Water-saving closestool siphon structure fluid simulation optimization method and system

Abstract

The invention provides a fluid simulation optimization method and system for a siphon structure of a water-saving closestool. According to the method, a three-dimensional flow field model is constructed by collecting pressure mutation data of a liquid-gas interface of a flow guide cavity of the closestool and vortex intensity data of a spiral area and combining the shrinkage rate characteristics of a trapezoidal cross section; iteratively adjusting a pressure gradient parameter based on a shrinkage rate parameter in the model, so that the flow velocity distribution of the simulation flow field is dynamically matched with the pressure field, and optimizing a pressure gradient balance negative pressure peak value according to the energy loss difference of the pipe section intersection and vortex intensity data; the inclination angles and the distances of the virtual blades are adjusted in an automatic linkage mode through vortex intensity feedback; verifying an inclination angle effect of a drain outlet by combining a water storage boundary, and establishing a positive correlation function of a negative pressure peak value and siphon time; and finally, integrating the collaborative parameter set, dynamically calibrating a negative pressure peak interval under a low water pressure working condition, and realizing closed-loop control of water saving and fluid kinetic energy conversion. According to the application, negative pressure peak value stable control and water-saving efficiency cooperative improvement in the siphon process are realized.

Description

Technical Field

[0001] This application relates to the field of fluid intelligent control technology, and in particular to a fluid simulation optimization method and system for a water-saving toilet siphon structure. Background Technology

[0002] In the design of water-saving toilet siphon structures under low water pressure conditions, it is urgent to solve core problems through real-time simulation of dynamic pressure distribution and multi-parameter correlation verification technology. Specifically, the requirements are to overcome the risk of siphon breakage caused by instantaneous pressure changes at the liquid-gas interface of the guide chamber, and to achieve coordinated control of energy loss in the spiral vortex region and the peak negative pressure at the drain outlet, thereby optimizing water-saving efficiency while ensuring the continuity of the siphon.

[0003] The current mainstream approach uses a static gradient threshold optimization simulation method. This method first establishes a two-dimensional simplified flow field model based on the single variable of the trapezoidal cross-section contraction rate and presets a fixed pressure gradient threshold. Then, the cross-section contraction rate parameter is adjusted through a finite number of iterations to make the simulated pressure value approach the preset threshold. Finally, under a fixed water storage height, discrete combinations of blade tilt angle parameters are manually selected to verify the sewage discharge effect and output a static optimization scheme.

[0004] However, this scheme has significant drawbacks: on the one hand, the static preset pressure threshold cannot respond to dynamic pressure changes in actual working conditions, causing the simulation model to frequently deviate from the real fluid behavior; on the other hand, the eddy current intensity adjustment and pressure gradient control are disconnected from each other, resulting in increased fluid energy loss in the spiral region, and the fixed combination of blade parameters lacks a dynamic coordination mechanism. Summary of the Invention

[0005] This application provides a method and system for fluid simulation optimization of the siphon structure of a water-saving toilet, in order to solve the problems in the prior art.

[0006] Firstly, this application provides a method for fluid simulation optimization of a water-saving toilet siphon structure, including: Pressure abrupt change data at the liquid-gas interface of the guide cavity and eddy intensity data in the spiral region were collected. Combined with the contraction rate characteristics of the trapezoidal cross section, a three-dimensional flow field model including cross section contraction rate parameters, pressure gradient parameters and eddy intensity was constructed. Based on the contraction rate parameter in the three-dimensional flow field model, the pressure gradient parameter of the trapezoidal section is adjusted by numerical iteration to dynamically match the velocity distribution of the simulated flow field with the pressure field. The pressure gradient parameter is dynamically optimized according to the energy loss difference at the pipe segment intersection and the eddy current intensity data to balance the negative pressure peak. Based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and the preset threshold, the virtual blade group in the spiral region of the guide section is driven in the simulation environment, and the blade tilt angle and spacing parameters are adjusted in conjunction with the eddy current intensity feedback mechanism. The adjusted blade tilt angle and spacing parameters are imported into the flow field model. Combined with the water storage height boundary condition, the enhancement effect of the sewage outlet tilt angle on the pressure distribution is verified through multiple iterations, and a positive correlation function between the negative pressure peak and the siphon duration is established. Based on the positive correlation function and the velocity field pressure compensation characteristics, a set of coordinated parameters including contraction rate parameters, pressure gradient, and blade spacing is integrated. By matching the coordinated parameter set with the flow channel inclination angle, the negative pressure peak range is dynamically calibrated under low water pressure conditions, thereby achieving closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion.

[0007] Optionally, based on the contraction rate parameter in the three-dimensional flow field model, the pressure gradient parameter of the trapezoidal cross-section is adjusted through numerical iteration to dynamically match the velocity distribution of the simulated flow field with the pressure field. Furthermore, the pressure gradient parameter is dynamically optimized based on the energy loss difference at the pipe segment intersection and the eddy current intensity data to balance the negative pressure peak, including: In the three-dimensional flow field model, the correlation constraint between the contraction rate parameter and the pressure gradient parameter is established. The trapezoidal cross section is divided into calculation units along the flow direction, and the initial pressure gradient distribution is determined based on the unit contraction characteristics. Based on the contraction rate parameter, the pressure gradient correction is calculated through numerical iteration, and based on the pressure gradient correction, the pressure gradient parameter of the trapezoidal section is adjusted synchronously. The initial pressure gradient distribution is used as the starting point of the iteration to gradually achieve dynamic matching between the velocity distribution and the pressure field. Extract the energy loss difference at the pipe segment intersection node, and combine it with the eddy current intensity data to construct a multi-parameter evaluation function. Input the dynamically matched pressure gradient parameters into the multi-parameter evaluation function. Based on the evaluation results of the multi-parameter evaluation function, a gradient correction strategy is adopted to optimize the pressure gradient parameters, balance node energy loss and reduce the fluctuation range of negative pressure peak, and balance the negative pressure peak through multiple iterations.

[0008] Optionally, based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and a preset threshold, the virtual blade group in the spiral region of the guide section is driven in the simulation environment. The blade inclination angle and spacing parameters are adjusted in conjunction with the eddy current intensity feedback mechanism, including: Based on the optimized pressure gradient parameters, and combined with the deviation between the pressure mutation data and the preset threshold, a quantitative analysis is performed to obtain the deviation distribution characteristics. The deviation distribution characteristics are projected onto the spiral region of the guide section to identify the key adjustment section where the pressure fluctuation exceeds the preset threshold. The position parameters of the key adjustment section are associated with the multi-parameter evaluation function of the negative pressure fluctuation to establish a vortex intensity feedback mechanism between real-time vortex intensity data and blade tilt angle and spacing parameters. In the simulation environment, the virtual blade group is driven to perform linkage adjustment. For the key adjustment section, according to the eddy current intensity feedback mechanism, the compensation amount of blade tilt angle and the scaling ratio of spacing are calculated. Based on the compensation amount of blade tilt angle and the scaling ratio of spacing, the blade tilt angle and spacing parameters in the key adjustment section are adjusted synchronously.

[0009] Optionally, the adjusted blade tilt angle and spacing parameters are imported into the flow field model. Combined with the water storage height boundary condition, the enhancing effect of the discharge outlet tilt angle on pressure distribution is verified through multiple iterations. A positive correlation function between the negative pressure peak and the siphon duration is established, including: The adjusted blade tilt angle and spacing parameters are imported into the flow field model to reconstruct the geometry of the spiral region of the guide section. The rotation reference plane is defined by the blade tilt angle, and the flow channel width is determined by the spacing parameters. By combining the dynamic boundary conditions of water storage height, the enhancement effect of the outlet inclination angle on pressure distribution was verified through multiple rounds of iteration, and pressure distribution data and outlet flow data of the spiral region were collected simultaneously. Based on the pressure distribution data, the spatiotemporal evolution characteristics of the negative pressure peak in the changing region under different sewage outlet inclination angles are analyzed. Combined with the sewage outlet flow data, a four-dimensional feature map is constructed, which includes inclination angle parameters, spatiotemporal evolution characteristics of negative pressure peak, rotational reference plane angle and flow channel width. Based on the data point distribution in the four-dimensional feature map, a positive correlation function between the negative pressure peak and the siphon duration, including the rotation reference plane and the channel width correction term, is generated.

[0010] Optionally, based on the positive correlation function and the velocity field pressure compensation characteristics, a coordinated parameter set integrating the contraction rate parameter, pressure gradient, and blade spacing is integrated. Through a matching strategy between the coordinated parameter set and the flow channel inclination angle, the negative pressure peak range is dynamically calibrated under low water pressure conditions to achieve closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion, including: Based on the slope characteristics of the positive correlation function, a pressure compensation coefficient is defined, and the effective range of the shrinkage rate parameter, pressure gradient, and blade spacing interval are integrated to form a set of synergistic parameters. Based on the aforementioned collaborative parameter set, an adaptive flow channel tilt angle matching strategy is established, and the tilt angle adjustment amount is calculated according to the flow channel tilt angle matching strategy. Based on the tilt adjustment amount, the flow channel angle is adjusted, and the negative pressure peak range is calibrated under low water pressure conditions based on the pressure compensation coefficient to generate an optimized peak interval. The optimized peak interval is used as a dynamic constraint boundary to form a closed-loop control flow, thereby realizing closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion.

