Physical anti-counterfeiting method based on magnetic field atlas
By doping microparticles into the material to generate a unique magnetic field map and combining it with blockchain evidence storage, the high cost and narrow application problems of existing anti-counterfeiting technologies are solved, and the trusted binding and anti-counterfeiting traceability of physical entities and digital identities are achieved.
Patent Information
- Application Number
- CN202510746862.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-05
- Publication Date
- 2025-09-26
AI Technical Summary
Existing anti-counterfeiting technologies have the disadvantages of high implementation costs, high complexity, narrow application scope and susceptibility to counterfeiting methods, making it difficult to effectively cover diverse entities.
By doping detectable particles into the creation or manufacturing materials, we can obtain three-dimensional spatial distribution characteristics, generate unique and tamper-resistant digital fingerprints, and combine them with blockchain for evidence storage to achieve the binding of physical entities and digital identities.
It realizes the trusted association between physical entities and digital identities, and has uniqueness, stability and fault tolerance. It is applicable to a variety of materials and detection technologies, and is suitable for anti-counterfeiting traceability and ownership confirmation of diverse entities such as artworks, high-end consumer products, and important documents.
Smart Images
Figure CN120707152A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of anti-counterfeiting technology, and in particular to a physical anti-counterfeiting method based on magnetic field maps. Background Art
[0002] Anti-counterfeiting technology refers to measures taken to prevent counterfeiting, making it easier for users to distinguish authenticity. It accurately identifies authenticity within a certain range and is difficult to copy or replicate. Simply put, it prevents counterfeiting and imitation. It's a preventative measure taken to protect corporate brands, markets, and the legitimate rights and interests of consumers.
[0003] However, existing anti-counterfeiting technologies generally have the following limitations: first, limited scalability, which is manifested in high implementation costs, complex system integration, and strict requirements for specific materials or production processes; second, a narrow scope of application, making it difficult to effectively cover diverse entities such as artworks, high-end consumer goods, and important documents, and vulnerable to increasingly sophisticated counterfeiting methods. Summary of the Invention
[0004] The purpose of the present invention is to provide a physical anti-counterfeiting method based on magnetic field maps to solve the technical problem of how to bind a physical entity with a digital identity and thereby achieve physical anti-counterfeiting.
[0005] The present invention is implemented by adopting the following technical solution: a physical anti-counterfeiting method based on magnetic field spectrum, comprising the following steps: S1: Doping the material being created or processed with particles having detectable properties; S2: Data collection on works or products doped with detectable particles to obtain original detection data reflecting the three-dimensional spatial distribution characteristics of the particles; S3: Process the collected raw detection data to extract and compress it to generate a digital fingerprint that is unique, fault-tolerant, and tamper-resistant; S4: Upload the digital information of the digital fingerprint to the blockchain to form the original data; S5: Collect data on the work or product to be tested, and compare the similarity with the original data to determine the authenticity of the work or product to be tested.
[0006] Furthermore, the particles need to meet the following requirements: Microscopic size, can be evenly or unevenly mixed into the creative material without changing the material's physical properties, appearance or the feel of the creation; Long-lasting stability, which can maintain the relative stability of shape and distribution position for a long time under different creative environments and preservation conditions; Doping controllability, which can achieve the desired doping concentration and distribution; Non-destructive, non-contact or minimally invasive detection can be performed through external equipment, and the detection process does not cause significant damage to the work itself.
[0007] Furthermore, the data collection uses high-precision magnetic field detection instruments to scan a preset sampling area of the work or product or perform precise measurements at selected points. At each collection point, the magnetic field information of that point is recorded, including the direction and intensity of the magnetic field; wherein the magnetic field detection instrument includes a magnetometer, a fluxgate sensor array, or a superconducting quantum interference device (SQUID).
[0008] Furthermore, the data collection uses a universal mobile device with an integrated magnetic sensor, which, in conjunction with a corresponding magnetic field measurement application, can achieve preliminary detection of the surface or near-field magnetic field distribution of the work or product; wherein, the universal mobile device includes a smartphone or tablet computer.
