Traffic data hierarchical evidence storage and auditing device

Abstract

The invention relates to the technical field of traffic data processing, in particular to a traffic data hierarchical evidence storage and auditing device which comprises a master control coordination module, a data preprocessing module, a hierarchical evidence storage module, a supervision node module, a token economy module and a visual auditing module which are interconnected through data lines. And all the modules cooperate to realize algorithm hardware processing of the traffic data in the whole life cycle. A hierarchical evidence storage module is adopted, and a sovereignty link access unit stores key hash values, so that the performance bottleneck caused by full-amount data uplink is avoided; a private chain storage unit adopts an RAID6NVMeSSD array, periodic root Hash is generated through a Merkle tree construction algorithm and anchored to a sovereign block chain, it is ensured that data cannot be tampered and traceable, and the core contradiction that performance and credibility cannot be achieved at the same time in a traditional scheme is solved.

Description

Technical Field

[0001] This invention relates to the field of traffic data processing technology, and in particular to a device for hierarchical storage and auditing of traffic data. Background Technology

[0002] In intelligent transportation systems, traffic data (such as vehicle trajectories, road conditions, and accident information) is characterized by multi-source heterogeneity, significant differences in sensitivity, and high real-time requirements. Therefore, hierarchical management is necessary for efficient storage and secure control. The core requirements for hierarchical storage and auditing of traffic data are: classifying data according to its importance, usage scenarios, and security requirements; and using technologies such as blockchain, timestamps, and hash algorithms to achieve tamper-proof storage and full-process traceability auditing, ensuring data authenticity and integrity, and providing reliable data support for urban traffic planning, accident liability determination, and law enforcement supervision.

[0003] Existing technologies rely heavily on pure software for data processing, which suffers from low efficiency, vulnerability to malicious attacks, and the possibility of tampering with results, failing to meet usage requirements. Therefore, it is necessary to design a device for hierarchical storage and auditing of traffic data. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a device for hierarchical storage and auditing of traffic data, comprising a main control coordination module, a data preprocessing module, a hierarchical storage module, a regulatory node module, a token economy module, and a visualization auditing module. The modules are interconnected via data lines, and the modules work together to achieve hardware-based algorithmic processing of traffic data throughout its entire lifecycle. The main control and coordination module consists of an ARM Cortex-A76 processor, an integrated 5G industrial module, and a 25Gbps Ethernet interface. The data transmission latency is ≤3ms. It is used for high-speed access of multi-source traffic data, priority distribution, and algorithm execution scheduling of each module to ensure the overall coordinated and efficient operation of the device. The data preprocessing module consists of an FPGA unit and a hardware control unit, and is used to standardize multi-source heterogeneous traffic data and generate a unique data fingerprint. The hierarchical evidence storage module consists of a sovereign chain entry unit and a private chain storage unit, which is used to realize hierarchical management of key data on-chain evidence storage and full data local storage, balancing storage performance and data trustworthiness. The regulatory node module consists of a zk-SNARKs dedicated ASIC chip, an HSM unit, and a dual-mode audit interface unit, and is used to complete compliance verification and penetrating supervision while protecting data privacy. The token economy module consists of a contribution evaluation unit and a token allocation execution unit, which is used to quantify the contribution of participants and realize the allocation of credible tokens to support the marketization of data elements. The visual audit module consists of a touch screen, a GPU data tracing unit, and an anomaly alarm unit. It is used to generate data tracing maps, compliance reports, and trigger anomaly alarms, thereby improving the convenience and efficiency of auditing.

[0005] As a further description of the above technical solution: The FPGA unit consists of a Z-score hardware acceleration core, a Min-Max normalization hardware acceleration core, a PCA hardware acceleration core, and a SHA-256 hardware acceleration core. The Z-score hardware acceleration core uses the Z-score algorithm to remove data with invalid timestamps, geographical coordinates outside the reasonable range, and abnormal values ​​to ensure data authenticity. The Min-Max normalization hardware acceleration core uses the Min-Max normalization algorithm to map vehicle speed data, traffic flow data, and energy consumption data to the [0,1] interval, reducing the computational complexity of subsequent algorithms. The PCA hardware acceleration core uses the PCA algorithm to reduce the dimensionality of high-dimensional operation log data containing multiple features, reducing storage and transmission overhead. The SHA-256 hardware acceleration core uses the SHA-256 algorithm to generate data block hash values ​​to support traceability and verification.

