A fresh stewed yolk single product grade traceability method, system and device

Abstract

This invention discloses a single-product-level traceability method, system, and equipment for freshly stewed bird's nest, belonging to the field of food traceability data processing technology. The method includes acquiring the fingerprint texture of the empty bottle bottom, extracting the fingerprint and encoding it onto the inside of the bottle cap to form a physical binding; during the stewing process, collecting temperature data from the stewing pot, calculating the temperature residual based on the theoretical temperature field, expanding it into a residual field, compressing it to obtain a batch signature, and storing it; printing a multi-threshold thermally sensitive dot matrix on the inside of the bottle cap, where each pixel irreversibly changes color upon heating, forming a thermally sensitive pattern; acquiring the bottle bottom fingerprint texture and the thermally sensitive pattern, reconstructing the theoretical temperature curve based on the signature from the bottle bottom fingerprint, and inferring the measured temperature curve from the thermally sensitive pattern; comparing the consistency of the two curves and the matching of the texture encoding to determine authenticity. This invention establishes an unforgeable mathematical constraint relationship between the product's physical identifier and production parameters, achieving extremely low single-product storage overhead, physically unclonable anti-counterfeiting, and cross-scale mutual verification for precise single-product-level traceability.

Description

Technical Field

[0001] This invention belongs to the field of food traceability data processing technology, specifically relating to a single-product traceability method, system and equipment for fresh stewed bird's nest. Background Technology

[0002] In the food industry, traceability technology is a crucial means of ensuring food safety and protecting consumer rights. Existing traceability solutions mainly include technologies such as QR code identification, blockchain-based evidence storage, and spectral fingerprint database comparison. While these technologies achieve product traceability to a certain extent, their underlying logic all follows the same paradigm of "feature collection → feature storage → feature comparison."

[0003] The fundamental flaw in this paradigm lies in the lack of a physically unbreakable, mandatory binding relationship between the collected features and the actual product. Counterfeiters can pass verification by copying or simulating the same feature information (such as forging QR codes or simulating spectral data), resulting in a structural vulnerability in the anti-counterfeiting capabilities of the traceability system. For high-value foods like freshly stewed bird's nest, the temperature history during its production process directly determines the product's quality and safety. However, current technologies cannot achieve precise traceability of the temperature history at the individual product level, nor can they establish a mathematical constraint relationship between the product's physical identifiers and production process parameters.

[0004] Furthermore, traditional traceability solutions require storing fingerprint data for each individual product, with storage costs increasing linearly with the number of products, placing enormous pressure on distributed storage systems such as blockchain. At the same time, existing verification processes heavily rely on centralized databases, meaning consumers' trust in traceability information must be based on the credibility of the institution, rather than on the laws of physics themselves.

[0005] Therefore, there is an urgent need for a new method that can establish an unforgeable mathematical constraint relationship between product identification and production parameters from the perspective of physical principles, so as to achieve accurate traceability at the single-item level and extremely low single-item storage overhead. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a single-item traceability method, system, and device for freshly stewed bird's nest, thereby resolving the technical problems in the prior art where identifiers are easily copied, data sources are simply stacked, and single-item self-consistent verification cannot be achieved.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a single-product traceability method for freshly stewed bird's nest, the method comprising: Obtain the fingerprint texture of the bottom of the empty bottle, extract the fingerprint and encode it onto the inside of the bottle cap to form a physical binding; In the stewing process, temperature data of the stewing pot is collected, and the temperature residual is calculated in combination with the theoretical temperature field. The temperature residual is expanded into a residual field, compressed to obtain a batch signature and stored. A multi-threshold thermal dot matrix is ​​printed on the inside of the bottle cap. When the dot matrix is ​​heated, each pixel changes color irreversibly, forming a thermal pattern. Collect the fingerprint texture of the bottle bottom and the thermal pattern, reconstruct the theoretical temperature curve based on the fingerprint signature, and deduce the measured temperature curve from the thermal pattern. Compare the consistency of the two curves and the matching of texture codes to determine authenticity.

[0009] Optionally, obtaining the fingerprint texture of the bottom of the empty bottle includes: Scan the bottom surface of the bottle to obtain three-dimensional morphological data; The three-dimensional topography data is denoised and smoothed to generate a depth map; The depth map is subjected to median filtering and binarization to obtain the fingerprint; The fingerprint is encoded into a QR code and burned onto the inside of the bottle cap, forming a physical bond with the bottle.

[0010] Optionally, the calculation of the temperature residual by combining the theoretical temperature field includes: A theoretical temperature field model is established based on the stewing set temperature, the input steam temperature, and the spatial characteristic time constant obtained through fluid simulation. The temperature data at each sensor location is compared with the calculated value of the theoretical temperature field model at the corresponding location to obtain the temperature residual at each location.

[0011] Optionally, expanding the temperature residual into a residual field and compressing it to obtain the batch signature includes: Using the temperature residual at each location as the node value, radial basis function interpolation is used to calculate the temperature residual value of each grid point on the whole-cell space grid, and the residual field on the whole grid is obtained. The residual field is organized into a four-dimensional residual tensor according to the time series. The four-dimensional residual tensor is subjected to high-order singular value decomposition to obtain a batch signature consisting of a core tensor and a mode matrix. The batch signatures are stored in the blockchain.

