Multi-source data fusion water conservancy slope protection construction parameter optimization method and system

Abstract

The application discloses a water conservancy slope protection construction parameter optimization method and system based on multi-source data fusion, relates to the technical field of water conservancy construction, and comprises the following steps: acquiring adaptive construction design standards and adaptive construction control precision; determining adaptive construction adjustment duration and adaptive construction error threshold; optimizing concrete pouring parameters by taking the adaptive construction error threshold as a constraint, and taking approximation of the adaptive construction design standards and maximization of construction efficiency as double optimization targets; performing water conservancy slope protection construction control according to the optimal concrete pouring parameters, and iteratively optimizing and controlling the construction until the water conservancy slope protection construction of the target construction area is completed. The technical problems of poor adaptability of the existing water conservancy slope protection construction parameters to dynamic working conditions, difficulty in balancing construction precision and efficiency, and lack of dynamic iterative optimization mechanism for construction error control, which lead to poor stability of the slope protection structure, long construction period and low quality control precision, are solved.

Description

Technical Field

[0001] This application relates to the field of water conservancy construction technology, specifically to a method and system for optimizing water conservancy slope protection construction parameters through multi-source data fusion. Background Technology

[0002] With the continuous expansion of water conservancy project construction and the increasing requirements for ecological and environmental protection, water conservancy slope protection, as a key engineering structure for river management, embankment reinforcement and ecological restoration, has its construction quality directly related to the safety, stability and durability of the project.

[0003] However, traditional methods for determining construction parameters for hydraulic slope protection often rely on empirical formulas or static design standards, failing to fully consider the dynamic characteristics of water flow impact, differences in geological conditions, and real-time monitoring data during construction. This results in insufficient adaptability of construction parameters to actual working conditions. During construction, adjustments to concrete pouring parameters lag behind changes in the site environment, making it difficult to balance construction accuracy and efficiency. This can easily lead to problems such as insufficient concrete strength, poor slope protection structural stability, or excessively long construction periods, affecting the overall quality and economic benefits of hydraulic slope protection projects.

[0004] Meanwhile, existing technologies mostly rely on single threshold judgments to control construction errors, lacking a dynamic iterative optimization mechanism based on multi-source monitoring information. This makes it impossible to achieve refined management of the construction process and fails to meet the high standards required for hydraulic slope protection construction under complex hydrological conditions. Summary of the Invention

[0005] This application provides a method and system for optimizing construction parameters of hydraulic slope protection by integrating multi-source data. It solves the technical problems of insufficient adaptability of existing hydraulic slope protection construction parameters to dynamic working conditions, difficulty in balancing construction accuracy and efficiency, and lack of dynamic iterative optimization mechanism for construction error control, which lead to poor slope protection structure stability, long construction period and low quality control accuracy.

[0006] The technical solution to the above-mentioned technical problems in this application is as follows:

[0007] Firstly, this application provides a method for optimizing construction parameters of hydraulic slope protection by fusing multi-source data, the method comprising:

[0008] Adjust the construction design standards and construction control precision according to the predicted water flow impact characteristics of the target construction area, and obtain suitable construction design standards and construction control precision;

[0009] The adaptation construction adjustment time and adaptation construction error threshold are determined based on the aforementioned adaptation construction control accuracy.

[0010] Based on the adaptive construction adjustment time, and constrained by the adaptive construction error threshold, with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1 construction adjustment period are optimized based on the construction monitoring information of the Nth construction adjustment period to obtain the optimal concrete pouring parameters.

[0011] The hydraulic slope protection construction control is executed according to the optimal concrete pouring parameters within the N+1th construction adjustment cycle, and iterative construction optimization control is carried out until the hydraulic slope protection construction of the target construction area is completed.

[0012] Secondly, this application provides a multi-source data fusion system for optimizing hydraulic slope protection construction parameters, including:

[0013] The construction adaptation and adjustment module is used to adjust the construction design standards and construction control precision according to the predicted water flow impact characteristics of the target construction area, and to obtain the adapted construction design standards and adapted construction control precision.

[0014] The parameter threshold setting module is used to determine the adaptive construction adjustment time and the adaptive construction error threshold based on the adaptive construction control accuracy.

[0015] The concrete pouring parameter optimization module is used to optimize the concrete pouring parameters for the N+1 construction adjustment period based on the adaptive construction adjustment time, with the adaptive construction error threshold as a constraint, and with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency.

[0016] The iterative construction control module is used to execute the hydraulic slope protection construction control within the N+1th construction adjustment cycle according to the optimal concrete pouring parameters, and to perform iterative construction optimization control until the hydraulic slope protection construction of the target construction area is completed.

[0017] This application provides one or more technical solutions, which have at least the following technical effects or advantages:

[0018] This application provides a method and system for optimizing hydraulic slope protection construction parameters through multi-source data fusion. First, by dynamically adjusting design standards and construction precision in each area based on water flow impact prediction, the protection strength of the project is precisely matched with the actual risk, optimizing resource input while ensuring safety. Second, by monitoring key parameters in real time and comparing them with dynamic error thresholds, an iterative optimization algorithm automatically adjusts the pouring process, ensuring that construction quality continuously converges to design standards and significantly reducing defects. Finally, under strict quality constraints, the system autonomously balances quality and efficiency goals, dynamically planning the construction rhythm and parameter combinations to maximize comprehensive benefits, propelling hydraulic construction into a new stage of predictable, controllable, and intelligent development.

[0019] Through the above technical solutions, this application realizes closed-loop management of the entire process of water conservancy slope protection construction from static design to dynamic optimization, forming a dynamic cycle mechanism of "prediction-design-construction-feedback-optimization", which effectively solves the problems of parameter solidification and response lag in traditional construction, and improves the quality stability and construction economy of water conservancy slope protection projects. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the method for optimizing hydraulic slope protection construction parameters through multi-source data fusion provided in this application embodiment;

[0022] Figure 2 This is a schematic diagram of the structure of the hydraulic slope protection construction parameter optimization system with multi-source data fusion provided in the embodiments of this application.

