Vibration temperature control collaborative control method and system for concrete forming

Abstract

The application discloses a kind of concrete forming vibration temperature control collaborative control method and system, the method includes: first using concrete mix proportion parameter, historical vibration control parameter and initial temperature field data constructs the initial prediction model of vibration temperature control two-way coupling effect, then the model is dynamically revised by the real-time acquisition vibration time series data and temperature field space data and obtains real-time coupling effect prediction model, and then use this real-time model to predict the density deviation and temperature gradient of next control period, subsequently through multi-objective collaborative optimization algorithm based on predicted value calculation vibration parameter adjustment quantity and temperature control measure intervention intensity to generate collaborative control instruction, finally according to the instruction realizes the collaborative optimization of vibration and temperature control in concrete pouring process.The application can realize the accurate collaborative optimization of vibration and temperature control in concrete pouring process, effectively guarantee the stability of concrete forming quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a method and system for coordinated vibration and temperature control in concrete molding. Background Technology

[0002] In the field of concrete forming, existing technologies often employ relatively independent control methods for vibration and temperature control during concrete pouring. Specifically, vibration control typically involves pre-setting fixed vibration parameters (including vibration frequency, amplitude, and duration) based on the type of concrete (such as strength grade and slump) and the structural characteristics of the pouring location. During pouring, the vibration equipment is activated according to these preset parameters, using mechanical vibration to compact the concrete. Temperature control, on the other hand, usually involves developing a preliminary temperature control plan before pouring based on the ambient temperature and the heat of hydration characteristics of the concrete. This might include pre-embedding cooling water pipes or installing insulation layers inside the concrete. During pouring, temperature sensors monitor the internal temperature of the concrete. When the temperature exceeds a preset threshold, the flow rate of the cooling water pipes is manually or semi-automatically adjusted, or the coverage of the insulation measures is adjusted, to control the temperature rise and temperature gradient within the concrete. Furthermore, some existing technologies attempt to establish simple correlations between vibration parameters and temperature data, such as appropriately reducing the vibration frequency when the temperature is too high. However, such correlations are often empirical linear adjustments, lacking systematic modeling and dynamic response analysis of the complex coupling relationship between vibration and temperature. Therefore, existing technologies cannot achieve precise and coordinated optimization of vibration and temperature control during concrete pouring, making it difficult to effectively guarantee the stability of concrete forming quality. Summary of the Invention

[0003] This invention provides a vibration and temperature control method and system for concrete molding, which can achieve precise and coordinated optimization of vibration and temperature control during concrete pouring, effectively ensuring the stability of concrete molding quality.

[0004] An embodiment of the present invention provides a vibration and temperature control method for concrete forming, comprising:

[0005] Based on concrete mix proportion parameters, historical vibration control parameters, and initial temperature field data, an initial prediction model for the two-way coupling effect of vibration and temperature control is constructed.

[0006] Vibration time-series data and temperature field spatial data during the concrete pouring process are collected in real time, and the vibration time-series data and temperature field spatial data are input into the initial prediction model for dynamic correction to obtain a real-time coupling effect prediction model.

[0007] Using the real-time coupling effect prediction model, the density evolution trend and temperature gradient of concrete pouring in the next control cycle are predicted, and the density deviation and temperature gradient prediction values ​​of concrete pouring in the next control cycle are generated.

[0008] Based on the predicted density deviation and the predicted temperature gradient, the vibration parameter adjustment amount and the intervention intensity of temperature control measures are calculated through a multi-objective collaborative optimization algorithm. Based on the calculation results, the collaborative control command for the next control cycle is obtained. The collaborative control command includes a vibration control command and a temperature control adjustment command.

[0009] Based on the aforementioned coordinated control command, the concrete pouring system is controlled to perform coordinated optimization of vibration and temperature control during concrete pouring in the next control cycle.

[0010] As an improvement to the above scheme, the initial prediction model for the two-way coupling effect of vibration and temperature control, based on concrete mix proportion parameters, historical vibration control parameters, and initial temperature field data, includes the following sub-steps:

[0011] The concrete mix proportion parameters are processed by feature encoding to obtain material property feature vectors.

[0012] Based on the material property feature vector and the historical vibration control parameters, a vibration response prediction sub-model is constructed.

[0013] Based on the material property feature vector and the initial temperature field data, a temperature evolution prediction sub-model is constructed;

[0014] The vibration response prediction sub-model and the temperature evolution prediction sub-model are bidirectionally coupled to obtain the initial prediction model of the vibration-temperature control bidirectional coupling effect.

[0015] As an improvement to the above scheme, the real-time acquisition of vibration time-series data and temperature field spatial data during the concrete pouring process, and the input of the vibration time-series data and temperature field spatial data into the initial prediction model for dynamic correction, to obtain a real-time coupling effect prediction model, includes the following sub-steps:

[0016] Vibration time sequence data during concrete pouring is collected in real time by a vibration sensor array to obtain the vibration signal time sequence.

[0017] The temperature field spatial data during the concrete pouring process is collected in real time by a temperature sensor array to obtain the temperature distribution spatial matrix.

[0018] The vibration signal time sequence and the temperature distribution spatial matrix are input into the initial prediction model to calculate the model prediction error.

[0019] Based on the model prediction error, update the coupling parameters of the initial prediction model;

[0020] The convergence of the initial prediction model after parameter updates is verified to obtain the real-time coupling effect prediction model.

[0021] As an improvement to the above scheme, the step of using the real-time coupling effect prediction model to predict the density evolution trend and temperature gradient of concrete pouring in the next control cycle, and generating the predicted values ​​of density deviation and temperature gradient of concrete pouring in the next control cycle, includes the following sub-steps:

[0022] The control cycle time window is divided according to the pouring progress, and the prediction time interval of the next control cycle is determined.

[0023] The real-time coupling effect prediction model is performed in forward iterative calculation within the prediction time interval to obtain the density evolution trend curve and the temperature gradient distribution surface.

[0024] The density evolution trend curve is subjected to target density deviation quantification processing to generate the predicted density deviation value of concrete pouring in the next control cycle.

[0025] The maximum gradient value is extracted from the temperature gradient distribution surface to generate the predicted temperature gradient value for concrete pouring in the next control cycle.

