Power pipe jacking path optimization method and system based on multi-objective optimization

Abstract

The application relates to the field of electric power pipe jacking path optimization, and discloses an electric power pipe jacking path optimization method and system based on multi-target optimization. The method comprises the following steps: constructing a three-dimensional underground space model according to a BIM design drawing of a region where an electric power pipe jacking path is located, geological radar data, existing pipeline coordinates and obstacle coordinates; generating a plurality of alternative electric power pipe jacking paths in the three-dimensional underground space model based on safety constraint conditions of the electric power pipe jacking path; and screening the plurality of alternative electric power pipe jacking paths based on economic targets, construction period targets, environmental protection targets and operation and maintenance targets to obtain an optimized target electric power pipe jacking path. The application can take into account construction cost, construction period and maintenance cost and other factors of the electric power pipe jacking construction path, and improves the optimization effect of the electric power pipe jacking path.

Description

Technical Field

[0001] This application relates to the field of power pipe jacking path optimization technology, and more specifically, to a power pipe jacking path optimization method and system based on multi-objective optimization. Background Technology

[0002] Power pipe jacking is a trenchless underground pipeline laying technology. Its core principle is to use hydraulic jacking equipment to push prefabricated pipe sections from a starting shaft into the ground, forming an underground utility tunnel. Traditional construction methods typically employ manual guidance or simple mechanical assistance. Before construction, jacking parameters must be manually calculated based on geological survey data, and the jacking machine's posture adjusted based on experience. The jacking machine head often uses an open or grid-extrusion structure, relying on operators to monitor soil pressure, correction angles, and jacking speed in real time. Especially in complex geological formations, frequent shutdowns are necessary to handle obstacles or adjust the trajectory. During construction, the sealing of pipe section interfaces relies on rubber rings for water sealing or grouting reinforcement, and the control of surface settlement mainly depends on experience-based predictions and subsequent remedial measures, resulting in limitations such as low accuracy, large efficiency fluctuations, and high safety risks.

[0003] Furthermore, traditional power pipe jacking guidance and measurement technologies largely rely on optical theodolites or laser targets for manual positioning, resulting in data feedback lag, especially during long-distance jacking where cumulative errors are prone to occur. The construction process requires a large workforce for underground collaborative operations, leading to high labor intensity and harsh working environments. Simultaneously, when jacking pipes through sensitive areas, traditional methods lack sufficient control over surrounding soil disturbance, easily causing ground subsidence or pipeline deviation, resulting in high subsequent maintenance costs. These methods are poorly adaptable to soft soil, sandy layers, or water-rich strata, often requiring auxiliary dewatering, grouting, or ground reinforcement processes, further increasing construction time and costs, and failing to meet the demands of modern power engineering for efficiency, environmental protection, and intelligence. Currently, there are methods that automatically detect collisions between cable lines and survey results, ensuring cable lines meet the safety distance requirements of various underground facilities; however, the construction path for power pipe jacking struggles to balance construction costs, schedule, and maintenance costs. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for optimizing the path of power pipe jacking based on multi-objective optimization, which solves the technical problem that it is difficult to take into account factors such as construction cost, construction period and maintenance cost in the construction path of power pipe jacking, and achieves the technical effect that the construction path of power pipe jacking can take into account factors such as construction cost, construction period and maintenance cost.

[0005] This application provides a method for optimizing power jacking pipe paths based on multi-objective optimization. The method includes: constructing a three-dimensional underground space model based on BIM design drawings, ground-penetrating radar data, existing pipeline coordinates, and obstacle coordinates of the area where the power jacking pipe path is located; generating multiple candidate power jacking pipe paths within the three-dimensional underground space model based on safety constraints of the power jacking pipe path; and screening the multiple candidate power jacking pipe paths based on economic objectives, schedule objectives, environmental objectives, and operation and maintenance objectives to obtain the optimized target power jacking pipe path.

[0006] In one possible implementation, multiple candidate power pipe jacking routes are screened based on economic, schedule, environmental, and operation and maintenance objectives to obtain optimized target power pipe jacking routes. This includes: obtaining the material cost, labor cost, equipment cost, and construction risk indicators corresponding to the economic objectives of each candidate power pipe jacking route; constructing an economic objective matrix based on these indicators; obtaining the construction schedule indicators, approval schedule indicators, and emergency schedule indicators corresponding to the schedule objectives of each candidate power pipe jacking route; constructing a schedule objective matrix based on these indicators; obtaining the carbon emission indicators and ecological impact indicators corresponding to the environmental objectives of each candidate power pipe jacking route; and obtaining the inspection indicators corresponding to the operation and maintenance objectives of each candidate power pipe jacking route. The system employs a multi-stage approach to power pipe jacking. It constructs an operation and maintenance (O&M) target matrix based on the O&M targets and expected failure rate for each candidate power pipe jacking route. A cost control factor is determined using a cost detection unit based on the economic and project schedule target matrices. This cost control factor represents the cost of each candidate power pipe jacking route; a larger cost control factor indicates a higher cost. Similarly, a benefit target factor is determined using a benefit detection unit based on the environmental and O&M target matrices. This benefit target factor represents the benefit of each candidate power pipe jacking route; a larger benefit target factor indicates a higher benefit. The system also acquires the project priority control attributes for the region where the power pipe jacking route is located. These attributes include cost priority and benefit priority. When the project priority control attribute is cost priority, the candidate power pipe jacking route corresponding to the lowest cost control factor among all cost control factors is selected as the target power pipe jacking route. When the project priority control attribute is benefit priority, the candidate power pipe jacking route corresponding to the highest benefit target factor among all benefit target factors is selected as the target power pipe jacking route.

[0007] In another possible implementation, the method further includes: obtaining the short-term maintenance cost factor and long-term maintenance cost factor of the area where the power jacking path is located, where the short-term maintenance cost factor represents the average maintenance cost of the power lines in the area where the power jacking path is located in the short term, and the long-term maintenance cost factor represents the average maintenance cost of the power lines in the area where the power jacking path is located in the long term; determining the product of the cost control factor and the short-term maintenance cost factor as the modified cost control factor; determining the product of the benefit target factor and the long-term maintenance cost factor as the modified benefit target factor; when the project priority control attribute is cost priority, determining the candidate power jacking path corresponding to the smallest modified cost control factor among all modified cost control factors as the target power jacking path; when the project priority control attribute is benefit priority, determining the candidate power jacking path corresponding to the largest modified benefit target factor among all modified benefit target factors as the target power jacking path.

