Multi-modal dynamic coupling mass concrete temperature control prediction analysis method and system

Abstract

The application specifically provides a multi-modal dynamic coupling mass concrete temperature control prediction analysis method and system. The method of the application comprises obtaining real-time monitoring data and preset temperature control index data; based on the real-time monitoring data, a dynamic prediction result is determined, wherein the dynamic prediction result at least comprises dynamic prediction parameter values in different time periods; based on the dynamic prediction result and the preset temperature control index data, dynamic prediction temperature control data is determined, wherein the dynamic prediction temperature control data at least comprises temperature control reliability values in different time periods; based on the dynamic prediction temperature control data, a temperature control measure adjustment scheme is selectively generated, wherein the temperature control measure adjustment scheme at least comprises different types of temperature control adjustment parameter values, adjustment costs and predicted temperature control effects. The temperature control measure adjustment scheme that is more suitable for the current mass concrete structure is continuously generated, and the adjusted temperature control effect is verified, so that the response timeliness and scientificity of the temperature control are continuously improved.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, specifically to the field of bridge engineering technology, and specifically provides a multimodal dynamic coupling method and system for predictive analysis of temperature control in large-volume concrete. Background Technology

[0002] In the construction of large-volume concrete, the large temperature difference and temperature stress generated by the heat of hydration reaction of concrete can easily lead to cracks if effective temperature control measures are not taken, resulting in a reduction in the durability and safety of the concrete structure.

[0003] Traditional temperature control decisions rely heavily on early-stage simulations and temperature monitoring during construction. However, in actual temperature control, data such as concrete thermal parameters and pipe cooling method parameters will change dynamically, leading to inconsistencies with the parameters simulated in the early stages. Therefore, it is difficult to accurately predict the changes in temperature control data in the next stage and make reasonable adjustments to temperature control measures in advance. Temperature control commands will be severely delayed, affecting the final temperature control effect.

[0004] Accordingly, there is a need in this field for a new multimodal dynamic coupling temperature control prediction and analysis scheme for large-volume concrete to overcome the shortcomings of existing temperature control technologies and solve the above problems. Summary of the Invention

[0005] To overcome the above-mentioned defects, this invention is proposed to provide a multimodal dynamic coupling method and system for predictive analysis of temperature control in large-volume concrete, which solves or at least partially solves the technical problem that the inability of temperature monitoring to predict accurately in real time in the prior art leads to a serious lag in temperature control commands and affects the final temperature control effect.

[0006] In a first aspect, the present invention provides a multimodal dynamic coupling method for predictive analysis of temperature control in large-volume concrete, the method comprising:

[0007] Acquire real-time monitoring data and preset temperature control index data;

[0008] Based on real-time monitoring data, dynamic prediction results are determined, wherein the dynamic prediction results include at least the dynamic prediction parameter values ​​for different time periods;

[0009] Based on the dynamic prediction results and the preset temperature control index data, the dynamic predicted temperature control data is determined. The dynamic predicted temperature control data includes at least the temperature control reliability values ​​for different time periods.

[0010] The temperature control reliability prediction calculation includes: obtaining the design limits of the temperature control index and the dynamic prediction values ​​of the response surface model at different time periods after pouring; obtaining the theoretical calculation values ​​of the temperature control design scheme at different time periods after pouring; and determining the predicted temperature control reliability index at different time periods after pouring based on the design limits of the temperature control index, the dynamic prediction values ​​of the response surface model at different time periods after pouring, and the theoretical calculation values ​​of the temperature control design scheme at different time periods after pouring. Specifically, the temperature control reliability prediction calculation formula is as follows:

[0011]

[0012] In the formula, The predictive indicator is the reliability of temperature control t hours after pouring. The design limits for temperature control parameters can be set here based on relevant specifications, engineering structural characteristics, and construction conditions. This represents the dynamic predicted value from the response surface model t hours after pouring. This is the theoretically calculated value in the temperature control design scheme t hours after pouring;

[0013] Based on dynamically predicted temperature control data, temperature control adjustment schemes are selectively generated. These schemes include at least different types of temperature control adjustment parameter values, adjustment costs, and predicted temperature control effects.

[0014] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete, the step of determining the dynamic prediction result based on real-time monitoring data includes:

[0015] The initial finite element model is dynamically corrected based on real-time monitoring data to obtain the corrected prediction model.

[0016] Based on the revised prediction model, the prediction results are obtained.

[0017] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete, the step of dynamically correcting the initialized finite element model based on real-time monitoring data to obtain the corrected prediction model includes:

[0018] Obtain the preset initial finite element model;

[0019] Based on real-time monitoring data, the parameters to be corrected in the preset initial finite element model are dynamically corrected to obtain the corrected model parameters. The parameters to be corrected include at least the cooling pipe inlet water temperature, cooling water flow rate, and thermal insulation material convection coefficient.

[0020] Based on the corrected model parameters, the corrected prediction model is obtained.