[0026] As an improvement to the above scheme, the step of calculating the vibration parameter adjustment amount and the temperature control intervention intensity based on the predicted density deviation value and the predicted temperature gradient value using a multi-objective collaborative optimization algorithm, and obtaining the collaborative control command for the next control cycle based on the calculation results, wherein the collaborative control command includes a vibration control command and a temperature control adjustment command, includes the following sub-steps:

[0027] Based on the predicted values ​​of density deviation and temperature gradient, density optimization weights and temperature control optimization weights are configured to obtain a multi-objective collaborative optimization weight matrix for density optimization and temperature control optimization.

[0028] By setting constraints on the adjustment range of vibration parameters and the intervention intensity of temperature control measures, the optimization constraints for multi-objective collaborative optimization are obtained.

[0029] Using the multi-objective collaborative optimization weight matrix and the optimization constraints as input, the optimal solution set for the vibration parameter adjustment amount and the temperature control intervention intensity is obtained by solving the non-dominated sorting genetic algorithm, thus obtaining the optimal vibration control command and the optimal temperature control adjustment command for the next control cycle.

[0030] Another embodiment of the present invention provides a vibration and temperature control coordinated control system for concrete molding, comprising:

[0031] The module is used to build an initial prediction model of the two-way coupling effect of vibration and temperature control based on concrete mix proportion parameters, historical vibration control parameters and initial temperature field data.

[0032] The correction module is used to collect vibration time-series data and temperature field spatial data in real time during the concrete pouring process, and input the vibration time-series data and temperature field spatial data into the initial prediction model for dynamic correction to obtain a real-time coupling effect prediction model.

[0033] The prediction module is used to predict the density evolution trend and temperature gradient of concrete pouring in the next control cycle using the real-time coupling effect prediction model, and generate the predicted values ​​of density deviation and temperature gradient of concrete pouring in the next control cycle.

[0034] The calculation module is used to calculate the vibration parameter adjustment amount and the temperature control intervention intensity based on the predicted density deviation value and the predicted temperature gradient value through a multi-objective collaborative optimization algorithm, and to obtain the collaborative control command for the next control cycle based on the calculation results. The collaborative control command includes a vibration control command and a temperature control adjustment command.

[0035] The collaborative control module is used to control the concrete pouring system to perform collaborative optimization of vibration and temperature control during concrete pouring in the next control cycle based on the collaborative control instructions.

[0036] As an improvement to the above solution, the building module is specifically used for:

[0037] The concrete mix proportion parameters are processed by feature encoding to obtain material property feature vectors.

[0038] Based on the material property feature vector and the historical vibration control parameters, a vibration response prediction sub-model is constructed.

[0039] Based on the material property feature vector and the initial temperature field data, a temperature evolution prediction sub-model is constructed;

[0040] The vibration response prediction sub-model and the temperature evolution prediction sub-model are bidirectionally coupled to obtain the initial prediction model of the vibration-temperature control bidirectional coupling effect.

[0041] As an improvement to the above solution, the correction module is specifically used for:

[0042] Vibration time sequence data during concrete pouring is collected in real time by a vibration sensor array to obtain the vibration signal time sequence.

[0043] The temperature field spatial data during the concrete pouring process is collected in real time by a temperature sensor array to obtain the temperature distribution spatial matrix.

[0044] The vibration signal time sequence and the temperature distribution spatial matrix are input into the initial prediction model to calculate the model prediction error.

[0045] Based on the model prediction error, update the coupling parameters of the initial prediction model;

[0046] The convergence of the initial prediction model after parameter updates is verified to obtain the real-time coupling effect prediction model.

[0047] As an improvement to the above scheme, the prediction module is specifically used for:

[0048] The control cycle time window is divided according to the pouring progress, and the prediction time interval of the next control cycle is determined.

[0049] The real-time coupling effect prediction model is performed in forward iterative calculation within the prediction time interval to obtain the density evolution trend curve and the temperature gradient distribution surface.

[0050] The density evolution trend curve is subjected to target density deviation quantification processing to generate the predicted density deviation value of concrete pouring in the next control cycle.

[0051] The maximum gradient value is extracted from the temperature gradient distribution surface to generate the predicted temperature gradient value for concrete pouring in the next control cycle.

[0052] As an improvement to the above solution, the calculation module is specifically used for:

[0053] Based on the predicted values ​​of density deviation and temperature gradient, density optimization weights and temperature control optimization weights are configured to obtain a multi-objective collaborative optimization weight matrix for density optimization and temperature control optimization.

[0054] By setting constraints on the adjustment range of vibration parameters and the intervention intensity of temperature control measures, the optimization constraints for multi-objective collaborative optimization are obtained.

[0055] Using the multi-objective collaborative optimization weight matrix and the optimization constraints as input, the optimal solution set for the vibration parameter adjustment amount and the temperature control intervention intensity is obtained by solving the non-dominated sorting genetic algorithm, thus obtaining the optimal vibration control command and the optimal temperature control adjustment command for the next control cycle.

[0056] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0057] First, an initial prediction model of the vibration-temperature control bidirectional coupling effect is constructed based on concrete mix proportion parameters, historical vibration control parameters, and initial temperature field data. Then, by real-time acquisition of vibration time-series data and temperature field spatial data during the pouring process, the initial prediction model is dynamically corrected to obtain a real-time coupling effect prediction model, reflecting the actual state of the current pouring. This real-time model is then used to predict the compaction deviation and temperature gradient for the next control cycle. Based on these predictions, a multi-objective collaborative optimization algorithm is used to calculate the vibration parameter adjustment amount and the intervention intensity of temperature control measures, generating a collaborative control command. Finally, the pouring system is controlled according to this command to perform collaborative optimization of vibration and temperature control. The construction and dynamic correction of the vibration-temperature control bidirectional coupling effect model accurately reflects the mutual influence between the two, and the multi-objective collaborative optimization algorithm ensures the coordination of vibration and temperature control adjustments, thereby achieving precise collaborative control of vibration and temperature control during the concrete forming process. Compared to existing technologies that fail to construct a predictive model for the bidirectional coupling effect of vibration and temperature control and achieve synergistic optimization, the embodiments of this invention construct and dynamically correct the coupling model and combine multi-objective optimization to generate synergistic instructions, thereby achieving precise synergistic optimization of vibration and temperature control during concrete pouring and effectively ensuring the stability of concrete forming quality. Attached Figure Description

[0058] Figure 1 This is a schematic flowchart of a vibration and temperature control method for concrete molding according to an embodiment of the present invention.