[0008] In another possible implementation, the method further includes: obtaining the operation and maintenance cycle of the power pipeline in the area where the power jacking path is located, and obtaining the discount rate corresponding to the long-term maintenance cost factor within the operation and maintenance cycle. The discount rate corresponding to the long-term maintenance cost factor represents the discount ratio of the power jacking path under long-term maintenance to the corresponding short-term maintenance. The product of the benefit target factor, the long-term maintenance cost factor, and the discount rate corresponding to the long-term maintenance cost factor is determined as the modified benefit target factor.

[0009] In another possible implementation, the method further includes: determining multiple segmented maintenance cycles for the area where the power jacking pipeline is located based on the historical maintenance cycles of existing power lines in the area; obtaining the discount rate corresponding to the long-term maintenance cost factor within the multiple segmented maintenance cycles; determining the sum of the products of the long-term maintenance cost factor and the discount rate within each segmented maintenance cycle as the discounted maintenance cost; and determining the product of the benefit target factor and the discounted maintenance cost as the modified benefit target factor.

[0010] In another possible implementation, the method further includes: using a construction risk and schedule impact model, determining the emergency schedule impact factor for each alternative power pipe jacking route based on the construction risk indicators and construction risk impact factors for each alternative power pipe jacking route; and constructing a schedule target matrix based on the construction schedule indicators, approval schedule indicators, emergency schedule indicators, and emergency schedule impact factors corresponding to the schedule target of each alternative power pipe jacking route.

[0011] In another possible implementation, the method further includes: using a construction period carbon emission impact model, determining the construction period carbon emission impact factor for each candidate power pipe jacking route based on the emergency construction period index and the carbon emission parameters during the construction of each candidate power pipe jacking route; and constructing an environmental protection target matrix based on the carbon emission index, construction period carbon emission impact factor, and ecological impact index corresponding to the environmental protection target of each candidate power pipe jacking route.

[0012] In another possible implementation, the method further includes: using a construction period ecological impact model, determining the construction period ecological impact factor of each candidate power pipe jacking path based on the emergency construction period indicators of each candidate power pipe jacking path and the ecological environment information of the area where each candidate power pipe jacking path is located; and constructing an environmental protection target matrix based on the carbon emission indicators, construction period carbon emission impact factors, ecological impact indicators, and construction period ecological environment impact factors corresponding to the environmental protection targets of each candidate power pipe jacking path.

[0013] In another possible implementation, the method further includes: obtaining the residential characteristics of the area where the power pipe jacking path is located; determining the noise pollution factor of each candidate power pipe jacking path based on the residential characteristics using a noise diffusion model; the noise pollution factor characterizing the noise pollution level of each candidate power pipe jacking path during construction; and determining the product of the noise pollution factor, the cost control factor, and the short-term maintenance cost factor as a modified cost control factor.

[0014] This application also provides a power pipe jacking path optimization system based on multi-objective optimization, including a unit for performing the method described in any of the preceding claims.

[0015] The beneficial effects of the embodiments in this application compared with the prior art are: This application provides a multi-objective optimization method for power pipe jacking path optimization. The method includes: constructing a three-dimensional underground space model based on BIM design drawings, ground-penetrating radar data, existing pipeline coordinates, and obstacle coordinates of the area where the power pipe jacking path is located; generating multiple candidate power pipe jacking paths within the three-dimensional underground space model based on safety constraints of the power pipe jacking path; and filtering the multiple candidate power pipe jacking paths based on economic, schedule, environmental, and operation and maintenance objectives to obtain the optimized target power pipe jacking path. The multi-objective optimization method for power pipe jacking path optimization in this application can construct an optimized target power pipe jacking path that meets economic, schedule, environmental, and operation and maintenance objectives while taking safety into account, thus improving the optimization effect of power pipe jacking path optimization in terms of economy, schedule, environmental protection, and operation and maintenance. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the first power pipe jacking path optimization method based on multi-objective optimization provided in this application embodiment; Figure 2 A flowchart illustrating the second power pipe jacking path optimization method based on multi-objective optimization provided in this application embodiment; Figure 3 A flowchart illustrating the third power pipe jacking path optimization method based on multi-objective optimization provided in this application embodiment; Figure 4 This is a schematic diagram of the logical structure of a power pipe jacking path optimization system based on multi-objective optimization, provided in an embodiment of this application. Detailed Implementation

[0018] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0019] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0021] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0023] Currently, there are methods to automatically detect the collision between cable lines and survey results and ensure that cable lines meet the safety distance requirements of various underground facilities. However, the construction path of power pipe jacking is difficult to balance factors such as construction cost, construction period, and maintenance cost.

[0024] Based on the above reasons, this application provides a multi-objective optimization method for power pipe jacking path optimization. The method includes: constructing a three-dimensional underground space model based on BIM design drawings, ground-penetrating radar data, existing pipeline coordinates, and obstacle coordinates of the area where the power pipe jacking path is located; generating multiple candidate power pipe jacking paths within the three-dimensional underground space model based on safety constraints of the power pipe jacking path; and filtering the multiple candidate power pipe jacking paths based on economic, schedule, environmental, and operation and maintenance objectives to obtain the optimized target power pipe jacking path. The multi-objective optimization method for power pipe jacking path optimization in this application can construct an optimized target power pipe jacking path that meets economic, schedule, environmental, and operation and maintenance objectives while taking safety into account, thus improving the optimization effect in terms of economy, schedule, environmental protection, and operation and maintenance of power pipe jacking paths.

[0025] In some scenarios, the multi-objective optimization-based power pipeline jacking path optimization method of this application embodiment can be applied to the path optimization of power pipeline jacking construction, which can improve the optimization effect of power pipeline jacking construction.

[0026] The following describes in detail, with specific examples, a method for optimizing the path of power pipe jacking based on multi-objective optimization provided in the embodiments of this application.

[0027] Figure 1 A flowchart illustrating the first multi-objective optimization-based power pipe jacking path optimization method provided in this application embodiment is shown below. Figure 1 As shown, the above method includes S110 to S130, and S110 to S130 will be described in detail below.

[0028] S110. Based on the BIM design drawings, ground-penetrating radar data, coordinates of existing pipelines and obstacles in the area where the power jacking pipeline route is located, construct a three-dimensional underground space model.

[0029] In the specific implementation of this method, the construction process of the three-dimensional underground space model can be completed based on the spatial registration and fusion technology of multi-source data. Specifically, BIM design drawings can provide structured building information model data of underground space, including the distribution of soil and rock layers, coordinates and elevation information of underground structures; ground-penetrating radar data can supplement geological anomalies, aquifer distribution and geotechnical parameters through electromagnetic wave reflection signal analysis; coordinates of existing pipelines can be imported through the coordinate conversion interface of geographic information system, and coordinates of obstacles can be extracted through on-site surveying or digital extraction of existing engineering drawings.

