Mountain city modular water plant layout optimization method and system based on topographic features

Abstract

The invention provides a mountain city modular water plant layout optimization method and system based on topographic features, and belongs to the technical field of industrial data processing. The method comprises the following steps: analyzing topographic features of a mountain environment, extracting gradient, elevation difference data and geological stability data, and calculating a topographic adaptability index according to the gradient, elevation difference and geological stability data; comparing the terrain adaptability index with a preset threshold value to generate a terrain adaptability level; analyzing the water plant function modules, and calculating a module priority index according to the inter-module association degree, the water demand distribution adaptation degree and the operation maintenance frequency of the water plant function modules; and generating a module layout signal and / or a module adjustment signal according to the terrain adaptability index and the module priority index, and if the module adjustment signal is generated, redistributing the target adjustment module to the optimal terrain area. By making full use of mountain terrain characteristics, scientific distribution of water plant function modules is achieved, and construction difficulty and operation cost are reduced.

Description

Technical Field

[0001] This application relates to the field of industrial data processing technology, and in particular to a method and system for optimizing the layout of modular water plants in mountainous cities based on terrain features. Background Technology

[0002] With the acceleration of urbanization, the issues of water resource management and water plant layout optimization in mountainous cities are becoming increasingly prominent. In mountainous environments with complex terrain and limited space, how to efficiently utilize terrain features to achieve a scientific layout of modular water plants has become a key research focus and challenge. However, existing water plant layout schemes still have significant shortcomings in combining terrain features, modular design, and overall system optimization, making it difficult to meet the needs of mountainous cities for highly adaptable, low-cost, and eco-friendly water plants.

[0003] Existing technologies propose a comprehensive system for a fully underground water treatment plant. This system organically integrates the water tank with the underground water treatment plant through a sunken plaza, along with lighting, ventilation, and water diversion systems, forming an ecological complex. However, this approach primarily focuses on the design of fully underground water plants and does not fully consider the topographical characteristics of mountainous cities, lacking the ability to optimize modular layouts for complex terrain conditions. Furthermore, its heavy reliance on underground space may increase construction difficulty, especially in geologically complex mountainous environments, where its applicability and economic viability may be significantly limited. Existing technologies still have considerable room for improvement in integrating mountainous terrain features, enhancing the flexibility of modular design, and reducing construction and operating costs. Summary of the Invention

[0004] To address the aforementioned technical challenges, this application provides a method and system for optimizing the layout of modular water plants in mountainous cities based on terrain features. This method fully utilizes the characteristics of mountainous terrain to achieve a scientific distribution of functional modules in the water plant, reducing construction difficulty and operating costs, and enhancing the overall adaptability and ecological benefits of the water plant. This, in turn, meets the needs of mountainous cities for efficient and sustainable water plant construction.

[0005] In a first aspect, this application provides a method for optimizing the layout of modular water plants in mountainous cities based on terrain features, the method comprising: The terrain analysis module analyzes the terrain features of the mountainous environment, extracts slope data, elevation difference data, and geological stability data, and calculates a terrain adaptability index based on the slope data, elevation difference data, and geological stability data; the terrain adaptability index is compared with a preset threshold to generate a terrain adaptability level, and the terrain adaptability level is sent to the layout optimization module. The water plant's functional modules are analyzed through a priority analysis module. Based on the inter-module correlation, water demand distribution adaptability, and operation and maintenance frequency of the water plant's functional modules, a module priority index is calculated and sent to the layout optimization module. The layout optimization module generates a module layout signal and / or a module adjustment signal based on the terrain adaptability index and the module priority index. If the module adjustment signal is generated, the functional module corresponding to the module adjustment signal is marked as the target adjustment module, and the target adjustment module is reassigned to the optimal terrain area.

