Sprayed concrete construction optimization method and system combined with space positioning

Abstract

The invention discloses a sprayed concrete construction optimization method and system in combination with space positioning, and relates to the technical field related to sprayed concrete.The method comprises the steps that a tunnel section to be constructed is divided in combination with the model of a wet spraying machine, and a plurality of construction areas are determined; surface point cloud data are collected for three-dimensional modeling, and spraying operation sequence optimization is carried out with the purpose of minimizing the idle time length of equipment and the repeated spraying coverage rate; with the minimum concrete springback value as the target, according to the optimal block spraying sequence, optimal analysis is conducted on the spraying construction parameters of all the construction areas in sequence; and according to the optimal construction parameter sequence, spraying operation of the to-be-constructed tunnel section is executed. The technical problems that in the prior art, the springback amount is large, compactness is poor, durability is poor, the using amount of rubber materials is high, and material adaptability is unstable, so that the shotcrete construction quality and efficiency are poor, and material consumption is large are solved, and the technical effects of reducing material consumption and improving the shotcrete construction quality and efficiency are achieved.

Description

Technical Field

[0001] This application relates to the technical field of shotcrete, and specifically relates to an optimization method and system for shotcrete construction combined with spatial positioning. Background Art

[0002] In recent years, shotcrete technology has been widely used in tunnel construction projects, mainly due to its simple process, high construction efficiency, unique effect, and economic cost. However, although shotcrete technology has significant advantages in some aspects, compared with traditional formwork concrete materials and technologies, its development level still fails to fully meet the requirements of modern engineering technology development. Especially during the construction process of shotcrete, the key material for shotcrete preparation - the accelerating agent for shotcrete has developed rapidly, and the technology of alkali-free liquid accelerating agent has gradually matured, providing a material basis for the high-performance development of shotcrete. At the same time, the shotcrete forming process has gradually transformed from dry spraying to wet spraying, and the construction equipment is gradually achieving localization and intelligent control, and the working environment and project quality have also been significantly improved. However, due to the inherent characteristics of shotcrete construction, there are still problems such as large rebound, poor density, high binder consumption, and unstable material adaptability, which result in poor durability performance of shotcrete and affect the overall construction quality and efficiency of shotcrete.

[0003] Therefore, in the current related technologies, there are technical problems such as large rebound, poor density, poor durability, high binder consumption, and unstable material adaptability, which in turn lead to poor construction quality and efficiency of shotcrete and large material consumption. Summary of the Invention

[0004] This application provides an optimization method and system for shotcrete construction combined with spatial positioning, solving the technical problems in the prior art such as large rebound, poor density, poor durability, high binder consumption, and unstable material adaptability, which in turn lead to poor construction quality and efficiency of shotcrete and large material consumption, and achieving the technical effects of reducing material consumption and improving the construction quality and efficiency of shotcrete.

[0005] The present application provides an optimized shotcrete construction method combined with spatial positioning. The method includes: dividing the tunnel section to be constructed according to the type of wet shotcreting machine to determine multiple construction areas; collecting the surface point cloud data of the multiple construction areas for three-dimensional modeling, and using the three-dimensional models of the multiple construction areas, aiming at minimizing the equipment idle time and the repeated spraying coverage rate, optimizing the spraying operation sequence to obtain the optimal block spraying sequence; combining the current concrete mix ratio, surrounding rock characteristic information, construction environment information and the three-dimensional models of the multiple construction areas, aiming at minimizing the concrete rebound volume, and according to the optimal block spraying sequence, successively optimizing and analyzing the spraying construction parameters of each construction area to obtain the optimal construction parameter sequence; and performing the shotcrete operation on the tunnel section to be constructed according to the optimal construction parameter sequence.

[0006] In a possible implementation manner, the optimized shotcrete construction method combined with spatial positioning further performs the following processing: obtaining the types of several available wet shotcreting machines and their maximum spraying coverage ranges; based on the maximum spraying coverage ranges, performing collaborative simulation operation analysis on the tunnel section to be constructed to determine multiple area division schemes; evaluating the division quality of the multiple area division schemes, and selecting the area division scheme corresponding to the maximum division quality coefficient to obtain multiple construction areas.

[0007] In a possible implementation manner, the optimized shotcrete construction method combined with spatial positioning further performs the following processing: obtaining multiple area division schemes, where each area division scheme is marked with the number of areas and the number of operations of several wet shotcreting machines; calculating the variances of the number of operations of several wet shotcreting machines for each area division scheme respectively to determine multiple operation number variances; calculating multiple division quality coefficients through weighted calculation based on the multiple area numbers and the multiple operation number variances, where the division quality coefficient is negatively correlated with the area number and the operation number variance.

[0008] In a possible implementation manner, the optimized shotcrete construction method combined with spatial positioning further performs the following processing: based on the multiple construction areas, enumerating the spraying operation sequences in combination with several wet shotcreting machines to generate multiple initial block spraying sequences; randomly selecting a first initial block spraying sequence, in the spraying operation simulation space, using the three-dimensional models of the multiple construction areas, and combining several wet shotcreting machines to perform spraying operation simulation, counting the simulated idle time of several wet shotcreting machines and the repeated spraying coverage area of the multiple construction areas, and calculating to obtain the first equipment idle time and the first repeated spraying coverage rate; calculating the first sequence fitness according to the first equipment idle time and the first repeated spraying coverage rate; successively analyzing the multiple sequence fitnesses of the multiple initial block spraying sequences, and selecting the initial block spraying sequence with the maximum sequence fitness as the optimal block spraying sequence.

[0009] In a possible implementation manner, the shotcrete construction optimization method combining spatial positioning further performs the following processing: configuring a first weight and a second weight according to the construction operation requirements, where the first weight is the operation efficiency weight and the second weight is the material loss weight; according to the first weight and the second weight, performing weighted calculation on the reciprocal of the idle time of the first device and the reciprocal of the first repeated spraying coverage rate to obtain the first order fitness.

[0010] In a possible implementation manner, the shotcrete construction optimization method combining spatial positioning further performs the following processing: selecting a first spraying operation area and a first wet shotcreting machine in the optimal block spraying sequence, and obtaining first spraying construction parameters of the first wet shotcreting machine; based on the three-dimensional model of the first construction area, the current concrete mix ratio, the surrounding rock characteristic information, and the construction environment information, constructing a first concrete rebound prediction plug-in in combination with a feedforward neural network; using the first concrete rebound prediction plug-in, aiming at minimizing the concrete rebound amount, performing optimization of the first spraying construction parameters, obtaining the first optimal spraying construction parameters, and adding them to the optimal construction parameter sequence in order.

[0011] In a possible implementation manner, the shotcrete construction optimization method combining spatial positioning further performs the following processing: obtaining the first surface feature distribution of the three-dimensional model of the first construction area, and respectively expanding the first surface feature distribution, the current concrete mix ratio, the surrounding rock characteristic information, and the construction environment information according to a predetermined tolerance interval to obtain similarity comparison conditions; taking the similarity comparison conditions as conditional constraints and the first wet shotcreting machine as equipment constraints, retrieving a sample spraying construction parameter set based on big data, and counting the concrete rebound amounts after different sample spraying construction parameter operations to construct a sample concrete rebound amount set; training a feedforward neural network using the sample spraying construction parameter set and the sample concrete rebound amount set until the model converges to obtain the first concrete rebound prediction plug-in.