[0030] It should be understood that BIM design drawings, ground-penetrating radar data, existing pipeline coordinates, and obstacle coordinates can be aligned in spatial coordinate systems using a coordinate transformation module. A composite model, including geological structural layers, obstacle spatial envelopes, and pipeline topology, can then be generated using 3D modeling algorithms. Optionally, model construction can employ a fusion of point cloud data and the BIM model. A 3D geological surface can be generated through interpolation of the ground-penetrating radar point cloud data, and Boolean operations can be performed with the building components in the BIM model to accurately characterize the underground spatial features of the construction area.

[0031] S120. Based on the safety constraints of the power pipe jacking path, multiple alternative power pipe jacking paths are generated in the three-dimensional underground space model.

[0032] In this implementation, the safety constraints can include three sets of core parameters: path curvature constraints, burial depth constraints, and obstacle avoidance constraints. Specifically, the path curvature constraint sets a minimum turning radius threshold based on the mechanical performance of the pipe jacking machine; the burial depth constraint determines the vertical safety distance based on the groundwater level and the distribution of the permafrost layer; and the obstacle avoidance constraint ensures that the path maintains a minimum safe distance from existing pipelines and obstacles through a three-dimensional spatial collision detection algorithm.

[0033] When generating alternative paths based on constraints, a path search algorithm can be used for three-dimensional path search, or a genetic algorithm can be used to generate a population of paths that meet multiple constraints. Optionally, the path generation module can set dynamic constraint parameters to automatically enhance obstacle avoidance constraint weights when special geological areas are detected, such as increasing the path deviation coefficient threshold in quicksand areas, thereby generating a set of alternative paths that comply with engineering safety specifications.

[0034] S130. Based on economic, schedule, environmental and operation and maintenance objectives, multiple alternative power pipe jacking routes are screened to obtain the optimized target power pipe jacking route.

[0035] Specifically, during the screening process, multiple candidate power pipe jacking routes can be selected based on economic, schedule, environmental, and operation and maintenance objectives to obtain an optimized target power pipe jacking route. A target weight matrix can be set to dynamically adjust the contribution of each objective function according to project priority. For example, when constructing in a core urban area, the weight coefficient of the environmental protection objective can be increased, ultimately outputting a target power pipe jacking route that meets multi-dimensional project requirements.

[0036] The beneficial effects of the above implementation method are that it can significantly improve the accuracy of underground space representation through deep fusion modeling of multi-source heterogeneous data, and reduce the path planning deviation rate compared with traditional methods; and it can reduce the overall engineering cost by using key indicators such as economy and environmental protection to achieve collaborative optimization.

[0037] The aforementioned implementation method also has the beneficial effect that the path generation algorithm based on dynamic constraints effectively avoids geologically risky areas, significantly reducing the incidence of construction safety accidents. Through the synergistic application of three-dimensional spatial modeling, multi-constraint path generation, and multi-objective optimization screening techniques, it effectively solves the problems of single-objective optimization and insufficient multi-factor coupling analysis in traditional power pipe jacking path planning.

[0038] In some implementation methods, in S130 above, based on economic objectives, construction period objectives, environmental protection objectives, and operation and maintenance objectives, multiple alternative power pipe jacking routes are screened to obtain optimized target power pipe jacking routes, including S131 to S133. S131 to S133 will be explained in detail below.

[0039] S131. Obtain the material cost, labor cost, equipment cost, and construction risk indicators corresponding to the economic objectives of each candidate power pipe jacking route. Construct an economic objective matrix based on these indicators. Obtain the construction period, approval period, and emergency period indicators corresponding to the construction period objectives of each candidate power pipe jacking route. Construct a construction period objective matrix based on these indicators. Obtain the carbon emission and ecological impact indicators corresponding to the environmental objectives of each candidate power pipe jacking route. Construct an environmental objective matrix based on these indicators. Obtain the maintenance and operation indicators and expected failure rate indicators corresponding to the operation and maintenance objectives of each candidate power pipe jacking route. Construct an operation and maintenance objective matrix based on these indicators.

[0040] In the specific implementation of multi-objective optimization screening, the index parameters of each candidate path can be structured through a multi-dimensional data acquisition module. Specifically, the construction of the economic objective matrix can be achieved based on the bill of quantities decomposition technology. Material cost indicators can be quantified through pipe type, earthwork volume, and support structure usage; labor cost indicators are calculated based on the work hour quota and labor unit price model; equipment cost indicators cover pipe jacking machine rental costs and energy consumption costs; and construction risk indicators are assessed using the product of risk event probability and loss. Optionally, the calculation of construction risk indicators can be combined with ground-penetrating radar detection data to dynamically correct pipeline settlement risks in soft soil areas.

[0041] During the construction of the project schedule target matrix, multi-parameter coupled analysis can be achieved through a construction progress simulation engine. Construction schedule indicators can be based on a jacking speed prediction model, comprehensively considering pipe section assembly efficiency and grouting curing time; approval schedule indicators can automatically match the planning approval process time based on policy and regulatory databases of different administrative regions; emergency schedule indicators are probabilistically estimated using Monte Carlo simulation methods to predict the handling cycle for abnormal conditions such as sudden changes in groundwater levels or equipment failures. In practical implementation, a schedule correlation factor matrix can be set, automatically triggering a schedule overlap correction algorithm when parallel construction sections are detected.

[0042] To construct the environmental protection target matrix, a life cycle assessment method can be used to model the environmental impact of the construction process. Carbon emission indicators can be calculated across the entire chain of building material production, transportation emissions, and construction machinery exhaust emissions. Ecological impact indicators are analyzed using remote sensing image interpretation technology to spatially overlay changes in vegetation cover and the extent of water disturbance within the construction area. Optionally, the calculation of ecological impact indicators can incorporate a biodiversity index, setting an impact amplification factor for special ecological protection areas. For example, the ecological impact weight of paths surrounding wetland protection areas can be increased to 1.5 times that of conventional areas.

[0043] In terms of constructing the operation and maintenance target matrix, full life-cycle performance prediction can be achieved through digital twin technology. The inspection and maintenance indicators can be calculated based on topological characteristics such as the number of abrupt changes in path curvature and the frequency of changes in burial depth to determine the inspection difficulty coefficient, while the expected failure rate indicator is predicted by combining the pipe fatigue life model and the soil corrosion rate.

[0044] For example, in specific implementation, a pipeline stress monitoring data feedback mechanism can also be set up so that when the displacement of the pipe joint exceeds the threshold, the dynamic update of the operation and maintenance indicators is automatically triggered.

