Spatial-temporal consistency data association modeling and verification method for heterogeneous data sources

Abstract

This invention relates to the field of electronic digital data processing technology and discloses a method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources. The method includes: acquiring the spatiotemporal feature benchmark of the heterogeneous data source and generating encrypted positioning anchors; extracting feature parameters of data entities and calculating state feature vectors; generating cascaded association fingerprints by calling the state feature vectors of preceding nodes when responding to data write requests; mapping business logic timing to topological constraints of the data storage structure based on the cascaded association fingerprints; constructing a directed acyclic graph model and generating an index matrix with unidirectional association characteristics. This invention establishes a unified spatiotemporal alignment benchmark for asynchronous heterogeneous data through encrypted positioning anchors, solidifies the causal timing of the storage structure using a cascaded association mechanism, transforms timing verification into logical rejection of the underlying topology, and improves the addressing efficiency and verification reliability of cross-source data.

Description

Technical Field

[0001] This invention relates to a method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources, belonging to the field of electronic digital data processing technology. Background Technology

[0002] Currently, building data indexes and association models is the foundation for maintaining the logical consistency of multi-source heterogeneous data. The mainstream technical path adopts a centralized aggregation model, in which a centralized platform collects the original data from each participant and uses business identifiers to establish the association relationship of data entities. As the scale of data processing expands, multi-source data exhibits asynchronous generation time and dispersed storage location. Due to the isolation between the states of different systems, this static matching method based on business identifiers is difficult to reflect the spatiotemporal trajectory of data. When processing data streams with causal logic, the data association relationship only exists in the logical mapping of the application layer. At the storage level, there is a lack of structured physical connection between data entities, which leads to the system tolerating potential temporal logical deviations and generating high post-verification overhead.

[0003] Conventional improvement approaches attempt to correct the aforementioned deviations by increasing global time synchronization accuracy or audit frequency. Analysis shows that this linear improvement method does not address the root cause of system state isolation; instead, it generates redundant communication load and increases the risk of sensitive data leakage. Existing technologies struggle to solidify the causal dependencies of heterogeneous data entities in the underlying storage structure without aggregating the original data. Furthermore, in addition to the limitations of the distributed physical architecture, existing software-level control logic also has shortcomings. For example, Chinese invention patent application CN117130550A discloses a distributed data storage method, apparatus, device, and storage medium that filters available nodes based on historical reuse metrics and performs independent hash signatures on uploaded data. A deeper analysis of storage reveals that the selection logic, which relies on static scoring based on the historical state of nodes, implicitly assumes that the network topology and node states are relatively stable. However, when facing extremely asynchronous and high-concurrency heterogeneous data networks, such as cross-domain business and concurrent financial flows, the objective physical laws of nodes manifest as high-frequency nonlinear fluctuations in clock drift and network latency. In contrast, the fragmented hash signature mechanism in the comparison scheme can only maintain the static integrity of single-point data, stripping away the spatiotemporal causal constraints that should exist between data entities. Because it fails to forcibly bind the running state of the preceding node to the generation of subsequent data signatures in terms of physical mechanism, when encountering out-of-order concurrency caused by asynchronous latency or deliberate timestamp tampering, the independent signature defense line faces a fundamental mismatch between the core preset premise and the actual boundary conditions, and cannot spontaneously form a temporal rejection in the underlying data structure.

[0004] Therefore, how to construct a topological index structure with physical causal constraints to achieve spatiotemporal consistency association and decentralized verification of cross-source heterogeneous data while ensuring data privacy has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of this invention is as follows: A method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources, comprising the following steps: Step S1: Obtain the spatiotemporal feature benchmark corresponding to the heterogeneous data source. The spatiotemporal feature benchmark includes timing signals, digital identifiers of participating parties, and data metadata. Generate encrypted positioning anchor points based on the spatiotemporal feature benchmark. Step S2: Extract the feature parameters of the initial data entity, and calculate and output the initial state feature vector by hash concatenating the feature parameters with the encrypted positioning anchor point. Step S3: In response to the write request of the subsequent data entity, extract the attribute bits of the subsequent data entity and call the state feature vector solidified by the logical preceding node associated with it. Step S4: The hash parameters of subsequent data entities are used as input payloads. By converting the state feature vector of the logical preceding node into the initialization vector of the hash operation, a one-way encryption transformation is performed to generate a cascaded association fingerprint. Based on the cascaded association fingerprint, the business logic sequence is solidified into the topological constraints of the data storage structure. Step S5: Using the encrypted positioning anchor point as the root index, the cascaded association fingerprints generated at each level are used as the primary keys of the sub-indexes to construct a directed acyclic graph topology model and generate an index matrix with unidirectional association characteristics.

