A heat dissipation performance testing system and testing method of a control board crystal press block

Abstract

The application discloses a kind of control board crystal briquettes heat dissipation performance test system and test method, it is related to electronic equipment heat dissipation test technical field, including in crystal briquettes specified position deployment multiple temperature detection device;By loading device, preset contact pressure and heat power are applied to simulate actual working condition;Continuous acquisition real-time temperature variation data, form time-related temperature data sequence;According to the sequence, the overall temperature rise slope in preset time window is calculated and the hotspot position coordinates of the maximum temperature rise slope are identified;Based on this, a dynamic three-dimensional heat conduction model is constructed;The model is compared and analyzed in multiple dimensions with the preset standard heat dissipation model in the reference heat dissipation performance database;Based on the analysis result, the overall heat dissipation efficiency parameter and hotspot heat dissipation coefficient parameter are calculated;According to the parameter, the heat dissipation performance evaluation grade is determined and the quantitative test report is generated.The method realizes the dynamic, quantitative and standardized evaluation of heat dissipation performance.

Description

Technical Field

[0001] This invention belongs to the field of heat dissipation testing technology for electronic devices, specifically a heat dissipation performance testing system and method for control board crystal pressure blocks. Background Technology

[0002] The control board is the core control unit of electronic equipment, and the crystal holder is a key component on the control board used to fix crystal components and ensure stable crystal operation. It needs to be tightly fitted to the crystal for positioning and heat conduction, preventing the crystal from accumulating heat and affecting the overall performance of the control board. In the research and development and production of electronic equipment, especially highly integrated control boards, the heat dissipation performance of the crystal holder, as a critical component, directly affects the long-term stability and reliability of the entire circuit. Current technologies for evaluating the heat dissipation performance of such holders typically rely on measuring their steady-state surface temperature under specific loads or using a thermal imager to capture the temperature field distribution at a certain moment. These methods mainly judge the performance by recording the highest temperature after reaching thermal equilibrium or observing a rough temperature cloud map; the testing process is relatively static.

[0003] Existing technical solutions have shortcomings. Static temperature measurements or single-moment thermal imaging cannot fully reflect the dynamic thermal response characteristics of the heat dissipation structure during loading. This makes it difficult to predict potential thermal failure risks in advance. Furthermore, conventional methods lack accurate extraction of dynamic parameters of thermal behavior and fail to systematically correlate and compare real-time test data with standardized heat dissipation benchmarks. Test results are mostly qualitative descriptions or isolated data, unable to output quantitative heat dissipation coefficients for horizontal comparison. The evaluation process relies on engineer experience, is highly subjective, and lacks repeatability and consistency. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art; Therefore, this invention proposes a method for testing the heat dissipation performance of a control board crystal clamp, comprising: S1: Deploy multiple temperature detection devices at designated locations on the crystal block of the control board under test to obtain real-time temperature change data of the crystal block during the test process; S2: Apply a preset contact pressure and thermal power to the control board crystal block using a loading device to simulate the actual working load state of the control board crystal block. Continuously collect real-time temperature change data obtained by the multiple temperature detection devices during the application of the contact pressure and thermal power to form a time-related temperature data sequence. S3: Based on the temperature data sequence, calculate the overall temperature rise slope of the control board crystal block within a preset time window, and identify the coordinates of the hot spot with the largest temperature rise slope. S4: Construct a dynamic three-dimensional heat conduction model based on the overall temperature rise slope and the hot spot location coordinates; S5: Perform a multi-dimensional comparative analysis between the dynamic three-dimensional heat conduction model and the preset standard heat dissipation model in the reference heat dissipation performance database; S6: Based on the results of the multi-dimensional comparative analysis, the overall heat dissipation efficiency parameter and hot spot heat dissipation coefficient parameter of the control board crystal block are calculated. S7: Based on the overall heat dissipation efficiency parameter and the hot spot heat dissipation coefficient parameter, determine the heat dissipation performance evaluation level corresponding to the control board crystal block in the reference heat dissipation performance database, and generate a final test report containing quantitative parameters and heat dissipation performance evaluation level.

[0005] Further, step S1 specifically includes: Obtain a three-dimensional structural model of the control board crystal block, and analyze the geometry and material distribution of the contact surface between the crystal and the block in the three-dimensional structural model; Based on the geometry and material distribution, a preliminary simulation was performed using the thermal conductivity finite element analysis method to predict the potential temperature distribution region of the control board crystal block under working load. Based on the predicted potential temperature distribution area, regions with significant temperature gradient changes and key nodes on the heat dissipation path are selected as the designated locations. At each of the designated locations, a high-response-speed and high-precision temperature detection device is installed to ensure that the thermal probe of the temperature detection device forms a tight physical contact with the surface of the crystal block of the control board; The installed temperature detection devices were numbered and their location coordinates were recorded, and a physical channel connection was established with the temperature acquisition system.

[0006] Further, step S2 specifically includes: Fix the control board crystal block onto a special test fixture, ensuring that the bottom heat dissipation surface of the control board crystal block is aligned with the cooling plate of the test platform; The pressure control module of the loading device is activated, and a uniformly distributed contact pressure is applied to the surface of the crystal block of the control board according to the preset pressure loading curve, and the actual pressure value is monitored to achieve a steady state. At the same time, the thermal power loading device is activated, and electrical energy with preset power and frequency is integrated into the crystal of the control board crystal block to simulate the actual heat generation of the crystal during operation. The contact pressure and thermal power are monitored and adjusted in real time to keep them stable within the set tolerance range until the preset loading duration is reached. The data acquisition function of all temperature detection devices is activated simultaneously to record the temperature data of their respective locations at a uniform high-frequency sampling rate; The temperature data collected by all temperature detection devices at each sampling time are aggregated to form a raw data point set containing timestamps, detection device numbers, and temperature values. Based on the timestamp, all data points in the original data point set are sorted by time and synchronized to ensure that data from different temperature detection devices correspond strictly on the time axis. The sorted and aligned data is grouped according to the detection device number to form a list of temperature readings arranged in chronological order at each specified location. By integrating the lists of temperature readings at all specified locations, a multi-channel temperature data sequence correlated with time is constructed.

[0007] Furthermore, step S3 specifically includes: From the multi-channel temperature data sequence, extract the data segment from the start of thermal power loading to the end of the preset time window; The overall average temperature at each sampling time is calculated by averaging the temperature readings of all temperature detection devices in the captured data segment. Plot the curve of the overall average temperature changing over time with time on the horizontal axis and the overall average temperature on the vertical axis. The curve of the overall average temperature changing with time is subjected to piecewise linear fitting, and the rising stage with the highest linearity is selected. The slope of the rising stage is calculated as the overall temperature rising slope. Meanwhile, for the data from each temperature detection device in the intercepted data segment, the corresponding temperature rise rate is calculated respectively. Compare the temperature rise rate of all temperature detection devices, and identify the location of the temperature detection device with the maximum temperature rise rate as a hot spot. Read the serial number of the temperature detection device identified as a hotspot, query and obtain the installation coordinates of the temperature detection device based on the serial number, and the installation coordinates are the location coordinates of the hotspot.

