Layout optimization method and system for assembly type drainage pipe

Abstract

The invention relates to the technical field of pipeline layout, and discloses a layout optimization method and system for a fabricated drainage pipe, and the system comprises a collection module which is used for collecting soil geological data of a to-be-constructed region, and judging whether the to-be-constructed region is suitable for drainage pipeline construction according to the soil geological data; the simulation prediction module is used for dividing a to-be-constructed area into a plurality of monitoring areas, predicting the predicted soil bearing capacity variation in each monitoring area during drainage pipeline construction by using the soil bearing capacity prediction model, and establishing a first soil bearing capacity data set; a monitoring analysis module; and an optimization module. According to the construction method, the scientificity and safety of drainage pipeline construction can be remarkably improved, the construction cost is reduced, the service life of the drainage pipeline is prolonged, and remarkable practical value and economic benefits are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline layout, and in particular to a layout optimization method and system for assembled drainage pipes. Background Art

[0002] In the field of pipeline engineering, especially in the construction and layout of prefabricated drainage pipes, accurate assessment and optimization of soil bearing capacity is crucial. Traditional methods often rely on empirical judgment and simple geological surveys, which are difficult to fully and accurately reflect the dynamic changes of soil in the construction area during the construction of drainage pipes. This may not only lead to low construction efficiency, but also cause safety hazards such as foundation settlement and pipe damage.

[0003] Therefore, the present invention aims to provide a more scientific and efficient method and system for optimizing the layout of prefabricated drainage pipes to overcome the above-mentioned technical difficulties. Summary of the invention

[0004] In view of this, the present invention proposes a layout optimization method and system for prefabricated drainage pipes, aiming to solve the problem that it is difficult to comprehensively and accurately reflect the dynamic changes of the soil in the construction area during the construction of drainage pipes in the current technology, which may not only lead to low construction efficiency but also cause safety hazards such as foundation settlement and pipe damage.

[0005] In one aspect, the present invention provides a layout optimization system for assembled drainage pipes, comprising: A collection module is used to collect soil geological data of the area to be constructed, and judge whether the area to be constructed is suitable for drainage pipeline construction according to the soil geological data; when it is judged that the area to be constructed is suitable for drainage pipeline construction, collect meteorological data of the area to be constructed, and construct a soil bearing capacity prediction model according to the soil geological data and the meteorological data; A simulation prediction module is used to divide a plurality of monitoring areas in the area to be constructed, use a soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each of the monitoring areas during the construction of the drainage pipe, and establish a first soil bearing capacity data set; determine the layout path, layout depth and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set; A monitoring and analysis module, used to collect actual soil bearing capacity changes in each monitoring area during the construction of the drainage pipe and establish a second soil bearing capacity data set; and also used to calculate a bearing capacity influence factor based on the first soil bearing capacity data set and the second soil bearing capacity data set, compare the bearing capacity influence factor with historical data, and determine whether to optimize the layout depth and sand cushion thickness of the drainage pipe based on the comparison result; An optimization module is used to obtain an average actual bearing capacity change of the soil according to the second soil bearing capacity data set when it is determined that the layout depth and the sand cushion thickness of the drainage pipe are to be optimized, optimize the layout depth and the sand cushion thickness of the drainage pipe according to the difference between the average actual bearing capacity change and the average predicted bearing capacity change, and store the optimized layout depth and sand cushion thickness of the drainage pipe.

[0006] Furthermore, collecting soil geological data of the area to be constructed and judging whether the area to be constructed is suitable for the construction of drainage pipes according to the soil geological data includes: Comparing the soil geological data of each point in the area to be constructed with the data threshold respectively, and judging whether it is suitable for drainage pipeline construction according to the comparison results; When there is data greater than the data threshold in the soil geological data of each point in the area to be constructed, it is determined that it is not suitable for drainage pipe construction; When the soil geological data of each point in the area to be constructed is less than or equal to the data threshold, it is determined to be suitable for drainage pipeline construction.

[0007] Furthermore, the soil geological data include soil type, soil density, soil water content, soil strength, soil permeability and soil strength; the meteorological data include temperature, humidity, wind speed, precipitation and sunshine duration.

