A blockchain-enabled technology project full-process evidence platform

Abstract

The application discloses a blockchain-enabled science and technology project whole-process evidence storage platform, and relates to the technical field of blockchains, comprising an evidence storage equipment space-time reference management module: using high-precision surveying and mapping GNSS-PPP technology to register accurate physical space-time coordinates for each evidence storage equipment node in the network, calculating the theoretical time difference according to the theory of relativity physical model, and establishing a digital twin file for each evidence storage equipment through reference testing, including recording the static identification information and dynamic performance fingerprint of the evidence storage equipment; the application unifies the local time of the equipment to the absolute reference system under the geocentric coordinate time by introducing high-precision space-time reference and relativity time difference correction, solves the inherent time sequence error caused by physical laws, improves the time sequence credibility of the evidence storage data, and can intelligently distinguish abnormality caused by malicious tampering and equipment performance by creating the digital twin file of the equipment and binding the equipment state proof at the data source.

Description

A blockchain-enabled technology project end-to-end evidence storage platform Technical Field

[0001] This invention relates to the field of blockchain technology, specifically to a blockchain-enabled technology project end-to-end evidence storage platform. Background Technology

[0002] Blockchain technology, with its decentralized, immutable, and traceable characteristics, has become an important tool for storing scientific research data. In science and technology project management, using blockchain to store data throughout the entire process, from project initiation and evaluation to experimental procedures, R&D iterations, and output, can effectively improve the transparency, credibility, and auditing efficiency of scientific research data. However, existing blockchain-based data storage solutions have inherent shortcomings when applied to large-scale, cross-regional, and highly sophisticated science and technology projects, such as limitations in spatiotemporal consistency, device heterogeneity, and consensus mechanisms.

[0003] For example, for the problem of spatiotemporal consistency, existing solutions usually rely on the Network Time Protocol (NTP) or the device's local clock for timestamp synchronization. This ignores the fact that in a distributed network of devices at different latitudes / longitudes and altitudes, the relativistic effects of gravitational time dilation and velocity time dilation can cause nanosecond-level deviations in the inherent clocks of the devices, resulting in disordered order of events and undermining the foundation of traceability of evidence.

[0004] Regarding the issue of device heterogeneity, the evidence storage devices in existing technology projects, such as sensors, experimental instruments, or storage servers, are highly heterogeneous in terms of performance such as I / O speed, processing latency, and clock accuracy. Blockchain can only guarantee that the data after it is uploaded to the chain is immutable, but it cannot guarantee against delays, out-of-order issues, or errors introduced by device performance bottlenecks in the generation, processing, and transmission of data before it is uploaded to the chain.

[0005] Regarding the limitations of blockchain consensus mechanisms, existing blockchain consensus mechanisms, such as PoW and PoS, aim to solve the global consistency of on-chain data, but cannot effectively verify the generation logic and temporal consistency of off-chain physical world data.

[0006] Therefore, when existing technologies face shortcomings in technology project management such as spatiotemporal consistency, device heterogeneity, and limitations of consensus mechanisms, there is an urgent need to provide a blockchain-enabled end-to-end evidence storage platform for technology projects. This platform can fundamentally solve the problems of the physical world and the blockchain world when they are interfacing, thereby providing truly reliable end-to-end evidence storage services for high-value technology projects.

[0007] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to provide a blockchain-enabled technology project end-to-end evidence storage platform. This invention solves the problems in the background technology by introducing high-precision spatiotemporal benchmarks and relativistic time difference correction, creating digital twin archives of equipment and binding equipment status proofs at the data source, and integrating high-precision surveying, theoretical physics, machine learning and blockchain consensus technologies.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a blockchain-enabled technology project end-to-end evidence storage platform, including an evidence storage device spatiotemporal benchmark management module: using high-precision mapping GNSS-PPP technology to register precise physical spatiotemporal coordinates for each evidence storage device node in the network, calculating theoretical time difference based on relativistic physical models, and establishing a digital twin archive for each evidence storage device through benchmark testing, including recording the static identification information and dynamic performance fingerprint of the evidence storage device;

[0010] Preprocessed data trusted binding module: When data is generated, the local timestamp of the evidence storage device is collected, and the absolute reference system timestamp is calculated by calling the theoretical time difference. The internal operating status data of the evidence storage device is collected synchronously, and the status hash value is calculated. The original data, the absolute reference system timestamp, and the status hash value are used to calculate and generate an integrity hash certificate.

[0011] Asynchronous verification and consensus coordination module: When the blockchain verification node receives the evidence storage request, it starts the asynchronous verification window. During the window period, it calls the digital twin file and state hash value of the evidence storage device, simulates and calculates the theoretical delay range of the data, performs global sorting of all data in the entire process of the technology project based on the absolute reference system timestamp, performs constraint diagnosis on time sequence anomalies, generates diagnostic results, and performs dynamic consensus coordination on the time sequence consistency of the evidence storage data.

