Defect detection method and system for cooling pipeline of heat dissipation module and medium

Abstract

The invention provides a heat dissipation module cooling pipeline defect detection method and system and a medium, and relates to the technical field of pipeline defect detection.The method comprises the steps that a heat dissipation mode identifier is received, an excitation signal configuration type is recognized, an excitation source of an MOSFET switching circuit is controlled according to the excitation signal configuration type, and a heat dissipation module cooling pipeline defect detection result is obtained. The method comprises the steps of obtaining an excitation input signal for defect detection, injecting the excitation input signal into a cooling pipeline of a heat dissipation module for detection, collecting an excitation response signal, constructing a heat dissipation mode-feature extraction template, performing defect identification on the excitation response signal, and outputting a defect detection result. The technical problem of poor defect identification accuracy and reliability caused by obvious defects in the aspect of cooling pipeline defect detection in multiple heat dissipation modes in the prior art is solved. The technical effects that self-adaptive and accurate defect detection of the cooling pipeline is realized in different heat dissipation modes by using the signal sensing detection of the cooling pipeline, so that the accuracy and reliability of defect detection are improved are achieved.

Description

Technical Field

[0001] This invention relates to the field of pipeline defect detection technology, specifically to a method, system, and medium for detecting defects in cooling pipelines of heat dissipation modules. Background Technology

[0002] Traditional methods for detecting defects in cooling pipes of heat dissipation modules typically employ a uniform detection stimulus or a single feature extraction approach. However, due to the existence of various heat dissipation modes, such as fluorinated refrigerants, water cooling, different refrigerants, or mixed modes, the physical environment and signal propagation characteristics differ significantly between these modes. A uniform cooling pipe detection method is ill-suited to all modes and cannot accurately capture defect features under different conditions. Furthermore, single feature extraction methods focus only on one aspect of the signal, resulting in a high false positive rate and insufficient sensitivity in cooling pipe defect detection. Moreover, the cooling pipes of heat dissipation modules involve different media, and the electrical, thermal, and acoustic characteristics of these media vary considerably, leading to differences in the optimal frequency band, amplitude, and encoding of the detection signal across different media. Using fixed signal sensing parameters, which cannot be flexibly adjusted based on media characteristics and sensor feedback, further limits the accuracy and reliability of cooling pipe defect detection.

[0003] In summary, existing technologies have significant shortcomings in detecting defects in cooling pipes under multiple heat dissipation modes. They lack adaptive detection strategies, resulting in sensor detection signals failing to effectively reflect defect characteristics, thus causing technical problems such as poor accuracy and reliability in identifying cooling pipe defects. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and medium for detecting defects in cooling pipes of heat dissipation modules, in order to solve the technical problems that existing technologies have significant shortcomings in detecting defects in cooling pipes under multiple heat dissipation modes, lack adaptive detection strategies, and cause sensor detection signals to fail to effectively reflect defect characteristics, resulting in poor accuracy and reliability of cooling pipe defect identification.

[0005] In view of the above problems, this application provides a method, system and medium for detecting defects in the cooling pipes of heat dissipation modules.

[0006] The first aspect of this application provides a method for detecting defects in the cooling pipes of a heat dissipation module. The method includes: receiving a heat dissipation mode identifier from a heat dissipation module controller, wherein the heat dissipation module includes cooling pipes and multiple heat dissipation modes, and the multiple heat dissipation modes share the cooling pipes for heat dissipation; identifying the excitation signal configuration type of the heat dissipation mode identifier, controlling the excitation source of a MOSFET switching circuit according to the excitation signal configuration type, and obtaining an excitation input signal for defect detection corresponding to the heat dissipation mode identifier; injecting the excitation input signal into the cooling pipes of the heat dissipation module for detection, and acquiring an excitation response signal; constructing a heat dissipation mode-feature extraction template, calling the corresponding feature extraction template based on the heat dissipation mode identifier to perform defect identification on the excitation response signal, and outputting a defect detection result, wherein the defect detection result includes defect type, defect location, and defect level.

[0007] Optionally, the excitation source of the MOSFET switching circuit includes a waveform generator and a bridge drive structure. The waveform generator is electrically connected to the bridge drive structure. The bridge drive structure includes a first bridge arm composed of MOSFET1 and MOSFET2 and a second bridge arm composed of MOSFET3 and MOSFET4. The first bridge arm controls the voltage amplitude of the excitation source under the drive of the controller, and the second bridge arm switches the signal output mode of the excitation source under the mode command of the controller.

[0008] Optionally, MOSFET1 and MOSFET2 are connected back-to-back, and MOSFET3 and MOSFET4 are connected back-to-back.

[0009] Optionally, the information of the heat dissipation mode identifier is parsed, including PWM encoding, digital bus instructions, and voltage level signal configuration; an identifier-excitation configuration table is constructed, which is used to match the corresponding excitation signal configuration type according to the parsed information of the heat dissipation mode identifier. The items stored in the identifier-excitation configuration table include heat dissipation mode identifier, excitation type adapted to the mode, injection port, and threshold constraint.

[0010] Optionally, based on the excitation signal configuration type, an initial excitation candidate set corresponding to the heat dissipation mode identifier is constructed; excitation is performed on each excitation candidate in the initial excitation candidate set and an excitation simulation response set is collected; defect response is calculated on the excitation simulation response set; and a response score for each excitation candidate is output based on the true positive count ratio, the mean of true positive responses, and the standard deviation of true positive responses; and an optimal excitation candidate solution is selected from the initial excitation candidate set based on the response score of each excitation candidate as the excitation input signal for defect detection.

[0011] Optionally, based on the response score of each incentive candidate, the top k incentive candidates with a preset response score are obtained; multiple rounds of cross-mutation are performed on the k incentive candidates to obtain a mutated incentive candidate set; the defect response is calculated from the mutated incentive candidate set; and the optimal incentive candidate solution is reselected as the incentive input signal for defect detection.

[0012] Optionally, representative defect feature samples under the multiple heat dissipation modes are obtained; based on the excitation response signal samples of the representative defect feature samples, a preferred feature set corresponding to the excitation response signal samples is extracted, and a heat dissipation mode-feature extraction template is constructed based on the preferred feature set.

