A method and system for visualizing a trajectory of a risk verification task

Abstract

The present application relates to the field of risk data visualization traceability, and provides a kind of risk check task visual track traceability method and system, method includes: parsing heterogeneous data stream separates source metadata set, calculates original structure entropy fingerprint and intercepts end feature embedding global tracking identifier, generates encapsulation data packet;Transformed business data is generated using dynamic bytecode insertion, runtime execution context and differential snapshot are constructed, and track node object is instantiated according to shunt delivery strategy transmission;Based on hash slice mapping instantiation logical topology execution graph, mark pollution state when abnormal and generate full-factor execution link graph;Topological summary data packet is generated, real-time state view is rendered, search request data packet is constructed in response to physical interaction operation, full-amount business load is reconstructed by topological traceability and reverse data evolution, and attribution view is generated in combination with static code and rule description.The present application constructs the dynamic track traceability and full-link risk visualization mechanism of mass heterogeneous data.

Description

Technical Field

[0001] This invention relates to the field of risk data visualization and tracing, and in particular to a method and system for visual trajectory tracing of risk verification tasks. Background Technology

[0002] With the widespread application of the Internet of Things (IoT) and microservice architecture, modern data integration platforms face massive amounts of heterogeneous data and complex ETL processes when handling reports and decision analysis. In high-concurrency, multi-node complex data flow environments, traditional batch processing modes easily lead to black-box processing, missing intermediate states, and difficulties in anomaly localization. Therefore, there is an urgent need for a visual trajectory tracing method and system that can achieve end-to-end transparency in heterogeneous data processing, possess instance-level accurate traceability capabilities, and support efficient visual interaction for risk verification tasks.

[0003] Chinese patent application CN118646144A discloses an automatic verification method and system for the status of power plant equipment. The method includes: automatically extracting data from a set verification scope and objects according to a verification task using an established data model, enabling accurate and rapid acquisition of data related to the verification task; calculating status data for different periods using a clustering algorithm; judging the calculation results using alarm rules; automatically triggering an alarm when the alarm rule's trigger condition is met; and further calculating the associated equipment in the alarm rule to find the cause of the alarm. Therefore, this invention can automatically verify the extracted data using alarm rules.

[0004] However, current technologies still face numerous challenges. In the ETL process of large-scale data integration platforms, when risk verification tasks process massive amounts of heterogeneous data through a distributed microservice architecture, traditional synchronous or batch processing log recording mechanisms are typically used. If the data on a certain node is tampered with due to logical conflicts or numerical anomalies, technicians can often only conduct post-event investigations by reviewing discretely stacked text logs, making it difficult to establish a strong logical correlation between the final abnormal result and the intermediate processing state. This method of full data dumping not only leads to I / O bottlenecks, causing the processing process to become black-boxed and state fragmented, but also makes it impossible to quickly reproduce the dynamic trajectory of data flow in a high-concurrency environment. This results in the inability to promptly locate the source and rules of the contaminated data, thus missing the golden window for risk control and triggering data compliance risks or decision-making errors. Summary of the Invention

[0005] To achieve the above objectives, this invention provides a visual trajectory tracing method for risk verification tasks, the specific technical solution of which is as follows:

[0006] Parse the original heterogeneous data stream to generate the original structural entropy fingerprint, construct a global tracing identifier based on the original structural entropy fingerprint, and write the global tracing identifier, the original structural entropy fingerprint, and the original heterogeneous data stream into a hierarchical storage topology to generate an encapsulated data packet.

[0007] Dynamic bytecode instrumentation technology is used to process encapsulated data packets to generate transformed business data. Physical execution parameters are read to construct a runtime execution context. Based on the encapsulated data packets and transformed business data, an entropy encoding function is called to generate differential snapshots. A shadow transmission link is constructed to serialize instantiated trajectory node objects into binary trajectory messages. The direct memory access controller is driven to perform zero-copy transmission of binary trajectory messages and differential snapshots, and data splitting and transmission are completed.

[0008] The binary trajectory message is routed to the memory time-series aggregation bucket based on the hash sharding mapping mechanism to generate an unordered node set. The node objects in the unordered node set are parsed by the hash connection algorithm to instantiate the logical topology execution graph. If the execution status code of the node object is detected to be abnormal, the depth-first search algorithm is called to mark the potential pollution status. The memory pointer mounting method is used to write the pre-set rule description text and static code fragments into the logical topology execution graph to generate a full-element execution link graph.

[0009] The system performs a structural projection transformation on the link diagram of all elements to generate a topology summary data package. It then uses a vector graphics rendering engine to generate a real-time status view. In response to hardware interrupt signals triggered by physical interaction operations on the real-time status view, it constructs a retrieval request data package. Based on the link diagram of all elements, it performs topology backtracking addressing to obtain baseline input data. It then reconstructs the full workload after restoration by reversing the data evolution of differential snapshots and baseline input data. Finally, it combines static code snippets and rule description text to generate an attribution view.

[0010] Furthermore, the method for generating the encapsulated data packet includes:

[0011] The original heterogeneous data stream is parsed to separate the source metadata set and the payload data. The payload data is then subjected to a depth-first traversal algorithm and ascending order reordering to generate a topological feature sequence. The service feature summary of the topological feature sequence and the channel feature summary of the source metadata set are calculated respectively, and multiplication and bitwise XOR operations are performed sequentially to generate the original structural entropy fingerprint.

[0012] The system calculates the time difference between the access timestamp and a preset epoch start time constant. It then performs a binary left shift operation on the time difference and node topology parameters to construct time slices and node topology coordinates. A preset binary mask is used to extract the terminal feature vector of the original structural entropy fingerprint, and a bitwise XOR operation is performed with the anti-collision auto-incrementing sequence generated by the memory atomic counter to generate a composite content anchor. This anchor is then combined with the time slices and node topology coordinates to generate a global tracking identifier. The node topology parameters include a data center identifier and a worker node identifier.

[0013] A hierarchical storage topology is constructed within the physical address space. The internal round-robin key is read to perform a hash message authentication code operation on the global tracking identifier and the original structure entropy fingerprint to generate an encapsulation verification code. The encapsulation verification code, the current encapsulation timestamp, the source metadata set encoded in big-endian order, and the original heterogeneous data stream are written into the corresponding storage area of ​​the hierarchical storage topology to generate an encapsulated data packet.

[0014] Furthermore, the hierarchical storage topology is a contiguous binary buffer allocated in random access memory based on the operating system's memory allocation instructions. The contiguous binary buffer is divided into independent storage areas including a header storage area, a metadata storage area, and a load storage area.

[0015] The header storage area is configured as a pre-allocated fixed-byte-width storage space, located at the starting address of the continuous binary buffer, and stores the global tracking identifier, the current encapsulation timestamp, and the encapsulation check code.

[0016] The metadata storage area is configured as a variable-length storage space based on the type-length-value encoding rule, storing the source metadata dataset encoded in big-endian order;

[0017] The load storage area is configured as an unformatted binary stream storage space to store raw heterogeneous data streams.

[0018] Furthermore, the physical execution parameters include the server network address, thread identifier, stack frame depth, effective rule version identifier, and execution time data;

[0019] The steps for reading the physical execution parameters include: driving the asynchronous daemon thread to call the memory state extraction operator, and loading the thread context handle, the effective rule version identifier, and the instruction sequence start time and instruction sequence end time into the arithmetic register;

[0020] The memory state extraction operator is a set of computer program instructions running in an asynchronous daemon thread; the thread context handle is a memory address pointer in the operating system kernel that maintains the state of the current main business processing thread, including the server network address, thread identifier, and stack frame depth.

[0021] The system addresses the process control block in the operating system kernel space using the thread context handle pointer, reads the server network address and thread identifier, reads the memory values ​​of the base pointer register and stack pointer register of the current main business processing thread, and generates the stack frame depth by calculating the difference between the memory values ​​of the base pointer register and the stack pointer register, reads the version identifier of the effective rule loaded in the arithmetic register, and uses the arithmetic logic unit to calculate the difference between the start time and end time of the instruction sequence to generate execution time data.

[0022] Furthermore, the instantiation method of the trajectory node object includes:

[0023] Dynamic bytecode instrumentation technology is used to configure non-blocking monitoring instructions as the memory entry address of predefined business rule functions. If the program counter of the main business processing thread points to the memory entry address, the asynchronous daemon thread is activated to execute the business rule function on the encapsulated data packet to generate transformed business data. At the same time, the physical execution parameters of the main business processing thread are read to construct the runtime execution context.

[0024] The system identifies the physical format attributes of the encapsulated data packets and the transformed business data. If the physical format attribute is unstructured data, it drives the arithmetic logic unit to execute a bitwise XOR instruction to construct a bit-flipping sequence. If the physical format attribute is structured data, it calls the memory parser to execute recursive differential operations to extract the logical topology change set. It uses an entropy encoding function to map the bit-flipping sequence or logical topology change set to a differential snapshot and calculates the cryptographic hash digest of the differential snapshot as a snapshot reference index.

[0025] The execution fingerprint is calculated by calling a non-cryptographic hash algorithm based on the runtime execution context, and a trajectory node object containing a topology key set and a state value set is instantiated. The global tracking identifier, execution fingerprint, and parent node identifier parsed from the header storage area of ​​the encapsulated data packet are mapped to the topology key set using memory address assignment instructions. Simultaneously, the effective rule version identifier, snapshot reference index, execution status code corresponding to the function return value in the general-purpose register, and execution time data parsed based on the runtime execution context are written to the state value set.

[0026] Furthermore, the data offloading transmission steps include: constructing a shadow transmission link based on a preset lock-free circular buffer and an I / O offloading daemon thread; if the I / O offloading daemon thread detects that the memory start address pointers of the trajectory node object and the differential snapshot are written to the lock-free circular buffer, it calls the binary serialization protocol to serialize the trajectory node object to generate a binary trajectory message, and drives the direct memory access controller to perform zero-copy transmission; and according to the offloading delivery strategy, the binary trajectory message and the differential snapshot are mapped to the message middleware and the distributed object storage system, respectively.

[0027] Furthermore, the method for generating the full-element execution link graph includes:

[0028] Extract the global tracking identifier of the binary trajectory message and call the hash sharding mapping mechanism to generate a memory logical index. Based on the memory logical index, route the binary trajectory message to the corresponding memory time-series aggregation bucket. If the cumulative number or residence time of the binary trajectory message in the memory time-series aggregation bucket exceeds the preset threshold, call the binary deserialization operator to generate an unordered set of nodes.

[0029] The logical topology execution graph is instantiated by parsing the parent node identifier of the unordered node set based on the hash connection algorithm. If an execution status code abnormality is detected in the node object in the logical topology execution graph, the node object is defined as the risk transmission root node. The depth-first search algorithm is called to perform recursive traversal. The state overwrite operation is performed on the downstream descendant nodes that have a directed connected path with the risk transmission root node to mark the potential pollution state.