[0023] The components represented by each number in the attached diagram are explained below:

[0024] Construction adaptation and adjustment module 11, parameter threshold setting module 12, pouring parameter optimization module 13, iterative construction control module 14. Detailed Implementation

[0025] This application provides a method and system for optimizing hydraulic slope protection construction parameters by fusing multi-source data. It addresses the technical problems of insufficient adaptability of existing hydraulic slope protection construction parameters to dynamic working conditions, difficulty in balancing construction accuracy and efficiency, and lack of dynamic iterative optimization mechanism for construction error control, which lead to poor slope protection structure stability, long construction period, and low quality control accuracy.

[0026] 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.

[0027] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0028] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid unnecessarily obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0029] Example 1, as Figure 1 As shown in the embodiments of this application, a method for optimizing construction parameters of hydraulic slope protection by multi-source data fusion is provided, including:

[0030] S10: Adjust the construction design standards and construction control precision according to the predicted water flow impact characteristics of the target construction area, and obtain suitable construction design standards and suitable construction control precision;

[0031] In this embodiment, firstly, the construction design standards and construction control precision are adjusted. Based on the topographic data, hydrological monitoring data, and historical flood records of the target construction area, a hydrodynamic simulation model is used to predict the distribution of water flow velocity, direction, and impact force under different seasons and water levels. Combined with the geological structural stability assessment results of the target construction area, the water flow impact characteristics are quantified into indicators such as impact pressure level, impact frequency distribution, and duration of impact.

[0032] Then, the appropriate construction design standards and appropriate construction control precision are obtained. Based on the above quantitative indicators, the concrete strength grade, impermeability grade and slope protection structure thickness in the basic construction design standards are dynamically adjusted to form appropriate construction design standards. At the same time, according to the spatiotemporal differences in water flow impact characteristics, the construction control precision levels are divided. Millimeter-level deformation monitoring precision is set for high-impact areas, and centimeter-level monitoring precision is used for low-impact areas to achieve differentiated allocation of construction control resources.

[0033] Among them, obtaining the predicted water flow impact characteristics of the target construction area includes:

[0034] Multi-source data collection is performed on the target construction area to obtain information on the riverbank structure, river channel structure, and river flow characteristics of the target construction area.

[0035] Within the river flow impact simulation platform, the predicted flow impact characteristics of the target construction area are obtained based on the riverbank structure information, river channel structure information, and river flow characteristics. The predicted flow impact characteristics include predicted bed shear stress and predicted near-wall velocity.

[0036] In this embodiment of the application, firstly, multi-source data of the target construction area is collected, specifically including 1:500 three-dimensional point cloud data of the riverbank obtained by UAV oblique photography, cross-sectional data of the river channel collected by ultrasonic depth sounder, continuous 72-hour river channel flow profile data recorded by ADCP current meter, and the maximum flood peak flow data of the past 5 years provided by hydrological station.

[0037] Furthermore, riverbank structure information determines the flow angle, run-up, and area of ​​action, directly affecting flow resistance and scour patterns; channel structure information constitutes the lower boundary of flow motion, determining flow energy and direction; channel flow characteristics serve as the driving conditions for model calculations, representing external hydraulic loads at different return periods.

[0038] Secondly, parameters such as slope ratio, vegetation cover, and soil type from riverbank structure information, and parameters such as river width, water depth, and radius of curvature from river channel structure information, along with average flow velocity, discharge process curve, and sediment concentration data from river channel flow characteristics, were input into Fluent fluid simulation software to construct a three-dimensional river flow impact simulation model. By setting up the RNG k-ε turbulence model and the VOF free surface tracking method, the flow motion state under different flood frequencies was simulated, such as P=1%, P=5%, and P=10%. The bed shear stress and near-wall velocity of each computational unit on the slope protection surface were extracted as core indicators for predicting the flow impact characteristics. The calculation accuracy of the bed shear stress was controlled within ±0.5 Pa, and the spatial resolution of the near-wall velocity reached 0.2 m × 0.2 m.

[0039] Specifically, step S10 in the method is used for:

[0040] The predicted water flow impact intensity is determined based on the predicted water flow impact characteristics, wherein the predicted water flow impact intensity is positively correlated with the predicted bed surface shear stress and the predicted near-wall velocity;

[0041] Based on the preset impact intensity-construction operation standard comparison table, the appropriate construction design standard and appropriate construction control accuracy are obtained according to the predicted water flow impact intensity. The construction design standard includes standard slope protection thickness, standard concrete flatness and standard concrete density. The appropriate construction control accuracy is positively correlated with the predicted water flow impact intensity.

[0042] In this embodiment, firstly, the predicted water flow impact intensity is determined based on the predicted water flow impact characteristics. Then, the predicted bed shear stress and the predicted near-wall velocity are summed using a weighted summation formula to calculate the impact intensity index. The predicted bed shear stress is expressed in Pa, and the predicted near-wall velocity is expressed in m / s.

[0043] For example, the weighting coefficients are determined based on the unified standard for the reliability design of hydraulic engineering structures. If the weight of the predicted bed shear stress is 0.6, then the weight of the predicted near-wall velocity is 0.4. The impact intensity index = 0.6 × predicted bed shear stress + 0.4 × predicted near-wall velocity. The calculated impact intensity index is divided into five levels: Level 1 (impact intensity index ≤ 5), corresponding to the low impact region; Level 2 (5 < impact intensity index ≤ 10), corresponding to the low-to-medium impact region; Level 3 (10 < impact intensity index ≤ 15), corresponding to the medium impact region; Level 4 (15 < impact intensity index ≤ 20), corresponding to the medium-to-high impact region; and Level 5 (impact intensity index > 20), corresponding to the high impact region.