[0012] Optimize the credit module for evidence storage device management: Based on the diagnostic results, use online machine learning algorithms to periodically optimize and update the dynamic performance fingerprint and theoretical time difference of the evidence storage devices, and establish a dynamic credit score for each evidence storage device. Adjust the credit score based on the consistency of the device's historical evidence storage behavior to adjust the length of the asynchronous verification window and the strictness of the verification strategy.

[0013] Optionally, the steps for calculating the theoretical time difference using the relativistic physical model are as follows:

[0014] Using high-precision GNSS-PPP mapping technology, the precise latitude, longitude, and altitude coordinates of the antenna phase center of the evidence storage device are obtained, and the geographical coordinates are converted into a geocentric rectangular coordinate system.

[0015] The velocity information of the evidence storage device under relativistic effects is calculated using the Earth's center of mass as the reference frame.

[0016] Substituting the coordinates and velocity of the evidence storage device into a relativistic physical model, the theoretical time difference between the local clock of the evidence storage device and Earth time was calculated.

[0017] The calculated theoretical time difference is recorded in the digital twin archive of the evidence storage device.

[0018] Optionally, the steps for creating the digital twin archive are as follows:

[0019] When a new evidence storage device is connected to the platform, the system will send a standardized benchmark test program to the new evidence storage device. The benchmark test program will comprehensively test the various I / O performance of the new evidence storage device, including storage I / O test, network I / O test, clock test and CPU performance test.

[0020] During operation, the new evidence storage device continuously collects internal operating status data and combines it with performance data collected from benchmark tests to establish a digital twin model of the evidence storage device.

[0021] The static identification information and dynamic performance fingerprints obtained from the new evidence storage device are collected and integrated into a digital twin archive, and a unique identifier is generated for each digital twin archive.

[0022] The digital twin archives of the new evidence storage device are uploaded to the blockchain platform for evidence storage. Performance tests and dynamic performance fingerprint data collection are carried out on the new evidence storage device regularly. The dynamic performance fingerprints and digital twin model parameters in the digital twin archives are updated. At the same time, the update history of the digital twin archives of the new evidence storage device is recorded on the blockchain.

[0023] Optionally, the calculation steps for the absolute reference frame timestamp are as follows:

[0024] When data is generated during the execution of a technology project, the evidence storage process is triggered. At the same time, the time information is immediately read from the local clock of the evidence storage device to capture the local timestamp.

[0025] The theoretical time difference is pre-calculated and stored on the evidence storage device by invoking a relativistic physics model;

[0026] Based on the collected local timestamps and the calculated theoretical time difference, the absolute reference system timestamp of the evidence storage device in the absolute reference frame is calculated. The formula for calculating the absolute reference system timestamp is as follows: In the formula, Represented as an absolute reference frame timestamp, This represents the local timestamp of the evidence storage device.

[0027] Optionally, the steps for generating the integrity hash certificate are as follows:

[0028] While generating data from science and technology projects, the system collects operational status data from the storage device and formats the data into one of the following formats: JSON, XML, or CSV.

[0029] Choose a hash algorithm and input the formatted running status data to calculate the status hash value;

[0030] The raw data of scientific and technological projects collected by the evidence storage device, the absolute reference system timestamp, and the status hash value of the evidence storage device's operating status data are concatenated into a single data block in a predefined, fixed order.

[0031] The concatenated data block is input into a newly selected cryptographic hash function for calculation, and the integrity hash certificate is output.

[0032] Optionally, the asynchronous verification window startup steps are as follows:

[0033] The blockchain verification node monitors the network in real time. When it receives a storage request from the storage device, it triggers an asynchronous verification process.

[0034] Record the moment when the evidence storage request is received as the start time of the asynchronous verification window;

[0035] Based on the business needs of the enterprise's technology projects, the duration of the asynchronous verification window can be preset, which determines how long the verification node will perform subsequent verification operations.

[0036] Start the asynchronous verification window based on the moment the evidence storage request is received, and calculate the end time of the asynchronous verification window.

[0037] Optionally, the steps for generating the diagnostic results are as follows:

[0038] After the asynchronous verification window is started, all data packets within the time window are collected, and the absolute reference frame timestamp of each data packet is extracted. The packets are then globally sorted according to the absolute reference frame timestamps from smallest to largest to obtain a theoretical event sequence arranged in chronological order.

[0039] For each data packet, the verification node retrieves the digital twin file and status hash value of the evidence storage device from the storage server, including querying the digital twin file of the evidence storage device and obtaining the performance benchmark parameters of the evidence storage device;

[0040] Based on the read status hash value of the evidence storage device, parse the status data of the internal operation of the evidence storage device corresponding to the status hash stored off-chain in the data packet;

[0041] The acquired performance benchmark parameters and status data are input into the digital twin model of the evidence storage device to calculate the theoretical delay range from the generation of the performance benchmark parameters and status data to the preparation for transmission.