[0012] In a possible implementation manner, the shotcrete construction optimization method combining spatial positioning further performs the following processing: obtaining a first spraying construction parameter threshold of the first wet shotcreting machine, where the first spraying construction parameters include the wet shotcreting machine wind speed, spraying distance, spraying angle, and layer thickness; randomly selecting a plurality of initial construction parameters within the first spraying construction parameter threshold; using the first concrete rebound prediction plug-in to respectively predict the plurality of initial construction parameters and output a plurality of predicted rebound amounts; based on the plurality of predicted rebound amounts, performing optimization of the first spraying construction parameters to obtain the first optimal spraying construction parameters.

[0013] In a possible implementation manner, the shotcrete construction optimization method combining spatial positioning further performs the following processing: taking the initial construction parameters as the initial solution, sorting the multiple initial construction parameters in ascending order according to the predicted rebound amount to generate an initial solution sequence; setting the first K solutions of the initial solution sequence as excellent solutions and the last J solutions as inferior solutions, where J is M times of K and M is an integer greater than 2; using the K excellent solutions to perform equivalent clustering on the J inferior solutions to obtain K solution thresholds, and within the K solution thresholds, adjusting the inferior solutions in the direction of the excellent solutions according to the optimization step size to obtain K updated solution thresholds, where if the adjusted inferior solutions do not meet the first shotcrete construction parameter threshold, this adjustment is not performed; using the first concrete rebound prediction plugin to predict the rebound amount of the inferior solutions of the K updated solution thresholds respectively, and identifying the K updated solution thresholds, if the predicted rebound amount of the updated inferior solutions within the same updated solution threshold is less than the predicted rebound amount of the excellent solutions, then using the inferior solutions to replace the excellent solutions; performing iterative optimization until a predetermined number of convergence times is reached, outputting the K current solution thresholds, and selecting the solution with the current minimum predicted rebound amount as the first optimal shotcrete construction parameter.

[0014] The present application further provides a shotcrete construction optimization system combining spatial positioning, including: a construction area determination module, configured to divide the tunnel section to be constructed in combination with the wet shotcrete machine model to determine multiple construction areas; an optimal block shotcrete sequence acquisition module, configured to collect the surface point cloud data of the multiple construction areas for three-dimensional modeling, and use the three-dimensional models of the multiple construction areas to optimize the shotcrete operation sequence with the goal of minimizing the equipment idle time and the repeated shotcrete coverage rate to obtain the optimal block shotcrete sequence; an optimal construction parameter sequence acquisition module, configured to combine the current concrete mix ratio, surrounding rock characteristic information, construction environment information and the three-dimensional models of the multiple construction areas, and optimize and analyze the shotcrete construction parameters of each construction area in turn according to the optimal block shotcrete sequence with the goal of minimizing the concrete rebound amount to obtain the optimal construction parameter sequence; a tunnel shotcrete operation execution module, configured to execute the shotcrete operation of the tunnel section to be constructed according to the optimal construction parameter sequence.

[0015] The optimized shotcrete construction method and system combining spatial positioning proposed in this application divides the tunnel section to be constructed according to the shotcrete machine model, and determines multiple construction areas; collects surface point cloud data for 3D modeling, and optimizes the shotcreting operation sequence with the goal of minimizing equipment idle time and repeated shotcrete coverage rate; with the goal of minimizing the concrete rebound volume, according to the optimal block-by-block shotcreting sequence, optimizes and analyzes the shotcrete construction parameters of each construction area in turn; performs the shotcreting operation on the tunnel section to be constructed according to the optimal construction parameter sequence. It solves the technical problems of large rebound volume, poor compactness, poor durability, high binder consumption and unstable material adaptability existing in the prior art, which in turn lead to poor shotcrete construction quality and efficiency and large material consumption, and achieves the technical effects of reducing material consumption and improving shotcrete construction quality and efficiency. Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the application. It should be understood that the operations before or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0017] Figure 1 Schematic flow chart of the optimized shotcrete construction method combining spatial positioning provided by the embodiment of the present application.

[0018] Figure 2 Schematic structural diagram of the optimized shotcrete construction system combining spatial positioning provided by the embodiment of the present application.

[0019] Explanation of reference numerals in the drawings: Construction area determination module 10, Optimal block-by-block shotcreting sequence acquisition module 20, Optimal construction parameter sequence acquisition module 30, Tunnel shotcreting operation execution module 40. Detailed Embodiments

[0020] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the detailed embodiments of this application.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the following will further describe this application in detail with reference to the drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0022] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first" and "second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0023] The embodiments of this application provide an optimized shotcrete construction method combined with spatial positioning, as Figure 1 shown. The method includes: Step S100, divide the tunnel section to be constructed in combination with the wet shotcrete machine model to determine multiple construction areas.

[0024] Preferably, according to the model parameters of the wet shotcrete machine and the specific situation of the tunnel to be constructed, the entire tunnel construction section is reasonably divided into several relatively independent construction units. Among them, the wet shotcrete machine is a mechanical device used for shotcrete construction, which is used to transport the wet concrete mixed in a certain proportion to the nozzle through a concrete pump, add a quick-setting agent at the nozzle, and use compressed air to spray the concrete at high speed onto the sprayed surface to form a concrete support layer, effectively reducing the dust and rebound amount during the spraying process, and improving the density and strength of the concrete. Specifically, according to the model performance parameters of the wet shotcrete machine (including spraying capacity, spraying distance and height, working efficiency, etc.), and considering the characteristics of the tunnel project (such as tunnel shape and size, geological conditions, construction difficulty, etc.) at the same time, the tunnel section to be constructed is divided into multiple construction areas. For example, according to the spraying capacity of the wet shotcrete machine, the concrete spraying volume of each construction area is matched with the spraying capacity of the wet shotcrete machine within a certain period of time to avoid the situation of excessive fatigue operation or excessive spraying capacity of the wet shotcrete machine.

[0025] Preferably, for large-section tunnels or tunnels with a relatively high height, a wet shotcreting machine with a larger spraying distance and height is selected, and the tunnel is divided into different construction areas along the height and width directions according to its performance to ensure that the wet shotcreting machine can effectively cover the construction surface; for circular tunnels, the circumferential direction of the tunnel can be divided into several fan-shaped construction areas according to the spraying range of the wet shotcreting machine; for horseshoe-shaped tunnels, it can be divided into different construction areas such as the crown and side walls to facilitate the targeted spraying operation of the wet shotcreting machine; in sections with complex geological conditions and broken rocks, the construction area may need to be divided smaller to facilitate the control of the thickness and quality of the shotcrete and ensure construction safety; while in sections with better geological conditions, the construction area can be appropriately increased; the construction difficulties and risk levels in different parts of the tunnel are different, such as the intersection and variable cross-section parts of the tunnel, where the construction difficulty is relatively large and the risk is relatively high. By comprehensively considering the wet shotcreting machine model and various factors of the tunnel project, scientifically and reasonably dividing the tunnel section, and determining multiple construction areas, the shotcrete construction can be carried out more efficiently and orderly, improving the construction quality and efficiency and reducing the construction cost and risk.

[0026] Further, step S100 further includes step S110 of obtaining several wet shotcreting machine models of several operable wet shotcreting machines and several maximum spraying coverage ranges; step S120 of performing collaborative simulation operation analysis on the to-be-constructed tunnel section based on the several maximum spraying coverage ranges to determine multiple area division schemes; and step S130 of performing division quality evaluation on the multiple area division schemes and selecting the area division scheme corresponding to the maximum division quality coefficient to obtain multiple construction areas.

