Intelligent certificate self-service receiving method, system and device and storage medium

Abstract

The invention relates to the technical field of data processing, and particularly provides an intelligent certificate self-service receiving method, system and device and a storage medium, and the method comprises the steps: reading the information of a certificate placed in a cabinet through a radio frequency chip, and carrying out the mapping of a hash value of the certificate and a cabinet number, and carrying out the local storage; binding the access cabinet ID with all local hash values, uploading the bound access cabinet ID and local hash values to a block chain, and associating the pre-stored biological characteristic hash values; multi-dimensional biological characteristics of a user are obtained, quality verification is carried out, and a biological characteristic hash value passing verification and a source access cabinet ID are uploaded to a block chain for comparison; and receiving a biological characteristic comparison result and a certificate information hash value returned by the block chain, and if the comparison result is consistent with the certificate information hash value, opening a corresponding cabinet door according to local mapping. According to the invention, the convenience and safety of the automatic certificate access cabinet are improved.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, specifically relating to a method, system, device, and storage medium for intelligent self-service document collection. Background Technology

[0002] Currently, the issuance and management of certificates mainly rely on two models: The manual counter model involves setting up manual windows in government service halls, social security centers, and other locations, where staff verify document information before issuing certificates. While this model can handle complex scenarios, it has significant drawbacks: efficiency bottlenecks: users need to queue in person, resulting in excessively long waiting times during peak periods.

[0003] Basic self-service terminals use simple devices (such as self-service lockers) that scan barcodes or verify passwords to partially replace manual services. While these devices improve basic automation, they still have key shortcomings: Weak security: relying on static passwords or physical barcodes makes them easy to copy or impersonate. Limited functionality: only supporting one-to-one collection, unable to dynamically link user biometrics to the locker's identification documents.

[0004] In summary, the existing methods of issuing certificates are inefficient and lack security. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method, system, device and storage medium for intelligent self-service document collection to solve the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a method for self-service collection of intelligent certificates, comprising: The document information of the document placed in the cabinet is read by the radio frequency chip reading device, the hash value of the document information and the cabinet number are used to construct a mapping and the mapping is saved locally; After binding the locker ID with all locally stored hash values, it is uploaded to the blockchain so that the blockchain can associate the pre-stored corresponding biometric hash values ​​with the locker ID. Obtain the multidimensional biometrics of the user being investigated, and perform quality verification on the multidimensional biometrics; The biometric hash value of the multidimensional biometric feature that has passed the quality inspection and the source access cabinet ID are uploaded to the blockchain so that the blockchain can compare the consistency of the biometric hash value with the pre-stored corresponding biometric hash value based on the source access cabinet ID. The system receives information returned from the blockchain, which includes biometric comparison results and the hash value of the corresponding document information. If the biometric comparison matches, the system opens the corresponding locker door based on the locally stored mapping and the document information hash value.

[0007] In an optional implementation, the document information of the document placed in the locker is read by an RFID chip reader, a mapping is constructed between the hash value of the document information and the locker number, and the mapping is saved locally, including: Receive storage instructions and control the opening of empty cabinet doors; Once it is confirmed that the opened cabinet door has been closed, the radio frequency chip reader is activated to read the document information; Based on the pre-stored correspondence between the device number and the counter number of the RFID chip reader and the device number of the RFID chip reader that sends the document information, the counter number corresponding to the document information is determined. Calculate the hash value of the document information and establish a mapping between the hash value and the corresponding counter number; Save the mapping to the specified local storage.

[0008] In one optional implementation, the locker ID is bound to the hash values ​​of all locally stored data and then uploaded to the blockchain, including: Monitor data updates in local storage; If a new mapping exists, the hash value of the document information in the new mapping will be bound to the locker ID stored in its own register and then uploaded to the blockchain. If a deleted mapping exists, a deletion marker is added to the hash value of the document information in the deleted mapping, and the hash value with the deletion marker is bound to the locker ID stored in its own register and then uploaded to the blockchain.