[0059] Figure 2 This is a schematic diagram of the structure of a vibration and temperature control system for concrete molding provided in an embodiment of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] See Figure 1 This is a schematic flowchart of a vibration and temperature control method for concrete molding according to an embodiment of the present invention. The vibration and temperature control method for concrete molding includes:

[0062] S10, based on concrete mix proportion parameters, historical vibration control parameters and initial temperature field data, an initial prediction model for the two-way coupling effect of vibration and temperature control is constructed.

[0063] S11, Real-time acquisition of vibration time-series data and temperature field spatial data during concrete pouring process, and input of the vibration time-series data and temperature field spatial data into the initial prediction model for dynamic correction, to obtain a real-time coupling effect prediction model;

[0064] S12, using the real-time coupling effect prediction model, predict the density evolution trend and temperature gradient of concrete pouring in the next control cycle, and generate the predicted value of density deviation and temperature gradient of concrete pouring in the next control cycle.

[0065] S13, based on the predicted density deviation and the predicted temperature gradient, the vibration parameter adjustment amount and the temperature control intervention intensity are calculated by a multi-objective collaborative optimization algorithm, and the collaborative control command for the next control cycle is obtained based on the calculation results. The collaborative control command includes a vibration control command and a temperature control adjustment command.

[0066] S14, based on the cooperative control command, control the concrete pouring system to perform cooperative optimization of vibration and temperature control during concrete pouring in the next control cycle.

[0067] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0068] First, an initial prediction model of the vibration-temperature control bidirectional coupling effect is constructed based on concrete mix proportion parameters, historical vibration control parameters, and initial temperature field data. Then, by real-time acquisition of vibration time-series data and temperature field spatial data during the pouring process, the initial prediction model is dynamically corrected to obtain a real-time coupling effect prediction model, reflecting the actual state of the current pouring. This real-time model is then used to predict the compaction deviation and temperature gradient for the next control cycle. Based on these predictions, a multi-objective collaborative optimization algorithm is used to calculate the vibration parameter adjustment amount and the intervention intensity of temperature control measures, generating a collaborative control command. Finally, the pouring system is controlled according to this command to perform collaborative optimization of vibration and temperature control. The construction and dynamic correction of the vibration-temperature control bidirectional coupling effect model accurately reflects the mutual influence between the two, and the multi-objective collaborative optimization algorithm ensures the coordination of vibration and temperature control adjustments, thereby achieving precise collaborative control of vibration and temperature control during the concrete forming process. Compared to existing technologies that fail to construct a predictive model for the bidirectional coupling effect of vibration and temperature control and achieve synergistic optimization, the embodiments of this invention construct and dynamically correct the coupling model and combine multi-objective optimization to generate synergistic instructions, thereby achieving precise synergistic optimization of vibration and temperature control during concrete pouring and effectively ensuring the stability of concrete forming quality.

[0069] As one example, the initial prediction model for the vibration-temperature control bidirectional coupling effect, based on concrete mix proportion parameters, historical vibration control parameters, and initial temperature field data, includes the following sub-steps:

[0070] The concrete mix proportion parameters are processed by feature encoding to obtain material property feature vectors.

[0071] Based on the material property feature vector and the historical vibration control parameters, a vibration response prediction sub-model is constructed.

[0072] Based on the material property feature vector and the initial temperature field data, a temperature evolution prediction sub-model is constructed;

[0073] The vibration response prediction sub-model and the temperature evolution prediction sub-model are bidirectionally coupled to obtain the initial prediction model of the vibration-temperature control bidirectional coupling effect.

[0074] In this embodiment, the concrete mix proportion parameters are first feature-encoded and transformed into feature vectors that characterize the concrete material properties. Then, based on these material property feature vectors and historical vibration control parameters, a vibration response prediction sub-model is constructed to predict the concrete's response under vibration. Simultaneously, combining the material property feature vectors and initial temperature field data, a temperature evolution prediction sub-model is constructed to reflect the concrete's temperature change process. Finally, the vibration response prediction sub-model and the temperature evolution prediction sub-model are bidirectionally coupled, enabling the two sub-models to reflect the influence of vibration on temperature and the reaction of temperature to vibration response, thus forming an initial prediction model of the two-way coupling effect of vibration and temperature control. Therefore, this embodiment extracts key material information through feature encoding, constructs sub-models separately, and then couples them, considering both the individual variation patterns of vibration and temperature and reflecting their interaction, which is beneficial for overall coordinated control. In summary, this embodiment accurately extracts material properties affecting vibration response and temperature evolution by encoding the features of concrete mix proportion parameters; the constructed vibration response prediction sub-model and temperature evolution prediction sub-model can specifically reflect the changing patterns of vibration and temperature; and the bidirectional coupling between the two enables the initial prediction model to accurately reflect the mutual influence between vibration and temperature control, providing a reliable initial model framework for subsequent real-time model correction and the synergistic optimization of vibration and temperature control, thereby improving the accuracy and applicability of the initial prediction model.

[0075] Specifically, the working process of this embodiment is exemplified as follows:

[0076] When performing feature encoding on concrete mix proportion parameters, 10 core parameters are first extracted, including cement strength grade, water-cement ratio, sand ratio, fly ash content, slag content, water-reducing agent content, coarse aggregate particle size distribution, and fineness modulus of fine aggregate. For continuous parameters (such as water-cement ratio), min-max normalization is used; for discrete parameters (such as cement strength grade), unique thermal encoding conversion is used; and for distributed parameters (such as coarse aggregate particle size distribution), the particle size distribution uniformity index is calculated as the feature value. Seven parameters strongly correlated with vibration response and temperature evolution are selected using the Pearson correlation coefficient, constructing a 7×1 material property feature vector. The standardized formula for the continuous parameter is: In the formula These are the original parameter values. These are the historical extreme values ​​of this parameter.