[0013] Optionally, a feature extraction template is loaded based on the heat dissipation mode identifier; defect features are extracted from the excitation response signal according to the feature extraction template, including the signal response peak energy index, the signal response phase sequence, and the signal response phase difference; modal localization is performed using the signal response peak energy index, the signal response phase sequence, and the signal response phase difference to determine the defect detection result.

[0014] A second aspect of this application provides a defect detection system for cooling pipes of a heat dissipation module. The system includes: a heat dissipation identifier receiving module for receiving a heat dissipation mode identifier from a heat dissipation module controller, wherein the heat dissipation module includes cooling pipes and multiple heat dissipation modes, and the multiple heat dissipation modes share the cooling pipes for heat dissipation; a circuit switching module for identifying the excitation signal configuration type of the heat dissipation mode identifier, controlling the excitation source of a MOSFET switching circuit according to the excitation signal configuration type, and obtaining an excitation input signal corresponding to the heat dissipation mode identifier for defect detection; a signal acquisition module for injecting the excitation input signal into the cooling pipes of the heat dissipation module for detection, and acquiring an excitation response signal; and a defect identification module for constructing a heat dissipation mode-feature extraction template, calling the corresponding feature extraction template based on the heat dissipation mode identifier to perform defect identification on the excitation response signal, and outputting a defect detection result, wherein the defect detection result includes defect type, defect location, and defect level.

[0015] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed, implements the steps of the heat dissipation module cooling pipe defect detection method described in any one of the first aspects above.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] The method provided in this application embodiment receives a heat dissipation mode identifier from a heat dissipation module controller. The heat dissipation module includes cooling pipes and multiple heat dissipation modes, and the multiple heat dissipation modes share the cooling pipes for heat dissipation. The method identifies the excitation signal configuration type of the heat dissipation mode identifier, controls the excitation source of the MOSFET switching circuit according to the excitation signal configuration type, and obtains an excitation input signal corresponding to the heat dissipation mode identifier for defect detection. The method injects the excitation input signal into the cooling pipes of the heat dissipation module for detection and collects the excitation response signal. A heat dissipation mode-feature extraction template is constructed, and based on the heat dissipation mode identifier, the corresponding feature extraction template is called to perform defect identification on the excitation response signal, outputting a defect detection result. The defect detection result includes defect type, defect location, and defect level. This method achieves the technical effect of improving the accuracy and reliability of defect detection by using a switching circuit connected to a MOSFET, changing the power supply parameters of the circuit according to the type of heat dissipation mode, analyzing the characteristics of the defect detection signal input to the cooling pipe, and utilizing cooling pipe signal sensing detection to achieve adaptive and accurate defect detection of the cooling pipe under different heat dissipation modes.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the method for detecting defects in the cooling pipes of the heat dissipation module provided in this application.

[0021] Figure 2 A schematic diagram of the connection between the waveform generator and the bridge drive structure in the defect detection method for the cooling pipe of the heat dissipation module provided in this application.

[0022] Figure 3 This is a schematic diagram of the structure of the heat dissipation module cooling pipe defect detection system provided in this application.

[0023] Explanation of reference numerals in the attached diagram: Heat dissipation indicator receiving module 11, circuit switching module 12, signal acquisition module 13, defect identification module 14. Detailed Implementation

[0024] This application provides a method, system, and medium for detecting defects in cooling pipes of a heat dissipation module. It addresses the significant shortcomings of existing technologies in detecting cooling pipe defects under multiple heat dissipation modes, lacking adaptive detection strategies, resulting in sensor signals failing to effectively reflect defect characteristics, and consequently causing poor accuracy and reliability in cooling pipe defect identification. The method achieves the technical effect of utilizing cooling pipe signal sensing to realize adaptive and accurate defect detection in cooling pipes under different heat dissipation modes, thereby improving the accuracy and reliability of defect detection.

[0025] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0026] Example 1, as Figure 1 As shown, this application provides a method for detecting defects in the cooling pipes of a heat dissipation module, the method comprising:

[0027] The system receives a heat dissipation mode identifier from the heat dissipation module controller. The heat dissipation module includes a cooling pipe and multiple heat dissipation modes, and the multiple heat dissipation modes share the cooling pipe for heat dissipation.

[0028] Specifically, a heat dissipation module controller is a component used to monitor and regulate the operating status of heat dissipation equipment. The heat dissipation module is the core component of the equipment's heat dissipation, including cooling pipes and multiple heat dissipation modes. The cooling pipes are used for heat conduction, and the multiple heat dissipation modes include fluorinated refrigerant, water cooling, different refrigerants, or a mixed mode. Although the multiple heat dissipation modes are different, they all share the same cooling pipes for heat dissipation. Therefore, different heat dissipation modes cause different types of defects to the cooling pipes. For example, water cooling is prone to scale blockage and / or gas lock, fluorinated refrigerant is prone to micro-leakage and / or interface aging, and mixed modes are prone to phase separation and / or stratification. For each heat dissipation mode, based on historical experimental data and experience, the corresponding heat dissipation mode parameter range is obtained, such as temperature, flow rate, and pressure values, and multiple heat dissipation mode parameters are pre-stored in the heat dissipation module controller's storage unit. When the heat dissipation module starts running, the heat dissipation module controller activates multiple high-precision sensors, including temperature sensors, speed sensors, and pressure sensors, to monitor various operating data of the heat dissipation module in real time, including temperature, flow rate, and pressure within the cooling pipes. The heat dissipation module controller compares and analyzes the collected real-time data with preset heat dissipation mode parameters one by one, calculating the similarity or matching degree between the real-time data and the corresponding parameters of each heat dissipation mode. This accurately identifies the current heat dissipation mode of the heat dissipation equipment and generates a unique corresponding heat dissipation mode identifier. Various comparison methods are used, such as Euclidean distance and cosine similarity. For example, Euclidean distance is used to calculate the Euclidean distance between the real-time data vector and multiple heat dissipation mode parameter vectors; a smaller distance indicates a higher similarity. By receiving the heat dissipation mode identifier from the heat dissipation module controller, the current heat dissipation mode can be clearly identified, achieving accurate identification of the heat dissipation mode and thus improving the accuracy and reliability of cooling pipe defect identification.

