A service scenario data management system, method, and storage medium

Abstract

The application provides a kind of business scene data management system, method and storage medium, belong to data management field, including server, block chain and client, server is used to import multiple business scene data, and all business scene data are encrypted and analyzed to obtain pending encrypted data set and encryption proof information, and pending encrypted data set and encryption proof information are sent to block chain;Block chain is used to encrypt the pending encrypted data set and encryption proof information to obtain the target encrypted data set and decryption key;Client is used to input data decryption request instruction, and data decryption request instruction is sent to server through block chain.The application solves the tampering and counterfeiting problems existing in the traditional business data management method, improves the credibility, transparency, security of data and the automation level of operation, at the same time, also reduces human error, improves the user experience and operation efficiency.

Description

Technical Field

[0001] This invention mainly relates to the field of data management technology, specifically to a business scenario data management system, method, and storage medium. Background Technology

[0002] In many business scenarios, recording and verifying the authenticity and integrity of operations is crucial. For example, in logistics management, it's essential to ensure that every step of the goods' journey is accurately recorded; in financial auditing, it's vital to ensure the authenticity and immutability of every transaction. However, traditional business scenario management methods are susceptible to tampering or forgery, making it difficult to ensure data reliability and transparency. Furthermore, with the diversification of business needs, existing management methods often struggle to meet higher-level requirements such as real-time verification and privacy protection. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing a business scenario data management system, method and storage medium.

[0004] The technical solution of this invention to solve the above-mentioned technical problems is as follows: a business scenario data management system, including a server, a blockchain, and a client.

[0005] The server is used to import data from multiple business scenarios, perform encrypted analysis on all the business scenario data to obtain encrypted datasets to be processed and encrypted proof information, and send the encrypted datasets to be processed and the encrypted proof information to the blockchain;

[0006] The blockchain is used to encrypt the encrypted dataset to be processed and the encrypted proof information to obtain the target encrypted dataset and the decryption key;

[0007] The client is used to input a data decryption request command and send the data decryption request command to the server through the blockchain;

[0008] The server is also used to verify the data decryption request instruction. If the verification is successful, a decryption instruction is generated, and the decryption instruction and the encryption proof information are sent to the blockchain.

[0009] The blockchain is also used to send the encrypted proof information, the target encrypted dataset, and the decryption key to the client according to the decryption instruction;

[0010] The client is used to perform decryption analysis on the target encrypted dataset based on the encryption proof information and the decryption key to obtain the decryption result of the business scenario data.

[0011] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: A business scenario data management method, comprising the following steps:

[0012] Import data from multiple business scenarios and perform encrypted analysis on all of the aforementioned business scenario data to obtain the encrypted dataset to be processed and encrypted proof information;

[0013] The dataset to be encrypted and the encryption proof information are encrypted to obtain the target encrypted dataset and the decryption key.

[0014] Input a data decryption request command and verify the data decryption request command. If the verification is successful, perform decryption analysis on the target encrypted dataset based on the encryption proof information and the decryption key to obtain the data decryption result for the business scenario.

[0015] The beneficial effects of this invention are as follows: by performing encrypted analysis on business scenario data, a data set to be processed and encrypted proof information are obtained; by performing encrypted processing on the data set to be processed and encrypted proof information, a target encrypted data set and decryption key are obtained; the data decryption request instruction is verified; if the verification is successful, the business scenario data decryption result is obtained by performing decryption analysis on the target encrypted data set based on the encrypted proof information and decryption key. This solves the problems of tampering and forgery existing in traditional business data management methods, improves the credibility, transparency, security and automation level of data operations, and at the same time reduces human error, improving user experience and operational efficiency. Attached Figure Description

[0016] Figure 1 This is a block diagram of a business scenario data management system provided in an embodiment of the present invention;

[0017] Figure 2 This is a flowchart illustrating a business scenario data management method provided in an embodiment of the present invention. Detailed Implementation

[0018] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0019] Figure 1 This is a block diagram of a business scenario data management system provided in an embodiment of the present invention.

[0020] like Figure 1 As shown, a business scenario data management system includes a server, a blockchain, and a client.

