Adaptive Energy-saving Control Method and System for Water-cooled Structure Heater

Abstract

The present application provides an adaptive energy-saving control method and system for a heater with a water-cooled structure, which relates to the technical field of energy-saving control and includes: sensing the heating layer inside the heater according to a temperature sensor and outputting a temperature sensing data set; analyzing 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, performing a heat conduction analysis on the temperature sensing data set to determine the heat conduction region of the heating layer; performing an adaptive energy-saving optimization in a limited space on the cooling layer in the heat conduction region and outputting a first response result including a first sliding position; and a guide rail mechanism controlling the cooling layer to slide according to the first sliding position. The present application solves the technical problem in the prior art that due to the lack of detailed analysis of the heat conduction situation of the heating layer and the fixed position of the cooling layer, the cooling effect is uneven and the energy consumption is high, and achieves the technical effects of improving the temperature control accuracy and reducing the energy consumption.

Description

Technical Field

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

[0002] As a common thermal management device, a heater is widely used in various high-temperature environments. To improve the heating efficiency and reduce energy waste, many modern heaters adopt a water-cooled structure to achieve more efficient heat dissipation and temperature control. The water-cooled structure usually consists of a heating layer and a cooling layer, where the cooling layer uses a cooling medium (such as water) to take away the excess heat generated during the heating process, ensuring the stable operation of the device at high temperatures.

[0003] However, existing water-cooled structure heaters usually adopt a cooling layer design with a fixed position. This design method fails to fully consider the heat conduction situation 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 over-cooling or under-cooling in certain areas, thus affecting the heating effect and overall energy efficiency. In addition, most existing heater control methods are based on simple temperature thresholds for on-off control, lacking a detailed analysis of the heat conduction situation of the heating layer and unable to perform dynamic adjustment according to actual temperature changes, resulting in energy waste and a decrease in temperature control accuracy. Summary of the Invention

[0004] This 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 that due to the lack of detailed analysis of the heat conduction situation of the heating layer and the fixed position of the cooling layer, the cooling effect is uneven and the energy consumption is high, and achieves the technical effects of improving the temperature control accuracy and reducing the energy consumption.

[0005] In view of the above problems, on the one hand, this application provides an adaptive energy-saving control method for a water-cooled structure heater. The method includes: a water-cooled structure is provided inside the heater, the water-cooled 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 the vertical direction through the guide rail mechanism; the heating layer is sensed by a temperature sensor to output a temperature sensing data set; 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 a preset temperature index, a heat conduction analysis is performed on the temperature sensing data set to determine the heat conduction region of the heating layer; an adaptive energy-saving optimization in a limited space is performed on the cooling layer in the heat conduction region to output a first response result, and 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.

[0006] On the other hand, the present application also provides an adaptive energy-saving control system for a water-cooled structure heater, and the system includes: 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 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 the heat conduction region 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 region and outputting a first response result, where the first response result includes a first sliding position; and a sliding control module for controlling the guide rail mechanism to control the sliding of the cooling layer according to the first sliding position.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] A water-cooled structure including a heating layer and a cooling layer is provided inside the heater, and 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 through 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, heat conduction analysis is performed on the temperature sensing data set to determine the heat conduction region 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 region, adaptive energy-saving optimization is performed on the position of the cooling layer within a limited space to find the optimal sliding position. The position of the cooling layer is optimized through an adaptive algorithm, effectively realizing dynamic adjustment, thereby reducing unnecessary energy consumption and improving the cooling efficiency. The guide rail mechanism controls the sliding position of the cooling layer according to the first response result after adaptive optimization to achieve the best cooling of the heat conduction region, ensuring uniform cooling effect and avoiding energy waste.

[0009] In summary, the present application sets a special water-cooled structure inside the heater, uses a temperature sensor to obtain data, deeply analyzes the temperature sensing data set to determine the heat conduction region, then performs adaptive energy-saving optimization to obtain the optimal sliding position of the cooling layer, and finally controls the sliding of the cooling layer by the guide rail mechanism, realizing precise temperature control and efficient energy-saving management of the water-cooled structure heater, not only improving the temperature control accuracy but also significantly reducing energy consumption.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically describes the specific embodiments of this application. Description of the Drawings

[0011] Figure 1 It is a schematic flowchart of an adaptive energy-saving control method for a heater with a water-cooled structure provided by an embodiment of this application.

