CN118377683A - Multi-mode data-based full life cycle archiving and tracing method - Google Patents
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Abstract
The invention discloses a method based on multi-mode data full life cycle filing traceability, which comprises a data acquisition, data storage, data processing, data access, data filing, data destruction, data security and real-time monitoring and early warning system.
Description
Technical Field
The invention relates to the technical field of information, in particular to a full life cycle archiving and tracing method based on multi-mode data.
Background
With the rapid development of information technology, multi-modal data generated by various industries, such as text, images, video and audio, has shown explosive growth.
The prior art has some problems in management, archiving and tracing of multi-modal data: the collection, storage, processing and access processes of the data lack of systematic management and automatic support, the data operation record is easy to tamper, the data security and privacy protection are insufficient, the data life cycle management is imperfect, and the integrity, traceability and usability of the data cannot be effectively ensured. Therefore, a need exists for a multi-modal data full lifecycle archiving and tracing method based on intelligent contracts and blockchain technology to improve the efficiency, security and transparency of data management.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a full life cycle archiving and tracing method based on multi-mode data, which aims to overcome the technical problems in the prior art.
For this purpose, the invention adopts the following specific technical scheme:
a method for filing and tracing a source based on multi-mode data full life cycle comprises the following steps:
s101, data acquisition: the method comprises the steps of collecting multi-mode data through a sensor, a camera and a microphone, preprocessing the data, and triggering early warning and maintenance operation when the state of the collecting equipment is monitored and equipment faults or anomalies are detected;
S102, data storage: uploading the preprocessed data to a centralized storage system, and recording the hash value and metadata of the data to a block chain;
S1021 centralized storage preparation: configuring IPFS nodes to ensure normal operation, wherein the type of codes used is Go language IPFS configuration;
S1022 data uploading: uploading the preprocessed data to IPFS, and obtaining CID of the data, wherein the used code types are Python script and IPFS HTTP API;
S1023, recording hash values and metadata of the data into a blockchain by using intelligent contracts to ensure the non-tamper property of the data, wherein the type of codes used is Solidity intelligent contract language, and the algorithm name used is Merkle tree;
S1024 data checksum confirmation: checking the data by using the hash value to ensure that no error exists in the uploading process, wherein the type of the used code is Python script;
S1025 metadata record: recording the data type, the acquisition time and the metadata information of the acquisition equipment to a blockchain, wherein the used code type is Solidity intelligent contract language;
s103, data processing: triggering data processing operations by using intelligent contracts, including data fusion, feature extraction and analysis;
S104, data access: verifying the data access authority and recording the log through the intelligent contract;
S105, data archiving: periodically triggering data archiving operation, storing the compressed and encrypted data to a long-term storage system, and recording a storage position to a blockchain;
s106, data destruction: according to the data life cycle or compliance requirements, executing data destruction operation, and recording destruction logs to a blockchain;
S107 data security: the security of the data is ensured by homomorphic encryption and zero knowledge proof technology;
S108, real-time monitoring and early warning: and monitoring the data and the system state, and triggering early warning under abnormal conditions.
Further, the data acquisition includes:
s1011 data acquisition initialization: confirming normal connection and operation of a sensor, a camera and microphone equipment, and setting equipment resolution, sampling rate and data format parameters;
S1012 data acquisition start: writing and deploying a device driver, starting data acquisition, wherein the code types are Python script and C++ driver;
s1013 data preprocessing: noise filtering is carried out on the original data by using a signal processing algorithm, the code type is Python script, the algorithm name is median filtering algorithm, the collected data is converted into a unified format for subsequent processing and storage, the data conversion code type is Python script, and the algorithm is data format conversion algorithm;
S1014 data upload preparation: and checking the preprocessed data to ensure the integrity and accuracy of the data, wherein the type of the used code is Python script, and the name of the algorithm is checking CRC.
Furthermore, the data processing step adopts a multi-mode deep learning model to carry out intelligent fusion and feature extraction.
Further, the data access step includes user authentication based on multi-factor authentication.
Further, the data archiving step comprises compressing and encrypting the data, so that the storage efficiency and the security of the archived data are ensured.
Further, the data storage step uses a decentralised storage system and records data hash values and metadata to the blockchain via smart contracts.
Furthermore, in the data processing step, self-supervision learning and transfer learning technology is utilized, so that the training efficiency and generalization capability of the data processing model are improved.
