Mpc-based concrete curing system temperature and humidity control method, system, device and medium
Patent Information
- Application Number
- CN202610792287.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-03
- Publication Date
- 2026-08-18
AI Technical Summary
[0004]本申请是为了解决冬期施工条件下，混凝土养护环境的温湿度调控困难的问题，现提供基于MPC的混凝土养护系统温湿度控制方法、系统、设备及介质，以提升冬期混凝土养护系统的整体性能与节能水平
[0035] This application constructs a dynamic model of temperature and humidity in the curing shed and introduces a model predictive control (MPC) strategy to achieve coordinated optimization control of temperature and humidity. This enables refined adjustment under multivariate coupling conditions, enhancing the system's stability and adaptability under complex working conditions. While ensuring concrete curing quality, it effectively reduces system energy consumption, making it suitable for intelligent curing control at winter construction sites. Furthermore, by introducing a comprehensive loss function for temperature and humidity control errors and energy consumption during the control process, it achieves unified optimization of control accuracy and energy-saving goals, improving the overall operating efficiency of the system.
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Figure CN122593530A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of concrete curing technology. Background Technology
[0002] Under winter construction conditions, the low ambient temperature significantly reduces the hydration rate of concrete, leading to slow early strength development and even frost damage, thus affecting the overall quality and durability of the engineering structure. To ensure proper concrete hardening, enclosed or semi-enclosed curing sheds are typically erected on the construction site, equipped with heating and humidification devices to regulate the temperature and humidity of the curing environment. However, the concrete curing process involves not only external heat input but also the release of heat from the hydration reaction, resulting in a curing environment exhibiting significant nonlinearity, time-varying characteristics, and temperature-humidity coupling features, increasing the difficulty of control. Therefore, achieving stable control of the curing environment under complex conditions has become one of the key technical challenges in winter construction.
[0003] In existing technologies, temperature and humidity in curing sheds are mostly regulated using experience-based control strategies or traditional PID control methods. However, these methods typically treat temperature and humidity as independent control objects, making it difficult to effectively handle the coupling relationship between them, resulting in limited control accuracy. Furthermore, existing control methods lack dynamic utilization of the heat release process during concrete hydration, often relying on continuous high-power heating and humidification, leading to low energy efficiency and high operating costs. In addition, the temperature and humidity requirements vary at different curing stages, and traditional control strategies struggle to adaptively adjust based on actual deviations and changes in operating conditions, making it difficult to achieve a balance between ensuring control accuracy and reducing energy consumption. Summary of the Invention
[0004] This application aims to address the difficulty in controlling the temperature and humidity of the concrete curing environment under winter construction conditions. It provides a method, system, equipment, and medium for temperature and humidity control of concrete curing systems based on MPC, in order to improve the overall performance and energy efficiency of concrete curing systems during winter.
[0005] The first aspect of this application provides a method for temperature and humidity control in a concrete curing system based on MPC, including:
[0006] Collect actual temperature and humidity data at different locations within the concrete curing environment;
[0007] Using heating power and humidification power as inputs, and temperature and humidity in the concrete curing environment as outputs, a dynamic temperature and humidity model is constructed.
[0008] Based on the error between the actual temperature and humidity data and the output of the temperature and humidity dynamic model, the weight coefficients of the temperature and humidity dynamic model loss function are dynamically adjusted so that when the error increases, the error weight of the temperature and humidity dynamic model loss function increases and / or the control weight decreases, and when the error decreases, the control weight of the temperature and humidity dynamic model loss function increases and / or the error weight decreases.
[0009] Based on the adjusted loss function, a model predictive control algorithm is used to solve for the optimal heating power and humidification power, thereby achieving temperature and humidity control of the concrete curing environment.
[0010] In one possible design, after collecting actual temperature and humidity data at different locations within the concrete curing environment, the following is also included:
[0011] The actual temperature and humidity data are preprocessed, including outlier removal, missing value handling, and data normalization.
