Database management method for heterogeneous cloud environment

Abstract

The application discloses a heterogeneous cloud environment database management method, compared with a traditional heterogeneous cloud database management method based on a hard threshold value, the scheme introduces a structure causal model, topology data analysis and formal verification technology, constructs a complete closed loop architecture covering causal cognition, topology feature extraction and safety verification, solves the limit cycle oscillation, control quantity step jump and brain split safety risk problems existing in the prior art, realizes the overall technical upgrade from passive response to predictive scheduling, from hard threshold switching to smooth transition, from experience safety to mathematically provable safety, improves the automation level, resource utilization and operation reliability of database management in the heterogeneous cloud environment, and the topology feature extraction step realizes the leap from the appearance value to the structure feature by introducing algebraic topology and persistent homology technology.

Description

Technical Field

[0001] This application relates to the field of data management technology, and in particular to a basic database management method for heterogeneous cloud environments. Background Technology

[0002] The field of cloud computing and distributed database management technology involves the basic database operation and maintenance system for heterogeneous cloud environments across regions and architectures. In this technical system, the segmented hysteresis threshold control and resource scheduling mechanism for database instance load fluctuations is the core component to ensure the continuity of data services in heterogeneous cloud environments. It aims to achieve adaptive allocation and consistency maintenance of cross-cloud database resources by setting multi-level control intervals such as rapid expansion, smooth transition and precise control, and with the help of hysteresis buffers to prevent frequent state jitter.

[0003] Existing heterogeneous cloud database management methods generally employ hard threshold judgment mechanisms based on fixed values. This discrete switching approach leads to step jumps in control variables at segment boundaries, causing mechanical shocks to actuators and oscillations in resource allocation. Simultaneously, due to cross-domain network latency, sensor noise, and state synchronization errors in heterogeneous cloud environments, the system is prone to limit cycle oscillations near hysteresis zone boundaries, resulting in ineffective consumption of cloud resource costs. Furthermore, existing methods lack formal security verification for the split-brain risk in network partitioning scenarios, making it difficult to ensure data consistency and severely limiting the level of automated management of heterogeneous cloud databases in high-concurrency business scenarios. Summary of the Invention

[0004] The main objective of this application is to provide a basic database management method for heterogeneous cloud environments to solve the problems mentioned in the background.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] A basic database management method for heterogeneous cloud environments, with the following specific steps: S1. Heterogeneous Data Awareness: Deploy probes across cloud nodes to collect database performance, network transmission and physical resource indicators. Construct a directed acyclic graph based on a structural causal model to distinguish the causal links between real load and network disturbances, and generate a high-confidence causal dataset. S2. Load simulation and prediction: Construct a dual-path simulation model of intervention path and natural evolution path, identify false overload by combining load change rate, and generate suppression command or pre-intervention command. S3. Topological Feature Extraction: Map time-series data to high-dimensional point clouds and construct simple complexes, calculate homology groups to extract bottleneck features, and generate a topological health index. S4. Smooth Hysteresis Decision: Integrates pre-intervention instructions and health indices, sets an asymmetric double-threshold hysteresis band, and uses continuous interpolation in the critical interval to achieve smooth transition of control quantity. After formal verification, it outputs resource adjustment instructions. S5. Execution Feedback Optimization: Monitor execution response, optimize inference threshold, filtering parameters and hysteresis coefficient, update feature library and retrain causal graph to achieve closed-loop self-evolution.

[0007] Preferably, in step S1, the specific method for heterogeneous data perception is as follows: S1.1 Deploy multi-dimensional status probes on distributed nodes deployed across clouds to collect in real time the CPU utilization, query rate per second, number of active connections, cross-domain network transmission latency and packet loss rate, underlying storage input and output throughput and gas source pressure value of database instances. Perform timestamp alignment and unit standardization processing on heterogeneous data sources to eliminate differences in collection frequency and protocol format deviations, and build a unified time series data base. S1.2 Based on a unified time series data base, the structural causal model algorithm is used to mine the real causal dependencies between various state indicators, and a directed acyclic causal graph is constructed to explicitly characterize the causal direction and promiscuous paths between business load and network disturbance. The original time series data is subjected to depromiscuous processing based on the backdoor criterion to generate a high-confidence causal dataset that eliminates spurious correlation interference.

[0008] Preferably, in step S2, the specific method for load projection and prediction is as follows: S2.1 Receive a high-confidence causal dataset and construct a dual-channel inference architecture based on a potential outcome framework. The architecture includes a parameter Siamese network with shared structure. Input the observed load feature vectors into the intervention channel and the natural evolution channel respectively. The intervention channel simulates the state transition probability after performing the expansion action through the do-operator. The natural evolution channel keeps the current policy unchanged. The two channels calculate the system state sequence for the next N time steps in parallel to form a comparative state pair containing the intervention trajectory and the counterfactual trajectory. S2.2 Extract the load change rate and acceleration features, calculate the load change rate using the exponentially weighted moving average algorithm, and calculate the load acceleration using the second-order difference; combine the comparison state to calculate the individual treatment effect estimate. When the counterfactual trajectory shows that the natural load drop exceeds the set threshold and lasts for M cycles under no-intervention conditions, it is determined to be a false overload and an inhibition instruction is generated. When the counterfactual trajectory shows that the number of connections is growing exponentially, the optimal intervention lead time is calculated and a pre-intervention instruction is generated.

[0009] Preferably, in step S3.1, the CPU utilization, query rate per second, and active connection rate are received and mapped into three-dimensional state vectors and embedded into a high-dimensional state space to construct a dynamic point cloud set that evolves over time. Adjacency relationships are established based on the Euclidean distance metric of each state vector within the sliding time window. A distance threshold ε is set to expand layer by layer to form a simple complex structure. The simple complex is output as a multi-scale topological skeleton to the coherence calculation stage. S3.2 Receive simple complex structure, calculate zero-dimensional homology group to track the evolution of the number of connected components over time, calculate one-dimensional homology group to capture the birth and death times of loops caused by resource competition, construct a persistence graph to record the birth and death times of each homology class, calculate the duration and set a noise filtering threshold, screen stable topological features with duration greater than the threshold, extract persistent topological invariants corresponding to deadlock precursor modes and resource competition modes, quantify and generate a topological health index and output it to the hysteresis decision-making stage.

