Chip-type radiator heat dissipation efficiency monitoring method based on multi-modal data fusion
By using multimodal data fusion and flow path topology modeling, the problem of difficulty in identifying local anomalies in plate heat sinks in existing technologies has been solved. This enables early identification of local heat transfer performance degradation and accurate assessment of the overall heat dissipation response, improving the stability and accuracy of monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHONGSHAN YUHAO METAL PROD CO LTD
- Filing Date
- 2026-05-21
- Publication Date
- 2026-07-21
AI Technical Summary
Existing monitoring methods are unable to accurately distinguish between normal hysteresis response and localized heat dissipation performance degradation caused by load fluctuations and changes in operating conditions such as the start and stop of cooling devices. Furthermore, they are unable to locate localized anomalies in finned heat sinks, resulting in insufficient accuracy in early identification of localized degradation and assessment of overall heat dissipation response.
By collecting load data, top oil temperature, ambient temperature, infrared thermal images, and visible light images, and combining them with the flow path topology model, thermal state alignment and local thermal resistance increment distribution inversion are performed to correct the overall heat dissipation response parameters and identify local heat transfer performance degradation.
It improves the ability to identify local radiator anomalies, reduces misjudgments caused by differences in thermal inertia, and enhances the stability and accuracy of monitoring results.
Smart Images
Figure CN122432935A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of transformer heat dissipation monitoring technology, specifically to a method for monitoring the heat dissipation efficiency of a plate-type radiator based on multimodal data fusion. Background Technology
[0002] Plate-type radiators are widely used in oil-immersed transformers and other oil-circulating cooling equipment, and their heat dissipation status directly affects the equipment's temperature rise and long-term operational reliability. Existing monitoring methods typically rely on operating data such as load, top oil temperature, ambient temperature, and fan or oil pump status, or combine infrared thermography to inspect and judge the radiator surface temperature. These methods can reflect the overall thermal state of the equipment, but during changes in operating conditions such as load fluctuations and cooling device start-up and shutdown, there are significant differences in thermal inertia between electrical losses, oil temperature, and radiator surface temperature. If comparisons are made directly at the same sampling time, normal delayed responses can easily be misjudged as abnormal heat dissipation.
[0003] Furthermore, plate-type radiators consist of multiple plate units, with the oil dissipating heat step by step along the flow path. When early anomalies occur in local plate groups due to dust accumulation, blocked oil channels, or deteriorated heat transfer, they often first manifest as changes in the temperature drop distribution between plates and local heat transfer capacity, while the average temperature of the entire unit may not have yet increased significantly. Traditional evaluation methods based on overall unit temperature rise or single-point temperature are difficult to accurately distinguish between normal fluctuations caused by changes in operating conditions and localized declines in heat dissipation performance, and are also difficult to locate abnormal plate groups. Existing methods do not make sufficient collaborative use of visible light structural information, infrared temperature information, and operational sequence data, and lack a heat dissipation efficiency monitoring mechanism oriented towards the spatial arrangement of plate units and the direction of oil flow, resulting in the need to improve the accuracy of early localized degradation identification and overall unit heat dissipation response assessment. Summary of the Invention
[0004] This application provides a method for monitoring the heat dissipation efficiency of a chip heat sink based on multimodal data fusion, so as to at least solve some of the technical problems existing in the related technologies described above.
[0005] According to a first aspect of the embodiments of this application, a method for monitoring the heat dissipation efficiency of a chip heat sink based on multimodal data fusion is provided, including: Collect load data, top oil temperature, ambient temperature, cooling status information, infrared thermal images, and visible light images, and uniformly mark the time; Identify the sheet unit boundaries and sheet unit arrangement order from the visible light image, register the infrared thermal image to the visible light image, and generate the sheet unit surface temperature rise sequence according to the sheet unit boundaries and ambient temperature; The operating event window is determined by load data, top oil temperature and cooling status information. Within the operating event window, a heat input proxy sequence is constructed from the load data. Using the heat input proxy sequence as a reference sequence, the top oil temperature and the surface temperature rise sequence of the wafer unit are thermally aligned to obtain a thermally aligned data set. A flow path topology model is constructed based on the chip unit boundaries and chip unit arrangement order; the initial overall heat dissipation response parameters and local thermal resistance increment distribution are obtained by using the thermal state aligned data set and the flow path topology model. The overall heat dissipation response parameters are fitted and corrected by attenuating the temperature rise weight of the unit according to the local thermal resistance increment distribution; the corrected overall heat dissipation response parameters are used to update the inversion, and the corrected local thermal resistance increment distribution and the corrected overall heat dissipation response parameters are obtained and output.
[0006] As an optional approach, generating the surface temperature rise sequence of the chip unit includes: performing grayscale and contrast enhancement processing on the visible light image that has passed quality inspection; extracting the outer contour of the radiator and the inter-chip separation line using edge detection to obtain the chip unit boundary; determining the arrangement order of the chip units according to the direction from the upper oil collecting pipe area to the lower oil collecting pipe area and the horizontal arrangement position between the chip groups; extracting radiator corner points or significant structural feature points from the infrared thermal image and the visible light image; calculating the spatial transformation matrix according to the correspondence of feature points; and completing the registration according to the spatial transformation matrix; taking the area-weighted average of the infrared temperature pixel values in each registered chip unit area and subtracting the ambient temperature to obtain the surface temperature rise sequence of the chip unit.
[0007] As an optional approach, determining the running event window includes: when the change in the load rate corresponding to the load data exceeds a preset threshold within a preset time period, when the fan start-stop state changes, or when the absolute value of the slope of the top layer oil temperature change exceeds a preset slope threshold, the corresponding time is determined as the starting point of the running event window; when the corresponding trigger quantity recovers to stability and the change rate of the surface temperature rise sequence of the chip unit drops below a preset convergence threshold, the corresponding time is determined as the ending point of the running event window.
[0008] As an optional approach, constructing the heat input proxy quantity sequence includes: within the running event window, calculating an estimated load loss value based on the load data and the rated short-circuit loss parameter, and using the estimated load loss value as the principal component of the heat input proxy quantity sequence; when obtaining the core loss estimate, merging the core loss estimate into an additional component of the heat input proxy quantity sequence.