[0012] Figure 2 It is a schematic structural diagram of a heater in an adaptive energy-saving control method for a heater with a water-cooled structure provided by an embodiment of this application.

[0013] Figure 3 It is a schematic flowchart of adaptively optimizing energy saving in a limited space for a cooling layer in a heat conduction region in an adaptive energy-saving control method for a heater with a water-cooled structure provided by an embodiment of this application.

[0014] Figure 4 It is a schematic structural diagram of an adaptive energy-saving control system for a heater with a water-cooled structure provided by an embodiment of this application.

[0015] Description 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 Embodiments

[0016] By providing an adaptive energy-saving control method and system for a heater with a water-cooled structure in an embodiment of this application, the technical problem in the prior art that due to the lack of detailed analysis of the heat conduction situation of the heating layer and the fixed position of the cooling layer, the cooling effect is uneven and the energy consumption is high is solved, and the technical effects of improving the temperature control accuracy and reducing the energy consumption are achieved.

[0017] Embodiment 1, as Figure 1 shown, an embodiment of this application provides an adaptive energy-saving control method for a heater with a water-cooled structure, and the method includes:

[0018] Step S1: A water-cooled structure is provided inside the heater, and the water-cooled 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 the vertical direction through the guide rail mechanism.

[0019] Specifically, a heater is a heating device, and its main function is to convert electrical energy or other energy into heat energy to raise the temperature of the surrounding environment or object. As 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 contains heating elements that convert electrical energy into heat for the device to use for heating. The heating layer 20 is close to the usage surface of the Heater. The cooling layer 10, on the other hand, takes away the excess heat generated by the heating layer 20 by contacting with water flow. The cooling layer 10 is close to the atmosphere end, located between the atmosphere end and the heating layer 20, and can slide in the vertical direction through a guide rail mechanism. For example, linear guide rails and sliders can be used to achieve 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.

[0020] Step S2: Sense the heating layer 20 according to the temperature sensor and output a temperature sensing data set.

[0021] Specifically, a plurality of temperature sensors are arranged around the heating layer 20, such as thermocouples, thermistors, infrared temperature sensors, etc. These sensors continuously monitor the temperature of the heating layer 20 and transmit the data to the control unit. For example, thermocouple sensors can be used to transmit the temperature data to a computer or 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 and can be used to analyze the temperature change trend. By continuously monitoring the temperature of the heating layer 20, accurate data support can be provided for subsequent temperature control and the position adjustment of the cooling layer 10.

[0022] 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, conduct a heat conduction analysis on the temperature sensing data set to determine the heat conduction region of the heating layer 20.

[0023] Specifically, the real-time temperature index refers to the temperature value or related parameter obtained through the temperature sensor at the current moment and after certain processing, reflecting the actual temperature state of the heating layer 20 at a certain moment. The preset temperature index is a preset temperature threshold used to determine whether cooling is required, usually set based on the safe operating temperature or the optimal operating temperature of the heater. The heat conduction region refers to the region 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.

[0024] Analyze the temperature sensing data set output in step S2 to extract the current real-time temperature index. Compare the real-time temperature index with the preset temperature index. If the real-time temperature index is greater than or equal to the preset temperature index, start the heat conduction analysis. The heat conduction analysis uses mathematical models and algorithms to determine the heat conduction region of the heating layer 20 according to the heat distribution of the heating layer 20. In these regions, heat transfer is concentrated and the temperature is higher than the threshold, requiring stronger cooling support.

[0025] Through precise temperature monitoring and heat conduction analysis, the high-temperature areas 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 of the cooling effect and the accuracy of temperature control.

[0026] Step S4: Perform adaptive energy-saving optimization in a limited space for the cooling layer 10 in the heat conduction region, and output a first response result, where the first response result includes a first sliding position.

[0027] Specifically, the first response result is an adjustment suggestion for the cooling layer 10 obtained according to the optimization result, including the sliding position of the cooling layer 10 in the vertical direction, that is, the first sliding position.

[0028] Within the determined heat conduction region, through calculation and optimization algorithms, considering 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, adjust the position of the cooling layer 10 in a limited space to achieve the best effects 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 simulate natural selection or swarm behavior, gradually optimize the position of the cooling layer 10, and finally output the first response result. Taking the genetic algorithm as an example, different positions of the cooling layer 10 are regarded as individuals, and through simulating the biological evolution process, after operations such as crossover and mutation in multiple generations, the position of the cooling layer 10 with the lowest energy consumption is found, and this position is the first sliding position in the first response result.