[0006] Preferably, step S2 includes: extracting the business attribute features of the initial data entity, converting the business attribute features into a high-dimensional space feature vector through a preset mapping rule; using the timestamp parameter contained in the encrypted positioning anchor point and the weight value of the participants to perform a nonlinear perturbation transformation on the high-dimensional space feature vector; after each participant node completes the consensus writing of the initial data entity, the vector result after the nonlinear perturbation transformation is solidified into the initial state feature vector, which serves as the logical root node for subsequent data entity cascade calculations.

[0007] Preferably, the cascaded associated fingerprint has asymmetric and unidirectional logical link characteristics; the state feature vector of the logical predecessor node is obtained through the memory addressing interface and participates in the generation calculation of the cascaded associated fingerprint, generating a causal binding between the fingerprint generation of subsequent data entities and the running state of the logical predecessor node; in response to the change of the state feature vector of the logical predecessor node, the generated cascaded associated fingerprint is logically broken with the existing index primary key in the index matrix, and a logical rejection of the time-series tampering request is generated at the data storage layer.

[0008] Preferably, data penetration verification is performed based on the index matrix; during the data integrity verification process, the root index is located according to the encrypted location anchor point corresponding to the target business order number, and the address is performed along the unidirectional pointer chain formed by the cascaded associated fingerprints; the associated heterogeneous business data is extracted through the encrypted dependency relationship between the index primary keys.

[0009] Preferably, the cascaded associated fingerprints follow the calculation rules below: ,in, For subsequent data entities, the cascaded association fingerprint is generated at level i, and Hash is a one-way hash function. The hash parameter for the extracted subsequent data entities, This is a cascading symbol. The obtained logical predecessor node is the fixed state feature vector at level i-1.

[0010] Preferably, the heterogeneous data sources include contract data, business flow data, financial data, and invoice data in flexible employment scenarios. After each participant generates heterogeneous data sources in its independent business system, it extracts the feature parameters of the data through the decentralized association module and initiates a modeling request to the distributed ledger node.

[0011] Preferably, the method further includes: monitoring the resource utilization rate of each participating node when writing data, and adjusting the generation frequency of cascaded fingerprints according to the fluctuation trend of resource utilization rate; in response to the resource utilization rate exceeding a preset threshold, reducing the number of index entries generated by merging the hash parameters of similar business entities.

[0012] Preferably, the method further includes: during the verifiable verification process, the verification module acquires the heterogeneous dataset to be tested, and recursively reconstructs the causal relationships in the heterogeneous dataset to be tested using the cascaded association fingerprints at each level in the index matrix; in response to the fingerprint sequence generated by the reconstruction being consistent with the fingerprint primary key fixed in the index matrix, it is determined that the heterogeneous dataset to be tested meets the authenticity requirements.

[0013] Preferably, the index matrix construction process includes compensation for spatiotemporal anchor point offset: by comparing the deviation between the local time of the participating node and the timing signal, a clock deviation compensation factor is generated, and the clock deviation compensation factor is used to correct the timestamp characteristics of the encrypted positioning anchor point.

[0014] Preferably, the index matrix is ​​stored in a decentralized consensus node cluster; the consensus node cluster achieves consensus on data modeling by verifying the validity of the cascaded association fingerprint; no single node in the consensus node cluster can modify the cascaded association fingerprint without affecting the directed acyclic graph topology model.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In spatiotemporal consistency data association modeling, a globally unified spatiotemporal association benchmark is established. By integrating high-precision time synchronization signals, digital identity parameters of participating parties, and data metadata, cryptographic anchors with uniqueness and unforgeability are generated. This mechanism provides a standardized spatiotemporal imprint for asynchronous heterogeneous data scattered in different physical storage spaces and different logical systems, enabling the originally isolated data streams to have a unified logical affiliation and time alignment benchmark, eliminating the fragile association problem caused by system differences, and ensuring that the data in the entire link converges to the same root node.

