A temperature control method, device, intelligent device and computer readable storage medium

Abstract

The present application relates to temperature control technical field, especially a kind of temperature control method, device, intelligent equipment and computer readable storage medium.Control temperature method includes: for each temperature control unit, the cost function corresponding to its current temperature and target temperature is obtained, target control function is generated based on cost function and heating power;According to the thermal conductivity of temperature control panel, the thermal conductivity of heater, heating power and ambient temperature, the temperature dynamic equation of temperature control unit temperature change rate is generated;The power constraint condition and / or temperature constraint condition of temperature control panel is obtained, based on power constraint condition and / or temperature constraint condition, the heating power of target control function minimum under the condition of satisfying temperature dynamic equation is solved using optimal control method, the heating power of solution is used as the target heating power of heater.The present application can effectively improve the uniformity of heating, and control energy consumption.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and in particular to a temperature control method, apparatus, intelligent device, and computer-readable storage medium. Background Technology

[0002] The accuracy and stability of temperature control are crucial for ensuring the quality of heated components, especially for the heating, curing, and welding of power electronic components, as well as the baking and drying of power batteries. The quality of temperature control directly affects the consistency of product quality.

[0003] The commonly used temperature control method on the market is to place the temperature-controlling thermocouple in the heating field and use a closed-loop heating method. Closed-loop control can achieve real-time monitoring and adjustment of temperature, which can achieve good results for small heaters. However, for large heating bodies, when the heating area is large, it is difficult to achieve temperature control uniformity when using a single thermocouple for closed-loop temperature control due to differences in the heat absorption of the heated parts and the power distribution of the heating plate. Summary of the Invention

[0004] The technical problem to be solved by the embodiments of the present invention is to provide a temperature control method, device, intelligent device and computer-readable storage medium to solve the problem of uneven heating in the prior art.

[0005] This invention discloses a temperature control method applied to a temperature control panel. The temperature control panel includes at least one temperature control unit, and the temperature control unit includes at least one set of interconnected temperature sensors and a heater. The heater adjusts the heating power in real time according to the temperature feedback from the temperature sensors.

[0006] The temperature control method includes:

[0007] For each temperature control unit, obtain the cost function corresponding to its current temperature and target temperature, and obtain a preset target control function based on the cost function and the heating power;

[0008] A temperature dynamic equation for the temperature change rate of the temperature control unit is generated based on the thermal conductivity coefficient of the temperature control panel, the thermal conductivity coefficient of the heater, the heating power, and the ambient temperature.

[0009] Obtain the power constraints and / or temperature constraints of the temperature control panel. Based on the power constraints and / or temperature constraints, calculate the optimal heating power by combining the temperature dynamic equation and the target control function using the optimal control method, so as to complete the temperature regulation.

[0010] Optionally, the optimal control method is a dynamic programming algorithm;

[0011] The step of calculating the optimal heating power by combining the temperature dynamic equation and the target control function using the optimal control method includes:

[0012] The time step and boundary conditions are obtained, and a recursive relationship is obtained based on the target control function and the temperature dynamic equation. The relationship between the heating power of adjacent time steps is obtained based on the recursive relationship and the boundary conditions, and the heating power of the current time step is obtained based on the heating power of adjacent time steps.

[0013] Optionally, the step of obtaining the recursive relationship based on the target control function and the temperature dynamic equation includes:

[0014] The recursive relation is described by the Bellman equation, which is expressed as:

[0015] V i [t] = min(u i [t]·{c(T i [t],T i ^)+αu i [t] 2 +V i [t+1])}

[0016] Among them, u i [t] represents the value function of the temperature control unit i at time step t, and min represents the value of the control input variable u at time step t. i Find the minimum value within the range of [t]; c(T) i [T],T i ^) represents the current temperature T of the temperature control unit i at the current moment. i [t] and target temperature T i The cost of the difference between ^; αu i [t] 2 Indicates control of heating power u i The energy cost of [t]; V i [t+1] represents the value function of the temperature control unit i at the next time step t+1.

[0017] Optionally, the temperature dynamic equation is a discretized equation.

[0018] Optionally, the step of generating the temperature dynamic equation for the temperature change rate of the temperature control unit based on the thermal conductivity coefficient of the temperature control panel, the thermal conductivity coefficient of the heater, the heating power, and the ambient temperature includes:

[0019] A temperature disturbance term for the neighboring temperature control unit is added to the discretized temperature dynamic equation. The temperature dynamic equation with the added temperature disturbance term is:

[0020] T i [k+1]=T i [k]+Δt×(β[u i -γ(T i -T amp )])

[0021] Where k represents the k-th discrete time step, Δt represents the step size of each discrete time step, β is the thermal conductivity coefficient of the temperature control panel, γ is the thermal conductivity coefficient of the heater, and T amp It is the ambient temperature, u i It refers to the heating power.