[0011] Optionally, pressure abrupt change data at the liquid-gas interface of the guide cavity and eddy intensity data in the spiral region are collected. Combined with the contraction rate characteristics of the trapezoidal cross-section, a three-dimensional flow field model is constructed, including cross-sectional contraction rate parameters, pressure gradient parameters, and eddy intensity, including: Pressure mutation data is collected by arranging a point array structure at the liquid-gas interface of the guide cavity, and eddy current intensity data is calculated by deploying a grid cell structure in the spiral region. The width change of the trapezoidal cross section is measured and divided by the corresponding pipe section length value to generate the contraction rate characteristic. The pressure mutation data, the eddy current intensity data, and the contraction rate characteristics are input into a three-dimensional coordinate system and spatial mapping processing is performed to generate a three-dimensional parameter point set. Based on the three-dimensional parameter point set, calculate and derive the sequence of cross-sectional contraction rate parameter value points, pressure gradient parameter value points, and eddy current intensity value points; By integrating the sequence of cross-sectional contraction rate parameter values, pressure gradient parameter values, and eddy current intensity values, a three-dimensional flow field model containing cross-sectional contraction rate parameters, pressure gradient parameters, and eddy current intensity is established.

[0012] Optionally, the energy loss difference at the pipe segment intersection is extracted, and combined with the eddy current intensity data, a multi-parameter evaluation function is constructed. The dynamically matched pressure gradient parameters are input into the multi-parameter evaluation function, including: Control zones are defined at the junctions of pipe sections. Pressure, velocity and fluid density data of upstream and downstream of the control zone nodes are extracted, and the energy difference between upstream and downstream is calculated. Simultaneously, eddy current intensity data of the control zone nodes are extracted through eddy current field analysis. The eddy current intensity data includes eddy current amplitude and eddy current range. The energy loss difference and the eddy current intensity data are processed to unify the dimensions and normalized and mapped with a preset safety threshold to construct a multi-parameter evaluation function that includes a pressure fluctuation deviation term, an energy dissipation ratio term, and a turbulence intensity index term. The parameters of the multi-parameter evaluation function are coupled through fluid dynamics correlation. The dynamically matched pressure gradient parameters are input into the multi-parameter evaluation function, where the weighting coefficients are dynamically adjusted according to the influence of each physical quantity under the current flow field conditions, providing a quantitative basis for subsequent pressure gradient parameter optimization.

[0013] Secondly, this application provides a fluid simulation optimization system for a water-saving toilet siphon structure, comprising: The acquisition module collects pressure change data at the liquid-gas interface of the guide cavity and eddy intensity data in the spiral region. Combined with the contraction rate characteristics of the trapezoidal cross section, it constructs a three-dimensional flow field model that includes cross section contraction rate parameters, pressure gradient parameters, and eddy intensity. The matching module, based on the contraction rate parameter in the three-dimensional flow field model, adjusts the pressure gradient parameter of the trapezoidal section through numerical iteration to dynamically match the velocity distribution of the simulated flow field with the pressure field. It also dynamically optimizes the pressure gradient parameter according to the energy loss difference at the pipe segment intersection node and the eddy current intensity data to balance the negative pressure peak. The adjustment module, based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and the preset threshold, drives the virtual blade group in the spiral region of the guide section in the simulation environment, and adjusts the blade tilt angle and spacing parameters in conjunction with the eddy current intensity feedback mechanism. The verification module imports the adjusted blade tilt angle and spacing parameters into the flow field model, and, in conjunction with the water storage height boundary condition, verifies the enhancing effect of the sewage outlet tilt angle on the pressure distribution through multiple iterations, and establishes a positive correlation function between the negative pressure peak and the siphon duration. The integration module, based on the positive correlation function and the velocity field pressure compensation characteristics, integrates a set of coordinated parameters including the contraction rate parameter, pressure gradient, and blade spacing. Through the matching strategy of the coordinated parameter set and the flow channel inclination angle, it dynamically calibrates the negative pressure peak range under low water pressure conditions, thereby achieving closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion.

[0014] Thirdly, embodiments of this application provide a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement the fluid simulation optimization method for a water-saving toilet siphon structure as described in the first aspect above.

[0015] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a computer, implements a fluid simulation optimization method for a water-saving toilet siphon structure as described in the first aspect.

[0016] In this embodiment, a three-dimensional flow field model is constructed by collecting pressure abrupt changes and eddy current intensity data at the liquid-gas interface and fusing them with cross-sectional contraction characteristics. This step establishes a precise simulation foundation for multi-physics coupling. Based on the contraction rate parameter in the model, the pressure gradient parameter is dynamically iteratively optimized, enabling dynamic matching between the velocity field and the pressure field. Simultaneously, the energy loss difference data of the pipe section is combined to reduce energy dissipation in the eddy current region. This process directly promotes dynamic equilibrium of the negative pressure peak and effectively prevents siphon breakage. Furthermore, based on the deviation between the pressure abrupt change data and a preset threshold, the virtual blade assembly is driven to adjust the tilt angle and spacing parameters in the simulation environment, through eddy current intensity feedback. The mechanism enhances the fluid guidance and control capability of the spiral zone in real time. Subsequently, the optimized parameters are imported into the flow field model and integrated with the water storage height boundary conditions. After multiple rounds of iteration to verify the sewage outlet inclination effect, a positive correlation function between the negative pressure peak and the siphon duration is finally established. This measure quantifies the synergistic law between siphon stability and pressure distribution. Based on the above function model and the velocity field pressure compensation characteristics, the contraction rate parameter, pressure gradient parameter, and blade spacing parameter are integrated to form a synergistic parameter set. Through the flow channel inclination angle matching strategy, the negative pressure peak interval is calibrated in a closed loop under low water pressure conditions, simultaneously achieving the dual goals of maximizing water-saving efficiency and efficiently converting fluid kinetic energy.

[0017] Furthermore, by establishing the correlation constraint between the contraction rate parameter and the pressure gradient parameter in the three-dimensional flow field model, the trapezoidal cross section is divided into independent calculation units along the flow direction, and the pressure gradient distribution is initialized based on the unit contraction characteristics. Subsequently, the pressure gradient correction is calculated numerically using the contraction rate parameter as input, and the parameters are adjusted synchronously to achieve dynamic matching between the velocity field and the pressure field. During this process, the energy loss difference at the pipe segment intersection nodes is extracted and eddy current intensity data is fused to construct a multi-parameter evaluation function. Finally, a gradient correction strategy is used to perform multiple rounds of iterative optimization of the pressure gradient parameters, thereby balancing node energy loss and compressing the fluctuation range of negative pressure peak, achieving dynamic balance control of negative pressure peak. This scheme improves the accuracy of flow field simulation through a unit-based iterative mechanism, establishes optimization criteria using a multi-physics collaborative evaluation function, and stabilizes the negative pressure peak to a safe range based on the gradient correction strategy, simultaneously reducing the risk of siphon interruption and pipe segment energy loss, and completely solving the siphon breakage problem caused by pressure oscillation in traditional schemes.

[0018] These or other aspects of this application will become more apparent from the description of the following embodiments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The flowchart of a fluid simulation optimization method for a water-saving toilet siphon structure provided in this application is shown. Figure 2 A scenario diagram is shown, illustrating a fluid simulation optimization method for a water-saving toilet siphon structure provided in this application. Figure 3 This application provides a schematic diagram of the structure of a water-saving toilet siphon structure fluid simulation optimization system. Figure 4 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0022] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0023] Researchers have discovered significant shortcomings in existing siphon structure fluid simulation methods: firstly, statically preset pressure thresholds cannot respond to dynamic pressure surges in actual operating conditions, causing simulation models to frequently deviate from real fluid behavior; secondly, eddy current intensity adjustment and pressure gradient control are disconnected, leading to increased fluid energy loss in the spiral region, and the fixed combination of blade parameters lacks a dynamic coordination mechanism. Therefore, a method for optimizing water-saving toilet siphon structures that can achieve dynamic coordination of multiple parameters is urgently needed.

[0024] To address the aforementioned issues, this invention proposes a fluid simulation optimization method for the siphon structure of a water-saving toilet. The core of this method lies in establishing a three-dimensional dynamic coupling and synergistic mechanism involving the cross-sectional contraction rate, pressure gradient, and eddy current intensity. Specifically, after constructing a three-dimensional flow field model including contraction rate parameters, pressure gradient parameters, and eddy current intensity, the pressure gradient parameters are dynamically optimized through numerical iteration to achieve velocity distribution matching with the pressure field and negative pressure peak balance. The virtual blade inclination angle and spacing are adjusted based on the pressure mutation threshold deviation to control eddy current intensity. A positive correlation function between the drain outlet inclination angle and siphon duration is established through multiple iterations. Finally, key parameters are integrated to form a synergistic parameter set, which, combined with a flow channel inclination angle matching strategy, dynamically calibrates the negative pressure peak range under low water pressure conditions, achieving closed-loop control of water-saving efficiency and fluid kinetic energy conversion. This method simultaneously solves the problems of static pressure threshold inaccuracy and eddy current intensity control separation, improving simulation accuracy and optimizing fluid energy conversion efficiency.

[0025] The technical solutions of the embodiments 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. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Figure 1 This application provides a flowchart of a fluid simulation optimization method for a water-saving toilet siphon structure, as shown in the embodiments of this application. Figure 1 As shown, the method includes: 101. Collect pressure change data at the liquid-gas interface of the guide cavity and eddy intensity data in the spiral region. Combine the contraction rate characteristics of the trapezoidal cross section to construct a three-dimensional flow field model that includes cross section contraction rate parameters, pressure gradient parameters, and eddy intensity. Optionally, step 101 may specifically include the following steps: 1011. Arrange a point array structure at the liquid-gas interface of the guide cavity to collect pressure change data. At the same time, deploy a grid unit structure in the spiral region to calculate eddy current intensity data. Measure the width change of the trapezoidal cross section and divide it by the corresponding pipe section length value to generate the contraction rate characteristic. 1012. Input the pressure mutation data, the eddy current intensity data and the contraction rate characteristics into a three-dimensional coordinate system, perform spatial mapping processing, and generate a three-dimensional parameter point set; 1013. Based on the three-dimensional parameter point set, calculate and derive the sequence of cross-sectional contraction rate parameter values, the sequence of pressure gradient parameter values, and the sequence of eddy current intensity values; 1014. Integrate the sequence of cross-sectional contraction rate parameter values, pressure gradient parameter values, and eddy current intensity values ​​to establish a three-dimensional flow field model that includes cross-sectional contraction rate parameters, pressure gradient parameters, and eddy current intensity.

