Digital asset intelligent analysis platform based on block chain intelligent contract technology

Abstract

The invention relates to the technical field of digital asset management analysis, in particular to a digital asset intelligent analysis platform based on a block chain intelligent contract technology. The computing power resource state coupling module is used for obtaining the saturation degree of computing resources by capturing information of a task control block and combining the load rate of a processor, and embedding and collecting mapping data streams to generate a management and control data packet; the abnormal data screening module is used for executing local serial processing when the saturation degree of the computing resources is lower than an I / O throughput threshold value, and otherwise, hardware acceleration is carried out, and a to-be-verified digital sequence is constructed; generating an abrupt change collection feature vector based on the to-be-verified digital sequence; and the component resume anchoring module is used for carrying out data binding operation on the abrupt change collection feature vector, generating a single-piece digital traceability certificate, carrying out aggregation compression, obtaining a batch verification root value and outputting a quality signal digital right. According to the method, a load awareness and distributed storage partitioning strategy is constructed through indexes, and the I / O frequency and transmission bandwidth occupation of whole library retrieval are reduced.

Description

Technical Field

[0001] This invention relates to the field of digital asset management and analysis technology, specifically a digital asset intelligent analysis platform based on blockchain smart contract technology. Background Technology

[0002] In the storage and retrieval of multimedia data objects related to cultural heritage, and in the instantiation and distribution of digital entities (such as cultural digital assets and metaverse assets), version lineage tracking of digital content and integrity verification of the data generation process are crucial for ensuring the value of data objects and the flexibility of metadata exchange. Current digital resource management architectures tend to utilize high-precision acquisition and digital twin technologies to generate structured digital copies of cultural relics or artworks, and introduce blockchain technology to construct distributed, immutable index logs. This aims to optimize cross-node data traceability and access control systems through the immutability of data summaries.

[0003] However, existing digital entity generation and persistence processes still have significant logical flaws in the construction of data trust models and the execution loop of rule engines. First, in the current data collection and encapsulation process, the generation of metadata indexes is often completed independently by the centralized processing unit of the technology provider. This single-point-dominated data entry mode lacks a real-time consistency verification mechanism involving multiple nodes. Before the raw unstructured data is transformed into on-chain structured records, the lack of effective third-party audit interfaces or joint verification protocols makes the source input data susceptible to being cleaned, artificially modified, or algorithmically generated during extraction, transformation, and loading, resulting in a lack of mutual trust between the trading platform and the user regarding the digital twin archive before writing. Second, the system's I / O operation architecture is mostly in the form of "asynchronous delayed write," and the execution logic (smart contracts) and the control loop of the data collection terminal are decoupled, making it impossible to achieve atomic control and real-time interruption of physical collection operations based on distributed state machines. Furthermore, there is a contradiction between the inherent characteristic of blockchain technology that "transaction confirmation is slow" and the rigid requirement of "instant response" in the data collection process. When performing native active asset ownership verification on high-frequency, high-volume unstructured point cloud and spectral data, the peak CPU load of the edge gateway may be too high, causing the delay in data on-chain ownership verification to exceed the time window allowed by the data collection cycle, thus causing the automated compliance verification function to fail.

[0004] To address this, a digital asset intelligent analysis platform based on blockchain smart contract technology is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a digital asset intelligent analysis platform based on blockchain smart contract technology to solve the problems mentioned in the background.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a digital asset intelligent analysis platform based on blockchain smart contract technology, comprising: The computing resource status coupling module receives the acquisition mapping data stream from the cultural relic digitization acquisition terminal; it constructs an index status feature set by capturing the task queue depth and context switching frequency of the task control block and combining it with the processor load rate; based on the index status feature set, it obtains the computing resource saturation through weighted concatenation and embeds it into the acquisition mapping data stream to generate a control data package. The abnormal data screening module: Based on the control data packet, when the computing resource saturation is lower than the I / O throughput threshold, it performs local serial processing; otherwise, it performs a sharding mechanism and performs parallel processing through the hardware acceleration channel to construct the digital sequence to be verified. Based on the digital sequence to be verified, it calculates the feature space distance parameter between the collected mapping data stream and the historical benchmark model through a sliding window. When the feature space distance parameter exceeds the preset change threshold, it generates a mutation collection feature vector. Component history anchoring module: In the independent metadata management partition, the mutation collection feature vector is bound to the data through the pre-set storage node physical addressing identifier to generate a single digital traceability certificate; the single digital traceability certificate is aggregated and compressed through tree topology aggregation logic to obtain the batch verification root value and output the quality assurance digital certificate.

[0007] Preferably, the acquired mapping data stream specifically includes an unstructured index data segment and a structured index maintenance log segment; the unstructured index data segment includes the spatial coordinate displacement data of the scanning probe and the spectral response value of the light sensor; the structured index maintenance log segment includes the globally unique identifier of the data object, the tuple access popularity count, and the index tree node split record.

[0008] Preferably, the specific generation process of the index state feature set includes: reading the task control block in the operating system memory through the storage engine performance probe, extracting the task queue depth and context switching frequency of the running queue where the task control block is located, and collecting the processor load rate; performing a mapping operation on the task queue depth, context switching frequency, and load rate through a Gaussian normalization function to output the processor load feature vector; synchronously reading the cache dirty block counter to obtain the number of pages marked as dirty pages and calculating and generating the dirty page write-back saturation; retrieving the preset disk IO throughput limit threshold and the actual disk write rate to obtain the IO load rate, and performing a weighted summation of the dirty page write-back saturation and the IO load rate to generate the IO pressure ratio; performing gradient mapping on the IO pressure ratio using a nonlinear function to output the storage blocking coefficient; and summing the processor load feature vector and the storage blocking coefficient to construct the index state feature set.

[0009] Preferably, the specific generation process of the control data packet includes: calling the feature aggregation interface to receive the index status feature set, performing a weighted concatenation operation on the processor load feature vector and the storage blocking coefficient, and outputting the computing resource saturation; synchronously intercepting the acquisition mapping data stream and parsing the data transmission frame header of the acquisition mapping data stream; locating the reserved metadata attribute field in the data transmission frame header and filling the reserved metadata attribute field with the computing resource saturation; performing a serialization and packet assembly operation on the data transmission frame header and the original payload data segment, and outputting the control data packet.

[0010] Preferably, the specific generation process of the digital sequence to be verified includes: parsing the control data packet to obtain the computing resource saturation; comparing the computing resource saturation with a preset I / O throughput threshold and outputting the comparison result; when the comparison result is in a normal state, performing local serial computation on the control data packet and synchronously archiving it to the digital asset management database; when the comparison result is in a high-concurrency state, triggering a sharding mechanism, specifically including: control data packets with a data volume exceeding a preset sharding threshold are sharded and scheduled to edge index nodes for distributed computation; for control data packets with a sampling frequency higher than a preset frequency threshold, calling the FPGA hardware acceleration unit to perform parallel aggregation computation; receiving the processing results returned by the edge index node and the FPGA hardware acceleration unit, and outputting the digital sequence to be verified.

[0011] Preferably, the specific generation process of the mutation acquisition feature vector includes: importing the digital sequence to be verified into the circular buffer of the memory database, generating a continuous instantaneous data view through a cursor sliding read operation; performing dimensionality reduction mapping on the instantaneous data view to output the query feature index key; retrieving the standard benchmark feature vector from the historical benchmark model; calculating the feature difference metric between the query feature index key and the standard benchmark feature vector in the feature space, and outputting the feature space distance parameter; performing amplitude comparison between the feature space distance parameter input condition filtering operator and a preset change threshold; locking the corresponding query feature index key when the feature space distance parameter exceeds the preset change threshold; adding an abnormal timestamp index and dimension type identifier to the locked query feature index key, and outputting the mutation acquisition feature vector.

[0012] Preferably, the specific generation process of the single digital traceability certificate includes: establishing an instruction interaction channel with the independent metadata management partition, transmitting the mutation collection feature vector to the isolation buffer of the independent metadata management partition; retrieving the physical addressing identifier of the storage node, performing an association mapping operation on the mutation collection feature vector and the physical addressing identifier of the storage node, and outputting a feature fusion data block containing device identity attributes; performing standardized encoding on the feature fusion data block, and outputting a transaction operation log with a digital signature of the bound content; retrieving the credit endorsement identifier inside the independent metadata management partition, performing digital credit registration on the transaction operation log, and generating a version consistency verification code; calling the data encapsulator to receive the version consistency verification code and the feature fusion data block, appending redundant verification bits to the index field of the feature fusion data block, and outputting the single digital traceability certificate.