[0009] In one optional implementation, the blockchain associates a pre-stored corresponding biometric hash value with the locker ID, including: When the certificate is issued, the blockchain stores the mapping between the certificate information hash value and its corresponding biometric hash value for each certificate; After receiving the binding data of the locker ID and the document information hash value, the blockchain uses the document information hash value to retrieve the pre-stored biometric hash value, and then binds the locker ID, the document information hash value and the retrieved biometric hash value as a new entry for storage. After receiving the binding data of the locker ID and the document information hash value with the deletion mark, the blockchain will delete the data entry containing the locker ID, the document information hash value and the corresponding biometric hash value.

[0010] In one optional implementation, the multidimensional biometrics of the user being investigated are acquired, and the quality of the multidimensional biometrics is verified, including: Collect facial images, fingerprints, and behavioral images of users for evidence collection; The quality scores of the face image and fingerprint are generated based on preset evaluation indicators. Extract facial features from face images and generate confidence scores for those features; Extract fingerprint features from the fingerprint and generate the confidence level of the fingerprint features; Extract liveness features from behavioral images and generate risk coefficients for these liveness features; If the risk coefficient reaches the set risk threshold, the verification is deemed unsuccessful; if the risk coefficient does not reach the set risk threshold, a comprehensive feature score is calculated. Score = (α·S 指纹 ·Q 指纹 ) + (β·S 人脸 ·Q 人脸 ) + (γ·S 活体 ·R) Where α, β, and γ are the respective weights of the features, and S 指纹 Q represents the confidence level of fingerprint features. 指纹 S represents the quality score of the fingerprint. 人脸 Confidence level of facial features, Q 人脸 S is the quality score of a face image. 活体 For living organisms, R is the risk coefficient; If the overall feature score reaches the set score threshold, the verification is deemed successful; if the overall feature score does not reach the set score threshold, the verification is deemed unsuccessful.

[0011] In one optional implementation, the blockchain compares the biometric hash value with the pre-stored corresponding biometric hash value based on the source access locker ID, including: The blockchain receives biometric hash values ​​and source locker IDs; The blockchain smart contract query includes the target data entry containing the source locker ID, extracts the document information hash value and the pre-stored biometric hash value from the target data entry, calculates the similarity between the received biometric hash value and the pre-stored biometric hash value, and returns the similarity and the extracted document information hash value to the locker controller corresponding to the source locker ID.

[0012] In an optional implementation, if the biometric feature match, the corresponding locker door is opened based on the locally stored mapping and the hash value of the identification information, including: If the similarity reaches the set similarity threshold, the target counter is determined based on the mapping between the locally stored document information hash value and the counter and the document information hash value received from the blockchain, and the target counter is opened. If the similarity does not reach the set similarity threshold, a verification failure message will be generated.

[0013] Secondly, the present invention provides an intelligent self-service document collection system, comprising: The document recognition module is used to read the document information of the documents placed in the cabinet through the radio frequency chip reader, construct a mapping between the hash value of the document information and the cabinet number, and save the mapping locally; The first on-chain module is used to bind the locker ID with all locally stored hash values ​​and then upload it to the blockchain, so that the blockchain can associate the pre-stored corresponding biometric hash values ​​with the locker ID. The feature verification module is used to acquire the multidimensional biometric features of the user being investigated and to perform quality verification on the multidimensional biometric features. The second on-chain module is used to upload the biometric hash value of the multidimensional biometric features that have passed the quality inspection and the source storage cabinet ID to the blockchain, so that the blockchain can compare the consistency of the biometric hash value with the pre-stored corresponding biometric hash value based on the source storage cabinet ID. The door opening control module is used to receive information returned by the blockchain. The information includes biometric comparison results and the hash value of the corresponding document information. If the biometric comparison matches, the corresponding cabinet door is opened based on the locally stored mapping and document information hash value.

[0014] Thirdly, a device is provided, comprising: Storage device for storing the smart document self-service collection program; A processor is configured to implement the steps of the smart document self-service collection method as provided in the first aspect when executing the smart document self-service collection program.

[0015] Fourthly, a computer-readable storage medium is provided, on which a smart document self-service collection program is stored, wherein when the smart document self-service collection program is executed by a processor, the steps of the smart document self-service collection method as provided in the first aspect are implemented.

