Temperature control method and graphene heating pad system applying same

Abstract

The invention provides a temperature control method and a graphene heating pad system applying the method, and relates to the technical field of temperature control, and the method comprises the steps: dividing a region to be subjected to temperature control into a plurality of subregions, obtaining the target temperature data and the real-time temperature data of each subregion, based on the real-time temperature data of each subarea, calculating the temperature gradient of the adjacent subareas; and constructing a heat transfer attenuation model, calculating a temperature adjustment value required by each partition based on the target temperature data, the temperature gradient and the heat transfer attenuation model, and adjusting the temperature of each partition according to the temperature adjustment value required by each partition. The accuracy and precision of temperature control can be improved.

Description

Technical Field

[0001] This application relates to the technical field of temperature control, and in particular to a temperature control method and a graphene heating pad system using the method. Background Technology

[0002] Temperature control is a critical and fundamental requirement in numerous industrial manufacturing, scientific research experiments, and daily life scenarios. Precise temperature control plays an indispensable role in ensuring product quality, improving the reliability of experimental results, and creating a comfortable living environment.

[0003] Related technologies employ a globally unified control mode, deploying a small number of temperature sensors within the area to be controlled to acquire the average temperature information of the entire area. Then, based on a preset target temperature, a uniform heating or cooling operation is implemented across the entire area to regulate the temperature. However, this globally unified control mode completely ignores the temperature differences at different locations within the area to be controlled, failing to meet the individualized temperature requirements of each local area. This results in extremely low temperature control accuracy, making it difficult to achieve ideal control effects in complex and variable real-world scenarios. Summary of the Invention

[0004] To improve the accuracy or precision of temperature control, this application provides a temperature control method and a graphene heating pad system using the method.

[0005] Firstly, this application provides a temperature control method, which adopts the following technical solution: A temperature control method includes the following steps: The area to be controlled is divided into multiple partitions, and the target temperature data and real-time temperature data of each partition are obtained. The temperature gradient between adjacent partitions is calculated based on the real-time temperature data of each partition. A heat transfer attenuation model is constructed. Based on the target temperature data, temperature gradient, and heat transfer attenuation model, the required temperature adjustment value for each zone is calculated. The temperature of each zone is then adjusted according to the required temperature adjustment value.

[0006] This application divides the area to be temperature-controlled into multiple zones, allowing for more detailed consideration of temperature differences at different locations. By acquiring real-time temperature data for each zone and calculating the temperature gradient between adjacent zones, it can accurately identify areas with uneven temperature distribution. Based on this information, this application makes targeted temperature adjustments to each zone, effectively reducing temperature differences between areas and making the temperature more uniform throughout the entire temperature-controlled area. This application pays attention to the unique situation of each zone; by calculating the temperature gradient, it can discover the direction and intensity of heat transfer, and then take corresponding adjustment measures to improve temperature distribution and enhance temperature uniformity at the microscopic level.

[0007] Subsequently, this application constructs a heat transfer attenuation model and calculates the required temperature adjustment value for each zone based on the target temperature data, temperature gradient, and the model. By introducing the heat transfer attenuation model, various factors affecting temperature are incorporated into the calculation, making the calculation of the temperature adjustment value more scientific and reasonable, thereby improving the control accuracy.

[0008] Optionally, the method further includes: treating each partition as a particle, where the current position of the i-th particle represents the current temperature adjustment strategy of the i-th partition, the velocity of the i-th particle represents the strategy adjustment rate, constructing a fitness function based on the absolute difference between the target temperature data and the real-time temperature data and the temperature gradient of adjacent partitions, and outputting the temperature adjustment strategy of the i-th partition using a particle swarm optimization algorithm.

[0009] This application treats partitions as particles, with their positions corresponding to temperature adjustment strategies. By continuously optimizing particle positions, it can accurately find strategies that bring the temperature of each partition close to the target temperature. The particle swarm optimization algorithm simulates the foraging behavior of bird flocks, continuously searching for the optimal solution in the solution space. The fitness function uses the absolute difference between the target temperature and the real-time temperature as an important indicator, guiding particles to move in directions that reduce the difference, thereby achieving precise temperature regulation.

[0010] Optionally, the method further includes: Set physical constraint boundaries, and based on these boundaries, add a penalty term to the fitness function to determine whether the temperature adjustment strategy exceeds the physical constraint boundaries. If so, the temperature adjustment strategy that exceeds the physical constraint boundary is marked as an out-of-bounds strategy, and the out-of-bounds strategy is mapped to the nearest feasible point of the physical constraint boundary to obtain a new temperature adjustment strategy. If not, no action will be taken.

[0011] This application improves safety by setting physical constraint boundaries to minimize the risk of temperature adjustment strategies exceeding safe limits. These physical constraint boundaries are set based on the equipment's safe operating range and process requirements. Limiting temperature adjustment strategies within these boundaries reduces potential safety hazards caused by excessively high or low temperatures, thus enhancing safety at the source.

[0012] This application penalizes temperature adjustment strategies that exceed physical constraint boundaries by adding a penalty term to the fitness function, resulting in lower fitness values ​​for these strategies during the algorithm's search process. This causes the particle swarm optimization algorithm to be more inclined to search for strategies with higher fitness values, i.e., those that do not exceed the physical constraint boundaries, thus guiding the algorithm's search direction towards the space of feasible solutions.