[0027] Preferably, obtain the specific model information of all shotcrete machines used in tunnel construction. Since shotcrete machines of different models have different maximum spraying coverage ranges due to differences in design, performance, etc., obtain the maximum spraying coverage range of each shotcrete machine. Here, the maximum spraying coverage range refers to the maximum spatial range within which the shotcrete machine can effectively spray concrete under normal working conditions, usually measured by parameters such as horizontal distance and vertical distance. For example, for some models of shotcrete machines, the maximum horizontal spraying distance can reach 15 meters, and the maximum vertical spraying height can reach 10 meters. Then, using the determined maximum spraying coverage range data of the shotcrete machines, combined with the specific shape, size, geological conditions, etc. of the tunnel section to be constructed, conduct collaborative simulation operation analysis on the computer simulation platform, that is, consider how multiple shotcrete machines cooperate to ensure that the entire tunnel section can be effectively sprayed with concrete, while improving construction efficiency as much as possible, reducing equipment idle time and repetitive operations, etc. Through different combination and planning methods, multiple different regional division schemes (i.e., determine multiple regional division schemes) will be obtained. For example, divide the tunnel along the length direction into several sections according to the horizontal spraying distance of the shotcrete machine, or divide the tunnel into layers along the height direction in combination with the height of the tunnel and the vertical spraying height of the shotcrete machine, etc.

[0028] Preferably, for the determined multiple regional division schemes, comprehensively consider multiple factors such as equipment utilization rate, construction efficiency, uniformity of concrete spraying, and whether there are spraying blind spots, evaluate the division quality of the multiple regional division schemes, calculate a division quality coefficient for each scheme, and the higher this coefficient, the better the scheme. Finally, select the scheme with the largest division quality coefficient from all the schemes, divide the tunnel section to be constructed into multiple relatively independent construction areas according to this scheme, ensure that each area is within the effective spraying coverage range of the shotcrete machine, and the connection between each area is reasonable, facilitating the subsequent spraying operation to proceed smoothly, thereby improving the quality and efficiency of tunnel shotcrete construction.

[0029] Furthermore, step S130 further includes step S131 of obtaining multiple regional division schemes, where each regional division scheme is marked with the number of regions and several shotcrete machine operation times; step S132 of calculating the variance of the several shotcrete machine operation times for each regional division scheme respectively to determine multiple operation time variances; step S133 of calculating multiple division quality coefficients through weighted calculation based on the multiple numbers of regions and multiple operation time variances, where the division quality coefficient is negatively correlated with the number of regions and the operation time variance.

[0030] Preferably, for each regional division plan, the number of regions into which the tunnel segment is divided is clearly recorded. For example, Plan A may divide the tunnel into 5 regions, and Plan B may divide it into 8 regions, etc. At the same time, under this division plan, the number of operations required for each wet shotcreting machine will also be determined. Different regional divisions will result in different task assignments for the wet shotcreting machines, so the number of operations of the wet shotcreting machines in each plan will also vary. For example, in a certain plan, a wet shotcreting machine may need to operate in 3 regions, and the time and workload of each operation will also vary according to factors such as the size and shape of the region. Then, the variance of the number of operations of several wet shotcreting machines for each regional division plan is calculated separately to understand the distribution uniformity of the number of operations of the wet shotcreting machines under this regional division plan. Among them, variance is a statistic used to measure the degree of dispersion of a set of data. Taking Plan A as an example, if there are 3 wet shotcreting machines with the number of operations being 5 times, 6 times, and 7 times respectively, the variance of this set of data can be obtained through the variance calculation formula. The larger the variance, the greater the difference in the number of operations between the wet shotcreting machines, indicating that some wet shotcreting machines may have an overly heavy workload while some are relatively idle; the smaller the variance, the more uniform the distribution of the number of operations, and the workload of each wet shotcreting machine is relatively balanced.

[0031] Preferably, weighted calculations are performed on multiple regional quantities and multiple variances of the number of operations to obtain corresponding multiple division quality coefficients. Specifically, different weights are assigned to the regional quantity and the variance of the number of operations, and then weighted calculations are carried out. Generally speaking, the more regions there are, the greater the difficulty of construction organization and coordination may be, and the relatively smaller the area of each region may lead to more time and resource waste when the wet shotcreting machine switches regions, so the division quality coefficient will be lower; the larger the variance of the number of operations, the more unbalanced the task assignment of the wet shotcreting machines, which affects the construction efficiency and overall quality, and will also reduce the division quality coefficient. For example, if Plan B has a larger number of regions and a larger variance of the number of operations, the calculated division quality coefficient will be relatively small, indicating that the quality of this plan is relatively poor; while if Plan A has a moderate number of regions and a small variance of the number of operations, its division quality coefficient will be larger, indicating that Plan A is better. Through scientific quantification methods, factors such as the rationality of regional division and the balance of wet shotcreting machine operations are comprehensively considered to evaluate the quality of different regional division plans in order to select the optimal plan and improve the overall efficiency and quality of tunnel shotcrete construction.

[0032] Step S200, collect the surface point cloud data of the multiple construction regions for 3D modeling, and use the 3D models of the multiple construction regions to optimize the spraying operation sequence with the goal of minimizing the equipment idle time and the repeated spraying coverage rate, and obtain the optimal block spraying sequence.

[0033] Preferably, surface point cloud data of multiple construction areas are obtained through specific measuring devices (such as laser scanners, etc.), that is, a series of discrete points, which contain the spatial coordinate information of the object surface. Specifically, in tunnel construction, for the multiple construction areas that have been divided, devices such as laser scanners are used to scan the surface of each area, collect the three-dimensional coordinates (x, y, z coordinates) of a large number of points on the surface, as well as possible information such as color and reflection intensity, so as to obtain the surface point cloud data of each construction area. And using the collected point cloud data, through specialized three-dimensional modeling software (such as AutoCAD, Revit, 3ds Max, etc.), these discrete points are processed and fitted to construct a three-dimensional model of each construction area, intuitively displaying information such as the shape, size, and spatial position of the construction area.

[0034] Preferably, aiming to minimize the equipment idle time and the repeated spraying coverage rate, in the tunnel shotcrete construction, the use cost of equipment such as wet shotcreters is relatively high. Excessive equipment idle time will lead to low construction efficiency and increased construction costs. It is hoped that by reasonably arranging the spraying operation sequence, the equipment can work continuously as much as possible, reducing the waiting and idle time of the equipment; repeated spraying will cause waste of concrete materials and may affect the quality and structural performance of the concrete. It is necessary to avoid excessive repeated spraying in the same area as much as possible, so that the concrete spraying coverage of each area reaches the best effect without unnecessary repeated coverage; then based on the two goals, using the three-dimensional models of multiple construction areas that have been constructed, combined with the performance parameters of equipment such as wet shotcreters (such as spraying range, spraying speed, etc.) and construction process requirements (such as concrete initial setting time, etc.), an optimization algorithm (such as genetic algorithm, simulated annealing algorithm, etc.) is used to analyze and evaluate all possible spraying operation sequences, calculate indicators such as the equipment idle time and the repeated spraying coverage rate under each operation sequence, and through continuous comparison and screening, find the spraying operation sequence that can minimize the equipment idle time and the repeated spraying coverage rate; the finally determined spraying operation sequence that can simultaneously meet the goals of minimizing the equipment idle time and the repeated spraying coverage rate is the optimal block spraying sequence. Spraying operations are carried out according to this optimal sequence, which can improve construction efficiency, reduce costs, ensure construction quality, and realize the optimization of tunnel shotcrete construction.