[0027] In the above steps, the pressure change data at the liquid-gas interface of the guide cavity refers to the rapid dynamic change information of the pressure at the liquid-gas interface in the guide cavity, used to capture instantaneous fluctuations in fluid pressure. The eddy current intensity data in the spiral region refers to the numerical value of the rotational intensity of the fluid eddy currents in the spiral region, used to quantify the energy level of the eddy currents. The contraction rate characteristic of the trapezoidal cross-section refers to the characteristic calculated by dividing the width change measurement by the corresponding pipe segment length value, describing the rate at which the width of the trapezoidal cross-section changes over time. The point array structure refers to the lattice-like structure arranged at the liquid-gas interface of the guide cavity, used for fixed-point acquisition of pressure change data. The grid cell structure refers to the grid-like cell structure deployed in the spiral region, used to calculate eddy current intensity data. The width change measurement refers to the process of measuring the numerical change in the width of the trapezoidal cross-section. The corresponding pipe segment length value refers to the pipe segment length value related to the width change of the trapezoidal cross-section, used to calculate the contraction rate characteristic in conjunction with the width change. The three-dimensional coordinate system refers to the three-dimensional spatial coordinate system used for data spatial mapping. Spatial mapping processing refers to the coordinate transformation processing of the input data in the three-dimensional coordinate system to generate a spatially distributed set. A three-dimensional parameter point set refers to a set of points in a three-dimensional coordinate system, where each point represents the spatial location of a parameter value. A cross-sectional contraction rate parameter value sequence refers to a sequence of points, each containing the calculated value of the cross-sectional contraction rate parameter. A pressure gradient parameter value sequence refers to a sequence of points, each containing the calculated value of the pressure gradient parameter. A eddy current intensity value sequence refers to a sequence of points, each containing the calculated value of the eddy current intensity. The cross-sectional contraction rate parameter is a parameter representing the cross-sectional contraction rate in a three-dimensional flow field model. The pressure gradient parameter is a parameter representing the pressure gradient in a three-dimensional flow field model. The eddy current intensity is a parameter representing the eddy current intensity in a three-dimensional flow field model.

[0028] In this embodiment, firstly, in step 1011, a point array structure is arranged at the liquid-gas interface of the guide cavity to collect pressure change data. Simultaneously, a grid cell structure is deployed in the spiral region to calculate eddy current intensity data. The width change of the trapezoidal cross-section is measured and divided by the corresponding pipe segment length to generate a contraction rate feature. Specifically, a point array structure is arranged at the liquid-gas interface of the guide cavity, and a pressure sensor is used to collect pressure change data at fixed points. Secondly, a grid cell structure is deployed in the spiral region, and eddy current intensity data is calculated using a flow field calculation algorithm. Finally, the width change of the trapezoidal cross-section is measured, and the measurement result is divided by the corresponding pipe segment length to generate the contraction rate feature of the trapezoidal cross-section, thus completing data acquisition and feature generation.

[0029] Secondly, in step 1012, the pressure mutation data, eddy current intensity data, and contraction rate characteristics obtained in step 1011 are input into a three-dimensional coordinate system for spatial mapping processing to generate a three-dimensional parameter point set. Specifically, the process is as follows: first, the pressure mutation data, eddy current intensity data, and contraction rate characteristics are input into a three-dimensional coordinate system; second, a spatial mapping processing algorithm is applied to transform these data; finally, this processing generates a three-dimensional parameter point set, which is distributed in three-dimensional space and contains the coordinate information of the data points.

[0030] Next, in step 1013, based on the three-dimensional parameter point set generated in step 1012, the sequence points of cross-sectional contraction rate parameter value, pressure gradient parameter value, and eddy current intensity value are calculated and derived. Specifically, the process is as follows: first, coordinate data is extracted from the three-dimensional parameter point set; second, a parameter calculation algorithm is used to calculate the cross-sectional contraction rate parameter value, pressure gradient parameter value, and eddy current intensity value for each point; finally, these calculated values ​​are organized into a sequence point format, thus obtaining the sequence points of cross-sectional contraction rate parameter value, pressure gradient parameter value, and eddy current intensity value.

[0031] Finally, in step 1014, the sequence points of cross-sectional contraction rate parameter, pressure gradient parameter, and eddy current intensity obtained in step 1013 are integrated to establish a three-dimensional flow field model that includes cross-sectional contraction rate parameters, pressure gradient parameters, and eddy current intensity. The specific process is as follows: First, the sequence points of cross-sectional contraction rate parameter, pressure gradient parameter, and eddy current intensity are collected; second, a flow field modeling algorithm is applied to integrate these sequence points; finally, a complete three-dimensional flow field model is constructed based on the integration results. This model includes cross-sectional contraction rate parameters, pressure gradient parameters, and eddy current intensity as key parameters.

[0032] In a practical application, during an experiment on a flow guiding device in an industrial pipeline, a point array structure was used to collect pressure change data at 10 equidistant measurement points at the liquid-gas interface of the flow guiding cavity. The pressure change range at each point was recorded as 0.5 kPa to 2.0 kPa. Simultaneously, a 20×20 grid cell structure, with each cell size of 0.1 m, was deployed in the spiral region to calculate eddy current intensity data. The obtained intensity values ​​ranged from 0.2 s⁻¹ to 0.8 s⁻¹. The inlet width of the trapezoidal cross-section was set to 500 mm, and the outlet width to 300 mm. By measuring the width change and dividing it by the corresponding pipe section length of 2000 mm, a contraction rate characteristic of 0.1 mm / mm was generated. These pressure change data, eddy current intensity data, and contraction rate characteristics were input into a three-dimensional coordinate system and spatially mapped to form a three-dimensional parameter point set of 100 points. Based on this point set, a sequence of cross-sectional contraction rate parameter values ​​was calculated and derived, such as 0.09 to 0.11. Twenty discrete values ​​of mm / mm, pressure gradient parameter values ​​(e.g., gradient variation from -0.2 to -0.6 kPa / m), and eddy current intensity values ​​(e.g., numerical distribution from 0.21 to 0.79 s⁻¹) were collected. Finally, these values ​​were integrated to construct a three-dimensional flow field model that includes cross-sectional contraction rate parameters, pressure gradient parameters, and eddy current intensity, for simulating fluid flow behavior.

[0033] In the overall scheme of step 101 above, pressure change data of the liquid-gas interface in the guide cavity is collected by arranging a point array structure, and eddy intensity data of the spiral region is measured by combining the grid unit structure. At the same time, the contraction rate characteristics are calculated based on the change in the width of the trapezoidal section and the length of the pipe section. The three are then input into a three-dimensional coordinate system for spatial mapping to generate a three-dimensional parameter point set. Based on this, the sequence of cross-sectional contraction rate parameter value points, the sequence of pressure gradient parameter value points, and the sequence of eddy intensity value points are derived. Finally, they are integrated to form a three-dimensional flow field model that includes the cross-sectional contraction rate parameter, the pressure gradient parameter, and the eddy intensity.

[0034] 102. Based on the contraction rate parameter in the three-dimensional flow field model, the pressure gradient parameter of the trapezoidal section is adjusted by numerical iteration to dynamically match the velocity distribution of the simulated flow field with the pressure field. The pressure gradient parameter is dynamically optimized according to the energy loss difference at the pipe segment intersection node and the eddy current intensity data to balance the negative pressure peak. Optionally, step 102 may specifically include the following steps: 1021. Establish the correlation constraint between the contraction rate parameter and the pressure gradient parameter in the three-dimensional flow field model, divide the trapezoidal cross section into calculation units along the flow direction, and determine the initial pressure gradient distribution based on the unit contraction characteristics; 1022. Based on the contraction rate parameter, the pressure gradient correction amount is calculated through numerical iteration, and based on the pressure gradient correction amount, the pressure gradient parameter of the trapezoidal section is adjusted synchronously. The initial pressure gradient distribution is used as the starting point of the iteration to gradually realize the dynamic matching between the flow velocity distribution and the pressure field. 1023. Extract the energy loss difference at the pipe segment intersection node, and combine it with the eddy current intensity data to construct a multi-parameter evaluation function. Input the dynamically matched pressure gradient parameters into the multi-parameter evaluation function.

[0035] Step 1023 may specifically include the following processes: dividing the control area at the pipe segment intersection node, extracting pressure, velocity, and fluid density data upstream and downstream of the control area node, calculating the energy difference between upstream and downstream, and simultaneously extracting eddy current intensity data of the control area node through eddy current field analysis, wherein the eddy current intensity data includes eddy current amplitude and eddy current range; performing dimensional unification processing on the energy loss difference and the eddy current intensity data, normalizing and mapping them with a preset safety threshold, and constructing a multi-parameter evaluation function including pressure fluctuation deviation term, energy dissipation ratio term, and turbulence intensity index term, wherein each parameter term of the multi-parameter evaluation function establishes coupling constraints through fluid dynamics correlation; inputting the dynamically matched pressure gradient parameters into the multi-parameter evaluation function, wherein the weight coefficients are dynamically adjusted according to the influence degree of each physical quantity under the current flow field conditions, providing a quantitative basis for subsequent pressure gradient parameter optimization.

