Silicon carbide chip radiation resistance test system and test method thereof

Abstract

The invention relates to the technical field of silicon carbide chips, in particular to a silicon carbide chip anti-radiation test system and a test method thereof.The silicon carbide chip anti-radiation test system comprises a sensor module, a data collecting and processing module, a data prediction module, an early warning and control module, a communication module, a power management module and a safety protection module; through cooperation of the sensor module, the data collecting and processing module, the data prediction module, the early warning and control module, the communication module, the power management module and the safety protection module, automatic monitoring, quick response and accurate early warning can be realized, and the automatic requirements of different fields on the radiation resistance test of the silicon carbide chip are met; through cooperation of the data collection and processing module and the data prediction module, data collection and preprocessing, feature extraction, model selection and training, model evaluation, prediction and anomaly detection are utilized, accurate prediction and anomaly detection of data are realized, and powerful support is provided for decision making.

Description

Technical Field

[0001] The present invention belongs to the technical field of silicon carbide chips, and in particular relates to a silicon carbide chip radiation resistance testing system and a testing method thereof. Background Art

[0002] In the production of silicon carbide chips, in order to ensure their stability and reliability in extreme environments, radiation-resistant testing equipment is usually used to perform radiation-resistant testing on silicon carbide chips. Radiation-resistant treatment helps maintain key performance indicators of the chip, such as threshold voltage and dynamic characteristics, and can maintain normal operation even in high-radiation environments.

[0003] For example, the prior art patent application number is 202311741512.9, which discloses a silicon carbide chip anti-radiation test and early warning device, including: a machine body; an anti-radiation compartment door, which is connected to the outside of the machine body; an irradiation device, which is arranged on the inner wall of the machine body; a contact mechanism, which is arranged inside the machine body; the invention arranges an installation plate~stretching seat, a placement plate seat and a stretching spring, so that the elastic expansion and contraction between the sliding plate and the stretching spring and the setting of the sliding mechanism can increase the stability of the sliding plate during sliding and expansion, so that the silicon carbide chip can be fixed and clamped by the abutment clamping of the abutment plate, and then the limit seat is stretched and rotated, so that the contact probe can be quickly powered on and connected in the motor area of ​​the silicon carbide chip to be tested, thereby effectively increasing the rapid anti-radiation test operation of the silicon carbide chip.

[0004] However, the above-mentioned radiation resistance test and its early warning equipment include early warning mechanisms, but these early warning mechanisms are mainly based on current data indicators for power-off and alarm operations, and lack the ability to automatically monitor the entire test process in real time, which means that if a problem occurs during the test, manual intervention may be required to discover and solve it in time, which is not convenient for automatic monitoring. Therefore, we need to propose a silicon carbide chip radiation resistance test system and its testing method to solve the above-mentioned problems, so that it can monitor the working status and test data of the equipment in real time, improve the accuracy and timeliness of the early warning, and facilitate the automatic monitoring of the system. Summary of the invention

[0005] In view of the above problems, the present invention provides a silicon carbide chip radiation resistance test system, comprising: a sensor module, the sensor module is used to monitor the working state of the system and the test environment parameters in real time; A data collection and processing module, which is used to receive data transmitted by the sensor module and perform preliminary processing; A data prediction module, which uses machine learning algorithms to perform in-depth analysis on the collected data and predict possible abnormal situations; An early warning and control module, which determines whether it is necessary to issue an early warning signal based on the analysis results of the data prediction module, and automatically performs power-off or alarm operations in abnormal situations; A communication module, which is used for data transmission and communication between the sensor module, the data collection and processing module, the data prediction module, the early warning and control module, the power management module and the safety protection module; A power management module, which is used to provide stable power for the sensor module, the data collection and processing module, the data prediction module, the early warning and control module, the communication module and the safety protection module; A safety protection module is used to automatically cut off the power supply and take protective measures when a serious fault or abnormality occurs, so as to minimize the risk of equipment damage and personal injury.

[0006] Furthermore, the sensor module includes a sensor device, a signal conditioning circuit for amplifying and filtering the signal of the sensor device, an analog-to-digital converter for converting the analog signal of the signal conditioning circuit into a digital signal, and a microcontroller for controlling the operation of the sensor device, and the sensor device includes a thermocouple, a resistance temperature detector, a piezoelectric sensor, a photoelectric sensor and a current sensor.