[0042] By comparing the theoretical event sequence with the actual order in which they arrive at the blockchain, a timing anomaly diagnosis is triggered when the notarization request sequence and time sequence of data packets are found to be inconsistent.

[0043] Constraint diagnosis is performed on the detected timing anomalies, and diagnostic results are generated. For the detected timing anomalies, the constraint diagnosis is used to check whether the timing anomalies are within the tolerance range that the equipment performance can explain.

[0044] Optionally, the steps for establishing the dynamic credit score of the evidence storage device are as follows:

[0045] Each new evidence storage device is assigned an initial credit score upon connection;

[0046] Collect historical evidence storage data from the evidence storage device, including the time of each storage, the original data content, and diagnostic results.

[0047] Based on historical evidence storage data, the behavioral consistency index of the evidence storage device is calculated by statistically analyzing the ratio of normal evidence storage occurrences to the total number of evidence storage occurrences. The formula for calculating the behavioral consistency index is as follows: ,and In the formula, This is represented as a behavioral consistency indicator. This represents the number of times the evidence storage device has successfully stored evidence throughout its historical evidence storage process. This represents the total number of times the evidence storage device has stored evidence. This represents the number of abnormal evidence storage instances during the historical evidence storage process of the evidence storage device;

[0048] The credit score of the evidence storage device is updated based on the behavioral consistency index. The formula for calculating the updated credit score is as follows: ,and In the formula, This represents the credit score after the evidence storage device has been updated. This represents the old credit score of the evidence storage device. This represents the adjustment factor used to control the magnitude of credit score updates. This is represented as a baseline value for behavioral consistency.

[0049] The updated credit score is written into the digital twin file of the corresponding evidence storage device, and the length of the asynchronous verification window and the strictness of the verification strategy are adjusted according to the updated credit score.

[0050] A computer device includes: a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described blockchain-enabled technology project end-to-end evidence storage platform.

[0051] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned blockchain-enabled technology project end-to-end evidence storage platform.

[0052] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0053] This invention introduces a high-precision spatiotemporal reference and relativistic time difference correction to unify the device's local time to an absolute reference system such as geocentric coordinate time, thus solving the inherent time sequence errors caused by physical laws. This provides nanosecond-level reliable time sequence assurance for high-precision scientific research collaboration across regions and improves the time sequence reliability of evidence data. By creating digital twin archives of devices and binding device status proofs at the data source, it can intelligently distinguish between malicious tampering and anomalies caused by device performance. Based on physical constraints, it understands and verifies the rationality of data, achieving accurate identification and inclusion of anomalies caused by device heterogeneity, greatly enhancing the platform's usability and robustness. By integrating high-precision surveying, theoretical physics, machine learning, and blockchain consensus technologies, it constructs a digital twin model, a dynamic credit model, and adopts an online learning machine model. This enables the platform to continuously learn from changes in device performance, optimize its own model, and self-adjust model parameters, making the platform more and more accurate with use. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0055] Figure 1 is a block diagram of the blockchain-enabled technology project end-to-end evidence storage platform of the present invention.

[0056] Figure 2 is a flowchart of the data storage method of the full-process evidence storage platform for scientific and technological projects of this invention. Detailed Implementation

[0057] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0058] This invention provides a blockchain-enabled technology project end-to-end evidence storage platform, as shown in Figure 1, including a spatiotemporal benchmark management module for evidence storage devices: using high-precision GNSS-PPP mapping technology to register precise physical spatiotemporal coordinates for each evidence storage device node in the network, calculating theoretical time difference based on relativistic physical models, and establishing a digital twin archive for each evidence storage device through benchmark testing, including recording the static identification information and dynamic performance fingerprint of the evidence storage device;

[0059] Specifically, the steps for calculating theoretical time difference using relativistic physics models are as follows:

[0060] Using high-precision GNSS-PPP mapping technology, the precise latitude, longitude, and altitude coordinates of the antenna phase center of the evidence storage device are obtained, and the geographical coordinates are converted into a geocentric rectangular coordinate system.

[0061] Using the Earth's center of mass as a reference frame, the motion velocity information of the evidence storage device under relativistic effects is calculated. For evidence storage devices that are stationary on the ground, the motion velocity is accurately calculated based on the Earth's rotation and the geocentric rectangular coordinate position. For evidence storage devices that move on the ground, the motion velocity needs to be estimated by integrating the velocity information provided by the GNSS receiver.

[0062] Substituting the coordinates and velocity of the evidence storage device into a relativistic physical model, the theoretical time difference between the device's local clock and Earth time is calculated. The formula for calculating the theoretical time difference is as follows: ,and In the formula, This is expressed as a time rate deviation caused by relativistic effects. Expressed as theoretical time difference, Expressed as the speed of light in a vacuum, and , This is expressed as the speed of the evidence storage device relative to a ground-based reference clock. Represented as the gravitational potential on the geoid. It represents the difference between the actual gravitational potential of the evidence storage device and the gravitational potential on the geoid. This represents the actual gravitational position of the evidence storage device. Expressed as the Earth's rotational angular velocity, Represented as the major radius of the Earth's reference ellipsoid. Represented as the geocentric radius of the evidence storage device. This represents the geographical latitude and longitude of the evidence storage device. Represented as higher-order microterms;

[0063] The calculated theoretical time difference is recorded in the digital twin file of the evidence storage device. The digital twin file includes the static identification information and dynamic performance fingerprint of the evidence storage device.

