PUF (Physical Unclonable Function) key generation and equipment identity authentication system for cold chain environment

Abstract

The invention discloses a PUF (Physical Unclonable Function) key generation and equipment identity authentication system for a cold chain environment, and particularly relates to the technical field of information security, comprising temperature change data perception, drift modeling, dynamic error correction and closed-loop optimization, and collecting original response data of PUF hardware and temperature sensor readings by simulating the cold chain environment in a temperature-controllable experiment module; learning a nonlinear mapping relation between the dynamic temperature change and PUF response drift, and outputting a drift compensation matrix; constructing a two-stage error correction mechanism based on the drift compensation matrix; the first-stage error correction unit carries out nonlinear calibration on the original PUF response sequence according to the drift compensation matrix; the second-stage error correction unit adopts a temperature sensing type reconfigurable LDPC coding architecture, an LDPC check matrix is obtained based on a temperature-drift joint constraint model, the error correction performance of a high-drift scene is optimized through the LDPC check matrix, and the problem of key instability caused by PUF hardware response drift in a cold chain environment is solved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology. More specifically, the present invention relates to a PUF key generation and device identity authentication system for cold chain environments. Background Art

[0002] Cold chain transportation is a key link in ensuring the quality of temperature-sensitive products such as food and medicine. Its environmental temperature is usually maintained between -20°C and 4°C and exhibits significant dynamic fluctuation characteristics; during the cold chain logistics process, factors such as the frequent opening and closing of refrigerated truck doors, temperature differences between regions, and the periodic operation of refrigeration equipment result in a temperature change rate that can be as high as ±5°C / min, forming a complex dynamic temperature field; however, in the cold chain environment, the response of PUF hardware is sensitive to temperature changes, and dynamic temperature fluctuations directly affect transistor delay, oscillation frequency, or the flip probability of storage units, thereby causing drift in the PUF response. This drift significantly reduces the consistency of key generation and may lead to device identity authentication failures, posing a serious threat to the security of cold chain equipment.

[0003] In the prior art, regarding the temperature drift problem of PUF response, research mainly focuses on the stability analysis under static low-temperature conditions. Usually, the PUF response at different temperatures is measured through experiments, a static drift model is constructed, and traditional error-correcting codes (ECC) such as BCH codes or Hamming codes are used for correction; traditional ECC is based on the assumption of a linear noise model and cannot effectively cope with the non-linear drift effect caused by dynamic temperature changes in the cold chain environment. For example, transistor delay or the flip probability of SRAM cells may change exponentially rather than linearly with temperature, resulting in insufficient error-correcting ability of traditional methods in high-drift scenarios.

[0004] Therefore, there is an urgent need for a technical solution that can model and correct the non-linear drift effect caused by dynamic temperature changes in the cold chain environment to improve the robustness and reliability of the PUF key generation and device identity authentication system. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a PUF key generation and device identity authentication system for cold chain environments, through non-linear drift modeling and an adaptive error correction algorithm, to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solution: A PUF key generation and device identity authentication system for cold chain environments, comprising: The temperature-variable data perception module collects the original response data of the PUF hardware and the readings of the temperature sensor by simulating the cold chain environment in a temperature-controllable experimental chamber; by analyzing the original PUF response sequence, it extracts the joint feature vector; and outputs the temperature time-series data, the original PUF response sequence, and the joint feature vector. The drift modeling module learns the non-linear mapping relationship between the temperature dynamic change and the PUF response drift through the joint feature vector, and outputs the drift compensation matrix. The drift compensation matrix includes the expected offset and confidence level of each response bit in each temperature interval. The expected offset represents the probability of bit drift, and the confidence level represents the credibility of the expected offset, with a range of [0, 1]. The dynamic error correction module constructs a two-level error correction mechanism based on the output of the drift modeling module; the first-level error correction unit non-linearly calibrates the original PUF response sequence according to the drift compensation matrix; the second-level error correction unit adopts a temperature-aware reconfigurable LDPC coding architecture to obtain the LDPC parity-check matrix based on the temperature-drift joint constraint model, including the column weight, redundancy ratio, and number of iterations, and optimizes the error correction performance in high-drift scenarios through the LDPC parity-check matrix.

[0007] Preferably, the process of obtaining the drift compensation matrix includes the following steps: According to the temperature range of the cold chain environment, the temperature time-series data is segmented into multiple temperature intervals, and the PUF response sequence at a stable reference temperature is selected as the reference response sequence. Denote the reference response sequence of the i-th bit as ; For each temperature interval, the original PUF response sequence is measured multiple times, and the temperature gradient change rate is used as the weight factor , and the dynamic drift amount of each bit relative to the reference response sequence is calculated to generate a preliminary drift distribution. Suppose Q tests are performed, and s is used to represent the index of the test order, represents the value of the i-th bit in the s-th measurement; the expected offset is calculated through the following formula; , and the way to obtain the confidence level is: ; where represents the variance of the expected offset in multiple measurements; represents the maximum allowable variance of the bit.

[0008] Preferably, the drift compensation matrix is obtained based on a dual-channel drift model. A dual-channel drift model is built by combining a long short-term memory network and a self-attention mechanism, which processes the temperature time-series data and the original PUF response sequence respectively, and outputs the drift compensation matrix, including: Taking the temperature time-series data, the original PUF response sequence, and the joint feature vector as structured training data, and inputting them into the dual-channel drift model; The long short-term memory network captures the cumulative effect of temperature dynamic changes through the temperature sensitivity coefficient, and the attention mechanism is used to locate the influence of temperature mutation points on sensitive response bits; After training, the dual-channel drift model predicts the drift probability and prediction reliability of each response bit in each temperature range. The drift probability is denoted as the expected offset, and the prediction reliability is denoted as the confidence level. Finally, a drift compensation matrix is output.