Further, the data access step includes a dynamic rights management system that implements a combination of role-based access control and attribute-based access control.
Further, data security is ensured through homomorphic encryption, zero knowledge proof and secure multiparty computing techniques.
Furthermore, the real-time monitoring and early warning system integrates a machine learning algorithm, intelligent early warning is realized, and the potential risk is discovered in advance.
The invention provides a full life cycle archiving and tracing method based on multi-mode data, which solves the problem of multi-mode data management in the prior art by introducing intelligent contract and blockchain technology and combining an efficient data compression and encryption mechanism, an intelligent data fusion and feature extraction, a real-time monitoring and early warning system and a user-friendly data access and sharing platform.
The method has the specific beneficial effects that:
1. The method based on the multi-mode data full life cycle archiving and tracing is transparent in data operation, all data operation records are recorded on a block chain through intelligent contracts, and operation non-tampering and transparent traceability are achieved;
2. The method based on the multi-mode data full life cycle filing traceability has high data security, and adopts a high-efficiency data compression and encryption mechanism to ensure the security of data in the transmission and storage processes;
3. The method for archiving and tracing the multi-modal data based on the full life cycle of the multi-modal data is used for intelligent data processing, deep learning and self-supervision learning technologies are used for realizing intelligent fusion and feature extraction of the multi-modal data, and the efficiency and accuracy of data processing are improved;
4. The method based on the full life cycle filing and tracing of the multi-mode data monitors and early warns in real time, monitors the data operation and the system state in real time, discovers anomalies in time and early warns in time, and improves the stability and safety of the system;
5. the method for archiving and tracing the full life cycle of the multi-mode data is high in user friendliness, provides a user-friendly data access and sharing platform, simplifies the data operation flow, and meets diversified access and sharing requirements.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart illustrating steps of a method for full lifecycle archive tracing based on multi-modal data according to a first embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, a method for full life cycle filing and tracing based on multi-mode data according to an embodiment of the invention includes the following steps:
s101, data acquisition: the method comprises the steps of collecting multi-mode data through a sensor, a camera and microphone equipment, and preprocessing the data;
S1011 data acquisition initialization: confirming normal connection and operation of a sensor, a camera and microphone equipment, and setting equipment resolution, sampling rate and data format parameters, wherein an OpenCV library and a PyAudio library are used for data acquisition initialization, and the specific implementation step A is as follows:
a1, importing an OpenCV library for camera data acquisition, and key codes: import cv2;
a2, importing PyAudio libraries for audio data acquisition, and key codes: import pyaudio;
a3, initializing camera equipment, and starting video acquisition through cv2.video capture (0);
a4, starting a video acquisition cycle, reading camera frame data, and calling a processing function process_frame (frame) to perform data processing;
a5, initializing an audio device, and starting audio collection through audio.open (format=pyaudio.paint16, channels=1, rate=44100, input=true);
A6, starting an audio acquisition cycle, reading audio data, and calling a processing function process_audio (data) to perform data processing;
S1012 data acquisition start: writing and deploying a device driver, starting data acquisition, wherein the code types are Python script and C++ driver;
S1013 data preprocessing: noise filtering is carried out on the original data by using a signal processing algorithm, the code type is a Python script, the algorithm name is a median filtering algorithm, the collected data is converted into a unified format for subsequent processing and storage, the data conversion code type is the Python script, and the algorithm is a data format conversion algorithm, and the specific steps B are as follows:
b1, importing NumPy libraries and Scipy libraries, performing noise filtration on the original data by using a median filtering algorithm, and performing key codes: import numpy as np, from scipy.signal import medfilt;
b2, realizing a noise filtering function remove_noise (signal), returning processed data and key codes: medfilt (signal, kernel_size=3);
B3, converting the acquired data into a unified format for subsequent processing and storage;
B4, importing a JSON library, converting the data into a JSON format, and key codes: importjson, json. Dump (data);
S1014 data upload preparation: the preprocessed data is checked, the integrity and the accuracy of the data are ensured, the type of the used code is Python script, the name of the algorithm is checking CRC, and the CRC algorithm is used for checking the data, and the specific steps are as follows:
Leading in zlib library to realize data checking function calculation_crc (data), key code: importzlib, zlib. Crc32 (data. Encode ('utf-8'));
s102, data storage: uploading the preprocessed data to a decentralised storage system, and recording the hash value and metadata of the data to a blockchain;