[0030] The logical topology execution graph is traversed to extract the effective rule version identifier of each node object. The effective rule version identifier is used as the reverse index key. Combined with the pre-set static metadata repository, the memory address handles pointing to static code fragments and rule description text are obtained. The memory address handles are written into the pre-allocated metadata reference pointer field of the node object using a zero-copy mount operation to generate a full-element execution link graph.

[0031] Furthermore, the method for generating the attribution view includes:

[0032] Based on the full-duplex network communication channel to maintain the connection state, the hierarchical layout algorithm is called to parse the parent node identifier of the full-element execution link graph to generate the layout coordinate parameters of the node object. The structural projection transformation operation is performed on the full-element execution link graph to generate the topology summary data packet. Combined with the vector graphics rendering engine, a real-time status view is generated.

[0033] The system captures hardware interrupt signals generated by the user's physical interaction on the real-time status view to extract the user interaction coordinates. It then maps these coordinates with a preset view transformation matrix to obtain logical coordinate points. A geometric collision detection algorithm is used to determine whether the logical coordinate points fall into the geometric bounding box constructed based on geometric node primitives. If they do, the system extracts the global tracking identifier and snapshot reference index associated with the geometric node primitives to encapsulate them into logical interaction instructions. Finally, it combines these instructions with an access permission authentication token to construct a retrieval request data packet.

[0034] In response to the retrieval request data packet, a differential snapshot is extracted. Based on the full-element execution link diagram, topology backtracking is performed to obtain the baseline input data. The reverse data evolution reconstruction algorithm is used to perform a reverse merging operation on the differential snapshot and the baseline input data to generate the restored full service payload. Static code snippets and rule description text are aggregated to generate an attribution view.

[0035] Furthermore, the method for generating the topology summary data packet includes:

[0036] The processor is invoked to perform structural projection transformation logic on the full-element execution link graph, traversing each node object of the full-element execution link graph;

[0037] Extract and retain the pre-defined structured attribute fields in the node object, including the global tracking identifier, parent node identifier, execution status code, and layout coordinate parameters;

[0038] Filter the unstructured field data stored in the node object, the unstructured field data including rule description text and static code snippets;

[0039] Perform binary serialization encoding on the structured attribute fields to generate a topology summary data packet;

[0040] The method for generating the layout coordinate parameters of the node objects includes: parsing the full-element execution link graph, identifying the parent node identifier of each node object, and establishing a hierarchical dependency topology between node objects; traversing the hierarchical dependency topology to determine the vertical hierarchy depth of each node object in the vertical direction of the visualization canvas, and quantizing and mapping it to the ordinate value on the visualization canvas; calculating the arrangement order of node objects in the same level in the horizontal direction, optimizing the arrangement order through a cross-minimization algorithm, and quantizing and mapping the optimized order result to the abscissa value on the visualization canvas; and combining the ordinate value and the abscissa value to generate the layout coordinate parameters.

[0041] A visual trajectory tracing system for risk verification tasks is provided to implement the aforementioned visual trajectory tracing method for risk verification tasks. The system includes a data encapsulation module, a trajectory generation module, a link construction module, and a visual interaction module.

[0042] The data encapsulation module is used to parse the original heterogeneous data stream to generate the original structural entropy fingerprint, construct a global tracking identifier based on the original structural entropy fingerprint, and write the global tracking identifier, the original structural entropy fingerprint, and the original heterogeneous data stream into the hierarchical storage topology to generate an encapsulated data packet.

[0043] The trajectory generation module is used to process encapsulated data packets using dynamic bytecode instrumentation technology to generate transformed business data, read physical execution parameters to construct a runtime execution context, call an entropy encoding function based on the encapsulated data packets and transformed business data to generate differential snapshots, construct a shadow transmission link to serialize instantiated trajectory node objects into binary trajectory messages, drive the direct memory access controller to perform zero-copy transmission of binary trajectory messages and differential snapshots, and complete data splitting transmission.

[0044] The link construction module: Based on the hash sharding mapping mechanism, it routes binary trajectory messages to memory time-series aggregation buckets to generate an unordered node set. It then uses a hash connection algorithm to parse node objects in the unordered node set to instantiate a logical topology execution graph. If an execution status code anomaly is detected in a node object, it calls a depth-first search algorithm to mark potential pollution states. Finally, it uses a memory pointer mounting method to write the pre-set rule description text and static code fragments into the logical topology execution graph to generate a full-element execution link graph.

[0045] The visual interaction module is used to perform structural projection transformation operations on the link diagram of all elements to generate a topology summary data package, generate a real-time status view in conjunction with a vector graphics rendering engine, construct a retrieval request data package in response to hardware interrupt signals triggered by physical interaction operations on the real-time status view, perform topology backtracking addressing based on the link diagram of all elements to obtain baseline input data, reconstruct the restored full business payload through the inverse data evolution of differential snapshots and baseline input data, and generate an attribution view in conjunction with static code snippets and rule description text.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] This invention utilizes bit-level XOR operations to embed the entropy fingerprint representing the original heterogeneous data topology into a global tracking identifier constructed from spatiotemporal parameters. This transforms a simple logical index into a deterministic carrier of embedded content integrity verification attributes, avoiding the problems of confusion of the original state and broken traceability caused by the logical decoupling of data entities and identity identifiers in high-frequency ETL processes.

[0048] This invention addresses the I / O blocking and performance bottleneck issues caused by the full data synchronous dumping during high-frequency risk verification tasks by constructing a shadow transmission link independent of the main business thread in the memory of the microservice node and employing lock-free circular buffers and zero-copy technology.

[0049] This invention reconstructs the spatiotemporal topology of discrete nodes using a hash connection algorithm and combines zero-copy pointer mounting technology to aggregate static code metadata and dynamic running status at the address level, constructing a full-element execution link graph containing complete code context. This solves the problems of difficulty in reproducing faults and lack of backtracking caused by the lack of instance-level dynamic trajectories in high-concurrency environments.

[0050] This invention addresses the technical contradiction between high latency in visualization interaction and high cost in data tracing in large-scale risk verification tasks by constructing a summary data push mechanism based on structural projection transformation and an on-demand reverse reconstruction logic based on physical interaction events. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating the principle of a visual trajectory tracing method for risk verification tasks according to the present invention.

[0053] Figure 2 This is a schematic diagram of the runtime execution context for building the present invention;

[0054] Figure 3 This is a functional module diagram of a visual trajectory tracing system for risk verification tasks according to the present invention. Detailed Implementation

[0055] 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.

[0056] Example 1:

[0057] Please see Figure 1 As shown, this embodiment provides a method for visually tracing the trajectory of a risk verification task, including:

[0058] Step S1000: Parse the original heterogeneous data stream. To generate the original structural entropy fingerprint Based on the original structural entropy fingerprint Construct global tracking identifiers and global tracking identifier Original structural entropy fingerprint and raw heterogeneous data streams Write to the hierarchical storage topology and generate encapsulated data packets. .

[0059] Specifically, this step aims to transform non-standardized raw heterogeneous data streams Converted into standardized, encapsulated data packets for unified processing within the system. Furthermore, it uses mathematical calculations to bind the content characteristics and identity identifiers of the data, providing an immutable index benchmark for subsequent steps.

[0060] Further, step S1000 includes:

[0061] Step S1100: Parse the original heterogeneous data stream. To separate the source metadata collection The payload data is then subjected to a depth-first traversal algorithm followed by ascending order rearrangement to generate a topological feature sequence. Calculate the topological feature sequences respectively Business feature summary and the source metadata set The channel feature summary is then processed sequentially using multiplication and bitwise XOR operations to generate the original structural entropy fingerprint. .

[0062] Specifically, this step aims to use structured parsing based on preset rules to transform raw, heterogeneous data streams with different physical formats, such as tree-structured JSON, nested XML, and linear binary data, into structured data streams. Deconstructed into a standardized set of source metadata and topological feature sequences Based on this deconstruction result, a unique original structural entropy fingerprint is calculated and generated. This establishes digital anchors for data content and spatiotemporal environment before the data enters the data cleaning and transformation logic, preventing subsequent processing nodes from confusing the original state.

[0063] In the specific implementation process, in order to establish digital anchors for data entities in the risk verification task, this step instantiates a multi-protocol parsing interface in the computer memory buffer and accesses the raw heterogeneous data stream to be verified through the I / O channel. Read the original heterogeneous data stream The header binary segment at the beginning of the storage address is XORed with a preset protocol feature library. When the result is all zeros, the original heterogeneous data stream is identified. The transmission protocol type. Based on the message header length defined by the transmission protocol type, the byte offset is calculated, and accordingly, the original heterogeneous data stream is stored in physical storage space. The physical segmentation consists of a source metadata set containing the sender's network address and gateway timestamp, located prior to the stated byte offset. and payload data located after the byte offset.

[0064] To eliminate hash calculation inconsistencies caused by the randomness of serialization order in non-linear hierarchical structures such as tree-like JSON and nested XML, and to achieve unified dimensional processing for linear binary streams and non-linear structured data, this step calls the Depth-First Search (DFS) algorithm to scan the memory address space of the payload data. For non-linear hierarchical structured data, all terminal node elements that no longer contain child nodes are identified and extracted, and a full path identifier containing the complete hierarchical relationship from the root node to the terminal node element is constructed. The full path identifier and the actual value of the terminal node element are combined to form a path-value tuple. For linear binary stream data, it is divided into logical fragments according to a preset byte step size, and a corresponding index sequence number is generated as the full path identifier. This index sequence number is then combined with the corresponding logical fragment content to form a path-value tuple. Based on this, the ASCII codes of the full path identifiers in all the path-value pairs are obtained. All path-value pairs are then rearranged in ascending order according to a preset lexicographical order rule. The sorted full path identifiers and their corresponding actual values ​​are then concatenated byte-by-byte using a predefined binary concatenation operator to generate a topological feature sequence that eliminates serialization randomness differences and is uniquely determined at the bit level. .

[0065] To simultaneously characterize content completeness and source authenticity using a single feature value in the high-frequency workflow of risk verification tasks, this step constructs an entropy calculation logic based on weighted orthogonal hashing. The specific logic of the entropy calculation logic is as follows:

[0066] Call the processor's arithmetic logic unit to load the topological feature sequence The process proceeds to the first register, where a non-encrypted hash function is executed to generate a business feature digest. This digest is then multiplied by a preset business sensitivity weighting coefficient to obtain the first intermediate feature value. Simultaneously, the arithmetic logic unit is invoked to load the source metadata set. The data is fed into the second register, where a cyclic redundancy check (CRC) operation is performed to generate a channel feature digest. This digest is then multiplied by a preset channel sensitivity weighting coefficient to obtain a second intermediate feature value. Subsequently, a binary bitwise XOR operation is performed on the first and second intermediate feature values. This operation orthogonally superimposes the integrity features of the service content and the source authenticity features of the transmission channel at the bit level, generating a hybrid feature sequence. Finally, this hybrid feature sequence is used as an input parameter to a secure hash function, which performs multiple rounds of compression and permutation operations, outputting a fixed-length hexadecimal string to lock the original heterogeneous data stream. Original structural entropy fingerprint .