[0009] As an optional approach, the thermal state alignment includes: within the running event window, performing constrained dynamic time warping on the thermal input proxy sequence and the top-layer oil temperature to form a first alignment relationship; performing constrained dynamic time warping on the thermal input proxy sequence and the wafer unit surface temperature rise sequence to form a second alignment relationship; based on the first alignment relationship and the second alignment relationship, mapping the top-layer oil temperature and the wafer unit surface temperature rise sequence to a unified thermal state time axis, and associating the ambient temperature and the cooling state information with the same time marker to the unified thermal state time axis to obtain the thermal state aligned data set; wherein, the constraints of the constrained dynamic time warping include: the local delay of the oil temperature response relative to the thermal input proxy sequence is not negative, the local delay of the wafer unit surface temperature rise sequence is not less than the local delay of the oil temperature response, and each local delay changes continuously along the time direction.
[0010] As an optional approach, the flow path topology model includes nodes, longitudinal connecting edges, and transverse connecting edges corresponding to the plate unit or plate unit partition. The node attributes include location identifier, plate unit surface temperature rise value after thermal state alignment, and effective heat dissipation area. The longitudinal connecting edge represents the flow path direction relationship between adjacent nodes along the oil flow direction, and the transverse connecting edge represents the parallel relationship between nodes at the same oil flow level and spatially adjacent nodes. When inverting the local heat transfer resistance increment distribution, the local heat transfer resistance increment of each node is taken as the unknown quantity, the theoretical temperature rise is calculated node by node along the flow path topology model, and the estimated value of the local heat transfer resistance increment of each node is solved based on the deviation between the theoretical temperature rise and the plate unit surface temperature rise value.
[0011] As an optional approach, when solving for the estimated local heat transfer resistance increment of each node, flow direction constraints, adjacency constraints, sparsity constraints, and non-negative lower bound constraints are applied. The flow direction constraints stipulate that the estimated temperature drop difference between adjacent nodes in the same oil flow direction is arranged in descending order of temperature drop from the upper oil gathering pipe region to the lower oil gathering pipe region. The adjacency constraints add the sum of squares of the local heat transfer resistance increment differences between adjacent nodes to the objective function. The sparsity constraints add the L1 norm of the local heat transfer resistance increment vector composed of the local heat transfer resistance increments of each node to the objective function. The non-negative lower bound constraints stipulate that the local heat transfer resistance increment of each node is not less than zero.
[0012] As an optional approach, obtaining the initial overall thermal response parameters includes: weighting the surface temperature rise of each node's chip unit after thermal alignment according to the effective heat dissipation area to generate an overall average temperature rise curve; establishing the input-output relationship between the overall average temperature rise curve and the heat input proxy sequence; fitting the input-output relationship using a first-order inertial model, and using the fitted steady-state gain and time constant as the initial overall thermal response parameters.
[0013] As an optional approach, fitting and correcting the overall heat dissipation response parameters includes: comparing the local heat transfer resistance increment of each node in the local heat transfer resistance increment distribution with an anomaly determination threshold; identifying the chip unit corresponding to the node whose local heat transfer resistance increment exceeds the anomaly determination threshold as an abnormal chip unit; and identifying the region composed of consecutive abnormal chip units as an abnormal chip group; keeping the chip unit temperature rise weight of normal chip units unchanged, and reducing the chip unit temperature rise weight of abnormal chip units according to the degree of deviation of their local heat transfer resistance increment from the anomaly determination threshold; regenerating the overall average temperature rise curve based on the reduced chip unit temperature rise weight, and fitting the corrected overall heat dissipation response parameters.
[0014] As an optional approach, updating the inversion with the corrected overall heat dissipation response parameters includes: replacing the initial overall heat dissipation response parameters with the corrected overall heat dissipation response parameters as the inversion background condition, re-performing the inversion of the local thermal resistance increment distribution to obtain the corrected local thermal resistance increment distribution; stopping the correction when the change in the corrected overall heat dissipation response parameters obtained from two adjacent correction rounds is less than a preset convergence threshold, and outputting the steady-state gain, the time constant, and the corrected local thermal resistance increment distribution corresponding to the spatial layout of the finned heat sink.
[0015] This application collects load data, top oil temperature, ambient temperature, cooling status information, infrared thermal images, and visible light images. Based on the running event window and heat input proxy quantity, it aligns various data types with their thermal states, enabling analysis of oil temperature and radiator surface temperature rise under load changes, fan start-up and shutdown conditions, and other conditions that may exhibit response lag, all under the same thermal state reference, reducing misjudgments caused by differences in thermal inertia. Simultaneously, by identifying the boundaries and arrangement order of the plate units through visible light images, the infrared temperature data is converted into a plate unit surface temperature rise sequence with spatial location meaning. Combined with the flow path topology model to invert the local thermal resistance increment distribution, it can identify local plate group heat transfer performance degradation before the overall average temperature has significantly increased. Furthermore, this application attenuates the weight of abnormal plate unit temperature rises based on local thermal resistance increments, corrects the overall heat dissipation response parameters, and updates the local inversion with the corrected parameters. This allows for mutual correction between the overall heat dissipation efficiency assessment and local anomaly location, improving the stability and accuracy of the monitoring results.
[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Furthermore, no embodiment in this disclosure is required to achieve all the effects described above. Attached Figure Description
[0017] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0018] Figure 1 This is a schematic diagram of a method for monitoring the heat dissipation efficiency of a chip heat sink based on multimodal data fusion, provided in an embodiment of this disclosure.
[0019] Figure 2 A flowchart illustrating the acquisition of thermal state alignment data sets provided in embodiments of this disclosure.
[0020] Figure 3 A flowchart illustrating the acquisition of initial heat dissipation response parameters and local thermal resistance increment distribution provided in the embodiments of this disclosure.
[0021] Figure 4 A flowchart illustrating the local thermal resistance increment distribution and overall heat dissipation response parameter correction provided in the embodiments of this disclosure.
[0022] Figure 5 This is a schematic diagram of a heat dissipation efficiency monitoring system for a chip heat sink based on multimodal data fusion, provided as an embodiment of this disclosure. Detailed Implementation
[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0024] According to embodiments of this disclosure, a method for monitoring the heat dissipation efficiency of a plate-type radiator based on multimodal data fusion is provided. This method is applicable to oil-immersed transformers or similar oil-circulating cooling equipment equipped with plate-type radiators. Typically, in this scenario, sensors and image acquisition devices are deployed on-site. The monitoring system can acquire transformer load data, top oil temperature, ambient temperature, cooling status information, as well as infrared thermal images and visible light images of the plate-type radiator area from the corresponding viewing angle. The plate-type radiator consists of multiple plate units arranged along the oil flow direction. Oil enters each plate unit from the upper oil collector and dissipates heat step by step, eventually flowing into the lower oil collector. After load changes, electrical losses change rapidly, the top oil temperature changes slightly slower, and the radiator surface temperature changes even slower, with varying degrees of time lag among these three due to differences in heat capacity. Simultaneously, early anomalies caused by dust accumulation, blocked oil passages, or deteriorated heat transfer in localized plate groups often first alter the order of temperature drop distribution between plates, rather than immediately manifesting as a significant increase in the overall average temperature. This method addresses these characteristics by monitoring heat dissipation efficiency without adding dedicated detection hardware.
