Rebar raw material quality assurance data identification system based on image analysis
By collecting the micro three-dimensional morphology of the steel bar surface to generate rolled pattern gene codes and calibrating them with environmental corrosion coefficients, the problem of separation from the steel bar body and identification of forged documents is solved, and the accurate identification of the steel bar identity and dynamic consistency verification of the warranty certificate is achieved, which improves the safety and accuracy of the system.
Patent Information
- Application Number
- CN202510770978.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-10
- Publication Date
- 2025-09-02
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In the existing reinforcement quality assurance system, the warranty certificate and the physical characteristics of the steel bars lack direct coupling, easy to be separated or maliciously replaced, the forged documents cannot be identified, and it is difficult to maintain effective information in extreme environments, and the warranty information lacks dynamic consistency verification.
The microscopic three-dimensional morphology of the steel bar surface is collected through a laser interferometer, the rolled pattern gene code is generated, and the compensation calibration is performed in combination with the environmental corrosion coefficient, and an encrypted digital watermark is generated to achieve reverse synthesis and dynamic consistency verification of the warranty certificate.
It has improved the security and tamper resistance of the steel bar quality assurance system, can accurately identify the identity of the steel bar in a variety of corrosion scenarios, reduce the false alarm rate and cracking success rate, and has good actual project deployment capabilities.
Smart Images
Figure CN120580593A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of image recognition, and in particular to a steel bar raw material quality assurance data recognition system based on image analysis. Background Art
[0002] As a key structural material in construction and infrastructure projects, the quality traceability of rebar directly impacts the project's structural safety and accountability. Existing rebar quality assurance systems typically rely on manually managed scans of warranty certificates or barcode labels to bind each batch of rebar to its conformity certification documents. However, this binding mechanism has the following significant technical flaws: Currently, warranty documents are often distributed with goods in paper, PDF, or QR code format. These documents lack direct connection to the physical characteristics of the rebar itself, making them susceptible to separation from the original material or malicious substitution during storage, construction, and other distribution processes. This is particularly true in scenarios where contractors have complex hierarchical management or where recycled and reused materials are used. Warranty data can be easily distorted, making it difficult to trace quality risks.
[0003] Commonly used paint spray codes, coding strips or external RFID tags are easily worn, rusted or removed on site. They are difficult to retain effective information for a long time in extreme construction site environments such as acid rain, humidity, and high dust, making it difficult to extract reliable identity tags from the steel bars later.
[0004] The warranty information lacks a dynamic consistency verification mechanism, making it impossible to identify forged documents or mismatched roll information. Even if a rebar can be successfully scanned and its warranty document is obtained, the document's contents may still indicate forgery, diversion, or rebar produced by a different rolling mill. Existing systems cannot verify the consistency of the rebar's surface physical information with the warranty document by inverting it. In particular, it is difficult to determine whether production parameters such as roll wear and rolling batches are truly consistent. Summary of the Invention
[0005] The present invention provides a steel bar raw material quality assurance data identification system based on image analysis, which can extract unique and stable identity information from the microscopic features of the steel bar itself, and can realize reverse authentication and dynamic consistency verification with the quality assurance certificate, thereby fundamentally improving the security, anti-tampering ability and automation level of the steel bar quality assurance system.
[0006] A steel bar raw material quality assurance data identification system based on image analysis, the system performs the following: S1, rolling grain gene code extraction: The microscopic three-dimensional morphology of the specified area of the steel bar surface is collected by laser interferometer, and the morphology data is compensated and calibrated in combination with the real-time environmental corrosion coefficient to extract the peak and valley distribution characteristics of the rolling grain and encode it to generate the steel bar gene code; S2: Reverse synthesis of the warranty watermark: Generate an encrypted digital watermark based on the rebar genetic code, and reversely search the cloud-based warranty database for an encrypted quality assurance document that matches the digital watermark; S3: Cross-media attenuation verification: Decrypt the encrypted quality assurance document to obtain the original warranty certificate, and simultaneously obtain the historical roller wear data when the original warranty certificate was issued. Compare the roller number recorded in the original warranty certificate with the theoretical attenuation consistency of the wear characteristics of the steel bar gene code in a time-varying environment, and output the cross-media binding result.