[0008] Furthermore, step S4 specifically includes: Based on the geometric model of the control board crystal block, material thermal properties, applied contact pressure, applied thermal power, and environmental boundary conditions, a basic three-dimensional heat conduction model is established. From the multi-channel temperature data sequence, extract snapshots of the transient temperature distribution of the entire control board crystal block at several discrete key time points; The overall temperature rise slope is used as a global constraint condition of the model to optimize the overall equivalent thermal conductivity of the material in the basic three-dimensional heat conduction model. Using the hot spot location coordinates and the transient temperature distribution snapshot, an inversion algorithm is used to locally correct the thermal resistance parameters and heat source distribution in the contact area between the block and the crystal in the basic three-dimensional heat conduction model; By combining global constraints and local correction results, the basic three-dimensional heat conduction model is iteratively updated so that the temperature field evolution process simulated by the model matches the measured temperature data sequence within a preset error range. Finally, a dynamic three-dimensional heat conduction model that can accurately reflect the heat dissipation characteristics of the crystal block of the specific control board under test is obtained.

[0009] Furthermore, step S5 specifically includes: The multi-dimensional comparative analysis includes a comparison of the initial temperature distribution, the formation time of the steady-state temperature field, and the hotspot temperature values ​​at specific time points. From the reference heat dissipation performance database, a preset standard heat dissipation model with the same geometric configuration and basic material specifications as the crystal block of the control board under test is retrieved; Temperature field data for the entire simulation time domain from the start of loading to reaching quasi-steady state are extracted from the dynamic three-dimensional heat conduction model. Similarly, temperature field data in the corresponding time domain is extracted from the preset standard heat dissipation model; Align the temperature field data extracted from the two models along the time dimension; By comparing the three-dimensional temperature fields of the two models at corresponding time points, the average and root mean square values ​​of the temperature differences at various points in space are calculated to evaluate the differences in the initial temperature distribution. Compare the time required for the two models to reach the preset steady-state temperature threshold to evaluate the difference in the steady-state temperature field formation time. During the simulation of the two models, the hotspot locations and their temperature values ​​were identified and recorded respectively. The absolute values ​​of the hotspot temperatures at the same key time points were compared to analyze the differences in hotspot temperature values ​​at specific time points. Based on the evaluation results of the differences between the dynamic three-dimensional heat conduction model and the preset standard heat dissipation model, a multi-dimensional comparative analysis was completed.

[0010] Furthermore, step S6 specifically includes: Based on the ratio of the overall temperature rise slope to the corresponding standard overall temperature rise slope in the preset standard heat dissipation model, the normalized overall heat dissipation efficiency parameter is defined and calculated. Based on the difference in the steady-state temperature field formation time, the percentage of the steady-state establishment speed of the dynamic three-dimensional heat conduction model relative to the preset standard heat dissipation model is calculated. This percentage is used as a correction factor for the overall heat dissipation efficiency parameter, and the normalized overall heat dissipation efficiency parameter is weighted and corrected to obtain the final overall heat dissipation efficiency parameter. The difference between the hot spot temperature rise and the hot spot temperature value simulated by the dynamic three-dimensional heat conduction model at key time points is calculated. Simultaneously, the deviation distance between the hotspot location coordinates and the standard hotspot location of the preset standard heat dissipation model is considered; By combining the hot spot temperature rise difference value and the deviation distance, the hot spot heat dissipation coefficient parameter, which characterizes the local heat dissipation capability, is calculated through a preset hot spot heat dissipation coefficient mapping function.

[0011] Furthermore, step S7 specifically includes: A heat dissipation performance evaluation standard table is established in the reference heat dissipation performance database. The heat dissipation performance evaluation standard table divides multiple consecutive heat dissipation performance evaluation levels according to the overall heat dissipation efficiency parameter range and the hot spot heat dissipation coefficient parameter range. The calculated overall heat dissipation efficiency parameters are compared with the range of overall heat dissipation efficiency parameters in the heat dissipation performance evaluation standard table to preliminarily determine the grade range corresponding to the overall heat dissipation efficiency parameters. The calculated hotspot heat dissipation coefficient parameters are compared with the range of hotspot heat dissipation coefficient parameters in the heat dissipation performance evaluation standard table to preliminarily determine the grade range corresponding to the hotspot heat dissipation coefficient parameters. A two-parameter weighted decision rule is adopted to comprehensively evaluate the grade range initially determined by the two parameters, and finally determine a unique heat dissipation performance evaluation grade.

[0012] Furthermore, the present invention also includes a heat dissipation performance testing system for a control board crystal block, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the heat dissipation performance testing method for a control board crystal block as described above.

[0013] Compared with the prior art, the beneficial effects of the present invention are: Precisely pinpointing critical heat dissipation weaknesses in the crystal clamping block ensures stable crystal operation: Addressing the characteristic of the control board's crystal clamping block where the contact surface between the crystal and the clamping block forms the core heat conduction path, this technology calculates the overall temperature rise slope within a preset time window using continuously acquired temperature data sequences and accurately identifies the coordinates of the hotspot location with the largest slope. This technique directly captures the dynamic process of heat accumulation in the initial heating stage of the heat sink, rather than relying solely on conventional steady-state temperature results. The overall temperature rise slope quantifies the immediate response capability of the heat dissipation system, while the identification of hotspot coordinates precisely locates the weakest link in heat dissipation from the perspective of dynamic change rate, rather than simply relying on absolute temperature levels. This shifts the evaluation of heat dissipation performance from static result judgment to dynamic process analysis, enabling earlier identification of heat dissipation design flaws.

[0014] The system outputs specific quantitative parameters for crystal compacts to support their selection and process optimization: A dynamic three-dimensional heat conduction model is constructed based on real-time test data and compared with preset standard models in a reference database from multiple dimensions. The constructed model is rooted in the dynamic temperature field evolution data of actual tests, reflecting the real heat conduction path under specific pressure and power loads. Unlike comparisons with theoretical models with fixed parameters or historical data of a single product, multi-dimensional comparisons with standard heat dissipation models establish a quantitative gap between the current test object and the ideal or qualified benchmark. Through this comparison, the effective overall heat dissipation efficiency parameters and hotspot heat dissipation coefficient parameters are automatically calculated, transforming complex temperature field information into comparable indicators, ultimately mapping them to standardized performance evaluation levels. This realizes the transformation of test results from qualitative description to quantitative grading, and the evaluation process no longer relies on manual interpretation of thermal images or temperature curves. Attached Figure Description

[0015] Figure 1 This is a step diagram of the heat dissipation performance testing method for the control board crystal block described in this invention; Figure 2 A flowchart for simulating workload states; Figure 3 A bar chart comparing the evaluation parameters of the heat dissipation performance of the control board's crystal pressure block; Figure 4 The temperature rise characteristic curve of the control board crystal pressing block under thermal power loading is shown in the figure. Figure 5 A bar chart comparing the heat dissipation performance parameters of five control board crystal block samples. Detailed Implementation

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 This invention provides a method for testing the heat dissipation performance of a control board crystal pressure block, the implementation of which includes: S1: Deploy multiple temperature detection devices at designated locations on the crystal block of the control board under test to obtain real-time temperature change data of the crystal block during the test process; S2: Apply a preset contact pressure and thermal power to the control board crystal block using a loading device to simulate the actual working load state of the control board crystal block. Continuously collect real-time temperature change data obtained by the multiple temperature detection devices during the application of the contact pressure and thermal power to form a time-related temperature data sequence. S3: Based on the temperature data sequence, calculate the overall temperature rise slope of the control board crystal block within a preset time window, and identify the coordinates of the hot spot with the largest temperature rise slope. S4: Construct a dynamic three-dimensional heat conduction model based on the overall temperature rise slope and the hot spot location coordinates; S5: Perform a multi-dimensional comparative analysis between the dynamic three-dimensional heat conduction model and the preset standard heat dissipation model in the reference heat dissipation performance database; S6: Based on the results of the multi-dimensional comparative analysis, the overall heat dissipation efficiency parameter and hot spot heat dissipation coefficient parameter of the control board crystal block are calculated. S7: Based on the overall heat dissipation efficiency parameter and the hot spot heat dissipation coefficient parameter, determine the heat dissipation performance evaluation level corresponding to the control board crystal block in the reference heat dissipation performance database, and generate a final test report containing quantitative parameters and heat dissipation performance evaluation level.