[0016] 2. Achieve causal temporal locking of data storage structure. Through state entanglement mechanism, the fingerprint generation of subsequent data entities is forcibly bound to the state vector of the preceding entity. This ensures that the hash operation at each level consumes the feature value output by the preceding node. This asymmetric encrypted topological dependency relationship builds a directed acyclic graph structure with sequential logic at the bottom layer. The lagging verification that originally depended on the upper layer application logic is transformed into physical rejection of the bottom layer storage structure. Any request that attempts to reverse the time sequence or forge the preceding data will fail because it cannot obtain the correct initialization parameters, thus avoiding the risk of time sequence tampering.

[0017] 3. Optimize the addressing efficiency and retrieval performance of related data. The dynamic index matrix formed by state entanglement naturally maps the physical storage distribution of heterogeneous data to the business occurrence path. When performing data homology or integrity verification, the retrieval logic does not need to perform full traversal or multi-table join queries in massive isolated datasets. Instead, it directly completes the penetrating address along the deterministic unidirectional pointer chain formed by the entangled fingerprint. This structured constraint significantly reduces the resource consumption of processor memory hits. Even when facing high-concurrency writes and large-scale verification requests, it can still maintain a stable processing throughput. Attached Figure Description

[0018] Figure 1 This is a flowchart of the spatiotemporal consistency correlation modeling method for heterogeneous data according to the present invention; Figure 2 This is a logic diagram for the generation of data entity feature cascading and associated fingerprints in this invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] A method for modeling and validating spatiotemporal consistent data association for heterogeneous data sources includes the following steps: Step S1: Obtain the spatiotemporal feature benchmark corresponding to the heterogeneous data source. The spatiotemporal feature benchmark includes timing signals, digital identifiers of participating parties, and data metadata. Generate encrypted positioning anchor points based on the spatiotemporal feature benchmark. Step S2: Extract the feature parameters of the initial data entity, and calculate and output the initial state feature vector by hash concatenating the feature parameters with the encrypted positioning anchor point. Step S3: In response to the write request of the subsequent data entity, extract the attribute bits of the subsequent data entity and call the state feature vector solidified by the logical preceding node associated with it. Step S4: The hash parameters of subsequent data entities are used as input payloads. By converting the state feature vector of the logical preceding node into the initialization vector of the hash operation, a one-way encryption transformation is performed to generate a cascaded association fingerprint. Based on the cascaded association fingerprint, the business logic sequence is solidified into the topological constraints of the data storage structure. Step S5: Using the encrypted positioning anchor point as the root index, the cascaded association fingerprints generated at each level are used as the primary keys of the sub-indexes to construct a directed acyclic graph topology model and generate an index matrix with unidirectional association characteristics.

[0022] Preferably, step S2 includes: extracting the business attribute features of the initial data entity, converting the business attribute features into a high-dimensional space feature vector through a preset mapping rule; using the timestamp parameter contained in the encrypted positioning anchor point and the weight value of the participants to perform a nonlinear perturbation transformation on the high-dimensional space feature vector; after each participant node completes the consensus writing of the initial data entity, the vector result after the nonlinear perturbation transformation is solidified into the initial state feature vector, which serves as the logical root node for subsequent data entity cascade calculations.

[0023] Preferably, the cascaded associated fingerprint has asymmetric and unidirectional logical link characteristics; the state feature vector of the logical predecessor node is obtained through the memory addressing interface and participates in the generation calculation of the cascaded associated fingerprint, generating a causal binding between the fingerprint generation of subsequent data entities and the running state of the logical predecessor node; in response to the change of the state feature vector of the logical predecessor node, the generated cascaded associated fingerprint is logically broken with the existing index primary key in the index matrix, and a logical rejection of the time-series tampering request is generated at the data storage layer.

[0024] Preferably, data penetration verification is performed based on the index matrix; during the data integrity verification process, the root index is located according to the encrypted location anchor point corresponding to the target business order number, and the address is performed along the unidirectional pointer chain formed by the cascaded associated fingerprints; the associated heterogeneous business data is extracted through the encrypted dependency relationship between the index primary keys.

[0025] Preferably, the cascaded associated fingerprints follow the calculation rules below: ,in, For subsequent data entities, the cascaded association fingerprint is generated at level i, and Hash is a one-way hash function. The hash parameter for the extracted subsequent data entities, This is a cascading symbol. The obtained logical predecessor node is the fixed state feature vector at level i-1.