[0035] Further, step S200 further includes step S210, enumerating the spraying operation sequence based on the multiple construction areas and combining several shotcreters to generate multiple initial block spraying sequences; step S220, randomly selecting a first initial block spraying sequence, in the spraying operation simulation space, using the three-dimensional models of the multiple construction areas, combining several shotcreters to conduct spraying operation simulation, counting the simulated idle time of several shotcreters and the repeated spraying coverage area of the multiple construction areas, and calculating to obtain the first equipment idle time and the first repeated spraying coverage rate; step S230, calculating a first sequence fitness according to the first equipment idle time and the first repeated spraying coverage rate; step S240, sequentially analyzing the multiple sequence fitnesses of the multiple initial block spraying sequences, and selecting the initial block spraying sequence with the maximum sequence fitness as the optimal block spraying sequence.

[0036] Preferably, based on the multiple construction areas that have been divided, considering several shotcreters participating in the construction, by enumerating all possible combinations of spraying operation sequences, multiple different initial block spraying sequences are obtained. For example, assuming there are 3 construction areas (A, B, C) and 2 shotcreters, the possible spraying sequences are A→B→C, A→C→B, B→A→C, etc. These different sequence combinations are the initial block spraying sequences; then randomly select one from the generated multiple initial block spraying sequences as the first sequence to be analyzed (i.e., the first initial block spraying sequence), in a simulated spraying operation space (which can be a virtual environment created by computer software), according to the three-dimensional models of the multiple construction areas established before (including information such as the shape, size, and spatial position of the areas), and at the same time considering the performance parameters of several shotcreters participating in the construction (such as spraying range, spraying speed, etc.), simulate the process of spraying operation according to this initial sequence.

[0037] Preferably, during the simulated spraying operation, record the time when each wet spraying machine is in an idle state (i.e., not performing spraying operations), and add up the idle times of all wet spraying machines to obtain the total simulated idle duration, which is the first equipment idle duration. At the same time, count the area of repeated spraying in each construction area when spraying operations are carried out in this order, calculate the proportion of the repeated spraying area to the total area of the area, obtain the repeated spraying coverage areas of multiple construction areas, and then calculate the overall first repeated spraying coverage rate. Calculate the weighted sum of the first equipment idle duration and the first repeated spraying coverage rate to obtain the first order fitness of the initial block spraying order. Among them, the shorter the equipment idle duration and the lower the repeated spraying coverage rate, the higher the order fitness, which means higher construction efficiency and less material waste. Simulate, count, and calculate for each initial block spraying order to obtain their respective corresponding order fitnesses. Then, compare the magnitudes of these order fitnesses, find the initial block spraying order corresponding to the order fitness with the largest value, and determine this order as the optimal block spraying order. In subsequent actual tunnel shotcrete construction, operate according to this optimal block spraying order to expect to achieve the best construction effect, improve construction efficiency and quality, and reduce costs.

[0038] Further, step S230 further includes step S231 of configuring a first weight and a second weight according to the construction operation requirements, where the first weight is the operation efficiency weight and the second weight is the material loss weight; step S232 of calculating the weighted sum of the reciprocals of the first equipment idle duration and the first repeated spraying coverage rate according to the first weight and the second weight to obtain the first order fitness.

[0039] Preferably, in the construction of tunnel shotcrete, the construction operation requirements may have different emphases. For example, sometimes more attention is paid to construction efficiency, and sometimes more attention is paid to material loss. According to the actual construction requirements, weight values are set for the two aspects of operation efficiency and material loss respectively. Among them, the operation efficiency weight (the first weight) is used to measure the importance of the operation efficiency factor when evaluating the spraying sequence; the material loss weight (the second weight) measures the importance of the material loss factor; the value ranges of these two weight values are usually between 0 and 1, and their sum is 1. For example, if the current construction particularly emphasizes operation efficiency, the first weight may be set to 0.7 and the second weight to 0.3; take the reciprocal of the idle time of the first device. The shorter the idle time of the device, the larger its reciprocal, which means the higher the operation efficiency; similarly, take the reciprocal of the first repeated spraying coverage rate. The lower the repeated spraying coverage rate, the larger its reciprocal, indicating less material loss; according to the set first weight (operation efficiency weight) and second weight (material loss weight), these two reciprocals are weighted and calculated to obtain the corresponding first-order fitness. The larger this value is, the better the comprehensive performance of the spraying sequence in meeting operation efficiency and controlling material loss.

[0040] Step S300: Combining the current concrete mix ratio, surrounding rock characteristic information, construction environment information, and the three-dimensional models of the multiple construction areas, with the goal of minimizing the concrete rebound amount, and in accordance with the optimal block spraying sequence, the spraying construction parameters of each construction area are optimized and analyzed in sequence to obtain an optimal construction parameter sequence.

[0041] Preferably, different concrete mix ratios will affect its properties such as fluidity and adhesiveness, and thus affect the rebound amount during spraying. For example, when the proportions of components such as cement, aggregate, and admixture are different, the consistency and setting speed of the concrete will vary. A reasonable mix ratio can make the concrete better adhere to the surface of the surrounding rock and reduce rebound; the properties of the surrounding rock, such as hardness, surface roughness, and fracture development, have a significant impact on the concrete rebound amount. On a hard and smooth surrounding rock surface, it is difficult for the concrete to adhere, and the rebound amount may be large, while on a surrounding rock with a certain roughness or fractures, the concrete is more likely to be embedded, and the rebound amount is relatively small; the construction environment information includes environmental factors such as the temperature, humidity, and wind speed of the construction site. In a high-temperature and dry environment, the water in the concrete may evaporate too quickly, affecting its adhesion performance and resulting in an increase in the rebound amount, while strong wind weather may blow away the sprayed concrete, also increasing the rebound amount; the three-dimensional models of the multiple construction areas can visually display the geometric information such as the shape, size, and spatial position of each construction area. Different area shapes and spatial layouts will affect construction parameters such as spraying angles and distances, and thus affect the rebound amount.

[0042] Preferably, aiming at minimizing the rebound of concrete, where concrete rebound is a common problem in shotcreting construction. Excessive rebound not only causes material waste and increases construction costs, but may also affect the quality and structural performance of shotcrete. Then, according to the optimal block-by-block shotcreting sequence, the shotcreting construction parameters of each construction area are optimized and analyzed in turn to ensure the orderliness and efficiency of the construction process. Specifically, the shotcreting construction parameters mainly include shotcreting pressure, shotcreting distance, shotcreting angle, shotcreting speed, etc. These parameters are interrelated and have a direct impact on the concrete rebound. For example, excessive shotcreting pressure may cause the concrete to rebound after impacting the surrounding rock surface, increasing the rebound; too far a shotcreting distance will cause the concrete to disperse during flight, reducing its adhesion and also increasing the rebound. For each construction area, combining the above-mentioned multi-faceted information, analyze the concrete rebound under different combinations of construction parameters, and by continuously adjusting the parameter values, find the parameter combination that minimizes the rebound; then obtain the optimal construction parameters corresponding to each area, and arrange these parameters in the shotcreting sequence to form an optimal construction parameter sequence. Construction personnel can use this sequence to adopt the corresponding optimal parameters for shotcreting operations in different construction areas, thereby effectively reducing the concrete rebound and improving the construction quality and efficiency.

