Block chain and privacy computing collaborative verification system

Abstract

The invention relates to the technical field of block chains, and discloses a block chain and privacy computing collaborative verification system, which comprises a data fragmentation module, a privacy fusion module, a collaborative verification module, a consensus correction module and a fragmentation graph optimization module. The data fragmentation module constructs a multi-stage data collaborative topology through a preprocessing node and a dynamic fragmentation protocol; the privacy fusion module realizes privacy protection by using a ciphertext analysis unit to switch links in a multi-state manner and a zero-knowledge verification unit; a collaborative verification module generates a reference verification strategy through a consensus modeling node multi-dimensional fusion model, and a state optimization unit dynamically aggregates and sorts fragmentation states; the consensus correction module corrects the fragmentation state through an isolation verification unit and a delay compensation unit and calibrates ciphertext parameters; and the fragment atlas optimization module constructs a stability evaluation network monitoring synchronization rate and performs closed-loop adjustment on a verification strategy. The system realizes dynamic fragmentation, privacy fusion and intelligent verification, and improves the efficiency, security and stability of block chain data processing.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and specifically to a blockchain and privacy computing collaborative verification system. Background Art

[0002] In the application and development of blockchain technology, with the explosive growth of data scale and the increasing complexity of cross-chain interaction scenarios, traditional blockchain systems face many challenges in data processing efficiency, privacy security and system stability.

[0003] Regarding privacy and security, while the open and transparent nature of blockchain ensures data immutability, it also poses the risk of privacy breaches. In blockchain applications involving sensitive data (such as user identity information and trade secrets), traditional encryption technologies struggle to effectively protect privacy during data processing. For example, during data fusion and verification, traditional methods may require exposing sensitive data in plaintext to multiple nodes for processing, creating the potential for privacy breaches. Furthermore, with the development of privacy-preserving computing technology, how to deeply integrate it with blockchain technology to achieve "available but invisible" data remains a pressing challenge.

[0004] In terms of system stability, traditional blockchain systems lack effective dynamic adjustment and self-repair mechanisms in the face of disruptions such as network fluctuations, node failures, or external attacks. For example, when a shard node fails or becomes overloaded, traditional systems struggle to quickly transfer data to other nodes, resulting in performance degradation and even service interruptions. Furthermore, during cross-chain interactions, differences in consensus mechanisms, data formats, and update frequencies across blockchains can easily lead to data inconsistencies, impacting system stability and reliability.

[0005] While some existing research has explored blockchain sharding and privacy-preserving computing, most approaches lack comprehensive solutions that organically integrate the two and enable collaborative verification. For example, some sharding technologies focus solely on data storage and processing efficiency while neglecting privacy protection. Meanwhile, some privacy-preserving computing solutions fail to fully leverage the distributed nature of blockchain, making efficient collaborative verification difficult in practical applications. Furthermore, existing systems lack flexible policy adjustment mechanisms to address dynamically changing network environments and business needs, hindering dynamic optimization of data sharding status and verification strategies.

[0006] Therefore, there is an urgent need for a system that can combine blockchain with privacy computing to achieve efficient data sharding, privacy protection and collaborative verification, so as to solve the shortcomings of traditional blockchain systems in data processing efficiency, privacy security and system stability, and meet the increasingly complex needs of blockchain application scenarios. Summary of the Invention

[0007] The purpose of the present invention is to provide a blockchain and privacy computing collaborative verification system to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a blockchain and privacy computing collaborative verification system, the system comprising:

[0009] Data sharding module, privacy fusion module and collaborative verification module;

[0010] The data sharding module includes a preprocessing node and a dynamic sharding protocol; the privacy fusion module includes a ciphertext parsing unit and a zero-knowledge verification unit; the collaborative verification module includes a consensus modeling node and a state optimization unit; the preprocessing node is used to collect the original data hash value and cross-chain interaction parameters in real time; according to the block height and timestamp sequence, a multi-level data collaborative topology is constructed through the dynamic sharding protocol; the dynamic sharding protocol is composed of multiple sharding nodes based on hash thresholds in parallel; based on the real-time response characteristics of multiple sharding nodes, a benchmark verification strategy is generated through the consensus modeling node, and combined with the verification signal output by the zero-knowledge verification unit, the data sharding status is dynamically aggregated and sorted through the state optimization unit.

[0011] Preferably, the system further comprises a consensus correction module and a sharding graph optimization module; the consensus correction module comprises an isolation verification unit and a delay compensation unit;

[0012] According to the generated benchmark verification strategy and dynamic aggregation sorting results, the data sharding status is subjected to distributed barrier correction through the isolation verification unit, and the ciphertext parameters of the privacy fusion module are dynamically calibrated through the delay compensation unit; at the same time, the on-chain stability evaluation network constructed by the sharding graph optimization module monitors the sharding synchronization rate in real time, and the monitoring results are fed back to the consensus modeling node and the state optimization unit to perform closed-loop adjustments to the verification strategy.

[0013] Preferably, the isolation verification unit is composed of state control circuits corresponding to parallel sharding nodes in the dynamic sharding protocol; the steps of constructing the multi-level data collaboration topology include:

[0014] Collect the original data hash value and cross-chain fluctuation signal, input it into the transfer learning model configured in each level of sharding node, generate the data consistency compensation coefficient and store it in the corresponding sharding node;

[0015] Based on the consistency compensation coefficient stored in shard nodes at all levels and combined with the instantaneous change rate of the timestamp sequence, the data shard status is distributed to multiple shard nodes through a dynamic hash detection algorithm to form a dynamic collaborative matching topology.

[0016] Preferably, the sharding node construction conditions configured in the pre-processing node include: the original data hash value is within a preset range, the synchronization index corresponding to the sharding node is greater than a threshold, and the block height is less than an allowable deviation range;

[0017] The ciphertext parsing unit is also configured with a polymorphic switching link; the polymorphic switching link includes a steady-state sharding sub-link and a verification avoidance sub-link; the steady-state sharding sub-link is generated based on the matching degree between the on-chain stability signal of the privacy fusion module and the benchmark verification strategy; the verification avoidance sub-link is constructed based on the correlation between the instantaneous verification risk of the data sharding state and the external interference event.

[0018] Preferably, the consensus modeling node is configured with a multi-dimensional fusion model; the multi-dimensional fusion model includes a hash-time mapping table corresponding to the data sharding module and a verification avoidance parameter library of the privacy fusion module; the step of generating the benchmark verification strategy includes:

[0019] Based on the real-time hash data and aggregate sorting results of the data sharding module, the theoretical synchronization range upper limit of each level of sharding nodes in the steady-state sharding sub-link is calculated;

[0020] The upper limit of the theoretical synchronization range and the transient timestamp are input into the multi-dimensional fusion model to generate an on-chain verification reference map, and the verification risk of the reference map is corrected through the verification avoidance sub-link to form a benchmark verification strategy.