[0018] In one embodiment of the present invention, step S1 is specifically implemented as follows: A three-dimensional structural model of the control board crystal block is obtained. The geometry and material distribution of the contact surface between the crystal and the block in the three-dimensional structural model are analyzed. Based on the geometry and material distribution, a preliminary simulation is performed using the thermal conductivity finite element analysis method to predict the potential temperature distribution area of ​​the control board crystal block under working load. Based on the predicted potential temperature distribution area, regions with significant temperature gradient changes and key nodes on the heat dissipation path are selected as designated locations. A high-response-speed and high-precision temperature detection device is installed at each designated location, and the thermal probe of the temperature detection device is ensured to form a tight physical contact with the surface of the control board crystal block. The multiple temperature detection devices are numbered and their position coordinates are recorded, and a physical channel connection with the temperature acquisition system is established.

[0019] In practical implementation, a method for testing the heat dissipation performance of a control board crystal clamping block involves the deployment of a temperature detection device. The deployment process begins with acquiring a three-dimensional structural model of the control board crystal clamping block under test. The three-dimensional structural model is usually derived from computer-aided design drawings or three-dimensional scanning data. Analyzing the geometry and material distribution of the contact surface between the crystal and the clamping block in the three-dimensional structural model is the basis for determining the measurement points. The acquired three-dimensional structural model is preliminarily simulated using the finite element method of thermal conductivity. The boundary conditions of the simulation are set to the same contact pressure and thermal power loading values ​​as those in subsequent actual tests. The simulation calculations predict the potential temperature distribution area of ​​the control board crystal clamping block under working load conditions. The simulation results will display the surface and internal temperature gradients in the form of a contour map.

[0020] Based on the predicted potential temperature distribution area, regions with significant temperature gradient changes and key nodes on the heat dissipation path are selected as designated locations for deploying temperature sensing devices. In some embodiments, regions with significant temperature gradient changes refer to areas with sharp color transitions in the simulation cloud map. Key nodes on the heat dissipation path include the contact center between the pressure block and the heat sink substrate, the crystal edge, and the area around the pressure block fixing screws. At each designated location, a high-response and high-precision temperature sensing device is installed. During installation, it is ensured that the thermal probe of the temperature sensing device forms a tight physical contact with the measured surface of the control board crystal pressure block. Thermal grease can be used to fill the microscopic gaps between the probe and the surface to reduce contact thermal resistance. All installed temperature sensing devices are uniquely numbered and their location coordinates are recorded. The mapping relationship between the number and the physical location coordinates is stored in a configuration file. Simultaneously, the signal output terminal of each temperature sensing device is connected to the corresponding physical input channel of the temperature acquisition system, thereby completing the deployment and preparing for subsequent data acquisition.

[0021] In some embodiments, the selection of a specified location can be based on a quantified decision function that comprehensively considers the simulated predicted temperature gradient value and the structural representativeness of the location. It can be understood that the decision function can be constructed by calculating and weighting the spatial derivative of the simulated temperature field data. For example, an optional location selection priority index can be evaluated using the following relationship: ; in: Represents coordinate points The deployment priority index indicates that the higher the index value, the greater the likelihood that a point will be selected as the designated location. This represents the magnitude of the temperature gradient at this coordinate point in the preliminary simulation, reflecting the degree of drastic temperature change at that point. It is a structural characteristic function, and its value depends on whether the point is located at a characteristic position such as the crystal edge, screw hole, or geometric center of the compact; and These are pre-set weighting coefficients used to balance the relative importance of temperature gradient factors and structural feature factors in the decision-making process. It can be understood that by calculating and ranking the priority indices of all candidate points on the surface of the control plate crystal block, the most representative set of temperature monitoring points can be objectively selected.

[0022] In practical implementation, the model selection of the temperature detection device must meet the temperature range, response time, and accuracy requirements of the test, such as using a T-type thermocouple or a platinum resistance temperature sensor. Installation and fixing methods can include high-temperature adhesive bonding, mechanical clamping, or pre-embedding in a miniature pressure block with a spring structure. The core purpose is to maintain the stability and consistency of the contact state between the thermal probe and the pressure block surface during the test. When numbering and recording the position coordinates of the temperature detection devices, the coordinate recording can be based on a two-dimensional or three-dimensional coordinate system established by the control board's crystal pressure block itself, recording the coordinates of the center point of each probe. With number Accurately associate and input data into the database. After establishing physical channel connections, channel configuration is required in the temperature acquisition software. Each physical input channel must be bound to its corresponding sensor number and location coordinates to ensure that every set of data acquired subsequently can be accurately traced to its specific spatial location on the control board's crystal block.

[0023] In one embodiment of the present invention, step S2 is implemented as follows: See Figure 2The control board crystal block is fixed on a special test fixture, and the bottom heat dissipation surface of the control board crystal block is aligned with the cooling plate of the test platform. The pressure control module of the loading device is started, and a uniformly distributed contact pressure is applied to the surface of the control board crystal block according to the preset pressure loading curve. At the same time, the actual pressure value is monitored to reach a steady state. Simultaneously, the thermal power loading device is started, and electrical energy with preset power and frequency is integrated into the crystal of the control board crystal block to simulate the actual heat generation of the crystal during operation. The contact pressure and thermal power are monitored and adjusted in real time to keep them stable within the set tolerance range until the preset loading duration is reached. The data acquisition functions of all temperature detection devices are simultaneously activated, and temperature data at their respective locations are recorded at a uniform high-frequency sampling rate. The temperature data collected by all temperature detection devices at each sampling moment are aggregated to form a raw data point set containing timestamps, detection device numbers, and temperature values. All data points in the raw data point set are sorted and synchronized according to timestamps to ensure that the data from different temperature detection devices correspond strictly on the time axis. The sorted and aligned data are grouped according to the detection device number to form a temperature reading list arranged in chronological order at each specified location. The temperature reading lists at all specified locations are integrated to construct a time-correlated multi-channel temperature data sequence.