[0013] Optionally, the method further includes: The real-time temperature data of each partition is mapped to a two-dimensional Riemannian manifold. The curvature distribution is solved by conformal coordinate transformation. Based on the curvature distribution, heat flow singularities are identified. The heat source singularities are used as the partition boundary control points. Non-uniform partitions are generated through conformal mapping.

[0014] This application maps real-time temperature data onto a two-dimensional Riemannian manifold and solves for the curvature distribution through conformal coordinate transformation, enabling precise identification of heat flux singularities. These singularities correspond to heat sources or key locations where heat flows converge or diverge within the system. This application can accurately identify these heat source locations, providing precise target points for subsequent temperature control. The two-dimensional Riemannian manifold better describes the geometric structure of the temperature field in space, while the conformal coordinate transformation preserves angular relationships, allowing the curvature distribution to accurately reflect the changing characteristics of the temperature field. Significant changes in curvature occur at heat flux singularities; detecting these changes allows for the accurate location of heat sources or key heat flow points.

[0015] This application generates non-uniform partitions by using heat flow singularities as partition boundary control points. Based on the distribution of heat sources and the direction of heat flow, the temperature control area can be divided into partitions of different sizes and shapes. Using non-uniform partitions allows for setting more refined temperature control strategies for areas with severe heat generation, while control resources can be appropriately reduced in areas with less heat generation. This improves the overall accuracy of temperature control and reduces temperature control errors caused by unreasonable partitioning.

[0016] Optionally, the method further includes: A time dimension is embedded in the two-dimensional Riemannian manifold to construct a spatiotemporal temperature field. The first r-order modes of the spatiotemporal temperature field are extracted using the POD algorithm to construct a reduced-order subspace. Real-time temperature data is linearly projected onto the reduced-order subspace to obtain a low-dimensional feature vector. When the L2 norm of the low-dimensional feature vector exceeds a preset threshold, it is determined to be a temperature anomaly, and the temperature anomaly partition is marked as a temperature anomaly partition. The temperature anomaly partition is then re-partitioned.

[0017] This application constructs a spatiotemporal temperature field by embedding a time dimension in a two-dimensional Riemannian manifold. This allows for the simultaneous consideration of the spatial distribution of temperature and its changes over time. The spatiotemporal temperature field can accurately capture these changes, thereby more accurately determining whether the temperature is abnormal.

[0018] Subsequently, this application extracts the first r-order modes of the spatiotemporal temperature field using the POD algorithm and constructs a reduced-order subspace, which can remove redundant information in the temperature field and retain the most important features. By extracting key modes, the normal range and abnormal change patterns of temperature can be accurately identified, thereby more accurately detecting temperature anomalies.

[0019] Subsequently, this application linearly projects the real-time temperature data into a reduced-order subspace to obtain a low-dimensional feature vector, thereby reducing the impact of noise on the temperature data. Through dimensionality reduction, this application can remove or weaken the noise component in the temperature data, making the low-dimensional feature vector more reflective of the true temperature changes, thus improving the accuracy of temperature anomaly detection.

[0020] After detecting a temperature anomaly, this application further re-partitions the temperature anomaly zone, which can accurately allocate control resources according to the specific situation of the anomaly zone, and avoid taking uniform and excessive control measures on all zones as much as possible, thereby saving energy and control resources and improving resource utilization efficiency.

[0021] Optionally, the method further includes: recalculating the basis functions of the POD algorithm based on newly added real-time temperature data every preset period.

[0022] Temperature fields change constantly over time; new equipment operating conditions and environmental factors can all cause new characteristics in temperature distribution. This application recalculates the basis functions of the POD algorithm every preset period, enabling it to promptly capture these latest temperature change patterns. When new real-time temperature data reflects new changes, recalculating the basis functions updates these main patterns, making them more consistent with the actual current temperature field and improving the accuracy of anomaly detection.

[0023] Optionally, the method further includes: Obtain historical energy consumption data for each partition, calculate the weighting factor for partition i based on the historical energy consumption data of partition i, and use the product of the temperature adjustment value of partition i and the weighting factor of partition i as the new temperature adjustment value for partition i.

[0024] Historical energy consumption data reflects the energy consumption of a zone under different operating conditions. Zones with high energy consumption are typically more sensitive to temperature changes or require stricter temperature control; therefore, they are given a larger weighting factor, resulting in larger temperature adjustment values ​​and thus achieving differentiated control. Historical energy consumption data contains information on the energy consumption of a zone at different times and under different operating conditions, reflecting the zone's operating characteristics and needs. Combining this data with temperature adjustment values ​​allows for a comprehensive consideration of the impact of multiple factors on temperature control, leading to the development of more reasonable temperature adjustment strategies.