[0025] The implementation process of the method described in this application will be described in detail below with reference to specific embodiments. It should be noted that this embodiment is only used to explain this application and is not intended to limit the scope of protection of this application. Conventional adjustments or substitutions of each step by those skilled in the art without departing from the concept of this application should be included in the scope of protection of this application.
[0026] Please see Figure 1 , Figure 1 A flowchart of a method for monitoring the heat dissipation efficiency of a plate-type heat sink based on multimodal data fusion according to an embodiment of the present invention is shown, as follows: Figure 1 As shown, the method includes steps S1-S5: In step S1, load data, top oil temperature, ambient temperature, cooling status information, infrared thermal image and visible light image are collected and time is uniformly marked.
[0027] In some embodiments, the monitoring system acquires the following types of data according to a preset acquisition cycle: load data, top oil temperature, ambient temperature, cooling status information, infrared thermal images of the radiator area, and visible light images from corresponding viewpoints. Load data refers to load current or load rate; cooling status information includes fan start / stop status and oil pump operating level. When the load data is load current, the system determines the corresponding load rate based on the ratio of the load current to the transformer's rated current; when the load data is load rate, the system directly uses this load rate as the input for subsequent operation event window judgment and heat input proxy quantity calculation. All the above data acquired within the same acquisition cycle correspond to the same time marker, thereby ensuring the correspondence of data from each channel in the time dimension during subsequent processing.
[0028] Optionally, a quality check is performed on the collected data after a unified marking time. Specifically, the quality check includes: whether there is missing data in each sensor channel; whether the infrared thermal image meets the preset blurring conditions, the preset occlusion ratio, or whether there is temperature saturation; and whether the values of the top oil temperature and the ambient temperature are within the preset physical range. The preset blurring conditions, preset occlusion ratio, and preset physical range can all be calibrated by maintenance personnel based on the field equipment parameters, or determined based on the statistical distribution of historical collected data. Collected data that fails the quality check is marked as invalid samples, and the system stops the subsequent processing of the sample and records the reason for the anomaly. Collected data that passes the quality check enters the step of generating the surface temperature rise sequence of the chip unit.
[0029] In step S2, the sheet unit boundaries and sheet unit arrangement order are identified from the visible light image, the infrared thermal image is registered to the visible light image, and the sheet unit surface temperature rise sequence is generated according to the sheet unit boundaries and ambient temperature.
[0030] In some embodiments, the system performs grayscale and contrast enhancement processing on the visible light image that has passed quality inspection. Then, it uses an edge detection operator to extract the outer contour of the heat sink and the inter-plate separation lines, thereby determining the position region of each plate unit in the image coordinate system, i.e., the plate unit boundary. For example, the edge detection operator can be the Canny operator or the Sobel operator. The Canny operator extracts fine edges by calculating the gradient magnitude and applying non-maximum suppression and double thresholding, suitable for scenarios where the heat sink separation lines are relatively clear. The Sobel operator calculates the horizontal and vertical gradients through convolution operations, suitable for contour extraction with high contrast. The plate unit arrangement order is determined according to the direction from the upper oil collecting pipe region to the lower oil collecting pipe region and the horizontal arrangement position between plate groups. This arrangement order is directly used as the node arrangement basis when constructing the flow path topology model later.
[0031] After completing the edge recognition of the chip unit, the system extracts corner points or significant structural feature points of the heat sink from the infrared thermal image and the visible light image, respectively. Based on the correspondence of these feature points, a spatial transformation matrix is calculated, and the infrared thermal image is registered to the coordinate system of the visible light image according to this spatial transformation matrix. In some embodiments, the spatial transformation matrix adopts an affine transformation model. Affine transformation preserves straight lines and parallel relationships, which is suitable for situations where the shooting angles of the two images are similar. When the shooting angles of the two images differ significantly, a perspective transformation model can be used to compensate for perspective distortion. After registration, the system takes the area-weighted average of the infrared temperature pixel values within each chip unit region, and then subtracts the current ambient temperature to obtain the surface temperature rise value of that chip unit. The surface temperature rise values of each chip unit are organized into a chip unit surface temperature rise sequence according to the chip unit arrangement order. This sequence retains both the temperature rise value of each chip unit and the spatial position information of the chip unit on the heat sink. Optionally, for pixels in the chip unit edge region that may be affected by reflections from the metal frame, the system assigns them a lower weight or excludes them when calculating the area-weighted average to reduce the impact of edge effects on the accuracy of temperature rise extraction.
[0032] In step S3, the running event window is determined by the load data, top oil temperature and cooling status information. Within the running event window, a heat input proxy quantity sequence is constructed from the load data. Using the heat input proxy quantity sequence as a reference sequence, the top oil temperature and the surface temperature rise sequence of the wafer unit are thermally aligned to obtain a thermally aligned data set.
[0033] Specifically, please refer to Figure 2 , Figure 2 A flowchart illustrating the acquisition of thermal state alignment data sets provided in an embodiment of this disclosure is shown. Figure 2 As shown in box 201, the running event window is determined by load data, top oil temperature and cooling status information.
[0034] In some embodiments, after a load change, the response speeds of electrical losses, oil temperature, and radiator surface temperature decrease sequentially. If the data from each channel are directly combined and judged at the same sampling time, the timing misalignment caused by the difference in thermal inertia will cause normal thermal hysteresis to be misjudged as a decrease in heat dissipation efficiency. Therefore, in this embodiment, the running event window is determined before efficiency judgment, and then thermal state alignment is performed within the window.
[0035] Specifically, the system continuously monitors the load rate corresponding to the load data. When the change in load rate within a preset time period exceeds a preset threshold, that moment is determined as the starting point of the running event window; or, when the fan start / stop state switches, the switching moment is determined as the starting point; or, when the absolute value of the slope of the top oil temperature change exceeds a preset slope threshold, the corresponding moment is determined as the starting point. The end point of the event window is the moment when the corresponding trigger quantity returns to stability and the rate of change of the surface temperature rise sequence of the chip unit drops below a preset convergence threshold, at which point the radiator surface temperature has basically approached a new steady state. Optionally, the starting point can be extended forward by a baseline acquisition time to obtain a steady-state reference before the event. This baseline data is used to provide a reference state before the event during thermal state alignment; various thresholds can be calibrated by maintenance personnel according to the transformer's rated capacity and cooling configuration, and recalibrated when the equipment operating conditions change significantly.