[0007] Furthermore, the data collection and processing module performs data collection and preliminary processing as follows: A1. Obtain real-time sensed environmental parameter data from the sensor module through the network interface and convert it into corresponding electrical signals or electrical parameters; A2. Preprocess the data, including removing noise, filling missing values, and standardizing or normalizing the data; A3. After data preprocessing is completed, the correlation coefficient between the data is calculated to measure the correlation between the features. The correlation coefficient calculation formula is: , , , where x and y represent two features, r is the correlation coefficient, n is the number of samples; i is the index variable of the summation symbol, starting from 1 and ending at n; A4. Select the data with high correlation system to form a feature data set, and then use the conversion formula to convert the feature data. The conversion formula is: ,in, is a continuous variable in the original data, and c is a constant used to ensure that the parameter in the logarithmic function is a positive number.

[0008] Furthermore, the machine learning algorithms used in the data prediction module include linear regression algorithm, logistic regression algorithm, decision tree algorithm and random forest algorithm.

[0009] Furthermore, the process of the data prediction module predicting possible abnormal situations is as follows: B1. Establish a new feature set based on the data processed by the data collection and processing module; B2. Use the prediction formula in the new feature set to predict continuous variables and establish a prediction model. The prediction formula is: + ,in are model parameters, is the error term, x is the input feature vector, is the predicted value; The prediction model establishment formula is: , where m is the number of samples, is the hypothesis function; B3. Use the logistic regression formula to find the predicted value in the prediction model that is closest to the actual value , the logistic regression formula is: ; B4. The predicted value closest to the actual value Divide into training set and test set, and use the training set to train the prediction model; B5. Use accuracy, recall, and F1 score indicators to evaluate the performance of the prediction model to verify the prediction ability of the prediction model; B6. After the prediction model training is completed, the trained prediction model is used to predict the new data, and based on the prediction results, it is determined whether the system is in a normal state or has an abnormal situation. When the prediction result is a normal state, it returns to step B1. When the prediction result is an abnormal situation, it is necessary to further analyze the cause of the abnormality and stop the radiation resistance test of the silicon carbide chip.

[0010] Furthermore, the process of the early warning and control module in determining whether it is necessary to issue an early warning signal is as follows: D1. receiving processed and analyzed data from the data prediction module, the data including environmental parameters measured by the sensor module and prediction results based on these parameters; D2. Set the warning threshold used to determine when to trigger the warning; D3. Based on the received data and the preset threshold, the early warning and control module will make a decision on whether to issue an early warning signal. If the data shows normal, no action will be taken; if the data shows abnormal, an early warning will be issued; D4. When it is determined that an early warning is required, the early warning and control module will activate the early warning signal, which includes an audible alarm, a visual alarm or sending a notification message to relevant personnel; D5. While issuing the warning signal, the warning and control module also performs some control operations, including power off, starting the backup power supply, and shutting down certain parts of the system; D6. After executing the control operation, the early warning and control module will continue to monitor the data and adjust its strategy according to the new data. If the problem is solved, the system will return to normal operation; if the problem persists, it will be necessary to continue to execute some control operations; D7. Record the issuance of warnings and the execution of control operations, and generate reports.

[0011] Furthermore, the power management module includes an input power receiving unit for receiving external power input, a voltage conversion unit for converting the input voltage into a required voltage, a current distribution unit for reasonably distributing current and ensuring that each module can obtain sufficient power supply, a monitoring and adjustment unit for maintaining output voltage stability, a voltage protection unit for overvoltage or undervoltage protection, a thermal management unit for thermal protection and a standby power consumption management unit. When the device is on standby, the standby power consumption management unit can reduce power consumption, extend battery life or reduce energy waste.

[0012] Furthermore, the input power receiving unit receives an external input voltage, which can be direct current or alternating current. The received external voltage is converted into the voltage required by each unit through the voltage conversion unit, and then the current is distributed to each unit through the current distribution unit according to the needs of each internal unit. The monitoring and adjustment unit monitors the output voltage and current in real time, and adjusts the internal parameters through a feedback mechanism to maintain the stability of the output. When an abnormality such as overvoltage, undervoltage, overcurrent or short circuit is detected, the voltage protection unit will immediately cut off the power supply or limit the current to protect the safety of the equipment. When the temperature is too high, the thermal management unit will automatically reduce the output power.