[0012] In one possible design, the outlier removal includes: identifying and removing mutation data using the 3σ criterion;
[0013] The missing value processing includes: using a linear interpolation method to complete the missing values;
[0014] The data normalization includes mapping the actual temperature and humidity data to a uniform scale range to eliminate dimensional differences.
[0015] In one possible design, the expression for the dynamic temperature and humidity model is:
[0016] ,
[0017] ,
[0018] in, The equivalent heat capacity of air within the concrete curing environment. Humidity response coefficient The air temperature within the concrete curing environment. The relative humidity within the concrete curing environment. For heating power, Humidification power, The heat exchange coefficient between the concrete curing environment and the external environment. The moisture exchange coefficient between the concrete curing environment and the external environment. is the coupling coefficient between humidity and temperature. This is the coupling coefficient between temperature and humidity. For time.
[0019] In one possible design, the loss function expression of the temperature and humidity dynamic model is:
[0020] ,
[0021] in, For loss function, For temperature error, Due to humidity error, For heating power, Humidification power, This represents the change in heating power. This represents the change in humidification power. , , and All are weighting coefficients.
[0022] In one possible design, the weighting coefficients of the loss function of the temperature and humidity dynamic model are dynamically adjusted according to the following formula:
[0023] ,
[0024] ,
[0025] in, The state weight matrix is... The initial matrix of state weights, To control the weight matrix, The initial matrix for controlling the weights, It is the identity matrix. This is the current temperature error. This is the current humidity error. for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient.
[0026] In one possible design, the parameters of the temperature and humidity dynamic model are obtained by using the least squares method and combining the preprocessed actual temperature and humidity data.
[0027] The second aspect of this application provides a temperature and humidity control system for a concrete curing system based on MPC, including:
[0028] Data acquisition unit: Used to collect actual temperature and humidity data at different locations within the concrete curing environment;
[0029] Modeling unit: used to construct a dynamic temperature and humidity model by taking heating power and humidification power as inputs and temperature and humidity in the concrete curing environment as outputs;
[0030] Weight adjustment unit: used to dynamically adjust the weight coefficients of the temperature and humidity dynamic model loss function according to the error between the actual temperature and humidity data and the output of the temperature and humidity dynamic model, so that when the error increases, the error weight of the temperature and humidity dynamic model loss function increases and / or the control weight decreases, and when the error decreases, the control weight of the temperature and humidity dynamic model loss function increases and / or the error weight decreases.
[0031] Control unit: Used to solve for the optimal heating power and humidification power based on the adjusted loss function and the model predictive control algorithm, so as to realize the temperature and humidity control of the concrete curing environment.
[0032] A third aspect of this application provides a temperature and humidity control device for a concrete curing system based on MPC. The MPC-based temperature and humidity control device for a concrete curing system includes a processor and a memory. The memory stores at least one instruction, which is loaded and executed by the processor to implement the temperature and humidity control method for a concrete curing system based on MPC as described above.
[0033] A fourth aspect of this application provides a computer storage medium storing at least one instruction, which is loaded and executed by a processor to implement the temperature and humidity control method for a concrete curing system based on MPC as described above.
[0034] The beneficial effects of this application are:
[0035] This application constructs a dynamic model of temperature and humidity in the curing shed and introduces a model predictive control (MPC) strategy to achieve coordinated optimization control of temperature and humidity. This enables refined adjustment under multivariate coupling conditions, enhancing the system's stability and adaptability under complex working conditions. While ensuring concrete curing quality, it effectively reduces system energy consumption, making it suitable for intelligent curing control at winter construction sites. Furthermore, by introducing a comprehensive loss function for temperature and humidity control errors and energy consumption during the control process, it achieves unified optimization of control accuracy and energy-saving goals, improving the overall operating efficiency of the system.