[0016] The intelligent self-service document collection method, system, device, and storage medium provided by this invention have the following beneficial effects: Dual security barriers: Verifying the authenticity of documents through RFID chips and identifying the holder's identity through biometrics prevents single-point attacks such as document misuse or biometric forgery, reducing the risk of fraudulent claims; On-chain trusted evidence storage: The blockchain dynamically binds the hash value of the certificate in the cabinet with the biometric template to realize the "person-certificate-cabinet" three-element association evidence storage, and the possibility of tampering is close to 0 (based on hash irreversibility and chain structure). Seamless and efficient verification: Users only need to input their biometric features once, and the system automatically completes on-chain comparison and cabinet door control, greatly reducing the average time consumption; Zero privacy breach: Original biometric data and identification information are always offline, only the hash value is uploaded to the blockchain, which complies with GDPR privacy regulations. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a method according to an embodiment of the present invention.

[0019] Figure 2 This is a schematic block diagram of a system according to an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the structure of a device provided in an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0023] The intelligent document self-service collection method provided in this embodiment of the invention is executed by a computer device, and correspondingly, the intelligent document self-service collection system runs on the computer device.

[0024] Figure 1 This is a schematic flowchart illustrating a method according to an embodiment of the present invention. Wherein, Figure 1 The implementing entity can be an intelligent self-service document collection system. Depending on different needs, the order of the steps in this flowchart can be changed, and some can be omitted.

[0025] like Figure 1 As shown, the method includes: S1. Read the document information of the document placed in the cabinet using the radio frequency chip reading device, construct a mapping between the hash value of the document information and the cabinet number, and save the mapping locally; S2. After binding the locker ID with all locally stored hash values, upload it to the blockchain so that the blockchain can associate the pre-stored corresponding biometric hash values ​​with the locker ID; S3. Obtain the multidimensional biometrics of the user being investigated, and perform quality verification on the multidimensional biometrics; S4. Upload the biometric hash value of the multidimensional biometric feature that has passed the quality inspection and the source access cabinet ID to the blockchain, so that the blockchain can compare the consistency of the biometric hash value with the pre-stored corresponding biometric hash value based on the source access cabinet ID. S5. Receive information returned by the blockchain, which includes biometric comparison results and the hash value of the corresponding document information. If the biometric comparison matches, open the corresponding cabinet door based on the locally stored mapping and the document information hash value.

[0026] In one embodiment of the present invention, based on step S1, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0027] S101. Receive storage instructions and control the opening of the vacant cabinet door.

[0028] The system receives storage instructions via a human-machine interface. Upon receiving the instruction, the central controller (using an STM32H743 microprocessor) immediately invokes the cabinet status monitoring module (composed of a distributed Hall sensor array and a real-time database). It iterates through a pre-defined list of cabinet numbers to select cabinets in an "empty" state (i.e., no object detected by the sensors and the door control feedback is closed). The selection logic employs a greedy algorithm, prioritizing the empty cabinet with the earliest recent operation time to balance the usage frequency of each cabinet. After determining the target cabinet, the controller sends a voltage level signal (DC12V, lasting 500ms) to the corresponding cabinet's electromagnetic lock drive module, simultaneously triggering the door status feedback circuit. The photoelectric sensor monitors the cabinet door's opening status in real time, ensuring the door opens within 3 seconds. If the timeout occurs, an audible and visual alarm is activated, and an anomaly log is recorded.

[0029] S102. Confirm that the opened cabinet door has been closed, and activate the radio frequency chip reader to read the document information.

[0030] The cabinet door closure confirmation mechanism employs dual verification: first, the Hall sensor built into the electromagnetic lock detects the latch reset signal; second, the infrared beam sensor installed inside the cabinet compartment is blocked (indicating the door is closed). These two signals are ANDed using hardware logic gates, and the result is fed back to the controller, which determines the door is validly closed. Subsequently, the controller sends a start command via RS485 bus to the corresponding RFID chip reader (a high-frequency card reader using the ISO / IEC 14443 Type B standard, operating at 13.56MHz). After waking up, the card reader executes an anti-collision algorithm (based on the ISO / IEC 18092 standard NFC protocol), reading the unique identifier (UID) and stored data (such as document number and holder information digest) of the built-in RFID chip within 500ms. This data is then uploaded to the controller via an encrypted transmission channel (using the AES-128 algorithm).

[0031] S103. Based on the pre-stored correspondence between the device number and the counter number of the RFID chip reader and the device number of the RFID chip reader that sends the document information, determine the counter number corresponding to the document information.

