheme, so that it can quickly converge to the line-temporary collaborative optimization scheme, meeting the needs of line-temporary collaborative optimization in complex line selection environments.

[0063] The present invention also discloses a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned line-large temporary engineering collaborative site selection optimization method is implemented.

[0064] The present invention also discloses an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned line-large temporary engineering collaborative site selection optimization method by executing the executable instructions.

[0065] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0067] Figure 1 It is a flow chart of the line-Dalin engineering collaborative site selection optimization method according to an embodiment of the present invention;

[0068] Figure 2 is a schematic diagram of the gravitational field of the line selection environment in an embodiment of the present invention;

[0069] Figure 3 It is a schematic diagram of the gravitational field layout of a large temporary project in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0071] Example

[0072] A line-Dalin project collaborative site selection optimization method, specifically a composite gravitational field-guided optimization method for line-Dalin project collaborative site selection, comprising the following steps:

[0073] Select evaluation indicators for route accessibility and temporary project layout suitability, establish a quantitative evaluation model for each indicator, and propose an indicator fusion method to construct a route accessibility and temporary project layout suitability field based on this;

[0074] Cluster high and low suitable line selection environments, quantify the attraction (repulsion) of high (low) suitable line selection environments on the line, and abstract the line traffic suitability field as a first-level gravitational field; cluster voxel clusters where large temporary projects can or cannot be deployed, quantify the attraction (repulsion) of the environment where large temporary projects can (cannot) be deployed on large temporary projects, and abstract the suitability of large temporary projects as a second-level gravitational field;

[0075] The above-mentioned composite suitability gravity field is integrated into the search process of the particle swarm algorithm to guide the particle swarm to the search area suitable for the layout of the line-to-line scheme, and the optimization is continuously iterated until a line-to-line collaborative optimization scheme group is generated.

[0076] See Figure 1 , the line-temporary project collaborative site selection optimization method includes three major parts: line traffic-temporary project layout suitability distribution field, line traffic-temporary project and temporary project layout suitability gravity field, and composite gravity field guided particle swarm line-temporary project optimization. Among them: line traffic-temporary project layout suitability distribution field includes line selection area voxel discretization and line traffic & temporary project layout suitability evaluation; line traffic-temporary project and temporary project layout suitability gravity field includes voxel suitability clustering and line traffic & temporary project gravity field quantitative calculation; composite gravity field guided particle swarm line-temporary project optimization includes initial line-temporary scheme particle swarm and scheme particle swarm iterative update. Specifically includes the following steps:

[0077] Step S1, discretize the line selection area into a voxel set; obtain the line traffic suitability evaluation index of the voxels in the voxel set and the large temporary engineering layout suitability evaluation index of the voxels; obtain the The distribution field and The distribution field of ; among which: is the route suitability value, Set suitability values ​​for large temporary projects;

[0078] Before obtaining the route traffic suitability evaluation index of the voxels in the voxel set and the large temporary engineering layout suitability evaluation index of the voxels, it also includes:

[0079] According to the difference between the center elevation of each voxel in the voxel set and the surface elevation, which corresponds to the threshold of the set bridge, tunnel, and roadbed, the voxels are divided into different structural layers. The structural layer includes bridges, roadbeds, and tunnels, as follows:

[0080] ;

[0081] in, is the difference between the voxel center elevation and the surface elevation; is the maximum bridge height; is the bridge-fill boundary height; It is the depth of the excavation-tunnel boundary.

[0082] In this embodiment, the evaluation index of line traffic suitability includes the cost , structural stability And short straight direction deviation Three indicators; the evaluation indicators for the suitability of the layout of the large temporary project include the length of the vertical / inclined shaft , Construction access road transportation efficiency And the construction period of the temporary project Three indicators. The calculation expressions of the above six indicators are as follows:

[0083] Cost The calculation formula is as follows:

[0084] ;

[0085] in: is the unit price of the bridge, in ¥ / m; For a single piece of roadbed, unit is ¥ / m 3 ; is the unit price of the tunnel, in ¥ / m; is the width of the voxel; is the volume of the voxel;

[0086] Structural stability The calculation formula is as follows:

[0087] ;

[0088] in: The slope of the fill / cut for the roadbed; , and It is the surrounding rock attribute parameter determined by the surrounding rock grade, specifically: The tunnel is overburdened with heavy rock mass. is the lateral pressure coefficient of surrounding rock, is the friction angle on both sides of the tunnel roof soil column; is the tunnel portal width;

[0089] Short straight direction deviation The calculation formula is as follows:

[0090] ;

[0091] in: It is the voxel level deviation from the spatial connection line value of the starting and ending points; It is the vertical deviation of the voxel from the spatial connection line of the starting and ending points;

[0092] Construction access road transportation efficiency The calculation formula is as follows:

[0093] ;

[0094] in: It is the straight-line distance between the starting and ending points of the construction access road; is the construction access road extension coefficient; It is the height difference between the starting point and the end point of the construction access road; The speed of the transport vehicle is determined by the slope of the spatial connection between the starting point and the end point;

[0095] Construction period of temporary project The calculation formula is as follows:

[0096] ;

[0097] in: The construction period of the vertical / inclined shaft; is the excavation speed of the vertical / inclined shaft; The construction period of the construction access road is determined by the length of the construction access road.

[0098] Step S2: based on all voxels in the line selection area obtained in step S1 The distribution field of the route is obtained by dividing the voxels into high-suitability voxel clusters and low-suitability voxel clusters. The attractive effect of high suitability on the route is abstracted as the first attractive force, and the repulsive effect of low suitability on the route is abstracted as the first repulsive force. The first attractive force and the first repulsive force are integrated to form the first-level gravitational field of route suitability.

[0099] In this embodiment, the first attraction has a high suitability for the voxel Gravity The calculation expression is as follows:

[0100] ;

[0101] in: For voxels located in high-suitability environments of value; is the number of voxels in the high suitability environment; Voxel To Voxel distance; Voxel Point to voxel The unit vector of Voxel The distance to the end of the line; Voxel The distance to the end of the line;

[0102] The first repulsive force has low fitness for voxels The repulsive force The calculation expression is as follows:

[0103] ;

[0104] in: For voxels located in low-suitability environments of value; is the number of voxels in the low suitability environment; Voxel To Voxel distance; Voxel Point to voxel The unit vector of Voxel The distance to the end of the line;

[0105] The starting and ending sections of the tunnel are regarded as the layout scope of large temporary projects. According to the maximum and minimum spacing requirements for the layout of large temporary projects, all voxels in the line selection area are divided into voxel clusters where large temporary projects can be laid out and voxel clusters where large temporary projects cannot be laid out. The attraction of voxels where large temporary projects can be laid out on large temporary projects is abstracted as the second attraction, and the attraction of voxels where large temporary projects cannot be laid out on large temporary projects is abstracted as the second repulsion. The second attraction and the second repulsion are integrated to form a secondary gravitational field for the suitability of large temporary project layout.

[0106] In this embodiment, the second attraction may be provided with a large temporary engineering voxel pair voxel Gravity The calculation expression is as follows:

[0107] ;

[0108] in: For voxels located in a large temporary engineering environment of value; is the number of voxels that can be deployed in a large temporary engineering environment; Voxel To Voxel distance; Voxel Point to voxel The unit vector of

[0109] The second repulsive force cannot be placed on large temporary engineering voxels. The repulsive force The calculation expression is as follows:

[0110] ;

[0111] in: The number of voxels that cannot be arranged is large temporary engineering; Voxel To Voxel distance; Voxel Point to voxel The unit vector of .

[0112] Step S3, integrate the first-level gravity field of line traffic suitability and the second-level gravity field of large temporary project layout suitability obtained in step S2 into the search process of the particle swarm algorithm, guide the particle swarm to the search area suitable for the line-temporary scheme layout, and iterate and optimize to obtain the optimal line-temporary collaborative optimization scheme.

[0113] In this embodiment, step S3 specifically includes:

[0114] Step S3-1, randomly generate a line plan, the geometric shape of the line plan is determined by the position of its spatial control point; integrate multiple line-adjacent plans, regard a line-adjacent plan as a particle, and obtain an initial particle group of the line-adjacent plan.