[0010] Preferably, step S3.1 is performed as follows: S3.11 Receive the high-confidence causal dataset after decontamination, extract the CPU utilization, query rate per second and cross-domain network latency indicators to construct a three-dimensional state vector, embed it into a high-dimensional Euclidean space to form a temporal state snapshot, and construct a dynamic point cloud set that evolves over time. S3.12. Set the width of the sliding time pane to W time periods, extract a local state subset of the dynamic point cloud set, calculate the Euclidean distance between each state vector to establish adjacency relationships, set the distance threshold ε=0.5σ, where σ is the standard deviation of the state vector, connect neighboring points with a distance less than ε to expand layer by layer to form a Vietoris-Rips complex structure, and terminate the expansion when the complex dimension reaches the preset upper limit or the coverage reaches the threshold, construct a multi-scale topological skeleton representing the evolution trajectory of the system's healthy state and output it to the coherence calculation stage.

[0011] Preferably, step S3.2 is performed in the following manner; S3.21. Receive the multi-scale topological skeleton, perform orientation processing on each simplification in the simplex and assign integer coefficients, construct a linear combination to generate a chain group, establish the incidence matrix representation of the boundary operator, calculate the surface relations of the simplification to obtain the boundary matrix, calculate the rank of the matrix by performing Smith canonical decomposition on the boundary matrix, obtain the zero-dimensional homology group to track the generation and dissolution time of connected components, obtain the one-dimensional homology group to capture the birth and death time of loop structures, and construct a set of algebraic invariants characterizing the topological defects of the state space. S3.22. Construct a persistent graph based on the set of algebraic invariants. The horizontal axis records the birth time of topological features, and the vertical axis records the extinction time. Calculate the duration of each homology class, set a noise filtering threshold, and select stable homology classes with a duration greater than the threshold. Extract persistent loop features corresponding to deadlock precursors and connected component splitting features corresponding to resource contention. Map persistent topological invariants to normalized values, generate a topological health index, and output it to the hysteresis decision-making process.

[0012] Preferably, in step S4, the specific method for smoothing hysteresis decision-making is as follows: S4.1 Receive pre-intervention instructions and topology health index, construct an asymmetric dual-threshold hysteresis band with expansion threshold T_up and reduction threshold T_down, set the asymmetric difference Δ=T_up-T_down=15% and T_up=75% and T_down=30%, dynamically adjust the center position μ and boundary width δ of the hysteresis band according to the load change trend, and use the Sigmoid continuous interpolation function to perform smooth and gradual processing on the control quantity within the critical interval [μ-δ, μ+δ] to generate a control decision to be verified that satisfies the continuity constraint; S4.2 Receive the control decision to be verified, define the state space as normal, expanding, shrinking, network partition risk, and dual-master write risk, construct a state transition system model to characterize the transition relationship between each state, use the timing logic TLA+ to perform formal verification on the state switching action, check whether there are abnormal migration paths that violate split-brain avoidance and data consistency maintenance, and only generate incremental resource adjustment instructions and output them to the feedback optimization stage after the verification is passed.

[0013] Preferably, step S4.1 is performed as follows: S4.11. Integrate pre-intervention instructions and topological health index to construct an asymmetric dual-threshold hysteresis band, configure the asymmetric difference Δ=15% between the expansion boundary T_up and the shrinkage boundary T_down and the initial boundary width δ0=5%, set the initial center position μ0 of the hysteresis band according to the historical load fluctuation variance, and establish a hysteresis control base for the load characteristics of heterogeneous cloud databases. S4.12. Based on the load change trend v, perform an upward offset of the hysteresis band center position, with an offset amount Δμ=-k·v, where k is the system response coefficient. Dynamically adjust the boundary width δ=δ0·(1+|v| / v_max). Within the critical interval [μ-δ, μ+δ], use the Sigmoid function σ(x)=1 / (1+e^{-x}) to perform a smooth and gradual change on the control quantity, eliminating the step jump caused by hard threshold switching, and generating a control decision to be verified that satisfies the continuity constraint.

[0014] Preferably, step S4.2 is performed as follows: S4.21 Receive control decisions to be verified, define state variables including current master node identifier, replica synchronization delay, network partition flag, and data consistency status, and construct a state transition system model covering network partition and dual-master write anomaly scenarios; establish a security specification based on time-series logic TLA+, define the atomic proposition Safe≡(master node unique∧replica delay<threshold), characterize the critical constraints for split-brain avoidance and data consistency maintenance, and construct a computable and verifiable logical specification base; S4.22. Based on the state transition system model, perform formal verification of the state switching actions in the control decision to be verified. Use the symbolic model detection algorithm to traverse the state space and check whether there is a reachable path from the initial state to the Safe state. Only when the verification is passed and no violation risk is confirmed, generate the incremental resource adjustment instruction and output it to the feedback optimization stage.