[0077] When constructing a vibration response prediction sub-model based on material property eigenvectors and historical vibration control parameters, the historical vibration frequency is selected. ,amplitude Vibration duration Vibration acceleration Distance between vibration points The input matrix consists of 5 parameters. ( (For the number of historical samples), an improved gated recurrent unit (GRU) model is adopted, and an adaptive weighting module based on material properties is added to the input layer. This is achieved through calculation... and The mutual information values ​​of each parameter determine the weighting coefficients. The influence weights of vibration parameters under different material properties are dynamically adjusted. The model output is the concrete density growth rate. The expression is: In the formula Here is the weight matrix of the GRU network. For bias terms, This is a weighted diagonal matrix.

[0078] When constructing a temperature evolution prediction sub-model based on material property feature vectors and initial temperature field data, the initial temperature field data is discretized into a 10×10 grid temperature matrix. , combined thermal conductivity Specific heat capacity To obtain the same thermal parameters, a spatiotemporal convolutional network (STCN) architecture is adopted. First, the spatial distribution features of the temperature field are extracted through 2D convolutional layers, and then the temporal evolution is captured through 1D convolutional layers. At the same time, a material property correction factor is introduced. ( For the Sigmoid function, To correct the weight matrix, the temperature conduction rate is dynamically adjusted. The model output is... Temperature field matrix at time ,satisfy: In the formula For STCN network parameters, As a bias term, this model can effectively reflect the differences in temperature field evolution of concrete with different mix proportions.

[0079] When bidirectionally coupling the vibration response prediction sub-model and the temperature evolution prediction sub-model, design the coupling coefficient matrix. , The coefficient representing the influence of temperature change on vibration response (obtained through linear regression of temperature gradient and density change rate in historical data). The coefficient representing the influence of vibration on temperature evolution (determined based on theoretical calculations of vibration energy consumption converted into heat energy). A two-way feedback mechanism is established: the output of the vibration response sub-model. pass The initial temperature matrix of the modified temperature evolution sub-model ( Output of the temperature evolution sub-model pass Adjust the weighting coefficients of the vibration response sub-model ( , (For the temperature gradient). The final initial prediction model expression is: This enables dynamic coupling simulation of vibration and temperature fields.

[0080] As one example, the real-time acquisition of vibration time-series data and temperature field spatial data during the concrete pouring process, and the input of the vibration time-series data and temperature field spatial data into the initial prediction model for dynamic correction to obtain a real-time coupling effect prediction model, includes the following sub-steps:

[0081] Vibration time sequence data during concrete pouring is collected in real time by a vibration sensor array to obtain the vibration signal time sequence.

[0082] The temperature field spatial data during the concrete pouring process is collected in real time by a temperature sensor array to obtain the temperature distribution spatial matrix.

[0083] The vibration signal time sequence and the temperature distribution spatial matrix are input into the initial prediction model to calculate the model prediction error.

[0084] Based on the model prediction error, update the coupling parameters of the initial prediction model;

[0085] The convergence of the initial prediction model after parameter updates is verified to obtain the real-time coupling effect prediction model.

[0086] In this embodiment, vibration time-series data and temperature field spatial data during the concrete pouring process are first collected in real time using a vibration sensor array and a temperature sensor array, respectively, forming a vibration signal time-series sequence and a temperature distribution spatial matrix that reflect the real-time state. Next, these two types of real-time data are input into an initial prediction model. The model prediction error is calculated by comparing the model output with the actual monitoring data, thereby quantifying the deviation between the model and the actual pouring state. Then, based on this prediction error, the coupling parameters in the initial prediction model are updated in a targeted manner, enabling the model to adapt to the dynamic changes of the current pouring process. Finally, the convergence of the updated model is verified to ensure its stability and reliability, thus obtaining a real-time coupling effect prediction model that accurately reflects the real-time pouring state. Therefore, this embodiment utilizes vibration sensor arrays and temperature sensor arrays to comprehensively and accurately collect real-time vibration and temperature data during the pouring process. By calculating the model prediction error and updating the coupling parameters, the initial prediction model can dynamically adapt to the real-time changes in the concrete pouring process. Convergence verification ensures the stability and reliability of the model after parameter updates. The resulting real-time coupling effect prediction model accurately reflects the coupling relationship between vibration and temperature under the current pouring state, providing a model basis for prediction and coordinated control in the next control cycle and improving the model's dynamic adaptability to the actual pouring process.

[0087] In this embodiment, its working process is exemplarily as follows:

[0088] When collecting real-time vibration time-series data during concrete pouring using a vibration sensor array, eight sets of piezoelectric vibration sensors are first deployed on the outside of the pouring formwork and near the vibrator inside. The sensor sampling frequency is set to 2kHz, and the acquisition cycle is 10s / time. The acquired parameters include vibration acceleration, vibration frequency, and vibration displacement. During the acquisition process, an anti-interference filtering circuit is used to remove mechanical noise from the pouring equipment (filtering frequency range 0.5-500Hz). The data from 100 consecutive cycles collected by each set of sensors are then stitched together in chronological order to form a vibration signal time sequence with a dimension of 8×2000. ( (The sampling time is 1-2000), where Indicates the first Group of sensors The fused value of the vibration signal at any given time.

[0089] When collecting real-time spatial data of the temperature field during concrete pouring using a temperature sensor array, a distributed fiber optic temperature sensor array is employed. The sensor array is deployed in a 5cm×5cm grid across the pouring area, covering the top, middle, and bottom surfaces of the pouring section (a total of 3 layers, with 36 monitoring points per layer). The sensors collect temperature values ​​from each monitoring point every 5 minutes. The temperature data from the 3 layers of monitoring points at the same time are mapped spatially into a 3×6×6 three-dimensional matrix. Then, a spatial interpolation algorithm (using inverse distance weighted interpolation) is used to complete the temperature values ​​in the edge areas, ultimately converting the data into a 10×10 temperature distribution spatial matrix. Each element in the matrix Indicates the first Line 1 The actual temperature values ​​of the grid points.