[0036] This application proposes an energy-saving optimization control method for temperature and humidity in winter construction concrete curing systems based on MPC. Through preprocessing and dynamic modeling of temperature and humidity data, it effectively improves the system's responsiveness to environmental changes and hydration heat release processes. By dynamically adjusting weight parameters, it achieves adaptive switching of control strategies at different curing stages, enabling reasonable regulation between the heating, stabilization, and critical curing stages. Through multi-objective optimization and rolling predictive control, it significantly improves the accuracy of temperature and humidity control and reduces energy consumption. This application combines model predictive control with the mechanism of concrete curing processes, realizing intelligent regulation and energy-saving operation of the curing environment. It provides an efficient and reliable optimized control scheme for winter construction concrete curing, possessing significant engineering application value and promising prospects for widespread adoption. Attached Figure Description
[0037] Figure 1 The flowchart is a description of the temperature and humidity control method for a concrete curing system based on MPC, as described in Specific Implementation Method 1.
[0038] Figure 2 This is a structural diagram of the temperature and humidity control system of the MPC-based concrete curing system described in Specific Implementation Method 2;
[0039] Figure 3 This is a schematic diagram of the temperature and humidity control device for the MPC-based concrete curing system described in Specific Implementation Method 3;
[0040] Figure 4 This is a schematic diagram of the computer storage medium described in Specific Implementation Method Four. Detailed Implementation
[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.
[0042] Specific implementation method one: Refer to Figure 1 This embodiment describes a temperature and humidity control method for a concrete curing system based on MPC, which includes:
[0043] Collect actual temperature and humidity data at different locations within the concrete curing environment;
[0044] Using heating power and humidification power as inputs, and temperature and humidity in the concrete curing environment as outputs, a dynamic temperature and humidity model is constructed.
[0045] Based on the error between the actual temperature and humidity data and the output of the temperature and humidity dynamic model, the weight coefficients of the temperature and humidity dynamic model loss function are dynamically adjusted so that when the error increases, the error weight of the temperature and humidity dynamic model loss function increases and / or the control weight decreases, and when the error decreases, the control weight of the temperature and humidity dynamic model loss function increases and / or the error weight decreases.
[0046] Based on the adjusted loss function, a model predictive control algorithm is used to solve for the optimal heating power and humidification power, thereby achieving temperature and humidity control of the concrete curing environment.
[0047] In one embodiment, after collecting actual temperature and humidity data at different locations within the concrete curing environment, the method further includes:
[0048] The actual temperature and humidity data are preprocessed, including outlier removal, missing value handling, and data normalization.
[0049] In one implementation, the outlier removal includes: identifying and removing mutation data using the 3σ criterion;
[0050] The missing value processing includes: using a linear interpolation method to complete the missing values;
[0051] The data normalization includes mapping the actual temperature and humidity data to a uniform scale range to eliminate dimensional differences.
[0052] In one embodiment, the expression for the temperature and humidity dynamic model is:
[0053] ,
[0054] ,
[0055] in, The equivalent heat capacity of air within the concrete curing environment. Humidity response coefficient The air temperature within the concrete curing environment. The relative humidity within the concrete curing environment. For heating power, Humidification power, The heat exchange coefficient between the concrete curing environment and the external environment. The moisture exchange coefficient between the concrete curing environment and the external environment. is the coupling coefficient between humidity and temperature. This is the coupling coefficient between temperature and humidity. For time.
[0056] In one implementation, the loss function expression of the temperature and humidity dynamic model is:
[0057] ,
[0058] in, For loss function, For temperature error, Due to humidity error, For heating power, Humidification power, This represents the change in heating power. This represents the change in humidification power. , , and All are weighting coefficients.
[0059] In one implementation, the weighting coefficients of the temperature and humidity dynamic model loss function are dynamically adjusted according to the following formula:
[0060] ,
[0061] ,
[0062] in, The state weight matrix is... The initial matrix of state weights, To control the weight matrix, The initial matrix for controlling the weights, It is the identity matrix. This is the current temperature error. This is the current humidity error. for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient.
[0063] In one embodiment, the parameters of the temperature and humidity dynamic model are obtained by using the least squares method and combining the preprocessed actual temperature and humidity data.