[0015] Preferably, in step S5, the feedback optimization is performed in the following specific way: S5.1 Collect the execution response data of resource adjustment instructions, construct a decision effect evaluation vector containing response time, resource utilization rate and switching frequency, construct a reward signal R=α·utilization rate+β·(1 / response time)-γ·switching frequency based on the evaluation vector, and use the strategy gradient algorithm to perform online collaborative optimization on the counterfactual inference threshold, topology filter parameters and hysteresis boundary coefficient to realize adaptive dynamic adjustment of control parameters. S5.2 Set the retraining cycle to T time units, extract the newly identified bottleneck topology patterns and update them to the feature knowledge base. Based on incremental observation data, use the online Bayesian update algorithm to periodically retrain the causal graph node relationships and causal link weights, continuously optimize the causal structure to adapt to the business evolution trend, and realize the global closed-loop self-evolution of the management method.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. Compared with traditional heterogeneous cloud database management methods based on hard threshold judgment, this solution introduces structural causal models, topological data analysis, and formal verification techniques to construct a complete closed-loop architecture covering causal cognition, topological feature extraction, and security verification. This solves the security risks of limit cycle oscillation, control variable step jump, and split-brain in existing technologies. It achieves a comprehensive technical upgrade from passive response to predictive scheduling, from hard threshold to smooth transition, and from empirical security to mathematically provable security, thereby improving the automation level, resource utilization, and operational reliability of database management in heterogeneous cloud environments.

[0017] 2. Compared to traditional monitoring methods based on single-dimensional thresholds or simple statistical analysis, the above-mentioned topological feature extraction step, by introducing algebraic topology and continuous cohomology techniques, achieves a leap from apparent numerical values ​​to structural features. This method overcomes the limitation of traditional methods that can only detect linear out-of-range anomalies. By constructing a high-dimensional topological model to capture the connectivity and loop structure of the system's state space, it identifies deep fault modes such as deadlock precursors. This solves the problems of difficulty in fusing multi-source indicators and insufficient early warning of complex faults in heterogeneous cloud environments. It achieves the technical effect of accurately characterizing system structural risks and improving the depth of anomaly detection, providing deep-level structured feature support for the accurate scheduling and high-reliability operation of heterogeneous cloud databases, and optimizing the accuracy of resource bottleneck prediction. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of the method described in this application. Detailed Implementation

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

[0020] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] Example 1: Please refer to Figure 1 A basic database management method for heterogeneous cloud environments, with the following specific steps: S1. Heterogeneous Data Awareness: Deploy probes across cloud nodes to collect database performance, network transmission and physical resource indicators. Construct a directed acyclic graph based on a structural causal model to distinguish the causal links between real load and network disturbances, and generate a high-confidence causal dataset. S2. Load simulation and prediction: Construct a dual-path simulation model of intervention path and natural evolution path, identify false overload by combining load change rate, and generate suppression command or pre-intervention command. S3. Topological Feature Extraction: Map time-series data to high-dimensional point clouds and construct simple complexes, calculate homology groups to extract bottleneck features, and generate a topological health index. S4. Smooth Hysteresis Decision: Integrates pre-intervention instructions and health indices, sets an asymmetric double-threshold hysteresis band, and uses continuous interpolation in the critical interval to achieve smooth transition of control quantity. After formal verification, it outputs resource adjustment instructions. S5. Execution Feedback Optimization: Monitor execution response, optimize inference threshold, filtering parameters and hysteresis coefficient, update feature library and retrain causal graph to achieve closed-loop self-evolution.

[0023] In this embodiment: In step S1, distributed probes are deployed across cloud nodes to collect database instance performance, cross-domain network transmission and physical resource status indicators in real time. A directed acyclic causal graph is constructed using a structural causal model algorithm to clearly distinguish the causal links between real business load and network disturbances. Decontamination preprocessing is performed on time-series data to complete high-confidence fusion of heterogeneous data sources and identification of true and false signals, thereby achieving the purpose of eliminating false correlation interference and effectively avoiding false overload misjudgments caused by cross-domain network jitter.

[0024] In step S2, a dual-channel inference architecture of intervention path and natural evolution path is constructed based on high-confidence causal dataset. The system state evolution trajectory under resource regulation and the counterfactual state trajectory under no-intervention conditions are calculated in parallel. The false overload scenario is identified by combining load change rate and acceleration characteristics, and suppression instructions or pre-intervention instructions with optimal intervention lead are generated. This completes the upgrade of the decision-making mode from passive response to causal prediction, and achieves the purpose of pre-allocating resources before the actual overload occurs, reducing the frequency of ineffective expansion and contraction caused by misjudgment.

[0025] In step S3, multi-dimensional time-series data is mapped to a high-dimensional state space to construct a dynamic point cloud set. Adjacency relationships are established based on distance metrics and expanded layer by layer to form a simple complex structure. Zero-dimensional and one-dimensional homology groups are calculated to track the evolution of connected components and the occurrence of loops. Long-lifecycle topological features are screened using persistent barcodes. Topological invariants corresponding to deadlock precursor patterns and resource contention patterns are extracted to generate a topological health index. This completes the deep characterization of the bottleneck structure of complex systems and achieves the goal of identifying nonlinear abnormal patterns that cannot be captured by traditional scalar thresholds.

[0026] In step S4, the pre-intervention command and the topology health index are integrated to construct a double-threshold hysteresis band with asymmetric width. The center position and boundary width of the hysteresis band are dynamically adjusted according to the load change trend. Within the critical transition interval, a continuous interpolation function is used to perform smooth and gradual processing on the control quantity to eliminate step jumps. A state transition system model is constructed using timing logic to perform formal verification of the state switching action, and to check the risks of network partitioning and dual master writes. Only after the verification is passed is a progressive resource adjustment command generated, which completes the dual guarantee of smoothness and security, and achieves the goal of flexible resource allocation under the premise of ensuring data consistency. This solves the safety hazards of limit loop oscillation and split-brain.

[0027] In step S5, the execution response data of resource adjustment instructions are collected to construct a feedback loop. A reward signal is constructed based on the decision effect evaluation. Online collaborative optimization is performed on the counterfactual inference threshold, topology filter parameters, and hysteresis boundary coefficients. Newly identified bottleneck topology patterns are extracted and updated to the feature knowledge base. Based on incremental observation data, the causal graph node relationships and causal link weights are periodically retrained. This completes the adaptive dynamic adjustment of control parameters and the continuous optimization of the causal structure, achieving the goal of the management system automatically converging to the optimal state as business evolves. This realizes true closed-loop self-evolution and long-term autonomous operation.