[0021] The server is used to import data from multiple business scenarios, perform encrypted analysis on all the business scenario data to obtain encrypted datasets to be processed and encrypted proof information, and send the encrypted datasets to be processed and the encrypted proof information to the blockchain;

[0022] The blockchain is used to encrypt the encrypted dataset to be processed and the encrypted proof information to obtain the target encrypted dataset and the decryption key;

[0023] The client is used to input a data decryption request command and send the data decryption request command to the server through the blockchain;

[0024] The server is also used to verify the data decryption request instruction. If the verification is successful, a decryption instruction is generated, and the decryption instruction and the encryption proof information are sent to the blockchain.

[0025] The blockchain is also used to send the encrypted proof information, the target encrypted dataset, and the decryption key to the client according to the decryption instruction;

[0026] The client is used to perform decryption analysis on the target encrypted dataset based on the encryption proof information and the decryption key to obtain the decryption result of the business scenario data.

[0027] It should be understood that authorized verifiers (i.e., clients) issue data decryption requests (i.e. data decryption request instructions) through the blockchain, using smart contracts to verify their permissions.

[0028] In the above embodiments, the encrypted dataset to be processed and the encrypted proof information are obtained by the encrypted analysis of the business scenario data. The encrypted processing of the encrypted dataset to be processed and the encrypted proof information yields the target encrypted dataset and the decryption key. The data decryption request instruction is verified. If the verification is successful, the decryption result of the business scenario data is obtained by the decryption analysis of the target encrypted dataset based on the encrypted proof information and the decryption key. This solves the problems of tampering and forgery in traditional business data management methods, improves the credibility, transparency, security and automation level of the data, and reduces human error, thereby improving user experience and operational efficiency.

[0029] Optionally, as an embodiment of the present invention, the process of performing encrypted analysis on all the business scenario data in the server to obtain the encrypted dataset to be processed and the encrypted proof information includes:

[0030] All the business scenario data are preprocessed, and all the preprocessed business scenario data are combined to obtain a business scenario dataset;

[0031] Multiple sensitive information items of individuals are extracted from the business scenario dataset, and all of the sensitive information items of individuals are combined to obtain a set of sensitive information items of individuals;

[0032] The sensitive information set of the person is encrypted using a zero-knowledge proof protocol to obtain encrypted proof information;

[0033] The sensitive information set of the person is encrypted using the MPC multi-party secure computation algorithm to obtain the encrypted dataset to be processed.

[0034] It should be understood that the sensitive information of the person may include personal identification information, financial data, etc.

[0035] It should be understood that zero-knowledge proof protocols are cryptographic techniques that allow one party to prove to another that a statement is true without revealing the specific content of that statement. This protocol was first proposed in 1985 by Shafi Goldwasser, Silvio Micali, and Charles Rackoff, and has been developed and applied over the following decades. Zero-knowledge proofs have a wide range of applications, including but not limited to blockchain, secure communication, electronic voting, access control, and gaming. With technological advancements, zero-knowledge proofs have demonstrated enormous potential in protecting personal privacy and improving system security.

[0036] Specifically, Secure Multi-Party Computation (MPC) is a cryptographic technique that allows multiple participants to collaboratively complete a computational task while protecting their own data privacy. The core idea of ​​MPC is to use cryptographic methods to encrypt the participants' data before computation, ensuring that each participant can only obtain the computation result and their own encrypted data, and cannot access the data of other participants, thus achieving secure computation without the need for a trusted third party.

[0037] It should be understood that sensitive information requiring encryption protection, such as personal identification information and financial data, is extracted from the generated structured data packet B5 (i.e., the business scenario dataset) to form sensitive data C1 (i.e., the set of sensitive personal information).

[0038] Specifically, the encryption process is as follows:

[0039] Zero-knowledge proof (ZKP) (i.e., zero-knowledge proof protocol): Performs zero-knowledge proof processing on sensitive data C1 (i.e., a set of sensitive information about a person). A proof G1 (i.e., encrypted proof information) is generated using a zero-knowledge proof protocol (such as zk-SNARKs), which can verify the correctness of the sensitive data C1 (i.e., the set of sensitive information about a person) without exposing it.

