Hash chain-based vehicle chip detection full-link credible trace method

Abstract

The present application relates to the technical field of industrial detection, and more particularly to a vehicle gauge chip detection full-link credible trace method based on a hash chain, which obtains network transmission parameters, event metadata and running environment data of a current operation event, determines whether there is an abnormal risk in the equipment operation based on the comparison between the running environment trend parameters of the to-be-detected vehicle gauge chip at a detection process node and the event metadata and the calibration cutoff date, performs hash operation on the event metadata of the detection process node to determine event data digest, performs combined encryption operation on the event data digest of the previous detection process node to generate a chained hash value of the current detection process node, and determines whether to allow the issuance in response to a report issuance instruction of the to-be-detected vehicle gauge chip. The present application realizes adaptively triggering the recovery mechanism according to the actual detection situation of the to-be-detected vehicle gauge chip, and improves the reliability and efficiency of the vehicle gauge chip detection full-link credible trace.

Description

Technical Field

[0001] This invention relates to the field of industrial testing technology, and in particular to a trusted end-to-end traceability method for automotive-grade chip testing based on hash chains. Background Technology

[0002] Automotive-grade chips are core components of automotive electronic systems, and their reliability directly affects the safe operation of vehicles. The International Automotive Electronics Association (IAEA) has established automotive-grade integrated circuit reliability testing and certification standards, setting hundreds of test items across dimensions such as environmental adaptability, lifespan, and reliability. These standards require that test data be traceable and tamper-proof. In traditional laboratory testing management models, testing processes often rely on manual recording and paper document circulation. Test data is scattered across forms, paper reports, and equipment systems, making it difficult to achieve complete traceability and reliable evidence storage throughout the entire process. In recent years, laboratory information management systems have been gradually applied to the chip testing field, and some advanced systems have begun to integrate technologies such as blockchain evidence storage and audit trails. However, the audit trails in traditional laboratory testing management models mostly focus on recording operation logs, lacking cryptographic protection for the original test data files themselves. The end-to-end trusted evidence storage and anti-tampering capabilities are insufficient. At the same time, the management of equipment calibration validity period is mostly based on static ledger records, which cannot determine whether the equipment is compliant in real time at the moment the test task is executed. Moreover, the test data file does not carry an environmental snapshot at the moment the data is generated, making it impossible to verify whether the data was generated within a period of environmental compliance. Automotive-grade chip testing has strict requirements for temperature and humidity stability. If the temperature fluctuates frequently within the tolerance range, even if the average value is qualified, the temperature fluctuation itself may cause uncontrollable drift in the chip electrical parameter measurement results. The lack of dynamic judgment and pre-intervention of the instantaneous environmental and equipment compliance at the moment of testing, and the lack of multi-dimensional integrated quantitative evaluation of key electrical parameters within batches in the automotive-grade chip testing report issuance process, makes it difficult to determine whether the anomaly is due to inherent quality defects or occasional disturbances. Therefore, improving the reliability and efficiency of end-to-end trusted traceability in automotive-grade chip testing is an urgent technical problem to be solved.

[0003] For example, Chinese patent application publication number CN112948086A discloses a trusted PLC control system. By incorporating trusted hardware and software technologies, a hardware and software isolation zone is established on the basis of a security isolation module. A complete set of trusted PLC hardware and software solutions is established from aspects such as hardware memory isolation, lightweight trusted startup, embedded trusted operation, real-time dynamic monitoring and protection, and secure communication. The embedded trusted hardware includes secure area hardware, isolation area hardware, and non-secure area hardware. The secure area hardware constitutes the embedded minimum operable system of the PLC control system. The non-secure area hardware realizes the external communication and storage of the PLC control system. The isolation area hardware uses the security isolation module as the root of trust and connects the secure area hardware and the non-secure area hardware through pipeline protection and isolation technology to form a trusted communication interface and communication link.

[0004] The following problems still exist in the existing technology: Existing technologies do not consider the impact of the device's operating status at the moment of detection on the reliability of the hash digest of the original test data file. Existing technologies cannot quickly identify device operating risks and cannot adaptively trigger recovery mechanisms based on the actual testing conditions of the automotive-grade chip under test, thus affecting the reliability and efficiency of trusted traceability throughout the entire automotive-grade chip testing chain. Summary of the Invention

[0005] To address this, the present invention provides a hash chain-based end-to-end trusted traceability method for automotive-grade chip testing, which overcomes the problems of existing technologies being unable to quickly identify equipment operation risks and unable to adaptively trigger recovery mechanisms based on the actual testing conditions of the automotive-grade chip under test, thus affecting the reliability and efficiency of end-to-end trusted traceability for automotive-grade chip testing.

[0006] To achieve the above objectives, this invention provides a trusted end-to-end traceability method for automotive-grade chip testing based on hash chains, comprising: In response to the trigger command of the automotive-grade chip under test at the testing process node, the network transmission parameters, event metadata, and operating environment data of the current operation event are obtained; The event metadata includes a test data file and a timestamp associated with the test data file; the operating environment data includes a calibration deadline, ambient temperature parameters, and ambient humidity parameters. The operating environment trend parameters are determined by comparing the operating environment data of the equipment at several monitoring times within the preset monitoring period. Based on the comparison between the operating environment trend parameters and event metadata of the automotive-grade chip under test at the testing process node and the calibration deadline, it is determined whether there is an abnormal risk in the operation of the equipment at the current testing process node; In response to the determination that there is no abnormal risk in the operation of the equipment, the event metadata of the automotive-grade chip to be tested at the testing process node is hashed to determine the event data digest. The event data digest is then combined with the event data digest of the previous testing process node for encryption to generate the chained hash value of the current testing process node. In response to the report issuance instruction for the automotive-grade chip to be tested, the chip batch quality control parameters of the automotive-grade chip to be tested are obtained to determine whether issuance is allowed; Based on the yield rate marking feature nodes of each testing process node, the abnormal tendency category is determined according to the distribution of feature nodes. This allows for the selection of issuing an abnormal warning signal, or calling the network transmission parameters of the feature nodes to determine whether to reacquire the event metadata of the automotive-grade chip to be tested and re-issue the report.