[0024] In practice, the heat dissipation performance test method of the control board crystal block enters the load simulation and data acquisition stage. The operator places the control board crystal block under test, which has been equipped with temperature detection device, on the carrier platform of the special test fixture. The special test fixture is designed with adjustable clamping arms and positioning pins. By tightening the fastening screws on the clamping arms and aligning with the positioning pin holes, the control board crystal block is accurately fixed and free from drift constraints in the test plane. At the same time, the bottom heat dissipation surface of the control board crystal block is fully aligned and parallel to the surface of the circulating liquid cooling plate inside the test platform by manual visual inspection or with the help of alignment instruments, so as to establish controllable heat dissipation boundary conditions. The pressure control module of the loading device is activated. The pressure control module drives the servo motor or pneumatic actuator to push the pressure head downward. A pressure equalizing pad matching the shape of the pressure block surface is installed at the bottom of the pressure head. According to the preset pressure loading curve, a uniformly distributed contact pressure is applied to the upper surface of the control board crystal pressure block. The preset pressure loading curve defines the time history and stable holding stage of the pressure from zero to the target value. The pressure sensor feeds back the actual pressure value to the control system in real time. The control system makes the actual pressure value fluctuate around the target value through closed-loop regulation and eventually reach a steady state.

[0025] Simultaneously, the operator activates an independent thermal power loading device, which is a programmable DC power supply or a high-frequency current source. Its output electrode is connected via leads to the electrodes of a crystal element integrated inside the crystal block on the control board, inputting electrical energy with a preset power and frequency into the crystal. The electrical energy is converted into heat energy within the crystal due to resistance or dielectric loss, thus simulating the actual heat generated by the crystal during real circuit operation. Throughout the load simulation, the data acquisition system monitors and records the actual values ​​of the contact pressure and thermal power output in real time. The control algorithm dynamically adjusts the output commands of the loading device based on the deviation between the monitored values ​​and their respective set values, ensuring that the contact pressure and thermal power remain stable within the set tolerance range. For example, the contact pressure fluctuation should not exceed ±2% of the target value, and the thermal power fluctuation should not exceed ±1% of the preset power. This loading state is maintained until the preset loading duration is reached, which should cover the entire thermal transient process from the initial temperature rise to the entry into a quasi-steady state.

[0026] In some embodiments, the preset pressure loading curve is not a simple step function, but a function containing a slow rising segment and a holding segment, designed to avoid impact loads and simulate actual assembly conditions. It can be understood that the pressure loading curve can be expressed as a piecewise function, and an optional piecewise function form is as follows: ; in: Indicates time The contact pressure setting value at any given time; This represents the target contact pressure value that ultimately needs to be achieved and maintained; This is the ramp time required for the pressure to increase linearly from zero to the target pressure value. By setting an appropriate ramp time, contact between the pressure block and the heat dissipation surface can be established smoothly, reducing the impact of mechanical shock on the initial state of the test system. In practice, the pressure control module generates a time-varying pressure setting command based on this function and combines it with feedback from the pressure sensor to achieve precise tracking.

[0027] In practical implementation, once the contact pressure and thermal power reach a stable maintenance phase, the data acquisition system synchronously triggers all temperature sensing devices to begin operation. The data acquisition function of each temperature sensing device records temperature data at its respective location using a uniform high-frequency sampling rate, which can be set from several times to hundreds of times per second depending on the thermal response speed. At each sampling moment, the data acquisition system reads a temperature voltage value from the signal channel of each temperature sensing device. After analog-to-digital conversion and scaling transformation, a temperature value with a timestamp and channel identifier is obtained. Collecting the data from all channels at that moment forms a raw data point set containing a timestamp, sensing device number, and temperature value. Based on the timestamp sequence, the post-processing software of the data acquisition system performs time sorting and synchronization alignment on all data points in the raw data point set. This sorting and alignment ensures that data from different temperature sensing devices strictly correspond on the timeline. Even if there is a slight hardware trigger delay in the acquisition of each channel, the software will use an interpolation algorithm to correct the data to a unified time reference point.

[0028] The sorted and aligned data is grouped according to the detector number, which matches the number recorded during deployment. The grouping operation extracts temperature readings obtained by the same detector at different sampling times, arranging them chronologically to form a one-dimensional temperature time series for that specified location—a temperature reading list. This grouping operation is repeated to generate a corresponding temperature reading list for each specified location where a temperature detector is deployed. Finally, all temperature reading lists for specified locations are integrated to construct a multi-channel temperature data sequence strictly correlated with time. This multi-channel temperature data sequence is a two-dimensional array or matrix data structure, where row indices correspond to sampling time points, column indices correspond to detector numbers, and each element in the matrix is ​​the temperature measurement value at a specific time and location. This data sequence forms the basis for all subsequent calculations and analyses.

[0029] In one embodiment of the present invention, the specific implementation of steps S3-S4 is as follows: A data segment is extracted from the multi-channel temperature data sequence, from the start of thermal power loading to the end of the preset time window. The temperature readings of all temperature detection devices in the extracted data segment are arithmetically averaged to calculate the overall average temperature at each sampling moment. A curve of the overall average temperature changing with time is plotted with time as the horizontal axis and the overall average temperature as the vertical axis. Piecewise linear fitting is performed on the curve of the overall average temperature changing with time, and the rising stage with the highest linearity is selected to calculate the slope of the rising stage as the overall temperature rise slope. At the same time, the temperature rise rate of each temperature detection device in the extracted data segment is calculated. The temperature rise rates of all temperature detection devices are compared, and the location of the temperature detection device with the maximum temperature rise rate is identified as a hot spot. The number of the temperature detection device identified as a hot spot is read, and the installation coordinates of the temperature detection device are retrieved based on the number to be used as the hot spot location coordinates. A basic three-dimensional heat conduction model is established based on the geometric model of the control board crystal compact, material thermal properties, applied contact pressure, applied heat power, and environmental boundary conditions. Transient temperature distribution snapshots of the entire control board crystal compact at several discrete key time points are extracted from multi-channel temperature data sequences. The overall temperature rise slope is used as a global constraint to optimize the overall equivalent thermal conductivity of the material in the basic three-dimensional heat conduction model. Using the hotspot coordinates and transient temperature distribution snapshots, an inversion algorithm is employed to locally correct the thermal resistance parameters and heat source distribution in the contact area between the compact and the crystal in the basic three-dimensional heat conduction model. The basic three-dimensional heat conduction model is iteratively updated by combining the global constraint and local correction results to match the simulated temperature field evolution process with the measured temperature data sequence within a preset error range. Finally, a dynamic three-dimensional heat conduction model that accurately reflects the heat dissipation characteristics of the specific control board crystal compact under test is obtained.

[0030] In practical implementation, the process of processing multi-channel temperature data sequences to extract feature parameters and build a model begins. The system extracts corresponding data segments from the complete multi-channel temperature data sequence based on the start and end times of a preset time window. The start time of the preset time window is typically defined as the moment when thermal power loading begins, while the end time is set according to the thermal time constant of the control board's crystal pressing block to ensure coverage of the main temperature rise phase. The arithmetic mean of the temperature readings from all temperature detection devices in the extracted data segments at each sampling moment is calculated. This involves summing the temperature values ​​of all channels at each sampling moment and dividing by the total number of channels to calculate the overall average temperature for each sampling moment. The calculated time-overall average temperature data points are plotted as a curve with time on the horizontal axis and the overall average temperature on the vertical axis, yielding a curve showing the overall average temperature changing over time.