[0029] Identify the excitation signal configuration type of the heat dissipation mode identifier, control the excitation source of the MOSFET switching circuit according to the excitation signal configuration type, and obtain the excitation input signal for defect detection corresponding to the heat dissipation mode identifier.

[0030] Specifically, after receiving the heat dissipation mode identifier from the heat dissipation module controller, the system accurately identifies the excitation signal configuration type corresponding to the current heat dissipation mode identifier through an excitation signal configuration type recognition mechanism. For example, by pre-constructing an identifier-excitation configuration table corresponding to heat dissipation modes and excitation signal configuration types, the received heat dissipation mode identifier is precisely compared and matched with the identifier-excitation configuration table to obtain the excitation signal configuration type. Based on the matched excitation signal configuration type, the excitation source of the MOSFET switching circuit is controlled to obtain the excitation input signal corresponding to the heat dissipation mode identifier. Using cooling pipe signal sensing detection, adaptive and accurate defect detection of the cooling pipe is achieved.

[0031] Furthermore, such as Figure 2 As shown, the excitation source of the MOSFET switching circuit includes a waveform generator and a bridge drive structure. The waveform generator is electrically connected to the bridge drive structure. The bridge drive structure includes a first bridge arm composed of MOSFET1 and MOSFET2 and a second bridge arm composed of MOSFET3 and MOSFET4. The first bridge arm controls the voltage amplitude of the excitation source under the drive of the controller, and the second bridge arm switches the signal output mode of the excitation source under the mode command of the controller.

[0032] Specifically, the excitation source of the MOSFET switching circuit includes a waveform generator and a bridge drive structure. The waveform generator is a data signal generator capable of producing various waveform signals, such as sine waves, square waves, and triangular waves. In this application, the waveform generator generates an initial excitation signal based on a heat dissipation mode identifier. The bridge drive structure consists of two bridge arms, each composed of two MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors). Specifically, the first bridge arm consists of MOSFET1 and MOSFET2, and the second bridge arm consists of MOSFET3 and MOSFET4. The bridge drive structure, composed of these two bridge arms, effectively controls signal transmission and conversion. The first bridge arm controls the voltage amplitude of the excitation source. Under the drive of the controller, the voltage value at the output terminal of the bridge arm is changed by adjusting the conduction level of MOSFET1 and MOSFET2, thereby achieving precise control of the excitation source voltage amplitude. The second bridge arm is used to switch the signal output mode of the excitation source. Different heat dissipation modes correspond to different types of cooling pipe defect characteristics, therefore, different excitation signal modes are required for effective detection. The controller determines the required signal output mode based on the heat dissipation mode identifier and sends corresponding mode commands to MOSFET3 and MOSFET4 in the second bridge arm to switch the signal output mode. For example, it switches from a sine wave output mode to a square wave output mode to adapt to the detection of defects in cooling pipes under different heat dissipation modes. The waveform generator and the bridge drive structure are electrically connected by wires. For example, the waveform generator is connected to the HIN and LIN input pins of the bridge drive structure, and the HO and LO output pins of the bridge drive structure are connected to the gates of MOSFET1 and MOSFET3 respectively through a 10Ω gate resistor. The sources of MOSFET1 and MOSFET2 are connected, and the sources of MOSFET3 and MOSFET4 are connected, forming a back-to-back connection to ensure that the generated signal characteristics can be smoothly transmitted to the bridge drive structure for further processing.

[0033] By connecting to the switching circuit of the MOSFET and controlling the excitation source of the MOSFET switching circuit according to the excitation signal configuration type, the power supply parameters of the circuit are dynamically adjusted according to the heat dissipation mode type, thereby adjusting the defect detection signal characteristics of the input cooling pipe for analysis of typical defects in the corresponding mode, thus improving the accuracy and reliability of cooling pipe defect detection.

[0034] Furthermore, MOSFET1 and MOSFET2 are connected back-to-back, and MOSFET3 and MOSFET4 are connected back-to-back.

[0035] Specifically, a back-to-back connection between MOSFET1 and MOSFET2 means that the drain (D) of MOSFET1 is connected to the source (S) of MOSFET2. Simultaneously, the source (S) of MOSFET1 and the drain (D) of MOSFET2 serve as the connection ports for this bridge arm to the external circuit. Similarly, a back-to-back connection between MOSFET3 and MOSFET4 means that the drain of MOSFET3 is connected to the source of MOSFET4. The source of MOSFET3 and the drain of MOSFET4 serve as the connection ports for the second bridge arm to the external circuit. This back-to-back connection effectively prevents current backflow. During circuit operation, when it is necessary to control the current flow or block the current, by reasonably controlling the on / off state of each MOSFET, precise adjustment of the excitation source voltage amplitude and flexible switching of the signal output mode can be achieved. This provides an excitation input signal that meets the requirements of a specific heat dissipation mode for cooling pipe defect detection, improving the accuracy and reliability of cooling pipe defect detection.

[0036] Furthermore, the method for identifying the excitation signal configuration type of the heat dissipation mode identifier includes: parsing the information of the heat dissipation mode identifier, including PWM encoding, digital bus instructions, and voltage level signal configuration; constructing an identifier-excitation configuration table, which is used to match the corresponding excitation signal configuration type according to the parsed information of the heat dissipation mode identifier, and the items stored in the identifier-excitation configuration table include the heat dissipation mode identifier, the excitation type adapted to the mode, the injection port, and the threshold constraint.

[0037] Specifically, the information identifying the heat dissipation mode is parsed to obtain information including PWM encoding, digital bus instructions, and voltage level signal configuration. For example, using timer and counter circuits, the pulse width, period, and duty cycle of the PWM signal are measured to decode the information represented by the PWM encoding; different duty cycles of the PWM signal correspond to different heat dissipation modes. According to the communication protocol of the digital bus, such as I2C or SPI, data frames on the bus are read, and the specific content and parameters of the instructions are parsed. Voltage level signal configuration uses different combinations of voltage levels to represent the heat dissipation mode; different voltage values ​​or combinations correspond to specific heat dissipation modes. By detecting the voltage level of the signal and comparing it with a preset threshold, the configuration information represented by the voltage level signal is determined.