[0007] Optionally, the S1 specifically includes: S11, dynamic area calibration: using laser interferometer to delineate on the steel bar surface The detection area is automatically avoided, and invalid areas with rust areas greater than 20% are avoided. By emitting white light interference fringes with a wavelength of 650nm, the microscopic three-dimensional topography point cloud data in the area is collected to construct a data set: ; Indicates the The spatial position of a three-dimensional coordinate point, where: is the horizontal position of the point, Indicates the vertical position of the point. Indicates the topography value of a point in the height direction, reflecting the concave and convex changes of the steel bar surface. is the serial number of the point, a total of 1024 points are sampled. It is a set symbol, which represents the set of all sampling points in the entire detection area.
[0008] S12 environmental corrosion compensation: real-time collection of environmental corrosion coefficients , which is calculated as follows: ; And the height value of each point in the point cloud is compensated and calibrated. The correction formula is: ; in, Indicates the first The height component of each point, Indicates the height value after compensation for corrosion and pollution, represents the environmental corrosion coefficient, represents the chloride ion concentration, Indicates the exposure time of steel bars, Represents the empirical coefficient, corresponding to ion concentration and time sensitivity, is the pollution compensation coefficient, which is used to adjust the sensitivity of the interference of attached dirt. Indicates a point The Laplacian operator of the region is used to estimate the local curvature change to identify non-metallic attachments.
[0009] S13, peak and valley feature extraction: the height sequence after compensation along the steel bar rolling direction Scan and identify peak sequences The following characteristic indicators are further calculated with the trough sequence: Standard deviation of the distance between adjacent peaks: ; Coefficient of variation of trough depth: ; in, For the The location of the peak, is the standard deviation of the distance between adjacent peaks, , represents the mean and standard deviation of the trough depth, Indicates the coefficient of variation of trough depth.
[0010] S14, Gene code generation: the feature pairs Input the normalized encoder and output the 256-bit rebar gene code: The first 64 bits are defined as the roller wear fingerprint, which is generated as follows: ; Or based on frequency domain processing, it is constructed mainly with low-frequency components: ; The low-frequency energy is calculated as follows: ; Among them, SHA256 represents a 256-bit hash function. is the signature XOR operator, express The fast Fourier transform of The complex modulus value of the frequency point, The sum of low-frequency energy representing the distance between peaks.
[0011] Optionally, the S2 specifically includes: S21, chaotic watermark generation: comprising dividing the rebar gene code generated in S1 into a plurality of bit segments, and using the bit segments as initial state inputs of a Lorenz chaotic system, wherein the Lorenz chaotic system is defined by a set of differential equations; S22, watermark phase modulation: including iteratively generating state variables of the Lorentz chaotic system Perform mapping transformation to generate frequency domain phase perturbation matrix : And inject it into the phase component of the original warranty image in the frequency domain to form a modulated digital watermark image ; S23, quantum secure retrieval: including encoding the phase characteristics of the modulated digital watermark image into a fixed-length hash key , and perform post-quantum encryption fuzzy retrieval based on number theory transformation in the cloud database.
[0012] Optionally, the quantum encryption fuzzy retrieval includes similarity determination, and the similarity determination formula is: ;in, represents number theory transformation operations, represents element-wise product, The similarity threshold is set to identify the encrypted warranty document corresponding to the current rebar gene code from the database. Indicates the first The feature vector of the encrypted watermark.
[0013] Optionally, the state quantity Perform mapping transformation to generate frequency domain phase perturbation matrix Specifically include: Chaos state value Zoom in 10 times to enhance the perturbation accuracy of its decimal part; Perform a modulo 256 operation on the result, i.e. take the remainder after dividing it by 256, to ensure that the perturbation value range is between 0 and 255; Normalize the perturbation value to The interval is used to construct the phase offset in angular units, which can be directly added to the frequency domain phase of the image, and the final result is Represents a position in the frequency domain The phase perturbation angle on .
