Construction parameter recommendation method and system for municipal greening engineering assembly type irrigation system

Abstract

The application discloses a kind of municipal afforestation engineering assembly type irrigation system's construction parameter recommendation method and system, including using field data and historical data cluster analysis, predict the irrigation demand of green area in future state, determine the initial recommended value of irrigation parameter;Establish the three-dimensional hydraulic model of irrigation area pipe network and carry out grid division, carry out steady-state analysis and transient analysis to determine the compensation value of irrigation parameter, obtain target recommended value after optimization, to determine the design pressure and flow of pipe network, measure the actual water pressure and flow of key node after pipeline installation is completed, according to the deviation of measured value and design value determine the specification of pressure reducing valve that needs to be added in the same pipe section or adjust the model of nozzle, to calibrate the operating parameter of irrigation system.The application quickly obtains the initial recommended value of irrigation parameter by multivariate data clustering, and is optimized and compensated by combining hydraulic simulation analysis, to accurately recommend construction parameter, improve the accuracy and water-saving efficiency of irrigation system construction installation.

Description

Technical Field

[0001] This invention relates to the field of engineering construction technology, and in particular to a method and system for recommending construction parameters for a prefabricated irrigation system for municipal greening projects. Background Technology

[0002] In the construction and maintenance of municipal greening projects, efficient and water-saving irrigation systems are crucial for ensuring healthy plant growth, enhancing landscape effects, and achieving sustainable water resource utilization. With the popularization of modular and standardized construction concepts, prefabricated irrigation systems are gradually becoming an industry trend due to their advantages such as quick construction, easy maintenance, and reusability. These systems typically involve prefabricating components such as pipes, controllers, and sprinklers in a factory, which are then quickly assembled on-site.

[0003] However, in the actual deployment of prefabricated irrigation systems, the scientific and precise determination of construction parameters such as pipeline design pressure, sprinkler / dripper selection, rotational irrigation group division, and irrigation regime directly determines the irrigation uniformity, water-saving efficiency, and long-term operational stability of the system after completion. Currently, the conventional methods relied upon in determining these key construction parameters in this field still have significant shortcomings, mainly in the following aspects:

[0004] Existing methods largely rely on designers' past experience or simplified empirical formulas for calculations. They lack comprehensive consideration of soil texture, plant community water requirements, micro-topographical changes, and future weather conditions, resulting in often coarse-grained recommended irrigation parameters such as irrigation depth and cycle. Differences in experience between different projects and designers directly lead to large fluctuations in design results, making it difficult to guarantee optimal irrigation effects, especially in accurately adapting to the complex actual needs of specific sites. Traditional designs are mostly based on steady-state hydraulic calculations, making it difficult to simulate transient processes such as water hammer and drastic pressure fluctuations caused by frequent pump starts and stops and valve openings and closings during actual operation. This may lead to the neglect of the risk of excessive or insufficient pressure in certain pipe sections under dynamic conditions, creating safety hazards such as pipe bursts or uneven irrigation, and also affecting the service life of the equipment. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this invention provides a method and system for recommending construction parameters for prefabricated irrigation systems in municipal greening projects.

[0006] In a first aspect, the present invention provides a method for recommending construction parameters for a prefabricated irrigation system for municipal greening projects, the method comprising:

[0007] Acquire basic on-site data, including soil characteristics, plant community data, and environmental data of the target greening area and the construction site. The environmental data includes meteorological data, soil moisture data, and topographic data for a future preset period.

[0008] Cluster analysis is performed on a database constructed using on-site basic data and historical greening irrigation project data. Based on the clustering results, the expected irrigation demand of the current greening area under future operation is predicted to determine the initial recommended values ​​of key construction parameters of the irrigation system. The key construction parameters include at least the target irrigation depth, irrigation cycle and pipeline design working pressure.

[0009] A three-dimensional hydraulic model of the irrigation area pipe network was established and adaptive meshing was performed. Steady-state and transient analyses were conducted on the meshed three-dimensional model using hydraulic simulation analysis. Based on the analysis results, compensation values ​​for key construction parameters were determined. The initial recommended values ​​were optimized based on the compensation values ​​to obtain the target recommended values ​​for key construction parameters.

[0010] On-site measurements and layout were conducted, and the design pressure and flow rate of each level of the pipeline network were determined based on the target recommended values ​​of key construction parameters. After pipeline installation and preliminary commissioning, the actual pressure and flow rate of key nodes in the pipeline network system were measured, and the actual operating parameters of the irrigation system were calibrated based on the deviation between the actual values ​​and the design values.

[0011] Secondly, the present invention also provides a construction parameter recommendation system for a prefabricated irrigation system for municipal greening projects, the system comprising:

[0012] The data acquisition unit is used to acquire basic on-site data, including soil characteristics of the target greening area, plant community data, and environmental data of the construction site. The environmental data includes meteorological data, soil moisture data, and topographic data for a future preset time period.