[0021] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete, obtaining the preset temperature control index data includes:

[0022] Obtain engineering data;

[0023] Based on engineering data, the limiting range of temperature control data is determined in order to obtain the preset temperature control index data.

[0024] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete, the step of determining the dynamic predicted temperature control data based on the dynamic prediction results and preset temperature control index data includes:

[0025] The dynamic prediction results and preset temperature control index data are used to calculate the temperature control reliability prediction, and the predicted temperature control reliability is obtained for different time periods.

[0026] Based on the predictive reliability of temperature control at different time periods, dynamic predictive temperature control data are determined.

[0027] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete, the theoretical calculated values ​​of the temperature control design scheme at different time periods after pouring include:

[0028] Obtain the current temperature control design scheme;

[0029] Based on the current temperature control design scheme, determine the theoretical calculated values ​​of the temperature control design scheme for different time periods after pouring.

[0030] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete, the step of selectively generating temperature control adjustment schemes based on dynamically predicted temperature control data includes:

[0031] If the dynamically predicted temperature control data is lower than the preset temperature control adjustment threshold, a temperature control adjustment plan will be generated.

[0032] Otherwise, continue with the steps of "acquiring real-time monitoring data and preset temperature control index data" and subsequent steps.

[0033] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing temperature control in large-volume concrete, the step of generating an adjustment scheme for temperature control measures includes:

[0034] The number of times the temperature control measure adjustment plan is generated is initialized to zero;

[0035] Based on dynamic predicted temperature control data, the current temperature control design scheme, dynamic prediction results, and preset temperature control index data, an initial temperature control measure adjustment scheme is generated, and the generation count of the temperature control measure adjustment scheme is incremented by 1. The initial temperature control measure adjustment scheme includes at least different types of temperature control adjustment parameter values.

[0036] Based on the initial plan for adjusting temperature control measures, determine the set of adjustment decision parameters;

[0037] Based on the adjustment of the decision parameter set, a temperature control measure adjustment plan is selectively output.

[0038] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete, the determination of the adjustment decision parameter set based on the initial scheme of temperature control measures includes:

[0039] Based on the initial plan for adjusting temperature control measures, the estimated construction costs corresponding to different types of temperature control adjustment parameter values ​​are determined;

[0040] Based on the estimated construction costs corresponding to different types of temperature control adjustment parameter values, determine the estimated cost of adjusting temperature control measures;

[0041] Based on the initial plan for adjusting temperature control measures and the prediction model, we obtained the estimated effect data of temperature control adjustment.

[0042] Based on the estimated cost and effect data of temperature control measures, a set of adjustment decision parameters is obtained.

[0043] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing temperature control in large-volume concrete, the step of selectively outputting temperature control adjustment schemes based on adjusting the decision parameter set includes:

[0044] If the estimated effect data of temperature control adjustment reaches the preset threshold range of temperature control effect, then based on the estimated cost of temperature control measure adjustment, a temperature control measure adjustment plan will be selectively output.

[0045] Otherwise, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed.

[0046] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete, the selective output of temperature control adjustment schemes based on the estimated cost of adjusting temperature control measures includes:

[0047] When the number of times the temperature control measure adjustment plan is generated is 1:

[0048] If the estimated cost of adjusting the temperature control measures exceeds the preset adjustment cost threshold of the temperature control measures, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed.

[0049] Otherwise, the initial temperature control measure adjustment plan will be output as the final temperature control measure adjustment plan.

[0050] or,

[0051] When the number of times the temperature control measure adjustment plan is generated is greater than 1:

[0052] If the estimated cost of the initial temperature control measure adjustment plan is the lowest among the historical initial temperature control measure adjustment plans, then the initial temperature control measure adjustment plan is output as the temperature control measure adjustment plan.

[0053] Otherwise, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed.

[0054] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing temperature control in large-volume concrete, the method further includes:

[0055] Implement the temperature control measures adjustment plan;

[0056] After a preset inspection and adjustment period, the current measured data is obtained;

[0057] Based on the current measured data, determine the measured temperature control data;

[0058] Based on measured temperature control data, the temperature control measures adjustment plan is selectively adjusted.

[0059] In one technical solution of the above-mentioned multimodal dynamic coupling method for predicting and analyzing temperature control in large-volume concrete, the selective adjustment scheme of temperature control measures based on measured temperature control data includes:

[0060] If the deviation between the measured temperature control data and the estimated temperature control adjustment effect data corresponding to the temperature control adjustment plan exceeds the preset deviation threshold, then the preset threshold range for adjusting the temperature control effect will be adjusted.

[0061] Based on the current measured data, a new temperature control adjustment plan is generated to implement the adjusted temperature control measures.

