A bridge span arrangement main girder automatic type selection design method based on an ant colony algorithm

Abstract

The application belongs to the technical field of bridge engineering intelligent design, and discloses a bridge span arrangement main girder automatic selection design method based on an ant colony algorithm. The method comprises the following steps: screening control points according to control point information to determine main control points; determining ant colony parameters according to the main control points and a beam type library; performing beam arrangement processing on the main control points based on the ant colony algorithm to generate a main girder arrangement strategy, wherein the ant colony algorithm adopts the ant colony parameters; obtaining a total distance of the main girder corresponding to the main girder arrangement strategy; screening a target main girder arrangement strategy from the main girder arrangement strategy according to the total distance of the main girder; adding the target main girder arrangement strategy to a main girder arrangement result list and updating the iteration number; when the iteration number is greater than or equal to a preset iteration threshold, sorting and deduplicating the main girder arrangement result list to obtain a target main girder arrangement result list. In the above manner, time is saved, efficiency is improved, and the influence of global factors can be considered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent design of bridge engineering, and in particular to a bridge span girder automatic selection design method based on an ant colony algorithm. BACKGROUND

[0002] In order to reduce the impact of construction of railway, highway, rail transit and other traffic lines on people's life, most traffic lines adopt the form of viaduct, and the proportion of bridges in newly built traffic lines is increasing, so the design and construction technology of traffic line bridges has become one of the key technologies. In the case of a determined traffic line, a bridge design engineer needs to consider various control points in the line, such as rivers, pipelines, urban roads, railways, etc. during scheme design, and the bridge engineer must rely on strong professional knowledge and rich design experience to design a main girder to span each main control point, and then design a bridge hole layout design drawing that meets the professional specifications through bridge design. However, the traditional manual selection of girder type is low in efficiency and cannot be automatically designed, and it is too dependent on the personal experience of engineers. The main girder layout method based on the greedy method uses the standard bridge construction library in the building information model and uses the greedy strategy to arrange the main girder, which can complete the selection of the main girder matching the main control point with high efficiency, but the main girder layout method based on the greedy method has the inherent defects of the greedy method, that is, it is easy to fall into a local optimum, and it is difficult to ensure the optimal selection, and it only considers the lowest cost of the current control point girder arrangement, and fails to consider the influence of the arrangement of non-standard girders on the cost of the bridge.

[0003] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a bridge span girder automatic selection design method based on an ant colony algorithm, which aims to solve the technical problem that the traditional main girder layout method based on the greedy method is easy to fall into a local optimum and difficult to ensure the optimal selection in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides a bridge span girder automatic selection design method based on an ant colony algorithm, which comprises the following steps:

[0006] Obtain the control points and the control point information corresponding to the control points, filter the control points according to the control point information, determine the main control points, and determine the ant colony parameters according to the main control points;

[0007] Perform girder arrangement on the main control points based on the ant colony algorithm to generate a main girder layout strategy, and the ant colony algorithm uses the ant colony parameters;

[0008] obtaining a total distance of the main girder arrangement strategy, screening a target main girder arrangement strategy from the main girder arrangement strategy according to the total distance of the main girder arrangement strategy, adding the target main girder arrangement strategy to a main girder arrangement result list, and updating the iteration number;

[0009] when the iteration number is greater than or equal to a preset iteration threshold, sorting and deduplicating the main girder arrangement result list to obtain a main girder selection design strategy.

[0010] Optionally, the control point information includes a control point length, a control point angle, a control point height, a gray region coefficient, a safety distance threshold, a girder arrangement priority, and a control point attribute, and the screening of the control points according to the control point information to determine a main control point includes:

[0011] determining whether the control point meets a main control point attribute according to a control point attribute of the control point;

[0012] determining whether the control point meets a preset length condition according to a control point length of the control point;

[0013] determining whether the control point meets a preset priority condition according to a girder arrangement priority of the control point;

[0014] when the control point meets the main control point attribute, meets the preset length condition, and meets the preset priority condition, determining that the control point is a main control point, and generating a main control point list.

[0015] Optionally, the ant colony parameters include a total number of ants, an information heuristic factor, an expected heuristic factor, an information evaporation factor, a single pheromone increment, and a pheromone matrix, and the determination of the ant colony parameters according to the main control points includes:

[0016] determining the total number of ants according to a number of the main control points and a preset number multiple;

[0017] determining the information heuristic factor, the expected heuristic factor, the information evaporation factor, and the single pheromone increment according to a preset requirement;

[0018] determining a row and column number of the pheromone matrix according to the number of the main control points and a number of beam types in a beam type library;

[0019] obtaining a greedy pheromone increment, and initializing the pheromone matrix according to the greedy pheromone increment.

[0020] Optionally, the girder arrangement processing of the main control points to generate the main girder arrangement strategy includes:

[0021] determine a beam type corresponding to the main control point in the beam type library according to a preset beam arrangement condition, and determine a selection probability of the beam type;

[0022] assign ants to the beam type according to the selection probability of the beam type;

[0023] determine a target beam type of the main control point according to a preset quantity threshold and the number of ants of the beam type, and arrange the target beam type to generate the main beam arrangement strategy.

[0024] Optionally, the determining of the beam type corresponding to each main control point in the beam type library according to the preset beam arrangement condition comprises:

[0025] determine an un-treadable distance corresponding to the main control point according to a control point length, a control point angle, a safety distance threshold and a gray region coefficient of the main control point;

[0026] compare a main span length of the beam type in the beam type library with the un-treadable distance, compare a beam type priority of the beam type with the beam arrangement priority, and compare an arrangement state of the beam type with a preset arrangement state;

[0027] when the main span length of the beam type corresponding to the main control point is greater than the un-treadable distance, the beam type priority of the beam type corresponding to the main control point is greater than the beam arrangement priority, and the arrangement state of the beam type corresponding to the main control point meets the preset arrangement state, determine that the beam type is the beam type that can be arranged for the main control point.

[0028] Optionally, the determining of the selection probability of each beam type comprises:

[0029] obtain line mileage data, a beam type priority of the beam type and a beam type length of the beam type;

[0030] determine a beam type distance of the beam type according to the line mileage data, the beam type priority, the beam type length and a corresponding relationship between the line mileage data, the beam type priority, the beam type length and a beam type distance;

[0031] obtain an information concentration corresponding to the beam type in the pheromone matrix;

[0032] determine the selection probability of the beam type according to the beam type distance, the information concentration of the beam type, the beam type distance of the beam type, and a corresponding relationship between the beam type distance, the information concentration and the selection probability.