[0013] Preferably, the specific generation process of the quality assurance digital certificate includes: collecting single digital traceability certificates from the same database transaction submission window, inputting them one by one into the index generation logic, and outputting the underlying data page fingerprint sequence; extracting adjacent elements from the underlying data page fingerprint sequence according to the storage block sequence order to form an associated index block; performing tuple aggregation and compression operations on the associated index block, inputting the concatenated code back into the index generation logic to generate a directory-level aggregated key value; recursively performing pairing, concatenation, and index generation operations on the directory-level aggregated key value until a unique full-database retrieval root node identifier is generated; locking the full-database retrieval root node identifier as the batch verification root value; calling the file system encapsulator to receive the batch verification root value, associating it with the blockchain smart contract address and the index submission timestamp, performing structured serialization, and outputting the quality assurance digital certificate.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By capturing the task queue depth and context switching frequency of the task control block, the micro-level congestion state of the processor is quantified. By embedding the quantified computing resource saturation into the acquisition and mapping data stream in real time to construct management data packets, the business data stream is endowed with self-describing computing power awareness attributes. This mechanism allows the data stream itself to carry the load information of the current computing environment, enabling the assessment of processing risks at the data receiving end and allowing for proactive flow control or policy adjustments before the CPU load approaches its physical limit. This not only effectively ensures the stability of edge gateway computing resources, preventing system crashes or task backlogs due to overload, but also allows downstream nodes to independently obtain the upstream load status without frequently polling the central monitoring server, thereby reducing the interaction overhead of network-wide control signaling and improving the self-healing capability and robustness of the distributed network when facing data surges.

[0015] 2. By using computing resource saturation as the core indicator for dynamic scheduling, adaptive switching of computing modes between hardware and software is achieved. On the one hand, under high load conditions, a sharding mechanism can be automatically triggered and hardware acceleration channels can be invoked to significantly improve data throughput through parallel computing, ensuring that the data processing speed always matches the output rate of high-speed digitization scanning. On the other hand, by monitoring feature space distance parameters through a sliding window and comparing them with preset change thresholds, regular steady-state data can be automatically filtered, locking and extracting only the mutation feature vectors with analytical value. This strategy, which combines hierarchical processing and intelligent filtering, significantly reduces the total amount of data processed and the bandwidth occupied, ensuring the accurate capture of key feature data, while effectively avoiding the ineffective occupation of storage space by redundant data, providing an optimized technical path for low-cost management of digital assets throughout their entire lifecycle.

[0016] 3. By deploying tree-structured topology aggregation logic in an independent off-chain metadata management partition, efficient batch-based rights confirmation of massive amounts of collected data is achieved. Numerous individual digital traceability certificates generated within the same database transaction commit window are efficiently aggregated and compressed into a unique batch verification root value, which is then used as a quality-assured digital certificate for on-chain operations. This mechanism merges the high-frequency on-chain transactions that would otherwise require hundreds or thousands of operations into a single operation, significantly mitigating the processing bottlenecks inherent in blockchain consensus mechanisms. Simultaneously, by utilizing the physical addressing identifiers of storage nodes to bind data at the underlying level, the aggregated root value retains its uniqueness and tamper-proof capability, ensuring traceability to the physical source. This achieves highly reliable digital asset rights confirmation while meeting the requirements of high-concurrency data collection, and reduces the storage pressure and gas cost consumption of the blockchain network.

[0017] 4. By organically linking the computing resource status coupling module, the anomaly data screening module, and the component history anchoring module, an impedance matching system is constructed to transform high-frequency physical world data into low-frequency, high-value assets. The computing resource status coupling module acts as a front-end filter, preventing surges in instantaneous traffic by controlling data packets; the anomaly screening module acts as an intermediate frequency converter, transforming massive raw data streams into sparse key feature streams through mutation feature extraction; and the history anchoring module acts as a back-end aggregator, further compressing the feature streams into single quality-based digital certificates. These three modules support each other, forming a closed-loop flow system that ensures the platform can withstand high concurrency pressure on the edge side while adapting to low TPS limitations on the on-chain side, achieving smooth flow and value anchoring across the entire chain from underlying sensor data to top-level digital assets. Attached Figure Description

[0018] Figure 1 This is a structural diagram of the digital asset intelligent analysis platform based on blockchain smart contract technology proposed in an embodiment of this invention application; Figure 2This is a flowchart illustrating the generation process of computing power status and control package according to an embodiment of this invention. Figure 3 This is a flowchart of the abnormal data screening and segmentation mechanism proposed in an embodiment of this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figures 1-3 The digital asset intelligent analysis platform based on blockchain smart contract technology provided by this invention has the following specific modules: The computing resource status coupling module receives the acquisition mapping data stream from the cultural relic digitization acquisition terminal; it constructs an index status feature set by capturing the task queue depth and context switching frequency of the task control block and combining it with the processor load rate; based on the index status feature set, it obtains the computing resource saturation through weighted concatenation and embeds it into the acquisition mapping data stream to generate a control data package. The abnormal data screening module: Based on the control data packet, when the computing resource saturation is lower than the I / O throughput threshold, it performs local serial processing; otherwise, it performs a sharding mechanism and performs parallel processing through the hardware acceleration channel to construct the digital sequence to be verified. Based on the digital sequence to be verified, it calculates the feature space distance parameter between the collected mapping data stream and the historical benchmark model through a sliding window. When the feature space distance parameter exceeds the preset change threshold, it generates a mutation collection feature vector. Component history anchoring module: In the independent metadata management partition, the mutation collection feature vector is bound to the data through the pre-set storage node physical addressing identifier to generate a single digital traceability certificate; the single digital traceability certificate is aggregated and compressed through tree topology aggregation logic to obtain the batch verification root value and output the quality assurance digital certificate.

[0021] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0022] Example 1 This application discloses a digital asset intelligent analysis platform based on blockchain smart contract technology. (See also...) Figure 1The specific modules proposed in this invention include: a computing resource status coupling module: receiving the acquisition mapping data stream from the cultural relic digitization acquisition terminal; constructing an index status feature set by capturing the task queue depth and context switching frequency of the task control block, combined with the processor load rate; obtaining the computing resource saturation by weighted concatenation based on the index status feature set, and embedding it into the acquisition mapping data stream to generate a control data package; an abnormal data screening module: performing local serial processing when the computing resource saturation is lower than the I / O throughput threshold based on the control data package, and otherwise performing a sharding mechanism, performing parallel processing through a hardware acceleration channel to construct a digital sequence to be verified; calculating the feature space distance parameter between the acquisition mapping data stream and the historical benchmark model through a sliding window based on the digital sequence to be verified, and generating a mutation acquisition feature vector when the feature space distance parameter exceeds a preset change threshold; and a component history anchoring module: performing data binding operations on the mutation acquisition feature vector through a preset storage node physical addressing identifier in an independent metadata management partition to generate a single digital traceability certificate; aggregating and compressing the single digital traceability certificate through tree topology aggregation logic to obtain the batch verification root value and output the quality assurance digital certificate.

[0023] Furthermore, it receives the acquisition and mapping data stream from the cultural relic digitization acquisition terminal; by capturing the task queue depth and context switching frequency of the task control block, and combining this with the processor load rate, it constructs an index state feature set; corresponding to the computing resource state coupling module; see [link / reference]. Figure 2 The specific implementation process includes: The acquired and mapped data stream specifically includes an unstructured index data segment and a structured index maintenance log segment; the unstructured index data segment includes the spatial coordinate displacement data of the scanning probe and the spectral response value of the light sensor; the structured index maintenance log segment includes the globally unique identifier of the data object, the tuple access popularity count, and the index tree node split record.

[0024] The storage engine performance probe, i.e., the kernel probe, reads the task control block in the operating system memory, extracts the task queue depth and context switching frequency of the running queue where the task control block is located, and collects the processor load rate. A Gaussian normalization function is used to map the task queue depth, context switching frequency, and load rate, outputting a processor load feature vector. Simultaneously, the cache dirty block counter is read to obtain the number of pages marked as dirty pages and calculate the dirty page write-back saturation. A preset disk I / O throughput limit threshold and the actual disk write rate are retrieved to obtain the I / O load rate. The dirty page write-back saturation and the I / O load rate are weighted and summed to generate the I / O pressure ratio. A non-linear function is used to perform gradient mapping on the I / O pressure ratio, outputting a storage blocking coefficient. The processor load feature vector and the storage blocking coefficient are summarized and constructed into an index state feature set.