s1021, performing decentralization storage preparation: configuring IPFS nodes to ensure normal operation, wherein the used code type is Go language IPFS configuration, and the specific step C is as follows:
Initializing IPFS nodes and key codes: ipfs init;
And C2, starting IPFS daemon processes and key codes: ipfs daemon;
S1022 data uploading: uploading the preprocessed data to IPFS, and obtaining CID of the data, wherein the code type is Python script, IPFS HTTP API, and the specific step D is as follows:
d1, uploading data to a IPFS node running locally through an HTTP POST request;
Key code: requests.post ('http:// localhost:5001/api/v0/add', files = { 'file': data });
D2, analyzing the returned JSON data to obtain CID;
key code: response.json () [ 'Hash' ];
S1023, recording hash values and metadata of data into a blockchain by using intelligent contracts to ensure the non-tamper property of the data, wherein the type of codes used is Solidity intelligent contract language, the algorithm name used is Merkle tree, and the specific step E is as follows:
e1, writing an intelligent contract DataRecord, and defining a data entry structure DATAENTRY;
E2, implement function addDataRecord for adding data records in contracts, key code ：pragma solidity ^0.8.0、struct DataEntry、mapping(address => DataEntry[ ]) public dataRecords、block.timestamp;
E3, realizing a function getDataRecords for acquiring the data record;
S1024 data checksum confirmation: and checking the data by using the hash value to ensure that no error exists in the uploading process, wherein the type of the used code is a Python script, and the specific steps are as follows:
Uploading data to IPFS and comparing CIDs to verify data consistency, key code: upper_to_ipfs (data), ASSERT VERIFY _data (formatted_data, cid);
S1025 metadata record: recording the data type, the acquisition time and the metadata information of the acquisition equipment into a blockchain, wherein the used code type is Solidity intelligent contract language, and the specific step F is as follows:
f1, writing an intelligent contract MetadataRecord, and defining a Metadata structure Metadata;
f2, realizing a function addMetadataRecord for adding metadata records in the contract, and key codes: struct Metadata, mapping (address= > Metadata [ ]) public metadataRecords;
F3, realizing a function getMetadataRecords for acquiring metadata records;
s103, data processing: triggering data processing operations by using intelligent contracts, including data fusion, feature extraction and analysis;
s104, data access: data access authority verification and log recording are carried out through intelligent contracts, so that the legality and traceability of data access are ensured;
S105, data archiving: periodically triggering data archiving operation, storing the compressed and encrypted data to a long-term storage system, and recording a storage position to a blockchain;
s106, data destruction: according to the data life cycle or compliance requirements, executing data destruction operation, and recording destruction logs to a blockchain;
s107 data security: ensuring the safety of data by homomorphic encryption and zero knowledge proof technology;
s108, a real-time monitoring and early warning system: and integrating a real-time monitoring and early warning system, monitoring data and system states, and triggering early warning under abnormal conditions.
The data acquisition step comprises the steps of monitoring the state of acquisition equipment, and triggering early warning and maintenance operation when equipment faults or anomalies are detected.
The data processing step adopts a multi-mode deep learning model to carry out intelligent fusion and feature extraction.
The data access step includes user authentication based on multi-factor authentication.
The data archiving step comprises the steps of compressing and encrypting the data, so that the storage efficiency and the security of the archived data are ensured.
The data storage step uses a decentralised storage system and records data hash values and metadata to the blockchain through smart contracts.
In the data processing step, self-supervision learning and transfer learning technology is utilized, so that the training efficiency and generalization capability of the data processing model are improved.
The data access step includes a dynamic rights management system that implements a combination of role-based access control and attribute-based access control.
Data security is ensured by homomorphic encryption, zero knowledge proof and secure multiparty computing techniques.
The real-time monitoring and early warning system integrates a machine learning algorithm, intelligent early warning is realized, and the potential risk is discovered in advance.
Example two
In this embodiment, the data acquisition and storage acquire video, audio and environmental data through devices such as a camera, a microphone, a temperature sensor, a pressure sensor and the like, perform preprocessing, including data cleaning, format conversion and preliminary data analysis, upload the preprocessed data to the decentralizing storage system IPFS, record hash values and metadata of the data to the ethernet blockchain through an intelligent contract, set triggering conditions of the data acquisition, including device online, periodic acquisition operation and device state monitoring, trigger early warning and maintenance operation when detecting device faults or anomalies, and in addition, the data acquisition can integrate unmanned aerial vehicle or satellite devices to perform large-scale data acquisition so as to improve data coverage.
