Self-adaptive energy-saving control method and system for water-cooling structure heat

Abstract

The invention provides a self-adaptive energy-saving control method and system for a water cooling structure heat, and relates to the technical field of energy-saving control, and the method comprises the steps: carrying out the sensing of a heating layer in the heat through a temperature sensor, and outputting a temperature sensing data set; analyzing the temperature sensing data set to obtain a real-time temperature index, and if the real-time temperature index is greater than or equal to a preset temperature index, performing heat conduction analysis on the temperature sensing data set to determine a heat conduction area of the heating layer; self-adaptive energy-saving optimization of a limited space is carried out on the cooling layer in the heat conduction area, and a first response result including the first sliding position is output; the guide rail mechanism controls the cooling layer to slide according to the first sliding position. The technical problems that in the prior art, due to the fact that careful analysis on the heat conduction condition of the heating layer is lacked and the position of the cooling layer is fixed, the cooling effect is uneven, and energy consumption is high are solved, and the technical effects of improving the temperature control precision and reducing energy consumption are achieved.

Description

Technical Field

[0001] The present application relates to the field of energy-saving control technology, and in particular to an adaptive energy-saving control method and system for a water-cooled structure heater. Background Art

[0002] Heaters are common thermal management devices and are widely used in various high-temperature environments. In order to improve heating efficiency and reduce energy waste, many modern heaters use water-cooling structures to achieve more efficient heat dissipation and temperature control. The water-cooling structure usually consists of a heating layer and a cooling layer, in which the cooling layer uses a cooling medium (such as water) to remove excess heat generated during the heating process to ensure that the equipment can still maintain stable operation under high temperature conditions.

[0003] However, existing water-cooled structure heaters usually adopt a fixed-position cooling layer design. This design method fails to fully consider the heat conduction and temperature changes of the heating layer, resulting in uneven cooling effects. For example, in some areas, due to differences in heat conduction, the fixed position of the cooling layer may cause some areas to be overcooled or undercooled, thus affecting the heating effect and overall energy efficiency. In addition, most of the existing heater control methods are based on simple temperature thresholds for switch control, lack a detailed analysis of the heat conduction of the heating layer, and cannot be dynamically adjusted according to actual temperature changes, resulting in energy waste and reduced temperature control accuracy. Summary of the invention

[0004] The present application provides an adaptive energy-saving control method and system for a water-cooled structure heater, which solves the technical problems in the prior art of uneven cooling effect and high energy consumption due to the lack of detailed analysis of the heat conduction of the heating layer and the fixed position of the cooling layer, thereby achieving the technical effect of improving temperature control accuracy and reducing energy consumption.

[0005] In view of the above problems, on the one hand, the present application provides an adaptive energy-saving control method for a water-cooled structure heater, the method comprising: a water-cooling structure is provided inside the heater, the water-cooling structure comprises a heating layer and a cooling layer, the cooling layer comprises a guide rail mechanism, and the cooling layer is adjusted to slide in a vertical direction by the guide rail mechanism; the heating layer is sensed according to a temperature sensor, and a temperature sensing data set is output; the temperature sensing data set is analyzed to obtain a real-time temperature index, and if the real-time temperature index is greater than or equal to a preset temperature index, a heat conduction analysis is performed on the temperature sensing data set to determine the heat conduction area of ​​the heating layer; the cooling layer is adaptively optimized for energy saving in a limited space in the heat conduction area, and a first response result is output, the first response result comprises a first sliding position; the guide rail mechanism controls the cooling layer to slide according to the first sliding position.

[0006] On the other hand, the present application also provides an adaptive energy-saving control system for a water-cooled structure heater, the system comprising: a temperature sensing module, for sensing the heating layer according to a temperature sensor, and outputting a temperature sensing data set; a heat conduction analysis module, for analyzing the temperature sensing data set, and obtaining a real-time temperature index; if the real-time temperature index is greater than or equal to a preset temperature index, performing a heat conduction analysis on the temperature sensing data set, and determining a heat conduction area of ​​the heating layer; an energy-saving optimization module, for performing adaptive energy-saving optimization in a limited space on the cooling layer in the heat conduction area, and outputting a first response result, the first response result comprising a first sliding position; and a sliding control module, for controlling the guide rail mechanism to control the cooling layer to slide according to the first sliding position.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: A water-cooling structure including a heating layer and a cooling layer is provided inside the heater, wherein the cooling layer slides in the vertical direction through a guide rail mechanism. This structure makes it possible to dynamically adjust the position of the cooling layer according to different situations in the future. Through the dynamic adjustment of the guide rail mechanism, the cooling effect can be accurately controlled according to the actual temperature of the heating layer. The temperature change of the heating layer is monitored in real time by a temperature sensor, and a temperature sensing data set is output to accurately obtain the real-time temperature of the heating layer, thereby providing data support for subsequent heat conduction analysis. The temperature sensing data set is analyzed to obtain a real-time temperature index. If the real-time temperature index is greater than or equal to the preset temperature index, a heat conduction analysis is performed on the temperature sensing data set to determine the heat conduction area of ​​the heating layer. This step is an accurate judgment of the thermal state of the heating layer, which can ensure that the position of the cooling layer is adjusted for the overheated area, thereby improving the accuracy of the cooling effect. After determining the heat conduction area, the position of the cooling layer is adaptively optimized in a limited space to find the best sliding position. The position of the cooling layer is optimized through an adaptive algorithm, and dynamic adjustment is effectively achieved, thereby reducing unnecessary energy consumption and improving cooling efficiency. The guide mechanism controls the sliding position of the cooling layer according to the first response result after adaptive optimization, achieves optimal cooling of the heat conduction area, ensures uniform cooling effect and avoids energy waste.