[0014] Optionally, the S3 specifically includes: S31, dual-channel decryption: using the first 128 bits of the rebar gene code as the private key component and the site GPS coordinate hash value as the public key component, the original warranty is obtained through elliptic curve encryption and decryption, and the smart contract is simultaneously triggered to retrieve the initial wear of the roller and the rolling parameter set at the time of issuance of the warranty; S32, time-varying wear modeling: Based on the dynamic wear evolution mechanism of the roller, a theoretical wear model is established to calculate the current theoretical wear amount; S33, attenuation consistency judgment: Analyze the measured wear amount from the rebar gene code and construct an environment-adaptive deviation threshold. If the difference between the measured wear amount and the theoretical wear amount exceeds the environment-adaptive deviation threshold, output "binding invalid" and lock the warranty. S34, dynamic label generation: Encode the verification result into an identification label and write it into the end face of the steel bar.
[0015] Optionally, the theoretical wear model is established as: ; in, The theoretical wear amount at the current time t, Indicates the initial wear amount recorded when the warranty is issued. The number of rolling operations is the total number of rolling operations from the date of issuance to the present. Indicates a moment The rolling stress value, represents the environmental corrosion coefficient, represents the dynamic coefficient of roller wear, The time when the warranty certificate is issued. The current verification time.
[0016] Optionally, the identification tag includes a ciphertext of a four-tuple of {binding state, theoretical wear amount, measured wear amount, actual deviation amount}.
[0017] Beneficial effects of the present invention: The present invention collects the microscopic three-dimensional morphological data of the steel bar surface through laser interference, and combines it with environmental corrosion factors for dynamic compensation, thereby constructing a "rolling pattern gene code" for the first time. This gene code has uniqueness and anti-pollution capabilities, and can be used as the physical identity fingerprint of the steel bar to achieve reverse binding with the quality assurance document.
[0018] This method uses the Lorenz chaos equation to perform nonlinear transformations on the rebar genetic code and dynamically injects the entropy of the construction site's electromagnetic environment spectrum into the chaotic parameters, achieving a synchronous integration of the site's spatiotemporal characteristics with the security watermark. Combined with a post-quantum fuzzy retrieval algorithm using frequency-domain phase perturbation and NTT acceleration, it can reliably identify non-original rebar even in attack scenarios such as screenshot forgery and warranty certificate reuse. This improves the accuracy of warranty certificate tampering identification and reduces the success rate of cracking.
[0019] The present invention establishes an integral model of the accelerating effect of environmental corrosion on roller wear, integrates the number of rolling times recorded in the blockchain and the grain inversion stress, constructs a traceable "theoretical wear amount-measured genetic wear amount" comparison system, and introduces an error tolerance threshold that is adaptively adjusted based on the degree of corrosion, so that the system has good environmental adaptability. In tests under various corrosion scenarios, compared with the fixed error threshold mechanism, the false alarm rate is reduced, and it has strong practical engineering deployment capabilities and judicial evidence effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0021] Figure 1 A schematic diagram of execution steps of an identification system according to an embodiment of the present invention; Figure 2 Schematic diagram of watermark phase modulation according to an embodiment of the present invention. DETAILED DESCRIPTION
[0022] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art may also implement some known technologies in other alternative ways. The accompanying drawings are only for describing the embodiments in more detail and are not intended to limit the present invention in any specific way.
[0023] like Figure 1-Figure 2 As shown in the figure, the steel bar raw material quality assurance data identification system based on image analysis performs the following: S1, rolling grain gene code extraction: The microscopic three-dimensional morphology of the specified area of the steel bar surface is collected by laser interferometer, and the morphology data is compensated and calibrated in combination with the real-time environmental corrosion coefficient to extract the peak and valley distribution characteristics of the rolling grain and encode it to generate the steel bar gene code; S2: Reverse synthesis of the quality assurance certificate watermark: Generate an encrypted digital watermark based on the rebar genetic code, and reversely search the cloud-based quality assurance certificate database for the encrypted quality assurance document that matches the digital watermark; S3: Cross-media attenuation verification: Decrypt the encrypted quality assurance document to obtain the original warranty certificate, and simultaneously obtain the historical roller wear data when the original warranty certificate was issued. Compare the theoretical attenuation consistency of the roller number recorded in the original warranty certificate with the wear characteristics of the steel bar gene code in a time-varying environment, and output the cross-media binding result.