[0064] To address the shortcomings of existing temperature and humidity control methods in winter concrete curing, such as insufficient consideration of the temperature and humidity coupling relationship, poor adaptive capability of the control strategy, and high energy consumption, this application provides an energy-saving optimization control method for temperature and humidity in a winter concrete curing system based on MPC. This method includes steps 1 to 5, where the numbering of each step does not necessarily restrict the order of execution. Each step is described in detail below:
[0065] S1. Temperature and humidity data acquisition and preprocessing:
[0066] Temperature and humidity sensors installed inside the concrete curing shed are used to collect real-time data on temperature and relative humidity changes under the action of heating and humidification devices, forming a raw environmental dataset.
[0067] The temperature and humidity sensors are arranged in a distributed manner, with multiple measuring points set at the center of the maintenance shed and at different heights to improve the representativeness and reliability of the collected data.
[0068] The collected raw data undergoes preprocessing, specifically including:
[0069] (1) Outlier removal: The 3σ criterion is used to identify and remove outlier data;
[0070] (2) Missing value handling: Missing values generated during the data acquisition process are filled in using linear interpolation.
[0071] (3) Data normalization: Map temperature and humidity data to a uniform scale range to eliminate dimensional differences and improve the stability of subsequent model calculations.
[0072] Through the above processing, high-quality temperature and humidity time-series data are obtained.
[0073] S2. Construct a dynamic temperature and humidity model:
[0074] A dynamic temperature and humidity model was constructed using heating power and humidification power as inputs, and air temperature and relative humidity inside the curing shed as outputs. During the modeling process, air density and specific heat capacity were treated as constants, and the higher-order effects of condensation and nonlinear evaporation were ignored. Dynamic equations were established using the laws of energy conservation and mass conservation.
[0075] According to the law of conservation of energy, we have:
[0076] ,
[0077] in, The temperature of the air inside the greenhouse. The equivalent heat capacity of the air inside the greenhouse. For heating power, The heat exchange coefficient between the curing shed and the outside environment, is the coupling coefficient between humidity and temperature. The relative humidity inside the greenhouse. For time.
[0078] According to the law of conservation of water vapor mass, we have:
[0079] ,
[0080] in, The humidity response coefficient reflects the inertial characteristics of changes in air humidity and can be obtained through experimentation. Humidification power, The wet exchange coefficient, This is the coupling coefficient between temperature and humidity.
[0081] This leads to a state-space model that couples temperature and humidity.
[0082] Furthermore, based on the experimental data obtained in step S1, the least squares method is used to identify the model parameters, thereby completing the construction of the dynamic temperature and humidity model.
[0083] S3. Design the loss function:
[0084] In this embodiment, a multi-objective loss function is constructed that balances control accuracy and energy consumption:
[0085] ,
[0086] in, The loss function; This is for temperature error; This is due to humidity error; This refers to the heating power. Humidification power; This represents the change in heating power. This represents the change in humidification power. , , , These are the weighting coefficients for the corresponding parameters.
[0087] S4. Weight adaptive adjustment mechanism based on real-time error:
[0088] Let the errors in temperature and humidity be respectively... and The dynamic weighting function can then be expressed as:
[0089] ,
[0090] ,
[0091] in, This is the state (output) weight matrix, used in the MPC loss function to penalize state errors such as temperature and humidity. The initial matrix for state weights is usually a fixed diagonal matrix; The control (input) weight matrix is used to penalize control variables (such as heating power and humidification power) in the MPC loss function. The initial matrix for controlling the weights; It is the identity matrix; for right The adaptive adjustment coefficient; for right The adaptive adjustment coefficient; for right The adaptive adjustment coefficient; for right The adaptive adjustment coefficient.
[0092] As can be seen from the above formula, when the error increases, the corresponding error weight increases and the control quantity weight decreases, allowing for larger control actions to quickly approach the reference value; while when the error decreases, the control quantity weight increases, optimizing the control action and thus reducing energy consumption.