[0028] Compared to traditional heterogeneous cloud database management methods based on hard threshold judgment, this solution introduces structural causal models, topological data analysis, and formal verification techniques to construct a complete closed-loop architecture covering causal cognition, topological feature extraction, and security verification. This solves the security risks of limit cycle oscillation, control variable step transition, and split-brain in existing technologies. It achieves a comprehensive technical upgrade from passive response to predictive scheduling, from hard threshold switching to smooth transition, and from empirical security to mathematically provable security, thereby improving the automation level, resource utilization, and operational reliability of database management in heterogeneous cloud environments.

[0029] Example 2: Please refer to Figure 1 In step S1, the specific method for heterogeneous data perception is as follows: S1.1 Deploy multi-dimensional status probes on distributed nodes deployed across clouds to collect in real time the CPU utilization, query rate per second, number of active connections, cross-domain network transmission latency and packet loss rate, underlying storage input and output throughput and gas source pressure value of database instances. Perform timestamp alignment and unit standardization processing on heterogeneous data sources to eliminate differences in collection frequency and protocol format deviations, and build a unified time series data base. S1.2 Based on a unified time series data base, the structural causal model algorithm is used to mine the real causal dependencies between various state indicators, and a directed acyclic causal graph is constructed to explicitly characterize the causal direction and promiscuous paths between business load and network disturbance. The original time series data is subjected to depromiscuous processing based on the backdoor criterion to generate a high-confidence causal dataset that eliminates spurious correlation interference.

[0030] In this embodiment: S1.1, multi-dimensional status probes are deployed on distributed nodes across clouds to achieve comprehensive real-time collection of database instance CPU utilization, query rate per second, active connections, cross-domain network transmission latency and packet loss rate, underlying storage input / output throughput, and gas source pressure values. By performing timestamp alignment and dimensional standardization on heterogeneous data sources, the differences in collection frequency and protocol format deviations between different cloud nodes are effectively eliminated, constructing a unified time-series data foundation. This solves the problem of collection synchronization loss and format conflict caused by the heterogeneity of multi-source data in heterogeneous cloud environments, achieving the goal of providing standardized, high-quality data support for subsequent causal analysis and improving the availability and consistency of the original data.

[0031] S1.2. Based on a unified time-series data foundation, a structural causal model algorithm is used to deeply mine the true causal dependencies between various state indicators, constructing a directed acyclic causal graph to explicitly characterize the causal direction and confounding paths between business load and network disturbances. By performing deconfounding processing on the original time-series data based on the backdoor criterion, the spurious influence of external factors such as cross-domain network latency on load assessment is effectively removed, generating a high-confidence causal dataset that eliminates spurious correlation interference. This process completes the transformation from raw data to causal understanding, achieving the goal of accurately distinguishing between real business pressure and network noise, laying a reliable causal logical foundation for subsequent accurate inference and prediction, and reducing the risk of misjudgment caused by spurious correlations.

[0032] Compared to traditional data processing methods based on simple correlation analysis, the above-mentioned heterogeneous data perception step, by introducing a structural causal model and a backdoor criterion decontamination mechanism, achieves a leap from superficial data to essential causality. This method not only solves the technical challenges of inconsistent data formats and asynchronous data collection in heterogeneous cloud environments, but also clarifies the true causal links between business load and network disturbances by constructing a directed acyclic causal graph. This eliminates false overload misjudgments caused by contamination factors in traditional methods, achieving the technical effects of improving data confidence and reducing false alarm rates. It provides a high-quality data cognition foundation for the accurate management of heterogeneous cloud databases, enhancing the system's data perception capabilities and decision reliability in complex heterogeneous environments.

[0033] Example 3: Please refer to Figure 1 In step S2, the specific method for load projection and prediction is as follows: S2.1 Receive a high-confidence causal dataset and construct a dual-channel inference architecture based on a potential outcome framework. The architecture includes a parameter Siamese network with shared structure. Input the observed load feature vectors into the intervention channel and the natural evolution channel respectively. The intervention channel simulates the state transition probability after performing the expansion action through the do-operator. The natural evolution channel keeps the current policy unchanged. The two channels calculate the system state sequence for the next N time steps in parallel to form a comparative state pair containing the intervention trajectory and the counterfactual trajectory. S2.2 Extract the load change rate and acceleration features, calculate the load change rate using the exponentially weighted moving average algorithm, and calculate the load acceleration using the second-order difference; combine the comparison state to calculate the individual treatment effect estimate. When the counterfactual trajectory shows that the natural load drop exceeds the set threshold and lasts for M cycles under no-intervention conditions, it is determined to be a false overload and an inhibition instruction is generated. When the counterfactual trajectory shows that the number of connections is growing exponentially, the optimal intervention lead time is calculated and a pre-intervention instruction is generated.

[0034] In this embodiment: S2.1 receives a high-confidence causal dataset and constructs a dual-channel inference architecture for intervention paths and natural evolution paths. Through parallel computation of the system state evolution trajectory under resource regulation actions and the counterfactual state trajectory under no-intervention conditions, a comparative state pair containing state differences and evolutionary trends is formed. This process completes a dual simulation and quantitative comparison of the system's future evolution path, achieving the goal of rehearsing the effects of different strategies before decision-making. It effectively overcomes the limitations of traditional single-path prediction, providing a comprehensive decision-making basis for accurately identifying real load demands and improving the comprehensiveness and accuracy of predictions.

[0035] S2.2 extracts load change rate and acceleration characteristics to perform dynamic trend extrapolation. Combined with comparative state analysis, it deeply identifies false overload scenarios caused by cross-domain network latency. If the counterfactual state indicates the system can self-heal, it generates suppression instructions to avoid ineffective operations. If it indicates connection resource exhaustion, it calculates the optimal intervention lead time and generates pre-intervention instructions. This process achieves accurate quantification of load change trends and strict differentiation between true and false overloads, achieving the goal of precise intervention before a real crisis occurs. It effectively avoids resource waste and business interruption caused by misjudgment, and improves the timeliness and accuracy of resource allocation.