[0009] Furthermore, step S3 includes the following sub-steps: S31: Feature extraction, extracting features from the original collected data that are insensitive to external interference and can effectively characterize the physical fingerprint; S32: Data standardization: unifying and quantizing the numerical domain of the feature-extracted data to reduce the impact of noise; S33: Order-independent processing, reordering all data points after normalization and concatenating the sorted data into a unified feature vector; S34: Digital fingerprint generation, performing fuzzy hash calculation on the feature vector to generate the final digital fingerprint representing the physical fingerprint of the work.
[0010] Furthermore, step S32 includes the following sub-steps: S321: Normalization processing, independently scaling the feature data of each dimension to ensure that the numerical range and distribution of data in different dimensions are comparable; S322: Discretization processing, numerically quantizing the normalized data, mapping continuous or large-range values to a finite, discrete set of values to enhance the data's tolerance to slight changes.
[0011] Furthermore, fuzzy hashing can be performed using the SimHash algorithm, or other fuzzy hashing algorithms with similar properties. Unlike traditional exact hashing, the key to fuzzy hashing is its insensitivity to small changes in the input. The similarity between the output hash values (for example, measured by Hamming distance) can reflect the similarity between the original data. This means that the hash distance of a digital fingerprint generated from slightly different physical fingerprint data will fall within a preset tolerance, thus supporting similarity-based verification.
[0012] Furthermore, the digital information includes a digital summary of the work or product (e.g., a SimHash value obtained through fuzzy hashing); basic metadata, and key sampling and processing strategies. Basic metadata includes essential information used to identify and describe the work, including one or more of the work's title, author identification, and completion date. To ensure the repeatability and accuracy of subsequent verification processes, the key information relied upon to generate the digital fingerprint should also be documented. This includes, but is not limited to, the sampling strategy used for data collection (e.g., designated sampling areas, quantity, and general layout, the type of primary detection equipment used, or the range of technical parameters), as well as key data processing parameters or version information used to generate the digital fingerprint. This information provides essential guidance for independent third-party verification.
[0013] Furthermore, step S5 is specifically as follows: data collection and processing are performed on the work or product to be tested, and its current digital fingerprint is generated according to the parameters and rules for generating the original fingerprint stored on the blockchain; then, the original digital fingerprint and related verification parameters of the work or product stored on the blockchain are obtained; finally, the authenticity of the work is determined by comparing the similarity between the currently generated digital fingerprint and the original digital fingerprint on the chain.
[0014] The beneficial effects of the present invention are: This invention utilizes the random three-dimensional spatial distribution of tiny particles with specific detectable properties, incorporated into various creative or manufactured materials, to create a unique, unreplicable "physical fingerprint" for an item. Through specific non-destructive detection methods (e.g., magnetic field mapping of magnetic particles, optical scanning, X-ray imaging, etc.) and robust data processing, a unique, stable, and fault-tolerant digital summary (i.e., a digital physical fingerprint) is generated. Furthermore, by integrating distributed ledger technologies such as blockchain, this digital physical fingerprint is linked to the item's basic information and stored as trusted evidence on-chain, thus achieving a strong link between the item's uniqueness and trusted verification in the physical and digital worlds.
[0015] The technology of this invention provides a practical material-level identification and trusted verification method to address the core issue of "trusted association between physical entities and digital identities." This solution benefits from the following technical advantages: the inherent uniqueness of the physical layer generated by the random distribution of particles ensures the natural anti-counterfeiting properties of physical fingerprints; the integration of fingerprint formation into the creation or manufacturing process realizes a mechanism where "creation / manufacturing is the granting of physical identity"; on-chain evidence storage ensures the immutability and traceability of digital fingerprints and related information; the solution design has a certain degree of local fault tolerance, which can cope with the impact of data collection or minor damage to objects; and the technology is highly scalable and applicable to a variety of material carriers and different detection technologies.