[0013] The clustering analysis unit is used to perform clustering analysis on a database constructed using on-site basic data and historical greening irrigation project data. Based on the clustering results, it predicts the expected irrigation demand of the current greening area under future operation conditions, so as to determine the initial recommended values ​​of key construction parameters of the irrigation system. The key construction parameters include at least the target irrigation depth, irrigation cycle and pipeline design working pressure.

[0014] The compensation optimization unit is used to establish a three-dimensional hydraulic model of the irrigation area pipe network and perform adaptive mesh generation. The hydraulic simulation analysis method is used to perform steady-state and transient analysis on the divided three-dimensional model. Based on the analysis results, the compensation values ​​of key construction parameters are determined. The initial recommended values ​​are optimized based on the compensation values ​​to obtain the target recommended values ​​of key construction parameters.

[0015] The parameter calibration unit is used for on-site measurement and layout, determining the design pressure and flow rate of each level of the pipeline network based on the target recommended values ​​of key construction parameters, and measuring the actual pressure and flow rate of key nodes in the pipeline network system after pipeline installation and initial commissioning. Based on the deviation between the actual values ​​and the design values, the actual operating parameters of the irrigation system are calibrated.

[0016] Thirdly, the present invention also provides an electronic device including a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs the method as described in the first aspect above and any possible implementation thereof.

[0017] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor of an electronic device, cause the processor to perform a method as described in the first aspect above and any possible implementation thereof.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] 1) Traditional, experience-based, extensive design is replaced by initial recommendations based on cluster analysis of multi-source data, including soil, vegetation, meteorological, and historical data. This provides a scientific and quantitative starting point for construction. Subsequently, by establishing a three-dimensional hydraulic model for steady-state and transient simulations, dynamic pressure fluctuations that cannot be considered in static design can be exposed and compensated for in advance, making the final target recommended values ​​more consistent with actual operating conditions. This fundamentally solves the shortcomings mentioned in the background technology, namely "over-reliance on experience in parameter determination" and "ignoring dynamic operating conditions," ensuring irrigation uniformity and system safety from the source.

[0020] 2) A feedback calibration mechanism based on measured data has been introduced. By measuring the actual pressure and flow rate at key nodes after installation and accurately comparing and analyzing the deviations with the design values, construction errors, equipment performance deviations, or field factors not covered by the model can be diagnosed. Subsequently, the system is precisely fine-tuned through guided corrective measures (such as adjusting the pressure reducing valve and replacing the nozzle). This process ensures that the actual effect of construction and installation can accurately achieve or even optimize the design goals, solving the problem of traditional "rough installation and commissioning with a lack of quantitative calibration methods," and significantly improving the controllability and reliability of project delivery quality.

[0021] 3) On the one hand, data-driven and model-optimized methods reduce the time spent on repeated calculations and on-site trial and error in traditional design, accelerating the design process. Accurate bill of materials recommendations can avoid waste or shortages in material procurement. On the other hand, precise parameter settings and calibration ensure the system operates under optimal conditions, preventing over-irrigation in some areas and insufficient water supply in others due to pressure imbalances, thus directly saving water resources and pumping energy. Simultaneously, stable pressure operation reduces the risk of pipe bursts and equipment damage, lowering long-term maintenance costs.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0025] Figure 1 A flowchart illustrating a method for recommending construction parameters for a prefabricated irrigation system in municipal greening projects, provided by an embodiment of the present invention;

[0026] Figure 2 This is a structural diagram of a construction parameter recommendation system for a prefabricated irrigation system for municipal greening projects, provided as an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] Please see Figure 1, Figure 1 This is a flowchart illustrating a method for recommending construction parameters for a prefabricated irrigation system in municipal greening projects, as provided in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0030] S10. Obtain basic on-site data, including soil characteristics of the target greening area, plant community data, and environmental data of the construction location. The environmental data includes meteorological data, soil moisture data, and topographic data for a future preset time period.

[0031] S20. Using the database constructed from on-site basic data and historical greening irrigation project data, perform cluster analysis, and predict the expected irrigation demand of the current greening area under future operation based on the clustering results, so as to determine the initial recommended values ​​of key construction parameters of the irrigation system. The key construction parameters include at least the target irrigation depth, irrigation cycle and pipeline design working pressure.

[0032] S30. Establish a three-dimensional hydraulic model of the irrigation area pipe network and perform adaptive mesh generation. Use hydraulic simulation analysis to perform steady-state and transient analysis on the divided three-dimensional model. Determine the compensation values ​​of key construction parameters based on the analysis results. Optimize the initial recommended values ​​based on the compensation values ​​to obtain the target recommended values ​​of key construction parameters.

[0033] S40. Conduct on-site measurement and layout, determine the design pressure and flow rate of each level of the pipeline network based on the target recommended values ​​of key construction parameters, and after pipeline installation and preliminary commissioning, measure the actual pressure and flow rate of key nodes in the pipeline network system. Based on the deviation between the actual value and the design value, calibrate the actual operating parameters of the irrigation system.