[0036] Compared to traditional load management methods based on fixed thresholds or simple statistical predictions, the aforementioned load extrapolation and prediction steps, by introducing counterfactual reasoning and dynamic trend extrapolation mechanisms, achieve a paradigm shift from passive response to proactive prediction. This method not only overcomes the limitations of traditional approaches that can only react laggingly based on the current state, but also accurately identifies false overloads caused by cross-domain network latency through a dual-channel comparison of intervention and natural processes. It eliminates the frequent false triggering problems caused by noise interference in traditional threshold judgments, achieving the technical effects of predicting resource demand in advance and accurately suppressing ineffective operations. This reduces the risk of system instability and resource waste, provides intelligent decision support for the stable operation of heterogeneous cloud databases in highly dynamic environments, and significantly improves the response speed and decision reliability of the overall management system. Example 4: Please refer to Figure 1 In step S3, the specific method for topological feature extraction is as follows: S3.1 Receive time-series data stream, map the CPU utilization, query rate per second and active connection rate into three-dimensional state vectors and embed them into a high-dimensional state space, construct a dynamic point cloud set that evolves over time, establish adjacency relationships based on the Euclidean distance metric of each state vector within the sliding time window, set a distance threshold ε to expand layer by layer to form a simple complex structure, and output the simple complex as a multi-scale topological skeleton to the coherence calculation stage. S3.2 Receive simple complex structure, calculate zero-dimensional homology group to track the evolution of the number of connected components over time, calculate one-dimensional homology group to capture the birth and death times of loops caused by resource competition, construct a persistence graph to record the birth and death times of each homology class, calculate the duration and set a noise filtering threshold, screen stable topological features with duration greater than the threshold, extract persistent topological invariants corresponding to deadlock precursor modes and resource competition modes, quantify and generate a topological health index and output it to the hysteresis decision-making stage.

[0037] Compared to traditional monitoring methods based on single-dimensional thresholds or simple statistical analysis, the aforementioned topological feature extraction step, by introducing algebraic topology and continuous cohomology techniques, achieves a leap from apparent numerical values ​​to structural features. This method overcomes the limitation of traditional methods that can only detect linear out-of-range anomalies. By constructing a high-dimensional topological model to capture the connectivity and loop structure of the system's state space, it identifies deep-seated fault modes such as deadlock precursors. This solves the problems of difficulty in fusing multi-source indicators and insufficient early warning of complex faults in heterogeneous cloud environments, achieving the technical effect of accurately characterizing system structural risks and improving the depth of anomaly detection. It provides deep-level structured feature support for the precise scheduling and high-reliability operation of heterogeneous cloud databases and optimizes the accuracy of resource bottleneck prediction.

[0038] Example 5: Please refer to Figure 1 The specific method for step S3.1 is as follows: S3.11 Receive the high-confidence causal dataset after decontamination, extract the CPU utilization, query rate per second and cross-domain network latency indicators to construct a three-dimensional state vector, embed it into a high-dimensional Euclidean space to form a temporal state snapshot, and construct a dynamic point cloud set that evolves over time. S3.12. Set the width of the sliding time pane to W time periods, extract a local state subset of the dynamic point cloud set, calculate the Euclidean distance between each state vector to establish adjacency relationships, set the distance threshold ε=0.5σ, where σ is the standard deviation of the state vector, connect neighboring points with a distance less than ε to expand layer by layer to form a Vietoris-Rips complex structure, and terminate the expansion when the complex dimension reaches the preset upper limit or the coverage reaches the threshold, construct a multi-scale topological skeleton representing the evolution trajectory of the system's healthy state and output it to the coherence calculation stage.

[0039] In this embodiment: S3.11 receives a high-confidence causal dataset after decontamination processing, extracts heterogeneous indicators such as CPU utilization, query rate per second, and cross-domain network latency to construct a multi-dimensional state vector, embeds it into a high-dimensional state space to form a temporal state snapshot, and constructs a dynamic point cloud set that evolves over time. This process completes the high-dimensional topological transformation of heterogeneous monitoring data, transforming discrete time-series indicators into a spatial distribution that retains topological correlation characteristics. It achieves the goal of fusing heterogeneous indicators under a unified mathematical framework, solves the fusion difficulties and information fragmentation problems caused by the heterogeneity of multi-source data in traditional methods, and provides a standardized data structure foundation for subsequent topological analysis.

[0040] S3.12 uses a sliding time pane to extract a subset of local states from a dynamic point cloud dataset, calculates the spatial proximity between state vectors to establish adjacency relationships, and expands the connections of neighboring points layer by layer to form a complex structure, constructing a multi-scale topological skeleton capable of representing the evolution trajectory of the system's healthy state. This process achieves structured abstraction of high-dimensional point cloud data, transforming complex state evolution into computable topological objects, thus capturing both local correlations and global structural features of the system. It overcomes the limitation of traditional thresholding methods, which can only identify linear outliers, and provides a reliable mathematical representation for extracting deep bottleneck features.

[0041] Compared to traditional monitoring methods based on single-dimensional thresholds or simple statistical analysis, the aforementioned topology construction steps, by introducing high-dimensional space mapping and complex construction techniques, achieve topological modeling of the operational status of heterogeneous cloud databases. This method overcomes the limitations of traditional methods, which can only examine single indicators in isolation and cannot characterize the correlation structure between indicators. By constructing dynamic point clouds and multi-scale topological skeletons, it preserves the continuity and correlation of the system's operational trajectory, solves the technical challenges of multi-source data fusion and complex state representation in heterogeneous environments, and achieves the technical effect of accurately characterizing the system's state evolution structure and extracting deep topological correlation features. This provides a solid structural foundation for identifying nonlinear anomaly patterns such as deadlock precursors and optimizes the accuracy and reliability of heterogeneous cloud database health status assessment.

