Data traceability visualization interactive system, method and storage medium based on blockchain

Abstract

The present invention relates to the field of data traceability technology, and discloses a data traceability visualization interaction system, method and storage medium based on blockchain. The system includes an edge computing module, a dynamic desensitization module, an augmented reality interaction module and a blockchain trusted verification module. The system compresses the video stream and adjusts the frame rate through edge computing. The dynamic desensitization module identifies and processes sensitive information in the video in real time. The augmented reality interaction module supports visual rendering and user interaction. The blockchain trusted verification module realizes data encryption storage and authenticity verification. Users can complete video traceability and data authenticity verification based on blockchain, and trigger automatic compensation when necessary. The invention is suitable for video data traceability in multiple scenarios such as production, processing, circulation and use, and has the advantages of data verifiability, video visualization interaction and privacy protection.

Description

Technical Field

[0001] The present invention relates to the field of data traceability technology, and in particular to a blockchain-based data traceability visualization interactive system, method, and storage medium. Background Art

[0002] With the development of technologies such as the Internet of Things, big data, and blockchain, data traceability has been widely applied in fields such as supply chain management, food safety, pharmaceutical distribution, industrial production, and energy management. Traditional data traceability relies primarily on text, images, or simple data information displays. These technologies suffer from limitations such as limited information, poor real-time performance, and insufficient user interactivity. This makes it difficult to meet the demand for transparent, visual, and verifiable traceability in complex modern scenarios.

[0003] Especially in scenarios involving production and processing, circulation and transportation, on-site operations, etc. that require real-time monitoring and multi-dimensional information interaction, video data, as an important traceability information carrier, has the advantages of strong intuitiveness and large amount of information.

[0004] Existing video provenance tracing technologies generally face the following technical bottlenecks:

[0005] Lack of real-time performance: The amount of video data is large, and traditional centralized storage and processing methods are prone to high transmission delays and unsmooth interactions, making it difficult to achieve real-time traceability and interaction.

[0006] Weak data credibility: Videos and related data are mostly stored on centralized platforms, which are easily tampered with and lack effective anti-counterfeiting and non-repudiation mechanisms. It is difficult for users to directly verify the authenticity of traceability data.

[0007] High privacy and security risks: Directly publishing raw videos of scenes such as production workshops, medical facilities, and logistics warehouses may leak personnel privacy, equipment parameters, or key process information.

[0008] Poor user experience: Traditional traceability methods have weak interactivity. Users can usually only passively view text or video content, lacking means to verify data authenticity and visual interactive experience.

[0009] Existing solutions are limited to text or image hashing on the chain, making it difficult to process high-frequency, high-capacity video data, and even more unable to achieve real-time chain-up, desensitization processing, and visual interaction of video streams. Summary of the Invention

[0010] The purpose of this invention is to provide a blockchain-based data traceability visualization interaction system, which integrates edge computing, blockchain, video processing and visualization interaction, and can realize real-time processing, trusted evidence storage and verifiable interaction of large-scale video data for multiple industries and multiple scenarios, thereby improving the reliability, real-time performance and user experience of the data traceability system.

[0011] The above technical objectives of the present invention are achieved through the following technical solutions:

[0012] The blockchain-based data traceability visualization interactive system includes:

[0013] An edge computing module includes a 5G private network communication unit and an edge server cluster. The 5G private network communication unit is used to receive video stream data from the traceability scenario and transmit the video stream data to the edge server cluster via the 5G private network. The edge server cluster compresses and encodes the received video stream and dynamically adjusts the video stream frame rate.

[0014] A dynamic desensitization module is connected to the edge server cluster, and is used to detect sensitive areas in the video stream and replace the sensitive areas;

[0015] An augmented reality interaction module is connected to the dynamic desensitization module, and is used to render the processed video stream and three-dimensional scene on the user's browser side to achieve scene visualization interaction;

[0016] A blockchain trusted verification module is connected to the dynamic desensitization module. The blockchain trusted verification module is used to hash the desensitized video frames and biometric verification records and generate anchor points to store on the blockchain, and supports data authenticity verification based on zero-knowledge proof.

[0017] Further settings:

[0018] The edge server cluster is configured with a video stream processing module, which is connected to the 5G private network communication unit and is used to encode and compress the video stream data transmitted by the 5G private network communication unit to generate a compressed video stream;

[0019] The video stream processing module is connected to a frame rate control module, and the frame rate control module is used to dynamically adjust the transmission frame rate of the compressed video stream based on the network bandwidth status;

[0020] The video stream processing module transmits the compressed video stream after compression processing and frame rate adjustment to the dynamic desensitization module.

