Blockchain-based component supply chain risk traceability management method
Patent Information
- Application Number
- CN202611176148.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-04
- Publication Date
- 2026-10-27
AI Technical Summary
[0003]然而，现有方法在比对验证环节普遍采用静态比对策略，将出厂时提取的物理特征作为固定基准，与后期实测特征进行直接匹配，往往忽视了元器件在供应链流转及实际使用过程中，因温度、湿度、电应力等环境因素持续作用而发生的性能退化与老化漂移，导致后期实测特征与出厂基准之间产生系统性偏差，使得在静态阈值判定机制下，极易造成正品因特征漂移超出容差而被误判为假冒，或假冒品通过单一基准匹配而蒙混过关，严重削弱了溯源验证的可信度
[0064]与现有技术相比，本发明的有益效果是：通过获取元器件的初始物理特征参数，生成唯一的数字身份码，并与生产信息绑定为数据包后上传至区块链存证，选取样本元器件并进行加速老化实验，基于实时记录的样本元器件退化数据拟合建立老化轨迹模型，并将模型参数固化于区块链智能合约中，通过持续采集环境应力数据，并实时上传至区块链进行记录存储，形成该元器件自出厂时刻起至当前时刻为止的完整累积环境应力数据链，当该元器件流转至供应链中的任一验证节点需要进行真伪验证或风险排查时，提取元器件当前时刻的实测物理特征参数，同时以数字身份码为索引，调取全部累积环境应力数据，按照预设的等效老化折算规则进行加权累计处理，得到归一化累计老化当量，并代入到老化轨迹模型中，输出预测置信区间，将实测物理特征参数进行比较，根据比较结果输出匹配程度指标；通过引入老化轨迹模型并融合区块链技术，将元器件供应链溯源验证从传统的静态比对转变为基于累积环境应力驱动的动态轨迹匹配，有效克服了因元器件正常老化漂移导致的误判问题，同时利用区块链的不可篡改性保障了环境应力数据与验证过程的可信度，为供应链风险溯源提供了更加科学可靠的技术手段。
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Figure CN122989135A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of blockchain risk management technology, specifically to a blockchain-based method for tracing and managing risks in the component supply chain. Background Technology
[0002] Blockchain-based risk traceability and control methods for component supply chains typically use unique physical identifiers as the core. By extracting the inherent physical characteristics of components, an uncopyable digital identity code is generated and bound to data from production, circulation, and testing processes on the blockchain. Finally, during verification, real-time features are extracted and compared with evidence stored on the blockchain to achieve component authenticity identification and risk traceability.
[0003] However, existing methods generally adopt a static comparison strategy in the comparison and verification stage, using the physical features extracted at the time of manufacture as a fixed benchmark and directly matching them with the later measured features. This often ignores the performance degradation and aging drift of components during the supply chain flow and actual use due to environmental factors such as temperature, humidity, and electrical stress. This leads to a systematic deviation between the later measured features and the factory benchmark. Under the static threshold judgment mechanism, it is very easy for genuine products to be misjudged as counterfeit due to feature drift exceeding the tolerance, or for counterfeit products to pass through by matching a single benchmark, which seriously weakens the credibility of traceability verification.
[0004] Therefore, in view of this, the present invention proposes a blockchain-based method for tracing and controlling risks in the component supply chain to make up for and improve the shortcomings of existing technologies. Summary of the Invention
[0005] To address the aforementioned technical problems, this invention provides a blockchain-based method for tracing and managing risks in the component supply chain, thereby resolving the corresponding technical issues raised in the background section.
[0006] To achieve the above objectives, the technical solution adopted by this invention is: a blockchain-based method for tracing and managing risks in the component supply chain, comprising the following steps:
[0007] Step 1: Obtain the initial physical characteristic parameters of the component during the manufacturing stage, perform hash calculation to generate a unique digital identity code for the component, bind the initial physical characteristic parameters, digital identity code and production information into a data packet and upload it to the blockchain for evidence storage.