[0021] Preferably, the parameter updating step of the multi-dimensional fusion model includes:

[0022] When it is detected that the hash value of the original data exceeds the preset range or the shard synchronization rate exceeds the threshold, the first verification avoidance instruction is triggered, that is, the replacement shard unit is started and the parsing granularity of the privacy fusion module is adjusted;

[0023] If the sharding status has not recovered to the allowed range after the first verification avoidance instruction is executed, the second verification avoidance instruction is triggered, that is, switching to the verification avoidance sub-link through the polymorphic switching link, and redistributing the topological weight of the data sharding status based on the duration of the external interference event.

[0024] Preferably, the parameter updating step of the multi-dimensional fusion model further includes:

[0025] When the synchronization index of any first-level sharding node in the data sharding module is lower than the threshold, the third verification avoidance instruction is triggered, that is, the sharding output channel of the node is blocked and the corresponding data is transferred to other sharding nodes through the isolation verification unit;

[0026] During the execution of the third verification avoidance instruction, if it is detected that other shard nodes are overloaded, the fourth verification avoidance instruction is triggered, that is, the shard graph optimization module is called to downgrade the output strategy of the state optimization unit and limit the synchronization requirements of the edge state.

[0027] Preferably, the degradation processing logic of the sharding graph optimization module includes:

[0028] Based on the synchronization rate data output by the on-chain stability evaluation network and the off-chain ciphertext indicators, a shard status security level table is constructed;

[0029] When the fourth verification avoidance instruction is triggered, the shard path priority is dynamically downgraded according to the security level table, and the parsing frequency of the privacy fusion module is synchronously adjusted to match the downgraded verification strategy.

[0030] Preferably, the step of constructing the shard node further includes:

[0031] Generate a hash gradient adaptability index table based on the spatial distribution of the original data hash value and the historical cross-chain fluctuation range;

[0032] According to the differences in consistency compensation coefficients of each shard node in the index table, the state control circuits of multiple shard nodes are topologically weighted using a dynamic weight distribution algorithm, and redundant transmission links are established between shard nodes.

[0033] Preferably, the process of generating the on-chain verification reference graph includes:

[0034] After inputting the theoretical synchronization range upper limit and transient timestamp into the multi-dimensional fusion model, the preset slice medium database and external interference event layer are loaded simultaneously;

[0035] Based on the encrypted peak frequency data output by the ciphertext parsing unit, the verification threshold of the data fragmentation status in the reference spectrum is corrected, and the time gradient parameter is superimposed in the verification avoidance sub-link for secondary calibration.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] In terms of data processing efficiency, the data sharding module collects raw data hash values ​​and cross-chain interaction parameters in real time through pre-processing nodes, and uses a dynamic sharding protocol to build a multi-level data collaborative topology. This topology is based on the real-time response characteristics of multiple sharding nodes and can dynamically adjust the sharding strategy according to data traffic and cross-chain interaction requirements, achieving efficient data distribution and parallel processing. For example, the data sharding state is distributed to multiple sharding nodes through a dynamic hash detection algorithm, forming a dynamic collaborative matching topology, effectively improving the throughput and real-time performance of data processing and shortening the delay of cross-chain data synchronization. At the same time, the consensus modeling node generates a benchmark verification strategy and combines it with the state optimization unit to dynamically aggregate and sort the data sharding state, further optimizing the data processing process and ensuring efficient collaborative processing of data between different sharding nodes.

[0038] In terms of privacy protection, the ciphertext parsing unit of the privacy fusion module is equipped with a polymorphic switching link, including a steady-state sharding sub-link and a verification avoidance sub-link. The steady-state sharding sub-link is generated based on the matching degree between the on-chain stability signal of the privacy fusion module and the baseline verification strategy, and can achieve stable parsing and processing of ciphertext data under normal circumstances. The verification avoidance sub-link is constructed based on the correlation between the instantaneous verification risk of the data sharding state and external interference events. When a verification risk is detected, it can switch to this sub-link in a timely manner, adjust the parsing strategy, avoid the plaintext exposure of sensitive data, and effectively protect data privacy. In addition, the application of the zero-knowledge verification unit enables the verification process to be completed without revealing the specific content of the data, further enhancing privacy protection capabilities.

[0039] In terms of system stability and reliability, the consensus correction module's isolated verification unit, comprised of state control circuits corresponding to parallel shard nodes in the dynamic sharding protocol, performs distributed barrier corrections on data shard states and promptly handles node failures or anomalies. The delay compensation unit dynamically calibrates the ciphertext parameters of the privacy fusion module, ensuring their accuracy and consistency and mitigating system instability caused by parameter deviations. The on-chain stability assessment network constructed by the sharding graph optimization module monitors shard synchronization rates in real time and feeds the results back to the consensus modeling nodes and state optimization unit, enabling closed-loop adjustments to verification strategies. This allows the system to dynamically optimize strategies based on real-time monitoring data to adapt to changing network environments and business needs. In the event of node overload or failure, the system triggers verification avoidance instructions, such as blocking the faulty node's output channel, transferring data to other nodes, or invoking the sharding graph optimization module for downgrade processing. This ensures continued stable operation and improves the system's fault tolerance and reliability.

[0040] The multi-dimensional fusion model combines the hash-time mapping table of the data sharding module and the verification avoidance parameter library of the privacy fusion module. It can generate an on-chain verification reference map based on real-time data and perform verification risk correction through the verification avoidance sub-link to form a precise baseline verification strategy. At the same time, the model's parameter update mechanism can trigger corresponding verification avoidance instructions based on different abnormal situations, dynamically adjust the sharding strategy and privacy protection parameters, and enable the system to quickly adapt to changes in the original data hash value and abnormal shard synchronization rates. This achieves dynamic optimization and adaptive adjustment of system strategies, improving the system's operating efficiency and stability in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a diagram showing the working principle of the blockchain and privacy computing collaborative verification system described in the present invention;

[0042] Figure 2 This is the design diagram of the data sharding module structure;

[0043] Figure 3 This is a design diagram of the privacy fusion module.

[0044] Figure 4 This is the workflow diagram of the collaborative verification module;

[0045] Figure 5 A design diagram of the consensus revision module's operating mechanism;

[0046] Figure 6 Design diagram for a multi-level data collaboration topology. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] See also Figures 1-6 The present invention relates to a blockchain and privacy computing collaborative verification system, which includes:

[0049] Data sharding module, privacy fusion module, and collaborative verification module. The data sharding module consists of a preprocessing node and a dynamic sharding protocol; the privacy fusion module includes a ciphertext parsing unit and a zero-knowledge verification unit; and the collaborative verification module includes a consensus modeling node and a state optimization unit. Each module implements the system's collaborative verification function through the following methods:

[0050] The pre-processing node collects the hash value of the original data and cross-chain interaction parameters in real time, and builds a multi-level data collaborative topology based on the block height and timestamp sequence through the dynamic sharding protocol. The dynamic sharding protocol consists of multiple sharding nodes in parallel based on hash thresholds. Each sharding node performs data sharding tasks according to the interval division of the original data hash value. Specifically, the pre-processing node first performs a hash calculation on the original data to generate a unique hash value. At the same time, it collects parameters in the cross-chain interaction process such as cross-chain transaction amount, transaction timestamp, etc. Subsequently, based on the block height of the current blockchain (i.e., the length of the current blockchain) and the continuity of the timestamp sequence, the dynamic sharding protocol is activated, and the parallel sharding nodes are divided into different levels according to the hash threshold. For example, nodes with the same first 16 bits of the hash value are divided into the same level, forming the basic architecture of the multi-level data collaborative topology.

