Enterprise cloud data autonomous governance system supported by polymorphic cognitive engine

Abstract

The invention provides an enterprise cloud data autonomous governance system supported by a polymorphic cognitive engine, which relates to the technical field of enterprise data systems, is deployed in an enterprise-level hybrid cloud environment and is composed of a data acquisition module, a governance engine module, a storage module, a monitoring module, a privacy adaptive module, a sustainability evaluation module and a distributed consensus unit. According to the system, structured, unstructured and real-time streaming data are acquired in a multi-modal acquisition mode, and heterogeneous data are converted into a pulse sequence which can be identified by a neuromorphic processing unit through a pulse precoding unit. A multi-state cognitive engine is arranged in the governance engine, the governance engine is composed of a pulse neural network structure and a normal form switching mechanism, and self-iteration optimization can be achieved through generative sample construction and evolution parameter updating according to the statistical characteristics of a pulse mode. And the storage module expresses a complex business relationship by adopting an associated storage mode based on a hypergraph structure. According to the invention, autonomy, structuralization and credibility of data governance in the enterprise cloud environment are realized.

Description

Technical Field

[0001] This invention relates to the field of enterprise data system technology, specifically to an enterprise cloud data autonomous governance system supported by a polymorphic cognitive engine. Background Technology

[0002] As enterprises become increasingly digitalized and data sources diversify, enterprise cloud environments simultaneously contain structured data, unstructured data, and high-frequency real-time streaming data. Traditional data governance systems generally rely on rule bases, offline modeling, and manual strategy configuration. Their governance processes are fixed, their cognitive capabilities are limited, and they struggle to cope with the dynamic evolution and complex interactions of data. When enterprise business models, data distribution, or behavioral patterns change, existing systems often require manual adjustments to governance strategies or retraining of models, resulting in insufficient real-time governance, weak autonomy, and an inability to meet the high reliability, low latency, and high adaptability requirements of enterprise-level governance scenarios.

[0003] At the same time, enterprise cloud environments are evolving towards hybrid cloud and multi-node architectures, significantly increasing the communication complexity between edge nodes, cloud nodes, and distributed services. Data processing links are more susceptible to disturbances caused by abnormal inputs, sudden traffic spikes, and cross-domain computing. Traditional governance models struggle to maintain stable data structure relationships in such environments, lack sufficient privacy protection capabilities, and lack reliable guarantees for cross-organizational and cross-system data collaboration.

[0004] The development of new technologies such as neuromorphic computing, spiking neural networks, generative modeling, hypergraph storage, and blockchain trust mechanisms has made the construction of a data self-governance system—capable of proactively identifying changes in data patterns, automatically adjusting governance paradigms, autonomously evolving parameters, and simultaneously providing privacy protection and trusted verification capabilities—of great significance for the data quality, business stability, and security of enterprise cloud environments. This invention is proposed against this technological backdrop, aiming to address the problems of weak governance capabilities, insufficient adaptability, and lack of autonomy in existing systems. Summary of the Invention

[0005] Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides an enterprise cloud data autonomous governance system supported by a polymorphic cognitive engine, thus solving the problems of existing technologies.

[0007] Technical solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: an enterprise cloud data autonomous governance system supported by a multi-modal cognitive engine, the system comprising a data acquisition module, a governance engine module, a storage module, and a monitoring module; the governance engine module includes a multi-modal cognitive engine, which includes a neuromorphic processing unit and a paradigm switching unit, the neuromorphic processing unit representing the input signal as a pulse based on a spiking neural network structure, and the paradigm switching unit selecting a cognitive paradigm based on a preset paradigm set; the storage module includes an associative storage unit based on a hypergraph structure; the monitoring module includes a monitoring data acquisition unit and a monitoring strategy execution unit; the data acquisition module includes a multi-modal acquisition unit and a pulse precoding unit.

[0009] Preferably, the paradigm switching unit includes a Bayesian parameter generation unit and a paradigm selection control unit. The Bayesian parameter generation unit generates paradigm parameters based on a statistical model, and the paradigm selection control unit selects a preset paradigm set based on the paradigm parameters.

[0010] Preferably, the polymorphic cognitive engine includes a self-iterative module, which includes a generative sample construction unit and an evolutionary parameter update unit. The generative sample construction unit constructs a sample sequence based on a generative model, and the evolutionary parameter update unit iteratively updates the engine parameters based on genetic operators.

[0011] Preferably, the system includes a privacy adaptive module, which includes a zero-knowledge proof unit, a homomorphic encryption unit, and a noise adjustment unit. The noise adjustment unit adds noise signals to the data to be processed based on a preset noise strategy.

[0012] Preferably, the monitoring module includes a threat simulation submodule, which includes a search tree construction unit and a risk node generation unit. The search tree construction unit constructs a search tree based on Monte Carlo search rules, and the risk node generation unit generates a set of risk nodes based on the node data in the search tree.

[0013] Preferably, the associated storage unit of the storage module includes a hypergraph node construction unit and a hyperedge generation unit, wherein the hypergraph node construction unit generates a node set based on governance data, and the hyperedge generation unit generates a hyperedge set based on the association information between nodes.

[0014] Preferably, the system is deployed in a hybrid cloud architecture, which includes edge nodes and cloud nodes. The edge nodes include lightweight neuromorphic units, and the cloud nodes include rule synchronization units. The edge nodes and the cloud nodes exchange data based on a low-latency protocol.

[0015] Preferably, the multimodal acquisition unit of the data acquisition module includes a structured data interface, an unstructured data interface, and a streaming data interface, and the pulse precoding unit performs pulse format conversion on the acquired signal based on pulse coding rules.

[0016] Preferably, the governance engine module includes a sustainability assessment unit, which includes a life cycle parameter acquisition unit, a resource metering unit, and a parameter output unit.

[0017] Preferably, the system includes a distributed consensus unit, which includes a blockchain ledger unit and a smart contract verification unit. The smart contract verification unit verifies the decision records of the governance nodes based on preset contract rules.