[0036] In box 202, a heat input surcharge sequence is constructed from load data within the running event window. Specifically, within the defined running event window, the system calculates the estimated load loss value at each sampling time based on the load data and the transformer's rated short-circuit loss parameters, and uses this value as the principal component of the heat input surcharge sequence. Specifically, the system first determines the load rate at each sampling time. When the load data is the load current, This is the ratio of load current to rated current; when the load data is the load factor, Take the load rate directly; estimate the load loss value according to... Calculation, where The rated short-circuit loss parameter is typically given on the transformer's nameplate or test report. The heat input surcharge sequence characterizes the change in heat power input to the cooling system over time within an event window. Specifically, when obtaining the core loss estimate, the system incorporates the core loss estimate as an additional component of the heat input surcharge sequence, making the heat input surcharge more comprehensively reflect the total heat loss generated by the transformer. The core loss remains essentially constant under load fluctuations, and its contribution to the heat input surcharge is mainly reflected as an approximately constant base component.
[0037] In box 203, using the heat input agent sequence as a reference sequence, the top oil temperature and the surface temperature rise sequence of the wafer unit are thermally aligned to obtain a thermally aligned data set.
[0038] In some embodiments, the system uses the thermal input proxy sequence as a reference sequence to perform thermal state alignment on the top oil temperature and wafer unit surface temperature rise sequence within the running event window. The purpose is to organize the data with different response speeds under the same thermal state reference rather than at the same clock moment.
[0039] Specifically, the system first performs constrained dynamic time warping (DTW) on the heat input proxy sequence and the top oil temperature to form a first alignment relationship. DTW is an algorithm that measures the similarity between two time series through nonlinear time axis transformation: given two sequences, DTW searches for the alignment path that minimizes the cumulative distance under the conditions of satisfying boundary constraints and monotonically increasing constraints. In this embodiment, the distance metric of DTW uses Euclidean distance. The width of the constraint window is set according to the oil thermal time constant of the transformer. This time constant can be obtained by performing step response analysis on historical event window data during the system initialization phase, or it can be estimated according to the rated parameters of the transformer and can be configured to be 1.5 to 3 times the corresponding thermal time constant.
[0040] The system performs constrained time-delay warping (DTW) on the heat input proxy sequence and the surface temperature rise sequence of the heat sink unit to form a second alignment relationship. The constraint window width of the second alignment relationship is set according to the thermal time constant of the radiator metal structure, and its value is usually larger than the constraint window of the first alignment relationship. Both DTW operations apply the following physical constraints: the local delay of the oil temperature response relative to the heat input proxy sequence is not negative; the local delay of the surface temperature rise sequence of the heat sink unit is not less than the local delay of the oil temperature response; each local delay changes continuously along the time direction without any abrupt changes unrelated to the operating conditions. The physical meaning of these constraints is: heat is first transferred to the oil and then from the oil to the radiator surface; the response order is irreversible, and the response delay should change smoothly within an event window.
[0041] After establishing both the first and second alignment relationships, the system maps the top oil temperature and wafer unit surface temperature rise sequences to a unified thermal state time axis based on their alignment paths. It also associates the ambient temperature and cooling status information with the same time marker on this unified thermal state time axis, resulting in a thermal state aligned data set. Specifically, the unified thermal state time axis is based on the original time axis of the heat input proxy sequence. The first alignment relationship records the matching relationship between each sampling point of the heat input proxy sequence and the corresponding sampling point of the top oil temperature sequence. The second alignment relationship records the matching relationship between each sampling point of the heat input proxy sequence and the corresponding sampling point of the wafer unit surface temperature rise sequence. Based on these matching relationships, the system rearranges the sampled values of the top oil temperature and wafer unit surface temperature rise sequences onto the time axis where the heat input proxy sequence is located. When a one-to-many mapping occurs in the alignment path, weighted interpolation is used. In the thermal state aligned data set, the top oil temperature value corresponding to a given heat input state, the surface temperature rise value of each wafer unit, the ambient temperature, and the cooling status information are all under the same thermal state reference.
[0042] Through the above alignment process, the timing misalignment caused by the oil temperature not keeping up with the load change or the radiator temperature not keeping up with the oil temperature change during load step or fan start-stop switching is eliminated, reducing misjudgments caused by thermal inertia differences.
[0043] In step S4, a flow path topology model is constructed based on the chip unit boundaries and chip unit arrangement order; using the thermal state alignment data set and the flow path topology model, the initial overall heat dissipation response parameters and local thermal resistance increment distribution are obtained.
[0044] Specifically, please refer to Figure 3 , Figure 3 A flowchart illustrating the acquisition of initial heat dissipation response parameters and local thermal resistance increment distribution provided in an embodiment of this disclosure is shown. Figure 3 As shown in box 301, a flow path topology model is constructed based on the slice unit boundaries and the slice unit arrangement order.
[0045] In this model, each finned unit or finned unit partition corresponds to a node, and there are two types of connecting edges between nodes: longitudinal connecting edges represent the flow path direction relationship between adjacent nodes along the oil flow direction, i.e., the heat transfer order from the upper oil collecting pipe region to the lower oil collecting pipe region; lateral connecting edges represent the parallel relationship between nodes at the same oil flow level and spatially adjacent. In some embodiments, each independent heat sink corresponds to one node; in other embodiments, based on the spatial resolution of the infrared image, each heat sink can be divided into three partitions along the height direction: upper, middle, and lower, with each partition corresponding to one node, to provide more refined information on the longitudinal temperature drop distribution.
[0046] Each node's attributes include a location identifier, the surface temperature rise of the tile unit after thermal state alignment, the effective heat dissipation area, and the baseline equivalent thermal resistance determined by the health baseline state. Among these, the location identifier is used to determine the spatial coordinates of the node on the radiator, the surface temperature rise of the tile unit after thermal state alignment comes from the thermal state alignment data set output in the previous steps, and the effective heat dissipation area is determined based on the area of the tile unit boundary in the image and the radiator structural parameters. The attributes of the connection edges include the connection type identifier and the connection direction. This model transforms the radiator from a whole temperature object into a node network that retains the internal oil flow direction and spatial adjacency relationships.
[0047] In box 302, the initial overall heat dissipation response parameters and the local thermal resistance increment distribution are obtained by using the thermal state aligned data set and the flow path topology model.