[0007] Furthermore, the process of determining the operating environment trend parameters based on the comparison of operating environment data of the equipment at several monitoring times within a preset monitoring period includes: Obtain the ambient temperature and humidity parameters of the automotive-grade chip under test at each monitoring time; The ratio of the variance of the ambient temperature parameter to the variance threshold of the ambient temperature parameter is determined as the first environmental characteristic. The ratio of the difference between the maximum and minimum values ​​of the environmental humidity parameter to the threshold value of the maximum and minimum environmental humidity parameter is determined as the second environmental characteristic. The weighted sum of the first environmental feature and the second environmental feature is used to determine the operating environment trend parameter.

[0008] Furthermore, the process of determining whether there is any abnormal risk in the operation of the equipment at the current testing process node includes, If the automotive-grade chip under test meets the normal operating conditions at each testing process node, it is determined that there is no abnormal risk in the operation of the equipment at the current testing process node; If the automotive-grade chip to be tested does not meet the normal operating conditions at a certain node in the testing process, it is determined that there is an abnormal risk in the operation of the equipment at the current testing process node and an abnormal warning signal is issued. The normal operating conditions are that the operating environment trend parameter does not exceed the preset operating environment trend parameter threshold, and the timestamp associated with the test data file of the current operation event does not belong to the characteristic time period. The lower limit of the characteristic time period is the calibration deadline date, and the upper limit of the interval is the repair completion date.

[0009] Furthermore, in response to the report issuance instruction for the automotive-grade chip to be tested, the process of obtaining the chip batch quality control parameters of the automotive-grade chip to be tested includes, Several standby currents, several operating frequencies, and several leakage currents of the automotive-grade chip under test are obtained respectively. The ratio of the maximum standby current to the minimum standby current is defined as the standby current characterization value; the ratio of the maximum operating frequency to the minimum operating frequency is defined as the operating frequency characterization value; and the ratio of the maximum leakage current to the minimum leakage current is defined as the leakage current characterization value. The ratio of the standby current characterization value to the standby current characterization value threshold is determined as the first quality control feature, the ratio of the operating frequency characterization value to the operating frequency characterization value threshold is determined as the second quality control feature, and the ratio of the leakage current characterization value to the leakage current characterization value threshold is determined as the third quality control feature. The first quality control feature, the second quality control feature, and the third quality control feature are weighted and summed to obtain the batch quality control parameters for the chip.

[0010] Furthermore, the process of determining whether issuance is permitted based on the batch quality control parameters of the automotive-grade chip to be tested includes: If the quality control parameters of the automotive-grade chip to be tested exceed the preset chip batch quality control parameter threshold, then it is determined that issuance is not allowed. If the quality control parameters of the automotive-grade chip to be tested do not exceed the preset chip batch quality control parameter threshold, then issuance is permitted.

[0011] Furthermore, in response to the prohibition of issuance, the process of marking feature nodes based on the yield rate of each inspection process node includes, If the yield rate of a detection process node does not exceed a preset yield rate threshold, then the detection process node is marked as a feature node.

[0012] Furthermore, the process of determining the anomaly tendency category based on the distribution of feature nodes includes, If the number of feature nodes exceeds a preset threshold, the abnormal tendency category is determined to be a strong explicit abnormal tendency category. If the number of feature nodes does not exceed the preset threshold, the abnormal tendency category is determined to be the weakly explicit abnormal tendency category.

[0013] Furthermore, if the abnormal tendency category is the strongly dominant abnormal tendency category, an abnormal warning signal is issued; If the abnormal tendency category is the weakly dominant abnormal tendency category, the network transmission parameters of the feature node are called to determine whether to reacquire the event metadata of the automotive-grade chip to be detected at the feature node and to re-determine the report issuance.

[0014] Furthermore, the process of determining the network transmission parameters of the feature nodes includes, The out-of-order TCP packet rate of the feature node at each collection time is obtained within the preset monitoring period. The variance of the TCP packet out-of-order rate is determined as the network transmission parameter.

[0015] Furthermore, the process of determining whether to reacquire the event metadata of the automotive-grade chip under test at the feature node and re-issue the report includes, If the network transmission parameters of the feature node do not exceed the preset network transmission parameter threshold, it is determined to reacquire the event metadata of the automotive-grade chip to be detected at the feature node and to re-determine the report issuance.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: In response to the trigger command of the automotive-grade chip under test at a testing process node, the present invention acquires the network transmission parameters, event metadata, and operating environment data of the current operation event. Based on the comparison between the operating environment trend parameters of the automotive-grade chip under test at the testing process node and the event metadata and calibration deadline, it determines whether there is an abnormal risk in the operation of the equipment at the current testing process node. It performs a hash operation on the event metadata of the automotive-grade chip under test at the testing process node to determine the event data digest. It then combines and encrypts the event data digest with the event data digest of the previous testing process node to generate a chained hash value for the current testing process node. In response to the report issuance command of the automotive-grade chip under test, it determines whether issuance is allowed. Based on the distribution of feature nodes, it selects to issue an abnormal warning signal, or determines whether to reacquire the event metadata of the automotive-grade chip under test and re-determine the report issuance. Thus, it achieves the identification of equipment operation risks and adaptively triggers a recovery mechanism based on the actual testing situation of the automotive-grade chip under test, improving the reliability and efficiency of the end-to-end trusted traceability of automotive-grade chip testing.