[0038] By collecting and organizing the heat dissipation mode identifiers emitted by the heat dissipation module controller under different heat dissipation modes, such as fluorinated refrigerant, water cooling, different refrigerants, or mixed modes, and testing each heat dissipation mode, the required excitation signal characteristics are determined, including excitation type, voltage amplitude, frequency range, suitable injection port, and safety threshold constraints. A unique identifier is assigned to each heat dissipation mode. The heat dissipation mode identifier, the corresponding excitation type, precise injection port information, and reasonable threshold constraints are stored as data items in a specific format, such as a table, indexed by the heat dissipation mode identifier, constructing an identifier-excitation configuration table. This identifier-excitation configuration table is used to match the corresponding excitation signal configuration type based on the parsed information of the heat dissipation mode identifier. The items stored in the identifier-excitation configuration table include the heat dissipation mode identifier, the mode-adapted excitation type, the injection port, and the threshold constraints. The excitation type specifies the excitation type to be used for each heat dissipation mode. For example, in the water cooling (deionized water / ethylene glycol water) mode, the excitation type is low-frequency pressure sweep frequency and ECT dielectric spectroscopy. Low-frequency pressure sweep can disturb the fluid inside the pipe. ECT dielectric spectroscopy is used to detect changes in electrical properties caused by disturbances and defects, such as abrupt changes in reflection phase, ECT volume fraction differences, and temperature rise hysteresis, thereby determining whether the cooling pipe has defects such as easy fouling and / or gas lock. In refrigerant mode, the excitation types are TDR short pulses and micro-thermal pulse coding. TDR short pulses can quickly detect changes in electrical properties inside the pipe, while micro-thermal pulse coding assists in detecting interface aging, thereby obtaining features such as reflection peak drift, group delay changes, and thermal pulse peak-time delay coupling, enabling the detection of micro-leakage and interface aging defects. In mixed / phase change mode, the excitation types are ECT cross-sectional tomography and multi-frequency duty cycle perturbation. ECT cross-sectional tomography presents the distribution of electrical properties of the pipe cross-section, while multi-frequency duty cycle perturbation excites defect characteristic responses, obtaining features such as poor reconstruction of cross-sectional phase distribution, estimation of layered interface height, and pressure ripple nonlinearity, detecting phase separation / layering defects. The injection port refers to the specific interface location where the excitation signal is input to the cooling pipe. Different heat dissipation modes may require injecting the excitation signal at different locations to achieve the best detection results. Threshold constraints set the parameter range of the excitation signal, including amplitude, bandwidth / center frequency, pulse width, duty cycle, etc.

[0039] Furthermore, the method involves controlling the excitation source of the MOSFET switching circuit according to the excitation signal configuration type to obtain an excitation input signal for defect detection corresponding to the heat dissipation mode identifier. This includes: constructing an initial excitation candidate set corresponding to the heat dissipation mode identifier based on the excitation signal configuration type; performing excitation on each excitation candidate in the initial excitation candidate set and collecting an excitation simulation response set; calculating the defect response on the excitation simulation response set; and outputting a response score for each excitation candidate based on the true positive count ratio, the mean of true positive responses, and the standard deviation of true positive responses; and selecting the optimal excitation candidate solution from the initial excitation candidate set based on the response score of each excitation candidate as the excitation input signal for defect detection.

[0040] Specifically, the excitation signal configuration type is matched from the identifier-excitation configuration table, and an initial excitation candidate set corresponding to the heat dissipation mode identifier is constructed based on the excitation signal configuration type. Each excitation in the initial excitation candidate set is represented by a triple X = (excitation type T, injection port P, threshold constraint θ), where excitation type T includes such as TDR, voltage sweep frequency, thermal pulse, multi-frequency duty cycle perturbation, etc. The injection port P is determined according to the heat dissipation mode and detection requirements, such as different ports such as ECT loop 1, TDR port, and hot plate in ECT-related detection. Then, for each excitation candidate in the initial excitation candidate set, the controller controls the excitation source of the MOSFET switching circuit according to its corresponding configuration. The MOSFET switching circuit precisely adjusts the on and off states of the internal MOSFET according to the type T and injection port P information in the excitation candidate, accurately applying the excitation signal to the cooling pipe. For example, when the excitation type T is TDR (time domain reflection), the controller first generates a digital control signal according to the preset TDR signal parameters, including pulse width, rise time, etc., and converts it into an analog voltage signal through a high-speed digital-to-analog converter (DAC) to control the gate voltage of the MOSFET. By controlling and adjusting the gate voltage, the MOSFET switching circuit switches between on and off states in a very short time, thereby generating a high-speed electrical pulse signal at the injection port P, thus applying the excitation signal. During the excitation process, using the applied excitation signal as a trigger, voltage, current, and temperature sensors, such as a vector network analyzer, oscilloscope, or high-speed acquisition card, are used to simultaneously acquire the port reflection signal. The sampling rate follows the Nyquist sampling theorem and is set to at least twice the highest frequency of the excitation signal, with a resolution of no less than 12 bits. The sampling range is selected according to the signal amplitude range to ensure signal integrity without overloading. The acquired signal is processed in the time domain, frequency domain, or phase domain. Time domain processing includes extracting characteristic parameters of the signal, such as peak value, mean, standard deviation, rise time, and fall time, to reflect the amplitude changes and dynamic characteristics of the signal. For example, the peak value of the reflected signal is analyzed to determine the signal strength, and the rise time is analyzed to obtain the rate of change of the signal. The time-domain signal is converted into a frequency-domain signal using Fourier transform, yielding the signal's spectral distribution, including the amplitude spectrum and phase spectrum. The amplitude spectrum reflects the energy distribution of the signal at different frequencies, while the phase spectrum reflects the phase relationship between the frequency components of the signal. The time-domain and frequency-domain characteristics of the acquired signal are integrated to form an excitation simulation response set, reflecting the real-time state of the cooling pipe under specific excitation.