[0026] Preferably, the heterogeneous data sources include contract data, business flow data, financial data, and invoice data in flexible employment scenarios. After each participant generates heterogeneous data sources in its independent business system, it extracts the feature parameters of the data through the decentralized association module and initiates a modeling request to the distributed ledger node.

[0027] Preferably, the method further includes: monitoring the resource utilization rate of each participating node when writing data, and adjusting the generation frequency of cascaded fingerprints according to the fluctuation trend of resource utilization rate; in response to the resource utilization rate exceeding a preset threshold, reducing the number of index entries generated by merging the hash parameters of similar business entities.

[0028] Preferably, the method further includes: during the verifiable verification process, the verification module acquires the heterogeneous dataset to be tested, and recursively reconstructs the causal relationships in the heterogeneous dataset to be tested using the cascaded association fingerprints at each level in the index matrix; in response to the fingerprint sequence generated by the reconstruction being consistent with the fingerprint primary key fixed in the index matrix, it is determined that the heterogeneous dataset to be tested meets the authenticity requirements.

[0029] Preferably, the index matrix construction process includes compensation for spatiotemporal anchor point offset: by comparing the deviation between the local time of the participating node and the timing signal, a clock deviation compensation factor is generated, and the clock deviation compensation factor is used to correct the timestamp characteristics of the encrypted positioning anchor point.

[0030] Preferably, the index matrix is ​​stored in a decentralized consensus node cluster; the consensus node cluster achieves consensus on data modeling by verifying the validity of the cascaded association fingerprint; no single node in the consensus node cluster can modify the cascaded association fingerprint without affecting the directed acyclic graph topology model.

[0031] Example 1: In a distributed database cluster environment handling cross-domain concurrent business flows for flexible employment, the system faces a technical contradiction between high-frequency concurrent writing of massive heterogeneous data and causal time-series verification of business data. This application scenario is a specific operational example of the spatiotemporal consistency data association modeling and verification method for heterogeneous data sources described above. Specifically, contract data, business flow data, financial data, and invoice data belong to independent storage nodes of different participants, and their generation times are extremely asynchronous. Traditional centralized databases generally use the upper-layer application server to initiate cross-table join queries based on business order numbers after the fact to verify the order of business logic. This data organization based on static characteristics... The architecture results in the underlying physical storage lacking native awareness of the causal relationships generated by the data. When malicious nodes exploit network communication latency or system clock differences to perform illegal reverse data injection, such as preferentially writing to the underlying fund settlement data, generating forged pre-contract data by tampering with the node's local timestamp, and attaching it to the conventional Merkle tree parallel leaf nodes, the underlying database system will unconditionally accept this illegal data. Preventing such time-sequence logic reversal tampering relies on an extremely large and lagging full data traversal script, which not only drastically increases the processor's addressing overhead but also leads to a significant risk of time-sequence disorder when the distributed system is dealing with millions of concurrent transaction injections.

[0032] To address the issue of the underlying data structure being unaware of causal timing, the decentralized association module extracts spatiotemporal feature benchmarks from heterogeneous data sources. It then integrates timing signals, participant digital identifiers, and data metadata to generate globally unique encrypted positioning anchors, which serve as the absolute spatiotemporal benchmark for dynamic modeling. The system generates a clock deviation compensation factor by comparing the deviation between the local time of the participant nodes and the timing signal. The specific underlying measurement and calibration steps are as follows: the system continuously sends 10 probe messages with local hardware timestamp register values ​​to the timing node, records the timestamp difference for each round trip, calculates 10 basic offsets based on symmetrical delay distribution logic, and feeds them into a moving average algorithm with a capacity of 10. The calculation unit uses a comparator to identify and directly discard abnormal offset data whose absolute value is greater than three times the standard deviation of the sample mean. Then, it calculates the compensation factor by averaging the remaining data. The clock deviation compensation factor is used to correct the timestamp characteristics of the encrypted positioning anchor point. To ensure the one-way irreversible property of the underlying encrypted topology operation, the system limits the timestamp characteristic correction action to be triggered before the hash solidification of the encrypted anchor point. The local hardware clock register value at the time of generation of the data entity to be associated is read; the local hardware clock register value is added to the clock deviation compensation factor to calculate and output the physical alignment reference time variable; the reference time variable is extracted as the only time dimension input parameter to the one-way hash function, and the output is solidified to generate the encrypted positioning anchor point.