[0033] Optionally, the assigning of the ants to the beam type according to the selection probability of the beam type comprises:

[0034] generating a random value in a preset value interval;

[0035] determining a matching probability interval corresponding to the random value according to the random value and the selection probability of the arrangeable beam type;

[0036] determining an ant selection object in the arrangeable beam type according to the matching probability interval;

[0037] allocating ants to the ant selection object and counting the number of ants corresponding to the arrangeable beam type.

[0038] Optionally, the obtaining of the total distance of the main beam arrangement strategy comprises:

[0039] obtaining a main beam distance corresponding to a main control point in the main beam arrangement strategy;

[0040] determining the total distance of the main beam arrangement strategy according to the main beam distance corresponding to the main control point;

[0041] updating the pheromone matrix according to the information volatility factor;

[0042] determining a single pheromone concentration increment according to the single pheromone increment and the total distance of the main beam arrangement strategy, and determining the pheromone concentration corresponding to the main beam in the main beam arrangement strategy according to the single pheromone concentration increment.

[0043] Optionally, after the target main beam arrangement strategy is added to the main beam arrangement result list and the iteration number is updated, the method further comprises:

[0044] when the iteration number is less than a preset iteration threshold, returning to perform beam arrangement processing on the main control point based on the ant colony algorithm to generate a main beam arrangement strategy, wherein the ant colony algorithm adopts the ant colony parameters.

[0045] Optionally, before the control point and the control point information corresponding to the control point are obtained, the control point is screened according to the control point information to determine a main control point, and the ant colony parameters are determined according to the main control point, the method further comprises:

[0046] information modeling is performed on the beam type library, the control point and the beam arrangement route, wherein the beam arrangement route comprises a beam arrangement height and a beam arrangement position.

[0047] In addition, to achieve the above object, the application further provides a bridge beam arrangement main beam automatic selection design device based on an ant colony algorithm, which comprises:

[0048] The acquisition module is configured to acquire a control point and control point information corresponding to the control point, filter the control point according to the control point information, determine a main control point, and determine ant colony parameters according to the main control point.

[0049] The beam arrangement module is configured to perform beam arrangement processing on the main control point based on an ant colony algorithm to generate a main beam arrangement strategy, and the ant colony algorithm uses the ant colony parameters.

[0050] The beam arrangement module is further configured to acquire a total distance of the main beam arrangement strategy, filter a target main beam arrangement strategy from the main beam arrangement strategy according to the total distance of the main beam arrangement strategy, add the target main beam arrangement strategy to a main beam arrangement result list, and update an iteration number.

[0051] The beam arrangement module is further configured to sort and remove duplicates from the main beam arrangement result list when the iteration number is greater than or equal to a preset iteration threshold to obtain a main beam selection design strategy.

[0052] In addition, to achieve the above-mentioned purposes, the present application further provides a bridge beam arrangement main beam automatic selection design device based on an ant colony algorithm, which comprises a memory, a processor, and a bridge beam arrangement main beam automatic selection design program based on an ant colony algorithm stored on the memory and executable on the processor. The bridge beam arrangement main beam automatic selection design program based on an ant colony algorithm is configured to implement the steps of the bridge beam arrangement main beam automatic selection design method based on an ant colony algorithm as described above.

[0053] In addition, to achieve the above-mentioned purposes, the present application further provides a storage medium having a bridge beam arrangement main beam automatic selection design program based on an ant colony algorithm stored thereon. The bridge beam arrangement main beam automatic selection design program based on an ant colony algorithm is executed by a processor to implement the steps of the bridge beam arrangement main beam automatic selection design method based on an ant colony algorithm as described above.

[0054] In the application, by acquiring control point information corresponding to the control points, the control points are screened according to the control point information, the main control points are determined, the ant colony parameters are determined according to the main control points and the beam type library, the main control points are processed by the beam arrangement based on the ant colony algorithm, the main beam arrangement strategy is generated, the ant colony algorithm adopts the ant colony parameters, the total distance of the main beam arrangement strategy is acquired, and the target main beam arrangement strategy is screened in the main beam arrangement strategy according to the total distance of the main beam; the target main beam arrangement strategy is added to the main beam arrangement result list, and the iteration number is updated, when the iteration number is greater than or equal to the preset iteration threshold, the main beam arrangement result list is sorted and de-duplicated to obtain the target main beam arrangement result list. Compared with the traditional manual design, the application can efficiently and high-quality complete the main beam selection scheme design of bridge layout, and can accept the correction of the scheme by the human, access the subsequent bridge process to complete the design of the whole hole arrangement project, greatly improve the design efficiency, reduce the time and labor cost, compared with the use of traditional greedy method, the application can consider the influence of global factors, ensure the efficiency, and design the globally optimal main beam selection design scheme, in addition, the application uses more simply supported beam with the highest priority to bridge, which can greatly reduce the cost and improve the engineering quality. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a structural schematic diagram of a bridge layout main beam automatic selection design device based on an ant colony algorithm of a hardware running environment related to an embodiment scheme of the application;

[0056] Figure 2 is a flowchart of a first embodiment of a bridge layout main beam automatic selection design method based on an ant colony algorithm of the application;

[0057] Figure 3 is a beam type selection schematic diagram of an embodiment of a bridge layout main beam automatic selection design method based on an ant colony algorithm of the application;

[0058] Figure 4 is a schematic diagram of the overall flow of an embodiment of a bridge layout main beam automatic selection design method based on an ant colony algorithm of the application;

[0059] Figure 5 is an ant distribution flowchart of a second embodiment of a bridge layout main beam automatic selection design method based on an ant colony algorithm of the application;

[0060] Figure 6 is an ant distribution flowchart of an embodiment of a bridge layout main beam automatic selection design method based on an ant colony algorithm of the application;

[0061] Figure 7 is a schematic diagram of the overall flow of an embodiment of a bridge layout main beam automatic selection design method based on an ant colony algorithm of the application;

[0062] Figure 8 The figure is a structural block diagram of the first embodiment of the bridge span arrangement main girder automatic selection design device based on the ant colony algorithm.

[0063] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are intended to explain the present application, but not to limit the present application.

[0065] Referring to Figure 1 , Figure 1 The figure is a structural block diagram of the bridge span arrangement main girder automatic selection design device based on the ant colony algorithm of the hardware running environment involved in the embodiment scheme of the present application.

[0066] As Figure 1 shown, the bridge span arrangement main girder automatic selection design device based on the ant colony algorithm can include a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize the connection and communication between the components. The user interface 1003 can include a display screen, an input unit such as a keyboard, and an optional user interface 1003 can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a wireless fidelity (Wi-Fi) interface). The memory 1005 can be a high-speed random access memory (RAM) memory, or a stable non-volatile memory (NVM), such as a magnetic disk memory. The memory 1005 can also be an independent storage device from the aforementioned processor 1001.