[0041] Defect response calculations are performed on the collected stimulus simulation response set. Specifically, the calculation process involves comparing the stimulus simulation response with known defect characteristic patterns. For example, in water-cooling mode, if the stimulus causes features such as abrupt changes in reflection phase, the results are compared with preset defect characteristic thresholds and patterns. The true positive count percentage, the mean of the true positive response, and the standard deviation of the true positive response are calculated. The true positive count percentage refers to the proportion of samples that correctly detect defects out of the total number of samples tested, reflecting the stimulus candidate's ability to accurately identify defects in actual testing. For example, if a stimulus candidate correctly detects defects 80 times in 100 tests, its true positive count percentage is 80%. The mean of the true positive response is the average intensity of the stimulus simulation response, reflecting the average effect of the stimulus candidate when triggering defect responses. The standard deviation of the true positive response reflects the dispersion of the true positive response intensity, calculated using the standard deviation formula. A smaller standard deviation indicates a more stable response intensity, while a larger standard deviation indicates greater fluctuations in response intensity. Combining the true positive count percentage, the mean of the true positive response, and the standard deviation of the true positive response, a weighted average response score is output for each stimulus candidate. The weights of the true positive count percentage, the true positive response mean, and the true positive response standard deviation are set as w1, w2, and w3, respectively, with w1 + w2 + w3 = 1. The specific weights are set according to actual needs and expert experience. The incentive candidate's response score S = w1 × true positive count percentage + w2 × true positive response mean + w3 × (1 - true positive response standard deviation). The true positive response mean and true positive response standard deviation are normalized, and the response score ranges from 0 to 1. The higher the score, the better the incentive candidate performs in detecting defects in the corresponding heat dissipation mode.

[0042] Based on the response score of each stimulus candidate, the optimal stimulus candidate is selected from the initial stimulus candidate set as the optimal stimulus candidate solution. This optimal stimulus candidate solution is determined after considering multiple factors, including the stimulus signal configuration type, hardware security constraints, and defect detection performance. It maximizes the ability to accurately detect cooling pipe defects under the current heat dissipation mode. This optimal solution is then used as the stimulus input signal for defect detection, applied to the cooling pipes to perform actual defect detection, further improving the accuracy and reliability of cooling pipe defect detection.

[0043] Furthermore, the optimal solution for selecting an incentive candidate from the initial incentive candidate set based on the response score of each incentive candidate includes: obtaining the top k incentive candidates with a response score greater than a preset response score based on the response score of each incentive candidate; performing multiple rounds of cross-mutation on the k incentive candidates to obtain a mutated incentive candidate set; calculating the defect response from the mutated incentive candidate set; and reselecting the optimal solution for selecting an incentive candidate as the incentive input signal for defect detection.

[0044] Specifically, a preset response score is established. By comparing the response score of each incentive candidate with the preset response score, the incentive candidates are sorted from highest to lowest response score, and the top k incentive candidates with a response score greater than the preset response score are obtained. k is a predetermined value, greater than or equal to 5 and less than or equal to 10. By selecting the top k incentive candidates focusing on their superior performance, such as amplitude, frequency, pulse width, phase, and duty cycle, unnecessary computation is reduced while ensuring that enough high-quality candidates enter the next stage. Then, multiple rounds of crossover and mutation are performed on the selected k incentive candidates. Crossover and mutation is a common operation in genetic algorithms. It involves exchanging and combining some features of different incentive candidates and randomly changing some parameters of the incentive candidates to generate a new candidate set, i.e., a mutated incentive candidate set. Specifically, multiple rounds of crossover and mutation are performed on the selected k incentive candidates, using single-point crossover or multi-point crossover. Single-point crossover uses a random number generator to randomly select a feature location as the crossover point, exchanging only that feature location. For example, two excitation candidates are randomly selected, and their amplitude parameters are swapped to form a new offspring. Multi-point crossover randomly selects multiple feature locations as crossover points, randomly exchanging features such as amplitude, frequency, pulse width, phase, and duty cycle to form a new offspring. For example, the random number generator determines three crossover points, namely amplitude, frequency, and duty cycle feature locations. Then, the amplitude, frequency, and duty cycle parameters of the first excitation candidate are swapped with the corresponding parameters of the second excitation candidate. Based on the analysis of the impact of historical data on pipeline defects, it was determined that amplitude and frequency have a significant impact on the response signal of pipeline defect detection, and amplitude and frequency are used as parameters participating in the variation. Based on the parameters involved in the mutation, Gaussian perturbation is used to adjust the offspring after crossover. Gaussian perturbation is a random perturbation method based on Gaussian distribution. For parameters that need adjustment, such as amplitude parameters, their current value is determined as the mean. Based on a pre-set perturbation intensity according to the standard deviation, a random number following a Gaussian distribution is generated as the amplitude parameter perturbation amount. The current parameter value is added to the parameter perturbation amount to obtain the adjusted new parameter value. Similarly, the same method is used to adjust the frequency parameters using Gaussian perturbation, forming a heterogeneous excitation candidate set.

[0045] Then, defect response calculations are performed on each mutated stimulus candidate from the mutated stimulus candidate set. This calculation process is the same as that used for the initial stimulus candidate set, namely, comparing the simulated stimulus response with known defect characteristic patterns, calculating the percentage of true positives, the mean and standard deviation of true positive responses, and recalculating the response score for each mutated stimulus candidate based on the response metric. Finally, based on the recalculated response scores, the best-performing stimulus candidate, i.e., the one with the highest response score, is selected again from the mutated stimulus candidate set as the optimal stimulus candidate solution and used as the stimulus input signal for defect detection.

[0046] By first selecting high-performing stimulus candidates and then performing cross-mutation and re-evaluation, the stimulus candidates can be further optimized while ensuring computational efficiency. This increases the likelihood of finding stimulus input signals that are more suitable for detecting defects in cooling pipes under the current heat dissipation mode, thereby improving the accuracy and reliability of defect detection and ensuring the effective implementation of the entire detection scheme.

[0047] The excitation input signal is injected into the cooling pipe of the heat dissipation module for detection, and the excitation response signal is collected.