[0042] Example 6: Please refer to Figure 1 The specific method for step S3.2 is as follows; S3.21. Receive the multi-scale topological skeleton, perform orientation processing on each simplification in the simplex and assign integer coefficients, construct a linear combination to generate a chain group, establish the incidence matrix representation of the boundary operator, calculate the surface relations of the simplification to obtain the boundary matrix, calculate the rank of the matrix by performing Smith canonical decomposition on the boundary matrix, obtain the zero-dimensional homology group to track the generation and dissolution time of connected components, obtain the one-dimensional homology group to capture the birth and death time of loop structures, and construct a set of algebraic invariants characterizing the topological defects of the state space. S3.22. Construct a persistent graph based on the set of algebraic invariants. The horizontal axis records the birth time of topological features, and the vertical axis records the extinction time. Calculate the duration of each homology class, set a noise filtering threshold, and select stable homology classes with a duration greater than the threshold. Extract persistent loop features corresponding to deadlock precursors and connected component splitting features corresponding to resource contention. Map persistent topological invariants to normalized values, generate a topological health index, and output it to the hysteresis decision-making process.

[0043] In this embodiment: S3.21 performs chain group construction and boundary operator mapping for the simple complex structure, calculates the zero-dimensional homology group to track the generation and breakage evolution trajectory of the system's connected components, calculates the one-dimensional homology group to capture the emergence and disappearance of loops caused by resource competition, and constructs a set of algebraic topological invariants characterizing state-space connectivity defects and cyclic dependencies. This process completes the algebraic abstraction of the system's structural stability, transforms geometric topological features into computable invariants, achieves the goal of accurately quantifying connectivity changes and cyclic dependency formation mechanisms, and breaks through the limitation of traditional monitoring that can only observe surface values, providing a mathematical basis for identifying deep structural faults.

[0044] S3.22 constructs a persistent graph based on the homology calculation results to record the generation and annihilation parameters of topological features. It then selects stable homology classes with long lifecycles to suppress transient noise interference, extracts persistent topological invariants corresponding to deadlock precursor modes and resource contention modes, quantifies and maps them to generate a topological health index, and outputs it to the decision-making stage. This process achieves time-series tracking and noise filtering of topological feature stability, distinguishes between transient abnormal fluctuations and structural faults, and achieves the goal of extracting persistent bottleneck features and quantifying the system's health. It avoids misjudgments caused by transient jitter and provides a high-confidence topological assessment basis for accurate decision-making.

[0045] Compared to traditional monitoring methods based on threshold judgment or statistical analysis, the above-mentioned topological feature extraction step, by introducing homology algebra and continuous homology techniques, achieves a deep characterization of system structural evolution. This method breaks through the superficial model of traditional methods that can only detect single-indicator exceedances. By tracking the evolution of connected components and changes in loop structure, it accurately captures the topological essence of complex faults such as resource contention and deadlock precursors, solving the technical challenge of early identification of deep bottlenecks in heterogeneous cloud environments. This achieves the technical effect of accurately identifying structural risks and filtering transient noise interference, providing reliable topological support for the refined management and fault early warning of heterogeneous cloud databases, and improving the depth and accuracy of system health assessment.

[0046] Example 7: Please refer to Figure 1 In step S4, the specific method for smoothing hysteresis decision-making is as follows: S4.1 Receive pre-intervention instructions and topology health index, construct an asymmetric dual-threshold hysteresis band with expansion threshold T_up and reduction threshold T_down, set the asymmetric difference Δ=T_up-T_down=15% and T_up=75% and T_down=30%, dynamically adjust the center position μ and boundary width δ of the hysteresis band according to the load change trend, and use the Sigmoid continuous interpolation function to perform smooth and gradual processing on the control quantity within the critical interval [μ-δ, μ+δ] to generate a control decision to be verified that satisfies the continuity constraint; S4.2 Receive the control decision to be verified, define the state space as normal, expanding, shrinking, network partition risk, and dual-master write risk, construct a state transition system model to characterize the transition relationship between each state, use the timing logic TLA+ to perform formal verification on the state switching action, check whether there are abnormal migration paths that violate split-brain avoidance and data consistency maintenance, and only generate incremental resource adjustment instructions and output them to the feedback optimization stage after the verification is passed.

[0047] In this embodiment: Step S4.1 receives the pre-intervention command and the topology health index, constructs a dual-threshold hysteresis band with asymmetric width to distinguish between expansion and contraction trigger boundaries, and dynamically adjusts the center position and boundary width of the hysteresis band according to the load change trend. Within the critical transition interval, a continuous interpolation function is used to perform smooth and gradual processing on the control quantity, generating a control decision to be verified that conforms to the continuity constraint. This process completes the flexible transformation of the hard threshold switching mechanism, eliminates the step jump of the control quantity and the mechanical shock of the actuator, achieves the purpose of suppressing the limit cycle oscillation, and provides a decision basis for smooth transition in subsequent stages.

[0048] Step S4.2 receives the control decisions to be verified, constructs a state transition system model covering network partitioning and dual-master write scenarios, and uses timing logic to perform formal verification of the state switching actions to check for security constraint violations. Only when the verification passes and confirms there are no abnormal migration paths is a progressive resource adjustment instruction generated and output to the feedback loop. This process completes the mathematical security verification of the control decisions, establishes a critical security barrier, achieves the goal of ensuring data consistency in heterogeneous cloud split-brain scenarios, and prevents the risk of conflict between partition tolerance and consistency assurance.