[0008] In summary, the present application sets up a special water-cooling structure inside the heater, uses temperature sensors to obtain data, deeply analyzes the temperature sensor data set to determine the heat conduction area, and then performs adaptive energy-saving optimization to obtain the optimal cooling layer sliding position. Finally, the guide rail mechanism controls the sliding of the cooling layer, thereby achieving precise temperature control and efficient energy-saving management of the water-cooled structure heater, which not only improves the temperature control accuracy, but also significantly reduces energy consumption.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of an adaptive energy-saving control method for a water-cooled structure heater provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of the structure of a heater in an adaptive energy-saving control method for a water-cooled structure heater provided in an embodiment of the present application.

[0012] Figure 3 A schematic diagram of a flow chart of adaptive energy-saving optimization of a cooling layer in a limited space in a heat conduction area in an adaptive energy-saving control method for a water-cooled structure heater provided in an embodiment of the present application.

[0013] Figure 4 A schematic diagram of the structure of an adaptive energy-saving control system for a water-cooled structure heater provided in an embodiment of the present application.

[0014] Explanation of reference numerals: cooling layer 10 , heating layer 20 , temperature sensing module 30 , heat conduction analysis module 40 , energy-saving optimization module 50 , sliding control module 60 . DETAILED DESCRIPTION

[0015] The embodiment of the present application provides an adaptive energy-saving control method and system for a water-cooled structure heater, thereby solving the technical problems in the prior art of uneven cooling effect and high energy consumption due to the lack of detailed analysis of the heat conduction of the heating layer and the fixed position of the cooling layer, thereby achieving the technical effect of improving temperature control accuracy and reducing energy consumption.

[0016] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides an adaptive energy-saving control method for a water-cooled structure heater, the method comprising: Step S1: a water cooling structure is provided inside the heater, the water cooling structure comprises a heating layer and a cooling layer, the cooling layer comprises a guide rail mechanism, and the cooling layer is adjusted to slide in a vertical direction by the guide rail mechanism.

[0017] Specifically, a heater is a heating device whose main function is to increase the temperature of the surrounding environment or objects by converting electrical energy or other energy into thermal energy. Figure 2As shown, a water cooling structure is designed inside the heater, including a heating layer 20 and a cooling layer 10. The heating layer 20 includes a heating element, which converts electrical energy into thermal energy for heating the equipment. The heating layer 20 is close to the use surface of the heater. The cooling layer 10 takes away the excess heat generated by the heating layer 20 by contacting with the water flow. The cooling layer 10 is close to the atmospheric end, located between the atmospheric end and the heating layer 20, and can slide in the vertical direction through the control of the guide rail mechanism. For example, a linear guide rail and a slider can be used to realize the sliding of the cooling layer 10. By adjusting the position of the cooling layer 10, the cooling effect can be accurately controlled according to the temperature change of the heating layer 20.

[0018] Step S2: sensing the heating layer 20 using a temperature sensor and outputting a temperature sensing data set.

[0019] Specifically, a plurality of temperature sensors, such as thermocouples, thermistors, infrared temperature sensors, etc., are arranged around the heating layer 20. These sensors monitor the temperature of the heating layer 20 in real time and transmit the data to the control unit. For example, a thermocouple sensor can be used to transmit the temperature data to a computer or a microcontroller through a data acquisition card. These data are collected and form a temperature sensing data set. The temperature sensing data set includes temperature values ​​at multiple time points, which can be used to analyze the temperature change trend. By monitoring the temperature of the heating layer 20 in real time, accurate data support can be provided for subsequent temperature control and position adjustment of the cooling layer 10.

[0020] Step S3: Analyze the temperature sensing data set to obtain a real-time temperature index. If the real-time temperature index is greater than or equal to a preset temperature index, perform heat conduction analysis on the temperature sensing data set to determine a heat conduction area of ​​the heating layer 20 .

[0021] Specifically, the real-time temperature index refers to the temperature value or related parameters obtained by the temperature sensor at the current moment and after certain processing, which reflects the actual temperature state of the heating layer 20 at a certain moment. The preset temperature index is a pre-set temperature threshold used to determine whether cooling is required, usually based on the safe operating temperature or optimal operating temperature setting of the heater. The heat conduction area refers to the area in the heater where heat transfer is concentrated and the temperature is higher than the threshold, which is used to guide the optimization of the sliding position of the cooling layer 10.

[0022] The temperature sensing data set outputted in step S2 is analyzed to extract the current real-time temperature index. The real-time temperature index is compared with the preset temperature index. If the real-time temperature index is greater than or equal to the preset temperature index, the heat conduction analysis is started. The heat conduction analysis uses mathematical models and algorithms to determine the heat conduction areas of the heating layer 20 according to the heat distribution of the heating layer 20. These areas have concentrated heat transfer and temperatures above the threshold, and require stronger cooling support.

[0023] Through precise temperature monitoring and heat conduction analysis, the high-temperature area in the heating layer 20 can be accurately identified, providing a basis for the precise positioning of the cooling layer 10, thereby improving the uniformity of the cooling effect and the accuracy of temperature control.

[0024] Step S4: performing limited space adaptive energy-saving optimization on the cooling layer 10 in the heat conduction region, and outputting a first response result, wherein the first response result includes a first sliding position.