[0016] Based on the above technical advantages, the present invention has broad application prospects in many fields, such as: Artwork field: used for anti-counterfeiting, traceability and ownership confirmation of various types of artworks such as oil paintings, calligraphy, ceramics, sculptures, etc., to solve the problem of counterfeit products in the art market.
[0017] High-end consumer goods: Provide highly secure anti-counterfeiting labels for famous liquors, cosmetics, luxury goods, cultural and creative products, etc., to achieve full life cycle traceability of products.
[0018] Important documents and bills: Applicable to senior certificates, graduation certificates, ID cards, various bills, documents, etc., to prevent forgery and illegal copying, and ensure their authenticity and authority.
[0019] Traceable packaging and logistics: Embed physical fingerprints on logistics packaging materials or product packaging to achieve unique identification of individual items and reliable traceability in the supply chain.
[0020] Association between digital assets and physical objects: As the technical basis for connecting physical entities (such as artworks, collectibles, limited edition goods, etc.) with digital assets (for example, non-fungible tokens NFT), it provides a reliable physical fingerprint anchor to achieve mutual verification of physical ownership and digital certificates.
[0021] Other scenarios requiring unique identification and trusted verification of physical entities: This invention has potential application value in any field where a physical object needs to be given a unique digital identity that is closely associated with its entity and difficult to forge, and trusted verification is required. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.
[0023] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION
[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.
[0025] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.
[0026] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.
[0027] See also Figure 1 , a physical anti-counterfeiting method based on magnetic field pattern, comprising the following steps: S1: Doping the material being created or processed with particles having detectable properties; S2: Data collection on works or products doped with detectable particles to obtain original detection data reflecting the three-dimensional spatial distribution characteristics of the particles; S3: Process the collected raw detection data to extract and compress it to generate a digital fingerprint that is unique, fault-tolerant, and tamper-resistant; S4: Upload the digital information of the digital fingerprint to the blockchain to form the original data; S5: Collect data on the work or product to be tested, and compare the similarity with the original data to determine the authenticity of the work or product to be tested.
[0028] Step S1 specifically involves doping various materials suitable for artistic creation or production with an appropriate amount of tiny particles with specific detectable properties. During the subsequent creative or processing process (e.g., writing, painting, sculpting, high-temperature firing, etc.), these particles naturally form a highly random yet fixed three-dimensional spatial distribution structure within the material due to the material's flow, deformation, and solidification. This unique particle distribution, naturally formed during the creative process and difficult to precisely replicate or imitate, constitutes the work's unique "physical fingerprint" at the material level. The formation of this "physical fingerprint" is closely integrated with the creative process, achieving the effect of "creating is conferring physical identity."
[0029] The selected particle material should meet the following basic requirements: Microscopic size: Particle size should be small enough (e.g., nanometer or micrometer) to be incorporated uniformly or non-uniformly into commonly used creative materials without significantly changing the physical properties, appearance, or feel of the creative work.
[0030] Long-term stability: Particles should have good physical and chemical stability and be able to maintain their relative stability in shape and distribution position for a long time under different creative environments and preservation conditions (for example, temperature, humidity, light, chemical erosion, etc.).
[0031] Doping controllability: Particles should be able to be doped into the material in a controllable manner to achieve the desired doping concentration and distribution (e.g., uniform doping overall, or non-uniform doping in specific areas).
[0032] Non-destructive detection: The particles should possess properties that allow for non-contact or minimally destructive detection by external equipment (e.g., magnetic, optical, electrical, radioactive, etc.), and the detection process should not cause significant damage to the work itself.
[0033] As a non-limiting example of the present invention, this solution can use magnetic nanoparticles as particles to achieve the detectable properties. For example, Fe3O4 magnetic nanoparticles (particle size is usually in the range of 10-20 nm) are cost-effective superparamagnetic particles with relatively stable physical and chemical properties, which are very suitable for use as detection particles in the present invention. Fe3O4 nanoparticles have flexible applications and can be added to substrates such as clay, gypsum, concrete, pulp or 3D printing consumables in the form of dry powder to make sculptures, building components, paper products or objects of complex shapes; they can also be added to fluid media such as ink, pigment, paint, glue, etc. in the form of liquid suspension or dispersion after appropriate dispersion treatment, and are suitable for application scenarios such as calligraphy, painting, signing, printing or bonding.