[0064] Specifically, the steps for creating a digital twin profile are as follows:

[0065] When a new evidence storage device is connected to the platform, the system will send a standardized benchmark test program to the new evidence storage device. The benchmark test program will comprehensively test the various I / O performance of the new evidence storage device, including storage I / O test, network I / O test, clock test and CPU performance test. Among them, the storage I / O test records the throughput and latency of the new evidence storage device by sequentially / randomly reading and writing data blocks of different sizes.

[0066] Network I / O testing measures the uplink / downlink bandwidth, latency, and jitter of new storage devices.

[0067] Clock testing involves comparing the local clock of the new evidence storage device with a trusted time source to measure the drift rate of the new evidence storage device's clock.

[0068] CPU performance testing involves recording the completion time after the new evidence storage device performs a standard computational task.

[0069] During operation, the new evidence storage device continuously collects internal operating status data. Combined with performance data collected from benchmark tests, a digital twin model of the evidence storage device is established. The collected internal operating status data is sent to the preprocessing data trusted binding module to calculate the status hash value. At the same time, it is used to update the digital twin model. That is, the established digital twin model is verified and calibrated by using actual measurement data to ensure that the digital twin model of the evidence storage device can accurately reflect the actual performance of the evidence storage device. Furthermore, by comparing the difference between the prediction results of the digital twin model and the actual measurement results, the parameters of the digital twin model are adjusted and optimized.

[0070] The static identification information and dynamic performance fingerprints obtained from the new evidence storage device are collected and integrated into a digital twin archive. A unique identifier is generated for each digital twin archive to facilitate management and querying on the blockchain platform.

[0071] The digital twin archives of the new evidence storage devices are uploaded to the blockchain platform for evidence storage. Performance tests and dynamic performance fingerprint data collection are conducted on the new evidence storage devices regularly to update the dynamic performance fingerprints and digital twin model parameters in the digital twin archives. At the same time, the update history of the digital twin archives of the new evidence storage devices is recorded on the blockchain to ensure the real-time performance and accuracy of the data.

[0072] To further clarify, the static identification information is collected through the product manual, nameplate, or built-in electronic tag of the evidence storage device, including the device's manufacturer, model, serial number, production date, and hardware configuration.

[0073] Dynamic performance fingerprints are obtained through benchmark testing of evidence storage devices, including I / O read / write rate fluctuation range, storage latency information, clock drift rate, measured operating frequency of the evidence storage device, and power consumption. The quantification formula for the I / O read / write rate fluctuation range is as follows: In the formula, The standard deviation of latency reflects the degree of latency fluctuation; the larger the standard deviation of latency, the more unstable the I / O read / write speed performance. This represents the total number of I / O read / write operations collected within the time window. This is represented as the first time within the time window. One I / O read / write rate operation, Represented as the first Latency of each I / O read / write operation Indicated within the time window Average latency per I / O read / write operation;

[0074] The quantization formula for clock drift rate is: In the formula, Expressed as clock drift rate, This represents a period of elapsed time measured by the local clock of the evidence storage device. This represents the same elapsed time as measured by a reference clock;

[0075] The prediction expression for the theoretical delay range is: In the formula, This is expressed as the theoretical delay of the prediction. Represented as the current average write latency based on the digital twin archive. Expressed as the current write latency standard deviation based on the digital twin archive. This is expressed as a 99.7% confidence interval. This is represented as a queue compensation item based on state data.

[0076] Preprocessed data trusted binding module: When data is generated, the local timestamp of the evidence storage device is collected, and the absolute reference system timestamp is calculated by calling the theoretical time difference. The internal operating status data of the evidence storage device is collected synchronously, and the status hash value is calculated. The original data, the absolute reference system timestamp, and the status hash value are used to calculate and generate an integrity hash certificate.

[0077] Specifically, the calculation steps for an absolute reference frame timestamp are as follows:

[0078] When data is generated during the execution of a technology project, the evidence storage process is triggered. At the same time, the time information is immediately read from the local clock of the evidence storage device to capture the local timestamp. Before reading the local timestamp, the local clock needs to be checked and calibrated to ensure that the read local timestamp has high accuracy.