[0025] Specifically, the acquired and mapped data stream includes an unstructured index data segment and a structured index maintenance log segment. The unstructured index data segment includes the spatial coordinate displacement data of the scanning probe and the spectral response values ​​of the light sensor. The structured index maintenance log segment includes globally unique identifiers for data objects, tuple access popularity counts, and index tree node split records. In this embodiment, operational data from a six-axis high-precision robotic arm artifact scanning robot is received in real time via a high-speed industrial fieldbus (such as EtherCAT) to construct a binary-structured acquired and mapped data stream. The spatial coordinate displacement data of the scanning probe in the unstructured index data segment originates from the laser interferometer and grating ruler installed on the end effector of the robotic arm. The sampling frequency is set to 20kHz (i.e., one data point is captured every 50μs). Its output value maps the absolute micron-level displacement of the scanning probe relative to the surface of the artifact in the X / Y / Z three-dimensional space. For example, at the moment of close scanning of the texture of the porcelain surface, the captured displacement coordinate offset is (12.50μm, -3.20μm, 50.15μm). The spectral response value of the light sensor is obtained through the multispectral CMOS imaging sensor integrated inside the probe. This value reflects the spectral reflectance intensity of the artifact surface in a specific wavelength band. Under strong light supplementation conditions, the measured peak response value can reach 65535 (16-bit quantization full range). Meanwhile, the structured index maintenance log segment is synchronously written by the Digital Asset Management System (DAMS) via the gRPC protocol. It contains a globally unique identifier for the unique data object of the current batch (such as "MINT-2025-X7"), as well as a tuple access heat count read directly from the non-volatile memory of the device firmware. This value records the cumulative number of exposures completed since the optical lens shutter was put into use (such as 1,540,230 times). Together with the "SCANNING_RUN" or "CALIBRATION" status codes in the device operation status change log, it ensures strict alignment between physical acquisition actions and asset casting business logic on the microsecond-level timestamp.

[0026] Specifically, the storage engine performance probe reads the task control block in the operating system memory, extracts the task queue depth and context switching frequency of the run queue where the task control block resides, and collects the processor load rate. A Gaussian normalization function is used to map the task queue depth, context switching frequency, and load rate, outputting a processor load feature vector. In this embodiment, the computing resource state coupling module deploys an eBPF probe program at the operating system kernel layer of the edge index node. It reads the structure of the dedicated daemon process responsible for processing the collected and mapped data stream at 10ms intervals. The probe directly reads the `nr_running` variable in the scheduler run queue as the task queue depth. For example, if this value is 6 during peak data throughput, it indicates that 6 threads are in a ready-waiting state. Simultaneously, by reading the sum of the increments of the voluntary and involuntary context switching counters, the context switching frequency is measured to be 1200Hz. Combined with the CPU time slice duty cycle parsed from the ` / proc / stat` file, a real-time load rate of 0.85 (i.e., 85%) is calculated. To eliminate dimensional differences and achieve dimensionless processing, a Gaussian normalization function is used to map the three original indicators. The specific calculation logic is as follows: subtract the historical statistical mean from the real-time collected value and then divide by the standard deviation. For example, subtracting the mean of 2.0 from the task queue depth of 6 and dividing by the standard deviation of 1.5 yields a standardized value of 2.66. Subsequently, the standardized value is mapped to a closed interval of 0 to 1 using the Sigmoid nonlinear activation function, ultimately outputting a processor load feature vector [0.93, 0.99, 0.85] containing three normalized components. This vector quantifies the instantaneous congestion level of the CPU when processing high-frequency data streams.

[0027] Based on this, this embodiment can introduce micro-trend prediction logic. Task queue depth data for the most recent 5 scheduling cycles is temporarily stored in a circular buffer in the kernel space, and the instantaneous acceleration of the task queue depth is calculated using a second-order difference algorithm. If the acceleration value is positive and exceeds a preset congestion warning line (e.g., +2.5 tasks / ms), the algorithm will determine the instantaneous acceleration. 2 This indicates that the system is in a period of accelerated deterioration due to task backlog. A congestion prediction penalty coefficient greater than 1.0 will be automatically assigned to the collected load rate to amplify the current load signal. Monitoring of the system load change rate has been added, resolving the lag issue of only being able to passively respond after an overload occurs.

[0028] Specifically, the system synchronously reads the cache dirty block counter to obtain the number of pages marked as dirty pages and calculates the dirty page write-back saturation. It then retrieves a preset disk I / O throughput limit threshold and the actual disk write rate to obtain the I / O load rate. A weighted sum of the dirty page write-back saturation and the I / O load rate is generated to produce an I / O pressure ratio. A non-linear function is used to perform gradient mapping on the I / O pressure ratio, outputting a storage blocking coefficient. The processor load feature vector and the storage blocking coefficient are then summarized to construct an index state feature set. In this embodiment, the storage pressure sensing logic is executed in the kernel memory management subsystem. By calling a kernel function to read the global dirty page state variable, the number of physical memory pages currently marked as modified but not yet written to disk is obtained. Assuming that 1200MB of dirty page data is currently detected remaining in the memory buffer, it is compared with a preset system dirty page threshold (e.g., 1600MB) to calculate a dirty page write-back saturation of 0.75. Next, a preset disk I / O throughput limit threshold and the actual disk write rate are retrieved. This threshold is set to 1500MB / s based on the measured continuous write peak. The system monitors the current real-time IO write rate (e.g., 1275 MB / s). The IO load ratio (1275 / 1500 = 0.85) is calculated by dividing the real-time IO rate by the maximum threshold. Then, a weighted sum of the dirty page write-back saturation and IO load ratio is generated (e.g., each weighted at 0.5) to produce the overall IO stress ratio R. io =0.5×0.75+0.5×0.85=0.80. To provide a more sensitive control signal at the storage congestion threshold, a nonlinear gradient mapping of the I / O pressure ratio is performed using an exponential function with the natural constant e as its base, transforming the linear pressure ratio into an exponentially growing coefficient. The specific formula is as follows: α is the basic sensitivity coefficient, preferably ranging from [0.01, 0.1]. This coefficient sets the noise tolerance threshold of the system under low load conditions. Its value should be slightly larger than the background noise fluctuation amplitude when the system's I / O is idle to prevent false triggering of the suppression signal in non-blocking conditions. β is the pressure gain factor, preferably ranging from [1.5, 3.0]. This factor determines the non-linear growth rate of the blocking coefficient as the I / O pressure ratio increases. Its value is positively correlated with the random read / write latency tolerance of the storage device. This function ensures that the coefficient grows slowly when the I / O pressure ratio is low, and increases exponentially when approaching the limit. The calculated result is a storage blocking coefficient C of approximately 0.198 (rounded to 0.20). Finally, the previously generated three-dimensional processor load feature vector [0.93, 0.99, 0.85] is normalized and concatenated with the one-dimensional storage blocking coefficient 0.20 after bit width alignment to construct a four-dimensional index state feature set.

[0029] By refining data dimensions and delving into kernel-level computational feature extraction, the accuracy of perception and control capabilities have been improved. On one hand, by combining physical sensor data with management data, a comprehensive digital acquisition panorama has been constructed. This allows subsequent data fidelity analysis to not only be based on physical parameters but also to correlate device health status with business context, improving the accuracy of attribution analysis. On the other hand, by using storage engine performance probes to delve into the underlying operating system, task queues, dirty page write-backs, and IO pressure are precisely quantified. Gaussian normalization and nonlinear mapping techniques are used to output an index status feature set that reflects the true load. This deep integration of hardware and software monitoring mechanisms can predict system I / O bottlenecks in advance, preventing scan data frame loss or write delays caused by storage blockage, ensuring the robustness and stability of the digital asset analysis platform in complex digital acquisition laboratory environments.

[0030] Furthermore, based on the index state feature set, the computing resource saturation is obtained through weighted concatenation, and the collected mapping data stream is embedded to generate a control data package; this corresponds to the computing resource state coupling module; the specific implementation process includes: The system calls the feature aggregation interface to receive the index status feature set, performs a weighted concatenation operation on the processor load feature vector and the storage blocking coefficient, and outputs the computing resource saturation. It synchronously intercepts the acquisition mapping data stream, parses the data transmission frame header of the acquisition mapping data stream, locates the reserved metadata attribute field in the data transmission frame header, fills the reserved metadata attribute field with the computing resource saturation, and performs a serialization and packet assembly operation on the data transmission frame header and the original payload data segment to output the control data packet.