[0067] The unencrypted hash function is a fast hash algorithm configured in the processor instruction set, used to hash topological feature sequences within a microsecond time window. The avalanche effect operation is performed to extract discrete business feature summaries of the business content; the business sensitivity weighting coefficient is a preset positive integer configuration parameter used to linearly amplify the weight of business content features in the final fingerprint at the bit level, thereby adjusting the system's detection threshold for business value tampering; the cyclic redundancy check is a verification algorithm for detecting data transmission errors, used to extract the source metadata set. The channel feature digest is used to lock the integrity status of data at the network transport layer; the channel sensitivity weighting coefficient is a preset positive integer configuration parameter used to linearly amplify the weight of channel environment features in the final fingerprint at the bit level, so as to adjust the system's detection threshold for data source fraud; the binary bitwise XOR operation is a bit-level operation performed by the computer's arithmetic logic unit (ALU), used to realize orthogonal data fusion of service feature digest and channel feature digest, ensuring that the features of two independent dimensions are superimposed in the same data space and do not interfere with each other; the secure hash function is an encryption algorithm conforming to the FIPS PUB 180-4 standard, used to map the mixed feature sequence after XOR fusion to an irreversible final fingerprint, preventing the derivation of the original service data through reverse engineering.

[0068] Step S1200: Calculate the system access timestamp. and the preset epoch start time constant The time difference is used to perform a binary left shift operation on the time difference and the node topology parameters respectively to construct time slices and node topology coordinates; a preset binary mask is then used. Extracting the original structural entropy fingerprint The terminal feature vector, and the anti-collision auto-incrementing sequence generated by the memory atomic counter. Perform a bitwise XOR operation to generate composite content anchors, combine them with time slices and node topology coordinates to perform a concatenation operation, and generate a global tracking identifier. The system access timestamp Both node topology parameters are obtained by CPU instructions; the node topology parameters include the data center identifier. and work node identifier .

[0069] Specifically, this step aims to utilize the processor's register bitwise operation capabilities to process the raw heterogeneous data stream. System access timestamp The node topology parameters and the original structural entropy fingerprint output in step S1100 A global tracking identifier is generated by compressing the data into a fixed-length integer storage space through a non-linear bit-level mapping. This process will globally track identifiers. The transformation from a simple logical index to a deterministic carrier with embedded integrity verification attributes allows the risk verification system to avoid retrieving large amounts of underlying raw data packets during high-concurrency ETL processes; it only needs to parse this global tracking identifier. The binary bit field can be used to verify the spatiotemporal ownership and content integrity status of data entities at the instruction cycle level with low latency.

[0070] In the specific implementation process, in order to build a self-verifying tracing index in a distributed high-concurrency environment, this step first initializes the parameter loading instruction, reading the preset epoch start time constant from the read-only memory (ROM) or registry of the computing server executing the risk verification task. and the identifier used to define global tracking The left shift constant for the bit length of each internal logic segment. This left shift constant includes the timestamp left shift value. Data center left shift bits and the number of bits shifted left by the working node The preset epoch start time constant is mentioned above. This indicates the preset baseline time point of the risk verification platform, such as midnight on the day the platform went online; the timestamp shifted left by a certain number of bits. Data center left shift bits and the number of bits shifted left by the working node Indicates the global tracking identifier for each data segment. The starting bit position in the bit-level storage space is used to divide non-overlapping storage areas within the 64-bit or 128-bit integer space.

[0071] At the same time, CPU instructions are invoked to obtain the raw heterogeneous data stream. System access timestamp at the time of access It also reads uniquely assigned node topology parameters from the identity profile of the computing server, which include a data center identifier. and work node identifier The data center identifier is mentioned above. A unique code representing a physical data center or logical area within a distributed cluster, used for cross-data center traceability; the worker node identifier A unique identifier representing the compute server instance that performs a specific task within a single data center, used to locate microservice instances.

[0072] Subsequently, this step calls the Arithmetic Logic Unit (ALU) to calculate the system access timestamp. and the preset epoch start time constant The timing difference is loaded into the high-order bits of a long integer register and then shifted left by a number of bits based on the timestamp. Perform a binary left shift operation to construct a time slice that guarantees a monotonically increasing macroscopic time sequence; simultaneously, identify the data center. and work node identifier Based on the left shift bits of the data center Left shift bits of the working node Perform a binary left shift operation to obtain the data center identifier after the binary left shift operation. and work node identifier Mapping to the median range of a long integer register, construct the node topology coordinates that characterize the physical origin of the data.

[0073] Next, the existing distributed ID generation mechanism can only guarantee sequence uniqueness but cannot verify data content integrity, leading to the global tracking of identifiers during the high-frequency ETL process of risk verification tasks. and raw heterogeneous data stream This step introduces a technical flaw in logical decoupling. It invokes a register load instruction to read a preset binary mask from the system constant area. The original structural entropy fingerprint output in step S1100 Perform a bitwise AND operation to physically extract the entropy fingerprint of the original structure. The last bit segment, whose bit width and preset check code length are consistent, is defined as the end feature vector. The binary mask... It is a predefined bit-field filter whose end-of-bit parity region of the binary sequence contains consecutive "1" values, and the bit width of the "1" values ​​corresponds to the system's preset checksum length, used to mask the original structural entropy fingerprint during the instruction cycle. Interference in mid-to-high order bits.

[0074] Synchronously, access the atomic counter in memory to obtain the anti-collision auto-increment sequence within the current millisecond time slice. and the anti-collision auto-increment sequence A binary bitwise XOR operation is performed with the terminal feature vector. This operation utilizes the reversibility and obfuscation properties of the XOR operation to transform the original heterogeneous data stream. The hash features of the business content are modulated into the anti-collision auto-incrementing sequence. In the bits, generate a heterogeneous data stream from the original. The business content is strongly coupled with the composite content anchor point. Among them, the anti-conflict auto-incrementing sequence... It distinguishes between multiple raw heterogeneous data streams that are accessed concurrently within the same millisecond. A circular integer whose value is atomically incremented after each ID generation request, and whose maximum value range is limited by the reserved binary width of the sequence number.

[0075] Finally, a concatenation operation is performed on the generated time slices, node topology coordinates, and composite content anchors to merge the scattered binary segments into a contiguous memory word, generating the final global tracking identifier. .

[0076] Step S1300: Construct a hierarchical storage topology within the physical address space and read the internal round-robin key. Global tracking identifier and the original structure entropy fingerprint Perform hash message authentication code calculation to generate encapsulation verification code. Encapsulate the verification code Current encapsulation timestamp Source metadata set encoded in big-endian order and raw heterogeneous data streams Write the corresponding storage area of ​​the hierarchical storage topology to generate an encapsulated data packet. .

[0077] Specifically, this step aims to use a predefined binary serialization protocol to process the global tracking identifier generated in step S1200. The original structural entropy fingerprint output in step S1100 Step S1100: Separation of the source metadata set and raw heterogeneous data streams It performs physical-level reconfiguration mapping to construct a standardized encapsulated data packet with a three-layer logical protection structure. This process constructs the smallest atomic unit of data flow, enabling subsequent steps to route and verify data through header information without parsing business content. This solves the problem of black-box processing and provides a unified data carrier for end-to-end transparency.

[0078] In the specific implementation process, this step calls the operating system's memory allocation instructions to allocate a contiguous binary buffer in Random Access Memory (RAM). Based on a predefined serialization protocol, the binary buffer is divided into three independent storage regions with fixed byte offsets in the physical address space to construct a hierarchical storage topology. The three independent storage regions are: 1. Header storage region: configured as a pre-allocated fixed-byte width storage space, used to store routing control parameters and global tracing identifiers obtained from route risk verification. The index information is used in subsequent steps when encapsulating data packets. The first part, when parsed and read, has the highest memory addressing priority to support fast routing decisions that can be completed without unpacking; the second part, the metadata storage area, is configured as a variable-length storage space based on the Type-Length-Value (TLV) encoding rule to store the serialized source metadata set. To adapt to environment description information of different lengths; 3. Load storage area: a binary large object container configured as an unformatted binary stream storage space, used to store unprocessed raw heterogeneous data streams. The binary entity enables physical isolation between business data and control information.

[0079] To prevent data packet encapsulation If the data is forged or tampered with during cross-node transmission in a distributed computing cluster, this step performs a cryptographic integrity signature operation, invoking the cryptographic coprocessor to read the internal round-robin key stored in the Hardware Security Module (HSM). Execute binary concatenation instructions to add the global tracking identifier. and the original structure entropy fingerprint The data is merged into a bitstream to be verified and combined with the internal round-robin key. Perform a Hash-based Message Authentication Code (HMAC) operation to generate a non-repudiable encapsulated verification code. .

[0080] Finally, the system clock interrupt instruction is invoked to obtain the current millisecond value as the current encapsulation timestamp. ; global tracking identifier Current encapsulation timestamp and encapsulation verification code Write to the header storage area; use big-endian encoding rules to encode the source metadata set. After being converted into a byte stream, it is written to the metadata storage area; the byte stream copy instruction is called to copy the original heterogeneous data stream. The data is directly copied and written to the payload storage area in the form of a binary stream; finally, the starting addresses and total lengths of the three consecutive independent storage areas are combined to construct a standardized structure object, which encapsulates the data packet. .

[0081] Step S2000: Process the encapsulated data packet using dynamic bytecode instrumentation technology. To generate transformed business data And read physical execution parameters to construct the runtime execution context. According to the encapsulated data packet and transformed business data Call the entropy encoding function to generate a differential snapshot And construct the shadow transmission link to instantiate trajectory node objects. Serialized into binary trajectory message and drive the direct memory access controller to handle binary trajectory messages. Sum of differences snapshots Zero-copy transmission is performed, and data is distributed and transmitted according to the distribution and delivery strategy.

[0082] Specifically, this step aims to resolve the technical contradiction between the process black box and I / O performance bottleneck in the high-frequency ETL flow of risk verification tasks. This is achieved by constructing a shadow transmission link independent of the main business thread in the server's memory space during the risk verification task, upon receiving the encapsulated data packet constructed in step S1300. Then, dynamic bytecode instrumentation technology is used to transform the business logic and capture the runtime execution context. Synchronization is based on the encapsulated data packets and runtime execution context The physical format properties are used to generate a minimized differential snapshot using an entropy coding function. Finally, through structured encapsulation of memory objects and direct memory access mechanisms, the binary serialization of data is completed in user-space memory to obtain the binary trace message. It performs zero-copy transmission and outputs binary trajectory messages according to the diversion and delivery strategy. and differential snapshot To construct a digital trajectory record with dual integrity verification of runtime environment and data content.

[0083] Further, step S2000 includes:

[0084] Step S2100: Using dynamic bytecode instrumentation technology, non-blocking monitoring instructions are configured to the memory entry address of a predefined business rule function. If the program counter of the main business processing thread points to the memory entry address, the asynchronous daemon thread is activated to encapsulate the data packet. Execute business rule functions to generate transformed business data Simultaneously, it reads the physical execution parameters of the main business processing thread to construct the runtime execution context. .