[0090] When inputting the vibration signal time series and temperature distribution spatial matrix into the initial prediction model to calculate the model prediction error, first analyze the vibration signal time series... Feature extraction is performed (extracting features such as time-domain peak value, root mean square value, and frequency-domain dominant frequency), which are then converted into vibration feature vectors that match the input dimensions of the initial prediction model. ,Will and temperature distribution space matrix The data from the previous moment is input into the initial prediction model to obtain the predicted temperature field matrix output by the model. and predict vibration response characteristics An improved weighted error formula is used to calculate the model prediction error. The formula is: In the formula Temperature error weight (value 0.6, set according to the priority of temperature control and vibration control). For the characteristic dimension of vibration response, , The first and second parts of the actual and predicted temperature field matrices are respectively... Line 1 Column elements, , The first and second parts of the actual and predicted vibration response characteristics are respectively Each feature value.

[0091] When updating the coupling parameters of the initial prediction model based on the model prediction error, the coupling coefficient matrix in the initial prediction model is considered. Gradient descent is used to update parameters in order to minimize prediction error. Let the objective function be , and calculate the error. For coupling coefficients , The partial derivatives are updated using the following formula: , In the formula For learning rate, This represents the current update iteration number, and also the weight parameters of the GRU and STCN networks in the initial prediction model. , Fine-tuning is performed, with the adjustment increment being 1 / 5 of the initial training step size, to ensure the stability of the model's core architecture.

[0092] When verifying the convergence of the initial prediction model after parameter updates, prediction errors were collected for three consecutive update cycles. , , Set a convergence threshold (Set according to engineering accuracy requirements), if satisfied and If the convergence condition is met, the model parameter update is considered to have converged; otherwise, the number of iterations is increased, and the parameter update step is repeated until the convergence condition is met. After convergence, the model with updated parameters is determined as the real-time coupling effect prediction model, which can match the vibration and temperature field coupling state of the current pouring process in real time.

[0093] As one example, the method of using the real-time coupling effect prediction model to predict the density evolution trend and temperature gradient of concrete pouring in the next control cycle, and generating predicted values ​​for the density deviation and temperature gradient of concrete pouring in the next control cycle, includes the following sub-steps:

[0094] The control cycle time window is divided according to the pouring progress, and the prediction time interval of the next control cycle is determined.

[0095] The real-time coupling effect prediction model is performed in forward iterative calculation within the prediction time interval to obtain the density evolution trend curve and the temperature gradient distribution surface.

[0096] The density evolution trend curve is subjected to target density deviation quantification processing to generate the predicted density deviation value of concrete pouring in the next control cycle.

[0097] The maximum gradient value is extracted from the temperature gradient distribution surface to generate the predicted temperature gradient value for concrete pouring in the next control cycle.

[0098] In this embodiment, the control cycle time window is first divided according to the concrete pouring progress, clarifying the prediction time interval for the next control cycle and setting a clear time boundary for the prediction process. Then, the real-time coupling effect prediction model is used for forward iterative calculation within this prediction time interval. Through dynamic model deduction, a density evolution trend curve and a temperature gradient distribution surface reflecting changes in the internal state of the concrete are obtained. Next, for the density evolution trend curve, a target density deviation quantification method is used to calculate the degree of deviation from the preset target density, generating a predicted density deviation value for the next control cycle. Simultaneously, for the temperature gradient distribution surface, key temperature gradient indicators are obtained through maximum gradient value extraction, generating a predicted temperature gradient value for the next control cycle. This process, constrained by a time interval, relies on the real-time coupling effect prediction model for iterative calculation, and then obtains specific predicted values ​​through targeted data processing, providing a clear quantitative basis for subsequent collaborative optimization and control.

[0099] In this embodiment, specifically, when determining the predicted time interval for the next control cycle by dividing the control cycle time window according to the pouring progress, 1 / 3 of the initial setting time of the concrete is first used as the base cycle length. The pouring volume per unit time is calculated in combination with the real-time pouring rate (obtained through the pouring pump flow sensor). Then, the cycle length is dynamically adjusted according to the difference between the designed volume of the area to be poured and the volume already poured: if the remaining volume is less than the base cycle pouring volume, the cycle is shortened by the ratio of the remaining volume to the pouring rate; if the remaining volume is greater than the base cycle pouring volume, the base length is maintained, and the predicted time interval is set from the current time to the end of the base length. At the same time, a certain buffer time is reserved at the end of the interval to cope with the rate fluctuations during the pouring process, ensuring that the predicted interval is accurately matched with the actual pouring progress. When performing forward iterative calculations on the real-time coupling effect prediction model within the prediction time interval to obtain the density evolution trend curve and temperature gradient distribution surface, an improved variable step-size iterative method is adopted: a small step size (e.g., 10s / step) is used in the early stage of prediction to capture initial change details; a medium step size (e.g., 30s / step) is used in the middle stage to balance accuracy and efficiency; and a large step size (e.g., 60s / step) is used in the later stage to focus on the final trend. Each iteration uses the vibration parameters and temperature field data of the current step as model input, and outputs the density value and temperature field matrix of that step. The density values ​​of all steps are sorted by time to form the evolution curve; the gradient value of each step's temperature field matrix is ​​calculated using a three-dimensional gradient algorithm (combining spatial coordinates and time dimension), and superimposed to form the temperature gradient distribution surface. This variable step-size design improves computational efficiency while maintaining prediction accuracy. When quantifying the target density deviation of the density evolution trend curve and generating the density deviation prediction value, an innovative dynamic weight deviation algorithm is adopted: first, the target density threshold range is determined. (As specified in the design specifications), extract the predicted density value of the curve at the end of the prediction interval. Calculate the deviation value : In the formula To reduce the penalty weight for insufficient density, To mitigate the impact of excessive compaction, differentiated weights are used to prioritize undercompacted areas, making deviation quantification more aligned with actual engineering needs. The maximum gradient value is extracted from the temperature gradient distribution surface. When generating predicted temperature gradient values, a spatiotemporal weighted extraction method is employed: first, the gradient values ​​at all spatiotemporal points on the surface are traversed. ( For spatial coordinates, (For time), calculate the weight coefficients for each point. ,in Spatial weights (1.0 for core regions, 0.6 for edge regions). Time weights are assigned (1.0 for later stages and 0.7 for earlier stages), and then calculated using the formula. Determine the final predicted value.