[0067] Those skilled in the art can understand Figure 1 that the structure shown in the figure does not constitute a limitation on the bridge span arrangement main girder automatic selection design device based on the ant colony algorithm, and can include more or fewer components than the figure, or combine certain components, or different component arrangements.

[0068] As Figure 1 shown, the memory 1005 as a storage medium can include an operating system, a network communication module, a user interface module, and a bridge span arrangement main girder automatic selection design program based on the ant colony algorithm.

[0069] In Figure 1 The network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the bridge span main girder automatic selection design device based on an ant colony algorithm can be arranged in the bridge span main girder automatic selection design device based on an ant colony algorithm; the bridge span main girder automatic selection design device based on an ant colony algorithm calls the bridge span main girder automatic selection design program based on an ant colony algorithm stored in the memory 1005 through the processor 1001, and executes the bridge span main girder automatic selection design method based on an ant colony algorithm provided in the embodiment.

[0070] The embodiment of the present application provides a bridge span main girder automatic selection design method based on an ant colony algorithm. Figure 2 , Figure 2 The embodiment of the present application provides a bridge span main girder automatic selection design method based on an ant colony algorithm.

[0071] In the embodiment, the bridge span main girder automatic selection design method based on an ant colony algorithm comprises the following steps:

[0072] Step S10: acquiring a control point and control point information corresponding to the control point, screening the control point according to the control point information, determining a main control point, and determining an ant colony parameter according to the main control point.

[0073] It should be noted that the execution subject of the embodiment is a computer, the computer is provided with a bridge span main girder automatic selection design program based on an ant colony algorithm, and the automatic selection of the main girder is realized through the bridge span main girder automatic selection design program based on an ant colony algorithm.

[0074] It can be understood that before step S10, information modeling of the beam type library, the control point and the beam arrangement line is further included. The information modeling adopts a building information modeling (BIM) technology. The control point refers to various control points appearing in the beam arrangement line that needs to be considered in current bridge design, for example, rivers, pipelines, urban roads, railways and the like. The beam type library refers to a set of standard beam type library that is pre-manufactured and templated in conventional use, and generally includes all beam types that can be put into use. The beam arrangement line refers to the overall main girder arrangement line in current bridge design, and at least includes beam arrangement height and position, and the height and position of the main girder arrangement.

[0075] It should be understood that after information modeling is performed, relevant information corresponding to the control points, i.e., control point information, can be obtained therefrom. In order to screen the control points, the control point information required by the embodiment at least includes control point length, control point angle, control point height, gray region coefficient, safety distance threshold, beam arrangement priority, and control point attribute. The safety distance threshold is usually a safety distance threshold to the left and right of the control point. The beam arrangement priority refers to the minimum priority allowed for beam arrangement. The control point attribute refers to whether the control point can be a main control point.

[0076] Further, the screening of the control points according to the control point information and the determination of the main control points include: determining, according to the control point attribute of the control point, whether the control point meets the main control point attribute; determining, according to the control point length of the control point, whether the control point meets a preset length condition; determining, according to the beam arrangement priority of the control point, whether the control point meets a preset priority condition; and determining the control point as a main control point and generating a main control point list when the control point meets the main control point attribute, meets the preset length condition, and meets the preset priority condition.

[0077] It should be noted that the determination of whether the control point meets the main control point attribute refers to the judgment of whether the control point can be a main control point. The control point meets the main control point attribute means that the control point can be a main control point. The preset length condition refers to a condition that needs to be met by the length of the main control point at the time of current bridge design, for example, greater than / less than a length threshold or within a length threshold interval, which is determined according to actual conditions / design requirements. The embodiment does not limit this. The control point meets the preset length condition means that the control point length of the control point meets the requirement for the length when it is a main control point. The preset priority condition refers to a condition that needs to be met by the minimum priority allowed for beam arrangement of the main control point at the time of current bridge design, for example, greater than / less than a priority threshold, which is determined according to actual conditions / design requirements. The embodiment does not limit this. The control point meets the preset priority condition means that the beam arrangement priority of the control point meets the requirement for the beam arrangement priority when it is a main control point. The main control point list refers to a list composed of the main control points screened out, which is usually sorted by mileage.

[0078] In a specific implementation, according to whether the control point can be a main control point, and considering the control point length and the minimum priority allowed for beam arrangement, the control points that meet the preset length condition and the preset priority condition and can be main control points are screened out from the control point list as the main control points required at the time of current bridge design, and a corresponding main control point list is generated.

[0079] Further, the determining the ant colony parameters according to the master control points comprises: determining the total number of ants according to a multiple of a preset number of the master control points, determining the information heuristic factor, the expected heuristic factor, the information evaporation factor and the single pheromone increment according to a preset requirement, determining the row and column number of the pheromone matrix according to the number of the master control points and the number of beam types in the beam type library, and initializing the pheromone matrix according to the greedy pheromone increment.

[0080] It can be understood that the ant colony algorithm is used in the subsequent steps, and before this, the related parameters required by the ant colony algorithm, i.e., the ant colony parameters, need to be initialized. The ant colony parameters in the embodiment at least include the total number of ants, the information heuristic factor, the expected heuristic factor, the information evaporation factor, the single pheromone increment and the pheromone matrix. The single pheromone increment refers to the single ant pheromone increment.

[0081] It should be understood that in the initialization stage, the larger the information heuristic factor is, the greater the possibility of the path selected by the ant in the subsequent beam laying stage is, and the randomness of the search path is reduced. When the information heuristic factor is reduced, the search range of the ant colony is reduced, so that the algorithm is prone to fall into a local optimal solution. The information heuristic factor generally takes a value in the range of [0, 5]. The greater the expected heuristic factor value is, the easier the ant colony is to select a locally shorter path. At this time, the convergence speed of the algorithm is accelerated, but the randomness is reduced, and it is more likely to fall into a local relative optimum. The expected heuristic factor generally takes a value in the range of [0, 5]. The greater the total number of ants is, the more accurate the optimal solution obtained is, but a large number of repeated solutions are generated, and as the algorithm approaches the optimal value of the convergence, the information positive feedback effect is reduced, a large amount of repeated work is consumed, and the time complexity is increased. When the information evaporation factor is too small, the pheromone remaining on each path will be too much, which will lead to the invalid path being continuously searched, and then affect the convergence speed of the algorithm. However, when the information evaporation factor is too large, although the convergence speed is ensured, the effective path may also be abandoned, which affects the search of the optimal solution.