[0044] Then, matching is performed based on a preset impact strength-construction operation standard comparison table. Specifically, for the standard slope protection thickness, a base thickness of 0.3m is used in the Level 1 area, and the thickness increases by 0.05m for each additional impact strength level, reaching 0.5m in the Level 5 area; regarding the standard concrete flatness, the allowable deviation is ±5mm in the Level 1-2 area, ±3mm in the Level 3 area, and ±2mm in the Level 4-5 area; the standard concrete density requirement is 95% in the Level 1-3 area and 98% in the Level 4-5 area.

[0045] Furthermore, the accuracy of adaptive construction control is positively correlated with the predicted water flow impact intensity. For Level 1 areas, a monitoring frequency of 2 hours / time and a displacement monitoring accuracy of ±10mm are used; for Level 2 areas, it's 1 hour / time and ±5mm; for Level 3 areas, it's 30 minutes / time and ±3mm; for Level 4 areas, it's 15 minutes / time and ±2mm; and for Level 5 areas, it's 10 minutes / time and ±1mm, ensuring that the construction process in high-impact areas is under real-time monitoring. The accuracy of adaptive construction control can be quantified numerically, such as 100% or 95%.

[0046] S20: Determine the adaptation construction adjustment time and adaptation construction error threshold based on the aforementioned adaptation construction control accuracy;

[0047] In this embodiment, the adjustment duration for adaptive construction is determined by comprehensively considering the monitoring frequency and data processing timeliness of the adaptive construction control accuracy, while the adaptive construction error threshold is determined by combining the indicators in the adaptive construction design standard.

[0048] Specifically, step S20 in the method includes:

[0049] The ratio of the preset standard construction control accuracy to the adapted construction control accuracy is set as the control compensation coefficient.

[0050] The product of the control compensation coefficient and the preset construction adjustment time is used as the adaptive construction adjustment time.

[0051] The initial construction error threshold of the adapted construction design standard is obtained, and the upper and lower limits of the initial construction error threshold are adjusted according to the control compensation coefficient to obtain the adapted construction error threshold. The initial construction error threshold includes the initial thickness error threshold, the initial flatness error threshold, and the initial density error threshold.

[0052] In this embodiment, the preset standard construction control accuracy is first set to a baseline value of 1.0, corresponding to the standard monitoring accuracy under normal working conditions. When the adapted construction control accuracy is 1.05, it represents an accuracy improvement of 5%. The control compensation coefficient = 1.0 / 1.05 ≈ 0.952.

[0053] If the preset construction adjustment time is 4 hours under normal working conditions, then the adapted construction adjustment time = 0.952 × 4 ≈ 3.81 hours, or about 3 hours and 49 minutes. By shortening the adjustment interval, a rapid response under high-precision control can be achieved.

[0054] Secondly, obtain the initial construction error thresholds from the applicable construction design standards, such as initial thickness error threshold ±5mm, initial flatness error threshold ±3mm, and initial density error threshold ±2%. If the control coefficient is greater than 1, the upper and lower limits are expanded proportionally; if the control coefficient is less than 1, the upper and lower limits are contracted proportionally. The adjusted upper limit is the product of the initial upper limit and the control coefficient; the adjusted lower limit is the ratio of the initial lower limit to the control coefficient.

[0055] For example, the initial threshold is compressed and adjusted based on the control compensation coefficient of 0.952. The adjusted upper limit of the thickness error threshold is 5mm×0.952≈4.76mm, and the lower limit is -5mm / 0.952≈-5.25mm; the upper limit of the flatness error threshold is 3mm×0.952≈2.86mm, and the lower limit is -3mm / 0.952≈-3.15mm; the upper limit of the density error threshold is 2%×0.952≈1.90%, and the lower limit is -2% / 0.952≈-2.10%.

[0056] The dynamic compensation mechanism ensures that the construction error threshold matches the actual control capability, avoiding over-adjustment due to excessively high precision requirements or quality control failure due to excessively wide thresholds.

[0057] S30: Based on the adaptive construction adjustment time, and constrained by the adaptive construction error threshold, with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters of the N+1 construction adjustment period are optimized according to the construction monitoring information of the Nth construction adjustment period to obtain the optimal concrete pouring parameters.

[0058] In this embodiment, construction monitoring information for the Nth construction adjustment cycle is transmitted to a construction parameter optimization platform via an IoT gateway to construct a relational database containing construction process parameters and quality indicators. A time window for parameter optimization is set based on the adapted construction adjustment duration, and an adapted construction error threshold is used as a hard constraint. An improved particle swarm optimization algorithm is employed to perform multi-objective optimization of concrete pouring parameters. The initial construction error threshold includes an initial thickness error threshold, an initial flatness error threshold, and an initial density error threshold.

[0059] The algorithm's objective function is set to maximize pouring efficiency and the highest approximation to the design standard. The priorities of these two objectives are balanced by weighting factors: in high-impact areas, the weight for approximation to the design standard is 0.7, and the weight for pouring efficiency is 0.3; in low-impact areas, the weighting is reversed. During the optimization process, the construction simulation module is invoked in real time to perform virtual pouring verification on candidate parameter combinations, eliminating parameter schemes that may trigger error threshold exceedances, and finally outputting the optimal concrete pouring parameters.