[0034] This embodiment first collects and integrates multi-source data from the field, and uses historical data for cluster analysis to intelligently predict future irrigation needs, thereby scientifically generating initial construction parameters and changing the reliance on manual experience. Secondly, a detailed three-dimensional hydraulic model is established. Through steady-state and transient simulations, the system's operational performance under various working conditions is simulated and optimized in advance, and the initial parameters are precisely compensated and optimized to obtain target recommended values ​​to cope with complex situations such as dynamic pressure fluctuations. Finally, during on-site construction, layout and installation are carried out based on the target values. By measuring the actual operating data of key nodes, comparison and deviation analysis are performed with the design values ​​to guide specific equipment adjustments and calibrate the final operating parameters, ensuring that the construction results accurately match the design intent. This embodiment enables the recommendation of construction parameters to shift from "experience-based estimation" to "data and model-driven." Through the dual guarantee of "simulation optimization" and "actual measurement feedback," it is ensured that the system can operate efficiently and stably as expected after completion. Precise irrigation parameters and uniform system pressure distribution help achieve water and energy conservation, and reduce operating and maintenance costs.

[0035] In one embodiment, step S10 comprehensively acquires and structures the basic data of the target greening area by integrating multiple methods such as on-site surveys, data procurement, sensor monitoring, and design document analysis. Preferably, when acquiring the on-site basic data, the soil characteristics include soil texture, field water holding capacity, and permeability coefficient; the plant community data includes plant species, canopy size, root depth, and water requirement patterns; and the meteorological data includes evaporation, precipitation, temperature, and wind speed during the forecast period. The specific data collection process is as follows:

[0036] Soil property data acquisition: Sampling points are established in the target greening area using a grid method or a feature point method (e.g., different terrains, vegetation zones). Undisturbed or disturbed soil samples are collected at different depths (e.g., 0-30cm, 30-60cm) using a soil auger or sampler. The soil samples are sent to the laboratory, where key physical and hydraulic parameters such as soil texture (percentage of sand, silt, and clay content), field water holding capacity, saturated water content, and permeability coefficient are determined according to standards such as the "Standard for Geotechnical Testing Methods". The measurement results will generate a spatial distribution map or zoning attribute table of soil properties.

[0037] Plant community data acquisition: Based on the landscape design and construction drawings, verify and record the plant species, specifications (such as diameter at breast height, crown width, and height), planting density, and layout in each zone on-site. By consulting authoritative plant databases, local maintenance experience data, or specific research literature, obtain or calculate the main root distribution depth, canopy size, and water requirements (such as crop coefficient Kc or evapotranspiration ET reference values) for various plant types. Establish a project plant inventory database, linking each plant with its corresponding ecological and water requirement parameters to provide a basis for calculating zoned irrigation systems.

[0038] Environmental Data Acquisition: Obtain forecast meteorological data for a complete irrigation season or typical year for the project site from the local meteorological bureau, professional meteorological service providers, or micro-weather stations deployed on-site. Core data include: daily reference crop evapotranspiration, precipitation, maximum / minimum temperature, wind speed, sunshine duration, and air humidity. This data is used to calculate the net water requirement of the plants. Soil moisture sensors are deployed in key areas to monitor dynamic changes in soil volumetric moisture content before planting and during construction to understand the initial soil moisture conditions and spatial variability of the site.

[0039] Topographic data: Using total stations, RTK GPS, or UAV aerial surveying technology, a digital elevation model of the irrigation area is mapped to accurately obtain the elevation, slope, and aspect of each point. On-site surveys are conducted to record the location of micro-topographic features that may affect pipeline laying and irrigation uniformity, such as steep slopes, ditches, and paved roads.

[0040] Finally, all acquired heterogeneous data (drawings, tables, monitoring values, spatial coordinates) were standardized (unifying coordinate systems, units, and data formats). Using a geographic information system, soil property maps, vegetation zoning maps, digital elevation models, and meteorological data were spatially overlaid and fused to construct a multi-layered "project site digital twin base" containing spatial attributes. This provides a directly accessible and consistent data foundation for subsequent cluster analysis and hydraulic modeling.

[0041] In one embodiment, the cluster analysis performed in step S20 using the database constructed from on-site basic data and historical greening irrigation project data includes:

[0042] Outliers were removed, missing values ​​were filled, and data normalization was performed on the field basic data. Principal component analysis was then used to reduce the dimensionality of the processed field basic data.

[0043] Pearson correlation coefficient analysis was used to analyze the correlation between the dimensionality-reduced features and irrigation uniformity or water-saving rate. Features with correlation below a preset threshold were removed to generate target features.

[0044] Cluster analysis was performed using a database constructed from target features and historical greening irrigation project data.

[0045] In this embodiment, all field basic data obtained in step S10 are cleaned, including removing obvious outliers, such as identifying and eliminating abnormal soil permeability coefficients through box plots, and filling in reasonable missing values, such as filling missing soil characteristics with the mean of neighboring data or spatial interpolation. Data normalization is then performed to eliminate the influence of dimensions. Due to the high dimensionality of the data (multiple features such as soil, plants, meteorology, and topography), direct clustering is inefficient and easily affected by noise. Principal component analysis is used to convert the processed high-dimensional data into a set of linearly uncorrelated principal component features, significantly reducing the feature dimensionality while retaining most of the data variability. To focus on core influencing factors, Pearson correlation coefficient analysis is used to calculate the correlation between each principal component feature after dimensionality reduction and the "irrigation effect evaluation indicators" (such as irrigation uniformity and water saving rate) recorded in the historical database. Features with an absolute correlation coefficient lower than a preset threshold (e.g., 0.3) are considered weakly correlated features and are removed, ultimately generating a target feature set for clustering.