[0085] Specifically, this step aims to improve the encapsulated data packet constructed in step S1300. Application pre-built business rule library Within the millisecond-level execution window for cleaning or transformation, a strong correlation is established between business logic operation instructions and the physical execution environment state. Through memory state atomic capture technology, the transformed business data is output. And will identify the version of the abstract effective rules. With concrete runtime execution context Physical binding is performed within the same time window, thereby eliminating the black-box phenomenon of the risk verification system in the entire ETL process and providing irrefutable runtime environment evidence for subsequent steps.

[0086] In the specific implementation process, this step loads and instantiates a predefined business rule function in the heap memory area of ​​the risk verification processing server that performs the risk verification task. This occurs when encapsulating data packets. When an instruction is scheduled to the instruction execution queue of the risk verification processing server, a non-blocking monitoring instruction is written at the memory entry address in the virtual memory space of the business rule function using Dynamic Bytecode Instrumentation (DBI). This non-blocking monitoring instruction is configured to trigger the start of an asynchronous daemon thread that executes concurrently with the main business processing thread when the program counter (PC) of the main business processing thread points to this memory entry address.

[0087] Subsequently, the central processing unit is invoked to execute the machine instruction sequence contained in the business rule function to encapsulate the data packet. Perform at least one data processing operation, including data cleaning, format conversion, and numerical aggregation. During the loading and execution of the machine instruction sequence by the central processing unit, the data is retrieved from a pre-built business rule base. The currently effective set of business rule parameters is used as the business rule logic operator. Extract the corresponding effective rule version identifier. Next, the central processing unit is invoked to encapsulate the data packet. The business rule logic operator is read into the arithmetic register as an input operand and then loaded. The input operands are used as control parameters in the calculation; the central processing unit performs logical transformations on the input operands according to the control parameters, and the register status value after the calculation is completed is the output transformed business data. The machine instruction sequence is a set of opcodes executed by the central processing unit, used to define specific ETL processing algorithms for risk verification tasks, including but not limited to regular expression matching algorithms, numerical range filtering algorithms, or data type casting algorithms; the business rule logic operators The value is determined by the business rule base. The configuration items of the effective rule version determine the operation boundaries and judgment logic of the machine instruction sequence during instruction execution to determine the specific direction of data transformation.

[0088] During the same time the logical transformation operation is performed, the asynchronous daemon thread reads the physical execution parameters of the host process undertaking the risk verification task in real time by directly reading the process control block (PCB) in the operating system kernel space, without interrupting the main business processing thread, and constructs the runtime execution context. .

[0089] The runtime execution context It includes the server network address, thread identifier, stack frame depth, and the version identifier of the effective rule. And a binary state vector of execution time data, used to identify the abstract effective rule version within the same time window of instruction execution. The system physically binds the network address and thread identifier of the specific server executing the current risk verification task. The specific construction logic is as follows: It drives an asynchronous daemon thread to call the memory state extraction operator. With thread context handle Effective rule version identifier Instruction sequence start time and the end time of the instruction sequence The memory state extraction operator is loaded into the arithmetic register as an input parameter. Four operations are executed in parallel: First, physical addressing. This is based on the thread context handle. First, the pointer address is addressed to the process control block in the operating system kernel space, and the server network address and thread identifier are read. Second, stack frame calculation. The memory values ​​of the Extended Base Pointer (EBP) and Extended Stack Pointer (ESP) registers of the current main business processing thread are read, and the stack frame depth is generated by calculating the difference between the two. Third, rule anchoring. The version identifier of the effective rule recorded in the register is read. Fourth, performance calculation. The start time of the instruction sequence is calculated using the arithmetic logic unit. and instruction sequence end time The difference is used to generate execution time data. Finally, the server network address, thread identifier, stack frame depth, and effective rule version identifier are used. In addition, execution time-consuming data is encapsulated in memory using binary serialization to generate a runtime execution context. .

[0090] Among them, the memory state extraction operator It is a set of computer program instructions that runs in an asynchronous daemon thread and is used to perform non-blocking memory read operations; the thread context handle This is a pointer to a memory address in the operating system kernel that maintains the state of the current main business processing thread. It includes the server network address, thread identifier, and stack frame depth. The server network address is the network interconnection protocol address representing the physical location of the risk verification processing server. The thread identifier is a unique ID assigned by the operating system, representing the logical execution unit. The stack frame depth represents the stack usage of the current main business processing thread at the call level, used to determine if there is a risk of recursion overflow. The instruction sequence start time... It is the timestamp counter of the central processing unit in the business rule logic operator. The register value at the moment execution begins is used to mark the physical start time of the risk verification calculation task; the end time of the instruction sequence. It is the timestamp counter of the central processing unit in the business rule logic operator. The register value at the moment of completion is used to mark the physical end time of the risk verification calculation task.

[0091] Further, please refer to Figure 2 As shown, Figure 2 This is a schematic diagram of the runtime execution context for building the present invention.

[0092] Step S2200: Identify and encapsulate data packets and transformed business data The physical format attribute is determined. If the physical format attribute is unstructured data, the arithmetic logic unit executes a bitwise XOR instruction to construct a bit-flipping sequence; if the physical format attribute is structured data, the memory parser is called to perform recursive differential operations to extract the logical topology change set; the bit-flipping sequence or logical topology change set is mapped to a differential snapshot using an entropy encoding function. And calculate the differential snapshot Cryptographic hash digest as a snapshot reference index .

[0093] Specifically, this step aims to address the I / O throughput bottleneck caused by intermediate data in the high-frequency ETL process of full recording. It calculates the encapsulated data packet constructed in step S1300 by calling the processor's arithmetic logic unit. The transformed business data generated in step S2100 Differences at the bit level or logic structure level between them generate differential snapshots with extremely small volumes. and snapshot reference index This reduces storage space overhead and improves system bus utilization in high-concurrency scenarios.

[0094] In the specific implementation process, to avoid blocking the I / O channel due to the full dumping of intermediate data during the high-frequency flow of risk verification tasks, this step abandons the traditional full copy mode and instead executes a differential computation strategy based on in-situ comparison. This differential computation strategy schedules different hardware computing resources according to the physical format type of the input data, as follows:

[0095] For unstructured linear binary data, the Memory Management Unit (MMU) is invoked to encapsulate the data packets. Load storage area and transformed business data The data is mapped to adjacent CPU cache lines. Then, the driving arithmetic logic unit (ALU) performs a binary bitwise XOR instruction on the two sets of data mapped to the cache lines. Utilizing the bitwise characteristic of XOR ("0 for the same, 1 for different"), the differences between the two data at the bit level are quickly extracted, constructing a bit-flipping sequence.

[0096] For structured data such as tree-like JSON or nested XML, a memory parser is invoked to perform structured recursive difference operations. The logic of this recursive difference operation is as follows: first, the encapsulated data packet is... Load storage area and transformed business data The objects are parsed into hierarchical object tree structures in memory, defined as a baseline hierarchical object tree and a target hierarchical object tree, respectively. Then, a depth-first search (DFS) algorithm is used to simultaneously scan and compare the corresponding nodes in the baseline and target hierarchical object trees, identifying and extracting insertion, deletion, or numerical modification operations on the nodes. Finally, the extracted node address paths that have changed are used as keys, and the corresponding node value changes are used as values, encapsulated into key-value pairs to construct a logical topology change set.

[0097] The initialized entropy coding function loads the bit-flipping sequence (constructed for unstructured data) or the logical topology change set (constructed for structured data) into the compression buffer as the input data stream. Based on the principle of information entropy, the entropy coding function identifies statistically high-frequency bit patterns or structured labels (i.e., high-frequency features) in the input data stream and maps them to short codewords in variable-length prefix coding form. By removing statistical redundancy from the input data stream, it outputs a binary sequence with minimal volume, i.e., a differential snapshot. .

[0098] At the same time, the cryptographic coprocessor is invoked to execute a secure hash function, such as the SHA-256 algorithm, to perform a differential snapshot. The complete binary stream is loaded into the hash calculation register, and multiple rounds of compression and permutation operations are performed to generate a fixed-length cryptographic hash digest. This cryptographic hash digest is directly defined as a snapshot reference index. It is used for addressing and retrieving the differential snapshot in a Distributed Object Storage System (DOSS). The evidence file uses a unique content addressing storage key.

[0099] Step S2300, based on runtime execution context Call a non-cryptographic hash algorithm to calculate the execution fingerprint and instantiate a set containing topological keys. and set of state values trajectory node object The global trace identifier is assigned using a memory address assignment instruction. Execute fingerprint and based on encapsulated data packets Parent node identifier resolved from the header storage area Mapped to the aforementioned topology key set, the version identifier of the effective rule is synchronously applied. Snapshot Reference Index The execution status code corresponding to the function return value in the general-purpose register and the execution context based on the runtime execution context. The parsed execution time data is written into the state value set. .

[0100] Specifically, this step aims to use the global tracking identifier generated in step S1200. The encapsulated data packet constructed in step S1300 The runtime execution context generated in step S2100 Snapshot reference index defined in step S2200 and the effective rule version identifier extracted in step S2100 The mapping is encapsulated into a standardized output entity, namely the trajectory node object. The trajectory node object As a lightweight index carrier, it eliminates the need for subsequent steps to load heavy raw heterogeneous data streams. It can quickly construct a complete Directed Acyclic Graph (DAG) execution path, reducing I / O overhead and solving the interface lag problem in real-time visualization interaction.

[0101] In the specific implementation, to transform discrete, heterogeneous processing states into structured trajectories that can be indexed by the system, this step allocates physical memory addresses in the temporary memory buffer of the distributed computing instance carrying the risk verification task to store structure objects. The system calls a non-cryptographic hash algorithm, such as MurmurHash3, to the runtime execution context. As the input bitstream, a fixed-length hash value unique within the current global task's lifecycle is calculated and generated, which is defined as the execution fingerprint. This execution fingerprint is used to uniquely identify the physical execution instance of this atomic operation in the spatiotemporal coordinate system, providing a microscopic anchor point for trajectory reconstruction.

[0102] Subsequently, this step, based on a predefined graph data topology protocol, allocates a contiguous block of addresses in the heap memory space of the service process executing the current risk verification task, and instantiates a set of topology keys. With the set of state values Composite data structure objects, namely trajectory node objects .

[0103] The set of topological keys The instantiation logic is as follows: parse and encapsulate the data packet. From the header storage area, extract the upstream node identifier recorded therein as the parent node identifier. Subsequently, the global tracing identifier is assigned a memory address using a memory address assignment instruction. Execute fingerprint and parent node identifier Write them to the trajectory node objects respectively. topological key set In this field, the topological connection relationship of the current risk verification calculation instance in the directed acyclic graph (DAG) is established, and a trajectory skeleton for visualizing the task flow path is constructed.

