Embedded label for preventing exchange of concrete test piece and exchange prevention method

Abstract

The invention discloses an embedded label for preventing replacement of a concrete test piece and an anti-replacement method. The method comprises the following steps: S1, embedding labels at different depths in three stages; s2, embedding a third-layer label into a position which is 80-90% deep away from the surface, wherein the third-layer label adopts an embedded electronic chip; s3, embedding a second-layer label at a position 50% deep away from the surface of the test piece, wherein the second-layer label adopts a label based on an optical fiber sensing technology; s4, embedding the first-layer label into the surface layer of the test piece; s5, performing coding processing on each layer of labels; s6, verifying each layer of label by using equipment, and performing analysis and anomaly detection on verification data by adopting an artificial intelligence model based on combination of a support vector machine and a clustering algorithm to obtain a verification result of each layer; and S7, recording the verification result of each layer through the Internet of Things technology and carrying out data tracing. According to the invention, through the multi-layer embedded label and the intelligent algorithm, the anti-exchange safety and the data tracing accuracy of the concrete test piece are improved.

Description

Technical Field

[0001] The invention relates to the technical field of construction engineering, and in particular to an embedded label for preventing concrete specimens from being replaced and an anti-replacement method. Background Art

[0002] In the field of construction engineering, the production, transportation, testing and acceptance of concrete specimens are important links to ensure the quality of construction. Concrete specimens are usually used to measure key indicators such as concrete strength, compression resistance, durability, etc. to evaluate the overall quality of the project. However, as the scale of construction projects continues to expand and the interests involved become increasingly complex, the replacement and tampering of concrete specimens also occur from time to time, which poses a serious hidden danger to the control of construction quality and engineering safety.

[0003] Traditional concrete specimen management methods mainly rely on manual records and single-level identification technologies, such as simple barcodes, QR codes or RFID tags. Although these identification methods can provide basic information about the specimens to a certain extent, their anti-substitution and anti-tampering capabilities are limited. First, barcodes and QR codes are easy to copy and forge, and once the labels on the surface of the specimens are damaged or replaced, their information is difficult to be correctly identified. Secondly, although a single RFID tag has a certain anti-counterfeiting ability, it is also easy to be illegally copied or its internal data tampered with by technical means. These traditional methods are powerless in the specimen management process and are difficult to cope with the current complex engineering environment and strict quality control requirements.

[0004] In addition, the concrete specimen management in the existing technology also faces the problem of lacking a multi-level verification mechanism. In the entire life cycle of the specimen, from production to transportation, to final testing and acceptance, there may be a risk of specimen substitution at each link. However, the traditional specimen management method usually only verifies at a certain fixed link, which makes it possible for criminals to replace specimens in other links, resulting in inaccurate test results and even affecting the safety of the entire project.

[0005] In addition, existing concrete specimen management systems generally lack efficient data traceability and verification systems. Even if some systems can provide basic verification functions, there is still a risk of information being tampered with or lost during the storage, transmission and traceability of data. Especially in complex construction environments, due to the large amount of data and the cumbersome verification process, traditional methods are difficult to achieve full life cycle management of specimens and cannot effectively record and track data changes in each link. This lack of data management makes it difficult to trace back to the specific responsible link when quality problems occur, and thus cannot provide strong evidence support.

[0006] Therefore, how to provide an embedded label and an anti-replacement method for concrete specimens is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0007] One purpose of the present invention is to propose an embedded tag and anti-replacement method for concrete specimens. The present invention adopts multi-layer embedded tag technology combined with an artificial intelligence model of support vector machine and clustering algorithm to achieve all-round anti-replacement management of concrete specimens. By embedding QR codes, fiber optic sensor tags and embedded electronic chips at different depths in the specimens, and performing intelligent analysis and anomaly detection on the data, the present invention significantly improves the anti-counterfeiting ability of the specimens and the accuracy of data traceability. At the same time, the integrated Internet of Things technology ensures the data management of the entire process of the specimen from manufacturing to acceptance, and has the advantages of high security, intelligent detection and full life cycle traceability.

[0008] According to an embodiment of the present invention, an embedded label and an anti-replacement method for concrete specimens include the following steps:

[0009] S1. During the production of concrete specimens, labels are embedded in three stages at different depths, including the first layer of labels embedded in the surface layer, the second layer of labels embedded in the middle layer, and the third layer of labels embedded in the deep layer;

[0010] S2. In the initial stage of concrete pouring, a third layer of labels is embedded at a depth of 80-90% from the surface, wherein the third layer of labels uses an embedded electronic chip;

[0011] S3. When the concrete is poured to the middle of the specimen, a second layer of labels is embedded at a depth of 50% from the surface of the specimen. The second layer of labels is a label based on optical fiber sensing technology.

[0012] S4. When the concrete pouring is nearly completed, a first layer of labels is embedded into the surface of the specimen, wherein the first layer of labels is a QR code or a barcode label;

[0013] S5. Encode each layer of labels, the first layer of labels generates a basic identification code, the second layer of labels stores detailed information and intermediate verification data of the test piece, and the third layer of labels stores the core verification information of the test piece;

[0014] S6. During the manufacturing, transportation, inspection and acceptance of the test piece, the equipment is used to verify the labels of each layer, and the artificial intelligence model based on the combination of support vector machine and clustering algorithm is used to analyze the verification data and detect anomalies to obtain the verification results of each layer;

[0015] S7. Record the verification results of each layer through the Internet of Things technology and conduct data traceability to form a complete traceability chain of the test piece.