[0024] S1 specifically includes: S11, dynamic area calibration: using laser interferometer to delineate on the steel bar surface The detection area is automatically avoided, and invalid areas with rust areas greater than 20% are avoided. By emitting white light interference fringes with a wavelength of 650nm, the microscopic three-dimensional topography point cloud data in the area is collected to construct a data set: ; Indicates the The spatial position of a three-dimensional coordinate point, where: is the horizontal position of the point, Indicates the vertical position of the point. Indicates the topography value of a point in the height direction, reflecting the concave and convex changes of the steel bar surface. is the serial number of the point, a total of 1024 points are sampled. It is a set symbol, which represents the set of all sampling points in the entire detection area.
[0025] S12 environmental corrosion compensation: real-time collection of environmental corrosion coefficients , which is calculated as: ; And the height value of each point in the point cloud is compensated and calibrated. The correction formula is: ; in, Indicates the first The height component of each point, Indicates the height value after compensation for corrosion and pollution, represents the environmental corrosion coefficient, represents the chloride ion concentration, Indicates the exposure time of steel bars, Represents the empirical coefficient, corresponding to ion concentration and time sensitivity, is the pollution compensation coefficient, which is used to adjust the sensitivity of the interference of attached dirt. Indicates a point The Laplacian operator of the region is used to estimate the local curvature change to identify non-metallic attachments.
[0026] S13, peak and valley feature extraction: the height sequence after compensation along the steel bar rolling direction Scan and identify peak sequences and trough sequence , further calculate the following characteristic indicators: Standard deviation of the distance between adjacent peaks: ; Coefficient of variation of trough depth: ; in, For the The location of the peak, is the standard deviation of the distance between adjacent peaks, , represents the mean and standard deviation of the trough depth, Indicates the coefficient of variation of trough depth.
[0027] S14, Gene code generation: the feature pairs Input the normalized encoder and output the 256-bit rebar gene code: The first 64 bits are defined as the roller wear fingerprint, which is generated as follows:
[0028] Or based on frequency domain processing, it is constructed mainly with low-frequency components: ; The low-frequency energy is calculated as follows: ; Among them, SHA256 represents a 256-bit hash function. is the signature XOR operator, express The fast Fourier transform of The complex modulus value of the frequency point, The sum of low-frequency energy representing the distance between peaks.
[0029] S2 specifically includes: S21, chaotic watermark generation: The rebar gene code is divided into 8 groups of 32-bit segments, which are used as the initial state input of the Lorenz chaotic system to generate a highly irreversible chaotic trajectory, which is expressed as follows:
[0030] In order to enhance the dynamics of the system, the electromagnetic environment disturbance spectrum of the construction site is introduced into the chaotic system, and the parameters are adjusted by spectral entropy. : ; in, 、 represents the fixed parameter of the Lorentz system, represents the adjustment parameter, which is determined by the electromagnetic power spectrum density, represents the electromagnetic power spectrum density of the construction site environment, Represents spectral entropy, which is used to characterize randomness in the frequency domain.
[0031] S22, watermark phase modulation: obtained by iterative Lorentz system Sequence construction of frequency domain phase modulation matrix: ; This matrix is used to perturb the phase domain representation of the digital guarantee certificate watermark:
[0032] in, represents the first Step value, Represents the complex representation of the original warranty image in the frequency domain, representing the amplitude spectrum, represents the original phase spectrum, Represents the phase perturbation matrix, which is used to enhance the robustness of the watermark. : Modulo operation, remainder.
[0033] S23, Quantum Secure Retrieval: Watermarking the Perturbation The phase features are extracted and compressed into a 384-bit hash key. , used to perform lattice-based post-quantum fuzzy retrieval in cloud databases.
[0034] Encrypted retrieval function definition: ; in, represents number theoretic transformations, Indicates the first Feature vector of encrypted watermark (NTT domain), similarity threshold Defined as: , the above encryption retrieval function is used to retrieve the mathematical expression of records similar to the target watermark in the encrypted database, specifically: , filter out those that match the current query key After element-wise product in the number theory transformation (NTT) domain, The records form the set Result5et, which is the encrypted watermark that matches successfully.
[0035] And use number theory transformation (NTT) to accelerate watermark similarity calculation: ; in, Indicates the multiplication of corresponding elements, are 1-norm and 2-norm respectively, Indicates the watermark similarity retrieval threshold. A value lower than this is considered a valid match. represents the quantum cryptographic hash key extracted from the watermark, Represents the watermark characteristics of each encrypted warranty in the database.