[0008] Step 2: Select sample components from the production batch to which the component belongs, and conduct accelerated aging experiments under multiple stress conditions. Record the degradation data of the physical characteristic parameters of the sample components under each stress condition with the aging time in real time. Based on the degradation data, fit and establish an aging trajectory model of the physical characteristic parameters of the components in this batch as the environmental stress factor changes, and solidify the model parameters in the blockchain smart contract.
[0009] Step 3: Through the micro-sensor group built into the component, during the flow and use of the component in the supply chain, continuously collect the environmental stress data actually experienced by the component at a preset sampling frequency, and upload it to the blockchain in real time for recording and storage, forming a complete cumulative environmental stress data chain of the component from the time of manufacture to the current time. The cumulative environmental stress data chain is used to quantitatively reflect the total aging stress load that the component bears throughout the entire supply chain cycle.
[0010] Step 4: When the component is transferred to any verification node in the supply chain and needs to be verified for authenticity or risk investigation, the measured physical characteristic parameters of the component at the current moment are extracted through the physical characteristic acquisition device. At the same time, using the component's digital identity code as an index, all the cumulative environmental stress data recorded by the component since it left the factory are retrieved from the blockchain. The data is then weighted and accumulated according to the preset equivalent aging conversion rules to obtain the normalized cumulative aging equivalent. This is then substituted into the aging trajectory model to output the predicted confidence interval in which the physical characteristic parameters of the component should be under the current cumulative environmental stress.
[0011] Step 5: Compare the measured physical characteristic parameters with the predicted confidence interval. Based on the comparison results, output the matching degree index between the current physical state of the component and the normal change law represented by the aging trajectory model. The matching degree index is used as the basis for determining the authenticity of the component and assessing the supply chain risk level.
[0012] Preferably, in step three:
[0013] The microsensor group includes a temperature sensor, a humidity sensor, a voltage sensor, and a current sensor;
[0014] The environmental stress data includes temperature, humidity, voltage, and current.
[0015] Preferably, step one specifically includes:
[0016] After the components complete the factory testing, the initial physical characteristic parameters of the components are extracted by physical characteristic acquisition equipment. The initial physical characteristic parameters include one or more of the following: internal resistance, threshold voltage, signal delay time, and resonant frequency.
[0017] After serializing and splicing the initial physical characteristic parameters, seed data is formed by combining the production batch number, production date, and production line number of the component.
[0018] The seed data is hashed using the SM3 hash algorithm to generate a fixed-length hash value as the unique digital identification code of the component.
[0019] The initial physical characteristic parameters, digital identity code and production information are packaged into a data packet, digitally signed and uploaded to the blockchain network. After being verified by the consensus mechanism, the data packet is written into a block to complete the evidence storage.
[0020] At the same time, the digital identification code is attached to the outer packaging of the component using RFID tags.
[0021] Preferably, step two specifically includes:
[0022] A predetermined number of sample components are randomly selected from the production batch to which the component belongs. The samples are divided into multiple groups and placed under different stress conditions for accelerated aging experiments. The stress conditions include one or more combinations of temperature stress, voltage stress, current stress and humidity stress.
[0023] During the experiment, samples were taken out periodically at preset time intervals, and their physical characteristic parameters were measured under standard test conditions. The degradation data of each sample at each measurement time was recorded.
[0024] Based on the collected degradation data, a multivariate regression fitting model was used to establish the aging trajectory model of the physical characteristic parameters of this batch of components:
[0025]
[0026] in, These are the characteristic parameter values after aging time t;
[0027] These are the initial feature parameter values;
[0028] This is the overall degradation rate function.
[0029] Preferably, step three specifically includes:
[0030] During the component packaging stage, a micro-sensor group is integrated inside the component package. The micro-sensor group is automatically initialized after power-on and bound to the digital identification code of the component.
[0031] Based on the component type and supply chain cycle length, the sampling frequency is preset. The micro-sensor group synchronously collects the environmental stress data actually experienced by the component according to the preset sampling frequency. Each data collection is accompanied by a timestamp and bound to the component's digital identity code.
[0032] The collected environmental stress data is encrypted and uploaded to the blockchain network in real time after being appended with a digital identity code and a timestamp.