[0043] Further, step S300 further includes step S310, selecting a first shotcreting operation area and a first wet shotcreting machine in the optimal block-by-block shotcreting sequence, and obtaining the first shotcreting construction parameters of the first wet shotcreting machine; step S320, based on the three-dimensional model of the first construction area, the current concrete mix ratio, the surrounding rock characteristic information and the construction environment information, constructing a first concrete rebound prediction plug-in in combination with a feedforward neural network; step S330, using the first concrete rebound prediction plug-in, aiming at minimizing the concrete rebound, performing optimization of the first shotcreting construction parameters, and obtaining the first optimal shotcreting construction parameters, and adding them to the optimal construction parameter sequence in order.

[0044] Preferably, in the already determined optimal block-by-block shotcreting sequence, select the first area that needs to be shotcreted, and at the same time select a wet shotcreting machine for operation in this area, that is, the first wet shotcreting machine, and obtain the currently set shotcreting construction parameters of the first wet shotcreting machine, which may include shotcreting pressure, shotcreting speed, shotcreting angle, nozzle diameter, etc., directly affecting the effect and quality of shotcrete; use the three-dimensional model of the first construction area to understand the shape, size, spatial position, etc. of this area; the current concrete mix ratio determines the performance of the concrete, such as strength, fluidity, etc.; the surrounding rock characteristic information includes the type, hardness, stability, etc. of the surrounding rock, and different surrounding rock conditions have a great impact on the adhesion and rebound of shotcrete; construction environment information such as temperature, humidity, ventilation conditions, etc. will also affect the setting and rebound of concrete.

[0045] Preferably, the feedforward neural network is a unidirectional multi-layer neural network, including an input layer, a hidden layer, and an output layer. By passing the input data layer by layer, after weighted summation and activation function processing of neurons in each layer, the output result is finally obtained. Specifically, the three-dimensional model of the first construction area, concrete mix ratio, surrounding rock characteristic information, and construction environment information collected are used as the input of the feedforward neural network, and the concrete rebound amount is used as the output. The neural network is trained with a large amount of sample data to enable it to learn the complex mapping relationship between these input factors and the concrete rebound amount, thereby constructing the first concrete rebound prediction plugin. Then, the first concrete rebound prediction plugin is used, and the first spraying construction parameters of the first wet shotcreting machine are used as the input to predict the concrete rebound amount under these parameters. Through optimization algorithms (such as genetic algorithms, particle swarm algorithms, etc.), the first spraying construction parameters are continuously adjusted and input into the prediction plugin again to observe the change in the concrete rebound amount, so as to find a set of parameters that minimize the concrete rebound amount, that is, the first optimal spraying construction parameters. Finally, this set of first optimal spraying construction parameters is added to the optimal construction parameter sequence in order, so as to perform spraying operations on the entire tunnel section to be constructed according to this optimized parameter sequence in the future, thereby improving the construction quality and reducing material waste and costs.

[0046] Further, step S320 further includes step S321, obtaining the first surface feature distribution of the three-dimensional model of the first construction area, and respectively expanding the first surface feature distribution, the current concrete mix ratio, the surrounding rock characteristic information, and the construction environment information according to a predetermined tolerance interval to obtain similarity comparison conditions; step S322, using the similarity comparison conditions as conditional constraints and the first wet shotcreting machine as equipment constraints, retrieving a sample spraying construction parameter set based on big data, and counting the concrete rebound amounts after operations with different sample spraying construction parameters to construct a sample concrete rebound amount set; step S323, training the feedforward neural network with the sample spraying construction parameter set and the sample concrete rebound amount set until the model converges to obtain the first concrete rebound prediction plugin.

[0047] Preferably, by analyzing the 3D model of the first construction area, various characteristic information on the surface of this area is extracted, such as surface roughness, flatness, concavity and convexity, fissure distribution, etc., and this characteristic information is quantified and represented in a certain way to form the first surface characteristic distribution. A tolerance interval is preset in advance for appropriately expanding each item of information. Specifically, for the first surface characteristic distribution, based on its quantified value, it fluctuates within a certain range according to the tolerance interval to obtain possible surface characteristic distribution situations; for the current concrete mix ratio, the proportions of each component (such as cement, aggregate, admixture, etc.) are slightly adjusted within the tolerance interval; for the surrounding rock characteristic information, such as indexes of the hardness and stability of the surrounding rock, they are expanded within the tolerance interval; for the construction environment information, such as temperature, humidity, etc., they also change within the tolerance interval. Similarity comparison conditions are obtained through information expansion for subsequent retrieval of sample data similar to them.

[0048] Preferably, restricted by the similarity comparison conditions, construction cases similar to these conditions are searched in the big data platform or database. At the same time, with the first wet shotcreting machine as the equipment constraint, it is ensured that the equipment used in the retrieved sample cases is the same as or similar to the first wet shotcreting machine used in the current construction; the shotcreting construction parameters, such as shotcreting pressure, shotcreting speed, shotcreting angle, etc., are extracted from the retrieved sample cases to form a sample shotcreting construction parameter set; for each sample shotcreting construction parameter, the concrete rebound amount generated after actual operation is statistically analyzed, and these rebound amount data are collected to construct a sample concrete rebound amount set; then the sample shotcreting construction parameter set is used as the input data of the feedforward neural network, and the sample concrete rebound amount set is used as the corresponding output data to train the feedforward neural network. During the training process, the neural network will continuously adjust its own weights and thresholds according to the relationship between the input data and the output data to minimize the error between the predicted value and the actual value. As the training progresses, when the error of the model reaches an acceptable range, that is, when the model converges, the training process ends. At this time, the obtained feedforward neural network has the ability to predict the concrete rebound amount based on the input shotcreting construction parameters, and it is encapsulated into the first concrete rebound prediction plug-in for subsequent construction parameter optimization and concrete rebound amount prediction.

[0049] Further, step S330 further includes step S331 of obtaining the first shotcreting construction parameter threshold of the first wet shotcreting machine, where the first shotcreting construction parameters include the wet shotcreting machine wind speed, shotcreting distance, shotcreting angle, and layering thickness; step S332 of randomly selecting multiple initial construction parameters within the first shotcreting construction parameter threshold; step S333 of using the first concrete rebound prediction plug-in to respectively predict the multiple initial construction parameters and output multiple predicted rebound amounts; step S334 of performing optimization of the first shotcreting construction parameters based on the multiple predicted rebound amounts to obtain the first optimal shotcreting construction parameters.

[0050] Preferably, according to the performance of the wet shotcreting machine, the characteristics of the concrete materials, the construction specifications, and past construction experience, etc., determine the first spraying construction parameter threshold of the first wet shotcreting machine, that is, determine the wind speed, spraying distance, spraying angle, and layer thickness of the wet shotcreting machine, which limits the value range of the construction parameters and ensures the safety and quality controllability of the construction process; within the range specified by the first spraying construction parameter threshold, randomly select multiple different parameter combinations as the initial construction parameters to more comprehensively explore the influence of different parameter combinations on the concrete rebound amount and avoid missing possible optimal solutions; use the constructed first concrete rebound prediction plug-in, and input each initial construction parameter into the plug-in for calculation respectively, that is, according to the input construction parameters and the model trained inside it, predict the possible concrete rebound amount during spraying operation under this parameter, so as to obtain multiple predicted rebound amounts; finally, optimize the first spraying construction parameters based on multiple predicted rebound amounts, and find the first spraying construction parameters that minimize the rebound amount, that is, by comparing the predicted rebound amounts corresponding to different initial construction parameters, analyze the differences and laws between them, and finally determine a set of construction parameters that can make the concrete rebound amount reach the optimum (such as the minimum), that is, the first optimal spraying construction parameters, for actual spraying operation to achieve the purposes of reducing concrete rebound, improving construction quality and efficiency, etc.

