Intelligent planning construction method for multi-layer spraying of heavy-duty anticorrosive coating of concrete pool

Abstract

The present application relates to the technical field of concrete pool heavy-duty anticorrosive coating construction, and discloses a multi-layer spraying intelligent planning construction method for concrete pool heavy-duty anticorrosive coating, which determines coating spraying time sequence parameters, material combination and basic spraying parameter set based on pool structure parameters and anticorrosion grade; generates a spraying path planning set combining equipment attributes and environmental data and determines the execution priority; generates a spraying parameter correction list and a quality check scheme list according to construction cycle, drying rate and the like; and forms an intelligent construction overall scheme in association with real-time data. The method also covers area process boundary determination, construction mode switching, path optimization, quality check and construction data management and the like. Through multi-factor fusion and intelligent algorithm, the method realizes precise control and quality optimization of multi-layer spraying, improves construction efficiency and anticorrosion performance, and is suitable for concrete pool heavy-duty anticorrosion construction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of concrete pool heavy-duty coating construction, in particular to a multi-layer spraying intelligent planning construction method for concrete pool heavy-duty coating. BACKGROUND

[0002] In industrial and civil fields, concrete pools are important facilities for storing and treating corrosive media such as chemical wastewater, industrial wastewater, acid and alkali solutions, etc. They are subjected to medium corrosion and environmental erosion for a long time, resulting in pool structure damage, leakage, and even affecting safe operation. Traditional concrete pool corrosion construction relies on manual experience for process planning and spraying operation, which has low construction efficiency, poor quality stability, serious material waste, and other problems, and cannot meet the needs of modern engineering for heavy-duty corrosion performance and intelligent construction.

[0003] From the process planning perspective, the traditional method lacks systematic analysis of concrete pool structure parameters (such as pool size, inside and outside corner structure, concave and convex surface distribution, etc.) and corrosion grade requirements. For example, for different shapes of pool areas, it is difficult to accurately determine the spraying timing parameters (such as spraying interval threshold, curing time window) and material combination scheme (reasonable matching of primer, intermediate layer, and topcoat materials), which can easily lead to insufficient adhesion between coatings, incomplete curing, and other problems, affecting the overall corrosion performance. At the same time, the traditional process planning does not fully consider the influence of construction environmental factors (such as temperature, humidity, wind speed, etc.) on the drying rate and curing effect of the coating, and it is difficult to dynamically adjust the construction parameters, resulting in large fluctuations in construction quality.

[0004] In terms of spraying parameters, the traditional method mainly relies on the experience of construction personnel to set basic spraying parameters (such as dry film thickness, spraying angle, gun speed, etc.), lacks scientific decision-making algorithms and data support. When the dry film thickness of the coating is required to be high, it is difficult to clearly distinguish the applicable conditions of the reinforced construction mode and the standard construction mode, and it is also difficult to reasonably classify the materials according to the material viscosity, curing temperature, etc. This leads to the inability to fully utilize the performance of the materials. In addition, for different surface morphologies of the construction area (such as high porosity, low porosity, or intermediate transition morphology of the concrete surface), the traditional method cannot accurately plan the path based on the surface roughness data, which can easily lead to unreasonable spraying paths, uneven coating thickness, and other problems, affecting the flatness and corrosion resistance of the corrosion layer.

[0005] In terms of quality verification and construction management, traditional construction lacks intelligent quality detection and dynamic adjustment mechanisms. For example, it is impossible to monitor coating thickness and flatness in real time, and it is difficult to discover problems such as thickness deviation or surface waviness exceeding the standard during the construction process and trigger compensation spraying procedures. At the same time, the recording and management of construction data are relatively scattered, and there is a lack of effective use of historical data, which makes it impossible to establish a coating performance degradation model to predict the maintenance period, resulting in a lack of scientific basis for later maintenance work, increasing the maintenance cost and safety risk.

[0006] With the popularization of intelligent technology in the field of building construction, the limitations of traditional concrete pool heavy-duty corrosion coating construction methods are increasingly prominent. How to integrate intelligent planning, dynamic adjustment, data-driven technology into the construction process to achieve precise control and quality optimization of multi-layer spraying has become a technical problem to be solved. In the existing technology, although there are some intelligent researches on corrosion coating construction, most of them focus on the spraying control of a single coating layer, and lack of systematic intelligent planning for the whole process of multi-layer spraying, especially in the coordination of process planning, path optimization, quality verification and construction data management. Therefore, it is urgent to develop a multi-layer spraying intelligent planning construction method that can comprehensively consider structural parameters, corrosion grade, equipment attributes, environmental data and other factors to improve the efficiency and quality of concrete pool heavy-duty corrosion construction, and reduce the construction cost and later maintenance difficulty. SUMMARY

[0007] The purpose of the present application is to provide a multi-layer spraying intelligent planning construction method for concrete pool heavy-duty corrosion coating to solve the problems raised in the background art.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solution: a multi-layer spraying intelligent planning construction method for concrete pool heavy-duty corrosion coating, the method comprising:

[0009] Based on the structural parameters and corrosion grade requirements of the concrete pool, the process planning of the multi-layer corrosion coating spraying construction process is carried out, the spraying time sequence parameters and material combination scheme of each coating layer are determined, the time sequence parameters include spraying interval threshold and curing time window, and the material combination scheme includes bottom coating material identification, intermediate layer material identification and surface coating material identification;

[0010] According to the structural parameters and corrosion grade requirements, a set of basic spraying parameters of each coating layer is determined;

[0011] According to the set of basic spraying parameters, spraying equipment attribute parameters and environmental monitoring data, a set of coating spraying path planning is generated, and the execution priority of the set of spraying path planning is determined based on a preset optimization criterion;

[0012] According to the preset construction period, the coating drying rate curve and the equipment moving parameters, a spraying parameter correction list is generated at each coating construction stage, and a quality checking scheme list is generated according to the coating thickness detection rule and the surface flatness detection rule;

[0013] The spraying parameter correction list and the quality checking scheme list are associated with real-time construction data based on the execution priority, and a multi-layer spraying intelligent construction overall scheme is generated.

[0014] Preferably, the method is based on the structure parameters and the corrosion protection grade requirements of the concrete pool, and the spraying construction process of the multi-layer corrosion protection coating is planned, and the spraying sequence parameters and the material combination scheme of each coating are determined, including:

[0015] For the current construction area, the process planning boundary of the current area is determined according to the historical construction data of the adjacent area and the preset area division rule;

[0016] According to the process planning boundary and the preset spatial grid division strategy, a coating parameter configuration subset of the current area is extracted;

[0017] The data content of the spraying sequence parameters and the material combination scheme in the coating parameter configuration subset is determined as the reference construction parameters of the current area.

[0018] Preferably, the method determines a basic spraying parameter set of each coating according to the structure parameters and the corrosion protection grade requirements, including:

[0019] For the current coating, when the dry film thickness of the coating is greater than a preset thickness threshold, it is determined that the coating belongs to a strengthened construction mode;

[0020] When the dry film thickness is not greater than the preset thickness threshold, the material viscosity parameter and the solidification temperature parameter of the coating are classified based on a first spraying decision algorithm to generate a material attribute feature vector;

[0021] A material adaptation degree coefficient is determined according to the construction environment temperature and humidity data of the current coating;

[0022] When the matching degree of the material attribute feature vector and the predetermined material standard exceeds a preset adaptation threshold, it is determined to enable a standard construction mode;

[0023] When the matching degree does not exceed the preset adaptation threshold, it is determined to enable a self-defined construction mode.