[0033] After receiving the data, the blockchain node verifies it through consensus and writes it into a new block. The data is organized and stored using the digital identity code of the component as an index. All environmental stress data records arranged in chronological order together constitute the complete cumulative environmental stress data chain of the component from the time it was manufactured to the present time.
[0034] Preferably, step four specifically includes:
[0035] When a component moves to a verification node in the supply chain, the physical feature acquisition device extracts the measured physical feature parameters of the component at the current moment under standard test conditions, and temporarily stores the measured values in a local cache.
[0036] Using the component's digital identification code as the primary key, a query request is initiated to the blockchain network to obtain the complete cumulative environmental stress data chain of the component since it left the factory.
[0037] According to the preset equivalent aging conversion rules, the environmental stress data at each sampling time is converted into the equivalent aging time under the corresponding reference stress conditions, and then summed over time intervals to obtain the normalized cumulative aging equivalent. The calculation formula is:
[0038]
[0039] Where Q represents the total number of cumulative sampling points;
[0040] For the j-th sampling interval;
[0041] , , These represent the temperature, humidity, and voltage values at the j-th sampling time;
[0042] As a comprehensive acceleration factor;
[0043] Normalized cumulative aging equivalent Substituting the aging trajectory model embedded in the smart contract, the theoretical predicted center value of the physical characteristic parameters of the component under the current cumulative aging equivalent is calculated. ;
[0044] Considering model fitting error and individual differences, the theoretical predicted central value... Based on this, the confidence interval width is added to calculate the predicted confidence interval:
[0045]
[0046] in, This is the critical value for the standard normal distribution;
[0047] Preset significance level;
[0048] The standard error of the model fit;
[0049] Standard deviation of individual differences within the batch;
[0050] The predicted confidence interval includes an upper confidence limit and a lower confidence limit, which are used to characterize the reasonable fluctuation range of the current physical characteristic parameters of the component under normal aging and degradation.
[0051] Preferably, step five specifically includes:
[0052] measured physical characteristic parameters With prediction confidence interval To perform a read comparison, first determine... Does it fall within the predicted confidence interval? Within the range, calculate simultaneously Compared to The standardized residual z is given by the following formula:
[0053]
[0054] when When it falls within the predicted confidence interval, the degree of matching index Calculate using the following formula:
[0055]
[0056] in, This is the preset maximum permissible relative deviation;
[0057] when When it falls outside the predicted confidence interval, the degree of matching index Calculate using the following formula:
[0058]
[0059] in, The preset standardized residual critical threshold;
[0060] Matching degree index Output as verification result:
[0061] when If the value exceeds the preset acceptance threshold, it is determined to be a genuine product;
[0062] when If the value is below the preset acceptance threshold but is positive, it is determined to be a genuine product but has abnormal aging.
[0063] when When the value is negative, it is judged as a suspected counterfeit product or a serious anomaly, triggering a supply chain risk warning.
[0064] Compared with existing technologies, the beneficial effects of this invention are as follows: By acquiring the initial physical characteristic parameters of components, a unique digital identity code is generated and bound to production information as a data packet, which is then uploaded to the blockchain for evidence storage. Sample components are selected and accelerated aging experiments are conducted. Based on the real-time recorded degradation data of the sample components, an aging trajectory model is fitted and established, and the model parameters are solidified in a blockchain smart contract. By continuously collecting environmental stress data and uploading it to the blockchain in real time for recording and storage, a complete cumulative environmental stress data chain of the component from the moment it leaves the factory to the present moment is formed. When the component flows to any verification node in the supply chain and needs to be verified for authenticity or risk investigation, the measured physical characteristic parameters of the component at the current moment are extracted. Using digital identity codes as indexes, all accumulated environmental stress data is retrieved, and weighted cumulative processing is performed according to preset equivalent aging conversion rules to obtain normalized accumulated aging equivalents. These are then substituted into the aging trajectory model to output predicted confidence intervals. The measured physical characteristic parameters are compared, and matching degree indicators are output based on the comparison results. By introducing the aging trajectory model and integrating blockchain technology, the traceability verification of the component supply chain is transformed from traditional static comparison to dynamic trajectory matching driven by accumulated environmental stress. This effectively overcomes the misjudgment problem caused by normal aging drift of components. At the same time, the immutability of blockchain ensures the credibility of environmental stress data and the verification process, providing a more scientific and reliable technical means for supply chain risk traceability. Attached Figure Description
[0065] Figure 1 This is a schematic diagram of the overall structure of a preferred embodiment of the present invention;
[0066] Figure 2 This is a flowchart illustrating the process of forming a complete cumulative environmental stress data chain as shown in this invention;
[0067] Figure 3 This is a flowchart illustrating the output prediction confidence interval of the present invention. Detailed Implementation
[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0069] Embodiments of the present invention:
[0070] Please refer to Figures 1 to 3 As shown, the blockchain-based method for tracing and managing risks in the electronic component supply chain includes the following steps:
[0071] Step 1: Obtain the initial physical characteristic parameters of the component during the manufacturing stage, perform hash calculation to generate a unique digital identity code for the component, bind the initial physical characteristic parameters, digital identity code and production information into a data packet and upload it to the blockchain for evidence storage.