[0031] The system analyzes the curve of the overall average temperature changing over time. A piecewise linear fitting algorithm is used to identify the temperature rise stage with the highest linearity in the curve. This algorithm calculates the linear correlation coefficient for different intervals using a sliding time window to determine the best-fit segment. The interval with the linear correlation coefficient closest to 1 is selected, and univariate linear regression analysis is performed on the data within this interval. The slope of the calculated regression line is then determined as the overall temperature rise slope. Simultaneously, the system independently processes the data from each temperature sensing device channel within the extracted data segment. For each channel, the temperature-time data is also linearly fitted in the corresponding main temperature rise stage. The slope of the fitted line is then the temperature rise rate at that location. After calculating the temperature rise rate for all channels, the system compares the values ​​of all channels and identifies the temperature sensing device channel with the highest temperature rise rate as the hotspot channel. The system reads the number of the identified hotspot channel and queries a pre-stored number-location coordinate mapping table to obtain the installation coordinates of the temperature sensing device on the control board crystal block corresponding to that number. These installation coordinates are then defined as the hotspot location coordinates.

[0032] In some embodiments, the rate of temperature rise can be calculated by time-difference of the temperature data. An optional method for calculating the instantaneous rate of temperature rise is as follows: ; in: Indicates the first Temperature detection device number in time interval The average rate of temperature rise within; Representing the Temperature detection device at time Temperature values ​​recorded at all times; time points and Selected from a linear interval within the overall temperature rise phase, the values ​​of all channels were calculated. By comparing the magnitudes, the hottest channels with the fastest temperature rise can be quickly located. This method can be understood as providing an alternative calculation scheme besides linear fitting.

[0033] In practical implementation, the process begins with constructing a dynamic three-dimensional heat conduction model based on the obtained overall temperature rise slope and hotspot location coordinates. First, a basic three-dimensional heat conduction model is established in finite element analysis software based on the computer-aided design geometric model of the control plate crystal block, material thermal properties such as thermal conductivity and specific heat capacity of the block and crystal materials, the contact pressure applied during testing, the thermal power input to the crystal, and boundary conditions such as the environmental convective heat transfer coefficient. From the multi-channel temperature data sequence, several discrete key time points representing the characteristics of the temperature rise process are selected, such as the initial, middle, and near-steady-state time points. Temperature values ​​recorded by all temperature detection devices at these time points are extracted, and combined with the known spatial coordinates of the detection points, a snapshot of the transient temperature distribution of the entire control plate crystal block at these moments is reconstructed using a spatial interpolation algorithm. The calculated overall temperature rise slope is used as a global constraint and substituted into the parameter optimization process of the basic three-dimensional heat conduction model. By adjusting the overall equivalent thermal conductivity of the encapsulation material in the model, the difference between the rise slope of the simulated overall average temperature curve and the measured overall temperature rise slope is minimized.

[0034] Using the identified hotspot coordinates and reconstructed snapshots of multiple transient temperature distributions, an inversion algorithm is employed to locally correct the thermal resistance parameters of the contact area between the pressure block and the crystal, as well as the uniformity of heat source distribution within the crystal, in the basic three-dimensional heat conduction model. The inversion algorithm adjusts these local parameters by minimizing the difference between the model-predicted temperature field and the measured temperature snapshots at various points in space (especially in hotspot areas). Combining the global optimization results of the overall equivalent thermal conductivity with the local correction results of parameters such as contact thermal resistance, a complete iterative update of the basic three-dimensional heat conduction model is performed. The updated model is then re-simulated transiently, and its output temperature field evolution data (including spatial temperature distributions at different times and temperature histories at specific points) is compared with the measured multi-channel temperature data sequence to calculate the error norm. If the error norm exceeds a preset error range, the iterative process of parameter optimization, local correction, and model update is repeated until the model simulation results match the measured data within the preset error range. Ultimately, a dynamic three-dimensional heat conduction model that accurately reflects the heat dissipation characteristics of the crystal pressure block of the specific control board under test is obtained.

[0035] See Figure 3This is a bar chart comparing the thermal performance evaluation parameters of the control board crystal chip, showing the normalized parameter values ​​of "overall thermal efficiency" and "hot spot thermal coefficient" under different thermal performance levels. Both parameters decrease synchronously with the decrease in evaluation level, showing a significant positive correlation, reflecting the consistency between overall thermal performance and local hot spot thermal performance. At the same level, the overall thermal efficiency is always slightly higher than the hot spot thermal coefficient, indicating that the thermal performance of the hot spot area is the weak point of the entire control board crystal chip. From "excellent" to "unsatisfactory," the overall thermal efficiency decreases from 0.92 to 0.62, and the hot spot thermal coefficient decreases from 0.88 to 0.60, a decrease of nearly 33%, reflecting the quantitative basis of the level classification. This chart can serve as a rapid assessment tool for thermal performance. Testers only need to compare the measured values ​​of the two parameters with the corresponding level ranges in the chart to complete the preliminary performance rating, and it can also intuitively reflect the consistency level of the product in batch testing.

[0036] In one embodiment of the present invention, the specific implementation of steps S5-S6 is as follows: Multi-dimensional comparative analysis includes comparing the initial temperature distribution, the formation time of the steady-state temperature field, and the hot spot temperature values ​​at specific time points. A preset standard heat dissipation model with the same geometric configuration and basic material specifications as the crystal block of the control board under test is retrieved from a reference heat dissipation performance database. Temperature field data for the entire simulation time domain from the start of loading to reaching quasi-steady state is extracted from the dynamic three-dimensional heat conduction model. Similarly, temperature field data for the corresponding time domain is extracted from the preset standard heat dissipation model. The temperature field data extracted from the two models are aligned in the time dimension. The three-dimensional temperature fields at corresponding time points of the two models are compared, and the average and root mean square values ​​of the temperature differences at various points in space are calculated to assess the differences in the initial temperature distribution. The time required for the two models to reach the preset steady-state temperature threshold is compared to assess the differences in the steady-state temperature field formation time. During the simulation process of the two models, the hot spot locations and their temperature values ​​are identified and recorded respectively, and the absolute values ​​of the hot spot temperatures at the same key time points are compared to analyze the differences in hot spot temperature values ​​at specific time points. The comprehensive evaluation results of the differences between the dynamic three-dimensional heat conduction model and the preset standard heat dissipation model are used to complete the multi-dimensional comparative analysis. The normalized overall heat dissipation efficiency parameter is defined and calculated based on the ratio of the overall temperature rise slope to the corresponding standard overall temperature rise slope in the preset standard heat dissipation model. The percentage of steady-state establishment speed of the dynamic three-dimensional heat conduction model relative to the preset standard heat dissipation model is calculated based on the difference in steady-state temperature field formation time, and this percentage is used as a correction factor for the normalized overall heat dissipation efficiency parameter to obtain the final overall heat dissipation efficiency parameter. The hotspot temperature rise difference value is calculated based on the difference between the hotspot temperature value simulated by the dynamic three-dimensional heat conduction model at key time points and the hotspot temperature value simulated by the preset standard heat dissipation model at key time points. Simultaneously, the deviation distance between the hotspot location coordinates and the standard hotspot location in the preset standard heat dissipation model is considered. Combining the hotspot temperature rise difference value and the deviation distance, the hotspot heat dissipation coefficient parameter, representing the local heat dissipation capacity, is calculated through a preset hotspot heat dissipation coefficient mapping function.