[0018] Beneficial effects

[0019] This invention provides an enterprise cloud data autonomous governance system supported by a polymorphic cognitive engine. It has the following beneficial effects:

[0020] 1. This invention introduces a neuromorphic processing unit into the governance engine module and employs a spiking neural network to perform pulse representation and time-domain computation on multi-source data. This allows complex data patterns to be mapped into pulse sequences with temporal and spatial structures during the encoding stage. Due to the event-driven, sparse distribution, and low computational cost characteristics of spiking neural networks, compared to traditional data governance processes based on batch processing or centralized feature extraction, this invention can complete pattern aggregation and feature expression within a single time window after data enters the system. This enables the governance engine to have millisecond-level response capabilities when handling data bursts, pattern changes, or high-frequency events. Through this technique, the overall data governance latency of the system is significantly reduced, and it is more stable when handling traffic fluctuations, supporting the online governance needs of enterprise-level real-time business scenarios.

[0021] 2. This invention introduces a paradigm switching unit into the governance engine module and inputs impulse pattern features into the Bayesian paradigm selection model. Dynamic switching between multiple governance paradigms is achieved through likelihood scoring, prior distribution, and time smoothing mechanisms. Under different data distributions, data quality, association structures, and time series fluctuations, the system can automatically select different governance paths such as rule-based governance, predictive inference, structural association governance, or cluster analysis based on posterior probabilities. This autonomous switching mechanism avoids the limitations of traditional governance systems that rely on manually configured strategies or preset fixed models, enabling the system to adapt to concept drift, data mutations, and structural anomalies. Simultaneously, through energy consumption correction terms and feedback from self-iterative modules, the governance process maintains a balance between performance, resource consumption, and stability, improving the system's long-term robustness. Attached Figure Description

[0022] Figure 1This is a diagram of the overall system architecture of the present invention;

[0023] Figure 2 This is a flowchart of the data acquisition and pulse precoding process of the present invention;

[0024] Figure 3 This is a flowchart illustrating the internal structure and paradigm switching decision-making process of the polymorphic cognitive engine of the present invention.

[0025] Figure 4 This is a structural diagram of the self-iterative module of the present invention;

[0026] Figure 5 This is a diagram showing the relationship between the hypergraph storage structure and the nodes / hyperedges of the present invention.

[0027] Figure 6 This is a diagram illustrating the privacy adaptive module and data flow protection path of the present invention.

[0028] Figure 7 This is a flowchart illustrating the distributed consensus unit and blockchain ledger recording process of the present invention.

[0029] Figure 8 This is a system deployment diagram under the hybrid cloud architecture of the present invention;

[0030] Figure 9 This is a screenshot of the system in operation according to the present invention;

[0031] Figure 10 This is a screenshot of the system in operation according to the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0034] like Figures 1 to 10As shown, an enterprise cloud data self-governance system supported by a polymorphic cognitive engine is presented. This system operates in an enterprise-level hybrid cloud environment. Its overall architecture comprises a data acquisition module, a governance engine module, a storage module, a monitoring module, a privacy adaptation module, a sustainability assessment module, and a distributed consensus unit. Each module operates as a distributed microservice in engineering deployment and maintains data synchronization and state consistency through an internal highly reliable communication protocol. In practical engineering implementation, the system typically uses highly reliable communication frameworks based on gRPC, MQ, Raft streaming replication, or QUIC. Different modules interact using structured message formats, such as Protocol Buffers or Avro, to serialize data packets and ensure stable and reliable data structures transmitted across nodes. The system includes a transaction number, version number, and module state summary with each data synchronization for conflict detection and eventual consistency recovery.

[0035] The system's data processing flow begins with multimodal data acquisition. The data acquisition module is configured with multiple acquisition channels within the enterprise cloud environment to access structured data, unstructured data, and real-time streaming data. Each acquisition channel contains a data buffer, a data integrity verification unit, and a time calibration unit, ensuring that data has a unified time reference and structural format before entering the unified processing pipeline. For structured data, the system ensures data continuity through field-level hash verification and transaction number verification; for unstructured data, it ensures no slices are lost through file slice sequence number and content digest verification; and for real-time streaming data, a time window rolling calibrator ensures strict alignment of batch divisions.

[0036] During the structured data acquisition process, the system retrieves records from business databases, data warehouses, or reporting systems through database drivers or change capture channels, and converts the raw row records into standardized structures containing field names, field values, logical primary keys, timestamps, and field sensitivity levels. The field sensitivity levels here are derived from a predefined sensitivity model defined by the enterprise, which categorizes fields into P0 to P4, where P0 represents public data and P4 represents data requiring strong privacy protection. The system automatically parses the meaning of the fields and extracts the sensitivity levels from the policy library, using them in subsequent privacy adaptive modules. For example, the range of field value variations and the correlation between the field and the user's identity affect the sensitivity level. Field values ​​are stored internally using a unified dynamic type structure (using the union type), supporting multiple types such as strings, integers, floating-point numbers, booleans, and nested structures, ensuring seamless integration into subsequent governance pipelines.

[0037] In unstructured data acquisition, the system segments textual content using a file listener or log agent, and then performs word segmentation, feature extraction, and content summarization on the segmented data. Content summarization typically employs vector summarization algorithms based on TF-IDF or pre-trained Transformer models to compress long text into fixed-length vector features, making them suitable for pulse coding. Simultaneously, the system establishes semantic tags for logs and unstructured textual content, such as "abnormal," "warning," "transaction," and "risk," for subsequent paradigm identification. The log agent can also identify metadata such as line numbers, source files, thread numbers, and transaction numbers, enabling the pulse coding unit to more precisely control the location and frequency of textual pulse generation.