[0072] Step 2: Select sample components from the production batch to which the component belongs, and conduct accelerated aging experiments under multiple stress conditions. Record the degradation data of the physical characteristic parameters of the sample components under each stress condition with the aging time in real time. Based on the degradation data, fit and establish an aging trajectory model of the physical characteristic parameters of the components in this batch as the environmental stress factor changes, and solidify the model parameters in the blockchain smart contract.
[0073] Step 3: Through the micro-sensor group built into the component, the environmental stress data actually experienced by the component is continuously collected at a preset sampling frequency during the flow and use of the component in the supply chain, and uploaded to the blockchain in real time for recording and storage, forming a complete cumulative environmental stress data chain of the component from the time of manufacture to the current time. The cumulative environmental stress data chain is used to quantitatively reflect the total aging stress load that the component has borne throughout the entire supply chain cycle.
[0074] Step 4: When the component is transferred to any verification node in the supply chain and needs to be verified for authenticity or risk investigation, the measured physical characteristic parameters of the component at the current moment are extracted through the physical characteristic acquisition device. At the same time, using the component's digital identity code as an index, all the cumulative environmental stress data recorded by the component since it left the factory are retrieved from the blockchain. The data is then weighted and accumulated according to the preset equivalent aging conversion rules to obtain the normalized cumulative aging equivalent. This is then substituted into the aging trajectory model to output the predicted confidence interval in which the physical characteristic parameters of the component should be under the current cumulative environmental stress.
[0075] Step 5: Compare the measured physical characteristic parameters with the predicted confidence interval. Based on the comparison results, output the matching degree index between the current physical state of the component and the normal change law represented by the aging trajectory model. The matching degree index is used as the basis for determining the authenticity of the component and assessing the supply chain risk level.
[0076] In step three:
[0077] The micro-sensor group includes a temperature sensor, a humidity sensor, a voltage sensor, and a current sensor;
[0078] Environmental stress data include temperature, humidity, voltage, and current.
[0079] Step one specifically includes:
[0080] After the components complete the factory testing, the initial physical characteristic parameters of the components are extracted by physical characteristic acquisition equipment. The initial physical characteristic parameters include one or more of the following: internal resistance, threshold voltage, signal delay time, and resonant frequency.
[0081] After serializing and splicing the initial physical characteristic parameters, seed data is formed by combining the production batch number, production date, and production line number of the component.
[0082] The seed data is hashed using the SM3 hash algorithm to generate a fixed-length hash value as the unique digital identification code of the component.
[0083] The initial physical characteristic parameters, digital identity code and production information are packaged into a data packet, digitally signed and uploaded to the blockchain network. After being verified by the consensus mechanism, the data packet is written into a block to complete the evidence storage.
[0084] At the same time, the digital identification code is attached to the outer packaging of the component using RFID tags.
[0085] Step two specifically includes:
[0086] A predetermined number of sample components are randomly selected from the production batch to which the component belongs. The samples are divided into multiple groups and placed under different stress conditions for accelerated aging experiments. The stress conditions include one or more combinations of temperature stress, voltage stress, current stress and humidity stress.