[0115] In this embodiment, the spatial control points include plane intersections and longitudinal section slope change points. According to the line plan, the locations of temporary projects (such as tunnel portals and bridge piers) may be generated, and the locations of temporary projects such as vertical shafts, inclined shafts, and construction access roads are randomly generated in the temporary project layout sections in the tunnel divided according to the minimum and maximum spacing of temporary projects. The geometric shape of the temporary project is determined by the spatial control points of the vertical shafts, inclined shafts, and construction access roads, including the three-dimensional polyline inflection points.

[0116] Step S3-2, line-to-line scheme particle swarm iterative optimization, to obtain the optimal line-to-line collaborative optimization scheme.

[0117] Further preferably, the step S3-2 specifically includes:

[0118] Step S3-2-1, updating the movement speed of each spatial control point of the particle line of the line-proximate scheme according to the line passing attraction and repulsion stored in the voxel where the spatial control point of the line-proximate scheme is located;

[0119] Step S3-2-2, updating the position of each spatial control point of the line-protocol particle;

[0120] Step S3-2-3, calculating the cumulative fitness value of the current particle passing through the voxel;

[0121] Step S3-2-4: judge the accumulated fitness value obtained in step S3-2-3. If the fitness value of the current particle is higher than , that is, the optimal position of the individual line-contingent scheme, and the current particle is set to If its suitability value is higher than , that is, the global optimal position of the line-contingent scheme, then the current particle is set to , proceed to step S3-2-6; wherein: For particles The individual optimal position of is the global optimal position of all particles;

[0122] Step S3-2-5, return to step S3-2-1 for the next particle until all particles are processed;

[0123] Step S3-2-6: Determine whether the number of iterations meets the requirement. If so, It is output as the optimal line-to-line collaborative optimization solution, otherwise, returns to step S3-2-1.

[0124] In this embodiment, the motion speed of each spatial control point of the line-to-line scheme particle line is updated by the following formula:

[0125] ;

[0126] in: is the inertia weight, which controls the influence of the particle's previous velocity on the current velocity; and Respectively The particle Linear spatial control points are Daihe The speed of generation; For the population The particle Linear spatial control points are The position of the generation; is the individual learning factor, which is used to adjust the influence of the particle’s own experience; is a social learning factor used to regulate the influence of group experience, usually = =2; and are the influence factors of attraction and repulsion, both of which are 2; , , as well as are all random numbers distributed in the range [0, 1]; For the The particle The individual optimal position of the control point in the linear space; For all particles The global optimal position of the line space control point; , For the The particle The attraction and repulsion of the route stored in the voxel where the line space control point is located;

[0127] In this embodiment, the motion speed of each spatial control point of the line-to-particle system is updated by the following formula:

[0128] ;

[0129] in: and Respectively The particle A large temporary engineering space control point is Daihe The speed of generation; For the population The particle A large temporary engineering space control point is The position of the generation; For the The particle The individual optimal positions of the control points in the large temporary engineering space; For all particles The global optimal position of the control points in the large temporary engineering space; , For the The particle The gravitational and repulsive effects of the large temporary engineering layout stored in the voxel where the large temporary engineering space control point is located;

[0130] In this embodiment, the position of each spatial control point of the line-proximity scheme particle is updated by the following formula:

[0131] ;

[0132] in: For the population Particles in The position of the generation; For the population Particles in The position of the generation; For the population Particles in The speed of generation.

[0133] This embodiment also discloses a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned line-large temporary project collaborative site selection optimization method is implemented.

[0134] This embodiment also discloses an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned line-large temporary engineering collaborative site selection optimization method by executing the executable instructions.

[0135] The technical solution of the present invention is further explained by taking a railway case with a complex line selection environment, specifically:

[0136] The selected line area is discretized into a set of voxels of 90m cubes. According to the voxel center elevation and the structure layering threshold, the specific is 100m, 20m; The voxels are divided into bridges, roadbeds and tunnels.

[0137] Using CRITIC multi-criteria decision-making method to , structural stability And short straight direction deviation The three indicators are integrated into the route suitability value of this voxel , integrating all voxels of the line selection environment Worth it Distribution field.