[0045] S132. Through the cost detection unit, based on the economic target matrix and the construction period target matrix, determine the cost control factor. The cost control factor represents the cost of each candidate power pipe jacking route; the larger the cost control factor, the greater the cost. Through the benefit detection unit, based on the environmental protection target matrix and the operation and maintenance target matrix, determine the benefit target factor. The benefit target factor represents the benefit of each candidate power pipe jacking route; the larger the benefit target factor, the greater the benefit.

[0046] In this implementation, when conducting cost assessment, a cost control factor can be determined by a cost detection unit based on the economic target matrix and the schedule target matrix. The cost control factor represents the cost of each alternative power pipe jacking route. The larger the cost control factor, the greater the cost. The cost of the power pipe jacking route can be assessed through the cost control factor.

[0047] In this implementation, when conducting benefit assessment, the benefit detection unit can determine the benefit target factor based on the environmental protection target matrix and the operation and maintenance target matrix. The benefit target factor represents the benefit of each candidate power pipe jacking route. The larger the benefit target factor, the greater the benefit. The benefit of the power pipe jacking route can be assessed through the benefit target factor.

[0048] It should be noted that the cost detection unit and the benefit detection unit can be deep learning units based on neural networks. The cost detection unit can be trained by labeled economic target matrix, schedule target matrix and cost control factors; the benefit detection unit can be trained by labeled environmental protection target matrix, operation and maintenance target matrix and benefit target factors.

[0049] S133. Obtain the project priority control attributes for the area where the power pipe jacking path is located. The project priority control attributes include cost priority and benefit priority. When the project priority control attribute is cost priority, determine the candidate power pipe jacking path corresponding to the lowest cost control factor among all cost control factors, and use it as the target power pipe jacking path. When the project priority control attribute is benefit priority, determine the candidate power pipe jacking path corresponding to the highest benefit target factor among all benefit target factors, and use it as the target power pipe jacking path.

[0050] During the path decision-making phase, adaptive strategy selection can be achieved through the priority response module. When the project's priority control attribute is cost priority, the alternative power pipeline jacking path corresponding to the lowest cost control factor among all cost control factors is determined as the target power pipeline jacking path, so that the power pipeline jacking construction is carried out according to the target power pipeline jacking path with the lowest cost.

[0051] Similarly, in the path decision-making stage, when the project's priority control attribute is benefit priority, the alternative power pipeline jacking path corresponding to the maximum benefit target factor among all benefit target factors is determined as the target power pipeline jacking path, so that the power pipeline jacking construction is carried out according to the target power pipeline jacking path with the greatest benefit.

[0052] The beneficial effects of the above implementation method are that by establishing a multi-dimensional target matrix system, the full-element digital expression of engineering parameters can be realized, significantly improving the objectivity and systematicness of path evaluation; and by comprehensively applying optimization algorithms, it can be ensured that the final selected path meets the requirements of engineering economy.

[0053] In some implementations, the above method also includes S134 to S135, which will be explained in detail below.

[0054] S134. Obtain the short-term maintenance cost factor and long-term maintenance cost factor of the area where the power pipe jacking path is located. The short-term maintenance cost factor represents the average maintenance cost of the power pipeline in the area where the power pipe jacking path is located in the short term, and the long-term maintenance cost factor represents the average maintenance cost of the power pipeline in the area where the power pipe jacking path is located in the long term.

[0055] In practice, the maintenance cost of the entire life cycle of the power pipe jacking route can be dynamically evaluated through the maintenance cost analysis module.

[0056] For example, the short-term maintenance cost factor can be quantitatively calculated based on construction quality inspection data and geological stability parameters. The construction quality inspection data may include indicators such as the compactness of pipe joints and the uniformity of grouting layers, while the geological stability parameters can be obtained through real-time monitoring of soil moisture content and lateral pressure coefficient.

[0057] For example, the long-term maintenance cost factor can be calculated by combining the soil corrosion rate model and the pipeline stress distribution map, wherein the soil corrosion rate model can integrate multi-dimensional environmental parameters such as soil resistivity, pH value and chloride ion concentration.

[0058] Optionally, the calculation of the short-term maintenance cost factor can incorporate construction process monitoring data, such as the jacking trajectory deviation value obtained through distributed fiber optic sensing technology. When the detected trajectory deviation exceeds a preset threshold, the maintenance cost coefficient can be dynamically corrected. The long-term maintenance cost factor can also integrate climate prediction data to predict and model pipeline deformation under freeze-thaw cycles, thereby more accurately reflecting the impact of climate characteristics in different regions on pipeline durability.

[0059] S135. Determine the product of the cost control factor and the short-term maintenance cost factor as the modified cost control factor. Determine the product of the benefit target factor and the long-term maintenance cost factor as the modified benefit target factor. When the project's priority control attribute is cost priority, determine the alternative power jacking route corresponding to the smallest modified cost control factor among all modified cost control factors, as the target power jacking route. When the project's priority control attribute is benefit priority, determine the alternative power jacking route corresponding to the largest modified benefit target factor among all modified benefit target factors, as the target power jacking route.

[0060] During the cost control factor correction process, the product of the cost control factor and the short-term maintenance cost factor can be used as the corrected cost control factor. It should be noted that the calculation of the corrected cost control factor can also include setting a construction quality weighting coefficient, automatically increasing the contribution of the short-term maintenance cost factor when a high-risk geological section is detected. For example, during construction in soft soil areas, the settlement of pipe sections can be predicted using a soil rheology model, thereby dynamically adjusting the proportion of the short-term maintenance cost factor in the product calculation.

[0061] To refine the benefit target factor, the product of the benefit target factor and the long-term maintenance cost factor can be determined as the refined benefit target factor. It should be noted that a time decay function can be used to further couple the long-term maintenance cost with environmental benefits. The generation of the refined benefit target factor can be combined with a pipeline service life prediction model, where the pipeline service life prediction can be iteratively calculated based on pipe fatigue life test data and stress concentration factors.

[0062] In practice, when the path is detected to pass through a highly corrosive soil area, the maintenance cost superposition module of the anti-corrosion coating can be automatically activated to realize the dynamic correction of the long-term maintenance cost factor.

[0063] When the project's priority control attribute is cost priority, the alternative power pipe jacking path corresponding to the smallest modified cost control factor among all modified cost control factors can be identified as the target power pipe jacking path to reduce pipeline construction costs. When the project's priority control attribute is benefit priority, the alternative power pipe jacking path corresponding to the largest modified benefit target factor among all modified benefit target factors can be identified as the target power pipe jacking path to improve the efficiency of pipeline construction.

[0064] The beneficial effects of the above implementation method are that by introducing a dual-dimensional evaluation mechanism of short-term maintenance costs and long-term maintenance costs, the full life-cycle consideration dimensions of path selection decision-making are effectively improved; and by adopting a dynamic correction algorithm, the construction period cost and operation and maintenance period cost are organically unified, so that the selected path not only meets the current economic requirements but also has long-term service reliability.