[0087] During the experiment, samples were taken out periodically at preset time intervals, and their physical characteristic parameters were measured under standard test conditions. The degradation data of each sample at each measurement time was recorded.
[0088] Based on the collected degradation data, a multivariate regression fitting model was used to establish the aging trajectory model of the physical characteristic parameters of this batch of components:
[0089]
[0090] in, These are the characteristic parameter values after aging time t;
[0091] These are the initial feature parameter values;
[0092] This is the overall degradation rate function.
[0093] Step three specifically includes:
[0094] During the component packaging stage, the micro-sensor group is integrated inside the component package. After power-on, the micro-sensor group is automatically initialized and bound to the digital identification code of the component.
[0095] Based on the component type and supply chain cycle length, the sampling frequency is preset. The micro-sensor group synchronously collects the environmental stress data actually experienced by the component according to the preset sampling frequency. Each data collection is accompanied by a timestamp and bound to the component's digital identity code.
[0096] The collected environmental stress data is encrypted and uploaded to the blockchain network in real time after being appended with a digital identity code and a timestamp.
[0097] After receiving the data, the blockchain node verifies it through consensus and writes it into a new block. The data is organized and stored using the digital identity code of the component as an index. All environmental stress data records arranged in chronological order together constitute the complete cumulative environmental stress data chain of the component from the time it was manufactured to the present time.
[0098] Step four specifically includes:
[0099] When a component moves to a verification node in the supply chain, the physical feature acquisition device extracts the measured physical feature parameters of the component at the current moment under standard test conditions, and temporarily stores the measured values in a local cache.
[0100] Using the component's digital identification code as the primary key, a query request is initiated to the blockchain network to obtain the complete cumulative environmental stress data chain of the component since it left the factory.
[0101] According to the preset equivalent aging conversion rules, the environmental stress data at each sampling time is converted into the equivalent aging time under the corresponding reference stress conditions, and then summed over time intervals to obtain the normalized cumulative aging equivalent. The calculation formula is:
[0102]
[0103] Where Q represents the total number of cumulative sampling points;
[0104] For the j-th sampling interval;
[0105] , , These represent the temperature, humidity, and voltage values at the j-th sampling time;
[0106] As a comprehensive acceleration factor;
[0107] Normalized cumulative aging equivalent Substituting the aging trajectory model embedded in the smart contract, the theoretical predicted center value of the physical characteristic parameters of the component under the current cumulative aging equivalent is calculated. ;
[0108] Considering model fitting error and individual differences, the theoretical predicted central value... Based on this, the confidence interval width is added to calculate the predicted confidence interval:
[0109]
[0110] in, This is the critical value for the standard normal distribution;
[0111] Preset significance level;
[0112] The standard error of the model fit;
[0113] Standard deviation of individual differences within the batch;
[0114] The prediction confidence interval includes an upper confidence limit and a lower confidence limit, which are used to characterize the reasonable fluctuation range of the current physical characteristic parameters of the component under normal aging and degradation.
[0115] Step five specifically includes:
[0116] measured physical characteristic parameters With prediction confidence interval To perform a read comparison, first determine... Does it fall within the predicted confidence interval? Within the range, calculate simultaneously Compared to The standardized residual z is given by the following formula:
[0117]
[0118] when When it falls within the predicted confidence interval, the degree of matching index Calculate using the following formula:
[0119]
[0120] in, This is the preset maximum permissible relative deviation;
[0121] when When it falls outside the predicted confidence interval, the degree of matching index Calculate using the following formula:
[0122]
[0123] in, The preset standardized residual critical threshold;
[0124] Matching degree index Output as verification result:
[0125] when If the value exceeds the preset acceptance threshold, it is determined to be a genuine product;
[0126] when If the value is below the preset acceptance threshold but is positive, it is determined to be a genuine product but has abnormal aging.
[0127] when When the value is negative, it is judged as a suspected counterfeit product or a serious anomaly, triggering a supply chain risk warning.