[0036] 1024. Based on the evaluation results of the multi-parameter evaluation function, a gradient correction strategy is adopted to optimize the pressure gradient parameters, balance the node energy loss and reduce the fluctuation range of the negative pressure peak, and balance the negative pressure peak through multiple iterations.

[0037] In the above steps, the contraction rate parameter refers to the parameter representing the cross-sectional contraction rate in the three-dimensional flow field model. The pressure gradient parameter refers to the parameter representing the pressure gradient in the three-dimensional flow field model. The velocity distribution refers to the spatial distribution of fluid velocity in the simulated flow field. The pressure field refers to the spatial distribution of fluid pressure in the simulated flow field. The pipe segment junction node refers to the connection point region in the fluid flow pipeline system. The energy loss difference refers to the difference in energy dissipation between upstream and downstream of the fluid at the junction. The eddy current intensity data refers to the measured intensity of eddies in the fluid. The negative pressure peak value refers to the highest intensity fluctuation value in the negative pressure region of the fluid pressure. Numerical iteration refers to the numerical calculation method of gradually optimizing parameters through repeated calculations. The element contraction characteristics refer to the characteristic parameters related to cross-sectional contraction in the computational element. The initial pressure gradient distribution refers to the distribution of the pressure gradient parameters initially set in the computational element. The pressure gradient correction amount refers to the adjustment amount of the pressure gradient parameters obtained from the numerical iteration calculation. The multi-parameter evaluation function refers to the mathematical function used to quantify the degree of influence of multiple physical quantities. The eddy current effect range refers to the spatial range of the eddy current effect. The energy dissipation ratio term refers to the part of the multi-parameter evaluation function that represents the proportion of energy loss. The turbulence intensity index refers to the portion of the multi-parameter evaluation function that represents the level of turbulence intensity. Fluid dynamic correlations refer to the constraints between physical quantities established based on fluid dynamics principles. Coupling constraints are the restrictions imposed on the interdependencies of the terms in the multi-parameter evaluation function.

[0038] In this embodiment, firstly, through step 1021, a constraint relationship between the contraction rate parameter and the pressure gradient parameter is established in the three-dimensional flow field model. The trapezoidal cross-section is divided into computational units along the flow direction, and the initial pressure gradient distribution is determined based on the unit contraction characteristics. Specifically, the process involves: establishing a constraint relationship between the contraction rate parameter and the pressure gradient parameter using fluid dynamics relationships to form mathematical constraints; secondly, applying mesh generation techniques along the flow direction of the trapezoidal cross-section to divide the cross-section into multiple computational units; and then, based on the unit contraction characteristics of each computational unit, using numerical calculation methods to determine the initial pressure gradient distribution as the starting point for subsequent adjustments.

[0039] Secondly, in step 1022, based on the contraction rate parameter, the pressure gradient correction is calculated through numerical iteration. Then, based on the pressure gradient correction, the pressure gradient parameter of the trapezoidal cross-section is simultaneously adjusted. The initial pressure gradient distribution is used as the starting point for iteration, gradually achieving dynamic matching between the velocity distribution and the pressure field. Specifically, a numerical iterative algorithm, such as the Newton-Raphson method, is used to calculate the pressure gradient correction based on the contraction rate parameter. Next, based on this pressure gradient correction, the pressure gradient parameter of the trapezoidal cross-section is simultaneously adjusted. Then, starting from the initial pressure gradient distribution, the parameters are gradually optimized through multiple iterations, enabling the velocity distribution and pressure field of the simulated flow field to achieve a dynamic matching state.

[0040] Next, in step 1023, the energy loss difference at the pipe segment junction is extracted, and combined with the eddy current intensity data, a multi-parameter evaluation function is constructed. The dynamically matched pressure gradient parameters are then input into the multi-parameter evaluation function. Specifically, a control region is defined at the pipe segment junction. Data extraction techniques are used to obtain pressure, velocity, and fluid density data upstream and downstream of the control region nodes, and the upstream-downstream energy difference is calculated. Simultaneously, eddy current intensity data, including eddy current amplitude and eddy current range, is extracted from the control region nodes using eddy current field analysis techniques. Next, the energy loss difference and eddy current intensity data are processed to unify their dimensions. Then, the processed data is normalized and mapped to a preset safety threshold. Next, a multi-parameter evaluation function is constructed, including a pressure fluctuation deviation term, an energy dissipation ratio term, and a turbulence intensity index term. Each term is coupled through fluid dynamics relationships. Finally, the dynamically matched pressure gradient parameters are input into the multi-parameter evaluation function, where the weighting coefficients are dynamically adjusted according to the influence of each physical quantity under the current flow field conditions, providing a quantitative basis for subsequent optimization.

[0041] Finally, in step 1024, based on the evaluation results of the multi-parameter evaluation function, a gradient correction strategy is adopted to optimize the pressure gradient parameters, balance node energy loss, and reduce the fluctuation range of negative pressure peaks. Multiple iterations are then used to balance the negative pressure peak. Specifically, the process is as follows: First, based on the output of the multi-parameter evaluation function, a gradient correction strategy, such as gradient descent, is applied to optimize the pressure gradient parameters. Second, the parameters are adjusted to balance node energy loss. Third, the peak fluctuation is controlled by reducing the fluctuation range of negative pressure peaks. Finally, through multiple iterations, the parameters are gradually optimized to ultimately achieve the goal of balancing the negative pressure peak.

[0042] In practical applications, during the flow field optimization of an industrial pipeline, the cross-sectional contraction rate parameter distribution ranged from 0.08 to 0.12 mm per millimeter based on the three-dimensional flow field model. First, the trapezoidal cross-section was divided into 100 computational units along the flow direction, and the initial pressure gradient distribution was set to present a gradient sequence of -0.3 to -0.8 kPa per meter. Subsequently, the pressure gradient correction was calculated through three numerical iterations, with each correction controlled at 0.04 kPa per meter. The pressure gradient parameters of each unit were adjusted synchronously, ultimately stabilizing the flow velocity distribution in the main flow area within the range of 2.5 ± 0.3 m / s, and forming a dynamic distribution of 20 to 80 kPa in the pressure field cloud map. A 1.2-meter diameter spherical control area was delineated at the pipe section intersection 1.5 meters from the inlet. The pressure difference between upstream and downstream nodes was measured at 18 kPa, the velocity difference at 0.9 m / s, and the fluid density difference at 3 kg / m³. Based on these measurements, an energy loss difference of 5.4 kJ was calculated. Simultaneously, vortex intensity data were collected at this node, including a vortex amplitude of 0.45 m / s and a vortex core radius of 0.25 m. Through dimensional standardization and matching with safety thresholds, a multi-parameter evaluation function containing three core indicators was constructed: a pressure fluctuation deviation term recording a dynamic offset of ±0.7 kPa; an energy dissipation ratio term related to the 3.2% kinetic energy conversion rate between upstream and downstream; and a turbulence intensity indicator term corresponding to the time-varying characteristics of vortex from 0.3 to 0.6 m / s. The weighting coefficients for these three parameters were dynamically allocated according to the current Reynolds number as 0.45:0.35:0.20. Based on the evaluation results of the function, gradient optimization is performed: the first round of correction increases the pressure gradient parameter by 0.15 kPa per meter, reducing the negative pressure peak from 43 kPa to 31 kPa; in the subsequent three iterations, the gradient distribution curvature is gradually fine-tuned, and the negative pressure peak is finally stabilized in the range of 20 to 28 kPa, reducing the nodal energy loss difference to 1.8 kJ, and achieving dynamic equilibrium of the flow field.

[0043] In the overall scheme of step 102 above, after establishing the correlation constraint between the contraction rate parameter and the pressure gradient parameter based on the three-dimensional flow field model and dividing the calculation unit along the flow direction to determine the initial pressure gradient distribution, the pressure gradient parameter of the trapezoidal section is adjusted synchronously by calculating the pressure gradient correction amount through numerical iteration based on the contraction rate parameter. The dynamic matching of the velocity distribution and the pressure field is gradually realized with the initial pressure gradient distribution as the starting point of the iteration. For the pipe segment intersection node, the control area is divided to extract the energy loss difference and eddy current intensity data. Through dimension unification and threshold normalization, a multi-parameter evaluation function containing pressure fluctuation deviation term, energy dissipation ratio term and turbulence intensity index term is constructed. The dynamically matched pressure gradient parameter is input into the function. Based on the function evaluation result, the pressure gradient parameter is optimized by using a gradient correction strategy. Through multiple rounds of iteration, the node energy loss is balanced and the negative pressure peak fluctuation range is reduced so that the negative pressure peak reaches dynamic equilibrium.

[0044] 103. Based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and the preset threshold, the virtual blade group in the spiral region of the guide section is driven in the simulation environment, and the blade tilt angle and spacing parameters are adjusted in conjunction with the eddy current intensity feedback mechanism. Optionally, step 103 may specifically include the following steps: 1031. Based on the optimized pressure gradient parameters, and combined with the deviation between the pressure mutation data and the preset threshold, a quantitative analysis is performed to obtain the deviation distribution characteristics. 1032. Project the deviation distribution characteristics onto the spiral region of the guide section, identify the key adjustment section where the pressure fluctuation exceeds the preset threshold, associate the position parameters of the key adjustment section with the multi-parameter evaluation function of the negative pressure fluctuation, and establish a vortex intensity feedback mechanism between real-time data of vortex intensity and blade tilt angle and spacing parameters. 1033. Drive the virtual blade group to perform linkage adjustment in the simulation environment. For the key adjustment section, calculate the compensation amount of blade tilt angle and the scaling ratio of spacing according to the eddy current intensity feedback mechanism. Based on the compensation amount of blade tilt angle and the scaling ratio of spacing, adjust the blade tilt angle and spacing parameters in the key adjustment section synchronously.