[0060] Specifically, step S30 in the method includes:

[0061] Based on the adapted construction adjustment duration, the expected construction time range of the target construction area is divided into several construction adjustment cycles, and any construction adjustment cycle is randomly selected as the Nth construction adjustment cycle, where N is an integer;

[0062] The monitoring acquires multi-source construction monitoring data for the Nth construction adjustment cycle, wherein the multi-source construction monitoring data includes concrete material state information, process execution information, and molding quality process information;

[0063] To meet the adaptive construction error threshold as a constraint, and with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1th construction adjustment cycle are optimized based on the multi-source construction monitoring data to obtain the optimal concrete pouring parameters.

[0064] The concrete material state information includes real-time slump, real-time spread, and pouring temperature; the process execution information includes pouring rate, layer thickness, vibration frequency, and vibration amplitude; and the molding quality process information includes surface smoothness, actual slope protection thickness, internal density, and ambient temperature and humidity.

[0065] In this embodiment, firstly, based on the adapted construction adjustment duration, the expected total construction period of the target construction area is divided into continuous and non-overlapping construction adjustment cycles, with the duration of each cycle equal to the adapted construction adjustment duration. For example, if the adapted construction adjustment duration is 3 hours and 49 minutes, then each construction adjustment cycle is set to 3 hours and 49 minutes, and sequentially marked as cycle 1, cycle 2, ... cycle N, starting from the start time of construction. One cycle is randomly selected as the Nth construction adjustment cycle so that the parameters of subsequent cycles can be optimized based on the actual construction data of that cycle.

[0066] Secondly, multi-source construction monitoring data is acquired during the Nth construction adjustment cycle. Data collection is achieved through an intelligent sensor network deployed at the construction site. For example, concrete material condition information is acquired in real time using a mobile concrete performance testing instrument, recording slump every 5 minutes (measurement range 10-300mm, accuracy ±1mm), spread measurement range 200-600mm, accuracy ±2mm, and placement temperature measurement range 0-80℃, accuracy ±0.5℃.

[0067] Furthermore, the process execution information is collected by the IoT module built into the construction machinery. The pouring rate is recorded by the flow sensor of the concrete delivery pump with an accuracy of ±1%. The layer thickness is monitored in real time by a laser height gauge with an accuracy of ±2mm. The vibration frequency and vibration amplitude are collected by the built-in sensor of the smart vibrator with a frequency accuracy of ±0.1Hz and an amplitude accuracy of ±0.5mm.

[0068] Furthermore, the forming quality process information is obtained by scanning the slope surface every 30 minutes using a 3D laser scanner to calculate the surface flatness, with the maximum deviation within a 3m range as the benchmark and an accuracy of ±0.5mm. The actual slope thickness is calculated by comparing point cloud data with the design model, with an accuracy of ±1mm. The internal density is tested using an ultrasonic flaw detector with a grid of 50cm×50cm, a testing depth of 0-500mm, and a defect identification accuracy of ≥2mm. The ambient temperature and humidity are collected by a weather station every 10 minutes, with a temperature accuracy of ±0.2℃ and a humidity accuracy of ±2%RH.

[0069] Finally, parameter optimization was performed based on multi-source construction monitoring data. Adapted construction error thresholds were used as hard constraints, meaning that the concrete pouring parameters for the N+1th cycle must ensure that the thickness error does not exceed the adapted thickness error threshold (e.g., ±4.76mm to ±5.25mm), the flatness error does not exceed the adapted flatness error threshold (e.g., ±2.86mm to ±3.15mm), and the density error does not exceed the adapted density error threshold (e.g., ±1.90% to ±2.10%).

[0070] Specifically, the dual optimization objectives are constructed by setting an objective function. The design standard approximation objective is calculated using a weighted Euclidean distance, with the weight allocation related to the impact intensity level. The smaller the distance, the closer it is to the high-impact area of ​​the design standard. For example, the thickness weight is 0.4, the flatness weight is 0.3, and the density weight is 0.3. The construction efficiency objective is characterized by the volume of concrete poured per unit time. The larger the value, the higher the efficiency.

[0071] For example, an improved particle swarm optimization algorithm is used for optimization. The initial particle swarm size is set to 50, the learning factors are c1=1.5 and c2=1.7, the inertia weight decreases linearly from 0.9 to 0.4 with the number of iterations, and the maximum number of iterations is set to 100. In each iteration, the generated concrete pouring parameter combination is used, including pouring rate of 3-8 m³ / h, layer thickness of 200-400 mm, vibration frequency of 20-50 Hz, and vibration amplitude of 3-8 mm.

[0072] Virtual verification is performed using a construction simulation module. The simulation process calls upon the material constitutive model and the construction dynamics model to calculate the predicted value of the forming quality. If the predicted value exceeds the error threshold, the parameter combination is directly eliminated. After multiple rounds of iterative optimization, the optimal concrete pouring parameters that simultaneously satisfy the constraints and the dual-objective optimization are finally output.

[0073] Furthermore, constrained by the adaptive construction error threshold, and with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1th construction adjustment cycle are optimized based on the multi-source construction monitoring data to obtain the optimal concrete pouring parameters, including:

[0074] Within the concrete pouring parameter adjustment space, the first initial pouring parameters are randomly selected, including the single pouring thickness, pouring rate, vibration duration, vibration frequency, and vibration amplitude.

[0075] A first pouring scheme is formed based on the multi-source construction monitoring data and the first initial pouring parameters;

[0076] Within the concrete pouring simulation platform, construction simulation is performed based on the first pouring plan, and the first simulated construction result is output.

[0077] If the first simulated construction result meets the adaptive construction error threshold, then the first initial pouring parameter standard is set as the first qualified pouring parameter.

[0078] With the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the fitness of the first parameter is calculated based on the first simulated construction results.

[0079] Based on the concrete pouring parameter adjustment space, the initial pouring parameters are iteratively selected, iteratively judged, and iteratively calculated until the preset number of optimization convergences is reached. The qualified pouring parameters corresponding to the maximum parameter fitness in the optimization process are output as the optimal concrete pouring parameters for the N+1th construction adjustment cycle.