[0104] The set of state values The instantiation logic is as follows: Read the function return value stored in the general-purpose register after the central processing unit executes the machine instruction sequence contained in the business rule function in step S2100; then, parse the function return value to identify the execution status of the business logic, including but not limited to: logic pass, threshold block, or abnormal overflow, and define it as an execution status code; next, through a key-value pair association mechanism, identify the effective rule version that represents the logic criterion. Snapshot reference index representing data evidence Execution status codes representing logical final states and execution contexts from runtime execution context The execution time data, which represents the physical performance, is parsed and mapped into the trajectory node object. set of state values Within the field. This operation embeds logical criteria, data transformation facts, logical final states, and physical performance indicators into the same memory structure object, populating the business attribute payload of the trajectory node.

[0105] Step S2400: Based on the preset lock-free circular buffer and I / O offloading daemon thread, a shadow transmission link is constructed. If the I / O offloading daemon thread detects the trajectory node object... Sum of differences snapshots If the memory start address pointer is written to the lock-free circular buffer, then the binary serialization protocol is invoked to process the trajectory node object. Perform serialization to generate binary trajectory messages It also drives the direct memory access controller to perform zero-copy transfer, and distributes binary trace messages according to the diversion and delivery strategy. Sum of differences snapshots These are mapped to message middleware and distributed object storage systems, respectively.

[0106] Specifically, this step aims to use the trajectory node objects generated in step S2300. and the differential snapshot generated in step S2200 As input, a shadow transport link, independent of the business logic execution path and built in the memory space of the risk verification processing server, is used in conjunction with Direct Memory Access (DMA) and zero-copy technologies to process trajectory node objects. Invoke the binary serialization protocol to generate binary track messages This process physically separates the heavy serialization computation and network data transmission tasks from the main business processing thread, ensuring that the main business process completes its response within milliseconds and releases computing resources, while guaranteeing the complete retention and persistence of massive trajectory data.

[0107] In the specific implementation process, in order to achieve complete decoupling between business logic execution and data storage and transmission, this step pre-allocates a lock-free circular buffer based on memory barrier technology in the user-mode memory space of the risk verification processing server that executes the risk verification task, and initializes an I / O offloading daemon thread residing in the background. The lock-free circular buffer and the I / O offloading daemon thread together constitute a shadow transmission link independent of the main business processing thread.

[0108] The main business processing thread first obtains the trajectory node object. Sum of differences snapshots The memory start address pointers are then written to the lock-free circular buffer via atomic operation instructions. Once the pointer writing is complete, the main business processing thread immediately releases control of the computing resources for the current risk verification task and returns the transformed business data generated in step S2100. This enables business processes to flow to downstream nodes with zero I / O wait time at the physical level.

[0109] Subsequently, the I / O offloading daemon thread detects the arrival of the memory start address pointer of the lock-free circular buffer through a semaphore mechanism and takes over the subsequent data processing tasks. It invokes the binary serialization protocol and accesses the trajectory node object based on the detected memory start address pointer. The set of topological keys in its memory structure With the set of state values The data mapping in the middle is converted into a compact binary trajectory message. .

[0110] When the I / O offloading daemon thread completes, the trajectory node object Convert to binary trajectory message The serialization operation is then performed and the data enters the network physical transmission stage. The system utilizes the Direct Memory Access (DMA) controller to perform data transfer. Through memory page table remapping technology, the binary trace message is stored... Sum of differences snapshots The user-mode memory space is directly mapped to the DMA descriptor circular queue of the Network Interface Controller (NIC). This Direct Memory Access (DMA) controller uses zero-copy technology to directly transfer memory data to the NIC's transmit buffer without occupying the CPU's L2 cache or requiring CPU involvement in data copy instruction execution. This avoids the copying overhead during context switching between operating system kernel mode and user mode.

[0111] Finally, a data-characteristic-based triage and delivery strategy is implemented, as follows: For latency-sensitive binary trajectory messages... The system pushes these data via the TCP / IP protocol stack to specific topics in a low-latency, high-throughput message middleware for real-time consumption by the downstream trajectory reconstruction engine; for throughput-sensitive differential snapshots... The system asynchronously writes it to the high-capacity distributed object storage system DOSS via the HTTP / RESTful protocol, and sets the snapshot reference index defined in step S2200. This serves as the unique content addressing key for the stored object.

[0112] Step S3000: Based on the hash sharding mapping mechanism, the binary trajectory message is... Routing to in-memory time-series aggregation buckets to generate unordered sets of nodes Combining hash join algorithm to parse unordered node set The node object is used to instantiate the logical topology execution graph. If an execution status code anomaly is detected in a node object, a depth-first search algorithm is invoked to mark the potentially polluted state, and a memory pointer mounting method is used to write the pre-defined rule description text and static code snippets into the logical topology execution graph. Generate a full-element execution chain diagram .

[0113] Specifically, this step aims to utilize the asynchronous concurrent processing capabilities of computers and graph theory topology algorithms to transmit binary trajectory messages, which are physically discrete and are transmitted through the shadow transmission link constructed in step S2400. Logically, it is reconstructed into an execution chain diagram that has spatiotemporal continuity and includes a complete code context and risk propagation path. .

[0114] Further, step S3000 includes:

[0115] Step S3100: Extract binary trajectory message Global tracking identifier It also invokes the hash sharding mapping mechanism to generate a memory logical index. Based on memory logical index binary trajectory message The route is routed to the corresponding memory time-series aggregation bucket. If a binary trace message is detected within the memory time-series aggregation bucket... If the cumulative number or dwell time exceeds a preset threshold, the binary deserialization operator is invoked to generate an unordered set of nodes. .

[0116] Specifically, this step aims to receive the discrete binary trajectory messages generated in step S2400. As input, a deterministic hash sharding mapping mechanism is used to force all discrete messages, i.e., binary trajectory messages, belonging to the same original risk verification task to be associated with the same task. The data is routed and aggregated to the same physical memory address region, thus achieving physical data merging without using distributed locks, and outputting an aggregated unordered set of nodes. This provides a complete data foundation for subsequent steps and eliminates processing delays caused by multi-threaded lock contention.

[0117] In the specific implementation process, in order to solve the problems of data out-of-order and context fragmentation caused by multi-threaded concurrent consumption, this step builds a memory-based time-series aggregation mechanism based on direct memory addressing in the trajectory reconstruction engine. The specific logic is as follows:

[0118] First, the trajectory reconstruction engine starts high-concurrency consumer threads to continuously listen for discrete binary trajectory messages from the message middleware. Upon receiving any message, this step first parses the message header and extracts the global tracking identifier contained therein. As a hash sharding key.

[0119] Subsequently, this step invokes the hash sharding mapping mechanism to process the extracted global tracking identifier. Perform mapping to generate binary trajectory messages. Logical index of the memory belonging to The memory logical index It is a physical address pointer to a pre-allocated processing slot in the computer's memory space, used to indicate the binary trace message belonging to the current risk verification task. Data should be routed to a specific CPU cache-associated region to achieve physical aggregation and isolation. The specific generation logic is as follows: call the memory routing mapping function to globally trace the identifier. As input operands; the memory routing mapping function utilizes a cyclic redundancy check hash operator on the global tracking identifier. A polynomial hash operation is performed to generate an intermediate integer feature value with pseudo-random characteristics; subsequently, the arithmetic logic unit is used to hash the intermediate integer feature value against a preset constant for the total number of parallel processing slots. Perform a modulo operation; finally, define the remainder obtained from the modulo operation as a logical memory index. .

[0120] The memory routing mapping function is used to map global tracing identifiers in a high-dimensional discrete space. A mathematical transformation function mapping to a low-dimensional finite contiguous memory space; the cyclic redundancy check hash operator is a high-throughput hash algorithm based on polynomial division, used for globally tracing identifiers with structured characteristics. Perform avalanche effect calculations to map numerically similar identifiers to pseudo-random integers with significantly different bit values; the total number of parallel processing slots is constant. It is a preset positive integer constant used to define the concurrency granularity and memory shard size of the trajectory reconstruction engine.

[0121] Based on the memory logical index This step performs direct memory addressing, which will link all binary trace messages belonging to the same original risk verification task. Routes are distributed to the corresponding memory time-series aggregation buckets. Based on the deterministic nature of the memory routing mapping function in the aforementioned hash sharding mechanism, it is guaranteed that they have the same global tracing identifier. All discrete data fragments, regardless of their arrival time or out-of-order status in the distributed network transmission link, will eventually be written to the same physical memory slot, reducing the overhead of context switching across CPU cores.

[0122] Within the memory time-series aggregation bucket, this step configures dual-mode batch processing triggering logic based on buffer saturation and latency constraints. A memory counter is used to monitor the accumulated binary trajectory messages within the memory time-series aggregation bucket in real time. The cumulative number is used to monitor the first binary trajectory message in the memory timing aggregation bucket using a hardware timer. The system determines the dwell time. When the accumulated quantity reaches a preset batch processing capacity threshold, or the dwell time exceeds a preset maximum latency window, the system immediately generates a batch processing interrupt signal. In response to the batch processing interrupt signal, the system calls the binary deserialization operator to process the binary trajectory messages accumulated in the bucket. Perform batch decoding operations, and by parsing the binary bitstream, instantiate and reconstruct directly addressable trajectory node objects in memory in batches. Through this process, the system constructs an unordered set of nodes in memory that are aggregated by task but have not yet established temporal connections. .

[0123] The binary deserialization operator is a set of computer program instructions stored in the instruction register, used to deserialize compact binary trace messages. That is, the binary bitstream is mapped back to a structured object entity in memory, namely the trajectory node object. .

[0124] Step S3200: Parse the unordered node set based on the hash join algorithm. Parent node identifier Logical topology execution graph constructed by instantiation If a logical topology execution graph is detected If a node object has an execution status code exception, then the node object is defined as the risk propagation root node. A depth-first search algorithm is called to perform recursive traversal. The state overwrite operation is performed on the downstream descendant nodes that have a directed connected path with the risk propagation root node to mark the potential pollution state.

[0125] Specifically, this step aims to receive the unordered set of nodes constructed in step S3100. As input, this unordered set of nodes is parsed. The implicit parent node identifier in the attribute fields of each discrete node object. By establishing strict logical constraints, discrete nodes are reorganized into a directed acyclic graph with spatiotemporal continuity, i.e., the output logical topology execution graph. Based on this, a graph theory traversal algorithm is used to perform risk transmission analysis, automatically identifying and marking downstream nodes affected by upstream anomalies, thereby achieving a technological leap from single-point fault monitoring to end-to-end risk situation awareness.

[0126] In the specific implementation process, for unordered node sets This step executes a memory-based algorithm for constructing a directed acyclic graph (DAG). Unlike traditional linear sorting that relies solely on timestamps, this step utilizes an unordered set of nodes. Each discrete node in the process, i.e., the trajectory node object. Parent node identifier carried internally and execute fingerprint Establish strict reference constraints and construct a logical topology execution graph that accurately reflects the business flow logic. .