[0032] During the initialization phase, the system establishes a mapping table between the RF chip reader device number (64-bit unique identifier) ​​and the rack number (8-bit binary code) using a configuration tool. This table is stored in the controller's non-volatile memory (NAND Flash) and supports dynamic updates (via OTA). When the controller receives the document information, it first parses the reader device number in the data packet and then calls a hash lookup algorithm (time complexity O(1)) to match it in the mapping table. To avoid device number conflicts, the system employs a three-level verification mechanism: device physical address verification, factory serial number verification, and network MAC address verification, ensuring that each reader device uniquely corresponds to a rack number. After matching, the controller marks the rack number as "occupied" and updates it to the real-time database.

[0033] S104. Calculate the hash value of the document information and establish a mapping between the hash value and the corresponding counter number.

[0034] For the read document information (including UID and stored data), the system uses the SHA-256 hash algorithm for one-way encryption to generate a 256-bit hash value. During the hash calculation, a random salt (128 bits, generated by a hardware random number generator) is introduced to enhance collision resistance; the salt is stored in conjunction with the hash result. Subsequently, the controller constructs a hash value-cabinet number mapping table in memory (using a chained hash structure to resolve hash collisions). The mapping relationship is established following the atomicity principle, i.e., using a mutex to ensure data consistency in a multi-threaded environment. Simultaneously, the system performs a secondary verification of the hash value, comparing the data integrity before and after the hash calculation. If the verification fails, the document information is reread and the calculation process is repeated, with a maximum of three retries. If the verification fails again, an error handling mechanism is triggered.

[0035] S105. Save the mapping to the designated local storage.

[0036] The hash value-store number mapping table in memory is asynchronously written to local storage (using an embedded storage chip with the eMMC 5.1 standard and a capacity of 64GB). The writing process employs a log-structured file system to improve writing efficiency and data reliability. To prevent data loss due to sudden power outages, the system uses a power failure protection mechanism supported by an uninterruptible power supply (UPS). Upon detecting a power anomaly, it immediately triggers a data persistence operation, forcibly writing the mapped data in memory to the spare area of ​​the storage chip. The storage file uses an encrypted format (based on the XTS-AES algorithm), with the key generated and managed by the hardware security module (HSM). Each storage block includes a CRC32 checksum for data integrity verification. After the write operation is complete, the controller sends a successful operation response (including a timestamp, hash value digest, and store number) to the host computer and updates the system operation log (compliant with the ISO / IEC 27001 information security standard).

[0037] In one embodiment of the present invention, based on step S2, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0038] S201. After binding the locker ID with the hash values ​​of all locally stored data, upload it to the blockchain for subsequent biometric verification. This includes the following steps: The system uses a file system monitor (based on the inotify mechanism) to capture changes to mapped data in local storage in real time. Triggering conditions include the creation of new mapped files (addition of inode nodes) or the deletion of existing files (link count reaches zero). For new mappings, the controller reads a 128-bit unique storage cabinet ID from a register (stored in EEPROM, retained even after power loss), generates a digital signature using elliptic curve cryptography (ECCsecp256r1), and packages the storage cabinet ID, document information hash value, and signature into a JSON format transaction (compliant with the ERC-721 standard extension protocol). The transaction is sent to the blockchain node via a P2P network, employing a batch submission strategy (triggered every 10 new records or a 30-second timeout) to reduce on-chain storage costs. For deletion operations, the system generates an 8-bit deletion flag (0xDEADBEEF) for the target hash value, binds it to the hash value via XOR operation, and uploads it along with the storage cabinet ID and signature to ensure the traceability of on-chain data changes.

[0039] S202. The blockchain associates the pre-stored corresponding biometric hash value with the access locker ID, including: When the certificate is issued, the blockchain stores the mapping between the certificate information hash value and its corresponding biometric hash value for each certificate; After receiving the binding data of the locker ID and the document information hash value, the blockchain uses the document information hash value to retrieve the pre-stored biometric hash value, and then binds the locker ID, the document information hash value and the retrieved biometric hash value as a new entry for storage. After receiving the binding data of the locker ID and the document information hash value with the deletion mark, the blockchain will delete the data entry containing the locker ID, the document information hash value and the corresponding biometric hash value.