[0031] Specifically, for the feature aggregation interface to receive the index status feature set, a weighted concatenation operation is performed on the processor load feature vector and the storage blocking coefficient to output the computing resource saturation. In this embodiment, the edge index node first receives the index status feature set through the shared memory interface. This feature set is instantiated in memory as a double-precision floating-point structure array, containing a normalized processor load feature vector [0.93, 0.99, 0.85] (corresponding to task queue depth, context switching frequency, and CPU load rate, respectively) and a storage blocking coefficient of 0.20. Based on the sensitivity of high-fidelity scan feedback to computational latency, the feature aggregation interface defines a set of heterogeneous weight vectors W. The values ​​of these weight vectors are calculated based on the analytic hierarchy process: predetermined during the system initialization phase, a 4th-order judgment matrix is ​​constructed, and the importance scale of task queue depth and context switching frequency relative to the storage blocking coefficient is set to 3 (i.e., slightly stronger importance), and the importance scale relative to CPU load rate is set to 1 (i.e., equally important). By calculating the eigenvector corresponding to the largest eigenvalue of the judgment matrix and performing normalization, the weight vector W = [0.30, 0.30, 0.25, 0.15] is obtained. This weight allocation strategy focuses on the CPU's concurrent processing capability (queueing and switching) and slightly reduces the sensitivity to disk I / O. The fusion calculation process adopts a weighted linear projection algorithm, which performs vector dot product operation through the arithmetic logic unit: multiplying each component of the eigenvector with the corresponding weight vector and then summing the results. The specific calculation logic is described as: 0.93 multiplied by 0.30 plus 0.99 multiplied by 0.30 plus 0.85 multiplied by 0.25 plus 0.20 multiplied by 0.15, i.e., 0.279 + 0.297 + 0.2125 + 0.03. Finally, a double-precision floating-point scalar value of 0.8185 (i.e., 81.85%) is output, which is used as the computing resource saturation.

[0032] Specifically, for synchronously intercepting the acquisition and mapping data stream, the data transmission frame header of the acquisition and mapping data stream is parsed; the reserved metadata attribute field in the data transmission frame header is located, and the computing resource saturation is filled into the reserved metadata attribute field. This embodiment adopts a zero-copy network driver architecture. Direct memory access technology is used to directly lock the transmitting acquisition and mapping data stream frame in the ring receive buffer of the network card. This data stream follows a custom dedicated high-speed imaging transmission protocol, and its header contains a 64-byte data transmission frame header. The parser reads the starting byte of the header, identifies the protocol version number, and then uses a pointer offset algorithm to locate the "reserved metadata attribute field" with a byte offset of 48 to 49. This field is reserved as 16 bytes of unsigned integer space in the protocol definition, specifically for carrying in-band telemetry data. A linear quantization mapping operation is performed to map the saturation range of 0.0 to 1.0 to an integer space of 0 to 65535. The specific calculation formula is as follows: multiply the saturation value 0.8185 by the quantization order 65535, round the result up to obtain the decimal integer 53641, which is 0xD189 in hexadecimal. Then, through atomic bit manipulation instructions, this hexadecimal value 0xD189 is written to the memory address of the reserved metadata attribute field.

[0033] Specifically, in this embodiment, after performing serialization and packet assembly operations on the data transmission frame header and the original payload data segment to output a control data packet, the reassembly and integrity verification logic of the data packet is triggered after the metadata injection of the message header is completed. Since the original acquisition mapping data stream contains 4096 bytes of high-frequency sensor physical payload, a distributed-aggregated IO model is adopted. A new socket buffer descriptor points the header pointer to the modified 64-byte data transmission frame header containing saturation 0xD189, and the data pointer points to the original 4096-byte physical payload memory area. Subsequently, using the checksum and offload function supported by the direct memory access (DMA) controller, a frame checksum sequence is generated synchronously during data transmission. The checksum is generated and appended to the tail, and the data unit is then marked as a valid control data packet.

[0034] By employing a feature aggregation interface to embed computing power status into reserved metadata attribute fields in the data transmission frame header, a separate heartbeat monitoring channel is eliminated, thus saving network bandwidth resources. This "data and status go hand in hand" encapsulation method allows downstream nodes to know the upstream computing power health status while receiving data, achieving efficient collaboration between the communication and control layers. Furthermore, standardized serialization packet assembly operations ensure the uniformity of data packet structure, facilitating high-speed parsing and routing by network devices at different levels and reducing protocol conversion overhead for cross-system interactions.

[0035] Furthermore, based on the controlled data packets, local serial processing is executed when the computing resource saturation is below the I / O throughput threshold; otherwise, a fragmentation mechanism is executed for parallel processing via a hardware acceleration channel to construct a sequence of numbers to be verified; this corresponds to the abnormal data screening module; see [link to module]. Figure 3 The specific implementation process includes: The computing resource saturation is obtained by parsing the control data packets; the computing resource saturation is compared with a preset I / O throughput threshold, and the comparison result is output; when the comparison result is in a normal state, the control data packets are executed in local serial computation and synchronously archived to the digital asset management database; when the comparison result is in a high-concurrency state, a sharding mechanism is triggered, the specific process of which includes: control data packets with a data volume exceeding a preset sharding threshold are sharded and scheduled to edge index nodes for distributed computation; for control data packets with a sampling frequency higher than a preset frequency threshold, the FPGA hardware acceleration unit is invoked to perform parallel aggregation computation; the processing results returned by the edge index nodes and the FPGA hardware acceleration unit are received, and the digital sequence to be verified is output.

[0036] Specifically, the computational resource saturation is obtained by parsing the control data packets. This saturation is then compared with a preset I / O throughput threshold, and the comparison result is output. In this embodiment, a high-priority real-time message parsing thread is first started to extract a 16-bit hexadecimal signature (e.g., 0xD189) from the extended field of the control data packet header. Reverse calculation logic is executed to restore the current edge node's computational resource saturation to 0.8185 (i.e., 81.85%). Subsequently, a conditional filtering operator is invoked to compare this real-time saturation value with the preset I / O throughput threshold of 0.80 in the system configuration register. To prevent frequent switching between serial and parallel modes due to small fluctuations near the threshold, this embodiment uses a hysteresis comparison algorithm to set the trigger threshold for entering a high-concurrency state to 0.80, and the release threshold for returning to the normal state to 0.75. Since the current calculated value of 0.8185 is greater than the trigger threshold of 0.80, it is determined that the current state is "high concurrency" and a high-level interrupt control signal is generated. This signal will activate the routing logic of the fragment scheduler within the next 10µs.

[0037] Specifically, when the comparison result is in a normal state, the control data packet is processed locally serially and synchronously archived to the digital asset management database. In this embodiment, assuming that the computing resource saturation dropped to 0.65 (below the I / O throughput threshold) at the previous moment, it will automatically switch to local serial processing mode. At this time, the edge gateway's main processor directly takes over the control data packet and uses a time-slice round-robin scheduling algorithm to map the acquired mapping data stream to a single computing thread. This thread sequentially performs data unpacking, verification, and basic statistics, and writes the processed physical sensing data (such as probe displacement coordinates (12.50, -3.20, 50.15) and spectral response value 52000) and management data (such as casting task codes) into the locally deployed time-series database using a structured query language. The archiving process adopts a batch write strategy, setting a disk write operation to be triggered every 100 cached records or every 500ms to balance IO overhead.

[0038] Specifically, when the comparison result indicates a high-concurrency state, a sharding mechanism is triggered. The process includes: control data packets with data volumes exceeding a preset sharding threshold are sharded and scheduled to edge index nodes for distributed computing. In this embodiment, given that the current saturation has reached 0.8185, the sharding mechanism is activated immediately. The control data packets with data volumes exceeding the preset sharding threshold refer to the "structured index maintenance log segments" (such as equipment calibration logs, surface texture maps of cultural relics captured by high-definition industrial cameras, etc.) that are relatively large in size but have relatively low real-time requirements. The sharding threshold refers to a single data packet exceeding 4MB. A consistent hashing algorithm is used for sharding scheduling. This algorithm maps the globally unique identifier of the data object in the data packet (e.g., MINT-2025-X7) to a number between 0 and 2. 32 The data is routed along a hash ring of -1 and the nearest available edge index node is found clockwise based on the hash value. For example, the large data packet is routed to "edge node-03" within the local area network, which currently has a CPU load of only 30%. The data is transmitted at a rate of 1Gbps, and log parsing and image feature extraction tasks are performed independently on the target node, thereby offloading approximately 40% of the network and computing pressure from the master node.