[0049] Compared to traditional heterogeneous cloud database management methods based on hard threshold judgments and lacking security verification, the aforementioned smooth hysteresis decision-making steps, by introducing asymmetric dynamic hysteresis and formal verification mechanisms, achieve a leap from empirical decision-making to mathematically provable secure decision-making. This method solves the problems of control variable step jumps and limit cycle oscillations caused by traditional hard thresholds, and eliminates the split-brain risk in network partitioning scenarios through time-series logic verification. Thus, it achieves a balance between the smoothness and security of resource regulation, ensuring high system availability while enabling gradual and flexible expansion, and optimizing the automated management level and operational reliability of heterogeneous cloud databases in complex network environments.

[0050] Example 8: Please refer to Figure 1 The specific method for step S4.1 is as follows: S4.11. Integrate pre-intervention instructions and topological health index to construct an asymmetric dual-threshold hysteresis band, configure the asymmetric difference Δ=15% between the expansion boundary T_up and the shrinkage boundary T_down and the initial boundary width δ0=5%, set the initial center position μ0 of the hysteresis band according to the historical load fluctuation variance, and establish a hysteresis control base for the load characteristics of heterogeneous cloud databases. S4.12. Based on the load change trend v, perform an upward offset of the hysteresis band center position, with an offset amount Δμ=-k·v, where k is the system response coefficient. Dynamically adjust the boundary width δ=δ0·(1+|v| / v_max). Within the critical interval [μ-δ, μ+δ], use the Sigmoid function σ(x)=1 / (1+e^{-x}) to perform a smooth and gradual change on the control quantity, eliminating the step jump caused by hard threshold switching, and generating a control decision to be verified that satisfies the continuity constraint.

[0051] In this embodiment: S4.11 integrates pre-intervention commands and topology health indices to construct an asymmetric dual-threshold hysteresis band. By configuring the asymmetric difference and width parameters of the expansion and contraction boundaries, and setting the initial center position of the hysteresis band based on load fluctuation characteristics, a hysteresis control base oriented towards the load characteristics of heterogeneous cloud databases is established. This process completes the flexible transformation of the fixed symmetric threshold, distinguishes the trigger sensitivity of expansion and contraction, achieves the purpose of suppressing state jitter caused by load fluctuations, provides a configurable parameter basis for subsequent dynamic adjustments, and avoids the frequent false triggering problem caused by traditional single-threshold strategies.

[0052] S4.12 performs an upward offset of the hysteresis band center position and adaptive adjustment of the boundary width based on the load change trend. Within the critical transition range, a continuous interpolation function is used to perform smooth and gradual processing on the control quantity. This process eliminates the step jump in control quantity and mechanical shock to the actuator caused by hard threshold switching, completes the transformation from discrete switching control to continuous smooth control, achieves the purpose of suppressing limit cycle oscillation and ensuring the life of the actuator, and generates a control decision to be verified that meets the continuity constraint, providing a control command basis for a smooth transition in the safety verification stage.

[0053] Compared to traditional heterogeneous cloud database resource scheduling methods based on fixed hard thresholds, the aforementioned smooth hysteresis construction step, by introducing an asymmetric dual-threshold design and continuous interpolation mechanism, achieves a leap from rigid switching to flexible adjustment in control strategy. This method overcomes the technical limitations of traditional approaches that cause abrupt changes in control quantity at threshold boundaries. By dynamically adjusting the hysteresis center position and boundary width, it solves the problems of frequent switching and mechanical shocks in load fluctuation scenarios. This achieves the technical effects of smooth control quantity transition and suppression of limit loop oscillations, optimizing the control quality and actuator lifespan of heterogeneous cloud database resource scheduling, and providing a flexible decision support foundation for automated resource management in complex dynamic environments.

[0054] Example 9: Please refer to Figure 1 The specific method for step S4.2 is as follows: S4.21 Receive control decisions to be verified, define state variables including current master node identifier, replica synchronization delay, network partition flag, and data consistency status, and construct a state transition system model covering network partition and dual-master write anomaly scenarios; establish a security specification based on time-series logic TLA+, define the atomic proposition Safe≡(master node unique∧replica delay<threshold), characterize the critical constraints for split-brain avoidance and data consistency maintenance, and construct a computable and verifiable logical specification base; S4.22. Based on the state transition system model, perform formal verification of the state switching actions in the control decision to be verified. Use the symbolic model detection algorithm to traverse the state space and check whether there is a reachable path from the initial state to the Safe state. Only when the verification is passed and no violation risk is confirmed, generate the incremental resource adjustment instruction and output it to the feedback optimization stage.

[0055] In this embodiment: After receiving the control decision to be verified in step S4.21, a state transition system model covering network partitioning and dual-master write anomaly scenarios is constructed. By defining the state space and the set of transition rules, a formal description of the security specification based on temporal logic is established. This process characterizes the critical constraints for avoiding split-brain and maintaining data consistency, constructs a computable and verifiable logical specification base, and completes the mathematical modeling of security-critical attributes in a heterogeneous cloud environment. It achieves the goal of transforming security requirements into verifiable logical specifications, providing a rigorous mathematical foundation for subsequent automated verification and avoiding the ambiguity and vulnerability risks of traditional empirical security strategies.

[0056] S4.22 performs formal verification of state transition actions in the control decisions to be verified based on the state transition system model. A model checking algorithm traverses the state space to examine for any abnormal transition paths that violate safety protocols. Only when the verification passes and no violation risk is confirmed, a progressive resource adjustment instruction is generated and output to the feedback optimization stage. This process completes the mathematical rigor verification of the control decisions, establishes a critical safety barrier, and achieves the goal of ensuring zero safety violations in automated resource scheduling. It effectively prevents the risk of split-brain and data inconsistency issues in network partitioning scenarios, ensuring business continuity.