[0036] S3 specifically includes: S31, dual-channel decryption: Use the first 128 bits of the rebar gene code as the private key component, the hash value of the current GPS coordinates of the construction site as the public key component, and use the elliptic curve cryptography (ECC) method to decrypt the encrypted warranty document retrieved from the cloud. At the same time, the smart contract is automatically triggered to call the initial wear of the roller recorded when the warranty was issued. And the rolling parameter set: .
[0037] S32, time-varying wear modeling: Based on the dynamic wear evolution mechanism of the roller, a theoretical wear model is established as follows: ; in, The theoretical wear amount at the current time t, Indicates the initial wear amount recorded when the warranty is issued. The number of rolling operations is the total number of rolling operations from the date of issuance to the present (obtained through blockchain evidence), Indicates a moment The rolling stress value (obtained by inversion of the steel bar gene code), represents the environmental corrosion coefficient, represents the dynamic coefficient of roll wear, The time when the warranty certificate is issued. is the current verification time. In this model, the second It characterizes the cumulative mechanical wear caused by the operating frequency. The third integral term is the exponentially weighted integral of the rolling stress in a corrosive environment, reflecting the nonlinear wear process caused by the accelerated corrosion of the environment.
[0038] S33, attenuation consistency judgment: the measured wear amount is obtained by inverting the rolling grain characteristics in the steel bar gene code , and construct the following environment-adaptive deviation tolerance threshold: ; If present: ; The system determines that the binding relationship between the current steel bar and the warranty is invalid, outputs the "binding invalid" result, and performs a lock operation on the warranty. Indicates the wear amount deduced from the steel bar gene code, represents the adaptive error tolerance threshold, 、 Indicates the upper and lower limits of the system's preset corrosion environment intensity (clean room: 0.05, coastal area: 0.30).
[0039] S34, dynamic tag generation: The determination result is written in encrypted form into the NFC security tag at the end of the steel bar. The written content includes the following four-tuple: Binding status, ; Among them, the binding status: indicates whether to discuss the attenuation consistency verification, Indicated as actual deviation.
[0040] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.
[0041] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.
Claims
1. A steel bar raw material quality assurance data identification system based on image analysis, characterized in that: The system performs the following: S1, rolling grain gene code extraction: The microscopic three-dimensional morphology of the specified area of the steel bar surface is collected by laser interferometer, and the morphology data is compensated and calibrated in combination with the real-time environmental corrosion coefficient to extract the peak and valley distribution characteristics of the rolling grain and encode it to generate the steel bar gene code; S2: Reverse synthesis of the warranty watermark: Generate an encrypted digital watermark based on the rebar genetic code, and reversely search the cloud-based warranty database for an encrypted quality assurance document that matches the digital watermark; S3: Cross-media attenuation verification: Decrypt the encrypted quality assurance document to obtain the original warranty certificate, and simultaneously obtain the historical roller wear data when the original warranty certificate was issued. Compare the roller number recorded in the original warranty certificate with the theoretical attenuation consistency of the wear characteristics of the steel bar gene code in a time-varying environment, and output the cross-media binding result.
2. The steel bar raw material quality assurance data identification system based on image analysis according to claim 1 is characterized in that: Said S1 specifically includes: S11, dynamic area calibration: using a laser interferometer to delineate the detection area on the steel bar surface, avoiding the ineffective rust area; emitting white light interference fringes to collect microscopic 3D topography point cloud data; S12, environmental corrosion compensation: real-time acquisition of environmental corrosion coefficient; compensation calibration of topography data; S13, peak and trough feature extraction: Scan the compensated data along the rolling direction, identify the peak sequence and trough sequence, and calculate the standard deviation of the distance between adjacent peaks and the coefficient of variation of the trough depth; S14, gene code generation: the standard deviation of the distance between adjacent peaks and the coefficient of variation of the trough depth are input into the normalized encoder, and a 256-bit binary steel bar gene code is output. The first 64 bits of the gene code are the roller wear fingerprint.
3. The steel bar raw material quality assurance data identification system based on image analysis according to claim 2, characterized in that: The microscopic three-dimensional point cloud data is expressed as: ; Indicates the The spatial position of a three-dimensional coordinate point, where: is the horizontal position of the point, Indicates the vertical position of the point. Indicates the topography value of a point in the height direction, reflecting the concave and convex changes of the steel bar surface. is the point number, It is a collection symbol.