[0051] Based on the real-time response characteristics of multiple sharding nodes, the consensus modeling node generates a baseline verification strategy. The specific process is as follows: after receiving a data sharding task, each sharding node provides real-time feedback on its processing status (such as processing speed, resource utilization, etc.). The consensus modeling node collects this response data and analyzes the reliability and efficiency of each node through a preset algorithm (such as a neural network algorithm or fuzzy logic algorithm). It then generates a baseline verification strategy for the current data sharding task, such as determining the verification order and verification weight of each node.

[0052] At the same time, the zero-knowledge verification unit verifies the data sharding process and outputs a verification signal. Using zero-knowledge proof technology, the zero-knowledge verification unit verifies the correctness and integrity of the data sharding without revealing the specific data content. For example, by constructing a verification circuit, it verifies whether the data sharding complies with pre-set logical rules, such as whether the hash value range is correct and whether the sharded data can be fully restored. The state optimization unit dynamically aggregates and sorts the data sharding status based on the baseline verification strategy and the verification signal. Based on the node weights and verification order set in the baseline verification strategy and the verification results (e.g., pass or fail) output by the zero-knowledge verification unit, the state optimization unit adjusts the data sharding status of each sharding node in real time. For example, nodes that pass verification and have high processing efficiency are given higher aggregation priority, while nodes that fail verification or have low processing efficiency are re-sharded or marked as abnormal, ensuring that the data sharding status is always optimal.

[0053] Example 1:

[0054] The consensus correction module and sharding graph optimization module set up by the system work together. The consensus correction module includes the isolation verification unit and the delay compensation unit. The specific implementation is as follows:

[0055] After generating the baseline verification strategy and completing the dynamic aggregation and sorting of the data shard states, the Isolation Verification Unit initiates the distributed barrier correction mechanism. The Isolation Verification Unit is composed of state control circuits corresponding to each shard node in the dynamic sharding protocol, connected in parallel. Each shard node's state control circuit is independently configured, providing real-time monitoring and independent correction capabilities. When the data sharding status of a sharding node is abnormal (such as the original data hash value exceeds the preset range, the synchronization index is lower than the threshold, or the block height exceeds the allowable deviation range), the state control circuit of the node performs corrections through the following steps: First, the current data sharding task of the abnormal node is isolated and its data interaction channel with other nodes is suspended to prevent the spread of the abnormal status; second, the local verification process is initiated to recalculate the original data hash value of the node, verify the integrity of the cross-chain interaction parameters (such as whether the cross-chain transaction amount and timestamp match the blockchain record), and compare the consistency of the block height and timestamp sequence; if a logical error is found in the data sharding (such as an incorrect hash value calculation resulting in an incomplete sharding), the local data reconstruction program is triggered to regenerate the correct sharding data based on the original data hash value and cross-chain interaction parameters; if the verification result shows that the node hardware resources are insufficient (such as CPU utilization continuously exceeding 90% causing processing delays), the state control circuit sends a resource scheduling request to the system management module to dynamically allocate additional computing resources or migrate some tasks to other idle nodes. During the entire distributed barrier correction process, the state control circuits of each shard node work in parallel without interfering with each other, ensuring the efficiency and independence of anomaly correction.

[0056] The delay compensation unit dynamically calibrates the ciphertext parameters of the privacy fusion module. When the privacy fusion module processes ciphertext data, ciphertext parameters (such as the key version of the encryption algorithm, the ciphertext length of the data sharding, and the circuit parameters of zero-knowledge verification) may deviate due to network transmission delays, multi-node consensus delays, or cross-chain interaction delays. The delay compensation unit achieves calibration through the following mechanism: First, timestamp monitoring points are deployed along the ciphertext parameter transmission path to collect real-time data on the transmission time of the parameters from the ciphertext parsing unit of the privacy fusion module to the consensus modeling node of the collaborative verification module. The difference between the actual transmission time and the preset time threshold (i.e., the delay difference) is calculated. Then, based on the type of delay difference (e.g., network delay-dominated or computational delay-dominated), the corresponding compensation strategy is invoked. If network delay is the cause, the output buffer queue length of the ciphertext parsing unit is adjusted, increasing the parameter cache time at the sender to balance network fluctuations. If computational delay is the cause, the verification circuit complexity of the zero-knowledge verification unit is dynamically adjusted, for example, by simplifying some non-critical verification steps to reduce computational time. In addition, the delay compensation unit also monitors special delays in cross-chain interaction scenarios. When a ciphertext parameter delay caused by a cross-chain transaction is detected, the delay compensation unit analyzes the block confirmation time of the cross-chain transaction to predict the delay trend of the subsequent ciphertext parameter and adjust the parsing rhythm of the privacy fusion module in advance. For example, during peak cross-chain transaction periods, the number of parallel processing threads of the ciphertext parsing unit is automatically reduced to avoid delays exacerbated by thread competition.

[0057] The on-chain stability assessment network, built by the sharding graph optimization module, monitors shard synchronization rates in real time through multi-dimensional data collection and analysis. The on-chain stability assessment network's architecture comprises a data collection layer, a feature extraction layer, and a status assessment layer. The data collection layer captures synchronization logs from each shard node in real time, including raw data such as synchronization start and end times, the amount of data successfully synchronized, and the number of synchronization failures. The feature extraction layer preprocesses this raw data and calculates key characteristic metrics, such as the number of synchronizations per unit time (synchronization frequency), synchronization success rate (number of successful synchronizations / total number of synchronizations), and average synchronization time (total synchronization time / number of synchronizations). The status assessment layer dynamically analyzes these characteristic metrics based on a pre-defined evaluation model (such as a sliding window model or exponential smoothing model) to generate a synchronization rate stability score for each shard node. For example, a sliding window is used to measure the fluctuation amplitude of the synchronization frequency over the past 10 minutes. If the fluctuation amplitude exceeds a preset threshold, the node's synchronization rate is deemed unstable.