[0006] In one embodiment, the slope data includes a slope adaptability factor, the elevation difference data includes an elevation difference adaptability factor, and the geological stability data includes a geological stability factor. The calculation of the terrain adaptability index based on the slope data, the elevation difference data, and the geological stability data includes: The terrain adaptability index is calculated using the following formula: ; TAI represents the terrain adaptability index, S represents the slope adaptability factor, H represents the elevation difference adaptability factor, G represents the geological stability factor, α is the weighting coefficient of the slope adaptability factor, β is the weighting coefficient of the elevation difference adaptability factor, and λ is the weighting coefficient of the geological stability factor, and satisfies the following conditions: ; The calculation of the module priority index based on the inter-module correlation, water demand distribution adaptability, and operation and maintenance frequency of the water plant's functional modules includes: The priority index of the module is calculated using the following formula: ; Wherein, MPI represents the module priority index, C represents the inter-module correlation degree, D represents the water demand distribution adaptability, F represents the operation and maintenance frequency, λ is the weighting coefficient of the correlation degree between the water plant functional modules, μ is the weighting coefficient of the water demand distribution adaptability, and ν is the weighting coefficient of the operation and maintenance frequency, and satisfies the following conditions: , In one embodiment, the method further includes: The slope adaptability factor is calculated using the following formula: ; Where P represents the slope value, P max This indicates the maximum permissible slope, which is set based on the construction equipment capacity and the water plant construction requirements.

[0007] In one embodiment, the method further includes: The elevation difference adaptability factor is calculated using the following formula: ; in, Indicates the difference in elevation. This represents the maximum permissible elevation difference, where the maximum permissible elevation... The difference is determined based on the pump's head capacity.

[0008] In one embodiment, the method further includes: The geological stability factor is calculated using the following formula: ; Where T represents the geological stability score, T max This represents the highest geological stability score, which is a comprehensive assessment based on factors such as the regional soil and rock mechanical properties and the frequency of seismic activity. In one embodiment, the method further includes: The degree of correlation between the modules is calculated using the following formula: ; in, Indicates the number of connections between modules. This represents the total number of possible connections, where the number of inter-module connections represents the physical connection relationships and functional dependencies between the functional modules of the water plant. In one embodiment, the method further includes: The water demand distribution fit is calculated using the following formula: ; Wherein, Y represents the area of ​​the hot spot covered by water demand, and Y1 represents the total area of ​​the hot spot covered by water demand. The area of ​​the hot spot covered by water demand indicates the degree of matching between the location of the functional module and the hot spot area of ​​water demand.

[0009] In one embodiment, the method further includes: The operation and maintenance frequency is calculated using the following formula: ; Where Z represents the average number of maintenance operations per year, and Z1 represents the number of designed maintenance cycles. The average number of maintenance operations per year is calculated based on actual operation records.

[0010] In one embodiment, the step of reassigning the target adjustment module to the optimal terrain area includes: Identify the optimal terrain region and evaluate the module layout within the optimal terrain region; Adjust the module connection relationship of the optimal terrain region so as to place the target adjustment module into the optimal terrain region; The terrain adaptability level is divided into high-level adaptability area, medium-level adaptability area and low-level adaptability area. The high-level adaptability area is used to arrange core functional modules, the medium-level adaptability area is used to arrange auxiliary functional modules, and the low-level adaptability area is an area where no modules are arranged.

[0011] Secondly, this application provides a modular water plant layout optimization system for mountainous cities based on terrain features, the modular water plant layout optimization system for mountainous cities based on terrain features includes: The terrain analysis module is used to analyze the terrain features of the mountainous environment, extract slope data, elevation difference data, and geological stability data, calculate the terrain adaptability index based on the slope data, elevation difference data, and geological stability data, compare the terrain adaptability index with a preset threshold to generate a terrain adaptability level, and send the terrain adaptability level to the layout optimization module. The priority analysis module is used to analyze the functional modules of the water plant. Based on the inter-module correlation, water demand distribution adaptability, and operation and maintenance frequency of the functional modules of the water plant, the module priority index is calculated and the module priority index is sent to the layout optimization module. The layout optimization module is used to generate a module layout signal and / or a module adjustment signal based on the terrain adaptability index and the module priority index. If the module adjustment signal is generated, the functional module corresponding to the module adjustment signal is marked as the target adjustment module, and the target adjustment module is reassigned to the optimal terrain area.

[0012] The method and system for optimizing the layout of modular water plants in mountainous cities based on terrain features, as provided in this application, effectively addresses the problem of neglecting terrain complexity in existing technologies. Through scientific terrain analysis, functional module priority evaluation, and layout optimization strategies, it achieves the goals of efficient, economical, and sustainable water plant construction. In practical applications, this method not only reduces construction difficulty and costs but also improves the ecological benefits and operational efficiency of water plants, providing crucial technical support for water resource management in mountainous cities. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation on the scope of protection of this application. In the various drawings, similar components are numbered similarly.