[0025] Specifically, the first response result is an adjustment suggestion for the cooling layer 10 obtained based on the optimization result, and includes the sliding position of the cooling layer 10 in the vertical direction, that is, the first sliding position.

[0026] In the determined heat conduction area, through calculation and optimization algorithms, the influence of the distance between the cooling layer 10 and the heating layer 20 on the cooling effect and energy consumption, the cooling water flow rate, the water temperature and other factors are taken into consideration, and the position of the cooling layer 10 in the limited space is adjusted to achieve the best effect of energy saving and temperature control. The optimization process can use genetic algorithms or particle swarm optimization algorithms to find the optimal sliding position of the cooling layer 10. These algorithms gradually optimize the position of the cooling layer 10 by simulating natural selection or group behavior, and finally output the first response result. Taking the genetic algorithm as an example, the different positions of the cooling layer 10 are regarded as individuals. By simulating the biological evolution process, after multiple generations of crossover, mutation and other operations, the position of the cooling layer 10 with the lowest energy consumption is found. This position is the first sliding position in the first response result.

[0027] Through the adaptive optimization algorithm, the position of the cooling layer 10 can be adjusted within a limited space to optimize the temperature control accuracy and reduce energy consumption, ensuring that the entire heater system operates in the most energy-efficient manner.

[0028] Step S5: the guide rail mechanism controls the cooling layer 10 to slide according to the first sliding position.

[0029] Specifically, according to the first sliding position output by the adaptive energy-saving optimization algorithm, the guide rail mechanism controls the cooling layer 10 to slide. For example, a stepper motor can be used to drive the guide rail mechanism to accurately control the movement of the cooling layer 10. The stepper motor moves the cooling layer 10 to the first sliding position according to the instruction of the control unit. By accurately controlling the position of the cooling layer 10, dynamic cooling is achieved, the cooling efficiency and energy-saving effect are improved, and the temperature of the heating layer 20 is ensured to always remain within a preset range, thereby improving the accuracy of temperature control and ensuring energy-saving effect.

[0030] Further, such as Figure 3 As shown, step S4 includes: Step S41: obtaining the adjustable area of ​​the cooling layer 10 inside the heater.

[0031] Step S42: obtaining an overlapping area between the heat conduction area and the adjustable area, and performing random sampling in the overlapping area to obtain a sliding position particle group, wherein each particle represents a sliding position of the cooling layer 10 .

[0032] Step S43: constructing an adaptive optimization model, wherein the adaptive optimization model is used to perform gradient optimization on the cooling layer 10 in the sliding position particle group and output a first response result.

[0033] Specifically, by reading or measuring the design parameters of the internal structure of the heater, the boundary position of the cooling layer 10 in the vertical direction is determined, thereby obtaining the adjustable area in which the cooling layer 10 can move. This adjustable area is the spatial range in which the cooling layer 10 can be adjusted (here, vertical sliding) inside the heater. Clarifying the adjustable range of the cooling layer 10 provides a spatial constraint for the subsequent search for the optimal sliding position, which helps to improve the efficiency and accuracy of the optimization.

[0034] After obtaining the adjustable area of ​​the cooling layer 10, the overlapping part of the two is calculated in combination with the heat conduction area obtained by the heat conduction analysis to determine the overlapping area. The heat conduction area is a local range of the heating layer 20, and the adjustable area of ​​the cooling layer 10 is a specific vertical spatial range. The intersection of the two is the overlapping area. In this overlapping area, sampling is performed according to certain random rules. For example, a computer program can be used to generate random numbers, which correspond to the position coordinates in the vertical direction. The positions corresponding to these coordinates are defined as particles (sliding positions), thereby obtaining a sliding position particle group. By determining the overlapping area and randomly sampling therein to obtain a sliding position particle group, the scope of optimization is narrowed, focusing on the area related to the heat conduction area and within the adjustable range, improving the targeted optimization, and reducing unnecessary calculations.

[0035] When constructing an adaptive optimization model, first determine the objective function, which is related to factors such as the energy consumption and cooling effect of the cooling layer 10 at different sliding positions. For example, the objective function can be a function of energy consumption, which is related to factors such as the distance from the cooling layer 10 to the center of the heat conduction area. Then calculate the gradient of the objective function based on the particles in the sliding position particle group (i.e., the sliding position). Mathematical calculation software (such as Matlab, etc.) or a special optimization algorithm library can be used to implement the gradient calculation and optimization process. According to the gradient direction, the particles (sliding position) are continuously adjusted until the optimal sliding position that meets the stop condition (such as reaching a certain number of iterations or the convergence of the objective function value) is found. This position is the first response result. By constructing an adaptive optimization model for gradient optimization, the most energy-saving sliding position of the cooling layer 10 can be found while meeting the cooling requirements, thereby improving energy efficiency and temperature control accuracy.

[0036] Further, step S43 includes: Step S431: obtaining cooling control parameters of the cooling layer 10, wherein the cooling control parameters include cooling flow, cooling temperature and cooling medium.

[0037] Step S432: define a first group of input samples with the sliding position particle group, define a second group of input samples with the cooling control parameters, and define label samples of cooling effects for function fitting to generate a cooling objective function, wherein the cooling objective function is used to maximize the cooling effect of the cooling layer 10.

[0038] Step S433: the adaptive optimization model receives the first optimization instruction, takes the real-time cooling control parameter as input to quantitatively perform gradient optimization in the sliding position particle swarm, and outputs a first response result that satisfies the cooling objective function.