[0022] Optionally, the step of adding the temperature disturbance term of the neighboring temperature control unit to the discretized temperature dynamic equation includes:

[0023] The weighting coefficients of the temperature disturbance term are obtained by at least one of the following methods: experimental measurement, numerical simulation, and optimization algorithm.

[0024] Optionally, a temperature control interference term of the neighboring temperature control unit is added to the target control function, and the coefficients of each term of the target control function are calculated by minimizing the target control function.

[0025] The target control function, incorporating a temperature control interference term, is:

[0026] J(u)=∑(γ1×u i 2 +γ2×(T i -T i ^) 2 +γ3×(T i -T i-1 -ΔT) 2 )

[0027] Among them, u i T represents the current heating power of the i-th temperature control unit. i T is the current temperature of the i-th temperature control unit. i ^ represents the target temperature, and ΔT represents the temperature difference between adjacent temperature control units. γ1, γ2, and γ3 are the weighting coefficients for energy consumption, temperature deviation, and interference terms between adjacent temperature control units, respectively.

[0028] The present invention also discloses a temperature control device applied to a temperature control panel, the temperature control panel including multiple temperature control units, each of the temperature control units including a temperature sensor and a heater connected to each other, the heater being able to adjust the heating power according to the current temperature fed back by the temperature sensor;

[0029] The temperature control device includes:

[0030] The invention relates to a temperature control panel, the temperature control panel including at least one temperature control unit, each temperature control unit including at least one set of interconnected temperature sensors and heaters, the heaters being able to adjust heating power according to the current temperature fed back by the temperature sensors;

[0031] The temperature control device includes:

[0032] The function module is used to obtain the cost function corresponding to the current temperature and the target temperature for each temperature-controlled unit, and generate the target control function based on the cost function and the heating power;

[0033] The equation module is used to generate a dynamic temperature equation based on the thermal conductivity coefficient of the temperature control panel, the thermal conductivity coefficient of the heater, the heating power, and the ambient temperature.

[0034] The solution module is used to obtain power constraints and / or temperature constraints, and based on the power constraints and / or temperature constraints, calculates the optimal heating power by combining the temperature dynamic equation and the target control function through the optimal control method, so as to complete the temperature regulation.

[0035] The present invention also discloses an intelligent device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0036] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0037] Compared with existing technologies, the temperature control method provided in this invention has the following advantages: Since each temperature control unit of the temperature control panel can be individually controlled, the uniformity of heating and the consistency of heating effect can be ensured. For each temperature control unit, a temperature dynamic equation for the rate of temperature change of the temperature control unit is established, which can model and predict temperature changes. Based on the cost function corresponding to the current temperature and the target temperature, a target control function is generated based on the cost function and the heating power. An optimal control method is used to solve for the heating power at which the target control function is minimized under the condition of satisfying the temperature dynamic equation. This achieves energy consumption optimization, that is, minimizing energy consumption while maintaining temperature control accuracy and ensuring heating uniformity. Simultaneously, power constraints and / or temperature constraints are considered. These constraints can be taken into account when solving the target control function to ensure that the obtained optimal heating power satisfies the constraints. This ensures that the temperature control unit operates within a safe and reliable range. Attached Figure Description

[0038] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0039] Figure 1 This is a schematic flowchart of an embodiment of the temperature control method provided by the present invention;

[0040] Figure 2 This is a schematic diagram of the structure of an embodiment of the temperature control panel provided by the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of an embodiment of the temperature control unit provided by the present invention;

[0042] Figure 4 This is a schematic diagram of an embodiment of the temperature control device provided by the present invention;

[0043] Figure 5 This is an internal structural diagram of a smart device in one embodiment of the present invention.

[0044] The labels for the attached figures are as follows:

[0045] 10. Temperature control panel; 11. Temperature control unit; 111. Temperature sensor; 112. Heater; 20. Temperature control device; 21. Function module; 22. Equation module; 23. Solver module. Detailed Implementation

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0047] Please refer to the following: Figure 1 , Figure 2 and Figure 3 , Figure 1 This is a schematic flowchart of an embodiment of the temperature control method provided by the present invention. Figure 2 This is a schematic diagram of an embodiment of the temperature control panel provided by the present invention. Figure 3 This is a schematic diagram of an embodiment of the temperature control unit provided by the present invention. Figure 2 As shown, the temperature control panel 10 includes multiple temperature control units 11, in Figure 2 In the scenario shown, there are M*N temperature control units 11, where M and N are both integers greater than 1, and the M*N temperature control units 11 are arranged in a matrix. Each temperature control unit 11 includes at least one set of interconnected temperature sensors 111 and heaters 112 to form a heating closed loop.