[0025] Optionally, the method further includes: Based on the historical energy consumption data and impedance of the i-th partition, an energy consumption impedance correlation model for the i-th partition is constructed. The aging sensitivity coefficient of the i-th partition is extracted from the energy consumption impedance correlation model of the i-th partition. The aging sensitivity coefficient of the i-th partition is used to correct the Arrhenius acceleration model, and the corrected Arrhenius acceleration model is obtained. Calculate the impedance change rate of the i-th partition. Based on the impedance change rate of the i-th partition, calculate the aging rate of the i-th partition using the modified Arrhenius accelerated model. Calculate the remaining lifetime of the i-th partition using the aging rate of the i-th partition and the real-time temperature data of the i-th partition. Issue an alarm signal when the remaining lifetime is lower than a preset lifetime threshold.

[0026] Due to differences in operating conditions and load, the aging characteristics of equipment in different zones vary. By constructing an energy consumption-impedance correlation model based on historical energy consumption data and impedance of the i-th zone, this application can accurately capture the unique aging characteristics of that zone. The energy consumption-impedance correlation model is built based on a large amount of actual data and can accurately reflect the relationship between energy consumption, impedance, and equipment aging. Through a specific algorithm to extract the aging sensitivity coefficient from this model, this application can accurately characterize the intrinsic connection between equipment aging and changes in energy consumption and impedance.

[0027] This application modifies the Arrhenius accelerated model using the aging sensitivity coefficient of the i-th partition. The modified model more accurately reflects the aging behavior of equipment under actual operating conditions. Besides temperature increases accelerating aging, high-energy-consumption operation and impedance changes can also significantly impact aging. The modified Arrhenius accelerated model comprehensively considers these factors, improving the accuracy of aging prediction. The aging rate reflects the rate of aging of the equipment under its current condition, while real-time temperature data reflects the current operating environment temperature. Combining these two metrics allows for a more accurate simulation of the aging process during actual operation, thereby calculating a more accurate remaining lifespan.

[0028] Optionally, the method further includes: Construct a standard resistor aging curve that includes the entire life cycle; Based on the impedance of each partition, a corresponding real-time resistance aging curve is constructed. The DTW algorithm is used to calculate the similarity between the real-time resistance aging curve of each partition and the standard resistance aging curve. Partitions with similarity below a preset similarity threshold are marked as aging abnormal partitions.

[0029] This application constructs a standard resistor aging curve covering the entire life cycle, providing a complete reference benchmark for evaluating resistor aging. This standard curve encompasses the entire process from initial use to final failure, reflecting the normal aging characteristics and variation patterns of the resistor at different stages. Based on the impedance of each partition, this application constructs a corresponding real-time resistor aging curve and calculates its similarity to the standard resistor aging curve using the DTW algorithm, enabling accurate identification of partitions with abnormal aging.

[0030] Subsequently, this application periodically calculates the similarity between the real-time resistance aging curve of each partition and the standard resistance aging curve, and marks the partition as an aging abnormal partition when the similarity is lower than a preset similarity threshold, which can issue an early warning before the resistance aging problem develops to a serious stage.

[0031] Secondly, this application provides a graphene heating pad system using the aforementioned temperature control method, employing the following technical solution: A graphene heating pad system employing the temperature control method described in the first aspect, characterized in that it comprises: a processor, and a memory communicatively connected to the processor; The memory is provided with a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the processor processes a computer program stored on the computer-readable storage medium, it implements the method as described in the first aspect.

[0032] In summary, this application includes at least one of the following beneficial technical effects: 1. This application divides the area to be temperature-controlled into multiple zones, allowing for more detailed consideration of temperature differences at different locations. By acquiring real-time temperature data for each zone and calculating the temperature gradient between adjacent zones, it can accurately identify areas with uneven temperature distribution. Based on this information, this application makes targeted temperature adjustments to each zone, effectively reducing temperature differences between areas and making the temperature more uniform throughout the entire temperature-controlled area. This application pays attention to the unique situation of each zone. By calculating the temperature gradient, it can discover the direction and intensity of heat transfer, and then take corresponding adjustment measures to improve temperature distribution and enhance temperature uniformity at the microscopic level.

[0033] 2. This application constructs a heat transfer attenuation model and calculates the required temperature adjustment value for each zone based on the target temperature data, temperature gradient, and the model. By introducing the heat transfer attenuation model, various factors affecting temperature are included in the calculation, making the calculation of the temperature adjustment value more scientific and reasonable, thereby improving the control accuracy. Attached Figure Description

[0034] Figure 1 This is a flowchart of Embodiment 1 of this application; Figure 2 This is a flowchart of Embodiment 2 of this application; Figure 3 This is a flowchart of Embodiment 3 of this application; Figure 4 This is a flowchart of Embodiment 4 of this application. Detailed Implementation

[0035] The following combination Figures 1 to 4 This application will be described in further detail.

[0036] Example 1: This example discloses a temperature control method, referring to... Figure 1 The method includes: S11 calculating the temperature gradient and S12 temperature control. The execution process of each step in this embodiment is as follows: S11 calculates the temperature gradient and divides the area to be controlled into multiple partitions using a grid partitioning method or a functional partitioning method, based on the spatial geometry of the area to be controlled. The partitions can be uniform or non-uniform.

[0037] Obtain target temperature data and real-time temperature data for each zone. The target temperature data can be a temperature value set in advance when the product is manufactured, or a temperature value set by the user in real time.