[0006] As a further description of the above technical solution: The formula for the Min-Max normalization algorithm is: , Where x is a single original data sample to be processed. The minimum value in the entire dataset X to be processed. The maximum value in the entire dataset X to be processed.

[0007] As a further description of the above technical solution: The data preprocessing module's block period is dynamically adjusted by a hardware control unit. During peak hours, the block period is 30 minutes, and during off-peak hours, it is 2 hours. This is used to adapt to the differences in the spatiotemporal distribution of traffic flow and optimize data processing and on-chain efficiency.

[0008] As a further description of the above technical solution: The sovereign blockchain access unit uses a PBFT consensus ASIC chip with a transaction throughput of ≥2000 TPS, for efficient on-chaining and authoritative notarization of key behavior hashes; the private blockchain storage unit uses a RAID6 NVMe SSD array, generating periodic root hashes through a Merkle tree construction algorithm and anchoring them to the sovereign blockchain, for high-speed storage and trusted traceability of all data; the formula for the Merkle tree construction algorithm is as follows:

[0009] Among them, hi Represents the i-th data block, hash(h i ) represents the hash value of the data block, and root_hash is the Merkle tree root hash; The specific process is as follows: When ownership changes, authorization grants, or rule updates occur, the system generates a JSON description of the action and calculates its SHA-256 hash value. Then, it packages the action into a transaction and submits it to the sovereign blockchain. Merkle tree anchoring: Each participant divides the full operation log into blocks according to time periods. Each block of data generates a hash value and constructs a Merkle tree. The root hash is calculated and serves as a periodic anchor point, which is periodically submitted to the sovereign blockchain to form a trusted traceability mechanism.

[0010] As a further description of the above technical solution: The zk-SNARKs dedicated ASIC chip employs the zk-SNARKs algorithm for compliance verification without original data leakage. The formula for the zk-SNARKs algorithm is as follows:

[0011] Where condition(x) is the computation logic function, x is the input variable, and public is the zero-knowledge proof; The HSM unit is used to store the key and the verification private key to prevent key leakage and ensure the security of the supervision process. The dual-mode audit interface unit includes a PCIe 4.0 white-box interface and a 25Gbps encrypted Ethernet black-box interface, which are used for end-to-end data transmission after authorization and privacy-protected verification interaction, respectively, to achieve synergy between privacy and supervision.

[0012] As a further description of the above technical solution: The contribution evaluation unit employs a weighted scoring algorithm to accurately quantify the contributions of data, algorithms, and computing power providers. The formula for the weighted scoring algorithm is as follows:

[0013] Among them, S i Let C be the total score for the i-th party. i For data contribution, U i V represents the number of times it is used. i For data value, the weighting coefficients are 0.4, 0.3, and 0.3, respectively; The token allocation execution unit supports the ERC-20 standard algorithm, with a token issuance delay of ≤1.5 seconds, and is used to realize the issuance and circulation of trusted tokens, supporting the market-based trading of traffic data elements.

[0014] As a further description of the above technical solution: The GPU data tracing unit has an embedded data tracing and compliance report generation algorithm, with a compliance report generation time of ≤5 minutes, which is used to quickly output standardized audit basis; the anomaly alarm unit has an anomaly alarm response speed of ≤1 second, which is used to monitor data anomalies and algorithm execution failures in real time and improve regulatory response efficiency.

[0015] The present invention has the following beneficial effects: 1. Compared with existing technologies, this invention employs a hierarchical evidence storage module. The sovereign chain input unit stores key hash values, avoiding the performance bottleneck caused by uploading all data to the blockchain. The private chain storage unit uses a RAID6 NVMe SSD array, generating periodic root hashes through a Merkle tree construction algorithm and anchoring them to the sovereign blockchain, ensuring data immutability and traceability. This resolves the core contradiction of traditional solutions where performance and trustworthiness are mutually exclusive. Simultaneously, the sovereign chain input unit uses the PBFT consensus mechanism, ensuring data consistency even when distributed nodes fail or are malicious, with block intervals controlled within 10 seconds, meeting the high real-time evidence storage requirements of traffic data.