[0062] In a second aspect, the present invention provides a multimodal dynamic coupling temperature control prediction and analysis system for large-volume concrete, used to implement the multimodal dynamic coupling temperature control prediction and analysis method for large-volume concrete as described in any of the above claims, the system comprising:

[0063] The data acquisition module is used to acquire real-time monitoring data and preset temperature control index data;

[0064] The prediction module is used to generate dynamic prediction results based on real-time monitoring data and to determine dynamic predicted temperature control data based on the dynamic prediction results and preset temperature control index data.

[0065] The decision-making module is used to determine whether to generate a temperature control adjustment plan based on dynamically predicted temperature control data.

[0066] The scheme generation module is used to generate temperature control measure adjustment schemes.

[0067] In a third aspect, a control device is provided, comprising a processor and a storage device, the storage device being adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to execute the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method as described in any of the above-described technical solutions of the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method.

[0068] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method described in any of the above-described technical solutions.

[0069] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:

[0070] By using a dynamically coupled prediction model based on real-time monitoring data, more accurate temperature control parameter predictions are provided. The current temperature control status is evaluated using the temperature control reliability calculation formula based on the dynamic prediction results and preset temperature control index data. This dynamic prediction of temperature control data, combined with cost optimization screening decision formulas, automatically generates temperature control adjustment schemes and continuously optimizes them. This enables the continuous generation of temperature control adjustment schemes that are more suitable for the current large-volume concrete structure, and verifies the temperature control effect after adjustment, thereby continuously improving the timeliness and scientific nature of temperature control response.

[0071] By using the "multimodal dynamic coupling" method, the limitations of traditional simulation and on-site monitoring are overcome. The finite element model is corrected in real time using on-site measured data. This not only solves the problem of inaccurate prediction caused by dynamic changes in concrete thermal parameters and pipe cooling parameters, but also greatly improves the calculation efficiency. It can predict the temperature control data of the next stage in real time and accurately, thus gaining time to adjust the temperature control measures in advance.

[0072] By proposing the quantitative indicator of "temperature control reliability" and constructing a screening decision formula in conjunction with construction cost factors, this invention can automatically generate the optimal temperature control adjustment scheme (such as adjusting cooling water flow rate, inlet water temperature, etc.) based on real-time reliability, and perform effect verification and parameter self-optimization after execution. This transforms traditional experience-based decision-making into data-driven intelligent decision-making, significantly improving the scientific nature of temperature control measures.

[0073] By constructing a closed-loop control process of "monitoring-prediction-decision-verification-optimization", this invention can automatically adjust the decision threshold and regenerate the optimization scheme when the actual temperature control effect deviates significantly from the expected result. This enables continuous iterative optimization of model parameters and temperature control measures, making the temperature control system more robust and reliable in the face of complex and ever-changing construction environments.

[0074] In implementing the technical solution of this invention, during the construction phase, this technology avoids crack repair or even structural scrapping caused by temperature control failure through accurate prediction, directly saving a large amount of material costs, labor costs, and construction delay costs. At the same time, its dynamic optimization algorithm can accurately control parameters such as cooling water flow, effectively reducing direct costs such as water pump operation, water replenishment, and insulation materials, achieving cost reduction and efficiency improvement. From the perspective of the entire life cycle, due to the improvement of the durability of the engineering structure, the frequency of later operation, maintenance, and major repairs will be greatly reduced, thereby saving huge amounts of long-term maintenance funds.

[0075] Through precise dynamic temperature control prediction and intelligent decision-making, the generation of temperature cracks in large-volume concrete can be fundamentally suppressed, significantly improving the structural durability and safety of infrastructure such as bridges, dams, and super high-rise buildings, extending the lifespan of projects, thereby effectively protecting public life and property safety and reducing social risks caused by structural defects. As a model of deep integration between traditional civil engineering and big data and artificial intelligence technologies, this invention will powerfully promote the transformation and upgrading of the construction industry from experience-driven extensive management to data-driven refined and intelligent models, promote the training of technical personnel in the industry, enhance the overall technological content of the industry, and actively respond to the national call for green construction and sustainable development by optimizing the allocation of temperature control resources and reducing the waste of building materials and energy caused by crack repair and reconstruction, thus having significant environmental value. Attached Figure Description

[0076] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:

[0077] Figure 1 This is a schematic diagram of the main steps of a multimodal dynamic coupling method for predictive analysis of temperature control in large-volume concrete according to an embodiment of the present invention.

[0078] Figure 2 This is a flowchart illustrating the decision analysis process for generating temperature control measure adjustment schemes in a multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method according to an embodiment of the present invention.

[0079] Figure 3 This is a flowchart illustrating a multimodal dynamic coupling method for predicting and analyzing temperature control in large-volume concrete according to an embodiment of the present invention.

[0080] Figure 4 This is a schematic diagram of the main structure of a multimodal dynamic coupling large-volume concrete temperature control prediction and analysis system according to an embodiment of the present invention. Detailed Implementation

[0081] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0082] In the description of this invention, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0083] like Figures 1 to 3 As shown, the multimodal dynamic coupling method for predicting and analyzing the temperature control of large-volume concrete in this embodiment of the invention mainly includes the following steps S101-S104.