[0021] Further settings:

[0022] The dynamic desensitization module is configured with a target detection unit and a replacement processing unit. The target detection unit is used to detect sensitive areas of the compressed video stream transmitted by the edge server cluster, identify faces, work badges and equipment numbers in the video stream, and generate corresponding sensitive area marking information;

[0023] The replacement processing unit is used to perform content replacement processing on the sensitive area based on the detection result of the target detection unit, generate a desensitized video stream, and transmit the desensitized video stream to the augmented reality interaction module.

[0024] Further settings:

[0025] The replacement processing unit is configured with a content generation unit, which is used to generate virtual graphic content based on the sensitive area marking information generated by the target detection unit, and overlay the virtual graphic content on the corresponding sensitive area to complete the replacement processing of the sensitive area.

[0026] Further settings:

[0027] The augmented reality interaction module is configured with a rendering unit, which is used to receive the desensitized video stream transmitted by the dynamic desensitization module, and perform augmented reality rendering processing on the desensitized video stream and three-dimensional scene model based on the browser environment.

[0028] Further settings:

[0029] The augmented reality interaction module is configured with a neural radiation field compression unit, which includes a feature extraction unit and a voxel compression unit. The feature extraction unit is used to extract spatial features and color features in the three-dimensional scene model. The voxel compression unit is used to voxelize the three-dimensional scene model based on the features extracted by the feature extraction unit and generate a compressed three-dimensional scene model, and transmit the compressed three-dimensional scene model to the rendering unit for rendering processing.

[0030] Further settings:

[0031] The augmented reality interaction module is configured with a gesture recognition unit, which includes a gesture capture unit and a gesture analysis unit. The gesture capture unit is used to collect the user's interactive gesture data, and the gesture analysis unit is used to perform feature extraction and pattern recognition on the gesture data collected by the gesture capture unit, identify the user's zoom operation or rotation operation, and control the rendering unit to perform corresponding interactive rendering processing.

[0032] Further settings:

[0033] The blockchain trusted verification module includes a homomorphic encryption unit and a smart contract unit. The homomorphic encryption unit is used to encrypt the desensitized video data and biometric verification records and generate encrypted data that can be stored on the blockchain;

[0034] The smart contract unit is connected to the homomorphic encryption unit. The smart contract unit is used to call the encrypted data in the blockchain for verification after receiving the user's verification request, and automatically execute the preset compensation process if the verification is passed.

[0035] Another object of the present invention is to provide a blockchain-based data traceability visualization interaction method, which is applied to a blockchain-based data traceability visualization interaction system, comprising the following steps:

[0036] S1: Obtain video stream data in the traceability scenario through the 5G private network communication unit, and transmit the video stream data to the edge server cluster;

[0037] S2: performing encoding compression processing and dynamic frame rate adjustment on the video stream data in the edge server cluster to generate a compressed video stream;

[0038] S3: transmitting the compressed video stream to a dynamic desensitization module, performing desensitization processing on sensitive areas in the video stream based on target detection and content replacement, and generating a desensitized video stream;

[0039] S4: transmitting the desensitized video stream to an augmented reality interaction module, and performing augmented reality rendering in combination with a three-dimensional scene model;

[0040] S5: homomorphically encrypting the desensitized video stream and biometric verification record and storing them in the blockchain;

[0041] S6: Based on the blockchain data storage, the user's verification request is received, the smart contract is called to verify the authenticity of the data, and the augmented reality display is completed if the verification is passed. If the verification fails, the compensation process is automatically triggered.

[0042] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, can realize a blockchain-based data traceability visualization interactive system.

[0043] In summary, the present invention has the following beneficial effects:

[0044] 1. Obtain video stream data related to the production, processing, transportation, warehousing or use of the target object through the 5G private network communication unit and transmit it to the edge server cluster; achieve rapid acquisition of high-definition video streams and low-latency private network transmission; ensure the real-time and security of the video data transmission process, laying the foundation for subsequent efficient processing and trusted traceability.

[0045] 2. The edge server cluster encodes and compresses the video stream data and dynamically adjusts the frame rate based on the network conditions, compresses the video data volume, dynamically adapts to the network environment to optimize the frame rate, effectively reduces network transmission pressure, ensures video smoothness, and improves the system's adaptability to different network environments, realizes real-time processing of large-scale video data, enhances the stability and reliability of the system, and improves user experience.