[0038] In streaming data acquisition, the system reads event data in batches from message queues or event streaming platforms, using time windows as units. Each event is appended with a source identifier and stream sequence number, placing it into a unified internal buffer. The streaming data then passes through an event granularity parser, breaking down events into multiple key-value pairs and mapping them to structural information such as topic, event key, value type, occurrence order, and business tag. Simultaneously, a batch summary is appended to the acquired event batches, using metrics such as minimum timestamp, maximum timestamp, and event count, to ensure the timing consistency and window precision of pulse generation.

[0039] All types of data are ultimately integrated into an initial data volume that meets preprocessing requirements and enters the pulse precoding unit. The pulse precoding unit is responsible for converting heterogeneous data into pulse sequences recognizable by the neuromorphic processing unit. This unit first extracts features from the input data, processes numerical fields through normalization, processes discrete fields through label mapping, processes text content through embedding vectors or keyword compression methods, and extracts key parameters of streaming events based on event type. The normalization process uses mean-variance standardization or min-max linear transformation to avoid extreme values ​​causing pulse density shifts when mapping numerical fields to pulse frequencies. Label mapping uses hash embedding or discrete encoding to map discrete fields to fixed-dimensional integer indices. Text embedding can use SentenceBERT or a lightweight TextCNN model to generate 128–512 dimensional vectors for generating pulse intensity or pulse distribution structures.

[0040] Subsequently, the system divides the data into time sequences according to a set time window, maps the features to the pulse domain space, and generates pulse events through amplitude mapping, event triggering, or delay mapping, so that each data point generates a pulse with a time position on a specified virtual neuron channel. For example, when a numerical feature is a continuously monitored value, the system maps its value to a pulse firing frequency; when a field is event-triggered, using "alarm" or "payment action," the system generates a pulse spike of fixed amplitude; when a feature has temporal continuity, using a device vibration sequence, the system uses delay encoding to map the feature value to a pulse delay amount, thus forming a time-sensitive pulse pattern.

[0041] The encoded pulse sequence is organized into pulse time packets and fed into the governance engine module. The pulse time packet contains fields such as neuron ID, pulse occurrence time, pulse type, pulse intensity, feature source, original data index, and window number, enabling the neuromorphic unit to completely reconstruct the input structure.

[0042] The polymorphic cognitive engine within the governance engine module is the core of the system, comprising a neuromorphic processing unit and a paradigm switching unit. The neuromorphic processing unit constructs a spiking neural network structure. In its engineering implementation, it stores state parameters such as neuronal membrane potential, firing threshold, leakage factor, and reset value, enabling each neuron to accumulate and decay input pulses over time. In its implementation, the neuromorphic processing unit uses simplified models such as LIF (Leaky Integrate and Fire), Izhikevich, or Hodgkin-Huxley as the mathematical basis for neurons, simulating membrane potential changes through discrete time steps. The synaptic model describes the weights, delays, short-term enhancements, and decays between neurons, storing the synaptic connection matrix in an adjacency list, allowing the system to scale to millions of virtual neurons.

[0043] When a pulse data packet enters the unit, the system first integrates all pulses within the current time window and maps the input pulses according to the synaptic connections of each neuron, causing an increment in the neuron's membrane potential within that time slice. If a neuron reaches a set threshold after updating its state, it generates a pulse firing event and triggers synaptic propagation, affecting the membrane potential of downstream neurons and forming a multi-level pulse propagation chain. During propagation, the system adds pulse events to a synaptic delay queue based on synaptic delay parameters and uses different weight symbols for different types of synapses (excitatory and inhibitory), enabling the network to form rich dynamic behavioral patterns.

[0044] At the end of a time window, the system summarizes the firing status of all neurons in the network, forming a pulse pattern structure that reflects the current data pattern. This structure includes neuron activation distribution, temporal characteristics, and cross-layer propagation information, which are used for paradigm determination in the next stage. The pulse pattern contains multi-dimensional structures, such as neuron activation histograms, pulse temporal entropy, inter-layer propagation matrices, and population activation peak positions, enabling the paradigm switching unit to recognize different input structures.

[0045] After receiving the pulse pattern, the paradigm switching unit enters the cognitive paradigm selection process. The Bayesian parameter generation section within this unit calculates the posterior fit of each governance paradigm based on the statistical characteristics of the pulse pattern. Here, a multidimensional Gaussian model, Dirichlet distribution, or a Bayesian network based on latent variables is used to model the pattern, ensuring that different paradigms correspond to different probability structures. By calculating the likelihood of the high-dimensional features of the pulse pattern, a "paradigm pattern adaptation vector" is generated.

[0046] The system first extracts high-level features from the pulse patterns, such as activation density, temporal fluctuations, concentration of activation regions, and synchronicity of neuron groups. Then, under a pre-defined probability model, it constructs a corresponding likelihood function for each governance paradigm, enabling the system to determine the probability of a particular pulse pattern occurring under a given paradigm. By combining long-accumulated prior paradigm distributions, the system obtains the posterior probability of each paradigm based on Bayesian inference and eliminates the influence of short-term noise through stability correction, ultimately forming the fitness distribution of each paradigm.

[0047] The self-iterative module runs continuously in the system background to maintain the engine's long-term performance and adaptability. The generative sample construction part generates various types of simulated samples based on historical governance records, pulse pattern changes, anomaly trigger frequencies, and monitoring indicator numbers. These include simulated burst traffic samples, rare correlation samples, high-noise data samples, and complex time-series samples, enabling the system to learn complex behavioral patterns even in non-realistic environments. The generation process is typically based on GANs, VAEs, or time-series generative models. By sampling the pulse domain or feature domain, it generates synthetic pulse sequences or hypergraph structure simulated data, resulting in higher coverage and complexity of the training data.

[0048] The evolutionary parameter update unit converts the neuron parameters, synaptic weights, paradigm priority thresholds, and governance strategy weights in the governance engine into an evolvable parameter vector. It then evaluates parameter fitness in simulated samples and updates the engine's internal parameters through iterative methods such as parameter perturbation, parameter combination, parameter recombination, and fitness comparison. After evolution, the optimal parameters are written back to the system, and updates are performed in a gray-scale manner to ensure the stability of the governance process. Fitness calculations include metrics such as impulse stability, paradigm consistency, governance time, and energy consumption. Parameter updates are arranged using genetic algorithms, differential evolution algorithms, or multi-objective optimization orchestration.