[0040] Confidential computation (i.e., MPC multi-party secure computation algorithm): If data needs further analysis or computation, confidential computation technology (such as Intel SGX or MPC multi-party secure computation) is used to encrypt sensitive data C1 (i.e., the set of sensitive personal information) to ensure that data is not leaked during the computation process. The processed data generates encrypted data C2 (i.e., the encrypted dataset to be processed).

[0041] In the above embodiments, encrypted analysis of all business scenario data is performed to obtain encrypted datasets and encrypted proof information to be processed, ensuring that data will not be leaked during the calculation process. This solves the problems of tampering and forgery in traditional business data management methods and improves the credibility, transparency, security and automation level of the data.

[0042] Optionally, as an embodiment of the present invention, the business scenario data includes original business scenario images and original business voucher images.

[0043] In the server, the process of preprocessing all the business scenario data and aggregating all the preprocessed business scenario data to obtain a business scenario dataset includes:

[0044] Each of the original business scenario images is preprocessed to obtain a target business scenario image corresponding to each of the original business scenario images.

[0045] Target detection is performed on all the target business scene images using a pre-constructed convolutional neural network to obtain multiple business scene category labels;

[0046] Anomaly detection is performed on all the business scenario category labels using a pre-built deep learning model to obtain multiple business scenario anomaly data;

[0047] Text analysis is performed on all the original business voucher images to obtain the target business scenario text set. The target business scenario text set, all the business scenario category tags, and all the business scenario abnormal data are then combined to obtain the business scenario dataset.

[0048] It should be understood that the pre-built convolutional neural network can be a YOLO model or a Faster R-CNN model, and the pre-built deep learning model can be an autoencoder or a variational autoencoder (VAE).

[0049] It should be understood that high-resolution images or text data are acquired from mobile devices, including photos of logistics scenes (i.e., original business scene images), images of financial documents (i.e., original business document images), etc.

[0050] Specifically, convolutional neural networks (CNNs) (i.e. pre-built convolutional neural networks) such as YOLO or Faster R-CNN are used to detect key targets (such as goods, transportation vehicles, and operators) (i.e. target business scene images) in logistics scenarios. The results of the identification generate a series of bounding box coordinates and classification labels, forming data B1 (i.e. business scene category labels).

[0051] Specifically, anomaly detection verification is performed on detected objects (i.e., business scenario category labels) using a trained deep learning model (such as an autoencoder or variational autoencoder VAE) (i.e., a pre-built deep learning model). If the features of a target exceed the normal range of the model, it will be marked as anomalous data B2 (i.e., anomalous business scenario data).

[0052] It should be understood that the above detection and review results (B1, B2, B3, B4) (i.e., target business scenario text set, business scenario category label, and business scenario abnormal data) are integrated into structured data and packaged into a data package B5 (i.e., business scenario dataset).

[0053] In the above embodiments, all business scenario data are preprocessed, and all preprocessed business scenario data are combined to obtain a business scenario dataset. This solves the problems of tampering and forgery in traditional business data management methods and improves the credibility, transparency, security and automation level of the data.

[0054] Optionally, as an embodiment of the present invention, the process of performing image preprocessing on each of the original business scene images in the server to obtain target business scene images corresponding to each of the original business scene images includes:

[0055] Each of the original business scene images is denoised to obtain a denoised business scene image corresponding to each of the original business scene images.

[0056] The Canny edge detection algorithm is used to crop each of the denoised business scene images to obtain cropped business scene images corresponding to each of the original business scene images.

[0057] The perspective transformation algorithm is used to perform image correction processing on each of the cropped business scene images to obtain corrected business scene images corresponding to each of the original business scene images.

[0058] The nearest neighbor interpolation algorithm is used to perform image standardization processing on each of the corrected business scene images to obtain target business scene images corresponding to each of the original business scene images.

[0059] It should be understood that each of the original business scene images can be denoised using a Gaussian blur filter or a bilateral filter.

[0060] It should be understood that the Canny edge detection algorithm is a classic edge detection technique proposed by John F. Canny in 1986. It is a multi-stage algorithm designed to detect edges in images while minimizing false positives and false negatives.

[0061] It should be understood that the perspective transformation algorithm is a geometric transformation in computer graphics and computer vision used to simulate the perspective effect of a camera lens, that is, mapping a three-dimensional scene onto a two-dimensional plane. This transformation takes into account the size changes of objects at different depths and distances, conforming to the perspective rules observed by the human eye when viewing the real world.