[0039] Specifically, the cooling flow rate is the volume of the cooling medium (such as coolant) flowing through the cooling layer 10 per unit time in the cooling layer 10. The cooling temperature is the initial temperature of the cooling medium when it enters the cooling layer 10. The cooling medium is a substance used to absorb heat in the cooling system to achieve the purpose of cooling. Common cooling media include water, air, etc. In special industrial environments or high-performance equipment, special cooling media such as liquid nitrogen are also used. The cooling control parameters such as the cooling flow rate, cooling temperature and physical properties of the cooling medium of the cooling layer 10 are collected in real time through sensors or control systems. These cooling control parameters directly affect the cooling efficiency and cooling effect. For the cooling flow rate, its value can be obtained by a flow sensor installed on the cooling pipe; the cooling temperature can be measured by a temperature sensor located at the inlet of the cooling medium; the type of cooling medium is usually determined when the cooling system is designed, and can be determined by checking the technical documentation or system identification of the equipment.

[0040] When constructing the cooling objective function, we must first determine the input samples used to provide independent variable data and the label samples used to represent the expected output results. Among them, the input samples are divided into a first group of input samples (sliding position particle group, representing the possible sliding position of the cooling layer 10) and a second group of input samples (cooling control parameters, reflecting various cooling conditions). According to the first group of input samples, the second group of input samples and the label samples, mathematical or statistical methods are used to fit the function, such as using a multivariate linear regression method. The sliding position particle group is represented by vector x, the cooling control parameter is represented by vector y, and the cooling effect is represented by z. The multivariate linear regression model can be expressed as z=β0+β1x1+β2x2+…+β n x n +γ1y1+γ2y2+…+γ m y m +ϵ, where β0, β1, β2, β n and γ1, γ2, γ m is the coefficient determined by fitting, and ϵ is the error term. By minimizing the error term, a suitable coefficient is found to generate a cooling objective function. The purpose of this cooling objective function is to find a combination of sliding position and cooling control parameters so that the cooling effect of the cooling layer 10 is maximized. By generating the cooling objective function through function fitting, the quantitative relationship between the cooling effect and the sliding position particle group and the cooling control parameters can be clarified, providing a target orientation for subsequent optimization operations.

[0041] In the optimization process, the control of the cooling layer 10 is divided into two aspects, one is the adjustment of the position of the cooling layer 10, and the other is the adjustment of the cooling control parameters, which together determine the final cooling effect. When the cooling layer 10 is working, there is usually an initial cooling control parameter. Since the adjustment of the cooling control parameters (cooling flow, cooling temperature and cooling medium) is more complicated than the adjustment of the position of the cooling layer 10, involving flow adjustment, temperature adjustment, medium replacement and other aspects, and consuming more energy, in the optimization process, firstly, the position of the cooling layer 10 is optimized while keeping the initial cooling control parameters unchanged, and the position of the cooling layer 10 that can meet the cooling effect is found. When the position adjustment alone does not meet the cooling requirements of the heating layer 20, the cooling control parameters are optimized to save energy consumption during the adjustment process.

[0042] The first optimization instruction is a signal used to start the adaptive optimization model to start the optimization process. This instruction contains some initial setting information, such as the accuracy requirements of the optimization, the upper limit of the number of iterations, etc. The real-time cooling control parameter is the cooling control parameter at the current actual operating moment, which reflects the current working state of the cooling system. After receiving the first optimization instruction, the adaptive optimization model uses the real-time cooling control parameter as a fixed input. For example, the real-time cooling flow is 5L / min, the cooling temperature is 20°C, and the cooling medium is water. These cooling control parameter values ​​are input into the cooling objective function, and then the gradient of each sliding position particle (i.e., each possible sliding position of the cooling layer 10) is calculated according to the cooling objective function. Mathematical calculation software (such as Matlab, etc.) or a special optimization algorithm library can be used to calculate the gradient. The sliding position particles are continuously adjusted along the gradient direction until a sliding position that satisfies the cooling objective function (i.e., maximizing the cooling effect) is found. This position is the first response result. By using the real-time cooling control parameter as an input quantitatively to perform gradient optimization in the sliding position particle group, the sliding position that maximizes the cooling effect of the cooling layer 10 under the current cooling conditions can be found, thereby improving the efficiency and effectiveness of the cooling system.

[0043] Furthermore, after step S433, the following steps are further included: Step S434: obtaining a first cooling effect corresponding to the first response result.

[0044] Step S435: If the first cooling effect is greater than or equal to a preset threshold, output a first response result.

[0045] Step S436: If the first cooling effect is less than the preset threshold, a second optimization instruction is obtained, and the adaptive optimization model receives the second optimization instruction. The adaptive optimization model receives the second optimization instruction and performs segmented optimization on the cooling control parameters and the sliding position to obtain an updated first sliding position.

[0046] Specifically, after optimizing the position of the cooling layer 10, the cooling effect is further verified, and the sliding position and cooling control parameters in the first response result are substituted into the constructed cooling objective function for calculation to obtain the first cooling effect corresponding to the first response result. By calculating the first cooling effect, it is possible to evaluate whether the first response result meets the cooling requirements, thereby determining whether to accept the first response result or perform further optimization.

[0047] The preset threshold is a critical value of a cooling effect that is set in advance. For example, in a specific heater cooling system, a minimum standard value of the cooling effect is set as the preset threshold according to the operating requirements, safety standards or production process requirements of the equipment. The calculated first cooling effect is compared with the preset threshold. If the first cooling effect is greater than or equal to the preset threshold, it means that the first response result meets the cooling requirements. Directly outputting the first response result can ensure that the cooling system operates normally while meeting the requirements, avoid unnecessary further optimization operations, and improve the system operation efficiency.