[0039] Specifically, for control data packets with sampling frequencies higher than a preset frequency threshold, the FPGA hardware acceleration unit is invoked to perform parallel aggregation operations. In this embodiment, for unstructured index data segments (containing spatial coordinate displacement and spectral intensity waveforms, where the preset frequency threshold is 20kHz) with sampling frequencies as high as 20kHz, the original waveform data is pushed to the onboard DDR4 memory of the Xilinx Alveo U200 FPGA accelerator card in a "zero-copy" manner via the AXI4-Stream streaming interface using a high-speed bus and direct memory access technology. The FPGA has 16 pre-built parallel digital signal processing (DSP48E2) pipelines that use an adder tree structure to perform hardware-level parallel aggregation. Specifically, the system maintains a circular buffer in the off-chip DDR4 memory. When the accumulated data reaches a preset batch window size (e.g., 80,000 historical sampling points), a DMA burst transfer is triggered. The data is then streamed into the FPGA's on-chip buffer (BRAM) via the AXI4-Stream bus. The FPGA internally incorporates a data distribution logic unit that divides the incoming data stream into 16 sub-data streams using a polling method. These 16 sub-data streams are then fed into 16 parallel-instantiated DSP48E2 computing cores. Each DSP core is configured in pipelined accumulation mode, independently calculating the local mean and variance of its sub-stream. The outputs of all DSP cores are connected to a 4-stage pipelined adder tree. The first-stage adder merges the 16 local results pairwise into 8 streams, the second stage merges them into 4 streams, and so on, finally outputting the global statistical result at the fourth stage. The entire process is pipelined under the drive of the system clock (e.g., 200MHz), ensuring that 80,000 points of fully parallel aggregation are completed within 50µs, and that data processing does not experience backlog at a high saturation level of 0.8185.

[0040] More specifically, the data access and distribution logic of the FPGA hardware acceleration unit is as follows: The FPGA receives the control data packet stream from the PCIe bus through the AXI4-Stream slave interface. Within the FPGA's programmable logic area, a message parsing IP core based on a finite state machine (FSM) is deployed. This state machine monitors the TVALID and TREADY handshake signals of the AXI bus, marking the start of a frame when the TUSER signal goes high. Internally, the state machine maintains a byte counter. When the counter value reaches the offset address of the reserved metadata attribute field in the data transmission frame header (i.e., the 48th byte), the subsequent 16 bits of data are latched on the rising edge of the clock, extracting the embedded computing resource saturation feature code (e.g., 0xD189). Subsequently, the latched saturation feature code is sent to a hardware comparator and compared with a preset hardware start threshold in the configuration register in a single cycle. If the signature value is higher than the threshold, the state machine pulls up the internal ENABLE_DSP signal, activating the multiplexer to import the subsequent Payload data stream into the input FIFO of the DSP48E2 parallel computing array; if it is lower than the threshold, the data stream is routed to the bypass BRAM buffer or discarded directly, thereby realizing hardware-level dynamic computing power scheduling based on in-band control data.

[0041] Specifically, for the processing results returned by the edge index node and the FPGA hardware acceleration unit, a digital sequence to be verified is output. In this embodiment, an asynchronous association matching mechanism based on a time sliding window is set up. The spectral / displacement waveform feature vector output by the FPGA hardware acceleration unit is temporarily stored in a high-speed circular queue, and the system monitors the log analysis results from "edge node-03" in real time. Once the analysis data from the edge node is received, the waveform feature vector within the corresponding time window is extracted from the circular queue according to the global timestamp index, and the two are logically merged. For example, the spectral intensity waveform variance "0.05" calculated by the FPGA is associated with the device calibration status "normal" parsed by the edge node to construct a digital sequence to be verified containing the unique serial number "BATCH-SEQ-88492". This sequence contains only highly condensed feature information, rather than the original massive data.

[0042] By employing a dynamic resource saturation-based allocation mechanism, computing resources are allocated on demand, overcoming the waste or insufficiency of computing power caused by traditional static resource allocation. Under normal conditions, local serial computing reduces system energy consumption and network overhead; while under high concurrency, intelligent triggering of edge index node sharding scheduling and FPGA hardware acceleration leverages the inherent advantages of FPGAs in parallel aggregation operations, shortening the processing time for high-frequency data. This heterogeneous computing architecture, combining software and hardware, not only ensures the real-time generation of the digital sequence to be verified but also effectively smooths out peaks and troughs in computing power, extending the average lifespan of edge devices and ensuring high throughput and low latency in digital asset generation under high-fidelity digitization scanning cycles.

[0043] Furthermore, based on the sequence of numbers to be verified, the feature space distance parameter between the collected mapping data stream and the historical benchmark model is calculated using a sliding window. When the feature space distance parameter exceeds a preset change threshold, a mutation collection feature vector is generated; this corresponds to the abnormal data screening module. The specific implementation process includes: The sequence of numbers to be verified is imported into the circular buffer of the in-memory database, and a continuous instantaneous data view is generated through a cursor sliding read operation. A dimensionality reduction mapping is performed on the instantaneous data view to output the query feature index key. A standard benchmark feature vector is retrieved from the historical benchmark model. The feature difference metric between the query feature index key and the standard benchmark feature vector in the feature space is calculated, and the feature space distance parameter is output. The feature space distance parameter is input to a conditional filtering operator and compared with a preset change threshold. When the feature space distance parameter exceeds the preset change threshold, the corresponding query feature index key is locked. An abnormal timestamp index and dimension type identifier are added to the locked query feature index key, and a mutation collection feature vector is output.

[0044] Specifically, for the circular buffer that imports the digital sequence to be verified into the memory database, a continuous instantaneous data view is generated through a cursor sliding read operation. In this embodiment, a circular buffer is allocated in memory to receive the digital sequence to be verified output from the previous steps. This sequence is essentially high-frequency time series data preprocessed by the hardware acceleration channel, mainly containing spectral intensity response waveform data of a light sensor with a sampling frequency of 20kHz. A fixed-length circular buffer of the memory database is configured, with a window size set to 1000 data points, corresponding to a physical time length of 50ms. The sliding step size of the window is set to 200 data points, that is, a truncation operation is performed every 10ms, thereby ensuring an 80% data overlap rate between adjacent windows. This high overlap rate design is to prevent transient anomalies from being missed at the window edges. Through pointer arithmetic operations, 1000 floating-point values ​​within the current window are continuously read from the buffer to construct a 1000x1 instantaneous acquisition segment matrix, which completely preserves the spectral texture and radiation energy distribution characteristics within the current time slice.

[0045] Specifically, for outputting query feature index keys by performing dimensionality reduction mapping on the instantaneous data view, this embodiment uses the linear projection operator of the principal component analysis algorithm to perform online dimensionality reduction. A pre-loaded projection matrix is ​​generated by a historical benchmark model, which is constructed as follows: it is obtained by offline training on massive historical data generated by the device running continuously for 72 hours under standard calibration conditions. The projection matrix has a dimension of 1000 x 5. This means that the projection matrix extracts the five principal component directions with the highest variance contribution rate in the original data. In the actual processing, the computing unit performs matrix multiplication, multiplying the transpose of the 1000-dimensional instantaneous acquisition segment vector with the 1000 x 5 projection matrix, thereby mapping the high-dimensional data into a 5-dimensional low-dimensional feature space. The output acquisition feature vector contains only five double-precision floating-point numbers, such as [12.50, -3.20, 0.85, 4.10, 0.05]. These five values ​​represent the overall radiative energy of the spectral waveform, low-frequency color temperature drift, high-frequency thermal noise texture, and two specific harmonic component characteristics, respectively. This greatly compresses the data volume (compression ratio of 200:1) while retaining key information entropy sufficient to distinguish between normal scanning and abnormal interference states.

[0046] Specifically, for retrieving the standard benchmark feature vector from the historical benchmark model; calculating the feature difference metric between the query feature index key and the standard benchmark feature vector in the feature space; and outputting the feature space distance parameter, in this embodiment, a preset "standard benchmark feature vector" is read from the cache. This benchmark vector is generated by the historical benchmark model and is the arithmetic mean feature vector calculated after dimensionality reduction mapping of multiple sets of operating condition data collected within a set time window (e.g., 10 minutes) when the device has just completed blackboard calibration and is in optimal operating condition. Assume its value is [12.00, -3.00, 0.50, 4.00, 0.00]. The distance calculation logic calls a mathematical library function to perform Euclidean distance calculation, that is, first calculating the difference between the corresponding elements of the query feature index key and the benchmark feature vector in 5 dimensions, then squaring each of these 5 differences, then summing the 5 squared values, and finally taking the square root of the sum. Taking specific numerical values ​​as an example, the calculation process for the sum of squares of the differences is as follows: 0.5 squared plus -0.2 squared plus 0.35 squared plus 0.1 squared plus 0.05 squared, resulting in a total of 0.425. Taking the square root of this sum yields a characteristic spatial distance parameter of approximately 0.652. This characteristic spatial distance parameter is a scalar value that intuitively quantifies the geometric distance of the current operating condition from the optimal spectral reference.