[0016] Optionally, the S2 specifically includes:

[0017] S21, selecting an embedded electronic chip, the internal of which integrates a secure data storage module and a multi-layer encryption algorithm, for storing core verification information of the test piece, including a unique identification code, a manufacturing date, and concrete mix ratio information;

[0018] S22. Determine the embedding position of the third layer of labels, the depth from the surface of the specimen is d3:

[0019]

[0020] Where h is the total thickness of the concrete specimen. and χ2 represents the adjustment coefficient, E chip Represents the elastic modulus of the electronic chip, E concrete represents the elastic modulus of concrete, ∈2 and δ2 represent exponents related to material properties, σ concrete Indicates the initial hardening strength of concrete material, σ chip Indicates the initial hardening strength of the chip, T chip Indicates the temperature of the chip, T ambient Indicates the temperature of the environment;

[0021] S23, embed the electronic chip at the initial stage of concrete pouring at time T4:

[0022]

[0023] Among them, T 初期 represents the starting time of concrete pouring, φ2 represents the adjustment coefficient, θ chip represents the angular acceleration of the chip, θ concrete represents the angular acceleration of concrete, ω chip represents the rotation speed of the chip, ω concrete represents the rotation speed of concrete, γ3 represents an index related to impedance, and β3 represents an index related to rotation speed;

[0024] S24. During the electronic chip embedding process, a laser alignment system and an inertial measurement device are used to monitor the positioning of the chip in real time. The angle θ3 is:

[0025]

[0026] Among them, F concrete Indicates the vertical force during concrete pouring, F chip Represents the horizontal force on the electronic chip, μ chip Indicates the friction coefficient of the optical fiber label, μ concrete represents the friction coefficient of concrete;

[0027] S25. After the chip is embedded, a preliminary communication test is performed with an external reader / writer through a wireless transmission device to verify the chip's signal strength and the integrity of data transmission.

[0028] Optionally, the S3 specifically includes:

[0029] S31, selecting a tag based on optical fiber sensing technology, and forming the core sensing part of the tag through a multimode optical fiber array, wherein the tag based on optical fiber sensing technology has the function of monitoring the internal stress and temperature of concrete;

[0030] S32. Determine the embedding position of the second layer of labels, the depth from the surface of the specimen is d2:

[0031]

[0032] Where h is the total thickness of the concrete specimen, P fiber Indicates the compressive strength of the optical fiber label, P concrete represents the compressive strength of concrete, σ concrete Indicates the initial hardening strength of concrete material, σ fiber represents the initial hardening strength of the optical fiber material, α1 and β1 represent material-related indexes, λ1 represents the adjustment coefficient, κ represents the adjustment coefficient related to the ambient temperature, T fiber Indicates the temperature of the optical fiber label, T ambient Indicates the temperature of the environment;

[0033] S33, embed the optical fiber label at time T3 when the concrete is poured to the middle of the specimen:

[0034]

[0035] Among them, T 中间 represents the time point when concrete is poured to the middle position, ω represents the adjustment coefficient, represents the index related to flow velocity, δ represents the index related to sensor response, ν concrete represents the flow rate of concrete, ν fiber represents the sensor response speed of the optical fiber tag, η fiber Indicates the viscosity of the optical fiber label, η concrete represents the viscosity of concrete, τ fiber represents the shear stress of the optical fiber label, τ concrete It represents the shear stress of concrete;

[0036] S34. During the embedding process of the optical fiber label, a vibration or laser ranging device is used to monitor the depth and angle of the label in real time to control the embedding speed and angle θ2:

[0037]

[0038] Among them, F fiber Indicates the axial force on the optical fiber label, F concrete Represents the vertical force during concrete pouring, μ fiber Indicates the friction coefficient of the optical fiber label, μ concrete represents the friction coefficient of concrete;

[0039] S35. After the tag is embedded, a preliminary functional test is performed on the optical fiber tag through a signal transmission device to monitor and record the internal stress and temperature changes of the concrete in real time;

[0040] S36. The concrete pouring continues, and the optical fiber label is completely wrapped inside the concrete.

[0041] Optionally, the S4 specifically includes:

[0042] S41, selecting a QR code or barcode label, generating a unique identification code through an adaptive coding algorithm, and dynamically adjusting it based on the physical parameters of the concrete specimen;

[0043] S42. When the concrete pouring is nearly completed, embed the label at time T1:

[0044]

[0045] Among them, T 初期 represents the starting time of concrete pouring, k represents the adjustment factor related to ambient temperature and humidity, η represents the viscosity of concrete, and μ represents the fluidity of concrete;

[0046] S43, embedding the QR code or barcode label into the concrete at an angle θ:

[0047]

[0048] Among them, F tag Indicates the horizontal force on the label, F concrete Indicates the vertical pressure during concrete pouring;

[0049] S44, fixing the tag using an adjustable fixing device, wherein the adjustable fixing device automatically detaches before the concrete hardens, and the tag maintains communication with an external device via wireless signals during the fixing process, and the displacement and angle of the tag are monitored in real time;

[0050] S45, continue pouring concrete after the tag is fixed, and use the fluid dynamics simulation model to adjust the pouring speed and direction in real time;

[0051] S46. At time T2 after the concrete has initially hardened, the embedded first layer of labels is initially verified:

[0052]

[0053] Among them, T 接近完成 Indicates the time point when concrete pouring is nearly completed, ΔT 硬化 represents the initial setting time of concrete, γ represents the adjustment factor, σ concrete Indicates the initial hardening strength of concrete material, σ tag Indicates the initial hardening strength of the label material.

[0054] Optionally, the S5 specifically includes:

[0055] S51, encode the first layer of labels, wherein the unique identification code C1 of the first layer of labels is generated by a hash function based on a chaotic sequence:

[0056]

[0057] Among them, x1 represents the initial parameters related to the specimen, a1, b1, c1 and d1 represent constants related to concrete mix and environmental conditions, and n1 represents the modulus value to ensure the coding length;

[0058] S52, encoding the second layer of labels, wherein the unique identification code C2 of the second layer of labels is generated through multivariate regression analysis based on the stress and temperature data acquired by the sensor:

[0059]

[0060] Among them, S represents the stress value monitored in real time by the optical fiber sensor, T represents the temperature, and α t , β t and γ t represents the regression coefficient, λ t Indicates the offset;

[0061] S53, encode the third-layer label, wherein the unique identification code C3 of the third-layer label is generated based on the elliptic curve encryption algorithm:

[0062] C3=k3·P3=(x3,y3);

[0063] Among them, k3 represents the private key, P3 represents the base point on the elliptic curve, (x3, y3) represents the encoded coordinate point, and the definition equation of the elliptic curve is:

[0064]

[0065] Where a3 and b3 represent elliptic curve parameters, and p represents the prime modulus;

[0066] S54, logically associate the generated unique identification code C1 of the first layer label, the unique identification code C2 of the second layer label, and the unique identification code C3 of the third layer label, store and verify them in the form of a hash tree based on a Merkle tree structure, and calculate the root hash value H root :

[0067] H root =H4(H2(C1)∥H3(C2)∥H5(C3));

[0068] Among them, H2, H3, H4 and H5 represent different hash functions, and ∥ represents a connection operation;

[0069] S55, the root hash value H root Stored in the third level tag.