[0008] Furthermore, when it is determined that the drainage pipe is suitable for construction, the meteorological data of the area to be constructed is collected, and a soil bearing capacity prediction model is constructed according to the soil geological data and the meteorological data, including: Selecting points in the area to be constructed where the soil geological data is less than or equal to the data threshold as valid sampling points; Using the soil geological data and meteorological data of the effective sampling points, a machine learning algorithm is used to construct the soil bearing capacity prediction model, and the soil bearing capacity prediction model is trained and verified by a cross-validation method; The constructed soil bearing capacity prediction model is applied to the area to be constructed.

[0009] Further, dividing the area to be constructed into a plurality of monitoring areas, using the soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each of the monitoring areas during the construction of the drainage pipeline, and establishing a first soil bearing capacity data set, includes: Use GIS technology to conduct spatial analysis of the area to be constructed to determine the key areas and sensitive areas for drainage pipeline construction; According to the distribution of the key areas and sensitive areas, the area to be constructed is divided into a number of monitoring areas in combination with the soil geological data; In each of the monitoring areas, using a soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each of the monitoring areas; The predicted soil bearing capacity changes are collated and established as the first soil bearing capacity data set.

[0010] Further, when determining the laying depth, laying path and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set, it includes: Obtaining an average predicted bearing capacity change of the soil according to the first soil bearing capacity data set, and determining a laying depth of the drainage pipe and a thickness of the sand cushion layer according to the average predicted bearing capacity change; Comparing the predicted soil bearing capacity change in each monitoring area with the average predicted bearing capacity change, and judging whether the drainage pipe passes through the monitoring area during construction according to the comparison result; When the predicted soil bearing capacity change is greater than or equal to the average predicted bearing capacity change, it is determined that the drainage pipe does not pass through the monitoring area during construction; When the predicted change in soil bearing capacity is less than the average predicted change in soil bearing capacity, it is determined that the drainage pipe passes through the monitoring area during construction, and the GIS technology is used to determine the layout path.

[0011] Furthermore, a bearing capacity influence factor is calculated according to the first soil bearing capacity data set and the second soil bearing capacity data set, and the bearing capacity influence factor is obtained by the following formula:

[0012] in, represents the bearing capacity influencing factor; represents the predicted soil bearing capacity change; It represents the actual soil bearing capacity change.

[0013] Furthermore, the bearing capacity influencing factor is compared with historical data, and it is determined whether to optimize the layout depth of the drainage pipe and the thickness of the sand cushion layer according to the comparison result, including: When the historical data contains the same data as the bearing capacity influencing factor, the layout depth of the drainage pipe and the thickness of the sand cushion layer are not optimized; When the historical data does not contain the same data as the bearing capacity influencing factor, it is determined to optimize the laying depth of the drainage pipe and the thickness of the sand cushion layer.

[0014] Furthermore, when optimizing the laying depth of the drainage pipe and the thickness of the sand cushion layer, the following steps are included: Obtaining an average actual bearing capacity change of the soil according to the second soil bearing capacity data set, and determining a laying depth of the drainage pipe and a thickness of the sand cushion layer according to a difference between the average actual bearing capacity change and the average predicted bearing capacity change; The difference is proportional to the laying depth of the drainage pipe; the difference is inversely proportional to the thickness of the sand cushion layer.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: the acquisition module of the layout optimization system of the assembled drainage pipe provided by the present invention can obtain detailed soil geological information of the area to be constructed in real time, thereby ensuring the feasibility and safety of the drainage pipe construction; the simulation prediction module can accurately predict the soil bearing capacity changes of each monitoring area during the construction of the drainage pipe through the soil bearing capacity prediction model; by establishing a first soil bearing capacity data set, the simulation prediction module can scientifically determine the layout path, layout depth and sand cushion thickness of the drainage pipe, thereby improving the stability and service life of the drainage pipe; the monitoring and analysis module establishes a second soil bearing capacity data set by real-time acquisition of the actual soil bearing capacity changes in each monitoring area, and the monitoring and analysis module can timely discover problems that may occur during the construction process; by calculating the bearing capacity influencing factor and comparing it with historical data, the monitoring and analysis module can provide optimization suggestions for the layout depth and sand cushion thickness of the drainage pipe to ensure the long-term stable operation of the drainage pipe; the optimization module uses a computer optimization algorithm to scientifically optimize the layout depth and sand cushion thickness of the drainage pipe according to the comparison results of the bearing capacity influencing factor and historical data; the optimized data storage function ensures that the optimization results can be recorded and referenced, providing valuable experience for similar projects in the future.