[0009] Preferably, the operation process of the first-level error correction unit includes the following steps: Denote the original PUF response sequence as , where i represents the sequential number of the bit, represents the original PUF response of the i-th bit, and N represents the total number of bits; Obtain the drift compensation vector for each temperature range by fusing the expected offset and the confidence level. The drift compensation vector is denoted as ; For each temperature range, generate a drift compensation vector and calibrate the original PUF response sequence through the following formula: ; The drift compensation vectors for all temperature ranges form a drift compensation matrix, which is stored in hardware in the form of a lookup table; Perform binary processing on each drift compensation vector through the threshold decision formula and output the calibrated PUF response sequence.

[0010] Preferably, the operation process of the second-level error correction unit includes the following steps: Input the calibrated PUF response sequence, the drift compensation matrix, and the real-time temperature; Calculate the drift severity S corresponding to the current temperature range by using the drift compensation matrix; Calculate the column weight and redundancy ratio through the temperature-drift joint constraint model, and calculate the number of iterations through the average confidence level; Generate the LDPC check matrix H according to the column weight, redundancy ratio, and number of iterations. The LDPC check matrix H is a binary sparse matrix used to define the error correction rules of the LDPC code; Perform LDPC encoding on the calibrated PUF response sequence based on the LDPC check matrix, add redundant bits, perform iterative decoding on the calibrated PUF response sequence through the belief propagation algorithm, and control the number of decoding loops based on the number of iterations; Output: Output the finally error-corrected PUF response sequence, and generate a key through the hash function for device identity authentication.

[0011] Preferably, the temperature-drift joint constraint model includes: ; ; Among them, represents the minimum redundancy ratio, β represents the adjustment coefficient, which is used to control the increase amplitude of the redundancy ratio; represents the minimum column weight, γ represents the adjustment coefficient, which is used to control the increase amplitude of the column weight; The calculation method of the drift severity S is: ; Among them, represents the absolute value of the expected offset of the i-th bit, reflecting the drift amplitude, represents the complement of the confidence level, highlighting the influence of the predicted unstable bits.

[0012] Preferably, the system further includes: A closed-loop optimization module, which builds an embedded verification platform and integrates the output of the dynamic error correction module into a programmable PUF controller implemented by an FPGA; by injecting the real cold chain logistics temperature curve, continuously monitors the key reconstruction success rate and the Hamming distance distribution; establishes a dual feedback loop: the short-term loop uses online learning to fine-tune the temperature sensitivity coefficient of the dual-channel drift model, including: according to the key reconstruction failure rate and the Hamming distance distribution, real-time fine-tuning the temperature sensitivity coefficient, and the optimization goal is to minimize the drift probability prediction error; the long-term loop generates adversarial training samples based on the cumulative failure data and iteratively optimizes the neural network structure of the dual-channel drift model.

[0013] Preferably, the closed-loop optimization module includes a periodic drift analysis unit, which is used to analyze the coupling effect of the time periodicity of temperature changes in the cold chain environment and the long-term aging effect of the PUF hardware, specifically including: Extract the periodic characteristics of the temperature time series data through Fourier transform. The periodic characteristics include the frequency, amplitude, and phase of the temperature change, and determine the main periodic components; Based on the cumulative running time and the key reconstruction failure rate, construct an aging effect model, and the aging effect model uses an exponential decay function to describe the transistor threshold voltage drift, where represents the threshold voltage drift amount changing with time t, is the initial drift amplitude, is the aging rate, and t is the running time; Through the periodic characteristics and the aging effect model, predict the long-term drift trend of the PUF response and dynamically update the drift compensation matrix.

[0014] Preferably, the closed-loop optimization module includes an adaptive time window adjustment unit, which is used to dynamically adjust the training time window of the dual-channel drift model according to the temperature periodicity and the aging stage. The specific steps include: In the initial operation stage, the dual-channel drift model is trained with a short time window, focusing on capturing the drift driven by temperature periodicity; In the intermediate operation stage, the model is trained with a medium time window to balance the effects of temperature periodicity and aging; In the long-term operation stage, the model is trained with a long time window, focusing on capturing the drift dominated by aging effects; Through online learning, the time window length is dynamically switched according to the key reconstruction failure rate to ensure the effectiveness of the drift compensation matrix over time.

[0015] Preferably, the closed-loop optimization module includes an aging compensation factor generation unit for generating time-related aging compensation factors and integrating them into the drift compensation matrix. The specific steps include: Calculating the aging compensation factor based on the cumulative failure data ; where k is the aging influence coefficient, t is the operating time, is the nominal threshold voltage of the transistor for normalization; is the temperature periodicity influence coefficient for adjusting the contribution of periodicity to aging, and its value can be determined through experiments. For example, by comparing the key reconstruction failure rates under different temperature periodicities; A represents the amplitude, and f represents the frequency; reflects the accelerating effect of temperature change on aging; Applying the aging compensation factor to the expected offset in the drift compensation matrix, and the update formula is ; where, represents the updated expected offset; verifying the updated drift compensation matrix through an embedded verification platform and monitoring the key reconstruction success rate during long-term operation to ensure key consistency.

[0016] Technical effects and advantages of the present invention: The PUF key generation and device identity authentication system for the cold chain environment provided by the present invention application significantly improves the calibration and error correction capabilities of PUF responses in the cold chain environment by collecting dynamic temperature data through a temperature change data perception module, modeling the non-linear drift relationship with a dual-channel drift model (LSTM + self-attention mechanism), and implementing two-level error correction with temperature-aware LDPC coding. It solves the problem of key reconstruction failure in dynamic temperature change and high-drift scenarios, providing a stable and reliable key generation basis for device identity authentication.