[0048] When the first cooling effect is less than the preset threshold, it indicates that the first response result does not meet the cooling demand, and the second optimization instruction is obtained at this time. This second optimization instruction is an instruction triggered when the first cooling effect is less than the preset threshold, which is used to allow the adaptive optimization model to perform optimization again. This instruction contains some parameters or optimization strategy information that are different from the first optimization, such as a looser optimization range or a different number of iterations. After receiving the second optimization instruction, the adaptive optimization model performs segmented optimization on the cooling control parameters and the sliding position. The process of segmented optimization is to first adjust one variable to ensure that it can maximize the cooling effect, and then adjust another variable to achieve the optimal cooling effect. For example, if the cooling control parameters include cooling flow, cooling temperature, etc., and the sliding position is the position of the cooling layer 10 in the vertical direction, the cooling flow is first fixed during optimization, the cooling temperature and the sliding position are optimized, and then the cooling temperature is fixed, and the cooling flow and the sliding position are optimized. Through this segmented optimization strategy, the parameters are gradually adjusted until the optimal solution that meets the preset threshold is found, and the updated first sliding position is finally obtained.

[0049] By verifying the first cooling effect corresponding to the first response result and performing segmented optimization, we can explore the optimization space of cooling control parameters and sliding positions more comprehensively and deeply, increase the possibility of finding the best solution to meet cooling needs, ensure the best cooling effect with the lowest energy consumption, and significantly improve the energy-saving performance and cooling efficiency of the heater system.

[0050] Further, in step S3, heat conduction analysis is performed on the temperature sensing data set to determine the heat conduction area of ​​the heating layer 20, including: Step S31: collecting a temperature sensing sample data set of the heating layer 20, and generating a continuous two-dimensional temperature field of the heating layer 20 based on an interpolation algorithm.

[0051] Step S32: Calculate the heat flux density distribution of the continuous two-dimensional temperature field.

[0052] Step S33: based on the heat flux density threshold, filter out the area in the heat flux density distribution where the heat flux density is higher than the set value, and define it as the heat conduction area of ​​the heating layer 20.

[0053] Specifically, the temperature sensing sample data set is a set of a series of discrete data points about the temperature of the heating layer 20 obtained from the temperature sensor. First, the temperature sensing sample data set of the heating layer 20 is collected from the temperature sensor. Since the sensor cannot cover every point of the heating layer 20, it is necessary to use an interpolation algorithm (such as bilinear interpolation or spline interpolation) to convert these discrete temperature data into a continuous two-dimensional temperature field. The goal of interpolation is to obtain a smooth temperature distribution graph that reflects the temperature change on the surface of the heating layer 20. When the data points are distributed more evenly and the changes are more linear, a linear interpolation algorithm can be selected; if a smoother temperature field curve is required, a spline interpolation algorithm can be selected. Taking the linear interpolation algorithm as an example, for any point (x, y) on a two-dimensional plane, assuming that four adjacent data points (x1, y1), (x2, y2), (x3, y4), (x4, y4) and their temperature values ​​T1, T2, T3, T4 are known, the temperature value of the point is calculated by the linear interpolation formula, thereby constructing a continuous two-dimensional temperature field. The discrete temperature data is converted into a continuous two-dimensional temperature field through an interpolation algorithm, which can more accurately describe the temperature distribution on the surface of the heating layer 20, thereby providing more refined data support for subsequent heat flux density calculation and heat conduction area analysis.

[0054] According to Fourier's law of heat conduction, heat flux is proportional to temperature gradient. In a continuous two-dimensional temperature field, the temperature gradient is obtained by calculating the partial derivative of temperature with respect to the coordinates, and then the heat flux distribution is calculated based on parameters such as the thermal conductivity of the material. The heat flux distribution describes the distribution of the intensity and direction of heat flow in a continuous two-dimensional temperature field. Heat flux is a vector whose magnitude represents the amount of heat passing through a unit area per unit time, and whose direction represents the direction of heat flow. For example, for a two-dimensional temperature field T (x, y), the component of heat flux in the x direction is , the component in the y direction , where k is the thermal conductivity of the material. By calculating the heat flux density component of each point, the heat flux density distribution of the entire continuous two-dimensional temperature field can be obtained. Calculating the heat flux density distribution can reveal the flow law of heat in the heating layer 20, help to find the key area of ​​heat transfer, and provide an important basis for determining the heat conduction area.

[0055] The heat flux density threshold is a pre-set value used to distinguish the boundaries of high and low heat flux densities. This value is usually set according to experimental results or system design requirements. The heat flux density value of each point in the calculated heat flux density distribution is compared with the pre-set heat flux density threshold. If the heat flux density value of a point is higher than the threshold, the area where the point is located is defined as the heat conduction area. By setting the heat flux density threshold to screen out the heat conduction area, the area with more active heat conduction in the heating layer 20 can be accurately located, providing a targeted target area for subsequent adaptive energy-saving optimization operations.

[0056] Furthermore, before analyzing the temperature sensing data set in step S3 to obtain the real-time temperature index, the following steps are further included: The state of the heater is detected to obtain a state return result of the heater; if the state return result is a standby state, the guide rail mechanism controls the cooling layer 10 to slide to a standby position, wherein the standby position is a preset position away from the heating layer 20.