[0012] Optionally, the higher-order singular value decomposition of the four-dimensional residual tensor includes: Set the truncation rank for each mode and perform dimensionality reduction decomposition on each mode of the residual tensor; Calculate the core tensor and modal matrix to reduce the total storage requirement from the product of the number of spatial grid points and the number of temporal sampling points of the complete tensor to the sum of the number of elements of the core tensor and the modal matrix.

[0013] Optionally, printing a multi-threshold thermal dot matrix on the inside of the bottle cap includes: Pixels are arranged in a preset grid on the inside of the bottle cap, and each pixel is coated with a cholesteric liquid crystal ink. The cholesteric liquid crystal ink includes three types, each with a different phase transition temperature threshold. Each pixel is compared with the highest temperature experienced by its bottle position during the stewing process and the phase transition temperature threshold of the coated ink type. When the highest temperature reaches or exceeds the phase transition temperature threshold, the pixel undergoes an irreversible phase transition from a colorless state to a colored state. The colored state is used as a discrete sample value of the pixel for the temperature history of the bottle position.

[0014] Optionally, the step of retrieving the measured temperature curve from the thermal pattern includes: Based on the ink type and color rendering state of each pixel in the thermal pattern, a measured indicator function is constructed for each ink type. The value is 1 when the pixel presents the corresponding color rendering state, and 0 otherwise. For the candidate temperature curve, the highest temperature of each pixel is calculated by time integration, and the simulated indicator function is calculated. The loss function is the sum of the absolute values ​​of the differences between the measured indicator function and the simulated indicator function. The loss function is minimized using a simulated annealing algorithm to obtain the measured temperature curve.

[0015] Optionally, comparing the consistency of the two curves includes: Find the optimal alignment path between the theoretical temperature curve and the measured temperature curve that satisfies the boundary conditions and monotonicity conditions; Calculate the sum of squares of the differences between corresponding points on the alignment path, and take the arithmetic square root of the sum of squares as the dynamic time-normalized distance; When the dynamic time warp distance is less than the preset temperature threshold, the two curves are determined to be consistent.

[0016] In a second aspect, the present invention provides a single-product traceability system for freshly stewed bird's nest, used to perform the method described in any one of the first aspects, comprising: The acquisition module is used to acquire the fingerprint texture of the bottom of the empty bottle, extract the fingerprint and encode it to the inside of the bottle cap to form a physical binding; The calculation module is used to collect temperature data of the stewing pot during the stewing process, calculate the temperature residual by combining it with the theoretical temperature field, expand the temperature residual into a residual field, compress it to obtain the batch signature and store it. The processing module is used to print a multi-threshold thermal dot matrix on the inside of the bottle cap. When the dot matrix is ​​heated, each pixel changes color irreversibly to form a thermal pattern. The verification module is used to collect the fingerprint texture of the bottle bottom and the thermal pattern, reconstruct the theoretical temperature curve based on the fingerprint of the bottle bottom, deduce the measured temperature curve from the thermal pattern, compare the consistency of the two curves and the matching of texture codes, and determine the authenticity.

[0017] Thirdly, the present invention provides an electronic device, characterized in that the electronic device comprises: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method described in any one of the first aspects above.

[0018] Compared with the closest existing technology, the present invention has the following advantages: This application proposes a single-product traceability method, system, and device for freshly stewed bird's nest. It utilizes the micron-level random texture naturally formed during the glass bottle molding process as a fingerprint on the bottle bottom. This fingerprint cannot be copied or transferred, fundamentally solving the problem of easily counterfeited external identification codes. Simultaneously, the fingerprint is engraved on the inside of the bottle cap, forming a physical bond between the bottle body and the cap; any replacement of the cap will result in verification failure.

[0019] Temperature residuals are collected by a limited number of sensors, expanded into a full residual field, and then compressed into batch signatures through high-order singular value decomposition. A batch of hundreds of bottles only requires storing about 10,000 floating-point numbers, which significantly reduces the blockchain storage overhead compared to storing fingerprints for each bottle. At the same time, the residual field itself does not contain product information, so leakage does not compromise security.

[0020] A multi-threshold thermosensitive ink dot matrix is ​​printed on the inside of the bottle cap, automatically forming a unique color pattern corresponding to the temperature history during the stewing process. Consumers can participate in the verification simply by taking a photo with their mobile phone, without the need for professional equipment, greatly reducing the verification threshold.

[0021] During verification, the theoretical temperature curve reconstructed from the residual field and the measured temperature curve derived from the thermosensitive pattern corroborate each other. A fraudster would have to simultaneously falsify physical texture, hydrodynamic residual field, thermosensitive material response, and glycoprotein denaturation patterns, which is completely impractical in engineering.

[0022] This invention performs texture extraction, residual correlation, and thermal pattern solidification on each bottle of product on the production line, enabling precise traceability of every single item in the entire batch and overcoming the limitations of sampling inspection. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0024] Figure 1 This is a flowchart of a single-product traceability method for freshly stewed bird's nest provided by the present invention; Figure 2 This is a schematic diagram of the process for extracting and encoding micron-level random textures at the bottom of a bottle in this invention; Figure 3 This is a schematic diagram of a single-product traceability system for freshly stewed bird's nest provided by the present invention.