4. The steel bar raw material quality assurance data identification system based on image analysis according to claim 3 is characterized in that: The environmental corrosion coefficient is expressed as: ;in, is the environmental corrosion coefficient, Represents the empirical coefficient, corresponding to ion concentration and time sensitivity, represents the chloride ion concentration, Indicates the exposure time of steel bars; based on Compensate and calibrate the height value of each point in the point cloud and correct it to: ;in, Indicates the first The height component of each point, Indicates the height value after compensation for corrosion and pollution, is the pollution compensation coefficient, which is used to adjust the sensitivity of the interference of attached dirt. Indicates a point The Laplacian operator of the region is used to estimate the local curvature change to identify non-metallic attachments.
5. The steel bar raw material quality assurance data identification system based on image analysis according to claim 1, characterized in that: The S2 specifically includes: S21, chaotic watermark generation: comprising dividing the rebar gene code generated in S1 into a plurality of bit segments, and using the bit segments as initial state inputs of a Lorenz chaotic system, wherein the Lorenz chaotic system is defined by a set of differential equations; S22, watermark phase modulation: including iteratively generating state variables of the Lorentz chaotic system Perform mapping transformation to generate frequency domain phase perturbation matrix : And inject it into the phase component of the original warranty image in the frequency domain to form a modulated digital watermark image ; S23, quantum secure retrieval: including encoding the phase characteristics of the modulated digital watermark image into a fixed-length hash key , and perform post-quantum encryption fuzzy retrieval based on number theory transformation in the cloud database.
6. The steel bar raw material quality assurance data identification system based on image analysis according to claim 5, characterized in that: The quantum encryption fuzzy retrieval includes similarity determination, and the similarity determination formula is: ;in, represents number theory transformation operations, represents element-wise product, The similarity threshold is set to identify the encrypted warranty document corresponding to the current rebar gene code from the database. Indicates the first The feature vector of the encrypted watermark.
7. The steel bar raw material quality assurance data identification system based on image analysis according to claim 6, characterized in that: The state quantity Perform mapping transformation to generate frequency domain phase perturbation matrix Specifically include: Chaos state value Zoom in 10 times to enhance the perturbation accuracy of its decimal part; Perform a modulo 256 operation on the result, that is, take the remainder after dividing it by 256 to ensure that the disturbance value range is between 0 and 255; normalize the disturbance value to The interval is used to construct the phase offset in angular units, which can be directly added to the frequency domain phase of the image, and the final result is Represents a position in the frequency domain The phase perturbation angle on .
8. The steel bar raw material quality assurance data identification system based on image analysis according to claim 1, characterized in that: The S3 specifically includes: S31, dual-channel decryption: using the first 128 bits of the rebar gene code as the private key component and the site GPS coordinate hash value as the public key component, the original warranty is obtained through elliptic curve encryption and decryption, and the smart contract is simultaneously triggered to retrieve the initial wear of the roller and the rolling parameter set at the time of issuance of the warranty; S32, time-varying wear modeling: Based on the dynamic wear evolution mechanism of the roller, a theoretical wear model is established to calculate the current theoretical wear amount; S33, attenuation consistency judgment: The measured wear amount is parsed from the rebar genetic code and an environmental adaptive deviation threshold is established. If the difference between the measured wear amount and the theoretical wear amount exceeds the environmental adaptive deviation threshold, a "binding invalidation" signal is output and the warranty is locked. S34, dynamic label generation: Encode the verification result into an identification label and write it into the end face of the steel bar.
9. The steel bar raw material quality assurance data identification system based on image analysis according to claim 8, characterized in that: The theoretical wear model is established as follows: ;in, The theoretical wear amount at the current time t, Indicates the initial wear amount recorded when the warranty is issued. The number of rolling operations is the total number of rolling operations from the date of issuance to the present. Indicates a moment The rolling stress value, represents the environmental corrosion coefficient, represents the dynamic coefficient of roll wear, The time when the warranty certificate is issued. The current verification time.
10. The steel bar raw material quality assurance data identification system based on image analysis according to claim 9, characterized in that: The identification tag includes a four-tuple ciphertext of {binding state, theoretical wear amount, measured wear amount, actual deviation amount}.