[0049] The privacy-adaptive module protects sensitive data during governance. This module includes zero-knowledge proof mechanisms, homomorphic encryption mechanisms, and intelligent noise conditioning mechanisms. Zero-knowledge proofs use Groth16 or Bulletproofs protocols to generate short proofs with a complexity of O(n) or O(logn), efficiently verifying the correctness of business rule execution. Homomorphic encryption can employ Paillier or BFV / BGV schemes, enabling encrypted data to support addition or multiplication operations. In noise conditioning, the system controls output sensitivity through differential privacy noise Δ (e.g., Laplace noise) to protect data security.

[0050] The monitoring module continuously collects system operation metrics, including data processing speed, pulse generation density, paradigm shift frequency, node growth trend, and resource consumption level. It also uses the threat simulation submodule to predict potential future governance risks. In its implementation, the monitoring module internally maintains multiple real-time samplers and metric calculators. Samplers read system status at fixed time slices (50ms, 100ms, and 500ms), including input data volume, neuron firing rate, thread queue latency, storage write latency, and block write confirmation latency. All sampled data is fed into a sliding window structure, and short-term and long-term trends are calculated using an exponentially weighted moving average (EWMA) model to capture sudden changes and anomalous inflection points. When a metric change exceeds a threshold (e.g., a continuous spike in paradigm shift frequency or a pulse density exceeding historical peaks within a short period), the monitoring module immediately triggers the threat simulation submodule to perform in-depth risk calculations.

[0051] The threat simulation submodule constructs a multi-layered search tree to predict potential state changes in the governance system within several future windows. It measures node state mutations, impulsive pattern anomalies, and hypergraph structure anomalies to identify high-risk nodes from the search tree and output a risk map. This allows the governance engine and monitoring module to adjust strategies in advance, preventing potential risks from escalating. Algorithmically, the search tree construction is based on Monte Carlo Tree Search (MCTS). First, the current governance state is encoded as a root node. The node state includes the impulsive pattern, paradigm activation sequence, resource consumption status, and storage growth rate of the most recent N windows. During simulation, the system generates several possible future evolution paths for each state based on historical statistical patterns and prediction models, generating child nodes through random sampling and probability transitions. When a node expands, the system uses a heuristic function (e.g., a risk assessment function R = α·impulsive anomaly + β·paradigm instability + γ·hypergraph anomaly growth rate) to calculate the node's potential risk value. In the backpropagation phase, the risk value is propagated upwards along the tree, allowing the root node to obtain an overall score for future risk trends. In this way, the system can predict risks in advance and adjust the parameters of the neuromorphic network, limit input, reduce paradigm sensitivity, or trigger privacy-enhancing modes based on simulation results, thereby achieving proactive governance.

[0052] The storage module adopts a hypergraph-based associative storage method, constructing entities related to governance results as nodes and complex relationships between multiple entities as hyperedges. The node construction unit extracts node identifiers, related attributes, and association times from business entities in the data and writes them into storage according to a unified structure. The hyperedge generation unit constructs relationship units based on the association behavior between multiple entities, recording relationship types, participating entity sets, and timestamps, enabling the storage system to represent complex business relationships in a high-dimensional way and supporting fast querying and tracing. At the engineering level, the hypergraph adopts a sparse matrix and list hybrid storage structure: nodes are stored in the node table with incremental IDs, and hyperedges are stored in the hyperedge table with hyperedge IDs. An inverted index is used to store the "node → hyperedge" relationship, enabling queries to achieve a complexity of approximately O(1). In the hypergraph construction algorithm, the system performs consistency checks on entity attributes before generating hyperedges, for example, determining whether participating entities belong to the same business session, whether they share the same time window, and whether there are logical association links. Hyperedge generation uses "set hashing" to calculate hyperedge signatures (e.g., sorting the node ID array and taking the hash), which is used to quickly detect isomorphic hyperedges and avoid duplicate writes. When writing to the hypergraph, the system triggers a lifecycle maintenance procedure, which uses a time decay function to de-weight or archive inactive nodes and hyperedges, keeping the hypergraph size stable and avoiding resource pressure caused by explosive growth.

[0053] The system continuously utilizes node indexes and hyperedge indexes to maintain efficient data retrieval capabilities. During the query process, the system first finds the corresponding node through the primary node index, then retrieves all hyperedges it participates in through the inverted index, and then uses graph traversal algorithms (BFS, DFS, or heuristic multi-level jump search) to extend the links, which can quickly construct complete association links for risk analysis or data tracing.

[0054] The system's hybrid cloud deployment ensures collaborative governance between the edge and the cloud. Edge nodes deploy lightweight neuromorphic processing components and some paradigm-switching logic to handle business scenarios with high real-time requirements; cloud nodes deploy a complete governance engine, hypergraph storage, and self-iterative modules to process global data and execute parameter updates. Data synchronization between the two is achieved through a low-latency protocol. Edge nodes can submit incremental data to the cloud and receive the latest policy and parameter updates. The synchronization mechanism is typically based on a lightweight streaming replication protocol, transmitting changes in binary differential packets. Every fixed time slice (30ms), edge nodes collect summaries of recently processed data pulses, paradigm judgment results, and resource usage, encapsulate them into "incremental snapshots," and push them to the cloud in a non-blocking stream. The cloud aggregates the edge snapshots to generate a global state and employs a two-stage synchronization approach during parameter updates: first, a "shadow parameter set" is established in the cloud and pushed to some edge nodes for canary testing; only after successful testing is the official parameter set pushed to all nodes. The synchronization protocol includes a version conflict detection mechanism. When an edge node detects that the cloud update is inconsistent with the local state, it will automatically enter "version coordination mode" to roll back parameters or resynchronize with the cloud as the primary mode.