[0046] To identify the most similar cases to the current project from historical data and transfer its success parameters, further cluster analysis is required. The "target feature set" obtained from the current project's processing is used as a new sample and merged with similar feature data from all completed projects in the historical database, forming a complete dataset for cluster analysis. A suitable unsupervised clustering algorithm (such as K-Means clustering or hierarchical clustering) is selected to perform cluster analysis on this dataset. The algorithm automatically divides all projects (including the current new project) into several clusters, ensuring that projects within the same cluster have the highest similarity across all features. After cluster analysis, the clustering results are reviewed to determine which cluster the current new project is assigned to. From this cluster, several completed projects with the best historical irrigation performance (e.g., highest uniformity and reasonable water usage) are selected as "similar success cases." Statistical analysis (e.g., taking weighted averages or optimal values) is performed on the key construction parameters (target irrigation depth, irrigation cycle, pipeline design working pressure) used in these "similar success cases." These statistical values ​​are used as the initial recommended values ​​for the predicted key construction parameters applicable to the current project.

[0047] This embodiment transforms specific field conditions into computable features through a standardized data mining process. It then automatically searches for "similar cases" in historical big data using clustering algorithms, thereby shifting parameter estimation from reliance on personal experience to scientific recommendations based on collective intelligence and empirical data. This provides a high-quality, rational computational starting point for subsequent precise simulation optimization, effectively addressing the problems of insufficient data utilization and coarse design starting points in traditional methods.

[0048] Step S30 is the core of the "model simulation optimization" stage, aiming to refine and improve the initial recommended values ​​obtained in step S20. It involves establishing a digital twin model and conducting multi-condition simulations to quantitatively evaluate system performance, thereby calculating scientific compensation values ​​and ultimately obtaining "target recommended values" that can be directly used to guide construction. The specific implementation is as follows:

[0049] Using professional hydraulic simulation software, based on the digital elevation model and vegetation zoning map obtained in step S10 and the initial recommended values ​​obtained in step S20, a three-dimensional pipe network model of the irrigation area is constructed within the software. Specifically, this includes:

[0050] Component definition: Based on the initially recommended pipeline design working pressure and target irrigation depth, select the corresponding pipe material, diameter, valve type, pump curve, and the flow-pressure characteristic curve of the initially selected sprinkler / dripper from the software component library.

[0051] Topology connection: According to the design drawings, the water source, water pump, main pipe, branch pipe, valve and water injector are precisely connected in three-dimensional space, and the actual elevation of each node is set according to the terrain data.

[0052] Furthermore, the established pipeline network model is discretized. Adaptive mesh generation technology is employed, automatically refining the computational mesh in key areas: pipe connections (tees, elbows), before and after valves, pipe diameter changes, and the end of the pipeline network. This ensures higher computational accuracy in areas with large pressure gradients and complex flow conditions, while using a sparser mesh in straight pipe sections to improve overall computational efficiency.

[0053] In one embodiment, the step of using hydraulic simulation analysis to perform steady-state and transient analyses on the divided three-dimensional model, and determining the compensation values ​​of key construction parameters based on the analysis results, includes:

[0054] Steady-state analysis is used to determine the hydraulic state of the pipeline network under constant flow, and the pressure and flow distribution at each node are calculated.

[0055] Transient analysis was used to simulate the hydraulic transition process of the pipeline network under the conditions of pump start-up and shutdown and valve opening and closing to obtain the peak pressure fluctuation and low pressure point location.

[0056] Obtain the pressure and flow rate at the most unfavorable point under steady-state analysis, as well as the maximum pressure fluctuation value under transient analysis, to comprehensively evaluate the irrigation uniformity and safety of the pipeline system, and determine the compensation value for the target irrigation depth or the design working pressure of the pipeline based on the evaluation results.

[0057] The initial recommended values ​​are optimized based on the compensation values ​​to obtain the target recommended values ​​for key construction parameters.

[0058] When constructing the 3D hydraulic model, the GIS base map containing topographic data generated in step S10 and the initial recommended values ​​determined in step S20 are imported, especially the total flow rate converted from the pipeline design working pressure and the target irrigation depth. In professional hydraulic simulation software, the pipeline topology is established strictly according to the design drawings. Material, diameter, length, and roughness coefficient are defined for each pipeline; local resistance coefficients are defined for each valve and filter; performance curves are defined for water pumps; and the inherent flow-pressure relationship formula is defined for each sprinkler / dripper. Based on the digital elevation model obtained in step S10, each node in the pipeline network is assigned realistic elevation coordinates to ensure that the model accurately reflects the gravity effects caused by topographic elevation differences. Discretization is performed before model solving. The software uses an adaptive mesh generation algorithm, automatically refining the calculation mesh in the following areas based on the initial hydraulic calculation results: pipe intersections and diameter changes, near components with large local head losses such as valves, water pumps, and elbows, and at the ends of the pipeline where the pressure gradient is expected to be large. A sparser mesh is used in sections where the pipeline is straight and the water flow is stable. This method significantly improves overall simulation efficiency while ensuring the calculation accuracy of key areas.