[0013] Furthermore, when a serious fault or abnormality occurs in the safety protection module, the specific protection process is as follows: E1. When the device is abnormal, the safety protection module will receive abnormal information through the sensor module. The abnormal information includes excessive temperature, excessive current and abnormal voltage; E2. After receiving the abnormal information, the security protection module needs to analyze the abnormal data and quickly determine the severity and impact of the problem; E3. When no immediate action is required, repeat the E2 operation. When immediate action is required, the safety protection module should immediately suspend the operation of the equipment; E4. After suspending the operation of the equipment, the security protection module needs to conduct a thorough analysis of the anomaly by checking the logs and comparing the normal working status of the equipment to confirm the type and cause of the anomaly; E5. According to the type and cause of the exception, the security protection module needs to formulate a corresponding processing plan; E6. According to the formulated processing plan, the security protection module executes corresponding steps and measures to repair the abnormal problem of the equipment. During the execution process, it is necessary to follow the relevant operating procedures and safe operating steps; E7. After the abnormal problem of the device is solved, the security protection module needs to restore the device to ensure that the device can operate normally. During the recovery process, the various functions and performances of the device need to be checked step by step; E8. After the equipment exception processing is completed, the security protection module needs to record and summarize the entire processing process; E9. Based on the summary information of equipment exception handling, the security protection module needs to formulate corresponding preventive measures.

[0014] Based on the above-described silicon carbide chip radiation resistance test system, the present invention also provides a silicon carbide chip radiation resistance test method, comprising the following steps: S1, start the system, perform self-check and initialization settings on the system; S2. Real-time monitoring of test environment parameters through sensor modules, the test environment parameters including temperature, humidity and radiation intensity; S3, the data collection and processing module receives the data from the sensor module and processes it; S4, the data prediction module uses machine learning algorithms to analyze data and predict abnormal situations; S5. Based on the analysis results, the warning and control module decides whether to issue a warning signal or perform a power-off operation; S5.1. When no immediate action is required, repeat S3. S5.2. When immediate action is required, the safety protection module shall immediately suspend the operation of the equipment; S6. After the test is completed, the system automatically enters the standby mode or shuts down state.

[0015] The beneficial effects of the present invention are: 1. The present invention can realize automatic monitoring, rapid response and accurate early warning through the cooperation of sensor module, data collection and processing module, data prediction module, early warning and control module, communication module, power management module and safety protection module, so as to meet the automation requirements of different fields for the anti-radiation performance test of silicon carbide chips; 2. The present invention realizes accurate prediction and anomaly detection of data through the cooperation of data collection and processing module and data prediction module, and utilizes data collection and preprocessing, feature extraction, model selection and training, model evaluation, prediction and anomaly detection, so as to provide strong support for decision-making.

[0016] 3. Through the setting of the early warning and control module, the present invention can respond to abnormal situations in a timely manner and take necessary measures to protect the safety of equipment and personnel, thereby ensuring the stable and reliable operation of the silicon carbide chip radiation resistance test system.

[0017] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A system block diagram according to an embodiment of the present invention is shown; Figure 2 A flowchart of data collection and preliminary processing according to an embodiment of the present invention is shown; Figure 3 A flowchart of possible abnormal situation prediction by a data prediction module according to an embodiment of the present invention is shown; Figure 4 A flowchart of the early warning and control module in determining whether it is necessary to issue an early warning signal according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The embodiment of the present invention provides a silicon carbide chip radiation resistance testing system, such as Figure 1-4 As shown, it includes a sensor module, a data collection and processing module, a data prediction module, an early warning and control module, a communication module, a power management module and a safety protection module; The sensor module is used to monitor the working status and test environment parameters of the system in real time, and provides basic information for subsequent data analysis and early warning by sampling the working status and test environment parameters.