[0079] The theoretical time difference is pre-calculated and stored on the evidence storage device by invoking a relativistic physics model;

[0080] Based on the collected local timestamps and the calculated theoretical time difference, the absolute reference system timestamp of the evidence storage device in the absolute reference frame is calculated. The formula for calculating the absolute reference system timestamp is as follows: In the formula, Represented as an absolute reference frame timestamp, This is represented as the local timestamp of the evidence storage device. Since the theoretical time difference is not fixed and changes with the location and movement of the evidence storage device, the theoretical time difference needs to be updated periodically to account for the errors of the local clock and the calculation of the theoretical time difference. Therefore, when calculating the absolute reference system timestamp, the errors of the local clock and the calculation of the theoretical time difference need to be considered. Finally, the corrected absolute reference system timestamp is output together with the original data generated by the science and technology project and the local timestamp.

[0081] Specifically, the steps for generating an integrity hash credential are as follows:

[0082] While generating data from science and technology projects, the system collects operational status data from the internal storage device and formats the operational status data, converting it into one of the following data formats: JSON, XML, or CSV. The formatting process is used to ensure a uniform format and structure.

[0083] Choose either SHA-256 or SHA-512 hash algorithm, input the formatted runtime data, and calculate the runtime hash value. The runtime hash value is a fixed-length binary string that uniquely represents the content of the runtime data.

[0084] The raw data of scientific and technological projects collected by the evidence storage device, the absolute reference system timestamp, and the status hash value of the evidence storage device's operating status data are concatenated into a single data block in a predefined, fixed order.

[0085] The concatenated data block is input into a newly selected cryptographic hash function for calculation, outputting an integrity hash credential. This credential ensures the integrity and immutability of data during transmission and storage. The formula for calculating the integrity hash credential is as follows: ,and In the formula, Represented as an integrity hash credential. This is represented as a cryptographic hash function, and you can choose either SHA-256 or SHA-512. Represented as a serialization function, This refers to the raw data generated by science and technology projects. Represented as an absolute reference frame timestamp, Represented as a state hash value, This represents the internal operating status data of the evidence storage device;

[0086] Lightweight metadata such as integrity hash credentials and local timestamps are sent to the blockchain network for storage.

[0087] The raw data and complete internal state data generated by large-scale technology projects are stored in off-chain distributed storage servers, and the storage identifier index of the distributed storage server is associated with the integrity hash certificate.

[0088] To further clarify, the internal operating status data of the evidence storage device includes disk write queue depth, network buffer status, CPU utilization, memory usage, and disk I / O rate.

[0089] Asynchronous verification and consensus coordination module: When the blockchain verification node receives the evidence storage request, it starts the asynchronous verification window. During the window period, it calls the digital twin file and state hash value of the evidence storage device, simulates and calculates the theoretical delay range of the data, performs global sorting of all data in the entire process of the technology project based on the absolute reference system timestamp, performs constraint diagnosis on time sequence anomalies, generates diagnostic results, and performs dynamic consensus coordination on the time sequence consistency of the evidence storage data.

[0090] Specifically, the steps to start the asynchronous verification window are as follows:

[0091] The blockchain verification node monitors the network in real time. When it receives a storage request from the storage device, it triggers an asynchronous verification process.

[0092] Record the moment when the evidence storage request is received as the start time of the asynchronous verification window;

[0093] Based on the business needs of the enterprise's technology projects, the duration of the asynchronous verification window can be preset, which determines how long the verification node will perform subsequent verification operations.

[0094] Starting from the moment the evidence storage request is received, an asynchronous verification window is initiated, and the end time of the asynchronous verification window is calculated. The formula for calculating the end time of the asynchronous verification window is as follows: In the formula, This represents the end time of the asynchronous verification window. This represents the start time point when the evidence preservation request is received. This indicates the preset duration of the asynchronous verification window.

[0095] Specifically, the steps for generating the diagnostic results are as follows:

[0096] After the asynchronous verification window is started, all data packets within the time window are collected, and the absolute reference frame timestamp of each data packet is extracted. The packets are then globally sorted according to the absolute reference frame timestamps from smallest to largest to obtain a theoretical event sequence arranged in chronological order.

[0097] For each data packet, the verification node retrieves the digital twin profile and status hash value of the evidence storage device from the storage server, including querying the digital twin profile of the evidence storage device and obtaining the performance benchmark parameters of the evidence storage device, such as average storage latency and latency standard deviation.

[0098] Based on the state hash value of the evidence storage device, the internal operating status data of the evidence storage device corresponding to the state hash stored off-chain in the data packet is parsed, such as disk write queue depth, network buffer status, CPU utilization, memory usage, and disk I / O rate.

[0099] The acquired performance benchmark parameters and status data are input into the digital twin model of the evidence storage device to calculate the theoretical delay range from the generation of the performance benchmark parameters and status data to the readiness for transmission. The formula for calculating the theoretical delay range is as follows: ,and In the formula, Represented as the first The theoretical minimum latency generated by a data packet Represented as the first The average I / O processing latency of each data packet recorded in the digital twin archive of the evidence storage device. This represents the total number of data packets. Represented as the first The standard deviation of I / O latency recorded in the digital twin archive of the evidence storage device for each data packet. Represented as the first The theoretical maximum latency generated by each data packet Represented as a queue compensation item based on state data;

[0100] By comparing the theoretical event sequence with the actual order in which they arrive at the blockchain, a timing anomaly diagnosis is triggered when the notarization request sequence and time sequence of data packets are found to be inconsistent.