[0017] In particular, this invention determines operating environment trend parameters based on the comparison of operating environment data of equipment at several monitoring times within a preset monitoring period. It can be understood that by determining operating environment trend parameters through comparison of operating environment data at several monitoring times, environmental compliance judgment is upgraded from static threshold checking to dynamic stability assessment. Operating environment trend parameters can specifically capture the intensity of temperature and humidity fluctuations during the testing process, effectively identifying hidden environmental anomalies missed by traditional instantaneous threshold judgments, such as temperature control system oscillations and repeated humidity boundary overshoots. This provides a more rigorous environmental guarantee for the credibility of automotive-grade chip testing data. By determining operating environment trend parameters to characterize multi-dimensional environmental features, it retains the independent physical meaning of each dimension while providing an automated judgment basis for issuance verification. It can also more flexibly adapt to the differentiated environmental sensitivity requirements of different test items such as three-temperature electrical testing and HAST damp heat testing, improving the system's applicability in multi-product line and multi-test type scenarios. Therefore, it realizes the determination of operating environment trend parameters of equipment at testing process nodes, improving the reliability and efficiency of credible traceability throughout the entire automotive-grade chip testing chain.

[0018] In particular, this invention determines whether there is an abnormal risk in the operation of the equipment at the current testing process node based on the comparison between the operating environment trend parameters of the automotive-grade chip under test at each testing process node and the event metadata and calibration deadline. It can be understood that by constructing a dual-condition logic of operating environment and calibration validity period, a comprehensive judgment of the abnormal risk of equipment operation at the moment of testing is achieved. This considers both the equipment's operating environment and the calibration status of the equipment itself. If the timestamp associated with the test data file falls within this characteristic time period, it indicates that the equipment was in a state of calibration invalidation when the data was generated. Through the joint determination of operating environment trend parameters and calibration deadline, the risk of abnormal equipment operation at the moment of testing is achieved. The automated pre-emptive interception of equipment malfunction risks prevents invalid data generated under abnormal conditions from entering the subsequent hash storage and report issuance process at the source of data generation. This avoids invalid data contaminating the evidence chain and reducing the credibility of the storage system. When equipment calibration expires and there is no repair completion date, it is automatically identified as abnormal and an alert is issued. This fills the blind spot in the judgment of traditional static ledger management in the case of indefinite invalidation. It ensures that the testing tasks are always executed under compliant equipment conditions. From the process perspective, it guarantees the legality and credibility of automotive-grade chip testing data. In turn, it enables rapid identification of equipment operation risks and improves the reliability and efficiency of credible traceability of the entire automotive-grade chip testing chain.

[0019] In particular, in response to the report issuance command of the automotive-grade chip under test, this invention obtains the chip batch quality control parameters of the automotive-grade chip under test to determine whether issuance is allowed. It can be understood that standby current, operating frequency, and leakage current respectively characterize the static power consumption level, maximum operating speed, and off-state leakage characteristics of the chip in a low-power state, covering three orthogonal dimensions of chip static power consumption, dynamic performance, and reliability. The range ratio of each parameter is used as a consistency metric, which can characterize the difference between the worst and best individuals in the batch, and characterize extreme outliers caused by process fluctuations. The chip batch quality control parameters comprehensively reflect the overall consistency level of the batch in multiple dimensions. The smaller the chip batch quality control parameters, the better the consistency within the batch. The larger the chip batch quality control parameters, the more dispersed there is in at least one dimension, and the batch quality does not meet the issuance requirements. Thus, the determination of report issuance is realized, improving the reliability and efficiency of the whole-chain reliable traceability of automotive-grade chip testing.

[0020] In particular, under the condition of weakly dominant anomaly tendency, this invention calls the network transmission parameters of the feature node to determine whether to reacquire the event metadata of the automotive-grade chip under test at the feature node and re-perform the report issuance determination. It can be understood that, under the condition of weakly dominant anomaly tendency, by analyzing the network transmission parameters to determine whether to trigger data retransmission, intelligent attribution and adaptive recovery are achieved in the scenario of issuance failure. The distribution of the number of feature nodes is used to distinguish between systemic process defects and occasional transmission failures, avoiding unnecessary batch scrapping caused by misjudging network problems as chip quality problems, reducing the misjudgment rate, and using a method that reduces TCP packet out-of-order rate. Using the difference as a criterion for judging network transmission parameters, it can specifically capture intermittent fluctuations in link quality. Excessive network transmission parameters indicate that the link itself is unstable, and retransmission is unlikely to guarantee integrity. Relatively small network transmission parameters indicate that the link is basically healthy and the probability of successful retransmission is high. This avoids the waste of system resources and time delays caused by repeated retransmissions on unstable links. In the high-throughput scenario of mass production testing of automotive-grade chips, it improves the efficiency of anomaly handling and shortens production line downtime. Furthermore, it enables the adaptive triggering of recovery mechanisms based on the actual testing conditions of the automotive-grade chips under test, thereby improving the reliability and efficiency of trusted traceability of the entire automotive-grade chip testing link. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the steps of the end-to-end trusted traceability method for automotive-grade chip testing based on hash chains, as described in this embodiment of the invention. Figure 2 A flowchart illustrating the logic of determining whether there is an abnormal risk in the operation of the device at the current detection process node in this embodiment of the invention. Figure 3 This is a flowchart illustrating the logic of determining whether issuance is permitted based on the batch quality control parameters of the automotive-grade chip to be tested, according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the logic of determining the anomaly tendency category based on the distribution of feature nodes in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0025] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Please see Figure 1 The diagram shows a flowchart of the steps in the trusted traceability method for end-to-end automotive-grade chip testing based on hash chains, according to an embodiment of the present invention. The trusted traceability method for end-to-end automotive-grade chip testing based on hash chains of the present invention includes: Step S100: In response to the trigger command of the automotive-grade chip under test at the test process node, obtain the network transmission parameters, event metadata, and operating environment data of the current operation event; The event metadata includes a test data file and a timestamp associated with the test data file; the operating environment data includes a calibration deadline, ambient temperature parameters, and ambient humidity parameters. Step S200: Determine the operating environment trend parameters based on the comparison of the operating environment data of the equipment at the detection process nodes during a preset monitoring period at several monitoring times. Step S300: Based on the comparison between the operating environment trend parameters and event metadata of the automotive-grade chip under test at the testing process node and the calibration deadline, determine whether there is an abnormal risk in the operation of the equipment at the current testing process node. Step S400: In response to the determination result that there is no abnormal risk in the operation of the equipment, the event metadata of the automotive-grade chip to be tested at the testing process node is hashed to determine the event data digest, and the event data digest is combined with the event data digest of the previous testing process node to generate the chained hash value of the current testing process node. Specifically, if the current testing process node is the first process node of automotive-grade chip testing, the event data digest of the current testing process node will not be combined with the event data digest of the previous testing process node for encryption.