[0046] 3. The dynamic desensitization module identifies and processes sensitive areas in the video stream based on target detection and content replacement algorithms, and automatically detects and replaces sensitive information such as faces, work badges, and equipment numbers. This prevents privacy leaks and core process exposure, enhances system compliance and data security, and improves the security and privacy protection capabilities of the video traceability process, adapting to more high-sensitivity scenarios such as medical and industrial applications.

[0047] 4. The augmented reality interaction module combines the three-dimensional scene model and the desensitized video stream for visual rendering, and supports user interaction, presenting the fusion effect of video data and three-dimensional models. It supports interactive functions such as user gesture operations, breaking through the passive viewing mode of traditional traceability, enhancing user participation and experience, realizing the visualization and operability of the video traceability process, and enhancing users' intuitive perception and trust in the authenticity of the data.

[0048] 5. Video data and biometric verification records are encrypted through homomorphic encryption units and stored in the blockchain. Authenticity verification and compensation are automatically completed in conjunction with smart contracts, achieving tamper-proof data storage and automated trusted verification and processing, preventing traceability data from being forged or tampered with. Users can independently verify the authenticity of the data, while improving the efficiency and transparency of exception handling. This significantly enhances the security, credibility, and automatic performance capabilities of the data traceability system to meet the complex traceability needs of multiple industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a schematic diagram of the system architecture of the invention;

[0050] Figure 2 It is a flow chart of the method of the invention. DETAILED DESCRIPTION

[0051] The present invention will be further described in detail below with reference to the accompanying drawings.

[0052] Example:

[0053] This embodiment applies this system to food traceability scenarios to meet the digital transformation of the modern food industry chain and consumers' demand for transparent and safe food traceability.

[0054] Data traceability visualization interactive system based on blockchain, such as Figure 1 As shown, including:

[0055] The edge computing module includes a 5G private network communication unit and an edge server cluster. The 5G private network communication unit is used to receive video stream data from the traceability scenario and transmit the video stream data to the edge server cluster through the 5G private network. The edge server cluster compresses and encodes the received video stream and dynamically adjusts the video stream frame rate.

[0056] The dynamic desensitization module is connected to the edge server cluster and is used to detect sensitive areas in the video stream and replace them;

[0057] The augmented reality interaction module is connected to the dynamic desensitization module. The augmented reality interaction module is used to render the processed video stream and three-dimensional scene on the user's browser side to achieve scene visualization interaction;

[0058] The blockchain trusted verification module is connected to the dynamic desensitization module. The blockchain trusted verification module is used to hash the desensitized video frames and biometric verification records and generate anchor points to store on the blockchain, and supports data authenticity verification based on zero-knowledge proof.

[0059] The edge server cluster is equipped with a video stream processing module, which is connected to the 5G private network communication unit. The video stream processing module is used to encode and compress the video stream data transmitted by the 5G private network communication unit to generate a compressed video stream;

[0060] The video stream processing module is connected to a frame rate control module, which is used to dynamically adjust the transmission frame rate of the compressed video stream based on the network bandwidth status;

[0061] The video stream processing module transmits the compressed video stream after compression processing and frame rate adjustment to the dynamic desensitization module.

[0062] This embodiment further defines the video stream processing process of the edge server cluster, which specifically includes the following steps:

[0063] Video stream access:

[0064] The edge server cluster receives the original video stream of the food processing workshop transmitted by the 5G private network communication unit, with a resolution of 1920×1080, an initial frame rate of 60 frames per second (fps), and a bit rate of 8Mbps.

[0065] Encoding compression processing:

[0066] The original video stream is input to the video stream processing module, which executes the H.265 encoding algorithm based on Lagrangian optimization. Its goal is to maximize the bit rate compression while ensuring video quality. The optimization objective function is as follows:

[0067]

[0068] in:

[0069] Q is the quantization parameter (QP), which ranges from 22 to 37;

[0070] is the Lagrange multiplier;

[0071] is the quantized video distortion;

[0072] is the compressed video bit rate.

[0073] The final video compression bit rate is controlled within 1Mbps.

[0074] Network bandwidth detection and collection:

[0075] Real-time collection of current 5G private network communication bandwidth , assuming the current sampling result:

[0076] =1.5Mbps

[0077] System preset bandwidth threshold 1Mbps.