[0084] Step S101: Obtain real-time monitoring data and preset temperature control index data;

[0085] Specifically, the types of real-time monitoring data include at least ambient temperature, wind speed, maximum concrete temperature, cooling rate, concrete water temperature difference, surface air temperature difference, inner and outer surface temperature difference, inlet and outlet water temperature difference, inlet / outlet water temperature, and flow rate.

[0086] Specifically, obtaining the preset temperature control index data includes:

[0087] Obtain engineering data;

[0088] Based on engineering data, the limiting range of temperature control data is determined in order to obtain the preset temperature control index data.

[0089] Specifically, based on engineering data, the limitation range of temperature control data can be determined as follows: those skilled in the art can formulate the limitation range of temperature control data according to relevant specifications, engineering structural characteristics, and construction conditions.

[0090] Step S102: Based on real-time monitoring data, determine the dynamic prediction results, wherein the dynamic prediction results include at least the dynamic prediction parameter values ​​for different time periods;

[0091] Specifically, determining the dynamic prediction result based on real-time monitoring data includes:

[0092] The initial finite element model is dynamically corrected based on real-time monitoring data to obtain the corrected prediction model.

[0093] Based on the revised prediction model, the prediction results are obtained.

[0094] Specifically, the dynamic correction of the initialized finite element model based on real-time monitoring data to obtain the corrected prediction model includes:

[0095] Obtain the preset initial finite element model;

[0096] Based on real-time monitoring data, the parameters to be corrected in the preset initial finite element model are dynamically corrected to obtain the corrected model parameters. The parameters to be corrected include at least the cooling pipe inlet water temperature, cooling water flow rate, and thermal insulation material convection coefficient.

[0097] Based on the corrected model parameters, the corrected prediction model is obtained.

[0098] Specifically, the preset initial finite element model can be a pre-constructed hydration heat finite element model of large-volume concrete. Before modifying the model, the method further includes:

[0099] Based on real-time monitoring data of large-volume concrete and the running data of the preset initial finite element model, the expectation value of the preset initial finite element model is determined.

[0100] If the expected value is not within the preset expected value range, the preset initial finite element model will be corrected based on real-time monitoring data.

[0101] Otherwise, the preset initial finite element model will be used as the corrected prediction model for output.

[0102] Specifically, the model is modified using the following formula:

[0103]

[0104]

[0105] In the formula, The values ​​are calculated using the response surface methodology. These are the measured values ​​of the temperature control indicators. This represents the minimum value calculated by the response surface methodology. The maximum value of the response surface model calculation result is given by n, where n is the number of measured values ​​of the temperature control index.

[0106] The above formula enables dynamic correction of the initial model through real-time monitoring data, providing accurate data support for subsequent predictive analysis.

[0107] Specifically, the construction of the preset initial finite element model includes:

[0108] Construct a response surface model;

[0109] The fitting accuracy of the response surface model is checked, and based on the fitting accuracy results, the response surface is selectively refitted to form a preset initial finite element model.

[0110] The response surface model is constructed using the following formula:

[0111]

[0112] In the formula, For the constant term of the response surface model, The parameters to be corrected here can be ambient temperature, concrete adiabatic temperature rise, cold water pipe flow rate, cold water inlet temperature, etc. (see Table 1, input parameters for the hydration heat finite element model). , The coefficients corresponding to the parameters to be corrected. , These are the sequence numbers of the parameters to be corrected. y represents the number of parameters to be corrected, and y represents the key temperature control index (see Table 2, Key Temperature Control Indicators for Mass Concrete).

[0113] Table 1 Input parameters for the hydration heat finite element model

[0114]

[0115] Table 2 Key Indicators for Temperature Control of Mass Concrete

[0116]

[0117] Specifically, the fitting accuracy test of the response surface model is performed using the following formula:

[0118]

[0119] In the formula, The values ​​are calculated using the response surface methodology. These are values ​​calculated using the finite element model. This is the average value calculated by the finite element method. The number of data entries to be tested;

[0120] pass To verify the reliability of the fitted response surface model. The test result is between 0 and 1. If the value is too small, it indicates low fitting accuracy, and the response surface fitting should be repeated.

[0121] In the above embodiments, a multimodal dynamic coupling hydration heat simulation method is used to construct a parameter sensitivity model using the response surface methodology. The key thermal parameters of the finite element model are dynamically optimized and corrected by combining the Myers expectation function. By correcting the key parameters of the hydration heat model using the response surface methodology, the need to perform calculations in the finite element model for each iteration during the correction process is avoided. This reduces the finite element calculation cost while improving the correction efficiency and accuracy, making it more in line with the needs of practical applications and achieving a significant improvement in computational efficiency and prediction accuracy.

[0122] Step S103: Based on the dynamic prediction results and the preset temperature control index data, determine the dynamic prediction temperature control data, wherein the dynamic prediction temperature control data includes at least the temperature control reliability values ​​for different time periods.