[0017] The PUF key generation and device identity authentication system for cold chain environment provided by this invention application realizes the long-term dynamic update of the drift compensation matrix through the double feedback loops of the closed-loop optimization module, the trend prediction of the periodic drift analysis unit, the dynamic modeling of the adaptive time window adjustment unit, and the precise compensation of the aging compensation factor generation unit. It solves the problem of drift compensation failure caused by the coupling of temperature periodicity (such as daily door opening and closing, refrigeration cycle) and aging effect (such as transistor threshold voltage drift) in the cold chain environment, and ensures the robustness and key consistency of the system during long-term operation. Brief Description of the Drawings

[0018] Figure 1 It is the block diagram of the PUF key generation management structure for the cold chain environment of this invention; Figure 2 It is the operation flowchart of the second-level error correction unit of this invention; Figure 3 It is the block diagram of the PUF key generation management structure based on the closed-loop optimization module of this invention. Detailed Embodiment

[0019] Hereinafter, the exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0020] Meanwhile, it should be understood that for the convenience of description, the dimensions of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0021] The description of at least one exemplary embodiment hereinafter is actually only illustrative and in no way limits the application or use of this application.

[0022] The technologies, methods and devices known to those of ordinary skill in the relevant fields may not be discussed in detail, but where appropriate, the said technologies, methods and devices should be regarded as part of the specification.

[0023] For the convenience of those skilled in the art to understand and implement, the following terms are explained. The PUF response sequence is the binary output sequence generated by the PUF hardware unit (such as SRAM or ring oscillator structure) under specific environmental conditions (temperature, voltage), and is usually generated by the physical entropy source (such as transistor process deviation); the PUF response sequence is the raw material for key generation. In the low-temperature dynamic drift scenario, the stability of the PUF response sequence directly affects the key consistency (KRR), and its bit value is determined by the initial power-on state of the storage unit.

[0024] A response bit refers to a single binary digit (0 or 1) in the PUF response sequence, corresponding to the microstructure of the physical hardware (such as SRAM cell pairs, ring oscillator path delay comparison results); the response bit is the smallest unit for key generation, and the physical characteristics of the response bit (such as the transistor threshold voltage Vth) determine the bit value; in the low-temperature model, the drift behavior of each response bit is independently modeled; for example, the 25th bit of a certain PUF hardware unit is determined by the process deviation of a storage unit in the upper left corner of the chip.

[0025] The response bit flip probability refers to the probability that the bit value (0→1 or 1→0) of the response bit changes during multiple measurements, reflecting the environmental sensitivity of the bit; for example, a bit with a response bit flip probability <3% can be regarded as a stable bit (for key generation), and a bit with a response bit flip probability >10% needs to be screened or compensated; in low-temperature drift analysis, the response bit flip probability increases exponentially with the temperature gradient; A sensitive response bit refers to a response bit that exhibits extremely high sensitivity to dynamic temperature changes, and its response bit flip probability or offset is significantly higher than the average level.

[0026] Example 1, refer to Figure 1 the block diagram of the PUF key generation management structure for the cold chain environment. The present invention provides a Figure 1 PUF key generation and device identity authentication system for the cold chain environment as shown in The temperature change data sensing module simulates the cold chain environment (-20°C to 4°C dynamic temperature cycle) in a controllable temperature experimental chamber, synchronously collects the original response data of the PUF hardware (such as SRAM startup value, ring oscillator frequency) and the temperature sensor readings, to obtain temperature time series data, the original PUF response sequence; the sampling frequency needs to cover the temperature mutation scenarios during cold chain transportation (such as ±5°C / min change caused by the opening and closing of the refrigerated truck door); high-frequency noise is removed through wavelet transform, and by analyzing the original PUF response sequence, joint feature vectors such as the temperature gradient change rate (dT / dt), response bit flip probability (BER), and time series features (such as the standard deviation of the oscillation period) are extracted, and the temperature time series data, the original PUF response sequence, and the joint feature vector are output.

[0027] In the embodiments of the present invention, it needs to be further explained that the temperature change data perception module is used to provide structured training data for the drift modeling module, where the temperature gradient change rate directly determines the time series window length of the LSTM network; the temperature gradient change rate reflects the severity of temperature fluctuations. When the LSTM (Long Short-Term Memory network) processes time series data, a "window length" needs to be specified (that is, how many consecutive time step data are input each time), and the window length determines the range of historical data that the model can "see"; the magnitude of the temperature gradient change rate dynamically adjusts the window length of the LSTM; simply put, a short window is used when the temperature changes rapidly, and a long window is used when the change is slow to adapt to the dynamic characteristics of the cold chain environment; in the specific implementation process, when the absolute value of the temperature gradient change rate is greater than 5 °C / min, the LSTM window length is set to 5 time steps to quickly respond to temperature mutations; when it is less than 2 °C / min, the window length is set to 20 time steps to capture long-term temperature dependencies; the intermediate value is determined by linear interpolation.

[0028] The drift modeling module learns the non-linear mapping relationship between temperature dynamic changes and PUF response drift through joint feature vectors, and outputs a drift compensation matrix. The drift compensation matrix contains the expected offset and confidence level of each response bit in each temperature range. The expected offset represents the probability of bit drift, and the confidence level represents the credibility of the expected offset, with a range of [0, 1]. The higher the value, the more stable the prediction.

[0029] In the embodiments of the present invention, it needs to be further explained that the acquisition method of the expected offset is as follows: According to the temperature range of the cold chain environment, the temperature time series data is segmented into multiple temperature ranges (such as -20 °C to -15 °C, -15 °C to -10 °C, etc.), and the PUF response sequence at a stable reference temperature (such as 0 °C) is selected as the reference response sequence Rref. The reference response sequence of the i-th bit is denoted as ; For each temperature range, the original PUF response sequence is measured multiple times, and the temperature gradient change rate is used as a weight factor , and the dynamic drift amount of each bit relative to Rref is calculated to generate a preliminary drift distribution; Suppose Q tests are performed, and s represents the index of the test order, represents the value (0 or 1) of the i-th bit in the s-th measurement; the expected offset is calculated through the following formula; , in a possible embodiment, the expected offset of each bit is output through the LSTM, and the attention mechanism weights the influence of key temperature points.

[0030] The acquisition method of the confidence level is as follows: ; where, represents the variance of the expected offset in multiple measurements; Represents the maximum allowable variance of bits.