[0016] 2. Compared with existing technologies, the regulatory node module of this invention adopts the zk-SNARKs algorithm to complete the compliance verification of the calculation process without obtaining the original traffic sensitive data, and the privacy protection is significantly improved compared with traditional regulatory solutions. In legally authorized scenarios, the key sharing mechanism unlocks the information of the entire chain, so as to protect privacy without hindering supervision, improve the efficiency of regulatory response, and meet the needs of traffic management departments for in-depth compliance inspections.

[0017] 3. Compared with existing technologies, the token economy module of this invention provides a credible basis for the market-based allocation of data, algorithms, and computing power by quantifying data contribution, usage frequency, and value. This breaks the traditional dilemma of treating traffic data as a resource and promotes its transformation into a factor-based resource. Data providers can exchange tokens for actual resources, incentivizing them to actively share high-quality data and promoting the formation of a closed-loop traffic data ecosystem. Calculations show that this can improve the efficiency of traffic data circulation and provide richer data support for urban traffic planning and intelligent scheduling. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall architecture of the device of the present invention; Figure 2 This is a flowchart of the hierarchical evidence storage module of the present invention; Figure 3 This is a flowchart of the regulatory node module of the present invention; Figure 4 This is a flowchart of the token economy module of the present invention. Detailed Implementation

[0019] Reference Figure 1-4The present invention provides a device for hierarchical storage and auditing of traffic data, including a main control coordination module, a data preprocessing module, a hierarchical storage module, a regulatory node module, a token economy module, and a visualization auditing module. The modules are interconnected through data lines, and the modules work together to realize the hardware-based algorithmic processing of traffic data throughout its entire lifecycle.

[0020] The main control and coordination module consists of an ARM Cortex-A76 processor, an integrated 5G industrial module, and a 25Gbps Ethernet interface. With a data transmission latency of ≤3ms, it is used for high-speed access to multi-source traffic data, priority-based distribution, and algorithm execution scheduling of each module, ensuring the overall efficient and coordinated operation of the device. During operation, the main control and coordination module's 25Gbps Ethernet interface and the 5G industrial module receive data. The ARM Cortex-A76 processor categorizes data according to data type and sensitivity level: classified accident data (highest priority), vehicle trajectory data (medium priority), and road status data (normal priority), and then distributes the data to the data preprocessing module.

[0021] The data preprocessing module consists of an FPGA unit and a hardware control unit, used to standardize multi-source heterogeneous traffic data and generate unique data fingerprints. The FPGA unit comprises a Z-score hardware acceleration core, a Min-Max normalization hardware acceleration core, a PCA hardware acceleration core, and a SHA-256 hardware acceleration core. The Z-score hardware acceleration core uses the Z-score algorithm to remove data with invalid timestamps, geographic coordinates outside a reasonable range, and abnormal values ​​to ensure data authenticity. The Min-Max normalization hardware acceleration core uses the Min-Max normalization algorithm to map vehicle speed data, traffic flow data, and energy consumption data to the [0,1] interval, reducing the computational complexity of subsequent algorithms. The PCA hardware acceleration core uses the PCA algorithm to reduce the dimensionality of high-dimensional operation log data containing multiple features, reducing storage and transmission overhead. The SHA-256 hardware acceleration core uses the SHA-256 algorithm to generate data block hash values ​​to support traceability and verification. The formula for the Min-Max normalization algorithm is: The data preprocessing module's block cycle is dynamically adjusted by the hardware control unit. During peak hours, the block cycle is 30 minutes, and during off-peak hours it is 2 hours. This is used to adapt to the differences in the spatiotemporal distribution of traffic flow and optimize data processing and on-chain efficiency.