[0029] Through the adaptive optimization algorithm, the position of the cooling layer 10 can be adjusted in a limited space, realizing the optimization of temperature control accuracy, reducing energy consumption, and ensuring that the entire heater system operates in the most energy-saving manner.

[0030] Step S5: The guide rail mechanism controls the cooling layer 10 to slide according to the first sliding position.

[0031] 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 precisely control the movement of the cooling layer 10. The stepper motor moves the cooling layer 10 to the first sliding position according to the instructions of the control unit. By precisely controlling the position of the cooling layer 10, dynamic cooling is realized, improving the cooling efficiency and energy-saving effect, ensuring that the temperature of the heating layer 20 always remains within the preset range, improving the accuracy of temperature control, and ensuring the energy-saving effect.

[0032] Further, as Figure 3 shown, step S4 includes:

[0033] Step S41: Obtain the adjustable region of the cooling layer 10 inside the heater.

[0034] Step S42: Obtain the overlapping region between the heat conduction region and the adjustable region, and perform random sampling in the overlapping region to obtain a sliding position particle swarm, where each particle represents the sliding position of the cooling layer 10.

[0035] Step S43: Construct an adaptive optimization model, which is used to perform gradient optimization on the cooling layer 10 in the sliding position particle swarm and output a first response result.

[0036] Specifically, by reading or measuring the design parameters of the internal structure of the heater, determine the boundary positions of the cooling layer 10 in the vertical direction, so as to obtain the adjustable region where the cooling layer 10 can move. This adjustable region is the spatial range within which the cooling layer 10 can be adjusted (here, sliding in the vertical direction) inside the heater. Defining the adjustable range of the cooling layer 10 provides a spatial constraint for subsequent searching for the optimal sliding position, which helps to improve the efficiency and accuracy of optimization.

[0037] After obtaining the adjustable region of the cooling layer 10, combine it with the heat conduction region obtained from heat conduction analysis, calculate the overlapping part of the two, and determine the overlapping region. The heat conduction region is a certain local range of the heating layer 20, and the adjustable region of the cooling layer 10 is a specific vertical spatial range. The intersecting part of the two is the overlapping region. In this overlapping region, perform sampling according to a certain random rule. For example, a computer program can be used to generate random numbers, which correspond to position coordinates in the vertical direction. Define the positions corresponding to these coordinates as particles (sliding positions), so as to obtain a sliding position particle swarm. By determining the overlapping region and performing random sampling in it to obtain a sliding position particle swarm, the optimization range is narrowed, concentrated in the region related to the heat conduction region and within the adjustable range, improving the pertinence of optimization and reducing unnecessary computational effort.

[0038] When constructing the 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 and is related to factors such as the distance from the cooling layer 10 to the center of the heat conduction region. Then calculate the gradient of the objective function according to the particles (i.e., sliding positions) in the sliding position particle swarm. Mathematical calculation software (such as Matlab, etc.) or a dedicated optimization algorithm library can be used to implement the gradient calculation and optimization process. Continuously adjust the particles (sliding positions) according to the gradient direction until the best 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, and this position is the first response result. By constructing an adaptive optimization model for gradient optimization, it is possible to find the most energy-efficient sliding position of the cooling layer 10 while meeting the cooling requirements, improving the energy utilization efficiency and temperature control accuracy.

[0039] Further, step S43 includes:

[0040] Step S431: Obtain the cooling control parameters of the cooling layer 10, where the cooling control parameters include cooling flow rate, cooling temperature, and cooling medium.

[0041] Step S432: Define the first group of input samples with the sliding position particle swarm, define the second group of input samples with the cooling control parameters, and perform function fitting with the label samples defining the cooling effect to generate a cooling objective function, which is used to maximize the cooling effect of the cooling layer 10.

[0042] Step S433: The adaptive optimization model receives the first optimization instruction, takes the real-time cooling control parameters as inputs, performs gradient optimization in the sliding position particle swarm, and outputs the first response result that meets the cooling objective function.

[0043] 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 in the cooling system to absorb heat to achieve the cooling purpose. 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 the control system, and these cooling control parameters directly affect the cooling efficiency and cooling effect. For the cooling flow rate, its value can be obtained through a flow sensor installed on the cooling pipeline; 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 during the design of the cooling system and can be determined by checking the technical documents or system identification of the equipment.