[0040] Specifically, the underlying blockchain adopts a consortium blockchain architecture (HyperledgerFabric 2.4). A pre-built mapping table of document information hash values ​​and biometric hash values ​​(based on LevelDB storage) enables access control via smart contracts. When a node receives the binding data uploaded from the storage locker, it calls the retrieval function in the chaincode to perform a range query (time complexity O(logN)) using the document information hash value as an index, matching the corresponding biometric hash value (generated using the SHA-3 algorithm, including fingerprint and facial feature fusion data). After successful verification, the smart contract automatically creates a new entry, associating the storage locker ID and the dual hash values ​​through a Merkle tree structure, and adding multi-signature from consensus nodes (PBFT algorithm, fault tolerance 1 / 3) when writing to the block. For transactions with deletion markers, the chaincode performs a logical deletion operation (retaining historical versions and marking them as invalid), and data traceability is achieved through block height and transaction ID, meeting GDPR compliance requirements.

[0041] In one embodiment of the present invention, based on step S3, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0042] S301. Collect facial images, fingerprints, and behavioral images of users for evidence collection; S302. Generate quality scores for face images and fingerprints based on preset evaluation metrics: The quality scoring system is based on a multi-dimensional feature fusion algorithm: Face image quality assessment: Sharpness: Based on Laplacian operator variance (threshold ≥ 80); Completeness: Detection rate of facial feature points (68-point Dlib model) (threshold ≥ 95%); Illumination uniformity: YCbCr color space luminance standard deviation (threshold ≤ 30); Attitude angles: Euler angle detection (pitch angle ≤ ±15°, yaw angle ≤ ±20°); Fingerprint quality assessment: Effective area ratio: the proportion of foreground pixels after binarization (threshold ≥ 60%). Ridge sharpness: Gabor filter response intensity (average amplitude ≥ 0.3); Breakpoint and bifurcation point density: Uniformity of feature point distribution (≤5 per square millimeter).

[0043] The quality score is calculated using a weighted fusion model: Q = 0.4Q 清晰度 + 0.3Q 完整性 + 0.2Q 光照 + 0.1Q 姿态 The final output is the standardized value in the interval [0,1].

[0044] S303. Extract facial features from face images and generate confidence scores for the facial features; feature extraction and confidence score calculation are implemented using a deep convolutional neural network (DenseNet-121 architecture).

[0045] S304. Extract fingerprint features from the fingerprint and generate the confidence level of the fingerprint features: Employing a minutiae feature extraction algorithm: Feature extraction process: Orientation field estimation: 8-neighborhood analysis based on gradient tensor; Frequency analysis: Gabor filtering enhances ridge structure; Minute point detection: Breakpoint / fork point localization based on the Poincare index.

[0046] Confidence assessment: Number of detail points: ≥40 valid detail points; Triangulation stability: based on the angular variance of Delaunay triangulation (threshold ≤ 15°); Matching stability: Jaccard similarity extracted three times independently (threshold ≥ 0.75).

[0047] Confidence model: C=min(1,0.5・N / 40+0.3・(1-σ)+0.2・J), ensuring feature stability quantification.

[0048] S305. Extract liveness features from behavioral images and generate risk coefficients for these liveness features: Spatiotemporal feature fusion analysis technology is used: Liveness feature extraction: Physiological characteristics: respiratory rate (0.1-0.5Hz), heart rate (0.8-2.5Hz); Motor characteristics: blinking frequency (8-20 times / minute), lip movement trajectory; Deep features: 3D deformation analysis of facial micro-expressions based on RGB-D; Risk coefficient calculation: Feature consistency: Euclidean distance change rate of features across multiple frames (threshold ≤ 0.15); Physiological signal periodicity: FFT spectrum energy concentration (threshold ≥ 0.6); Action naturalness: Action sequence probability based on HMM model (threshold ≥ 0.05).

[0049] Risk function: R = 1 - min(1, 0.4·C) 时间 +0.3·C 频率 +0.3·C 动作 Output the risk value in the interval [0,1]. When R≥0.7, a live attack warning is triggered.