[0047] To eliminate the linear drift effect on the baseline caused by physical wear and tear from long-term equipment operation, this embodiment introduces an adaptive compensation mechanism for equipment aging. The tuple access frequency count N in the structured index maintenance log segment is read synchronously, and a preset linear degradation function D(N) based on the wear mechanism is called to calculate the current equipment aging compensation bias vector δ. This bias vector is dynamically superimposed onto the low-frequency trend dimension of the standard baseline feature vector to generate a dynamic adaptive baseline vector that conforms to the current physical health state of the equipment. The equipment aging compensation bias vector quantifies the normal parameter shifts caused by increased CMOS thermal noise and attenuated light source flux during the current lifespan of the equipment. The function D(N) is defined as follows: Where k is the aging degradation coefficient (e.g., 1.5 × 10⁻⁶). -7 ), V wear This is a preset attenuation direction unit vector (corresponding to the low-frequency trend dimension in the standard baseline eigenvector). This calculation simulates the cumulative physical drift in the device's signal-to-noise ratio (SNR) as the number of scan exposures increases. By combining lifetime counting with an aging physics model, dynamic calibration of the baseline is achieved. This effectively distinguishes between normal optical aging and sudden acquisition accidents, reducing unnecessary downtime for inspections and improving acquisition efficiency.

[0048] Specifically, the feature space distance parameter is input into the conditional filtering operator and compared with a preset change threshold. When the feature space distance parameter exceeds the preset change threshold, the corresponding query feature index key is locked. An abnormal timestamp index and dimension type identifier are added to the locked query feature index key, and the mutation acquisition feature vector is output. In this embodiment, the conditional filtering operator reads the preset change threshold in the system configuration register, which is set to 0.50 based on the statistical 3-Sigma principle. The real-time calculated feature space distance parameter 0.652 is compared with the threshold 0.50. Since 0.652 is greater than 0.50, the comparator outputs an anomaly judgment signal, immediately triggering the locking mechanism. The current 5-dimensional acquisition feature vector [12.50, -3.20, 0.85, 4.10, 0.05] is written into a temporary register dedicated to anomaly handling. Next, the metadata appending engine obtains the global timestamp and identifies the feature dimension that contributes the most to the distance offset (in this example, the 3rd dimension, i.e., the high-frequency noise component), generating the dimension type identifier DIM_3_NOISE. Finally, the feature vector, time label, and dimension type identifier are encapsulated into a structured mutation acquisition feature vector package.

[0049] By constructing a mutation acquisition feature vector using a circular buffer in an in-memory database and dimensionality reduction mapping techniques, the sensitivity and accuracy of acquisition anomaly detection are improved. Utilizing a sliding window technique to capture continuous instantaneous acquisition segments preserves the temporal correlation of data and avoids misinterpretation. Through dimensionality reduction mapping and Euclidean distance comparison with historical benchmark models, genuine acquisition mutation signals can be accurately extracted from massive amounts of high-dimensional noisy data, effectively reducing the false alarm rate. Simultaneously, adding anomaly timestamp indexes and dimensionality type identifiers to the locked feature vectors provides spatiotemporal coordinates for subsequent data traceability, enabling data auditors to quickly pinpoint the specific time and associated parameter dimensions of the problem, shortening the cycle of troubleshooting and acquisition parameter optimization.

[0050] Furthermore, in the independent metadata management partition, the mutation collection feature vector is data-bound using a pre-defined storage node physical addressing identifier to generate a single-item digital traceability certificate; corresponding to the component history anchoring module; the specific implementation process includes: Establish an instruction interaction channel with the independent metadata management partition, and transmit the mutation collection feature vector to the isolation buffer of the independent metadata management partition; retrieve the physical addressing identifier of the storage node, perform an association mapping operation on the mutation collection feature vector and the physical addressing identifier of the storage node, and output a feature fusion data block containing device identity attributes; perform standardized encoding on the feature fusion data block, and output a transaction operation log with a digital signature of the bound content; retrieve the credit endorsement identifier inside the independent metadata management partition, perform digital credit registration on the transaction operation log, and generate a version consistency verification code; call the data encapsulator to receive the version consistency verification code and the feature fusion data block, append redundant verification bits to the index field of the feature fusion data block, and output a single digital traceability certificate.

[0051] Specifically, for establishing an instruction interaction channel with the independent metadata management partition, the mutation acquisition feature vector is transmitted to the isolation buffer of the independent metadata management partition. In this embodiment, a high-level secure interaction channel is first constructed between the host operating system of the edge inode and the hardware-protected Intel SGX enclave. A protected memory page cache of 128MB is initialized, and this area serves as an isolation buffer, completely shielded from external operating systems and privileged instructions. The memory pointer and length parameter of the feature vector are passed to the isolation buffer through the edge call interface.

[0052] Specifically, for retrieving the physical address identifier of the storage node, an association mapping operation is performed on the mutation acquisition feature vector and the physical address identifier of the storage node, outputting a feature fusion data block containing device identity attributes; the feature fusion data block is then subjected to standardized encoding, outputting a transaction operation log bound to the content digital signature. In this embodiment, the preset physical address identifier of the storage node is first retrieved from sealed storage. This fingerprint is a 256-bit unique hardware key, derived from the physical non-cloning function characteristic of the main control security chip of the digital acquisition terminal (such as a high-fidelity scanner), ensuring the non-forgeability of the physical device. Subsequently, a hash message authentication code algorithm is executed for association matching: using the physical address identifier of the storage node as the key and the binary stream of the mutation acquisition feature vector as the message, a 32-byte hash digest is calculated and generated. For example, the calculation result is a hexadecimal sequence 0xA1B2...C3D4, which is the feature fusion data block, mathematically binding the feature vector and the content digital signature. Next, the fused data block is standardized and encapsulated into a transaction operation log structure conforming to the ASN.1 standard, thereby outputting a transaction operation log with a unified format and a digital signature of bound content that can be parsed by heterogeneous systems.

[0053] Specifically, for retrieving the credit endorsement identifier within the independent metadata management partition, digital credit registration is performed on the transaction operation log to generate a version consistency check code. The data encapsulator is then invoked to receive the version consistency check code and the feature fusion data block, and redundant check bits are appended to the index field of the feature fusion data block to output a single digital traceability certificate. In this embodiment, SGX hardware instructions are further invoked to obtain the identity measurement value of the current isolation buffer. This includes a hash fingerprint representing the currently running code, such as 0x78F2..., and a hash of the signature public key representing the software publisher. These two values ​​constitute the credit endorsement identifier, proving the integrity and credibility of the processing logic. The aforementioned ASN.1 transaction operation log and hash fingerprint are concatenated, and a CRC32 cyclic redundancy check code (e.g., 0x8F1A2B3C) is calculated as the version consistency check code for rapid integrity verification. Subsequently, the data encapsulator initiates the signing process, using the elliptic curve digital signature algorithm private key generated within the isolation buffer to sign the combination of the feature fusion data block and the check code, generating 64 bytes of redundant check bits. Finally, the wrapper pushes the feature fusion data block (32 bytes), credit endorsement identifier (32 bytes), version consistency check code (4 bytes), and redundancy check bit (64 bytes) into a binary container in sequence, and outputs a single digital traceability certificate with a total length of 132 bytes.

[0054] By binding an Independent Metadata Management Enclosure (TEE) with the physical addressing identifier of the storage node, the absolute ownership and credit rating of individual digital asset data are established. Data processing is performed within the TEE isolation buffer, blocking malicious software or viruses from spying on and tampering with the core natively collected data at the hardware level, ensuring data confidentiality. Deeply integrating the physical addressing identifier of the storage node (unique identifiers such as PUF) with mutation collection characteristics strongly associates electronic credentials with specific physical collection devices, effectively preventing the risks of "AI-generated data impersonating physical scanning" or "forging collection sources." Furthermore, the generated version consistency check code and redundant check bits provide security endorsement for the circulation of cultural digital assets on the blockchain.

[0055] Furthermore, the individual digital traceability certificates are aggregated and compressed using tree-structured topology aggregation logic to obtain batch verification root values ​​and output quality assurance digital certificates; corresponding to the component history anchoring module; the specific implementation process includes: Collect single digital traceability certificates from the same database transaction commit window, input them one by one into the index generation logic, and output the underlying data page fingerprint sequence. Extract adjacent elements from the underlying data page fingerprint sequence according to the storage block sequence to form an associated index block. Perform tuple aggregation and compression operations on the associated index block, input the concatenated code back into the index generation logic to generate a directory-level aggregated key value. Recursively perform pairing, concatenation, and index generation operations on the directory-level aggregated key value until convergence generates a unique full-database retrieval root node identifier. Lock the full-database retrieval root node identifier as the batch verification root value. Call the file system wrapper to receive the batch verification root value, associate it with the blockchain smart contract address and index commit timestamp, perform structured serialization, and output the quality assurance digital certificate.