[0058] The feedback and closed-loop adjustment mechanism for monitoring results is as follows: The on-chain stability assessment network transmits each shard node's synchronization rate stability score, characteristic indicators, and abnormal events (such as a surge in synchronization failures) in real time to the consensus modeling node and the state optimization unit. Upon receiving this feedback, the consensus modeling node first updates the parameter library for the baseline verification strategy. For example, if a shard node's synchronization success rate consistently falls below 80%, the node's verification weight in the baseline verification strategy is lowered, reducing the proportion of verification tasks it undertakes. If the synchronization frequency of multiple nodes fluctuates periodically within a specific time period (e.g., a significant drop in synchronization frequency at 9:00 AM each day), the baseline verification strategy's scheduling rules are adjusted to automatically allocate more tasks to stable synchronization nodes during that time period. Based on the feedback from the synchronization rate data, the state optimization unit makes real-time adjustments to the dynamic aggregation ranking of data shard states: For nodes with stable synchronization rates, their priority in the aggregation ranking is increased, ensuring that their processed data enters the subsequent verification process first. For nodes with large synchronization rate fluctuations, their data shard status is marked as "under observation," extending their waiting time in the aggregation queue until the synchronization rate stabilizes. In addition, the closed-loop adjustment process also involves cross-module collaboration: when the consensus modeling node adjusts the baseline verification strategy, it will send a policy update signal to the zero-knowledge verification unit of the privacy fusion module. The zero-knowledge verification unit adjusts the resource allocation of the verification circuit according to the new verification weight and order. For example, it allocates more verification computing power to the data shards of high-weight nodes to ensure that the verification efficiency matches the strategy.

[0059] During implementation, the isolated verification unit, delay compensation unit, and sharding graph optimization module form a linkage mechanism. For example, after the isolated verification unit completes the correction of an abnormal node and restores its normal state, it will send a node status reset signal to the on-chain stability assessment network, which will re-collect the synchronization data of the node and update its stability score. If the delay compensation unit finds that the ciphertext parameter delay exceeds the system's compensable range during the dynamic calibration process, it will trigger the emergency monitoring process of the sharding graph optimization module and temporarily increase the monitoring frequency of the synchronization rate of the relevant nodes to promptly detect potential systemic risks. Through the above mechanism, the entire system realizes the dynamic correction of data sharding status, delay compensation of ciphertext parameters, and closed-loop optimization of sharding synchronization rate, ensuring the stability, accuracy, and efficiency of the collaborative verification process of blockchain and privacy computing.

[0060] Example 2:

[0061] The construction process of a multi-level data collaboration topology involves several key steps and technical implementations. The specific implementation methods are as follows:

[0062] The system collects raw data hash values ​​and cross-chain volatility signals. Raw data hash values ​​are generated by hashing various input data (such as transaction records, asset information, and user behavior data), ensuring data uniqueness and immutability. Cross-chain volatility signals are collected based on interactions between different blockchains, including parameters such as the frequency of cross-chain transactions, fluctuations in transaction amounts, and transaction intervals. These parameters are collected by deploying monitoring probes on each blockchain node. These probes capture cross-chain transaction data in real time and transmit it to pre-processing nodes for centralized processing.

[0063] The collected data is fed into a transfer learning model deployed at each sharding node level. This transfer learning model is pre-trained based on historical data and possesses the ability to predict data consistency. The model training process utilizes a large amount of historical data, including raw data hash values, corresponding cross-chain volatility signals, and the final data consistency results. By learning from this historical data, the model can identify potential correlation patterns between data hash values ​​and cross-chain volatility signals and generate data consistency compensation coefficients based on these patterns. For example, when the model detects that a certain type of hash value distribution is associated with a specific cross-chain transaction frequency pattern, it generates a corresponding compensation coefficient based on historical experience to adjust the processing strategy for subsequent data shards.

[0064] The generated compensation coefficients are stored in the corresponding shard nodes. Each shard node maintains a local compensation coefficient table, which is continuously updated based on real-time input data. Compensation coefficients are stored using a distributed storage mechanism to ensure data security and accessibility. Furthermore, to improve system responsiveness, the compensation coefficient table is regularly synchronized between shard nodes to ensure data consistency across all nodes.

[0065] Based on the consistency compensation coefficients stored by each shard node and the instantaneous rate of change of the timestamp sequence, a dynamic hashing algorithm distributes data shard status to multiple shard nodes. The instantaneous rate of change of the timestamp sequence reflects the real-time speed of data generation and transmission and is an important indicator of system dynamics. The dynamic hashing algorithm analyzes this rate of change and the consistency compensation coefficient to adjust the data sharding allocation strategy in real time.

[0066] The algorithm's workflow is as follows: First, the current timestamp sequence is analyzed and its instantaneous rate of change is calculated. This rate of change is calculated based on a sliding time window mechanism, comparing timestamp differences within different time windows to determine the rate of change in data generation and transmission. Next, the algorithm evaluates each shard node's processing capacity for the current data shard, combining the consistency compensation coefficients stored by shard nodes at all levels. Nodes with higher consistency compensation coefficients are considered to have greater data processing stability and accuracy, and therefore receive higher priority in allocation.

[0067] Based on the evaluation results, the dynamic hash detection algorithm distributes the data sharding state to multiple sharding nodes, forming a dynamic coordinated matching topology. During this distribution process, the algorithm considers multiple factors, such as node load, processing power, and network latency, to ensure balanced distribution and efficient processing of data shards. For example, nodes with lighter loads and higher processing power will be assigned more data sharding tasks; nodes with higher network latency will have fewer data sharding tasks assigned to them, which require more real-time performance.

[0068] The resulting dynamic, collaborative matching topology is adaptive and scalable. When the system load changes or new nodes are added, the topology automatically adjusts to the new environment. This dynamic adjustment mechanism is achieved by regularly collecting status information from each shard node (such as load, processing speed, and network connection quality) and recalculating the data shard allocation strategy based on this information.

[0069] The implementation process also involves real-time monitoring and adjustment of data sharding status. The system continuously monitors the data processing status of each sharding node, including data reception, processing, and transmission. If a node experiences processing delays or errors, the system immediately takes corrective action, such as reallocating data sharding tasks to that node or increasing the processing load on other nodes.

[0070] Furthermore, to ensure data security and privacy, various encryption technologies are employed during data sharding and transmission. Original data is encrypted before sharding to generate ciphertext data. During data transmission, secure communication protocols are employed to ensure data integrity and confidentiality. Furthermore, strict privacy protection rules are adhered to when processing data on each sharding node to prevent user data from being leaked.

[0071] The multi-level data collaboration topology is built with fault tolerance and recovery mechanisms in mind. When a sharding node fails or becomes unavailable, the system automatically identifies it and transfers the node's data sharding tasks to other functioning nodes. The system also records the status of the failed node for subsequent troubleshooting and repair.

[0072] The entire multi-level data collaboration topology construction process is a dynamic, continuously optimized one. By continuously collecting and analyzing system operation data and adjusting the parameters of the transfer learning model and the strategy of the dynamic hash detection algorithm, the system can continuously improve the efficiency and accuracy of data processing and adapt to changing business needs and network environments.

[0073] In practical applications, the construction of a multi-level data collaboration topology can effectively improve the performance and reliability of the blockchain and privacy computing collaborative verification system. By rationally allocating data sharding tasks and fully utilizing the processing power of each node, the system achieves efficient data processing and verification. Furthermore, dynamic adjustment mechanisms and fault-tolerant recovery mechanisms ensure that the system maintains stable operation in the face of various abnormal situations, providing users with reliable services.