[0059] Furthermore, steady-state and transient hydraulic simulation analyses were performed on the model:

[0060] Steady-state analysis:

[0061] Operating Condition Setting: Simulates the continuous operation of the system under its design maximum load condition. This condition typically corresponds to all irrigation emitters that need to be activated simultaneously.

[0062] Execution and Output: Run a steady-state simulation and solve the hydraulic balance equations for the pipe network. Core outputs include pressure distribution contour maps, flow rate lists, and pipe velocities for all nodes in the entire network. A key result is identifying the most unfavorable point (the emitter with the lowest pressure) and recording its pressure value. Compared with actual traffic .

[0063] Transient analysis:

[0064] Hazardous operating conditions: Simulates the most destructive dynamic processes of the system during actual operation. This mainly includes: rapid shutdown in case of pump power failure, and rapid opening and closing of the main control valve or the largest rotating irrigation group valve (opening and closing time set to 1-3 seconds).

[0065] Execution and Output: Run a transient water hammer simulation and solve the momentum and continuity equations. The core output is the pressure-time history curves of key nodes. By analyzing the curves, the maximum positive pressure peak of the entire pipeline network during the transient process is captured. and minimum negative pressure valley value Locate the position where the pressure fluctuations are most intense.

[0066] Compensation calculation based on steady-state uniformity:

[0067] Stress compensation: Comparison of stress at the most unfavorable point With the rated operating pressure of the selected water emitter .like This will generate pressure compensation. This is the verification value for the calculated head loss from the source point to this point. This value needs to be superimposed on the initial pipeline design working pressure.

[0068] Irrigation Depth / Time Compensation: If the pressure cannot be increased to the ideal value due to structural or energy limitations, irrigation time must be compensated. This is done by comparing the actual flow rate at the most unfavorable point. With rated flow Calculate the time compensation coefficient The time required to reach the initial target irrigation depth should be multiplied by [the factor]. Alternatively, the opening time of the irrigation group valves can be adjusted proportionally.

[0069] Compensation calculation based on transient safety:

[0070] Water hammer protection compensation: compared with the maximum water hammer pressure Permissible pressure of system components (pipelines, valves) .like In such cases, engineering measures must be taken to compensate, rather than simply increasing the design pressure. The compensation scheme should be based on the overpressure amount. Calculation determines, for example:

[0071] Compensation measure A: Add a slow-closing check valve after the pump. Its closing mechanism needs to be determined through iterative simulation. Reduce to a safe range.

[0072] Compensation measure B: Install air inlet and outlet valves at the starting point of the pipeline network to prevent negative pressure damage at low-pressure points.

[0073] Compensation measure C: Adjust the opening and closing time of the solenoid valve from... Seconds compensated extension to Within seconds, the minimum value that meets the safety requirements is found through software simulation. value.

[0074] Furthermore, the compensation amount calculated above is systematically applied to the initial recommended value, including:

[0075] ;

[0076] ;

[0077] At the same time, based on compensation measures A / B / C, a mandatory list of water hammer protection equipment and installation requirements will be generated.

[0078] This list of parameters and equipment, optimized through simulation and reinforced for safety, constitutes the target recommended values ​​for key construction parameters used to guide construction. Preferably, after obtaining the target recommended values ​​for key construction parameters, a bill of materials for the prefabricated irrigation system is automatically generated. The bill of materials includes recommended main pipe diameter, branch pipe diameter, sprinkler or dripper model, filter grade, valve type, and quantity.

[0079] This embodiment, through transient water hammer analysis, can accurately predict and quantify the pressure shocks caused by pump start-up and shutdown and rapid valve closure before construction. This allows for proactive design-based mitigation of pipe burst risks through compensatory measures (such as specifying slow-closing valve models and valve opening / closing times), significantly improving the system's inherent safety. Steady-state analysis precisely locates the "most unfavorable point" in the pipeline network and quantitatively calculates its pressure and flow gaps, scientifically determining the necessary compensation values ​​for system operating pressure or irrigation time. This ensures that after system completion, the water output of each emitter meets design requirements, achieving high uniformity irrigation globally and directly improving water conservation and green space maintenance quality. Traditional methods rely on engineers' qualitative judgments for trial-and-error adjustments. This embodiment directly calculates precise pressure compensation values, time compensation coefficients, and specific water hammer protection schemes through simulation results, making the optimization process data-driven, precise, and efficient, significantly improving design efficiency and the scientific basis of decision-making. Furthermore, since the system pressure test and optimization are completed in the model first, it is equivalent to a comprehensive rehearsal before actual construction. This makes the final delivered "target recommended value" and supporting measures highly reliable, reducing frequent on-site debugging, equipment replacement and even engineering rework caused by improper design, thus saving time and economic costs.