[0027] Step S500: In response to the report issuance instruction of the automotive-grade chip to be tested, obtain the chip batch quality control parameters of the automotive-grade chip to be tested to determine whether issuance is allowed. Step S600: Based on the yield rate of each inspection process node, mark the feature nodes, determine the abnormal tendency category according to the distribution of the feature nodes, and select to issue an abnormal warning signal, or call the network transmission parameters of the feature nodes to determine whether to reacquire the event metadata of the automotive-grade chip to be inspected and re-issue the report.

[0028] Specifically, the process of determining operating environment trend parameters based on the comparison of operating environment data of equipment at several monitoring times within a preset monitoring period includes: Obtain the ambient temperature and humidity parameters of the automotive-grade chip under test at each monitoring time; The ratio of the variance of the ambient temperature parameter to the variance threshold of the ambient temperature parameter is determined as the first environmental characteristic. The ratio of the difference between the maximum and minimum values ​​of the environmental humidity parameter to the threshold value of the maximum and minimum environmental humidity parameter is determined as the second environmental characteristic. The weighted sum of the first environmental feature and the second environmental feature is used to determine the operating environment trend parameter.

[0029] Specifically, the preset monitoring period can be defined by the trigger command of the current batch of automotive-grade chips at the testing process node as the lower limit of the preset monitoring period, and by the end command of the current batch of automotive-grade chips at the testing process node as the upper limit of the preset monitoring period.

[0030] Specifically, the monitoring time can be evenly distributed within a preset monitoring period. The interval between adjacent monitoring times can be set by those skilled in the art based on the accuracy requirements of reliable traceability of the entire automotive-grade chip detection chain. The higher the accuracy requirement, the shorter the interval should be. The value range can be [20, 120], with the interval unit being seconds. Preferably, it can be 30 seconds.

[0031] Specifically, in the testing of automotive-grade chips, the variance of the ambient temperature parameter reflects the stability of the temperature control system during the testing period. The larger the variance, the more drastic the temperature fluctuation, and the greater the impact on the reproducibility of the chip's electrical parameter measurement results. The range of ambient humidity reflects the risk of boundary overshoot of the humidity control system. An excessively large range means that there is a risk of condensation or electrostatic damage during the testing process. Given that the sensitivity of automotive-grade chip three-temperature electrical tests to temperature stability is much higher than that to humidity fluctuations, temperature stability characteristics are given priority in implementation. Therefore, the first environmental characteristic calculated based on temperature variance is given a slightly higher weight. Thus, when performing weighted summation, the weight of the first environmental characteristic can be set to 0.6, and the weight of the second environmental characteristic can be set to 0.4. In particular, for humidity-sensitive testing scenarios such as high-acceleration damp heat testing, the weight of the second environmental characteristic can be adjusted to 0.6, and the weight of the first environmental characteristic can be adjusted to 0.4 to adapt to the differentiated sensitivity requirements of different test items to environmental factors.

[0032] In this embodiment, the purpose of setting the environmental temperature parameter variance threshold and the environmental humidity maximum / minimum threshold is to characterize the critical value at which the adverse impact of the operating environment on the reliability of automotive-grade chip testing data has reached an unacceptable level. By calling the operating environment monitoring data corresponding to several batches judged as environmentally compliant in historical testing tasks, the historical data of the environmental temperature parameter variance and the historical data of the environmental humidity parameter range for each compliant batch within the same preset monitoring period are obtained. The historical average of the environmental temperature parameter variance and the historical average of the environmental humidity parameter range are calculated. Based on the purpose of setting the above two thresholds, the environmental temperature parameter variance threshold is determined. The environmental humidity extreme value threshold is determined as the product of the historical average of the variance of the environmental temperature parameter and the first deviation coefficient. The environmental humidity extreme value threshold is determined as the product of the historical average of the range of the environmental humidity parameter and the second deviation coefficient. The first deviation coefficient can be selected within the range [1.05, 1.2], and the second deviation coefficient can be selected within the range [1.1, 1.25]. Preferably, in the three-temperature test scenario of automotive-grade chip Grade 1, the first deviation coefficient can be 1.1 and the second deviation coefficient can be 1.15. This ensures the stringency of the compliance judgment of the testing environment while avoiding unnecessary false interception due to overly conservative threshold settings.

[0033] Specifically, this invention determines operating environment trend parameters based on the comparison of operating environment data of equipment at several monitoring times within a preset monitoring period. It can be understood that by comparing operating environment data at several monitoring times to determine operating environment trend parameters, environmental compliance judgment is upgraded from static threshold checking to dynamic stability assessment. Operating environment trend parameters can specifically capture the intensity of temperature and humidity fluctuations during testing, effectively identifying hidden environmental anomalies missed by traditional instantaneous threshold judgments, such as temperature control system oscillations and repeated humidity boundary overshoots. This provides a more rigorous environmental guarantee for the credibility of automotive-grade chip testing data. By determining operating environment trend parameters to characterize multi-dimensional environmental features, the independent physical meaning of each dimension is preserved, and an automated judgment basis is provided for issuance verification. It can also more flexibly adapt to the differentiated environmental sensitivity requirements of different test items such as three-temperature electrical testing and HAST damp heat testing, improving the system's applicability in multi-product line and multi-test type scenarios. Therefore, it realizes the determination of operating environment trend parameters for equipment at testing process nodes, improving the reliability and efficiency of credible traceability throughout the entire automotive-grade chip testing chain.