[0024] Preferably, the basic spraying parameter set further includes a spraying angle parameter and a gun speed parameter, the construction mode includes a continuous spraying mode, an intermittent spraying mode and a compensation spraying mode, and the generation of the coating spraying path planning set includes:

[0025] For the current construction area, the surface roughness data is hierarchically clustered based on the second spraying decision algorithm to generate surface morphology category data;

[0026] For the current surface morphology category, a path density correction factor is calculated according to the coating material permeability coefficient and the surface porosity;

[0027] According to the path density correction factor and the spraying equipment movement parameters, a region path planning matrix is generated.

[0028] Preferably, the method further comprises, after determining the surface morphology category of the current construction area:

[0029] If the surface morphology category belongs to the high-porosity morphology, a three-cross spraying path is additionally set;

[0030] If the surface morphology category belongs to the low-porosity morphology, a single-cycle coverage path is generated based on a preset path optimization rule;

[0031] If the surface morphology category belongs to the intermediate transition morphology, an optimal path template is matched according to historical construction data.

[0032] Preferably, the method further comprises, after generating the coating spraying path planning set:

[0033] According to real-time coating thickness detection data, the spraying parameter correction list of the current region is updated;

[0034] Based on the updated spraying parameter correction list, the path density correction factor is recalculated;

[0035] According to the recalculated path density correction factor, the execution priority of the coating spraying path planning set is dynamically adjusted.

[0036] Preferably, the surface flatness detection rule comprises:

[0037] A coating thickness fluctuation threshold matrix is established, and when the deviation of the real-time detection thickness value from the target thickness exceeds the set tolerance range, a compensation spraying program is automatically triggered;

[0038] A surface waviness detection period is set, and a multi-point scanning method is used to obtain surface morphology topological data.

[0039] Preferably, the generation rule of the quality verification scheme list further comprises:

[0040] A spraying equipment calibration verification mechanism is established, and before each layer of construction, the spraying equipment is parameter calibrated and compensated based on the thickness measurement value of the reference coating sample plate.

[0041] Preferably, the method further comprises, after generating the overall scheme of multi-layer intelligent spraying construction:

[0042] A construction process database is constructed, and the spraying parameter correction list of each coating layer, the quality check scheme list, real-time construction data and surface morphology category data are stored in the database;

[0043] The expired construction records and redundant data in the database are cleared according to a preset period;

[0044] A coating performance degradation model is established based on historical data in the database, and is used for predicting a coating maintenance period.

[0045] Preferably, the second spraying decision algorithm adopts a genetic optimization algorithm, and the specific steps for generating the surface morphology category data include:

[0046] Initializing a population set of surface roughness data;

[0047] Iteratively optimizing the path planning scheme through selection, crossover and mutation operations;

[0048] When a preset iteration number or a fitness function convergence threshold is reached, an optimal surface morphology classification result is output.

[0049] Compared with the prior art, the present application has the following beneficial effects:

[0050] In the process planning link, the spraying timing parameters and material combination scheme of each coating layer are systematically determined based on the structural parameters and corrosion protection grade requirements of the concrete pool. Through preset regional division rules and spatial grid division strategies, combined with historical construction data of adjacent regions, the process planning boundary of the current region is accurately defined and the coating parameter configuration subset is extracted, so that the construction parameters of different regions are highly matched with the structural features and corrosion protection requirements. For example, for complex structure regions such as inside and outside corners, concave and convex surfaces, the spraying timing and material combination can be adjusted accordingly to avoid coating defects caused by uniform parameter setting, and the scientificity and adaptability of process planning are improved, thereby ensuring the overall performance of the corrosion protection layer from the source.

[0051] In the process of determining the basic spraying parameters, the dry film thickness threshold is used to distinguish between the enhanced construction mode and the standard construction mode, and the first spraying decision algorithm is used to classify parameters such as material viscosity and solidification temperature to generate a material attribute feature vector. Combined with the construction environment temperature and humidity data, the material adaptation degree coefficient is calculated to realize intelligent switching of the construction mode. When the material attributes match the predetermined standard with high degree of matching, the standard construction mode is enabled to ensure construction efficiency; when the degree of matching is low, the self-defined construction mode is adopted to flexibly adjust the parameters to adapt to special material requirements, thereby avoiding the blindness of traditional experience setting parameters and improving the material utilization rate and the stability of coating performance.

[0052] In terms of coating spraying path planning, the second spraying decision algorithm (genetic optimization algorithm) is used to perform hierarchical clustering on the surface roughness data to generate surface morphology category data. In combination with the coating material penetration coefficient and the surface porosity, a path density correction factor is calculated to generate a regional path planning matrix. For surfaces with high porosity morphology, low porosity morphology and intermediate transition morphology, a three-cross spraying path, a single-cycle coverage path and a historical optimal path template are respectively used to realize dynamic optimization of the spraying path. For example, for high-porosity surfaces, multiple cross-spraying is used to improve the coating penetration effect, and for low-porosity surfaces, an optimized path is used to reduce the number of spraying times, thereby significantly improving the construction efficiency and reducing material waste while ensuring uniformity of the coating thickness.

[0053] The quality verification and dynamic adjustment mechanism monitors the coating thickness and flatness in real time by establishing a coating thickness fluctuation threshold matrix and a surface waviness detection period. When the detected value exceeds the tolerance range, the compensation spraying program is automatically triggered to ensure real-time controllability of the construction quality. At the same time, the spraying equipment is calibrated and compensated based on the reference coating sample before each layer of construction to avoid construction deviations caused by equipment errors, further improving the construction precision. In addition, the construction process database realizes digital management of all-process information such as spraying parameters, quality verification data and surface morphology data. By periodically removing expired data, the database is kept running efficiently. The coating performance degradation model is established using historical data to provide data support for scientific prediction of maintenance cycles, which helps to develop maintenance plans in advance and reduce maintenance costs and safety risks in the later stage.