[0057] Specifically, the heater state indicates the current operation of the heating device. The state of the heater is detected to determine the current working state of the heater and obtain a state return result. This state return result contains specific state information of the heater, such as operating state, standby state or off state. For example, the state of the heater can be judged by detecting electrical parameters such as current and voltage of the heater. If the values ​​of current and voltage are both at a very low level, it indicates that the heater is in a standby state, that is, the heater is currently in a state of waiting for instructions without performing a heating operation. When the state return result is a standby state, the guide rail mechanism will receive a corresponding control instruction to control the cooling layer 10 to slide to the standby position. This standby position is a pre-set position, which is the position where the cooling layer 10 should be when the heater is in a standby state. This position is away from the heating layer 20 to avoid unnecessary heat conduction or mutual influence during standby. Exemplarily, the guide rail mechanism may include components such as a motor, a transmission device (such as a belt, a chain or a screw rod, etc.) and a position sensor. The motor is started according to the control instruction, and drives the cooling layer 10 to slide along the guide rail through the transmission device. The position sensor monitors the position of the cooling layer 10 in real time. When the cooling layer 10 reaches a preset standby position away from the heating layer 20, the motor stops working.

[0058] Before analyzing the temperature sensing data set to obtain the real-time temperature index, the heater status is detected, and the action of the cooling layer 10 is controlled in combination with the status return result. When the heater is in the standby state, the cooling layer 10 is moved to the standby position away from the heating layer 20 through the guide mechanism, avoiding the heat interference between the cooling layer 10 and the heating layer 20, reducing unnecessary heat conduction and potential energy loss, and also helping to protect the equipment and extend the service life of the equipment.

[0059] Furthermore, after analyzing the temperature sensing data set to obtain the real-time temperature index in step S3, the following steps are further included: If the state return result is a non-standby state, and the real-time temperature index is less than the preset temperature index, the cooling layer 10 is adaptively energy-saving optimized in a limited space, and a second response result is output, and the second response result includes a second sliding position; the guide rail mechanism controls the cooling layer 10 to slide according to the second sliding position.

[0060] Specifically, when the status return result is not in standby state, the real-time temperature index and the preset temperature index are compared. If the real-time temperature index is less than the preset temperature index, it means that the current cooling effect is too strong and the position of the cooling layer 10 needs to be adjusted to reduce unnecessary heat exchange. When the adjustable area of ​​the cooling layer 10 is known, the position of the cooling layer 10 is optimized and calculated using the adaptive energy-saving optimization model, and the current temperature, cooling flow and other parameters are comprehensively considered to output a new position of the cooling layer 10 (i.e., the second sliding position) to reduce heat loss and energy waste.

[0061] After the optimization model calculates the second sliding position, the guide rail mechanism accurately adjusts the position of the cooling layer 10 according to the result. After the adjustment is completed, the cooling effect is evaluated again through real-time temperature feedback to ensure that the temperature gradually approaches the preset value.

[0062] The above steps further refine the logic of regulating the cooling layer 10. When the heater is not in standby mode and the temperature of the heating layer 20 does not reach the preset index, the position of the cooling layer 10 is adjusted through the adaptive energy-saving optimization model, the second sliding position is output, and the position adjustment is completed through the guide rail mechanism. This dynamic optimization mechanism can effectively reduce heat loss, improve heating efficiency, and save energy consumption.

[0063] Furthermore, there is a first distance between the first sliding position and the position where the heating layer 20 is located, and there is a second distance between the second sliding position and the position where the heating layer 20 is located, wherein the first distance is smaller than the second distance.

[0064] Specifically, when the real-time temperature index of the heating layer 20 is higher than the preset temperature index, the first sliding position is calculated by the adaptive energy-saving optimization model, so that the cooling layer 10 is close to the heating layer 20, thereby enhancing the cooling effect. When the real-time temperature index of the heating layer 20 is lower than the preset temperature index, the second sliding position is calculated by the adaptive energy-saving optimization model, so that the cooling layer 10 is away from the heating layer 20, and the cooling effect is weakened to maintain a suitable temperature. There is a first spacing between the first sliding position and the position where the heating layer 20 is located, and there is a second spacing between the second sliding position and the position where the heating layer 20 is located, and the first spacing is smaller than the second spacing.

[0065] By dynamically adjusting the first sliding position and the second sliding position, the distance between the cooling layer 10 and the heating layer 20 shows a significant difference (the first spacing is smaller than the second spacing). This control method effectively improves the cooling efficiency and adjustment accuracy of the cooling layer 10: when strong cooling is required, the cooling layer 10 is close to the heating layer 20; when weak cooling or heat preservation is required, the cooling layer 10 is far away from the heating layer 20. Ultimately, the temperature control is precise and the cooling energy consumption is minimized, further improving the energy-saving performance and intelligence level of the heater system.