[0080] In one embodiment, during the on-site measurement and layout in step S40, based on the site coordinate reference points obtained in step S10, a total station or RTK-GPS device is used to accurately mark the location of the water source, the center of the pumping station, the main pipeline axis, the branch pipe interface nodes, and the design plane position of each water injector on site. Then, based on the target pipeline design working pressure determined in step S30 and the hydraulic model calculation results, the design elevations of key nodes, especially the pipeline center at the system's starting point, high point, and end point, are calculated. Using a level, the design elevations are accurately transferred to the corresponding markers on site, and combined with the trench excavation depth, this guides the earthwork construction. A work instruction manual containing the following core contents is issued to the construction team:

[0081] System diagram and plan view: label all pipe diameters, node design pressures and flow rates.

[0082] Equipment and Materials List: This list specifies the exact models and specifications of the optimized water pumps, valves, filters, and nozzles / drippers. This list can be generated directly based on the output of step S30.

[0083] Detailed diagram of water hammer protection measures: such as the installation location and requirements of slow-closing check valves and air inlet / outlet valves.

[0084] Then, strictly follow the layout location and design elevation for trench excavation, pipe laying, welding or bonding, equipment installation (pumps, valves, filtration system, and irrigators), and backfilling. After the system installation is completed, conduct initial water supply. Turn on each irrigation group in sequence, check for leaks at pipe connections, confirm that all valves open and close normally, that the physical positions of the irrigators are correct, and that the system can operate according to the basic procedures.

[0085] Further, measurement point planning is conducted, and key measurement nodes are determined on-site based on the sensitivity analysis in the hydraulic model. These typically include: pump outlet, middle section of the main pipe, starting point of the most unfavorable branch pipe, location of the most unfavorable sprinkler, highest point of the terrain, and potential water hammer risk points identified through transient analysis. During measurement implementation, calibrated precision pressure gauges (or portable electronic pressure sensors) are installed at the planned key nodes to read the actual pressure values ​​of the system under stable operation at its design maximum condition (i.e., the condition analyzed in step S30). Ultrasonic flow meters are used to measure the actual flow rates at the pump outlet, main pipe, and typical branch pipes. For sprinklers, the water output per unit time can be measured using the collection bucket method to calculate the flow rate. The pressure and flow rate readings at each measurement point are recorded synchronously, and a data record table is created that corresponds one-to-one with the design target value (from the output of step S30) of that node.

[0086] After recording, perform deviation calculations. Calculate the absolute and relative deviations between the actual pressure and flow rate at each measuring point and the design values. Input or compare the deviation data with the hydraulic model used in step S30 for rapid verification simulation. Analyze the causes of the deviations: whether it is due to excessive local resistance (such as valves not fully open, filters clogged), changes in the actual friction coefficient caused by pipeline construction errors, or differences between equipment performance and calibration curves, etc., to further calibrate the actual operating parameters of the irrigation system.

[0087] In one embodiment, calibrating the actual operating parameters of the irrigation system based on the deviation between the actual and design values ​​includes:

[0088] The actual measured values ​​of pressure and flow at each key node in the pipeline network system are compared with their corresponding design values ​​to calculate the deviation; based on the pipeline network hydraulic model and the analysis of the deviation distribution pattern, the location of the pipe section causing the deviation and the potential hydraulic causes are diagnosed.

[0089] Based on the diagnostic results, formulate and perform at least one of the following corrective actions:

[0090] If the actual pressure of a certain pipeline section is higher than the design allowable upper limit for a duration exceeding the first preset duration, then a pressure reducing valve is added to or the set pressure is adjusted for that pipeline section.

[0091] If the actual pressure in a certain pipe section remains below the design lower limit for more than the second preset duration, then check and remove any abnormal resistance points upstream of that pipe section, or replace the pipe with a larger diameter fitting.

[0092] If the actual flow rate at a critical node deviates from the design flow rate beyond the allowable range, replace it with a nozzle or dripper model that matches the flow rate coefficient.

[0093] After performing the calibration operation, remeasure the actual pressure and flow rate of the key nodes until the measured values ​​of all key nodes reach the design allowable error range; record the system status parameters that finally meet the requirements as the calibrated actual operating parameters.