[0045] In the above steps, the deviation distribution characteristics refer to the set of parameters generated by quantitative analysis in step 1031 that describe the spatial distribution characteristics of the deviation between the pressure mutation data and the preset threshold. The key adjustment section is the identifier of the sub-region to be optimized in the spiral region of the guide flow where the pressure fluctuation exceeds the preset threshold. The eddy current intensity feedback mechanism refers to the dynamic control logic of the eddy current intensity data, blade inclination angle parameters, and spacing parameters established based on the multi-parameter evaluation function. The blade inclination angle compensation amount is the numerical adjustment amount calculated to correct the blade angle deviation. The spacing scaling ratio is the quantitative factor for correcting the blade spacing size. The virtual blade group is the blade system model used for parameter adjustment testing in the simulation environment.

[0046] In this embodiment of the application, firstly, through step 1031, based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and the preset threshold, a quantitative analysis is performed to obtain the deviation distribution characteristics. Specifically, firstly, statistical analysis techniques are used to process the optimized pressure gradient parameters and the pressure mutation data set to calculate and generate the deviation values ​​at each point. Secondly, a spatial interpolation algorithm is applied to convert the discrete deviation values ​​into continuous three-dimensional distribution data. Then, a feature extraction algorithm is used to identify high deviation clustering areas and construct a spatial topology structure. Finally, a deviation distribution characteristic data set representing the deviation law of pressure mutation is output.

[0047] Secondly, through step 1032, the deviation distribution characteristics are projected onto the spiral region of the guide section to identify the key adjustment section. The position parameters of the key adjustment section are associated with the multi-parameter evaluation function of negative pressure fluctuation to establish a vortex intensity feedback mechanism. The specific process is as follows: First, the coordinate mapping algorithm is executed to project the spatial topology data of the deviation distribution characteristics onto the coordinate system of the spiral region. Second, the threshold screening algorithm is applied to scan the spatial model of the spiral region to divide the boundary of the key adjustment section where the pressure fluctuation value exceeds the preset threshold. Then, the geometric position parameters of the key adjustment section are extracted and input into the pre-set multi-parameter evaluation function. Finally, the feedback response rules of the real-time vortex intensity data to the blade tilt angle parameters and spacing parameters are established based on the dynamic control algorithm to form the control logic of the vortex intensity feedback mechanism.

[0048] Finally, through step 1033, the virtual blade assembly is driven to perform coordinated adjustment in the simulation environment. For the key adjustment section, based on the eddy current intensity feedback mechanism, the compensation amount of the blade tilt angle and the scaling ratio of the spacing are calculated, and the blade tilt angle and spacing parameters are adjusted synchronously. The specific process is as follows: First, the fluid dynamics calculation module of the simulation environment is activated to load the virtual blade assembly model. Second, based on the control logic of the eddy current intensity feedback mechanism, the gradient optimization algorithm is used to calculate the tilt angle compensation value of each blade in the key adjustment section. Then, the geometric deformation algorithm is applied to calculate the blade spacing scaling factor. Then, the parameter adjustment module is driven to perform two operations simultaneously: the blade angle parameter is rotated and adjusted according to the tilt angle compensation amount, and the blade spacing size is scaled according to the scaling factor. Finally, the adjustment effect is verified by flow field iteration calculation to complete the coordinated adjustment process.

[0049] In practical applications, during the commissioning experiment of a turbine guide vane system, based on the optimized pressure gradient parameters distributed in the 20-28 kPa range, and comparing the pressure abrupt changes of 0.5 to 2.0 kPa collected from the liquid-gas interface of the guide vane cavity with the preset threshold of 1.2 kPa, a dynamic offset of -0.7 kPa to +0.8 kPa was measured. Through quantitative analysis of this offset distribution characteristics, projecting it onto the spiral region of the guide vane, a continuous over-threshold phenomenon was identified in the 0.8-1.2 m section from the inlet, with a maximum fluctuation of 1.8 kPa. The coordinate parameters of this key adjustment section were correlated with the preceding multi-parameter evaluation function: based on the current Reynolds number, weights of 0.45:0.35:0.20 were assigned, establishing a control mechanism with real-time eddy current intensity data of 0.3-0.6 s⁻¹ as the feedback core. For every 0.1 s⁻¹ change in eddy current amplitude, a linkage response of 0.5° blade tilt angle compensation and a pitch scaling ratio of 1:0.8 was triggered. Driven by the simulation environment, the virtual blade assembly performs a three-stage adjustment for key sections: In the first round, based on the measured vortex of 0.55 s⁻¹, the tilt angle compensation is increased by +1.5°, and the spacing is reduced by 20%, reducing pressure fluctuations to 60% of the original amplitude; in the second round, based on the feedback vortex of 0.42 s⁻¹, the tilt angle is adjusted back by 0.8°, and the spacing is expanded by 12%; in the final round, the tilt angle is finely adjusted by -0.3°, stabilizing the pressure in this section within the threshold ±0.3 kPa. Ultimately, the tilt angle of the 32 blades in the key section is adjusted from the initial 12° to 13.4°, and the spacing is optimized from 80 mm to 72 mm, forming a dynamic balance control mode.

[0050] In the overall scheme of step 103 above, the deviation distribution characteristics are obtained by quantitative analysis based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and the preset threshold. These characteristics are then projected onto the spiral region of the guide section to identify the key adjustment section where the pressure fluctuation exceeds the threshold. The location parameters of this section are associated with the multi-parameter evaluation function of the negative pressure fluctuation to establish a feedback mechanism between the real-time data of the eddy current intensity and the blade tilt angle and spacing parameters. In the simulation environment, the virtual blade group is driven to calculate the blade tilt angle compensation and spacing scaling ratio for the key adjustment section according to the feedback mechanism, and the blade tilt angle and spacing parameters in this section are adjusted synchronously.

[0051] 104. The adjusted blade tilt angle and spacing parameters are imported into the flow field model. Combined with the water storage height boundary condition, the enhancement effect of the sewage outlet tilt angle on the pressure distribution is verified through multiple iterations. A positive correlation function between the negative pressure peak and the siphon duration is established. Optionally, step 104 may specifically include the following steps: 1041. Import the adjusted blade tilt angle and spacing parameters into the flow field model, reconstruct the geometry of the spiral region of the guide section, define the rotation reference plane with the blade tilt angle, and determine the flow channel width with the spacing parameters; 1042. Combining the dynamic boundary conditions of water storage height, the enhancement effect of the outlet inclination angle on pressure distribution was verified through multiple iterations, and pressure distribution data and outlet flow data of the spiral region were collected simultaneously. 1043. Based on the pressure distribution data, analyze the spatiotemporal evolution characteristics of the peak negative pressure in the changing region of the rotating reference plane and the flow channel width under different discharge outlet inclination angles. Combine the discharge outlet flow data to construct a four-dimensional feature map including inclination angle parameters, spatiotemporal evolution characteristics of peak negative pressure, rotating reference plane angle and flow channel width. 1044. Based on the data point distribution in the four-dimensional feature map, generate a positive correlation function between the negative pressure peak and the siphon duration, which includes the rotation reference plane and the channel width correction term.

[0052] In the above steps, the rotating reference plane refers to a virtual geometric plane defined with the blade tilt angle as the reference, used to reconstruct the spatial orientation of the blades in the flow field model; the channel width is the lateral dimension of the fluid channel between adjacent blades determined by the blade spacing parameter, which directly affects the vortex generation efficiency; the four-dimensional feature map is a dynamic database integrating tilt angle parameters, spatiotemporal evolution characteristics of negative pressure peak, rotating reference plane angle, and channel width; the pressure distribution enhancement effect specifically refers to the optimization effect of adjusting the outlet tilt angle on the pressure field uniformity; the siphon duration is an indicator of the duration for which the outlet maintains continuous fluid discharge based on the negative pressure peak; the spatiotemporal evolution characteristics of negative pressure peak describe the variation law of negative pressure intensity with space and time in the region of change of rotating reference plane and channel width; the dynamic boundary condition of water storage height is a time-varying water level constraint parameter set to simulate actual working conditions; the outlet flow data refers to the monitoring dataset of fluid discharge rate; the positive correlation function includes rotating reference plane correction term and channel width correction term, establishing a quantitative mathematical relationship between negative pressure peak and siphon duration.

[0053] In this embodiment, firstly, through step 1041, the adjusted blade inclination angle and spacing parameters are imported into the flow field model to reconstruct the geometric structure of the spiral region of the guide section. Specifically, the blade inclination angle and spacing parameters output in step 1033 are first imported into the fluid dynamics calculation engine. Secondly, a non-uniform rational B-spline geometric reconstruction algorithm is applied to calculate the virtual plane normal vector based on the blade inclination angle parameters to define the spatial geometric reference of the rotating reference plane. Then, the minimum clearance size between adjacent blades is calculated based on the spacing parameters to generate the flow channel width value. Finally, the three-dimensional geometric mesh model of the spiral region of the guide section is reconstructed to update the flow channel structure data and the coordinates of the rotating reference plane, thus completing the geometric model reconstruction.

[0054] Secondly, through step 1042, combined with the dynamic boundary condition of the water storage height, the enhancement effect of the sewage outlet inclination angle on the pressure distribution is verified through multiple rounds of iteration. The specific process is as follows: First, the water storage height parameter is set as the transient calculation module for the time-varying boundary condition of the flow field. Then, the finite volume method numerical iterative algorithm is used to adjust the sewage outlet inclination angle value and perform multiple rounds of flow field simulation calculation. Next, a three-dimensional pressure distribution dataset of the spiral region is collected through a virtual pressure sensor array. At the same time, a flow monitoring algorithm is used to obtain the sewage outlet fluid discharge rate dataset. Finally, after five rounds of iteration, a pressure enhancement effect verification report is output, which includes a pressure gradient change spectrum and a flow change curve, thus completing the effect verification.