[0048] Specifically, after obtaining the excitation input signal, a MOSFET switching circuit is used to precisely control and inject the excitation input signal into the cooling pipes of the heat dissipation module. The MOSFET switching circuit can quickly and accurately adjust the on / off state of the internal MOSFETs according to the characteristics of the excitation input signal, ensuring that the excitation signal is injected stably with the expected parameters, such as amplitude, frequency, and waveform. Simultaneously with the excitation signal injection, various sensors deployed at different key locations in the cooling pipes, including but not limited to voltage sensors, current sensors, temperature sensors, and pressure sensors, collect the excitation response signals of the cooling pipes in real time, such as temperature changes, current changes, and pressure fluctuations. The collected excitation response signals comprehensively reflect the dynamic characteristics of the cooling pipes under the action of the excitation input signal, providing accurate and reliable data support for the detection of pipe defects.

[0049] A heat dissipation mode-feature extraction template is constructed. Based on the heat dissipation mode identifier, the corresponding feature extraction template is called to perform defect identification on the excitation response signal and output the defect detection result. The defect detection result includes defect type, defect location and defect level.

[0050] Specifically, representative defect feature samples from multiple heat dissipation modes are acquired. These samples reflect the typical response characteristics of cooling pipes under different heat dissipation modes. Based on the excitation response signals of the representative defect feature samples, a set of key features characterizing the defects is extracted, and a heat dissipation mode-feature extraction template is constructed. Then, based on the heat dissipation mode identifier, the corresponding feature extraction template is called to identify defects in the excitation response signals, and the defect detection results are output, including defect type, defect location, and defect level. For example, the current heat dissipation mode identifier indicates a water cooling mode. Based on this heat dissipation mode identifier, the pre-constructed heat dissipation mode-feature extraction template is called to obtain the feature extraction template corresponding to the water cooling mode. The heat dissipation mode-feature extraction template is constructed based on common defects such as scale blockage and air resistance in water cooling modes, and specifies the rules and algorithms for extracting key features such as response peak energy index, signal response phase sequence, and signal response phase difference from the excitation response signal. After collecting excitation response signals such as temperature change signals from temperature sensors and current change signals from current sensors, relevant features are extracted according to the template requirements. Scale blockage causes local impedance changes. The higher the energy peak of the reflected signal, the more prominent the frequency component is in the excitation response signal. Furthermore, the signal response phase sequences of different defects are also different. The reflected signal energy peak is compared with the defect parameters in the heat dissipation mode-feature extraction template. Simultaneously, the defect type and severity are identified based on the signal response phase sequence. When the reflected signal energy peak falls within the range of scale blockage defect parameters in the heat dissipation mode-feature extraction template, a scale blockage defect is identified. Further, the defect location is determined based on the signal response phase difference of the excitation response signal. Finally, the defect detection result is output, clearly indicating the defect type, defect location, and defect level, including severe, moderate, and mild levels. Identifying the excitation response signal improves the accuracy of cooling pipe defect identification. The output defect detection results, including defect type, location, and level, provide specific guidance for the repair and prevention of cooling pipe defects, enabling technicians to respond quickly and take appropriate measures, thereby improving the reliability and safety of equipment heat dissipation.

[0051] Furthermore, the method for constructing a heat dissipation mode-feature extraction template includes: obtaining representative defect feature samples under the multiple heat dissipation modes; extracting a preferred feature set corresponding to the excitation response signal samples based on the excitation response signal samples of the representative defect feature samples; and constructing a heat dissipation mode-feature extraction template based on the preferred feature set.

[0052] Specifically, representative defect feature samples of cooling pipes are collected using historical data under different heat dissipation modes, such as water cooling, fluorinated refrigerant cooling, and hybrid modes. These representative defect feature samples include various possible defect types, such as scale buildup, air blockage, seepage, aging, separation, and stratification. The collected representative defect feature samples are labeled, recording the type, location, and severity of each defect. Simultaneously, excitation response signal samples for each representative defect feature sample are obtained, including temperature changes and pressure fluctuations. Signal processing techniques, such as Fourier transform, wavelet transform, and time-frequency analysis, are used to extract features from the excitation response signals. The extracted features reflect the presence and characteristics of defects, such as signal peak values, frequency components, phase changes, and energy distribution. Statistical analysis, such as principal component analysis, is used to select a preferred set of features helpful for defect identification from the extracted features, such as response peak energy indices, signal response phase sequences, and signal response phase differences. Furthermore, based on the preferred feature combinations for each heat dissipation mode, corresponding feature extraction templates are constructed. Independent validation datasets are used to validate the constructed templates and evaluate their accuracy in defect identification. Based on the verification results, the template is adjusted and optimized, and the optimized template is used as the constructed heat dissipation mode-feature extraction template and stored in the template database to provide accurate feature extraction and recognition guidance for defect detection, thereby improving the accuracy and efficiency of defect detection.

[0053] Furthermore, based on the heat dissipation mode identifier, the corresponding feature extraction template is invoked to perform defect identification on the excitation response signal and output defect detection results. The method includes: loading a feature extraction template based on the heat dissipation mode identifier; extracting defect features from the excitation response signal according to the feature extraction template, including signal response peak energy index, signal response phase sequence, and signal response phase difference; performing modal localization through the signal response peak energy index, signal response phase sequence, and signal response phase difference to determine the defect detection results.