[0025] Figure 4 This is an internal structural diagram of the electronic device provided by the present invention. Detailed Implementation

[0026] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore merely examples, and should not be construed as limiting the scope of protection of the present invention.

[0027] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0028] This invention provides a method, system, and device for tracing the origin of freshly stewed bird's nest at the single-item level. Specifically, it relates to a method for tracing the origin of freshly stewed bird's nest at the single-item level based on physically unclonable identifiers, process residual field compression, and thermal pattern inversion. The embodiments of this invention are described below with reference to the accompanying drawings.

[0029] Example 1: As Figure 1 As shown, Embodiment 1 of the present invention provides a single-product traceability method for freshly stewed bird's nest, which specifically includes the following steps: S101 acquires the fingerprint texture of the bottom of the empty bottle, extracts the fingerprint and encodes it onto the inside of the bottle cap to form a physical binding; S102 In the stewing process, temperature data of the stewing pot is collected, temperature residual is calculated by combining with theoretical temperature field, the temperature residual is expanded into residual field, compressed to obtain batch signature and stored. S103 prints a multi-threshold thermal dot matrix on the inside of the bottle cap. When the dot matrix is ​​heated, each pixel changes color irreversibly, forming a thermal pattern. S104 collects the fingerprint texture of the bottle bottom and the thermal pattern, reconstructs the theoretical temperature curve based on the fingerprint of the bottle bottom and the signature, and reverses the measured temperature curve from the thermal pattern. The consistency of the two curves and the matching of texture codes are compared to determine the authenticity.

[0030] In step S101 above, obtaining the fingerprint texture of the bottom of the empty bottle includes: Scan the bottom surface of the bottle to obtain three-dimensional morphological data; The three-dimensional topography data is denoised and smoothed to generate a depth map; The depth map is subjected to median filtering and binarization to obtain the fingerprint; The fingerprint is encoded into a QR code and burned onto the inside of the bottle cap, forming a physical bond with the bottle.

[0031] In one embodiment, physical binding refers to burning the fingerprint of the bottle bottom texture onto the inside of the bottle cap in the form of a QR code, so that the bottle cap and the bottle body form a unique correspondence through an uncopyable physical texture, and any act of replacing the bottle cap will result in verification failure.

[0032] like Figure 2 In one embodiment, after bottle production is completed and before filling, a laser confocal microscope is used to scan and image the bottom surface of each empty bottle. The laser confocal microscope can acquire three-dimensional topographic data of the bottle bottom surface, and its resolution is sufficient to capture surface undulation features at the micrometer level.

[0033] The acquired 3D topography data undergoes denoising and smoothing preprocessing to eliminate noise interference during the imaging process, and then a depth map is generated. The depth map reflects the height information of each point on the bottom surface of the bottle relative to a reference plane.

[0034] The depth map is binarized using median filtering. Median filtering effectively removes isolated noise points while preserving edge features. Binarization converts the depth map into a binary matrix, where matrix elements are set to 0 or 1 depending on whether the height value at the corresponding location exceeds a preset threshold.

[0035] It is important to emphasize that the fingerprint pattern on the bottom of the bottle is not stored in any central database. Instead, it is directly encoded as a QR code and burned onto the inside of the bottle cap. This design achieves a physical binding between the bottle's unique, unclonable identifier and the digital code, making it impossible to forge the bottle's physical identity even if the database is compromised.

[0036] In step S102 above, the calculation of the temperature residual by combining the theoretical temperature field includes: A theoretical temperature field model is established based on the stewing set temperature, the input steam temperature, and the spatial characteristic time constant obtained through fluid simulation. The temperature data at each sensor location is compared with the calculated value of the theoretical temperature field model at the corresponding location to obtain the temperature residual at each location.

[0037] In the above embodiments, the theoretical temperature field model is simplified based on the one-dimensional lumped parameter method, equating the steam heating process to an exponential heating process. The characteristic time constant τ(x,y,z) is obtained through computational fluid dynamics (CFD) simulation: using ANSYS Fluent software, with the three-dimensional geometric model of the stewing vessel as the computational domain, the steam inlet as the velocity inlet boundary condition, the exhaust port as the pressure outlet boundary condition, and the vessel wall as a non-slip adiabatic wall, the unsteady Navier-Stokes equations and energy equations are solved, and the temperature response curves at the center points of each bottle are extracted and fitted using the e-exponential method to obtain τ(x,y,z). Those skilled in the art can also use other commercial CFD software to establish models with the same boundary condition type.

[0038] In step S102 above, the temperature residual is expanded into a residual field, and the batch signature is compressed to include: Using the temperature residual at each location as the node value, radial basis function interpolation is used to calculate the temperature residual value of each grid point on the whole-cell space grid, and the residual field on the whole grid is obtained. The residual field is organized into a four-dimensional residual tensor according to the time series. The four-dimensional residual tensor is subjected to high-order singular value decomposition to obtain a batch signature consisting of a core tensor and a mode matrix. The batch signatures are stored in the blockchain.