[0048] Specifically, the system performs a weighted average of the surface temperature rise values of each node's unit after thermal alignment, based on the effective heat dissipation area. The base temperature rise weight for each node is the ratio of its effective heat dissipation area to the total effective heat dissipation area, generating an overall average temperature rise curve that reflects the evolution of the radiator's overall temperature rise with thermal state. The system establishes an input-output relationship between the overall average temperature rise curve and the heat input surrogate sequence, and fits this relationship using a first-order inertial model. The first-order inertial model is a commonly used model in control engineering to describe a system with heat capacity gradually approaching a steady state under a step input. Its two parameters are steady-state gain and time constant. The steady-state gain reflects the magnitude of the steady-state temperature rise caused by a unit change in heat input; a larger value indicates weaker heat dissipation capacity. The time constant reflects the speed at which the temperature rise approaches steady state; a larger value indicates stronger thermal inertia. The fitted steady-state gain and time constant are the initial overall thermal response parameters.
[0049] Based on the flow path topology model, the system uses the local thermal resistance increment of each node as the unknown quantity for inversion. The initial overall thermal response parameters are used to constrain the effective heat dissipation area weighted average curve of the theoretical temperature rise of each node obtained by forward calculation, so that it is consistent with the overall average temperature rise response output by the first-order inertial model corresponding to the initial steady-state gain and the initial time constant; thus, the inversion of the local node temperature rise distribution maintains the same thermal state benchmark as the overall average thermal response.
[0050] The local thermal resistance increment refers to the change in the equivalent thermal resistance of each unit relative to the heat transfer capacity under the radiator's healthy baseline state. A positive value indicates a decrease in heat transfer capacity, while a zero value indicates consistency with the baseline state. The healthy baseline state is established as follows: during the initial commissioning of the radiator or after maintenance confirming normal operation, the system collects data from multiple event windows covering different load levels and ambient temperature conditions. After processing according to the complete process described above, the statistical distribution of the overall heat dissipation response parameters and the thermal resistance increment of each node is stored as the baseline. Optionally, the baseline data is re-collected after a major overhaul of the transformer or after radiator cleaning and maintenance. When establishing the healthy baseline state, the system also determines and stores the baseline equivalent thermal resistance of each node based on the unit surface temperature rise, ambient temperature, effective heat dissipation area, and corresponding heat input surcharge within the health event window. Its reciprocal is used as the baseline heat transfer coefficient for that node; subsequent local heat transfer resistance increments This indicates the current equivalent thermal resistance relative to the reference equivalent thermal resistance at that node. The increase.
[0051] In some embodiments, the forward calculation of the inversion process is as follows: the system uses the top oil temperature as the initial oil temperature condition at the flow path inlet, and calculates the theoretical temperature rise of each unit node by node along the longitudinal connecting edge of the flow path topology model; for each node in the flow path, its theoretical surface temperature rise is determined by the temperature of the upstream oil flowing into the node, the reference heat transfer coefficient and the local heat transfer resistance increment of the node, the effective heat dissipation area, and the ambient temperature. Specifically, the heat dissipation of each node is equal to the temperature difference between the node surface and the environment divided by the equivalent heat transfer resistance of the node, and the equivalent heat transfer resistance is equal to the reference heat transfer resistance plus the local heat transfer resistance increment; after the oil flows through the node, the temperature drop is equal to the heat dissipation divided by the product of the oil flow rate and the specific heat capacity flowing through the node.
[0052] Among them, the oil flow rate through the node is determined by calling the pre-calibrated flow rate of the oil pump operation level in the cooling status information; the specific heat capacity is determined by the oil parameters in the transformer oil parameter file, equipment technical data or maintenance record; for parallel nodes with transverse connections, the oil temperature after merging is weighted averaged according to the oil flow rate of each parallel branch.
[0053] When the local thermal resistance increment of a node increases, the equivalent thermal resistance increases, the heat dissipation of that node decreases, the temperature drop of the oil flowing through that node also decreases, and the inlet oil temperature of the downstream node increases accordingly. For parallel nodes connected laterally, the system calculates the oil temperature after merging according to the parallel thermal path. This calculation process maps the local thermal resistance increment distribution to the theoretical temperature rise distribution. The solution objective of the inversion is to find a set of local thermal resistance increment distributions that minimizes the deviation between the theoretical temperature rise of each node and the surface temperature rise value of the plate unit after aligning with the thermal state.
[0054] In some embodiments, the following four types of constraints are applied during the inversion solution: the flow direction constraint stipulates that the estimated temperature drop difference between adjacent nodes in the same oil flow direction is arranged in descending order of temperature drop from the upper oil gathering pipe region to the lower oil gathering pipe region; the estimated temperature drop difference is the difference between the estimated inlet oil temperature and the estimated outlet oil temperature of adjacent nodes; for upstream and downstream nodes in the same longitudinal flow path, the estimated temperature drop difference satisfies the descending order constraint according to the oil flow direction; the influence of the node heat transfer resistance increment on the theoretical temperature rise is reflected by the undetermined local heat transfer resistance increment and the objective function residual, without changing the above flow direction constraint itself; The adjacency constraint adds the sum of squares of the differences in local thermal resistance increments between adjacent nodes to the objective function as a regularization term to suppress single-node pseudo-anomalies caused by temperature measurement noise or registration errors, so that the inversion results maintain reasonable smoothness in space. The weight coefficient of this term is a preset parameter, and its value can be between 0.01 and 1, which is determined according to the spatial resolution of the infrared thermal image and the temperature measurement noise level.
[0055] The sparsity constraint will be composed of the vector of local heat transfer resistance increments at each node. The norm is added to the objective function, causing the inversion results to tend to concentrate anomalies on a few nodes; its weighting coefficients... The preset parameters are determined based on the total number of slice units and the expected maximum proportion of anomalous slice groups. Specifically, the system selects from the candidate weight set the minimum weight that ensures the proportion of non-zero anomalous nodes obtained from the inversion does not exceed the expected maximum proportion of anomalous slice groups, and the temperature rise fitting residual does not exceed the threshold corresponding to the infrared thermometry allowable error. value.
[0056] The non-negative lower bound constraint ensures that the local thermal resistance increment at each node is not less than zero, which physically means that the heat transfer capacity of the unit cell will not be better than the healthy baseline. Before substituting into the objective function, the system normalizes the temperature rise residual and the local thermal resistance increment. The temperature rise residual is calculated according to a preset temperature rise scale. Normalization, the local thermal resistance increment is based on the baseline equivalent thermal resistance of the corresponding node. Normalization yields ;in, The statistical average of the overall machine temperature rise within the health baseline event window or the baseline temperature rise under rated operating conditions can be obtained.