[0123] Furthermore, before determining the dynamically predicted temperature control data based on the dynamic prediction results and preset temperature control index data, the method further includes:

[0124] Obtain engineering data;

[0125] Based on engineering data, the limits of temperature control data are determined.

[0126] Specifically, determining the dynamically predicted temperature control data based on the dynamic prediction results and preset temperature control index data includes:

[0127] The dynamic prediction results and preset temperature control index data are used to calculate the temperature control reliability prediction, and the predicted temperature control reliability is obtained for different time periods.

[0128] Based on the predictive reliability of temperature control at different time periods, dynamic predictive temperature control data are determined.

[0129] Specifically, the temperature control reliability prediction calculation includes:

[0130] Obtain the design limits of temperature control indicators and the dynamic predicted values ​​of the response surface model at different time periods after pouring;

[0131] Obtain the theoretical calculation values ​​of the temperature control design scheme at different time periods after pouring;

[0132] Based on the design limits of temperature control indicators, the dynamic predicted values ​​of the response surface model at different times after pouring, and the theoretical calculated values ​​in the temperature control design scheme at different times after pouring, the reliability of the predicted temperature control indicators at different times after pouring is determined.

[0133] Specifically, the theoretical calculation values ​​in the temperature control design scheme for different time periods after pouring include:

[0134] Obtain the current temperature control design scheme;

[0135] Based on the current temperature control design scheme, determine the theoretical calculated values ​​of the temperature control design scheme for different time periods after pouring.

[0136] Specifically, the formula for predicting the reliability of temperature control is as follows:

[0137]

[0138] In the formula, The predictive indicator is the reliability of temperature control t hours after pouring. The design limits for temperature control parameters can be set here based on relevant specifications, engineering structural characteristics, and construction conditions. This represents the dynamic predicted value from the response surface model t hours after pouring. This is the theoretically calculated value in the temperature control design scheme for t hours after pouring.

[0139] Step S104: Based on the dynamic predicted temperature control data, selectively generate temperature control measure adjustment schemes, wherein the temperature control measure adjustment schemes include at least different types of temperature control adjustment parameter values, adjustment costs, and predicted temperature control effects.

[0140] Specifically, the selective generation of temperature control adjustment schemes based on dynamically predicted temperature control data includes:

[0141] If the dynamically predicted temperature control data is lower than the preset temperature control adjustment threshold, a temperature control adjustment plan will be generated.

[0142] Otherwise, continue with the steps of "acquiring real-time monitoring data and preset temperature control index data" and subsequent steps.

[0143] Specifically, the preset temperature control adjustment threshold can be 0.6. The setting of the preset temperature control adjustment threshold here is only an example. Those skilled in the art can set it according to the actual situation, and it will not be elaborated here.

[0144] Specifically, the steps for generating the temperature control adjustment plan include:

[0145] The number of times the temperature control measure adjustment plan is generated is initialized to zero;

[0146] Based on dynamic predicted temperature control data, the current temperature control design scheme, dynamic prediction results, and preset temperature control index data, an initial temperature control measure adjustment scheme is generated, and the generation count of the temperature control measure adjustment scheme is incremented by 1. The initial temperature control measure adjustment scheme includes at least different types of temperature control adjustment parameter values.

[0147] Based on the initial plan for adjusting temperature control measures, determine the set of adjustment decision parameters;

[0148] Based on the adjustment of the decision parameter set, a temperature control measure adjustment plan is selectively output.

[0149] Specifically, the adjustment decision parameter set based on the initial plan for temperature control measures includes:

[0150] Based on the initial plan for adjusting temperature control measures, the estimated construction costs corresponding to different types of temperature control adjustment parameter values ​​are determined;

[0151] Based on the estimated construction costs corresponding to different types of temperature control adjustment parameter values, determine the estimated cost of adjusting temperature control measures;

[0152] Based on the initial plan for adjusting temperature control measures and the prediction model, we obtained the estimated effect data of temperature control adjustment.

[0153] Based on the estimated cost and effect data of temperature control measures, a set of adjustment decision parameters is obtained.

[0154] Specifically, the estimated cost of adjusting temperature control measures is obtained using the following formula:

[0155]

[0156] In the formula, Adjustments to temperature control measures to reduce costs The inlet water temperature is The hourly water replenishment cost, The cooling water flow rate is The hourly operating cost of the water pump The convection coefficient of the insulation material is The cost of insulation materials at that time This refers to the inlet water temperature of the cooling pipe. For cooling water flow rate, This represents the convection coefficient of the insulation material.

[0157] Specifically, based on the initial plan for adjusting temperature control measures and the prediction model, the estimated effect data of the temperature control adjustment includes:

[0158] Based on the initial temperature control measure adjustment plan and prediction model, the dynamic prediction results and several key temperature control indicators corresponding to the initial temperature control measure adjustment plan are obtained.