[0039] The higher-order singular value decomposition of the four-dimensional residual tensor includes: Set the truncation rank for each mode and perform dimensionality reduction decomposition on each mode of the residual tensor; Calculate the core tensor and modal matrix to reduce the total storage requirement from the product of the number of spatial grid points and the number of temporal sampling points of the complete tensor to the sum of the number of elements of the core tensor and the modal matrix.

[0040] In step S103 above, printing a multi-threshold thermal dot matrix on the inside of the bottle cap specifically involves arranging pixels on the inside of the bottle cap according to a preset grid, with each pixel coated with a cholesteric liquid crystal ink.

[0041] Cholesteric liquid crystal inks comprise three types, each with a different phase transition temperature threshold. The first type undergoes a phase transition from a colorless state to a first colored state when the temperature reaches the first threshold; the second type undergoes a phase transition from a colorless state to a second colored state when the temperature reaches the second threshold; and the third type undergoes a phase transition from a colorless state to a third colored state when the temperature reaches the third threshold, with the three thresholds increasing sequentially.

[0042] During the stewing process, each pixel compares the highest temperature experienced by its location in the bottle with the phase transition temperature threshold of the coated ink type. When the highest temperature reaches or exceeds the phase transition temperature threshold, the pixel undergoes an irreversible phase transition from a colorless state to a colored state. The colored state is represented as a discrete sample value of the pixel's temperature history at that location. Specifically, the irreversible phase transition is caused by the irreversible color development reaction resulting from the thermal rupture of cholesteric liquid crystal microcapsules.

[0043] Because each bottle is located in a different spatial position within the vessel, its actual temperature history varies. Therefore, the thermal pattern formed on the inside of each bottle cap uniquely corresponds to the actual temperature history of that bottle.

[0044] In step S104 above, reconstructing the theoretical temperature curve based on the fingerprint of the bottle bottom includes: querying the corresponding batch signature based on the fingerprint of the bottle bottom texture, reconstructing the temperature residual curve of the bottle position through the inverse operation of high-order singular value decomposition, and superimposing the theoretical temperature field model to obtain the theoretical temperature curve.

[0045] In step S104 above, the process of retrieving the measured temperature curve from the thermal pattern includes: Based on the ink type and color rendering state of each pixel in the thermal pattern, a measured indicator function is constructed for each ink type. The value is 1 when the pixel presents the corresponding color rendering state, and 0 otherwise. For the candidate temperature curve, the highest temperature of each pixel is calculated by time integration, and the simulated indicator function is calculated. The loss function is the sum of the absolute values ​​of the differences between the measured indicator function and the simulated indicator function. The loss function is minimized using a simulated annealing algorithm to obtain the measured temperature curve.

[0046] In the simulated annealing algorithm, the candidate temperature curve is parameterized as a step function, and the temperature values ​​of 15 equal time intervals are used as optimization variables; the neighborhood search is achieved by applying a ±0.5℃ perturbation to a randomly selected interval temperature value.

[0047] In step S104 above, comparing the consistency of the two curves includes: Find the optimal alignment path between the theoretical temperature curve and the measured temperature curve that satisfies the boundary conditions and monotonicity conditions; Calculate the sum of squares of the differences between corresponding points on the alignment path, and take the arithmetic square root of the sum of squares as the dynamic time-normalized distance; When the dynamic time warp distance is less than the preset temperature threshold, the two curves are determined to be consistent.

[0048] In step S104 above, the determination of authenticity includes: calculating the Hamming distance between the fingerprint texture on the bottom of the bottle and the fingerprint texture on the bottom of the bottle coded on the inside of the bottle cap; when the distance is zero and the consistency of the two temperature curves is passed, the conclusion of authenticity is output; otherwise, the counterfeit risk level is output according to the number of items that fail.

[0049] In one embodiment, the method further includes: establishing a glycoprotein thermal denaturation model, and inferring the temperature change sequence based on the Raman spectrum of the finished bird's nest; during verification, the Raman spectrum of the finished bird's nest is collected, and the temperature curve is obtained by inferring the temperature through the model; the temperature curve is then compared with the theoretical temperature curve and the measured temperature curve for consistency.

[0050] In the above embodiments, establishing the glycoprotein thermal denaturation model includes: The Arrhenius rate equation was used to describe the changes in glycoprotein concentration with temperature and time, and an exponential relationship between the glycoprotein denaturation reaction rate and absolute temperature was established. A power-law quantitative relationship was established between the Raman spectral intensity ratio and the glycoprotein concentration, wherein the Raman spectral intensity ratio is the ratio of the intensity of the amide I band to the intensity of the CH2 band; The stewing time is discretized into multiple equal time intervals. Using the Raman spectral intensity ratio of the finished bird's nest as a known condition, the equivalent temperature of each time interval is solved by the Newton-Raphson iterative method to form the temperature change sequence.

[0051] In the above embodiments, the Newton-Raphson iterative method is used to solve for the equivalent temperature in each time interval, including: Set the initial temperature value for each time interval, and calculate the final concentration prediction value by forward recursion based on the glycoprotein denaturation reaction rate equation; Establish a residual function, which is the difference between the predicted final concentration and the actual final concentration calculated from the Raman spectrum intensity ratio; Calculate the Jacobian matrix of the residual function, the elements of which are obtained by numerically perturbing each temperature component; Solve the linear equation system to update the temperature vector. Stop iterating when the magnitude of the residual function is less than a preset convergence threshold, and output the temperature change sequence.