[0034] In the actual process of particle doping and material application, the randomness and unevenness of particle distribution can be intentionally enhanced by controlling the doping concentration and distribution of particles (for example, by employing different doping processes or using materials doped with different concentrations of particles in different parts of the work). This unique spatial distribution of particles, through their specific detectable properties (for example, magnetism), produces a unique spatial field pattern (for example, a magnetic field pattern) under the influence of an external field. This pattern is a macroscopic manifestation of the three-dimensional distribution of particles, is highly unique and difficult to replicate, and can serve as a reliable basis for generating digital physical fingerprints.
[0035] Step 2 involves collecting data on the work or product doped with detectable particles to obtain raw detection data reflecting the three-dimensional spatial distribution characteristics of the particles. The specific collection method depends on the type of detectable particles used and the corresponding detection technology.
[0036] As a preferred embodiment of the present invention, when magnetic particles are used as detectable particles, data collection is intended to obtain magnetic field distribution data within a specific area of the work or product. This process can be performed using any one of the following methods or a combination of methods: Use professional detection equipment: Utilize high-precision professional magnetic field detection instruments (e.g., magnetometers, fluxgate sensor arrays, superconducting quantum interference devices (SQUIDs), etc.) to systematically scan the preset sampling area of the work or product or perform precise measurements at selected points. At each collection point, record the magnetic field information at that point, including the direction and intensity of the magnetic field, which corresponds to a four-dimensional vector (X, Y, Z, M). Among them, X / Y / Z represent the components of the magnetic field intensity on the three axes of the spatial orthogonal coordinate system, and M is the total magnetic field intensity at that point: .
[0037] Using common mobile devices and external sensors: For scenarios where accuracy requirements are relatively low or where convenient verification is crucial, common mobile devices with integrated magnetic sensors (such as smartphones and tablets) can be used. By pairing these with appropriate magnetic field measurement applications, preliminary detection of the magnetic field distribution on the surface or near-field of the work can be achieved. To improve the resolution and accuracy of the collected data, external professional magnetic field detection modules or high-precision magnetic sensors can be connected to the common mobile device via wired or wireless communication (e.g., Bluetooth, Wi-Fi, USB, etc.), leveraging the computing and storage capabilities of the mobile device to complete data collection and preliminary processing.
[0038] Through the above data collection process, a raw data set describing the magnetic field distribution in a specific area of a work or product can be obtained. This data set reflects the three-dimensional spatial arrangement of the doped magnetic particles, laying the foundation for subsequent physical fingerprint feature extraction and comparison. For embodiments using other types of detectable particles, corresponding non-destructive detection techniques (e.g., optical scanning, X-ray imaging, ultrasonic detection, etc.) are used to obtain raw data reflecting the particle distribution.
[0039] Step S3 specifically processes the collected raw detection data, aiming to extract and compress it to generate a unique, fault-tolerant, and tamper-resistant digital summary, namely the digital fingerprint of the work. Data processing is a key technical link in this invention. Its core goal is to ensure that the digital fingerprint accurately represents the unique characteristics of the physical fingerprint of the work (strong anti-counterfeiting capabilities) while effectively addressing uncertainties in the data collection process (for example, environmental interference, equipment differences, slight changes in sampling angle and position, etc.) and slight changes in the work itself (for example, local wear), thereby significantly improving the stability and robustness of the subsequent verification process. This step typically includes one or more of the following sub-steps: Feature Extraction: This substep aims to extract features from the raw data that are insensitive to external interference and can effectively represent the physical fingerprint. For example, to eliminate bias in magnetic field vector measurements caused by changes in detection direction, methods such as principal component analysis (PCA) can be used to comprehensively analyze the three-dimensional magnetic field component vectors at all sampling points. This identifies and establishes a standardized coordinate system consistent with the global principal directions. All magnetic field vectors are then projected into this coordinate system to obtain a directionally standardized feature representation.