[0055] The sustainability assessment module acquires data on task execution frequency, data scale growth rate, storage overhead, and computational consumption through the lifecycle parameter acquisition unit. It also measures the system's CPU, memory, and network resource usage through the resource metering unit. Finally, the parameter output unit generates sustainability parameters to adjust governance strategies and paradigm selection logic, enabling the system to minimize energy consumption and efficiently utilize resources while ensuring governance quality. Algorithmically, the sustainability assessment module uses the LCA (Lifecycle Analysis) mathematical model to calculate the energy consumption cost C_life for each type of governance task, including computational energy consumption, storage energy consumption, and network energy consumption. Resource consumption is weighted and modeled using the formula C_life = w1·CPU + w2·MEM + w3·IO + w4·NET. The resource metering unit acquires the usage rate of each resource through real-time sampling and calculates the cumulative energy consumption over the task's lifecycle through time integration. The parameter output unit uses reinforcement learning algorithms (DQN and PPO) to adjust the governance strategy, reducing the trigger probability of high-energy-consuming paradigms and increasing the scheduling priority of low-energy-consuming paradigms, allowing the system to automatically evolve to a more energy-efficient paradigm path during long-term operation. The system also dynamically adjusts neuromorphic parameters such as the number of neurons, synapse density, pulse sampling rate, and window length, so that the governance task enters "energy-saving mode" during high-load periods and "enhanced model mode" during low-load periods.

[0056] Distributed consensus units are used to ensure consistency in governance decisions in a multi-node environment. Blockchain ledger units record key governance events and parameter updates in an immutable manner, including paradigm shift information, hypergraph node changes, and policy iteration trajectories, enabling each node to verify governance actions at different points in time. A ledger block consists of a block header and a block body. The block header records the hash of the previous block, timestamp, Merkle root, difficulty value, etc.; the block body records the serialized content of governance events. "Governance events" are stored in an immutable data structure, including event type, event time, node ID, paradigm number, version number, parameter digest, entity index, etc. The ledger writing process uses PBFT, Raft, or HotStuff consensus algorithms, confirming block validity through multi-node voting. Before each governance event is written to the ledger, its digest hash is generated, and then multiple nodes jointly sign it to form a multi-signature proof, giving it strong auditing capabilities.

[0057] The smart contract verification mechanism automatically verifies governance decisions before they are written to the ledger, ensuring that the written data meets the set governance rules, permission restrictions, and audit requirements. If verification fails, it prevents the record from being written and sends an alarm to the monitoring module, achieving traceability and trusted execution of governance decisions. The smart contract internally defines a decision verification process, such as: whether a certain paradigm is allowed to be activated in a certain business subdomain, whether a certain type of hyperedge is allowed to be written, whether the current sensitivity level meets encryption requirements, and whether the governance node has write permissions. Contract execution uses a deterministic virtual machine (using WASM), inputting the hash of the governance operation into the contract, and returning states such as "allow / reject," "requires additional proof," and "requires rollback" after executing the state machine. For privacy-sensitive decisions, the smart contract automatically calls a zero-knowledge proof verifier to ensure that the governance process does not violate privacy constraints. Finally, approved decisions are entered into the blockchain ledger, while unapproved decisions are sent to the alarm channel.

[0058] In the enterprise cloud data autonomous governance system supported by the polymorphic cognitive engine of this invention, core processes such as the neuromorphic processing unit, the pulse coding unit, and sustainability and risk assessment can all be described by explicit mathematical equations, thus providing a quantifiable theoretical basis for engineering implementation. For the neuromorphic processing unit, a leaky integrated-and-fire model can be used to model the state of a single spiking neuron, where the membrane potential of the i-th neuron at discrete time step t is denoted as... Its evolution can be represented as

[0059]

[0060] in Indicates the membrane potential leakage factor. This represents the weight of the synaptic connection from neuron j to neuron i. Indicates synaptic transmission delay, It is an indicator function for whether neuron j fires a pulse at time t (1 if firing, 0 otherwise). This represents the external input current. If at a certain time step, the following conditions are met:

[0061]

[0062] It is assumed that the i-th neuron fires a pulse at time t, and the pulse output can be set to... and reset the membrane potential to

[0063]

[0064] in Threshold potential, To reset the potential. For a pulse coding unit, mapping the input features to the pulse domain can be constructed as an explicit coding function. For example, the frequency coding of the numerical feature x can be written as:

[0065]

[0066] Within a given time window Within this range, the corresponding set of pulse firing times can be represented as:

[0067]

[0068] This yields the set of pulse time points within that window on a given neuron. For time-to-first-spike encoding, this can be achieved by defining a monotonically decreasing function. Mapping eigenvalues ​​to the initial pulse delay Only one pulse is emitted within the window. Text features are obtained through embedding vectors. Once obtained, the vector components can be mapped to the firing frequencies or firing probabilities of multiple neuron groups according to their dimension or principal component direction, by setting...

[0069]

[0070] The firing probability distribution of the corresponding neuron group within the current window is determined, where For the Sigmoid or Softmax function, These are encoding parameters.

[0071] In sustainability assessment, the resource consumption vector for a single governance task can be defined as follows: and define the lifecycle cost function.

[0072]

[0073] Among them each The coefficients are determined by the operation and maintenance strategy or energy consumption metering standards. The system accumulates the lifecycle costs of multiple governance tasks over a time axis to obtain the results within a time interval. Total cost within

[0074]

[0075] This is used as a penalty term in reinforcement learning to guide the governance strategy to gradually optimize to a lower-energy-consumption paradigm preference and parameter configuration while ensuring governance quality. In the paradigm switching decision-making process, this invention uses a Bayesian inference model to quantify the adaptability of different governance paradigms. The impulse patterns output by the neuromorphic processing unit are represented as feature vectors. The predefined set of paradigms is denoted as For each paradigm Define its likelihood function in the feature space. ,in This represents the statistical model parameters corresponding to this paradigm; for example, a Gaussian model can be used.