[0034] Specifically, it is understandable that automotive-grade chip testing requires not only instantaneous compliance with environmental standards but also process stability. Existing technologies typically only determine whether the instantaneous values ​​of environmental parameters are within tolerance ranges, but cannot identify hidden anomalies where the instantaneous values ​​are acceptable while the process experiences violent fluctuations. For example, frequent start-stop cycles of the compressor in the temperature control system cause rapid back-and-forth temperature fluctuations within the tolerance boundary. Although such fluctuations do not exceed the threshold, they are sufficient to cause dynamic drift of the chip's PN junction electrical parameters, making the measurement results unreproducible. By acquiring the operating environment trend parameters, the discrete characteristics of multi-dimensional environmental monitoring data in the time dimension are compressed into a single comprehensive scalar. The larger the operating environment trend parameter, the more the environment tends to be out of control. Since the core requirement for temperature in automotive-grade testing is stability, variance is sensitive to frequent oscillations and occasional jumps. Since humidity risks mainly come from exceeding boundaries, condensation, or static electricity risks, the range can characterize the most unfavorable deviation. Thus, the operating environment trend parameters of the equipment at each node of the testing process are determined, improving the reliability and efficiency of the entire chain of credible traceability in automotive-grade chip testing.

[0035] Please see Figure 2 As shown, this is a flowchart illustrating the logic of determining whether there is an abnormal risk in the operation of the device at the current detection process node according to an embodiment of the present invention. The process of determining whether there is an abnormal risk in the operation of the device at the current detection process node includes, If the automotive-grade chip under test meets the normal operating conditions at each testing process node, it is determined that there is no abnormal risk in the operation of the equipment at the current testing process node; If the automotive-grade chip to be tested does not meet the normal operating conditions at a certain node in the testing process, it is determined that there is an abnormal risk in the operation of the equipment at the current testing process node and an abnormal warning signal is issued. The normal operating conditions are that the operating environment trend parameter does not exceed the preset operating environment trend parameter threshold, and the timestamp associated with the test data file of the current operation event does not belong to the characteristic time period. The lower limit of the characteristic time period is the calibration deadline date, and the upper limit of the interval is the repair completion date.

[0036] Specifically, the calibration deadline is the expiration date of the equipment calibration of the current testing process node, and the repair completion date is the time when the equipment is recalibrated and updated after the equipment calibration expiration date. In particular, if the equipment does not have a repair completion date after the calibration deadline, all timestamps after the calibration deadline are considered to fall into the characteristic period, and the equipment operation of the current testing process node is judged to have an abnormal risk and an abnormal warning signal is issued. The repair completion date can be entered and confirmed by the testing engineer with the corresponding authority through the designated interface of the laboratory information management system after the calibration operation is completed, or the calibration equipment can send back the new calibration deadline through the automated interface after completing the calibration process with the calibrated equipment.

[0037] Specifically, if the automotive-grade chip to be tested does not meet the normal operating conditions at a certain node in the testing process, it is determined that there is an abnormal risk in the operation of the equipment at the current testing node and an abnormal warning signal is issued, waiting for maintenance personnel to repair and then retest.

[0038] Specifically, the preset threshold for the operating environment trend parameter is the product of the operating environment trend parameter reference value and the environmental factor. The operating environment trend parameter reference value is the average value of the operating environment trend parameter under the same working conditions in historical data. The environmental factor can be set by those skilled in the art based on the accuracy requirements of the reliable traceability of the entire chain of automotive-grade chip testing. The higher the accuracy requirement, the smaller the value should be. The value range can be [0.95, 1.15], preferably 1.05.

[0039] Specifically, this embodiment of the invention determines whether there is an abnormal risk in the operation of the equipment at the current testing process node based on the comparison between the operating environment trend parameters of the automotive-grade chip under test at each testing process node and the event metadata and calibration deadline. It can be understood that by constructing a dual-condition logic of operating environment and calibration validity period, a comprehensive judgment of the abnormal risk of equipment operation at the moment of testing is achieved. While considering the equipment's operating environment, the calibration status of the equipment itself is also taken into account. If the timestamp associated with the test data file falls within this characteristic time period, it indicates that the equipment was in a state of calibration invalidation when the data was generated. Through the joint determination of operating environment trend parameters and calibration deadline, the detection is achieved. The automated pre-emptive interception of risks associated with sudden equipment malfunctions prevents invalid data generated under abnormal conditions from entering the subsequent hash storage and report issuance process at the source of data generation. This avoids the problem of invalid data contaminating the evidence chain and reducing the credibility of the storage system. When equipment calibration expires and there is no repair completion date, it is automatically identified as abnormal and an alert is issued. This fills the blind spot in the judgment of traditional static ledger management in the case of indefinite invalidation. It ensures that the testing tasks are always executed under compliant equipment conditions. From the process perspective, it guarantees the legality and credibility of automotive-grade chip testing data. In turn, it enables rapid identification of equipment operation risks and improves the reliability and efficiency of credible traceability throughout the entire automotive-grade chip testing chain.

[0040] Specifically, the process of obtaining the batch quality control parameters of the automotive-grade chip to be tested in response to the report issuance instruction includes the following steps: Several standby currents, several operating frequencies, and several leakage currents of the automotive-grade chip under test are obtained respectively. The ratio of the maximum standby current to the minimum standby current is defined as the standby current characterization value; the ratio of the maximum operating frequency to the minimum operating frequency is defined as the operating frequency characterization value; and the ratio of the maximum leakage current to the minimum leakage current is defined as the leakage current characterization value. The ratio of the standby current characterization value to the standby current characterization value threshold is determined as the first quality control feature, the ratio of the operating frequency characterization value to the operating frequency characterization value threshold is determined as the second quality control feature, and the ratio of the leakage current characterization value to the leakage current characterization value threshold is determined as the third quality control feature. The first quality control feature, the second quality control feature, and the third quality control feature are weighted and summed to obtain the batch quality control parameters for the chip.