[0074] Example 3:

[0075] The sharding nodes configured by the preprocessing nodes must meet strict construction conditions to ensure the rationality of data sharding and system stability. Specific conditions include: the hash value of the original data is within the preset range, the synchronization index corresponding to the sharding node is greater than the threshold, and the block height is less than the allowable deviation range. The hash value of the original data is generated by the hash function H(data), and the preset range is pre-set by the system security policy and business requirements, for example, [H min ,H max ], only when H min ≤H(data)≤H max The synchronization index is used to measure the data synchronization ability of a shard node with other nodes. It is calculated based on the timeliness and integrity of data transmission between nodes. The formula is:

[0076]

[0077] Among them, S is the synchronization index, T sync T is the total time it takes for a node to successfully complete synchronization. total is the total synchronization time (including retry time), C comp is the synchronization data integrity coefficient (the value range is 0-1, 1 means completely complete). thresh (S thresh The node is allowed to participate in sharding only when the block height is the preset threshold. The allowable deviation range of the block height is determined by the consensus mechanism of the blockchain network. Suppose the current main chain block height is H main , the allowed deviation is ΔH, then the block height of the shard node is H node Need to meet H node ≤H main +ΔH to avoid data inconsistency caused by node lag.

[0078] The polymorphic switching link configured by the ciphertext parsing unit includes a stable sharding sub-link and a verification circumvention sub-link, both of which are generated based on different mechanisms and work together. The generation of the stable sharding sub-link depends on the matching degree between the on-chain stability signal of the privacy fusion module and the baseline verification strategy. The on-chain stability signal is generated by monitoring indicators such as transaction throughput and node online rate of the blockchain network. Let the stability index be σ, and the matching degree of the baseline verification strategy is calculated using cosine similarity:

[0079]

[0080] Among them, A is the on-chain stability indicator vector, and B is the parameter vector of the benchmark verification strategy. thresh (Sim thresh When the preset matching degree threshold is reached, the steady-state sharding sub-link is triggered. This link adopts a fixed parsing rule and verification order. For example, the ciphertext data is parsed in the order of the hash values ​​of the sharding nodes, and the data shards of the high-stability nodes are verified first.

[0081] The construction of the verification avoidance sub-link is based on the correlation between the instantaneous verification risk of the data sharding state and the external interference event. The instantaneous verification risk is determined by analyzing the hash value fluctuations of the data sharding, the abnormal ciphertext length and other indicators. Let the risk indicator be R, and the external interference event is generated by real-time monitoring of network attack logs, node failure reports, etc. Event vector E is generated. The system uses association rule algorithms (such as Apriori algorithm) to mine the association pattern between R and E. For example, when it is detected that R exceeds the threshold R thresh If a "network delay surge" event is present in the event vector, a verification risk is determined and the verification avoidance sub-link is triggered. This sub-link uses a dynamic adjustment strategy, including:

[0082] Verification rule enhancement: Increase the number of zero-knowledge verification rounds, for example, expand the default 1-round verification to 3 rounds, and use different verification circuit parameters for each round to improve the verification of the correctness of data sharding.

[0083] Enhanced encryption level: Ciphertext data is encrypted twice, a more complex encryption algorithm is used (such as switching from AES-128 to AES-256), and temporary keys are dynamically generated to reduce the risk of privacy leakage caused by external attacks.

[0084] Data shard rerouting: High-risk data shards are temporarily transferred to a dedicated verification node group for processing. This node group deploys independent computing resources and network channels, isolated from the main link to prevent interference spread.

[0085] During implementation, the pre-processing node dynamically screens eligible sharding nodes by monitoring the raw data hash value, synchronization index, and block height in real time. For example, when a node's raw data hash value exceeds a preset range, the node is immediately marked as "temporarily inactive" and refuses to accept new data sharding tasks until the hash value returns to normal. For nodes whose synchronization index falls below the threshold, the system automatically triggers a synchronization optimization process, including reconfiguring the node's network connection parameters and adjusting the data caching strategy. If the synchronization index still does not meet the standard after optimization, the node is removed from the sharding node list.

[0086] The polymorphic switching links of the ciphertext parsing unit achieve seamless switching through a state machine mechanism. The system maintains a link state register that records the currently activated link type (steady state or verification avoidance) and the switching conditions. When the matching degree of the steady-state sharding sub-link falls below the threshold, the state machine triggers the switching logic: first, the current ciphertext parsing task is paused, and the unfinished parsing state is saved to the cache; then the parameters of the verification avoidance sub-link (such as encryption algorithm and verification rules) are initialized; finally, the cached parsing state is imported into the new link to continue processing the ciphertext data. The entire switching process is completed in nanoseconds, ensuring the continuity of data processing.

[0087] In addition, the system also has a link state fallback mechanism. When the verification avoidance sub-link is processed or the external interference event is resolved, the system automatically detects the match between the on-chain stability signal and the baseline verification strategy. If the match rises above the threshold, the fallback logic is triggered, switching back to the stable sharding sub-link and performing a second verification on the data processed during the verification avoidance sub-link to ensure data consistency.

[0088] In cross-chain interaction scenarios, pre-processing nodes must additionally address the impact of cross-chain fluctuations on shard node construction conditions. For example, if a cross-chain transaction causes a shard node's block height to momentarily exceed the permitted deviation range, the system immediately initiates block synchronization, pulling the missing block data from neighboring nodes to quickly restore the node's block height to within the permitted range. Simultaneously, the node's synchronization index calculation method is dynamically adjusted, temporarily reducing the synchronization time weight during cross-chain transactions to avoid misjudgments of the synchronization index due to cross-chain delays.

[0089] When processing cross-chain ciphertext data, the ciphertext parsing unit adopts different parsing strategies based on the type of polymorphic switching link. In the stable sharding sub-link, a standardized parsing process is used for cross-chain ciphertext data. This process extracts information such as transaction amounts and timestamps according to a preset field order and compares them with blockchain records. In the verification avoidance sub-link, a source verification step is added to cross-chain ciphertext data. By parsing the traceability hash of cross-chain transactions, the data is verified to be from a trusted blockchain network, preventing forged cross-chain data from entering the verification process.

[0090] Throughout the implementation process, preprocessing nodes and ciphertext parsing units ensured the compliance of sharding nodes and the flexibility of ciphertext parsing through real-time data interaction and dynamic policy adjustments. Through quantified construction conditions and switching rules, the system avoids the randomness of human intervention, achieving automated and intelligent data sharding and ciphertext processing, providing a solid foundation for the collaborative verification of blockchain and privacy-preserving computing.

[0091] Example 4:

[0092] The multi-dimensional fusion model configured in the consensus modeling node integrates the hash-time mapping table of the data sharding module and the verification avoidance parameter library of the privacy fusion module to generate baseline verification strategies. For example, in a supply chain finance scenario, when the system processes blockchain transactions containing supplier accounts receivable data, the raw data is hashed to generate a unique identifier. The hash-time mapping table records timestamp information such as the transaction initiation time and block confirmation time corresponding to each hash value. The verification avoidance parameter library stores processing rules for different risk scenarios, such as automatically triggering verification process adjustments when abnormal hash value fluctuations are detected.