[0138] The vertical / inclined shaft length is determined by using the CRITIC multi-criteria decision method. , Construction access road transportation efficiency And the construction period of the temporary project The three indicators are integrated into the suitability value of the large temporary engineering layout of this voxel , integrating all voxels of the line selection environment Worth it Distribution field.

[0139] Based on all voxels in the selected line area The k-mean algorithm is used to divide voxels into high-suitability voxel clusters and low-suitability voxel clusters. The attraction of high suitability on the line is abstracted as the first attraction, and the repulsion of low suitability on the line is abstracted as the first repulsion. The first attraction and the first repulsion are integrated to form the first-level gravitational field of line suitability. For details, see Figure 2 .

[0140] The maximum and minimum spacing of temporary large-scale projects are set to 2.5 km and 5 km respectively. Based on this, all voxels in the selected line area are divided into voxel clusters where temporary large-scale projects can be deployed and voxel clusters where temporary large-scale projects cannot be deployed. The attraction of voxels where temporary large-scale projects can be deployed on temporary large-scale projects is abstracted as the second attraction, and the attraction of voxels where temporary large-scale projects cannot be deployed on temporary large-scale projects is abstracted as the second repulsion. The second attraction and the second repulsion are integrated to form the secondary gravitational field of the suitability of temporary large-scale project deployment. For details, see Figure 3 .

[0141] Randomly generate 100 line-to-temporary initial solution particle groups, update the movement speed of each spatial control point of the line and large temporary project in the line-to-temporary solution particles, and update the position of each spatial control point of the line-to-temporary solution particles. Continuously iterate and update the position of contemporary particles according to the accumulated value of the suitability of the line-to-temporary solution crossing the voxel. and , until all 100 line-to-proximate solution particles are updated. Enter the next generation of particle update until the line-to-proximate collaborative optimization solution generated after 200 iterations The fluctuation of the cumulative fitness value does not exceed 0.5%, and the As the ultimate online-online collaborative optimization solution.

[0142] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A line-large temporary project collaborative site selection optimization method, characterized in that: The following steps are involved: Step S1, discretize the line selection area into a voxel set; obtain the line traffic suitability evaluation index of the voxels in the voxel set and the large temporary engineering layout suitability evaluation index of the voxels; obtain the ES of all voxels in the line selection area A Distribution field and ES L The distribution field of ES A is the route suitability value, ES L Set suitability values ​​for large temporary projects; Step S2: ES of all voxels in the line selection area obtained in step S1 A The distribution field of the voxels is divided into high-suitability voxel clusters and low-suitability voxel clusters. The attraction of high suitability on the line is abstracted as the first attraction, and the repulsion of low suitability on the line is abstracted as the first repulsion. The first attraction and the first repulsion are integrated to form the first-level gravitational field of line suitability. The tunnel start and end sections are regarded as the layout range of temporary large-scale projects. According to the maximum spacing requirements and minimum spacing requirements for temporary large-scale projects, all voxels in the line selection area are divided into voxel clusters where temporary large-scale projects can be laid out and voxel clusters where temporary large-scale projects cannot be laid out. The attraction of voxels where temporary large-scale projects can be laid out on temporary large-scale projects is abstracted as the second attraction, and the attraction of voxels where temporary large-scale projects cannot be laid out on temporary large-scale projects is abstracted as the second repulsion. The second attraction and the second repulsion are integrated to form a secondary gravitational field for the suitability of temporary large-scale projects. Step S3, integrating the first-level gravity field of line traffic suitability and the second-level gravity field of large temporary project layout suitability obtained in step S2 into the search process of the particle swarm algorithm, guiding the particle swarm to the search area suitable for the line-temporary scheme layout, and iteratively optimizing to obtain the optimal line-temporary collaborative optimization scheme; Among them: The gravitational force F of high fitness on voxel i in the first attraction A The calculation expression of (i) is as follows: Among them: ES Af is the ES of voxel f located in a high-suitability environment A value; F is the number of voxels in the high suitability environment; d if is the distance from voxel i to voxel f; d i-f is the unit vector pointing from voxel i to voxel f; d i is the distance from voxel i to the end point of the line; d f is the distance from voxel f to the end point of the line; The repulsive force F of low fitness on voxel i in the first repulsive force R The calculation expression of (i) is as follows: Among them: ES Au is the ES of voxel u located in a low-suitability environment A value; U is the number of voxels in the low suitability environment; d ui is the distance from voxel u to voxel i; d u-i is the unit vector pointing from voxel u to voxel i; d u is the distance from voxel u to the end point of the line; In the second attraction, the gravitational force F of the large temporary engineering voxel on voxel i can be arranged LA (i) The calculation expression is as follows: Among them: ES Lp is the ES of voxel p located in the deployable large temporary engineering environment L value; P is the number of voxels that can be deployed in the large temporary engineering environment; d ip is the distance from voxel i to voxel p; d i-p is the unit vector pointing from voxel i to voxel p; The repulsive force F of the large temporary engineering voxel on voxel i cannot be arranged in the second repulsive force LR (i) The calculation expression is as follows: Where: Q is the number of voxels that cannot be deployed; d qi is the distance from voxel p to voxel i; d q-i is the unit vector pointing from voxel p to voxel i.