[0065] In some implementations, the above method also includes S136 to S137, which will be explained in detail below.

[0066] S136. Obtain the operation and maintenance cycle of the power pipelines in the area where the power jacking path is located, and obtain the discount rate corresponding to the long-term maintenance cost factor within the operation and maintenance cycle. The discount rate corresponding to the long-term maintenance cost factor represents the discount ratio of the power jacking path under long-term maintenance to the short-term maintenance.

[0067] In practice, the long-term maintenance costs can be converted into time value through a full lifecycle cost analysis module. Specifically, the maintenance cycle can be obtained based on the design service life of the power pipeline, combined with the average overhaul interval from historical maintenance data for comprehensive calculation.

[0068] It should be noted that the discount rate for the long-term maintenance cost factor can be dynamically calculated using a discounted cash flow model. The discount rate can be determined based on the current benchmark interest rate in the financial market, plus the risk premium parameter specific to the operation and maintenance of power facilities.

[0069] Optionally, the calculation of the discount rate can incorporate a risk assessment module for the operation and maintenance phase. By analyzing dynamic parameters such as the frequency of geological activity and the trend of soil corrosion rate changes in the area where the route is located, the basic discount rate can be periodically adjusted. For example, when the route is detected to cross an active seismic zone, a risk adjustment coefficient can be automatically increased to improve the discount rate value, thereby more accurately reflecting the time value decay characteristics of long-term maintenance costs.

[0070] In practical implementation, a dynamic discount rate adjustment mechanism can be set up. When the rate of performance degradation of the pipe material is detected to exceed expectations, data can be collected in real time through material performance monitoring sensors to trigger a dynamic update of the discount rate. For example, in a route scheme using new composite pipe materials, the material durability parameters in the discount rate calculation model can be automatically optimized based on the on-site measured pipe wall thickness attenuation rate.

[0071] S137. Determine the product of the benefit target factor, the long-term maintenance cost factor, and the discount rate corresponding to the long-term maintenance cost factor, and use it as the modified benefit target factor.

[0072] To optimize benefit assessment, a benefit decay model can be established by combining the time distribution characteristics of operation and maintenance costs. By using the product of the long-term maintenance cost factor and the discount rate as a time-weighted coefficient, the maintenance expenditures of future years can be converted into equivalent cost values ​​at the current decision-making point. This approach allows for a comparative analysis of the spatiotemporal distribution characteristics of environmental benefits and operation and maintenance costs within a unified dimension.

[0073] The beneficial effects of the above implementation method are that by introducing a time value conversion mechanism, it effectively solves the problem of the difference between the dimensions of long-term maintenance costs and short-term benefit assessments; by adopting a dynamic discount rate adjustment strategy, it can reflect the impact of changes in the engineering environment on operation and maintenance costs in real time, and can significantly improve the precision of full life cycle cost control.

[0074] In some implementations, the above method also includes S138 to S139, which will be explained in detail below.

[0075] S138. Based on the historical operation and maintenance cycle of existing power pipelines in the area where the power jacking pipeline is located, determine multiple segmented operation and maintenance cycles in the area where the power jacking pipeline is located, and obtain the discount rate corresponding to the long-term maintenance cost factor in the multiple segmented operation and maintenance cycles in the area where the power jacking pipeline is located.

[0076] In practical implementation, a segmented lifecycle assessment model can be used to conduct a refined analysis of the operation and maintenance costs of power pipe jacking routes. Specifically, the segmentation of operation and maintenance cycles can be based on the clustering analysis results of historical operation and maintenance data, and a time series segmentation algorithm can be used to identify high-frequency maintenance intervals and stable operation intervals.

[0077] For example, the K-means clustering method can be used to perform pattern recognition on the maintenance interval data of historical pipelines, and the operation and maintenance cycle can be divided into typical stages such as the initial commissioning period, the stable operation period, and the aging maintenance period, with each stage corresponding to different maintenance cost characteristics.

[0078] Optionally, the segmented maintenance cycle can be dynamically adjusted based on the pipeline material degradation curve. For pipelines using high-strength polyethylene, a segmentation duration prediction model can be established based on material creep test data; for metal pipes, segmentation standards can be established based on electrochemical corrosion rates. In practice, a segmentation cycle division scheme that conforms to actual working conditions can be automatically generated by extracting features from pipeline wall thickness monitoring data using a convolutional neural network.

[0079] In the discount rate calculation stage, differentiated economic models can be set for different operation and maintenance phases. The discount rate during the initial commissioning period can be dynamically adjusted based on construction quality acceptance data. For example, when the interface welding qualification rate is found to be lower than the threshold, the discount rate upward mechanism for this stage can be triggered. The discount rate during the stable operation period is periodically corrected based on regional power grid load fluctuation data, and precise control is achieved by establishing a correlation matrix between load fluctuation and maintenance costs.

[0080] S139. Determine the sum of the products of the long-term maintenance cost factor and the discount rate within each segmented maintenance cycle, as the discounted maintenance cost. Determine the product of the benefit target factor and the discounted maintenance cost, as the revised benefit target factor.

[0081] For calculating discounted maintenance costs, a phased cumulative method can be used to integrate costs across multiple periods. In practice, a time-weighted coefficient matrix can be set up, and the product results within each period can be weighted and summed according to the proportion of maintenance duration. For example, during the aging maintenance phase, the impact of costs in this phase on the overall assessment can be strengthened by increasing the time-weighted coefficient. Simultaneously, an inflation adjustment factor can be introduced to dynamically convert historical cost data to purchasing power parity.

[0082] In the process of generating the benefit target factors, a multidimensional benefit correlation map can be established. Specifically, when determining the sum of the products of the long-term maintenance cost factor and the discount rate within each segmented operation and maintenance cycle, the product operation can be decomposed into dual optimization in spatial and temporal dimensions: in the spatial dimension, the distribution data of sensitive facilities around the path are obtained through the GIS system; in the temporal dimension, the benefit time distribution curve is generated by combining the segmented maintenance plan.

[0083] The beneficial effects of the above implementation method are as follows: Through a segmented operation and maintenance cycle division mechanism, the fluctuation pattern of maintenance costs throughout the entire pipeline lifecycle can be accurately captured; the use of a differentiated discount rate calculation model effectively reflects the changes in economic characteristics at different operation and maintenance stages; and the establishment of a multi-dimensional weighted cumulative algorithm makes the time value conversion of long-term maintenance costs more in line with engineering economic laws, significantly improving the spatiotemporal adaptability of path optimization decisions. Through the synergistic effect of phased refined evaluation and dynamic adjustment mechanisms, optimal decision support that balances short-term benefits and long-term sustainability is provided for power pipe jacking projects.