[0031] Explanation: In the embodiments of the present invention, a preliminary drift compensation matrix is generated through experimental measurement as the training data for the dual-channel drift model; based on the preliminary drift compensation matrix, the dual-channel drift model is trained to predict the real-time drift compensation matrix.

[0032] In a possible embodiment, a dual-channel drift model is built by combining a long short-term memory network and a self-attention mechanism, which processes temperature time-series data and the original PUF response sequence respectively and outputs a drift compensation matrix, including: Using the temperature time-series data, the original PUF response sequence, and the joint feature vector as structured training data and inputting them into the dual-channel drift model; The long short-term memory network captures the cumulative effect of the dynamic change of temperature through the temperature sensitivity coefficient (such as the transistor hysteresis characteristics caused by repeated freezing and thawing), and the attention mechanism is used to locate the influence of temperature mutation points (such as the -10°C phase change critical region) on the sensitive response bits; when training the drift model, an adversarial sample generation technology is introduced to simulate extreme temperature fluctuation scenarios to enhance the robustness of the model, and the network hyperparameters are determined through Bayesian optimization; The temperature sensitivity coefficient refers to the parameter related to the temperature time-series data in the input gate weight matrix of the LSTM network in the dual-channel drift model, which is used to control the influence degree of temperature change on the hidden state; the temperature sensitivity coefficient is adjusted through online learning to enhance the model's response ability to temperature mutations (such as ±5°C / min) and improve the accuracy of drift probability prediction; After training, the dual-channel drift model predicts the drift probability and prediction reliability of each response bit in each temperature interval, records the drift probability as the expected offset, and records the prediction reliability as the confidence level, and finally outputs the drift compensation matrix for use by the dynamic error correction module.

[0033] In the embodiments of the present invention, it needs to be further explained that the drift compensation matrix of the drift modeling module is the basic mapping relationship of the ECC parameter space of the dynamic error correction module, and its confidence level parameter is used to dynamically adjust the number of iterative decoding times of the LDPC code; Explanation: In the embodiments of the present invention, the ECC parameter space mainly refers to the LDPC code parameters, which are used to control the error correction performance and calculation efficiency; the basic mapping relationship refers to the corresponding relationship between the expected offset and the confidence level in the drift compensation matrix and the LDPC code parameters; the LDPC code refers to the low-density parity-check code, which is an efficient error correction code that defines the error correction rules through a sparse parity-check matrix and is used to correct the residual errors in the PUF response; in the embodiments of the present invention, the basic mapping relationship refers to the temperature-drift joint constraint model.

[0034] The dynamic error correction module constructs a two - level error correction mechanism based on the output of the drift modeling module; the first - level error correction unit non - linearly calibrates the original PUF response sequence according to the drift compensation matrix; In the embodiments of the present invention, it needs to be further explained that the operation process of the first - level error correction unit includes the following steps: The operation process of the first - level error correction unit includes the following steps: Denote the original PUF response sequence as , where i represents the order number of the bit, represents the original PUF response of the i - th bit, and N represents the total number of bits; By fusing the expected offset and confidence, obtain the drift compensation vector for each temperature interval, and denote the drift compensation vector as ; For each temperature interval, generate a drift compensation vector, and calibrate the original PUF response sequence through the following formula: , and the drift compensation vectors of all temperature intervals form a drift compensation matrix, which is stored in hardware (such as FPGA or MCU memory) in the form of a look - up table; Through the threshold decision formula perform binary processing on each drift compensation vector to output the calibrated PUF response sequence.

[0035] Explanation: By introducing a confidence - weighted mechanism, dynamically adjust the intensity of drift compensation to ensure the robustness and consistency of the calibration result under the temperature fluctuation of the cold - chain environment, and finally output the calibrated PUF response sequence Rcal for subsequent key generation and device identity authentication.

[0036] The second - level error correction unit adopts a temperature - aware reconfigurable LDPC coding architecture, obtains the LDPC check matrix based on the temperature - drift joint constraint model, including column weight, redundancy ratio, and number of iterations, and optimizes the error - correction performance in high - drift scenarios through the LDPC check matrix.

[0037] Explanation: The column weight is the number of 1s in each column of the LDPC parity-check matrix, which represents the number of parity equations that the codeword bit participates in and directly affects the error correction ability. The higher the column weight, the more parity equations each codeword bit participates in, and the stronger the error correction ability of the LDPC code, but the higher the computational complexity. High-drift scenario (such as the phase transition point at -10°C, with a high drift severity S): Increase the column weight to enhance the error correction ability. Low-drift scenario (such as stable temperature, with a low drift severity S): Reduce the column weight to reduce the computational overhead. The column weight determines the sparsity and structure of the parity-check matrix, which directly affects LDPC encoding and decoding. When the number of 1s in each column of the parity-check matrix increases, a denser matrix is generated. A matrix with a high column weight can correct more errors and is suitable for high-drift scenarios. The redundancy ratio is the ratio of the redundant bits (parity bits) in the LDPC code to the total codeword length, which determines the amount of "spare information" used for error correction in the codeword. The higher the redundancy ratio, the more redundant bits, and the stronger the error correction ability, but the code rate (the ratio of information bits) decreases, and the computational and storage overheads increase. High-drift scenario: Increase the redundancy ratio, add more redundant bits, and enhance the error correction ability. Low-drift scenario: Reduce the redundancy ratio, reduce the redundant bits, and improve the efficiency. The redundancy ratio determines the number of rows of the parity-check matrix. More redundant bits provide more parity equations and enhance the error correction ability. The number of iterations is the number of loops of the iterative algorithm (such as the belief propagation algorithm) during LDPC decoding. Each iteration attempts to correct errors. The more iterations, the more opportunities the algorithm has to repair errors, and the stronger the error correction ability, but the greater the computational time and resource consumption. When the confidence level is low, the prediction is unstable, and the error distribution is complex, increase the number of iterations to ensure error correction. When the confidence level is high, the prediction is stable, and the error distribution is simple, reduce the number of iterations to save resources.