[0066] In summary, the adaptive energy-saving control method for a water-cooled structure heater provided in the embodiment of the present application has the following technical effects: By detecting the state of the heater, it is determined whether it is in standby state. If the heater is in standby state, the guide rail mechanism controls the cooling layer 10 to slide to the standby position, away from the heating layer 20, to reduce unnecessary cooling and energy consumption. This pre-step ensures that when the heater is not working, no energy is wasted, and the overall energy efficiency is improved. Next, the temperature sensing sample data set of the heating layer 20 is collected, and a continuous two-dimensional temperature field is generated based on the interpolation algorithm. By calculating the heat flux density distribution of the continuous two-dimensional temperature field, the area where the heat flux density is higher than the set value is screened out and defined as the heat conduction area. Through precise temperature monitoring and heat conduction analysis, the high-temperature area in the heating layer 20 can be accurately identified, providing a basis for the precise positioning of the cooling layer 10, and improving the uniformity and efficiency of the cooling effect. After determining the heat conduction area, the temperature sensing data set is analyzed to obtain the real-time temperature index. If the real-time temperature index is greater than or equal to the preset temperature index, the cooling layer 10 is subjected to limited space adaptive energy-saving optimization, and the first response result, including the first sliding position, is output. The guide rail mechanism controls the cooling layer 10 to slide according to the first sliding position to ensure the best cooling effect with the minimum energy consumption. The position of the cooling layer 10 is optimized through an intelligent algorithm, which significantly improves the energy-saving performance and cooling efficiency. If the heater is not in standby mode and the real-time temperature index is less than the preset temperature index, the cooling layer 10 is adaptively optimized for energy saving in a limited space, and a second response result is output, including a second sliding position. The guide rail mechanism controls the cooling layer 10 to slide according to the second sliding position. By designing the first spacing to be smaller than the second spacing, different optimization goals can be achieved under different operating conditions. When the cooling effect needs to be maximized, the cooling layer 10 is closer to the heating layer 20; when energy saving is needed, the cooling layer 10 is appropriately away from the heating layer 20 to reduce unnecessary cooling and energy consumption.

[0067] In general, the embodiment of the present application effectively reduces the energy consumption of the water-cooled structure heater and improves the energy-saving effect by finely controlling the position of the cooling layer 10 and the cooling control parameters, while enhancing the intelligence and flexibility of temperature control and optimizing the overall performance of the heater.

[0068] Embodiment 2, as Figure 4 As shown, based on the same inventive concept as that of the first embodiment, the embodiment of the present application further provides an adaptive energy-saving control system for a water-cooled structure heater, the system is applied to a heater, a water-cooled structure is provided inside the heater, the water-cooled structure includes a heating layer 20 and a cooling layer 10, the cooling layer 10 includes a guide rail mechanism, and the cooling layer 10 is adjusted to slide in a vertical direction by the guide rail mechanism, including: The temperature sensing module 30 is used to sense the heating layer 20 using a temperature sensor and output a temperature sensing data set.

[0069] The heat conduction analysis module 40 is used to analyze the temperature sensing data set to obtain a real-time temperature index. If the real-time temperature index is greater than or equal to a preset temperature index, a heat conduction analysis is performed on the temperature sensing data set to determine the heat conduction area of ​​the heating layer 20.

[0070] The energy-saving optimization module 50 is used to perform limited space adaptive energy-saving optimization on the cooling layer 10 in the heat conduction area and output a first response result, wherein the first response result includes a first sliding position.

[0071] The sliding control module 60 is used to control the guide rail mechanism to control the cooling layer 10 to slide according to the first sliding position.

[0072] Furthermore, the energy-saving optimization module 50 in the embodiment of the present application is also used to perform the following steps: Obtain the adjustable area of ​​the cooling layer 10 inside the heater; obtain the overlapping area between the heat conduction area and the adjustable area, perform random sampling in the overlapping area to obtain a sliding position particle group, wherein each particle represents the sliding position of the cooling layer 10; construct an adaptive optimization model, and the adaptive optimization model is used to perform gradient optimization on the cooling layer 10 in the sliding position particle group, and output a first response result.

[0073] Furthermore, the energy-saving optimization module 50 in the embodiment of the present application is also used to perform the following steps: The cooling control parameters of the cooling layer 10 are obtained, and the cooling control parameters include cooling flow, cooling temperature and cooling medium; a first group of input samples are defined by the sliding position particle group, a second group of input samples are defined by the cooling control parameters, and a label sample of the cooling effect is defined for function fitting to generate a cooling objective function, and the cooling objective function is used to maximize the cooling effect of the cooling layer 10; the adaptive optimization model receives a first optimization instruction, and quantitatively performs gradient optimization in the sliding position particle group using the real-time cooling control parameters as input, and outputs a first response result that satisfies the cooling objective function.

[0074] Furthermore, the energy-saving optimization module 50 in the embodiment of the present application is also used to perform the following steps: Obtain a first cooling effect corresponding to the first response result; if the first cooling effect is greater than or equal to a preset threshold, output the first response result; if the first cooling effect is less than the preset threshold, obtain a second optimization instruction, the adaptive optimization model receives the second optimization instruction, and uses the adaptive optimization model to receive the second optimization instruction to perform segmented optimization on the cooling control parameters and the sliding position to obtain an updated first sliding position.

[0075] Furthermore, the heat conduction analysis module 40 of the embodiment of the present application is also used to perform the following steps: A temperature sensing sample data set of the heating layer 20 is collected, and a continuous two-dimensional temperature field of the heating layer 20 is generated based on an interpolation algorithm; a heat flux density distribution of the continuous two-dimensional temperature field is calculated; and based on a heat flux density threshold, an area in the heat flux density distribution where the heat flux density is higher than a set value is screened and defined as a heat conduction area of ​​the heating layer 20.

[0076] Furthermore, the heat conduction analysis module 40 of the embodiment of the present application is also used to perform the following steps: The state of the heater is detected to obtain a state return result of the heater; if the state return result is a standby state, the guide rail mechanism controls the cooling layer 10 to slide to a standby position, wherein the standby position is a preset position away from the heating layer 20.

[0077] Furthermore, the heat conduction analysis module 40 of the embodiment of the present application is also used to perform the following steps: If the state return result is a non-standby state, and the real-time temperature index is less than the preset temperature index, the cooling layer 10 is adaptively energy-saving optimized in a limited space, and a second response result is output, and the second response result includes a second sliding position; the guide rail mechanism controls the cooling layer 10 to slide according to the second sliding position.