[0054] The generation of the intelligent construction overall scheme associates the spraying parameter correction list, the quality verification scheme list and the real-time construction data, and realizes dynamic collaborative control of the construction process based on the execution priority. In combination with the equipment movement parameters, the coating drying rate curve and the preset construction period, the spraying parameter correction list is generated in real time, so that the construction parameters can be adjusted in a timely manner according to environmental changes and construction progress, ensuring that each coating is completed under the best conditions. The spraying and curing are avoided due to changes in environmental factors, which leads to fluctuations in construction quality. This whole-process intelligent planning and dynamic adjustment not only improves the automation level of construction and reduces manual intervention, but also significantly improves the consistency and durability of the concrete pool heavy-duty coating, providing strong technical support for prolonging the service life of the pool and ensuring safe operation. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A work principle diagram of the multi-layer spraying intelligent planning construction method of the concrete pool heavy-duty coating described in the present application;

[0056] Figure 2 A work principle diagram for determining the coating construction mode;

[0057] Figure 3A working principle diagram for determining a spraying path based on a surface morphology category;

[0058] Figure 4 A working principle diagram for coating surface flatness detection;

[0059] Figure 5 A working principle diagram for construction process database construction and coating maintenance cycle prediction. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0061] Please refer to Figures 1-5 The concrete pool heavy-duty coating multi-layer spraying intelligent planning construction method according to the present application specifically comprises the following implementation steps:

[0062] A simulation calculation model of the high-pressure mud pump is established based on dynamic fluid-structure coupling parameters, soil multiphase flow variable parameters and pipeline transient resistance parameters. The dynamic fluid-structure coupling parameters include mud pulsating pressure and impeller dynamic stress; the soil multiphase flow variable parameters include particle phase content and viscous resistance coefficient; and the pipeline transient resistance parameters include transient pressure drop gradient and local resistance mutation coefficient. By integrating the above parameters, a basic model framework capable of reflecting the interaction of multiple physical fields in the working process of the mud pump is formed.

[0063] In the simulation calculation model, the factors of mud pump speed-power dynamic matching, mud flow stratification effect and pipeline transient cavitation risk are introduced. By quantifying the correlation between these factors and the operating performance of the mud pump, the operating envelope and critical failure threshold of the mud pump under different construction scenarios are calculated by using the model, thereby providing data support for subsequent safety assessment.

[0064] According to the simulation calculation model, a high-pressure mud pump multi-physical field coupling analysis system is developed, which includes a parameter configuration module, a dynamic simulation module and a safety assessment module. The dynamic simulation module integrates a transient flow field solver and a structure vibration solver to realize bidirectional coupling calculation of the flow field and the structure field.

[0065] The parameter configuration module of the system is used to input the measured transient performance data of the mud pump, self-defined soil rheological properties and pipeline topology parameters. The measured transient performance data includes pulsating power spectral density and dynamic head fluctuation amplitude, which are used to calibrate the initial parameters of the model; and the self-defined parameters are set according to the soil conditions and pipeline layout of the specific construction scenario, so as to ensure the applicability of the model.

[0066] The system-based dynamic simulation module performs fluid-solid two-way coupling calculation to obtain the dynamic flow velocity distribution, pressure pulsation propagation characteristics and structural fatigue damage index of the mud pump under different rotating speeds. Through analysis of these data, an optimized configuration scheme for high-pressure working conditions is generated to realize fine adjustment of the operating parameters of the mud pump.

[0067] The application will be further described below in conjunction with Examples 1 to 5:

[0068] Example 1:

[0069] In this example, the dynamic fluid-solid coupling parameters are further expanded to include the impeller gap leakage vortex intensity coefficient and the mud non-Newtonian fluid thixotropic index in addition to the mud pulsating pressure and the impeller dynamic stress. The impeller gap leakage vortex intensity coefficient is used to represent the vortex intensity formed by the fluid leakage at the gap between the impeller and the pump shell. This parameter is obtained based on the measured data of the internal flow field of the mud pump. Specifically, a micro pressure sensor or a flow rate measuring device is arranged at the gap between the impeller and the pump shell of the mud pump prototype, and the pressure fluctuation and flow rate distribution data at the gap under different working conditions are recorded by a high-speed data acquisition system. For example, during the operation of the mud pump, the fluid parameters at the gap region are collected at fixed time intervals (e.g. millisecond level), the collected pressure signals are analyzed by using digital signal processing technology, the characteristic frequency and corresponding intensity amplitude of the leakage vortex are extracted, and thus the impeller gap leakage vortex intensity coefficient is determined. The numerical range of this coefficient is closely related to the impeller gap size, the mud viscosity and the pump rotating speed, and a typical value is usually obtained through repeated tests to ensure the reliability of the parameter.

[0070] The mud non-Newtonian fluid thixotropic index describes the viscosity change of the mud over time under the action of shear. The mud, as a multiphase fluid composed of water, soil particles and other additives, has complex rheological properties and significant non-Newtonian fluid characteristics. The thixotropic index is a key parameter for characterizing the thixotropic behavior of the mud. The process of obtaining this parameter is as follows: first, according to the soil conditions that may be encountered in actual construction, mud samples with different proportions are prepared, such as changing the particle size distribution of soil particles, the ratio of clay to sand and the content of additives. Then, a rotary rheometer is used to perform shear tests on each mud sample. During the experiment, the shear rate is set to change over time according to a certain rule (e.g. linearly increasing, stepwise changing), and the shear stress response curve over time is recorded. The calculation of the thixotropic index is based on the constitutive model of thixotropic fluid. By fitting the shear stress-time curve, the thixotropic parameter in the model is determined, which is the thixotropic index described in the application. For example, for a certain mud sample, during the cycle of shear rate from low to high and then to low, the hysteresis loop area formed by the shear stress curve has a corresponding relationship with the thixotropic index. By analyzing the hysteresis loop by mathematical methods, the specific value of the thixotropic index can be obtained.

[0071] In constructing the simulation calculation model, the impeller gap leakage vortex intensity coefficient and the mud non-Newtonian fluid thixotropic index are included in the dynamic fluid-structure coupling parameter system. Specifically, in the flow field control equation, the impeller gap leakage vortex intensity coefficient is embodied by modifying the vortex viscosity term in the turbulence model. For example, in the standard k-ε turbulence model, the leakage vortex intensity coefficient is added as an additional source term to the equations of turbulent kinetic energy k and dissipation rate ε to reflect the influence of the gap leakage vortex on the development of turbulence. For the non-Newtonian fluid characteristics of the mud, the thixotropic index is used to adjust the viscosity expression in the constitutive equation. In the thixotropic fluid model, the viscosity is not only a function of shear rate, but also related to the shear duration, and the thixotropic index controls the rate term of the viscosity change with time, so that the model can accurately describe the viscosity change or thinning behavior of the mud under shear.

[0072] In the dynamic simulation process, the impeller gap leakage vortex intensity coefficient and the thixotropic index are transmitted to the flow field solver as input parameters. The flow field solver first calculates the leakage flow and vortex distribution in the gap region according to the input geometric parameters (such as impeller size, gap width) and operating parameters (such as pump speed, mud flow), combined with the leakage vortex intensity coefficient. The calculation of leakage flow uses a simplified model of gap flow, such as considering the gap flow as viscous flow between parallel plates, combining Bernoulli equation and continuity equation to establish the relationship between leakage flow and pressure difference on both sides of the gap, leakage vortex intensity coefficient. The calculated leakage flow is input as a boundary condition to the overall flow field calculation, affecting the flow distribution and flow field distribution at the inlet and outlet of the impeller.

[0073] At the same time, the flow field solver updates the viscosity parameters of the mud in real time according to the thixotropic index and the current shear history. For example, at a certain moment, when the shear rate suddenly increases, the thixotropic index controls the viscosity to gradually decrease over time until it reaches a stable value; when the shear rate decreases, the viscosity gradually increases over time. This dynamic viscosity updating mechanism enables the flow field calculation to truly reflect the thixotropic characteristics of the mud, avoiding the calculation errors caused by treating the mud as a constant viscosity fluid.