[0041] Specifically, the number of samples taken from the same production batch for electrical parameter testing must meet the statistical confidence level requirements for batch quality assessment of automotive-grade chips. In this embodiment, the range of the number of samples taken can be [300, 1000], and preferably, it can be 500.

[0042] Specifically, in the batch quality assessment of automotive-grade chips, the leakage current range ratio is associated with gate oxide layer defects and early failure risk, and is a core sensitive indicator of reliability. The operating frequency range ratio can reflect dynamic performance consistency and affect the system timing convergence margin. The standby current range ratio characterizes the static power consumption dispersion in low-power mode. Given that automotive-grade chips have the most stringent requirements for long-term reliability, leakage current consistency is given priority in implementation. Therefore, the third quality control feature calculated based on the leakage current range ratio is assigned the highest weight. Thus, when performing weighted summation in the general automotive-grade chip testing scenario, the weight of the first quality control feature is set to 0.2, the weight of the second quality control feature is set to 0.2, and the weight of the third quality control feature is set to 0.6. In particular, for high-performance computing chips such as intelligent driving domain controllers, the weight of the second quality control feature can be increased to 0.5, and the weight of the third quality control feature can be adjusted to 0.3 to adapt to the differentiated emphasis on chip performance dimensions in different application scenarios.

[0043] In this embodiment, the purpose of setting the standby current characterization threshold, the operating frequency characterization threshold, and the leakage current characterization threshold is to characterize the dispersion of the corresponding parameters within a batch, which has reached a critical level affecting automotive-grade reliability and consistency. By calling the electrical test data corresponding to several batches that were judged to be qualified in historical testing, the historical data of the standby current range ratio, the historical data of the operating frequency range ratio, and the historical data of the leakage current range ratio of each qualified batch are obtained, and the historical average values ​​of the three are calculated respectively. Based on the purpose of setting the above three thresholds, the standby current characterization threshold is determined to be the product of the historical average value of the standby current range ratio and the first characterization coefficient, the operating frequency characterization threshold is determined to be the product of the historical average value of the operating frequency range ratio and the second characterization coefficient, and the leakage current characterization threshold is determined to be the product of the historical average value of the leakage current range ratio and the third characterization coefficient. The first characterization coefficient can be selected in the interval [1.05, 1.15], the second characterization coefficient can be selected in the interval [0.95, 1.2], and the third characterization coefficient can be selected in the interval [1.1, 1.25]. Preferably, in the mass production testing scenario of automotive-grade MCU chips at the AEC-Q100 Grade 1 level, the first characterization coefficient is set to 1.1, the second characterization coefficient is set to 1.15, and the third characterization coefficient is set to 1.15. This ensures the stringency of batch consistency determination while taking into account the economic requirements of mass production of automotive-grade chips.

[0044] Please see Figure 3 The diagram shown is a flowchart illustrating the logic of determining whether issuance is permitted based on the batch quality control parameters of the automotive-grade chip under test, according to an embodiment of the present invention. The process of determining whether issuance is permitted based on the batch quality control parameters of the automotive-grade chip under test includes... If the quality control parameters of the automotive-grade chip to be tested exceed the preset chip batch quality control parameter threshold, then it is determined that issuance is not allowed. If the quality control parameters of the automotive-grade chip to be tested do not exceed the preset chip batch quality control parameter threshold, then issuance is permitted.

[0045] Specifically, the preset chip batch quality control parameter threshold is the product of the chip batch quality control parameter reference value and the quality control factor. The chip batch quality control parameter reference value is the average value of the chip batch quality control parameters under the same working conditions in historical data. The quality control factor can be calculated by those skilled in the art based on the average value of several historical experimental data, and the value range can be [1.05, 1.15], preferably 1.1.

[0046] Specifically, in response to the report issuance command of the automotive-grade chip under test, this embodiment of the invention obtains the chip batch quality control parameters of the automotive-grade chip under test to determine whether issuance is allowed. It can be understood that standby current, operating frequency, and leakage current respectively characterize the static power consumption level, maximum operating speed, and off-state leakage characteristics of the chip in a low-power state, covering three orthogonal dimensions: static power consumption, dynamic performance, and reliability. The range ratio of each parameter is used as a consistency metric, which can characterize the difference between the worst and best individuals in the batch, and characterize extreme outliers caused by process fluctuations. The chip batch quality control parameters comprehensively reflect the overall consistency level of the batch in multiple dimensions. The smaller the chip batch quality control parameters, the better the consistency within the batch. The larger the chip batch quality control parameters, the more it indicates that there is too large a dispersion in at least one dimension, and the batch quality does not meet the issuance requirements. Thus, the determination of report issuance is realized, improving the reliability and efficiency of the whole-chain reliable traceability of automotive-grade chip testing.

[0047] Specifically, in response to the prohibition of issuance, the process of marking characteristic nodes based on the yield rate at each testing process node includes, If the yield rate of a detection process node does not exceed a preset yield rate threshold, then the detection process node is marked as a feature node. If the yield rate of a detection process node exceeds a preset yield rate threshold, the detection process node will not be marked.

[0048] Specifically, the preset yield rate threshold is the product of the yield rate reference value and the yield factor. The yield rate reference value is the average yield rate under the same working conditions in historical data. The yield factor can be set by those skilled in the art based on the accuracy requirements of the reliable traceability of the entire chain of automotive-grade chip testing. The higher the accuracy requirement, the larger the value should be. The value range can be [1.1, 1.25], preferably 1.15.

[0049] Please see Figure 4The diagram shown is a flowchart illustrating the logic of determining the anomaly tendency category based on the distribution of feature nodes according to an embodiment of the present invention. The process of determining the anomaly tendency category based on the distribution of feature nodes includes: If the number of feature nodes exceeds a preset threshold, the abnormal tendency category is determined to be a strong explicit abnormal tendency category. If the number of feature nodes does not exceed the preset threshold, the abnormal tendency category is determined to be the weakly explicit abnormal tendency category.