[0100] As one example, the step of calculating the vibration parameter adjustment amount and the temperature control intervention intensity based on the predicted density deviation value and the predicted temperature gradient value using a multi-objective collaborative optimization algorithm, and obtaining the collaborative control command for the next control cycle based on the calculation results, wherein the collaborative control command includes a vibration control command and a temperature control adjustment command, includes the following sub-steps:

[0101] Based on the predicted values ​​of density deviation and temperature gradient, density optimization weights and temperature control optimization weights are configured to obtain a multi-objective collaborative optimization weight matrix for density optimization and temperature control optimization.

[0102] By setting constraints on the adjustment range of vibration parameters and the intervention intensity of temperature control measures, the optimization constraints for multi-objective collaborative optimization are obtained.

[0103] Using the multi-objective collaborative optimization weight matrix and the optimization constraints as input, the optimal solution set for the vibration parameter adjustment amount and the temperature control intervention intensity is obtained by solving the non-dominated sorting genetic algorithm, thus obtaining the optimal vibration control command and the optimal temperature control adjustment command for the next control cycle.

[0104] In this embodiment, firstly, based on the predicted values ​​of density deviation and temperature gradient, a multi-objective collaborative optimization weight matrix is ​​formed by configuring density optimization weights and temperature control optimization weights, clarifying the priority of the two types of optimization objectives in collaborative control. Next, constraints on the vibration parameter adjustment range and the intervention intensity of temperature control measures are set to construct boundary conditions for multi-objective collaborative optimization, ensuring the engineering feasibility of the optimization results. Finally, the multi-objective collaborative optimization weight matrix and optimization constraints are input into a non-dominated sorting genetic algorithm. The algorithm solves for the optimal solution set of vibration parameter adjustment and temperature control intervention intensity, which is then transformed into the optimal vibration control command and temperature control adjustment command for the next control cycle. This embodiment achieves scientific quantification and collaborative optimization of vibration and temperature control adjustment quantities by balancing optimization objectives through weight configuration, ensuring engineering applicability through constraint setting, and solving for the optimal solution through a specific algorithm.

[0105] Specifically, when configuring density optimization weights and temperature control optimization weights based on the predicted density deviation and temperature gradient, a dynamic adaptive weight adjustment mechanism is adopted: Let the predicted density deviation be... The predicted temperature gradient value is First, calculate the normalized deviation coefficients of the two. ( (to allow maximum density deviation) and ( (for temperature gradient limits), then using the formula , Determine the weights, where To optimize the weights for density, Weights are optimized for temperature control. Finally, a multi-objective collaborative optimization weight matrix is ​​constructed. This dynamic weighting mechanism can automatically adjust the optimization priority based on the actual degree of deviation. When the standard is exceeded Automatically increase to ensure that key indicators are optimized first.

[0106] When setting constraints on the vibration parameter adjustment range and the intervention intensity of temperature control measures, the vibration parameter adjustment range constraint includes: vibration frequency adjustment amount. Amplitude adjustment amount Vibration duration adjustment amount Temperature control intervention intensity constraints include: cooling water pipe flow rate adjustment coefficient. Insulation layer thickness adjustment amount Simultaneously, coupling constraints are set: the sum of the vibration power increment and the temperature control energy consumption increment does not exceed a predetermined proportion of the rated total energy consumption, forming a complete set of optimization constraints. .

[0107] Using the multi-objective collaborative optimization weight matrix and optimization constraints as input, the improved non-dominated sorting genetic algorithm is used to solve the problem. The following improvements are made to the traditional NSGA-II: First, a real-number encoding method is adopted to encode the vibration parameter adjustment amount. and the intensity of temperature control intervention measures The first is to encode a chromosome of length 5; the second is to design a weighted composite fitness function. ,in Optimize the objective function for density ( , (Adjusted prediction bias) Optimize the objective function for temperature control ( , (For adjusted gradient prediction). The algorithm process includes: initializing the population (e.g., size 100), calculating fitness and performing non-dominated sorting, selecting parents using improved crowding distance (introducing weight matrix correction), generating offspring through adaptive crossover (crossover probability dynamically adjusted with population diversity) and polynomial mutation, and performing constraint checks (eliminating violations). After the individual populations are eliminated, a new population is formed. After 50 iterations, the solution closest to the ideal point in the Pareto optimal front is taken as the optimal solution, and the vibration control command for the next control cycle (including...) is output. ) and temperature control adjustment commands (including ).

[0108] See Figure 2 This is a schematic diagram of a vibration and temperature control system for concrete molding according to an embodiment of the present invention. The vibration and temperature control system for concrete molding includes:

[0109] Module 10 is used to construct an initial prediction model of the vibration-temperature control bidirectional coupling effect based on concrete mix proportion parameters, historical vibration control parameters and initial temperature field data.

[0110] The correction module 11 is used to collect vibration time-series data and temperature field spatial data in real time during the concrete pouring process, and input the vibration time-series data and temperature field spatial data into the initial prediction model for dynamic correction to obtain a real-time coupling effect prediction model.

[0111] The prediction module 12 is used to predict the density evolution trend and temperature gradient of concrete pouring in the next control cycle using the real-time coupling effect prediction model, and generate the predicted value of the density deviation and temperature gradient of concrete pouring in the next control cycle.

[0112] The calculation module 13 is used to calculate the vibration parameter adjustment amount and the temperature control intervention intensity based on the predicted density deviation value and the predicted temperature gradient value through a multi-objective collaborative optimization algorithm, and to obtain the collaborative control command for the next control cycle based on the calculation results. The collaborative control command includes a vibration control command and a temperature control adjustment command.

[0113] The collaborative control module 14 is used to control the concrete pouring system to perform collaborative optimization of vibration and temperature control during concrete pouring in the next control cycle based on the collaborative control command.