[0082] It should be noted that the number of master control points is the total number of master control points, the preset number multiple refers to the relationship between the total number of master control points and the total number of ants, for example, 1.5 times, the preset requirement refers to some requirements and demands in the current bridge design. In the embodiment, the total number of ants is set to 1.5 times the total number of master control points, the information heuristic factor is set to 3, the expected heuristic factor is set to 2, the information evaporation factor is set to 0.3, and the single ant pheromone increment of each iteration is set to 20.

[0083] It can be understood that the number of beam types in the beam type library is the number of beam types contained in the beam type library. The number of rows and columns refers to the number of rows and columns of the pheromone matrix. The number of rows of the pheromone matrix is equal to the number of main control points, and the number of columns of the pheromone matrix is equal to the number of beam types. For example, the number of main control points is a, and the number of beam types in the beam type library is b. Then the pheromone matrix is a row and b columns. The greedy pheromone increment refers to the pheromone increment calculated by using the beam arrangement scheme found by the greedy algorithm. The greedy pheromone increment is used as the initial value to initialize the pheromone matrix.

[0084] In a specific implementation, the pheromone matrix is initialized, and the total number of ants, the information heuristic factor, the expected heuristic factor, the information volatility factor, and the single pheromone increment are set.

[0085] Step S20: performing beam arrangement processing on the main control points based on the ant colony algorithm to generate a main beam arrangement strategy, wherein the ant colony algorithm uses the ant colony parameters.

[0086] It should be understood that, as shown in Figure 3 When using the ant colony algorithm to arrange beams for the main control points, the core idea is to arrange beams for the control points sorted by mileage in turn. It can be regarded as starting from a city, passing through each city in a fixed order, and finally returning to the initial city. In the process of going from each city to the next city, there are multiple paths to choose from, that is, multiple beam types can be selected.

[0087] It should be understood that the ant colony algorithm (Ant Colony Optimization, ACO) is a swarm intelligence algorithm. It is a new possibility for solving complex problems by intelligent behavior of a group of individuals (agents) through mutual cooperation. The ant colony algorithm is a bionics algorithm inspired by the foraging behavior of ants in nature. In nature, during the foraging process of ants, the ant colony can always find an optimal path from the nest to the food source. The basic principle is that ants release pheromones on the path, and when they encounter a road junction that has not been passed, they randomly choose a path to walk, and at the same time, release pheromones related to the path length. The concentration of pheromones is inversely proportional to the path length. When the later ants encounter the junction again, they choose the path with higher pheromone concentration. The pheromone concentration on the optimal path becomes larger and larger, and finally the ant colony finds the optimal foraging path.

[0088] It can be understood that the main beam arrangement strategy refers to the generated main beam arrangement scheme, which contains the beam type arrangement of all main control points, that is, the arrangement of main beams. Each main control point has a corresponding arranged beam type.

[0089] In a specific implementation, the ant colony algorithm is used to perform beam arrangement processing on the main control points to obtain the beam type arrangement of all main control points and generate a main beam arrangement strategy.

[0090] Step S30: obtaining a total distance of the main girder corresponding to the main girder arrangement strategy, screening a target main girder arrangement strategy from the main girder arrangement strategy according to the total distance of the main girder, adding the target main girder arrangement strategy to a main girder arrangement result list, and updating the iteration number.

[0091] Further, the obtaining of the total distance of the main girder corresponding to the main girder arrangement strategy comprises: obtaining a main girder distance corresponding to a main control point in the main girder arrangement strategy, determining the total distance of the main girder corresponding to the main girder arrangement strategy according to the main girder distance corresponding to the main control point, updating the pheromone matrix according to the information volatilization factor, determining a single pheromone concentration increment according to the single pheromone increment and the total distance of the main girder of the main girder arrangement strategy, and determining a pheromone concentration corresponding to a main girder in the main girder arrangement strategy according to the single pheromone concentration increment.

[0092] It should be understood that the main girder distance corresponding to the main control point refers to the distance of the main girder laid on each main control point, and the total distance of the main girder refers to the total distance corresponding to the main girders laid in the main girder arrangement strategy, that is, the sum of the distances of the main girders laid on each main control point in the main girder arrangement strategy.

[0093] It should be noted that the updating of the pheromone matrix according to the information volatilization factor refers to the volatilization of the pheromone in the entire pheromone matrix, that is, setting each pheromone concentration τ in the pheromone matrix as (1-ρ)τ0, wherein ρ is the information volatilization factor, and τ0 is the initial pheromone concentration, that is, the previous pheromone concentration. Then, for each main girder arrangement strategy, the pheromone concentration corresponding to each main girder is updated, and in this embodiment, the update value of the pheromone concentration corresponding to each main girder is determined according to the pheromone concentration increment (single pheromone concentration increment) generated by a single ant. The calculation relationship of the single pheromone concentration increment is:

[0094] d k = η k

[0095] η k , wherein η k represents the total distance of the main girder corresponding to the kth main girder arrangement strategy, Q represents the single pheromone increment, and d k represents the pheromone concentration increment generated by the kth ant. The single pheromone increment and the total distance of the main girder of the main girder arrangement strategy are substituted into the formula to obtain the pheromone concentration increment generated by a single ant. At this time, the pheromone concentration τ i corresponding to each main girder in the main girder arrangement strategy is updated as τ i = τ i + dτ i, wherein i ∈ [1, s], and s represents the number of main girders in the kth main girder arrangement strategy. k i i i k

[0096] ​​​​​In a specific implementation, after the beam arrangement processing of all control points is completed, pheromone volatilization is performed on the entire information matrix, and the pheromone concentration corresponding to each main beam is updated for each main beam arrangement strategy.

[0097] It can be understood that the embodiment needs to use the ant colony algorithm for multiple iterations, one iteration can generate multiple main beam arrangement strategies, the target main beam arrangement strategy refers to the main beam arrangement strategy with the shortest total distance of main beams in this iteration, that is, the optimal main beam arrangement scheme selected from this iteration, the main beam arrangement result list refers to the final main beam design scheme, which is embodied in the form of a list. The iteration number refers to the number of iterations that have been completed, and the iteration number increases by 1 after each iteration is completed. Each time the target main beam arrangement strategy is selected, it is added to the main beam arrangement result list, representing the completion of one iteration.

[0098] In a specific implementation, the main beam arrangement scheme with the shortest distance in this iteration is selected and added to the main beam arrangement result list to complete one iteration.

[0099] Step S40: When the iteration number is greater than or equal to a preset iteration threshold, sorting and deduplication are performed on the main beam arrangement result list to obtain a main beam selection design strategy.