[0022] The sensor module includes a sensor device, a signal conditioning circuit for amplifying and filtering the signal of the sensor device, an analog-to-digital converter for converting the analog signal of the signal conditioning circuit into a digital signal, and a microcontroller for controlling the operation of the sensor device. The sensor device includes a thermocouple, a resistance temperature detector, a piezoelectric sensor, a photoelectric sensor, and a current sensor. The thermocouple is used for measuring the environment from -200°C to 2000°C, the resistance temperature detector is used for measuring the temperature within the range of -200°C-850°C, the piezoelectric sensor is used for measuring the physical quantities of pressure, force, acceleration and vibration, the photoelectric sensor is used for monitoring the radiation intensity, and the current sensor is used for monitoring the current flow in the circuit; The signal conditioning circuit receives the original electrical signal from the sensor device, and amplifies, filters and converts the original electrical signal into a digital signal. After receiving the converted digital signal, the microcontroller performs calculations and logical judgments to extract useful information.

[0023] The data collection and processing module is used to receive data transmitted by the sensor module and perform preliminary processing to ensure the accuracy and reliability of the data and provide a high-quality data source for subsequent analysis and decision-making.

[0024] The process of data collection and preliminary processing by the data collection and processing module is as follows: A1. Obtain real-time sensed environmental parameter data from the sensor module through the network interface and convert it into corresponding electrical signals or electrical parameters; A2. Preprocess the data, which includes removing noise, filling missing values, and standardizing or normalizing the data for subsequent analysis and modeling.

[0025] A3. After data preprocessing is completed, the correlation coefficient between the data is calculated to measure the correlation between the features. The correlation coefficient calculation formula is: , , , where x and y represent two features, r is the correlation coefficient, n is the number of samples, and i is the index variable of the summation symbol, starting from 1 and ending at n.

[0026] A4. Select the data with high correlation system to form a feature data set, and then use the conversion formula to convert the feature data. The conversion formula is: ,in, is a continuous variable in the original data, and c is a constant used to ensure that the parameter in the logarithmic function is positive, so as to facilitate in-depth analysis of the data prediction module to better understand the key patterns and trends in the data.

[0027] The data prediction module uses machine learning algorithms to conduct in-depth analysis of the collected data and predict possible abnormal situations, so as to facilitate the early detection of potential problems and provide a scientific basis for the early warning mechanism; The machine learning algorithms used in the data prediction module include linear regression algorithm, logistic regression algorithm, decision tree algorithm and random forest algorithm. The process of the data prediction module predicting possible abnormal situations is as follows: B1. Establish a new feature set based on the data processed by the data collection and processing module; B2. Use the prediction formula in the new feature set to predict continuous variables and establish a prediction model. The prediction formula is: + ,in are model parameters, is the error term, x is the input feature vector, is the predicted value; The prediction model establishment formula is: , where m is the number of samples, is the hypothesis function; B3. Use the logistic regression formula to find the predicted value in the prediction model that is closest to the actual value , the logistic regression formula is: ; B4. The predicted value closest to the actual value Divide into training set and test set, and use the training set to train the prediction model; B5. Use accuracy, recall, and F1 score indicators to evaluate the performance of the prediction model to verify the prediction ability of the prediction model; Among them, the accuracy is the ratio of the number of correctly predicted samples to the total number of samples, and the calculation formula is A= , A is the accuracy rate, TP is the true positive example, that is, the number of samples that are actually positive and predicted to be positive; TN is the true negative example, that is, the number of samples that are actually negative and predicted to be negative; FP is the false positive example, that is, the number of samples that are actually negative but predicted to be positive; FN is the false negative example, that is, the number of samples that are actually positive but predicted to be negative; The recall rate is the ratio of true positive examples to actual positive examples, and the F1 score is the harmonic mean of the ratio of true positive examples to predicted positive examples and the recall rate.

[0028] B6. After the prediction model training is completed, the trained prediction model is used to predict new data. The formula for the prediction value is: (where T is a transposition operation, which is used to swap the rows and columns of a matrix), and based on the prediction results, determine whether the system is in a normal state or has an abnormal situation. When the prediction result is a normal state, return to step B1. When the prediction result is an abnormal situation, further analyze the cause of the abnormality and stop the radiation resistance test of the silicon carbide chip. Through data collection and preprocessing, feature extraction, model selection and training, model evaluation, prediction and anomaly detection, accurate prediction of data and anomaly detection are achieved, providing strong support for decision-making.