[0038] The temperature gradient between adjacent partitions is calculated based on the real-time temperature data of each partition. Specifically, all partitions are traversed to determine the set of adjacent partitions for each partition. The temperature difference between the j-th adjacent partition in the set of adjacent partitions of the i-th partition is calculated. Combined with the boundary characteristics of the j-th adjacent partition, the temperature change rate per unit distance, i.e., the temperature gradient value, is calculated. The temperature gradient values ​​of all adjacent partitions are integrated into a gradient matrix. The element in the i-th row and j-th column of the gradient matrix represents the temperature gradient between the i-th partition and the j-th adjacent partition. The temperature gradient of non-adjacent partitions is zero.

[0039] The calculation model for the temperature gradient value is as follows:

[0040] in, Let be the temperature gradient value between the i-th partition and the j-th adjacent partition at time t; Let be the temperature difference between the i-th partition and the j-th adjacent partition at time t; It represents the distance between the center point of the i-th partition and the center point of the j-th adjacent partition.

[0041] S12 temperature control, constructing a heat transfer attenuation model. The heat transfer attenuation model is used to characterize the loss and delay characteristics of heat during the interval transfer process. The construction process of the heat transfer attenuation model is as follows: Based on the law of conservation of energy, a dynamic equation for heat transfer between adjacent partitions is constructed. Taking the heat transfer from the i-th partition to the j-th adjacent partition as an example, the heat flow rate satisfies the following calculation formula:

[0042] in, Let be the heat flow from the i-th partition to the j-th adjacent partition at time t; Thermal conductivity; Let be the shared boundary area between the i-th partition and the j-th adjacent partition; Let be the temperature difference between the i-th partition and the j-th adjacent partition; Let be the distance between the center point of the i-th partition and the center point of the j-th adjacent partition; Let be the convective heat transfer coefficient between the i-th partition and the j-th adjacent partition; It is the Stefan-Boltzmann constant; Let be the emissivity of the surface of the i-th partition; Let be the emissivity of the surface of the j-th adjacent partition; This represents the real-time temperature data of the i-th partition at time t. This represents the real-time temperature data of the j-th adjacent partition at time t.

[0043] Based on the target temperature data, temperature gradient, and heat transfer attenuation model, the required temperature adjustment value for each partition is calculated. This temperature adjustment value refers to the degree of temperature adjustment required for each partition to reach the target temperature data. Taking the i-th partition as an example, the calculation process for the temperature adjustment value is as follows: Based on the real-time temperature data and target temperature data of the i-th partition, calculate the basic adjustment value:

[0044] in, This is the base adjustment value for the i-th partition; The target temperature data for the i-th partition; In this embodiment, the temperature data of the i-th partition at time t is... It equals the value of the real-time temperature data.

[0045] Considering the heat transfer between adjacent partitions and partition i, if the temperature gradient between partition i and its i-th adjacent partition is not zero, the heat transfer will cause a temperature change in partition i. This embodiment calculates the temperature change caused by adjacent partitions of partition i based on a heat transfer attenuation model. The calculation model is as follows:

[0046] in, The temperature change caused by the adjacent partitions of the i-th partition; Let i be the set of adjacent partitions of the i-th partition; The heat flow from the i-th partition to the j-th adjacent partition; Specific heat capacity; This is the equivalent heat capacity mass.

[0047] By combining the base adjustment value and the temperature change, the temperature adjustment value for the i-th partition is obtained. The calculation model is as follows:

[0048] in, This is the temperature adjustment value for the i-th partition; This is the base adjustment value for the i-th partition; This represents the temperature change caused by the adjacent partitions of the i-th partition.

[0049] Finally, adjust the temperature of each zone according to the required temperature adjustment value.

[0050] Example 2: Refer to Figure 2 The difference between this embodiment and Embodiment 1 is that this embodiment uses the Particle Swarm Optimization (PSO) algorithm. By establishing a mapping relationship between partitions and particles, and taking temperature deviation and gradient as the core objectives, iteratively optimizes the temperature adjustment strategy of each partition to achieve better temperature control. This embodiment specifically includes S21 optimizing the adjustment strategy and S22 determining the boundary.

[0051] S21 optimizes the adjustment strategy by treating each partition as a particle. The current position of the i-th particle represents the current temperature adjustment strategy for the i-th partition, and the velocity of the i-th particle represents the strategy adjustment rate.

[0052] A fitness function is constructed based on the absolute difference between the target temperature data and the real-time temperature data, and the temperature gradient between adjacent zones. The fitness function calculation model includes a fitness function calculation model for a constant temperature scenario and a fitness function calculation model for a gradient temperature control scenario.

[0053] The fitness function calculation model under isothermal conditions is as follows:

[0054] in, This is the sum of the absolute differences between the target temperature data and the real-time temperature data for all partitions. The smaller the value, the better the temperature control; n represents the number of zones. This represents the real-time temperature data of the i-th partition at time t. The target temperature data for the i-th partition.

[0055]

[0056] in, This represents the sum of the temperature gradients between adjacent zones in a constant-temperature scenario. The smaller the value, the closer the temperature gradient between adjacent partitions is to the target state; n is the number of partitions. The temperature gradient value between the i-th partition and the j-th adjacent partition; Let i be the set of adjacent partitions of the i-th partition.