[0080] The first simulated construction results include the first simulated construction duration, the first simulated slope protection thickness, the first simulated flatness, and the first simulated density.

[0081] In this embodiment of the application, firstly, random sampling is performed within the concrete pouring parameter adjustment space to determine the value range of the first initial pouring parameter.

[0082] Secondly, the multi-source construction monitoring data of the Nth construction adjustment cycle, such as real-time slump, pouring temperature, and ambient humidity, are integrated with the first initial pouring parameters to form the first pouring scheme, which serves as the input conditions for construction simulation.

[0083] Subsequently, the material constitutive model and construction dynamics model are called in the concrete pouring simulation platform to simulate the virtual construction process of the first pouring scheme and output the first simulated construction results, including simulated construction time, simulated slope thickness, simulated flatness deviation, and simulated density.

[0084] Then, the first simulated construction results are compared with the adapted construction error threshold. If the simulated slope thickness error is within the adapted thickness error threshold, the flatness deviation is within the adapted flatness error threshold, and the density is within the adapted density error threshold, then the first initial pouring parameters are determined to be the first qualified pouring parameters.

[0085] Subsequently, the fitness of the first parameter is calculated with the dual objectives of approximating the construction design standard and maximizing construction efficiency. The approximation of the design standard is calculated using the weighted Euclidean distance formula. Construction efficiency is represented by the simulated pouring volume, which is normalized and then weighted and summed with the approximation of the design standard, taking the reciprocal, to obtain the fitness of the first parameter.

[0086] For example, if the first simulated construction time is 180 minutes and the simulated pouring volume is 50 m³, then the construction efficiency index is 50 / 180≈0.278 m³ / min. In the calculation of the approximation degree of the design standard, if the simulated slope protection thickness design standard is ±5 mm with an error of 2 mm, the flatness design standard is ±3 mm with a deviation of 1.5 mm, and the compaction design standard is ±2% and 1%, and the thickness weight is 0.4, the flatness weight is 0.3, and the compaction weight is 0.3 under the weight of the high impact area, the normalized values ​​of each index are (5-2) / 5=0.6, (3-1.5) / 3=0.5, and (2-1) / 2=0.5, respectively, and the weighted sum is 0.6×0.4+0.5×0.3+0.5×0.3=0.54.

[0087] Then, the design standard approximation score is 0.54. Assuming the maximum efficiency is 0.5 m³ / min, the normalized construction efficiency is 0.278 / 0.5 = 0.556. Taking the design standard weight of 0.7 and the efficiency weight of 0.3, the fitness of the first parameter is 0.54×0.7+0.556×0.3≈0.545.

[0088] After iterating this process 50 times, when the parameter combination in a certain iteration is 300mm single pour thickness, 6m³ / h pouring rate, 20s vibration duration, 35Hz vibration frequency, and 5mm vibration amplitude, the simulated construction efficiency reaches 0.3m³ / min, the design standard approximation degree is 0.6, and the calculated fitness is 0.6×0.7+0.6×0.3=0.6, which is the maximum value in the iteration process. This value is determined as the optimal concrete pouring parameters for the N+1th cycle.

[0089] Specifically, based on the iterative mechanism of the particle swarm optimization algorithm, new initial pouring parameters are continuously generated within the parameter adjustment space, and the above simulation, judgment, and fitness calculation process is repeated. The preset number of optimization convergence iterations is 50. This iteration continues until the maximum parameter fitness change is less than 0.01 in 10 consecutive iterations, at which point the optimization stops, and the qualified pouring parameters corresponding to the maximum fitness are output as the optimal concrete pouring parameters for the N+1th construction adjustment cycle, achieving synergistic optimization of construction quality and efficiency.

[0090] Specifically, the fitness of the first parameter is calculated based on the first simulated construction results, including:

[0091] Based on the aforementioned adaptive construction design standard, deviations were calculated for the first simulated slope thickness, the first simulated flatness, and the first simulated density to obtain the first thickness deviation, the first flatness deviation, and the first density deviation.

[0092] The fitness of the first parameter is calculated by weighting the first simulated construction time, the first thickness deviation, the first flatness deviation, and the first density deviation. The fitness of the first parameter is negatively correlated with the first simulated construction time, the first thickness deviation, the first flatness deviation, and the first density deviation.

[0093] In this embodiment, firstly, based on the standard values ​​of slope protection thickness, smoothness, and density specified in the adaptive construction design standard, the difference between the first simulated slope protection thickness and the standard value is calculated as the first thickness deviation, the difference between the first simulated smoothness and the standard value is calculated as the first smoothness deviation, and the difference between the first simulated density and the standard value is calculated as the first density deviation. The smaller the absolute value of the deviation, the closer the simulation result is to the design standard.

[0094] Secondly, the first simulated construction time is used as a direct indicator of construction efficiency; the shorter the time, the higher the efficiency. When calculating the fitness of the first parameter using weighted averages, the deviation values ​​and construction time are first normalized to eliminate the influence of dimensions. Specifically, the normalization formulas for thickness deviation, flatness deviation, and density deviation are (maximum value of the indicator - actual value) / (maximum value of the indicator - minimum value of the indicator), and the normalization formula for construction time is (maximum value of the indicator - actual value) / (maximum value of the indicator - minimum value of the indicator), ensuring that the normalization results of all indicators are within the range [0,1], and that a larger value indicates better performance for that indicator.

[0095] Subsequently, the weights of each indicator were assigned according to the impact intensity level of the target construction area.