[0048] Temperature sensor 111 measures the current temperature of temperature control unit 11 and feeds this measurement back to heater 112. Heater 112 compares the current temperature with the set temperature, calculates the temperature error, and corrects the temperature deviation by adjusting the output of heater 112. If the current temperature is lower than the set temperature, the output of heater 112 is increased; if the current temperature is higher than the set temperature, the output of heater 112 is decreased or paused. Temperature sensor 111 continuously measures the temperature of temperature control unit 11 and feeds the new measurement back to heater 112. Heater 112 compares the new temperature measurement with the set temperature, calculates the new temperature error, and adjusts its output. This process is continuously repeated, keeping the temperature of temperature control unit 11 near the set value. This closed-loop heating system can monitor and adjust the temperature in real time to ensure the stability and accuracy of temperature control unit 11.

[0049] In this embodiment, each temperature control unit 11 is an independent heating closed-loop system. Each temperature control unit 11 has at least one set of temperature sensors 111 and heaters 112, which independently measure and adjust the temperature. The heating closed-loop system of each temperature control unit 11 autonomously adjusts the output of the heaters 112 according to the error between the current temperature and the set temperature, so that the temperature of the temperature control unit 11 is kept near the set value.

[0050] The temperature control method provided by this invention includes the following steps:

[0051] S101: For each temperature-controlled unit, obtain the cost function corresponding to its current temperature and target temperature, and generate the target control function based on the cost function and heating power.

[0052] In this embodiment, since each temperature control unit 11 can perform independent temperature control, a temperature control method is executed for each temperature control unit 11. In a specific implementation scenario, the cost function corresponding to its current temperature and target temperature is obtained, and a target control function is generated based on the cost function and heating power.

[0053] The temperature sensor 111 can obtain the real-time current temperature of the temperature control unit 11. The target temperature can be set by the user and input when the temperature control panel 10 is turned on. The cost function is defined based on the difference between the current temperature and the target temperature and is used to measure the performance of temperature control. A common form of the cost function is to use the square of the temperature deviation as the cost term, i.e., c(T) i T i ^)=(T i -T i ^) 2 , among which, T i It is the current temperature of the i-th temperature control unit 11, T i^ represents the target temperature of the i-th temperature control unit, c(T) i T i ^) represents the cost function of the difference between the current temperature and the target temperature of the i-th temperature control unit 11. This cost function measures the square of the difference between the temperature of each temperature control unit and the target temperature, i.e., the square of the temperature deviation. By squarening the temperature deviation, the magnitude of the temperature deviation can be highlighted more, making the control system more sensitive to temperature deviations. A heating power term is also introduced into the objective control function to consider energy consumption optimization in the optimal control problem.

[0054] In this embodiment, the target control function is:

[0055] J(u)=∑[c(T i ,T i ^)+αu i 2 ]

[0056] Among them, u i The current heating power of the i-th temperature control unit 11 can be balanced by multiplying the square of the heating power by the heating weight coefficient α, thereby achieving the goal of minimizing energy consumption.

[0057] S102: Generate a dynamic temperature equation based on the thermal conductivity coefficient of the temperature control panel, the thermal conductivity coefficient of the heater, the heating power, and the ambient temperature.

[0058] In a specific implementation scenario, the thermal conductivity coefficient β of the temperature control panel 10 and the thermal conductivity coefficient γ of the heater 112 are obtained, and the ambient temperature T is simultaneously collected. amp A temperature dynamic equation is generated based on the current heating power. This temperature dynamic equation describes the temperature change of each temperature control unit 11 over time. In this embodiment, the temperature dynamic equation is:

[0059] △T i =β[u i -γ(T i -T amp )]

[0060] △T i This represents the rate of temperature change of the i-th temperature control unit 11. The temperature dynamic equation expresses the relationship between the rate of temperature change, heating power, and temperature difference. By analyzing the temperature dynamic equation, a suitable control strategy can be designed to achieve the desired temperature control performance. The heater 112 can adjust the control input according to the parameters and state variables in the temperature dynamic equation, so that the temperature can reach the target temperature quickly and accurately.

[0061] The thermal conductivity coefficient β of the temperature control panel 10 and the thermal conductivity coefficient γ of the heater 112 can be obtained experimentally or by calculation. These are physical property parameters of the materials, used to describe their heat transfer characteristics. The values ​​of thermal conductivity coefficients β and γ typically depend on the material's composition, structure, and temperature. For some common materials, the values ​​of thermal conductivity coefficients can be found in material handbooks, thermophysical property databases, or relevant literature.

[0062] S103: Obtain power constraints and / or temperature constraints, and based on the power constraints and / or temperature constraints, use the optimal control method to solve for the heating power when the target control function is minimized, and use the solved heating power as the current heating power of the heater.

[0063] In a specific implementation scenario, power constraints and / or temperature constraints are obtained. These constraints are set based on the characteristics of the temperature control panel 10 and the user's actual needs. Power constraints can set upper and lower limits for heating power to ensure the control input is within a reasonable range. Temperature constraints can set upper and lower limits for temperature to ensure the temperature is within a safe range, preventing damage to the temperature control panel or the device being heated.