[0056] Specifically, for a single digital traceability certificate collected from the same database transaction commit window, the index generation logic is input one by one, and the underlying data page fingerprint sequence is output. In this embodiment, a standard "database transaction commit window" is first defined as a continuous work shift in a cultural relic digitization acquisition laboratory, such as 2000 digital twin cultural digital asset copies produced continuously within eight hours. This is based on the acquisition generation timestamp (e.g., T0 to T...). 1999 The system retrieves these binary credential data blocks from the cache one by one in the following order and inputs them into the index generation logic based on the SHA-256 algorithm. Through cryptographic hashing, the variable-length input of 132 bytes is mapped to a fixed-length output of 256 bits (i.e., 32 bytes). For example, for credential number #001, its original binary data begins with 0xA1. After hashing, a unique hexadecimal sequence, such as 0xE9...03, is output. This sequence is the first "leaf node hash" of the Merkle tree. This process is repeated for 2000 credentials, eventually constructing a linear hash sequence containing 2000 elements in memory, occupying a total memory space of 2000 × 32 bytes.

[0057] Before the input logic is executed, this embodiment can introduce a physical entropy salting operation. The least significant bit sequence of the spectral response values ​​of the high-frequency sampled photosensor in the unstructured index data segment is extracted. Due to the complexity of the high-frequency electronic environment in the acquisition laboratory, this least significant bit sequence mainly consists of thermal noise and quantization error, containing unpredictable true random physical entropy. This 32-bit noise sequence is used as the physical salt R. phyThe data block of a single digital traceability certificate is subjected to bitwise XOR obfuscation. The processed salted data is then fed into the SHA-256 operation unit, outputting a low-level data page fingerprint sequence with high collision resistance. This ensures that even two digital copies with macroscopically identical spectral parameters can generate distinctly different binary fingerprints at the microscopic data level. Furthermore, it cleverly utilizes noisy data, transforming it into a secure asset, providing a technical foundation for refined traceability. This enhances the security level at the edge without increasing hardware costs or affecting system real-time performance.

[0058] Specifically, adjacent elements in the fingerprint sequence of the underlying data pages are retrieved in the order of the storage block sequence to form an associated index block. A tuple aggregation and compression operation is performed on the associated index block, and the concatenated code is input again into the index generation logic to generate a directory-level aggregated key value. Pairing, concatenation, and index generation operations are recursively performed on the directory-level aggregated key value until a unique root node identifier for the full database retrieval is generated. In this embodiment, a binary tree topology is used to perform recursive aggregation. Faced with the N=2000 leaf node hashes generated at the underlying level, the parity of the number of nodes is first checked. Since 2000 is even, no padding operation is needed (if it is odd, the last node is copied). In the first round of recursive operation, adjacent hash values ​​are retrieved in sequence to form 1000 associated index blocks. A strict tuple aggregation and compression operation is performed on each associated index block, concatenating the 32 bytes of data from H0 and H1 at the byte level in memory to construct a 64-byte data block. Subsequently, this 64-byte data block is input again into the SHA-256 index generation logic to calculate a new 32-byte hash value, namely the parent node H. 0-1 This process is executed recursively upwards through the tree structure, with the number of nodes at each level converging exponentially at a rate of N / 2: from 2000 nodes at Level 0, to 1000 at Level 1, 500 at Level 2, and so on, until Level 11 converges to a single, unique node. This final node is the Merkle tree root node, for example, with a calculated hash value of 0x7F...8A. This value is then locked as the "batch verification root value," which, mathematically compressed using an encryption algorithm, represents the original compliance status of all 2000 digital assets within the entire database transaction commit window and serves as the unique digital fingerprint for batch ownership confirmation.

[0059] Specifically, the process involves calling the file system encapsulator to receive the batch verification root value, associating it with the blockchain smart contract address and index submission timestamp, performing structured serialization, and outputting the Zhixin digital certificate. In this embodiment, the file system encapsulator receives the locked 32-byte batch verification root value 0x7F...8A, which is used as the core payload. Next, the "smart contract address" responsible for digital asset registration, such as 0x7a...88D, is obtained through an RPC interface query. Simultaneously, the current "index submission timestamp" is obtained and converted to a standard Unix timestamp format, such as 1716200400. The encapsulator serializes the above three core data items (root value, contract address, and timestamp) along with the production batch number (such as BATCH-20240520-A) to generate a standardized JSON data packet. This data packet is the Zhixin digital certificate.

[0060] By aggregating individual credentials using a Merkle tree structure, blockchain storage efficiency and verification speed are optimized. Faced with massive amounts of individual product data, uploading each item to the blockchain individually would lead to main chain congestion and high costs. However, by generating a unique batch verification root value through recursive hashing, and only uploading the root value and smart contract address to the blockchain, a highly efficient "off-chain data storage, on-chain fingerprint storage" model is achieved, reducing gas fees. Simultaneously, the Merkle tree structure supports rapid lightweight node verification. Third parties do not need to download complete data; only a minimal number of hash paths are required to verify whether a specific cultural digital asset belongs to the batch and has not been tampered with. This protects the privacy of enterprise asset creation while endowing the Zhixin digital certificate with strong credibility and traceability.

[0061] This invention provides a digital asset intelligent analysis platform based on blockchain smart contract technology. By constructing a computing power resource state coupling module, an abnormal data screening module, and a component history anchoring module, it achieves efficient integration of digital acquisition of cultural resources and digital asset ownership confirmation. By embedding the computing resource saturation of the acquired mapping data stream and dynamically adjusting the computing strategy, it avoids data backlog or loss caused by single node overload, ensuring the continuity of high-frequency scanning acquisition data. Secondly, it adopts a hierarchical processing mechanism of "local serial + hardware-accelerated parallel," ensuring low energy consumption while improving the throughput of sudden massive data bursts through a sharding mechanism, solving the problem of sluggish response in traditional systems when facing instantaneous peak data from scanning terminals. Finally, by combining independent metadata management partitioning and storage node physical addressing identification technology, it ensures the credibility and tamper-proof nature of the source acquisition data, generating unforgeable digital ownership certificates for each digital twin asset.

[0062] Example 2 This second embodiment demonstrates the specific process of using the present invention in a cultural relic digitization acquisition terminal while performing a high-precision scanning task; the specific implementation method is as follows: This embodiment is set as a high-precision multispectral cultural relic scanning automated platform based on a seven-axis robotic arm, with an acquisition scanning rate of 40 FPS (acquiring 40 frames of point cloud and spectral data per second). At this time, in the direct memory access (DMA) receive buffer of the edge gateway, the binary acquisition mapping data stream containing the spectral intensity response waveform of the light sensor with a sampling rate of 20 kHz and the micron-level spatial coordinate data of the probe has completed physical acquisition and is flowing into the system kernel space at a rate of 1.6 Mbit per second.

[0063] When the data packet arrives at the socket buffer in kernel space, the eBPF probe program residing in kernel space is immediately activated, taking a snapshot of the current edge node's computing power characteristics at a 10ms time granularity. By reading the structure, the probe detects that the run queue depth of the daemon currently responsible for data processing has backlogged to 12 pending threads, while the frequency of involuntary context switching of the processor core has surged to 2800Hz, indicating that the CPU is engaged in intense resource contention. Simultaneously, the memory management subsystem reports that the dirty page write-back volume has reached 1.2GB, causing the real-time IO pressure ratio to climb to 0.72. Based on the above micro-indicators, the internal feature aggregation interface calculates the current node's computing resource saturation as 0.925 (i.e., 92.5%) through weighted calculation. This saturation value is linearly mapped to a 16-bit hexadecimal signature code 0xECCD and filled into the reserved metadata attribute field in the header of the acquired data stream. At this point, the data stream transforms from a simple physical payload into a control data packet carrying "computing power awareness" attributes.

[0064] The control data packets then enter the abnormal data screening module. The scheduler first parses the 0xECCD signature in the packet header, deciphering the saturation level to 0.925, which far exceeds the preset I / O throughput threshold of 0.82. Determining that the current node is in a severely congested state, the scheduler immediately triggers a fragmentation offloading mechanism. For large (approximately 4MB) high-resolution texture maps of artifact surfaces and full-lifecycle calibration logs within the data packets, which have lower real-time requirements, they are routed to idle standby nodes within the local area network for asynchronous processing using a consistent hashing algorithm. For the core high-frequency unstructured index data segments (spectral and coordinate data), the data stream is directly injected into the onboard FPGA hardware acceleration card memory in a "zero-copy" manner via the PCIe 4.0 x16 bus and its DMA channel. Inside the FPGA, 16 parallel DSP48E2 pipelines perform hard-line aggregation operations on the data stream, completing the calculation of the mean, variance, and higher-order moments of 1000 sampling points within the current scan cycle in 50µs, outputting a 64-byte sequence of numbers to be verified.