[0096] For example, in high-impact areas, the weight of design standard approximation is higher than the weight of construction efficiency, and the weights of thickness deviation, flatness deviation, density deviation, and construction time can be set to 0.3, 0.25, 0.25, and 0.2 respectively. In low-impact areas, the weights of construction efficiency are appropriately increased and adjusted to 0.2 for thickness deviation, 0.2 for flatness deviation, 0.2 for density deviation, and 0.4 for construction time.

[0097] Finally, the normalized index values ​​are multiplied by their corresponding weights and summed to obtain the fitness of the first parameter. This fitness value comprehensively reflects the overall performance of the current pouring parameters in meeting design standards and improving construction efficiency, providing a quantitative evaluation basis for subsequent parameter optimization.

[0098] For example, in the parameter fitness calculation for the low-impact area, it is assumed that the adaptive construction error thresholds are thickness error ±6mm, flatness deviation ±4mm, and density ±3%, and the preset construction time is a maximum of 240 minutes and a minimum of 120 minutes. If the first simulated construction time is 150 minutes, the first thickness deviation is 3mm, the first flatness deviation is 2mm, and the first density deviation is 1%.

[0099] First, normalize the indicators. The normalized value of thickness deviation is (6-3) / (6-0)=0.5, the normalized value of flatness deviation is (4-2) / (4-0)=0.5, the normalized value of density deviation is (3-1) / (3-0)≈0.667, and the normalized value of construction time is (240-150) / (240-120)=0.75.

[0100] The first parameter, fitness, is calculated as follows: 0.5 × 0.2 + 0.5 × 0.2 + 0.667 × 0.2 + 0.75 × 0.4 = 0.1 + 0.1 + 0.1334 + 0.3 = 0.6334. This calculation transforms multi-dimensional indicators into a single fitness value, enabling a quantitative evaluation of the comprehensive performance of the casting parameters and providing direction for subsequent iterative optimization.

[0101] S40: Implement the hydraulic slope protection construction control within the N+1th construction adjustment cycle according to the optimal concrete pouring parameters, and perform iterative construction optimization control until the hydraulic slope protection construction of the target construction area is completed.

[0102] In this embodiment, after obtaining the optimal concrete pouring parameters for the N+1th construction adjustment cycle, the parameter set is sent to the intelligent control system at the construction site in real time. It is then transmitted to the control unit of construction machinery such as concrete pumps and vibration equipment via an industrial bus or wireless communication module, thereby realizing the automatic adjustment of parameters such as pouring rate, layer thickness, and vibration frequency.

[0103] During construction, the intelligent sensor network continuously monitors actual construction data, including real-time slump, pouring temperature, pouring volume, and surface smoothness, and synchronously feeds the data back to the parameter optimization module. If the monitoring data shows a deviation between the actual construction results and the predicted values ​​of the optimal parameters, a dynamic fine-tuning mechanism will be triggered to correct the vibration duration or pouring rate of the current cycle in real time based on the degree of deviation.

[0104] After each construction adjustment cycle, the actual construction data of that cycle is compared and analyzed with the simulation results corresponding to the optimized parameters. The deviation rate and fitness change trend are calculated. If the deviation rate is less than 3% for three consecutive cycles and the fitness value is stable above 0.8, the current parameter optimization model is considered to be stable, and the parameter update interval for subsequent cycles can be appropriately extended. If the deviation rate suddenly increases or the fitness decreases by more than 10%, the abnormal diagnosis process is initiated to check the calibration status of the sensing equipment, the parameters of the material constitutive model, or the coefficients of the construction dynamics model. After ruling out equipment failure or model drift, the parameters are re-optimized to ensure that the entire construction process is always in a closed-loop optimization control state.

[0105] Furthermore, when all construction adjustment cycles in the target construction area are completed in sequence and all quality indicators meet the design requirements, the entire construction control process for the water conservancy slope protection is completed.

[0106] In summary, compared with existing technologies, this application achieves synergistic optimization of quality and efficiency during construction by combining multi-source data fusion technology with dynamic parameter optimization algorithms.

[0107] In summary, the embodiments of this application have at least the following technical effects:

[0108] This application provides a method for optimizing hydraulic slope protection construction parameters through multi-source data fusion. First, by dynamically adjusting the design standards and construction precision of each area based on water flow impact prediction, the protection strength of the project is precisely matched with the actual risk, optimizing resource input while ensuring safety. Second, by monitoring key parameters in real time and comparing them with dynamic error thresholds, an iterative optimization algorithm is used to automatically adjust the pouring process, ensuring that the construction quality continuously converges to the design standards and significantly reducing defects. Finally, under strict quality constraints, the method autonomously balances quality and efficiency goals, dynamically plans the construction rhythm and parameter combinations, and maximizes comprehensive benefits, propelling hydraulic construction into a new stage of predictable, controllable, and intelligent construction. Through the above technical solution, this application achieves closed-loop management of the entire process of hydraulic slope protection construction from static design to dynamic optimization, forming a dynamic cycle mechanism of "prediction-design-construction-feedback-optimization," effectively solving the problems of parameter rigidity and response lag in traditional construction, and improving the quality stability and construction economy of hydraulic slope protection projects.

[0109] Example 2, as Figure 2 As shown, based on the same inventive concept as the multi-source data fusion method for optimizing hydraulic slope protection construction parameters provided in Embodiment 1, this application also provides a multi-source data fusion system for optimizing hydraulic slope protection construction parameters, including:

[0110] The construction adaptation adjustment module 11 is used to adjust the construction design standards and construction control precision according to the predicted water flow impact characteristics of the target construction area, and to obtain the adapted construction design standards and adapted construction control precision.

[0111] The parameter threshold setting module 12 is used to determine the adaptive construction adjustment time and the adaptive construction error threshold based on the adaptive construction control accuracy.