[0050] If the risk coefficient reaches the set risk threshold, the verification is deemed unsuccessful; if the risk coefficient does not reach the set risk threshold, a comprehensive feature score is calculated. Score = (α·S 指纹 ·Q 指纹 ) + (β·S 人脸 ·Q 人脸 ) + (γ·S 活体 ·R) Where α, β, and γ are the respective weights of the features, and S 指纹 Q represents the confidence level of fingerprint features. 指纹 S represents the quality score of the fingerprint. 人脸 Confidence level of facial features, Q人脸 S is the quality score of a face image. 活体 For living organisms, R is the risk coefficient; If the overall feature score reaches the set score threshold, the verification is deemed successful; if the overall feature score does not reach the set score threshold, the verification is deemed unsuccessful.

[0051] In one embodiment of the present invention, based on step S4, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0052] S401. Upload the biometric hash value of the multidimensional biometric feature that has passed the quality inspection and the source storage cabinet ID to the blockchain.

[0053] S402. The blockchain receives a biometric hash value and a source locker ID; the blockchain's smart contract queries a target data entry containing the source locker ID, extracts a document information hash value and a pre-stored biometric hash value from the target data entry, calculates the similarity between the received biometric hash value and the pre-stored biometric hash value, and returns the similarity and the extracted document information hash value to the locker controller corresponding to the source locker ID.

[0054] In one example, the following steps are included: (1) Generation of biometric hashes: Facial features: The 512-dimensional standardized feature vector is used to generate a hash value H using the SHA-3-256 algorithm. face ; Fingerprint features: The extracted minutiae coordinate set is encoded by Morton to generate a hash value H. finger ; Liveness characteristics: The spatiotemporal behavioral feature sequence is used to generate a hash value H using the BLAKE2b algorithm. liveness ; (2) Hash value fusion and encryption: The three-dimensional hash values ​​are integrated using a Merkle tree structure, with the root hash H. bio = Hash(H face || H finger ||H liveness ); Use the locker private key (ECDSA secp256k1) to access H bio Perform digital signature Sig=Sign sk (H bio ||ID cabinet ).

[0055] (3) On-chain transaction construction: Transaction structure: {"type": "VERIFICATION","timestamp": UTC timestamp,"cabinet_id": ID} cabinet "bio_hash": H bio `,"signature": Sig,"nonce": random number (to prevent replay attacks)}. Transactions are sent to the blockchain gateway via HTTPS (using TLS 1.3 encryption).

[0056] (4) Blockchain smart contract verification logic: Data retrieval mechanism: Smart contracts use IDs cabinet As a key, a range query is performed in the state database (a distributed storage based on IPFS); a Bloom filter is used to quickly determine whether the target data entry exists (false positive rate <0.1%).

[0057] Similarity calculation process: Extract the pre-stored biometric hash H on the blockchain bio chain With the newly uploaded H bio ; Calculate the Hamming distance D hamming = ∑(H bio ⊕ H bio chain Similarity conversion: Sim = 1 - (D) hamming ( / 256) (assuming a 256-bit hash).

[0058] Zero-knowledge proof verification: Smart contract verification of digital signature e(Sig, P) cabinet )==H(H bio || ID cabinet zk-SNARKs are used to prove the correctness of the similarity calculation process and ensure that there is no leakage off-chain.

[0059] (5) Response data generation: Smart contract return structure: {"status": "SUCCESS","similarity": Sim,"doc_hash":H doc "block_height": Current block height, "tx_id": Transaction ID}.

[0060] In one embodiment of the present invention, based on step S5, a possible embodiment will be given below, and its specific implementation will be described in a non-limiting manner.

[0061] The locker controller receives information returned from the blockchain and extracts biometric similarity and document information hash value. If the similarity reaches the set similarity threshold, the target locker is determined based on the mapping between the locally stored document information hash value and the locker and the document information hash value received from the blockchain, and the target locker is opened. If the similarity does not reach the set similarity threshold, a verification failure message is generated.

[0062] In some embodiments, the intelligent document self-service collection system may include multiple functional modules composed of computer program segments. The computer programs for each program segment in the intelligent document self-service collection system may be stored in the memory of a computer device and executed by at least one processor to perform (see details). Figure 1 (Description) The function of self-service collection of smart certificates.