2. The line-large temporary project collaborative site selection optimization method according to claim 1 is characterized in that: Before obtaining the route traffic suitability evaluation index of the voxels in the voxel set and the large temporary engineering layout suitability evaluation index of the voxels in step S1, the following steps are also included: According to the difference between the center elevation of each voxel in the voxel set and the surface elevation, which corresponds to the threshold of the set bridge, tunnel, and roadbed, the voxels are divided into different structural layers. The structural layer includes bridges, roadbeds, and tunnels, as follows: Among them, Δh is the difference between the voxel center elevation and the ground surface elevation; H bmax is the maximum bridge height; h bf h is the bridge-fill boundary height; ct It is the depth of the excavation-tunnel boundary.

3. The line-large temporary project collaborative site selection optimization method according to claim 2 is characterized in that: In step S1: the evaluation index of route traffic suitability includes the cost C C 、Structural stability C S And the short straight direction deviation C D Three indicators; the evaluation index of the suitability of the layout of the large temporary project includes the vertical / inclined shaft length L W , Construction access road transportation efficiency T A And the construction period of the temporary project T L Three indicators.

4. The line-large temporary project collaborative site selection optimization method according to claim 3 is characterized in that: Cost C C The calculation formula is as follows: Where: c b is the unit price of the bridge; c e A single piece of roadbed; c t is the unit price of the tunnel; w is the width of the voxel; v is the volume of the voxel; Structural stability C S The calculation formula is as follows: Where: β is the slope of the roadbed fill / cut; γ R is the weight of the surrounding rock overlying the tunnel; B is the lateral pressure coefficient of the surrounding rock; θ is the friction angle on both sides of the tunnel roof soil column; B is the width of the tunnel portal; Short straight direction deviation C D The calculation formula is as follows: Where: D H D is the voxel level deviation from the spatial connection value of the starting and ending points; V It is the vertical deviation of the voxel from the spatial connection line of the starting and ending points; Construction access road transportation efficiency T A The calculation formula is as follows: Where: L is the straight-line distance between the starting and ending points of the construction access road; γ L is the construction access road extension coefficient; Δh L is the height difference between the starting point and the end point of the construction access road; V is the speed of the transport vehicle; Construction period of temporary project T L The calculation formula is as follows: T L =max{T w ,T a }=max{L W / V W ,T a (γ L ·L)}; Where: T w , is the construction period of the vertical / inclined shaft; V W is the excavation speed of the vertical / inclined shaft; T a The construction period for the construction access road.

5. The line-large temporary project collaborative site selection optimization method according to claim 1 is characterized in that: The step S3 specifically includes: Step S3-1, randomly generate a route plan, the geometric shape of the route plan is determined by the position of its spatial control point; integrate multiple line-adjacent plans, regard a line-adjacent plan as a particle, and obtain an initial particle group of the line-adjacent plan; Step S3-2, line-to-line scheme particle swarm iterative optimization, to obtain the optimal line-to-line collaborative optimization scheme.