[0057]

[0058] in, This represents the sum of the temperature gradients of all adjacent partitions in a gradient temperature control scenario. The smaller the value, the closer the temperature gradient between adjacent partitions is to the target state; n is the number of partitions. The temperature gradient value between the i-th partition and the j-th adjacent partition; Let i be the set of adjacent partitions of the i-th partition; The target temperature gradient.

[0059]

[0060]

[0061] in, This is the fitness function under isothermal conditions; for Weighting coefficients; for The weighting coefficients.

[0062]

[0063]

[0064] in, For gradient temperature control scenarios; for Weighting coefficients; for The weighting coefficients.

[0065] The temperature adjustment strategy for the i-th partition is output using the particle swarm optimization algorithm, as follows: S211 position initialization, randomly generating initial temperature adjustment strategy for the i-th particle. Initial temperature adjustment strategy The initial value is equal to the base adjustment value calculated in S12 temperature control. The value after adding a small perturbation value, the small perturbation value being in the range of -0.2 to 0.2; the initial velocity of the i-th particle. Set it to 0.

[0066] Individual optimal position initialized as .

[0067] Global optimal position initialized as .

[0068] S212 iteratively updates particle position and velocity, with the velocity update formula as follows:

[0069] in, Let be the velocity of the i-th particle at time t+1; The inertial weight decreases linearly, with a value range of 0.4-0.9 and an initial value of 0.9. Let be the velocity of the i-th particle at time t; The individual learning factor has a value of 2. and A random number between 0 and 1; The social learning factor has a value of 2. Let be the optimal position of the i-th particle at time t; The temperature adjustment strategy for the i-th particle at time t; Let t be the globally optimal position at time t.

[0070] The position update formula is as follows:

[0071] in, The temperature adjustment strategy for the i-th particle at time t+1; The temperature adjustment strategy for the i-th particle at time t; Let be the velocity of the i-th particle at time t+1.

[0072] After the position is updated, calculate the temperature adjustment strategy of the i-th particle at time t+1. The fitness function value under the given conditions.

[0073] S213 updates the individual optimality and the global optimality, if the temperature adjustment strategy of the i-th particle at time t+1 is... The fitness function value is less than the optimal position of the i-th particle at time t. If the fitness function value is , then let Conversely, the optimal position of the original individual is preserved, that is, let .

[0074] Iterate through the individual optimal positions of all particles, and take the individual optimal position of the particle with the smallest fitness function value as the global optimal position at time t+1. .

[0075] S214 terminates the iteration. When any of the following termination conditions are met, the iteration stops and the global optimal position and the corresponding temperature adjustment strategy for each partition are output.

[0076] Condition 1: The number of iterations reaches a preset maximum value (e.g., 500 times); Condition 2: The change in the global optimal fitness function value over 20 consecutive iterations is less than 0.01; Condition 3: The temperature deviation of all zones meets the preset accuracy requirements (e.g., temperature deviation is less than 1).

[0077] S22 Out-of-bounds judgment: Set physical constraint boundaries. In this embodiment, the physical constraint boundaries are [L [H], for example, the maximum temperature rise is 3 degrees Celsius and the maximum temperature drop is -5 degrees Celsius.

[0078] A penalty term is added to the fitness function based on physical constraints, significantly worsening the fitness function value of out-of-bounds strategies and guiding the algorithm to optimize within the feasible region. The computational model of the fitness function after adding the penalty term is as follows:

[0079]

[0080] in, The fitness function after adding a penalty term; The original fitness function, including the fitness function under isothermal scenarios. Fitness function in gradient temperature control scenarios Two options are available; choose one based on the specific scenario. This is the set of temperature adjustment strategies for all partitions at the m-th iteration. This is the penalty coefficient, with a value ranging from 100 to 1000; This is the penalty term for the m-th iteration; This represents the lower limit of the physical constraints. This represents the upper limit of physical constraints; Let n be the temperature adjustment strategy for the i-th partition during the m-th iteration; n is the number of partitions.

[0081] Iteratively update particle position Then, check each zone individually to see if the temperature adjustment strategy exceeds the physical constraint boundaries. If so, the temperature adjustment strategy that exceeds the physical constraint boundary is marked as an out-of-bounds strategy. This out-of-bounds strategy is then mapped to the nearest feasible point on the physical constraint boundary, resulting in a new temperature adjustment strategy. In other words, the out-of-bounds strategy is adjusted to the boundary with the smallest absolute difference. The calculation model is as follows:

[0082] For example, if the temperature increase strategy for the i-th partition is 4℃ (exceeding the upper limit of 3℃), it will be corrected to 3℃; if the temperature decrease is -6℃ (exceeding the lower limit of -5℃), it will be corrected to -5℃.

[0083] If not, no action will be taken.

[0084] Example 3: Reference Figure 3 The difference between this embodiment and Embodiment 1 is that the method further includes: S31 non-uniform partitioning maps the real-time temperature data of each partition to a two-dimensional Riemannian manifold, as follows: Let the physical plane of the area to be temperature controlled be... The coordinates of any point in the physical plane are (x, y). The temperature data T(x, y, t) of all sampling points in the physical plane at time t are collected, and the temperature field function is constructed based on this data.