[0093] The baseline verification strategy is first generated based on the real-time hash data and aggregate ranking results from the data sharding module. Assume that the current system receives 100,000 accounts receivable data items. The preprocessing node distributes this data to 10 parallel sharding nodes using a dynamic sharding protocol, with each node processing 10,000 data items. Real-time hash data shows that the hash value distribution of node A is concentrated in the interval [0x1a2b3c, 0x1a2b4c], while the hash value distribution of node B is more dispersed. The aggregate ranking results prioritize nodes A through E based on their processing efficiency (e.g., data processed per second) and verification pass rate, and assign low priority to nodes F through J.

[0094] Based on this information, the system calculates the theoretical synchronization limit for each shard node in the steady-state shard sublink. For example, high-priority node A successfully processed 5,000 data items in the past five minutes, an average of approximately 17 items per second. To account for system resource fluctuations, the theoretical synchronization limit is set at 20 items per second. Similarly, node B, due to its lower processing efficiency, has a theoretical synchronization limit of 10 items per second. The theoretical synchronization limits for each node form a dynamic threshold list for subsequent policy generation.

[0095] The theoretical synchronization range upper limit and the instantaneous timestamp are input into the multi-dimensional fusion model. Assuming the current instantaneous timestamp is 14:30:00 on May 22, 2025, the model, combined with the hash-time mapping table, finds that transactions corresponding to the hash values ​​processed by Node A are mostly initiated in the morning. Since it is currently afternoon, it is speculated that data traffic may be reduced. Therefore, when generating the on-chain verification reference graph, the expected synchronization pressure on Node A is appropriately reduced. At the same time, the "higher network latency in the afternoon" rule recorded in the verification avoidance parameter library is triggered, and the model automatically adjusts the reference graph to allocate more buffer time windows for Nodes A and B, etc.

[0096] The generated on-chain verification reference map visually presents the synchronization range, verification priority, and risk warnings for each shard node. For example, the map area for node A shows that its theoretical synchronization range is 0-20 messages per second, with a current load of 15 messages per second, placing it in the green safety zone. Node B's theoretical synchronization range is 0-10 messages per second, with a current load of 8 messages per second, approaching the yellow warning zone. The reference map also identifies potential risk points, such as node C's hash value distribution matching historical cross-chain fluctuation data, indicating a potential risk of cross-chain interaction delays.

[0097] The verification risk of the reference graph is corrected through the verification avoidance sub-link. Assume that the system detects that the hash value fluctuations of node C are associated with the "cross-chain gateway temporary failure" event in the external interference event layer, triggering the verification avoidance sub-link. This sub-link first performs a secondary verification of the data shard status of node C, adding a zero-knowledge verification step, such as regenerating the verification circuit to verify that the data shard fully reflects the original accounts receivable information. At the same time, the verification priority of node C is adjusted, temporarily moving it from the high-priority queue to the low-priority queue until the cross-chain gateway failure is resolved.

[0098] In another scenario, if the system processes medical data privacy computing tasks, the original data contains hash values ​​of patient medical records, and the hash-time mapping table records the creation time and upload time of each record. When the real-time hash data of a shard node shows that a large number of hash values ​​correspond to recently created medical records (the transient timestamp is 16:00:00 on May 22, 2025), the theoretical upper limit of the synchronization range is set to 50 per second based on the efficiency of the node in processing historical data (due to the high encryption complexity of medical data, the processing speed is slow). The multi-dimensional fusion model is combined with the "sensitive data requires enhanced verification" rule in the verification avoidance parameter library, and a "double verification" mark is added to the data shard of the node in the on-chain verification reference map, requiring the zero-knowledge verification unit to perform two independent verifications on each piece of data.

[0099] The verification avoidance sub-link in this scenario is as follows: When a medical record's hash value matches a pre-defined sensitive data hash signature (e.g., containing a specific medical code segment), the verification avoidance process is automatically triggered, routing the data shard to a dedicated privacy-preserving node for processing. This node deploys encryption algorithms and verification mechanisms that comply with medical data security standards. Simultaneously, changes to the processing path of the data shard are annotated in real time in the reference graph to ensure traceability of the verification process.

[0100] After generating the baseline verification strategy, the consensus modeling node continuously monitors the execution status of each shard node. For example, when node D is processing e-commerce transaction data, the theoretical synchronization range is capped at 500 items per second, but the actual load suddenly increases to 600 items per second, exceeding the limit. The system captures this anomaly in real time through the on-chain stability assessment network. The multi-dimensional fusion model automatically adjusts the baseline verification strategy, dynamically migrating some of node D's tasks to node E, which has a lower load. At the same time, the status of node D is updated to "overload processing" in the reference map, and the state optimization unit is prompted to prioritize aggregating node E's data shards.

[0101] The verification avoidance sublink plays a role in the task migration process: To ensure the integrity of the migrated data, the sublink initiates a data verification process, comparing the hash values ​​of each data shard migrated from node D to node E to verify the consistency of the data before and after migration. If the hash value of a data shard is found to have changed during the migration process (possibly due to a network transmission error), the retransmission mechanism is triggered, and the data shard is retrieved from the original node and marked as "high risk", increasing the verification frequency in subsequent verification.

[0102] In the cross-quarterly financial data verification scenario, the system processes a large amount of historical transaction data. The hash-time mapping table shows a clear quarterly distribution of data hash values. The consensus modeling node divides the processing tasks of the shard nodes according to the quarterly time interval. For example, data from Q1 2024 is assigned to node F, and data from Q2 2024 is assigned to node G. The theoretical upper limit of the synchronization range is dynamically adjusted based on the data volume of each quarter: Q1 has a smaller data volume, so the upper limit for node F is set at 100 items per second; Q2 has a larger data volume, so the upper limit for node G is set at 300 items per second.

[0103] The multi-dimensional fusion model, combined with the "historical data requires regular archiving and verification" rule in the verification avoidance parameter library, adds an "archive verification" tag to the Q1 and Q2 data shards in the on-chain verification reference graph. This requires the ciphertext parsing unit of the privacy fusion module to prioritize the use of historically archived encryption key versions during parsing to ensure decryption correctness. In this scenario, the verification avoidance sub-link primarily addresses verification risks arising from data archiving. For example, if the encryption key for a piece of historical data cannot be decrypted due to an outdated version, the key rollback mechanism is automatically triggered, retrieving the corresponding key version from the blockchain's historical blocks and performing verification after decryption is complete.

[0104] Throughout the implementation process, a multi-dimensional fusion model dynamically generates and optimizes baseline verification strategies by combining real-time data with historical rules. The verification avoidance sub-link provides a flexible correction mechanism for potential risks in different scenarios, ensuring the accuracy and security of data shard verification. By integrating hash-time mapping tables, a verification avoidance parameter library, and real-time monitoring data, the system forms an intelligent decision-making system covering the entire data processing process, effectively improving the efficiency and reliability of blockchain and privacy-preserving computing collaborative verification.