[0112] The pouring parameter optimization module 13 is used to optimize the concrete pouring parameters for the N+1 construction adjustment period based on the adaptive construction adjustment time, with the adaptive construction error threshold as a constraint, and with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency.

[0113] The iterative construction control module 14 is used to execute the hydraulic slope protection construction control within the N+1th construction adjustment cycle according to the optimal concrete pouring parameters, and to perform iterative construction optimization control until the hydraulic slope protection construction of the target construction area is completed.

[0114] Furthermore, in one embodiment of the application, obtaining the predicted water flow impact characteristics of the target construction area includes:

[0115] Multi-source data collection is performed on the target construction area to obtain information on the riverbank structure, river channel structure, and river flow characteristics of the target construction area.

[0116] Within the river flow impact simulation platform, the predicted flow impact characteristics of the target construction area are obtained based on the riverbank structure information, river channel structure information, and river flow characteristics. The predicted flow impact characteristics include predicted bed shear stress and predicted near-wall velocity.

[0117] In one embodiment, the construction adaptation adjustment module 11 is specifically used for:

[0118] The predicted water flow impact intensity is determined based on the predicted water flow impact characteristics, wherein the predicted water flow impact intensity is positively correlated with the predicted bed surface shear stress and the predicted near-wall velocity;

[0119] Based on the preset impact intensity-construction operation standard comparison table, the appropriate construction design standard and appropriate construction control accuracy are obtained according to the predicted water flow impact intensity. The construction design standard includes standard slope protection thickness, standard concrete flatness and standard concrete density. The appropriate construction control accuracy is positively correlated with the predicted water flow impact intensity.

[0120] In one embodiment, the parameter threshold setting module 12 is specifically used for:

[0121] The ratio of the preset standard construction control accuracy to the adapted construction control accuracy is set as the control compensation coefficient.

[0122] The product of the control compensation coefficient and the preset construction adjustment time is used as the adaptive construction adjustment time.

[0123] The initial construction error threshold of the adapted construction design standard is obtained, and the upper and lower limits of the initial construction error threshold are adjusted according to the control compensation coefficient to obtain the adapted construction error threshold. The initial construction error threshold includes the initial thickness error threshold, the initial flatness error threshold, and the initial density error threshold.

[0124] In one embodiment, the casting parameter optimization module 13 is specifically used for:

[0125] Based on the adapted construction adjustment duration, the expected construction time range of the target construction area is divided into several construction adjustment cycles, and any construction adjustment cycle is randomly selected as the Nth construction adjustment cycle, where N is an integer;

[0126] The monitoring acquires multi-source construction monitoring data for the Nth construction adjustment cycle, wherein the multi-source construction monitoring data includes concrete material state information, process execution information, and molding quality process information;

[0127] To meet the adaptive construction error threshold as a constraint, and with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1th construction adjustment cycle are optimized based on the multi-source construction monitoring data to obtain the optimal concrete pouring parameters.

[0128] Furthermore, in one embodiment of the application, the concrete material state information includes real-time slump, real-time spread, and placement temperature; the process execution information includes pouring rate, layer thickness, vibration frequency, and vibration amplitude; and the molding quality process information includes surface smoothness, actual slope protection thickness, internal density, and ambient temperature and humidity.

[0129] Furthermore, constrained by the adaptive construction error threshold, and with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1th construction adjustment cycle are optimized based on the multi-source construction monitoring data to obtain the optimal concrete pouring parameters, including:

[0130] Within the concrete pouring parameter adjustment space, the first initial pouring parameters are randomly selected, including the single pouring thickness, pouring rate, vibration duration, vibration frequency, and vibration amplitude.

[0131] A first pouring scheme is formed based on the multi-source construction monitoring data and the first initial pouring parameters;

[0132] Within the concrete pouring simulation platform, construction simulation is performed based on the first pouring plan, and the first simulated construction result is output.

[0133] If the first simulated construction result meets the adaptive construction error threshold, then the first initial pouring parameter standard is set as the first qualified pouring parameter.

[0134] With the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the fitness of the first parameter is calculated based on the first simulated construction results.

[0135] Based on the concrete pouring parameter adjustment space, the initial pouring parameters are iteratively selected, iteratively judged, and iteratively calculated until the preset number of optimization convergences is reached. The qualified pouring parameters corresponding to the maximum parameter fitness in the optimization process are output as the optimal concrete pouring parameters for the N+1th construction adjustment cycle.

[0136] Furthermore, in one embodiment of the application, the first simulated construction result includes a first simulated construction duration, a first simulated slope protection thickness, a first simulated flatness, and a first simulated density.