[0070] Optionally, the S6 specifically includes:

[0071] S61. During the test piece manufacturing process, use a handheld device to perform preliminary verification of the first layer of labels, scan and read its unique identification code C1:

[0072]

[0073] Among them, H1(x′1) represents the recalculated hash value, x′1 represents the initial parameters extracted from the test piece, and if V1 verification passes, it is considered that the first layer label has not been tampered with;

[0074] S62. After transportation and arrival at the testing site, use a dedicated optical fiber sensing device to verify the second layer label, read the real-time monitored stress and temperature data, and calculate the expected identification code C′2:

[0075]

[0076] Among them, S′ represents the stress value measured on site, T′ represents the temperature measured on site, α t , β t and γ t represents the regression coefficient, λ t Indicates the offset;

[0077] S63. Before testing or accepting the test piece, the identification code C3 of the third-layer label is read by a dedicated encryption device, and the authenticity is verified by using an elliptic curve digital signature algorithm. If the verification is successful, the authenticity of the third-layer label is confirmed;

[0078] S64, normalizing the verification data D1, D2 and D3;

[0079] S65, extracting features from the preprocessed verification data based on a clustering algorithm, and calculating the distance between each data point and the cluster center;

[0080] S66. Use support vector machine to classify feature data, determine whether the data is abnormal, analyze the abnormal data in the classification results, and calculate the abnormal score A. s , if A s If the preset threshold is exceeded, it is determined that the test piece may have been replaced or tampered with, triggering an alarm mechanism;

[0081] S67. After each verification is completed, the verification result is generated by calculating the hash value and stored in the Internet of Things database, and the root hash value of the overall verification result is stored.

[0082] Optionally, the S7 specifically includes:

[0083] S71. Before the specimen solidifies, take a photo of the surface of the test block with the QR code on the label surface exposed on site to obtain a sampling photo, transmit the sampling photo and the shooting information to the platform, and send the test block to the testing unit;

[0084] S72. After receiving the test block, the testing unit shall check whether the anti-substitution label is intact and confirm whether the hollowed-out part of the label has the color characteristics after the water-soluble material is dissolved. If the label is incomplete or the hollowed-out part has no color, the test piece shall be deemed invalid;

[0085] S73. Scan the QR code on the anti-substitution label on the surface of the test block, obtain the sampling information of the test piece from the platform, and automatically compare the sampling photo with the comparison photo taken by the testing unit. If the comparison passes, the test piece is judged to be valid, otherwise the test piece is judged to be invalid.

[0086] At the same time, the present invention proposes an embedded label for preventing concrete specimens from being replaced. The label surface is a polygon with four sides hollowed out, and the bottom has a conical base. The hollowed-out part is filled with a colored water-soluble new material.

[0087] The beneficial effects of the present invention are:

[0088] First, the present invention uses multi-layer embedded label technology to effectively improve the safety and anti-counterfeiting ability of the specimen. By embedding different types of labels such as QR codes, fiber optic sensor labels and embedded electronic chips in the concrete specimen, the present invention achieves all-round protection from the surface to the deep layer. Each layer of label not only has an independent anti-counterfeiting function, but also forms a strict protection system through interrelated coding and data logic. This multi-layer embedded design ensures that even if a layer of labels is damaged or tampered with, the labels on other layers can still provide verification information, thereby greatly increasing the difficulty of replacing the specimen.

[0089] Secondly, the present invention introduces an artificial intelligence model that combines a support vector machine with a clustering algorithm to perform intelligent analysis and anomaly detection on the data generated by the test piece during the manufacturing, transportation, testing and acceptance process. This advanced artificial intelligence algorithm can automatically identify anomalies in the data and accurately locate possible tampering behaviors, significantly improving the accuracy and reliability of data verification. Compared with traditional static comparison methods, the application of artificial intelligence models makes the verification process more dynamic and intelligent, able to adapt to complex and changing engineering environments, and effectively prevent verification errors caused by human factors.

[0090] In addition, the present invention also integrates the Internet of Things technology to build a full life cycle data traceability and management system. By real-time recording and synchronously updating the verification results of each link, a complete data chain of the test piece from production to final acceptance is formed. This full life cycle management model not only improves the transparency of test piece management, but also provides reliable data support for engineering quality control. Once a problem is found in a certain link, the manager can quickly trace it back to the specific responsibility point through the system, thereby improving the efficiency of management and the timeliness of problem solving.

[0091] At the same time, the present invention has also made significant progress in data encryption and security assurance. The hash storage method using the elliptic curve encryption algorithm and the Merkle tree structure ensures the immutability and high security of the data stored in the embedded electronic chip. This encryption technology not only has higher computing efficiency, but also can operate efficiently in resource-constrained environments, ensuring the security of data throughout the life cycle. In addition, through multi-level encryption protection, data can be effectively protected even in harsh construction environments to prevent illegal reading and tampering. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0093] Figure 1 A flowchart of an embedded label and an anti-replacement method for concrete specimens proposed by the present invention;

[0094] Figure 2 A schematic diagram of a multi-layer embedded label structure of a concrete specimen for preventing replacement of an embedded label and an anti-replacement method for a concrete specimen proposed by the present invention;

[0095] Figure 3 The present invention provides an abnormality analysis and detection flow chart of an embedded tag for preventing concrete specimens from being replaced and an anti-replacement method. DETAILED DESCRIPTION

[0096] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0097] refer to Figure 1-3 , an embedded label and anti-replacement method for concrete specimens, comprising the following steps:

[0098] S1. During the production of concrete specimens, labels are embedded in three stages at different depths, including the first layer of labels embedded in the surface layer, the second layer of labels embedded in the middle layer, and the third layer of labels embedded in the deep layer;

[0099] S2. In the initial stage of concrete pouring, a third layer of labels is embedded at a depth of 80-90% from the surface, wherein the third layer of labels uses an embedded electronic chip;

[0100] S3. When the concrete is poured to the middle of the specimen, a second layer of labels is embedded at a depth of 50% from the surface of the specimen. The second layer of labels is a label based on optical fiber sensing technology.