[0033] When responding to a write request for the initial data entity, the system extracts the feature parameters of the initial data entity, concatenates them with the encrypted positioning anchor point using hash, calculates and outputs the initial state feature vector. The key technical intervention here lies in activating the state entanglement hash operation logic. Specifically, in response to a write request for a subsequent data entity, the system extracts the attribute bits of the subsequent data entity. To address the network congestion and retransmission issues inevitably caused by cross-physical server addressing, remote procedure call requests are prohibited here. Instead, an asynchronous event listening interface is established through a locally deployed distributed memory object caching component. The system pre-mirrors and locks the state feature vectors of associated preceding nodes in an independent address segment of the local server's physical memory, and calls the fixed state feature vectors of the associated logical preceding nodes through the memory addressing interface. The system uses the hash parameters of the subsequent data entity as the input payload and converts the state feature vectors of the logical preceding nodes into initialization vectors according to the formula... Output the one-way encryption transformation result, where For subsequent data entities, the cascaded association fingerprint is generated at level i, and Hash is a one-way hash function. The hash parameter for the extracted subsequent data entities, This is a cascading symbol. The obtained logical predecessor node's state feature vector is solidified at level i-1. The state feature vector output by the predecessor node provides an unavoidable mathematical initial condition for the fingerprint generation of subsequent nodes. In turn, the cascaded fingerprint generated by the subsequent nodes objectively solidifies the historical running state of the predecessor node in its own hash primary key. This encrypted dependency hardcodes the compliant sequential logic that originally belongs to the application layer into an asymmetric physical topology constraint of the underlying data storage structure. Any request that attempts to forcibly write subsequent business data before the predecessor state vector is generated will trigger the logical rejection of the underlying data storage layer due to the lack of a correct initialization vector.

[0034] Based on the aforementioned state entanglement mechanism, the system uses encrypted positioning anchors as root indexes and cascaded fingerprints generated at each level as sub-index primary keys. It constructs a directed acyclic graph topology model with unidirectional pointers and generates an index matrix with unidirectional association characteristics. Its physical storage distribution naturally maps to the actual occurrence path of business data, eliminating the need for retrieval logic to initiate feature traversal matching among massive isolated database form points. The verifier only needs to locate the root index based on the target business order number and directly complete a constant-time-complexity penetration addressing along the unidirectional pointer chain formed by the cascaded fingerprints. By extracting associated heterogeneous business data through encrypted dependencies between index primary keys, once the reconstructed fingerprint sequence matches the fingerprint primary keys fixed in the index matrix, it can be determined that the heterogeneous dataset under test meets the authenticity requirements. This scheme abandons the traditional static hash aggregation structure, reshaping the complex compliance verification problem of cross-source heterogeneous data into a deterministic connectivity test for the directed topology links of the underlying storage matrix. Through the physical dimension transformation of the data structure, it eliminates the necessity of centralized credit endorsement and establishes a high-concurrency data modeling and deterministic addressing path in a distributed heterogeneous database environment.

[0035] Example 2: In a test platform simulating concurrent writes of distributed, multi-source heterogeneous transactions, the system faces the risk of data timing disorder caused by the superposition of sudden network communication latency changes and node clock drift. This test platform is built on four independently configured physical servers, simulating contract data nodes, business data nodes, fund data nodes, and bill data nodes, respectively. Each node is equipped with computing resources including a sixteen-core processor and 256GB of memory. The test data source uses a publicly available financial transaction benchmark test set. To objectively represent the disturbance factors in a real industrial environment, the system actively introduces network latency fluctuations following a Gaussian distribution at the data injection interface, setting the latency range to 20ms to 150ms. A unidirectional 500ms clock drift is injected to reproduce the asynchronous interference state in the cross-domain network. When setting the key extraction parameters of the verification module, the memory addressing overhead of hash pointer calculation is balanced with the time granularity of anti-tampering. Based on the maximum amplitude of network latency fluctuation and the cache throughput of the node processor, the system sets the cascading update frequency of the state feature vector. When the transaction concurrency density increases and the system's data processing load approaches the upper limit, in order to avoid cross-node memory addressing blocking, the cascading update frequency of the state feature vector needs to approach the lower limit of the system cache threshold. Based on this parameter decision logic, the baseline transaction injection rate is set to 10000TPS and the cascading depth threshold is set to 1024 layers.