[0014] Figure 1A flowchart illustrating the modular water plant layout optimization method for mountainous cities based on terrain features provided in this application is shown. Figure 2 A schematic diagram of the modular water plant layout optimization system for mountainous cities based on terrain features provided in this application is shown.

[0015] Icons: 200 - Modular water plant layout optimization system for mountainous cities based on terrain features, 201 - Terrain analysis module, 202 - Priority analysis module, 203 - Layout optimization module. Detailed Implementation

[0016] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0017] The components of this application, typically described and illustrated in the accompanying drawings, can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0018] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0019] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0020] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0021] Example 1 This application provides a method for optimizing the layout of modular water plants in mountainous cities based on terrain features. It proposes a modular water plant layout optimization scheme that combines terrain features to achieve the goal of efficient, economical and sustainable water plant construction.

[0022] See Figure 1 The method for optimizing the layout of modular water plants in mountainous cities based on terrain features includes steps S101-S103, and each step is explained below.

[0023] Step S101: Analyze the terrain features of the mountainous environment through the terrain analysis module, extract slope data, elevation difference data and geological stability data, calculate the terrain adaptability index based on the slope data, elevation difference data and geological stability data, compare the terrain adaptability index with a preset threshold to generate a terrain adaptability level, and send the terrain adaptability level to the layout optimization module.

[0024] As an example, in practical application, a mountainous urban area was selected as the research object. This area has complex terrain features, including steep slopes, significant elevation differences, and local geologically unstable areas. To achieve a scientific layout, a digital elevation model (DEM) of the target area was first obtained using a high-precision geographic information system (GIS), and slope data, elevation difference data, and geological stability assessment data were further extracted. Slope data and elevation difference data can be directly extracted from the DEM or obtained through further calculations, but geological stability assessment data usually cannot be obtained directly from the DEM and requires combination with other data sources and professional analysis. Using the spatial analysis tools of GIS software, slope was extracted from the DEM data to obtain the slope values ​​of each point in the study area, reflecting the steepness of the terrain. The DEM itself stores the elevation information of the terrain. By calculating the differences in elevation values ​​between different areas or nodes, elevation difference data can be obtained, which is used to analyze the terrain undulation. The DEM only provides elevation information of the terrain surface and cannot directly reflect geological stability-related parameters such as underground geological structure, soil and rock mechanical properties, or seismic activity frequency. The geological stability assessment data needs to be derived by combining multiple sources of data, such as geological survey reports, geotechnical engineering test data, and historical earthquake activity records, through professional geological assessment models or expert experience analysis.

[0025] In one embodiment, the slope data includes a slope adaptability factor, the elevation difference data includes an elevation difference adaptability factor, and the geological stability data includes a geological stability factor. The calculation of the terrain adaptability index based on the slope data, the elevation difference data, and the geological stability data includes: The terrain adaptability index is calculated using the following formula: ; TAI represents the terrain adaptability index, S represents the slope adaptability factor, H represents the elevation difference adaptability factor, G represents the geological stability factor, α is the weighting coefficient of the slope adaptability factor, β is the weighting coefficient of the elevation difference adaptability factor, and λ is the weighting coefficient of the geological stability factor, and satisfies the following conditions: .

[0026] In this embodiment, the extracted raw data may contain noise or outliers, requiring correction through filtering, smoothing, and other methods. Median filtering is used to remove random noise points from the DEM, ensuring the accuracy of slope and elevation difference data calculations. Furthermore, the slope values ​​are compared with a preset maximum allowable slope (e.g., 30 degrees), and the elevation difference data is compared with the maximum allowable elevation difference for the pump's head capacity (e.g., 50 meters). The slope and elevation difference adaptation factors are then normalized to become dimensionless adaptation factors for calculating the Terrain Adaptability Index (TAI).

[0027] In this embodiment, the method further includes: The slope adaptability factor is calculated using the following formula: ; Where P represents the slope value, P max This indicates the maximum permissible slope, which is set based on the construction equipment capacity and the water plant construction requirements. The maximum permissible slope is usually no more than 30 degrees.

[0028] In this embodiment, the method further includes: The elevation difference adaptability factor is calculated using the following formula: ; in, Indicates the difference in elevation. This represents the maximum permissible elevation difference, where the maximum permissible elevation... The difference is determined based on the pump's head capacity, and the maximum allowable elevation difference generally does not exceed 50 meters.