[0078] Adaptive frame rate adjustment:

[0079] Calculate the current frame rate adjustment coefficient :

[0080]

[0081] According to the frame rate control strategy:

[0082]

[0083] because =1.5≥1, the system sets the transmission frame rate to 60fps.

[0084] Encoding and transmission output:

[0085] After the encoding compression unit and the frame rate control unit work together, the compressed video stream generated is:

[0086] Resolution: 1920×1080

[0087] Frame rate: 60fps

[0088] Bit rate: 0.95Mbps

[0089] The processed video stream is directly output to the dynamic desensitization module.

[0090] The dynamic desensitization module is equipped with a target detection unit and a replacement processing unit. The target detection unit is used to detect sensitive areas in the compressed video stream transmitted by the edge server cluster, identify faces, work badges and equipment numbers in the video stream, and generate corresponding sensitive area marking information;

[0091] The replacement processing unit is used to perform content replacement processing on the sensitive area based on the detection results of the target detection unit, generate a desensitized video stream, and transmit the desensitized video stream to the augmented reality interaction module.

[0092] The replacement processing unit is configured with a content generation unit, which is used to generate virtual graphic content based on the sensitive area marking information generated by the target detection unit, and overlay the virtual graphic content on the corresponding sensitive area to complete the replacement processing of the sensitive area.

[0093] This embodiment further defines the specific implementation of the dynamic desensitization module, which specifically includes the following steps:

[0094] (1) Receiving video stream

[0095] The dynamic desensitization module receives the video stream transmitted by the edge server cluster, with a video resolution of 1920×1080 and a frame rate of 60fps.

[0096] (2) Target detection in sensitive areas

[0097] The dynamic desensitization module calls the target detection unit and performs frame-by-frame target detection on each frame in the video stream based on the YOLOv8 Nano model. It identifies the face area, work badge area, and equipment number area, and generates marker box information for sensitive areas.

[0098] The confidence score formula for YOLOv8 detection is as follows:

[0099]

[0100] in:

[0101] is the probability of target existence;

[0102] is the model weight;

[0103] is the input feature vector;

[0104] is the bias term;

[0105] is the Sigmoid activation function.

[0106] like ≥0.8, it is determined as a sensitive target area and its location information is extracted.

[0107] (3) Generating Marking Information for Sensitive Areas

[0108] The detected sensitive area coordinates are stored as marking information:

[0109]

[0110] in:

[0111] For sensitive areas collection;

[0112] is the number of sensitive areas detected.

[0113] (IV) Desensitization and replacement processing

[0114] The desensitization module calls the replacement processing unit to generate a corresponding virtual cartoon avatar or abstract pattern based on the above-mentioned sensitive area marking information, cover the corresponding area, and realize dynamic desensitization.

[0115] Virtual content generation is based on StyleGAN3, and the loss function of the generated image is as follows:

[0116]

[0117] The desensitization process maintains the original frame rate of the video and ultimately generates a desensitized video stream.

[0118] in, is the total loss function of the generated adversarial network, are real image samples sampled from the actual food workshop scene dataset. is the probability distribution of real image samples; is a latent variable randomly sampled from the latent space, is the probability distribution of the latent variable; A generator network for generating virtual cartoon patterns or abstract graphics; is the discriminator network, which is used to judge the probability score of the input image as a real image or a generated image; It is the mathematical expectation symbol, which is used to characterize the average performance of the model in the entire sample space.

[0119] (V) Outputting the desensitized video stream

[0120] The processed video stream is directly transmitted to the augmented reality interaction module for subsequent AR rendering.

[0121] The augmented reality interaction module is configured with a rendering unit, which is used to receive the desensitized video stream transmitted by the dynamic desensitization module, and perform augmented reality rendering processing on the desensitized video stream and three-dimensional scene model based on the browser environment.

[0122] The augmented reality interaction module is configured with a neural radiation field compression unit, which includes a feature extraction unit and a voxel compression unit. The feature extraction unit is used to extract spatial features and color features in the three-dimensional scene model. The voxel compression unit is used to voxelize the three-dimensional scene model based on the features extracted by the feature extraction unit and generate a compressed three-dimensional scene model, and transmit the compressed three-dimensional scene model to the rendering unit for rendering processing.