[0036] To quantify and verify the boundary response patterns and synergistic effects of the technical solution, an experiment was conducted with three control groups: the present invention sample group, control group 1, and control group 2. The present invention sample group uses state entanglement hashing logic and a directed acyclic graph topology model to construct an index matrix. Control group 1 removes the memory cascading call steps of the preceding node state feature vectors and uses an independent hash timestamp aggregation method. Control group 2 increases the transaction injection rate to 500,000 TPS, exceeding the set optimal concurrent processing window. The test platform injects heterogeneous business data with out-of-order timestamps into each node at high frequency. When responding to the initial data entity, the present invention sample group extracts feature parameters and cascades them with encrypted positioning anchors, outputting the initial state feature vector according to a preset mapping rule. When responding to subsequent data entities, the system extracts attribute bits and reads the state feature vector of the logical preceding node, according to the formula... Output the one-way encryption transformation result, where, For subsequent data entities, the cascaded association fingerprint is generated at level i, and Hash is a one-way hash function. The hash parameter for the extracted subsequent data entities, This is a cascading symbol. The obtained logical predecessor node is the fixed state feature vector at level i-1.

[0037] After 36 hours of continuous operation, the system's backend monitoring data was extracted. In the initial input stage without state entanglement processing, due to clock drift interference, 12.5% ​​of the underlying financial data timestamps were earlier than the business contract data. The independent aggregation method in control group 1, lacking a causal binding mechanism, verified all the aforementioned inverted data, resulting in a tampering detection rate of 0%. When generating cascaded fingerprints in the sample group of this invention, subsequent nodes rely on the feature vectors of preceding nodes as initialization conditions. However, when faced with randomly injected underlying financial data, it is unable to match compliant business contract preceding vectors. The underlying data storage layer triggered a logic rejection, and the system's record tampering detection rate reached 100.0%. In the data penetration verification stage, the sample group of this invention completed the addressing along the unidirectional pointer chain formed by the cascaded associated fingerprints, and the average verification time was stable at 4.2ms. The control group one relied on the full form traversal, and the average verification time reached 135.6ms. The extreme working condition monitoring data of the control group two showed that when the concurrency rate climbed to 500,000 TPS, the average verification time showed a non-linear surge and reached 45.8ms. The processor utilization rate remained at a saturated state of 98.5%. The excessively high concurrency density caused queue accumulation in the cross-node memory addressing interface. The generation rate of the unidirectional pointer chain lagged behind the data writing rate, resulting in the degradation of system addressing performance and the response that the resource utilization rate exceeded the preset threshold.

[0038] The number of index entries generated is reduced by merging hash parameters of similar business entities. The hash parameter merging operation procedure is as follows: A buffer window with a set time span is opened in the node memory; the physical time threshold of this buffer window is fixed at 50 milliseconds, and the cache hit rate of the central processing unit is monitored at a sampling period of 1 millisecond. When the cache hit rate is lower than the physical warning line of 85% for 5 consecutive sampling periods, it is determined that the resource usage is overloaded and the merging mechanism is triggered; and when intercepting data, the hardware comparator requires that the participant digital identifier bits of multiple concurrent data entities and the business serial number prefix bits reach 100% physical matching; multiple concurrent data entities with the same participant digital identifier are intercepted within the buffer window; the attribute bits of each concurrent data entity are extracted as the bottom leaf nodes, and the hash value is calculated by concatenating adjacent leaf nodes pairwise according to the complete binary tree topology, and the single local aggregate root hash value is recursively output; the local aggregate root hash value is extracted as a whole as a formula. Input hash parameter ; Call the logical preceding node state feature vector The system cascades the two to generate a cascaded associated fingerprint, and converges batch concurrent writes into a single verification load to maintain the connectivity constraint of the underlying directed acyclic graph with a single pointer. The test data above confirms that the encrypted positioning anchor point and state entanglement hash mechanism based on spatiotemporal feature benchmarks have the ability to resist asynchronous injection interference caused by network latency and clock drift. The system extracts the state feature vector of the preceding node and generates a directed acyclic graph topology index, transforming the compliance verification of cross-source data into the underlying physical pointer connectivity test. The performance inflection point data defines the optimal throughput boundary of the system under specific hardware configurations. This dynamic modeling mechanism blocks the temporal logic inversion tampering path and establishes a spatiotemporal consistent addressing architecture for heterogeneous database clusters.