[0029] In this embodiment, the method further includes: The geological stability factor is calculated using the following formula: ; Where T represents the geological stability score, T max This indicates the highest geological stability score, which is a comprehensive assessment based on the regional soil and rock mechanical properties and the frequency of seismic activity.

[0030] The geological stability score is calculated using the following formula: ; in, This represents the weight of the i-th evaluation factor. This reflects the degree of influence of the factor on geological stability. This represents the quantization function for the i-th evaluation factor, which transforms the raw data into standardized scores (e.g., 0-10). The quantization functions for several key evaluation factors are explained below.

[0031] (1) Scoring function for geotechnical properties. The evaluation index of the scoring function for geotechnical properties is the type of geotechnical mass (such as bedrock, loose deposits).

[0032] ; (2) Seismic activity frequency scoring function, the evaluation indicators of which are seismic intensity and seismic frequency.

[0033] ; in x λ represents the number of earthquakes per unit time, and λ is the attenuation constant (determined by regional seismic activity).

[0034] (3) Fault activity factor scoring function.

[0035] ; (4) The groundwater level scoring function adopts the inverse distance weighting function.

[0036] ; Where x is the depth of the groundwater level (m), h min and h max For the groundwater buried in the area Minimum and maximum depth (the greater the burial depth, the higher the score).

[0037] Suppose the geological assessment factors and weights for a mountainous urban area are as follows: Geotechnical properties (weight w1 = 0.5): score 8; Seismic activity frequency (weight w2 = 0.3): score 6; Fault activity (weight w3 = 0.2): score 10; then the geological stability score is: 0.5 × 8 + 0.3 × 6 + 0.2 × 10 = 4 + 1.8 + 2 = 7.8. Further, using the formula, the geological stability factor G = 7.8 / 10 = 0.78.

[0038] For example, the weighting coefficients α, β, and γ are set to 0.4, 0.3, and 0.3, respectively. This set of weights reflects that slope has a greater impact on construction difficulty, followed by elevation difference, while geological stability mainly affects long-term operational safety. The calculated Terrain Adaptability Index (TAI) is compared with preset thresholds to generate a terrain adaptability level. For example, there can be multiple preset thresholds; the first preset threshold can be 0.8, and the second preset threshold can be 0.5. For example, when TAI > 0.8, it is defined as a high-level adaptability area, suitable for arranging core functional modules; when 0.5 ≤ TAI ≤ 0.80, it is defined as a medium-level adaptability area, suitable for arranging auxiliary functional modules; when TAI < 0.5, it is defined as a low-level adaptability area, and it is not recommended to arrange any modules.

[0039] Step S102: The water plant functional modules are analyzed by the priority analysis module. The module priority index is calculated based on the inter-module correlation, water demand distribution adaptability, and operation and maintenance frequency of the water plant functional modules. The module priority index is then sent to the layout optimization module.

[0040] In this embodiment, the calculation of the module priority index based on the inter-module correlation, water demand distribution adaptability, and operation and maintenance frequency of the water plant functional modules includes: The priority index of the module is calculated using the following formula: ; Wherein, MPI represents the module priority index, C represents the inter-module correlation degree, D represents the water demand distribution adaptability, F represents the operation and maintenance frequency, λ is the weighting coefficient of the correlation degree between the water plant functional modules, μ is the weighting coefficient of the water demand distribution adaptability, and ν is the weighting coefficient of the operation and maintenance frequency, and satisfies the following conditions: .

[0041] In practice, taking a modular water plant in a mountainous city as an example, this water plant includes multiple functional modules, such as water intake, water purification, water storage, and water transmission modules. The inter-module correlation degree C is quantified through the physical connections and functional dependencies between the modules. For example, if the water intake module and the water purification module are directly connected and highly functionally dependent, their correlation degree is high. The water demand distribution adaptability is calculated by the degree of matching between water demand hotspot areas and module locations; the larger the coverage area, the higher the adaptability. The operation and maintenance frequency is obtained through statistical analysis of the modules' daily inspection and maintenance records. If the actual number of maintenance operations is significantly lower than the design cycle number, the operation and maintenance frequency is low.

[0042] In this embodiment, the method further includes: The degree of correlation between the modules is calculated using the following formula: ; in, Indicates the number of connections between modules. The total number of possible connections is represented, where the number of connections between modules represents the physical connection relationship and functional dependency between the functional modules of the water plant.