[0101] S4. When the concrete pouring is nearly completed, a first layer of labels is embedded into the surface of the specimen, wherein the first layer of labels is a QR code or a barcode label;

[0102] S5. Encode each layer of labels, the first layer of labels generates a basic identification code, the second layer of labels stores detailed information and intermediate verification data of the test piece, and the third layer of labels stores the core verification information of the test piece;

[0103] S6. During the manufacturing, transportation, inspection and acceptance of the test piece, the equipment is used to verify the labels of each layer, and the artificial intelligence model based on the combination of support vector machine and clustering algorithm is used to analyze the verification data and detect anomalies to obtain the verification results of each layer;

[0104] S7. Record the verification results of each layer through the Internet of Things technology and conduct data traceability to form a complete traceability chain of the test piece.

[0105] In this implementation, S2 specifically includes:

[0106] S21, selecting an embedded electronic chip, the internal of which integrates a secure data storage module and a multi-layer encryption algorithm, for storing core verification information of the test piece, including a unique identification code, a manufacturing date, and concrete mix ratio information;

[0107] S22. Determine the embedding position of the third layer of labels, the depth from the surface of the specimen is d3:

[0108]

[0109] Where h is the total thickness of the concrete specimen. and χ2 represents the adjustment coefficient, E chip Represents the elastic modulus of the electronic chip, E concrete represents the elastic modulus of concrete, ∈2 and δ2 represent exponents related to material properties, σ concrete Indicates the initial hardening strength of concrete material, σ chip Indicates the initial hardening strength of the chip, T chip Indicates the temperature of the chip, T ambient Indicates the temperature of the environment;

[0110] S23, embed the electronic chip at the initial stage of concrete pouring at time T4:

[0111]

[0112] Among them, T 初期 represents the starting time of concrete pouring, φ2 represents the adjustment coefficient, θ chip represents the angular acceleration of the chip, θ concrete represents the angular acceleration of concrete, ω chip represents the rotation speed of the chip, ω concrete represents the rotation speed of concrete, γ3 represents an index related to impedance, and β3 represents an index related to rotation speed;

[0113] S24. During the electronic chip embedding process, a laser alignment system and an inertial measurement device are used to monitor the positioning of the chip in real time. The angle θ3 is:

[0114]

[0115] Among them, F concrete Indicates the vertical force during concrete pouring, F chip Represents the horizontal force on the electronic chip, μ chip Indicates the friction coefficient of the optical fiber label, μ concrete represents the friction coefficient of concrete;

[0116] S25. After the chip is embedded, a preliminary communication test is performed with an external reader / writer through a wireless transmission device to verify the chip's signal strength and the integrity of data transmission.

[0117] In this implementation, S3 specifically includes:

[0118] S31, selecting a tag based on optical fiber sensing technology, and forming the core sensing part of the tag through a multimode optical fiber array, wherein the tag based on optical fiber sensing technology has the function of monitoring the internal stress and temperature of concrete;

[0119] S32. Determine the embedding position of the second layer of labels, the depth from the surface of the specimen is d2:

[0120]

[0121] Where h is the total thickness of the concrete specimen, P fiber Indicates the compressive strength of the optical fiber label, P concrete represents the compressive strength of concrete, σ concrete Indicates the initial hardening strength of concrete material, σ fiber represents the initial hardening strength of the optical fiber material, α1 and β1 represent material-related indexes, λ1 represents the adjustment coefficient, κ represents the adjustment coefficient related to the ambient temperature, T fiber Indicates the temperature of the optical fiber label, T ambient Indicates the temperature of the environment;

[0122] S33, embed the optical fiber label at time T3 when the concrete is poured to the middle of the specimen:

[0123]

[0124] Among them, T 中间 represents the time point when concrete is poured to the middle position, ω represents the adjustment coefficient, represents the index related to flow velocity, δ represents the index related to sensor response, ν concrete represents the flow rate of concrete, ν fiber represents the sensor response speed of the optical fiber tag, η fiber Indicates the viscosity of the optical fiber label, η concrete represents the viscosity of concrete, τ fiber represents the shear stress of the optical fiber label, τ concrete Represents the shear stress of concrete;

[0125] S34. During the embedding process of the optical fiber label, a vibration or laser ranging device is used to monitor the depth and angle of the label in real time to control the embedding speed and angle θ2:

[0126]

[0127] Among them, F fiber Indicates the axial force on the optical fiber label, F concrete Represents the vertical force during concrete pouring, μ fiber Indicates the friction coefficient of the optical fiber label, μ concrete represents the friction coefficient of concrete;

[0128] S35. After the tag is embedded, a preliminary functional test is performed on the optical fiber tag through a signal transmission device to monitor and record the internal stress and temperature changes of the concrete in real time;

[0129] S36. The concrete pouring continues, and the optical fiber label is completely wrapped inside the concrete.