[0085] Constructing the Riemannian metric tensor of the temperature field The temperature field Riemannian metric tensor The amount It is positively correlated with the magnitude of the temperature gradient, and the relationship between the two is as follows:

[0086] in, Let i be the Kronecker function, and let i be the function of i = j. The value of is 1, and vice versa. The value of is 0; This is a proportionality coefficient, with a value ranging from 10 to 100; This represents the temperature gradient magnitude.

[0087] This embodiment measures tensors. Mapping the physical plane to a two-dimensional Riemannian manifold Two-dimensional Riemannian manifold Each point on the Riemannian manifold corresponds to a location on the physical plane. The degree of curvature of the two-dimensional Riemannian manifold is determined by the temperature gradient distribution; that is, the greater the temperature gradient, the more pronounced the curvature. The higher the curvature.

[0088] Curvature distribution can be solved by conformal coordinate transformation. The advantage of conformal coordinate transformation is that it is a conformal transformation, meaning that the angle between any two curves remains unchanged before and after the transformation. It can unfold the curved Riemannian manifold into a planar coordinate system that is easy to calculate without distorting the direction of heat flow. The specific steps are as follows: Choose a two-dimensional Riemannian manifold A reference point, such as the geometric center of the temperature field, is used as the origin to establish a conformal local coordinate system. Construct conformal transformation function Satisfying the conformal condition:

[0089] in, The pullback metric after transformation; For exponentiation; It is the conformal factor; It is a Euclidean metric.

[0090] Solving two-dimensional Riemannian manifolds based on conformal coordinates Gaussian curvature Gaussian curvature It is a core indicator characterizing the degree of local curvature of a manifold, and the calculation model is as follows:

[0091] in, For the Laplace operator.

[0092] Traversing a two-dimensional Riemannian manifold Generate curvature distribution maps for all points on the graph. The higher the Gaussian curvature value, the more drastic the temperature gradient change and the more complex the thermal behavior.

[0093] Identifying heat flux singularities based on curvature distribution: Heat flux singularities are locations where heat flux behavior abruptly changes in the temperature field, manifested as extreme points (including maxima and minima) in the curvature distribution map. The process is as follows: Curvature distribution map Perform extreme value detection, set curvature threshold, and filter out curvature distribution maps. Points whose absolute value is greater than the curvature threshold are denoted as the candidate point set, which includes maximum points (i.e., heat source singularities) and minimum points (i.e., heat sink singularities). In this embodiment, the heat source singularities are used as the partition boundary control points.

[0094] Finally, non-uniform partitions are generated through conformal mapping, as follows: Each heat source singularity is set as the core anchor point of the non-uniform partition. The partition boundary is constrained to pass through the line connecting adjacent heat source singularities, and the partition boundary is perpendicular to the heat flow direction. The Dirichlet domain partitioning method is used. With each heat source singularity as the center, the Riemann distance from any point on the manifold to that heat source singularity is calculated, and the region with the smallest Riemann distance is divided into a partition.

[0095] S32 repartitioning, S31 non-uniform partitioning only describes the spatial distribution of the temperature field at time t. The actual temperature field is a spatiotemporally coupled dynamic system. This step is performed on the two-dimensional Riemannian manifold. The time dimension is embedded to construct a spacetime temperature field. The process is as follows: the two-dimensional Riemannian manifold is transformed into a spatial temperature field. Combined with the time dimension t, a three-dimensional spacetime Riemannian manifold is constructed. Where I is the time window for temperature data acquisition. The corresponding one-dimensional manifold; within the acquisition time window, with a fixed sampling period. Collect temperature field data at time M+1. The temperature field data at each moment corresponds to a three-dimensional spatiotemporal Riemannian manifold. A spatial slice; organize all spatiotemporal sampled data into a spatiotemporal data matrix. The i-th column vector in the spatiotemporal data matrix U corresponds to The spatial temperature field vector at time t.

[0096] The first r-order modes of the spatiotemporal temperature field are extracted using the POD algorithm, and a reduced-order subspace is constructed as follows: The spatiotemporal data matrix U is centered, and the covariance matrix of the centered spatiotemporal data matrix is ​​calculated. The eigenvalues ​​and eigenvectors of the covariance matrix correspond to the energy distribution and dominant modes of the temperature field. The covariance matrix is ​​decomposed into eigenvalues ​​to obtain the eigenvalue sequence and the eigenvector corresponding to each eigenvalue in the eigenvalue sequence. Each eigenvector corresponds to a first-order POD mode, representing a typical spatial distribution pattern of the temperature field. The first r-order modes are selected to construct a reduced-order subspace. The selected first r-order modes satisfy a cumulative contribution rate greater than 0.95.

[0097] Real-time temperature data is linearly projected onto a reduced-order subspace to obtain low-dimensional feature vectors. The formula for calculating linear projection is as follows:

[0098] in, The low-dimensional feature coefficients corresponding to the i-th POD mode are the projection components of the real-time temperature data onto the i-th base mode, and also the values ​​of the i-th element in the low-dimensional feature vector A. This is a vector dot product operation used to calculate the projection relationship between two vectors; The mean vector of the historical temperature field has a dimension of M×1 and is equal to the average of all historical temperature field vectors. This is the centered real-time temperature field vector; This is a vector transpose operation; Let be the i-th basic mode vector, with dimension M×1, which is the i-th eigenvector after the covariance matrix decomposition.