[0044] When constructing the cooling objective function, it is first necessary to determine the input samples for providing independent variable data and the label samples for representing the desired output results. Among them, the input samples are divided into the first group of input samples (sliding position particle swarm, representing the possible sliding positions of the cooling layer 10) and the second group of input samples (cooling control parameters, reflecting various conditions of cooling). According to the first group of input samples, the second group of input samples and the label samples, mathematical or statistical methods are used for function fitting, such as using the multiple linear regression method. Let the sliding position particle swarm be represented by the vector x, the cooling control parameters be represented by the vector y, and the cooling effect be represented by z. The multiple 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 are coefficients determined by fitting, and ϵ is the error term. By minimizing the error term, appropriate coefficients are found, thereby generating the cooling objective function. The purpose of this cooling objective function is to find a combination of sliding position and cooling control parameters that maximizes the cooling effect of the cooling layer 10. Generating the cooling objective function through function fitting can clarify the quantitative relationship between the cooling effect, the sliding position particle swarm, and the cooling control parameters, providing a target orientation for subsequent optimization operations.

[0045] During 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. The two 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 rate, cooling temperature, and cooling medium) is more complex than the adjustment of the position of the cooling layer 10, involving multiple aspects such as flow rate adjustment, temperature adjustment, and medium replacement, and consuming more energy, during the optimization process, first, on the premise of keeping the initial cooling control parameters unchanged, the position of the cooling layer 10 is optimized and adjusted to find the position of the cooling layer 10 that can meet the cooling effect. When the separate position adjustment does not meet the cooling requirements of the heating layer 20, the cooling control parameters are then optimized and adjusted to save energy consumption during the adjustment process.

[0046] The first optimization instruction is a signal used to start the adaptive optimization model to begin the optimization process. This instruction contains some initial setting information, such as the precision requirement for 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, reflecting the current working state of the cooling system. After receiving the first optimization instruction, the adaptive optimization model takes the real-time cooling control parameter as a fixed input quantity. For example, if the real-time cooling flow rate is 5 L / 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 cooling layer 10) is calculated according to the cooling objective function. Mathematical calculation software (such as Matlab, etc.) or a dedicated optimization algorithm library can be used to calculate the gradient. Continuously adjust the sliding position particles along the gradient direction until the sliding position that satisfies the cooling objective function (i.e., maximizes the cooling effect) is found. This position is the first response result. By performing gradient optimization on the sliding position particle swarm with the real-time cooling control parameter as the input quantity, the sliding position that maximizes the cooling effect of cooling layer 10 can be found under the current cooling conditions, improving the efficiency and effectiveness of the cooling system.

[0047] Further, after step S433, it also includes:

[0048] Step S434: Obtain the first cooling effect corresponding to the first response result.

[0049] Step S435: If the first cooling effect is greater than or equal to the preset threshold, output the first response result.

[0050] Step S436: If the first cooling effect is less than the preset threshold, obtain the second optimization instruction. The adaptive optimization model receives the second optimization instruction and performs segmented optimization on the cooling control parameter and the sliding position to obtain the updated first sliding position.

[0051] Specifically, after optimizing the position of cooling layer 10, the cooling effect is further verified. The sliding position and the cooling control parameter 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 can be evaluated whether the first response result meets the cooling requirement, thereby determining whether to accept the first response result or perform further optimization.

[0052] The preset threshold is a critical value of the cooling effect 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 of the equipment, safety standards, or production process requirements. 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 indicates that the first response result meets the cooling requirements. Directly outputting the first response result can ensure the normal operation of the cooling system under the condition of meeting the requirements, avoid unnecessary further optimization operations, and improve the system operation efficiency.

[0053] When the first cooling effect is less than the preset threshold, it indicates that the first response result does not meet the cooling requirements. At this time, a second optimization instruction is obtained. This second optimization instruction is an instruction triggered when the first cooling effect is less than the preset threshold and is used to let the adaptive optimization model perform the optimization operation again. This instruction contains some parameters or optimization strategy information different from the first optimization, such as a looser optimization range or different iteration times settings, etc. 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 rate, cooling temperature, etc., and the sliding position is the position of the cooling layer 10 in the vertical direction, during optimization, first fix the cooling flow rate and optimize the cooling temperature and the sliding position, and then fix the cooling temperature and optimize the cooling flow rate and the sliding position. Through this segmented optimization strategy, the parameters are gradually adjusted until the optimal solution that meets the preset threshold is found, and finally the updated first sliding position is obtained.