[0052] In this embodiment, the thermal denaturation kinetic model of bird's nest glycoprotein is as follows (all parameters were determined by laboratory isothermal experiments): Reaction rate equation: dC / dt = -k(T)×C n Where C is the concentration of the native conformation glycoprotein (initial value C0), n = 1.2 (reaction order), and k(T) is the temperature-dependent denaturation rate constant; Arrhenius rate constant: k(T) = A×exp(-E a / (R×T)) A = 3.2 × 10 12 s -1 E a =118 kJ / mol, R=8.314 J / (mol·K), T is the absolute temperature (in K).

[0053] A is the pre-exponential factor, E a R is the activation energy, and R is the molar gas constant.

[0054] Raman spectral intensity ratio R = I_{amide I} / I_{CH2}, where amide I: amide I band, wavenumber 1650. -1 cm, CH2 band wavenumber 1450 cm - ¹. The relationship between concentration and intensity ratio was obtained through experimental calibration: C = C0 × (R / R0) β , β=0.85.

[0055] Where C0 is the initial concentration of the natural conformation glycoprotein of the raw material, R is the amide I band in the Raman spectrum, R0 is the initial Raman intensity ratio of the raw material, and β is the power exponent determined experimentally.

[0056] Inverse problem solution: Discretize the stewing time from 0 to 900 seconds into M = 15 equal-length intervals, each interval having a Δt = 60 seconds. Assume the temperature is constant within each interval for T1, T2, ..., T 15 Forward recursion: C {j+1} = C j -k(T j )×(C j ) n ×Δt; j represents the discrete-time interval index, j=1,2,...,M, where M is the total number of intervals; T j Let C represent the constant equivalent temperature within the j-th interval. j Let Δt represent the natural conformation concentration at the start of the j-th interval, and let Δt represent the length of each time interval.

[0057] The Raman spectral intensity ratio of finished bird's nest is known to be R final C was calculated. final =C0×(R final / R0) 0.85 Solve for T using the Newton-Raphson iteration. j Let the residual function F({T}) = C pred ({T})-C final Initial guess T j=121℃ (all intervals). The Jacobian matrix J is calculated in each iteration. {i,j} = F i / T j Numerical perturbation is used: F / T j ≈(F(..., T j +ε, ...)-F(..., T j Solve the linear system of equations J·δT=-F, and update T. new =T old +δT. Stop when |F| < 0.01 × C0, and output the reverse-calculated temperature vector T. inv =[T1,T2,...,T 15 Where {T} is the temperature vector; C pred C represents the final concentration prediction value calculated from the temperature vector {T} through forward recursion. final To determine the Raman spectral intensity ratio R of the finished bird's nest final The calculated final concentration; δT is the correction factor for the temperature vector.

[0058] This mathematical model completes parameter calibration offline, and during online inference, it only requires collecting the Raman spectrum of the finished product to quickly calculate T. inv .

[0059] In summary, the above embodiments, through the organic combination of physically unclonable textures, process residual tensor fields, and thermal pattern inversion, establish an unforgeable mathematical constraint relationship between product physical identification and production parameters, achieving true single-item-level precise traceability, which has significant creativity, practicality, and technological advancement.

[0060] Example 2: This example uses glass blowing technology to produce a special bottle for freshly stewed bird's nest. A 2mm × 2mm micro-roughened area is reserved in the center of the outer surface of the bottle bottom. During the glass forming process, the random flow of molten glass within the mold causes micron-level uneven textures to form in this area.

[0061] After each bottle was cooled to room temperature, it was moved to the laser confocal microscope scanning station. An Olympus OLS5000 laser confocal microscope was used with a 405nm laser as the light source to scan the slightly rough area on the bottom of the bottle, acquiring 512×512 pixel three-dimensional topographic data. Each pixel contained the height information of that point (accuracy ±0.5μm).

[0062] For the obtained 3D topography data, a Gaussian filter is first used to remove high-frequency noise, and then a median filter is used to smooth isolated spikes to generate a depth map. Then, the depth values ​​of all pixels in the depth map are sorted, and the median depth is taken as the threshold. Pixels with depth values ​​greater than the threshold are set to 1, and pixels with depth values ​​less than or equal to the threshold are set to 0, resulting in a 512×512 binary matrix, which is used as the bottom texture fingerprint of the bottle.

[0063] Using a high-power ultraviolet laser marking machine (wavelength 355nm, power 10W), the binary matrix is ​​encoded into a 3mm×3mm Data Matrix QR code, which is then engraved on the inside of the bottle cap. This QR code is heat-resistant (withstanding 130℃) and is not pre-associated with the bottle body—each bottle's QR code only records its own bottom fingerprint, without recording any batch or product information. This bottom fingerprint texture is not stored in any central database.

[0064] The stewing vessel used in this embodiment is an FMC rotary sterilizer with 8 layers of trays inside, each layer holding 45 bottles. It is symmetrically divided into two half-zones, for a total of 360 bottles.