[0127] Define the logical topology execution graph It is a pair, that is .in, Represents the logical topology execution graph The vertex set is the set of all trajectory node objects to be reconstructed. A finite set, i.e., an unordered set of nodes. ; Represents the logical topology execution graph The set of directed edges in logic is a logical topology execution graph. It is a set of all connections, where each edge represents a direct call or data flow relationship between two business nodes.

[0128] To achieve linear time complexity in constructing associations across massive data nodes, this step employs a hash join algorithm for node matching. The specific execution logic of the hash join algorithm is as follows: First, traverse the vertex set. , based on the execution fingerprint of each node Using the physical memory address of the node object as the hash key, a temporary node index hash table is constructed; subsequently, the vertex set is traversed again. Each target node in Extract the target node Parent node identifier And use the parent node identifier A key-value lookup operation is performed in the node index hash table. When the target node is found... corresponding source node At that time, the target node is determined. Parent node identifier and source node execution fingerprint If they are completely identical in terms of bits, then a slave node is instantiated in memory. Point to target node The logical directed edges are generated, and the target node is updated synchronously. The in-degree counter value. Through the memory pointer mapping logic based on the hash join algorithm described above, physically discrete memory objects are linked into an adjacency list structure with clear predecessor and successor relationships, completing the topological reconstruction from a discrete set of points to a connected graph.

[0129] The constructed logical topology execution graph Building upon this foundation, this step utilizes a graph theory traversal algorithm to perform risk propagation analysis. The specific execution logic of the graph theory traversal algorithm is as follows: Traversal Logic Topology Execution Graph All trajectory node objects to be reconstructed internal state value set The execution status code in the logical topology execution graph. The execution status code of a certain trajectory node object is marked as belonging to the set of "abnormal" or "error". This step designates this trajectory node object as the risk propagation root node. Subsequently, starting from this risk propagation root node, the Depth-First Search (DFS) algorithm is invoked to recursively traverse downstream along the logical directed edges in memory. Risk propagation analysis is performed using graph theory traversal algorithms to calculate the diffusion path of the risk state in the entire link topology, and all execution graphs in the logical topology are analyzed. Downstream descendant nodes with a directed connection path to the aforementioned risk propagation root node perform a state overwrite operation, forcibly correcting the execution state code of the downstream descendant nodes to a potential contamination state marker, thereby realizing dynamic association and boundary locking of risks at the logical level.

[0130] Step S3300: Traverse the logical topology execution graph Extract the effective rule version identifier of each node object Identified by the version of the effective rule As a reverse index key, memory address handles pointing to static code snippets and rule description texts are obtained from a pre-built static metadata repository. A zero-copy mount operation is then used to write these memory address handles into the pre-allocated metadata reference pointer field of the node object, generating a full-feature execution chain graph. .

[0131] Specifically, this step aims to receive the logical topology execution graph with potential contamination status markers generated in step S3200. and the execution graph encapsulated in this logical topology vertex set Version identifier of the rules for the effectiveness of node attributes By using reverse indexing addressing technology, the system achieves delayed fusion of dynamic runtime state and static logic definition, outputting a holographic full-element execution chain diagram. It provides users with code-level fault reproduction capabilities.

[0132] In the specific implementation process, in order to achieve a massive number of trajectory node objects To quickly complete information and reduce memory overhead, this step executes a full-element data reorganization process based on memory pointers in the background refactoring engine. The specific logic is as follows:

[0133] First, the graph traversal engine is started, and the logical topology is executed on the graph. Perform a full graph scan. Execute the graph scan for this logical topology. Each trajectory node object in Access its internally encapsulated set of state values Extract the version identifier of the effective rules stored therein. .

[0134] Subsequently, the version identifier of the effective rule is used. As a reverse index key, it initiates multi-threaded concurrent key-value retrieval commands to a pre-built static metadata repository. The static metadata repository is a high-speed storage medium built on memory caching or version control systems, specifically used for persistently storing rule description texts of different versions of business rules, i.e., business logic described in natural language, as well as static code snippets, i.e., Java / Python script source code.

[0135] When the system receives the memory address handles returned by the static metadata repository, pointing to the shared memory area where the rule description text and static code fragment reside in the static metadata repository process, this step performs a zero-copy mount operation. Specifically, this step does not perform a deep copy operation that copies the actual binary content of the rule description text and static code fragment from the shared memory area to the current process's heap memory area. Instead, it directly calls the memory address assignment instruction to write the value of the memory address handle into the logical topology execution graph. The metadata reference pointer field pre-allocated in the memory structure of each node object.

[0136] The cross-process memory address space mapping established through the aforementioned zero-copy mount operation maintains the logical topology execution graph. While maintaining the original node connections and topology, the attribute dimensions of individual node objects were expanded to generate a full-element execution link diagram. This full-element execution chain diagram Logically, it aggregates three types of heterogeneous data: first, dynamic runtime state, which originates from the set of state values ​​of node objects. The execution status codes and execution time data are used to characterize the quality and performance of task execution; secondly, the logical topology structure: that is, derived from the logical topology execution graph. Topological key collection of middle node objects Parent node identifier in The first is used to characterize the causal dependencies of task execution; the second is static logical definition: that is, the rule description text and static code fragments that are directly accessed through metadata reference pointers and reside in the shared memory area of ​​the static metadata repository, used to characterize the logical basis of task execution.

[0137] Step S4000: Perform a link diagram for all elements. Perform structural projection transformation operations to generate a topology summary data package. Combined with a vector graphics rendering engine, a real-time status view is generated. Responding to real-time status view Physical interaction operation The triggered hardware interrupt signal constructs a retrieval request data packet. Based on the full-element execution link diagram Perform topology backtracking addressing to obtain baseline input data. Through differential snapshot and benchmark input data Reverse data evolution reconstruction generates the full business load after restoration Attribution view is generated by combining static code snippets and rule description text. .

[0138] Specifically, this step aims to use the full-element execution link diagram generated in step S3300. Physical interaction with users As input, topology summary data is constructed. and the restored full service load The separate rendering mechanism utilizes a full-duplex network communication channel for real-time transmission and combines it with a backend reverse data evolution and reconstruction algorithm triggered by frontend interactive events to achieve on-demand backtracking of any historical node, ultimately outputting an attribution view. .

[0139] Further, step S4000 includes:

[0140] Step S4100: Based on the full-duplex network communication channel maintaining the connection state, the hierarchical layout algorithm is invoked to parse the full-element execution link diagram. Parent node identifier Execute a link graph on all features using the layout coordinate parameters of the generated node objects. Perform structural projection transformation operations to generate a topology summary data package. Combined with a vector graphics rendering engine to generate real-time status views .

[0141] Specifically, this step aims to use the full-element execution link diagram generated in step S3300. As input, the link graph is generated from this element using a structural projection transformation algorithm on the backend server. In the memory topology, the massive unstructured large-field data is stripped away, and only the topological skeleton representing the logical relationships between node objects and the health status indicators representing the execution results of node objects are extracted to generate a topology summary data package. This topology summary data package By using a full-duplex network communication channel to push data in real time to the front-end visualization component on the client terminal, users can instantly parse and render a real-time status view reflecting the overall health of the entire link without consuming a large amount of local computing power and graphics storage resources on the client terminal. .

[0142] In the specific implementation process, in order to achieve millisecond-level initial screen loading and smooth interaction of the visual interface, this step executes the following layered processing logic:

[0143] First, the front-end visualization component initiates a connection request to the back-end server, establishing a full-duplex network communication channel based on the TCP / IP protocol, such as the WebSocket protocol, and maintaining a long connection state to support proactive message push from the back-end server.

[0144] Subsequently, the backend server received the full-element execution chain diagram. To address the rendering performance bottleneck caused by the browser's main thread calculating graphic positions when rendering massive numbers of node objects in front-end visualization components, this step invokes a layered layout algorithm on the back-end server. This algorithm parses the full-feature execution chain diagram. Identify the parent node identifier stored in each node object. Based on this, a hierarchical dependency topology is established between node objects. Based on this hierarchical dependency topology, a layered iterative calculation is performed, with the following specific logic: First, the hierarchical dependency topology is traversed to determine the vertical hierarchy depth of each node object in the vertical direction of the visualization canvas, and this depth is quantized and mapped to a ordinate value on the visualization canvas. Then, the horizontal arrangement order of node objects within the same hierarchy is calculated, and the arrangement order is optimized using a cross-minimization algorithm to reduce line occlusion. The optimized order result is then quantized and mapped to a horizontal coordinate value on the visualization canvas. The ordinate and horizontal coordinate values ​​are combined to generate a two-dimensional Cartesian coordinate tuple representing the physical display position of the node object, and this tuple is defined as a layout coordinate parameter. This layout coordinate parameter is then temporarily appended to the corresponding node object's attribute.

[0145] Next, the processor is invoked to execute the link graph for all elements. Perform structural projection transformation operations to generate a topology summary data package. The execution logic of the structural projection transformation operation is as follows: traverse the entire element execution link diagram. For each node object in the process, the global tracking identifier used for indexing is extracted and retained. Parent node identifier used to draw topology connections The execution status codes used for color mapping and the layout coordinate parameters used for positioning are the core elements of visualization rendering. On the other hand, unstructured large-field data such as rule description text and static code snippets stored in node objects are removed. Finally, the retained topology data and state attribute data are binary serialized and encoded to generate a topology summary data package with minimal size. Specifically, removing unstructured large field data from node objects only applies to the network transmission packets sent to the front-end visualization component this time, namely the topology summary data packet. The complete execution chain diagram in the backend server's memory The node objects in the system remain intact, ready to respond to user interactive query requests later.

[0146] Finally, this step transmits the topology summary data packet through the established full-duplex network communication channel. The data is pushed to the front-end visualization component in real time. This component then calls a vector graphics rendering engine, such as a Canvas-based renderer, to parse the topology summary data package. To render and generate a real-time state view The real-time status view The specific generation logic is as follows: First, based on the global tracking identifier... Construct the corresponding geometric node primitives in the rendering buffer; secondly, based on the parent node identifiers between node objects... The process begins by drawing directed vector connections between related geometric nodes. The vector graphics rendering engine then maps these connections to the positional attributes of the geometric nodes in the screen coordinate system based on layout coordinate parameters. Next, the geometric nodes are rendered as preset RGB color values ​​according to their execution status codes; for example, a normal status code is mapped to green, an abnormal status code to red, and a blocked status code to yellow. This vector graphics rendering process ultimately generates a real-time status view that, while not containing specific business payloads, intuitively reflects the entire logical flow and risk distribution across the entire process. .

[0147] Step S4200: Capture user status in real-time view Physical interaction operation The generated hardware interrupt signal is used to extract the user interaction coordinates, which are then mapped using a preset view transformation matrix to obtain logical coordinate points. A geometric collision detection algorithm is then used to determine whether the logical coordinate points fall within the geometric bounding box constructed based on geometric node primitives. If they do, the global tracking identifier associated with the geometric node primitives is extracted. and snapshot reference index Encapsulated into logical interaction instructions Construct the retrieval request data packet by combining the access permission authentication token. .