[0057] Taking into account the above constraints, the objective function is:
[0058] in, This is a vector composed of the surface temperature rise values of each node element after thermal alignment. This is the theoretical temperature rise vector derived from the local thermal resistance increment distribution through forward calculation. This is the normalized local heat transfer resistance increment vector; This is the set of adjacent node pairs in the flow path topology model. These are the adjacency constraint weight coefficients. These are the sparsity constraint weighting coefficients; In some embodiments, the alternating direction multiplier method (ADMM) is used to minimize the objective function; and the following constraints are satisfied while minimizing the objective function: The estimated temperature drop difference between adjacent nodes in the same longitudinal flow path is arranged in descending order of temperature drop from the upper oil gathering pipe region to the lower oil gathering pipe region. ADMM decomposes the original problem into several easier-to-solve subproblems, and updates the main variables, dual variables and Lagrange multipliers in each iteration until convergence. It is suitable for handling mixed optimization problems involving L1 norm and non-negative constraints. The solution results are the estimated local heat transfer resistance increment values of each node, forming a local heat transfer resistance increment distribution corresponding to the spatial layout of the radiator.
[0059] Through the above inversion, the system can infer the changes in the heat transfer capacity of each unit from the observed temperature rise, rather than just looking at which unit has a high temperature. Even if the absolute temperature value of a certain unit is not prominent, if its heat transfer resistance increment is significantly larger than that of neighboring units, the early deterioration of the heat transfer performance of that unit can be identified. It is particularly effective in capturing early fault signs such as local dust accumulation and oil circuit blockage, because such faults usually disrupt the normal order of temperature drop distribution between units first, while the average temperature rise of the whole machine has not yet changed significantly.
[0060] In step S5, the temperature rise weight of the attenuator unit is attenuated according to the local thermal resistance increment distribution, and the overall heat dissipation response parameters are fitted and corrected. The corrected overall heat dissipation response parameters are used to update the inversion, and the corrected local thermal resistance increment distribution and the corrected overall heat dissipation response parameters are obtained and output.
[0061] Please see Figure 4 , Figure 4 A flowchart illustrating the local thermal resistance increment distribution and overall system heat dissipation response parameter correction provided in an embodiment of this disclosure is shown. Figure 4 As shown in box 401, the temperature rise weight of the attenuator unit is adjusted according to the distribution of local thermal resistance increment to fit and correct the overall heat dissipation response parameters.
[0062] In existing technologies, overall system efficiency assessment and local anomaly identification are often performed independently, lacking information exchange; temperature deviations in local anomaly areas can skew the calculation of the overall system's average temperature rise, while deviations in overall system assessment can affect the determination of the degree of local anomalies; to eliminate this mutual interference, this embodiment, based on the aforementioned local thermal resistance increment distribution, first attenuates the temperature rise weight of each unit before calculating and correcting the overall system's heat dissipation response parameters.
[0063] Specifically, the system compares the local heat transfer resistance increment of each node in the local heat transfer resistance increment distribution with an anomaly determination threshold. Nodes with local heat transfer resistance increments exceeding this threshold are identified as anomalous unit pieces, and regions consisting of consecutive anomalous unit pieces are identified as anomalous unit groups. The anomaly determination threshold can be determined based on the statistical distribution of the estimated local heat transfer resistance increments during the healthy operation phase of the radiator. Optionally, it can be the average heat transfer resistance increment during the healthy operation phase plus the standard deviation of a preset multiple, which can be configured to be between 2 and 4. To avoid instability in the weight decay calculation due to an excessively small threshold, the anomaly determination threshold should also not be less than a preset minimum positive threshold. That is, the anomaly detection threshold is taken as the statistical threshold and... The larger value in the range.
[0064] In this model, the temperature rise weight of normal chip cells remains unchanged, while the temperature rise weight of abnormal chip cells is reduced according to the degree of deviation of their local thermal resistance increment from the abnormality determination threshold. The chip cell temperature rise weight is based on the normalized weight of the effective heat dissipation area used to generate the overall average temperature rise curve in the aforementioned steps. If a node in the flow path topology model corresponds to a chip cell partition, the normalized weight of the effective heat dissipation area of each partition is used as the basic weight, and weight reduction is applied to abnormal partitions respectively. The weight reduction results of multiple partitions within the same chip cell together constitute the contribution of that chip cell to the overall average temperature rise curve.
[0065] In one implementation, for abnormal chip cells, the attenuation factor according to Confirmed, among which This is the threshold for anomaly detection. This represents the increase in local thermal resistance of the node corresponding to the abnormal plate unit; when hour The weights remain unchanged; when hour, Furthermore, the larger the increase in thermal resistance, the smaller the attenuation factor.
[0066] Continuous attenuation avoids the jump in the calculation of the average temperature rise of the whole machine that may be caused by hard rejection; the system regenerates the average temperature rise curve of the whole machine based on the attenuated temperature rise weight of the chip unit, and then uses the same first-order inertial model as the previous steps to fit and obtain the corrected heat dissipation response parameters of the whole machine; since the temperature deviation of the local abnormal area has been downweighted, the corrected steady-state gain and time constant more accurately reflect the actual heat dissipation capacity of the normal area of the heat sink, and are no longer skewed by the temperature deviation of a few deteriorated chip groups.
[0067] Optionally, when the number of abnormal chip groups is large and their proportion of the total effective heat dissipation area of the radiator exceeds a preset ratio threshold, the number of normal chip units may not be sufficient to support stable overall system parameter fitting. The system adds a mark to the output result to prompt maintenance personnel to conduct a special inspection of the radiator. The preset ratio threshold is determined according to the minimum normal effective heat dissipation area ratio required for overall system parameter fitting, so that the effective heat dissipation area of the normal chip units that have not been downweighted can still cover the main flow path of the radiator.
[0068] In box 402, the overall heat dissipation response parameters are updated and inverted to obtain and output the corrected local thermal resistance increment distribution and the corrected overall heat dissipation response parameters.
[0069] The initial overall thermal response parameters are replaced with the corrected overall thermal response parameters as the inversion background conditions, and the inversion of the local thermal resistance increment distribution is re-executed. Specifically, in the inversion model of the aforementioned steps, the overall thermal response benchmark used to constrain the effective heat dissipation area weighted average curve of the theoretical temperature rise of each node is updated from the initial steady-state gain and initial time constant to the corrected steady-state gain and corrected time constant, while the rest of the forward calculation process and constraints remain unchanged. The updated background conditions are closer to the actual thermal response level of the normal area of the radiator, and the corrected local thermal resistance increment distribution obtained is more accurate than the initial distribution.