[0029] The early warning and control module determines whether it is necessary to issue an early warning signal based on the analysis results of the data prediction module, and automatically performs power-off or alarm operations in abnormal situations, so as to respond to abnormal situations in a timely manner and protect the safety of equipment and personnel; The process of the early warning and control module in determining whether to issue an early warning signal is as follows: D1. receiving processed and analyzed data from the data prediction module, the data including environmental parameters measured by the sensor module and prediction results based on these parameters; D2. Set warning thresholds to determine when to trigger a warning, such as if the temperature exceeds a certain set value, or if the prediction model indicates an abnormal trend, a warning will be triggered; D3. Based on the received data and the preset threshold, the early warning and control module will make a decision on whether to issue an early warning signal. If the data shows normal, no action will be taken; if the data shows abnormal, an early warning will be issued; D4. When it is determined that an early warning is required, the early warning and control module will activate the early warning signal, which includes an audible alarm, a visual alarm or sending a notification message to relevant personnel; D5. While issuing the warning signal, the warning and control module also performs some control operations, including powering off, starting the backup power supply, and shutting down certain parts of the system to prevent further damage; D6. After executing the control operation, the early warning and control module will continue to monitor the data and adjust its strategy according to the new data. If the problem is solved, the system will return to normal operation; if the problem persists, it will be necessary to continue to execute some control operations; D7. Record the issuance of warnings and the execution of control operations, and generate reports for subsequent analysis and maintenance.

[0030] Throughout the process, the design of the early warning and control module must ensure that personnel safety is given priority in any case to avoid personal injury caused by system failure. Through the above workflow, the early warning and control module can respond to abnormal situations in a timely manner and take necessary measures to protect the safety of equipment and personnel, thereby ensuring the stable and reliable operation of the silicon carbide chip radiation resistance test system.

[0031] The communication module is used for data transmission and communication between the sensor module, the data collection and processing module, the data prediction module, the early warning and control module, the power management module and the safety protection module, ensuring real-time information sharing and collaborative work, improving the system integration and collaborative efficiency, and enhancing the overall performance; The communication module establishes a physical connection with the sensor module, the data collection and processing module, the data prediction module, the early warning and control module, the power management module and the security protection module through wired or wireless means. The physical connection can be a serial bus, Ethernet, Wi-Fi or other communication protocols. When a module needs to send data, the module will package the data into a message or a data packet and send it to the communication module through the communication interface. After the communication module receives the data, it forwards the data to the corresponding receiving module according to the target address and protocol to complete the real-time sharing and collaborative work of information between the modules. The communication module uses common knowledge technology in the field and will not be described in detail here.

[0032] The power management module is used to provide stable power to the sensor module, data collection and processing module, data prediction module, early warning and control module, communication module and safety protection module to ensure that they can work stably and prevent test interruption or equipment damage caused by power problems; The power management module includes an input power receiving unit for receiving an external power input, a voltage conversion unit for converting an input voltage into a required voltage, a current distribution unit for reasonably distributing current and ensuring that each module can obtain sufficient power supply, a monitoring and adjustment unit for maintaining output voltage stability, a voltage protection unit for overvoltage or undervoltage protection, a thermal management unit for thermal protection, and a standby power consumption management unit. The input power receiving unit receives an external input voltage, which may be direct current or alternating current. The voltage conversion unit converts the received external voltage into a voltage required by each unit, and then distributes current to each unit according to the requirements of each internal unit through the current distribution unit. The monitoring and adjustment unit monitors the output voltage and current in real time, and adjusts internal parameters through a feedback mechanism to maintain output stability. When an abnormality such as overvoltage, undervoltage, overcurrent or short circuit is detected, the voltage protection unit immediately cuts off the power supply or limits the current to protect the safety of the device. When the temperature is too high, the thermal management unit automatically reduces the output power to prevent the device from being overheated and damaged. When the device is on standby, the standby power consumption management unit can reduce power consumption, extend battery life or reduce energy waste.

[0033] Through precise voltage conversion, current distribution, real-time monitoring and regulation, and multiple safety protection mechanisms, the stable operation and long-term working capability of the silicon carbide chip radiation resistance test system are ensured.

[0034] The safety protection module is used to automatically cut off the power supply and take protective measures when a serious fault or abnormality occurs, so as to minimize the risk of equipment damage and personal injury.