[0123] The augmented reality interaction module is configured with a gesture recognition unit, which includes a gesture capture unit and a gesture analysis unit. The gesture capture unit is used to collect the user's interactive gesture data, and the gesture analysis unit is used to perform feature extraction and pattern recognition on the gesture data collected by the gesture capture unit, identify the user's zoom operation or rotation operation, and control the rendering unit to perform corresponding interactive rendering processing.

[0124] This embodiment further defines a specific implementation process of the neural radiation field compression unit in the augmented reality interaction module, which specifically includes the following steps:

[0125] (1) 3D scene model access

[0126] The augmented reality interaction module obtains a three-dimensional scene model of the food processing workshop. The model is generated based on multi-view image reconstruction. The original model size is about 100MB and contains spatial coordinates (x, y, z) and color information (R, G, B) data.

[0127] (2) Feature extraction and processing

[0128] The feature extraction unit in the neural radiation field compression unit extracts features from the 3D scene model. The extracted features include:

[0129] Spatial eigenvector P=(x,y,z)

[0130] Color feature vector C=(R,G,B)

[0131] The feature extraction process is represented by the following neural field function:

[0132]

[0133] in:

[0134] is the neural radiation field model;

[0135] is the input space coordinate;

[0136] is the corresponding color feature output.

[0137] (3) Voxel compression processing

[0138] After feature extraction is completed, it enters the voxel compression unit and uses a voxel processing algorithm to discretize the continuous spatial features into a regular voxel grid to compress the storage capacity.

[0139] The spatial partitioning formula for voxel compression is:

[0140]

[0141] in:

[0142] is the overall three-dimensional scene space;

[0143] is the i-th voxel unit;

[0144] is the total number of pixels, after compression Much smaller than the original number of points.

[0145] Through voxel compression, the model size is compressed to 10% of the original volume, about 10MB.

[0146] (IV) Compression model output

[0147] The compressed three-dimensional scene model is transmitted to the rendering unit in the augmented reality interaction module for subsequent synchronous rendering with the desensitized video stream to generate the final augmented reality display screen.

[0148] The blockchain trusted verification module includes a homomorphic encryption unit and a smart contract unit. The homomorphic encryption unit is used to encrypt the desensitized video data and biometric verification records and generate encrypted data that can be stored on the blockchain.

[0149] The smart contract unit is connected to the homomorphic encryption unit. After receiving the user's verification request, the smart contract unit is used to call the encrypted data in the blockchain for verification, and automatically execute the preset compensation process if the verification is passed.

[0150] This embodiment further defines the specific application process of the blockchain trusted verification module, which includes a homomorphic encryption unit and a smart contract unit, and specifically includes the following steps:

[0151] Data collection and encryption:

[0152] The system collects the following two types of data during the food traceability process:

[0153] The desensitized video stream data is 30 seconds long and about 20MB in size.

[0154] Biometric verification records, including the operator's fingerprint feature code and facial feature vector, have a data volume of approximately 1MB.

[0155] After the data is collected, the system uses the homomorphic encryption unit to encrypt the entire data, generating a ciphertext data packet of approximately 25MB. The encrypted data is computable, allowing subsequent verification operations to be completed directly in the ciphertext state.

[0156] Blockchain storage and anchoring:

[0157] After encryption is completed, the system uploads the encrypted data to the alliance blockchain platform. The platform adopts a blockchain structure based on the Merkle tree and generates a new block every 5 minutes.

[0158] This data packet was packaged into block #1058. The block information summary is as follows:

[0159] Block number: 1058

[0160] Storage timestamp: March 15, 2025, 14:32

[0161] Data hash: 0x9f8cba...e76

[0162] After the blockchain storage is completed, the system automatically generates a unique index code SRC202503151432001 for this traceability link, and users can directly query with this index.

[0163] User verification and smart contract automatic compensation process:

[0164] In the subsequent consumption process, users initiate traceability verification by scanning the QR code on the food packaging. The system automatically calls the blockchain data and starts the smart contract.

[0165] Smart contract processing flow:

[0166] Read the ciphertext data of block #1058;

[0167] Perform data integrity verification and compare it with the biometric information queried by the user;

[0168] Verification results:

[0169] The data has not been tampered with and the verification has passed;

[0170] If data verification fails or the verification result is abnormal, the smart contract will automatically enter the compensation phase.

[0171] Compensation Examples:

[0172] After this user verification fails, the smart contract triggers the following actions:

[0173] Deduct 200 yuan from the preset "Food Safety Compensation Fund Pool";

[0174] Real-time transfer to the user's reserved digital wallet account wallet_0x829abc...e2f9;

[0175] There is no human intervention in the entire process, and compensation is completed within 2 seconds.