[0100] It should be understood that the preset iteration threshold refers to the number of iterations required by the embodiment, which can be adjusted according to actual needs, and the embodiment does not limit this. The main beam selection design strategy refers to the final main beam selection design scheme, that is, the final output main beam arrangement result list.

[0101] Further, after the step S30, it further includes: when the iteration number is less than the preset iteration threshold, returning to perform the beam arrangement processing on the main control points based on the ant colony algorithm to generate the main beam arrangement strategy, and the ant colony algorithm uses the ant colony parameters.

[0102] In a specific implementation, if the iteration is completed, the main beam arrangement strategies in the main beam arrangement result list are arranged in ascending order according to the total distance of the main beams, and then the repeated beam arrangement results are removed. The main beam arrangement result list is output, and the generation of the main beam selection design scheme is completed. If the iteration is not completed, the next round of iteration is continued, and the ant colony algorithm is used to perform the beam arrangement processing on the main control points until the iteration is completed.

[0103] As Figure 4The whole flowchart shown is shown. The beam type library, the main control point list, the control point information, the beam layout line information are acquired, the pheromone matrix is initialized, the total number of ants is acquired, the parameters required by the ant colony algorithm are acquired, the ant colony algorithm is adopted, the main beam is selected for each main control point recursively, the distance is calculated, the pheromone matrix is updated, then the current optimal path is added to the main beam layout result list, the main beam layout of the main control point is repeatedly executed, after a specified number of times, the main beam layout result is sorted by distance, and the main beam layout result list is finally output, and the main beam selection design scheme is obtained.

[0104] In the embodiment, by acquiring the control point information corresponding to the control points, the control points are screened according to the control point information, the main control points are determined, the ant colony parameters are determined according to the main control points and the beam type library, the main control points are processed by the beam layout based on the ant colony algorithm, the main beam layout strategy is generated, the ant colony algorithm adopts the ant colony parameters, the total distance of the main beam layout strategy is acquired, the target main beam layout strategy is screened out in the main beam layout strategy according to the total distance of the main beam layout strategy, the target main beam layout strategy is added to the main beam layout result list, and the iteration number is updated. When the iteration number is greater than or equal to the preset iteration threshold, the main beam layout result list is sorted and de-duplicated to obtain the target main beam layout result list. Compared with the traditional manual design, the embodiment can efficiently and high-quality complete the main beam selection scheme design of the bridge layout, and can accept the correction of the scheme by the human, access the subsequent bridge process to complete the design of the whole hole layout project, greatly improve the design efficiency, reduce the time and manpower cost, compared with the use of the traditional greedy method, the embodiment can consider the influence of the global factor, ensure the efficiency, and design the globally optimal main beam selection design scheme. In addition, the present application uses more simply supported beams with the highest priority for bridging, which can greatly reduce the cost and improve the engineering quality.

[0105] Reference Figure 5 , Figure 5 The flowchart of the second embodiment of the bridge layout main beam automatic selection design method based on the ant colony algorithm is shown.

[0106] Based on the above embodiment, in the embodiment, the step S20 comprises:

[0107] Step S201: According to the preset beam layout condition, the beam type corresponding to the main control point is determined in the beam type library, and the selection probability of the beam type is determined.

[0108] It should be noted that the deployable beam type refers to the beam type that can be arranged by the main control point in the beam type library, each main control point has a corresponding deployable beam type, and the preset beam arrangement condition refers to a condition required to become a deployable beam type: the main span length is greater than the non-treadable distance, the beam type priority is greater than the minimum priority of the allowed arranged beam type, and the beam type will not tread on the control point and will not overlap with other arranged beam types after arrangement.

[0109] Further, the determining, according to the preset beam arrangement condition, of the deployable beam type corresponding to the main control point in the beam type library comprises: determining the non-treadable distance corresponding to the main control point according to the control point length, the control point angle, the safety distance threshold and the gray region coefficient, comparing the main span length of the beam type in the beam type library with the non-treadable distance, comparing the beam type priority of the beam type with the beam arrangement priority, comparing the arrangement state of the beam type with the preset arrangement state, and determining the beam type as the deployable beam type of the main control point when the main span length of the beam type corresponding to the main control point is greater than the non-treadable distance, the beam type priority of the beam type corresponding to the main control point is greater than the beam arrangement priority, and the arrangement state of the beam type corresponding to the main control point meets the preset arrangement state.

[0110] It can be understood that the non-treadable distance is calculated by the control point length, the control point angle, the safety distance threshold, the gray region coefficient and the line spacing, and the arrangement state refers to the relationship between the beam type after arrangement and the main control point and other arranged beam types, which is used to judge whether the main control point will be treaded on and whether the beam type will overlap with other arranged beam types after arrangement.

[0111] In a specific implementation, the beam types meeting the preset beam arrangement condition are screened out as the deployable beam types of the main control points, and a deployable beam type list is generated.

[0112] Further, the determining of the selection probability of each deployable beam type comprises: obtaining line mileage data, a beam type priority of the deployable beam type and a beam type length of the deployable beam type, determining a beam type distance of the deployable beam type according to the line mileage data, the beam type priority, the beam type length and a corresponding relationship between the line mileage data, the beam type priority, the beam type length and the beam type distance, obtaining an information element concentration corresponding to the deployable beam type in the information element matrix, and determining the selection probability of the deployable beam type according to the beam type distance, the information element concentration of the deployable beam type, the beam type distance of the deployable beam type and a corresponding relationship between the beam type distance, the information element concentration and the selection probability.

[0113] It should be understood that the selection probability refers to the probability of each beam type being selected by the ants in the ant colony algorithm. The route mileage data refers to the total route mileage, and the beam type distance is an index for measuring whether the beam type is suitable for being laid at the current master control point and whether the cost is low, which is calculated by the priority of the current beam type, the total route mileage minus the length of the current beam type, and the remainder obtained by taking the length of the highest priority beam type as the remainder. The calculation relationship between the route mileage data, the beam type priority, the beam type length, and the beam type distance is as follows:

[0114] η=Pr+3log3K

[0115] In the formula, Pr represents the beam type priority, K represents the remainder obtained by taking the length of the highest priority beam type as the remainder after the total route mileage is subtracted by the beam type length, and η represents the beam type distance. The beam type priority is determined by the engineer considering the cost of the beam type and combining the experience in daily use. The higher the priority, the smaller the priority value. The highest priority beam type is the beam type with the lowest manufacturing cost and the most preferred by the engineer in the conventional beam type design, and the corresponding length is the length of the highest priority beam type. According to the calculated beam type distance, the selection probability is calculated, and the corresponding relationship between the route mileage data, the beam type priority, the beam type length, and the beam type distance is the calculation relationship of the selection probability, as follows:

[0116]

[0117] In the formula, i represents the master control point subscript, j represents the layable beam type subscript, S represents the set of layable beam types, η i,j represents the beam type distance corresponding to the jth layable beam type of the ith master control point, τ i,j represents the pheromone concentration corresponding to the jth layable beam type of the ith master control point, α and β represent the information heuristic factor and the expected heuristic factor, respectively, P i,j represents the selection probability of the jth layable beam type of the ith master control point. The beam type distance, the pheromone concentration, the beam type distance information heuristic factor, and the expected heuristic factor are substituted into the calculation relationship of the selection probability to obtain the selection probability of each layable beam type.