[0038] In the embodiments of the present invention, it needs to be further explained that refer to Figure 2 the operation flowchart of the second-level error correction unit. The operation process of the second-level error correction unit includes the following steps: Input: The calibrated PUF response sequence after the first level, the drift compensation matrix, and the real-time temperature; Calculate the drift severity S corresponding to the current temperature range by using the drift compensation matrix; Calculate the column weight, redundancy ratio, and number of iterations through the temperature-drift joint constraint model; Generate an LDPC parity-check matrix according to the column weight and redundancy ratio. The LDPC parity-check matrix H is a binary sparse matrix used to define the error correction rules of the LDPC code. The rows of the LDPC parity-check matrix H represent the codeword length (information bits + redundant bits), and the columns represent the number of redundant bits (which is also equal to the number of parity equations); Perform LDPC encoding on the calibrated PUF response sequence based on the LDPC parity-check matrix, add redundant bits, and repair errors through iterative decoding. The specific implementation method is as follows: calculate the number of iterations through the average confidence, perform iterative decoding on the calibrated PUF response sequence through the belief propagation algorithm, and control the number of decoding loops based on the number of iterations. Output: Output the finally error-corrected PUF response sequence, and generate a key through a hash function (such as SHA-256) for device identity authentication.

[0039] Explanation: The first-level error correction unit is used to calibrate most errors, and the second-level error correction unit is used to correct residual errors. Explanation: Based on the temperature-drift joint constraint model, an algorithm for real-time calculating and optimizing the redundancy bit ratio of LDPC encoding is used to improve the error correction performance of the PUF response in high-drift scenarios in the cold chain environment. Taking the drift characteristics (expected offset and confidence) output by the drift compensation matrix as the input, quantify the drift severity S of the PUF response in the current temperature range, and dynamically adjust the redundancy ratio Re, column weight, and number of iterations of LDPC encoding through a predefined mapping function, so as to achieve the balance between error correction ability and computing efficiency. In the embodiments of the present invention, it needs to be further explained that the process of dynamically adjusting LDPC parameters according to temperature and drift characteristics includes the following steps: The larger the value of the drift severity indicates that the PUF response is more unstable at the current temperature. The calculation formula of the drift severity S is as follows: ; Wherein, represents the absolute value of the expected offset of the i-th bit, reflecting the drift amplitude, represents the complement of the confidence, highlighting the influence of predicting unstable bits; Based on the drift severity S, calculate the redundancy ratio through the mapping function : ; Wherein, represents the minimum redundancy ratio (such as 10%) to ensure the basic error correction ability, β represents the adjustment coefficient (such as 0.5) used to control the increase amplitude of the redundancy ratio; Re is the final redundancy ratio, and the range is usually 10%-30%, and the redundancy increases in high-drift scenarios.

[0040] Based on the drift severity S, calculate the column weight Wc through the mapping function: ; Wherein, represents the minimum column weight, and γ represents the adjustment coefficient used to control the increase amplitude of the column weight.

[0041] In the embodiments of the present invention, it should be further explained that the number of iteration times is calculated through the average confidence level to balance the error correction performance and the calculation efficiency: ; Among them, represents the maximum number of iterations, represents the average confidence level.

[0042] Explanation: The two-level error correction mechanism is based on a hardware-friendly parameter updater of the CORDIC algorithm to achieve temperature change-triggered microsecond-level code rate switching.

[0043] In a possible embodiment, in order to avoid the drift contribution being 0 when the confidence level C is 1, a small weight is introduced to optimize the calculation formula of the drift severity S: ; In this way, even when the confidence level is 1, 10% of the drift contribution is still retained.

[0044] Summary: The embodiments of the present invention provide the core architecture and operation mechanism of a PUF key generation and device identity authentication system for the cold chain environment, including a temperature change data perception module, a drift modeling module, and a dynamic error correction module; the temperature change data perception module simulates the cold chain environment in a controllable temperature experimental chamber, collects the original response data of the PUF hardware and the readings of the temperature sensor, extracts joint feature vectors such as the temperature gradient change rate and the response bit flip probability, outputs structured training data, and dynamically adjusts the time series window length of the LSTM through the temperature gradient change rate to adapt to the dynamic characteristics of the cold chain environment; the drift modeling module uses the joint feature vectors to learn the non-linear mapping relationship between the temperature dynamic change and the PUF response drift through a two-channel drift model, generates a drift compensation matrix, where the expected offset is calculated by combining experimental measurement and model prediction, the confidence level is calculated based on variance, and the drift compensation matrix provides a basic mapping relationship for the dynamic error correction module; the dynamic error correction module adopts a two-level error correction mechanism: the first-level error correction unit non-linearly calibrates the original PUF response sequence through the drift compensation matrix, and the second-level error correction unit adopts a temperature-aware reconfigurable LDPC coding architecture to dynamically adjust the LDPC parameters based on the temperature-drift joint constraint model, optimize the error correction performance in high-drift scenarios, and finally output the error-corrected response for key generation.

[0045] Embodiment 2. The difference between the embodiment of the present invention and Embodiment 1 is that the system further includes: Closed-loop optimization module, build an embedded verification platform, and integrate the output of the dynamic error correction module into the programmable PUF controller implemented by FPGA; by injecting the real cold chain logistics temperature curve (including door opening events, regional temperature differences, etc.), continuously monitor the key reconstruction success rate and Hamming distance distribution; establish a double feedback loop: the short-term loop uses online learning to fine-tune the temperature sensitivity coefficient of the dual-channel drift model, including: According to the key reconstruction failure rate and Hamming distance distribution, fine-tune the temperature sensitivity coefficient in real time, and the optimization goal is to minimize the drift probability prediction error; The long-term loop generates adversarial training samples based on the cumulative failure data, and iteratively optimizes the neural network structure of the dual-channel drift model.