[0101] Constraint diagnostics are performed on detected timing anomalies, and diagnostic results are generated. For each detected timing anomaly, the constraint diagnostics are used to check whether the timing anomaly is within the explainable tolerance range of the equipment performance. The expression for the diagnostic result is as follows: In the formula, This refers to reasonable anomalies caused by performance fluctuations in the evidence storage device. This indicates that the abnormal time difference falls within the theoretical delay range. This indicates an unexplained device performance / suspected timing anomaly.

[0102] To further clarify, the constraint diagnosis includes diagnosing the data packet's evidence storage request as a reasonable anomaly caused by performance fluctuations in the evidence storage device when the abnormal time difference falls within the theoretical delay range; and diagnosing the data packet's evidence storage request as an unexplained device performance / suspicious timing anomaly, suspected malicious tampering or device failure, and marking the data packet as suspicious data when the abnormal time difference far exceeds the theoretical delay range.

[0103] The triggering types of asynchronous verification windows include event-related triggering and event-related triggering. The triggering condition for event-related triggering is to wait for all data packets of a specific related event to arrive. The triggering condition for event-related triggering is to wait for a fixed number of blockchain blocks to be generated. The steps of event-related triggering are as follows: When an enterprise technology project is monitored online, the verification node receives a data packet's notarization request.

[0104] When the verification node parses the metadata of the evidence storage request, it finds that the data packet belongs to a known event ID;

[0105] The verification node queries the internal status of the evidence storage device to check whether it has received other relevant data packets under the known event ID;

[0106] When all expected data packets for a known event ID have arrived, an asynchronous verification window is immediately started for the entire event. If not all data packets have arrived, asynchronous verification is triggered only after the last expected data packet arrives.

[0107] The steps for event-related triggering are as follows: When an enterprise's technology project is monitored online, the verification node receives a certificate storage request for any data packet;

[0108] The verification node records the block height of the evidence storage request in the received data packet;

[0109] A fixed increment for the future block height is pre-defined;

[0110] When the blockchain height reaches the sum of the received block height and the fixed future block height increment, any data packet will initiate an asynchronous verification window.

[0111] Optimize the credit module for evidence storage device management: Based on the diagnostic results, use online machine learning algorithms to periodically optimize and update the dynamic performance fingerprint and theoretical time difference of the evidence storage devices, and establish a dynamic credit score for each evidence storage device. Adjust the credit score based on the consistency of the device's historical evidence storage behavior to adjust the length of the asynchronous verification window and the strictness of the verification strategy.

[0112] Specifically, the steps for periodically optimizing and updating online machine learning algorithms are as follows:

[0113] At the beginning of each cycle, diagnostic results are collected, which include timing anomaly information of the evidence storage device during the data evidence storage process, reflecting the actual operating status of the device;

[0114] Continuously extract the current dynamic performance fingerprint and theoretical time difference data of the evidence storage device as the true label for training the model;

[0115] Select a machine learning model for online gradient descent and initialize its parameters to determine its initial state.

[0116] The diagnostic results and true labels are input into the online gradient descent machine learning model. Based on the prediction results of the online gradient descent machine learning model and the actual diagnostic results, a loss function is calculated to measure the prediction error of the online gradient descent machine learning model. The formula for calculating the loss function is as follows: In the formula, Represented as a loss function, These are the parameters of the machine learning model. The input data represents the diagnostic results, dynamic performance fingerprint, and theoretical time difference data. This represents the actual diagnostic result. This is expressed as the number of data samples. Represented as the model in parameters Next to the Prediction results for each sample Represented as the first The actual diagnostic results of each sample Represented as the first Input data for each sample;

[0117] Based on the gradient of the loss function, the parameters of the online gradient descent machine learning model are learned and updated, enabling the online gradient descent machine learning model to better fit the data. The formula for calculating the updated parameters of the online gradient descent machine learning model is as follows: In the formula, This represents the parameters of the updated machine learning model. This represents the learning rate used to update the control parameters. This is represented as the gradient of the loss function with respect to the parameters of the machine learning model;

[0118] The updated online gradient descent machine learning model is used to predict the dynamic performance fingerprint and theoretical time difference of the evidence storage device, resulting in optimized dynamic performance fingerprint and theoretical time difference.

[0119] The optimized dynamic performance fingerprint and theoretical time difference are updated and recorded in the digital twin archive of the evidence storage device to complete the periodic optimization and update.

[0120] Specifically, the steps for establishing a dynamic credit score for evidence storage devices are as follows:

[0121] Each new evidence storage device is assigned an initial credit score upon connection;

[0122] Collect historical evidence storage data from the evidence storage device, including the time of each storage, the original data content, and diagnostic results.