[0118] It should be noted that if a certain beam type is not in the layable beam types of the master control point, the probability of selecting the beam type is 0.

[0119] It can be understood that the embodiment calculates the selection probability by setting the beam type distance, so that the beam selection is biased towards the beam type that can just span the current control point and has the lowest priority, and at the same time, the highest priority beam type can be used as much as possible in the bridge connection process after the main beam is completely laid.

[0120] Step S202: According to the selection probability of the layable beam type, ants are assigned to the layable beam type.

[0121] Further, the step S202 comprises: generating a random value in a preset value interval, determining a matching probability interval corresponding to the random value according to the selection probability of the deployable beam type and the random value, determining an ant selection object in the deployable beam type according to the matching probability interval, assigning an ant to the ant selection object, and counting the number of ants corresponding to the deployable beam type.

[0122] It should be understood that the random value refers to a randomly generated value, and the preset value interval refers to the generation interval of the random value. In the embodiment, the preset value interval is (0, 1). Each selection probability has a corresponding probability interval. For example, the selection probabilities of the deployable beam types b1, b2 and b3 are P1, P2 and P3 respectively, and the corresponding probability intervals are (0, P1], (P1, P1+P2] and (P1+P2, 1). The random value is compared with these probability intervals to find the matching probability interval. For example, the selection probabilities of the deployable beam types are P1, P2 and P3 respectively, and the corresponding probability intervals are (0, P1], (P1, P1+P2] and (P1+P2, 1). If the random value is a, it is determined which interval a matches in (0, P1], (P1, P1+P2] and (P1+P2, 1). The matching interval is the matching probability interval. The ant selection object refers to the deployable beam type selected by each ant. The matching probability interval is found, the corresponding selection probability is found, and the deployable beam type corresponding to the selection probability is the deployable beam type selected by the ant.

[0123] In a specific implementation, as shown in FIG. 2B, it is assumed that the number of ants existing at the current control point is m1. Then, the roulette method is used to select m1 times according to the selection probabilities of the deployable beam types, and the number of ants existing at the current control point is assigned to each selected beam type. Specifically, it is assumed that there are three deployable beam types between the current control point and the next control point, which are labeled as b1, b2 and b3, and the selection probabilities are P1, P2 and P3 respectively, and P1+P2+P3=1. In each selection, a random number a in the interval (0, 1) is generated. If a≤P1, the current ant selects b1. If a>P1 and a≤P1+P2, the current ant selects b2. If a>P1+P2, the current ant selects b3. The above steps are repeated m1 times to complete the assignment of all m1 ants. Figure 6 Step S203: determining the target beam type of the main control point according to the preset number threshold and the number of ants of the deployable beam type, arranging the target beam type, and generating the main beam arrangement strategy.

[0124]

[0125] ​It should be noted that the preset quantity threshold refers to the minimum number of selected ants, which can be 1 or adjusted according to actual needs. This embodiment does not make any adjustments. The target beam type refers to the beam type to be arranged. The beam type that can be arranged with a number of ants greater than the minimum number of ants (preset quantity threshold) is selected as the target beam type.

[0126] like Figure 7 The overall detailed process diagram shown involves obtaining information such as the list of main control points, initializing the pheromone matrix, and setting the ant colony algorithm parameters. Before reaching the specified number of iterations, it obtains the deployable beam types corresponding to the current main control point and calculates the selection probability. It selects a beam type selected by one or more ants and deploys it. If the current main control point is the last main control point, it saves the current main beam layout scheme (i.e., which beam types were selected sequentially from the first control point to the current control point) and the number of ants for the currently selected beam type, and then backtracks to process the previous main control point; otherwise, it processes the next main control point. After retreating from the next main control point, reset the beam layout of the current main control point. If there are still unselected beam types to be selected and placed, try the next unselected beam type; otherwise, if the current main control point is not the first main control point, retreat to the previous main control point. If the current main control point is already the first main control point, it means that the beam layout processing for all main control points has been completed. Calculate the total distance of the main beams, update the pheromone matrix, select the main beam layout scheme with the shortest distance and add it to the main beam layout result list, and continue to the next iteration. After the iteration is completed, sort and deduplicate the main beam layout result list before outputting it.

[0127] In this embodiment, based on preset beam layout conditions, the deployable beam types corresponding to the main control point are determined from the beam type library, and the selection probability of each deployable beam type is determined. Based on the selection probability of each deployable beam type, ants are assigned to each deployable beam type. Based on a preset quantity threshold and the number of ants for each deployable beam type, the target beam type of the main control point is determined, and the target beam type is deployed, generating a main beam layout strategy. Compared to traditional manual design, this embodiment can efficiently and effectively complete the main beam selection scheme design for bridge spans, and can accept manual corrections to the scheme. It can be integrated into subsequent bridging processes to complete the design of the entire span project, greatly improving design efficiency and reducing time, manpower, and material costs. Compared to using traditional greedy algorithms, this embodiment can better consider the influence of global factors, ensuring efficiency while designing the globally optimal main beam selection scheme. Furthermore, this invention uses more high-priority standard simply supported beams for bridging, which can significantly reduce construction costs and improve project quality.

[0128] In addition, the embodiment of the present application also provides a storage medium, wherein the storage medium stores a bridge span main girder automatic selection design program based on an ant colony algorithm, and the bridge span main girder automatic selection design program based on the ant colony algorithm is executed by a processor to realize the steps of the bridge span main girder automatic selection design method based on the ant colony algorithm.

[0129] With reference to Figure 8 , Figure 8 FIG. 1 is a structural block diagram of a bridge span main girder automatic selection design device based on an ant colony algorithm according to a first embodiment of the present application.

[0130] As shown in FIG. 2, the bridge span main girder automatic selection design device based on the ant colony algorithm according to the embodiment of the present application comprises: Figure 8

[0131] An acquisition module 10 is configured to acquire control points and control point information corresponding to the control points, to screen the control points according to the control point information, to determine main control points, and to determine ant colony parameters according to the main control points.