[0037] In practical implementation, a multi-dimensional comparative analysis is conducted based on the constructed dynamic three-dimensional heat conduction model. This analysis specifically includes comparing the initial temperature distribution, the formation time of the steady-state temperature field, and the hot spot temperature values ​​at specific time points. The system retrieves a pre-defined standard heat dissipation model with the same geometric configuration and basic material specifications from a reference heat dissipation performance database, indexed by the model and material code of the crystal block of the control board under test. This pre-defined standard heat dissipation model is a benchmark model established based on ideal design parameters and standard process conditions. Temperature field data for the entire simulation time domain, from the start of thermal load application to the quasi-steady-state temperature field, is extracted from the simulation result file of the dynamic three-dimensional heat conduction model. This temperature field data exists in the form of node temperature cloud map data files corresponding to a series of time steps. Similarly, the corresponding temperature field data under the same loading conditions and time domain is extracted from the retrieved simulation result library of the pre-defined standard heat dissipation model.

[0038] Two sets of time-domain temperature field data extracted from the dynamic 3D heat conduction model and the preset standard heat dissipation model are aligned in the time dimension. This alignment ensures that the two models use the same or corresponding simulation time points when compared. The 3D temperature fields of the two models at corresponding time points are compared, and the average and root mean square (RMS) values ​​of temperature differences at each node in space are calculated. The average temperature difference reflects the shift in the overall temperature distribution, while the RMS value characterizes the degree of dispersion in the temperature field distribution. Based on these calculated values, the differences in initial temperature distribution are evaluated. The time required for the dynamic 3D heat conduction model and the preset standard heat dissipation model to reach a preset steady-state temperature threshold is compared. The preset steady-state temperature threshold is defined as the overall average temperature change rate being less than a minimum value over a continuous number of time steps. The time difference between the two models reaching this condition is calculated, and the difference in the formation time of the steady-state temperature field is evaluated. In the simulation process data of the dynamic 3D heat conduction model and the preset standard heat dissipation model, the coordinates of their respective hotspot locations and their temperature values ​​at key time points (such as 60 seconds and 180 seconds after loading begins) are identified and recorded. The absolute values ​​of the hotspot temperatures of the two models at the same key time points are compared, and the differences in hotspot temperature values ​​at specific time points are analyzed. Based on the above assessment results of differences in initial temperature distribution, differences in steady-state temperature field formation time, and differences in hotspot temperature values ​​at specific time points, a multi-dimensional comparative analysis was completed. The analysis results can be summarized in a table and presented in Table 1.

[0039] Table 1: Results of Multidimensional Comparative Analysis

[0040] In some embodiments, the overall heat dissipation efficiency parameter is calculated using a step-by-step weighted approach. First, a normalized preliminary value for the overall heat dissipation efficiency parameter is defined and calculated based on the ratio of the measured and calculated overall temperature rise slope to the standard overall temperature rise slope obtained from a preset standard heat dissipation model. Then, based on the difference in steady-state temperature field formation time between the dynamic three-dimensional heat conduction model and the preset standard heat dissipation model, the percentage of steady-state establishment rate of the dynamic three-dimensional heat conduction model relative to the preset standard heat dissipation model is calculated. The formula for calculating the percentage of steady-state establishment rate can be expressed as: ; in: Represents the percentage of steady-state establishment rate; This indicates the formation time of the steady-state temperature field obtained from the dynamic three-dimensional heat conduction model simulation; This represents the formation time of the steady-state temperature field simulated by the preset standard heat dissipation model. It can be understood that when the steady-state formation time of the dynamic three-dimensional heat conduction model is longer than that of the preset standard heat dissipation model, The percentage of steady-state establishment rate will be less than 100%. This percentage will be used as a correction factor for the overall heat dissipation efficiency parameter. The initial value of the normalized overall heat dissipation efficiency parameter will be weighted and corrected according to the preset weights. The result of the weighted correction is the final overall heat dissipation efficiency parameter.

[0041] In practical implementation, the calculation of the hotspot heat dissipation coefficient parameter requires comprehensive temperature and spatial information. Based on the difference between the hotspot temperature value simulated by the dynamic three-dimensional heat conduction model at key time points and the hotspot temperature value simulated by the preset standard heat dissipation model at the same key time point, the hotspot temperature rise difference value is calculated. This difference value may vary at different key time points; the average or maximum value of the difference at each time point can be used as a representative value. Simultaneously, the deviation distance between the measured hotspot location coordinates and the standard hotspot location coordinates in the preset standard heat dissipation model is considered; this deviation distance is a spatial Euclidean distance. Combining the hotspot temperature rise difference value and the hotspot location deviation distance, the hotspot heat dissipation coefficient parameter, characterizing the local heat dissipation capacity, is calculated through a preset hotspot heat dissipation coefficient mapping function. In some embodiments, the hotspot heat dissipation coefficient mapping function can be designed as a linear combination of two terms: one is a normalized term for the hotspot temperature rise difference value, and the other is a normalized term for the hotspot location deviation distance. These two terms are multiplied by different coefficients, summed, and mapped to a specified parameter range, thereby obtaining the quantified hotspot heat dissipation coefficient parameter. Alternatively, the hotspot heat dissipation coefficient mapping function can also adopt other mathematical forms, such as evaluation methods based on expert rules or fuzzy logic. The core purpose is to integrate the differences between the two dimensions of temperature and location into a single index for evaluating local heat dissipation performance.

[0042] See Figure 4This is a temperature rise characteristic curve of the control board crystal pressure block under thermal power loading, visually showing the changes in the overall average temperature and hot spot temperature over time. In the initial stage (0-200 seconds), both curves show a rapid linear rise, with the hot spot temperature consistently higher than the overall average temperature. The difference between the two gradually widens from 0, indicating that the hot spot area is where heat accumulates most rapidly. In the middle stage (200-600 seconds), the temperature rise rate begins to slow down, the curve slope decreases, and the difference between the hot spot temperature and the overall average temperature stabilizes at around 20℃, reflecting that the heat dissipation system begins to function, and heat is gradually conducted to the surrounding area. In the later stage (after 600 seconds), both curves tend to flatten and enter a quasi-steady state. The overall average temperature stabilizes at around 73℃, and the hot spot temperature stabilizes at around 96℃, indicating that heat generation and dissipation have reached a dynamic balance.

[0043] In one embodiment of the present invention, step S7 is specifically implemented as follows: A heat dissipation performance evaluation standard table is established based on the reference heat dissipation performance database. The heat dissipation performance evaluation standard table divides multiple continuous heat dissipation performance evaluation levels according to the overall heat dissipation efficiency parameter range and the hot spot heat dissipation coefficient parameter range. The calculated overall heat dissipation efficiency parameter is compared with the overall heat dissipation efficiency parameter range in the heat dissipation performance evaluation standard table to preliminarily determine the level interval corresponding to the overall heat dissipation efficiency parameter. The calculated hot spot heat dissipation coefficient parameter is compared with the hot spot heat dissipation coefficient parameter range in the heat dissipation performance evaluation standard table to preliminarily determine the level interval corresponding to the hot spot heat dissipation coefficient parameter. A two-parameter weighted decision rule is used to comprehensively evaluate the level intervals preliminarily determined by the two parameters to finally determine a unique heat dissipation performance evaluation level.