[0051] Further, step S334 further includes step A: taking the initial construction parameters as the initial solution, sort the multiple initial construction parameters in ascending order of the predicted rebound amount to generate an initial solution sequence; step B: set the first K solutions of the initial solution sequence as the optimal solutions and the last J solutions as the inferior solutions, where J is M times of K and M is an integer greater than 2; step C: use the K optimal solutions to perform equivalent clustering on the J inferior solutions to obtain K solution thresholds, and within the K solution thresholds, adjust the inferior solutions in the direction of the optimal solutions according to the optimization step size, and obtain K updated solution thresholds, where if the adjusted inferior solutions do not meet the first spraying construction parameter threshold, this adjustment is not performed; step D: use the first concrete rebound prediction plug-in to predict the rebound amounts of the inferior solutions of the K updated solution thresholds respectively, and identify the K updated solution thresholds. If the predicted rebound amount of the updated inferior solutions within the same updated solution threshold is less than the predicted rebound amount of the optimal solution, use the inferior solution to replace the optimal solution; step E: perform iterative optimization until the predetermined convergence number is reached, output the K current solution thresholds, and select the solution with the current minimum predicted rebound amount as the first optimal spraying construction parameter.

[0052] Preferably, a plurality of randomly selected initial construction parameters are regarded as the initial solutions in the process of finding the optimal solution. According to the predicted rebound values obtained by the first concrete rebound prediction plug-in based on the initial construction parameters, the initial construction parameters are arranged in ascending order to form an initial solution sequence. The predicted rebound values corresponding to the initial construction parameters ranked in the front of the sequence are smaller, which means that the concrete rebound may be less under these parameters. Then, the first K solutions with smaller predicted rebound values are selected as the optimal solutions. These solutions are considered to be relatively better construction parameter combinations, while the last J solutions with larger predicted rebound values are set as the inferior solutions. It is stipulated that J is M times of K, where M, K, and J are all positive integers and M is greater than 2 to ensure that there are enough inferior solutions for adjustment and optimization, and at the same time, it reflects a proportional relationship between the optimal solutions and the inferior solutions in terms of quantity.

[0053] Preferably, K optimal solutions are used as the centers of clustering, and an equal-value clustering operation is performed on J inferior solutions. Specifically, through clustering, the inferior solutions are assigned to K different categories, and each category corresponds to a solution threshold. Within each solution threshold, according to the preset optimization step size, the inferior solutions are adjusted in the direction of the optimal solutions to make the inferior solutions closer to the optimal solutions, so as to possibly reduce the predicted rebound value. If the adjusted inferior solutions exceed the previously determined first shotcreting construction parameter thresholds (such as the value ranges of parameters such as the wet shotcreting machine wind speed and the spraying distance), this adjustment is not performed to ensure the rationality and safety of the construction parameters. Finally, K updated solution thresholds are obtained. For each inferior solution in the K updated solution thresholds, the first concrete rebound prediction plug-in is used again to predict its rebound value, and then the predicted rebound values of the updated inferior solutions and the corresponding optimal solutions within the same updated solution threshold are compared. If the predicted rebound value of the updated inferior solution is less than that of the optimal solution, it means that this inferior solution has become better after adjustment, and this inferior solution is used to replace the original optimal solution to continuously optimize the set of solutions. Multiple iterative optimizations are performed, and each iteration will further optimize the solutions. When the preset convergence number (i.e., the maximum number of iterations) is reached, the iteration is stopped. At this time, K current solution thresholds are output, and then the solution with the smallest predicted rebound value is selected from these solutions and determined as the first optimal shotcreting construction parameter, that is, the shotcreting construction parameter that can minimize the concrete rebound amount found in the current optimization process.

[0054] Step S400, perform the shotcreting operation on the to-be-constructed tunnel section according to the optimal construction parameter sequence.

[0055] Preferably, during the construction of tunnel shotcrete, according to the optimal block spraying sequence and the optimal construction parameter sequence, the spraying operation of the tunnel section to be constructed is carried out. Specifically, the construction personnel must strictly follow the optimal block spraying sequence to carry out the spraying operation in each construction area to ensure the efficiency and orderliness of the construction process and avoid problems such as frequent equipment adjustment, material waste, and construction quality decline caused by unreasonable spraying sequence. And when carrying out the spraying operation in each construction area, the operator needs to set and adjust the construction equipment such as the wet shotcreting machine according to the optimal parameters corresponding to the area in the sequence. For example, when constructing in area 1, according to the parameters corresponding to area 1 in the optimal construction parameter sequence, adjust the spraying pressure of the wet shotcreting machine to the specified value, and also adjust the spraying distance and angle to the corresponding optimal values, so as to ensure the best spraying effect in each construction area, reduce the concrete rebound volume, and improve the quality indexes such as the compactness of the concrete and the bonding strength with the surrounding rock. The construction team sprays the shotcrete on each area of the entire tunnel section to be constructed in turn according to the established optimal block spraying sequence and the optimal construction parameter sequence. During the operation process, it is also necessary to monitor the construction situation in real time to ensure the accurate execution of the construction parameters and the smooth progress of the spraying operation. If it is found that there is a deviation between the actual situation and the expectation, such as sudden change of the surrounding rock conditions, etc., it is necessary to timely adjust the construction parameters appropriately to ensure that the spraying operation can meet the engineering quality requirements and finally complete the shotcrete construction task of the entire tunnel section to be constructed.

[0056] In the above text, with reference to Figure 1 the optimized shotcrete construction method combined with spatial positioning according to the embodiments of the present invention is described in detail. Next, with reference to Figure 2 the optimized shotcrete construction system combined with spatial positioning according to the embodiments of the present invention will be described.

[0057] The optimized shotcrete construction system combined with spatial positioning according to the embodiments of the present invention is used to solve the technical problems existing in the prior art, such as large rebound volume, poor compactness, poor durability, high consumption of binder materials, and unstable material adaptability, which in turn lead to poor construction quality and efficiency of shotcrete and large material consumption, and achieves the technical effects of reducing material consumption and improving the construction quality and efficiency of shotcrete. As Figure 2 shown, the optimized shotcrete construction system combined with spatial positioning includes: a construction area determination module 10, an optimal block spraying sequence acquisition module 20, an optimal construction parameter sequence acquisition module 30, and a tunnel spraying operation execution module 40.

[0058] The construction area determination module 10 is used to divide the tunnel section to be constructed in combination with the shotcrete machine model to determine multiple construction areas; the optimal block spraying sequence acquisition module 20 is used to collect the surface point cloud data of the multiple construction areas for three-dimensional modeling, and use the three-dimensional models of the multiple construction areas to optimize the spraying operation sequence with the goal of minimizing the equipment idle time and the repeated spraying coverage rate, and obtain the optimal block spraying sequence; the optimal construction parameter sequence acquisition module 30 is used to combine the current concrete mix ratio, surrounding rock characteristic information, construction environment information and the three-dimensional models of the multiple construction areas, and optimize and analyze the spraying construction parameters of each construction area in turn according to the optimal block spraying sequence with the goal of minimizing the concrete rebound amount, and obtain the optimal construction parameter sequence; the tunnel spraying operation execution module 40 is used to execute the spraying operation of the tunnel section to be constructed according to the optimal construction parameter sequence.