[0074] In the fluid-structure coupling iteration process, the pulsating pressure field calculated by the flow field solver not only contains periodic pressure fluctuations generated by the rotation of the impeller, but also contains non-periodic pressure disturbances caused by the gap leakage vortex and the thixotropic characteristics of the mud. These pressure disturbances are transmitted to the structure vibration solver through the fluid-structure coupling interface, acting on the impeller surface grid nodes and causing dynamic response of the impeller. The structure vibration solver calculates the dynamic stress distribution of the impeller under complex load according to the elastic parameters and geometric model of the impeller material. Since the unsteady pressure load generated by the gap leakage vortex has a wide frequency characteristic, it may excite high-order vibration modes of the impeller, so in the structure vibration calculation, an algorithm capable of handling wideband excitation, such as modal superposition method or direct integration method, should be used to accurately capture the vibration response of the impeller.

[0075] The dynamic stress distribution calculation results of the impeller are fed back to the flow field solver for updating the geometry of the flow field calculation domain. For example, when the impeller deforms under high stress, the gap size between the impeller and the pump shell changes, thereby affecting the leakage vortex intensity coefficient and the leakage flow. The flow field solver recalculates the leakage vortex intensity coefficient according to the deformed gap size and adjusts the flow field parameters to realize the bidirectional interaction of fluid-structure coupling. This iterative process continues until the residual errors of the pressure pulsation amplitude and the structural vibration acceleration meet the convergence condition, ensuring that the simulation results can accurately reflect the dynamic coupling behavior of the flow field and the structure field in the actual operation process of the mud pump.

[0076] By incorporating the impeller gap leakage vortex intensity coefficient and the thixotropic index of the non-Newtonian fluid into the dynamic fluid-structure coupling parameters, the simulation calculation model can more comprehensively consider the complex physical phenomena inside the mud pump. The leakage of the impeller gap not only leads to a decrease in pump efficiency, but also may induce fluid-induced vibration and aggravate the fatigue damage of the impeller; while the thixotropic characteristics of the mud affect the flow resistance and pressure distribution of the fluid, thereby affecting the power consumption and operation stability of the mud pump. Therefore, accurately characterizing these two parameters is crucial for improving the prediction accuracy of the model. In practical applications, the measured data of these two parameters can be updated by regularly testing the performance of the mud pump and conducting rheological experiments on the mud, ensuring that the model can adapt to changes in different construction conditions and equipment states.

[0077] In the parameter input link, the impeller gap leakage vortex intensity coefficient and the thixotropic index can be manually input through the parameter configuration module of the system or called from the database. The parameter configuration module provides a user interface, allowing the operator to select the corresponding parameter value according to the specific working condition. For example, when the soil quality changes, the operator can select the thixotropic index matching the current soil quality from the soil rheological property database; when the mud pump is overhauled or the impeller wears out, causing the gap size to change, the model can be updated by inputting a new leakage vortex intensity coefficient. This flexible parameter input mechanism enhances the adaptability and scalability of the model, making it widely applicable to different types of high-pressure mud pumps and various construction scenarios.

[0078] Example 2:

[0079] The parameter configuration module of the high-pressure mud pump multi-physical field coupling analysis system has a complete process of database construction and geometry model generation, and the specific implementation is as follows:

[0080] A mud pump transient performance database, a soil rheological property database, and a pipeline topology database are established. The construction of the mud pump transient performance database is based on a mud pump bench test. During the test, pressure fluctuation time domain signals, power consumption data, and rotational speed fluctuation data under different rotational speed conditions are collected in real time through pressure sensors, power sensors, and rotational speed sensors installed on the inlet and outlet pipes of the mud pump. For example, during the incremental process of rotational speed from low to high, the output signals of the pressure sensors are recorded at a sampling frequency of several hundred times per second to form pressure fluctuation time domain data containing time series. After the collection is completed, the dynamic signal analyzer is used to perform fast Fourier transform (FFT) on the time domain signals to obtain the frequency domain energy distribution, i.e., the energy amplitude corresponding to different frequency components. These data are stored in association with the working condition parameters such as rotational speed and flow rate to form a structured transient performance database. The database can store test data of multiple mud pumps of different types, facilitating subsequent quick retrieval and matching according to the mud pump type.

[0081] The establishment of the soil rheological property database involves soil types involved in engineering practice and obtains key parameters through indoor rheological experiments. For different soils (such as clay, sand, silt, and mixed soil), representative mud samples are prepared, and the soil particle phase content (i.e., the ratio of the volume of soil particles to the total volume of mud), particle size distribution, and the types and contents of additives are specified in the samples. A rotational rheometer is used to perform rheological tests on each sample, including shear stress-shear rate curves and thixotropy tests. For example, during the cyclic process of linearly increasing the shear rate from 0 to a maximum value and then linearly decreasing it to 0, the shear stress-time curve is recorded, and the viscosity resistance coefficient and thixotropic index are obtained through curve analysis. These parameters are stored in association with the soil type and sample ratio information to form a soil rheological property database. The database can also set retrieval fields, such as the soil particle phase content interval and the viscosity resistance coefficient range, to quickly filter the required soil parameters.

[0082] The construction of the pipeline topology database is based on the pipeline layout scheme in actual construction and includes the geometric parameters and connection relationships of the pipeline system. Specific parameters include the total length of the pipeline, the diameters of each section (such as the inlet section, the outlet section, and the variable diameter section), the number and curvature radius of elbows, the type and installation position of valves, etc. For complex pipeline systems, the topology structure of the actual pipeline can be obtained through three-dimensional laser scanning technology, and the key parameters are extracted after being converted into a digital model. The pipeline topology parameters are stored in the form of a table, with each record corresponding to a pipeline system configuration, containing parameter names, numerical values, and units, such as "pipeline length: 500 m, pipe diameter: 150 mm, number of elbows: 3, curvature radius: 300 mm," etc.

[0083] When simulation calculation is needed, the corresponding impeller geometry parameter set is matched from the above database according to the mud pump model. The impeller geometry parameter set corresponds to the mud pump model one by one, and contains key parameters such as blade wrap angle, hub ratio and outlet setting angle. The blade wrap angle refers to the angle between the tangent at the inlet and outlet of the impeller blade and the circumferential tangent of the impeller, which directly affects the work capacity and energy conversion efficiency of the impeller on the mud; the hub ratio is the ratio of the hub diameter of the impeller to the outer diameter of the impeller, which mainly affects the structural strength and flow capacity of the impeller, the larger the hub ratio, the higher the strength of the impeller, but the flow area decreases, which may increase the flow resistance; the outlet setting angle is the angle between the tangent at the outlet of the blade and the circumferential tangent of the impeller, which determines the velocity direction of the mud leaving the impeller, and has a significant impact on the head and power characteristics of the pump. For example, the impeller geometry parameter set of a certain type of mud pump is: blade wrap angle 35°, hub ratio 0.4, outlet setting angle 28°, these parameters are pre-stored in the database and called by the mud pump model index.