[0016] The assembled drainage pipe layout optimization system provided by the present invention can significantly improve the scientificity and safety of drainage pipe construction, reduce construction costs, and extend the service life of drainage pipes, and has significant practical value and economic benefits.

[0017] In another aspect, the present invention also proposes a method for optimizing the layout of assembled drainage pipes, comprising the following steps: S100: collecting soil geological data of the area to be constructed, and judging whether the area to be constructed is suitable for construction of drainage pipes according to the soil geological data; when it is judged that the area to be constructed is suitable for construction of the drainage pipes, collecting meteorological data of the area to be constructed, and constructing a soil bearing capacity prediction model according to the soil geological data and the meteorological data; S200: Divide the area to be constructed into several monitoring areas, use the soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each monitoring area during the construction of the drainage pipe, and establish a first soil bearing capacity data set; determine the layout path, layout depth and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set; S300: collecting actual soil bearing capacity changes in each monitoring area during the construction of the drainage pipe, and establishing a second soil bearing capacity data set; also used to calculate the bearing capacity influence factor according to the first soil bearing capacity data set and the second soil bearing capacity data set, compare the bearing capacity influence factor with historical data, and determine whether to optimize the layout depth and sand cushion thickness of the drainage pipe according to the comparison result; S400: When it is determined that the layout depth and sand cushion thickness of the drainage pipe are to be optimized, the average actual bearing capacity change of the soil is obtained according to the second soil bearing capacity data set, and the layout depth and sand cushion thickness of the drainage pipe are optimized according to the difference between the average actual bearing capacity change and the average predicted bearing capacity change, and the optimized layout depth and sand cushion thickness of the drainage pipe are stored.

[0018] It is understandable that the above-mentioned layout optimization method and system for assembled drainage pipes have the same beneficial effects and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A structural block diagram of a layout optimization system for assembled drainage pipes provided in an embodiment of the present invention; Figure 2 A flow chart of a method for optimizing the layout of assembled sewer pipes provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] See also Figure 1 As shown, in some embodiments of the present application, this embodiment provides a layout optimization system for assembled drainage pipes, including: A collection module is used to collect soil geological data of the area to be constructed, and judge whether the area to be constructed is suitable for drainage pipeline construction according to the soil geological data; when it is judged that the area to be constructed is suitable for drainage pipeline construction, collect meteorological data of the area to be constructed, and construct a soil bearing capacity prediction model according to the soil geological data and the meteorological data; In some embodiments of the present invention, the soil bearing capacity prediction model is constructed by the following steps: The collected soil geological data and meteorological data are preprocessed by cleaning, denoising, normalization and other preprocessing operations; characteristic variables that significantly affect soil bearing capacity are extracted from the preprocessed data; appropriate machine learning algorithms (such as neural networks, decision trees, support vector machines, etc.) are selected, and the model is trained using historical data. During the training process, the model parameters are continuously adjusted to optimize the prediction effect; the trained model is verified using methods such as cross-validation; the trained soil bearing capacity prediction model is applied to the layout optimization system of prefabricated drainage pipes to predict the changes in soil bearing capacity during construction in real time.

[0022] A simulation prediction module is used to divide a plurality of monitoring areas in the area to be constructed, use a soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each of the monitoring areas during the construction of the drainage pipe, and establish a first soil bearing capacity data set; determine the layout path, layout depth and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set; A monitoring and analysis module, used to collect actual soil bearing capacity changes in each monitoring area during the construction of the drainage pipe and establish a second soil bearing capacity data set; and also used to calculate a bearing capacity influence factor based on the first soil bearing capacity data set and the second soil bearing capacity data set, compare the bearing capacity influence factor with historical data, and determine whether to optimize the layout depth and sand cushion thickness of the drainage pipe based on the comparison result; In some embodiments of the present invention, the actual change in soil bearing capacity is measured in real time by soil pressure sensors installed in each monitoring area. These sensors can accurately capture the dynamic changes in soil bearing capacity during construction and transmit the data in real time to the monitoring and analysis module.

[0023] An optimization module is used to obtain an average actual bearing capacity change of the soil according to the second soil bearing capacity data set when it is determined that the layout depth and the sand cushion thickness of the drainage pipe are to be optimized, optimize the layout depth and the sand cushion thickness of the drainage pipe according to the difference between the average actual bearing capacity change and the average predicted bearing capacity change, and store the optimized layout depth and sand cushion thickness of the drainage pipe.