[0084] Figure 2 A flowchart illustrating the second multi-objective optimization-based power pipe jacking path optimization method provided in this application embodiment is shown below. Figure 2 As shown, the above method also includes S210 to S220, which will be described in detail below.

[0085] S210. Using the construction risk and schedule impact model, based on the construction risk indicators and construction risk impact factors of each alternative power pipe jacking route, determine the emergency schedule impact factor for each alternative power pipe jacking route.

[0086] In practical implementation, the risk-schedule coupling analysis module can be used to dynamically correlate construction risks with schedule indicators. Specifically, the construction risk-schedule impact model can be a deep learning model based on neural networks, and construction risk indicators can include parameters from dimensions such as geological condition risk coefficient, equipment failure risk coefficient, and worker safety risk coefficient.

[0087] In this implementation, when determining the emergency construction period impact factor through the construction risk and construction period impact model, the calculation of the emergency construction period impact factor can be combined with the construction risk indicators and construction risk impact factors of each alternative power pipe jacking route.

[0088] Optionally, when determining the factors influencing construction risks, real-time monitoring data streams can be introduced. For example, multi-parameter sensors installed at the pipe jacking machine head can collect data such as changes in ground pressure and tool wear in real time, and dynamically adjust the risk impact weights. For areas with fluctuating groundwater levels, a nonlinear correlation model between hydrogeological parameters and emergency construction periods can be set up. When the permeability coefficient is detected to exceed a threshold, the emergency response mechanism can be automatically triggered.

[0089] S220. Construct a schedule target matrix based on the construction schedule indicators, approval schedule indicators, emergency schedule indicators, and emergency schedule influencing factors corresponding to the schedule target of each alternative power pipe jacking route.

[0090] In the process of constructing the project schedule target matrix, multi-source data can be integrated through four-dimensional index fusion technology. Emergency project schedule indicators can be dynamically weighted according to the emergency project schedule risk impact factors. For example, when a high-risk construction section is detected, the emergency buffer time can be automatically increased. The construction schedule indicators, approval schedule indicators, emergency schedule indicators and emergency project schedule impact factors are spliced ​​together to obtain the project schedule target matrix.

[0091] It should be noted that a dynamic adjustment mechanism can be set up for optimizing the matrix construction. When a deviation in construction progress is detected to exceed a preset threshold, the matrix parameters are automatically updated in real time. For example, a BIM progress simulation engine can be used to compare and analyze the actual construction progress with the planned progress, and machine learning algorithms can be used to dynamically optimize the weight allocation ratio of each indicator to ensure that the project schedule target matrix always reflects the latest project status.

[0092] The beneficial effects of the above implementation method are: by establishing a quantitative correlation model between risk and work mechanism, the accuracy and reliability of project schedule prediction are significantly improved; by adopting a multi-dimensional data fusion mechanism, the collaborative optimization of approval process, construction progress and emergency plan is achieved; and after constructing a dynamically adjusted project schedule target matrix, it can effectively cope with the uncertainties in complex underground engineering and provide a decision basis that takes into account both efficiency and safety for the optimal selection of power jacking pipeline routes.

[0093] In some implementations, the above method also includes S230 to S240, which will be described in detail below.

[0094] S230. Using the construction period carbon emission impact model, determine the construction period carbon emission impact factor for each alternative power pipe jacking route based on the emergency construction period index and the carbon emission parameters during construction of each alternative power pipe jacking route.

[0095] In practical implementation, a construction period carbon emission impact model can be used to dynamically couple construction progress with environmental indicators. Specifically, the construction period carbon emission impact model can be constructed using a deep learning-based model, decomposing emergency construction period indicators into parameters such as equipment standby time, frequency of backup power supply activation, and intensity of expedited measures. Carbon emission parameters can be obtained by integrating fuel consumption data of construction machinery, carbon emission factors of electricity supply, and energy consumption curves of emergency lighting systems, establishing a quantitative relationship between construction period extension and carbon emission growth through a data fusion model.

[0096] Optionally, the calculation of carbon emission parameters can incorporate a real-time energy consumption monitoring system. For example, instantaneous power data of the hydraulic system of the pipe jacking machine can be collected through IoT sensors, and the carbon emission coefficient can be dynamically corrected by combining the equipment load characteristic curves under different geological conditions. For emergency plans involving nighttime construction, a correlation model between light intensity and auxiliary equipment energy consumption can be set up, and a gradient adjustment mechanism for carbon emission parameters can be automatically triggered when an increase in lighting duration is detected.

[0097] S240. Construct an environmental protection target matrix based on the carbon emission indicators, construction period carbon emission impact factors, and ecological impact indicators corresponding to the environmental protection targets of each candidate power pipe jacking route.

[0098] In this implementation method, the carbon emission indicators, construction period carbon emission impact factors, and ecological impact indicators corresponding to the environmental protection objectives of each candidate power pipe jacking route can be directly spliced ​​together to obtain the constructed environmental protection objective matrix.

[0099] As an optimization, a comprehensive assessment of multi-source environmental impacts can be achieved through a three-dimensional index weighting algorithm during the construction of the environmental protection target matrix. The baseline value for carbon emission indicators can be initialized based on the national emission standards for construction machinery, while the carbon emission impact factor during the construction period serves as a dynamic adjustment coefficient, amplifying or reducing the baseline value according to actual construction progress deviations. The calculation of ecological impact indicators can incorporate satellite remote sensing vegetation index change data to spatially model ecological degradation caused by disturbances such as construction vibration and noise.

[0100] The beneficial effects of the above implementation method are: by establishing a quantitative correlation model between construction period and carbon emissions, the comprehensive coverage of environmental assessment is significantly improved; by adopting a dynamic weight allocation mechanism, the intelligent adaptation of construction progress fluctuations and environmental protection indicators is achieved, enhancing the accuracy of assessment under complex working conditions; and after constructing a multi-dimensional integrated environmental target matrix, it can provide a decision-making basis for the optimal selection of power pipe jacking routes that takes into account both construction efficiency and ecological protection, effectively promoting the application of green construction technologies.

[0101] In some implementations, the above method also includes S250 to S260, which will be described in detail below.

[0102] S250. Using the construction period ecological impact model, based on the emergency construction period indicators of each candidate power pipe jacking route and the ecological environment information of the area where each candidate power pipe jacking route is located, the construction period ecological environment impact factor of each candidate power pipe jacking route is determined.