[0040] Data normalization: This substep normalizes and quantizes the numerical domain of feature-extracted (or raw) data to reduce the impact of noise and establish a solid foundation for subsequent processing (especially fuzzy hashing). This process typically includes: Normalization: Independently scale the feature data of each dimension, for example, using Min-Max or other methods to map the values to a preset standard interval (such as [-1, 1]) to ensure that the numerical range and distribution of data in different dimensions are comparable. Discretization: Quantize the normalized data, mapping continuous or wide-ranging values to a finite, discrete set of values (for example, to integer values in the range [0, 255]) to enhance the data's tolerance to small changes.
[0041] Order-independent processing: To ensure that the final digital fingerprint is not affected by the order in which data is collected, all normalized data points can be reordered, for example, by the magnitude of one or more dimensions of each point (ignoring the sign), and the sorted data are then concatenated into a unified feature vector. This processing makes the solution robust to the order in which data is collected.
[0042] Digital Fingerprint Generation: Finally, a fuzzy hash calculation is performed on the preprocessed feature vectors to generate a final digital summary representing the physical fingerprint of the work. The present invention preferably utilizes the SimHash algorithm or other fuzzy hash algorithms with similar properties. Unlike traditional exact hashing, the key to fuzzy hashing is its insensitivity to minor changes in the input. The similarity between the output hash values (for example, measured by Hamming distance) can reflect the similarity between the original data. This means that the hash distance of digital fingerprints generated from slightly different physical fingerprint collection data will fall within a preset tolerance, thus supporting similarity-based verification.
[0043] In one embodiment, a SimHash algorithm can be used to calculate, for example, a 256-bit digital fingerprint. To enhance security and attack resistance, the random seed used to construct the SimHash projection matrix can be generated based on the work's unique identifier (e.g., a hash value of information such as the work's title, author, and creation date). This approach links the work's information with the fingerprint generation process, facilitating public verification. By also introducing work-related randomness, it increases the difficulty for malicious attackers to construct similar physical fingerprints to generate near-hash collisions.
[0044] Step S4 specifically involves uploading the key digital information representing the physical fingerprint of the work to the blockchain to achieve secure, reliable, and tamper-proof on-chain evidence. By leveraging the core characteristics of blockchain technology, such as decentralization, openness and transparency, data immutability, and timestamp proof, the present invention establishes a unique and strong binding relationship between the physical individual of the work and the digital identity on the chain, providing a solid foundation for subsequent public verification. At the same time, this solution chooses not to store the original detailed detection data (for example, the complete magnetic field map data file) on the chain, thereby effectively protecting the privacy and security of the relevant information. Specifically, the following core information is anchored on the blockchain as part of the transaction record or through smart contracts, etc.: Digital fingerprint of the work: the unique digital summary representing the physical fingerprint of the work generated by the aforementioned data processing steps (for example, the SimHash value obtained through fuzzy hash calculation).
[0045] Basic metadata of works: includes basic information used to identify and describe works, such as the work title, author identification (e.g., email or public key), creation completion date, etc. These metadata help establish the identity information of the work on the chain.
[0046] Key Sampling and Processing Strategies: To ensure the repeatability and accuracy of subsequent verification processes, the key information relied upon to generate the digital fingerprint should be documented. This includes, but is not limited to, the sampling strategy used for data collection (e.g., designated sampling areas, quantity, and general layout, primary detection equipment types or technical parameter ranges used), as well as key data processing parameters or version information used to generate the digital fingerprint. This information provides essential guidance for independent third-party verification.
[0047] Given that some sampling or processing strategies may contain extensive details (for example, complex scan path diagrams, detailed parameter configuration files, or video operating instructions), digital summaries of these detailed files (for example, hash values calculated using standard cryptographic hash algorithms such as SHA-256) can be directly stored on-chain. Simultaneously, the original detailed files are stored in a publicly accessible, secure, and reliable external storage system. By comparing the hash values stored on-chain with those of the externally stored files, the integrity and integrity of the detailed files can be verified.