[0055] Next, through step 1043, the spatiotemporal evolution characteristics of the negative pressure peak are analyzed and a four-dimensional feature map is constructed. The specific process is as follows: First, the pressure distribution dataset and the sewage outlet flow dataset collected in step 1042 are input. Then, the temporal convolutional neural network algorithm is used to analyze the spatiotemporal migration characteristics of the negative pressure peak in the region of change of the rotating reference plane and the flow channel width under different sewage outlet inclination angles. Then, the four-dimensional parameters of inclination angle parameter value, spatiotemporal evolution characteristic index of negative pressure peak, rotating reference plane angle value, and flow channel width value are integrated. Finally, the four types of parameters are mapped into a dynamic coordinate point set of the four-dimensional feature map through tensor decomposition modeling technology to complete the map construction.

[0056] Finally, through step 1044, a positive correlation function between the negative pressure peak and the siphon duration is generated. The specific process is as follows: first, input the set of four-dimensional feature map data points; second, apply the least squares function fitting algorithm with regularization constraints to calculate the coefficients of the rotation reference plane correction term and the flow channel width correction term; then, establish the exponential decay mapping relationship between the siphon duration and the negative pressure peak intensity and add linear compensation of the correction term; finally, generate the mathematical equation of the positive correlation function and output a quantitative model verification report containing four variables: rotation reference plane angle correction, flow channel width correction, negative pressure peak intensity, and siphon duration.

[0057] In practical applications, during the verification of a flow diversion system in a water treatment plant, the optimized spiral zone blade inclination angle of 13.4 degrees and spacing of 72 mm were imported into the flow field model. A 16.5-degree rotating reference plane was established based on the blade inclination angle, and 18 groups of flow channel structures ranging from 68 to 75 mm were divided according to the spacing. Combined with the boundary condition of a 7.5-meter water storage height, two rounds of iterative tests on the discharge outlet inclination angle revealed that: when the initial inclination angle was set to -12 degrees, the peak negative pressure in the spiral zone was 19.2 kPa and the discharge flow rate was 45 liters per second; when the inclination angle was adjusted to -19 degrees, the peak negative pressure increased to 22.8 kPa and the flow rate increased to 58 liters per second. The analysis of the constructed four-dimensional feature map reveals key patterns: as the rotation reference plane increases from 15 degrees to 18 degrees, and the flow channel width narrows from 80 mm to 75 mm, the peak negative pressure jumps from 20.3 kPa to 24.1 kPa under the condition of a discharge inclination angle of -18.5 degrees, and the siphon duration is correspondingly shortened from 85 seconds to 76 seconds. The 200 data points in the map show that every 3-degree increase in the rotation reference plane increases the peak negative pressure by approximately 2.2 kPa, and every 5-mm reduction in the flow channel width reduces the siphon time by 7 seconds; when the rotation reference plane reaches 17.5 degrees and is combined with a 73 mm flow channel width, the siphon time under the condition of a peak negative pressure of 22.5 kPa can be further reduced to 68 seconds.

[0058] In the overall scheme of step 104 above, the adjusted blade tilt angle and spacing parameters are imported into the flow field model to reconstruct the geometric structure of the spiral region of the guide section. The rotation reference plane is defined by the blade tilt angle and the flow channel width is determined by the spacing parameter. The enhanced effect of the discharge outlet tilt angle on the pressure distribution is verified through multiple iterations in combination with the dynamic boundary conditions of the water storage height, and the pressure distribution data of the spiral region and the discharge outlet flow data are collected simultaneously. Based on the pressure distribution data, the spatiotemporal evolution characteristics of the negative pressure peak in the region of the rotation reference plane and the flow channel width change area under different discharge outlet tilt angles are analyzed. A four-dimensional feature map containing the spatiotemporal evolution characteristics of the negative pressure peak of the tilt angle parameter and the flow channel width is constructed in combination with the discharge outlet flow data. Finally, a positive correlation function between the negative pressure peak and the siphon duration containing the correction terms of the rotation reference plane and the flow channel width is generated according to the distribution of the map data points.

[0059] 105. Based on the positive correlation function and the velocity field pressure compensation characteristics, a set of coordinated parameters including contraction rate parameters, pressure gradient, and blade spacing is integrated. By matching the coordinated parameter set with the flow channel inclination angle, the negative pressure peak range is dynamically calibrated under low water pressure conditions to achieve closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion.

[0060] Optionally, step 105 may specifically include the following steps: 1051. Based on the slope characteristics of the positive correlation function, define the pressure compensation coefficient, integrate the effective range of the contraction rate parameter, the pressure gradient and the blade spacing interval to form a set of synergistic parameters; 1052. Based on the aforementioned collaborative parameter set, an adaptive flow channel tilt angle matching strategy is established, and the tilt angle adjustment amount is calculated according to the aforementioned flow channel tilt angle matching strategy. 1053. Adjust the flow channel angle according to the tilt adjustment amount, and calibrate the negative pressure peak range under low water pressure conditions based on the pressure compensation coefficient to generate an optimized peak interval. Use the optimized peak interval as a dynamic constraint boundary to form a closed-loop control flow, thereby realizing closed-loop simulation control of water saving efficiency and fluid kinetic energy conversion.

[0061] In the aforementioned steps, the collaborative parameter set refers to a dynamic optimization parameter set that integrates the effective range of the contraction rate parameter, the pressure gradient interval, and the blade spacing interval; the pressure compensation coefficient is a negative pressure calibration weighting factor defined based on the slope characteristics of the positive correlation function; the flow channel tilt angle matching strategy is a rule model established for adaptive adjustment of the flow channel angle; the tilt angle adjustment amount is a geometric angle correction value generated through strategy calculation; the optimized peak interval is the allowable fluctuation range of the negative pressure peak after calibration by the pressure compensation coefficient; the closed-loop control flow is a feedback control link with the optimized peak interval as the boundary; the water-saving efficiency parameter refers to the water saving rate index of unit fluid kinetic energy conversion; and the fluid kinetic energy conversion rate describes the efficiency value of fluid potential energy to kinetic energy conversion.

[0062] In this embodiment, firstly, through step 1051, a pressure compensation coefficient is defined based on the slope characteristics of the positive correlation function. The effective range of the contraction rate parameter, the pressure gradient, and the blade spacing interval are integrated to form a collaborative parameter set. Specifically, the mathematical equation of the positive correlation function is first analyzed to extract the slope characteristics related to pressure compensation. The weighted average algorithm is used to define the value of the pressure compensation coefficient. Secondly, the threshold boundary of the contraction rate parameter under the target working condition is extracted to determine the effective range. Then, the allowable range of the pressure gradient and the constraint range boundary of the blade spacing are integrated. Finally, a collaborative parameter set output dataset containing three parameters is generated through a parameter aggregation algorithm.

[0063] Secondly, through step 1052, an adaptive flow channel tilt angle matching strategy is established based on the collaborative parameter set and the tilt angle adjustment amount is calculated. Specifically, the collaborative parameter set dataset is first input into the strategy learning framework, then a multivariate regression model of the three parameters of flow channel tilt angle, contraction rate, pressure gradient, and blade spacing is constructed, then optimization calculation technology is used to calculate the flow channel tilt angle matching strategy and output the tilt angle adjustment amount, and finally a strategy adjustment instruction set is generated to provide input basis for step 1053.

[0064] Finally, through step 1053, the flow channel angle is adjusted according to the tilt adjustment amount, and the negative pressure peak range is calibrated based on the pressure compensation coefficient to achieve closed-loop simulation control. The specific process is as follows: First, the geometric drive algorithm is applied to adjust the flow channel entity angle parameter according to the tilt adjustment amount. Second, the low water pressure working condition threshold condition is set to activate the pressure compensation calibration module. Based on the pressure compensation coefficient, the negative pressure peak fluctuation range is remapped to generate the optimized peak range value range. Then, the optimized peak range is used as the dynamic boundary to construct a closed-loop control flow framework. The water saving efficiency parameter and the fluid kinetic energy conversion rate parameter are input into the control framework to perform real-time feedback adjustment. Finally, a continuously iterative closed-loop simulation control link output system optimization report is formed.