[0064] The objective control function and the temperature dynamic equation are linked together through the relationship between the system state and the control input. The objective control function is an index used to measure the system performance. By linking the temperature dynamic equation and the objective control function, an optimal control problem can be obtained: minimize the objective control function J(u) by adjusting the control input (heating power) while satisfying the temperature dynamic equation.

[0065] During the solution process, the optimization algorithm iteratively adjusts the magnitude of the control input (heating power) and simulates the response of the temperature control unit based on the temperature dynamic equation, calculating the value of the target control function. By continuously optimizing the control input (heating power), the optimal control input (target heating power) that minimizes the target control function is finally obtained.

[0066] As described above, in this embodiment, since each temperature control unit of the temperature control panel can be individually controlled, a temperature dynamic equation for the rate of temperature change of each unit can be established. This allows for modeling and prediction of temperature changes. Based on the cost function corresponding to the current temperature and the target temperature, a target control function is generated based on the cost function and the heating power. An optimal control method is then used to solve for the heating power that minimizes the target control function while satisfying the temperature dynamic equation. This optimizes energy consumption, minimizing energy consumption while maintaining temperature control accuracy. Furthermore, power constraints and / or temperature constraints are considered when solving the target control function, ensuring that the obtained optimal heating power meets these constraints. This guarantees that the temperature control unit operates within a safe and reliable range.

[0067] In another implementation scenario, a discretized temperature dynamic equation can be used to describe temperature changes, dividing the continuous time domain into discrete time intervals for simulating and calculating the changing processes of a dynamic system. The discretized temperature dynamic equation can be expressed as:

[0068] T i [k+1]=T i [k]+Δt×(β[u i -γ(T i -T amp )])

[0069] Where k represents the k-th discrete time step, Δt represents the step size of each discrete time step, β is the thermal conductivity coefficient of the temperature control panel 10, γ is the thermal conductivity coefficient of the heater 112, and T amp It refers to the ambient temperature. By discretizing the continuous-time temperature dynamic equation, the temperature state change of the temperature control panel 10 can be iteratively calculated at discrete time steps, thereby simulating and controlling the heating operation of the temperature control unit 11.

[0070] In practical temperature control systems, heat conduction exists between adjacent temperature control units; that is, a temperature change in one temperature control unit affects the temperature changes of its neighboring units. Therefore, in an implementation scenario, a temperature disturbance term from neighboring temperature control units is added to the discretized temperature dynamic equation. By introducing this disturbance, temperature changes within the temperature control units can be simulated and predicted more accurately. When the temperature of one temperature control unit changes, heat conduction between it and its neighboring units causes heat flow, which in turn affects the temperature changes of the neighboring units. Considering the disturbance from neighboring temperature control units better reflects the heat conduction effect in the actual system, improving the accuracy of temperature control.

[0071] The temperature dynamic equation after introducing the temperature disturbance term of the adjacent grid temperature control unit is:

[0072] T i [k+1]=T i [k]+Δt×(α i ×u i [k]-θ i ×(T i [k]-T amp )+λ×(T i-1 [k]-2×T i [k]+T i+1 [k])

[0073] Where k represents the k-th discrete time step, Δt represents the step size of each discrete time step, and θ i It is the heat loss of the i-th temperature control unit 11, T amp The ambient temperature is λ, which is the weighting coefficient for interference from neighboring temperature control units, and α is the ambient temperature. i It is the thermal conductivity coefficient of the i-th temperature control unit 11.

[0074] The weighting coefficient λ of the temperature disturbance term is obtained through at least one method: experimental measurement, numerical simulation, or optimization algorithm. Specifically, based on the physical characteristics and heat conduction principle of the temperature control panel 10, a theoretical model can be established to describe the heat conduction relationship between adjacent temperature control units. In the model, the weighting coefficient of the temperature disturbance term can be introduced as a parameter, and the values ​​of these parameters can be estimated through experiments or simulations to obtain the weighting coefficient.

[0075] The thermal conductivity relationship between adjacent temperature control units can also be obtained through experimental measurement. Experiments can be designed to measure the temperature changes between adjacent temperature control units 11 under different heating powers and initial temperatures, and the data can be analyzed to estimate the weighting coefficient of the temperature disturbance term.

[0076] Numerical simulations can also be performed using computational fluid dynamics (CFD) or heat conduction simulation software to simulate the temperature distribution of the temperature control panel 10 and the heat conduction relationship between adjacent temperature control units 11. By adjusting the weighting coefficients of the temperature disturbance term and comparing the simulation results with actual measurement data, appropriate weighting coefficients can be determined.