[0130] In this implementation, S4 specifically includes:

[0131] S41, selecting a QR code or barcode label, generating a unique identification code through an adaptive coding algorithm, and dynamically adjusting it based on the physical parameters of the concrete specimen;

[0132] S42. When the concrete pouring is nearly completed, embed the label at time T1:

[0133]

[0134] Among them, T 初期 represents the starting time of concrete pouring, k represents the adjustment factor related to ambient temperature and humidity, η represents the viscosity of concrete, and μ represents the fluidity of concrete;

[0135] S43, embedding the QR code or barcode label into the concrete at an angle θ:

[0136]

[0137] Among them, F tag Indicates the horizontal force on the label, F concrete Indicates the vertical pressure during concrete pouring;

[0138] S44, fixing the tag using an adjustable fixing device, wherein the adjustable fixing device automatically detaches before the concrete hardens, and the tag maintains communication with an external device via wireless signals during the fixing process, and the displacement and angle of the tag are monitored in real time;

[0139] S45, continue pouring concrete after the tag is fixed, and use the fluid dynamics simulation model to adjust the pouring speed and direction in real time;

[0140] S46. At time T2 after the concrete has initially hardened, the embedded first layer of labels is initially verified:

[0141]

[0142] Among them, T 接近完成 Indicates the time point when concrete pouring is nearly completed, ΔT 硬化 represents the initial setting time of concrete, γ represents the adjustment factor, σ concrete Indicates the initial hardening strength of concrete material, σ tag Indicates the initial hardening strength of the label material.

[0143] In this implementation manner, S5 specifically includes:

[0144] S51, encode the first layer of labels, wherein the unique identification code C1 of the first layer of labels is generated by a hash function based on a chaotic sequence:

[0145]

[0146] Among them, x1 represents the initial parameters related to the specimen, a1, b1, c1 and d1 represent constants related to concrete mix and environmental conditions, and n1 represents the modulus value to ensure the coding length;

[0147] S52, encoding the second layer of labels, wherein the unique identification code C2 of the second layer of labels is generated through multivariate regression analysis based on the stress and temperature data acquired by the sensor:

[0148]

[0149] Among them, S represents the stress value monitored in real time by the optical fiber sensor, T represents the temperature, and α t , β t and γ t represents the regression coefficient, λ t Indicates the offset;

[0150] S53, encode the third-layer label, wherein the unique identification code C3 of the third-layer label is generated based on the elliptic curve encryption algorithm:

[0151] C3=k3·P3=(x3,y3);

[0152] Among them, k3 represents the private key, P3 represents the base point on the elliptic curve, (x3, y3) represents the encoded coordinate point, and the definition equation of the elliptic curve is:

[0153]

[0154] Where a3 and b3 represent elliptic curve parameters, and p represents the prime modulus;

[0155] S54, logically associate the generated unique identification code C1 of the first layer label, the unique identification code C2 of the second layer label, and the unique identification code C3 of the third layer label, store and verify them in the form of a hash tree based on a Merkle tree structure, and calculate the root hash value H root :

[0156] H root =H4(H2(C1)∥H3(C2)∥H5(C3));

[0157] Among them, H2, H3, H4 and H5 represent different hash functions, and ∥ represents a connection operation;

[0158] S55, the root hash value Hroot Stored in the third level tag.

[0159] In this implementation manner, S6 specifically includes:

[0160] S61. During the test piece manufacturing process, use a handheld device to perform preliminary verification of the first layer of labels, scan and read its unique identification code C1:

[0161]

[0162] Among them, H1(x′1) represents the recalculated hash value, x′1 represents the initial parameters extracted from the test piece, and if V1 verification passes, it is considered that the first layer label has not been tampered with;

[0163] S62. After transportation and arrival at the testing site, use a dedicated optical fiber sensing device to verify the second layer label, read the real-time monitored stress and temperature data, and calculate the expected identification code C′2:

[0164]

[0165] Among them, S′ represents the stress value measured on site, T′ represents the temperature measured on site, α t , β t and γ t represents the regression coefficient, λ t Indicates the offset;

[0166] S63. Before testing or accepting the test piece, the identification code C3 of the third-layer label is read by a dedicated encryption device, and the authenticity is verified by using an elliptic curve digital signature algorithm. If the verification is successful, the authenticity of the third-layer label is confirmed;

[0167] S64, normalizing the verification data D1, D2 and D3;

[0168] S65, extracting features from the preprocessed verification data based on a clustering algorithm, and calculating the distance between each data point and the cluster center;

[0169] S66. Use support vector machine to classify feature data, determine whether the data is abnormal, analyze the abnormal data in the classification results, and calculate the abnormal score A. s , if A s If the preset threshold is exceeded, it is determined that the test piece may have been replaced or tampered with, triggering an alarm mechanism;

[0170] S67. After each verification is completed, the verification result is generated by calculating the hash value and stored in the Internet of Things database, and the root hash value of the overall verification result is stored.

[0171] In this implementation manner, the S7 specifically includes:

[0172] S71. Before the specimen solidifies, take a photo of the surface of the test block with the QR code on the label surface exposed on site to obtain a sampling photo, transmit the sampling photo and the shooting information to the platform, and send the test block to the testing unit;

[0173] S72. After receiving the test block, the testing unit shall check whether the anti-substitution label is intact and confirm whether the hollowed-out part of the label has the color characteristics after the water-soluble material is dissolved. If the label is incomplete or the hollowed-out part has no color, the test piece shall be deemed invalid;

[0174] S73. Scan the QR code on the anti-substitution label on the surface of the test block, obtain the sampling information of the test piece from the platform, and automatically compare the sampling photo with the comparison photo taken by the testing unit. If the comparison passes, the test piece is judged to be valid, otherwise the test piece is judged to be invalid.

[0175] At the same time, the present invention proposes an embedded label for preventing concrete specimens from being replaced. The label surface is a polygon with four sides hollowed out, and the bottom has a conical base. The hollowed-out part is filled with a colored water-soluble new material.

[0176] Embodiment 1:

[0177] In order to verify the feasibility of the present invention in implementation, the present invention was applied to a large infrastructure project, and the construction party put forward strict requirements on the management of concrete specimens. The project is located in a coastal city, with a construction period from March 2023 to June 2024, involving various types of concrete mixes and specimens of different strength grades. Due to the importance of the project, ensuring the authenticity and anti-replacement of concrete specimens has become a top priority in construction quality control.