[0176] A data traceability visualization interaction method based on blockchain is applied to a data traceability visualization interaction system based on blockchain, such as Figure 2 As shown, the following steps are included:

[0177] S1: Obtain video stream data in the traceability scenario through the 5G private network communication unit and transmit the video stream data to the edge server cluster;

[0178] S2: In the edge server cluster, the video stream data is encoded, compressed, and dynamically frame-rate adjusted to generate a compressed video stream.

[0179] S3: The compressed video stream is transmitted to the dynamic desensitization module, which desensitizes the sensitive areas in the video stream based on target detection and content replacement to generate a desensitized video stream;

[0180] S4: Transmitting the desensitized video stream to the augmented reality interaction module and performing augmented reality rendering in combination with the three-dimensional scene model;

[0181] S5: Perform homomorphic encryption on the desensitized video stream and biometric verification record and store them in the blockchain;

[0182] S6: Based on the blockchain data storage, the user's verification request is received, the smart contract is called to verify the authenticity of the data, and the augmented reality display is completed if the verification is passed. If the verification fails, the compensation process is automatically triggered.

[0183] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, a data traceability visualization interactive system based on blockchain can be implemented.

[0184] This invention builds a blockchain-based data traceability visualization interactive system, combining multiple core technologies such as 5G private network communication, edge computing, dynamic desensitization, augmented reality visualization, and blockchain trusted verification to achieve a full-link closed loop from video data collection, processing, evidence storage to interactive verification. It has the following significant technical effects and advantages:

[0185] First, through the collaboration of the 5G private network communication unit and the edge computing module, it is possible to quickly collect video stream data from the production, processing, transportation and other processes of the target objects in multiple scenarios, and realize localized compression processing and dynamic frame rate adjustment, solving the real-time transmission and processing bottlenecks in large-scale video tracing, effectively reducing bandwidth pressure and improving system processing efficiency.

[0186] Secondly, the dynamic desensitization module introduces target detection and content replacement mechanisms, which can identify and dynamically process sensitive information such as faces, work badges, equipment numbers, etc. in real time during video stream processing, ensuring that data meets privacy protection and compliance requirements during traceability and display, thereby reducing legal risks for enterprises and platforms.

[0187] Furthermore, the augmented reality interaction module combines three-dimensional scene modeling and rendering capabilities to integrate desensitized video data with the three-dimensional environment in real time, supports user interactive operations, and realizes the visualization, operability and experience of the video tracing process, breaking through the limitations of traditional tracing systems that are limited to passive viewing, and significantly enhancing user participation and credibility of the tracing process.

[0188] Furthermore, the system combines homomorphic encryption and blockchain technology to encrypt desensitized video streams and biometric verification records before storing them on-chain, ensuring data integrity and immutability. Users can independently initiate verification requests based on blockchain data, and smart contracts automatically perform authenticity verification and trigger compensation mechanisms when necessary, comprehensively enhancing the system's security, credibility, and automated contract fulfillment capabilities.

[0189] In summary, the present invention effectively solves the shortcomings of existing video tracing technology in terms of real-time performance, data security, privacy protection, and user trusted verification, and realizes the visualization, verifiability, traceability, and interactivity of full-chain video data, which has broad industry application value and promotion prospects.

[0190] The above-described embodiments do not constitute a limitation on the scope of protection of this technical solution. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the above-described embodiments shall be included in the scope of protection of this technical solution.

Claims

1. The data traceability visualization interactive system based on blockchain is characterized by: include: An edge computing module includes a 5G private network communication unit and an edge server cluster. The 5G private network communication unit is used to receive video stream data from the traceability scenario and transmit the video stream data to the edge server cluster via the 5G private network. The edge server cluster compresses and encodes the received video stream and dynamically adjusts the video stream frame rate. A dynamic desensitization module is connected to the edge server cluster, and is used to detect sensitive areas in the video stream and replace the sensitive areas; An augmented reality interaction module is connected to the dynamic desensitization module, and is used to render the processed video stream and three-dimensional scene on the user's browser side to achieve scene visualization interaction; A blockchain trusted verification module is connected to the dynamic desensitization module. The blockchain trusted verification module is used to hash the desensitized video frames and biometric verification records and generate anchor points to store on the blockchain, and supports data authenticity verification based on zero-knowledge proof.