[0077] Optimization algorithms can also be used to estimate the weighting coefficients of disturbances. By collecting temperature data and control input data from actual operation, an optimal control problem is established, and optimization algorithms are used to solve for the optimal weighting coefficients, so that the temperature predicted by the model matches the actual measured temperature as closely as possible.

[0078] In other implementation scenarios, the discretized temperature dynamics equation can be expressed as:

[0079]

[0080] Where α is the thermal conductivity coefficient of the temperature control panel 10, β is the influence coefficient of heating power, and u i T is the heating power applied to the i-th temperature control unit. amp γ is the ambient temperature, and γ is the influence coefficient of ambient temperature.

[0081] In practical temperature control systems, heat transfer may occur between adjacent temperature control units, such as due to temperature gradients caused by non-uniform thermal conductivity of the heating plate's structure or materials. This heat transfer can lead to temperature interactions between adjacent temperature control units, thereby affecting the temperature distribution and stability of the entire temperature control panel 10. This interaction can be accounted for by adding a term representing interference from adjacent temperature control units to the target control function.

[0082] The target control function with added temperature control disturbance term is:

[0083] J(u)=Σ(γ1×u i 2 +γ2×(T i -T i ^) 2 +γ3×(T i -T i-1 -ΔT) 2 )

[0084] Among them, u i T represents the current heating power of the i-th temperature control unit 11. i It is the current temperature of the i-th temperature control unit 11, T i ^ represents the target temperature, and ΔT is the temperature difference between adjacent temperature control units 11. γ1, γ2, and γ3 are the weighting coefficients for energy consumption, temperature deviation, and interference terms between adjacent temperature control units, respectively.

[0085] After generating the target control function, temperature data and input heating power data are collected. During the operation of the temperature control panel 10, the temperature and corresponding heating power of each temperature control unit 11 are recorded. Using the collected temperature and heating power data, the parameters in the function are estimated by minimizing the target control function.

[0086] By adjusting the parameters in the model, the control input strategy and the weighting coefficients of disturbances can be changed, thereby minimizing the value of the objective control function. Minimizing the objective control function can be achieved using optimization algorithms, such as nonlinear programming and quadratic programming. These optimization algorithms automatically adjust the parameters based on the objective control function and constraints to reduce the value of the objective control function as much as possible. Through iterative calculations and parameter adjustments, the optimization algorithm gradually approaches the minimum value of the objective control function, thus obtaining the optimal control heating power strategy and the weighting coefficients of the disturbances.

[0087] By minimizing the objective control function to estimate the parameters in the model, the temperature control of the temperature control system can be made more accurate and stable. Adjusting the parameters can optimize the control input strategy, reduce temperature deviation, and take into account the temperature interactions between adjacent temperature control units. Simultaneously, minimizing the energy consumption term can reduce system energy consumption and improve energy efficiency. Through iterative optimization, the accuracy and performance of the model can be gradually improved to achieve a more optimized temperature control effect.

[0088] The optimized control input strategy is applied to the control panel, and new temperature data is collected for verification. The predicted temperature is compared with the actual measured temperature to evaluate the accuracy and feasibility of the formula. If there is a large deviation between the predicted and actual measured temperatures, parameters can be adjusted based on the verification results. The parameters in the formula are adjusted according to the verification results. In particular, the weighting coefficient γ3 of the disturbance from adjacent temperature control units 11 is adjusted, and the optimization solution is repeated until a satisfactory control effect is achieved. This process is iterative, continuously collecting data, building models, estimating parameters, and verifying models to gradually optimize the performance and accuracy of the temperature control panel.

[0089] After determining the parameters, the optimal control method is used to solve for the heating power at the minimum of the target control function under the condition of satisfying the temperature dynamic equation.

[0090] In one implementation scenario, the optimal control method is dynamic programming. Dynamic programming is a method that divides a problem into subproblems and solves them step by step. In the temperature control problem of the temperature control panel 10, time can be discretized, and the control process can be divided into multiple time steps. The length of the time step needs to be determined based on the specific requirements of the problem and the dynamic characteristics of the temperature control panel 10. Specifically, it can be set by referring to at least one of the following: system response speed, computational efficiency, control requirements, and system inertia.

[0091] Define value function V i [t], value function V i [t] refers to the temperature of a temperature control unit 11i in the temperature control panel 10 at time step t, starting from the current moment, through the optimal control input strategy, so that the temperature of the temperature control unit changes from the current temperature T. i [t] Reaching the target temperature T i ^ The desired value of the target control function J(u) to be minimized. Value function V i [t] can be viewed as a measure of the temperature control problem of the temperature control unit 11i starting from time step t, using the optimal control input strategy. It represents the degree of optimization to which the system can achieve the target temperature starting from the current moment using the optimal control strategy.