[0065] After hardware-accelerated cleaning, the data stream is then pushed into the mutation acquisition and detection stage. The sequence to be verified is imported into a circular buffer of a 1000-point in-memory database, and the high-dimensional waveform data is mapped to a 5-dimensional feature space using a pre-trained PCA projection matrix. The computing unit rapidly retrieves the standard baseline feature vector [12.0, -3.0, 0.5, 4.0, 0.0] of the artifact material type from the L3 cache and calculates the feature difference metric between the real-time acquisition vector and its feature space. The calculation results show that the feature space distance parameter is 0.75, significantly exceeding the preset change threshold of 0.60, which physically corresponds to a sudden strong light interference in the acquisition environment or a slight jitter of the robotic arm. The abnormal vector is immediately locked, and a timestamp of 1716200400.005 and a "spectral noise / jitter" dimension type identifier are added to generate a mutation acquisition feature vector. This vector is then transmitted through an encrypted channel to the Intel SGX independent metadata management partition isolation area inside the CPU.

[0066] Within an independent metadata management partition, mutation-collected feature vectors are hashed and fused with the Physically Unclonable Function (PUF) fingerprint inherent in the security chip of the acquisition terminal to generate a unique feature-fused data block. This data block is then digitally signed using an ECDSA signature via a private key within the enclave, generating a 132-byte single-item digital traceability credential. Due to the high-speed operation of the scanning job, each credential is not individually uploaded to the blockchain; instead, 4500 single-item credentials generated within the same acquisition shift (8 hours) are aggregated into an aggregation buffer. A tree-structured topology aggregation logic is then initiated, employing the SHA-256 algorithm to perform layer-by-layer hash calculations on the credential stream. After 13 rounds of recursive computation, the 4500 discrete raw fidelity data points converge into a unique 32-byte batch verification root value. This root value, serving as a "summary" of the entire database transaction commit window data, is encapsulated within the blockchain transaction payload and sent to the smart contract address 0x8B...F2. With the achievement of consensus on the blockchain network, this transaction hash is permanently recorded on the distributed ledger.

[0067] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital asset intelligent analysis platform based on blockchain smart contract technology, characterized in that: include: Computing resource status coupling module: Receives the acquisition and mapping data stream from the cultural relic digitization acquisition terminal; By capturing the task queue depth and context switching frequency of the task control block, and combining them with the processor load rate, an index state feature set is constructed. Based on the index status feature set, the computing resource saturation is obtained by weighted concatenation, and the collected mapping data stream is embedded to generate a control data package; The abnormal data screening module: Based on the control data packet, when the computing resource saturation is lower than the I / O throughput threshold, it performs local serial processing; otherwise, it performs a sharding mechanism and performs parallel processing through the hardware acceleration channel to construct the digital sequence to be verified. Based on the digital sequence to be verified, it calculates the feature space distance parameter between the collected mapping data stream and the historical benchmark model through a sliding window. When the feature space distance parameter exceeds the preset change threshold, it generates a mutation collection feature vector. Component history anchoring module: It performs data binding operations on the mutation collection feature vector through the pre-set storage node physical addressing identifier to generate a single digital traceability certificate; it aggregates and compresses the single digital traceability certificate through tree topology aggregation logic, and calls the blockchain smart contract address to associate and confirm the rights, and outputs the quality digital certificate.

2. The digital asset intelligent analysis platform based on blockchain smart contract technology according to claim 1, characterized in that, The acquired and mapped data stream specifically includes an unstructured index data segment and a structured index maintenance log segment; the unstructured index data segment includes the spatial coordinate displacement data of the scanning probe and the spectral response value of the light sensor; the structured index maintenance log segment includes the globally unique identifier of the data object, the tuple access popularity count, and the index tree node split record.

3. The digital asset intelligent analysis platform based on blockchain smart contract technology according to claim 1, characterized in that, The specific generation process of the index state feature set includes: reading the task control block in the operating system memory through the storage engine performance probe, extracting the task queue depth and context switching frequency of the running queue where the task control block is located, and collecting the processor load rate; mapping the task queue depth, context switching frequency, and load rate through a Gaussian normalization function to output the processor load feature vector; synchronously reading the cache dirty block counter to obtain the number of pages marked as dirty pages and calculating the dirty page write-back saturation; retrieving the preset disk IO throughput limit threshold and the actual disk write rate to obtain the IO load rate, and performing a weighted summation of the dirty page write-back saturation and the IO load rate to generate the IO pressure ratio; performing gradient mapping on the IO pressure ratio using a nonlinear function to output the storage blocking coefficient; and summing the processor load feature vector and the storage blocking coefficient to construct the index state feature set.

4. The digital asset intelligent analysis platform based on blockchain smart contract technology according to claim 1, characterized in that, The specific generation process of the control data packet includes: calling the feature aggregation interface to receive the index status feature set, performing a weighted concatenation operation on the processor load feature vector and the storage blocking coefficient, and outputting the computing resource saturation; synchronously intercepting the acquisition mapping data stream and parsing the data transmission frame header of the acquisition mapping data stream; locating the reserved metadata attribute field in the data transmission frame header and filling the reserved metadata attribute field with the computing resource saturation; performing a serialization and packet assembly operation on the data transmission frame header and the original payload data segment, and outputting the control data packet.

5. The digital asset intelligent analysis platform based on blockchain smart contract technology according to claim 1, characterized in that, The specific generation process of the digital sequence to be verified includes: parsing the control data packet to obtain the computing resource saturation; comparing the computing resource saturation with a preset I / O throughput threshold and outputting the comparison result; when the comparison result is in a normal state, performing local serial computation on the control data packet and synchronously archiving it to the digital asset management database; when the comparison result is in a high-concurrency state, triggering the sharding mechanism, specifically including: control data packets with a data volume exceeding a preset sharding threshold are sharded and scheduled to edge index nodes for distributed computation; for control data packets with a sampling frequency higher than a preset frequency threshold, calling the FPGA hardware acceleration unit to perform parallel aggregation computation; receiving the processing results returned by the edge index node and the FPGA hardware acceleration unit, and outputting the digital sequence to be verified.

6. The digital asset intelligent analysis platform based on blockchain smart contract technology according to claim 1, characterized in that, The specific generation process of the mutation acquisition feature vector includes: importing the digital sequence to be verified into the circular buffer of the memory database, generating a continuous instantaneous data view through a cursor sliding read operation; performing dimensionality reduction mapping on the instantaneous data view to output the query feature index key; retrieving the standard benchmark feature vector from the historical benchmark model; calculating the feature difference metric between the query feature index key and the standard benchmark feature vector in the feature space, and outputting the feature space distance parameter; performing amplitude comparison between the feature space distance parameter input condition filtering operator and a preset change threshold; locking the corresponding query feature index key when the feature space distance parameter exceeds the preset change threshold; adding an abnormal timestamp index and dimension type identifier to the locked query feature index key, and outputting the mutation acquisition feature vector.

7. The digital asset intelligent analysis platform based on blockchain smart contract technology according to claim 1, characterized in that, The specific generation process of the single digital traceability certificate includes: establishing an instruction interaction channel with the independent metadata management partition, transmitting the mutation collection feature vector to the isolation buffer of the independent metadata management partition; retrieving the physical addressing identifier of the storage node, performing an association mapping operation on the mutation collection feature vector and the physical addressing identifier of the storage node, and outputting a feature fusion data block containing device identity attributes; performing standardized encoding on the feature fusion data block, and outputting a transaction operation log with a digital signature of the bound content; retrieving the credit endorsement identifier inside the independent metadata management partition, performing digital credit registration on the transaction operation log, and generating a version consistency verification code; calling the data encapsulator to receive the version consistency verification code and the feature fusion data block, appending redundant verification bits to the index field of the feature fusion data block, and outputting the single digital traceability certificate.

8. The digital asset intelligent analysis platform based on blockchain smart contract technology according to claim 1, characterized in that, The specific generation process of the quality assurance digital certificate includes: collecting single digital traceability certificates from the same database transaction submission window, inputting them one by one into the index generation logic, and outputting the underlying data page fingerprint sequence; extracting adjacent elements from the underlying data page fingerprint sequence according to the storage block sequence to form an associated index block; performing tuple aggregation and compression operations on the associated index block, inputting the concatenated code back into the index generation logic to generate a directory-level aggregated key value; recursively performing pairing, concatenation, and index generation operations on the directory-level aggregated key value until a unique full-database retrieval root node identifier is generated; locking the full-database retrieval root node identifier as the batch verification root value; calling the file system encapsulator to receive the batch verification root value, associating it with the blockchain smart contract address and the index submission timestamp, performing structured serialization, and outputting the quality assurance digital certificate.

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