[0070] The process involves first attenuating the weights of the local thermal resistance increment distribution and correcting the overall system parameters. Then, the corrected overall system parameters are used to update and invert the local thermal resistance increment distribution to obtain the corrected local thermal resistance increment distribution. The system stops correcting when the change in the corrected overall system thermal response parameters obtained from two consecutive correction rounds is less than a preset convergence threshold. The change in the corrected overall system thermal response parameters is the maximum relative change between the steady-state gain and the time constant, i.e., the [missing information - likely a specific value]. After the wheel correction, the following conditions are met:
[0071] in, For the first Steady-state gain obtained by wheel correction, For the first The time constant obtained by the round correction, To avoid the default small positive number with a denominator of zero; when When the value is less than the preset convergence threshold, the correction stops. The convergence threshold can be configured to be one percent to five percent of the previous round of parameter values. In actual implementation, the system can set the maximum number of correction rounds and stop the correction when the preset convergence threshold or the maximum number of correction rounds is reached. The first round of correction reduces the impact of local abnormal chip groups on the overall parameter estimation, and subsequent corrections are used to adjust the residual deviation.
[0072] After correction, the system outputs steady-state gain, time constant, and the corrected local thermal resistance increment distribution corresponding to the spatial layout of the heat sink. The steady-state gain and time constant constitute the corrected overall heat dissipation response parameters. An increase in the former indicates a decrease in the overall heat dissipation capacity, while an increase in the latter indicates a slower thermal response. The corrected local thermal resistance increment distribution uses the heat sink spatial layout as coordinates, and the value at each position is the estimated value of the thermal resistance increment of the corresponding heat sink unit. Maintenance personnel can directly observe which heat sink groups have deteriorated in heat transfer capacity and the relative degree of deterioration.
[0073] Optionally, the system can also compare the processing results of historical health event windows under the same load range, similar ambient temperature, and the same cooling conditions with the same operating conditions, and output the changing trends of the corrected overall heat dissipation response parameters and the corrected local thermal resistance increment distribution. The comparison with the same operating conditions helps to distinguish between normal fluctuations caused by differences in operating conditions and real changes caused by equipment deterioration. When the corrected overall heat dissipation response parameters of multiple consecutive event windows under the same operating conditions show a continuous upward trend, the system can output a warning of the heat dissipation efficiency change trend. This function is an optional extension. When the load change amplitude within the event window is too small, causing the change in the surface temperature of the radiator to be lower than the range that the infrared image temperature measurement accuracy can distinguish, the system pauses the efficiency evaluation of that window and waits for the next valid event window to avoid outputting unreliable results under low signal-to-noise ratio conditions.
[0074] Therefore, this method performs event window-based thermal state alignment before efficiency judgment, bringing load data, top-layer oil temperature, and surface temperature rise of the finned units under the same thermal state reference. This reduces misjudgments caused by differences in thermal inertia during load changes, fan start-stop switching, and other operating condition changes. By establishing a flow path topology model and inverting the local thermal resistance increment distribution under flow direction constraints, adjacency constraints, sparsity constraints, and non-negative lower bound constraints, this method decomposes the radiator from a whole temperature object into a node network that retains the oil flow direction and spatial adjacency relationships. This allows for the identification of local finned unit heat transfer performance degradation before the overall average temperature of the system has significantly increased. Simultaneously, by correcting the overall heat dissipation response parameters according to the weight of the finned unit temperature rise attenuation based on the local thermal resistance increment, and then updating the local inversion with the corrected overall parameters, the overall efficiency assessment and local anomaly location are mutually corrected under the same set of thermal state data and the same set of structural constraints. This avoids the accumulation of biases when processing independently, resulting in more stable and accurate output results.
[0075] Please see Figure 5 , Figure 5 This is a schematic diagram of a multi-modal data fusion-based heat sink heat dissipation efficiency monitoring system provided in an embodiment of this application. As shown in the figure, the system includes: The data acquisition module 501 is used to acquire load data, top oil temperature, ambient temperature, cooling status information, infrared thermal images and visible light images, and uniformly mark the time of the acquired data. The temperature rise sequence generation module 502 is used to identify the sheet unit boundary and sheet unit arrangement order from the visible light image, register the infrared thermal image to the visible light image, and generate a sheet unit surface temperature rise sequence according to the sheet unit boundary and the ambient temperature. The thermal state alignment module 503 is used to determine the running event window based on the load data, the top oil temperature and the cooling state information, construct a thermal input proxy sequence based on the load data in the running event window, and perform thermal state alignment on the top oil temperature and the plate unit surface temperature rise sequence using the thermal input proxy sequence as a reference sequence to obtain a thermal state alignment data group. The inversion processing module 504 is used to construct a flow path topology model based on the chip unit boundary and the chip unit arrangement order, and to obtain the initial whole machine heat dissipation response parameters and local thermal resistance increment distribution using the thermal state aligned data group and the flow path topology model. The correction output module 505 is used to fit the corrected overall heat dissipation response parameters according to the attenuation weight of the local heat transfer resistance increment distribution attenuation plate unit, and update the inversion with the corrected overall heat dissipation response parameters to obtain and output the corrected local heat transfer resistance increment distribution and the corrected overall heat dissipation response parameters.
[0076] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.
[0077] In particular, the methods and / or embodiments in this application can be implemented as computer software programs. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. When the computer program is executed by a processor, it performs the functions defined in the methods of this application.
[0078] Another embodiment of this application provides a storage medium storing computer program instructions thereon, which can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of this application.
[0079] In the above embodiments, the descriptions of each embodiment have different focuses. Parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. The above descriptions are merely preferred embodiments of this application and explanations of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by specific combinations of the above technical features, but should also cover other technical solutions formed by arbitrary combinations of the above technical features or their equivalent features without departing from the inventive concept.