[0044] In practical implementation, the process of determining the heat dissipation performance evaluation level of the control board crystal pressure block begins with referencing a structured heat dissipation performance evaluation standard table established in the heat dissipation performance database. This table, stored in tabular form, defines columns including the lower limit of the overall heat dissipation efficiency parameter range, the upper limit of the overall heat dissipation efficiency parameter range, the lower limit of the hotspot heat dissipation coefficient parameter range, the upper limit of the hotspot heat dissipation coefficient parameter range, and the corresponding heat dissipation performance evaluation level. Based on the combination of the overall heat dissipation efficiency parameter range and the hotspot heat dissipation coefficient parameter range, multiple consecutive and non-overlapping heat dissipation performance evaluation levels are defined, such as Level A, Level B, Level C, and Level D. The calculated overall heat dissipation efficiency parameter is then compared one by one with the overall heat dissipation efficiency parameter range in the heat dissipation performance evaluation standard table. Each row of the table is traversed to determine if the overall heat dissipation efficiency parameter is greater than or equal to the lower limit of the overall heat dissipation efficiency parameter range for a given row and less than or equal to the upper limit of the overall heat dissipation efficiency parameter range for that row. This initially determines the level range that the overall heat dissipation efficiency parameter falls within, and this level range corresponds to one or more rows in the heat dissipation performance evaluation standard table.

[0045] Simultaneously, the calculated hotspot heat dissipation coefficient parameters are compared one by one with the hotspot heat dissipation coefficient parameter ranges in the heat dissipation performance evaluation standard table. Similarly, by determining whether the hotspot heat dissipation coefficient parameter falls within the lower and upper limits of the hotspot heat dissipation coefficient parameter range for a specific row, the grade range corresponding to the hotspot heat dissipation coefficient parameter is initially determined. In some embodiments, the design of the heat dissipation performance evaluation standard table allows the ranges of the two parameters to exist in the form of intervals rather than fixed values, which allows the evaluation to adapt to continuous changes in parameter values. A two-parameter weighted decision rule is used to comprehensively evaluate the grade ranges initially determined by the two parameters. The two-parameter weighted decision rule defines the relative importance weights of the overall heat dissipation efficiency parameter and the hotspot heat dissipation coefficient parameter in the final grade determination.

[0046] It is understandable that one implementation of the two-parameter weighted decision rule is to convert the level interval into a level index before calculation. An optional formula for calculating the comprehensive level index is as follows: ; in: This represents the comprehensive rating index used for the final determination; This indicates the level index corresponding to the level range initially determined by the overall heat dissipation efficiency parameters. For example, level A is mapped to index 4, and level B is mapped to index 3. This represents the level index corresponding to the level range initially determined by the hotspot heat dissipation coefficient parameter; It is a weighting coefficient assigned to the overall heat dissipation efficiency parameter; It is a weighting coefficient assigned to the heat dissipation coefficient parameter of the hot spot, and satisfies... The comprehensive rating index was calculated. Then, it is compared with the preset level index threshold range to finally determine a unique heat dissipation performance evaluation level.

[0047] In practice, if the two initially determined grade ranges are consistent, the grade can be directly used as the final heat dissipation performance evaluation grade. If the two initially determined grade ranges are inconsistent, a two-parameter weighted decision rule must be used for arbitration. In some embodiments, the two-parameter weighted decision rule can be configured to select the worse of the two initial grades as the final grade, or to assign a higher weight to the hotspot heat dissipation coefficient parameter based on product reliability requirements, making it dominant in the decision. After determining the final heat dissipation performance evaluation grade, the system automatically generates a final test report containing quantitative parameters and the heat dissipation performance evaluation grade. The final test report is output in the form of a structured document or electronic record, and its content includes at least the identification of the tested component, the overall heat dissipation efficiency parameter value, the hotspot heat dissipation coefficient parameter value, the version of the heat dissipation performance evaluation standard table used, and the final determined heat dissipation performance evaluation grade. It can be understood that the final test report serves as an objective record of the testing process and is used for product quality judgment and process improvement analysis.

[0048] See Figure 5 This is a bar chart comparing the heat dissipation performance parameters of five control board crystal compact samples, showing the normalized values ​​of "overall heat dissipation efficiency" and "hot spot heat dissipation coefficient," which can be used to preliminarily determine the heat dissipation performance level of the samples. Both parameters show a synchronous decreasing trend in all samples, with sample 1 exhibiting the best performance and sample 5 the weakest. In each sample, the hot spot heat dissipation coefficient is consistently lower than the overall heat dissipation efficiency, with the difference remaining stable between 0.03 and 0.05, indicating that the local heat dissipation capacity of the hot spot area is the key bottleneck restricting overall performance. Based on common heat dissipation performance evaluation standards, sample 1 meets the "excellent" level, sample 2 meets the "good" level, sample 3 meets the "qualified" level, sample 4 meets the "needs improvement" level, and sample 5 belongs to the "unqualified" level. This chart can serve as a performance screening tool for batch samples, quickly identifying samples with unqualified performance and providing data support for process optimization.

[0049] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for testing the heat dissipation performance of a control board crystal pressure block, characterized in that, include: S1: Deploy multiple temperature detection devices at designated locations on the crystal block of the control board under test to obtain real-time temperature change data of the crystal block during the test process; S2: Apply a preset contact pressure and thermal power to the control board crystal block using a loading device to simulate the actual working load state of the control board crystal block. Continuously collect real-time temperature change data obtained by the multiple temperature detection devices during the application of the contact pressure and thermal power to form a time-related temperature data sequence. S3: Based on the temperature data sequence, calculate the overall temperature rise slope of the control board crystal block within a preset time window, and identify the coordinates of the hot spot with the largest temperature rise slope. S4: Construct a dynamic three-dimensional heat conduction model based on the overall temperature rise slope and the hot spot location coordinates; S5: Perform a multi-dimensional comparative analysis between the dynamic three-dimensional heat conduction model and the preset standard heat dissipation model in the reference heat dissipation performance database; S6: Based on the results of the multi-dimensional comparative analysis, the overall heat dissipation efficiency parameter and hot spot heat dissipation coefficient parameter of the control board crystal block are calculated. S7: Based on the overall heat dissipation efficiency parameter and the hot spot heat dissipation coefficient parameter, determine the heat dissipation performance evaluation level corresponding to the control board crystal block in the reference heat dissipation performance database, and generate a final test report containing quantitative parameters and heat dissipation performance evaluation level.

2. The method for testing the heat dissipation performance of a control board crystal pressure block according to claim 1, characterized in that, Step S1 specifically includes: Obtain a three-dimensional structural model of the control board crystal block, and analyze the geometry and material distribution of the contact surface between the crystal and the block in the three-dimensional structural model; Based on the geometry and material distribution, a preliminary simulation was performed using the thermal conductivity finite element analysis method to predict the potential temperature distribution region of the control board crystal block under working load. Based on the predicted potential temperature distribution area, regions with significant temperature gradient changes and key nodes on the heat dissipation path are selected as the designated locations. At each of the designated locations, a high-response-speed and high-precision temperature detection device is installed to ensure that the thermal probe of the temperature detection device forms a tight physical contact with the surface of the crystal block of the control board; The installed temperature detection devices were numbered and their location coordinates were recorded, and a physical channel connection was established with the temperature acquisition system.