[0065] 32 locations (x) were pre-selected inside the vessel. s , y s , z s Each location point is equipped with a PT100 platinum resistance temperature sensor with an accuracy of ±0.1℃ and a sampling frequency of 1Hz.

[0066] Based on previous fluid dynamics simulations, the characteristic time constant τ(x,y,z) for each spatial location (x, y, z) inside the vessel was obtained. The theoretical temperature field model is as follows: T theory(x,y,z,t) = T set +(T steam -T set ) × exp(-t / τ(x,y,z)) Among them, T set =121℃, T steam =125℃. T theory(x,y,z,t) This represents the theoretical temperature at position (x, y, z) at time t. set Indicates the set temperature for stewing; T steam The input steam temperature is represented by τ(x,y,z); τ(x,y,z) represents the characteristic time constant at the spatial location (x,y,z).

[0067] In one production batch, the stewing time was 15 minutes (900 seconds). The actual temperature T was collected every second throughout the stewing process from 32 sensor locations. actual(xs,ys,zs,t) At each sensor location, calculate the temperature residual at each time point: Δ(x s,y s ,z s ,t) = T actual(xs,ys,zs,t) -T theory(xs,ys,zs,t) Where, Δ(x) s ,y s ,z s ,t) represents the sensor position (x s , y s , z s The temperature residual at time t; T actual(xs,ys,zs,t) This indicates the actual temperature measured by the sensor. The subscript 's' represents the sensor number, where 's' = 1, 2, ..., 32.

[0068] Using the residuals at 32 discrete points as node values, radial basis function interpolation is employed (the basis function is a Gaussian kernel). φ(r)=exp(-(εr) 2 ), where φ(r) is the radial basis function value, r is the distance from the interpolation point to the sensor position, and ε is the shape parameter), interpolated to all 360 bottle position grid points to obtain the full grid residual field Δ(x,y,z,t), with dimensions of 8×45×2×900.

[0069] The residual field is organized as a four-dimensional tensor X. X is compressed using higher-order singular value decomposition: the truncation rank r for each mode is set. x =3,r y =5,r z =1,r t =10, decomposed into: X = G ×1U x ×2U y ×3U z ×4U t Among them, U x U y U z U t These correspond to the modality matrices of the layer, column, half-region, and time dimensions, respectively; U x It is an 8×3 matrix, U y It is a 45×5 matrix, U z U is a 2×1 matrix. t The matrix is ​​900×10, and G is the core tensor (3×5×1×10). The total data size after compression is: 8×3+45×5+2×1+900×10+3×5×1×10=24+225+2+9000+150=9401 floating-point numbers. {U x U y U z U t, G} is used as the signature data for this batch and written into the corresponding transaction in the Hyperledger Fabric blockchain.

[0070] In one embodiment, the truncation rank of each mode is set based on the minimum rank that retains at least 95% of the cumulative singular value energy after the mode expansion. Specifically, singular value decomposition is performed on the expansion matrix of each mode of the four-dimensional residual tensor, according to σ... i / Σσ j ≥0.95 determines r x r y r z r t , where σ i For the i-th largest singular value, Σσ j This represents the sum of all singular values ​​in this mode.

[0071] Furthermore, in the printing and curing steps of the thermosensitive dot matrix on the bottle cap, this embodiment uses three types of cholesteric liquid crystal microcapsule inks: Ink A: Color change range 118~120℃, colorless below 118℃, turns blue at 120℃ (irreversible); Ink B: Color-changing range 120~122℃, colorless below 120℃, turns green at 122℃ (irreversible); Ink C: Color change range 122~124℃, colorless below 122℃, turns red at 124℃ (irreversible).

[0072] A 15×15 grid of dots with a dot spacing of 0.5mm is formed by screen printing on the inside of the bottle cap (the side facing inwards). Before printing, 225 random numbers are generated using a pseudo-random number generator and evenly distributed on {1,2,3}, corresponding to inks A, B, and C respectively. A screen printing mask is then created based on these numbers. Each pixel contains only one type of ink.

[0073] The bottle caps with printed thermal dot matrix are attached to the bottles already filled with the ingredients to be stewed, tightened, and then placed into the stewing pot. During the stewing process, the temperature of each pixel increases over time. When the temperature reaches or exceeds the color-changing threshold of its ink, an irreversible color change occurs; if it remains below the threshold, it remains colorless. Due to the differences in the actual temperature curves of different bottle locations within the pot (affected by the residual field in step two), the final colored pattern formed in each bottle is different. For example, if the internal temperature of a bottle reaches 123°C quickly, some ink C pixels turn red; if the temperature only reaches 121°C, ink B turns green while ink C remains colorless.

[0074] After stewing, the pot cools naturally, and the heat-sensitive pattern remains unchanged.

[0075] Finally, the verification process involves a comprehensive assessment: after purchasing the product, consumers use a mobile app to photograph the fingerprint area on the bottom of the bottle (through the bottom of the bottle) and the inner thermal pattern after unscrewing the cap.

[0076] The mobile application uploads two images to the verification server. The server first reads the pre-engraved fingerprint of the bottle bottom from the QR code inside the bottle cap. bottle_code And extract the actual texture fingerprint F from the bottle bottom photo. bottom If the Hamming distance between the two is not 0, return "Fake - Bottle Cap Mismatch" directly.