6. The line-large temporary project collaborative site selection optimization method according to claim 5 is characterized in that: The step S3-2 specifically includes: Step S3-2-1, updating the movement speed of each spatial control point of the particle line of the line-adjacent scheme according to the line passing attraction and repulsion stored in the voxel where the spatial control point of the line-adjacent scheme is located; Step S3-2-2, updating the position of each spatial control point of the line-protocol particle; Step S3-2-3, calculating the cumulative fitness value of the current particle passing through the voxel; Step S3-2-4: judge the accumulated fitness value obtained in step S3-2-3. If the fitness value of the current particle is higher than P s , set the current particle to P s ; If its fitness value is higher than G, the current particle is set to G and the process goes to step S3-2-6; where: P s is the individual optimal position of particle s, and G is the global optimal position of all particles; Step S3-2-5, return to step S3-2-1 for the next particle until all particles are processed; Step S3-2-6: Determine whether the number of iterations meets the requirement. If so, output G as the optimal line-to-line collaborative optimization solution; otherwise, return to step S3-2-1.

7. The line-large temporary project collaborative site selection optimization method according to claim 6 is characterized in that: In step S3-2-1: the motion speed of each spatial control point of the line-protocol particle line is updated by the following formula: V s (j,t+1)=w·V s (j,t)+c1·r1·[P s (j)-x s (j,t)]+c2·r2·[G(j)-x s (j,t)]+c A ·r A ·F As (j)+c R ·r R ·F Rs (j); Where: w is the inertia weight; V s (j, t) is the velocity of the j-th line-space control point of the s-th particle at generation t; V s (j, t+1) is the velocity of the j-th line-space control point of the s-th particle at the t+1 generation; x s (j, t) is the position of the j-th line space control point of the s-th particle in the population in the t-th generation; c1 is the individual learning factor; c2 is the social learning factor; c A is the influence factor of gravity; c R is the influencing factor of repulsion; r1, r2, r A and r R are all random numbers distributed in the range [0,1]; P s (j) is the individual optimal position of the j-th line position space control point of the s-th particle; G(j) is the global optimal position of the j-th line position space control point of all particles; F As (j) is the gravitational force of the line stored in the voxel where the j-th line space control point of the s-th particle is located; F Rs (j) is the repulsive force of the line passage stored in the voxel where the j-th line position space control point of the s-th particle is located; Update the movement speed of each spatial control point of the line-protocol particle project by the following formula: V s (x,t+1)=w·V s (x,t)+c1·r1·[P s (x)-x s (x,t)]+c2·r2·[G(x)-x s (x,t)]+c A ·r A ·F LAs (x)+c R ·r R ·F LRs (x); Where: V s (x, t) is the velocity of the xth control point in the engineering space of the sth particle at the tth generation; Vs ( x, t+1) is the velocity of the xth large-scale engineering space control point of the sth particle at the t+1 generation; x s (x, t) is the position of the xth large-scale engineering space control point of the sth particle in the population in the tth generation; P s (x) is the individual optimal position of the xth large temporary engineering space control point of the sth particle; G(x) is the global optimal position of the xth large temporary engineering space control point of all particles; F LAs (x) is the gravitational effect of the large temporary engineering layout stored in the voxel where the x-th large temporary engineering space control point of the s-th particle is located; F LRs (x) is the repulsive force of the large temporary engineering layout stored in the voxel where the x-th large temporary engineering spatial control point of the s-th particle is located; In step S3-2-2, the position of each spatial control point of the line-protocol particle is updated by the following formula: x s (t+1)=x s (t)+V s (t+1); Where: x s (t+1) is the position of the sth particle in the population at generation t+1; x s (t) is the position of the sth particle in the population at generation t; V s (t+1) is the velocity of the sth particle in the population at generation t+1.

8. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the line-large temporary project collaborative site selection optimization method described in any one of claims 1 to 7 is implemented.

9. An electronic device, characterized in that: include: processor; and a memory for storing executable instructions for the processor; Wherein, the processor is configured to execute the line-large temporary project collaborative site selection optimization method as described in any one of claims 1-7 by executing the executable instructions.

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