[0048] Step S5 specifically involves verification and comparison. This solution allows any third party who legally possesses the work or product being tested to verify its authenticity. The verification process primarily involves collecting and processing data about the work or product being tested, generating its current digital fingerprint based on the parameters and rules for generating the original fingerprint stored on the blockchain; then, obtaining the work's original digital fingerprint stored on the blockchain and related verification parameters (e.g., recommended fault tolerance thresholds); and finally, determining the work's authenticity by comparing the currently generated digital fingerprint with the original digital fingerprint on the blockchain.
[0049] Specifically, the verifier collects data on the work to be tested based on the sampling strategy and processing rules of the on-chain evidence, performs corresponding data processing, and calculates the digital fingerprint SimHash(F*) of the work to be tested. The verifier then obtains the original digital fingerprint SimHash(F) and the fault tolerance threshold k from the blockchain. By calculating the similarity between the two fingerprints (for example, the Hamming distance) and comparing it with the threshold k, the verifier determines whether the work has passed verification. If the Hamming distance is less than or equal to k, the work is considered to have passed verification; otherwise, the work is considered to have failed verification: Hamming(SimHash(F*), SimHash(F)) ≤ k.
[0050] The above embodiments mainly record technical details such as Fe3O4 magnetic nanoparticles, specific data acquisition methods, robust algorithms (such as PCA, standardization, order-independent processing, fuzzy hashing) and related parameter settings, and are intended to provide a specific, feasible and easy-to-understand embodiment of the present invention. However, it should be emphasized that the scope of protection of the present invention is not limited to these specific material selections, detection technologies, algorithm details or parameter settings. Those skilled in the art can, based on the core principles and teachings of the present invention, select other detectable particles with similar functions (for example, particles with optical, electrical, acoustic or other detectable properties), adopt other corresponding detection equipment and technologies, select or modify data processing algorithms and adjust parameters to achieve the purpose of the present invention. All variations and improvements based on the basic principles and solutions of the present invention and achieved by equivalent technical means should fall within the scope of protection of the present invention.
[0051] Furthermore, the physical anti-counterfeiting and digital authentication scheme provided by the present invention is not limited to the details described in the above specific embodiments, but has broad applicability and flexibility. Those skilled in the art can modify and optimize various aspects of the scheme based on factors such as specific application scenario requirements, work type, expected verification accuracy and convenience, cost budget, and robustness requirements. The following lists some variations and optimization directions in the implementation of the scheme of the present invention: Verification levels correspond to parameters: Different levels of verification standards can be set. For example, for high-value works requiring high-precision authentication, high-precision detection equipment can be required, with a smaller error tolerance threshold and a higher discretization level. Meanwhile, for applications that prioritize accessibility and convenience, general portable devices can be used for collection, with a relatively loose error tolerance threshold and a lower discretization level.
[0052] Enhance the stability of physical fingerprints: By optimizing material selection and creation / manufacturing processes, such as selecting particle materials with better chemical and physical stability, and adopting particle surface coating or material encapsulation technologies, the long-term stability of particles in the material and their ability to resist environmental interference can be enhanced, thereby improving the stability and repeatability of physical fingerprints.
[0053] Standardized operations and supporting tools: Develop detailed, standardized data collection and processing protocols, and develop corresponding software tools to assist in the verification process. This will help reduce errors introduced by different verifiers and improve the consistency and reliability of verification results. Detailed sampling guidelines for on-chain evidence storage also provide important support for this.
[0054] For the sake of simplicity, the aforementioned embodiments are described as a series of actions. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions involved are not necessarily required by this application.
[0055] The above embodiments describe the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Without departing from the spirit and scope of the present invention, modifications and variations made by those skilled in the art without departing from the spirit and scope of the present invention should be within the scope of protection of the appended claims.