[0099] When the L2 norm of a low-dimensional feature vector exceeds a preset threshold, it is determined to be a temperature anomaly, and the temperature anomaly partition is marked as a temperature anomaly partition. The temperature anomaly partition is then re-partitioned, that is, the temperature anomaly partition is treated as a new partition set, and the S31 non-uniform partitioning is re-executed.

[0100] In this embodiment, every preset period (e.g., 24 hours), the newly added real-time temperature data is added to the original spatiotemporal data matrix, and the temperature data with the earliest timestamp in the original spatiotemporal data matrix is ​​deleted to obtain a new spatiotemporal data matrix. Based on the new spatiotemporal data matrix, the eigenvalue decomposition and mode extraction steps of the POD algorithm are repeated to obtain new first r order POD basis functions. The original basis functions are replaced with the new first r order POD basis functions to update the reduced order subspace.

[0101] Based on the heat flow distribution characteristics of the temperature field, this embodiment generates a non-uniform partition with fine heat flow dense areas and coarse heat flow sparse areas through Riemannian manifold modeling and conformal mapping technology, thereby achieving precise allocation of temperature control resources.

[0102] Example 4: Reference Figure 4 The difference between this embodiment and Embodiment 1 is that the method further includes: S41 calculates the new temperature adjustment value, obtains the historical energy consumption data for each partition, and then uses the historical energy consumption data of the i-th partition as a basis for... The weight factor for the i-th partition is calculated as follows:

[0103]

[0104] in, is the weighting factor for the i-th partition; C is the number of historical energy consumption data for the i-th partition; This represents the kth historical energy consumption data after normalization. This represents the k-th historical energy consumption data. The minimum historical energy consumption data for the i-th partition; This represents the historical energy consumption data for the i-th partition.

[0105] The product of the temperature adjustment value of the i-th partition and the weight factor of the i-th partition is used as the new temperature adjustment value of the i-th partition. In this embodiment, the partition with high energy consumption will obtain a smaller weight factor, and the temperature adjustment value will be reduced by using the smaller weight factor to save energy; the partition with low energy consumption will obtain a larger weight factor, and the new temperature adjustment value will be close to the original temperature adjustment value after the larger weight factor is multiplied by the original temperature adjustment value.

[0106] S42 marks the abnormal aging partition, obtains the changes in historical impedance values ​​of similar products, obtains the cumulative running time corresponding to each historical impedance value, and uses a nonlinear fitting algorithm to construct a standard resistor aging curve that includes the entire life cycle based on the correspondence between historical impedance values ​​and cumulative running time.

[0107] Based on the impedance of each partition, a real-time resistance aging curve for each partition is constructed. The DTW algorithm is used to calculate the similarity between the real-time resistance aging curve of each partition and the standard resistance aging curve, thereby solving the problem of time axis misalignment caused by the difference in aging rate between the real-time resistance aging curve and the standard resistance aging curve.

[0108] S43 Similarity judgment: Determine whether there are partitions with similarity lower than the preset similarity threshold. If so, mark the partitions with similarity lower than the preset similarity threshold as aging abnormal partitions and issue an aging abnormal signal. If not, execute S44 to calculate the remaining lifetime.

[0109] S44 calculates the remaining lifetime. Based on the historical energy consumption data and impedance values ​​of the i-th partition, it constructs an energy consumption-impedance correlation model for the i-th partition. The calculation model for the energy consumption-impedance correlation model of the i-th partition is as follows:

[0110] in, Let be the energy consumption impedance correlation model for the i-th partition, i.e., the energy consumption data of the i-th partition at time t; Let be the initial energy consumption of the i-th partition; The aging sensitivity coefficient is obtained by fitting accelerated aging tests (such as 85℃ / 85%RH environment), and its value ranges from 0.3 to 0.6. Let be the impedance value of the i-th partition at time t; Let be the initial impedance value of the i-th partition.

[0111] Extract the aging sensitivity coefficient of the i-th partition from the energy consumption impedance correlation model of the i-th partition. The higher the aging sensitivity coefficient, the greater the impact of changes in energy consumption on aging.

[0112] Using the aging sensitivity coefficient of the i-th partition The Arrhenius accelerated model is modified to obtain the modified Arrhenius accelerated model, as follows: In this embodiment, the computational model of the original Arrhenius accelerated model, i.e., the Arrhenius accelerated model before the modification, is as follows:

[0113] Where D is the aging rate; Pre-exponential factors; It is the activation energy; Boltzmann's constant; This refers to absolute temperature.

[0114] The revised computational model of the Arrhenius accelerated model is as follows:

[0115] in, This is the modified Arrhenius accelerated model; This refers to the aging sensitivity coefficient. Let be the impedance value at time t; This is the initial impedance value.

[0116] The impedance change rate of the i-th partition is calculated. Based on the impedance change rate of the i-th partition, the aging rate of the i-th partition is calculated using the modified Arrhenius accelerated model. The formula for calculating the aging rate of the i-th partition is as follows:

[0117] in, Let be the aging rate of the i-th partition.