[0065] In practical applications, during the simulation verification process of intelligent transformation of a large-scale water supply network, a slope-type pressure compensation coefficient of 0.36 was extracted based on the positive correlation between negative pressure peak and siphon time. This was integrated with key parameters of the trapezoidal cross-section—the effective range of the contraction rate (0.09 to 0.11 mm / mm), the dynamic range of the pressure gradient parameter (-0.25 to -0.65 kPa / m), and the blade spacing of the spiral zone (68 to 75 mm)—to construct a multi-dimensional collaborative parameter set including adaptive thresholds. A dynamic matching strategy for the flow channel inclination angle was established using this parameter set. When the inlet water pressure dropped to a low operating condition of 5 meters, the system captured pressure data from six monitoring points in the guide section in real time at a sampling frequency of 50 Hz. When a negative pressure peak of 28.5 kPa was detected deviating from the upper limit of the target range by 1.3 kPa, the system calculated, based on the pressure compensation coefficient and the eddy current intensity feedback mechanism, the required adjustment amount of 2.3 degrees to increase the flow channel reference inclination angle. Simultaneously, the rate of the third-stage contraction section was adjusted to 0.105 mm / mm, and the pressure gradient parameter was corrected to -0.38 kPa / m. After structural adjustments, a closed-loop verification process was initiated under a 6-meter baseline water pressure condition: The first simulation used 32.8 kPa as the target value for the negative pressure peak. By fine-tuning the impeller assembly tilt angle, the negative pressure fluctuation was converged to the optimized range of 32.5 ± 0.4 kPa, simultaneously driving the water-saving efficiency to increase to 2.8 cubic meters per hour, an improvement of 0.9 cubic meters per hour compared to the initial scheme; The second simulation, based on the 112% conversion rate data fed back by the kinetic energy sensor, dynamically expanded the boundary of the collaborative parameter set, widening the allowable range of the contraction rate to 0.08 to 0.12 mm per millimeter and the lower limit of the pressure gradient to -0.72 kPa per meter, thereby optimizing the tilt angle increment algorithm; The final simulation, targeting the extreme condition of a sudden 10% drop in pipeline pressure, applied the new parameter set to achieve stable control of the negative pressure peak within the range of 29.3 to 31.6 kPa, compressing the fluctuation range of water-saving efficiency to within 0.5 cubic meters per hour, and maintaining the fluid kinetic energy conversion rate at a level above 110% of the baseline condition. Ultimately, a three-dimensional closed-loop control system with the negative pressure peak range of 32±1.5 kPa as the core constraint was formed. Under the dual optimization objectives of pressure fluctuation self-suppression and energy consumption dynamic perception, after 480 minutes of continuous simulation verification, a deep synergistic control of fluid kinetic energy conversion and water-saving efficiency was achieved.

[0066] In the overall scheme of step 105 above, the pressure compensation coefficient is defined based on the slope characteristics of the positive correlation function, and the effective range of the contraction rate parameter, pressure gradient, and blade spacing interval are integrated to form a collaborative parameter set. On this basis, an adaptive flow channel tilt angle matching strategy is established to calculate the tilt angle adjustment amount. Then, the flow channel angle is adjusted and the negative pressure peak range is calibrated under low water pressure conditions in combination with the pressure compensation coefficient to generate an optimized peak interval. Finally, the peak interval is used as a dynamic constraint boundary to form a closed-loop control flow, thereby realizing closed-loop simulation control of water saving efficiency and fluid kinetic energy conversion.

[0067] The following is a complete embodiment for steps 101 to 105: like Figure 2 As shown, in the fluid simulation optimization process of a water-saving toilet siphon structure, 12 high-precision pressure sensor arrays were first arranged at the liquid-gas interface of the guide cavity to collect dynamic pressure change data ranging from 0.3 to 1.8 kPa. At the same time, a 20x20 grid cell was established in the spiral region to monitor the eddy current intensity data range of 0.4 to 0.9 per second. Combined with the shrinkage rate characteristic of 0.08 mm per millimeter as the inlet width of the trapezoidal section shrinks from 48 mm to 32 mm at the outlet, a three-dimensional flow field model including the section shrinkage rate parameter, pressure gradient parameter, and eddy current intensity value was constructed. Subsequently, based on the contraction rate parameter in the model, the trapezoidal cross section was divided into 80 computational units along the flow direction, and five rounds of gradient numerical iterations were performed. The correction range for each round was controlled within ±0.05 kPa per meter, ultimately stabilizing the flow velocity distribution in the main channel within the range of 1.8 ± 0.2 m / s, and forming a dynamic distribution cloud map of the pressure field from 15 to 35 kPa. At the pipe section intersection node 15 cm away from the sewage outlet, a multi-parameter evaluation function was constructed based on the pressure difference of 1.8 kPa and the peak vorticity data of 0.55 m / s. The weight ratio of the three terms, pressure fluctuation, energy dissipation, and turbulence intensity, was set to 0.4:0.4:0.2, optimizing the pressure gradient to -0.45 kPa per meter and balancing the negative pressure peak to the range of 28.5 ± 1.2 kPa.

[0068] Based on optimized parameter values ​​and the deviation of ±0.5 kPa between measured pressure data and the preset threshold of 0.9 kPa, the spiral region of the guide section is divided into three key adjustment sections: When fluctuations exceeding the threshold by 1.6 times occur in the core section 6 to 9 cm from the inlet, the eddy current intensity feedback mechanism is triggered—the blade tilt angle is adjusted by ±0.6 degrees and the spacing by ±5 percent for every 0.1 increase or decrease in eddy current amplitude per second; after three adjustment cycles, the blade tilt angle increases from the initial 15 degrees to 17.2 degrees, and the spacing decreases from 80 mm to 73 mm, so that the pressure fluctuation in the key area converges to the range of ±10 percent of the threshold. After importing the adjusted blade parameters into the flow field model, the effect of the outlet tilt angle was verified under the boundary condition of a water storage height of 9.5 liters: when the tilt angle was adjusted from -10 degrees to -15 degrees, the negative pressure peak increased from 25.1 kPa to 29.3 kPa, and the siphon time was shortened from 14 seconds to 11 seconds; based on 128 sets of experimental data, a positive correlation spectrum between the negative pressure peak and the siphon duration was established, revealing the physical law that for every 2 degrees increase in the rotation reference plane and every 3 mm narrowing of the flow channel width, the negative pressure peak increases by 1.8 kPa and the siphon time is reduced by 1.5 seconds.

[0069] Finally, based on this correlation, a pressure compensation coefficient of 0.31 was extracted, and a coordinated parameter set was formed by integrating the cross-sectional contraction rate (0.075 to 0.105 mm / mm), pressure gradient (-0.3 to -0.6 kPa / m), and blade spacing (70 to 77 mm). Under simulated low water pressure conditions with a water storage capacity of 7.8 liters, a flow channel inclination angle matching strategy was used to monitor the deviation of ±0.8 kPa in real time, triggering an adaptive inclination angle adjustment correction of -1.2 degrees, dynamically calibrating the negative pressure peak to the optimized range of 27.6 to 30.1 kPa. The established closed-loop control flow was rigorously verified under three operating conditions: the water-saving efficiency remained stable at 6.2 ± 0.3 liters per cycle, the fluid kinetic energy conversion rate reached 113% of the baseline level, and the negative pressure fluctuation during the siphon process was compressed to a range of ±0.7 kPa, achieving high-precision fluid dynamic control with a valve regulation response speed of 0.2 seconds.

[0070] Figure 3 This application provides a schematic diagram of a fluid simulation optimization system for a water-saving toilet siphon structure, as shown in the embodiment of the present application. Figure 3 As shown, the system includes: The acquisition module 31 acquires pressure change data at the liquid-gas interface of the guide cavity and eddy intensity data in the spiral region. Combined with the contraction rate characteristics of the trapezoidal cross section, it constructs a three-dimensional flow field model that includes cross section contraction rate parameters, pressure gradient parameters, and eddy intensity. The matching module 32, based on the contraction rate parameter in the three-dimensional flow field model, adjusts the pressure gradient parameter of the trapezoidal section through numerical iteration to dynamically match the flow velocity distribution of the simulated flow field with the pressure field, and dynamically optimizes the pressure gradient parameter according to the energy loss difference at the pipe segment intersection node and the eddy current intensity data to balance the negative pressure peak. The adjustment module 33, based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and the preset threshold, drives the virtual blade group in the spiral region of the guide section in the simulation environment, and adjusts the blade tilt angle and spacing parameters in conjunction with the eddy current intensity feedback mechanism. The verification module 34 imports the adjusted blade tilt angle and spacing parameters into the flow field model, and, in conjunction with the water storage height boundary condition, verifies the enhancing effect of the sewage outlet tilt angle on the pressure distribution through multiple iterations, and establishes a positive correlation function between the negative pressure peak and the siphon duration. The integration module 35, based on the positive correlation function and the flow velocity field pressure compensation characteristics, integrates the contraction rate parameter, pressure gradient, and blade spacing into a coordinated parameter set. Through the matching strategy of the coordinated parameter set and the flow channel inclination angle, it dynamically calibrates the negative pressure peak range under low water pressure conditions, thereby realizing closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion.

[0071] Figure 3 The aforementioned water-saving toilet siphon structure fluid simulation optimization system can perform... Figure 1 The implementation principle and technical effects of the water-saving toilet siphon structure fluid simulation optimization method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the water-saving toilet siphon structure fluid simulation optimization system in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0072] Simulation optimization systems can be implemented as computing devices, such as... Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42; The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 42.

[0073] The processing component 42 is used for the above Figure 1 The embodiment describes a fluid simulation optimization method for the siphon structure of a water-saving toilet.

[0074] The processing component 42 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0075] Storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0076] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0077] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0078] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0079] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0080] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a fluid simulation optimization method for the siphon structure of a water-saving toilet.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0083] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, 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 can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for fluid simulation optimization of a water-saving toilet siphon structure, characterized in that, include: Pressure abrupt change data at the liquid-gas interface of the guide cavity and eddy intensity data in the spiral region were collected. Combined with the contraction rate characteristics of the trapezoidal cross section, a three-dimensional flow field model including cross section contraction rate parameters, pressure gradient parameters and eddy intensity was constructed. Based on the contraction rate parameter in the three-dimensional flow field model, the pressure gradient parameter of the trapezoidal section is adjusted by numerical iteration to dynamically match the velocity distribution of the simulated flow field with the pressure field. The pressure gradient parameter is dynamically optimized according to the energy loss difference at the pipe segment intersection and the eddy current intensity data to balance the negative pressure peak. Based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and the preset threshold, the virtual blade group in the spiral region of the guide section is driven in the simulation environment, and the blade tilt angle and spacing parameters are adjusted in conjunction with the eddy current intensity feedback mechanism. The adjusted blade tilt angle and spacing parameters are imported into the flow field model. Combined with the water storage height boundary condition, the enhancement effect of the sewage outlet tilt angle on the pressure distribution is verified through multiple iterations, and a positive correlation function between the negative pressure peak and the siphon duration is established. Based on the positive correlation function and the velocity field pressure compensation characteristics, a set of coordinated parameters including contraction rate parameters, pressure gradient, and blade spacing is integrated. By matching the coordinated parameter set with the flow channel inclination angle, the negative pressure peak range is dynamically calibrated under low water pressure conditions, thereby achieving closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion.