[0148] Specifically, this step aims to monitor the real-time status view generated by the user in step S4100. Physical interaction operation The system utilizes a geometric collision detection algorithm to identify target geometric nodes that the user intends to focus on, i.e., geometric nodes rendered in red or yellow in the view to represent abnormal states. It then converts the user's hardware interaction actions into snapshot reference indices. Search request data packet This ensures that high-load queries on the backend are only triggered when the user explicitly expresses interest in a specific target geometric node primitive, thereby achieving human-machine collaborative optimization of system resource scheduling.

[0149] In the specific implementation, in order to capture the user's intent to verify specific target geometric nodes and trigger subsequent data backtracking processes, the front-end visualization component calls the processor to execute logical operations based on an event-driven interaction processing algorithm. The specific execution logic of this event-driven interaction processing algorithm is as follows:

[0150] First, the front-end rendering engine is used to display the real-time status view. Register human-computer interaction event listeners on the graphics rendering container. These listeners are activated when the user performs physical interaction operations using input devices such as mouse clicks or touchscreen presses. At that time, the human-computer interaction event listener captures the physical interaction operation. The generated hardware interrupt signal is converted into physical coordinates in the current display terminal screen coordinate system, i.e., user interaction coordinates. .in, This represents the horizontal pixel offset, which is the number of pixels from the origin of the coordinate system in the logical coordinate system of the graphics rendering container at the click position. This indicates the vertical pixel offset, which is the number of pixels from the origin of the coordinate system in the vertical direction of the logical coordinate system of the graphics rendering container when the clicked position is located.

[0151] Subsequently, the front-end visualization component calls the graphics processor to execute a geometric collision detection algorithm, which records the user's physical click actions, i.e., physical interaction operations. Mapped to logical interaction instructions The execution logic of the geometric collision detection algorithm is as follows: First, using a preset view transformation matrix, the captured user interaction coordinates are... The system maps and transforms the current display terminal screen coordinate system to the logical coordinate system of the visualization component to obtain the logical coordinate points to be detected; then, iterates through the real-time status view. For all rendered geometric nodes, based on the layout coordinate parameters and the preset geometric node radius, a rectangular region, or geometric bounding box, is constructed to enclose each geometric node using addition and subtraction operations along the horizontal and vertical coordinate axes. Next, it is checked whether each logical coordinate point falls within the coordinate range of any geometric bounding box. During this detection and determination process, the following logical branch processing is executed:

[0152] If the logical coordinate point is determined to fall within the coordinate range of the geometric bounding box, the geometric collision detection algorithm outputs the determination result and locks the topology summary data residing in the memory of the front-end visualization component that has a mapping relationship with the geometric node primitive. And extract a global tracking identifier from it for unique identification. And a snapshot reference index for subsequent data backtracking. ; wherein, the snapshot reference index Although generated in step S2200, the key value used as the correlation differential evidence is always retained in the topology summary data packet. The data is then transmitted to the front end; finally, the extracted global tracking identifier is... Snapshot Reference Index Combining and encapsulating to generate internal logical interaction instructions. This completes the transformation from geometric collision to business logic triggering.

[0153] If the logical coordinates are determined not to fall within the coordinate range of the geometric bounding box, for example, if the user clicks on a blank background area, then the geometric collision detection algorithm determines the physical interaction operation. If an event is identified as an invalid selection event or background roaming operation, the generation of logical interaction instructions will be immediately terminated. This avoids sending invalid network requests to the backend server.

[0154] Finally, the front controller parses the logical interaction instructions. Extract the global tracking identifiers contained therein. Snapshot Reference Index This information is defined as key index information for backend addressing. The access permission authentication token of the current session, stored in the frontend local storage area or memory, is read and logically merged with the key index information. Based on the full-duplex network communication channel pre-established in step S4100, the logically merged data is serialized into a standard binary stream to construct a retrieval request data packet. And it is sent asynchronously to the backend server via the network interface.

[0155] Step S4300, in response to the retrieval request data packet Extract differential snapshot Based on the full-element execution link diagram Perform topology backtracking addressing to obtain baseline input data. Using the reverse data evolution reconstruction algorithm to reconstruct differential snapshots and benchmark input data Perform a reverse merge operation to generate the restored full service load. It also aggregates static code snippets and rule description text to generate attribution views. .

[0156] Specifically, this step aims to respond to the explicit interaction intent of the front-end user by parsing the retrieval request data packet generated in step S4200. Locate and extract the differential snapshot generated in step S2200 and persisted in step S2400 from the distributed object storage system DOSS. And utilize the full-element execution link diagram generated in step S3300. The topological reference relationships constructed in the middle are used to execute a reverse data evolution reconstruction algorithm to convert highly compressed differential snapshots. Decompress and map to restore the complete, fully restored service payload. Finally, the restored full service payload The static code snippets and rule description text from step S3300 are aggregated in multiple dimensions to output an attribution view containing a complete chain of evidence. It enables atomic-level reproduction of the complete data at any execution timestamp without consuming normal business computing resources.

[0157] In the specific implementation process, the backend server receives and parses the retrieval request data packets sent by the frontend. Extract the encapsulated global tracking identifier. and snapshot reference index Reference index using this snapshot. For content-addressing key-value pairs, a read request is initiated to the high-throughput distributed object storage system DOSS to locate and extract the corresponding differential snapshot. To the memory buffer.

[0158] Accessing the full execution chain diagram residing in the backend server heap memory Based on global tracking identifiers Retrieved the parent node identifier of the current node object Based on the parent node identifier This step is part of the full-element execution chain diagram. The topology backtracking addressing operation is performed to locate its upstream parent node object, and the full service payload of the upstream parent node object after execution is read and defined as the baseline input data. .

[0159] Subsequently, the processor is invoked to execute the reverse data evolution and reconstruction algorithm. This algorithm calls different hardware computing resources to perform differentiated reverse merging operations based on the data type. The specific execution logic is as follows:

[0160] For unstructured binary data, the entropy decoding operator is called to perform differential snapshots. Decompression and restoration are performed to obtain the original bit-flipped sequence. This then drives the arithmetic logic unit to process the reference input data. Perform a binary bitwise XOR operation on the bit-flipped sequence. Utilizing the mathematical invertibility of the XOR operation, the differential snapshot... Superimposed on the baseline input data The above method losslessly restores the full workload of the node object after processing. .

[0161] For structured data such as JSON or XML, call the entropy decoding operator to perform differential snapshots. Perform reverse decompression to parse and generate a logical topology change set, then input the baseline data. The system is instantiated as a baseline document object model tree and a structured patch merging operation is performed. This operation, guided by the node addressing paths in the logical topology change set, applies the node value changes to specific node objects in the baseline document object model tree, reconstructing the complete, restored full business load. .

[0162] Finally, the baseline input data obtained through topology backtracking addressing will be... The restored full service payload generated by reverse merging operation , and through the full-element execution link diagram The static code snippets and rule description text obtained from the metadata reference pointers are logically aggregated. A multi-dimensional composite data comparison, i.e., an attribution view, is generated through a data visualization rendering engine. Then, return to the front end for display.

[0163] Example 2:

[0164] This embodiment, based on Embodiment 1, provides a visual trajectory tracing system for risk verification tasks, such as... Figure 3 As shown, the system includes a data encapsulation module, a trajectory generation module, a link construction module, and a visual interaction module;

[0165] The data encapsulation module is used to parse the original heterogeneous data stream. To generate the original structural entropy fingerprint Based on the original structural entropy fingerprint Construct global tracking identifiers and global tracking identifier Original structural entropy fingerprint and raw heterogeneous data streams Write to the hierarchical storage topology and generate encapsulated data packets. .

[0166] The trajectory generation module is used to process encapsulated data packets using dynamic bytecode instrumentation technology. To generate transformed business data And read physical execution parameters to construct the runtime execution context. According to the encapsulated data packet and transformed business data Call the entropy encoding function to generate a differential snapshot And construct the shadow transmission link to instantiate trajectory node objects. Serialization to generate binary track messages and drive the direct memory access controller to handle binary trajectory messages. Sum of differences snapshots Zero-copy transmission is performed, and data is distributed and transmitted according to the distribution and delivery strategy.

[0167] The link construction module: uses a hash sharding mapping mechanism to process binary trajectory messages. Routing to in-memory time-series aggregation buckets to generate unordered sets of nodes Combining hash join algorithm to parse unordered node set The node object is used to instantiate the logical topology execution graph. If an execution status code anomaly is detected in a node object, a depth-first search algorithm is invoked to mark the potentially polluted state, and a memory pointer mounting method is used to write the pre-defined rule description text and static code snippets into the logical topology execution graph. Generate a full-element execution chain diagram .

[0168] The visual interaction module is used to execute the link diagram of all elements. Perform structural projection transformation operations to generate a topology summary data package. Combined with a vector graphics rendering engine, a real-time status view is generated. Responding to real-time status view Physical interaction operation The triggered hardware interrupt signal constructs a retrieval request data packet. Based on the full-element execution link diagram Perform topology backtracking addressing to obtain baseline input data. Through differential snapshot and benchmark input data Reverse data evolution reconstruction generates the full business load after restoration Attribution view is generated by combining static code snippets and rule description text. .

[0169] The parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0170] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for visualizing a trace of a risk verification task, characterized in that, include: Parse the original heterogeneous data stream to generate the original structural entropy fingerprint, construct a global tracing identifier based on the original structural entropy fingerprint, and write the global tracing identifier, the original structural entropy fingerprint, and the original heterogeneous data stream into a hierarchical storage topology to generate an encapsulated data packet. Dynamic bytecode instrumentation technology is used to process encapsulated data packets to generate transformed business data. Physical execution parameters are read to construct a runtime execution context. Based on the encapsulated data packets and transformed business data, an entropy encoding function is called to generate differential snapshots. A shadow transmission link is constructed to serialize instantiated trajectory node objects into binary trajectory messages. The direct memory access controller is driven to perform zero-copy transmission of binary trajectory messages and differential snapshots, and data splitting and transmission are completed. The binary trajectory message is routed to the memory time-series aggregation bucket based on the hash sharding mapping mechanism to generate an unordered node set. The node objects in the unordered node set are parsed by the hash connection algorithm to instantiate the logical topology execution graph. If the execution status code of the node object is detected to be abnormal, the depth-first search algorithm is called to mark the potential pollution status. The memory pointer mounting method is used to write the pre-set rule description text and static code fragments into the logical topology execution graph to generate a full-element execution link graph. The system performs a structural projection transformation on the link diagram of all elements to generate a topology summary data package. It then uses a vector graphics rendering engine to generate a real-time status view. In response to hardware interrupt signals triggered by physical interaction operations on the real-time status view, it constructs a retrieval request data package. Based on the link diagram of all elements, it performs topology backtracking addressing to obtain baseline input data. It then reconstructs the full workload after restoration by reversing the data evolution of differential snapshots and baseline input data. Finally, it combines static code snippets and rule description text to generate an attribution view.