[0076]

[0077] in It is the mean vector. Let be the covariance matrix. For each normal form... Prior probability Prior information can be determined by historical usage frequency, business strategy configuration, or sustainability assessment results. According to Bayes' theorem, given the current feature vector... At that time, paradigm The posterior probability is

[0078]

[0079] To improve decision-making stability, a time smoothing mechanism is introduced, which adjusts the current time frame. posterior probability Probability compared to the previous moment Perform exponential smoothing to obtain the smoothed paradigm score vector.

[0080]

[0081] in This represents the smoothing coefficient. The basic decision rule of the paradigm switching control mechanism can be expressed as a selection...

[0082]

[0083] If and only if Exceeding the preset threshold Or, with the paradigm currently in use The score difference exceeds the minimum handover difference. Only when this happens will a paradigm shift be triggered, that is...

[0084]

[0085] Based on this, sustainability parameters can be introduced. The overall score, after "energy consumption correction," is constructed as a penalty factor.

[0086]

[0087]

[0088] in As energy consumption weight, The maximum energy consumption in the normalization set is used for normalization. In this case, normalization selection can be rewritten as...

[0089]

[0090] This approach balances cognitive matching with resource consumption, ensuring a mathematically clear optimization objective and constraints for the paradigm shift process. At the hypergraph storage structure level, this invention uses a hypergraph model to represent the governed associated data, representing the entire associated data space as a hypergraph. , in For a set of nodes, This is a set of hyperedges. Each node... It can be modeled as a triple.

[0091]

[0092] in As a unique identifier for the node, For node type identification, such as "user", "device", "order", "log entry", etc. This is the attribute vector for that node. The attribute vector can contain information such as timestamps, business tags, sensitivity levels, and source system markers. Each hyperedge... Used to describe the composite relationship between multiple nodes, it can be defined as follows:

[0093]

[0094] in As a unique identifier for the superedge, This represents the set of nodes participating in the relationship. This refers to relationship types, such as "multiple details within the same order" or "multiple accounts and devices involved in the same risk control event." The weights or confidence levels of this relationship can be obtained through statistical counting, business rules, or model scoring. This refers to the time of relation formation or update. To improve query and update efficiency, a mapping function from nodes to hyperedges can be introduced. :

[0095]

[0096] This way, when querying all relationships involving a certain node, you can... The relevant hyperedge set can be located using an index structure within a near-constant time. The global structure of the hypergraph can be numerically modeled using an adjacency tensor or a sparse adjacency list, for example, by defining a third-order tensor. Its elements Represents a node Do they appear together on the superedge? In the sparse implementation, only non-zero values ​​are stored, thus supporting subsequent graph mining or pattern recognition algorithms. During data writing, the system adds new node sets. and superedge set A consistency check will be performed first, including checking whether the node ID is unique, whether the node already exists, whether the node set participating in the hyperedge is not empty, and whether the relationship type meets preset constraints. Once the check passes, the node and hyperedge will be written to the corresponding storage area, and the node set will be updated accordingly. and superedge set Expand sequentially and update the mapping function simultaneously. The corresponding data structure, for example, adding a key to the key-value index. Mapping to the hyperedge ID set. For relation decay and archiving during temporal evolution, the hyperedge weights can be adjusted. Introducing a time decay function

[0097]

[0098] in For the first time the superedge is established, Let be the attenuation coefficient, when When the hyperedge falls below a certain threshold, the corresponding hyperedge can be archived or weakened, thereby controlling the size and temporal evolution characteristics of the hypergraph at the mathematical model level. Through the aforementioned equation-based expression, the Bayesian inference model of the paradigm shift process, and the data structure definition of the hypergraph storage structure, this invention not only possesses a clear engineering implementation path in terms of system architecture and functional modules, but also has a clear mathematical characterization at the algorithm and data levels, enabling the various states of this invention to... Specific Implementation Example 2:

[0100] Based on the technical solution of Specific Embodiment 1, the following further methods are proposed:

[0101] In the risk control operations of financial enterprises, the system needs to continuously process multiple types of data from the bank's core business system, payment clearing and settlement system, mobile payment platform, device fingerprint system, and regulatory data sources. This data includes transaction records, account behavior logs, user device characteristic information, abnormal alarm records, fraud sample information, and cross-system behavioral links. The implementation process of the system in this invention in this scenario begins with multimodal data acquisition. The acquisition module simultaneously monitors multiple data channels and accesses new transaction events and user behavior records with millisecond-level latency. The pulse precoding unit encodes continuous features such as transaction amount, transaction frequency, device stability coefficient, and geographical change probability into pulse amplitude or pulse time difference based on the business semantics of different data sources. It also maps discrete attributes such as device category, transaction method, account type, and payment scenario to designated pulse neuron channels, enabling similar types of data to form regular pulse sequences over time.

[0102] Upon receiving the latest pulse sequence, the neuromorphic processing unit accumulates, leaks, and propagates the pulses based on the synaptic connection structure, thereby generating a pulse pattern corresponding to the characteristics of the current transaction behavior. For transactions that may pose a fraud risk, such as multiple logins from different locations within a short period, high-frequency payment attempts, switching between multiple devices, or abnormal changes in device fingerprints, the pulse pattern will exhibit structural changes such as high-density jumps or activation across neuron groups. After these pulse patterns are identified by the paradigm switching unit, they often trigger a prediction paradigm or anomaly clustering paradigm, enabling the system to select a more suitable governance logic based on the current situation during real-time processing.