[0054] Specifically, based on the received heat dissipation mode identifier, the corresponding feature extraction template is loaded from the template database. The collected excitation response signal is then analyzed using this template. Signal processing algorithms, such as Fourier transform, are employed to convert the time-domain excitation response signal to the frequency domain. The peak value corresponding to the frequency point with the highest energy concentration in the excitation response signal is found. The energy within a certain frequency band around the peak value is then integrated to obtain the excitation response signal peak energy index. This index reflects the degree of energy concentration of the excitation response signal at a specific frequency; higher energy indicates a more prominent frequency component in the excitation response signal, thus indicating the size and severity of the defect. The signal response phase sequence can be obtained using the Hilbert transform phase extraction method to acquire the phase value of the excitation response signal at each sampling time. These phase values ​​are arranged chronologically to form the signal response phase sequence, which reflects the phase change of the excitation response signal over time. Different defects may cause specific fluctuation patterns in the phase sequence. Signal response phase difference is obtained by collecting excitation response signals at different locations on the cooling pipe using sensors, extracting the phase sequences of the excitation response signals at these two locations, and then calculating the difference between the two phase values ​​at the same moment. This phase difference is used to determine the differences in signal propagation characteristics at different locations, aiding in location determination. For example, when a defect occurs, the propagation of the excitation response signal at different locations will be affected, causing abnormal changes in the phase difference. Modal localization is performed using the extracted defect characteristic signal response peak energy index, signal response phase sequence, and signal response phase difference. The signal response peak energy index is compared with the feature parameters in the heat dissipation mode-feature extraction template. Similarity calculation methods, such as cosine similarity and Euclidean distance, are used to calculate the similarity between the input signal response peak energy index and the feature parameters of different defect types in the heat dissipation mode-feature extraction template. If the calculated similarity value is high, it indicates that the input index is highly matched with the feature parameters of a certain defect type. According to the preset similarity threshold, when the similarity exceeds the preset similarity threshold, it can be determined that there is a defect type in the cooling pipe that corresponds to the feature parameters of the heat dissipation mode-feature extraction template. Similarly, similarity comparison analysis is performed on the signal response phase sequence and the signal response phase difference. Finally, by combining the analysis results of the signal response peak energy index, the signal response phase sequence, and the signal response phase difference, defect detection results including defect type, defect location, and defect level are obtained.

[0055] By extracting key features from the excitation response signal, defects in cooling pipes can be accurately and effectively identified, improving the efficiency and accuracy of cooling pipe defect detection.

[0056] Example 2, based on the same inventive concept as the heat dissipation module cooling pipe defect detection method in the foregoing examples, such as... Figure 3As shown, this application provides a defect detection system for cooling pipes in a heat dissipation module, wherein the heat dissipation module cooling pipe defect detection system includes:

[0057] The heat dissipation identification receiving module 11 is used to receive the heat dissipation mode identification of the heat dissipation module controller. The heat dissipation module includes a cooling pipe and multiple heat dissipation modes, and the multiple heat dissipation modes share the cooling pipe for heat dissipation. The circuit switching module 12 is used to identify the excitation signal configuration type of the heat dissipation mode identification, control the excitation source of the MOSFET switching circuit according to the excitation signal configuration type, and obtain an excitation input signal for defect detection corresponding to the heat dissipation mode identification. The signal acquisition module 13 is used to inject the excitation input signal into the cooling pipe of the heat dissipation module for detection and acquire the excitation response signal. The defect identification module 14 is used to construct a heat dissipation mode-feature extraction template, call the corresponding feature extraction template based on the heat dissipation mode identification to perform defect identification on the excitation response signal, and output the defect detection result. The defect detection result includes defect type, defect location, and defect level.

[0058] Furthermore, the circuit switching module 12 in the heat dissipation module cooling pipe defect detection system is also used for: the excitation source of the MOSFET switching circuit includes a waveform generator and a bridge drive structure, the waveform generator is electrically connected to the bridge drive structure, the bridge drive structure includes a first bridge arm composed of MOSFET1 and MOSFET2 and a second bridge arm composed of MOSFET3 and MOSFET4; wherein, the first bridge arm controls the voltage amplitude of the excitation source under the drive of the controller, and the second bridge arm switches the signal output mode of the excitation source under the mode command of the controller.

[0059] Furthermore, the circuit switching module 12 in the heat dissipation module cooling pipe defect detection system is also used to: MOSFET1 and MOSFET2 are connected back to back, and MOSFET3 and MOSFET4 are connected back to back.

[0060] Furthermore, the circuit switching module 12 in the heat dissipation module cooling pipe defect detection system is also used to: parse the information of the heat dissipation mode identifier, including PWM encoding, digital bus instructions and voltage level signal configuration; construct an identifier-excitation configuration table, which is used to match the corresponding excitation signal configuration type according to the parsed information of the heat dissipation mode identifier. The items stored in the identifier-excitation configuration table include heat dissipation mode identifier, mode-adapted excitation type, injection port and threshold constraint.

[0061] Furthermore, the circuit switching module 12 in the heat dissipation module cooling pipe defect detection system is also used to: construct an initial excitation candidate set corresponding to the heat dissipation mode identifier based on the excitation signal configuration type; perform excitation on each excitation candidate in the initial excitation candidate set and collect an excitation simulation response set; calculate the defect response on the excitation simulation response set; output the response score of each excitation candidate based on the true positive count ratio, the mean of the true positive response, and the standard deviation of the true positive response; and select the optimal solution of the excitation candidate from the initial excitation candidate set based on the response score of each excitation candidate as the excitation input signal for defect detection.

[0062] Furthermore, the circuit switching module 12 in the heat dissipation module cooling pipe defect detection system is also used to: obtain the top k excitation candidates with a response score greater than the preset response score based on the response score of each excitation candidate; perform multiple rounds of cross mutation on the k excitation candidates to obtain a mutated excitation candidate set; perform defect response calculation from the mutated excitation candidate set; and reselect the optimal solution of the excitation candidate as the excitation input signal for defect detection.

[0063] Furthermore, the defect identification module 14 in the heat dissipation module cooling pipe defect detection system is also used to: acquire representative defect feature samples under the multiple heat dissipation modes; extract the preferred feature set corresponding to the excitation response signal sample based on the excitation response signal sample of the representative defect feature sample, and construct a heat dissipation mode-feature extraction template based on the preferred feature set.

[0064] Furthermore, the defect identification module 14 in the heat dissipation module cooling pipe defect detection system is also used to: load a feature extraction template based on the heat dissipation mode identifier; extract defect features from the excitation response signal according to the feature extraction template, including the signal response peak energy index, the signal response phase sequence, and the signal response phase difference; and perform modal localization through the signal response peak energy index, the signal response phase sequence, and the signal response phase difference to determine the defect detection result.