[0077] If a match is found, then the fingerprint F in the QR code will be used. bottle_code Query the pre-established mapping table to obtain the production batch number and the bottle's position coordinates (x, y, z) within the reactor. Based on the batch number, read the residual signature {U} of that batch from the blockchain. x U y U z U t , G}. The temperature residual curve Δ at the bottle location is reconstructed using HOSVD inverse operation. reconstructed(t) Then, the theoretical temperature field T is superimposed. theory(x,y,z,t) The theoretical temperature curve T of the bottle was obtained. theo(t) The theoretical temperature curve T is obtained by averaging the curve over 15 intervals. theo .

[0078] For the uploaded thermal pattern image, color segmentation is first performed to identify the color (blue / green / red / colorless) of each pixel. Given the ink distribution map of the random dot matrix in this batch, a measured indicator function is defined for each ink type: for example, for ink A, if a pixel is blue, then I_A_meas(p) = 1, otherwise 0; similarly, I_B_meas(p) and I_C_meas(p) are defined; p is the pixel index.

[0079] For a given candidate temperature curve T_candidate(t), simulate the highest temperature experienced by each pixel, thereby determining its simulated color and simulation indicator function I_A_sim(p), etc. Construct the loss function: Loss(T_candidate)=Σ p [|I_A_sim(p)-I_A_meas(p)|+|I_B_sim(p)-I_B_meas(p)|+ |I_C_sim(p)-I_C_meas(p)|] The simulated annealing algorithm (initial temperature 100℃, cooling rate 0.95, 500 iterations) was used to minimize this loss, resulting in the measured temperature curve T that minimizes the loss. obs(t)Discretized into 15 intervals, the measured temperature curves T obs .

[0080] Finally, the theoretical temperature curve T is calculated. theo Compared with the measured temperature curve T obs The dynamic time-warped distance between the two sequences is calculated. First, the cumulative distance matrix D(i,j) = |T_theo[i] - T_obs[j]| + min(D(i-1,j), D(i-1,j-1),D(i,j-1)) is constructed, and the optimal alignment path is obtained by backtracking. T_theo[i] represents the average temperature of the theoretical temperature curve in the i-th interval, and T_obs[j] represents the average temperature of the measured temperature curve in the j-th interval; D(i,j) represents the minimum cumulative distance from the starting point (1,1) to (i,j).

[0081] The DTW distance is the square root of the sum of the squares of the differences between corresponding points along the path. If DTW < 2.0℃ and the fingerprint on the bottom of the bottle matches, output "Genuine"; if DTW ≥ 2.0℃, output "Suspected Counterfeit - Temperature Curve Inconsistency".

[0082] Optionally, to further enhance reliability, a Raman spectroscopy verification step can be added: the user uses a portable Raman pen (e.g., 785nm excitation) to collect the spectrum of the finished bird's nest, uploads it, and then calculates the temperature vector T by inversely estimating the spectrum using the mathematical model in step three. raman T raman With T theo T obs Calculate the DTW distance separately. When the distance between any two of the three is less than 1.5℃, it is determined to be a highly reliable genuine product.

[0083] After each validation, the anonymized validation data (thermal pattern, Raman spectrum, and temperature curve comparison results) are stored in the feedback database. The system performs incremental learning weekly: using the most recent 1000 validation data points, it refines the parameters (β, n, A, E) of the kinetic model from step three. a Fine-tuning was performed using gradient descent to minimize the deviation between the predicted temperature curve and the back-calculated temperature during validation. Simultaneously, the DTW threshold in step five was adaptively adjusted based on actual batch fluctuations. With data accumulation, the model's accuracy and robustness continuously improved.

[0084] Example 3: Based on the same technical concept described above, Example 3 of this invention also provides a single-product traceability system for freshly stewed bird's nest, such as... Figure 3 As shown, it includes: an acquisition module 210, a calculation module 220, a processing module 230, and a verification module 240. Wherein: The acquisition module 210 is used to acquire the fingerprint of the bottom of the empty bottle, extract the fingerprint and encode it to the inside of the bottle cap to form a physical binding; The calculation module 220 is used to collect temperature data of the stewing pot during the stewing process, calculate the temperature residual by combining it with the theoretical temperature field, expand the temperature residual into a residual field, compress it to obtain a batch signature and store it. Processing module 230 is used to print a multi-threshold thermal dot matrix on the inside of the bottle cap. When the dot matrix is ​​heated, each pixel changes color irreversibly to form a thermal pattern. The verification module 240 is used to collect the fingerprint texture of the bottle bottom and the thermal pattern, reconstruct the theoretical temperature curve based on the fingerprint of the bottle bottom, deduce the measured temperature curve from the thermal pattern, compare the consistency of the two curves and the matching of texture codes, and determine the authenticity.

[0085] In one embodiment, an electronic device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements the single-product traceability method for freshly stewed bird's nest as described in any one of steps S101 to S104. The display screen can be a liquid crystal display or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the outer casing of the electronic device, or an external keyboard, touchpad, or mouse.