[0105] Example 5:

[0106] The parameter update mechanism of the multi-dimensional fusion model triggers corresponding verification avoidance instructions in different abnormal scenarios. This is explained using a logistics traceability system as an example:

[0107] When the system detects that the hash value of the original data of a batch of cargo traceability data exceeds the preset range (for example, the hash value prefix should be "0x4a" but "0x4b" appears), or the shard synchronization rate exceeds the threshold (for example, a node synchronizes 500 times per second, far exceeding the system average of 200 times per second), the first verification avoidance instruction is triggered. At this time, the system automatically starts the replacement sharding unit, which pre-stores several backup sharding nodes. For example, two backup nodes with lower loads (node ​​A and node B) are selected from 10 main nodes to take over the data sharding task of the abnormal node. At the same time, the parsing granularity of the privacy fusion module is adjusted from the default "block level" to "transaction level", that is, a more refined ciphertext parsing is performed on each transaction data, such as splitting the ciphertext data originally aggregated by block into a single transaction ciphertext, and verifying the integrity of the cargo traceability information one by one (such as logistics order number, storage node, transportation timestamp, etc.).

[0108] If the sharding state has not recovered to the allowed range after the execution of the first verification avoidance instruction (such as the hash value anomaly rate of the backup node A is still 30%), the second verification avoidance instruction will be triggered. The system switches from the steady-state sharding sub-link to the verification avoidance sub-link through the polymorphic switching link, and redistributes the topological weight of the data sharding state based on the duration of the external interference event (such as the duration of the network attack). For example, if a DDoS attack lasting 2 hours is detected, causing multiple nodes to synchronize abnormally, the topological weight distribution will tilt towards the unattacked nodes C and D, increasing their weight from the default 1.0 to 1.5, while the weight of the attacked node will be reduced to 0.5. At the same time, the verification avoidance sub-link starts the "attack mode" and adds a blockchain immutable verification step to all data shards, that is, by comparing the data hash value with the blockchain evidence hash value to ensure that the data has not been tampered with.

[0109] When the synchronization index of any level sharding node in the data sharding module is lower than the threshold (such as the synchronization index of node E drops from 0.9 to 0.6, which is lower than the threshold of 0.7), the third verification avoidance instruction is triggered. The system immediately blocks the sharding output channel of node E, prohibiting it from transmitting data to the collaborative verification module, and transfers the corresponding data to other sharding nodes (such as node F and node G) through the isolation verification unit. During the transfer process, the isolation verification unit verifies the data packet by packet, such as comparing the hash value of the original data with the hash value of the transferred data to ensure consistency. Assuming that node E is responsible for processing the warehouse data of a certain area, when it is transferred to node F (processing data in an adjacent area), the system automatically adjusts the parsing parameters of node F to make it compatible with the data format differences between the two areas.

[0110] During the execution of the third verification circumvention instruction, if node F and node G are detected to be overloaded (such as CPU utilization exceeding 85%), the fourth verification circumvention instruction is triggered. The sharding graph optimization module calls the pre-built sharding state security level table, which divides nodes into "high priority" (node ​​G), "medium priority" (node ​​F), and "low priority" (node ​​E) based on the on-chain synchronization rate (such as the synchronization rate of node F dropped from a stable 100 times / second to 50 times / second) and the off-chain ciphertext indicators (such as the ciphertext parsing delay increased from 20ms to 50ms). The system dynamically downgrades the sharding path priority according to the security level table, giving priority to the data of high-priority node G, temporarily suspending the processing of data of medium-priority node F, and limiting the edge state synchronization requirements of low-priority node E (such as synchronizing only key logistics node change data, rather than full transportation trajectory data). At the same time, the parsing frequency of the privacy fusion module is adjusted from real-time parsing to batch parsing, for example, low-priority data is parsed once every 10 minutes to match the downgraded verification strategy.

[0111] The shard node construction process also includes generating a hash gradient adaptive index table. Taking cross-border e-commerce data as an example, the spatial distribution of raw data hash values ​​shows that hash values ​​for European order data are concentrated in the "0xeu" prefix range, while those for the Americas are concentrated in the "0.us" prefix range. Historical cross-chain fluctuations indicate that interactions between the European and American chains are more frequent between 9:00 AM and 11:00 AM on weekdays. Based on this information, the system generates a hash gradient adaptive index table, classifying nodes in the "0xeu" range as the European shard group and nodes in the "0.us" range as the American shard group. The index table also records the differences in consistency compensation coefficients between the groups (e.g., the European group has a higher compensation coefficient due to frequent cross-chain interactions). Using a dynamic weight allocation algorithm, nodes in the European shard group are assigned a higher topological weight (e.g., a weight of 1.2). Redundant transmission links are established between nodes in the European and American groups, for example, by deploying dedicated lines through a cross-chain gateway. This ensures that data can be transmitted over the redundant link if the primary link is congested.

[0112] The generation process of the on-chain verification reference graph is further refined based on specific business scenarios. For example, when processing fresh food traceability data, the theoretical synchronization range limit (e.g., node H's upper limit for processing cold chain temperature data is 50 pieces per second) and the transient timestamp (8:00 AM on May 22, 2025, the peak period for fresh food transportation) are input into the multi-dimensional fusion model. The pre-set sharding media database (recording node H's storage capacity of 1TB with 800GB of remaining space) and the external interference event layer (displaying traffic control on a specific road section on that day) are simultaneously loaded. Based on the encrypted peak frequency data output by the ciphertext parsing unit (e.g., the encryption frequency of temperature data increases with the increase in the number of sensors), the verification threshold of the data sharding status in the reference graph is modified, tightening the hash value verification threshold of temperature data from the default ±0.5°C to ±0.2°C. In the verification avoidance sub-link, a time gradient parameter is superimposed (e.g., verification time increases by 30% during the morning rush hour) for secondary calibration to ensure that the verification accuracy of temperature data is not affected during peak transportation periods.

[0113] In another scenario, when processing financial transaction data, when the fourth verification avoidance instruction is triggered, resulting in the downgrade of the shard path priority, the system adopts a sampling verification strategy for low-priority historical transaction data. For example, 1,000 pieces of historical data are randomly selected from 100,000 pieces for zero-knowledge verification. If the verification passes, the remaining data is assumed to be normal to reduce the verification load. At the same time, the sharding graph optimization module generates a degradation processing report, recording the degradation cause (such as node overload), processing time (such as 4 hours), and the affected data range (such as part of the transaction data from Q3 to Q4 of 2024) for subsequent audit tracing.

[0114] The triggering and execution of verification avoidance instructions follows strict sequential logic. For example, the first verification avoidance instruction takes precedence over the second, and the second is initiated only after the first fails. The third and fourth verification avoidance instructions are considered peer responses and are dynamically selected for execution based on real-time monitoring data. The system ensures the atomicity of instruction execution through a state machine mechanism. For example, when blocking the output channel of an abnormal node, the system simultaneously records the operation log and triggers data preloading on the backup node to avoid data processing interruptions.