[0093] S5. Simulation Verification:
[0094] Based on the dynamic temperature and humidity model constructed in step S2, this embodiment is verified in a simulation environment. During the simulation, two typical operating conditions are set, including:
[0095] (1) Disturbance conditions where the external temperature and humidity change abruptly;
[0096] (2) The external temperature and humidity are subject to random disturbances.
[0097] Under the same initial conditions and constraints, this embodiment is compared and analyzed with the traditional PID control method and the conventional model predictive control (MPC) method. The PID control method employs a fixed-parameter control strategy, while the conventional MPC method uses a loss function with fixed weight coefficients. Simulation evaluation metrics include: response time for temperature and humidity; steady-state error; overshoot; and total system energy consumption (integral values of heating and humidification power).
[0098] Simulation results show that compared with the traditional PID control method, this embodiment has a faster dynamic response speed and smaller steady-state error in the temperature and humidity regulation process, while effectively reducing the system overshoot phenomenon. Compared with the conventional MPC method, this embodiment achieves optimized allocation of control input by introducing a weighted adaptive adjustment mechanism, while ensuring control accuracy, thereby reducing the total system energy consumption by about 3% to 5%. In addition, in the presence of external disturbances, this embodiment can quickly recover to the set state, demonstrating stronger anti-interference ability and robustness.
[0099] In summary, this application achieves an adaptive balance between temperature and humidity control accuracy and system energy consumption. While meeting the environmental control requirements for concrete curing, it effectively reduces the operating energy consumption of heating and humidifying devices, demonstrating superior energy-saving performance and robustness.
[0100] Specific Implementation Method Two: Refer to Figure 2 This embodiment describes a temperature and humidity control system for a concrete curing system based on MPC, comprising:
[0101] Data acquisition unit: Used to collect actual temperature and humidity data at different locations within the concrete curing environment;
[0102] Modeling unit: used to construct a dynamic temperature and humidity model by taking heating power and humidification power as inputs and temperature and humidity in the concrete curing environment as outputs;
[0103] Weight adjustment unit: used to dynamically adjust the weight coefficients of the temperature and humidity dynamic model loss function according to the error between the actual temperature and humidity data and the output of the temperature and humidity dynamic model, so that when the error increases, the error weight of the temperature and humidity dynamic model loss function increases and / or the control weight decreases, and when the error decreases, the control weight of the temperature and humidity dynamic model loss function increases and / or the error weight decreases.
[0104] Control unit: Used to solve for the optimal heating power and humidification power based on the adjusted loss function and the model predictive control algorithm, so as to realize the temperature and humidity control of the concrete curing environment.
[0105] Specific Implementation Method Three: Refer to Figure 3 This embodiment describes a temperature and humidity control device for a concrete curing system based on MPC. The device includes a processor and a memory. The memory stores at least one instruction, which is loaded and executed by the processor to implement the temperature and humidity control method for a concrete curing system based on MPC as described in Embodiment 1.
[0106] Specific Implementation Method Four: Refer to Figure 4 This embodiment describes a computer storage medium that stores at least one instruction, which is loaded and executed by a processor to implement the MPC-based temperature and humidity control method for concrete curing systems as described in Specific Embodiment 1.
[0107] While specific embodiments of this application have been described herein with reference to them, it should be understood that these embodiments are merely examples of the principles and applications of this application. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of this application as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.
Claims
1. A method for temperature and humidity control in a concrete curing system based on MPC, characterized in that, include: Collect actual temperature and humidity data at different locations within the concrete curing environment; Using heating power and humidification power as inputs, and temperature and humidity in the concrete curing environment as outputs, a dynamic temperature and humidity model is constructed. Based on the error between the actual temperature and humidity data and the output of the temperature and humidity dynamic model, the weight coefficients of the temperature and humidity dynamic model loss function are dynamically adjusted so that when the error increases, the error weight of the temperature and humidity dynamic model loss function increases and / or the control weight decreases, and when the error decreases, the control weight of the temperature and humidity dynamic model loss function increases and / or the error weight decreases. Based on the adjusted loss function, a model predictive control algorithm is used to solve for the optimal heating power and humidification power, thereby achieving temperature and humidity control of the concrete curing environment.