[0050] Specifically, the preset quantity threshold can be 10%-20% of the total number of nodes, preferably 15%.

[0051] Specifically, if the abnormal tendency category is the strongly dominant abnormal tendency category, an abnormal warning signal will be issued; If the abnormal tendency category is the weakly dominant abnormal tendency category, the network transmission parameters of the feature node are called to determine whether to reacquire the event metadata of the automotive-grade chip to be detected at the feature node and to re-determine the report issuance.

[0052] Specifically, the event metadata of the automotive-grade chip to be tested at the feature node can be re-acquired, and the event metadata can be hashed to determine the event data digest. The event data digest can be combined with the event data digest of the previous detection process node for encryption to generate the chained hash value of the current detection process node.

[0053] Specifically, the process of determining the network transmission parameters of feature nodes includes, The out-of-order TCP packet rate of the feature node at each collection time is obtained within the preset monitoring period. The variance of the TCP packet out-of-order rate is determined as the network transmission parameter.

[0054] Specifically, the TCP packet out-of-order rate can be determined by calling the network protocol stack statistics interface provided by the operating system or by deploying a network probe between the detection device and the server, using the ratio of the number of out-of-order packets to the total number of received packets within a preset monitoring period.

[0055] Specifically, the process of determining whether to reacquire the event metadata of the automotive-grade chip under test at the feature node and re-issue the report includes, If the network transmission parameters of the feature node do not exceed the preset network transmission parameter threshold, it is determined to reacquire the event metadata of the automotive-grade chip to be detected at the feature node and to re-determine the report issuance. If the network transmission parameters of a feature node exceed the preset network transmission parameter threshold, an abnormal warning signal will be issued.

[0056] Specifically, the preset network transmission parameter threshold is the product of the network transmission parameter reference value and the transmission factor. The network transmission parameter reference value is the average value of the network transmission parameters under the same working conditions in historical data. The transmission factor can be set by those skilled in the art based on the accuracy requirements of the whole-link reliable traceability of automotive-grade chip detection. The higher the accuracy requirement, the smaller the value should be. The value range can be [0.95, 1.15], and preferably 1.1.

[0057] Specifically, after retransmission, the hashes of the current node and all subsequent nodes can be recalculated, updating the entire chain.

[0058] Specifically, under the condition of weakly dominant anomaly tendency, this embodiment of the invention calls the network transmission parameters of the feature node to determine whether to reacquire the event metadata of the automotive-grade chip under test at the feature node and re-perform the report issuance determination. It can be understood that, under the condition of weakly dominant anomaly tendency, by analyzing the network transmission parameters to determine whether to trigger data retransmission, intelligent attribution and adaptive recovery are achieved in the scenario of issuance failure. The distribution of the number of feature nodes is used to distinguish between systemic process defects and occasional transmission failures, avoiding unnecessary batch scrapping caused by misjudging network problems as chip quality problems, reducing the misjudgment rate, and using out-of-order TCP packets. The variance of the rate is used as the basis for determining network transmission parameters. It can specifically capture intermittent fluctuations in link quality. Excessive network transmission parameters indicate that the link itself is unstable and retransmission is difficult to guarantee integrity. Relatively small network transmission parameters indicate that the link is basically healthy and the probability of successful retransmission is high. This avoids the waste of system resources and time delays caused by repeated retransmissions on unstable links. In the high-throughput scenario of mass production testing of automotive-grade chips, it improves the efficiency of anomaly handling and shortens production line downtime. Furthermore, it enables the adaptive triggering of recovery mechanisms based on the actual testing situation of the automotive-grade chip under test, thereby improving the reliability and efficiency of the trusted traceability of the entire automotive-grade chip testing link.

[0059] Specifically, it's understandable that when a report is rejected due to chip batch quality control parameters exceeding thresholds, the root cause of the anomaly typically falls into two categories: either the chip itself has a systemic process defect, leading to abnormally low yield rates at multiple testing nodes, or data transmission is corrupted due to network link quality issues, causing deviations in yield rate calculations at individual nodes. If the number of characteristic nodes with yield rates below the threshold is relatively large, it indicates a widespread distribution of the anomaly across multiple testing nodes, consistent with the statistical characteristics of systemic process defects, and is judged as a strong overt anomaly. If the number of characteristic nodes is relatively small, it indicates an isolated distribution of the anomaly, more likely due to a specific node experiencing issues during data transmission. When encountering occasional link disturbances, it is determined to be a weakly dominant anomaly tendency. Under the condition of weakly dominant anomaly tendency, the TCP packet out-of-order rate of the feature node at each collection time during the file transmission period is extracted to determine the network transmission parameters. TCP packet out-of-order is usually caused by network congestion, multipath delay difference, or switch load balancing conflict. The network transmission parameters can characterize the degree of fluctuation in link quality. The smaller the network transmission parameters, the more stable the link quality. Data anomalies are more likely to be caused by occasional packet loss that can be recovered in a single instance, and are suitable for recovery through retransmission. Thus, the recovery mechanism is adaptively triggered according to the actual testing situation of the automotive-grade chip under test, improving the reliability and efficiency of the whole-link trusted traceability of automotive-grade chip testing.