[0092] In dynamic programming algorithms, boundary conditions refer to the initial or termination conditions of the value function defined in the recurrence relation. In this implementation scenario, the value function V in the temperature control problem of the temperature control panel... i [t], the boundary conditions are set for the last time step. Specifically, in this implementation scenario, the boundary condition V i [T] = 0, where T represents the last time step, indicating that the value function is zero at the last time step. This boundary condition is set to ensure that the value function is at its minimum value at the last time step, meaning the target temperature has been reached and no further control is needed.

[0093] By setting boundary conditions V i When [t] = 0, the dynamic programming algorithm can be used to recursively calculate the value function and optimal control input strategy starting from the last time step. Thus, by recursively calculating from the last time step backwards, the optimal value function V for each temperature control unit i at each time step t can be obtained. i [t] and optimal control input strategy u i [t]*.

[0094] Based on the recurrence relation and boundary conditions, the relationship between heating power in adjacent time steps can be obtained, and the heating power in the current time step can be determined based on the heating power in adjacent time steps. By analyzing the recurrence relation, the minimum value function V can be found. i The optimal control input strategy (target heating power) u of [t] i [t]*. Once the optimal control input strategy (target heating power) u is determined... i [t] can determine the heating power of the current time step based on the relationship between the heating power of adjacent time steps.

[0095] Specifically, the optimal control input strategy u can be used. i The temperature dynamic equation [t] is used to calculate the temperature change between adjacent time steps. Then, based on the principles of temperature change and heat conduction, the relationship between heating power between adjacent time steps can be derived. This relationship can be used to determine the heating power for the current time step to achieve temperature control.

[0096] In one implementation scenario, the recurrence relation is described using the Bellman equation. The Bellman equation is used to relate the value function to the value function at the next time step. In the temperature control problem of the temperature control panel 10, the Bellman equation can be expressed as:

[0097] V i [t] = min(y i [t]·{c(T i [t],T i ^)+αui [t] 2 +V i [t+1])}

[0098] Among them, u i [t] represents the value function of temperature control unit i at time step t, that is, the value of the minimum target control function J(u) required to make the temperature control unit reach the target temperature through the optimal control input strategy. In the Bellman equation, min represents the value of the minimum target control function J(u) required to make the temperature control unit reach the target temperature through the optimal control input strategy. i The minimum value of [t] needs to be found within the range of possible control input strategies u. i Choose a strategy from [t] that minimizes the objective control function J(u).

[0099] c(T i [t],R i ^) represents the current temperature of temperature control unit i, the current temperature R. i [t] and target temperature R i The cost of the difference between ^. This item represents the degree to which the temperature is desired to be controlled near the target temperature. αu i [t] 2 Indicates the control input variable (heating power) u i [t] represents the energy cost. This term indicates the desired control input strategy (heating power) that minimizes energy consumption. V i [t+1] represents the value function for temperature control unit i at the next time step t+1. This term indicates that the control problem starting from the next time step has been considered and incorporated into the value function calculation for the current time step.

[0100] By solving the Bellman equation, the optimal value function V for each temperature control unit i at each time step t can be calculated recursively. i [t] and optimal control input strategy u i [t]*, thereby achieving precise temperature control and minimizing energy consumption.

[0101] Please see Figure 4 , Figure 4 This is a schematic diagram of an embodiment of the temperature control device provided by the present invention. The temperature control device 20 is applied to... Figure 2 The temperature control panel shown includes: function module 21, equation module 22, and solution module 23.

[0102] Function module 21 is used to obtain the cost function corresponding to the current temperature and the target temperature for each temperature-controlled unit, and generate a target control function based on the cost function and heating power; equation module 22 is used to generate a temperature dynamic equation based on the thermal conductivity coefficient of the temperature control panel, the thermal conductivity coefficient of the heater, the heating power and the ambient temperature; solution module 23 is used to obtain power constraints and / or temperature constraints, and calculate the optimal heating power based on the power constraints and / or temperature constraints by combining the temperature dynamic equation and the target control function through the optimal control method, so as to complete the temperature regulation.

[0103] The optimal control method is dynamic programming algorithm; the solution module 23 is used to obtain the time step and boundary conditions, obtain the recursive relationship based on the objective control function and temperature dynamic equation, obtain the relationship between the heating power of adjacent time steps based on the recursive relationship and boundary conditions, and thus obtain the heating power of the current time step based on the heating power of adjacent time steps.

[0104] Solver module 23 is used to describe the recurrence relation using the Bellman equation, which is expressed as:

[0105] V i [t] = min(u i [t]·{c(T i [t],T i ^)+αu i [t] 2 +V i [t+1])}

[0106] Among them, u i [t] represents the value function of the temperature control unit i at time step t, and min represents the value of the control input variable u at time step t. i Find the minimum value within the range of [t]; c(T) i [t],T i ^) represents the current temperature T of the temperature control unit i at the current moment. i [t] and target temperature T i The cost of the difference between ^; αu i [t] 2 Indicates control of heating power u i The energy cost of [t]; v i [t+1] represents the value function of the temperature control unit i at the next time step t+1.