[0043] In this embodiment, the method further includes: The water demand distribution fit is calculated using the following formula: ; Wherein, Y represents the area of ​​the hot spot covered by water demand, and Y1 represents the total area of ​​the hot spot covered by water demand. The area of ​​the hot spot covered by water demand indicates the degree of matching between the location of the functional module and the hot spot area of ​​water demand.

[0044] In this embodiment, the method further includes: The operation and maintenance frequency is calculated using the following formula: ; Where Z represents the average number of maintenance operations per year, and Z1 represents the number of designed maintenance cycles. The average number of maintenance operations per year is calculated based on actual operation records.

[0045] For example, the weighting coefficients λ, μ, and ν are set to 0.5, 0.3, and 0.2, respectively. This set of weights reflects that the inter-module correlation is of the highest importance to the overall system coordination, followed by the adaptability of water demand distribution, while the operation and maintenance frequency mainly affects the later operating costs. The calculated Module Priority Index (MPI) will be used for decision support in subsequent layout optimization modules.

[0046] Step S103: The layout optimization module generates a module layout signal and / or a module adjustment signal based on the terrain adaptability index and the module priority index. If the module adjustment signal is generated, the functional module corresponding to the module adjustment signal is marked as the target adjustment module, and the target adjustment module is reassigned to the optimal terrain area.

[0047] In practical implementation, the terrain adaptability level is first matched with the module priority index. For example, the water intake module has a high MPI value due to its high adaptability to water demand distribution and low operation and maintenance frequency, and therefore should be preferentially placed in a high-level adaptability area. If the current placement of a functional module does not meet its priority requirements, a module adjustment signal is generated, and the module is marked as the target adjustment module. The reallocation process of the target adjustment module includes the following steps: identifying the optimal terrain area, evaluating the placement of other modules in the area, and adjusting the connection relationships of related modules. For example, if the water storage module needs to be reallocated due to its low terrain adaptability level, a medium-level adaptability area is selected as the new placement location, ensuring that the connection path between this area and other modules is the shortest and does not affect the overall system's operating efficiency.

[0048] In this embodiment, the step of reallocating the target adjustment module to the optimal terrain area includes: Identify the optimal terrain region and evaluate the module layout within the optimal terrain region; Adjust the module connection relationship of the optimal terrain region so as to place the target adjustment module into the optimal terrain region; The terrain adaptability level is divided into high-level adaptability area, medium-level adaptability area and low-level adaptability area. The high-level adaptability area is used to arrange core functional modules, the medium-level adaptability area is used to arrange auxiliary functional modules, and the low-level adaptability area is an area where no modules are arranged.

[0049] In this embodiment, the layout optimization module matches the terrain adaptability level with the module priority index. For example, the water intake module has a high module priority index (MPI) due to its high adaptability to water demand distribution and low operation and maintenance frequency, so it should be preferentially placed in a high-level adaptability area (e.g., an area with a terrain adaptability index (TAI) > 0.8). This is because the terrain conditions in high-level adaptability areas are superior, better meeting the functional requirements of the water intake module and reducing construction and operation costs. If the current placement of a functional module does not meet its priority requirements, the layout optimization module will generate a module adjustment signal and mark the functional module whose current placement does not meet its priority requirements as the target adjustment module. For example, if the terrain adaptability level of the area where the water storage module is located is low (Terrain Adaptability Index (TAI) < 0.5), but its module priority index (MPI) indicates that it should be placed in a higher adaptability area, then the module adjustment signal will be triggered.

[0050] The process of reallocating the target adjustment module can include the following steps: (1) Identifying the optimal terrain area. For example, for the water storage module, a medium-level adaptability area (0.5≤TAI≤0.8) will be selected as the new location; then, the layout of other modules in the area will be evaluated to ensure that the new location will not conflict with the existing modules; finally, the connection relationship of the relevant modules will be adjusted to ensure that the connection path between the area and other modules is the shortest and does not affect the overall system's operating efficiency. Through these steps, the reasonable reallocation of the target adjustment module is achieved. The process of generating the module layout signal includes: the layout optimization module generates the module layout signal based on the above matching analysis and adjustment process. If all modules have been reasonably laid out, the generated module layout signal determines the final layout scheme of each functional module in the mountainous city area; if there is a target adjustment module, after reallocation, the final module layout signal is determined based on the adjustment result to complete the scientific layout of the modular water plant.