[0132] A bridge girder arrangement module 20 is configured to perform bridge girder arrangement processing on the main control points based on an ant colony algorithm to generate a main girder arrangement strategy, wherein the ant colony algorithm adopts the ant colony parameters.

[0133] The bridge girder arrangement module 20 is further configured to acquire a total distance of the main girder arrangement strategy, to screen a target main girder arrangement strategy from the main girder arrangement strategy according to the total distance of the main girder arrangement strategy, to add the target main girder arrangement strategy to a main girder arrangement result list, and to update an iteration number.

[0134] The bridge girder arrangement module 20 is further configured to sort and remove duplicates of the main girder arrangement result list when the iteration number is greater than or equal to a preset iteration threshold to obtain a main girder selection design strategy.

[0135] ​In the embodiment, by acquiring control point information corresponding to the control points, the control points are screened according to the control point information, the main control points are determined, the ant colony parameters are determined according to the main control points and the beam type library, the main control points are processed by the beam arrangement based on the ant colony algorithm, the main beam arrangement strategy is generated, the ant colony algorithm uses the ant colony parameters, the total distance of the main beam arrangement strategy is acquired, the target main beam arrangement strategy is screened from the main beam arrangement strategy according to the total distance of the main beam, the target main beam arrangement strategy is added to the main beam arrangement result list, and the iteration number is updated. When the iteration number is greater than or equal to the preset iteration threshold, the main beam arrangement result list is sorted and de-duplicated to obtain the target main beam arrangement result list. Compared with the traditional manual design, the embodiment can efficiently and high-quality complete the main beam selection scheme design of the bridge beam arrangement, and can accept the correction of the scheme by the human, access the subsequent bridge process to complete the design of the entire hole arrangement project, greatly improve the design efficiency, reduce the time and manpower and material cost. Compared with the use of the traditional greedy method, the embodiment can more consider the influence of the global factor, ensure the efficiency, and design the globally optimal main beam selection design scheme. In addition, the invention uses more simply supported beams with the highest priority to bridge, which can greatly reduce the cost and improve the engineering quality.

[0136] In an embodiment, the control point information includes control point length, control point angle, control point height, gray region coefficient, safety distance threshold, beam arrangement priority, and control point attribute. The acquisition module 10 is further configured to determine whether the control point meets the main control point attribute according to the control point attribute of the control point.

[0137] According to the control point length of the control point, it is determined whether the control point meets the preset length condition.

[0138] According to the beam arrangement priority of the control point, it is determined whether the control point meets the preset priority condition.

[0139] When the control point meets the main control point attribute, the preset length condition and the preset priority condition, the control point is determined as the main control point, and the main control point list is generated.

[0140] In an embodiment, the ant colony parameters include total number of ants, information heuristic factor, expected heuristic factor, information volatility factor, single pheromone increment, and pheromone matrix. The acquisition module 10 is further configured to determine the total number of ants according to the number of main control points and the preset number of multiples.

[0141] According to the preset requirement, the information heuristic factor, the expected heuristic factor, the information volatility factor and the single pheromone increment are determined.

[0142] determining a number of rows and columns of the pheromone matrix according to the number of the main control points and a number of beam types in the beam type library;

[0143] obtaining a greedy pheromone increment, and initializing the pheromone matrix according to the greedy pheromone increment.

[0144] In an embodiment, the beam arrangement module 20 is further configured to determine a beam type corresponding to the main control point in the beam type library according to a preset beam arrangement condition, and determine a selection probability of the beam type.

[0145] distributing ants to the beam type according to the selection probability of the beam type.

[0146] determining a target beam type of the main control point according to a preset number threshold and a number of ants of the beam type, and arranging the target beam type to generate the main beam arrangement strategy.

[0147] In an embodiment, the beam arrangement module 20 is further configured to determine an un-treadable distance corresponding to the main control point according to a control point length, a control point angle, a safety distance threshold and a gray region coefficient of the main control point.

[0148] comparing a main span length of a beam type in the beam type library with the un-treadable distance, comparing a beam type priority of the beam type with the beam arrangement priority, and comparing an arrangement state of the beam type with a preset arrangement state.

[0149] when the main span length of the beam type corresponding to the main control point is greater than the un-treadable distance, the beam type priority of the beam type corresponding to the main control point is greater than the beam arrangement priority, and the arrangement state of the beam type corresponding to the main control point meets the preset arrangement state, determining that the beam type is a beam type that can be arranged for the main control point.

[0150] In an embodiment, the beam arrangement module 20 is further configured to obtain line mileage data, a beam type priority of the beam type that can be arranged, and a beam type length of the beam type that can be arranged.

[0151] determining a beam type distance of the beam type that can be arranged according to the line mileage data, the beam type priority, the beam type length, and a corresponding relationship between the line mileage data, the beam type priority, the beam type length and the beam type distance.

[0152] obtaining a pheromone concentration corresponding to the beam type that can be arranged in the pheromone matrix;

[0153] determining a selection probability of the beam type that can be arranged according to the beam type distance, the pheromone concentration of the beam type that can be arranged, the beam type distance of the beam type that can be arranged, and a corresponding relationship between the beam type distance, the pheromone concentration and the selection probability.

[0154] In an embodiment, the beam arrangement module 20 is further configured to generate a random value within a preset value range.

[0155] According to the random value and the selection probability of the beam type, a matching probability interval corresponding to the random value is determined.

[0156] According to the matching probability interval, an ant selection object is determined in the beam type.

[0157] The ant selection object is assigned to an ant, and the number of ants corresponding to the beam type is counted.

[0158] In an embodiment, the beam arrangement module 20 is further configured to obtain a main beam distance corresponding to a main control point in the main beam arrangement strategy.

[0159] According to the main beam distance corresponding to the main control point, a total main beam distance corresponding to the main beam arrangement strategy is determined.

[0160] According to the information volatility factor, the pheromone matrix is updated.

[0161] According to the single pheromone increment and the total main beam distance of the main beam arrangement strategy, a single pheromone concentration increment is determined, and according to the single pheromone concentration increment, a pheromone concentration corresponding to a main beam in the main beam arrangement strategy is determined.

[0162] In an embodiment, the beam arrangement module 20 is further configured to, when the number of iterations is less than a preset iteration threshold, return to perform beam arrangement processing on the main control point based on the ant colony algorithm, generate a main beam arrangement strategy, and the ant colony algorithm uses the ant colony parameters.

[0163] In an embodiment, the obtaining module 10 is further configured to perform information modeling on the beam type library, the control point, and the beam arrangement route, wherein the beam arrangement route includes a beam arrangement height and a beam arrangement position.