[0065] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The method and specific examples for detecting defects in the cooling pipes of the heat dissipation module in Embodiment 1 are also applicable to the system for detecting defects in the cooling pipes of the heat dissipation module in this embodiment. Through the foregoing detailed description of the method for detecting defects in the cooling pipes of the heat dissipation module, those skilled in the art can clearly understand the system for detecting defects in the cooling pipes of the heat dissipation module in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0066] In Embodiment 3, based on the same inventive concept as the heat dissipation module cooling pipe defect detection method in the foregoing embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of the heat dissipation module cooling pipe defect detection method described in any one of Embodiment 1 above.

[0067] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0068] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for detecting defects in cooling pipes of a heat dissipation module, characterized in that, The method includes: The heat dissipation module receives a heat dissipation mode identifier from the heat dissipation module controller. The heat dissipation module includes a cooling pipe and multiple heat dissipation modes, and the multiple heat dissipation modes share the cooling pipe for heat dissipation. Identify the excitation signal configuration type of the heat dissipation mode identifier, control the excitation source of the MOSFET switching circuit according to the excitation signal configuration type, and obtain the excitation input signal for defect detection corresponding to the heat dissipation mode identifier; The excitation input signal is injected into the cooling pipe of the heat dissipation module for detection, and the excitation response signal is collected; A heat dissipation mode-feature extraction template is constructed. Based on the heat dissipation mode identifier, the corresponding feature extraction template is called to perform defect identification on the excitation response signal and output the defect detection result. The defect detection result includes defect type, defect location and defect level.

2. The method for detecting defects in cooling pipes of a heat dissipation module as described in claim 1, characterized in that, The excitation source of the MOSFET switching circuit includes a waveform generator and a bridge drive structure. The waveform generator is electrically connected to the bridge drive structure. The bridge drive structure includes a first bridge arm composed of MOSFET1 and MOSFET2 and a second bridge arm composed of MOSFET3 and MOSFET4. The first bridge arm controls the voltage amplitude of the excitation source under the drive of the controller, and the second bridge arm switches the signal output mode of the excitation source under the mode command of the controller.

3. The method for detecting defects in cooling pipes of a heat dissipation module as described in claim 2, characterized in that, MOSFET1 and MOSFET2 are connected back-to-back, and MOSFET3 and MOSFET4 are connected back-to-back.

4. The method for detecting defects in cooling pipes of a heat dissipation module as described in claim 1, characterized in that, The method for identifying the excitation signal configuration type of the heat dissipation mode identifier includes: The information of the heat dissipation mode identifier is parsed, including PWM encoding, digital bus commands, and voltage level signal configuration; An identifier-excitation configuration table is constructed. The identifier-excitation configuration table is used to match the corresponding excitation signal configuration type according to the parsing information of the heat dissipation mode identifier. The items stored in the identifier-excitation configuration table include heat dissipation mode identifier, mode-adapted excitation type, injection port, and threshold constraint.

5. The method for detecting defects in cooling pipes of a heat dissipation module as described in claim 1, characterized in that, The method involves controlling the excitation source of the MOSFET switching circuit according to the excitation signal configuration type to obtain an excitation input signal for defect detection corresponding to the heat dissipation mode identifier, the method comprising: Based on the excitation signal configuration type, an initial excitation candidate set corresponding to the heat dissipation mode identifier is constructed; Activate each incentive candidate in the initial incentive candidate set and collect the incentive simulation response set. Calculate the defect response on the incentive simulation response set. Output the response score for each incentive candidate based on the percentage of true positive responses, the mean of true positive responses, and the standard deviation of true positive responses. Based on the response score of each incentive candidate, the optimal incentive candidate solution is selected from the initial incentive candidate set as the incentive input signal for defect detection.

6. The method for detecting defects in cooling pipes of a heat dissipation module as described in claim 5, characterized in that, The optimal solution for selecting incentive candidates from the initial incentive candidate set is selected based on the response score of each incentive candidate, and the method includes: Based on the response score of each incentive candidate, the top k incentive candidates with a response score greater than the preset response score are obtained; Multiple rounds of cross-mutation are performed on the k excitation candidates to obtain a mutated excitation candidate set. Defect response is calculated from the mutated excitation candidate set, and a new optimal excitation candidate solution is selected as the excitation input signal for defect detection.

7. The method for detecting defects in cooling pipes of a heat dissipation module as described in claim 1, characterized in that, Constructing a heat dissipation pattern-feature extraction template, the methods include: Obtain representative defect feature samples under the multiple heat dissipation modes; Based on the excitation response signal sample representing the defect feature sample, extract the preferred feature set corresponding to the excitation response signal sample, and construct a heat dissipation mode-feature extraction template based on the preferred feature set.

8. The method for detecting defects in cooling pipes of a heat dissipation module as described in claim 1, characterized in that, Based on the heat dissipation mode identifier, the corresponding feature extraction template is invoked to perform defect identification on the excitation response signal, and the defect detection result is output. The method includes: Load the feature extraction template based on the heat dissipation mode identifier; Defect features are extracted from the excitation response signal based on the feature extraction template, including signal response peak energy index, signal response phase sequence, and signal response phase difference; Modal localization is performed using the signal response peak energy index, signal response phase sequence, and signal response phase difference to determine the defect detection result.

9. A defect detection system for cooling pipes in a heat dissipation module, characterized in that, The steps for implementing the method for detecting defects in cooling pipes of a heat dissipation module as described in any one of claims 1 to 8 include: A heat dissipation indicator receiving module is used to receive heat dissipation mode indicators from a heat dissipation module controller. The heat dissipation module includes cooling pipes and multiple heat dissipation modes, and the multiple heat dissipation modes share the cooling pipes for heat dissipation. The circuit switching module is used to identify the excitation signal configuration type of the heat dissipation mode identifier, control the excitation source of the MOSFET switching circuit according to the excitation signal configuration type, and obtain the excitation input signal for defect detection corresponding to the heat dissipation mode identifier. The signal acquisition module is used to inject the excitation input signal into the cooling pipe of the heat dissipation module for detection and to acquire the excitation response signal; The defect identification module is used to construct a heat dissipation mode-feature extraction template, call the corresponding feature extraction template based on the heat dissipation mode identifier to perform defect identification on the excitation response signal, and output the defect detection result, which includes defect type, defect location and defect level.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the steps of the method for detecting defects in cooling pipes of a heat dissipation module as described in any one of claims 1 to 8.

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