[0114] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0115] First, an initial prediction model of the vibration-temperature control bidirectional coupling effect is constructed based on concrete mix proportion parameters, historical vibration control parameters, and initial temperature field data. Then, by real-time acquisition of vibration time-series data and temperature field spatial data during the pouring process, the initial prediction model is dynamically corrected to obtain a real-time coupling effect prediction model, reflecting the actual state of the current pouring. This real-time model is then used to predict the compaction deviation and temperature gradient for the next control cycle. Based on these predictions, a multi-objective collaborative optimization algorithm is used to calculate the vibration parameter adjustment amount and the intervention intensity of temperature control measures, generating a collaborative control command. Finally, the pouring system is controlled according to this command to perform collaborative optimization of vibration and temperature control. The construction and dynamic correction of the vibration-temperature control bidirectional coupling effect model accurately reflects the mutual influence between the two, and the multi-objective collaborative optimization algorithm ensures the coordination of vibration and temperature control adjustments, thereby achieving precise collaborative control of vibration and temperature control during the concrete forming process. Compared to existing technologies that fail to construct a predictive model for the bidirectional coupling effect of vibration and temperature control and achieve synergistic optimization, the embodiments of this invention construct and dynamically correct the coupling model and combine multi-objective optimization to generate synergistic instructions, thereby achieving precise synergistic optimization of vibration and temperature control during concrete pouring and effectively ensuring the stability of concrete forming quality.

[0116] As one example, the building module is specifically used for:

[0117] The concrete mix proportion parameters are processed by feature encoding to obtain material property feature vectors.

[0118] Based on the material property feature vector and the historical vibration control parameters, a vibration response prediction sub-model is constructed.

[0119] Based on the material property feature vector and the initial temperature field data, a temperature evolution prediction sub-model is constructed;

[0120] The vibration response prediction sub-model and the temperature evolution prediction sub-model are bidirectionally coupled to obtain the initial prediction model of the vibration-temperature control bidirectional coupling effect.

[0121] As an improvement to the above solution, the correction module is specifically used for:

[0122] Vibration time sequence data during concrete pouring is collected in real time by a vibration sensor array to obtain the vibration signal time sequence.

[0123] The temperature field spatial data during the concrete pouring process is collected in real time by a temperature sensor array to obtain the temperature distribution spatial matrix.

[0124] The vibration signal time sequence and the temperature distribution spatial matrix are input into the initial prediction model to calculate the model prediction error.

[0125] Based on the model prediction error, update the coupling parameters of the initial prediction model;

[0126] The convergence of the initial prediction model after parameter updates is verified to obtain the real-time coupling effect prediction model.

[0127] As one example, the prediction module is specifically used for:

[0128] The control cycle time window is divided according to the pouring progress, and the prediction time interval of the next control cycle is determined.

[0129] The real-time coupling effect prediction model is performed in forward iterative calculation within the prediction time interval to obtain the density evolution trend curve and the temperature gradient distribution surface.

[0130] The density evolution trend curve is subjected to target density deviation quantification processing to generate the predicted density deviation value of concrete pouring in the next control cycle.

[0131] The maximum gradient value is extracted from the temperature gradient distribution surface to generate the predicted temperature gradient value for concrete pouring in the next control cycle.

[0132] As one example, the computing module is specifically used for:

[0133] Based on the predicted values ​​of density deviation and temperature gradient, density optimization weights and temperature control optimization weights are configured to obtain a multi-objective collaborative optimization weight matrix for density optimization and temperature control optimization.

[0134] By setting constraints on the adjustment range of vibration parameters and the intervention intensity of temperature control measures, the optimization constraints for multi-objective collaborative optimization are obtained.

[0135] Using the multi-objective collaborative optimization weight matrix and the optimization constraints as input, the optimal solution set for the vibration parameter adjustment amount and the temperature control intervention intensity is obtained by solving the non-dominated sorting genetic algorithm, thus obtaining the optimal vibration control command and the optimal temperature control adjustment command for the next control cycle.

[0136] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0137] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for vibration temperature control collaborative control of concrete forming, characterized in that, The application relates to a method for realizing real-time dynamic optimization of vibration and temperature control of a concrete pouring system. The method comprises the following steps: An initial prediction model of vibration and temperature control two-way coupling effect is constructed based on concrete mixing ratio parameters, historical vibration control parameters and initial temperature field data; Vibration time series data and temperature field space data in the concrete pouring process are collected in real time, and the vibration time series data and the temperature field space data are input into the initial prediction model for dynamic correction to obtain a real-time coupling effect prediction model; The real-time coupling effect prediction model is used to predict the density evolution trend and temperature gradient of the concrete pouring in the next control period, and a density deviation prediction value and a temperature gradient prediction value of the concrete pouring in the next control period are generated; According to the density deviation prediction value and the temperature gradient prediction value, a vibration parameter adjustment amount and a temperature control measure intervention intensity are calculated through a multi-objective collaborative optimization algorithm, and a collaborative control instruction of the next control period is obtained based on the calculation result, wherein the collaborative control instruction comprises a vibration control instruction and a temperature control adjustment instruction; 2. The method of claim 1, wherein the temperature of the concrete is controlled by a temperature control system. Based on the collaborative control instruction, the concrete pouring system is controlled to realize the collaborative optimization of vibration and temperature control of the concrete pouring in the next control period. The initial prediction model of vibration and temperature control two-way coupling effect is constructed based on concrete mixing ratio parameters, historical vibration control parameters and initial temperature field data, and comprises the following sub-steps: Material characteristic feature vectors are obtained by performing feature coding processing on the concrete mixing ratio parameters; A vibration response prediction sub-model is constructed based on the material characteristic feature vectors and the historical vibration control parameters; A temperature evolution prediction sub-model is constructed based on the material characteristic feature vectors and the initial temperature field data; 3. The method of claim 2, wherein the temperature of the concrete is controlled by the temperature control system. The vibration response prediction sub-model and the temperature evolution prediction sub-model are two-way coupled and associated to obtain the initial prediction model of vibration and temperature control two-way coupling effect. The vibration time series data and the temperature field space data in the concrete pouring process are collected in real time, and the vibration time series data and the temperature field space data are input into the initial prediction model for dynamic correction to obtain a real-time coupling effect prediction model, and the method comprises the following sub-steps: Vibration time series data in the concrete pouring process are collected in real time through a vibration sensor array to obtain a vibration signal time sequence; Temperature field space data in the concrete pouring process are collected in real time through a temperature sensor array to obtain a temperature distribution space matrix; The vibration signal time sequence and the temperature distribution space matrix are input into the initial prediction model to calculate a model prediction error; Based on the model prediction error, coupling parameters of the initial prediction model are updated; 4. The method of claim 3, wherein the temperature of the concrete is controlled by the temperature control unit. The initial prediction model after parameter updating is verified for convergence to obtain the real-time coupling effect prediction model. The real-time coupling effect prediction model is used to predict the density evolution trend and temperature gradient of the concrete pouring in the next control period, and a density deviation prediction value and a temperature gradient prediction value of the concrete pouring in the next control period are generated, and the method comprises the following sub-steps: A control period time window is divided according to a pouring progress, and a prediction time interval of the next control period is determined; The real-time coupling effect prediction model is calculated forwardly in the prediction time interval to obtain a density evolution trend curve and a temperature gradient distribution surface; The density evolution trend curve is subjected to target density deviation quantization processing to generate a density deviation prediction value of concrete pouring in a next control period; The temperature gradient distribution surface is subjected to maximum gradient value extraction processing to generate a temperature gradient prediction value of concrete pouring in the next control period.