[0103] When the system detects a high similarity between a certain type of impulse structure and known fraud samples, the self-iterative module automatically generates high-risk transaction simulation samples based on the current data. This further optimizes the neural network parameters and paradigm thresholds, thereby improving the sensitivity and accuracy of subsequent risk detection. After governance is completed, the system generates corresponding hypergraph nodes for multiple entities such as "account-device-geographical location-transaction pattern" and constructs transaction links with contextual relationships as hyperedges, enabling risk analysts to backtrack the complete risk link relationships in subsequent analyses. The privacy adaptive module dynamically encrypts user identity data when processing sensitive information and verifies whether the risk control strategy execution meets regulatory requirements through zero-knowledge proofs, ensuring that the entire risk control governance process is conducted under strict privacy protection. Finally, the distributed consensus unit writes key risk links and model decision records into the ledger, enabling financial institutions to prove the traceability and credibility of system behavior during internal audits or regulatory inspections. Specific Implementation Example 3:

[0105] Based on the technical solution of Specific Embodiment 1, the following further methods are proposed:

[0106] In large manufacturing enterprises, systems need to process large amounts of real-time data, including real-time sensor data generated by production line equipment, equipment operation logs, edge controller feedback, process parameter records, and personnel operation logs. The implementation of the system in this invention for this scenario focuses on pulsed representation of equipment status, hypergraph construction of complex process relationships, and anomaly prediction.

[0107] The operating data of production line equipment includes continuous features such as temperature, pressure, current, vibration amplitude, mechanical motion curves, and processing parameters. After acquisition, the system maps these continuous values ​​into pulse frequencies or pulse timing offsets through a pulse precoding unit, enabling the equipment's operating status to form a dynamic pulse structure with process characteristics in the neuromorphic layer. When the equipment experiences component wear, abnormal load, temperature surge, or abnormal vibration patterns, the activation distribution of the spiking neural network will produce non-stationary fluctuations in multiple neuron groups, forming abnormal pulse patterns. After these patterns are identified by the paradigm switching unit, the system automatically selects either a prediction paradigm or a process model paradigm for further analysis.

[0108] During continuous production line operation, the self-iterative module constructs simulated operating conditions based on accumulated equipment data over a long period, including disturbance modes under normal operating conditions, gradual failure modes, and sudden anomaly modes. This allows the governance model to learn the multi-stage failure behavior of complex equipment without affecting actual production. After identifying potential anomalies, the system automatically creates a relationship in the hypergraph structure of "equipment component - operating parameters - operation records - product batch," enabling engineers to quickly locate the source of the problem.

[0109] In this scenario, the privacy adaptive module primarily functions by encrypting and protecting key process data, production indicators, and equipment failure records during cross-departmental data sharing. This ensures that external collaborating departments, even with access to only necessary verification results, cannot obtain the company's core parameters. The sustainability assessment module analyzes the overall energy efficiency of the system based on mechanical load, energy consumption levels, and production rhythm, and feeds the results back to the paradigm switching unit. This allows for adjustments to governance strategies during high-energy-consumption phases, ensuring the system operates in an energy-optimal manner. Specific Implementation Example 4:

[0111] Based on the technical solution of Specific Embodiment 1, the following further methods are proposed:

[0112] In the medical setting, there are numerous highly sensitive data flows between hospitals, testing institutions, remote diagnostic platforms, and public health systems, including electronic medical records, imaging diagnostic results, medical orders, vital sign monitoring data, biochemical test reports, and cross-hospital consultation information. The system of this invention emphasizes privacy protection, cross-institutional data consistency, and the ability to construct complex connections between medical knowledge in this scenario.

[0113] Once medical data enters the system, the pulse precoding unit generates pulse sequences based on different data types. For example, it maps ECG data and vital sign monitoring data into high-density continuous pulse sequences, medical record text into sparse pulse events, and medical image metadata into multidimensional pulse groups. The neuromorphic processing unit then performs temporal evolution on this data to form pulse patterns reflecting the patient's health status. Deterioration of the condition may correspond to oversynchronization or abnormal activation of multiple key channels in the pulse pattern.

[0114] The paradigm switching unit selects either the medical knowledge graph paradigm or the clinical prediction paradigm based on the pulse pattern, thereby constructing a multidimensional relational structure of the patient's health status based on past case data and medical knowledge. The system establishes a composite association between "patient—examination report—diagnosis result—medication plan" through hypergraph storage, enabling doctors across departments or institutions to quickly understand the patient's condition's relational links.

[0115] Due to the highly sensitive nature of medical data, the privacy-adaptive module plays a crucial role in this embodiment. A zero-knowledge proof unit can be used to prove to other collaborating hospitals that a diagnostic rule has been correctly applied without providing the original medical records. A homomorphic encryption unit ensures that vital sign data and medical indicators can participate in calculations under encrypted conditions, while a noise conditioning unit perturbs the output statistical health trends, making them unusable for reconstructing sensitive individual information.

[0116] Distributed consensus units record the diagnostic decision-making process and data processing trajectory in cross-hospital collaboration, making every diagnostic step and data call traceable, which helps to establish a trustworthy data foundation in cross-institutional medical collaboration. Specific Implementation Example 5:

[0118] Based on the technical solution of Specific Embodiment 1, the following further methods are proposed:

[0119] In smart cities, systems need to process diverse data from transportation systems, public facilities, environmental monitoring, video surveillance, and IoT devices. This data is characterized by high real-time requirements, large scale, and strong uncertainty. The system of this invention is used in this scenario to support urban operation monitoring, emergency response triggering, and cross-system collaborative governance.

[0120] The system accesses multi-source data, including traffic flow data, environmental monitoring indices, feedback from road sensing devices, and camera event streams, through a multimodal acquisition unit, and converts them into pulse sequences. The neuromorphic processing unit transforms these pulse sequences into pulse patterns representing the city's operational status. When situations such as sudden increases in traffic congestion, sharp rises in pollution indices, or abnormal shutdowns of public facilities occur, the pulse patterns immediately exhibit characteristic changes, which are captured by the paradigm switching unit and switched to an event prediction or emergency response paradigm.

[0121] In the self-iterative module, the system automatically constructs various simulated scenarios such as "extreme weather," "large-scale events," and "traffic accidents" based on historical city operation data, enabling the governance engine to learn the pulse structure and correlation in complex contexts in advance and improve its actual prediction capabilities.