[0178] In this project, the construction party adopted the embedded label and anti-replacement method proposed in the present invention. At the beginning of the construction process, the concrete specimens were made according to the method of the present invention, and three different types of labels were embedded in the surface, middle and deep layers of the specimens. The surface layer was embedded with a QR code label for quick identification of the basic information of the specimen; the middle layer was embedded with a label based on optical fiber sensing technology to monitor the stress and temperature changes inside the concrete in real time; the deep layer was embedded with an embedded electronic chip with a high-security data storage module and a multi-layer encryption algorithm to store the core verification information of the specimen.

[0179] At the construction site, the production of concrete specimens is carried out in strict accordance with regulations. In the initial stage of pouring concrete, the construction workers will embed the embedded electronic chip into the deep position of the specimen (80%-90% from the surface). Later, when the concrete is poured to half the depth, the construction workers will embed the label based on fiber optic sensing technology at a depth of 50% from the surface of the specimen to ensure that the label can accurately monitor the physical state inside the concrete. When the pouring is nearly completed, the construction workers will embed the QR code label into the surface of the specimen to ensure that the label will not fail due to surface wear or external environmental influences, providing the highest level of anti-counterfeiting and data storage functions for the concrete specimen.

[0180] The construction environment of this project is complex, including exposure to high temperature and high humidity in the coastal environment, and there are multiple challenges in the preservation and transportation of the test pieces. During a construction phase from May to August 2023, some test pieces need to be transported to a remote laboratory for strength testing under high temperature exposure conditions. During transportation, the construction party verifies each layer of labels multiple times through the verification method of the present invention.

[0181] First, before the specimens leave the construction site, a handheld device is used to scan the QR code label on the surface of the specimens to read and verify their unique identification code. Since the QR code is generated by a chaotic sequence and dynamically adjusted based on the physical parameters of the specimens, the QR code can still be accurately read even in high temperature and mechanical vibration environments. Verification data shows that among 100 specimens, the QR code reading success rate reached 99%, and only one specimen could not be read due to surface damage. For the unreadable specimens, remedial measures were taken in a timely manner to ensure that the label information was repaired before transportation.

[0182] During the transportation of the test pieces, the construction party used a dedicated fiber optic sensor device to monitor the fiber optic sensor tags in the middle layer of the test pieces in real time. The data showed that the maximum stress on the test pieces during transportation was 45MPa, and the temperature reached 75 degrees Celsius. The monitoring data of the fiber optic sensor tags showed that the internal stress and temperature data of all test pieces remained within the normal range during transportation, without abnormal fluctuations. This shows that the test pieces were not subjected to excessive mechanical shock or abnormally high temperature during transportation, further verifying that the test pieces were not replaced.

[0183] After the specimens arrived at the laboratory, the inspectors used a dedicated encryption device to verify the electronic chip embedded deep inside the specimens. Using the elliptic curve digital signature algorithm, the inspectors verified the core data in the chip, including the specimen's manufacturing date, concrete mix information, and unique identification code. The data showed that the verification results of all electronic chips were consistent with the records at the construction site, and no abnormalities were found. This proves that even under complex transportation and storage conditions, electronic chips can still reliably protect data and ensure the authenticity of the specimens.

[0184] In this process, the artificial intelligence model based on support vector machine and clustering algorithm of the present invention played an important role. After the verification is completed, all verification data are input into the artificial intelligence model for comprehensive analysis. After the model normalizes the data, the clustering algorithm is applied to extract features, and the support vector machine is used to classify the data. The anomaly detection results show that among all 100 specimens, only the data of 2 specimens are marked as potential anomalies. The anomaly scores of these 2 specimens are 0.85 and 0.90 (full score is 1.0), respectively, which exceeds the preset alarm threshold of 0.8. Further manual inspection found that the two specimens did experience slight mechanical shock during transportation, but it did not affect their final test results. This analysis result verifies the efficiency and accuracy of the artificial intelligence model in anomaly detection and greatly improves the reliability of specimen management.

[0185] During the entire process, all verification results were recorded in real time through the IoT system and uploaded to the central database, forming a complete traceability chain. Data records show that the average verification time for each test piece is 3 minutes, and all verification data is uploaded within 5 minutes, with no data loss or delay. This efficient full life cycle management method ensures that every test piece in the project can be monitored and managed in real time.

[0186] Table 1 Comparative data of the traditional method and the method of the present invention in the management of concrete specimens

[0187]

[0188]

[0189] From the analysis of Table 1, it can be seen that there are significant differences between the traditional method and the method of the present invention in the management process of concrete specimens. First, the traditional method performs poorly in the reading success rate of the specimen QR code, with a success rate of only 85%, mainly because the QR code is easily affected by the environment, such as surface wear or mechanical vibration. The present invention increases the QR code reading success rate to 99% through an adaptive coding algorithm and a reasonable embedding position design, greatly reducing the reading failure caused by tag damage.

[0190] In terms of stress monitoring of specimens, traditional methods are usually unable to achieve real-time monitoring, resulting in the inability to detect abnormal stress fluctuations and temperature changes during transportation and storage. The present invention achieves real-time monitoring of internal stress and temperature of concrete by embedding tags based on fiber optic sensing technology in the specimens. During transportation, the fiber optic sensing tag successfully monitored a maximum stress of 45MPa and a maximum temperature of 75°C without abnormal fluctuations. This technical advantage ensures the integrity of the specimens in complex environments and the reliability of the data.

[0191] Traditional methods for verifying electronic chip data usually lack effective data support and cannot guarantee the security and integrity of data. The present invention ensures a 100% data verification success rate by embedding deep electronic chips and using elliptic curve encryption algorithms, thus ensuring the non-tamperability and high security of the core information of the test piece.

[0192] In terms of the number of abnormal test pieces detected, the accuracy of traditional methods is low and it is easy to make misjudgments, with the detection rate of abnormal test pieces as high as 20%. In contrast, the present invention combines the artificial intelligence model of support vector machine and clustering algorithm to classify and detect anomalies in verification data, which significantly improves the accuracy and reliability of detection, and only 2% of the test pieces are marked as potential anomalies. This result shows the significant advantages of the present invention in data analysis and anomaly detection, reducing the risk of misjudgment and missed detection.