[0022] Specifically, the data cleaning process involves the FPGA unit's Z-score hardware acceleration core reading the data, removing abnormal vehicle speeds and timestamps, and then transferring the cleaned data to the Min-Max normalization hardware acceleration core for data standardization. The standardized data undergoes dimensionality reduction via the PCA hardware acceleration core to reduce redundant features and improve subsequent storage and transmission efficiency. Next, the hardware control unit determines the current peak period (7:00-9:00) based on traffic flow and sets a 30-minute block period to create segmented data. This segmented data is then processed according to the aforementioned data processing method. Finally, the processed data is hashed using the SHA-256 hardware acceleration core, ensuring each piece of data has an immutable identifier, providing a reliable foundation for subsequent hierarchical evidence storage and auditing.

[0023] The tiered evidence storage module consists of a sovereign blockchain inbound unit and a private blockchain storage unit. It is used to achieve tiered management of on-chain evidence storage of critical data and local storage of all data, balancing storage performance and data trustworthiness. The sovereign blockchain inbound unit uses a PBFT consensus ASIC chip with a transaction throughput of ≥2000 TPS, used for efficient on-chain storage and authoritative evidence storage of key behavior hashes. The private blockchain storage unit uses a RAID6 NVMe SSD array, generating periodic root hashes through a Merkle tree construction algorithm and anchoring them to the sovereign blockchain, used for high-speed storage and trusted traceability of all data. The formula for the Merkle tree construction algorithm is as follows:

[0024] Among them, h i Represents the i-th data block, hash(h i ) is the hash value of the data block, and root_hash is the Merkle tree root hash.

[0025] Specifically, the sovereign blockchain's input unit receives the hash values ​​of the segmented data from the data preprocessing module and uses the PBFT consensus ASIC chip to complete the efficient on-chain operation, ensuring the authoritative notarization of critical behavioral data on the sovereign blockchain. Simultaneously, the private blockchain's storage unit stores all data at high speed using a RAID6 NVMe SSD array and generates periodic root hash values ​​through a Merkle tree construction algorithm, anchoring them to the sovereign blockchain to form a complete and trustworthy traceability chain. This process not only guarantees the immutability of the data but also significantly improves storage performance and data management efficiency, providing reliable technical support for subsequent supervision and auditing.

[0026] The regulatory node module consists of a dedicated zk-SNARKs ASIC chip, an HSM unit, and a dual-mode audit interface unit. It is used to complete compliance verification and penetrating supervision while protecting data privacy. The dedicated zk-SNARKs ASIC chip employs the zk-SNARKs algorithm for compliance verification without original data leakage. The formula for the zk-SNARKs algorithm is as follows:

[0027] Where condition(x) is the computation logic function, x is the input variable, and public is the zero-knowledge proof; The HSM unit is used to store the key and the verification private key to prevent key leakage and ensure the security of the supervision process; The dual-mode audit interface unit includes a PCIe 4.0 white-box interface and a 25Gbps encrypted Ethernet black-box interface, which are used for end-to-end data transmission after authorization and privacy-preserving verification interaction, respectively, to achieve synergy between privacy and supervision.

[0028] Specifically, the regulatory node module receives key behavior hash values ​​generated by the hierarchical evidence storage module. A dedicated zk-SNARKs ASIC chip verifies the compliance of the data processing logic using zero-knowledge proof technology, ensuring that regulatory requirements are met without exposing original sensitive information. The HSM unit protects key security through hardware-level encrypted storage mechanisms, preventing unauthorized access or tampering and further enhancing overall system security. The dual-mode audit interface unit dynamically switches operating modes according to the regulatory scenario: in conventional privacy-protected scenarios, a 25Gbps encrypted Ethernet black-box interface is used for data interaction; in legally authorized in-depth review scenarios, a PCIe 4.0 white-box interface provides full-link data access support, thus meeting the needs of different regulatory intensities while balancing privacy protection and penetrating auditing.

[0029] The token economy module consists of a contribution assessment unit and a token allocation execution unit. It quantifies the contributions of participants and implements trusted token allocation, supporting the marketization of data elements. The contribution assessment unit employs a weighted scoring algorithm to accurately quantify the contributions of data, algorithm, and computing power providers. The formula for the weighted scoring algorithm is as follows:

[0030] Among them, S i Let C be the total score for the i-th party. i For data contribution, U i V represents the number of times it is used. i For data value, the weighting coefficients are 0.4, 0.3, and 0.3, respectively; The token allocation execution unit supports the ERC-20 standard algorithm, with a token issuance delay of ≤1.5 seconds, and is used to realize the issuance and circulation of trusted tokens to support the market-based trading of transportation data elements.