2. The data traceability visualization interactive system based on blockchain according to claim 1 is characterized in that: The edge server cluster is configured with a video stream processing module, which is connected to the 5G private network communication unit and is used to encode and compress the video stream data transmitted by the 5G private network communication unit to generate a compressed video stream; The video stream processing module is connected to a frame rate control module, and the frame rate control module is used to dynamically adjust the transmission frame rate of the compressed video stream based on the network bandwidth status; The video stream processing module transmits the compressed video stream after compression processing and frame rate adjustment to the dynamic desensitization module.

3. The data traceability visualization interactive system based on blockchain according to claim 1 is characterized in that: The dynamic desensitization module is configured with a target detection unit and a replacement processing unit. The target detection unit is used to detect sensitive areas of the compressed video stream transmitted by the edge server cluster, identify faces, work badges and equipment numbers in the video stream, and generate corresponding sensitive area marking information; The replacement processing unit is used to perform content replacement processing on the sensitive area based on the detection result of the target detection unit, generate a desensitized video stream, and transmit the desensitized video stream to the augmented reality interaction module.

4. The data traceability visualization interactive system based on blockchain according to claim 3 is characterized in that: The replacement processing unit is configured with a content generation unit, which is used to generate virtual graphic content based on the sensitive area marking information generated by the target detection unit, and overlay the virtual graphic content on the corresponding sensitive area to complete the replacement processing of the sensitive area.

5. The data traceability visualization interactive system based on blockchain according to claim 1 is characterized in that: The augmented reality interaction module is configured with a rendering unit, which is used to receive the desensitized video stream transmitted by the dynamic desensitization module, and perform augmented reality rendering processing on the desensitized video stream and three-dimensional scene model based on the browser environment.

6. The data traceability visualization interactive system based on blockchain according to claim 5 is characterized in that: The augmented reality interaction module is configured with a neural radiation field compression unit, which includes a feature extraction unit and a voxel compression unit. The feature extraction unit is used to extract spatial features and color features in the three-dimensional scene model. The voxel compression unit is used to voxelize the three-dimensional scene model based on the features extracted by the feature extraction unit and generate a compressed three-dimensional scene model, and transmit the compressed three-dimensional scene model to the rendering unit for rendering processing.

7. The data traceability visualization interactive system based on blockchain according to claim 5 is characterized in that: The augmented reality interaction module is configured with a gesture recognition unit, which includes a gesture capture unit and a gesture analysis unit. The gesture capture unit is used to collect the user's interactive gesture data, and the gesture analysis unit is used to perform feature extraction and pattern recognition on the gesture data collected by the gesture capture unit, identify the user's zoom operation or rotation operation, and control the rendering unit to perform corresponding interactive rendering processing.

8. The data traceability visualization interactive system based on blockchain according to claim 1 is characterized in that: The blockchain trusted verification module includes a homomorphic encryption unit and a smart contract unit. The homomorphic encryption unit is used to encrypt the desensitized video data and biometric verification records and generate encrypted data that can be stored on the blockchain; The smart contract unit is connected to the homomorphic encryption unit. The smart contract unit is used to call the encrypted data in the blockchain for verification after receiving the user's verification request, and automatically execute the preset compensation process if the verification is passed.

9. A data traceability visualization interaction method based on blockchain, applied to a data traceability visualization interaction system based on blockchain according to any one of claims 1 to 8, characterized in that: The following steps are involved: S1: Obtain video stream data in the traceability scenario through the 5G private network communication unit, and transmit the video stream data to the edge server cluster; S2: performing encoding compression processing and dynamic frame rate adjustment on the video stream data in the edge server cluster to generate a compressed video stream; S3: transmitting the compressed video stream to a dynamic desensitization module, performing desensitization processing on sensitive areas in the video stream based on target detection and content replacement, and generating a desensitized video stream; S4: transmitting the desensitized video stream to an augmented reality interaction module, and performing augmented reality rendering in combination with a three-dimensional scene model; S5: homomorphically encrypting the desensitized video stream and biometric verification record and storing them in the blockchain; S6: Based on the blockchain data storage, the user's verification request is received, the smart contract is called to verify the authenticity of the data, and the augmented reality display is completed if the verification is passed. If the verification fails, the compensation process is automatically triggered.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the blockchain-based data traceability visualization interactive system as described in any one of claims 1 to 8 is implemented.

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