[0035] When a serious fault or abnormality occurs in the safety protection module, the specific protection process is as follows: E1. When the device is abnormal, the safety protection module will receive abnormal information through the sensor module. The abnormal information includes excessive temperature, excessive current and abnormal voltage; E2. After receiving abnormal information, the security protection module needs to analyze the abnormal data and quickly determine the severity and impact of the problem to determine whether immediate action is needed; E3. When no immediate action is required, repeat the E2 operation. When immediate action is required, the safety protection module should immediately suspend the operation of the equipment to prevent the problem from further expanding and ensure the safety of personnel; E4. After suspending the operation of the equipment, the security protection module needs to conduct a thorough analysis of the anomaly by checking the logs and comparing the normal working status of the equipment to confirm the type and cause of the anomaly; E5. According to the type and cause of the exception, the security protection module needs to formulate a corresponding processing plan, which includes the steps, measures and resources required to resolve the exception; E6. According to the formulated processing plan, the security protection module executes corresponding steps and measures to repair the abnormal problem of the equipment. During the execution process, it is necessary to follow the relevant operating procedures and safe operating steps; E7. After the abnormal problem of the device is solved, the security protection module needs to restore the device to ensure that the device can operate normally. During the recovery process, the various functions and performances of the device need to be checked step by step; E8. After the equipment exception is handled, the safety protection module needs to record and summarize the entire process, record the time, cause, solution, handling process and effect of the exception, and analyze and summarize it; E9. Based on the summary information of equipment abnormality handling, the safety protection module needs to formulate corresponding preventive measures; preventive measures include equipment maintenance, regular inspection and cleaning, and improvement of operating procedures.

[0036] Through the mutual cooperation of sensor modules, data collection and processing modules, data prediction modules, early warning and control modules, communication modules, power management modules and safety protection modules, automatic monitoring, rapid response and accurate early warning can be achieved to meet the needs of different fields for the radiation resistance performance testing of silicon carbide chips.

[0037] Based on the above-described silicon carbide chip radiation resistance test system, the present invention also provides a silicon carbide chip radiation resistance test method, comprising the following steps: S1, start the system, perform self-check and initialization settings on the system; S2. Real-time monitoring of test environment parameters through sensor modules, the test environment parameters including temperature, humidity and radiation intensity; S3, the data collection and processing module receives the data from the sensor module and processes it; S4, the data prediction module uses machine learning algorithms to analyze data and predict abnormal situations; S5. Based on the analysis results, the warning and control module decides whether to issue a warning signal or perform a power-off operation; S5.1. When no immediate action is required, repeat S3. S5.2. When immediate action is required, the safety protection module shall immediately suspend the operation of the equipment; S6. After the test is completed, the system automatically enters the standby mode or shuts down state.

[0038] In step S1, the system self-check and initialization settings are performed as follows: 1) The contents of the sensor module self-detection and initialization settings are as follows: Check whether the sensor connection is normal and ensure that the data transmission line is fault-free; Calibrate sensors to ensure the accuracy of measurement data; Initialize sensor parameters, such as range, accuracy, etc.

[0039] 2) The contents of self-detection and initialization settings for the data collection and processing module are as follows: Check whether the communication interface with the sensor module is normal; Initialize data processing algorithms and storage space; Set up the data receiving buffer to prepare for receiving sensor data.

[0040] 3) The contents of self-detection and initialization settings for the data prediction module are as follows: Load machine learning models or algorithm libraries; Initialize model parameters and states; Prepare to receive data from the data collection and processing module.

[0041] 4) The contents of self-detection and initialization settings for the early warning and control module are as follows: Check the communication interface with the data prediction module; Initialize warning thresholds and control strategies; Prepare to execute early warning signal issuance and control operations.

[0042] 5) The contents of the self-detection and initialization settings for the communication module are as follows: Check physical connections and communication protocols with other modules; Initialize network settings, such as IP address, subnet mask, etc.; Ensure that data transmission channels are unobstructed.

[0043] 6) The contents of self-detection and initialization settings for the power management module are as follows: Check the power input and output lines; Initialize voltage conversion, current distribution and monitoring regulation functions; Set the overvoltage, undervoltage, and overcurrent protection thresholds.