[0031] Specifically, the token economy module receives data processing results from the hierarchical storage module and the regulatory node module. The contribution evaluation unit quantifies the contributions of each participant based on a weighted scoring algorithm. The contribution of each data provider is calculated comprehensively based on its data quality, usage frequency, and data value, ensuring fair and transparent scoring results. The token allocation execution unit issues tokens according to the evaluation results and the ERC-20 standard algorithm. The entire process is efficient and reliable, with latency controlled within 1.5 seconds. In this way, data providers can obtain incentives commensurate with their contributions, thereby enhancing their enthusiasm for data sharing, promoting the transformation of traffic data resources into elements, and providing more comprehensive data support for urban traffic planning and intelligent scheduling.

[0032] The visual audit module consists of a touch screen, a GPU data tracing unit, and an anomaly alarm unit. It is used to generate data tracing maps, compliance reports, and trigger anomaly alarms, improving the convenience and efficiency of auditing. The GPU data tracing unit has embedded data tracing and compliance report generation algorithms, with a compliance report generation time of ≤5 minutes, which is used to quickly output standardized audit evidence. The anomaly alarm unit has an anomaly alarm response speed of ≤1 second, which is used to monitor data anomalies and algorithm execution failures in real time, improving regulatory response efficiency.

[0033] Specifically, the visual audit module provides an intuitive interface via a touchscreen, allowing users to easily view data traceability maps and compliance reports. The GPU data traceability unit utilizes built-in algorithms to quickly generate standardized audit evidence, ensuring compliance reports are output within 5 minutes, providing timely support for regulatory needs. The anomaly alarm unit monitors the system's operational status in real time, triggering an alarm within 1 second when data anomalies or algorithm execution failures are detected, alerting relevant personnel to take swift action. This efficient monitoring and response mechanism significantly improves overall regulatory efficiency while ensuring system stability and reliability, providing strong technical support for the full lifecycle management of traffic data.

[0034] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A device for hierarchical storage and auditing of traffic data, characterized in that: It includes a main control and coordination module, a data preprocessing module, a hierarchical evidence storage module, a regulatory node module, a token economy module, and a visualization audit module. The modules are interconnected through data lines, and the modules work together to realize the hardware-based algorithmic processing of traffic data throughout its entire lifecycle. The main control and coordination module consists of an ARM Cortex-A76 processor, an integrated 5G industrial module, and a 25Gbps Ethernet interface. The data transmission latency is ≤3ms. It is used for high-speed access of multi-source traffic data, priority distribution, and algorithm execution scheduling of each module to ensure the overall coordinated and efficient operation of the device. The data preprocessing module consists of an FPGA unit and a hardware control unit, and is used to standardize multi-source heterogeneous traffic data and generate a unique data fingerprint. The hierarchical evidence storage module consists of a sovereign chain entry unit and a private chain storage unit, which is used to realize hierarchical management of key data on-chain evidence storage and full data local storage, balancing storage performance and data trustworthiness. The regulatory node module consists of a zk-SNARKs dedicated ASIC chip, an HSM unit, and a dual-mode audit interface unit, and is used to complete compliance verification and penetrating supervision while protecting data privacy. The token economy module consists of a contribution evaluation unit and a token allocation execution unit, which is used to quantify the contribution of participants and realize the allocation of credible tokens to support the marketization of data elements. The visual audit module consists of a touch screen, a GPU data tracing unit, and an anomaly alarm unit. It is used to generate data tracing maps, compliance reports, and trigger anomaly alarms, thereby improving the convenience and efficiency of auditing.