[0063] In this embodiment, the intelligent self-service document collection system can be divided into multiple functional modules based on its functions, such as... Figure 2 As shown. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and is stored in memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0064] The document recognition module is used to read the document information of the documents placed in the cabinet through the radio frequency chip reader, construct a mapping between the hash value of the document information and the cabinet number, and save the mapping locally; The first on-chain module is used to bind the locker ID with all locally stored hash values ​​and then upload it to the blockchain, so that the blockchain can associate the pre-stored corresponding biometric hash values ​​with the locker ID. The feature verification module is used to acquire the multidimensional biometric features of the user being investigated and to perform quality verification on the multidimensional biometric features. The second on-chain module is used to upload the biometric hash value of the multidimensional biometric features that have passed the quality inspection and the source storage cabinet ID to the blockchain, so that the blockchain can compare the consistency of the biometric hash value with the pre-stored corresponding biometric hash value based on the source storage cabinet ID. The door opening control module is used to receive information returned by the blockchain. The information includes biometric comparison results and the hash value of the corresponding document information. If the biometric comparison matches, the corresponding cabinet door is opened based on the locally stored mapping and document information hash value.

[0065] Figure 3The smart document self-service collection method provided in the embodiments of this application can be applied to devices. Those skilled in the art will understand that the device structure involved in the embodiments of this invention does not constitute a limitation on the device. A device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the device includes, but is not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0066] The device 300 may include a processor 310, a memory 320, and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will understand that the server structure shown in the figure does not constitute a limitation of the present invention. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0067] The memory 320 can be used to store execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the device 300 is able to perform some or all of the steps in the above method embodiments.

[0068] The processor 310 serves as the control center of the storage device, connecting various parts of the electronic device via various interfaces and lines. It executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 310 may consist only of a central processing unit (CPU). In this embodiment of the invention, the CPU may have a single processing core or include multiple processing cores.

[0069] The communication unit 330 is used to establish a communication channel, enabling the storage device to communicate with other devices. It can receive or send forensic user data to other devices.

[0070] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, which, when executed, may include some or all of the steps provided in the embodiments of the present invention. The storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0071] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other medium capable of storing program code. It includes several instructions to cause a computer device (which may be a personal computer, a server, or a second device, network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0072] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0073] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some biometric features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or modules may be electrical, mechanical, or other forms.

[0074] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0076] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the present invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the present invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should also be covered within the protection scope of the present invention.

Claims

1. A method for self-service collection of intelligent certificates, wherein the biometric features include: The document information of the document placed in the cabinet is read by the radio frequency chip reading device, the hash value of the document information and the cabinet number are used to construct a mapping and the mapping is saved locally; After binding the locker ID with all locally stored hash values, it is uploaded to the blockchain so that the blockchain can associate the pre-stored corresponding biometric hash values ​​with the locker ID. Obtain the multidimensional biometrics of the user being investigated, and perform quality verification on the multidimensional biometrics; The biometric hash value of the multidimensional biometric feature that has passed the quality inspection and the source access cabinet ID are uploaded to the blockchain so that the blockchain can compare the consistency of the biometric hash value with the pre-stored corresponding biometric hash value based on the source access cabinet ID. The system receives information returned from the blockchain, which includes biometric comparison results and the hash value of the corresponding document information. If the biometric comparison matches, the system opens the corresponding locker door based on the locally stored mapping and the document information hash value.

2. The method according to claim 1, wherein the biological characteristic is that, The document information of the document placed in the locker is read using an RFID chip reader. A mapping is constructed between the hash value of the document information and the locker number, and the mapping is saved locally. This includes: Receive storage instructions and control the opening of empty cabinet doors; Once it is confirmed that the opened cabinet door has been closed, the radio frequency chip reader is activated to read the document information; Based on the pre-stored correspondence between the device number and the counter number of the RFID chip reader and the device number of the RFID chip reader that sends the document information, the counter number corresponding to the document information is determined. Calculate the hash value of the document information and establish a mapping between the hash value and the corresponding counter number; Save the mapping to the specified local storage.

3. The method according to claim 2, wherein the biological characteristic is that, After binding the locker ID with the hash values ​​of all locally stored data, upload it to the blockchain, including: Monitor data updates in local storage; If a new mapping exists, the hash value of the document information in the new mapping will be bound to the locker ID stored in its own register and then uploaded to the blockchain. If a deleted mapping exists, a deletion marker is added to the hash value of the document information in the deleted mapping, and the hash value with the deletion marker is bound to the locker ID stored in its own register and then uploaded to the blockchain.