[0062] Specifically, the nearest neighbor interpolation algorithm, also known as nearest point interpolation, is a simple image scaling or image reconstruction technique. It sets the value of an unknown pixel to the value of its nearest known neighbor pixel based on a distance criterion.

[0063] Specifically, the image preprocessing steps are as follows:

[0064] Image cleaning: Denoising images (i.e., original business scene images), including removing noise, blur, and other interfering information. Algorithms used can include Gaussian blur filters or bilateral filters.

[0065] Image cropping and correction: The image (i.e., the denoised business scene image) is automatically cropped based on the edge detection algorithm (such as Canny edge detection), and the perspective transformation technique is used to correct the tilt of the image (i.e., the cropped business scene image) to ensure that the effective area of ​​the image provides high-quality data for the next step of analysis.

[0066] Image standardization: Adjust the resolution and size of the images (i.e., the corrected images of the business scenario) to a uniform standard (e.g., 256x256 pixels) to facilitate subsequent model processing. The algorithm used can be nearest neighbor interpolation or bilinear interpolation.

[0067] In the above embodiments, image preprocessing is performed on the original business scene images to obtain target business scene images, eliminating noise, blur and other interference information in the images, ensuring that the effective area of ​​the image provides high-quality data for the next step of analysis, and solving the problems of tampering and forgery in traditional business data management methods.

[0068] Optionally, as an embodiment of the present invention, the process of performing text analysis on all the original business voucher images in the server to obtain the target business scenario text set includes:

[0069] The text of each original business voucher image is extracted using an optical character recognition algorithm to obtain multiple original business voucher texts corresponding to each original business voucher image.

[0070] The pre-built BERT model is used to perform text correction processing on each of the original business voucher texts to obtain the corrected business voucher texts corresponding to each of the original business voucher texts.

[0071] According to the preset text consistency detection rules, each of the corrected business voucher texts is subjected to text consistency detection to obtain the detected business voucher texts corresponding to each of the original business voucher texts.

[0072] According to the preset text validity detection rules, each of the detected business voucher texts is subjected to text validity detection to obtain the target business voucher text corresponding to each of the original business voucher texts. The target business scenario text set is obtained by combining all the original business voucher texts and all the target business voucher texts.

[0073] As should be understood, Optical Character Recognition (OCR) algorithms are technologies that convert printed or handwritten text into machine-readable text by scanning documents with electronic devices. The basic principles of OCR technology include three main steps: image preprocessing, feature extraction, and classifier classification.

[0074] It should be understood that the pre-built BERT model is a pre-trained language representation method proposed by Google in 2018. It is based on the Transformer architecture, and its main improvement lies in the adoption of a bidirectional training strategy. Compared with the traditional unidirectional (left to right or right to left) training method, BERT can more comprehensively understand the contextual information of the language.

[0075] Specifically, Optical Character Recognition (OCR) technology (i.e., optical character recognition algorithms) is used to extract text from financial document images (i.e., original business document images). Commonly used OCR engines such as Tesseract will generate the extracted text data B3 (i.e., the original business document text).

[0076] Specifically, named entity recognition (NER) and syntax parsers (such as SpaCy or BERT models) in natural language processing (NLP) are used to review the extracted text data (i.e., the original business voucher text), identify key entities such as amount, date, and signature, and perform consistency and legality checks on them to generate review result data B4 (i.e., the target business voucher text).

[0077] In the above embodiments, text analysis of all original business voucher images yields the target business scenario text set, which enables a more comprehensive understanding of the contextual information of the language. This solves the problems of tampering and forgery in traditional business data management methods and improves the credibility, transparency, security, and automation level of the data.

[0078] Optionally, as an embodiment of the present invention, in the blockchain, the process of encrypting the encrypted dataset to be processed and the encrypted proof information to obtain the target encrypted dataset and the decryption key includes:

[0079] The target encrypted dataset and the encrypted proof information are hashed using a hash algorithm to obtain the encrypted dataset and the decryption key.

[0080] It should be understood that the data packet (i.e., the encrypted dataset to be processed) is uploaded to the blockchain sidechain, generating a unique blockchain ID. The data packet (i.e., the encrypted dataset to be processed) is hashed using a hash algorithm (such as SHA-256) to ensure the integrity and immutability of the data.