[0107] The temperature dynamic equation is a discretized equation.

[0108] Equation module 22 is used to add a temperature disturbance term from the neighboring temperature control unit to the discretized temperature dynamic equation. The temperature dynamic equation with the added temperature disturbance term is:

[0109] T i [K+1]=t i [K]+ΔT×(β[u i -γ(T i -T amp )])

[0110] Where k represents the k-th discrete time step, Δt represents the step size of each discrete time step, β is the thermal conductivity coefficient of the temperature control panel, γ is the thermal conductivity coefficient of the heater, and T amp It is the ambient temperature, u i It refers to the heating power.

[0111] Equation module 22 is used to obtain the weighting coefficients of the temperature disturbance term through at least one of the following methods: experimental measurement, numerical simulation, and optimization algorithm.

[0112] The solver module 23 is used to add the temperature control disturbance term of the neighboring temperature control unit to the target control function, and calculate the coefficients of each term of the target control function by minimizing the target control function;

[0113] The temperature control interference term of the neighboring temperature control unit is added to the target control function, and the coefficients of each term of the target control function are calculated by minimizing the target control function.

[0114] The target control function, incorporating a temperature control interference term, is:

[0115] J(u)=∑(γ1×u i 2 +γ2×(T i -T i ^) 2 +γ3×(T i -T i-1 -ΔT) 2 )

[0116] Among them, u i T represents the current heating power of the i-th temperature control unit. i T is the current temperature of the i-th temperature control unit. i ^ represents the target temperature, and Δt represents the temperature difference between adjacent temperature control units. γ1, γ2, and γ3 are the weighting coefficients for energy consumption, temperature deviation, and interference terms between adjacent temperature control units, respectively.

[0117] As described above, in this embodiment, since each temperature control unit of the temperature control panel can be individually controlled, a temperature dynamic equation for the rate of temperature change of each unit can be established. This allows for modeling and prediction of temperature changes. Based on the cost function corresponding to the current temperature and the target temperature, a target control function is generated based on the cost function and the heating power. An optimal control method is then used to solve for the heating power that minimizes the target control function while satisfying the temperature dynamic equation. This optimizes energy consumption, minimizing energy consumption while maintaining temperature control accuracy. Furthermore, power constraints and / or temperature constraints are considered when solving the target control function, ensuring that the obtained optimal heating power meets these constraints. This guarantees that the temperature control unit operates within a safe and reliable range.

[0118] Please see Figure 5 , Figure 5 An internal structural diagram of a smart device in one embodiment is shown. This smart device can specifically be a terminal or a server. Figure 5 As shown, the smart device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement the temperature control method described above. The internal memory may also store a computer program. When executed by the processor, this computer program enables the processor to implement an age recognition method. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the solution of this application and does not constitute a limitation on the smart device to which the solution of this application is applied. A specific smart device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0119] In one embodiment, a smart device is provided, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform... Figure 1 The steps are shown.

[0120] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform... Figure 1 The steps are shown.

[0121] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] It should be understood that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Those skilled in the art can modify the technical solutions described in the above embodiments, or make equivalent substitutions for some of the technical features; and all such modifications and substitutions should fall within the protection scope of the appended claims of the present invention.

Claims

1. A temperature regulation method, characterized in that, The invention relates to a temperature control panel, the temperature control panel including at least one temperature control unit, the temperature control unit including at least one set of interconnected temperature sensors and a heater, the heater adjusting the heating power in real time according to the temperature feedback from the temperature sensors; The temperature regulation method includes: For each temperature control unit, a cost function corresponding to its current temperature and target temperature is obtained. Based on the cost function and the heating power, a preset target control function is obtained; the target control function is: in, The current heating power of the i-th temperature control unit 11 It is the current temperature of the i-th temperature control unit 11. It is the target temperature. It is the temperature difference between adjacent temperature control units 11. , and These are the weighting coefficients for energy consumption, temperature deviation, and interference from adjacent temperature control units, respectively. A temperature dynamic equation for the temperature change rate of the temperature control unit is generated based on the thermal conductivity coefficient of the temperature control panel, the thermal conductivity coefficient of the heater, the heating power, and the ambient temperature. Obtain the power constraint and / or temperature constraint of the temperature control panel, and calculate the optimal heating power based on the power constraint and / or temperature constraint by combining the temperature dynamic equation and the target control function through the optimal control method to complete the temperature regulation; The step of calculating the optimal heating power by combining the temperature dynamic equation and the target control function using the optimal control method includes: Collect temperature data and corresponding heating power data for each temperature control unit; The heating power is adjusted iteratively by optimizing the algorithm, and the response of the temperature control unit is simulated according to the temperature dynamic equation to calculate the value of the target control function; The heating power is optimized based on the collected temperature data and heating power data, and the weight coefficients of the control input strategy and disturbance terms are estimated and adjusted by minimizing the target control function, thereby obtaining the optimal heating power that minimizes the target control function; The optimized control input strategy is applied to the temperature control panel, and new temperature data is collected for verification. The predicted temperature is compared with the actual measured temperature. If there is a deviation between the predicted temperature and the actual measured temperature, the parameters in the target control function are adjusted, and the optimization solution is iterated again to complete the temperature regulation.