5. The method of claim 4, wherein the temperature of the concrete is controlled by the temperature control system. The density deviation prediction value and the temperature gradient prediction value are used to calculate a vibration parameter adjustment amount and a temperature control measure intervention intensity by a multi-objective collaborative optimization algorithm, and a collaborative control instruction of the next control period is obtained based on a calculation result, wherein the collaborative control instruction comprises a vibration control instruction and a temperature control adjustment instruction, and comprises the following sub-steps: The density deviation prediction value and the temperature gradient prediction value are used to configure density optimization weights and temperature control optimization weights to obtain a multi-objective collaborative optimization weight matrix of density optimization and temperature control optimization; A vibration parameter adjustment range constraint and a temperature control measure intervention intensity constraint are set to obtain optimization constraint conditions of multi-objective collaborative optimization; The multi-objective collaborative optimization weight matrix and the optimization constraint conditions are used as inputs to solve an optimal solution set of the vibration parameter adjustment amount and the temperature control measure intervention intensity by a non-dominated sorting genetic algorithm to obtain optimal vibration control instructions and optimal temperature control adjustment instructions of the next control period.

6. A vibration temperature control collaborative control system for concrete forming, characterized in that, It comprises: A construction module is configured to construct an initial prediction model of vibration and temperature control bidirectional coupling effect based on concrete mix proportion parameters, historical vibration control parameters and initial temperature field data; A correction module is configured to collect vibration time series data and temperature field space data in a concrete pouring process in real time, and input the vibration time series data and the temperature field space data into the initial prediction model for dynamic correction to obtain a real-time coupling effect prediction model; A prediction module is configured to use the real-time coupling effect prediction model to predict a density evolution trend and a temperature gradient of concrete pouring in a next control period to generate a density deviation prediction value and a temperature gradient prediction value of concrete pouring in the next control period; A calculation module is configured to use the density deviation prediction value and the temperature gradient prediction value to calculate a vibration parameter adjustment amount and a temperature control measure intervention intensity by a multi-objective collaborative optimization algorithm, and obtain a collaborative control instruction of the next control period based on a calculation result, wherein the collaborative control instruction comprises a vibration control instruction and a temperature control adjustment instruction; A collaborative control module is configured to control a concrete pouring system to perform collaborative optimization of vibration and temperature control of concrete pouring in the next control period based on the collaborative control instruction.

7. The concrete forming vibratory temperature control co-ordinated control system of claim 6, wherein, The construction module is specifically configured to: Perform feature coding processing on the concrete mix proportion parameters to obtain a material property feature vector; Construct a vibration response prediction sub-model based on the material property feature vector and the historical vibration control parameters; Construct a temperature evolution prediction sub-model based on the material property feature vector and the initial temperature field data; The vibration response prediction sub-model and the temperature evolution prediction sub-model are bidirectionally coupled and associated to obtain an initial prediction model of vibration-temperature control bidirectional coupling effect.

8. The concrete forming vibratory temperature control co-ordinated control system of claim 7, wherein, The correction module is specifically used for: The vibration sensor array is used to collect vibration time sequence data in the concrete pouring process in real time to obtain a vibration signal time sequence; The temperature sensor array is used to collect temperature field space data in the concrete pouring process in real time to obtain a temperature distribution space matrix; The vibration signal time sequence and the temperature distribution space matrix are input into the initial prediction model to calculate a model prediction error; Based on the model prediction error, the coupling parameters of the initial prediction model are updated; The initial prediction model after the parameter update is verified for convergence to obtain a real-time coupling effect prediction model.

9. The concrete forming vibratory temperature control co-ordinated control system of claim 8, wherein, The prediction module is specifically used for: According to a pouring progress, a control period time window is divided to determine a prediction time interval of a next control period; The real-time coupling effect prediction model is calculated forward in the prediction time interval to obtain a compactness evolution trend curve and a temperature gradient distribution surface; The compactness evolution trend curve is subjected to target compactness deviation quantization processing to generate a compactness deviation prediction value of the concrete pouring in the next control period; The temperature gradient distribution surface is subjected to maximum gradient value extraction processing to generate a temperature gradient prediction value of the concrete pouring in the next control period.

10. The concrete forming vibratory temperature control co-ordinated control system of claim 9, wherein, The calculation module is specifically used for: Based on the compactness deviation prediction value and the temperature gradient prediction value, compactness optimization weights and temperature control optimization weights are configured to obtain a multi-objective collaborative optimization weight matrix of compactness optimization and temperature control optimization; Vibration parameter adjustment range constraints and temperature control measure intervention intensity constraints are set to obtain optimization constraint conditions of multi-objective collaborative optimization; The multi-objective collaborative optimization weight matrix and the optimization constraint conditions are input, and a non-dominated sorting genetic algorithm is used to solve an optimal solution set of vibration parameter adjustment amount and temperature control measure intervention intensity to obtain optimal vibration control instructions and optimal temperature control adjustment instructions of the next control period.

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