2. The method of claim 1, wherein, The method for generating the encapsulated data packet includes: The original heterogeneous data stream is parsed to separate the source metadata set and the payload data. The payload data is then subjected to a depth-first traversal algorithm and ascending order reordering to generate a topological feature sequence. The service feature summary of the topological feature sequence and the channel feature summary of the source metadata set are calculated respectively, and multiplication and bitwise XOR operations are performed sequentially to generate the original structural entropy fingerprint. The system calculates the time difference between the access timestamp and a preset epoch start time constant. It then performs a binary left shift operation on the time difference and node topology parameters to construct time slices and node topology coordinates. A preset binary mask is used to extract the terminal feature vector of the original structural entropy fingerprint, and a bitwise XOR operation is performed with the anti-collision auto-incrementing sequence generated by the memory atomic counter to generate a composite content anchor. This anchor is then combined with the time slices and node topology coordinates to generate a global tracking identifier. The node topology parameters include a data center identifier and a worker node identifier. A hierarchical storage topology is constructed within the physical address space. The internal round-robin key is read to perform a hash message authentication code operation on the global tracking identifier and the original structure entropy fingerprint to generate an encapsulation verification code. The encapsulation verification code, the current encapsulation timestamp, the source metadata set encoded in big-endian order, and the original heterogeneous data stream are written into the corresponding storage area of ​​the hierarchical storage topology to generate an encapsulated data packet.

3. The method of claim 2, wherein, The hierarchical storage topology is a contiguous binary buffer allocated in random access memory based on the operating system's memory allocation instructions. The contiguous binary buffer is divided into independent storage areas, including a header storage area, a metadata storage area, and a load storage area. The header storage area is configured as a pre-allocated fixed-byte-width storage space, located at the starting address of the continuous binary buffer, and stores the global tracking identifier, the current encapsulation timestamp, and the encapsulation check code. The metadata storage area is configured as a variable-length storage space based on the type-length-value encoding rule, storing the source metadata dataset encoded in big-endian order; The load storage area is configured as an unformatted binary stream storage space to store raw heterogeneous data streams.

4. The method of claim 1, wherein, The physical execution parameters include the server network address, thread identifier, stack frame depth, effective rule version identifier, and execution time data; The steps for reading the physical execution parameters include: driving the asynchronous daemon thread to call the memory state extraction operator, and loading the thread context handle, the effective rule version identifier, and the instruction sequence start time and instruction sequence end time into the arithmetic register; The memory state extraction operator is a set of computer program instructions running in an asynchronous daemon thread; the thread context handle is a memory address pointer in the operating system kernel that maintains the state of the current main business processing thread, including the server network address, thread identifier, and stack frame depth. The system addresses the process control block in the operating system kernel space using the thread context handle pointer, reads the server network address and thread identifier, reads the memory values ​​of the base pointer register and stack pointer register of the current main business processing thread, and generates the stack frame depth by calculating the difference between the memory values ​​of the base pointer register and the stack pointer register, reads the version identifier of the effective rule loaded in the arithmetic register, and uses the arithmetic logic unit to calculate the difference between the start time and end time of the instruction sequence to generate execution time data.

5. The method of visualizing a trajectory of a risk verification task of claim 4, wherein, The instantiation methods for the trajectory node object include: Dynamic bytecode instrumentation technology is used to configure non-blocking monitoring instructions as the memory entry address of predefined business rule functions. If the program counter of the main business processing thread points to the memory entry address, the asynchronous daemon thread is activated to execute the business rule function on the encapsulated data packet to generate transformed business data. At the same time, the physical execution parameters of the main business processing thread are read to construct the runtime execution context. The system identifies the physical format attributes of the encapsulated data packets and the transformed business data. If the physical format attribute is unstructured data, it drives the arithmetic logic unit to execute a bitwise XOR instruction to construct a bit-flipping sequence. If the physical format attribute is structured data, it calls the memory parser to execute recursive differential operations to extract the logical topology change set. It uses an entropy encoding function to map the bit-flipping sequence or logical topology change set to a differential snapshot and calculates the cryptographic hash digest of the differential snapshot as a snapshot reference index. The execution fingerprint is calculated by calling a non-cryptographic hash algorithm based on the runtime execution context, and a trajectory node object containing a topology key set and a state value set is instantiated. The global tracking identifier, execution fingerprint, and parent node identifier parsed from the header storage area of ​​the encapsulated data packet are mapped to the topology key set using memory address assignment instructions. Simultaneously, the effective rule version identifier, snapshot reference index, execution status code corresponding to the function return value in the general-purpose register, and execution time data parsed based on the runtime execution context are written to the state value set.

6. The method of visualizing a trajectory of a risk verification task of claim 1, wherein, The data offloading and transmission steps include: constructing a shadow transmission link based on a pre-set lock-free circular buffer and an I / O offloading daemon thread; if the I / O offloading daemon thread detects that the memory start address pointers of the trajectory node object and the differential snapshot are written to the lock-free circular buffer, it calls the binary serialization protocol to serialize the trajectory node object to generate a binary trajectory message, and drives the direct memory access controller to perform zero-copy transmission; and according to the offloading and delivery strategy, the binary trajectory message and the differential snapshot are mapped to the message middleware and the distributed object storage system, respectively.

7. The method of visualizing a trajectory of a risk verification task of claim 1, wherein, The method for generating the full-element execution link diagram includes: Extract the global tracking identifier of the binary trajectory message and call the hash sharding mapping mechanism to generate a memory logical index. Based on the memory logical index, route the binary trajectory message to the corresponding memory time-series aggregation bucket. If the cumulative number or residence time of the binary trajectory message in the memory time-series aggregation bucket exceeds the preset threshold, call the binary deserialization operator to generate an unordered set of nodes. The logical topology execution graph is instantiated by parsing the parent node identifier of the unordered node set based on the hash connection algorithm. If an execution status code abnormality is detected in the node object in the logical topology execution graph, the node object is defined as the risk transmission root node. The depth-first search algorithm is called to perform recursive traversal. The state overwrite operation is performed on the downstream descendant nodes that have a directed connected path with the risk transmission root node to mark the potential pollution state. The logical topology execution graph is traversed to extract the effective rule version identifier of each node object. The effective rule version identifier is used as the reverse index key. Combined with the pre-set static metadata repository, the memory address handles pointing to static code fragments and rule description text are obtained. The memory address handles are written into the pre-allocated metadata reference pointer field of the node object using a zero-copy mount operation to generate a full-element execution link graph.

8. The method of visualizing a trajectory of a risk verification task of claim 1, wherein, The method for generating the attribution view includes: Based on the full-duplex network communication channel to maintain the connection state, the hierarchical layout algorithm is called to parse the parent node identifier of the full-element execution link graph to generate the layout coordinate parameters of the node object, and the structural projection transformation operation is performed on the full-element execution link graph to generate the topology summary data packet, which is combined with the vector graphics rendering engine to generate the real-time status view. The system captures hardware interrupt signals generated by the user's physical interaction on the real-time status view to extract the user interaction coordinates. It then maps these coordinates with a preset view transformation matrix to obtain logical coordinate points. A geometric collision detection algorithm is used to determine whether the logical coordinate points fall into the geometric bounding box constructed based on geometric node primitives. If they do, the system extracts the global tracking identifier and snapshot reference index associated with the geometric node primitives to encapsulate them into logical interaction instructions. Finally, it combines these instructions with an access permission authentication token to construct a retrieval request data packet. In response to the retrieval request data packet, a differential snapshot is extracted. Based on the full-element execution link diagram, topology backtracking is performed to obtain the baseline input data. The reverse data evolution reconstruction algorithm is used to perform a reverse merging operation on the differential snapshot and the baseline input data to generate the restored full service payload. Static code snippets and rule description text are aggregated to generate an attribution view.

9. The method of claim 8, wherein, The method for generating the topology summary data packet includes: The processor is invoked to perform structural projection transformation logic on the full-element execution link graph, traversing each node object of the full-element execution link graph; Extract and retain the pre-defined structured attribute fields in the node object, including the global tracking identifier, parent node identifier, execution status code, and layout coordinate parameters; Filter the unstructured field data stored in the node object, the unstructured field data including rule description text and static code snippets; Perform binary serialization encoding on the structured attribute fields to generate a topology summary data packet; The method for generating the layout coordinate parameters of the node objects includes: parsing the full-element execution link graph, identifying the parent node identifier of each node object, and establishing a hierarchical dependency topology between node objects; traversing the hierarchical dependency topology to determine the vertical hierarchy depth of each node object in the vertical direction of the visualization canvas, and quantizing and mapping it to the ordinate value on the visualization canvas; calculating the arrangement order of node objects in the same level in the horizontal direction, optimizing the arrangement order through a cross-minimization algorithm, and quantizing and mapping the optimized order result to the abscissa value on the visualization canvas; and combining the ordinate value and the abscissa value to generate the layout coordinate parameters.

10. A visualized track tracing system for a risk checking task, which is used to implement the visualized track tracing method for a risk checking task according to any one of claims 1-9, characterized in that, The system includes a data encapsulation module, a trajectory generation module, a link construction module, and a visual interaction module; The data encapsulation module is used to parse the original heterogeneous data stream to generate the original structural entropy fingerprint, construct a global tracking identifier based on the original structural entropy fingerprint, and write the global tracking identifier, the original structural entropy fingerprint, and the original heterogeneous data stream into the hierarchical storage topology to generate an encapsulated data packet. The trajectory generation module is used to process encapsulated data packets using dynamic bytecode instrumentation technology to generate transformed business data, read physical execution parameters to construct a runtime execution context, call an entropy encoding function based on the encapsulated data packets and transformed business data to generate differential snapshots, construct a shadow transmission link to serialize instantiated trajectory node objects into binary trajectory messages, drive the direct memory access controller to perform zero-copy transmission of binary trajectory messages and differential snapshots, and complete data splitting transmission. The link construction module: Based on the hash sharding mapping mechanism, it routes binary trajectory messages to memory time-series aggregation buckets to generate an unordered node set. It then uses a hash connection algorithm to parse node objects in the unordered node set to instantiate a logical topology execution graph. If an execution status code anomaly is detected in a node object, it calls a depth-first search algorithm to mark potential pollution states. Finally, it uses a memory pointer mounting method to write the pre-set rule description text and static code fragments into the logical topology execution graph to generate a full-element execution link graph. The visual interaction module is used to perform structural projection transformation operations on the link diagram of all elements to generate a topology summary data package, generate a real-time status view in conjunction with a vector graphics rendering engine, construct a retrieval request data package in response to hardware interrupt signals triggered by physical interaction operations on the real-time status view, perform topology backtracking addressing based on the link diagram of all elements to obtain baseline input data, reconstruct the restored full business payload through the inverse data evolution of differential snapshots and baseline input data, and generate an attribution view in conjunction with static code snippets and rule description text.

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