[0046] Explanation: If there are bits with low confidence or calibration failure, generate adversarial training samples based on the cumulative failure data (such as the temperature range and bit position where key reconstruction fails), and use the genetic algorithm to iteratively optimize the neural network structure of the dual-channel drift model (such as adjusting the number of LSTM hidden units and the number of self-attention heads); Or refine the temperature range granularity: If the confidence is lower than 0.5 or the calibration failure rate is higher than 10%, trigger the temperature range refinement; based on the statistical analysis of the cumulative failure data, identify the temperature range with a large drift probability prediction error (such as near -10°C), refine the temperature range granularity from 5°C to 2°C, and regenerate the drift compensation matrix to ensure the high precision and robustness of the drift compensation matrix in the cold chain dynamic environment.

[0047] In the embodiment of the present invention, the implementation method of building the embedded verification platform is as follows: First, select the hardware platform. Select a high-performance FPGA such as the Xilinx Zynq-7000 series, which supports the dual-core architecture of programmable logic (PL) and processing system (PS). The PL part implements the dynamic error correction module and the PUF controller, which are used for hardware acceleration of LDPC encoding and decoding. The PS part runs the embedded Linux system to execute online learning and data processing tasks; the FPGA integrates a high-precision temperature sensor such as DS18B20 to collect real-time temperature data with a resolution of 0.0625°C, which meets the requirements of the cold chain environment (-20°C to 4°C). Use an SD card or Flash to store the drift compensation matrix (stored in the form of a lookup table) and cold chain logistics temperature curve data, and integrate the UART or Ethernet interface for communication with the host computer to upload the key reconstruction success rate and Hamming distance distribution data; Next, perform software architecture design. Run embedded PetaLinux in the PS part of the FPGA to manage data acquisition, online learning, and logging. Develop a temperature sensor driver to collect temperature data in real-time. Develop an FPGA control driver to communicate with the PUF controller in the PL part through the AXI interface, implement a data preprocessing program to parse the temperature curve of the cold chain logistics (in CSV format), implement a calculation program for the success rate of key reconstruction and Hamming distance distribution, and record the results of each key generation. Then, implement a programmable PUF controller in the FPGA and implement a PUF circuit (SRAM-based PUF or ring oscillator PUF) in the PL part to generate an original PUF response sequence, such as a 128-bit PUF response sequence. Implement a dynamic error correction module, including a first-level error correction unit (fuzzy extractor), store a drift compensation matrix (in the form of a lookup table), query the drift compensation vector according to the real-time temperature, and the calibration formula is , and generate a threshold decision ; Implement a second-level error correction unit (LDPC error correction), calculate LDPC parameters (column weight, redundancy ratio, number of iterations) according to the temperature-drift joint constraint model, dynamically generate an LDPC parity-check matrix, perform encoding and decoding, implement a key generation module, and generate a key by passing the error-corrected PUF response through a hash function (such as SHA-256). Next, perform temperature curve injection and monitoring. Pre-collect real cold chain logistics temperature data (refrigerated truck operation data), including door opening events (temperature mutations, such as from -15°C to -5°C), regional temperature differences (temperature gradients at different locations); simulate door opening events (±5°C / min), inject the temperature curve into the system through software, control the analog output of the temperature sensor, monitor the success rate of key reconstruction, compare the matching rate between the generated key and the reference key (generated at a stable temperature), for example, success rate = number of successful reconstructions / total number of tests, monitor the Hamming distance distribution, calculate the Hamming distance between the error-corrected PUF response and the reference response, and analyze the drift calibration effect. Finally, integration and testing are carried out. The output of the dynamic error correction module (the corrected PUF response) is integrated into the PUF controller to generate a key. Data interaction between the PS and the PL is achieved through the AXI interface. The PS part records the monitoring data, and the PL part performs error correction and key generation. The system performance is tested under different temperature scenarios. In the stable scenario (constant temperature at -15°C), the basic error correction ability is verified. In the mutation scenario (rapid change from -15°C to -5°C), the dynamic adaptability is verified. The key reconstruction success rate and the Hamming distance distribution are recorded to evaluate the system robustness. The Xilinx Zynq-7020 FPGA is used, the DS18B20 temperature sensor is integrated, a 16GB SD card stores the temperature curve, PetaLinux runs the online learning program, Verilog is used to implement the PUF controller and the error correction module, the temperature curve (-15°C to -5°C, including door opening events) is injected, and the test results show that the key reconstruction success rate reaches 98% and the average Hamming distance is <2.

[0048] Summary: In Embodiment 2 of the present invention, a closed-loop optimization module is added on the basis of Embodiment 1 to further improve the robustness and adaptability of the system. The closed-loop optimization module integrates the output of the dynamic error correction module into the programmable PUF controller implemented by the FPGA by building an embedded verification platform, injects the real cold chain logistics temperature curve, and continuously monitors the key reconstruction success rate and the Hamming distance distribution. Through the short-term loop, online learning is used to fine-tune the temperature sensitivity coefficient of the dual-channel drift model in real time according to the key reconstruction failure rate and the Hamming distance distribution, and the optimization goal is to minimize the drift probability prediction error. The long-term loop generates adversarial training samples based on the cumulative failure data and uses the genetic algorithm to iteratively optimize the neural network structure of the dual-channel drift model.

[0049] Embodiment 3, refer to Figure 3 the block diagram of the PUF key generation management structure based on the closed-loop optimization module. The difference between the embodiment of the present invention and Embodiment 2 is that the closed-loop optimization module includes a periodic drift analysis unit for analyzing the coupling effect of the time periodicity of temperature changes in the cold chain environment and the long-term aging effect of the PUF hardware. The specific steps include: Extract the periodic characteristics of the temperature time series data through Fourier transform to determine the main periodic components (such as the daily door opening and closing cycle, the refrigeration cycle); Based on the cumulative running time and the key reconstruction failure rate, build an aging effect model, and the aging effect model uses an exponential decay function to describe the transistor threshold voltage drift, where represents the threshold voltage drift amount changing with time t, is the initial drift amplitude, is the aging rate, and t is the running time; Predict the long-term drift trend of the PUF response through the periodic characteristics and aging effect model, and dynamically update the drift compensation matrix.