[0081] Specifically, the hash algorithm is a mathematical function that converts data of arbitrary length into a fixed-length output, and is commonly used in fast data retrieval, integrity verification, data fingerprinting, and cryptography.

[0082] It should be understood that the encrypted data C2 (i.e., the encrypted dataset to be processed) and the generated proof G1 (i.e., the encrypted proof information) are uploaded to the blockchain via the sidechain, while recording metadata such as upload time and data source to form blockchain transaction T1.

[0083] In the above embodiments, a hash algorithm is used to hash the encrypted dataset to be processed and the encrypted proof information to obtain the target encrypted dataset and the decryption key, thus ensuring the integrity and immutability of the data.

[0084] Optionally, as an embodiment of the present invention, in the server, the process of verifying the data decryption request instruction, and generating a decryption instruction if the verification is successful, includes:

[0085] The data decryption request instruction is verified using a smart contract protocol. If the verification is successful, a decryption instruction is generated.

[0086] It should be understood that the smart contract protocol is a program that runs on a blockchain and can automatically execute contract terms when preset conditions are met. The smart contract protocol defines the rules for the creation, execution, and management of smart contracts.

[0087] In the above embodiments, the data decryption request instruction is verified using a smart contract protocol. If the verification is successful, a decryption instruction is generated, which solves the problems of tampering and forgery in traditional business data management methods and improves the credibility, transparency, security and automation level of the data.

[0088] Optionally, as an embodiment of the present invention, the process of decrypting and analyzing the target encrypted dataset based on the encryption proof information and the decryption key in the client to obtain the business scenario data decryption result includes:

[0089] The target encrypted dataset is decrypted for the first time using the decryption key to obtain the decryption dataset to be processed.

[0090] The encrypted proof information is used to verify the dataset to be decrypted. If the verification is successful, the dataset to be decrypted is decrypted a second time, and the result of the second decryption is used as the data decryption result for the business scenario. If the verification fails, a data error message is generated, and the data error message is used as the data decryption result for the business scenario, and sent to the server through the blockchain.

[0091] It should be understood that smart contracts control access permissions, and only authorized verifiers (i.e., clients) can decrypt the encrypted data (i.e., the target encrypted dataset) using the decryption key and verify the validity of the data by verifying G1 (i.e., the encrypted proof information) with zero-knowledge proof.

[0092] Specifically, the verifier (i.e. the client) obtains the decryption key through a smart contract, decrypts the encrypted data (i.e. the target encrypted dataset), verifies the zero-knowledge proof G1 (i.e. the encrypted proof information), and ensures the integrity and authenticity of the data.

[0093] In the above embodiments, the decryption analysis of the target encrypted dataset based on the encryption proof information and decryption key is used to obtain the decryption result of the business scenario data. This solves the problems of tampering and forgery in traditional business data management methods, improves the credibility, transparency, security and automation level of the data, and reduces human error, thereby improving user experience and operational efficiency.

[0094] Alternatively, as another embodiment of the present invention, the present invention relates to information recording and verification technology, specifically to a method and system that combines blockchain technology for recording and authenticating business operation scenarios, which is particularly suitable for fields that require highly reliable data recording, such as logistics tracking, financial auditing, and legal evidence preservation.

[0095] Optionally, as another embodiment of the present invention, the present invention aims to provide a method and system for recording and authenticating business scenario data by combining blockchain, artificial intelligence, cross-chain technology and privacy protection technology, to solve the problems of tampering and forgery in traditional recording methods, and to improve the credibility, transparency, security and automation level of data operations.

[0096] Alternatively, as another embodiment of the present invention, the technical solution of the present invention is as follows:

[0097] 1. Multi-layered blockchain architecture: The system adopts a multi-layered blockchain architecture consisting of a main chain and side chains. The main chain is used to record critical business data, while the side chains store more detailed data, such as high-resolution images and large data files, ensuring efficient and flexible data storage.

[0098] 2. Artificial Intelligence Analysis Module: This module integrates AI analysis to automatically detect and verify the authenticity and validity of uploaded data. The AI ​​module can detect anomalies in logistics scenarios through image recognition technology or review textual information in financial documents through natural language processing technology.