[0024] It can be seen that the acquisition module can obtain detailed soil geological information of the construction area in real time, so as to ensure the feasibility and safety of drainage pipe construction; the simulation prediction module can accurately predict the changes in soil bearing capacity of each monitoring area during the construction of the drainage pipe through the soil bearing capacity prediction model; by establishing the first soil bearing capacity data set, the simulation prediction module can scientifically determine the layout path, layout depth and sand cushion thickness of the drainage pipe, so as to improve the stability and service life of the drainage pipe; the monitoring and analysis module can establish the second soil bearing capacity data set by collecting the actual soil bearing capacity changes in each monitoring area in real time, and the monitoring and analysis module can timely discover possible problems in the construction process; by calculating the bearing capacity influencing factor and comparing it with historical data, the monitoring and analysis module can provide optimization suggestions for the layout depth and sand cushion thickness of the drainage pipe to ensure the long-term stable operation of the drainage pipe; the optimization module uses computer optimization algorithms to scientifically optimize the layout depth and sand cushion thickness of the drainage pipe according to the comparison results of the bearing capacity influencing factor and historical data; the optimized data storage function ensures that the optimization results can be recorded and referenced, providing valuable experience for similar projects in the future.

[0025] It can be understood that the prefabricated drainage pipe layout optimization system provided in this embodiment can significantly improve the scientificity and safety of drainage pipe construction, reduce construction costs, and extend the service life of drainage pipes, and has significant practical value and economic benefits.

[0026] Specifically, collecting soil geological data of the area to be constructed and judging whether the area to be constructed is suitable for drainage pipeline construction according to the soil geological data include: Comparing the soil geological data of each point in the area to be constructed with the data threshold respectively, and judging whether it is suitable for drainage pipeline construction according to the comparison results; When there is data greater than the data threshold in the soil geological data of each point in the area to be constructed, it is determined that it is not suitable for drainage pipe construction; When the soil geological data of each point in the area to be constructed is less than or equal to the data threshold, it is determined to be suitable for drainage pipeline construction.

[0027] It can be seen that by setting reasonable data thresholds, areas suitable for drainage pipe construction can be effectively screened out, thereby avoiding construction in areas with poor geological conditions and reducing construction risks and subsequent maintenance costs; the setting of data thresholds is based on a large amount of geological surveys and historical construction experience, ensuring the accuracy and reliability of the evaluation results; among them, the data thresholds are adjusted according to the geological characteristics and construction requirements of different regions to adapt to various complex construction environments, and the data thresholds are obtained through a combination of field collection and laboratory analysis: first, soil samples are collected in the area to be constructed using geological exploration equipment, and then the samples are subjected to detailed physical and chemical analysis in the laboratory to obtain the key parameters of the soil, which will be compared with historical construction data to determine soil conditions suitable for construction.

[0028] Specifically, the soil geological data include soil type, soil density, soil water content, soil strength, soil permeability and soil strength; the meteorological data include temperature, humidity, wind speed, precipitation and sunshine duration.

[0029] It can be seen that by comprehensively considering a variety of soil and meteorological factors, the system can comprehensively evaluate the construction conditions of the area to be constructed. Parameters such as soil type, density, water content, strength, permeability and strength can reflect the bearing capacity and stability of the soil, while meteorological data such as temperature, humidity, wind speed, precipitation and sunshine duration can affect the physical and chemical properties of the soil, and thus affect the feasibility and safety of drainage pipe construction.

[0030] Specifically, when it is determined that the construction of the drainage pipe is suitable, the meteorological data of the area to be constructed is collected, and a soil bearing capacity prediction model is constructed according to the soil geological data and the meteorological data, including: Selecting points in the area to be constructed where the soil geological data is less than or equal to the data threshold as valid sampling points; Using the soil geological data and meteorological data of the effective sampling points, a machine learning algorithm is used to construct the soil bearing capacity prediction model, and the soil bearing capacity prediction model is trained and verified by a cross-validation method; The constructed soil bearing capacity prediction model is applied to the area to be constructed.

[0031] It can be seen that by constructing a soil bearing capacity prediction model through machine learning algorithms, we can make full use of historical data and measured data to improve the prediction accuracy and reliability of the model; the application of cross-validation methods ensures the generalization ability of the model on different data sets, making the prediction results more stable and reliable; by applying the constructed model to the area to be constructed, the changes in soil bearing capacity can be predicted in real time, providing a scientific basis for the layout of drainage pipes.