4. The method according to claim 3, wherein the biological characteristic is that, The blockchain associates the pre-stored corresponding biometric hash value with the locker ID, including: When the certificate is issued, the blockchain stores the mapping between the certificate information hash value and its corresponding biometric hash value for each certificate; After receiving the binding data of the locker ID and the document information hash value, the blockchain uses the document information hash value to retrieve the pre-stored biometric hash value, and then binds the locker ID, the document information hash value and the retrieved biometric hash value as a new entry for storage. After receiving the binding data of the locker ID and the document information hash value with the deletion mark, the blockchain will delete the data entry containing the locker ID, the document information hash value and the corresponding biometric hash value.

5. The method according to claim 1, characterized in that, Obtaining multidimensional biometrics of the user being investigated, and performing quality verification on the multidimensional biometrics, including: Collect facial images, fingerprints, and behavioral images of users for evidence collection; The quality scores of the face image and fingerprint are generated based on preset evaluation indicators. Extract facial features from face images and generate confidence scores for those features; Extract fingerprint features from the fingerprint and generate the confidence level of the fingerprint features; Extract liveness features from behavioral images and generate risk coefficients for these liveness features; If the risk coefficient reaches the set risk threshold, the verification is deemed unsuccessful; if the risk coefficient does not reach the set risk threshold, a comprehensive feature score is calculated. Score = (α·S 指纹 ·Q 指纹 ) + (β·S 人脸 ·Q 人脸 ) + (γ·S 活体 ·R) Where α, β, and γ are the respective weights of the features, and S 指纹 Q represents the confidence level of fingerprint features. 指纹 S represents the quality score of the fingerprint. 人脸 Confidence level of facial features, Q 人脸 S is the quality score of a face image. 活体 For living organisms, R is the risk coefficient; If the overall feature score reaches the set score threshold, the verification is deemed successful; if the overall feature score does not reach the set score threshold, the verification is deemed unsuccessful.

6. The method according to claim 4, characterized in that, The blockchain compares the biometric hash value with the pre-stored corresponding biometric hash value based on the source access locker ID, including: The blockchain receives biometric hash values ​​and source locker IDs; The blockchain smart contract query includes the target data entry containing the source locker ID, extracts the document information hash value and the pre-stored biometric hash value from the target data entry, calculates the similarity between the received biometric hash value and the pre-stored biometric hash value, and returns the similarity and the extracted document information hash value to the locker controller corresponding to the source locker ID.

7. The method according to claim 6, characterized in that, If the biometric signature matches, the corresponding locker door will be opened based on the locally stored mapping and the hash value of the identification information, including: If the similarity reaches the set similarity threshold, the target counter is determined based on the mapping between the locally stored document information hash value and the counter and the document information hash value received from the blockchain, and the target counter is opened. If the similarity does not reach the set similarity threshold, a verification failure message will be generated.

8. A smart self-service document collection system, characterized in that, include: The document recognition module is used to read the document information of the documents placed in the cabinet through the radio frequency chip reader, construct a mapping between the hash value of the document information and the cabinet number, and save the mapping locally; The first on-chain module is used to bind the locker ID with all locally stored hash values ​​and then upload it to the blockchain, so that the blockchain can associate the pre-stored corresponding biometric hash values ​​with the locker ID. The feature verification module is used to acquire the multidimensional biometric features of the user being investigated and to perform quality verification on the multidimensional biometric features. The second on-chain module is used to upload the biometric hash value of the multidimensional biometric features that have passed the quality inspection and the source storage cabinet ID to the blockchain, so that the blockchain can compare the consistency of the biometric hash value with the pre-stored corresponding biometric hash value based on the source storage cabinet ID. The door opening control module is used to receive information returned by the blockchain. The information includes biometric comparison results and the hash value of the corresponding document information. If the biometric comparison matches, the corresponding cabinet door is opened based on the locally stored mapping and document information hash value.

9. A smart self-service document collection device, whose biometric features include: Storage device for storing the smart document self-service collection program; A processor is configured to implement the steps of the smart document self-service collection method as described in any one of claims 1-7 when executing the smart document self-service collection program.

10. A computer-readable storage medium storing a computer program, characterized in that the readable storage medium stores a smart document self-service collection program, which, when executed by a processor, implements the steps of the smart document self-service collection method as described in any one of claims 1-7.

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