[0044] 7) The contents of self-detection and initialization settings for the safety protection module are as follows: Check the security interface with each module; Initialize safety protection mechanisms, such as automatic power-off function; Prepare to trigger protection measures under abnormal circumstances; Through these self-test and initialization settings, each module can ensure that it is in the best working state and provide stable and reliable support for the subsequent silicon carbide chip radiation resistance test.

[0045] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A silicon carbide chip radiation resistance test system, characterized in that: include: A sensor module, which is used to monitor the working status of the system and test environment parameters in real time; A data collection and processing module, which is used to receive data transmitted by the sensor module and perform preliminary processing; A data prediction module, which uses machine learning algorithms to perform in-depth analysis on the collected data and predict possible abnormal situations; An early warning and control module, which determines whether it is necessary to issue an early warning signal based on the analysis results of the data prediction module, and automatically performs power-off or alarm operations in abnormal situations; A communication module, which is used for data transmission and communication between the sensor module, the data collection and processing module, the data prediction module, the early warning and control module, the power management module and the safety protection module; A power management module, which is used to provide stable power for the sensor module, the data collection and processing module, the data prediction module, the early warning and control module, the communication module and the safety protection module; A safety protection module is used to automatically cut off the power supply and take protective measures when a serious fault or abnormality occurs, so as to minimize the risk of equipment damage and personal injury.

2. The silicon carbide chip radiation resistance test system according to claim 1, characterized in that: The sensor module includes a sensor device, a signal conditioning circuit for amplifying and filtering the signal of the sensor device, an analog-to-digital converter for converting the analog signal of the signal conditioning circuit into a digital signal, and a microcontroller for controlling the operation of the sensor device. The sensor device includes a thermocouple, a resistance temperature detector, a piezoelectric sensor, a photoelectric sensor and a current sensor.

3. The silicon carbide chip radiation resistance test system according to claim 2, characterized in that: The process of data collection and preliminary processing by the data collection and processing module is as follows: A1. Obtain real-time sensed environmental parameter data from the sensor module through the network interface and convert it into corresponding electrical signals or electrical parameters; A2. Preprocess the data, including removing noise, filling missing values, and standardizing or normalizing the data; A3. After data preprocessing is completed, the correlation coefficient between the data is calculated to measure the correlation between the features. The correlation coefficient calculation formula is: ， , , where x and y represent two features, r is the correlation coefficient, n is the number of samples; i is the index variable of the summation symbol, starting from 1 and ending at n; A4. Select the data with high correlation system to form a feature data set, and then use the conversion formula to convert the feature data. The conversion formula is: ,in, is a continuous variable in the original data, and c is a constant used to ensure that the parameter in the logarithmic function is a positive number.

4. The silicon carbide chip radiation resistance testing system according to claim 3, characterized in that: The machine learning algorithms used in the data prediction module include linear regression algorithm, logistic regression algorithm, decision tree algorithm and random forest algorithm.

5. The silicon carbide chip radiation resistance testing system according to claim 4, characterized in that: The process of the data prediction module predicting possible abnormal situations is as follows: B1. Establish a new feature set based on the data processed by the data collection and processing module; B2. Use the prediction formula in the new feature set to predict continuous variables and establish a prediction model. The prediction formula is: + ,in are model parameters, is the error term, x is the input feature vector, is the predicted value; The prediction model establishment formula is: , where m is the number of samples, is the hypothesis function; B3. Use the logistic regression formula to find the predicted value in the prediction model that is closest to the actual value , the logistic regression formula is: ； B4. The predicted value closest to the actual value Divide into training set and test set, and use the training set to train the prediction model; B5. Use accuracy, recall, and F1 score indicators to evaluate the performance of the prediction model to verify the prediction ability of the prediction model; B6. After the prediction model training is completed, the trained prediction model is used to predict the new data, and based on the prediction results, it is determined whether the system is in a normal state or has an abnormal situation. When the prediction result is a normal state, it returns to step B1. When the prediction result is an abnormal situation, it is necessary to further analyze the cause of the abnormality and stop the radiation resistance test of the silicon carbide chip.