[0032] Specifically, a plurality of monitoring areas are divided in the area to be constructed, and a soil bearing capacity prediction model is used to predict the predicted soil bearing capacity change in each of the monitoring areas during the construction of the drainage pipeline, and a first soil bearing capacity data set is established, including: Use GIS technology to conduct spatial analysis of the area to be constructed to determine the key areas and sensitive areas for drainage pipeline construction; According to the distribution of the key areas and sensitive areas, the area to be constructed is divided into a number of monitoring areas in combination with the soil geological data; In each of the monitoring areas, using a soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each of the monitoring areas; The predicted soil bearing capacity changes are collated and established as the first soil bearing capacity data set.

[0033] It can be seen that through the spatial analysis of GIS technology, key locations and potential risk points in the construction area can be accurately identified, such as areas with high groundwater levels, loose soil or complex geological structures. The division of these key areas and sensitive areas not only provides clear guidance for subsequent monitoring work, but also ensures the stability and safety of drainage pipes during construction and operation. The use of soil bearing capacity prediction models is crucial to predict changes in soil bearing capacity. The accuracy of the prediction results directly determines the rationality and feasibility of subsequent construction plans. After the first soil bearing capacity data set is established, the data set is further used to conduct preliminary planning of drainage pipe layout paths. By comparing and analyzing the changes in soil bearing capacity under different layout paths, the most economical and stable layout plan can be selected. In order to verify the effectiveness of the preliminary plan, a small-scale test construction will be carried out in combination with the actual situation on site, and actual data will be collected and compared with the predicted data to further adjust and optimize the layout plan.

[0034] Specifically, determining the laying depth, laying path and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set includes: Obtaining an average predicted bearing capacity change of the soil according to the first soil bearing capacity data set, and determining a laying depth of the drainage pipe and a thickness of the sand cushion layer according to the average predicted bearing capacity change; Comparing the predicted soil bearing capacity change in each monitoring area with the average predicted bearing capacity change, and judging whether the drainage pipe passes through the monitoring area during construction according to the comparison result; When the predicted soil bearing capacity change is greater than or equal to the average predicted bearing capacity change, it is determined that the drainage pipe does not pass through the monitoring area during construction; When the predicted change in soil bearing capacity is less than the average predicted change in soil bearing capacity, it is determined that the drainage pipe passes through the monitoring area during construction, and the GIS technology is used to determine the layout path.

[0035] It can be seen that the changes in soil bearing capacity and the complexity of geological conditions were comprehensively considered when determining the layout plan of the drainage pipes. First, through the average predicted bearing capacity change in the first soil bearing capacity data set, the overall situation of the soil bearing capacity in the entire construction area can be roughly grasped, and then the layout depth of the drainage pipe and the thickness of the sand cushion layer can be reasonably set; the setting of the sand cushion layer, as a buffer layer, can effectively disperse the direct pressure of the pipeline on the soil, protect the soil structure from damage, and improve the stability of the pipeline; then, by carefully comparing the predicted soil bearing capacity change in each monitoring area with the average predicted bearing capacity change, it is possible to accurately determine which areas are "high-risk areas" that should be avoided during pipeline construction, and which are "suitable areas" that can be safely crossed; For monitoring areas that are determined to be unsuitable for crossing, we adopt the strategy of detouring or taking special reinforcement measures to ensure that construction activities will not cause irreversible damage to the soil environment. After determining the monitoring area that the pipeline needs to cross, we further refine the planning of the layout path by leveraging the powerful spatial analysis capabilities of GIS technology. GIS can not only intuitively display geographical information such as topography, soil type, etc., but also combine the results of the soil bearing capacity prediction model to intelligently recommend the optimal pipeline layout path. These paths can not only effectively avoid areas with harsh geological conditions, but also minimize construction difficulty and cost, thereby improving the economic benefits of the project.

[0036] Specifically, the bearing capacity influence factor is calculated according to the first soil bearing capacity data set and the second soil bearing capacity data set, and the bearing capacity influence factor is obtained by the following formula:

[0037] in, represents the bearing capacity influencing factor; represents the predicted soil bearing capacity change; It represents the actual soil bearing capacity change.