[0078] Furthermore, there is a first distance between the first sliding position and the position where the heating layer 20 is located, and there is a second distance between the second sliding position and the position where the heating layer 20 is located, wherein the first distance is smaller than the second distance.

[0079] Through the above-mentioned detailed description of the adaptive energy-saving control method for water-cooled structure heater in this specification, those skilled in the art can clearly understand the adaptive energy-saving control system for water-cooled structure heater in this embodiment. For the system disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional modules and beneficial effects, the relevant parts can be referred to the description of the method part.

[0080] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An adaptive energy-saving control method for water-cooled structure heater, characterized in that: The method comprises: A water cooling structure is provided inside the heater, the water cooling structure includes a heating layer and a cooling layer, the cooling layer includes a guide rail mechanism, and the cooling layer is adjusted to slide in a vertical direction by the guide rail mechanism; Sensing the heating layer according to a temperature sensor, and outputting a temperature sensing data set; Analyze the temperature sensing data set to obtain a real-time temperature index, and if the real-time temperature index is greater than or equal to a preset temperature index, perform heat conduction analysis on the temperature sensing data set to determine a heat conduction area of ​​the heating layer; Performing limited space adaptive energy-saving optimization on the cooling layer in the heat conduction region, and outputting a first response result, wherein the first response result includes a first sliding position; The guide rail mechanism controls the cooling layer to slide according to the first sliding position.

2. The method according to claim 1, characterized in that The method for performing limited space adaptive energy-saving optimization on the cooling layer in the heat conduction region includes: Obtaining an adjustable area of ​​the cooling layer inside the heater; Acquire an overlapping area between the heat conduction area and the adjustable area, and perform random sampling in the overlapping area to obtain a sliding position particle group, wherein each particle represents a sliding position of the cooling layer; An adaptive optimization model is constructed, wherein the adaptive optimization model is used to perform gradient optimization on the cooling layer in the sliding position particle group and output a first response result.

3. The method according to claim 2, characterized in that The adaptive optimization model is used to perform gradient optimization on the cooling layer in the sliding position particle group, and the method includes: Acquiring cooling control parameters of the cooling layer, wherein the cooling control parameters include cooling flow, cooling temperature and cooling medium; A first group of input samples is defined by the sliding position particle group, a second group of input samples is defined by the cooling control parameter, and a label sample of a cooling effect is defined to perform function fitting to generate a cooling objective function, wherein the cooling objective function is used to maximize the cooling effect of the cooling layer; The adaptive optimization model receives a first optimization instruction, takes the real-time cooling control parameter as an input quantity to perform gradient optimization in the sliding position particle swarm, and outputs a first response result that satisfies the cooling objective function.

4. The method according to claim 3, characterized in that After outputting the first response result satisfying the cooling objective function, the method further includes: Obtaining a first cooling effect corresponding to the first response result; If the first cooling effect is greater than or equal to a preset threshold, output a first response result; If the first cooling effect is less than the preset threshold, a second optimization instruction is obtained, the adaptive optimization model receives the second optimization instruction, and the cooling control parameters and the sliding position are optimized in sections using the adaptive optimization model to receive the second optimization instruction to obtain an updated first sliding position.

5. The method according to claim 1, characterized in that Performing heat conduction analysis on the temperature sensing data set to determine the heat conduction area of ​​the heating layer, the method comprising: Collecting a temperature sensing sample data set of the heating layer, and generating a continuous two-dimensional temperature field of the heating layer based on an interpolation algorithm; Calculating the heat flux density distribution of the continuous two-dimensional temperature field; Based on the heat flux density threshold, the area in the heat flux density distribution where the heat flux density is higher than the set value is screened and defined as the heat conduction area of ​​the heating layer.

6. The method according to claim 1, characterized in that Before analyzing the temperature sensing data set to obtain the real-time temperature index, the method further includes: Detect the status of the heater and obtain the status return result of the heater; If the state return result is a standby state, the guide rail mechanism controls the cooling layer to slide to a standby position, wherein the standby position is a preset position away from the heating layer.

7. The method according to claim 6, characterized in that After analyzing the temperature sensing data set to obtain the real-time temperature index, the method further includes: If the state return result is a non-standby state, and the real-time temperature index is less than the preset temperature index, the cooling layer is subjected to limited space adaptive energy-saving optimization, and a second response result is output, wherein the second response result includes a second sliding position; The guide rail mechanism controls the cooling layer to slide according to the second sliding position.

8. The method according to claim 7, characterized in that There is a first distance between the first sliding position and the position where the heating layer is located, and there is a second distance between the second sliding position and the position where the heating layer is located, wherein the first distance is smaller than the second distance.

9. An adaptive energy-saving control system for water-cooled structure heater, characterized in that: The system is used to execute the adaptive energy-saving control method for a water-cooled structure heater according to any one of claims 1 to 8, comprising: A temperature sensing module, used for sensing the heating layer according to a temperature sensor and outputting a temperature sensing data set; A heat conduction analysis module, used to analyze the temperature sensing data set to obtain a real-time temperature index, and if the real-time temperature index is greater than or equal to a preset temperature index, perform heat conduction analysis on the temperature sensing data set to determine a heat conduction area of ​​the heating layer; An energy-saving optimization module, used for performing limited space adaptive energy-saving optimization on the cooling layer in the heat conduction region, and outputting a first response result, wherein the first response result includes a first sliding position; The sliding control module is used to control the guide rail mechanism to control the cooling layer to slide according to the first sliding position.

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