Example III
According to the embodiment, the data processing and intelligent fusion adopt a multi-mode transducer model to carry out intelligent fusion and feature extraction on acquired text, image, video and audio data, the data processing operation is triggered through an intelligent contract, the data is analyzed by utilizing a deep learning technology, the analysis comprises data fusion, feature extraction and prediction analysis, a data processing result is stored in a decentralization storage system, hash values and metadata are recorded in a blockchain, in the data processing process, the self-supervision learning and transfer learning technology is adopted, the training efficiency and the generalization capability of the data processing model are improved, further, edge calculation is introduced, part of data processing tasks are lowered to edge equipment, and the instantaneity is improved.
Example IV
In this embodiment, the data access control and log includes recording that a user initiates a data access request through a user-friendly interface, the system uses multi-factor authentication to verify the user identity, and checks the user access authority through an intelligent contract, after the access authority passes the verification, the contract records the access log including the user ID, the access time and the access type, the user accesses the data according to the address returned by the contract, and simultaneously, the system realizes a dynamic authority management system combining role-based access control and attribute-based access control, the data access interface is diversified, and supports multiple data access modes such as REST API, graphQL and the like, so as to meet the requirements of different application scenes.
Example five
In this embodiment, data archiving and destruction includes periodically checking a data status, triggering a data archiving operation, compressing and encrypting archived data, and storing the data in a long-term storage system, and a data archiving log including a storage location and metadata recorded in a blockchain.
Example six
In this embodiment, the system integrates a real-time monitoring and early warning system, combines a machine learning algorithm to perform intelligent early warning, and the real-time monitoring system collects information such as a data operation log, a system state, a network flow, an equipment running state and the like, performs real-time analysis, automatically triggers early warning when abnormal behaviors or potential risks are found, performs emergency treatment measures, performs data isolation, permission recovery and notifies related personnel, and in addition, integrates more monitoring indexes, and provides more comprehensive monitoring and early warning functions by monitoring the temperature, the humidity, the atmospheric pressure and the like of data through a physical environment.
Besides the conventional cameras, microphones, temperature and pressure sensors, the data acquisition can be further expanded to use high-level sensors such as hyperspectral imaging equipment and laser radars, so that the dimensionality and the details of data are further enriched, meanwhile, the data acquisition frequency can be dynamically adjusted according to requirements, and high-frequency real-time data acquisition and low-frequency timing data acquisition modes are supported.
It should be further noted that in the data processing link, more advanced processing techniques may be introduced, such as a graph neural network for analysis of image and video data, reinforcement learning for dynamic data optimization, and meanwhile, the data processing process may employ a distributed computing architecture, and use cloud computing resources to improve processing efficiency and expansibility.
In addition, in the aspect of data access control, the method can be expanded to comprise time-based access control, time limit of access authority is realized, a data sharing platform can integrate social media functions, data sharing and collaboration among users are supported, and data utilization rate and user interactivity are enhanced.
Regarding the aspect of security, a quantum encryption technology is further introduced, the security of the data in a quantum computing environment is improved, and the aspect of privacy protection can be achieved by adopting a differential privacy technology, so that the user privacy is protected in the data processing and analyzing process, and the compliance of the data in the using process is ensured.