[0059] Next, the specific configuration of the construction area determination module 10 will be described in detail. The construction area determination module 10 further includes: obtaining several shotcrete machine models of several operable shotcrete machines and several maximum spraying coverage ranges; based on the several maximum spraying coverage ranges, performing collaborative simulation operation analysis on the tunnel section to be constructed to determine multiple area division schemes; evaluating the division quality of the multiple area division schemes, and selecting the area division scheme corresponding to the maximum division quality coefficient to obtain multiple construction areas.

[0060] Next, the specific configuration of the construction area determination module 10 will be further described in detail. The construction area determination module 10 further includes: obtaining multiple area division schemes, where each area division scheme is marked with the number of areas and several shotcrete machine operation times; calculating the variance of the several shotcrete machine operation times of each area division scheme respectively to determine multiple operation time variances; calculating multiple division quality coefficients by weighted calculation based on the multiple area numbers and multiple operation time variances, where the division quality coefficient is negatively correlated with the area number and the operation time variance.

[0061] Next, the specific configuration of the optimal block spraying sequence acquisition module 20 will be described in detail. The optimal block spraying sequence acquisition module 20 further includes: enumerating the spraying operation sequences by combining several shotcrete machines based on the multiple construction areas to generate multiple initial block spraying sequences; randomly selecting a first initial block spraying sequence, and in the spraying operation simulation space, using the three-dimensional models of the multiple construction areas and combining several shotcrete machines to conduct spraying operation simulation, counting the simulated idle time of several shotcrete machines and the repeated spraying coverage area of the multiple construction areas, and calculating to obtain the first equipment idle time and the first repeated spraying coverage rate; calculating a first sequence fitness based on the first equipment idle time and the first repeated spraying coverage rate; sequentially analyzing the multiple sequence fitnesses of the multiple initial block spraying sequences, and selecting the initial block spraying sequence with the maximum sequence fitness as the optimal block spraying sequence.

[0062] Next, the specific configuration of the optimal block spraying sequence acquisition module 20 will be further described in detail. The optimal block spraying sequence acquisition module 20 further includes: configuring a first weight and a second weight according to the construction operation requirements, where the first weight is the operation efficiency weight and the second weight is the material loss weight; according to the first weight and the second weight, performing weighted calculation on the reciprocals of the first equipment idle time and the first repeated spraying coverage rate to obtain the first sequence fitness.

[0063] Next, the specific configuration of the optimal construction parameter sequence acquisition module 30 will be described in detail. The optimal construction parameter sequence acquisition module 30 further includes: selecting a first spraying operation area and a first shotcrete machine in the optimal block spraying sequence, and obtaining the first spraying construction parameters of the first shotcrete machine; constructing a first concrete rebound prediction plug-in by combining a feedforward neural network based on the first construction area three-dimensional model, the current concrete mix ratio, the surrounding rock characteristic information, and the construction environment information; using the first concrete rebound prediction plug-in to optimize the first spraying construction parameters with the goal of minimizing the concrete rebound amount, and obtaining the first optimal spraying construction parameters, which are added to the optimal construction parameter sequence in order.

[0064] Next, the specific configuration of the optimal construction parameter sequence acquisition module 30 will be further described in detail. The optimal construction parameter sequence acquisition module 30 further includes: obtaining the first surface feature distribution of the three-dimensional model of the first construction area, and respectively expanding the first surface feature distribution, the current concrete mix ratio, the surrounding rock feature information, and the construction environment information according to a predetermined tolerance interval to obtain similarity comparison conditions; using the similarity comparison conditions as conditional constraints and the first shotcrete machine as equipment constraints, retrieving a sample shotcrete construction parameter set based on big data, and counting the concrete rebound amounts after the operations of different sample shotcrete construction parameters to construct a sample concrete rebound amount set; training a feedforward neural network using the sample shotcrete construction parameter set and the sample concrete rebound amount set until the model converges to obtain a first concrete rebound prediction plug-in.

[0065] Next, the specific configuration of the optimal construction parameter sequence acquisition module 30 will be further described in detail. The optimal construction parameter sequence acquisition module 30 further includes: obtaining the first shotcrete construction parameter threshold of the first shotcrete machine, where the first shotcrete construction parameters include the shotcrete machine wind speed, the shotcrete distance, the shotcrete angle, and the layer thickness; randomly selecting a plurality of initial construction parameters within the first shotcrete construction parameter threshold; using the first concrete rebound prediction plug-in to respectively predict the plurality of initial construction parameters and output a plurality of predicted rebound amounts; based on the plurality of predicted rebound amounts, performing optimization of the first shotcrete construction parameters to obtain the first optimal shotcrete construction parameters.

[0066] Next, the specific configuration of the optimal construction parameter sequence acquisition module 30 will be further described in detail. The optimal construction parameter sequence acquisition module 30 further includes: using the initial construction parameters as the initial solution, sorting the plurality of initial construction parameters in ascending order of the predicted rebound amount to generate an initial solution sequence; setting the first K solutions of the initial solution sequence as optimal solutions and the last J solutions as inferior solutions, where J is M times K and M is an integer greater than 2; using the K optimal solutions to perform equivalent clustering on the J inferior solutions to obtain K solution thresholds, and within the K solution thresholds, adjusting the inferior solutions in the direction of the optimal solutions according to the optimization step size to obtain K updated solution thresholds, where if the adjusted inferior solutions do not meet the first shotcrete construction parameter threshold, this adjustment is not performed; using the first concrete rebound prediction plug-in to respectively predict the rebound amounts of the inferior solutions of the K updated solution thresholds, and identifying the K updated solution thresholds. If the predicted rebound amount of the updated inferior solutions within the same updated solution threshold is less than the predicted rebound amount of the optimal solution, then use the inferior solution to replace the optimal solution for iterative optimization until a predetermined number of convergence times is reached, outputting the K current solution thresholds, and selecting the solution with the current minimum predicted rebound amount as the first optimal shotcrete construction parameter.

[0067] The shotcrete construction optimization system combined with spatial positioning provided by the embodiments of the present invention can execute the shotcrete construction optimization method combined with spatial positioning provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0068] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server. The included various units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0069] The above specific implementation manners do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. Optimization method for shotcrete construction combined with spatial positioning, characterized in that the method Including: Dividing the tunnel section to be constructed according to the wet shotcreting machine model to determine multiple construction areas; Collecting the surface point cloud data of the multiple construction areas for 3D modeling, and using the 3D models of the multiple construction areas, aiming at minimizing the equipment idle time and the repeated spraying coverage rate, optimizing the spraying operation sequence to obtain the optimal block spraying sequence; Combining the current concrete mix ratio, surrounding rock characteristic information, construction environment information and the 3D models of the multiple construction areas, aiming at minimizing the concrete rebound amount, and sequentially optimizing and analyzing the spraying construction parameters of each construction area according to the optimal block spraying sequence to obtain the optimal construction parameter sequence; Performing the spraying operation of the tunnel section to be constructed according to the optimal construction parameter sequence.

2. The optimization method for shotcrete construction combined with spatial positioning according to claim 1, characterized in that Dividing the tunnel section to be constructed according to the wet shotcreting machine model to determine multiple construction areas, including: Obtaining several wet shotcreting machine models of several operable wet shotcreting machines and several maximum spraying coverage ranges; Based on the several maximum spraying coverage ranges, performing collaborative simulation operation analysis on the tunnel section to be constructed to determine multiple area division schemes; Performing division quality evaluation on the multiple area division schemes, and selecting the area division scheme corresponding to the maximum division quality coefficient to obtain multiple construction areas.