[0084] Based on the matched impeller geometry parameter set, a three-dimensional grid model of the mud pump flow passage is generated using professional three-dimensional modeling software (such as ANSYS DesignModeler, SolidWorks, etc.). In the modeling process, first, a three-dimensional solid model of the impeller is drawn according to the parameters such as blade wrap angle, hub ratio and outlet setting angle, including blade, hub and front and rear cover plates; then the volute model is constructed, the spiral line shape of the volute is determined according to the outer diameter of the impeller and the design flow rate, to ensure smooth transition of the flow passage and reduce flow separation. After completing the geometric modeling, the flow passage model is meshed, and a combination of structured and unstructured meshes is used to increase the mesh density in complex flow areas such as the surface of the impeller blade and the volute tongue, to improve the accuracy of flow field calculation. For example, the boundary layer mesh technology is used on the surface of the blade, and multiple layers of prismatic mesh are set to ensure that the flow details in the boundary layer can be captured; for the main flow passage of the volute, tetrahedral unstructured mesh is used to ensure calculation accuracy while reducing the number of meshes and improving calculation efficiency.

[0085] The generated three-dimensional grid model is loaded into the flow field solver of the dynamic simulation module through a data interface (such as IGES, STEP format). Before loading, the grid quality needs to be checked, including grid distortion rate, aspect ratio, volume change rate and other indicators, to ensure that the grid meets the calculation requirements of the flow field solver. For example, through the grid checking tool, the grid elements with distortion rate exceeding the threshold (such as 0.9) are screened out, and local grid reconstruction is performed until the grid quality meets the standard. After being loaded into the flow field solver, the grid model serves as the geometric carrier for flow field calculation, and together with the input mud parameters and operating condition parameters forms the initial conditions for flow field solving.

[0086] The parameter configuration module also supports manual adjustment of the grid model by the user, such as modifying the grid density, re-dividing the grid of a specific area, etc., to adapt to different simulation accuracy requirements. At the same time, the system can automatically save the historical versions of the grid model, which facilitates comparison of the influence of different grid division schemes on the simulation results. In actual application, for a new type of mud pump or a special structure impeller, the geometric data of the impeller can be obtained through reverse engineering technology, such as using a three-dimensional scanner to scan the physical impeller, generating point cloud data, and then obtaining an accurate three-dimensional model through software processing, and then importing the parameter configuration module for grid division and subsequent simulation process.

[0087] Embodiment 3:

[0088] The process of the dynamic simulation module performing fluid-structure two-way coupling calculation is as follows: the flow field solver calculates the pulsating pressure field based on the input mud parameters and geometric model, and maps the pressure field to the impeller surface grid nodes of the structure vibration solver, realizing the transfer of flow field load to the structure field. After receiving the pressure load, the structure vibration solver calculates the dynamic stress distribution and modal participation factor of the impeller, wherein the dynamic stress distribution reflects the stress state of the impeller under the action of fluid load, and the modal participation factor is used to evaluate the contribution degree of different vibration modes to the response of the impeller. The calculated impeller deformation displacement field is fed back to the flow field solver through the coupling interface, and the flow field solver re-divides the grid and updates the flow field parameters according to the deformed calculation domain, realizing the influence feedback of structure deformation on the flow field.

[0089] The above process is calculated through iteration until the residual error of the pressure pulsation amplitude and the structure vibration acceleration converges to a preset threshold. The residual error convergence is realized by comparing the difference between the calculation results of adjacent iteration steps. When the relative error of the pressure pulsation amplitude and the structure vibration acceleration is less than a preset value (such as 1%), it is considered that the coupling calculation reaches a stable state, and the iteration is stopped. Through this two-way coupling mechanism, the model can accurately simulate the dynamic interaction between the flow field and the structure field during the operation of the mud pump, and provide reliable data for analyzing the vibration characteristics, fatigue damage, etc. of the mud pump.

[0090] In the flow solver, a mathematical model for mud flow is established based on the Navier-Stokes equations. Considering the non-Newtonian fluid characteristics of mud, a modified constitutive equation is used to describe the relationship between the viscous stress and the strain rate of mud. For example, for the Bingham fluid model, when the shear stress exceeds the yield stress, the viscous stress and the strain rate are linearly related; when the shear stress is lower than the yield stress, the fluid behaves as a rigid body. In numerical calculation, the finite volume method is used to discretize the calculation domain into multiple control volumes, and the mass and momentum conservation laws are applied to each control volume to establish a set of discrete equations. The implicit difference format is used for the time term to ensure the stability of the calculation. For the pressure-velocity coupling problem, the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm or its improved algorithm is used for solution, and the pressure field and velocity field are gradually corrected through iteration until the convergence condition is met.

[0091] During the calculation process, considering the complexity of the internal flow of the mud pump, especially the unsteady flow caused by the rotation of the impeller, the sliding mesh technique is used to handle the interface between the rotating region and the stationary region. The calculation domain is divided into a rotating domain (including the impeller) and a stationary domain (including the volute), and special boundary conditions are set at the interface to allow the mesh to slide relative to the interface. At each time step, the physical quantities of the rotating domain and the stationary domain are transferred at the interface through interpolation methods to ensure the continuity of the flow. In order to capture the turbulent flow phenomenon in the flow, appropriate turbulence models are used for simulation, such as the standard k-ε model, the RNG k-ε model, or the SST k-ω model, etc. According to the Reynolds number and the turbulent characteristics of the flow, appropriate turbulence model parameters are selected to improve the accuracy of the turbulent flow prediction.

[0092] After the flow solver calculates the fluctuating pressure field, it needs to map this pressure field to the impeller surface grid nodes of the structural vibration solver. Since the flow field grid and the structural grid usually use different discretization methods and grid densities, data interpolation processing is needed. The shape function interpolation method is used to calculate the pressure load on the structural grid nodes based on the pressure values of the flow field grid nodes. The weight coefficients are determined according to the distance and relative position between the nodes to ensure that the transfer of pressure load has physical meaning. During the mapping process, considering the dynamic characteristics of the pressure field, the pressure field at each time step is independently mapped to ensure that the structural vibration solver can accurately obtain the time-varying load.

[0093] After the pressure load is received by the structural vibration solver, a structural dynamics model of the impeller is established based on the theory of elasticity. The impeller is discretized into multiple elements by using the finite element method, and each element is connected by nodes. For the material properties of the impeller, parameters such as the elastic modulus, Poisson's ratio and density are considered, which are determined by material testing. In the dynamic equation, the effects of inertial force, elastic force and damping force are considered, and a second-order ordinary differential equation set is established:

[0094]

[0095] The physical meanings of the parameters in the above dynamic equation are explained as follows:

[0096] : represents the mass matrix of the impeller structure (unit: kg), which is consistent with the number of nodes of the impeller finite element grid, and the matrix elements are calculated by the material density of the impeller (the commonly used material density of the concrete water tank heavy-duty construction supporting slurry pump impeller is 7850 kg / m³) and the volume of the grid element, reflecting the mass distribution characteristics of each node of the impeller;

[0097] : represents the damping matrix of the impeller structure (unit: N·s / m), which is constructed by assuming proportional damping, i.e. (where , are damping coefficients, the value range is , which needs to be determined according to the material damping test of the slurry pump impeller), which is used to describe the energy dissipation degree in the vibration process of the impeller;