[0193] In terms of verification efficiency, the average verification time for each test piece in the traditional method is 10 minutes, while the method of the present invention shortens the verification time to 3 minutes, and the upload time of the verification data is also optimized from the delayed upload of the traditional method to within 5 minutes. This not only improves the efficiency of the verification work, but also ensures the real-time and accuracy of the data.

[0194] Data integrity is also a key issue. The traditional method has a high data loss rate, with about 5% of the data lost during transmission or storage, which poses a great challenge to the transparency and traceability of specimen management. The present invention integrates Internet of Things technology to ensure real-time recording and uploading of verification data, without data loss throughout the process, and realizes the full life cycle traceability of specimen management.

[0195] In summary, the present invention is significantly superior to traditional methods in many aspects. By improving the success rate of QR code reading, achieving real-time stress monitoring, ensuring data security, accurately detecting abnormal specimens, improving verification efficiency, and ensuring data integrity, the present invention provides a safer, more reliable, and more efficient solution for the management of concrete specimens. This innovative method not only improves the quality control level of specimen management, but also provides a solid guarantee for the overall safety of the project.

[0196] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An embedded label and anti-replacement method for concrete specimens, characterized in that: The steps include: S1. During the production of concrete specimens, labels are embedded in three stages at different depths, including the first layer of labels embedded in the surface layer, the second layer of labels embedded in the middle layer, and the third layer of labels embedded in the deep layer; S2. In the initial stage of concrete pouring, a third layer of labels is embedded at a depth of 80-90% from the surface, wherein the third layer of labels uses an embedded electronic chip; S3. When the concrete is poured to the middle of the specimen, a second layer of labels is embedded at a depth of 50% from the surface of the specimen. The second layer of labels is a label based on optical fiber sensing technology. S4. When the concrete pouring is nearly completed, a first layer of labels is embedded into the surface of the specimen, wherein the first layer of labels is a QR code or a barcode label; S5. Encode each layer of labels, the first layer of labels generates a basic identification code, the second layer of labels stores detailed information and intermediate verification data of the test piece, and the third layer of labels stores the core verification information of the test piece; S6. During the manufacturing, transportation, inspection and acceptance of the test piece, the equipment is used to verify the labels of each layer, and the artificial intelligence model based on the combination of support vector machine and clustering algorithm is used to analyze the verification data and detect anomalies to obtain the verification results of each layer; S7. Record the verification results of each layer through the Internet of Things technology and conduct data traceability to form a complete traceability chain of the test piece.

2. The embedded label and anti-replacement method for concrete specimens according to claim 1, characterized in that: The S2 specifically includes: S21, selecting an embedded electronic chip, the internal of which integrates a secure data storage module and a multi-layer encryption algorithm, for storing core verification information of the test piece, including a unique identification code, a manufacturing date, and concrete mix ratio information; S22. Determine the embedding position of the third layer of labels, the depth from the surface of the specimen is d3: Where h is the total thickness of the concrete specimen, ζ2 and χ2 are adjustment coefficients, and E chip Represents the elastic modulus of the electronic chip, E concrete represents the elastic modulus of concrete, ∈2 and δ2 represent exponents related to material properties, σ concrete Indicates the initial hardening strength of concrete material, σ chip Indicates the initial hardening strength of the chip, T chip Indicates the temperature of the chip, T ambient Indicates the temperature of the environment; S23, embed the electronic chip at the initial stage of concrete pouring at time T4: Among them, T 初期 represents the starting time of concrete pouring, φ2 represents the adjustment coefficient, θ chip represents the angular acceleration of the chip, θ concrete represents the angular acceleration of concrete, ω chip represents the rotation speed of the chip, ω concrete represents the rotation speed of concrete, γ3 represents an index related to impedance, and β3 represents an index related to rotation speed; S24. During the electronic chip embedding process, a laser alignment system and an inertial measurement device are used to monitor the positioning of the chip in real time. The angle θ3 is: Among them, F concrete Indicates the vertical force during concrete pouring, F chip Represents the horizontal force on the electronic chip, μ chip Indicates the friction coefficient of the optical fiber label, μ concrete represents the friction coefficient of concrete; S25. After the chip is embedded, a preliminary communication test is performed with an external reader / writer through a wireless transmission device to verify the chip's signal strength and the integrity of data transmission.

3. The embedded label and anti-replacement method for concrete specimens according to claim 1, characterized in that: The S3 specifically includes: S31, selecting a tag based on optical fiber sensing technology, and forming the core sensing part of the tag through a multimode optical fiber array, wherein the tag based on optical fiber sensing technology has the function of monitoring the internal stress and temperature of concrete; S32. Determine the embedding position of the second layer of labels, the depth from the surface of the specimen is d2: Where h is the total thickness of the concrete specimen, P fiber Indicates the compressive strength of the optical fiber label, P concrete represents the compressive strength of concrete, σ concrete Indicates the initial hardening strength of concrete material, σ fiber represents the initial hardening strength of the optical fiber material, α1 and β1 represent material-related indexes, λ1 represents the adjustment coefficient, κ represents the adjustment coefficient related to the ambient temperature, T fiber Indicates the temperature of the optical fiber label, T ambient Indicates the temperature of the environment; S33, embed the optical fiber label at time T3 when the concrete is poured to the middle of the specimen: Among them, T 中间 represents the time point when concrete is poured to the middle position, ω represents the adjustment coefficient, ζ represents the index related to flow velocity, δ represents the index related to sensor response, ν concrete represents the flow rate of concrete, ν fiber represents the sensor response speed of the optical fiber tag, η fiber Indicates the viscosity of the optical fiber label, η concrete represents the viscosity of concrete, τ fiber represents the shear stress of the optical fiber label, τ concrete Represents the shear stress of concrete; S34. During the embedding process of the optical fiber label, a vibration or laser ranging device is used to monitor the depth and angle of the label in real time to control the embedding speed and angle θ2: Among them, F fiber Indicates the axial force on the optical fiber label, F concrete Represents the vertical force during concrete pouring, μ fiber Indicates the friction coefficient of the optical fiber label, μ concrete represents the friction coefficient of concrete; S35. After the tag is embedded, a preliminary functional test is performed on the optical fiber tag through a signal transmission device to monitor and record the internal stress and temperature changes of the concrete in real time; S36. The concrete pouring continues, and the optical fiber label is completely wrapped inside the concrete.