[0137] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0138] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0139] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for optimizing construction parameters of hydraulic slope protection using multi-source data fusion, characterized in that the method... include: Adjust the construction design standards and construction control precision according to the predicted water flow impact characteristics of the target construction area, and obtain suitable construction design standards and construction control precision; The adaptation construction adjustment time and adaptation construction error threshold are determined based on the aforementioned adaptation construction control accuracy. Based on the adaptive construction adjustment time, and constrained by the adaptive construction error threshold, with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1 construction adjustment period are optimized based on the construction monitoring information of the Nth construction adjustment period to obtain the optimal concrete pouring parameters. The hydraulic slope protection construction control is executed according to the optimal concrete pouring parameters within the N+1th construction adjustment cycle, and iterative construction optimization control is carried out until the hydraulic slope protection construction of the target construction area is completed. This includes adjusting construction design standards and construction control precision based on the predicted water flow impact characteristics of the target construction area to obtain suitable construction design standards and construction control precision, including: The predicted water flow impact intensity is determined based on the predicted water flow impact characteristics, wherein the predicted water flow impact intensity is positively correlated with the predicted bed shear stress and the predicted near-wall velocity; Based on the preset impact intensity-construction operation standard comparison table, the appropriate construction design standard and appropriate construction control accuracy are obtained according to the predicted water flow impact intensity. The construction design standard includes standard slope protection thickness, standard concrete flatness and standard concrete density. The appropriate construction control accuracy is positively correlated with the predicted water flow impact intensity. The determination of the adaptation construction adjustment duration and the adaptation construction error threshold based on the accuracy of the adaptation construction control includes: The ratio of the preset standard construction control accuracy to the adapted construction control accuracy is set as the control compensation coefficient. The product of the control compensation coefficient and the preset construction adjustment time is used as the adaptive construction adjustment time. The initial construction error threshold of the adapted construction design standard is obtained, and the upper and lower limits of the initial construction error threshold are compensated and adjusted according to the control compensation coefficient to obtain the adapted construction error threshold. The initial construction error threshold includes the initial thickness error threshold, the initial flatness error threshold, and the initial density error threshold. Specifically, based on the adapted construction adjustment time, constrained by meeting the adapted construction error threshold, and with the dual optimization objectives of approximating the adapted construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1 construction adjustment period are optimized based on the construction monitoring information of the Nth construction adjustment period to obtain the optimal concrete pouring parameters, including: Based on the adapted construction adjustment duration, the expected construction time range of the target construction area is divided into several construction adjustment cycles, and any construction adjustment cycle is randomly selected as the Nth construction adjustment cycle, where N is an integer; The monitoring acquires multi-source construction monitoring data for the Nth construction adjustment cycle, wherein the multi-source construction monitoring data includes concrete material state information, process execution information, and molding quality process information; To meet the adaptive construction error threshold as a constraint, and with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1th construction adjustment cycle are optimized based on the multi-source construction monitoring data to obtain the optimal concrete pouring parameters. Specifically, with the constraint of meeting the adaptive construction error threshold and the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the concrete pouring parameters for the N+1th construction adjustment cycle are optimized based on the multi-source construction monitoring data to obtain the optimal concrete pouring parameters, including: Within the concrete pouring parameter adjustment space, the first initial pouring parameters are randomly selected, including the single pouring thickness, pouring rate, vibration duration, vibration frequency, and vibration amplitude. A first pouring scheme is formed based on the multi-source construction monitoring data and the first initial pouring parameters; Within the concrete pouring simulation platform, construction simulation is performed based on the first pouring plan, and the first simulated construction result is output. If the first simulated construction result meets the adaptive construction error threshold, then the first initial pouring parameter is marked as the first qualified pouring parameter; With the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency, the fitness of the first parameter is calculated based on the first simulated construction results. Based on the concrete pouring parameter adjustment space, the initial pouring parameters are iteratively selected, iteratively judged, and iteratively calculated until the preset number of optimization convergences is reached. The qualified pouring parameters corresponding to the maximum parameter fitness in the optimization process are output as the optimal concrete pouring parameters for the N+1th construction adjustment cycle.

2. The method for optimizing hydraulic slope protection construction parameters by multi-source data fusion according to claim 1, characterized in that, Obtain the predicted water flow impact characteristics of the target construction area, including: Multi-source data collection is performed on the target construction area to obtain information on the riverbank structure, river channel structure, and river flow characteristics of the target construction area. Within the river flow impact simulation platform, the predicted flow impact characteristics of the target construction area are obtained based on the riverbank structure information, river channel structure information, and river flow characteristics. The predicted flow impact characteristics include predicted bed shear stress and predicted near-wall velocity.

3. The method for optimizing hydraulic slope protection construction parameters by multi-source data fusion according to claim 1, characterized in that, The concrete material state information includes real-time slump, real-time spread, and pouring temperature; the process execution information includes pouring rate, layer thickness, vibration frequency, and vibration amplitude; and the molding quality process information includes surface smoothness, actual slope protection thickness, internal density, and ambient temperature and humidity.

4. The method for optimizing hydraulic slope protection construction parameters by multi-source data fusion according to claim 1, characterized in that, The first simulated construction results include the first simulated construction duration, the first simulated slope protection thickness, the first simulated flatness, and the first simulated density.

5. The method for optimizing hydraulic slope protection construction parameters by multi-source data fusion according to claim 4, characterized in that, The fitness of the first parameter is calculated based on the first simulated construction results, including: Based on the aforementioned adaptive construction design standard, deviations are calculated for the first simulated slope thickness, the first simulated flatness, and the first simulated density to obtain the first thickness deviation, the first flatness deviation, and the first density deviation. The fitness of the first parameter is calculated by weighting the first simulated construction time, the first thickness deviation, the first flatness deviation, and the first density deviation. The fitness of the first parameter is negatively correlated with the first simulated construction time, the first thickness deviation, the first flatness deviation, and the first density deviation.

6. A multi-source data fusion system for optimizing hydraulic slope protection construction parameters, characterized in that, The method for optimizing hydraulic slope protection construction parameters by multi-source data fusion as described in any one of claims 1-5 includes: The construction adaptation and adjustment module is used to adjust the construction design standards and construction control precision according to the predicted water flow impact characteristics of the target construction area, and to obtain the adapted construction design standards and adapted construction control precision. The parameter threshold setting module is used to determine the adaptive construction adjustment time and the adaptive construction error threshold based on the adaptive construction control accuracy. The concrete pouring parameter optimization module is used to optimize the concrete pouring parameters for the N+1 construction adjustment period based on the adaptive construction adjustment time, with the adaptive construction error threshold as a constraint, and with the dual optimization objectives of approximating the adaptive construction design standard and maximizing construction efficiency. The iterative construction control module is used to execute the hydraulic slope protection construction control within the N+1th construction adjustment cycle according to the optimal concrete pouring parameters, and to perform iterative construction optimization control until the hydraulic slope protection construction of the target construction area is completed.

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