In the aspect of system operation and maintenance, the system can automatically adjust parameters and optimize configuration according to the running state and environmental change by integrating the self-adaptive system technology, thereby realizing intelligent operation and maintenance, introducing an automatic operation and maintenance tool and improving operation and maintenance efficiency and response speed.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. The method for filing and tracing the source based on the full life cycle of the multi-mode data is characterized by comprising the following steps:
s101, data acquisition: the method comprises the steps of collecting multi-mode data through a sensor, a camera and a microphone, preprocessing the data, and triggering early warning and maintenance operation when the state of the collecting equipment is monitored and equipment faults or anomalies are detected;
S102, data storage: uploading the preprocessed data to a centralized storage system, and recording the hash value and metadata of the data to a block chain;
S1021 centralized storage preparation: configuring IPFS nodes to ensure normal operation of the IPFS nodes;
s1022 data uploading: uploading the preprocessed data to IPFS, and acquiring CID of the data;
s1023, recording hash values and metadata of the data into a blockchain by using an intelligent contract, wherein the algorithm name is Merkle tree;
S1024 data checksum confirmation: checking the data by using the hash value;
s1025 metadata record: recording the data type, the acquisition time and metadata information of acquisition equipment to a blockchain;
s103, data processing: triggering data processing operations by using intelligent contracts, including data fusion, feature extraction and analysis;
S104, data access: verifying the data access authority and recording the log through the intelligent contract;
S105, data archiving: periodically triggering data archiving operation, storing the compressed and encrypted data to a long-term storage system, and recording a storage position to a blockchain;
s106, data destruction: according to the data life cycle or compliance requirements, executing data destruction operation, and recording destruction logs to a blockchain;
S107 data security: the security of the data is ensured by homomorphic encryption and zero knowledge proof technology;
S108, real-time monitoring and early warning: and monitoring the data and the system state, and triggering early warning under abnormal conditions.
2. The method for full lifecycle archival traceability based on multi-modal data according to claim 1, wherein the data collection comprises:
S1011 data acquisition initialization, namely, confirming the normal connection and operation of a sensor, a camera and a microphone device, and setting the resolution, the sampling rate and the data format parameters of the device;
s1012 data acquisition start: writing and deploying a device driver, and starting data acquisition;
s1013 data preprocessing: noise filtering is carried out on the original data by using a signal processing algorithm, and the acquired data is converted into a unified format;
s1014 data upload preparation: and checking the preprocessed data, and ensuring the integrity and accuracy of the data.
3. The method for full life cycle archiving and tracing based on multi-modal data according to claim 2, wherein the data processing adopts a multi-modal deep learning model for intelligent fusion and feature extraction.
4. A method of full lifecycle archival traceability based on multimodal data according to claim 3, wherein the data access includes user authentication based on multi-factor authentication.
5. The method for tracing full lifecycle archiving of multimodal data according to claim 4, wherein the archiving of data comprises compressing and encrypting the data to ensure the storage efficiency and security of the archived data.
6. The multi-modal data full lifecycle archive tracing method of claim 5, wherein the data storage uses a centralized storage system and records data hash values and metadata to the blockchain via smart contracts.
7. The method for full life cycle archiving and tracing based on multi-modal data according to claim 6, wherein the data processing utilizes self-supervised learning and transfer learning technology to improve training efficiency and generalization capability of the data processing model.
8. The method for full lifecycle archival traceability of multimodal data according to claim 7, wherein the data access includes a dynamic rights management system implementing a combination of role-based access control and attribute-based access control.
9. The method for full lifecycle archiving and tracing of multi-modal data according to claim 8, wherein the data security is guaranteed by homomorphic encryption, zero knowledge proof and secure multiparty computing techniques.
10. The method for full life cycle filing and tracing based on multi-mode data according to claim 9, wherein a machine learning algorithm is integrated with a real-time monitoring and early warning system, intelligent early warning is achieved, and potential risks are found in advance.
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| CN113222625A (en) * | 2021-06-02 | 2021-08-06 | 安徽国科检测科技有限公司 | Multi-element heterogeneous data model of agricultural product detection result and construction method thereof |
| CN117951746A (en) * | 2024-03-26 | 2024-04-30 | 北京大学第三医院（北京大学第三临床医学院） | Medical data encryption system for multi-mode large language model |
| CN118152481A (en) * | 2024-05-10 | 2024-06-07 | 天津民祥生物医药股份有限公司 | Drug information storage method based on distributed edge calculation and multi-mode data |
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| CN113222625A (en) * | 2021-06-02 | 2021-08-06 | 安徽国科检测科技有限公司 | Multi-element heterogeneous data model of agricultural product detection result and construction method thereof |
| CN117951746A (en) * | 2024-03-26 | 2024-04-30 | 北京大学第三医院（北京大学第三临床医学院） | Medical data encryption system for multi-mode large language model |
| CN118152481A (en) * | 2024-05-10 | 2024-06-07 | 天津民祥生物医药股份有限公司 | Drug information storage method based on distributed edge calculation and multi-mode data |
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| CN119226236A (en) * | 2024-12-04 | 2024-12-31 | 杭州易康信科技有限公司 | A method and system for independent application of multimodal data fusion and archiving |
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