2. The temperature regulation method according to claim 1, characterized in that, The optimal control method is a dynamic programming algorithm; The step of calculating the optimal heating power by combining the temperature dynamic equation and the target control function using the optimal control method includes: The time step and boundary conditions are obtained, and a recursive relationship is obtained based on the target control function and the temperature dynamic equation. The relationship between the heating power of adjacent time steps is obtained based on the recursive relationship and the boundary conditions, and the heating power of the current time step is obtained based on the heating power of adjacent time steps.

3. The temperature regulation method according to claim 2, characterized in that, The step of obtaining the recursive relationship based on the target control function and the temperature dynamic equation includes: The recursive relation is described by the Bellman equation, which is expressed as: in, This represents the value function of the temperature control unit i at time step t, where min represents the value of the control input variable. Find the minimum value within the range of values; This indicates the current temperature of the temperature control unit i at the current moment. With target temperature The cost of the difference between them; Indicates control of heating power The energy cost; This represents the value function of the temperature control unit i at the next time step t+1.

4. The temperature regulation method according to claim 1, characterized in that, The temperature dynamic equation is a discretized equation.

5. The temperature regulation method according to claim 4, characterized in that, The step of generating the temperature dynamic equation for the temperature change rate of the temperature control unit based on the thermal conductivity coefficient of the temperature control panel, the thermal conductivity coefficient of the heater, the heating power, and the ambient temperature includes: A temperature disturbance term for the neighboring temperature control unit is added to the discretized temperature dynamic equation. The temperature dynamic equation with the added temperature disturbance term is: Where k represents the k-th discrete time step, Δt represents the step size of each discrete time step, β is the thermal conductivity coefficient of the temperature control panel, and γ is the thermal conductivity coefficient of the heater. It is the ambient temperature. It refers to the heating power.

6. The temperature regulation method according to claim 5, characterized in that, The step of adding the temperature disturbance term of the neighboring temperature control unit to the discretized temperature dynamic equation includes: The weighting coefficients of the temperature disturbance term are obtained by at least one of the following methods: experimental measurement, numerical simulation, and optimization algorithm.

7. The temperature regulation method according to claim 4, characterized in that, The step of generating a target control function based on the cost function and the heating power includes: The temperature control interference term of the neighboring temperature control unit is added to the target control function, and the coefficients of each term of the target control function are calculated by minimizing the target control function.

8. A temperature control device, characterized in that, The invention relates to a temperature control panel, the temperature control panel including at least one temperature control unit, each temperature control unit including at least one set of interconnected temperature sensors and heaters, the heaters being able to adjust heating power according to the current temperature fed back by the temperature sensors; The temperature control device includes: The function module is used to obtain the cost function corresponding to the current temperature and the target temperature for each temperature-controlled unit, and generate a target control function based on the cost function and the heating power; the target control function is: in, The current heating power of the i-th temperature control unit 11 It is the current temperature of the i-th temperature control unit 11. It is the target temperature. It is the temperature difference between adjacent temperature control units 11. , and These are the weighting coefficients for energy consumption, temperature deviation, and interference from adjacent temperature control units, respectively. The equation module is used to generate a dynamic temperature equation based on the thermal conductivity coefficient of the temperature control panel, the thermal conductivity coefficient of the heater, the heating power, and the ambient temperature. The solution module is used to obtain power constraints and / or temperature constraints, and based on the power constraints and / or temperature constraints, calculate the optimal heating power by combining the temperature dynamic equation and the target control function through the optimal control method to complete the temperature regulation; The step of calculating the optimal heating power by combining the temperature dynamic equation and the target control function using the optimal control method includes: Collect temperature data and corresponding heating power data for each temperature control unit; The heating power is adjusted iteratively by optimizing the algorithm, and the response of the temperature control unit is simulated according to the temperature dynamic equation to calculate the value of the target control function; The heating power is optimized based on the collected temperature data and heating power data, and the weight coefficients of the control input strategy and disturbance terms are estimated and adjusted by minimizing the target control function, thereby obtaining the optimal heating power that minimizes the target control function; The optimized control input strategy is applied to the temperature control panel, and new temperature data is collected for verification. The predicted temperature is compared with the actual measured temperature. If there is a deviation between the predicted temperature and the actual measured temperature, the parameters in the target control function are adjusted, and the optimization solution is iterated again to complete the temperature regulation.

9. A smart device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The device stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

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