[0099] 3. Cross-chain data interoperability: The system supports cross-chain technology, allowing data interoperability between different blockchain networks, enabling a wider range of application scenarios and data sharing capabilities.

[0100] 4. Hierarchical Access Control: A blockchain-based hierarchical access control mechanism was designed. Smart contracts control users' access permissions to data, including read, write, and delete operations, ensuring data security and privacy.

[0101] 5. Real-time dynamic data verification: The system supports real-time recording and verification of dynamic data. It combines IoT devices to achieve automated data collection and uploading, and performs real-time verification on the blockchain. It is suitable for complex business scenarios such as real-time logistics tracking.

[0102] 6. Privacy Protection Technology: The system uses zero-knowledge proof (ZKP) or confidential computing technology to ensure that data is encrypted during the upload process, and only specific verifiers can decrypt and view it, thus protecting data privacy.

[0103] 7. Smart Contract Extension: The system supports advanced smart contract functionality, allowing users to customize complex business processes and rules according to specific business needs, and supports the upgrading and extension of smart contracts to adapt to the ever-changing business environment.

[0104] Alternatively, as another embodiment of the present invention, the key points of the invention are as follows:

[0105] 1. Improve the efficiency and flexibility of data storage through a multi-layered blockchain architecture that combines the main chain and side chains.

[0106] 2. Integrates an artificial intelligence analysis module to automatically verify the authenticity and validity of data, reducing human error.

[0107] 3. Supports cross-chain data operations, expanding the system's application scope and data sharing capabilities.

[0108] 4. Adopt a hierarchical access control mechanism to ensure data security and privacy.

[0109] 5. Supports real-time dynamic data recording and verification, adapting to complex business scenarios.

[0110] 6. Introduce privacy protection technologies to ensure the security of data transmission and storage.

[0111] 7. Provide smart contract extension functionality to meet the automation needs of complex business processes.

[0112] Alternatively, as another embodiment of the present invention, the present invention has the following advantages and positive effects compared with the prior art:

[0113] 1. Data immutability: By utilizing the decentralized and encrypted technologies of blockchain, it ensures that all recorded data cannot be tampered with or deleted, fundamentally improving data security and reliability.

[0114] 2. High transparency: All data records are publicly available and transparent on the blockchain. Anyone can retrieve and verify them through the blockchain ID, which enhances the credibility and traceability of the data.

[0115] 3. Intelligent data verification: The integrated AI module improves the intelligence level of data authentication, reduces the workload of manual review, and enhances the accuracy and authenticity of the data.

[0116] 4. Flexible cross-chain interoperability: Supports cross-chain technology to achieve data interoperability between different blockchain networks and expand the application scenarios of the system.

[0117] 5. Privacy Protection: Ensure the privacy and security of sensitive data through zero-knowledge proofs or confidential computing technologies.

[0118] 6. Real-time and Immediacy: The system supports real-time recording and uploading of dynamic data to the blockchain, ensuring the timeliness and immediacy of the data, which helps to respond to and process business needs in a timely manner.

[0119] 7. Wide applicability: The system can be applied to multiple fields such as logistics tracking, financial auditing, and legal evidence storage, providing reliable solutions for various business scenarios that require high-credibility data recording and verification.

[0120] 8. Easy to operate: Users only need to take a photo with their mobile device, and the system will automatically process the overlay of all information and upload the data. No complicated operations are required, which improves the user experience and operational efficiency.

[0121] Alternatively, as another embodiment of the present invention, the logistics tracking system has the following structural composition:

[0122] 1. Mobile devices: smartphones or tablets equipped with high-resolution cameras and GPS positioning capabilities.

[0123] 2. Blockchain Network: The main chain stores key business data, while the side chains store detailed scenario data.

[0124] 3. Server: Used for processing photo information overlay, AI data analysis, and data upload operations.

[0125] Its working principle is as follows:

[0126] 1. Logistics operators use mobile devices to take photos of goods at transportation nodes.

[0127] 2. The system obtains the current GPS location information and records the shooting time.

[0128] 3. Automatically overlay information onto photos, including location, operator, time, device ID, blockchain ID, etc.

[0129] 4. Photos and related metadata are uploaded to the blockchain to generate a unique blockchain ID.