[0115] Throughout the implementation process, the multi-dimensional fusion model and the sharding graph optimization module interact with real-time data, forming a closed-loop feedback system. For example, new policies generated after parameter updates are injected into the consensus modeling node in real time to adjust the baseline verification strategy. Degraded sharding status data is fed back into the multi-dimensional fusion model to optimize the historical data training set of the hash-time mapping table. This collaborative mechanism enables the system to continuously optimize the verification process in complex business scenarios, addressing dynamically changing risk challenges while ensuring data privacy and system stability.

Claims

1. A blockchain and privacy computing collaborative verification system, characterized by: include: Data sharding module, privacy fusion module and collaborative verification module; The data sharding module includes a preprocessing node and a dynamic sharding protocol; the privacy fusion module includes a ciphertext parsing unit and a zero-knowledge verification unit; the collaborative verification module includes a consensus modeling node and a state optimization unit; The pre-processing node is used to collect the original data hash value and cross-chain interaction parameters in real time; based on the block height and timestamp sequence, a multi-level data collaborative topology is constructed through the dynamic sharding protocol; The dynamic sharding protocol is composed of multiple sharding nodes in parallel based on hash thresholds; based on the real-time response characteristics of multiple sharding nodes, a benchmark verification strategy is generated through the consensus modeling node, and combined with the verification signal output by the zero-knowledge verification unit, the data sharding status is dynamically aggregated and sorted through the state optimization unit.

2. The blockchain and privacy computing collaborative verification system according to claim 1, characterized in that: The system also includes a consensus correction module and a sharding graph optimization module; the consensus correction module includes an isolation verification unit and a delay compensation unit; According to the generated benchmark verification strategy and dynamic aggregation sorting results, the data sharding status is subjected to distributed barrier correction through the isolation verification unit, and the ciphertext parameters of the privacy fusion module are dynamically calibrated through the delay compensation unit; at the same time, the on-chain stability evaluation network constructed by the sharding graph optimization module monitors the sharding synchronization rate in real time, and the monitoring results are fed back to the consensus modeling node and the state optimization unit to perform closed-loop adjustments to the verification strategy.

3. The blockchain and privacy computing collaborative verification system according to claim 2, characterized in that: The isolation verification unit is composed of state control circuits corresponding to parallel sharding nodes in the dynamic sharding protocol; the steps of constructing the multi-level data collaboration topology include: Collect the original data hash value and cross-chain fluctuation signal, input it into the transfer learning model configured in each level of sharding node, generate the data consistency compensation coefficient and store it in the corresponding sharding node; Based on the consistency compensation coefficient stored in shard nodes at all levels and combined with the instantaneous change rate of the timestamp sequence, the data shard status is distributed to multiple shard nodes through a dynamic hash detection algorithm to form a dynamic collaborative matching topology.

4. The blockchain and privacy computing collaborative verification system according to claim 3, characterized in that: The shard node construction conditions configured by the pre-processing node include: the original data hash value is within a preset range, the synchronization index corresponding to the shard node is greater than a threshold, and the block height is less than the allowable deviation range; The ciphertext parsing unit is also configured with a polymorphic switching link; the polymorphic switching link includes a steady-state sharding sub-link and a verification avoidance sub-link; the steady-state sharding sub-link is generated based on the matching degree between the on-chain stability signal of the privacy fusion module and the benchmark verification strategy; the verification avoidance sub-link is constructed based on the correlation between the instantaneous verification risk of the data sharding state and the external interference event.

5. The blockchain and privacy computing collaborative verification system according to claim 4, characterized in that: The consensus modeling node is configured with a multi-dimensional fusion model; the multi-dimensional fusion model includes a hash-time mapping table corresponding to the data sharding module and a verification avoidance parameter library of the privacy fusion module; The steps of generating the benchmark verification strategy include: Based on the real-time hash data and aggregate sorting results of the data sharding module, the theoretical synchronization range upper limit of each level of sharding nodes in the steady-state sharding sub-link is calculated; The upper limit of the theoretical synchronization range and the transient timestamp are input into the multi-dimensional fusion model to generate an on-chain verification reference map, and the verification risk of the reference map is corrected through the verification avoidance sub-link to form a benchmark verification strategy.

6. The blockchain and privacy computing collaborative verification system according to claim 5, characterized in that: The parameter updating step of the multi-dimensional fusion model includes: When it is detected that the hash value of the original data exceeds the preset range or the shard synchronization rate exceeds the threshold, the first verification avoidance instruction is triggered, that is, the replacement shard unit is started and the parsing granularity of the privacy fusion module is adjusted; If the sharding status has not recovered to the allowed range after the first verification avoidance instruction is executed, the second verification avoidance instruction is triggered, that is, switching to the verification avoidance sub-link through the polymorphic switching link, and redistributing the topological weight of the data sharding status based on the duration of the external interference event.

7. The blockchain and privacy computing collaborative verification system according to claim 6, characterized in that: The parameter updating step of the multi-dimensional fusion model further includes: When the synchronization index of any first-level sharding node in the data sharding module is lower than the threshold, the third verification avoidance instruction is triggered, that is, the sharding output channel of the node is blocked, and the corresponding data is transferred to other sharding nodes through the isolation verification unit; During the execution of the third verification avoidance instruction, if it is detected that other shard nodes are overloaded, the fourth verification avoidance instruction is triggered, that is, the shard graph optimization module is called to downgrade the output strategy of the state optimization unit and limit the synchronization requirements of the edge state.

8. The blockchain and privacy computing collaborative verification system according to claim 7, characterized in that: The degradation processing logic of the sharding graph optimization module includes: Based on the synchronization rate data output by the on-chain stability evaluation network and the off-chain ciphertext indicators, a shard status security level table is constructed; When the fourth verification avoidance instruction is triggered, the shard path priority is dynamically downgraded according to the security level table, and the parsing frequency of the privacy fusion module is synchronously adjusted to match the downgraded verification strategy.

9. The blockchain and privacy computing collaborative verification system according to claim 3, characterized in that: The step of constructing the shard node further includes: Generate a hash gradient adaptability index table based on the spatial distribution of the original data hash value and the historical cross-chain fluctuation range; According to the differences in consistency compensation coefficients of each shard node in the index table, the state control circuits of multiple shard nodes are topologically weighted using a dynamic weight distribution algorithm, and redundant transmission links are established between shard nodes.

10. The blockchain and privacy computing collaborative verification system according to claim 5, characterized in that: The generation process of the on-chain verification reference graph includes: After inputting the theoretical synchronization range upper limit and transient timestamp into the multi-dimensional fusion model, the preset slice medium database and external interference event layer are loaded synchronously; Based on the encrypted peak frequency data output by the ciphertext parsing unit, the verification threshold of the data fragmentation status in the reference spectrum is corrected, and the time gradient parameter is superimposed in the verification avoidance sub-link for secondary calibration.

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