[0118] The remaining lifetime of the i-th partition is calculated using the aging rate of the i-th partition and the real-time temperature data of the i-th partition. The calculation process is as follows:

[0119]

[0120]

[0121] in, Let be the cumulative wear and tear lifetime of the i-th partition; The cumulative runtime of the i-th partition; Let be the total lifetime of the i-th partition; Let be the remaining lifetime of the i-th partition.

[0122] An alarm signal is issued when the minimum remaining lifespan of all partitions is lower than a preset lifespan threshold (such as 85% of the total lifespan).

[0123] Example 5: This example discloses a graphene heating pad system using the temperature control method described above. The system includes a processor and a memory communicatively connected to the processor. The memory is provided with a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the processor processes the computer program stored on the computer-readable storage medium, it implements the temperature control method as described above.

[0124] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A temperature control method, characterized in that, include: The area to be controlled is divided into multiple partitions, and the target temperature data and real-time temperature data of each partition are obtained. The temperature gradient between adjacent partitions is calculated based on the real-time temperature data of each partition. A heat transfer attenuation model is constructed. Based on the target temperature data, temperature gradient, and heat transfer attenuation model, the required temperature adjustment value for each zone is calculated. The temperature of each zone is then adjusted according to the required temperature adjustment value.

2. The temperature control method according to claim 1, characterized in that, The method further includes: Each partition is treated as a particle. The current position of the i-th particle represents the current temperature adjustment strategy of the i-th partition, and the velocity of the i-th particle represents the strategy adjustment rate. A fitness function is constructed based on the absolute difference between the target temperature data and the real-time temperature data, and the temperature gradient of the adjacent partitions. The particle swarm optimization algorithm is used to output the temperature adjustment strategy of the i-th partition.

3. The temperature control method according to claim 2, characterized in that, The method further includes: Set physical constraint boundaries, and based on these boundaries, add a penalty term to the fitness function to determine whether the temperature adjustment strategy exceeds the physical constraint boundaries. If so, the temperature adjustment strategy that exceeds the physical constraint boundary is marked as an out-of-bounds strategy, and the out-of-bounds strategy is mapped to the nearest feasible point of the physical constraint boundary to obtain a new temperature adjustment strategy. If not, no action will be taken.

4. The temperature control method according to any one of claims 1-3, characterized in that, The method further includes: The real-time temperature data of each partition is mapped to a two-dimensional Riemannian manifold. The curvature distribution is solved by conformal coordinate transformation. Based on the curvature distribution, heat flow singularities are identified. The heat source singularities are used as the partition boundary control points. Non-uniform partitions are generated through conformal mapping.

5. The temperature control method according to claim 4, characterized in that, The method further includes: A time dimension is embedded in the two-dimensional Riemannian manifold to construct a spatiotemporal temperature field. The first r-order modes of the spatiotemporal temperature field are extracted using the POD algorithm to construct a reduced-order subspace. Real-time temperature data is linearly projected onto the reduced-order subspace to obtain a low-dimensional feature vector. When the L2 norm of the low-dimensional feature vector exceeds a preset threshold, it is determined to be a temperature anomaly, and the temperature anomaly partition is marked as a temperature anomaly partition. The temperature anomaly partition is then re-partitioned.

6. The temperature control method according to claim 5, characterized in that, The method further includes: recalculating the basis functions of the POD algorithm based on newly added real-time temperature data every preset period.

7. The temperature control method according to any one of claims 1-3, characterized in that, The method further includes: Obtain historical energy consumption data for each partition, calculate the weighting factor for partition i based on the historical energy consumption data of partition i, and use the product of the temperature adjustment value of partition i and the weighting factor of partition i as the new temperature adjustment value for partition i.

8. The temperature control method according to claim 7, characterized in that, The method further includes: Based on the historical energy consumption data and impedance of the i-th partition, an energy consumption impedance correlation model for the i-th partition is constructed. The aging sensitivity coefficient of the i-th partition is extracted from the energy consumption impedance correlation model of the i-th partition. The aging sensitivity coefficient of the i-th partition is used to correct the Arrhenius acceleration model, and the corrected Arrhenius acceleration model is obtained. Calculate the impedance change rate of the i-th partition. Based on the impedance change rate of the i-th partition, calculate the aging rate of the i-th partition using the modified Arrhenius accelerated model. Calculate the remaining lifetime of the i-th partition using the aging rate of the i-th partition and the real-time temperature data of the i-th partition. Issue an alarm signal when the remaining lifetime is lower than a preset lifetime threshold.

9. The temperature control method according to claim 8, characterized in that, The method further includes: Construct a standard resistor aging curve that includes the entire life cycle; Based on the impedance of each partition, a corresponding real-time resistance aging curve is constructed. The DTW algorithm is used to calculate the similarity between the real-time resistance aging curve of each partition and the standard resistance aging curve. Partitions with similarity below a preset similarity threshold are marked as aging abnormal partitions.

10. A graphene heating pad system applying the temperature control method as described in any one of claims 1-9, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory is provided with a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the processor processes a computer program stored on the computer-readable storage medium, it implements the method as described in any one of claims 1-9.

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