3. The optimization method for shotcrete construction combined with spatial positioning according to claim 2, characterized in that Performing division quality evaluation on the multiple area division schemes, including: Obtaining multiple area division schemes, where each area division scheme is marked with the number of areas and several wet shotcreting machine operation times; Calculating the variance of the several wet shotcreting machine operation times of each area division scheme respectively to determine multiple operation time variances; Calculating multiple division quality coefficients by weighted calculation based on the multiple number of areas and the multiple operation time variances, where the division quality coefficient is negatively correlated with the number of areas and the operation time variance.

4. The optimization method for shotcrete construction combined with spatial positioning according to claim 2, characterized in that Using the 3D models of the multiple construction areas, aiming at minimizing the equipment idle time and the repeated spraying coverage rate, optimizing the spraying operation sequence to obtain the optimal block spraying sequence, including: Based on the multiple construction areas, enumerating the spraying operation sequence in combination with several wet shotcreting machines to generate multiple initial block spraying sequences; Randomly selecting a first initial block spraying sequence, in the spraying operation simulation space, using the 3D models of the multiple construction areas, and combining several wet shotcreting machines to perform spraying operation simulation, counting the simulated idle time of the several wet shotcreting machines and the repeated spraying coverage area of the multiple construction areas, and calculating to obtain the first equipment idle time and the first repeated spraying coverage rate; Calculating a first sequence fitness according to the first equipment idle time and the first repeated spraying coverage rate; Analyzing the multiple sequence fitnesses of the multiple initial block spraying sequences in turn, and selecting the initial block spraying sequence with the maximum sequence fitness as the optimal block spraying sequence.

5. The optimization method for shotcrete construction combined with spatial positioning according to claim 4, characterized in that Calculating a first sequence fitness according to the first equipment idle time and the first repeated spraying coverage rate, including: Configuring a first weight and a second weight according to the construction operation requirements, where the first weight is the operation efficiency weight and the second weight is the material loss weight; According to the first weight and the second weight, perform weighted calculation on the reciprocal of the idle duration of the first device and the reciprocal of the first repeated spraying coverage rate to obtain the first order fitness.

6. The optimization method for shotcrete construction combined with spatial positioning according to claim 1, characterized in that Combined with the current concrete mix ratio, surrounding rock characteristic information, construction environment information, and the three-dimensional models of the multiple construction areas, with the goal of minimizing the concrete rebound volume, optimize and analyze the spraying construction parameters of each construction area in sequence according to the optimal block spraying sequence to obtain an optimal construction parameter sequence, including: Select a first spraying operation area and a first wet spraying machine in the optimal block spraying sequence, and obtain the first spraying construction parameters of the first wet spraying machine; Based on the three-dimensional model of the first construction area, the current concrete mix ratio, surrounding rock characteristic information, and construction environment information, construct a first concrete rebound prediction plug-in in combination with a feedforward neural network; Using the first concrete rebound prediction plug-in, with the goal of minimizing the concrete rebound volume, perform optimization of the first spraying construction parameters to obtain the first optimal spraying construction parameters, and add them to the optimal construction parameter sequence in order.

7. The optimization method for shotcrete construction combined with spatial positioning according to claim 6, characterized in that Based on the three-dimensional model of the first construction area, the current concrete mix ratio, surrounding rock characteristic information, and construction environment information, construct a first concrete rebound prediction plug-in in combination with a feedforward neural network, including: Obtain the first surface feature distribution of the three-dimensional model of the first construction area, and expand the first surface feature distribution, the current concrete mix ratio, surrounding rock characteristic information, and construction environment information respectively according to a predetermined tolerance interval to obtain similarity comparison conditions; Taking the similarity comparison conditions as conditional constraints and the first wet spraying machine as equipment constraints, obtain a sample spraying construction parameter set based on big data retrieval, and count the concrete rebound volume after the operation of different sample spraying construction parameters to construct a sample concrete rebound volume set; Use the sample spraying construction parameter set and the sample concrete rebound volume set to train the feedforward neural network until the model converges to obtain the first concrete rebound prediction plug-in.

8. The optimized shotcrete construction method combined with spatial positioning according to claim 6, characterized in that Using the first concrete rebound prediction plug-in, with the goal of minimizing the concrete rebound volume, perform optimization of the first spraying construction parameters to obtain the first optimal spraying construction parameters, including: Obtain the first spraying construction parameter threshold of the first wet spraying machine, where the first spraying construction parameters include the wet spraying machine wind speed, spraying distance, spraying angle, and layering thickness; Randomly select multiple initial construction parameters within the first spraying construction parameter threshold; Use the first concrete rebound prediction plug-in to predict the multiple initial construction parameters respectively and output multiple predicted rebound volumes; Based on the multiple predicted rebound volumes, perform optimization of the first spraying construction parameters to obtain the first optimal spraying construction parameters.

9. The optimized shotcrete construction method combined with spatial positioning according to claim 8, characterized in that Based on the multiple predicted rebound volumes, perform optimization of the first spraying construction parameters to obtain the first optimal spraying construction parameters, including: Taking the initial construction parameters as the initial solution, sort the multiple initial construction parameters in ascending order according to the predicted rebound volume to generate an initial solution sequence; Set the first K solutions of the initial solution sequence as optimal solutions and the last J solutions as inferior solutions, where J is M times of K and M is an integer greater than 2; Use K optimal solutions to perform equivalent clustering on J inferior solutions, obtain K solution thresholds, and within the K solution thresholds, adjust the inferior solutions in the direction of the optimal solutions according to the optimization step size to obtain K updated solution thresholds. Among them, if the adjusted inferior solution does not meet the first shotcreting construction parameter threshold, this adjustment is not performed; Use the first concrete rebound prediction plugin to predict the rebound volume of the inferior solutions in the K updated solution thresholds respectively, and identify the K updated solution thresholds. If the predicted rebound volume of the updated inferior solutions within the same updated solution threshold is less than the predicted rebound volume of the optimal solution, use the inferior solution to replace the optimal solution; Perform iterative optimization until the predetermined number of convergence times is reached, output the K current solution thresholds, and select the solution with the current minimum predicted rebound volume as the first optimal shotcreting construction parameter.

10. The optimized shotcrete construction system combined with spatial positioning, characterized in that The system is used to implement the shotcreting construction optimization method combined with spatial positioning according to any one of claims 1 to 9. The system includes: A construction area determination module for dividing the tunnel section to be constructed in combination with the wet shotcreting machine model to determine multiple construction areas; An optimal block shotcreting sequence acquisition module for collecting the surface point cloud data of the multiple construction areas for 3D modeling, and using the 3D models of the multiple construction areas to optimize the shotcreting operation sequence with the goal of minimizing the equipment idle time and the repeated shotcreting coverage rate to obtain the optimal block shotcreting sequence; An optimal construction parameter sequence acquisition module for combining the current concrete mix ratio, surrounding rock characteristic information, construction environment information, and the 3D models of the multiple construction areas, and taking the minimization of the concrete rebound volume as the goal, and sequentially performing optimization analysis on the shotcreting construction parameters of each construction area according to the optimal block shotcreting sequence to obtain the optimal construction parameter sequence; A tunnel shotcreting operation execution module for executing the shotcreting operation of the tunnel section to be constructed according to the optimal construction parameter sequence.

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