[0054] By verifying the first cooling effect corresponding to the first response result and performing segmented optimization, the optimization space of the cooling control parameters and the sliding position can be explored more comprehensively and deeply, increasing the possibility of finding the best solution that meets the cooling requirements, ensuring the best cooling effect with the minimum energy consumption, and significantly improving the energy-saving performance and cooling efficiency of the heater system.

[0055] Further, in step S3, performing heat conduction analysis on the temperature sensing data set to determine the heat conduction region of the heating layer 20 includes:

[0056] Step S31: Collect the temperature sensing sample data set of the heating layer 20 and generate the continuous two-dimensional temperature field of the heating layer 20 based on the interpolation algorithm.

[0057] Step S32: Calculate the heat flux density distribution of the continuous two-dimensional temperature field.

[0058] Step S33: Screen the regions in the heat flux density distribution where the heat flux density is higher than the set value based on the heat flux density threshold, and define them as the heat conduction regions of the heating layer 20.

[0059] 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 temperature sensors. First, collect the temperature sensing sample data set of the heating layer 20 from the temperature sensors. Since the sensors cannot cover every point of the heating layer 20, interpolation algorithms (such as bilinear interpolation or spline interpolation) need to be used to convert these discrete temperature data into a continuous two-dimensional temperature field. The goal of interpolation is to obtain a smooth temperature distribution map that reflects the temperature changes on the surface of the heating layer 20. When the data points are relatively evenly distributed and the changes are relatively 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 the 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, calculate the temperature value of this point through the linear interpolation formula, so as to construct a continuous two-dimensional temperature field. Converting the discrete temperature data into a continuous two-dimensional temperature field through the interpolation algorithm can more accurately describe the temperature distribution on the surface of the heating layer 20, and further provide more refined data support for subsequent heat flux density calculation and heat conduction region analysis.

[0060] According to Fourier's law of heat conduction, the heat flux density is proportional to the temperature gradient. In a continuous two-dimensional temperature field, the temperature gradient is obtained by calculating the partial derivative of the temperature with respect to the coordinates, and then the heat flux density distribution is calculated according to parameters such as the thermal conductivity of the material. The heat flux density distribution describes the distribution of the intensity and direction of heat flow in the continuous two-dimensional temperature field. The heat flux density is a vector, whose magnitude represents the amount of heat passing through a unit area per unit time, and the direction represents the direction of heat flow. For example, for a two-dimensional temperature field T(x, y), the component of the heat flux density in the x direction , and the component in the y direction , where k is the thermal conductivity of the material. By calculating the heat flux density components 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 heat flow law in the heating layer 20, help to find out the key regions of heat transfer, and provide an important basis for determining the heat conduction regions.

[0061] The heat flux density threshold is a pre-set value used to distinguish the boundary between 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 certain 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 where heat conduction is more active in the heating layer 20 can be accurately located, providing a targeted target area for subsequent adaptive energy-saving optimization operations.

[0062] Further, before analyzing the temperature sensing data set in step S3 to obtain real-time temperature indicators, it also includes:

[0063] Detect the state of the heater to obtain the state return result of the heater; if the state return result is the standby state, the guide rail mechanism controls the cooling layer 10 to slide to the standby position, where the standby position is a preset position away from the heating layer 20.

[0064] Specifically, the heater state represents the current operating condition of the heating device. Detect the state of the heater to determine the current working state of the heater and obtain the state return result. This state return result contains specific state information of the heater, such as the operating state, standby state, or off state. For example, the state of the heater can be judged by detecting electrical parameters such as the current and voltage of the heater. If the values of the current and voltage are both at a very low level, it indicates that the heater is in the standby state, that is, the heater is currently in a state of not performing heating operations and waiting for instructions. When the state return result is the standby state, the guide rail mechanism will receive the 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 the 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 can include components such as a motor, a transmission device (such as a belt, a chain, or a lead screw, etc.), and a position sensor. The motor starts according to the control instruction, drives the cooling layer 10 to slide along the guide rail through the transmission device, and the position sensor monitors the position of the cooling layer 10 in real time. When the cooling layer 10 reaches the preset standby position away from the heating layer 20, the motor stops working.

[0065] Before analyzing the temperature sensing data set to obtain real-time temperature indicators, the detection of the heater status is introduced, and the operation 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 a standby position away from the heating layer 20 through the guide rail 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.