3. The method for testing the heat dissipation performance of a control board crystal pressure block according to claim 1, characterized in that, The S2 step is specifically as follows: Fix the control board crystal block onto a special test fixture, ensuring that the bottom heat dissipation surface of the control board crystal block is aligned with the cooling plate of the test platform; The pressure control module of the loading device is activated, and a uniformly distributed contact pressure is applied to the surface of the crystal block of the control board according to the preset pressure loading curve, and the actual pressure value is monitored to achieve a steady state. At the same time, the thermal power loading device is activated, and electrical energy with preset power and frequency is integrated into the crystal of the control board crystal block to simulate the actual heat generation of the crystal during operation. The contact pressure and thermal power are monitored and adjusted in real time to keep them stable within the set tolerance range until the preset loading duration is reached. The data acquisition function of all temperature detection devices is activated simultaneously to record the temperature data of their respective locations at a uniform high-frequency sampling rate; The temperature data collected by all temperature detection devices at each sampling time are aggregated to form a raw data point set containing timestamps, detection device numbers, and temperature values. Based on the timestamp, all data points in the original data point set are sorted by time and synchronized to ensure that data from different temperature detection devices correspond strictly on the time axis. The sorted and aligned data is grouped according to the detection device number to form a list of temperature readings arranged in chronological order at each specified location. By integrating the lists of temperature readings at all specified locations, a multi-channel temperature data sequence correlated with time is constructed.

4. The method for testing the heat dissipation performance of a control board crystal pressure block according to claim 3, characterized in that, The S3 step is specifically as follows: From the multi-channel temperature data sequence, extract the data segment from the start of thermal power loading to the end of the preset time window; The overall average temperature at each sampling time is calculated by averaging the temperature readings of all temperature detection devices in the captured data segment. Plot the curve of the overall average temperature changing over time with time on the horizontal axis and the overall average temperature on the vertical axis. The curve of the overall average temperature changing with time is subjected to piecewise linear fitting, and the rising stage with the highest linearity is selected. The slope of the rising stage is calculated as the overall temperature rising slope. Meanwhile, for the data from each temperature detection device in the intercepted data segment, the corresponding temperature rise rate is calculated respectively. Compare the temperature rise rate of all temperature detection devices, and identify the location of the temperature detection device with the maximum temperature rise rate as a hot spot. Read the serial number of the temperature detection device identified as a hotspot, query and obtain the installation coordinates of the temperature detection device based on the serial number, and the installation coordinates are the location coordinates of the hotspot.

5. The method for testing the heat dissipation performance of a control board crystal block according to claim 4, characterized in that, The S4 step is specifically as follows: Based on the geometric model of the control board crystal block, material thermal properties, applied contact pressure, applied thermal power, and environmental boundary conditions, a basic three-dimensional heat conduction model is established. From the multi-channel temperature data sequence, extract snapshots of the transient temperature distribution of the entire control board crystal block at several discrete key time points; The overall temperature rise slope is used as a global constraint condition of the model to optimize the overall equivalent thermal conductivity of the material in the basic three-dimensional heat conduction model. Using the hot spot location coordinates and the transient temperature distribution snapshot, an inversion algorithm is used to locally correct the thermal resistance parameters and heat source distribution in the contact area between the block and the crystal in the basic three-dimensional heat conduction model; By combining global constraints and local correction results, the basic three-dimensional heat conduction model is iteratively updated so that the temperature field evolution process simulated by the model matches the measured temperature data sequence within a preset error range. Finally, a dynamic three-dimensional heat conduction model that can accurately reflect the heat dissipation characteristics of the crystal block of the specific control board under test is obtained.

6. The method for testing the heat dissipation performance of a control board crystal pressure block according to claim 5, characterized in that, The S5 step is specifically as follows: The multi-dimensional comparative analysis includes a comparison of the initial temperature distribution, the formation time of the steady-state temperature field, and the hotspot temperature values ​​at specific time points. From the reference heat dissipation performance database, a preset standard heat dissipation model with the same geometric configuration and basic material specifications as the crystal block of the control board under test is retrieved; Temperature field data for the entire simulation time domain from the start of loading to reaching quasi-steady state are extracted from the dynamic three-dimensional heat conduction model. Similarly, temperature field data in the corresponding time domain is extracted from the preset standard heat dissipation model; Align the temperature field data extracted from the two models along the time dimension; By comparing the three-dimensional temperature fields of the two models at corresponding time points, the average and root mean square values ​​of the temperature differences at various points in space are calculated to evaluate the differences in the initial temperature distribution. Compare the time required for the two models to reach the preset steady-state temperature threshold to evaluate the difference in the steady-state temperature field formation time. During the simulation of the two models, the hotspot locations and their temperature values ​​were identified and recorded respectively. The absolute values ​​of the hotspot temperatures at the same key time points were compared to analyze the differences in hotspot temperature values ​​at specific time points. Based on the evaluation results of the differences between the dynamic three-dimensional heat conduction model and the preset standard heat dissipation model, a multi-dimensional comparative analysis was completed.

7. The method for testing the heat dissipation performance of a control board crystal block according to claim 6, characterized in that, The S6 step is specifically as follows: Based on the ratio of the overall temperature rise slope to the corresponding standard overall temperature rise slope in the preset standard heat dissipation model, the normalized overall heat dissipation efficiency parameter is defined and calculated. Based on the difference in the steady-state temperature field formation time, the percentage of the steady-state establishment speed of the dynamic three-dimensional heat conduction model relative to the preset standard heat dissipation model is calculated. This percentage is used as a correction factor for the overall heat dissipation efficiency parameter, and the normalized overall heat dissipation efficiency parameter is weighted and corrected to obtain the final overall heat dissipation efficiency parameter. The difference between the hot spot temperature rise and the hot spot temperature value simulated by the dynamic three-dimensional heat conduction model at key time points is calculated. Simultaneously, the deviation distance between the hotspot location coordinates and the standard hotspot location of the preset standard heat dissipation model is considered; By combining the hot spot temperature rise difference value and the deviation distance, the hot spot heat dissipation coefficient parameter, which characterizes the local heat dissipation capability, is calculated through a preset hot spot heat dissipation coefficient mapping function.

8. The method for testing the heat dissipation performance of a control board crystal pressure block according to claim 1, characterized in that, The S7 step is specifically as follows: A heat dissipation performance evaluation standard table is established in the reference heat dissipation performance database. The heat dissipation performance evaluation standard table divides multiple consecutive heat dissipation performance evaluation levels according to the overall heat dissipation efficiency parameter range and the hot spot heat dissipation coefficient parameter range. The calculated overall heat dissipation efficiency parameters are compared with the range of overall heat dissipation efficiency parameters in the heat dissipation performance evaluation standard table to preliminarily determine the grade range corresponding to the overall heat dissipation efficiency parameters. The calculated hotspot heat dissipation coefficient parameters are compared with the range of hotspot heat dissipation coefficient parameters in the heat dissipation performance evaluation standard table to preliminarily determine the grade range corresponding to the hotspot heat dissipation coefficient parameters. A two-parameter weighted decision rule is adopted to comprehensively evaluate the grade range initially determined by the two parameters, and finally determine a unique heat dissipation performance evaluation grade.

9. A heat dissipation performance testing system for a control board crystal pressing block, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the heat dissipation performance testing method for a control board crystal block as described in any one of claims 1 to 8.

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