[0103] In practical implementation, a construction period ecological impact model can be used to achieve dynamic coordination between construction progress and ecological protection. Specifically, the construction period ecological impact model can be a deep learning model, which can calculate the construction period ecological impact factor of each candidate power pipe jacking route by combining emergency construction period indicators and ecological environment information of the area where each candidate power pipe jacking route is located.

[0104] For example, the acquisition of ecological and environmental information can integrate vegetation cover layers, water system buffer data, and wildlife habitat heat maps from a geographic information system, and generate a three-dimensional ecological impact assessment grid through spatial interpolation algorithms.

[0105] Optionally, the processing of ecological and environmental information can incorporate high-precision remote sensing image interpretation technology to identify the type and vulnerability classification of the ecosystem within a 50-meter buffer zone around the construction path. For emergency plans involving nighttime construction, construction noise propagation data can be collected through an acoustic sensor network to establish a quantitative relationship model between sound pressure level and biological activity disturbance.

[0106] S260. Construct an environmental protection target matrix based on the carbon emission indicators, construction period carbon emission impact factors, ecological impact indicators, and construction period ecological environment impact factors corresponding to the environmental protection targets of each candidate power pipe jacking route.

[0107] Based on the carbon emission indicators, construction period carbon emission impact factors, ecological impact indicators, and construction period ecological environment impact factors corresponding to the environmental protection objectives of each candidate power pipe jacking route, the carbon emission indicators, construction period carbon emission impact factors, ecological impact indicators, and construction period ecological environment impact factors corresponding to the environmental protection objectives of each candidate power pipe jacking route can be spliced ​​together to construct an environmental protection objective matrix.

[0108] In the calculation of ecological and environmental impact factors during the construction period, a dynamic weight allocation mechanism can be adopted. Specifically, seasonal adjustment parameters can be set to automatically increase the weight of ecological impact factors during bird breeding seasons or plant growing seasons.

[0109] The beneficial effects of the above implementation method are that by establishing a quantitative correlation model between construction period and ecological protection, the spatiotemporal dimension coverage of environmental assessment is significantly improved; after constructing an environmental protection target matrix that integrates multi-source data, it can provide a scientific decision-making basis for the optimal selection of power pipe jacking routes that takes into account both construction efficiency and ecological sustainability.

[0110] Figure 3 A flowchart illustrating the third multi-objective optimization-based power pipe jacking path optimization method provided in this application embodiment is shown below. Figure 3 As shown, the above method also includes S310 to S320, which will be described in detail below.

[0111] S310. Obtain the residential characteristics of the area where the power pipe jacking path is located. Using a noise diffusion model, determine the noise pollution factor of each candidate power pipe jacking path based on the residential characteristics. The noise pollution factor represents the noise pollution level of each candidate power pipe jacking path during construction.

[0112] In practical implementation, a social factors integration module can be used to dynamically link residential characteristics with project costs. Specifically, the acquisition of residential characteristics can be combined with building distribution data in the urban planning database, and geofencing technology can be used to identify high-density residential areas, schools, hospitals, and other sensitive locations within a 200-meter buffer zone of the construction path.

[0113] In this implementation, the noise pollution factor can be calculated using a noise diffusion model based on an acoustic propagation model. Combined with the noise spectrum characteristics of the pipe jacking machine model and the sound barrier setting scheme, the equivalent sound level during the day and night can be spatially simulated.

[0114] Optionally, the analysis of population residential characteristics can incorporate mobile communication signaling data, using base station positioning to statistically analyze heatmaps of actual population distribution at different times. For example, when a path is detected traversing a mixed commercial and residential area, dynamic weights can be set for different time periods, increasing the weight of noise pollution factors during nighttime construction to 1.3 times that during the daytime. For particularly sensitive buildings, the secondary noise impact caused by construction vibration can be calculated using a building structure vibration response model.

[0115] S320. Determine the product of the noise pollution factor, cost control factor, and short-term maintenance cost factor as the modified cost control factor.

[0116] After obtaining the noise pollution factor, cost control factor, and short-term maintenance cost factor, the product of the noise pollution factor, cost control factor, and short-term maintenance cost factor can be determined as the modified cost control factor, thereby realizing the impact of the noise pollution factor on the pipe jacking construction path.

[0117] The beneficial effects of the above implementation method are that by integrating social residential characteristic parameters, the integrity of the social dimension of engineering cost assessment is significantly improved; and by establishing a dynamic correlation model between noise pollution and short-term maintenance costs, the impact of social coordination costs on the economics of the project is effectively quantified.

[0118] The beneficial effect of the above implementation method is that, after adopting the correction algorithm of multi-source data fusion, it can provide a decision-making basis for path optimization that takes into account both engineering benefits and social responsibility, and promote the harmonious development of engineering construction and community environment.

[0119] This application also provides a power pipe jacking path optimization system based on multi-objective optimization, including a unit for performing the method described in any of the preceding claims.

[0120] Figure 4 A schematic diagram of the logical structure of a power pipe jacking path optimization system based on multi-objective optimization is provided for an embodiment of this application, as shown below. Figure 4 As shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above-described method. The beneficial effects of the embodiments of this application have been described in the above-described method and will not be repeated here.

[0121] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0123] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0124] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0125] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0126] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing the path of power pipe jacking based on multi-objective optimization, characterized in that, The method includes: Based on the BIM design drawings, ground radar data, coordinates of existing pipelines and obstacles in the area where the power pipe jacking route is located, a three-dimensional underground space model is constructed. Based on the safety constraints of the power pipe jacking path, multiple alternative power pipe jacking paths are generated in the three-dimensional underground space model. Based on economic, schedule, environmental and operation and maintenance objectives, multiple alternative power pipe jacking routes were screened to obtain the optimized target power pipe jacking route. The BIM design drawings provide structured building information model data for underground space, including soil and rock layer distribution, coordinates and elevation information of underground structures; the ground-penetrating radar data is analyzed through electromagnetic wave reflection signals to supplement geological anomalies, aquifer distribution and soil and rock mechanical parameters; the coordinates of existing pipelines are imported through the geographic information system coordinate conversion interface, and the coordinates of obstacles are extracted through on-site surveying or digital extraction of existing engineering drawings. The BIM design drawings, ground-penetrating radar data, existing pipeline coordinates and obstacle coordinates are aligned in spatial coordinate system through a coordinate transformation module, and a composite model containing geological structure layers, obstacle spatial envelope and pipeline topology is generated using a three-dimensional modeling algorithm. The composite model is constructed by fusing point cloud data with BIM model. It generates a three-dimensional geological surface by interpolating ground-penetrating radar point cloud data and performs Boolean operations with building components in BIM model to accurately characterize the underground space features of the construction area. The safety constraints include three sets of core parameters: path curvature constraint, burial depth constraint, and obstacle avoidance constraint. The path curvature constraint sets the minimum turning radius threshold based on the mechanical performance of the pipe jacking machine. The burial depth constraint determines the vertical safety distance based on the groundwater level and the distribution of the permafrost layer. The obstacle avoidance constraint ensures that the path maintains a minimum safe distance from existing pipelines and obstacles through a three-dimensional spatial collision detection algorithm. When generating alternative paths based on safety constraints, a path search algorithm is used to perform a three-dimensional path search, or a genetic algorithm is used to generate a path population that meets multiple constraints. The path generation module sets dynamic constraint parameters, automatically enhances the obstacle avoidance constraint weight when a special geological area is detected, increases the path deviation coefficient threshold in the quicksand layer area, and generates a set of alternative paths that meet engineering safety specifications.