[0039] Example 3: In a distributed heterogeneous data network scenario with dynamic topology changes, the communication latency and business throughput of each participating node exhibit time-varying uncertainty. When the system extracts the business attribute features of the initial data entity and maps and transforms them, if the statically allocated participant weight values ​​are used to nonlinearly perturb the high-dimensional space feature vector, the static weights cannot reflect the real-time physical computing status and network reputation of the nodes at the time of data generation. Malicious nodes hijack low-reputation edge devices, use fixed static weights to generate forged business attribute features, inject false root indexes into the underlying index matrix, and cause the system to fail to verify the cascading association of subsequent data entities. The system calculates the dynamic participant weight values ​​and obtains the average response latency and consensus rate parameters of the current participating node within a preset time window of 1000ms. The system calculates the participant weight values ​​according to the formula W=S / D, where W represents the participant weight value, S represents the consensus success rate parameter, and D represents the average response latency parameter.

[0040] After extracting the business attribute features of the initial data entities, the system converts these features into a high-dimensional feature vector using a preset hash mapping rule. The preset hash mapping rule includes: extracting the business identifier field and the amount field from the business attribute features and concatenating them to form a continuous string; using the SHA-256 one-way hash function to calculate the continuous string and output a 256-bit discrete bit string; dividing the discrete bit string into 32 equal 8-bit blocks, converting each data block into a decimal floating-point value to generate a one-dimensional floating-point array with 32 independent dimensional components, establishing this as a high-dimensional feature vector; obtaining the timestamp parameter contained in the encrypted positioning anchor point; multiplying the timestamp parameter by the participant's weight value to generate a perturbation factor; and then applying the perturbation factor to the data. The dynamic factor, acting as an exponential term, acts on each dimensional component of the high-dimensional space feature vector, outputting a nonlinear perturbation transformation result. After each participating node completes the consensus writing of the initial data entity, the system solidifies the vector result after the nonlinear perturbation transformation into the initial state feature vector, which serves as the logical root node for subsequent data entity cascade computation. This operation establishes the specific computation steps for the nonlinear perturbation transformation of the high-dimensional space feature vector, and the generation of the initial state feature vector is controlled by the real-time dynamic computation state of the node. Through the exponential transformation of the perturbation factor, the system transforms the dynamic behavior characteristics of the node into the initial feature dimension for data modeling, blocks the false root index injection path of static weight guessing, and establishes the feature benchmark for verifying the spatiotemporal consistency of the initial data entity in a dynamic topology environment.

[0041] Example 4: In the initial deployment scenario of heterogeneous data source nodes first connecting to the distributed cluster, the system triggers a node behavior baseline calibration program before granting write permissions for business data. It continuously sends 1000 standard probe data packets to the participating nodes to be connected, records the round-trip time difference of the data packets, and discards boundary values ​​that deviate from the normal distribution. The physical judgment logic for these boundary values ​​is as follows: calculate the arithmetic mean and standard deviation of the 1000 round-trip time differences; identify and forcibly remove time difference sampling points whose absolute values ​​exceed the physical range of the arithmetic mean plus or minus three times the standard deviation through a hardware logic comparison circuit; calculate the arithmetic mean of the remaining time differences and output the basic response delay parameter; construct a test dataset containing logical conflicts and inject it into the participating nodes; calculate the initial consensus success rate parameter by calculating the ratio of the frequency of conflict interception by the participating nodes within a preset verification period of 10000ms to the total frequency; establish the initial participating weight value based on the basic response delay parameter and the initial consensus success rate parameter; and write the initial participating weight value into the identity registry to form the initial physical constraint boundary for the state entanglement hash operation.

[0042] After the participating nodes complete the baseline calibration procedure, obtain the permission to write formal business data, and access the directed acyclic graph topology model, the system switches to the dynamic weight update mode. During the continuous injection of heterogeneous business data, the participating weight values ​​in the identity registry are periodically overwritten based on the real-time collected average response delay parameter and consensus success rate parameter. The high-dimensional spatial feature vector output by the nonlinear perturbation transformation undergoes multi-dimensional deflection as the parameters change. The aforementioned calibration procedure provides the underlying measurement input for encrypted positioning anchor points and cascaded association fingerprint calculation. The topological constraint trajectory of the underlying data storage structure directly reflects the continuously quantized physical calculation state of each participating node.