[0050] Explanation: Voltage drift is a direct manifestation of the aging effect. Due to transistor aging (such as hot carrier injection or bias temperature instability), its threshold voltage will shift over time, resulting in changes in the response generated by the PUF; the aging effect model quantifies the law of this drift over time mathematically and verifies the accuracy of the model by combining the key reconstruction failure rate (i.e., the probability that the PUF outputs are inconsistent); by continuously monitoring and recording the key reconstruction failure rate of the PUF, use the aging effect model to predict the drift of the transistor threshold voltage over time, and deduce the change trend of the PUF response, such as the probability of certain bit flips increasing; compare the drift trend predicted by the aging effect model with the actually observed key reconstruction failure rate; if the predicted drift trend (such as the voltage drift increasing over time) should be consistent with the rising trend of the key reconstruction failure rate, it indicates that the aging effect model is effective; adjust the parameters (initial drift amplitude, aging rate) of the aging effect model according to the comparison result to make the prediction closer to the actual failure rate; the verified aging effect model is used for the dynamically updated drift compensation mechanism. For example, adjust the drift compensation matrix according to the predicted drift trend to ensure that the PUF response can still stably reconstruct the key during aging.

[0051] In the embodiment of the present invention, it needs to be further explained that the closed-loop optimization module includes an adaptive time window adjustment unit for dynamically adjusting the training time window of the dual-channel drift model according to temperature periodicity and aging stage. The specific steps include: In the initial operation stage (operation time less than 3 months), train the dual-channel drift model with a short time window (1 day) to focus on capturing the drift driven by temperature periodicity; In the middle operation stage (operation time from 3 months to 10 months), train the model with a medium time window (3 days) to balance the effects of temperature periodicity and aging effect; In the long-term operation stage (operation time greater than 10 months), train the model with a long time window (1 week) to focus on capturing the drift dominated by the aging effect.

[0052] In the embodiment of the present invention, it needs to be further explained that the closed-loop optimization module includes an aging compensation factor generation unit for generating a time-related aging compensation factor and integrating it into the drift compensation matrix. The specific steps include: Calculate the aging compensation factor based on the cumulative failure data, , where k is the aging influence coefficient, t is the operation time, is the nominal threshold voltage of the transistor for normalization; is the temperature periodicity influence coefficient, which is used to adjust the contribution of periodicity to aging, and its value can be determined through experiments. For example, by comparing the key reconstruction failure rates under different temperature periodicities; A represents the amplitude and f represents the frequency; reflects the accelerating effect of temperature change on aging; Apply the aging compensation factor to the expected offset in the drift compensation matrix, and the update formula is , where represents the updated expected offset; verify the updated drift compensation matrix through the embedded verification platform, monitor the key reconstruction success rate during long-term operation, and ensure key consistency; Explanation: The main function of the aging compensation factor generation unit is to generate an aging compensation factor related to time , and the aging compensation factor adjusts the expected offset in the drift compensation matrix to dynamically compensate for the response change caused by long-term aging of the PUF hardware (such as transistor threshold voltage drift); the aging compensation factor generation unit focuses on the long-term drift effect brought by hardware aging, generates a time-related compensation factor, and adjusts the drift compensation matrix to offset the aging effect.

[0053] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A PUF key generation and device identity authentication system for cold chain environment, characterized in that: include: The temperature change data perception module collects the original response data of the PUF hardware and the temperature sensor readings in a simulated cold chain environment in a temperature-controlled experimental cabin; extracts the joint feature vector by analyzing the original PUF response sequence; and outputs the temperature time series data, the original PUF response sequence, and the joint feature vector; The drift modeling module learns the nonlinear mapping relationship between temperature dynamic change and PUF response drift through the joint feature vector, and outputs a drift compensation matrix. The drift compensation matrix contains the expected offset and confidence of each response bit in each temperature range. The expected offset represents the probability of bit drift. The dynamic error correction module builds a two-level error correction mechanism based on the output of the drift modeling module. The first-level error correction unit performs nonlinear calibration on the original PUF response sequence according to the drift compensation matrix. The second-level error correction unit adopts a temperature-aware reconfigurable LDPC coding architecture. Based on the temperature-drift joint constraint model, the LDPC check matrix is ​​obtained, including column weights, redundancy ratios and number of iterations. The error correction performance of high drift scenarios is optimized through the LDPC check matrix. The temperature-drift joint constraint model can dynamically adjust the column weights, redundancy ratios and number of iterations of the LDPC check matrix to adapt to different temperatures and drift degrees.

2. According to claim 1, a PUF key generation and device identity authentication system for cold chain environment is characterized in that: The process of acquiring the drift compensation matrix includes the following steps: According to the temperature range of the cold chain environment, the temperature time series data is segmented into multiple temperature intervals, and the PUF response sequence under the stable reference temperature is selected as the reference response sequence. The reference response sequence of the i-th bit is recorded as ; For each temperature range, the original PUF response sequence is measured multiple times, with the temperature gradient change rate as the weight factor , calculate the dynamic drift of each bit relative to the reference response sequence and generate a preliminary drift distribution; Suppose Q tests are performed, and s is used to represent the index of the test sequence. represents the value of the i-th bit in the s-th measurement; the expected offset is calculated by the following formula; , the confidence degree is obtained as follows: ,in, represents the variance of the expected offset in multiple measurements; Indicates the maximum allowed bit variance.