Claims
1. A method for monitoring the heat dissipation efficiency of a plate-type heat sink based on multimodal data fusion, characterized in that, include: Collect load data, top oil temperature, ambient temperature, cooling status information, infrared thermal images, and visible light images, and uniformly mark the time; Identify the sheet unit boundaries and sheet unit arrangement order from the visible light image, register the infrared thermal image to the visible light image, and generate the sheet unit surface temperature rise sequence according to the sheet unit boundaries and ambient temperature; The operating event window is determined by load data, top oil temperature and cooling status information. Within the operating event window, a heat input proxy sequence is constructed from the load data. Using the heat input proxy sequence as a reference sequence, the top oil temperature and the surface temperature rise sequence of the wafer unit are thermally aligned to obtain a thermally aligned data set. A flow path topology model is constructed based on the chip unit boundaries and chip unit arrangement order; the initial overall heat dissipation response parameters and local thermal resistance increment distribution are obtained by using the thermal state aligned data set and the flow path topology model. The overall heat dissipation response parameters are fitted and corrected by attenuating the temperature rise weight of the unit according to the local thermal resistance increment distribution; the corrected overall heat dissipation response parameters are used to update the inversion, and the corrected local thermal resistance increment distribution and the corrected overall heat dissipation response parameters are obtained and output.
2. The method according to claim 1, characterized in that, Generating the surface temperature rise sequence of the chip unit includes: performing grayscale and contrast enhancement processing on the visible light image that has passed quality inspection; extracting the outer contour of the radiator and the inter-chip separation line using edge detection to obtain the chip unit boundary; determining the arrangement order of the chip units according to the direction from the upper oil pipe area to the lower oil pipe area and the horizontal arrangement position between the chip groups; extracting radiator corner points or significant structural feature points from the infrared thermal image and the visible light image; calculating the spatial transformation matrix according to the correspondence of feature points; and completing the registration according to the spatial transformation matrix; taking the area-weighted average of the infrared temperature pixel values in each registered chip unit area and subtracting the ambient temperature to obtain the surface temperature rise sequence of the chip unit.
3. The method according to claim 2, characterized in that, Determining the running event window includes: when the change in the load rate corresponding to the load data exceeds a preset threshold within a preset time period, when the fan start-stop state changes, or when the absolute value of the slope of the top oil temperature change exceeds a preset slope threshold, the corresponding time is determined as the starting point of the running event window; when the corresponding trigger quantity recovers to stability and the change rate of the surface temperature rise sequence of the chip unit drops below a preset convergence threshold, the corresponding time is determined as the ending point of the running event window.
4. The method according to claim 3, characterized in that, Constructing the heat input proxy quantity sequence includes: within the running event window, calculating an estimated load loss value based on the load data and the rated short-circuit loss parameter, and using the estimated load loss value as the principal component of the heat input proxy quantity sequence; when obtaining the core loss estimate, merging the core loss estimate as an additional component of the heat input proxy quantity sequence.
5. The method according to claim 4, characterized in that, The thermal state alignment includes: within the running event window, performing constrained dynamic time warping on the thermal input proxy sequence and the top oil temperature to form a first alignment relationship; performing constrained dynamic time warping on the thermal input proxy sequence and the chip unit surface temperature rise sequence to form a second alignment relationship; based on the first alignment relationship and the second alignment relationship, mapping the top oil temperature and the chip unit surface temperature rise sequence to a unified thermal state time axis, and associating the ambient temperature and the cooling state information with the same time marker to the unified thermal state time axis to obtain the thermal state alignment data group; wherein, the constraints of the constrained dynamic time warping include: the local delay of the oil temperature response relative to the thermal input proxy sequence is not negative, the local delay of the chip unit surface temperature rise sequence is not less than the local delay of the oil temperature response, and each local delay changes continuously along the time direction.
6. The method according to claim 5, characterized in that, The flow path topology model includes nodes, longitudinal connecting edges, and transverse connecting edges corresponding to the plate unit or plate unit partition. The node attributes include location identifier, plate unit surface temperature rise value after thermal state alignment, and effective heat dissipation area. The longitudinal connecting edge represents the flow path direction relationship between adjacent nodes along the oil flow direction, and the transverse connecting edge represents the parallel relationship between nodes at the same oil flow level and spatially adjacent nodes. When inverting the local heat transfer resistance increment distribution, the local heat transfer resistance increment of each node is taken as the unknown quantity, the theoretical temperature rise is calculated node by node along the flow path topology model, and the estimated value of the local heat transfer resistance increment of each node is solved based on the deviation between the theoretical temperature rise and the plate unit surface temperature rise value.
7. The method according to claim 6, characterized in that, When solving for the estimated local heat transfer resistance increment of each node, flow direction constraints, adjacency constraints, sparsity constraints, and non-negative lower bound constraints are applied. The flow direction constraint stipulates that the estimated temperature drop difference of adjacent nodes in the same oil flow direction is arranged in the order of decreasing temperature drop from the upper oil gathering pipe region to the lower oil gathering pipe region. The adjacency constraint adds the sum of squares of the local heat transfer resistance increment difference of adjacent nodes to the objective function. The sparsity constraint adds the L1 norm of the local heat transfer resistance increment vector composed of the local heat transfer resistance increments of each node to the objective function. The non-negative lower bound constraint stipulates that the local heat transfer resistance increment of each node is not less than zero.
8. The method according to claim 7, characterized in that, Obtaining the initial overall thermal response parameters includes: weighting the surface temperature rise of each node's chip unit after thermal alignment according to the effective heat dissipation area to generate an overall average temperature rise curve; establishing the input-output relationship between the overall average temperature rise curve and the thermal input proxy sequence; fitting the input-output relationship using a first-order inertial model, and using the fitted steady-state gain and time constant as the initial overall thermal response parameters.
9. The method according to claim 8, characterized in that, The fitting and correction of the overall heat dissipation response parameters includes: comparing the local heat transfer resistance increment of each node in the local heat transfer resistance increment distribution with an anomaly judgment threshold; identifying the chip unit corresponding to the node whose local heat transfer resistance increment exceeds the anomaly judgment threshold as an abnormal chip unit; and identifying the region composed of consecutive abnormal chip units as an abnormal chip group; the chip unit temperature rise weight of normal chip units remains unchanged, while the chip unit temperature rise weight of abnormal chip units is reduced according to the degree of deviation of its local heat transfer resistance increment from the anomaly judgment threshold; and regenerating the overall average temperature rise curve based on the reduced chip unit temperature rise weight, and fitting the corrected overall heat dissipation response parameters.
10. The method according to claim 9, characterized in that, The process of updating the inversion with the corrected overall heat dissipation response parameters includes: replacing the initial overall heat dissipation response parameters with the corrected overall heat dissipation response parameters as the inversion background condition, re-performing the inversion of the local thermal resistance increment distribution to obtain the corrected local thermal resistance increment distribution; stopping the correction when the change in the corrected overall heat dissipation response parameters obtained from two adjacent correction rounds is less than a preset convergence threshold, and outputting the steady-state gain, the time constant, and the corrected local thermal resistance increment distribution corresponding to the spatial layout of the finned heat sink.