[0094] In a pipeline network, pressure and flow rate are interrelated, following the laws of mass conservation and energy equations. An abnormal deviation at one node will propagate upstream or downstream along the network. By analyzing the distribution pattern of the deviation—for example, if all nodes on a branch have low pressure—the approximate location of the root cause (e.g., a clogged inlet filter on that branch) can be deduced. Adding / adjusting pressure-reducing valves when pressure is too high is based on the throttling and pressure-reducing principle in fluid mechanics. By increasing local resistance, excess head (energy) is dissipated, protecting downstream pipelines and equipment from overpressure damage and reducing water waste. When pressure is too low, check resistance or replace pipe fittings: According to the pipeline head loss formula (Darcy-Weisbach formula), low pressure is usually caused by abnormal friction (such as blockage, valves not fully open) or excessively high flow velocity due to insufficient pipe diameter. Clearing blockages or replacing with larger diameter fittings can directly reduce friction and local head loss, restoring pressure to the design level. Replace the emitter when there is a flow deviation: The flow rate of an emitter (sprinkler, dripper) is determined by its flow coefficient and operating pressure. If the flow rate still does not meet the standard after pressure calibration, it indicates that the emitter's flow characteristics are not compatible with its design. Replacing it with a model that matches the flow coefficient, based on its flow-pressure relationship curve, is the most direct way to ensure the accuracy of end-point water delivery and the uniformity of irrigation. The requirement that pressure exceeding the limit must persist for a certain period (first and second preset durations) before calibration is triggered is not arbitrary. This is based on distinguishing between transient fluctuations and steady-state faults. System start-up and shutdown, and valve switching will produce transient pressure fluctuations, which are normal transient processes. Only when the abnormal state persists does it indicate a steady-state defect in the system that needs correction (such as incorrect valve settings or permanent blockage). This avoids excessive and unnecessary adjustments due to normal transient phenomena.

[0095] Through a closed-loop process of "measurement-comparison-diagnosis-correction," the traditional extensive experience-based debugging is transformed into precise operation based on quantitative data. This ensures that the actual operating parameters of the final system strictly conform to the design goals optimized by simulation, fundamentally solving the problem of the disconnect between construction results and design intent. It can directly and specifically eliminate pressure anomalies and flow deviations in the pipeline network, thereby effectively ensuring the uniformity and operational safety of the entire irrigation system, directly improving water-saving effects and project reliability.

[0096] See Figure 2 In one embodiment, the present invention also provides a construction parameter recommendation system for a prefabricated irrigation system for municipal greening projects, the system comprising:

[0097] The data acquisition unit 100 is used to acquire basic on-site data, including soil characteristics of the target greening area, plant community data, and environmental data of the construction site. The environmental data includes meteorological data, soil moisture data, and topographic data for a future preset time period.

[0098] Cluster analysis unit 200 is used to perform cluster analysis using a database constructed from on-site basic data and historical greening irrigation project data. Based on the clustering results, it predicts the expected irrigation demand of the current greening area under future operation conditions, so as to determine the initial recommended values ​​of key construction parameters of the irrigation system. The key construction parameters include at least the target irrigation depth, irrigation cycle and pipeline design working pressure.

[0099] The compensation optimization unit 300 is used to establish a three-dimensional hydraulic model of the irrigation area pipe network and perform adaptive mesh generation. The hydraulic simulation analysis method is used to perform steady-state and transient analysis on the divided three-dimensional model. Based on the analysis results, the compensation values ​​of key construction parameters are determined. The initial recommended values ​​are optimized based on the compensation values ​​to obtain the target recommended values ​​of key construction parameters.

[0100] The parameter calibration unit 400 is used for on-site measurement and layout, determining the design pressure and flow rate of each level of the pipeline network based on the target recommended values ​​of key construction parameters, and measuring the actual pressure and flow rate of key nodes in the pipeline network system after pipeline installation and preliminary commissioning. Based on the deviation between the actual value and the design value, the actual operating parameters of the irrigation system are calibrated.

[0101] It is understood that the system provided in this embodiment has functions or includes modules that can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0102] The present invention also provides an electronic device including a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs a method as described in any of the above possible implementations.

[0103] The present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor of an electronic device, cause the processor to perform a method as described in any of the above possible implementations.

[0104] 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 implementations should not be considered beyond the scope of this invention.

Claims

1. A method for recommending construction parameters for a prefabricated irrigation system in municipal greening projects, characterized in that, The method includes: Acquire basic on-site data, including soil characteristics, plant community data, and environmental data of the target greening area and the construction site. The environmental data includes meteorological data, soil moisture data, and topographic data for a future preset period. Cluster analysis is performed on a database constructed using on-site basic data and historical greening irrigation project data. Based on the clustering results, the expected irrigation demand of the current greening area under future operation is predicted to determine the initial recommended values ​​of key construction parameters of the irrigation system. The key construction parameters include at least the target irrigation depth, irrigation cycle and pipeline design working pressure. A three-dimensional hydraulic model of the irrigation area pipe network was established and adaptive meshing was performed. Steady-state and transient analyses were conducted on the meshed three-dimensional model using hydraulic simulation analysis. Based on the analysis results, compensation values ​​for key construction parameters were determined. The initial recommended values ​​were optimized based on the compensation values ​​to obtain the target recommended values ​​for key construction parameters. On-site measurements and layout were conducted, and the design pressure and flow rate of each level of the pipeline network were determined based on the target recommended values ​​of key construction parameters. After pipeline installation and preliminary commissioning, the actual pressure and flow rate of key nodes in the pipeline network system were measured, and the actual operating parameters of the irrigation system were calibrated based on the deviation between the actual values ​​and the design values.