2. The device for hierarchical storage and auditing of traffic data according to claim 1, characterized in that: The FPGA unit consists of a Z-score hardware acceleration core, a Min-Max normalization hardware acceleration core, a PCA hardware acceleration core, and a SHA-256 hardware acceleration core. The Z-score hardware acceleration core uses the Z-score algorithm to remove data with invalid timestamps, geographical coordinates that are out of reasonable range, and abnormal values ​​to ensure data authenticity. The Min-Max normalization hardware acceleration core uses the Min-Max normalization algorithm to uniformly map vehicle speed data, traffic flow data, and energy consumption data to the [0,1] interval, reducing the computational complexity of subsequent algorithms; the PCA hardware acceleration core uses the PCA algorithm to reduce the dimensionality of high-dimensional operation log data containing multiple features, reducing storage and transmission overhead; the SHA-256 hardware acceleration core uses the SHA-256 algorithm to generate data block hash values ​​to support traceability and verification.

3. The device for hierarchical storage and auditing of traffic data according to claim 2, characterized in that: The formula for the Min-Max normalization algorithm is: ， Where x is a single original data sample to be processed. The minimum value in the entire dataset X to be processed. The maximum value in the entire dataset X to be processed.

4. The device for hierarchical storage and auditing of traffic data according to claim 1, characterized in that: The data preprocessing module's block period is dynamically adjusted by a hardware control unit. During peak hours, the block period is 30 minutes, and during off-peak hours, it is 2 hours. This is used to adapt to the differences in the spatiotemporal distribution of traffic flow and optimize data processing and on-chain efficiency.

5. The device for hierarchical storage and auditing of traffic data according to claim 1, characterized in that: The sovereign blockchain access unit uses a PBFT consensus ASIC chip with a transaction throughput of ≥2000 TPS, for efficient on-chaining and authoritative notarization of key behavior hashes; the private blockchain storage unit uses a RAID6 NVMe SSD array, generating periodic root hashes through a Merkle tree construction algorithm and anchoring them to the sovereign blockchain, for high-speed storage and trusted traceability of all data; the formula for the Merkle tree construction algorithm is as follows: ； Among them, h i Represents the i-th data block, hash(h i ) represents the hash value of the data block, and root_hash is the Merkle tree root hash; The specific process is as follows: When ownership changes, authorization grants, or rule updates occur, the system generates a JSON description of the action and calculates its SHA-256 hash value. Then, it packages the action into a transaction and submits it to the sovereign blockchain. Merkle tree anchoring: Each participant divides the full operation log into blocks according to time periods. Each block of data generates a hash value and constructs a Merkle tree. The root hash is calculated and serves as a periodic anchor point, which is periodically submitted to the sovereign blockchain to form a trusted traceability mechanism.

6. The device for hierarchical storage and auditing of traffic data according to claim 1, characterized in that: The zk-SNARKs dedicated ASIC chip employs the zk-SNARKs algorithm for compliance verification without original data leakage. The formula for the zk-SNARKs algorithm is as follows: ； Where condition(x) is the computation logic function, x is the input variable, and public is the zero-knowledge proof; The HSM unit is used to store the key and the verification private key to prevent key leakage and ensure the security of the supervision process. The dual-mode audit interface unit includes a PCIe 4.0 white-box interface and a 25Gbps encrypted Ethernet black-box interface, which are used for end-to-end data transmission after authorization and privacy-protected verification interaction, respectively, to achieve synergy between privacy and supervision.

7. The device for hierarchical storage and auditing of traffic data according to claim 1, characterized in that: The contribution evaluation unit employs a weighted scoring algorithm to accurately quantify the contributions of data, algorithms, and computing power providers. The formula for the weighted scoring algorithm is as follows: ； Among them, S i Let C be the total score for the i-th party. i For data contribution, U i V represents the number of times it is used. i For data value, the weighting coefficients are 0.4, 0.3, and 0.3, respectively; The token allocation execution unit supports the ERC-20 standard algorithm, with a token issuance delay of ≤1.5 seconds, and is used to realize the issuance and circulation of trusted tokens, supporting the market-based trading of traffic data elements.

8. The device for hierarchical storage and auditing of traffic data according to claim 1, characterized in that: The GPU data tracing unit has an embedded data tracing and compliance report generation algorithm, with a compliance report generation time of ≤5 minutes, which is used to quickly output standardized audit basis; the anomaly alarm unit has an anomaly alarm response speed of ≤1 second, which is used to monitor data anomalies and algorithm execution failures in real time and improve regulatory response efficiency.

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