3. A PUF key generation and device identity authentication system for cold chain environment according to claim 2, characterized in that: Based on the dual-channel drift model, the drift compensation matrix is ​​obtained. The dual-channel drift model is built by combining the long short-term memory network with the self-attention mechanism. The temperature timing data and the original PUF response sequence are processed respectively, and the drift compensation matrix is ​​output, including: The temperature time series data, the original PUF response sequence and the joint feature vector are used as structured training data and input into the dual-channel drift model; The long short-term memory network captures the cumulative effect of dynamic temperature changes through the temperature sensitivity coefficient, and the attention mechanism is used to locate the impact of temperature mutation points on sensitive response bits; After training, the dual-channel drift model predicts the drift probability and prediction reliability of each response bit in each temperature range, records the drift probability as the expected offset, records the prediction reliability as the confidence, and finally outputs the drift compensation matrix.

4. A PUF key generation and device identity authentication system for cold chain environment according to claim 3, characterized in that: The operation process of the first-level error correction unit includes the following steps: The original PUF response sequence is recorded as , i represents the sequential number of the bit, represents the original PUF response of the i-th bit, and N represents the total number of bits; The drift compensation vector for each temperature range is obtained by fusing the expected offset and confidence. The drift compensation vector is recorded as ; For each temperature range, a drift compensation vector is generated and the original PUF response sequence is calibrated using the following formula: ; The drift compensation vectors of all temperature intervals form a drift compensation matrix, which is stored in the hardware in the form of a lookup table; Through the threshold judgment formula For each drift compensation vector Perform binarization and output the calibrated PUF response sequence.

5. A PUF key generation and device identity authentication system for cold chain environment according to claim 4, characterized in that: The operation process of the second-level error correction unit includes the following steps: Input the calibrated PUF response sequence, drift compensation matrix, and real-time temperature; The drift severity S corresponding to the current temperature range is calculated using the drift compensation matrix; The column weight and redundancy ratio are calculated by the temperature-drift joint constraint model, and the number of iterations is calculated by the average confidence; Generate an LDPC check matrix H according to the column weight, the redundancy ratio and the number of iterations; the LDPC check matrix H is a binary sparse matrix used to define the error correction rules of the LDPC code; Perform LDPC encoding on the calibrated PUF response sequence based on the LDPC check matrix, add redundant bits, iteratively decode the calibrated PUF response sequence through the belief propagation algorithm, and control the number of decoding cycles based on the number of iterations; Output: Output the final error-corrected PUF response sequence and generate a key through a hash function for device authentication.

6. A PUF key generation and device identity authentication system for cold chain environment according to claim 5, characterized in that: The temperature-drift joint constraint model includes: ； ； in, represents the minimum redundancy ratio, and β represents the adjustment coefficient, which is used to control the increase of the redundancy ratio; represents the minimum column weight, and γ represents the adjustment coefficient, which is used to control the increase of column weight; The drift severity S is calculated as: ,in, Indicates the absolute value of the expected offset of the i-th bit, reflecting the drift amplitude, Represents the complement of the confidence level, highlighting the impact of unstable bits in prediction.

7. A PUF key generation and device identity authentication system for cold chain environment according to claim 3, characterized in that: The system further comprises: A closed-loop optimization module is built to build an embedded verification platform, and the output of the dynamic error correction module is integrated into the programmable PUF controller implemented by FPGA; by injecting the real cold chain logistics temperature curve, the key reconstruction success rate and Hamming distance distribution are continuously monitored; a dual feedback loop is established: the short-term loop uses online learning to fine-tune the temperature sensitivity coefficient of the dual-channel drift model, including: according to the key reconstruction failure rate and Hamming distance distribution, the temperature sensitivity coefficient is fine-tuned in real time, and the optimization goal is to minimize the drift probability prediction error; the long-term loop generates adversarial training samples based on the accumulated fault data, and iteratively optimizes the neural network structure of the dual-channel drift model.

8. A PUF key generation and device identity authentication system for cold chain environment according to claim 7, characterized in that: The closed-loop optimization module includes a periodic drift analysis unit for analyzing the coupling effect of the temporal periodicity of temperature changes in the cold chain environment and the long-term aging effect of the PUF hardware, specifically including: The periodic characteristics of temperature time series data are extracted through Fourier transform, and the periodic characteristics include the frequency, amplitude and phase of temperature change, and the main periodic components are determined; Based on the cumulative running time and key reconstruction failure rate, an aging effect model is constructed, and the aging effect model adopts an exponential decay function Describes the transistor threshold voltage drift, where represents the threshold voltage drift over time t, is the initial drift amplitude, is the aging rate, t is the operating time; Through the periodic characteristics and aging effect model, the long-term drift trend of PUF response is predicted and the drift compensation matrix is ​​dynamically updated.

9. A PUF key generation and device identity authentication system for cold chain environment according to claim 7, characterized in that: The closed-loop optimization module includes an adaptive time window adjustment unit for dynamically adjusting the training time window of the dual-channel drift model according to the temperature periodicity and the aging stage. The specific steps include: In the initial operation stage, the dual-channel drift model is trained with a short time window, focusing on capturing the drift driven by temperature periodicity; In the mid-term operation phase, the model is trained with a medium time window to balance the impact of temperature periodicity and aging effects; In the long-term operation phase, the model is trained with a long time window to focus on capturing drift dominated by aging effects; Through online learning, the time window length is dynamically switched according to the key reconstruction failure rate to ensure that the drift compensation matrix remains valid over time.

10. A PUF key generation and device identity authentication system for cold chain environment according to claim 8, characterized in that: The closed-loop optimization module includes an aging compensation factor generation unit, which is used to generate a time-related aging compensation factor and incorporate it into a drift compensation matrix. The specific steps include: Calculate aging compensation factor based on accumulated failure data ; Where k is the aging influence coefficient, t is the operating time, is the nominal threshold voltage of the transistor, used for normalization; is the temperature periodicity influence coefficient, which is used to adjust the contribution of periodicity to aging. Its value can be determined by experiment. A represents the amplitude and f represents the frequency. Aging compensation factor Applied to the desired offset in the drift compensation matrix, the update formula is: ;in, Indicates the expected offset after the update; verifies the updated drift compensation matrix through the embedded verification platform, monitors the key reconstruction success rate in long-term operation, and ensures key consistency.

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