[0051] In the modular water plant layout of mountainous cities, the need to reallocate target adjustment modules mainly stems from insufficient terrain adaptability or mismatched module priorities. The following are specific examples and analyses: 1. Inadequate Terrain Adaptability: Example 1: Core module located in a low-level adaptability area. Specific scenario description: The water purification module (core functional module, high priority) is initially located in an area with a slope of 35°, exceeding the maximum allowable slope of 30°, resulting in extremely difficult construction and a risk of landslides. The Terrain Adaptability Index (TAI) is 0.6 (medium level), failing to meet the requirement that the core module must be located in a high-level adaptability area (TAI>0.8). Adjustment basis: According to the terrain analysis module results, this area belongs to a low-level adaptability area and is unsuitable for locating the core module. Operation: Identify the optimal terrain area: Filter high-level areas with TAI>0.8 (e.g., a slope of 15°, a geologically stable, gently sloping area). Evaluate the placement of other modules: There are currently no other core modules in this area, and there is sufficient space. Adjust connection relationships: Re-plan the water supply pipeline between the water purification module and the water intake module to ensure the shortest water flow path and minimal head loss.

[0052] Example 2: Auxiliary modules occupy high-level adaptability areas. Specific scenario: The maintenance warehouse (an auxiliary function module, low priority) is initially placed in a high-level area with TAI=0.9, while this area is more suitable for the frequently operating water delivery module (high priority). Adjustment basis: The Module Priority Index (MPI) shows that the water delivery module has higher correlation (more connections to the water purification module) and water demand adaptability (covering major water hotspots), and should therefore occupy a high-level area first. Operation: Identify the optimal terrain area: Adjust the maintenance warehouse to a medium-level area with TAI=0.6 (slope 20°, meeting the auxiliary module placement requirements). Evaluate the placement of other modules: This medium-level area already has a power distribution module; ensure the distance between them meets safety regulations. Adjust connection relationships: Shorten the maintenance passage between the maintenance warehouse and the power distribution module, while freeing up a high-level area for the water delivery module and optimizing its physical connection with the water purification module.

[0053] 2. Module priority mismatch: Example 1: High-maintenance-frequency modules are located far from the maintenance access area. Specific scenario description: Chemical dosing module (high operation and maintenance frequency, F =0.8) The initial placement in a complex valley area, 300 meters from the maintenance access road, resulted in a 20% increase in average annual maintenance costs. Adjustment basis: Operation and maintenance frequency in the Module Priority Index (MPI). F The current solution has a low MPI value (MPI=0.6) due to maintenance inconvenience, and needs to be improved to MPI>0.7. Operation: Identify the optimal terrain area: Select a medium-adaptability area (TAI=0.7, slope 25°) near the maintenance access road. Evaluate the layout of other modules: The existing water storage module in this area needs to ensure that the length of the connecting pipeline between the dosing module and the water storage module does not increase by more than 10%. Adjust the connection relationship: Re-lay the dosing pipeline, and add a temporary storage point next to the maintenance access road to reduce maintenance path costs. Example 2: Over-concentration of low-association modules. Specific scenario description: The sludge treatment module (association with the water purification module C=0.3) and the water purification module (C=0.9) are initially arranged adjacently, resulting in complex pipeline intersections and increasing construction costs by 15%. Adjustment basis: The inter-module correlation C weight is 0.5. The sludge treatment module and the water purification module have low functional dependence and do not need to be arranged close together. They should be concentrated with other low-association modules (such as the drainage module, C=0.2). Operation: Identify the optimal terrain area: Select a medium-adaptability area at the edge of the plant (TAI=0.6, geological stability score 7). Evaluate the layout of other modules: This area has a planned drainage module, and the correlation between the two is C=0.6 (shared drainage pipeline), suitable for centralized layout. Adjust the connection relationship: Combine the sludge treatment module and the drainage module to reduce pipeline intersections and reduce construction costs to within the expected range.