[0122] In this embodiment, the hypergraph is used to construct a multi-dimensional correlation graph of "road nodes - traffic flow patterns - event types - regional environmental indices", enabling urban management departments to quickly understand the correlation paths between various factors in command and decision-making.

[0123] The privacy adaptive module encrypts and anonymizes sensitive information such as vehicle identity and personnel activity trajectories, enabling urban governance to protect citizens' privacy while safeguarding public interests. The sustainability assessment module optimizes governance strategies based on urban energy consumption and computing load, ensuring the system remains efficient even when operating on a large-scale urban platform. Specific Implementation Example Six:

[0125] Based on the technical solution of Specific Embodiment 1, the following further methods are proposed:

[0126] In multinational supply chain scenarios, there is a large amount of cross-system data interaction among manufacturing enterprises, logistics enterprises, customs departments, warehousing enterprises and retail enterprises. The system of this invention is used in this scenario for dynamic governance, risk warning and data consistency verification of supply chain information.

[0127] In this embodiment, the system accesses supply chain node status, material batch information, transportation process data, customs clearance information, warehousing environment data, and retail sales data from multiple enterprise data interfaces, and generates corresponding pulse patterns. The neuromorphic processing unit dynamically evolves these patterns to identify potential structural anomalies in the supply chain, such as delay risks, inventory abnormalities, cold chain damage, and batch contamination. It then triggers a paradigm switching unit to select an association graph governance paradigm to infer potentially affected downstream nodes.

[0128] Hypergraph structures are particularly important in this scenario. The system can construct a long-link hypergraph from "material batch—transport container—warehouse node—sales terminal," enabling enterprises to quickly trace back risks. Distributed consensus units record every data synchronization and decision in cross-enterprise data governance, enabling organizations to achieve collaborative operations based on a consistent and trusted ledger.

[0129] The privacy adaptive module is responsible for layered encryption of commercially sensitive information, ensuring that different enterprises can only access data within their authorized scope, while zero-knowledge proofs ensure that the governance process complies with business rules and contractual constraints.

[0130] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0131] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-state cognitive engine supported enterprise cloud data self-governance system, characterized in that: The system comprises a data acquisition module, a governance engine module, a storage module and a monitoring module; the governance engine module comprises a polymorphic cognitive engine, the polymorphic cognitive engine comprises a neuromorphic processing unit and a paradigm switching unit, the neuromorphic processing unit performs pulse representation on input signals based on a pulse neural network structure, and the paradigm switching unit selects a cognitive paradigm based on a preset paradigm set; the storage module comprises an association storage unit based on a hypergraph structure; the monitoring module comprises a monitoring data acquisition unit and a monitoring strategy execution unit; and the data acquisition module comprises a multi-modal acquisition unit and a pulse precoding unit.

2. The multi-state cognitive engine supported enterprise cloud data self-governance system of claim 1, wherein: The paradigm switching unit comprises a Bayesian parameter generation unit and a paradigm selection control unit, the Bayesian parameter generation unit generates paradigm parameters based on a statistical model, and the paradigm selection control unit selects a preset paradigm set based on the paradigm parameters.

3. The multi-state cognitive engine supported enterprise cloud data self-governance system of claim 2, wherein: The polymorphic cognitive engine comprises a self-iteration module, the self-iteration module comprises a generative sample construction unit and an evolutionary parameter updating unit, the generative sample construction unit constructs a sample sequence based on a generative model, and the evolutionary parameter updating unit iteratively updates engine parameters based on genetic operators.

4. The multi-state cognitive engine supported enterprise cloud data self-governance system of claim 1, wherein: The system comprises a privacy adaptive module, the privacy adaptive module comprises a zero-knowledge proof unit, a homomorphic encryption unit and a noise adjustment unit, and the noise adjustment unit adds a noise signal to the data to be processed based on a preset noise strategy.

5. The multi-state cognitive engine enabled enterprise cloud data self-governance system as claimed in claim 1, wherein: The monitoring module comprises a threat simulation sub-module, the threat simulation sub-module comprises a search tree construction unit and a risk node generation unit, the search tree construction unit constructs a search tree based on a Monte Carlo search rule, and the risk node generation unit generates a risk node set based on node data in the search tree.

6. The multi-state cognitive engine enabled enterprise cloud data self-governance system as claimed in claim 1, wherein: The association storage unit of the storage module comprises a hypergraph node construction unit and a hyperedge generation unit, wherein the hypergraph node construction unit generates a node set based on governance data, and the hyperedge generation unit generates a hyperedge set based on association information between nodes.

7. The multi-state cognitive engine enabled enterprise cloud data self-governance system as claimed in claim 1, wherein: The system is deployed in a hybrid cloud architecture, the hybrid cloud architecture comprises an edge node and a cloud node, the edge node comprises a lightweight neuromorphic unit, the cloud node comprises a rule synchronization unit, and the edge node and the cloud node exchange data based on a low-latency protocol.

8. The multi-state cognitive engine enabled enterprise cloud data self-governance system as claimed in claim 1, wherein: The multi-modal acquisition unit of the data acquisition module comprises a structured data interface, an unstructured data interface and a stream data interface, and the pulse precoding unit performs pulse format conversion on the acquisition signal based on a pulse coding rule.

9. The multi-state cognitive engine enabled enterprise cloud data self-governance system as claimed in claim 1, wherein: The governance engine module comprises a sustainability evaluation unit, the sustainability evaluation unit comprises a life cycle parameter acquisition unit, a resource metering unit and a parameter output unit.

10. The multi-state cognitive engine supported enterprise cloud data self-governance system of claim 1, wherein: The system comprises a distributed consensus unit, the distributed consensus unit comprises a blockchain ledger unit and a smart contract verification unit, and the smart contract verification unit verifies decision records of governance nodes based on a preset contract rule.

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