[0128] This invention introduces an aging trajectory model, treating the changes in the physical characteristic parameters of components as a dynamic evolution process with accumulated environmental stress. This overcomes the shortcomings of existing static comparison strategies that ignore aging drift. Simultaneously, it continuously collects environmental stress data experienced by components throughout the entire supply chain, quantifying the true aging load of each component. It uses predicted confidence intervals instead of fixed thresholds for dynamic verification, upgrading the verification logic from static matching to trajectory matching. This effectively reduces the false positive rate of genuine products due to normal aging and can identify abnormal aging states in genuine products. Furthermore, the aging trajectory model parameters are embedded in a blockchain smart contract, which automatically retrieves immutable environmental data from the chain and performs predictive calculations, eliminating the possibility of human intervention and data falsification, thus improving the credibility of the verification results. The output quantitative matching degree index can support tiered risk management, providing a more scientific and reliable technical means for the authenticity identification and risk traceability of the component supply chain.
[0129] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.
[0130] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.
[0131] In the two embodiments provided in this application, it should be understood that the disclosed apparatus and system can be implemented in other ways; for example, the apparatus embodiments described above are merely illustrative, and the division of modules is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; furthermore, the coupling or direct coupling or communication connection between the shown or discussed mutuals can be through some interfaces, and the indirect coupling or communication connection between the apparatus or modules can be electrical, mechanical or other forms.
[0132] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A blockchain-based method for risk traceability and control in the electronic component supply chain, characterized in that: Includes the following steps: Step 1: Obtain the initial physical characteristic parameters of the component during the manufacturing stage, perform hash calculation to generate a unique digital identity code for the component, bind the initial physical characteristic parameters, digital identity code and production information into a data packet and upload it to the blockchain for evidence storage. Step 2: Select sample components from the production batch to which the component belongs, and conduct accelerated aging experiments under multiple stress conditions. Record the degradation data of the physical characteristic parameters of the sample components under each stress condition with the aging time in real time. Based on the degradation data, fit and establish an aging trajectory model of the physical characteristic parameters of the components in this batch as the environmental stress factor changes, and solidify the model parameters in the blockchain smart contract. Step 3: Through the micro-sensor group built into the component, the environmental stress data actually experienced by the component is continuously collected at a preset sampling frequency during the flow and use of the component in the supply chain, and uploaded to the blockchain in real time for recording and storage, forming a complete cumulative environmental stress data chain of the component from the time of manufacture to the current time. Step 4: When the component is transferred to any verification node in the supply chain and needs to be verified for authenticity or risk investigation, the measured physical characteristic parameters of the component at the current moment are extracted through the physical characteristic acquisition device. At the same time, using the component's digital identity code as an index, all the cumulative environmental stress data recorded by the component since it left the factory are retrieved from the blockchain. The data is then weighted and accumulated according to the preset equivalent aging conversion rules to obtain the normalized cumulative aging equivalent. This is then substituted into the aging trajectory model to output the predicted confidence interval in which the physical characteristic parameters of the component should be under the current cumulative environmental stress. Step 5: Compare the measured physical characteristic parameters with the predicted confidence interval. Based on the comparison results, output the degree of matching between the current physical state of the component and the normal change pattern represented by the aging trajectory model.
2. The blockchain-based component supply chain risk traceability and control method according to claim 1, characterized in that, In step three: The microsensor group includes a temperature sensor, a humidity sensor, a voltage sensor, and a current sensor; The environmental stress data includes temperature, humidity, voltage, and current.
3. The blockchain-based component supply chain risk traceability and control method according to claim 1, characterized in that, Step one specifically includes: After the components have completed factory testing, the initial physical characteristic parameters of the components are extracted using physical characteristic acquisition equipment. After serializing and splicing the initial physical characteristic parameters, seed data is formed by combining the production batch number, production date, and production line number of the component. The seed data is hashed using the SM3 hash algorithm to generate a fixed-length hash value as the unique digital identification code of the component. The initial physical characteristic parameters, digital identity code and production information are packaged into a data packet, digitally signed and uploaded to the blockchain network. After being verified by the consensus mechanism, the data packet is written into a block to complete the evidence storage. At the same time, the digital identification code is attached to the outer packaging of the component using RFID tags.