[0123] Based on historical evidence storage data, the behavioral consistency index of the evidence storage device is calculated by statistically analyzing the ratio of normal evidence storage occurrences to the total number of evidence storage occurrences. The formula for calculating the behavioral consistency index is as follows: ,and In the formula, This is represented as a behavioral consistency indicator. This represents the number of times the evidence storage device has successfully stored evidence throughout its historical evidence storage process. This represents the total number of times the evidence storage device has stored evidence. This represents the number of abnormal evidence storage instances during the historical evidence storage process of the evidence storage device;

[0124] The credit score of the evidence storage device is updated based on the behavioral consistency index. The formula for calculating the updated credit score is as follows: ,and In the formula, This represents the credit score after the evidence storage device has been updated. This represents the old credit score of the evidence storage device. This represents the adjustment factor used to control the magnitude of credit score updates. This is represented as a baseline value for behavioral consistency.

[0125] The updated credit score is written into the digital twin file of the corresponding evidence storage device, and the length of the asynchronous verification window and the strictness of the verification strategy are adjusted according to the updated credit score.

[0126] To further clarify, storage devices with high credit scores enjoy a "fast track," with a shortened asynchronous verification window, reduced verification policy stringency, and omission of some verification processes, resulting in faster storage speeds and lower costs. Storage devices with low credit scores are listed as "key monitoring targets," requiring extended asynchronous verification windows, increased verification policy stringency, frequent submission of status proofs, and more complex multi-verification processes for each storage, leading to high storage costs.

[0127] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0128] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0129] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0130] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0131] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A blockchain-enabled end-to-end evidence storage platform for technology projects, characterized in that, The system includes a spatiotemporal reference management module for evidence storage devices: it uses high-precision mapping GNSS-PPP technology to register precise physical spatiotemporal coordinates for each evidence storage device node in the network, calculates theoretical time difference based on relativistic physical models, and establishes a digital twin profile for each evidence storage device through benchmark testing, including recording the static identification information and dynamic performance fingerprint of the evidence storage device. The preprocessed data trusted binding module collects the local timestamp of the evidence storage device when the data is generated, and calculates the absolute reference frame timestamp by calling the theoretical time difference. It also collects the internal operating status data of the evidence storage device and calculates the status hash value. The original data, the absolute reference frame timestamp, and the status hash value are used together to generate an integrity hash certificate. The asynchronous verification and consensus coordination module starts the asynchronous verification window after the blockchain verification node receives the evidence storage request. During the window period, it calls the digital twin file and status hash value of the evidence storage device, simulates and calculates the theoretical delay range of data generation, and performs global sorting of all data in the entire process of the technology project based on the absolute reference frame timestamp. It performs constraint diagnosis on time sequence anomalies, generates diagnostic results, and performs dynamic consensus coordination on the time sequence consistency of the evidence storage data. Optimize the credit module for evidence storage device management: Based on the diagnostic results, use online machine learning algorithms to periodically optimize and update the dynamic performance fingerprint and theoretical time difference of the evidence storage devices, and establish a dynamic credit score for each evidence storage device. Adjust the credit score based on the consistency of the device's historical evidence storage behavior to adjust the length of the asynchronous verification window and the strictness of the verification strategy.

2. The blockchain-enabled technology project end-to-end evidence storage platform according to claim 1, characterized in that, The steps for calculating the theoretical time difference using the relativistic physical model are as follows: Using high-precision GNSS-PPP mapping technology, obtain the precise latitude, longitude, and altitude coordinates of the antenna phase center of the evidence storage device, and convert these coordinates into a geocentric rectangular coordinate system; calculate the velocity information of the evidence storage device under relativistic effects using the Earth's center of mass as the reference frame; substitute the device's coordinates and velocity into the relativistic physical model to calculate the theoretical time difference between the device's local clock and Earth time; and record the calculated theoretical time difference value in the digital twin archive of the evidence storage device.

3. The blockchain-enabled technology project end-to-end evidence storage platform according to claim 2, characterized in that, The steps for establishing the digital twin archive are as follows: When a new evidence storage device is connected to the platform, the system sends a standardized benchmark test program to the new evidence storage device. The benchmark test program comprehensively tests the various I / O performance aspects of the new evidence storage device, including storage I / O testing, network I / O testing, clock testing, and CPU performance testing. During operation, the new evidence storage device continuously collects internal operating status data, and combines the performance data collected from the benchmark test to establish a digital twin model of the evidence storage device. The static identification information, dynamic performance fingerprints, and corresponding digital twin models obtained from the new evidence storage device are integrated into a digital twin archive, and a unique identifier is generated for each digital twin archive. The digital twin archive of the new evidence storage device is uploaded to the blockchain platform for evidence storage, and performance tests and dynamic performance fingerprint data collection are performed on the new evidence storage device periodically to update the dynamic performance fingerprints and digital twin model parameters in the digital twin archive. At the same time, the update history of the digital twin archive of the new evidence storage device is recorded on the blockchain.