[0038] Specifically, the bearing capacity influencing factor is compared with historical data, and judging whether to optimize the layout depth of the drainage pipe and the thickness of the sand cushion layer according to the comparison result includes: When the historical data contains the same data as the bearing capacity influencing factor, the layout depth of the drainage pipe and the thickness of the sand cushion layer are not optimized; When the historical data does not contain the same data as the bearing capacity influencing factor, it is determined to optimize the laying depth of the drainage pipe and the thickness of the sand cushion layer.

[0039] It can be seen that in order to further improve construction efficiency and project quality, the concept of bearing capacity influencing factor is introduced and used as an important basis for evaluating and optimizing drainage pipe layout schemes. The introduction of this indicator not only takes into account the difference between the prediction and the actual situation, but also guides subsequent construction adjustments by quantifying this difference. First of all, the calculation of the bearing capacity influencing factor is directly related to the difference ratio between the predicted soil bearing capacity change (ΔCd) and the actual soil bearing capacity change (ΔCa). This ratio intuitively reflects the degree of fit between the prediction model and the actual construction environment; when the factor is close to or reaches a certain threshold, it means that the prediction model is relatively accurate and the construction can be carried out according to the original plan; when it deviates far from the threshold, it indicates that the construction plan needs to be reviewed and optimized.

[0040] It is understandable that when the bearing capacity influencing factor is inconsistent with the historical data, if there is data matching the current bearing capacity influencing factor in the historical database, this often means that the previous construction experience is of reference value to the current project. Therefore, we can choose to maintain the original drainage pipe layout depth and sand cushion thickness unchanged to reduce unnecessary adjustments and cost increases; however, when there are no similar cases in the historical data, we need to pay more attention to the differences in the currently monitored data, and accurately locate the potential risk points in the construction through in-depth analysis of the specific deviations between the predicted soil bearing capacity changes and the actual changes; on this basis, the drainage pipe layout depth and sand cushion thickness are optimized in a targeted manner.

[0041] Specifically, when optimizing the layout depth and thickness of the sand cushion layer of the drainage pipe, the following steps are included: Obtaining an average actual bearing capacity change of the soil according to the second soil bearing capacity data set, and determining a laying depth of the drainage pipe and a thickness of the sand cushion layer according to a difference between the average actual bearing capacity change and the average predicted bearing capacity change; The difference is proportional to the laying depth of the drainage pipe; the difference is inversely proportional to the thickness of the sand cushion layer.

[0042] It can be seen that the average actual bearing capacity change of the soil is obtained through the second soil bearing capacity data set. This data reflects the actual change of the soil bearing capacity during the actual construction process. By comparing this actual change with the predicted bearing capacity change, the difference between the two can be found, and the layout depth of the drainage pipe and the thickness of the sand cushion layer can be optimized.

[0043] It can be understood that the difference is proportional to the laying depth of the drainage pipe, which means that if the actual bearing capacity change is greater than the predicted value, it indicates that the soil bearing capacity is stronger than expected. Therefore, the laying depth of the drainage pipe can be appropriately increased to ensure that the pipe runs in a more stable soil layer; the difference is inversely proportional to the thickness of the sand cushion layer, which means that if the actual bearing capacity change is less than the predicted value, it indicates that the soil bearing capacity is weaker than expected. Therefore, the thickness of the sand cushion layer needs to be increased to provide stronger support and buffering. In this way, the stability and safety of the drainage pipe under different soil conditions can be ensured, while reducing the risk of pipe damage due to insufficient soil bearing capacity.

[0044] See also Figure 2 As shown, in some embodiments of the present application, this embodiment provides a method for optimizing the layout of assembled drainage pipes, comprising the following steps: S100: collecting soil geological data of the area to be constructed, and judging whether the area to be constructed is suitable for construction of drainage pipes according to the soil geological data; when it is judged that the area to be constructed is suitable for construction of the drainage pipes, collecting meteorological data of the area to be constructed, and constructing a soil bearing capacity prediction model according to the soil geological data and the meteorological data; S200: Divide the area to be constructed into several monitoring areas, use the soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each monitoring area during the construction of the drainage pipe, and establish a first soil bearing capacity data set; determine the layout path, layout depth and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set; S300: collecting actual soil bearing capacity changes in each monitoring area during the construction of the drainage pipe, and establishing a second soil bearing capacity data set; also used to calculate the bearing capacity influence factor according to the first soil bearing capacity data set and the second soil bearing capacity data set, compare the bearing capacity influence factor with historical data, and determine whether to optimize the layout depth and sand cushion thickness of the drainage pipe according to the comparison result; S400: When it is determined that the layout depth and sand cushion thickness of the drainage pipe are to be optimized, the average actual bearing capacity change of the soil is obtained according to the second soil bearing capacity data set, and the layout depth and sand cushion thickness of the drainage pipe are optimized according to the difference between the average actual bearing capacity change and the average predicted bearing capacity change, and the optimized layout depth and sand cushion thickness of the drainage pipe are stored.