[0054] 3. Multiple Factors Conflicting. Example 1: Both Topography and Maintenance Requirements Fail to Meet Standards. Specific Scenario: The water intake module (highest priority, MPI=0.9) is initially located in a low-level area with TAI=0.4 (geological stability score of 5, with fault risk), and is 1 km from the water source, resulting in a 30% increase in water intake energy consumption, while maintenance vehicles cannot directly access it. Adjustment Basis: The terrain adaptability factor G=0.5 (below the threshold of 0.6), and the operation and maintenance frequency F=0.7 (requiring high-frequency inspections), together making the module layout infeasible. Action: Identify the optimal terrain area: Select a high-level area with TAI=0.8 (slope 18°, no active faults) near the water source. Evaluate the layout of other modules: This area is a blank plot that can accommodate the water intake module and its supporting pump house. Adjust the connection relationship: Shorten the water intake pipeline to 500 meters, and build a direct access road for maintenance to reduce energy consumption and maintenance costs.

[0055] It should be further noted that, in actual operation, the modules share information and collaborate through data interfaces. For example, the terrain analysis module sends the calculated terrain adaptability level to the layout optimization module, while the priority analysis module transmits the module priority index to the layout optimization module. The layout optimization module then generates the final module layout signal or adjustment signal based on the combined analysis results of both. The design of the entire system framework must meet the requirements of real-time performance, reliability, and scalability to address the layout optimization needs of water plants in mountainous cities of varying scales and complexities.

[0056] The modular water plant layout optimization method for mountainous cities based on terrain features provided in this embodiment effectively addresses the problem of neglecting terrain complexity in existing technologies. Through scientific terrain analysis, functional module priority evaluation, and layout optimization strategies, it achieves the goal of efficient, economical, and sustainable water plant construction. In practical applications, this method not only reduces construction difficulty and costs but also improves the ecological benefits and operational efficiency of water plants, providing important technical support for water resource management in mountainous cities.

[0057] Example 2 In addition, this application provides a modular water plant layout optimization system for mountainous cities based on terrain features.

[0058] like Figure 2 As shown, the modular water plant layout optimization system 200 for mountainous cities based on terrain features includes: The terrain analysis module 201 is used to analyze the terrain features of the mountainous environment, extract slope data, elevation difference data and geological stability data, calculate the terrain adaptability index based on the slope data, elevation difference data and geological stability data, compare the terrain adaptability index with a preset threshold to generate a terrain adaptability level, and send the terrain adaptability level to the layout optimization module. The priority analysis module 202 is used to analyze the functional modules of the water plant, calculate the module priority index based on the inter-module correlation, water demand distribution adaptability and operation and maintenance frequency of the functional modules of the water plant, and send the module priority index to the layout optimization module. The layout optimization module 203 is used to generate a module layout signal and / or a module adjustment signal based on the terrain adaptability index and the module priority index. If the module adjustment signal is generated, the functional module corresponding to the module adjustment signal is marked as the target adjustment module, and the target adjustment module is reassigned to the optimal terrain area.

[0059] The modular water plant layout optimization system 200 for mountainous cities based on terrain features provided in this embodiment can implement the modular water plant layout optimization method for mountainous cities based on terrain features provided in Embodiment 1. To avoid repetition, it will not be described again here.

[0060] The modular water plant layout optimization system for mountainous cities based on terrain features provided in this embodiment effectively addresses the problem of neglecting terrain complexity in existing technologies. Through scientific terrain analysis, functional module priority evaluation, and layout optimization strategies, it achieves the goals of efficient, economical, and sustainable water plant construction. In practical applications, this method not only reduces construction difficulty and costs but also improves the ecological benefits and operational efficiency of water plants, providing important technical support for water resource management in mountainous cities.

[0061] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0062] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0063] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for optimizing the layout of modular water plants in mountainous cities based on terrain features, characterized in that, The method includes: The terrain analysis module analyzes the terrain features of the mountainous environment, extracts slope data, elevation difference data, and geological stability data, and calculates a terrain adaptability index based on the slope data, elevation difference data, and geological stability data; the terrain adaptability index is compared with a preset threshold to generate a terrain adaptability level, and the terrain adaptability level is sent to the layout optimization module. The water plant's functional modules are analyzed through a priority analysis module. Based on the inter-module correlation, water demand distribution adaptability, and operation and maintenance frequency of the water plant's functional modules, a module priority index is calculated and sent to the layout optimization module. The layout optimization module generates a module layout signal and / or a module adjustment signal based on the terrain adaptability index and the module priority index. If the module adjustment signal is generated, the functional module corresponding to the module adjustment signal is marked as the target adjustment module, and the target adjustment module is reassigned to the optimal terrain area.