[0159] The dynamic prediction results corresponding to the initial temperature control measure adjustment plan and multiple key temperature control indicators are used to calculate the temperature control reliability prediction, and the predicted indicators of temperature control reliability for different key temperature control indicators are obtained.

[0160] Based on the predictive indicators of temperature control reliability for different key temperature control indicators, the estimated effect data of temperature control adjustment is determined.

[0161] Specifically, the selective output of temperature control adjustment schemes based on the set of adjustment decision parameters includes:

[0162] If the estimated effect data of temperature control adjustment reaches the preset threshold range of temperature control effect, then based on the estimated cost of temperature control measure adjustment, a temperature control measure adjustment plan will be selectively output.

[0163] Otherwise, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed.

[0164] Specifically, the preset threshold range for temperature control effect can be 0.8≤ ≤1.2, where, The temperature control reliability of each key temperature control indicator is represented by i, which is the indicator number in Table 2, and its value is the value of number 1 to 6.

[0165] Specifically, the selective output of temperature control adjustment schemes based on the estimated cost of temperature control measures includes:

[0166] When the number of times the temperature control measure adjustment plan is generated is 1:

[0167] If the estimated cost of adjusting the temperature control measures exceeds the preset adjustment cost threshold of the temperature control measures, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed.

[0168] Otherwise, the initial temperature control measure adjustment plan will be output as the final temperature control measure adjustment plan.

[0169] or,

[0170] When the number of times the temperature control measure adjustment plan is generated is greater than 1:

[0171] If the estimated cost of the initial temperature control measure adjustment plan is the lowest among the historical initial temperature control measure adjustment plans, then the initial temperature control measure adjustment plan is output as the temperature control measure adjustment plan.

[0172] Otherwise, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed.

[0173] Specifically, the method further includes:

[0174] Implement the temperature control measures adjustment plan;

[0175] After a preset inspection and adjustment period, the current measured data is obtained;

[0176] Based on the current measured data, determine the measured temperature control data;

[0177] Based on measured temperature control data, the temperature control measures adjustment plan is selectively adjusted.

[0178] Specifically, the preset inspection and adjustment time can be 1 hour. The preset inspection and adjustment time setting here is only an example. Those skilled in the art can set it according to the actual situation, and it will not be elaborated here.

[0179] Specifically, the adjustment scheme for selectively adjusting temperature control measures based on measured temperature control data includes:

[0180] If the deviation between the measured temperature control data and the estimated temperature control adjustment effect data corresponding to the temperature control adjustment plan exceeds the preset deviation threshold, then the preset threshold range for adjusting the temperature control effect will be adjusted.

[0181] Based on the current measured data, a new temperature control adjustment plan is generated to implement the adjusted temperature control measures.

[0182] Specifically, the measured temperature control data and the estimated effect data of the temperature control adjustment corresponding to the temperature control measure adjustment plan are obtained through the following formula:

[0183]

[0184] In the formula, The measured temperature control reliability index is given t hours after pouring. The design limits for temperature control parameters, The measured values ​​of temperature control parameters are given t hours after pouring. This is the theoretically calculated value in the temperature control design scheme for t hours after pouring.

[0185] Specifically, the preset deviation threshold can be 20%. The setting of the preset deviation threshold here is only an example. Those skilled in the art can set it according to the actual situation, and it will not be elaborated here.

[0186] Specifically, the preset threshold range for adjusting the temperature control effect includes:

[0187] If the current preset threshold range of temperature control effect does not reach the preset threshold range limit of temperature control effect, then the upper and lower limits of the preset threshold range of temperature control effect will be increased by the preset adjustment value.

[0188] Otherwise, the preset threshold range of the current temperature control effect will be used as the preset threshold range of the adjusted temperature control effect.

[0189] Specifically, the preset threshold range limit for the temperature control effect can be 1.6≤ ≤2.0, the preset adjustment value can be 0.1. The preset adjustment value and the preset threshold range limit of the temperature control effect are set here as an example only. Those skilled in the art can set them according to the actual situation, and will not be elaborated here.

[0190] In the above embodiments, by combining the prediction results of dynamic coupling with the temperature control reliability calculation formula, the temperature control measures adjustment scheme that best suits the current large-volume concrete structure is continuously generated, and the temperature control effect after adjustment is verified, so as to achieve continuous optimization of model parameters and temperature control measures.

[0191] Based on steps S101-S104 above, a dynamically coupled prediction model provides more accurate temperature control parameter predictions based on real-time monitoring data. Through dynamic prediction results and preset temperature control index data, the current temperature control status is evaluated using the temperature control reliability calculation formula, i.e., dynamic prediction of temperature control data. Combined with cost optimization screening decision formula, an automatic temperature control measure adjustment plan is generated and continuously optimized. This achieves the continuous generation of temperature control measure adjustment plans that are more suitable for the current large-volume concrete structure, and verifies the temperature control effect after adjustment, continuously improving the timeliness and scientific nature of temperature control response.

[0192] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of the present invention.