[0045] It can be seen that the embodiment provides a layout optimization method for assembled drainage pipes, which can achieve real-time optimization of the drainage pipe layout depth and sand cushion thickness, thereby improving construction efficiency and project quality. In practical applications, the system can be seamlessly integrated with existing construction equipment and management systems to achieve intelligent construction management. In addition, by continuously collecting and analyzing soil bearing capacity data, the system can continuously optimize the prediction model, improve prediction accuracy, and provide a more reliable reference for future construction projects.

[0046] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0047] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0049] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A layout optimization system for assembled drainage pipes, characterized in that: include: A collection module is used to collect soil geological data of the area to be constructed, and judge whether the area to be constructed is suitable for drainage pipeline construction according to the soil geological data; when it is judged that the area to be constructed is suitable for drainage pipeline construction, collect meteorological data of the area to be constructed, and construct a soil bearing capacity prediction model according to the soil geological data and the meteorological data; A simulation prediction module is used to divide a plurality of monitoring areas in the area to be constructed, use a soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each of the monitoring areas during the construction of the drainage pipe, and establish a first soil bearing capacity data set; determine the layout path, layout depth and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set; A monitoring and analysis module, used to collect actual soil bearing capacity changes in each monitoring area during the construction of the drainage pipe and establish a second soil bearing capacity data set; and also used to calculate a bearing capacity influence factor based on the first soil bearing capacity data set and the second soil bearing capacity data set, compare the bearing capacity influence factor with historical data, and determine whether to optimize the layout depth and sand cushion thickness of the drainage pipe based on the comparison result; An optimization module is used to obtain an average actual bearing capacity change of the soil according to the second soil bearing capacity data set when it is determined that the layout depth and the sand cushion thickness of the drainage pipe are to be optimized, optimize the layout depth and the sand cushion thickness of the drainage pipe according to the difference between the average actual bearing capacity change and the average predicted bearing capacity change, and store the optimized layout depth and sand cushion thickness of the drainage pipe.

2. The layout optimization system for assembled drainage pipes according to claim 1, characterized in that: Collecting soil geological data of the area to be constructed and judging whether the area to be constructed is suitable for drainage pipeline construction according to the soil geological data includes: Comparing the soil geological data of each point in the area to be constructed with the data threshold respectively, and judging whether it is suitable for drainage pipeline construction according to the comparison results; When there is data greater than the data threshold in the soil geological data of each point in the area to be constructed, it is determined that it is not suitable for drainage pipe construction; When the soil geological data of each point in the area to be constructed is less than or equal to the data threshold, it is determined to be suitable for drainage pipeline construction.

3. The layout optimization system for assembled drainage pipes according to claim 2, characterized in that: The soil geological data include soil type, soil density, soil water content, soil strength, soil permeability and soil strength; the meteorological data include temperature, humidity, wind speed, precipitation and sunshine duration.

4. The layout optimization system for assembled drainage pipes according to claim 3, characterized in that: When it is determined that the construction of the drainage pipe is suitable, the meteorological data of the area to be constructed is collected, and a soil bearing capacity prediction model is constructed according to the soil geological data and the meteorological data, including: Selecting points in the area to be constructed where the soil geological data is less than or equal to the data threshold as valid sampling points; Using the soil geological data and meteorological data of the effective sampling points, a machine learning algorithm is used to construct the soil bearing capacity prediction model, and the soil bearing capacity prediction model is trained and verified by a cross-validation method; The constructed soil bearing capacity prediction model is applied to the area to be constructed.