2. The method according to claim 1, characterized in that, The slope data includes a slope adaptability factor, the elevation difference data includes an elevation difference adaptability factor, and the geological stability data includes a geological stability factor. The calculation of the terrain adaptability index based on the slope data, the elevation difference data, and the geological stability data includes: The terrain adaptability index is calculated using the following formula: TAI represents the terrain adaptability index, S represents the slope adaptability factor, H represents the elevation difference adaptability factor, G represents the geological stability factor, α is the weighting coefficient of the slope adaptability factor, β is the weighting coefficient of the elevation difference adaptability factor, and λ is the weighting coefficient of the geological stability factor, and satisfies the following conditions: ; The calculation of the module priority index based on the inter-module correlation, water demand distribution adaptability, and operation and maintenance frequency of the water plant's functional modules includes: The priority index of the module is calculated using the following formula: Wherein, MPI represents the module priority index, C represents the inter-module correlation degree, D represents the water demand distribution adaptability, F represents the operation and maintenance frequency, λ is the weighting coefficient of the correlation degree between the water plant functional modules, μ is the weighting coefficient of the water demand distribution adaptability, and ν is the weighting coefficient of the operation and maintenance frequency, and satisfies the following conditions: .

3. The method according to claim 2, characterized in that, The method further includes: The slope adaptability factor is calculated using the following formula: Where P represents the slope value, P max This indicates the maximum permissible slope, which is set based on the construction equipment capacity and the water plant construction requirements.

4. The method according to claim 2, characterized in that, The method further includes: The elevation difference adaptability factor is calculated using the following formula: in, Indicates the difference in elevation. This represents the maximum permissible elevation difference, where the maximum permissible elevation... The difference is determined based on the pump's head capacity.

5. The method according to claim 2, characterized in that, The method further includes: The geological stability factor is calculated using the following formula: Where T represents the geological stability score, T max This indicates the highest geological stability score, which is a comprehensive assessment based on the regional soil and rock mechanical properties and the frequency of seismic activity.

6. The method according to claim 2, characterized in that, The method further includes: The degree of correlation between the modules is calculated using the following formula: in, Indicates the number of connections between modules. The total number of possible connections is represented, where the number of connections between modules represents the physical connection relationship and functional dependency between the functional modules of the water plant.

7. The method according to claim 2, characterized in that, The method further includes: The water demand distribution fit is calculated using the following formula: Wherein, Y represents the area of ​​the hot spot covered by water demand, and Y1 represents the total area of ​​the hot spot covered by water demand. The area of ​​the hot spot covered by water demand indicates the degree of matching between the location of the functional module and the hot spot area of ​​water demand.

8. The method according to claim 2, characterized in that, The method further includes: The operation and maintenance frequency is calculated using the following formula: Where Z represents the average number of maintenance operations per year, and Z1 represents the number of designed maintenance cycles. The average number of maintenance operations per year is calculated based on actual operation records.

9. The method according to claim 1, characterized in that, The step of reassigning the target adjustment module to the optimal terrain area includes: Identify the optimal terrain region and evaluate the module layout within the optimal terrain region; Adjust the module connection relationship of the optimal terrain region so as to place the target adjustment module into the optimal terrain region; The terrain adaptability level is divided into high-level adaptability area, medium-level adaptability area and low-level adaptability area. The high-level adaptability area is used to arrange core functional modules, the medium-level adaptability area is used to arrange auxiliary functional modules, and the low-level adaptability area is an area where no modules are arranged.

10. A modular water plant layout optimization system for mountainous cities based on terrain features, characterized in that, The system includes: The terrain analysis module is used to analyze the terrain features of the mountainous environment, extract slope data, elevation difference data, and geological stability data, calculate the terrain adaptability index based on the slope data, elevation difference data, and geological stability data, compare the terrain adaptability index with a preset threshold to generate a terrain adaptability level, and send the terrain adaptability level to the layout optimization module. The priority analysis module is used to analyze the functional modules of the water plant. Based on the inter-module correlation, water demand distribution adaptability, and operation and maintenance frequency of the functional modules of the water plant, the module priority index is calculated and the module priority index is sent to the layout optimization module. The layout optimization module is used to generate a module layout signal and / or a module adjustment signal based on the terrain adaptability index and the module priority index. If the module adjustment signal is generated, the functional module corresponding to the module adjustment signal is marked as the target adjustment module, and the target adjustment module is reassigned to the optimal terrain area.

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