[0066] Further, after analyzing the temperature sensing data set in step S3 to obtain real-time temperature indicators, it further includes:

[0067] If the status return result is a non-standby state and the real-time temperature indicator is less than the preset temperature indicator, an adaptive energy-saving optimization in a limited space is performed on the cooling layer 10, and a second response result is output. 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.

[0068] Specifically, when the status return result is a non-standby state, the real-time temperature indicator and the preset temperature indicator are compared. If the real-time temperature indicator is less than the preset temperature indicator, 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. In the case where the adjustable area of the cooling layer 10 is known, an adaptive energy-saving optimization model is used to optimize and calculate the position of the cooling layer 10, comprehensively considering parameters such as the current temperature and cooling flow rate, and outputting a new position of the cooling layer 10 (i.e., the second sliding position) to reduce heat loss and energy waste.

[0069] After the optimization model calculates the second sliding position, the guide rail mechanism accurately adjusts the position of the cooling layer 10 according to this result. After the adjustment is completed, the cooling effect is re-evaluated through real-time temperature feedback to ensure that the temperature gradually approaches the preset value.

[0070] The above steps further refine the logic of controlling the cooling layer 10. When the heater is in a non-standby state and the temperature of the heating layer 20 has not reached 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 at the same time.

[0071] Further, the first sliding position has a first distance from the position where the heating layer 20 is located, and the second sliding position has a second distance from the position where the heating layer 20 is located, where the first distance is less than the second distance.

[0072] 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 through the adaptive energy-saving optimization model, and the cooling layer 10 is moved closer to the heating layer 20 to enhance 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 through the adaptive energy-saving optimization, and the cooling layer 10 is moved away from the heating layer 20 to weaken the cooling effect to maintain a suitable temperature. Among them, there is a first distance between the first sliding position and the position of the heating layer 20, and a second distance between the second sliding position and the position of the heating layer 20, and the first distance is less than the second distance.

[0073] 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 an obvious difference (the first distance is less than the second distance). 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. Finally, the precision of temperature control and the minimization of cooling energy consumption are realized, further improving the energy-saving performance and intelligent level of the heater system.

[0074] In summary, the adaptive energy-saving control method for the heater facing the water-cooled structure provided by the embodiments of the present application has the following technical effects:

[0075] By detecting the status of the heater, it is determined whether it is in the standby state. If the heater is in the standby state, the guide rail mechanism controls the cooling layer 10 to slide to the standby position, away from the heating layer 20, reducing unnecessary cooling and energy consumption. This pre-step ensures that energy is not wasted when the heater is not working, improving the overall energy efficiency. Next, a 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 regions with heat flux density higher than the set value are screened out and defined as the heat conduction regions. Through accurate temperature monitoring and heat conduction analysis, the high-temperature regions 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 regions, the temperature sensing data set is analyzed to obtain real-time temperature indicators. If the real-time temperature indicator is greater than or equal to the preset temperature indicator, an adaptive energy-saving optimization in a limited space is performed on the cooling layer 10, and a first response result is output, including a first sliding position. The guide rail mechanism controls the cooling layer 10 to slide according to the first sliding position, ensuring the best cooling effect with the minimum energy consumption. By optimizing the position of the cooling layer 10 through intelligent algorithms, the energy-saving performance and cooling efficiency are significantly improved. If the heater is in a non-standby state and the real-time temperature indicator is less than the preset temperature indicator, an adaptive energy-saving optimization in a limited space is performed on the cooling layer 10, 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 less than the second spacing, different optimization goals can be achieved in different operating states. When maximizing the cooling effect is required, the cooling layer 10 is closer to the heating layer 20; while when energy saving is required, the cooling layer 10 is appropriately away from the heating layer 20, reducing unnecessary cooling and energy consumption.

[0076] Overall, in the embodiments of the present application, by precisely controlling the position of the cooling layer 10 and the cooling control parameters, the energy consumption of the heater of the water-cooling structure is effectively reduced, the energy-saving effect is improved, the intelligence and flexibility of temperature control are enhanced, and the overall performance of the heater is optimized.

[0077] Embodiment 2, as Figure 4 shown, based on the same inventive concept as Embodiment 1, the embodiments of the present application further provide an adaptive energy-saving control system for a heater of a water-cooling structure. The system is applied to the heater, and a water-cooling structure is provided inside the heater. The water-cooling 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 the vertical direction through the guide rail mechanism, including:

[0078] A temperature sensing module 30, configured to sense the heating layer 20 according to a temperature sensor and output a temperature sensing data set.