[0086] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for tracing the origin of freshly stewed bird's nest at a single-product level, characterized in that, The method includes: Obtain the fingerprint texture of the bottom of the empty bottle, extract the fingerprint and encode it onto the inside of the bottle cap to form a physical binding; In the stewing process, temperature data of the stewing pot is collected, and the temperature residual is calculated in combination with the theoretical temperature field. The temperature residual is expanded into a residual field, compressed to obtain a batch signature and stored. A multi-threshold thermal dot matrix is ​​printed on the inside of the bottle cap. When the dot matrix is ​​heated, each pixel changes color irreversibly, forming a thermal pattern. Collect the fingerprint texture of the bottle bottom and the thermal pattern, reconstruct the theoretical temperature curve based on the fingerprint signature, and deduce the measured temperature curve from the thermal pattern. Compare the consistency of the two curves and the matching of texture codes to determine authenticity.

2. The method according to claim 1, characterized in that, The process of obtaining the fingerprint texture of the bottom of the empty bottle includes: Scan the bottom surface of the bottle to obtain three-dimensional morphological data; The three-dimensional topography data is denoised and smoothed to generate a depth map; The depth map is subjected to median filtering and binarization to obtain the fingerprint; The fingerprint is encoded into a QR code and burned onto the inside of the bottle cap, forming a physical bond with the bottle.

3. The method according to claim 1, characterized in that, The calculation of the temperature residual by combining the theoretical temperature field includes: A theoretical temperature field model is established based on the stewing set temperature, the input steam temperature, and the spatial characteristic time constant obtained through fluid simulation. The temperature data at each sensor location is compared with the calculated value of the theoretical temperature field model at the corresponding location to obtain the temperature residual at each location.

4. The method according to claim 1, characterized in that, The step of expanding the temperature residual into a residual field and compressing it to obtain the batch signature includes: Using the temperature residual at each location as the node value, radial basis function interpolation is used to calculate the temperature residual value of each grid point on the whole-cell space grid, and the residual field on the whole grid is obtained. The residual field is organized into a four-dimensional residual tensor according to the time series. The four-dimensional residual tensor is subjected to high-order singular value decomposition to obtain a batch signature consisting of a core tensor and a mode matrix. The batch signatures are stored in the blockchain.

5. The method according to claim 4, characterized in that, The higher-order singular value decomposition of the four-dimensional residual tensor includes: Set the truncation rank for each mode and perform dimensionality reduction decomposition on each mode of the residual tensor; Calculate the core tensor and modal matrix to reduce the total storage requirement from the product of the number of spatial grid points and the number of temporal sampling points of the complete tensor to the sum of the number of elements of the core tensor and the modal matrix.

6. The method according to claim 5, characterized in that, The process of printing a multi-threshold thermal dot matrix on the inside of the bottle cap includes: Pixels are arranged in a preset grid on the inside of the bottle cap, and each pixel is coated with a cholesteric liquid crystal ink. The cholesteric liquid crystal ink includes three types, each with a different phase transition temperature threshold. Each pixel is compared with the highest temperature experienced by its bottle position during the stewing process and the phase transition temperature threshold of the coated ink type. When the highest temperature reaches or exceeds the phase transition temperature threshold, the pixel undergoes an irreversible phase transition from a colorless state to a colored state. The colored state is used as a discrete sample value of the pixel for the temperature history of the bottle position.

7. The method according to claim 1, characterized in that, The method of retrieving the measured temperature curve from the thermal pattern includes: Based on the ink type and color rendering state of each pixel in the thermal pattern, a measured indicator function is constructed for each ink type. The value is 1 when the pixel presents the corresponding color rendering state, and 0 otherwise. For the candidate temperature curve, the highest temperature of each pixel is calculated by time integration, and the simulated indicator function is calculated. The loss function is the sum of the absolute values ​​of the differences between the measured indicator function and the simulated indicator function. The loss function is minimized using a simulated annealing algorithm to obtain the measured temperature curve.

8. The method according to claim 1, characterized in that, The comparison of the consistency of the two curves includes: Find the optimal alignment path between the theoretical temperature curve and the measured temperature curve that satisfies the boundary conditions and monotonicity conditions; Calculate the sum of squares of the differences between corresponding points on the alignment path, and take the arithmetic square root of the sum of squares as the dynamic time-normalized distance; When the dynamic time warp distance is less than the preset temperature threshold, the two curves are determined to be consistent.

9. A single-product traceability system for freshly stewed bird's nest, used to execute the method according to any one of claims 1 to 8, characterized in that, include: The acquisition module is used to acquire the fingerprint texture of the bottom of the empty bottle, extract the fingerprint and encode it to the inside of the bottle cap to form a physical binding; The calculation module is used to collect temperature data of the stewing pot during the stewing process, calculate the temperature residual by combining it with the theoretical temperature field, expand the temperature residual into a residual field, compress it to obtain the batch signature and store it. The processing module is used to print a multi-threshold thermal dot matrix on the inside of the bottle cap. When the dot matrix is ​​heated, each pixel changes color irreversibly to form a thermal pattern. The verification module is used to collect the fingerprint texture of the bottle bottom and the thermal pattern, reconstruct the theoretical temperature curve based on the fingerprint of the bottle bottom, deduce the measured temperature curve from the thermal pattern, compare the consistency of the two curves and the matching of texture codes, and determine the authenticity.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-8.

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