2. The temperature and humidity control method for a concrete curing system based on MPC according to claim 1, characterized in that, After collecting actual temperature and humidity data at different locations within the concrete curing environment, the following is also included: The actual temperature and humidity data are preprocessed, including outlier removal, missing value handling, and data normalization.
3. The temperature and humidity control method for a concrete curing system based on MPC according to claim 2, characterized in that, The outlier removal includes: using the 3σ criterion to identify and remove mutation data; The missing value processing includes: using a linear interpolation method to complete the missing values; The data normalization includes mapping the actual temperature and humidity data to a uniform scale range to eliminate dimensional differences.
4. The temperature and humidity control method for a concrete curing system based on MPC according to claim 1, characterized in that, The expression for the dynamic temperature and humidity model is: ， ， in, The equivalent heat capacity of air within the concrete curing environment. Humidity response coefficient The air temperature within the concrete curing environment. The relative humidity within the concrete curing environment. For heating power, Humidification power, The heat exchange coefficient between the concrete curing environment and the external environment. The moisture exchange coefficient between the concrete curing environment and the external environment. is the coupling coefficient between humidity and temperature. This is the coupling coefficient between temperature and humidity. For time.
5. The temperature and humidity control method for a concrete curing system based on MPC according to claim 1, characterized in that, The loss function expression for the dynamic temperature and humidity model is: ， in, For loss function, For temperature error, Due to humidity error, For heating power, Humidification power, This represents the change in heating power. This represents the change in humidification power. , , and All are weighting coefficients.
6. The temperature and humidity control method for a concrete curing system based on MPC according to claim 1, characterized in that, The weighting coefficients of the loss function of the temperature and humidity dynamic model are dynamically adjusted according to the following formula: ， ， in, The state weight matrix is... The initial matrix of state weights, To control the weight matrix, The initial matrix for controlling the weights, It is the identity matrix. This is the current temperature error. This is the current humidity error. for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient, for right The adaptive adjustment coefficient.
7. The temperature and humidity control method for a concrete curing system based on MPC according to claim 2, characterized in that, The parameters of the temperature and humidity dynamic model are obtained by using the least squares method and combining the preprocessed actual temperature and humidity data.
8. A temperature and humidity control system for a concrete curing system based on MPC, characterized in that, include: Data acquisition unit: Used to collect actual temperature and humidity data at different locations within the concrete curing environment; Modeling unit: used to construct a dynamic temperature and humidity model by taking heating power and humidification power as inputs and temperature and humidity in the concrete curing environment as outputs; Weight adjustment unit: used to dynamically adjust the weight coefficients of the temperature and humidity dynamic model loss function according to the error between the actual temperature and humidity data and the output of the temperature and humidity dynamic model, so that when the error increases, the error weight of the temperature and humidity dynamic model loss function increases and / or the control weight decreases, and when the error decreases, the control weight of the temperature and humidity dynamic model loss function increases and / or the error weight decreases. Control unit: Used to solve for the optimal heating power and humidification power based on the adjusted loss function and the model predictive control algorithm, so as to realize the temperature and humidity control of the concrete curing environment.
9. A temperature and humidity control device for a concrete curing system based on MPC, characterized in that, The MPC-based concrete curing system temperature and humidity control device includes a processor and a memory. The memory stores at least one instruction, which is loaded and executed by the processor to implement the MPC-based concrete curing system temperature and humidity control method as described in any one of claims 1 to 7.
10. A computer storage medium, characterized in that, The computer storage medium stores at least one instruction, which is loaded and executed by a processor to implement the MPC-based temperature and humidity control method for concrete curing systems as described in any one of claims 1 to 7.