Claims
1. A physical anti-counterfeiting method based on magnetic field maps, characterized in that: The steps include: S1: Doping the material being created or processed with particles having detectable properties; S2: Data collection is performed on works or products doped with detectable particles to obtain original detection data reflecting the three-dimensional spatial distribution characteristics of the particles; S3: Process the collected raw detection data to extract and compress it to generate a digital fingerprint that is unique, fault-tolerant, and tamper-resistant; S4: Upload the digital information of the digital fingerprint to the blockchain to form the original data; S5: Collect data on the work or product to be tested, and compare the similarity with the original data to determine the authenticity of the work or product to be tested.
2. A physical anti-counterfeiting method based on magnetic field patterns according to claim 1, characterized in that: The microparticles need to meet the following requirements: Microscopic size, can be evenly or unevenly mixed into the creative material without changing the material's physical properties, appearance or the feel of the creation; Long-lasting stability, which can maintain the relative stability of shape and distribution position for a long time under different creative environments and preservation conditions; Doping controllability, which can achieve the desired doping concentration and distribution; Non-destructive, non-contact or minimally invasive detection can be performed through external equipment, and the detection process does not cause significant damage to the work itself.
3. A physical anti-counterfeiting method based on magnetic field pattern according to claim 1, characterized in that: The data collection uses a high-precision magnetic field detection instrument to scan a preset sampling area of the work or product or perform precise measurements at selected points. At each collection point, the magnetic field information of that point is recorded, including the direction and intensity of the magnetic field. The magnetic field detection instrument includes a magnetometer, a fluxgate sensor array, or a superconducting quantum interference device.
4. A physical anti-counterfeiting method based on magnetic field patterns according to claim 1, characterized in that: The data collection uses a general mobile device with an integrated magnetic sensor. By cooperating with a corresponding magnetic field measurement application, a preliminary detection of the surface or near-field magnetic field distribution of the work or product can be achieved; wherein, the general mobile device includes a smartphone or tablet computer.
5. The physical anti-counterfeiting method based on magnetic field pattern according to claim 1, characterized in that: Step S3 includes the following sub-steps: S31: Feature extraction, extracting features from the original collected data that are insensitive to external interference and can effectively characterize the physical fingerprint; S32: Data standardization: unifying and quantizing the numerical domain of the feature-extracted data to reduce the impact of noise; S33: Order-independent processing, reordering all data points after normalization and concatenating the sorted data into a unified feature vector; S34: Digital fingerprint generation, performing fuzzy hash calculation on the feature vector to generate the final digital fingerprint representing the physical fingerprint of the work.
6. A physical anti-counterfeiting method based on magnetic field patterns according to claim 5, characterized in that: Step S32 includes the following sub-steps: S321: Normalization processing, independently scaling the feature data of each dimension to ensure that the numerical range and distribution of data in different dimensions are comparable; S322: Discretization processing, numerically quantizing the normalized data, mapping continuous or large-range values to a finite, discrete set of values to enhance the data's tolerance to slight changes.
7. A physical anti-counterfeiting method based on magnetic field patterns according to claim 5, characterized in that: The SimHash algorithm is used for fuzzy hash calculation.
8. The physical anti-counterfeiting method based on magnetic field pattern according to claim 1, characterized in that: The digital information includes a digital summary of the work or product, basic metadata, key sampling and processing strategies.
9. A physical anti-counterfeiting method based on magnetic field patterns according to claim 8, characterized in that: The basic metadata includes basic information for identifying and describing the work, including one or more of the work title, author identification, and creation completion date.
10. The physical anti-counterfeiting method based on magnetic field pattern according to claim 1, characterized in that: Step S5 specifically includes: collecting and processing data on the work or product to be tested, generating its current digital fingerprint based on the parameters and rules for generating the original fingerprint stored on the blockchain; then, obtaining the original digital fingerprint and related verification parameters of the work or product stored on the blockchain; finally, determining the authenticity of the work by comparing the similarity between the currently generated digital fingerprint and the original digital fingerprint on the chain.