[0098] : represents the stiffness matrix of the impeller structure (unit: N / m), which is calculated based on the finite element method of elasticity, and is related to the elastic modulus (the commonly used elastic modulus of the impeller is 206 GPa), Poisson's ratio (the commonly used value is 0.3) and geometric dimensions (such as blade thickness, hub diameter) of the impeller material, reflecting the ability of the impeller to resist elastic deformation;

[0099] : represents the displacement vector of the impeller node (unit: m), which contains the displacement components of each finite element node in x, y and z three spatial directions, and is used to describe the deformation state of the impeller under the action of the flow field load;

[0100] : represents the velocity vector of the impeller node (unit: m / s), which is the first-order derivative of the displacement vector with respect to time, reflecting the rate of change of the displacement of the impeller node with respect to time;

[0101] : represents the acceleration vector of the impeller node (unit: m / s²), which is the first-order derivative of the displacement vector The second derivative of time, which is directly related to the inertial force experienced by the impeller (satisfies Newton's second law );

[0102] : represents the load vector varying with time (unit: N), which is derived from the mud fluctuating pressure field calculated by the flow field solver, transmitted to the impeller surface finite element nodes through the interpolation algorithm of the fluid-structure coupling interface, including the periodic load generated by the impeller rotation and the aperiodic load caused by the gap leakage vortex.

[0103] When solving the above dynamic equation, direct integration method or modal superposition method is used. Direct integration method directly solves the dynamic equation in each time step, which is suitable for nonlinear problems or complex load conditions; modal superposition method first solves the eigenvalue problem of the structure to obtain the natural frequency and modal shape of the structure, and then expresses the response as a linear combination of each mode, which is suitable for linear problems and has high computational efficiency. In the calculation process, the dynamic stress distribution of the impeller is calculated, and the equivalent stress such as Von Mises stress is calculated through the stress tensor to evaluate the strength of the impeller under dynamic load. At the same time, the modal participation factor is calculated, which represents the contribution of each mode to the total response. By analyzing the modal participation factor, it can be determined which mode plays a dominant role in the vibration response of the impeller, providing a basis for subsequent structural optimization.

[0104] The calculated deformation displacement field of the impeller is fed back to the flow field solver through the coupling interface. In the flow field solver, the flow field grid is re-divided according to the deformed impeller geometry. For small deformation, spring approximation method can be used, which establishes virtual springs between grid nodes and calculates the deformation of the grid according to the displacement of the nodes. For large deformation, the entire flow field grid needs to be regenerated to ensure that the grid quality meets the calculation requirements. After updating the grid, the flow field parameters including velocity field, pressure field, etc. are recalculated, considering the influence of structural deformation on the flow field. For example, the deformation of the impeller may change the geometry of the flow passage, leading to changes in flow resistance, and thus affecting the pressure distribution and velocity field.

[0105] In the fluid-structure coupling iteration process, the flow field solver and the structural vibration solver are calculated alternately until the convergence condition is met. The convergence is judged based on the residual error of the pressure pulsation amplitude and the structural vibration acceleration. For the pressure pulsation amplitude, the relative error between adjacent iteration steps is calculated, that is, the absolute value of the difference between the pressure pulsation amplitude of the current iteration step and the pressure pulsation amplitude of the last iteration step divided by the pressure pulsation amplitude of the last iteration step. For the structural vibration acceleration, the same method is used to calculate the relative error. When the relative errors of the two are less than a preset threshold (such as 1%), it is considered that the coupling calculation reaches the convergence state, and the iteration is stopped. In actual calculation, in order to improve the calculation efficiency, loose coupling or tight coupling strategy can be used. The loose coupling strategy only performs flow field and structure calculation once in each time step, and then exchanges data; the tight coupling strategy performs multiple iterations of flow field and structure calculation in each time step until the convergence condition is met. According to the characteristics of the problem and the limitation of the calculation resources, the appropriate coupling strategy is selected.

[0106] Through this two-way coupling mechanism, the model can accurately simulate the dynamic interaction between the flow field and the structure field during the operation of the mud pump. For example, the pressure pulsation in the flow field will cause the vibration of the impeller, and the vibration of the impeller will in turn affect the distribution of the flow field, forming a closed-loop interaction system. This interaction is very important in the actual operation of the mud pump, which may cause fatigue damage of the impeller, increase of vibration and noise, etc. Through the fluid-structure two-way coupling calculation method of the embodiment, the generation mechanism of these problems can be analyzed in depth, providing a theoretical basis for the design optimization of the mud pump.

[0107] In practical application, this method can be used to evaluate the running stability of the mud pump under different working conditions. For example, under high pressure working condition, the pressure pulsation amplitude of the mud may increase, causing the vibration of the impeller to intensify. By simulating the fluid-structure coupling process under different pressure conditions, the stress distribution and vibration response of the impeller can be predicted, and the reliability of the mud pump under high pressure can be evaluated. In addition, this method can also be used to optimize the structural design of the mud pump. For example, by analyzing the modal participation factor, the modes that contribute more to the vibration response are determined, and then the structure of the impeller is modified, such as adjusting the thickness of the blade, changing the shape of the hub, etc., to reduce the response of these modes and improve the anti-vibration performance of the mud pump.

[0108] In the calculation process, other factors that affect the fluid-structure coupling process can also be considered. For example, the bubbles in the mud may change the density and compressibility of the fluid, thereby affecting the characteristics of the flow field and the pressure pulsation. The influence of the existence of bubbles on the fluid-structure coupling process can be considered by introducing a two-phase flow model. In addition, the nonlinear characteristics of the material of the impeller, such as plastic deformation and fatigue damage, can also be considered in the model to more accurately predict the service life and reliability of the impeller.

[0109] Embodiment 4:

[0110] The safety evaluation module realizes the quantitative evaluation of the running safety of the mud pump by the following steps: extracting the equivalent stress peak value of the key position of the impeller and the critical pressure of the onset of mud cavitation according to the dynamic simulation results. The determination of the key position of the impeller is based on the mechanical analysis of the structural design of the mud pump, and usually includes the stress concentration parts such as the blade root, the transition area of the hub and the blade, and the edge of the inlet and outlet of the impeller. In the dynamic simulation process, the node stress data output by the structure vibration solver needs to be extracted after processing, for example, by setting the region of interest (ROI) and defining the stress threshold, automatically screening out the positions where the equivalent stress exceeds a certain percentage of the material yield strength as the key positions. The extraction of the equivalent stress peak value needs to consider the time history, that is, to track the stress change curve of each key position in the whole simulation period and record the maximum value and the corresponding working condition parameters (such as speed, head).

[0111] The determination of the critical pressure of the onset of mud cavitation is based on the calculation results of the pressure distribution of the flow field solver. The occurrence of cavitation phenomenon is related to the local pressure being lower than the saturated vapor pressure of the mud, so it is necessary to monitor the pressure value of each grid node in the flow field calculation in real time. In specific implementation, first, the saturated vapor pressure value of the mud is determined according to its temperature and composition as the judgment threshold of the onset of cavitation. During the simulation process, when the pressure value of a certain grid node is lower than the threshold for the first time, the position, pressure value and corresponding time point of the node are recorded, and the pressure value is defined as the critical pressure of the onset of cavitation. For multiple cavitation risk areas that may exist in a complex flow field, the pressure distribution cloud chart needs to be analyzed comprehensively to identify the typical area where cavitation is most likely to occur and needs to be monitored.