4. The embedded label and anti-replacement method for concrete specimens according to claim 1, characterized in that: The S4 specifically includes: S41, selecting a QR code or barcode label, generating a unique identification code through an adaptive coding algorithm, and dynamically adjusting it based on the physical parameters of the concrete specimen; S42. When the concrete pouring is nearly completed, embed the label at time T1: Among them, T 初期 represents the starting time of concrete pouring, k represents the adjustment factor related to ambient temperature and humidity, η represents the viscosity of concrete, and μ represents the fluidity of concrete; S43, embedding the QR code or barcode label into the concrete at an angle θ: Among them, F tag Indicates the horizontal force on the label, F concrete Indicates the vertical pressure during concrete pouring; S44, fixing the tag using an adjustable fixing device, wherein the adjustable fixing device automatically detaches before the concrete hardens, and the tag maintains communication with an external device via wireless signals during the fixing process, and the displacement and angle of the tag are monitored in real time; S45, continue pouring concrete after the tag is fixed, and use the fluid dynamics simulation model to adjust the pouring speed and direction in real time; S46. At time T2 after the concrete has initially hardened, the embedded first layer of labels is initially verified: Among them, T 接近完成 Indicates the time point when concrete pouring is nearly completed, ΔT 硬化 represents the initial setting time of concrete, γ represents the adjustment factor, σ concrete Indicates the initial hardening strength of concrete material, σ tag Indicates the initial hardening strength of the label material.

5. The embedded label and anti-replacement method for concrete specimens according to claim 1, characterized in that: The S5 specifically includes: S51, encode the first layer of labels, wherein the unique identification code C1 of the first layer of labels is generated by a hash function based on a chaotic sequence: Among them, x1 represents the initial parameters related to the specimen, a1, b1, c1 and d1 represent constants related to concrete mix and environmental conditions, and n1 represents the modulus value to ensure the coding length; S52, encoding the second layer of labels, wherein the unique identification code C2 of the second layer of labels is generated through multivariate regression analysis based on the stress and temperature data acquired by the sensor: Among them, S represents the stress value monitored in real time by the optical fiber sensor, T represents the temperature, and α t , β t and γ t represents the regression coefficient, λ t Indicates the offset; S53, encode the third-layer label, wherein the unique identification code C3 of the third-layer label is generated based on the elliptic curve encryption algorithm: C3=k3·P3=(x3,y3); Among them, k3 represents the private key, P3 represents the base point on the elliptic curve, (x3, y3) represents the encoded coordinate point, and the definition equation of the elliptic curve is: Where a3 and b3 represent elliptic curve parameters, and p represents the prime modulus; S54, logically associate the generated unique identification code C1 of the first layer label, the unique identification code C2 of the second layer label, and the unique identification code C3 of the third layer label, store and verify them in the form of a hash tree based on a Merkle tree structure, and calculate the root hash value H root : H root =H4(H2(C1)∥H3(C2)∥H5(C3)); Among them, H2, H3, H4 and H5 represent different hash functions, and ∥ represents a connection operation; S55, the root hash value H root Stored in the third level tag.

6. The embedded label and anti-replacement method for concrete specimens according to claim 1, characterized in that: The S6 specifically includes: S61. During the test piece manufacturing process, use a handheld device to perform preliminary verification of the first layer of labels, scan and read its unique identification code C1: Among them, H1(x′1) represents the recalculated hash value, x′1 represents the initial parameters extracted from the test piece, and if V1 verification passes, it is considered that the first layer label has not been tampered with; S62. After transportation and arrival at the testing site, use a dedicated optical fiber sensing device to verify the second layer label, read the real-time monitored stress and temperature data, and calculate the expected identification code C′2: Among them, S′ represents the stress value measured on site, T′ represents the temperature measured on site, α t , β t and γ t represents the regression coefficient, λ t Indicates the offset; S63. Before testing or accepting the test piece, the identification code C3 of the third-layer label is read by a dedicated encryption device, and the authenticity is verified by using an elliptic curve digital signature algorithm. If the verification is successful, the authenticity of the third-layer label is confirmed; S64, normalizing the verification data D1, D2 and D3; S65, extracting features from the preprocessed verification data based on a clustering algorithm, and calculating the distance between each data point and the cluster center; S66. Use support vector machine to classify feature data, determine whether the data is abnormal, analyze the abnormal data in the classification results, and calculate the abnormal score A. s , if A s If the preset threshold is exceeded, it is determined that the test piece may have been replaced or tampered with, triggering an alarm mechanism; S67. After each verification is completed, the verification result is generated by calculating the hash value and stored in the Internet of Things database, and the root hash value of the overall verification result is stored.

7. The embedded label and anti-replacement method for concrete specimens according to claim 1, characterized in that: The S7 specifically includes: S71. Before the specimen solidifies, take a photo of the surface of the test block with the QR code on the label surface exposed on site to obtain a sampling photo, transmit the sampling photo and the shooting information to the platform, and send the test block to the testing unit; S72. After receiving the test block, the testing unit shall check whether the anti-substitution label is intact and confirm whether the hollowed-out part of the label has the color characteristics after the water-soluble material is dissolved. If the label is incomplete or the hollowed-out part has no color, the test piece shall be deemed invalid; S73. Scan the QR code on the anti-substitution label on the surface of the test block, obtain the sampling information of the test piece from the platform, and automatically compare the sampling photo with the comparison photo taken by the testing unit. If the comparison passes, the test piece is judged to be valid, otherwise the test piece is judged to be invalid.

8. An embedded label for preventing concrete specimens from being replaced, characterized in that: The surface of the label is a polygon with four hollowed-out sides and a conical base at the bottom. The hollowed-out part is filled with a colored new water-soluble material.

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