5. The layout optimization system for assembled drainage pipes according to claim 4, characterized in that: The method includes dividing a plurality of monitoring areas in the area to be constructed, using a soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each of the monitoring areas during the construction of the drainage pipeline, and establishing a first soil bearing capacity data set, including: Use GIS technology to conduct spatial analysis of the area to be constructed to determine the key areas and sensitive areas for drainage pipeline construction; According to the distribution of the key areas and sensitive areas, the area to be constructed is divided into a number of monitoring areas in combination with the soil geological data; In each of the monitoring areas, using a soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each of the monitoring areas; The predicted soil bearing capacity changes are collated and established as the first soil bearing capacity data set.

6. The layout optimization system for assembled drainage pipes according to claim 5, characterized in that: Determining the laying depth, laying path and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set includes: Obtaining an average predicted bearing capacity change of the soil according to the first soil bearing capacity data set, and determining a laying depth of the drainage pipe and a thickness of the sand cushion layer according to the average predicted bearing capacity change; Comparing the predicted soil bearing capacity change in each monitoring area with the average predicted bearing capacity change, and judging whether the drainage pipe passes through the monitoring area during construction according to the comparison result; When the predicted soil bearing capacity change is greater than or equal to the average predicted bearing capacity change, it is determined that the drainage pipe does not pass through the monitoring area during construction; When the predicted change in soil bearing capacity is less than the average predicted change in soil bearing capacity, it is determined that the drainage pipe passes through the monitoring area during construction, and the GIS technology is used to determine the layout path.

7. The layout optimization system for assembled drainage pipes according to claim 6, characterized in that: The bearing capacity influence factor is calculated according to the first soil bearing capacity data set and the second soil bearing capacity data set. The bearing capacity influence factor is obtained by the following formula: in, represents the bearing capacity influencing factor; represents the predicted soil bearing capacity change; It represents the actual soil bearing capacity change.

8. The layout optimization system for assembled drainage pipes according to claim 7, characterized in that: When comparing the bearing capacity influencing factor with historical data and judging whether to optimize the layout depth of the drainage pipe and the thickness of the sand cushion layer according to the comparison result, it includes: When the historical data contains the same data as the bearing capacity influencing factor, the layout depth of the drainage pipe and the thickness of the sand cushion layer are not optimized; When the historical data does not contain the same data as the bearing capacity influencing factor, it is determined to optimize the laying depth of the drainage pipe and the thickness of the sand cushion layer.

9. The layout optimization system for assembled drainage pipes according to claim 8, characterized in that: Determining the optimization of the layout depth of the drainage pipe and the thickness of the sand cushion layer includes: Obtaining an average actual bearing capacity change of the soil according to the second soil bearing capacity data set, and determining a laying depth of the drainage pipe and a thickness of the sand cushion layer according to a difference between the average actual bearing capacity change and the average predicted bearing capacity change; The difference is proportional to the laying depth of the drainage pipe; the difference is inversely proportional to the thickness of the sand cushion layer.

10. A method for optimizing the layout of assembled drainage pipes, applied to the system for optimizing the layout of assembled drainage pipes as claimed in any one of claims 1 to 9, characterized in that: include: Collecting soil geological data of the area to be constructed, and judging whether the area to be constructed is suitable for construction of drainage pipes according to the soil geological data; when it is judged that the area to be constructed is suitable for construction of the drainage pipes, collecting meteorological data of the area to be constructed, and constructing a soil bearing capacity prediction model according to the soil geological data and the meteorological data; Divide the area to be constructed into several monitoring areas, use the soil bearing capacity prediction model to predict the predicted soil bearing capacity change in each monitoring area during the construction of the drainage pipe, and establish a first soil bearing capacity data set; determine the layout path, layout depth and sand cushion thickness of the drainage pipe according to the first soil bearing capacity data set; During the construction of the drainage pipe, the actual soil bearing capacity change in each monitoring area is collected, and a second soil bearing capacity data set is established; it is also used to calculate the bearing capacity influence factor according to the first soil bearing capacity data set and the second soil bearing capacity data set, compare the bearing capacity influence factor with the historical data, and determine whether to optimize the layout depth and sand cushion thickness of the drainage pipe according to the comparison result; When it is determined that the layout depth and sand cushion layer thickness of the drainage pipe are to be optimized, the average actual bearing capacity change of the soil is obtained according to the second soil bearing capacity data set, and the layout depth and sand cushion layer thickness of the drainage pipe are optimized according to the difference between the average actual bearing capacity change and the average predicted bearing capacity change, and the optimized layout depth and sand cushion layer thickness of the drainage pipe are stored.

Images