[0112] The three-dimensional safety boundary surface of speed-head-power in the running envelope of the mud pump is calculated. The construction of the surface is based on the multi-condition simulation data, and the specific steps are as follows: first, set the scanning range of speed, head and power in the parameter configuration module, for example, the speed range is 50%-120% of the rated speed, the head range is determined according to the performance curve of the pump, and the power range is determined by the combination of speed and head. Then, a series of working condition points are generated through automatic script, each working condition point corresponds to a set of speed, head and power parameters. For each working condition point, perform fluid-structure coupling simulation calculation to obtain the equivalent stress peak value of the key position of the impeller and the critical pressure of the onset of mud cavitation.

[0113] The calculation results of all operating points are imported into the data analysis unit of the safety assessment module, and a three-dimensional interpolation algorithm (such as Kriging interpolation or polynomial interpolation) is used to fit the safety boundary surface in the three-dimensional space of rotational speed-lift-power. The construction of the surface needs to meet two constraints: one is that the equivalent stress peak of the impeller does not exceed the allowable stress of the material (the allowable stress is obtained by dividing the yield strength of the material by the safety factor); the other is that the mud pressure is not lower than the critical pressure of cavitation inception. In the three-dimensional space, the operating points that meet the conditions form a safe operating area, and the surface itself is the boundary of the two constraints. For operating points that exceed the constraints, they are divided into a warning area (close to the boundary) and a high-risk area (far from the boundary) according to their deviation, and the high-risk area is marked as a critical failure area, which is highlighted in the visualization interface through color coding or grid line style.

[0114] Finally, based on the safety boundary surface, a set of allowed operating conditions and corresponding risk level labels are generated. The set of allowed operating conditions is obtained by filtering all operating points inside the safety boundary surface (i.e., satisfying the stress and pressure constraints), each operating point containing complete rotational speed, lift, power parameters and corresponding simulation results (such as efficiency, pulsation amplitude, fatigue life prediction value, etc.). The risk level labels are divided according to the distance of the operating points from the safety boundary surface, specifically by calculating the shortest geometric distance (or Euclidean distance) of the operating points in the three-dimensional space to the surface. Operating points with a distance greater than a set threshold (such as 10% of the characteristic length of the surface) are marked as low risk; those with a distance between the threshold and zero are marked as medium risk; and those outside the surface (i.e., not satisfying the constraints) are marked as high risk.

[0115] In practical applications, the generation of risk level labels needs to be calibrated in combination with engineering experience. For example, for medium-risk operating conditions close to the safety boundary, further analysis of stress distribution and pressure pulsation characteristics can be performed to determine whether there are local high stress or high cavitation risk, thereby adjusting the risk level. The set of allowed operating conditions can be exported in table form for operators to select appropriate operating parameters before the mud pump is running, and the system supports dynamically matching the set of allowed operating conditions based on real-time monitoring data to provide online safety warnings.

[0116] The safety assessment module also has data visualization functions, displaying the rotational speed-lift-power safety boundary surface and the distribution of operating points through a three-dimensional graphical interface. Operators can rotate and scale the three-dimensional model through interactive operations to view the safety area division from different perspectives. At the same time, the interface can superimpose stress distribution cloud maps of the impeller and flow field pressure contour lines to assist in analyzing the risk sources of operating points. For example, if an operating point is marked as medium risk, by viewing the stress cloud map, it is found that there is local stress concentration at the blade root, which can prompt the operator to pay attention to the fatigue damage of this part.

[0117] In terms of data storage, the safety assessment results are associated with the corresponding simulation working condition parameters and calculation results to establish a database, supporting historical data query and comparative analysis. For example, the safety boundary surface changes of the same mud pump at different times can be queried to evaluate the impact of equipment aging or wear on the safety of operation. In addition, the system supports exporting safety assessment reports in PDF or Excel format, including three-dimensional curved surface graphs, working condition set tables, risk level statistics, and other contents, facilitating archiving and reporting.

[0118] Embodiment 5:

[0119] The parameter configuration module includes a mud multiphase rheological model selection function, and the specific implementation is as follows:

[0120] A mud multiphase rheological model selector is set, which integrates the algorithm interfaces of common non-Newtonian fluid models such as Bingham fluid model, power-law fluid model, and thixotropic fluid model. The model selector automatically matches the rheological model through soil gradation parameters, including soil particle size distribution proportion (such as particle content less than 0.075 mm, sand content, etc.), clay mineral composition ratio, and water content, etc. The automatic matching rule is based on the engineering experience database of soil rheological properties, for example: when the clay content in soil particles exceeds 30% and the water content is high, it is determined as high cohesive soil, and the thixotropic fluid model is automatically matched; when the sand content exceeds 50% and the shear rate is sensitive, the power-law fluid model is matched; when the mud shows obvious yield stress characteristics (such as solid state at low shear rate), the Bingham fluid model is matched.

[0121] After selecting the rheological model, the model parameters are obtained through database call or user input. Taking the Bingham fluid model as an example, its constitutive equation is:

[0122]

[0123] wherein, is the shear stress (Pa), is the yield stress (Pa), is the plastic viscosity (Pa·s), is the shear rate (1 / s). The yield stress and the plastic viscosity are determined by soil rheological experiments, for example, using a rotary rheometer to measure the critical startup stress and steady-state shear stress at a fixed shear rate, corresponding to the yield stress and plastic viscosity parameters, respectively. For the power-law fluid model, the constitutive equation is:

[0124]

[0125] wherein, is the consistency coefficient (Pa·s ), is the flow index (dimensionless), obtained by fitting the logarithmic coordinate data of shear stress-shear rate curve, represents shear thinning behavior, represents shear thickening behavior. The thixotropic fluid model needs to consider the time effect additionally, and its viscosity expression is:

[0126]

[0127] where, is the viscosity changing with time (Pa·s), is the initial viscosity (Pa·s), is the shear stress and time related thixotropic coefficient function, determined by fitting the data of thixotropic loop experiment.

[0128] The selected rheological model and parameters are loaded to the flow field control equation of dynamic simulation module through data interface. In the flow field solver, the viscosity term in the momentum equation is modified according to the model type: for Bingham fluid, when the shear stress of the calculation unit is lower than the yield stress, the unit fluid is regarded as a rigid body and no flow occurs; for power-law fluid, the shear thinning or thickening behavior is simulated by adjusting the power change of the viscosity coefficient with the shear rate; for thixotropic fluid, the viscosity is updated by real-time integration of the thixotropic coefficient function, reflecting the influence of shear history on the fluid properties.