[0060] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A trusted end-to-end traceability method for automotive-grade chip inspection based on hash chains, characterized in that, include: In response to the trigger command of the automotive-grade chip under test at the testing process node, the network transmission parameters, event metadata, and operating environment data of the current operation event are obtained; The event metadata includes a test data file and a timestamp associated with the test data file; the operating environment data includes a calibration deadline, ambient temperature parameters, and ambient humidity parameters. The operating environment trend parameters are determined by comparing the operating environment data of the equipment at several monitoring times within the preset monitoring period. Based on the comparison between the operating environment trend parameters and event metadata of the automotive-grade chip under test at the testing process node and the calibration deadline, it is determined whether there is an abnormal risk in the operation of the equipment at the current testing process node; In response to the determination that there is no abnormal risk in the operation of the equipment, the event metadata of the automotive-grade chip to be tested at the testing process node is hashed to determine the event data digest. The event data digest is then combined with the event data digest of the previous testing process node for encryption to generate the chained hash value of the current testing process node. In response to the report issuance instruction for the automotive-grade chip to be tested, the chip batch quality control parameters of the automotive-grade chip to be tested are obtained to determine whether issuance is allowed; Based on the yield rate marking feature nodes of each testing process node, the abnormal tendency category is determined according to the distribution of feature nodes. This allows for the selection of issuing an abnormal warning signal, or calling the network transmission parameters of the feature nodes to determine whether to reacquire the event metadata of the automotive-grade chip to be tested and re-issue the report.

2. The trusted end-to-end traceability method for automotive-grade chip testing based on hash chains according to claim 1, characterized in that, The process of determining operating environment trend parameters based on the comparison of operating environment data of equipment at several monitoring times within a preset monitoring period includes: Obtain the ambient temperature and humidity parameters of the automotive-grade chip under test at each monitoring time; The ratio of the variance of the ambient temperature parameter to the variance threshold of the ambient temperature parameter is determined as the first environmental characteristic. The ratio of the difference between the maximum and minimum values ​​of the environmental humidity parameter to the threshold value of the maximum and minimum environmental humidity parameter is determined as the second environmental characteristic. The weighted sum of the first environmental feature and the second environmental feature is used to determine the operating environment trend parameter.

3. The trusted traceability method for end-to-end automotive-grade chip testing based on hash chains according to claim 2, characterized in that, The process of determining whether there is any abnormal risk in the operation of the equipment at the current detection process node includes, If the automotive-grade chip under test meets the normal operating conditions at each testing process node, it is determined that there is no abnormal risk in the operation of the equipment at the current testing process node; If the automotive-grade chip to be tested does not meet the normal operating conditions at a certain node in the testing process, it is determined that there is an abnormal risk in the operation of the equipment at the current testing process node and an abnormal warning signal is issued. The normal operating conditions are that the operating environment trend parameter does not exceed the preset operating environment trend parameter threshold, and the timestamp associated with the test data file of the current operation event does not belong to the characteristic time period. The lower limit of the characteristic time period is the calibration deadline date, and the upper limit of the interval is the repair completion date.

4. The trusted traceability method for end-to-end automotive-grade chip testing based on hash chains according to claim 3, characterized in that, The process of obtaining the batch quality control parameters of the automotive-grade chip to be tested in response to the report issuance instruction includes: Several standby currents, several operating frequencies, and several leakage currents of the automotive-grade chip under test are obtained respectively. The ratio of the maximum standby current to the minimum standby current is defined as the standby current characterization value; the ratio of the maximum operating frequency to the minimum operating frequency is defined as the operating frequency characterization value; and the ratio of the maximum leakage current to the minimum leakage current is defined as the leakage current characterization value. The ratio of the standby current characterization value to the standby current characterization value threshold is determined as the first quality control feature, the ratio of the operating frequency characterization value to the operating frequency characterization value threshold is determined as the second quality control feature, and the ratio of the leakage current characterization value to the leakage current characterization value threshold is determined as the third quality control feature. The first quality control feature, the second quality control feature, and the third quality control feature are weighted and summed to obtain the batch quality control parameters for the chip.

5. The method for end-to-end trusted traceability of automotive-grade chip testing based on hash chains according to claim 4, characterized in that, The process of determining whether to allow issuance based on the batch quality control parameters of the automotive-grade chip to be tested includes: If the quality control parameters of the automotive-grade chip to be tested exceed the preset chip batch quality control parameter threshold, then it is determined that issuance is not allowed. If the quality control parameters of the automotive-grade chip to be tested do not exceed the preset chip batch quality control parameter threshold, then issuance is permitted.

6. The trusted end-to-end traceability method for automotive-grade chip testing based on hash chains according to claim 5, characterized in that, In response to a decision not to issue a certificate, the process of marking characteristic nodes based on the yield rate at each testing process node includes: If the yield rate of a detection process node does not exceed a preset yield rate threshold, then the detection process node is marked as a feature node.

7. The trusted end-to-end traceability method for automotive-grade chip testing based on hash chains according to claim 6, characterized in that, The process of determining the anomaly tendency category based on the distribution of feature nodes includes, If the number of feature nodes exceeds a preset threshold, the abnormal tendency category is determined to be a strong explicit abnormal tendency category. If the number of feature nodes does not exceed the preset threshold, the abnormal tendency category is determined to be the weakly explicit abnormal tendency category.

8. The trusted end-to-end traceability method for automotive-grade chip testing based on hash chains according to claim 7, characterized in that, If the abnormal tendency category is the strongly dominant abnormal tendency category, an abnormal warning signal will be issued; If the abnormal tendency category is the weakly dominant abnormal tendency category, the network transmission parameters of the feature node are called to determine whether to reacquire the event metadata of the automotive-grade chip to be detected at the feature node and to re-determine the report issuance.

9. The trusted end-to-end traceability method for automotive-grade chip testing based on hash chains according to claim 8, characterized in that, The process of determining the network transmission parameters of feature nodes includes, The out-of-order TCP packet rate of the feature node at each collection time is obtained within the preset monitoring period. The variance of the TCP packet out-of-order rate is determined as the network transmission parameter.

10. The trusted end-to-end traceability method for automotive-grade chip testing based on hash chains according to claim 9, characterized in that, The process of determining whether to reacquire the event metadata of the automotive-grade chip under test at the feature node and re-issue the report includes: If the network transmission parameters of the feature node do not exceed the preset network transmission parameter threshold, it is determined to reacquire the event metadata of the automotive-grade chip to be detected at the feature node and to re-determine the report issuance.

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