6. The silicon carbide chip radiation resistance testing system according to claim 5, characterized in that: The process of the early warning and control module in determining whether to issue an early warning signal is as follows: D1. receiving processed and analyzed data from the data prediction module, the data including environmental parameters measured by the sensor module and prediction results based on these parameters; D2. Set the warning threshold used to determine when to trigger the warning; D3. Based on the received data and the preset threshold, the early warning and control module will make a decision on whether to issue an early warning signal. If the data shows normal, no action will be taken; if the data shows abnormal, an early warning will be issued; D4. When it is determined that an early warning is required, the early warning and control module will activate the early warning signal, which includes an audible alarm, a visual alarm or sending a notification message to relevant personnel; D5. While issuing the warning signal, the warning and control module also performs some control operations, including power off, starting the backup power supply, and shutting down certain parts of the system; D6. After executing the control operation, the early warning and control module will continue to monitor the data and adjust its strategy according to the new data. If the problem is solved, the system will return to normal operation; if the problem persists, it will be necessary to continue to execute some control operations; D7. Record the issuance of warnings and the execution of control operations, and generate reports.

7. The silicon carbide chip radiation resistance testing system according to claim 6, characterized in that: The power management module includes an input power receiving unit for receiving external power input, a voltage conversion unit for converting the input voltage into a required voltage, a current distribution unit for reasonably distributing current and ensuring that each module can obtain sufficient power supply, a monitoring and adjustment unit for maintaining output voltage stability, a voltage protection unit for overvoltage or undervoltage protection, a thermal management unit for thermal protection and a standby power consumption management unit. When the device is on standby, the standby power consumption management unit can reduce power consumption, extend battery life or reduce energy waste.

8. The silicon carbide chip radiation resistance testing system according to claim 7, characterized in that: The input power receiving unit receives an external input voltage, which can be direct current or alternating current. The voltage conversion unit converts the received external voltage into the voltage required by each unit, and then distributes current to each unit through the current distribution unit according to the needs of each internal unit. The monitoring and adjustment unit monitors the output voltage and current in real time, and adjusts internal parameters through a feedback mechanism to maintain output stability. When an abnormality such as overvoltage, undervoltage, overcurrent or short circuit is detected, the voltage protection unit will immediately cut off the power supply or limit the current to protect the safety of the equipment. When the temperature is too high, the thermal management unit will automatically reduce the output power.

9. The silicon carbide chip radiation resistance testing system according to claim 8, characterized in that: When a serious fault or abnormality occurs in the safety protection module, the specific protection process is as follows: E1. When the device is abnormal, the safety protection module will receive abnormal information through the sensor module. The abnormal information includes excessive temperature, excessive current and abnormal voltage; E2. After receiving the abnormal information, the security protection module needs to analyze the abnormal data and quickly determine the severity and impact of the problem; E3. When no immediate action is required, repeat the E2 operation. When immediate action is required, the safety protection module should immediately suspend the operation of the equipment; E4. After suspending the operation of the equipment, the security protection module needs to conduct a thorough analysis of the anomaly by checking the logs and comparing the normal working status of the equipment to confirm the type and cause of the anomaly; E5. According to the type and cause of the exception, the security protection module needs to formulate a corresponding processing plan; E6. According to the formulated processing plan, the security protection module executes corresponding steps and measures to repair the abnormal problem of the equipment. During the execution process, it is necessary to follow the relevant operating procedures and safe operating steps; E7. After the abnormal problem of the device is solved, the security protection module needs to restore the device to ensure that the device can operate normally. During the recovery process, the various functions and performances of the device need to be checked step by step; E8. After the equipment exception processing is completed, the security protection module needs to record and summarize the entire processing process; E9. Based on the summary information of equipment exception handling, the security protection module needs to formulate corresponding preventive measures.

10. A method for testing the radiation resistance of a silicon carbide chip, based on a system for testing the radiation resistance of a silicon carbide chip according to any one of claims 1 to 9, characterized in that: The steps include: S1, start the system, perform self-check and initialization settings on the system; S2. Real-time monitoring of test environment parameters through sensor modules, the test environment parameters including temperature, humidity and radiation intensity; S3, the data collection and processing module receives the data from the sensor module and processes it; S4, the data prediction module uses machine learning algorithms to analyze data and predict abnormal situations; S5. Based on the analysis results, the warning and control module decides whether to issue a warning signal or perform a power-off operation; S5.

1. When no immediate action is required, repeat S3. S5.

2. When immediate action is required, the safety protection module shall immediately suspend the operation of the equipment; S6. After the test is completed, the system automatically enters the standby mode or shuts down state.

Images