2. The method for recommending construction parameters for prefabricated irrigation systems in municipal greening projects according to claim 1, characterized in that, The method of using hydraulic simulation analysis to perform steady-state and transient analyses on the divided three-dimensional model, and determining the compensation values ​​of key construction parameters based on the analysis results, including: Steady-state analysis is used to determine the hydraulic state of the pipeline network under constant flow, and the pressure and flow distribution at each node are calculated. Transient analysis was used to simulate the hydraulic transition process of the pipeline network under the conditions of pump start-up and shutdown and valve opening and closing to obtain the peak pressure fluctuation and low pressure point location. Obtain the pressure and flow rate at the most unfavorable point under steady-state analysis, as well as the maximum pressure fluctuation value under transient analysis, to comprehensively evaluate the irrigation uniformity and safety of the pipeline system, and determine the compensation value for the target irrigation depth or the design working pressure of the pipeline based on the evaluation results.

3. The method for recommending construction parameters for prefabricated irrigation systems in municipal greening projects according to claim 1, characterized in that, The cluster analysis performed on the database constructed using on-site basic data and historical greening irrigation project data includes: Outliers were removed, missing values ​​were filled, and data normalization was performed on the field basic data. Principal component analysis was then used to reduce the dimensionality of the processed field basic data. Pearson correlation coefficient analysis was used to analyze the correlation between the dimensionality-reduced features and irrigation uniformity or water-saving rate. Features with correlation below a preset threshold were removed to generate target features. Cluster analysis was performed using a database constructed from target features and historical greening irrigation project data.

4. The method for recommending construction parameters for prefabricated irrigation systems in municipal greening projects according to claim 1, characterized in that, When acquiring the field basic data, the soil characteristics include soil texture, field water holding capacity, and permeability coefficient; the plant community data includes plant species, canopy size, root depth, and water requirement patterns; and the meteorological data includes evaporation, precipitation, temperature, and wind speed during the forecast period.

5. The method for recommending construction parameters for prefabricated irrigation systems in municipal greening projects according to claim 1, characterized in that, The process of calibrating the actual operating parameters of the irrigation system based on the deviation between the actual and design values ​​includes: The actual measured values ​​of pressure and flow at each key node in the pipeline network system are compared with their corresponding design values ​​to calculate the deviation; based on the pipeline network hydraulic model and the analysis of the deviation distribution pattern, the location of the pipe section causing the deviation and the potential hydraulic causes are diagnosed. Based on the diagnostic results, formulate and perform at least one of the following corrective actions: If the actual pressure of a certain pipeline section is higher than the design allowable upper limit for a duration exceeding the first preset duration, then a pressure reducing valve is added to or the set pressure is adjusted for that pipeline section. If the actual pressure in a certain pipe section remains below the design lower limit for more than the second preset duration, then check and remove any abnormal resistance points upstream of that pipe section, or replace the pipe with a larger diameter fitting. If the actual flow rate at a critical node deviates from the design flow rate beyond the allowable range, replace it with a nozzle or dripper model that matches the flow coefficient. After performing the calibration operation, remeasure the actual pressure and flow rate of the key nodes until the measured values ​​of all key nodes reach the design allowable error range; record the system status parameters that finally meet the requirements as the calibrated actual operating parameters.

6. The method for recommending construction parameters for prefabricated irrigation systems in municipal greening projects according to claim 1, characterized in that, The method further includes: After obtaining the target recommended values ​​for key construction parameters, a bill of materials for the prefabricated irrigation system is automatically generated. The bill of materials includes recommended main pipe diameter, branch pipe diameter, sprinkler or dripper model, filter grade, valve type and quantity.

7. A construction parameter recommendation system for a prefabricated irrigation system for municipal greening projects, characterized in that, The system includes: The data acquisition unit is used to acquire basic on-site data, including soil characteristics of the target greening area, plant community data, and environmental data of the construction site. The environmental data includes meteorological data, soil moisture data, and topographic data for a future preset time period. The clustering analysis unit is used to perform clustering analysis on a database constructed using on-site basic data and historical greening irrigation project data. Based on the clustering results, it predicts the expected irrigation demand of the current greening area under future operation conditions, so as to determine the initial recommended values ​​of key construction parameters of the irrigation system. The key construction parameters include at least the target irrigation depth, irrigation cycle and pipeline design working pressure. The compensation optimization unit is used to establish a three-dimensional hydraulic model of the irrigation area pipe network and perform adaptive mesh generation. The hydraulic simulation analysis method is used to perform steady-state and transient analysis on the divided three-dimensional model. Based on the analysis results, the compensation values ​​of key construction parameters are determined. The initial recommended values ​​are optimized based on the compensation values ​​to obtain the target recommended values ​​of key construction parameters. The parameter calibration unit is used for on-site measurement and layout, determining the design pressure and flow rate of each level of the pipeline network based on the target recommended values ​​of key construction parameters, and measuring the actual pressure and flow rate of key nodes in the pipeline network system after pipeline installation and initial commissioning. Based on the deviation between the actual values ​​and the design values, the actual operating parameters of the irrigation system are calibrated.

8. An electronic device, characterized in that, include: A processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs the method for recommending construction parameters for a prefabricated irrigation system for municipal greening projects as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor of an electronic device, cause the processor to perform the method for recommending construction parameters for a prefabricated irrigation system for municipal greening projects as described in any one of claims 1 to 6.

Images