2. The method according to claim 1, characterized in that, Based on the contraction rate parameter in the three-dimensional flow field model, the pressure gradient parameter of the trapezoidal cross-section is adjusted through numerical iteration to dynamically match the velocity distribution of the simulated flow field with the pressure field. Furthermore, the pressure gradient parameter is dynamically optimized based on the energy loss difference at the pipe segment intersection and the eddy current intensity data to balance the negative pressure peak, including: In the three-dimensional flow field model, the correlation constraint between the contraction rate parameter and the pressure gradient parameter is established. The trapezoidal cross section is divided into calculation units along the flow direction, and the initial pressure gradient distribution is determined based on the unit contraction characteristics. Based on the contraction rate parameter, the pressure gradient correction is calculated through numerical iteration, and based on the pressure gradient correction, the pressure gradient parameter of the trapezoidal section is adjusted synchronously. The initial pressure gradient distribution is used as the starting point of the iteration to gradually achieve dynamic matching between the velocity distribution and the pressure field. Extract the energy loss difference at the pipe segment intersection node, and combine it with the eddy current intensity data to construct a multi-parameter evaluation function. Input the dynamically matched pressure gradient parameters into the multi-parameter evaluation function. Based on the evaluation results of the multi-parameter evaluation function, a gradient correction strategy is adopted to optimize the pressure gradient parameters, balance node energy loss and reduce the fluctuation range of negative pressure peak, and balance the negative pressure peak through multiple iterations.

3. The method according to claim 1, characterized in that, Based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and a preset threshold, the virtual blade assembly in the spiral region of the guide section is driven in the simulation environment. The blade tilt angle and spacing parameters are adjusted in conjunction with the eddy current intensity feedback mechanism, including: Based on the optimized pressure gradient parameters, and combined with the deviation between the pressure mutation data and the preset threshold, a quantitative analysis is performed to obtain the deviation distribution characteristics. The deviation distribution characteristics are projected onto the spiral region of the guide section to identify the key adjustment section where the pressure fluctuation exceeds the preset threshold. The position parameters of the key adjustment section are associated with the multi-parameter evaluation function of the negative pressure fluctuation to establish a vortex intensity feedback mechanism between real-time vortex intensity data and blade tilt angle and spacing parameters. In the simulation environment, the virtual blade group is driven to perform linkage adjustment. For the key adjustment section, according to the eddy current intensity feedback mechanism, the compensation amount of blade tilt angle and the scaling ratio of spacing are calculated. Based on the compensation amount of blade tilt angle and the scaling ratio of spacing, the blade tilt angle and spacing parameters in the key adjustment section are adjusted synchronously.

4. The method according to claim 1, characterized in that, The adjusted blade tilt angle and spacing parameters were imported into the flow field model. Combined with the water storage height boundary condition, the enhancing effect of the discharge outlet tilt angle on pressure distribution was verified through multiple iterations. A positive correlation function between the negative pressure peak and the siphon duration was established, including: The adjusted blade tilt angle and spacing parameters are imported into the flow field model to reconstruct the geometry of the spiral region of the guide section. The rotation reference plane is defined by the blade tilt angle, and the flow channel width is determined by the spacing parameters. By combining the dynamic boundary conditions of water storage height, the enhancement effect of the outlet inclination angle on pressure distribution was verified through multiple rounds of iteration, and pressure distribution data and outlet flow data of the spiral region were collected simultaneously. Based on the pressure distribution data, the spatiotemporal evolution characteristics of the negative pressure peak in the changing region under different sewage outlet inclination angles are analyzed. Combined with the sewage outlet flow data, a four-dimensional feature map is constructed, which includes inclination angle parameters, spatiotemporal evolution characteristics of negative pressure peak, rotational reference plane angle and flow channel width. Based on the data point distribution in the four-dimensional feature map, a positive correlation function between the negative pressure peak and the siphon duration, including the rotation reference plane and the channel width correction term, is generated.

5. The method according to claim 1, characterized in that, Based on the aforementioned positive correlation function and velocity field pressure compensation characteristics, a coordinated parameter set integrating contraction rate parameters, pressure gradient, and blade spacing is used. Through a matching strategy between this coordinated parameter set and the flow channel inclination angle, the negative pressure peak range is dynamically calibrated under low water pressure conditions. This achieves closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion, including: Based on the slope characteristics of the positive correlation function, a pressure compensation coefficient is defined, and the effective range of the shrinkage rate parameter, pressure gradient, and blade spacing interval are integrated to form a set of synergistic parameters. Based on the aforementioned collaborative parameter set, an adaptive flow channel tilt angle matching strategy is established, and the tilt angle adjustment amount is calculated according to the flow channel tilt angle matching strategy. Based on the tilt adjustment amount, the flow channel angle is adjusted, and the negative pressure peak range is calibrated under low water pressure conditions based on the pressure compensation coefficient to generate an optimized peak interval. The optimized peak interval is used as a dynamic constraint boundary to form a closed-loop control flow, thereby realizing closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion.

6. The method according to claim 1, characterized in that, Pressure abrupt change data at the liquid-gas interface of the guide cavity and eddy intensity data in the spiral region were collected. Combined with the contraction rate characteristics of the trapezoidal cross-section, a three-dimensional flow field model was constructed, including cross-sectional contraction rate parameters, pressure gradient parameters, and eddy intensity. Pressure mutation data is collected by arranging a point array structure at the liquid-gas interface of the guide cavity, and eddy current intensity data is calculated by deploying a grid cell structure in the spiral region. The width change of the trapezoidal cross section is measured and divided by the corresponding pipe section length value to generate the contraction rate characteristic. The pressure mutation data, the eddy current intensity data, and the contraction rate characteristics are input into a three-dimensional coordinate system and spatial mapping processing is performed to generate a three-dimensional parameter point set. Based on the three-dimensional parameter point set, calculate and derive the sequence of cross-sectional contraction rate parameter value points, pressure gradient parameter value points, and eddy current intensity value points; By integrating the sequence of cross-sectional contraction rate parameter values, pressure gradient parameter values, and eddy current intensity values, a three-dimensional flow field model containing cross-sectional contraction rate parameters, pressure gradient parameters, and eddy current intensity is established.

7. The method according to claim 2, characterized in that, Extract the energy loss difference at the pipe segment intersection nodes and, in conjunction with the eddy current intensity data, construct a multi-parameter evaluation function. Input the dynamically matched pressure gradient parameters into the multi-parameter evaluation function, including: Control zones are defined at the junctions of pipe sections. Pressure, velocity and fluid density data of upstream and downstream of the control zone nodes are extracted, and the energy difference between upstream and downstream is calculated. Simultaneously, eddy current intensity data of the control zone nodes are extracted through eddy current field analysis. The eddy current intensity data includes eddy current amplitude and eddy current range. The energy loss difference and the eddy current intensity data are processed to unify the dimensions and normalized and mapped with a preset safety threshold to construct a multi-parameter evaluation function that includes a pressure fluctuation deviation term, an energy dissipation ratio term, and a turbulence intensity index term. The parameters of the multi-parameter evaluation function are coupled through fluid dynamics correlation. The dynamically matched pressure gradient parameters are input into the multi-parameter evaluation function, where the weighting coefficients are dynamically adjusted according to the influence of each physical quantity under the current flow field conditions, providing a quantitative basis for subsequent pressure gradient parameter optimization.

8. A fluid simulation and optimization system for a water-saving toilet siphon structure, characterized in that, include: The acquisition module collects pressure change data at the liquid-gas interface of the guide cavity and eddy intensity data in the spiral region. Combined with the contraction rate characteristics of the trapezoidal cross section, it constructs a three-dimensional flow field model that includes cross section contraction rate parameters, pressure gradient parameters, and eddy intensity. The matching module, based on the contraction rate parameter in the three-dimensional flow field model, adjusts the pressure gradient parameter of the trapezoidal section through numerical iteration to dynamically match the velocity distribution of the simulated flow field with the pressure field. It also dynamically optimizes the pressure gradient parameter according to the energy loss difference at the pipe segment intersection node and the eddy current intensity data to balance the negative pressure peak. The adjustment module, based on the optimized pressure gradient parameters and the deviation between the pressure mutation data and the preset threshold, drives the virtual blade group in the spiral region of the guide section in the simulation environment, and adjusts the blade tilt angle and spacing parameters in conjunction with the eddy current intensity feedback mechanism. The verification module imports the adjusted blade tilt angle and spacing parameters into the flow field model, and, in conjunction with the water storage height boundary condition, verifies the enhancing effect of the sewage outlet tilt angle on the pressure distribution through multiple iterations, and establishes a positive correlation function between the negative pressure peak and the siphon duration. The integration module, based on the positive correlation function and the velocity field pressure compensation characteristics, integrates a set of coordinated parameters including the contraction rate parameter, pressure gradient, and blade spacing. Through the matching strategy of the coordinated parameter set and the flow channel inclination angle, it dynamically calibrates the negative pressure peak range under low water pressure conditions, thereby achieving closed-loop simulation control of water-saving efficiency and fluid kinetic energy conversion.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the fluid simulation optimization method for the siphon structure of a water-saving toilet as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements the fluid simulation optimization method for the siphon structure of a water-saving toilet as described in any one of claims 1 to 7.

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