[0079] A heat conduction analysis module 40 is configured to analyze the temperature sensing data set to obtain real-time temperature metrics. If the real-time temperature metrics are greater than or equal to the preset temperature metrics, perform heat conduction analysis on the temperature sensing data set to determine the heat conduction region of the heating layer 20.

[0080] An energy-saving optimization module 50 is configured to perform adaptive energy-saving optimization in a limited space on the cooling layer 10 in the heat conduction region and output a first response result, where the first response result includes a first sliding position.

[0081] A sliding control module 60 is configured to control the guide rail mechanism to control the sliding of the cooling layer 10 according to the first sliding position.

[0082] Furthermore, the energy-saving optimization module 50 in the embodiment of the present application is further configured to perform the following steps:

[0083] Obtain the adjustable region of the cooling layer 10 inside the heater; obtain the overlapping region between the heat conduction region and the adjustable region, perform random sampling in the overlapping region to obtain a sliding position particle swarm, where each particle represents the sliding position of the cooling layer 10; construct an adaptive optimization model, and the adaptive optimization model is configured to perform gradient optimization on the cooling layer 10 in the sliding position particle swarm and output a first response result.

[0084] Furthermore, the energy-saving optimization module 50 in the embodiment of the present application is further configured to perform the following steps:

[0085] Obtain the cooling control parameters of the cooling layer 10, where the cooling control parameters include cooling flow rate, cooling temperature, and cooling medium; define a first group of input samples with the sliding position particle swarm, define a second group of input samples with the cooling control parameters, and perform function fitting with a label sample defining the cooling effect to generate a cooling objective function, where 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, takes the real-time cooling control parameters as input and performs gradient optimization in the sliding position particle swarm, and outputs a first response result that satisfies the cooling objective function.

[0086] Furthermore, the energy-saving optimization module 50 in the embodiment of the present application is further configured to perform the following steps:

[0087] Obtain the 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, and the adaptive optimization model receives the second optimization instruction to perform segmented optimization on the cooling control parameters and the sliding position, and obtain the updated first sliding position.

[0088] Further, the heat conduction analysis module 40 in the embodiment of the present application is further configured to perform the following steps:

[0089] Collect the temperature sensing sample data set of the heating layer 20, generate the continuous two-dimensional temperature field of the heating layer 20 based on the interpolation algorithm; calculate the heat flux density distribution of the continuous two-dimensional temperature field; screen the area where the heat flux density is higher than the set value in the heat flux density distribution based on the heat flux density threshold, and define it as the heat conduction area of the heating layer 20.

[0090] Further, the heat conduction analysis module 40 in the embodiment of the present application is further configured to perform the following steps:

[0091] Detect the state of the heater, and obtain the state return result of the heater; if the state return result is the standby state, the guide rail mechanism controls the cooling layer 10 to slide to the standby position, where the standby position is a preset position away from the heating layer 20.

[0092] Further, the heat conduction analysis module 40 in the embodiment of the present application is further configured to perform the following steps:

[0093] If the state return result is a non-standby state and the real-time temperature index is less than the preset temperature index, perform adaptive energy-saving optimization in a limited space on the cooling layer 10, and output a second response result, where 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.

[0094] Further, the first sliding position has a first distance from the position where the heating layer 20 is located, and the second sliding position has a second distance from the position where the heating layer 20 is located, where the first distance is less than the second distance.

[0095] Through the foregoing detailed description of the adaptive energy-saving control method for the heater facing the water-cooled structure in this specification, those skilled in the art can clearly know the adaptive energy-saving control system for the heater facing the water-cooled structure in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For the relevant parts, refer to the description in the method part.

[0096] The foregoing 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 readily 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. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded 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; 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 a preset temperature index, a heat conduction analysis is performed on the temperature sensing data set to determine a heat conduction area of ​​the heating layer. The method includes: 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, a region in the heat flux density distribution with a heat flux density higher than a set value is selected, and defined as a heat conduction region of the heating layer; 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; Constructing an adaptive optimization model, 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; 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 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.

3. The method according to claim 2, 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.

4. 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.

5. The method according to claim 4, 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.

6. The method according to claim 5, 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.

7. 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 6, 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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