4. The blockchain-enabled technology project end-to-end evidence storage platform according to claim 3, characterized in that, The calculation steps for the absolute reference frame timestamp are as follows: When data is generated during the execution of a scientific and technological project, the evidence storage process is triggered. At the same time, the time information is immediately read from the local clock of the evidence storage device to capture the local timestamp. The theoretical time difference is pre-calculated and stored on the evidence storage device by invoking a relativistic physics model; Based on the collected local timestamps and the calculated theoretical time difference, the absolute reference system timestamp of the evidence storage device in the absolute reference frame is calculated. The formula for calculating the absolute reference system timestamp is as follows: In the formula, Represented as an absolute reference frame timestamp, This represents the local timestamp of the evidence storage device.

5. A blockchain-enabled technology project end-to-end evidence storage platform according to claim 4, characterized in that, The steps for generating the integrity hash certificate are as follows: Simultaneously with the generation of the science and technology project data, the operational status data inside the evidence storage device is collected, and the operational status data is formatted and converted into one of the following data formats: JSON, XML, or CSV; a hash algorithm is selected, and the formatted operational status data is input to calculate the status hash value; the original data generated by the science and technology project collected from the evidence storage device, the absolute reference frame timestamp, and the status hash value of the operational status data of the evidence storage device are concatenated into a single data block in a predefined, fixed order. The concatenated data block is input into a newly selected cryptographic hash function for calculation, and the integrity hash certificate is output.

6. The blockchain-enabled technology project end-to-end evidence storage platform according to claim 5, characterized in that, The asynchronous verification window startup steps are as follows: The blockchain verification node listens to the network in real time. When it receives a storage request from the storage device, it triggers the asynchronous verification process; the moment the storage request is received is recorded as the start time of the asynchronous verification window. Based on the business needs of the enterprise's technology projects, the duration of the asynchronous verification window can be preset, which determines how long the verification node will perform subsequent verification operations; the asynchronous verification window is started from the moment the evidence storage request is received, and the end time of the asynchronous verification window is calculated.

7. A blockchain-enabled technology project end-to-end evidence storage platform according to claim 6, characterized in that, The steps for generating the diagnostic results are as follows: After the asynchronous verification window is started, all data packets within the time window are collected, and the absolute reference frame timestamp of each data packet is extracted. The packets are then globally sorted according to the absolute reference frame timestamps from smallest to largest to obtain a theoretical event sequence arranged in chronological order. For each data packet, the verification node retrieves the digital twin file and state hash value of the evidence storage device from the storage server, including querying the digital twin file of the evidence storage device to obtain the performance benchmark parameters of the evidence storage device. Based on the read state hash value of the evidence storage device, the state data of the evidence storage device's internal operation corresponding to the state hash stored off-chain in the data packet is parsed. The obtained performance benchmark parameters and state data are input into the digital twin model of the evidence storage device to calculate the theoretical delay range from the generation of the performance benchmark parameters and state data to the preparation for sending. The theoretical event sequence is compared with the actual order of arrival at the blockchain. When the evidence storage request sequence and time sequence of the data packet are found to be inconsistent, a timing anomaly diagnosis is triggered. Constraint diagnosis is performed on the detected timing anomalies, and a diagnostic result is generated. For the detected timing anomalies, the constraint diagnosis is used to check whether the timing anomalies are within the tolerance range of the device's performance interpretation.

8. A blockchain-enabled technology project end-to-end evidence storage platform according to claim 7, characterized in that, The steps for establishing the dynamic credit score for the evidence storage device are as follows: Each new evidence storage device is assigned an initial credit score upon connection; historical evidence storage behavior data of the device is collected, including the time of each storage, the original data content, and diagnostic results; based on the historical evidence storage behavior data, the behavioral consistency index of the evidence storage device is calculated by statistically analyzing the ratio of normal storage times to the total number of storage times. The formula for calculating the behavioral consistency index is as follows: ,and In the formula, This is represented as a behavioral consistency indicator. This represents the number of times the evidence storage device has successfully stored evidence throughout its historical evidence storage process. This represents the total number of times the evidence storage device has stored evidence. This represents the number of abnormal evidence storage instances during the historical evidence storage process of the evidence storage device; based on the behavioral consistency index, the credit score of the evidence storage device is updated, where the formula for calculating the updated credit score is... ,and In the formula, This represents the credit score after the evidence storage device has been updated. This represents the old credit score of the evidence storage device. This represents the adjustment factor used to control the magnitude of credit score updates. This is represented as a baseline value for behavioral consistency; the updated credit score is written into the digital twin file of the corresponding evidence storage device, and the length of the asynchronous verification window and the strictness of the verification strategy are adjusted according to the updated credit score.

9. A computer device, comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of a blockchain-enabled technology project end-to-end evidence storage platform as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the blockchain-enabled technology project end-to-end evidence storage platform as described in any one of claims 1 to 8.

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