[0130] 5. Logistics managers can retrieve and verify the status of goods through blockchain ID.

[0131] Its functions and effects are as follows:

[0132] 1. Ensure that goods are recorded and verified at each transportation node to prevent data tampering.

[0133] 2. Improve the transparency and efficiency of logistics management.

[0134] 3. Facilitates tracking and verification of the actual transportation status of goods.

[0135] Alternatively, as another embodiment of the present invention, the system structure of the financial audit is as follows:

[0136] 1. Mobile devices: smartphones or tablets equipped with high-resolution cameras.

[0137] 2. Blockchain network: main chain and side chain structure.

[0138] 3. Server: Handles photo information overlay, AI analysis, and data upload operations.

[0139] Its working principle is as follows:

[0140] 1. Finance staff take photos of vouchers after each transaction.

[0141] 2. The time and location of the shooting were recorded by the mobile device.

[0142] 3. The system automatically overlays information such as location, operator, time, device ID, and blockchain ID onto the photo.

[0143] 4. Upload photos and related metadata to the blockchain to generate a blockchain ID.

[0144] 5. Auditors use blockchain IDs to retrieve and verify the authenticity of transactions.

[0145] Its functions and effects are as follows:

[0146] 1. Ensure the authenticity and integrity of financial transaction records and prevent data tampering.

[0147] 2. Improve the transparency and efficiency of auditing.

[0148] 3. Facilitates auditors in tracking and verifying transaction details.

[0149] Alternatively, as another embodiment of the present invention, the present invention has broad application prospects and significant practical benefits in multiple fields. By combining blockchain technology and automated information processing, the system significantly improves the security, reliability and transparency of data recording in business scenarios.

[0150] Figure 2 This is a flowchart illustrating a business scenario data management method provided in an embodiment of the present invention.

[0151] Alternatively, as another embodiment of the present invention, such as Figure 2 As shown, a data management method for a business scenario includes the following steps:

[0152] Import data from multiple business scenarios and perform encrypted analysis on all of the aforementioned business scenario data to obtain the encrypted dataset to be processed and encrypted proof information;

[0153] The dataset to be encrypted and the encryption proof information are encrypted to obtain the target encrypted dataset and the decryption key.

[0154] Input a data decryption request command and verify the data decryption request command. If the verification is successful, perform decryption analysis on the target encrypted dataset based on the encryption proof information and the decryption key to obtain the data decryption result for the business scenario.

[0155] Optionally, another embodiment of the present invention provides a business scenario data management system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the business scenario data management method described above. This system can be a computer or similar system.

[0156] Optionally, another embodiment of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the business scenario data management method described above.

[0157] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0158] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0160] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0161] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0162] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This is understood to mean that the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0163] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A business scenario data management system, characterized in that, include: Servers, blockchain, and clients; The server is used to import data from multiple business scenarios, perform encrypted analysis on all the business scenario data to obtain encrypted datasets to be processed and encrypted proof information, and send the encrypted datasets to be processed and the encrypted proof information to the blockchain; The blockchain is used to encrypt the encrypted dataset to be processed and the encrypted proof information to obtain the target encrypted dataset and the decryption key; The client is used to input a data decryption request command and send the data decryption request command to the server through the blockchain; The server is also used to verify the data decryption request instruction. If the verification is successful, a decryption instruction is generated, and the decryption instruction and the encryption proof information are sent to the blockchain. The blockchain is also used to send the encrypted proof information, the target encrypted dataset, and the decryption key to the client according to the decryption instruction; The client is used to perform decryption analysis on the target encrypted dataset based on the encryption proof information and the decryption key to obtain the decryption result of the business scenario data.

2. The business scenario data management system according to claim 1, characterized in that, In the server, the process of performing encrypted analysis on all the business scenario data to obtain the encrypted dataset to be processed and the encrypted proof information includes: All the business scenario data are preprocessed, and all the preprocessed business scenario data are combined to obtain a business scenario dataset; Multiple sensitive information items of individuals are extracted from the business scenario dataset, and all of the sensitive information items of individuals are combined to obtain a set of sensitive information items of individuals; The sensitive information set of the person is encrypted using a zero-knowledge proof protocol to obtain encrypted proof information; The sensitive information set of the person is encrypted using the MPC multi-party secure computation algorithm to obtain the encrypted dataset to be processed.