[0043] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources, characterized in that, Includes the following steps: Step S1: Obtain the spatiotemporal feature benchmark corresponding to the heterogeneous data source. The spatiotemporal feature benchmark includes timing signals, digital identifiers of participating parties, and data metadata. Generate encrypted positioning anchor points based on the spatiotemporal feature benchmark. Step S2: Extract the feature parameters of the initial data entity, and calculate and output the initial state feature vector by hash concatenating the feature parameters with the encrypted positioning anchor point. Step S3: In response to the write request of the subsequent data entity, extract the attribute bits of the subsequent data entity and call the state feature vector solidified by the logical preceding node associated with it. Step S4: The hash parameters of subsequent data entities are used as input payloads. By converting the state feature vector of the logical preceding node into the initialization vector of the hash operation, a one-way encryption transformation is performed to generate a cascaded association fingerprint. Based on the cascaded association fingerprint, the business logic sequence is solidified into the topological constraints of the data storage structure. Step S5: Using the encrypted positioning anchor point as the root index, the cascaded association fingerprints generated at each level are used as the primary keys of the sub-indexes to construct a directed acyclic graph topology model and generate an index matrix with unidirectional association characteristics.

2. The method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, Step S2 includes: extracting the business attribute features of the initial data entity, converting the business attribute features into a high-dimensional space feature vector through a preset mapping rule; using the timestamp parameter contained in the encrypted positioning anchor point and the weight value of the participants, performing a nonlinear perturbation transformation on the high-dimensional space feature vector; after each participant node completes the consensus writing of the initial data entity, solidifying the vector result after the nonlinear perturbation transformation into the initial state feature vector, which serves as the logical root node for subsequent data entity cascade calculations.

3. The method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, Cascaded fingerprints possess asymmetric and unidirectional logical linking characteristics; The state feature vector of the logical predecessor node is obtained through the memory addressing interface and used to participate in the generation calculation of the cascade association fingerprint, so as to generate the fingerprint of the subsequent data entity and the causal binding of the running state of the logical predecessor node. In response to a change in the state feature vector of the logical predecessor node, the generated cascaded association fingerprint is logically broken from the existing index primary key in the index matrix, and a logical rejection of the time-series tampering request is generated at the data storage layer.

4. The method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, Data penetration verification is performed based on the index matrix; during the data integrity verification process, the root index is located according to the encrypted anchor point corresponding to the target business order number, and the address is performed along the unidirectional pointer chain formed by the cascaded associated fingerprints; the associated heterogeneous business data is extracted through the encrypted dependency relationship between the index primary keys.

5. The method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, Cascaded fingerprints follow the calculation rules as follows: ,in, For subsequent data entities, the cascaded association fingerprint is generated at level i, and Hash is a one-way hash function. The hash parameter for the extracted subsequent data entities, This is a cascading symbol. The obtained logical predecessor node is the fixed state feature vector at level i-1.

6. The method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, Heterogeneous data sources include contract data, business flow data, financial data, and invoice data in flexible employment scenarios. After each participant generates heterogeneous data sources in its independent business system, it extracts the feature parameters of the data through a decentralized association module and initiates a modeling request to the distributed ledger node.

7. The method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, The method also includes: monitoring the resource utilization rate of each participating node when writing data, and adjusting the generation frequency of cascaded fingerprints according to the fluctuation trend of resource utilization rate; in response to the resource utilization rate exceeding the preset threshold, reducing the number of index entries generated by merging the hash parameters of similar business entities.

8. The method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, The method also includes: during the verifiable verification process, the verification module acquires the heterogeneous dataset to be tested and recursively reconstructs the causal relationships in the heterogeneous dataset to be tested using the cascaded association fingerprints in the index matrix; in response to the fingerprint sequence generated by the reconstruction being consistent with the fingerprint primary key fixed in the index matrix, it is determined that the heterogeneous dataset to be tested meets the authenticity requirements.

9. The method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, The construction process of the index matrix includes compensation for the spatiotemporal anchor point offset: by comparing the deviation between the local time of the participating node and the timing signal, a clock deviation compensation factor is generated, and the clock deviation compensation factor is used to correct the timestamp characteristics of the encrypted positioning anchor point.

10. A method for spatiotemporal consistency data association modeling and verification for heterogeneous data sources according to claim 1, characterized in that, The index matrix is ​​stored in a decentralized consensus node cluster; the consensus node cluster achieves consensus on data modeling by verifying the validity of the cascaded association fingerprint; no single node in the consensus node cluster can modify the cascaded association fingerprint without affecting the directed acyclic graph topology model.

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