[0193] Furthermore, the present invention also provides a multimodal dynamic coupling temperature control prediction and analysis system for large-volume concrete.

[0194] like Figure 4 As shown, the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis system in this embodiment of the invention, used to implement the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method described above, mainly includes:

[0195] The data acquisition module is used to acquire real-time monitoring data and preset temperature control index data;

[0196] The prediction module is used to generate dynamic prediction results based on real-time monitoring data and to determine dynamic predicted temperature control data based on the dynamic prediction results and preset temperature control index data.

[0197] The decision-making module is used to determine whether to generate a temperature control adjustment plan based on dynamically predicted temperature control data.

[0198] The scheme generation module is used to generate temperature control measure adjustment schemes.

[0199] Specifically, the acquisition module is also used to acquire engineering data;

[0200] The prediction module is also used to construct and correct a preset initial finite element model to obtain a corrected prediction model.

[0201] Specifically, the acquisition module can be a sensor. The selection of the acquisition module here is only an example. Those skilled in the art can set it according to the actual situation, as long as it can acquire real-time monitoring data, preset temperature control index data, and engineering data. Further details will not be elaborated here.

[0202] Specifically, those skilled in the art can configure the scheme generation module according to the actual situation, as long as it can generate a temperature control adjustment scheme, which will not be elaborated here.

[0203] In one implementation, the specific function can be described in steps S101-S104.

[0204] The aforementioned multimodal dynamic coupling temperature control prediction and analysis system for large-volume concrete is used to perform... Figure 1 The embodiments of the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method shown are similar in technical principle, technical problem solved and technical effect produced. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis system can be found in the embodiments of the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method, and will not be repeated here.

[0205] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0206] Furthermore, the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis system of the present invention also includes a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method of the above-described method embodiments. The processor can be configured to execute the program in the storage device, which includes, but is not limited to, a program for executing the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method of the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The control device can be a control device device device comprising various electronic devices.

[0207] Furthermore, the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis system of the present invention also includes a computer-readable storage medium. In one embodiment of the present invention, the computer-readable storage medium can be configured to store a program for executing the multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described multimodal dynamic coupling large-volume concrete temperature control prediction and analysis method. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0208] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of software and hardware. Therefore, the number of modules shown in the figures is merely illustrative.

[0209] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of the present invention; therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.

[0210] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A multimodal dynamic coupling method for predicting and analyzing temperature control in large-volume concrete, characterized in that, The method includes: Acquire real-time monitoring data and preset temperature control index data; Based on real-time monitoring data, dynamic prediction results are determined, wherein the dynamic prediction results include at least the dynamic prediction parameter values ​​for different time periods; The step of determining the dynamic prediction result based on real-time monitoring data includes: dynamically correcting the initialized finite element model based on real-time monitoring data to obtain a corrected prediction model; and obtaining the prediction result based on the corrected prediction model. The process of dynamically correcting the initialized finite element model based on real-time monitoring data to obtain a corrected prediction model includes: acquiring a preset initialized finite element model; dynamically correcting the parameters to be corrected in the preset initialized finite element model based on real-time monitoring data to obtain corrected model parameters, wherein the parameters to be corrected include at least the cooling pipe inlet water temperature, cooling water flow rate, and thermal insulation material convection coefficient; and obtaining a corrected prediction model based on the corrected model parameters. The construction of the preset initial finite element model includes: constructing a response surface model; verifying the fitting accuracy of the response surface model; and selectively refitting the response surface based on the fitting accuracy results to form the preset initial finite element model. Based on the dynamic prediction results and the preset temperature control index data, the dynamic predicted temperature control data is determined. The dynamic predicted temperature control data includes at least the temperature control reliability values ​​for different time periods. The temperature control reliability prediction calculation includes: obtaining the design limits of the temperature control index and the dynamic prediction values ​​of the response surface model at different time periods after pouring; obtaining the theoretical calculation values ​​of the temperature control design scheme at different time periods after pouring; and determining the predicted temperature control reliability index at different time periods after pouring based on the design limits of the temperature control index, the dynamic prediction values ​​of the response surface model at different time periods after pouring, and the theoretical calculation values ​​of the temperature control design scheme at different time periods after pouring. Specifically, the temperature control reliability prediction calculation formula is as follows: In the formula, The predictive indicator is the reliability of temperature control t hours after pouring. The design limits for temperature control parameters, This represents the dynamic predicted value from the response surface model t hours after pouring. This is the theoretically calculated value in the temperature control design scheme t hours after pouring; Based on dynamic predicted temperature control data, temperature control adjustment schemes are selectively generated. The temperature control adjustment schemes include at least different types of temperature control adjustment parameter values, adjustment costs, and predicted temperature control effects. The method of selectively generating temperature control adjustment schemes based on dynamically predicted temperature control data includes: If the dynamically predicted temperature control data is lower than the preset temperature control adjustment threshold, a temperature control adjustment plan will be generated. Otherwise, continue with the steps of obtaining real-time monitoring data and preset temperature control index data, as well as subsequent steps.

2. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling as described in claim 1, characterized in that, Obtaining the preset temperature control index data includes: Obtain engineering data; Based on engineering data, the limiting range of temperature control data is determined in order to obtain the preset temperature control index data.

3. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling as described in claim 2, characterized in that, The determination of dynamic predicted temperature control data based on dynamic prediction results and preset temperature control index data includes: The dynamic prediction results and preset temperature control index data are used to calculate the temperature control reliability prediction, and the predicted temperature control reliability is obtained for different time periods. Based on the predictive reliability of temperature control at different time periods, dynamic predictive temperature control data are determined.

4. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling as described in claim 3, characterized in that, The theoretical calculation values ​​in the temperature control design scheme for different time periods after pouring include: Obtain the current temperature control design scheme; Based on the current temperature control design scheme, determine the theoretical calculated values ​​of the temperature control design scheme for different time periods after pouring.

5. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling as described in claim 4, characterized in that, The steps for generating the temperature control adjustment plan include: The number of times the temperature control measure adjustment plan is generated is initialized to zero; Based on dynamic predicted temperature control data, the current temperature control design scheme, dynamic prediction results, and preset temperature control index data, an initial temperature control measure adjustment scheme is generated, and the generation count of the temperature control measure adjustment scheme is incremented by 1. The initial temperature control measure adjustment scheme includes at least different types of temperature control adjustment parameter values. Based on the initial plan for adjusting temperature control measures, determine the set of adjustment decision parameters; Based on the adjustment of the decision parameter set, a temperature control measure adjustment plan is selectively output.

6. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling as described in claim 5, characterized in that, The initial adjustment plan based on temperature control measures determines the set of adjustment decision parameters, including: Based on the initial plan for adjusting temperature control measures, the estimated construction costs corresponding to different types of temperature control adjustment parameter values ​​are determined; Based on the estimated construction costs corresponding to different types of temperature control adjustment parameter values, determine the estimated cost of adjusting temperature control measures; Based on the initial plan for adjusting temperature control measures and the prediction model, we obtained the estimated effect data of temperature control adjustment. Based on the estimated cost and effect data of temperature control measures, a set of adjustment decision parameters is obtained.

7. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling as described in claim 6, characterized in that, The selective output of temperature control adjustment schemes based on the set of adjustment decision parameters includes: If the estimated effect data of temperature control adjustment reaches the preset threshold range of temperature control effect, then based on the estimated cost of temperature control measure adjustment, a temperature control measure adjustment plan will be selectively output. Otherwise, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed.

8. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling as described in claim 7, characterized in that, The method of selectively outputting temperature control adjustment schemes based on the estimated cost of temperature control measures includes: When the number of times the temperature control measure adjustment plan is generated is 1: If the estimated cost of adjusting the temperature control measures exceeds the preset adjustment cost threshold of the temperature control measures, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed. Otherwise, the initial temperature control measure adjustment plan will be output as the final temperature control measure adjustment plan. or, When the number of times the temperature control measure adjustment plan is generated is greater than 1: If the estimated cost of the initial temperature control measure adjustment plan is the lowest among the historical initial temperature control measure adjustment plans, then the initial temperature control measure adjustment plan is output as the temperature control measure adjustment plan. Otherwise, the adjustment decision parameter set of the initial temperature control measure adjustment plan is recorded, and the initial temperature control measure adjustment plan and subsequent steps are re-executed.

9. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling as described in claim 8, characterized in that, The method further includes: Implement the temperature control measures adjustment plan; After a preset inspection and adjustment period, the current measured data is obtained; Based on the current measured data, determine the measured temperature control data; Based on measured temperature control data, the temperature control measures adjustment plan is selectively adjusted.

10. The method for predicting and analyzing temperature control in large-volume concrete using multimodal dynamic coupling according to claim 9, characterized in that, The adjustment scheme for selectively adjusting temperature control measures based on measured temperature control data includes: If the deviation between the measured temperature control data and the estimated temperature control adjustment effect data corresponding to the temperature control adjustment plan exceeds the preset deviation threshold, then the preset threshold range for adjusting the temperature control effect will be adjusted. Based on the current measured data, a new temperature control adjustment plan is generated to implement the adjusted temperature control measures.

11. A multimodal dynamic coupling temperature control prediction and analysis system for large-volume concrete, applied to implement the multimodal dynamic coupling temperature control prediction and analysis method for large-volume concrete as described in any one of claims 1 to 10, characterized in that, The system includes: The data acquisition module is used to acquire real-time monitoring data and preset temperature control index data; The prediction module is used to generate dynamic prediction results based on real-time monitoring data and to determine dynamic predicted temperature control data based on the dynamic prediction results and preset temperature control index data. The decision-making module is used to determine whether to generate a temperature control adjustment plan based on dynamically predicted temperature control data. The scheme generation module is used to generate temperature control measure adjustment schemes.

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