4. The blockchain-based method for risk traceability and control of component supply chains according to claim 1, characterized in that, Step two specifically includes: A predetermined number of sample components are randomly selected from the production batch to which the component belongs. The samples are divided into multiple groups and placed under different stress conditions for accelerated aging experiments. During the experiment, samples were taken out periodically at preset time intervals, and their physical characteristic parameters were measured under standard test conditions. The degradation data of each sample at each measurement time was recorded. Based on the collected degradation data, a multivariate regression fitting model was used to establish the aging trajectory model of the physical characteristic parameters of this batch of components: in, These are the characteristic parameter values after aging time t; These are the initial feature parameter values; This is the overall degradation rate function.
5. The blockchain-based method for risk traceability and control of component supply chains according to claim 1, characterized in that, Step three specifically includes: During the component packaging stage, a micro-sensor group is integrated inside the component package. The micro-sensor group is automatically initialized after power-on and bound to the digital identification code of the component. Based on the component type and supply chain cycle length, the sampling frequency is preset. The micro-sensor group synchronously collects the environmental stress data actually experienced by the component according to the preset sampling frequency. Each data collection is accompanied by a timestamp and bound to the component's digital identity code. The collected environmental stress data is encrypted and uploaded to the blockchain network in real time after being appended with a digital identity code and a timestamp. After receiving the data, the blockchain node verifies it through consensus and writes it into a new block. The data is organized and stored using the digital identity code of the component as an index. All environmental stress data records arranged in chronological order together constitute the complete cumulative environmental stress data chain of the component from the time it was manufactured to the present time.
6. The blockchain-based method for risk traceability and control of component supply chains according to claim 1, characterized in that, Step four specifically includes: When a component moves to a verification node in the supply chain, the physical feature acquisition device extracts the measured physical feature parameters of the component at the current moment under standard test conditions, and temporarily stores the measured values in a local cache. Using the component's digital identification code as the primary key, a query request is initiated to the blockchain network to obtain the complete cumulative environmental stress data chain of the component since it left the factory. According to the preset equivalent aging conversion rules, the environmental stress data at each sampling time is converted into the equivalent aging time under the corresponding reference stress conditions, and then summed over time intervals to obtain the normalized cumulative aging equivalent. The calculation formula is: Where Q represents the total number of cumulative sampling points; For the j-th sampling interval; , , These represent the temperature, humidity, and voltage values at the j-th sampling time; As a comprehensive acceleration factor; Normalized cumulative aging equivalent Substituting the aging trajectory model embedded in the smart contract, the theoretical predicted center value of the physical characteristic parameters of the component under the current cumulative aging equivalent is calculated. ; Considering model fitting error and individual differences, the theoretical predicted central value... Based on this, the confidence interval width is added to calculate the predicted confidence interval: in, This is the critical value for the standard normal distribution; Preset significance level; The standard error of the model fit; Standard deviation of individual differences within the batch; The predicted confidence interval includes an upper confidence limit and a lower confidence limit, which are used to characterize the reasonable fluctuation range of the current physical characteristic parameters of the component under normal aging and degradation.
7. The blockchain-based method for risk traceability and control of component supply chains according to claim 1, characterized in that, Step five specifically includes: measured physical characteristic parameters With prediction confidence interval To perform a read comparison, first determine... Does it fall within the predicted confidence interval? Within the range, calculate simultaneously Compared to The standardized residual z is given by the following formula: when When it falls within the predicted confidence interval, the degree of matching index Calculate using the following formula: in, This is the preset maximum permissible relative deviation; when When it falls outside the predicted confidence interval, the degree of matching index Calculate using the following formula: in, The preset standardized residual critical threshold; Matching degree index Output as verification result: when If the value exceeds the preset acceptance threshold, it is determined to be a genuine product; when If the value is below the preset acceptance threshold but is positive, it is determined to be a genuine product but has abnormal aging. when When the value is negative, it is judged as a suspected counterfeit product or a serious anomaly, triggering a supply chain risk warning.