[0164] It should be understood that the above is only an example and does not constitute any limitation on the technical solutions of the present application. In specific applications, those skilled in the art can set it according to the needs, and the present application does not limit it.

[0165] It should be noted that the above-described workflow is only illustrative and does not limit the scope of protection of the present application. In actual application, those skilled in the art can select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited here.

[0166] In addition, technical details not described in detail in the present embodiment can be found in the bridge span main girder automatic selection design method based on the ant colony algorithm provided by any embodiment of the present application, which will not be described here.

[0167] Furthermore, it is to be understood that the terms "including", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or system that includes the element.

[0168] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0169] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and the necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk), and includes a number of instructions for making a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) execute the methods described in the various embodiments of the present application.

[0170] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields based on the content of the present application specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. A bridge span main girder automatic selection design method based on an ant colony algorithm, characterized in that, The bridge span arrangement main girder automatic selection design method based on the ant colony algorithm comprises: Obtaining control points and control point information corresponding to the control points, screening the control points according to the control point information, determining main control points, and determining ant colony parameters according to the main control points; Performing beam arrangement processing on the main control points based on an ant colony algorithm to generate a main girder arrangement strategy, wherein the ant colony algorithm uses the ant colony parameters; Obtaining a main girder total distance corresponding to the main girder arrangement strategy, screening a target main girder arrangement strategy from the main girder arrangement strategy according to the main girder total distance, adding the target main girder arrangement strategy to a main girder arrangement result list, and updating an iteration number; When the iteration number is greater than or equal to a preset iteration threshold, sorting and deduplicating the main girder arrangement result list to obtain a main girder selection design strategy; The control point information comprises a control point length, a control point angle, a control point height, a gray region coefficient, a safety distance threshold, a beam arrangement priority, and a control point attribute, and the screening of the control points according to the control point information to determine main control points comprises: Determining whether the control points meet main control point attributes according to the control point attributes of the control points; Determining whether the control points meet a preset length condition according to the control point lengths of the control points; Determining whether the control points meet a preset priority condition according to the beam arrangement priorities of the control points; When the control points meet the main control point attributes, the preset length condition, and the preset priority condition, determining the control points as main control points to generate a main control point list; The beam arrangement processing on the main control points based on the ant colony algorithm to generate the main girder arrangement strategy comprises: Determining a beam type that can be arranged corresponding to the main control points in a beam type library according to a preset beam arrangement condition, and determining a selection probability of the beam type that can be arranged; Assigning ants to the beam type that can be arranged according to the selection probability of the beam type that can be arranged; Determining a target beam type of the main control points according to a preset number threshold and an ant number of the beam type that can be arranged, and arranging the target beam type to generate the main girder arrangement strategy.

2. The method of claim 1, wherein, The ant colony parameters comprise a total number of ants, an information heuristic factor, an expected heuristic factor, an information volatility factor, a single pheromone increment, and a pheromone matrix, and the determination of the ant colony parameters according to the main control points comprises: Determining the total number of ants according to a number of the main control points and a preset number multiple; Determining the information heuristic factor, the expected heuristic factor, the information volatility factor, and the single pheromone increment according to a preset requirement; Determining a row and column number of the pheromone matrix according to the number of the main control points and a number of beam types in the beam type library; Obtaining a greedy pheromone increment, and initializing the pheromone matrix according to the greedy pheromone increment.

3. The method of claim 1, wherein, The determination of the beam type that can be arranged corresponding to each main control point in the beam type library according to the preset beam arrangement condition comprises: Determining an unstep distance corresponding to the main control points according to a control point length, a control point angle, a safety distance threshold, and a gray region coefficient of the main control points; comparing the main span length of the beam type in the beam type library with the non-treadable distance, comparing the beam type priority of the beam type with the beam arrangement priority, and comparing the arrangement state of the beam type with a preset arrangement state; when the main span length of the beam type corresponding to the main control point is greater than the non-treadable distance, the beam type priority of the beam type corresponding to the main control point is greater than the beam arrangement priority, and the arrangement state of the beam type corresponding to the main control point meets the preset arrangement state, determining that the beam type is a beam arrangement type of the main control point.

4. The method of claim 2, wherein, The determining of the selection probability of each beam arrangement type comprises: obtaining line mileage data, beam type priority of the beam arrangement type, and beam type length of the beam arrangement type; determining beam type distance of the beam arrangement type according to the line mileage data, the beam type priority, the beam type length, and a corresponding relationship between the line mileage data, the beam type priority, the beam type length, and beam type distance; obtaining pheromone concentration corresponding to the beam arrangement type in the pheromone matrix; determining the selection probability of the beam arrangement type according to the beam type distance, the pheromone concentration of the beam arrangement type, the beam type distance of the beam arrangement type, and a corresponding relationship between the beam type distance, the pheromone concentration, and the selection probability.

5. The method of claim 1, wherein, The assigning of the ant to the beam arrangement type according to the selection probability of the beam arrangement type comprises: generating a random value in a preset numerical interval; determining a matching probability interval corresponding to the random value according to the random value and the selection probability of the beam arrangement type; determining an ant selection object in the beam arrangement type according to the matching probability interval; assigning an ant to the ant selection object and counting the number of ants corresponding to the beam arrangement type.

6. The method of claim 2, wherein, The obtaining of the total beam distance corresponding to the main beam arrangement strategy comprises: obtaining main beam distance corresponding to the main control point in the main beam arrangement strategy; determining the total beam distance corresponding to the main beam arrangement strategy according to the main beam distance corresponding to the main control point; updating the pheromone matrix according to the information volatilization factor; determining a single pheromone concentration increment according to the single pheromone increment and the total beam distance of the main beam arrangement strategy, and determining the pheromone concentration corresponding to the main beam in the main beam arrangement strategy according to the single pheromone concentration increment.

7. The method of claim 2, wherein, Before the determining of the selection probability of each beam arrangement type, the obtaining of the control point and control point information corresponding to the control point, the screening of the control point according to the control point information, the determining of the main control point, and the determining of the ant colony parameter according to the main control point, the method further comprises: information modeling of the beam type library, the control point, and the beam arrangement line, wherein the beam arrangement line comprises beam arrangement height and beam arrangement position.

8. The method according to any one of claims 1 to 7, characterized in that, After the adding of the target main beam arrangement strategy into the main beam arrangement result list and the updating of the iteration number, the method further comprises: when the iteration number is less than a preset iteration threshold, returning to perform beam arrangement processing on the main control point based on the ant colony algorithm to generate a main beam arrangement strategy, wherein the ant colony algorithm adopts the ant colony parameter.

Images