[0129] The human-computer interaction interface design of the high-pressure mud pump multi-physics coupling analysis system includes the main control interface, parameter visualization interface and report generation interface. The main control interface integrates the working condition configuration function, allowing the operator to set the mud pump speed regulation range (such as 200-800 r / min), mud concentration gradient (such as particle phase content rate 5%-30%) and pipeline topology change sequence (such as elbow number 1-5). The system automatically generates a dynamic performance comparison matrix under different configuration combinations based on the simulation calculation model, the matrix rows and columns are each parameter variable, and the cell content includes efficiency-power curve coordinate data, fluctuation amplitude-frequency spectrum peak value and fatigue life prediction value (based on stress cycle number calculation).

[0130] The parameter visualization interface uses GPU accelerated three-dimensional rendering technology to synchronously display the flow field velocity cloud map, pressure contour and structural stress distribution. The velocity cloud map maps the flow field velocity size through the color scale, with red representing high-speed area and blue representing low-speed area; the pressure contour is drawn with different line types, and the interval is automatically adjusted according to the pressure gradient; the structural stress distribution is displayed through grid deformation and color superposition, with high stress area highlighted in red. The operator can switch the dynamic rendering results of different time steps through the interface controls to observe the evolution process of the physical field over time.

[0131] The report generation interface automatically integrates simulation results based on preset templates to generate documents containing working condition configuration details, performance index summary, and safety evaluation conclusions. The report content includes: screenshots of the three-dimensional safety boundary surface of rotational speed-head-power, a table of the allowable working condition configuration set (with risk level labels), stress history curves of key positions of the impeller, and flow field pressure distribution cloud diagrams. The report supports custom parameter filtering, such as outputting detailed analysis of high-risk working conditions or performance comparison data in a specific rotational speed range.

[0132] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0133] While embodiments of the present application have been shown and described, it is to be understood that the embodiments described are merely divergences, modifications, substitutions and variations of the embodiments and can be modified in various ways by those skilled in the art without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A multi-layer spraying intelligent planning construction method for concrete pool heavy-duty coating, characterized in that, The method comprises: Based on the structure parameters and corrosion grade requirements of the concrete pool, the process planning of the spraying construction process of the multi-layer corrosion protection coating is carried out, the spraying time sequence parameters and material combination scheme of each coating are determined, the time sequence parameters include spraying interval threshold and curing time window, and the material combination scheme includes bottom coating material identification, intermediate layer material identification and top coating material identification; According to the structure parameters and corrosion grade requirements, a set of basic spraying parameters of each coating is determined; According to the set of basic spraying parameters, spraying equipment attribute parameters and environmental monitoring data, a set of coating spraying path planning is generated, and the execution priority of the set of spraying path planning is determined based on a preset optimization criterion; According to the preset construction period, the coating drying rate curve and the equipment moving parameters, a spraying parameter correction list is generated in each coating construction stage, and a quality checking scheme list is generated according to the coating thickness detection rule and the surface flatness detection rule; Based on the execution priority, the spraying parameter correction list, the quality checking scheme list and the real-time construction data are associated to generate a whole scheme of multi-layer spraying intelligent construction.

2. The method according to claim 1, wherein the method is characterized by, Based on the structure parameters and corrosion grade requirements of the concrete pool, the process planning of the spraying construction process of the multi-layer corrosion protection coating is carried out, the spraying time sequence parameters and material combination scheme of each coating are determined, including: For the current construction area, the process planning boundary of the current area is determined according to the historical construction data of the adjacent area and the preset area division rule; According to the process planning boundary and the preset spatial grid division strategy, a coating parameter configuration subset of the current area is extracted; The data content of the spraying time sequence parameters and the material combination scheme in the coating parameter configuration subset is determined as the reference construction parameters of the current area.

3. The method according to claim 1, wherein the method is characterized by, According to the structure parameters and corrosion grade requirements, a set of basic spraying parameters of each coating is determined, including: For the current coating, when the dry film thickness of the coating is greater than a preset thickness threshold, it is determined that the coating belongs to a reinforced construction mode; When the dry film thickness is not greater than the preset thickness threshold, the material viscosity parameter and the curing temperature parameter of the coating are classified based on a first spraying decision algorithm to generate a material attribute feature vector; A material adaptation coefficient is determined according to the construction environment temperature and humidity data of the current coating; When the matching degree of the material attribute feature vector and the predetermined material standard exceeds a preset adaptation threshold, it is determined to enable a standard construction mode; When the matching degree does not exceed the preset adaptation threshold, it is determined to enable a self-defined construction mode.

4. The method according to claim 3, wherein the method is characterized by, The set of basic spraying parameters also includes spraying angle parameters and walking speed parameters, the construction mode includes continuous spraying mode, intermittent spraying mode and compensation spraying mode, and generating a set of coating spraying path planning includes: For the current construction area, the surface roughness data is hierarchically clustered based on a second spraying decision algorithm to generate surface morphology category data; For the current surface morphology category, a path density correction factor is calculated according to the coating material permeability coefficient and the surface porosity; The region path planning matrix is generated according to the path density correction factor and the spraying equipment moving parameters.

5. The method of claim 4, wherein the method further comprises: After determining the surface morphology category of the current construction area, it also includes: If the surface morphology category belongs to the high-pore morphology, a three-cross spraying path is additionally set; If the surface morphology category belongs to the low-pore morphology, a single-cycle coverage path is generated based on a preset path optimization rule; If the surface morphology category belongs to the intermediate transition morphology, an optimal path template is matched according to historical construction data.

6. The method of claim 5, wherein the method further comprises: After the coating spraying path planning set is generated, the following steps are further included: The spraying parameter correction list of the current area is updated according to real-time coating thickness detection data; The path density correction factor is recalculated based on the updated spraying parameter correction list; The execution priority of the coating spraying path planning set is dynamically adjusted according to the recalculated path density correction factor.

7. The method of claim 1, wherein the method further comprises: The surface flatness detection rule includes: A coating thickness fluctuation threshold matrix is established, and when the deviation of the real-time detection thickness value from the target thickness exceeds the set tolerance range, a compensation spraying program is automatically triggered; A surface waviness detection period is set, and a multi-point scanning method is used to obtain surface morphology topological data.

8. The method of claim 7, wherein the method further comprises: The generation rule of the quality verification scheme list further includes: A spraying equipment calibration verification mechanism is established, and the spraying equipment is parameter calibrated and compensated based on the thickness measurement value of the reference coating sample before each layer construction.

9. The method of claim 1, wherein the method further comprises: determining a number of layers of the coating to be applied to the concrete pool; and determining a number of passes of the coating to be applied to the concrete pool. After generating the overall scheme of multi-layer intelligent spraying construction, the following steps are further included: A construction process database is constructed, and the spraying parameter correction list, quality verification scheme list, real-time construction data and surface morphology category data of each coating are stored in the database; The expired construction records and redundant data in the database are cleared at a preset period; A coating performance degradation model is established based on the historical data of the database, which is used to predict the coating maintenance period.

10. The method of claim 4, wherein the method further comprises: The second spraying decision algorithm adopts a genetic optimization algorithm, and the specific steps of generating the surface morphology category data include: Initialize the population set of surface roughness data; Iteratively optimize the path planning scheme through selection, crossover and mutation operations; When the preset iteration number or the convergence threshold of the fitness function is reached, the optimal surface morphology classification result is output.

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