3. The business scenario data management system according to claim 2, characterized in that, The business scenario data includes original business scenario images and original business voucher images. In the server, the process of preprocessing all the business scenario data and aggregating all the preprocessed business scenario data to obtain a business scenario dataset includes: Each of the original business scenario images is preprocessed to obtain a target business scenario image corresponding to each of the original business scenario images. Target detection is performed on all the target business scene images using a pre-constructed convolutional neural network to obtain multiple business scene category labels; Anomaly detection is performed on all the business scenario category labels using a pre-built deep learning model to obtain multiple business scenario anomaly data; Text analysis is performed on all the original business voucher images to obtain the target business scenario text set. The target business scenario text set, all the business scenario category tags, and all the business scenario abnormal data are then combined to obtain the business scenario dataset.

4. The business scenario data management system according to claim 3, characterized in that, In the server, the process of performing image preprocessing on each of the original business scene images to obtain the target business scene image corresponding to each of the original business scene images includes: Each of the original business scene images is denoised to obtain a denoised business scene image corresponding to each of the original business scene images. The Canny edge detection algorithm is used to crop each of the denoised business scene images to obtain cropped business scene images corresponding to each of the original business scene images. The perspective transformation algorithm is used to perform image correction processing on each of the cropped business scene images to obtain corrected business scene images corresponding to each of the original business scene images. The nearest neighbor interpolation algorithm is used to perform image standardization processing on each of the corrected business scene images to obtain target business scene images corresponding to each of the original business scene images.

5. The business scenario data management system according to claim 3, characterized in that, In the server, the process of performing text analysis on all the original business voucher images to obtain the target business scenario text set includes: The text of each original business voucher image is extracted using an optical character recognition algorithm to obtain multiple original business voucher texts corresponding to each original business voucher image. The pre-built BERT model is used to perform text correction processing on each of the original business voucher texts to obtain the corrected business voucher texts corresponding to each of the original business voucher texts. According to the preset text consistency detection rules, each of the corrected business voucher texts is subjected to text consistency detection to obtain the detected business voucher texts corresponding to each of the original business voucher texts. According to the preset text validity detection rules, each of the detected business voucher texts is subjected to text validity detection to obtain the target business voucher text corresponding to each of the original business voucher texts. The target business scenario text set is obtained by combining all the original business voucher texts and all the target business voucher texts.

6. The business scenario data management system according to claim 1, characterized in that, In the blockchain, the process of encrypting the encrypted dataset to be processed and the encrypted proof information to obtain the target encrypted dataset and the decryption key includes: The target encrypted dataset and the encrypted proof information are hashed using a hash algorithm to obtain the encrypted dataset and the decryption key.

7. The business scenario data management system according to claim 1, characterized in that, In the server, the process of verifying the data decryption request command and generating a decryption command if the verification is successful includes: The data decryption request instruction is verified using a smart contract protocol. If the verification is successful, a decryption instruction is generated.

8. The business scenario data management system according to claim 1, characterized in that, In the client, the process of decrypting and analyzing the target encrypted dataset based on the encryption proof information and the decryption key to obtain the business scenario data decryption result includes: The target encrypted dataset is decrypted for the first time using the decryption key to obtain the decryption dataset to be processed. The encrypted proof information is used to verify the dataset to be decrypted. If the verification is successful, the dataset to be decrypted is decrypted a second time, and the result of the second decryption is used as the data decryption result for the business scenario. If the verification fails, a data error message is generated, and the data error message is used as the data decryption result for the business scenario, and sent to the server through the blockchain.

9. A data management method for a business scenario, characterized in that, Includes the following steps: Import data from multiple business scenarios and perform encrypted analysis on all of the aforementioned business scenario data to obtain the encrypted dataset to be processed and encrypted proof information; The dataset to be encrypted and the encryption proof information are encrypted to obtain the target encrypted dataset and the decryption key. Input a data decryption request command and verify the data decryption request command. If the verification is successful, the target encrypted dataset is decrypted and analyzed based on the encryption proof information and the decryption key to obtain the data decryption result for the business scenario.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the business scenario data management method as described in claim 9 is implemented.

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