2. The method as described in claim 1, characterized in that, Based on economic, schedule, environmental, and operation and maintenance objectives, multiple candidate power pipe jacking routes were screened to obtain optimized target power pipe jacking routes, including: For each candidate power pipe jacking route, obtain the material cost, labor cost, equipment cost, and construction risk indicators corresponding to the economic objectives. Construct an economic objective matrix based on these indicators. Similarly, for each candidate power pipe jacking route, obtain the construction period, approval period, and emergency period indicators corresponding to the project duration objectives. Construct a project duration objective matrix based on these indicators. Finally, for each candidate power pipe jacking route, obtain the carbon emission and ecological impact indicators corresponding to the environmental objectives. Construct an environmental objective matrix based on these indicators. Finally, for each candidate power pipe jacking route, obtain the maintenance and operation indicators and expected failure rate indicators corresponding to the operation and maintenance objectives. Construct an operation and maintenance objective matrix based on these indicators. The cost detection unit determines the cost control factor based on the economic target matrix and the schedule target matrix. The cost control factor represents the cost of each candidate power pipe jacking route. The larger the cost control factor, the greater the cost. The benefit detection unit determines the benefit target factor based on the environmental protection target matrix and the operation and maintenance target matrix. The benefit target factor represents the benefit of each candidate power pipe jacking route. The larger the benefit target factor, the greater the benefit. Obtain the project priority control attributes for the area where the power pipe jacking path is located. The project priority control attributes include cost priority and benefit priority. When the project priority control attribute is cost priority, determine the candidate power pipe jacking path corresponding to the minimum cost control factor among all cost control factors, and use it as the target power pipe jacking path. When the project priority control attribute is benefit priority, determine the candidate power pipe jacking path corresponding to the maximum benefit target factor among all benefit target factors, and use it as the target power pipe jacking path.

3. The method as described in claim 2, characterized in that, The method further includes: Obtain the short-term maintenance cost factor and long-term maintenance cost factor of the area where the power pipe jacking path is located. The short-term maintenance cost factor represents the average maintenance cost of the power pipeline in the area where the power pipe jacking path is located in the short term, and the long-term maintenance cost factor represents the average maintenance cost of the power pipeline in the area where the power pipe jacking path is located in the long term. The product of the cost control factor and the short-term maintenance cost factor is determined as the modified cost control factor; the product of the benefit target factor and the long-term maintenance cost factor is determined as the modified benefit target factor; when the project's priority control attribute is cost priority, the candidate power jacking path corresponding to the smallest modified cost control factor among all modified cost control factors is determined as the target power jacking path; when the project's priority control attribute is benefit priority, the candidate power jacking path corresponding to the largest modified benefit target factor among all modified benefit target factors is determined as the target power jacking path.

4. The method as described in claim 3, characterized in that, The method further includes: The operation and maintenance cycle of the power pipelines in the area where the power jacking pipeline is located is obtained, and the discount rate corresponding to the long-term maintenance cost factor within the operation and maintenance cycle is obtained. The discount rate corresponding to the long-term maintenance cost factor represents the discount ratio of the power jacking pipeline under long-term maintenance to the short-term maintenance. The benefit target factor, the long-term maintenance cost factor, and the product of the discount rates corresponding to the long-term maintenance cost factor are determined and used as the modified benefit target factor.

5. The method as described in claim 4, characterized in that, The method further includes: Based on the historical operation and maintenance cycle of existing power pipelines in the area where the power jacking pipeline route is located, determine multiple segmented operation and maintenance cycles in the area where the power jacking pipeline route is located, and obtain the discount rate corresponding to the long-term maintenance cost factor in the multiple segmented operation and maintenance cycles in the area where the power jacking pipeline route is located. The sum of the products of the long-term maintenance cost factor and the discount rate within each segmented operation and maintenance cycle is determined as the discounted maintenance cost; the product of the benefit target factor and the discounted maintenance cost is determined as the modified benefit target factor.

6. The method as described in claim 5, characterized in that, The method further includes: By using the construction risk and schedule impact model, based on the construction risk indicators and construction risk impact factors of each alternative power pipe jacking route, the emergency schedule impact factor of each alternative power pipe jacking route is determined. A schedule target matrix is ​​constructed based on the construction schedule indicators, approval schedule indicators, emergency schedule indicators, and emergency schedule influencing factors corresponding to the schedule targets of each alternative power pipe jacking route.

7. The method as described in claim 6, characterized in that, The method further includes: By using the construction period carbon emission impact model, the construction period carbon emission impact factor of each alternative power pipe jacking route is determined based on the emergency construction period index of each alternative power pipe jacking route and the carbon emission parameters during the construction of each alternative power pipe jacking route. An environmental protection target matrix is ​​constructed based on the carbon emission indicators, construction period carbon emission impact factors, and ecological impact indicators corresponding to the environmental protection targets of each alternative power pipe jacking route.

8. The method as described in claim 7, characterized in that, The method further includes: Based on the construction period ecological impact model, the construction period ecological impact factor of each candidate power pipe jacking route is determined according to the emergency construction period indicators of each candidate power pipe jacking route and the ecological environment information of the area where each candidate power pipe jacking route is located. An environmental protection target matrix is ​​constructed based on the carbon emission indicators, construction period carbon emission impact factors, ecological impact indicators, and construction period ecological environment impact factors corresponding to the environmental protection targets of each alternative power pipe jacking route.

9. The method as described in claim 8, characterized in that, The method further includes: The residential characteristics of the area where the power pipe jacking path is located are obtained. Based on the residential characteristics, the noise pollution factor of each candidate power pipe jacking path is determined by the noise diffusion model. The noise pollution factor represents the noise pollution level of each candidate power pipe jacking path during construction. The product of the noise pollution factor, cost control factor, and short-term maintenance cost factor is determined as the modified cost control factor.

10. A power pipe jacking path optimization system based on multi-objective optimization, characterized in that, Includes a unit for performing the method according to any one of claims 1 to 9.

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