[0057] Compared to traditional security management methods for heterogeneous cloud databases based on experience-based judgment or simple timeout mechanisms, the formal verification steps described above, by introducing a state transition system model and temporal logic reduction, achieve a leap from empirical security to mathematically provable security. This method overcomes the limitations of traditional approaches, which struggle to cover all abnormal scenarios and cannot rigorously prove security. By constructing a complete state space encompassing network partitioning and dual-master writes, it solves the technical challenge of insufficient security verification in heterogeneous cloud environments. This achieves the technical effect of ensuring data consistency and preventing split-brain scenarios in automated decision-making, providing verifiable security guarantees for the critical business operations of heterogeneous cloud databases, optimizing system reliability and data security in partitioned fault-tolerant scenarios, and achieving a harmonious balance between security and automation.

[0058] Example 10: Please refer to Figure 1 In step S5, the specific method for performing feedback optimization is as follows: S5.1 Collect the execution response data of resource adjustment instructions, construct a decision effect evaluation vector containing response time, resource utilization rate and switching frequency, construct a reward signal R=α·utilization rate+β·(1 / response time)-γ·switching frequency based on the evaluation vector, and use the strategy gradient algorithm to perform online collaborative optimization on the counterfactual inference threshold, topology filter parameters and hysteresis boundary coefficient to realize adaptive dynamic adjustment of control parameters. S5.2 Set the retraining cycle to T time units, extract the newly identified bottleneck topology patterns and update them to the feature knowledge base. Based on incremental observation data, use the online Bayesian update algorithm to periodically retrain the causal graph node relationships and causal link weights, continuously optimize the causal structure to adapt to the business evolution trend, and realize the global closed-loop self-evolution of the management method.

[0059] In this embodiment: S5.1 collects the execution response data of resource adjustment instructions to construct a closed-loop feedback loop. Based on the decision effect evaluation, a quantitative reward signal is constructed to evaluate the merits of previous decisions. Then, online collaborative optimization is performed on the counterfactual inference threshold, topology filter parameters, and hysteresis boundary coefficients. This process achieves dynamic matching between control parameters and operational effectiveness, continuously correcting the decision benchmarks of each stage based on actual responses. This achieves the goal of enabling system control parameters to adaptively adjust with the accumulation of operational data, avoiding the limitations of fixed parameters being unable to adapt to business changes, and ensuring that the management method has an inherent mechanism for continuous optimization.

[0060] S5.2 extracts newly identified bottleneck topology patterns and updates them to the feature knowledge base. Based on incremental observation data, it periodically retrains the causal graph node relationships and causal link weights. This process completes the learning and accumulation of new failure modes and the dynamic updating of the causal structure. It continuously corrects the causal understanding between business load and network disturbances based on the latest observations, achieving the goal of enabling the causal model to self-update with business evolution. This avoids cognitive biases caused by model solidification, ensures that the decision-making basis of management methods always remains consistent with the current business situation, and achieves global closed-loop self-evolution.

[0061] Compared to traditional heterogeneous cloud database management methods based on fixed parameters and static models, the aforementioned execution feedback optimization steps achieve dual dynamic updates of control parameters and cognitive models through the construction of an online collaborative optimization mechanism and periodic causal graph retraining. This method overcomes the limitations of traditional approaches, which rely on fixed configurations and cannot self-improve as business evolves. Through real-time feedback correction and incremental learning, it solves the technical challenge of management strategies gradually becoming ineffective in heterogeneous cloud environments, achieving the goal of enabling the system to have self-learning and continuous evolution capabilities. This ensures that the management method maintains optimal decision-making performance and business adaptability throughout long-term operation.

[0062] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0063] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A basic database management method for a heterogeneous cloud environment, characterized in that: The specific steps are as follows: S1. Heterogeneous Data Awareness: Deploy probes across cloud nodes to collect database performance, network transmission and physical resource indicators. Construct a directed acyclic graph based on a structural causal model to distinguish the causal links between real load and network disturbances, and generate a high-confidence causal dataset. S2. Load simulation and prediction: Construct a dual-path simulation model of intervention path and natural evolution path, identify false overload by combining load change rate, and generate suppression command or pre-intervention command. S3. Topological Feature Extraction: Map time-series data to high-dimensional point clouds and construct simple complexes, calculate homology groups to extract bottleneck features, and generate a topological health index. S4. Smooth Hysteresis Decision: Integrates pre-intervention instructions and health indices, sets an asymmetric double-threshold hysteresis band, and uses continuous interpolation in the critical interval to achieve smooth transition of control quantity. After formal verification, it outputs resource adjustment instructions. S5. Execution Feedback Optimization: Monitor execution response, optimize inference threshold, filtering parameters and hysteresis coefficient, update feature library and retrain causal graph to achieve closed-loop self-evolution.

2. The basic database management method for a heterogeneous cloud environment according to claim 1, characterized in that, In step S1, the specific method for heterogeneous data perception is as follows: S1.1 Deploy multi-dimensional status probes on distributed nodes deployed across clouds to collect in real time the CPU utilization, query rate per second, number of active connections, cross-domain network transmission latency and packet loss rate, underlying storage input and output throughput and gas source pressure value of database instances. Perform timestamp alignment and unit standardization processing on heterogeneous data sources to eliminate differences in collection frequency and protocol format deviations, and build a unified time series data base. S1.2 Based on a unified time series data base, the structural causal model algorithm is used to mine the real causal dependencies between various state indicators, and a directed acyclic causal graph is constructed to explicitly characterize the causal direction and promiscuous paths between business load and network disturbance. The original time series data is subjected to depromiscuous processing based on the backdoor criterion to generate a high-confidence causal dataset that eliminates spurious correlation interference.

3. The basic database management method for a heterogeneous cloud environment according to claim 2, characterized in that, In step S2, the specific method for load projection and prediction is as follows: S2.1 Receive a high-confidence causal dataset and construct a dual-channel inference architecture based on a potential outcome framework. The architecture includes a parameter Siamese network with shared structure. Input the observed load feature vectors into the intervention channel and the natural evolution channel respectively. The intervention channel simulates the state transition probability after performing the expansion action through the do-operator. The natural evolution channel keeps the current policy unchanged. The two channels calculate the system state sequence for the next N time steps in parallel to form a comparative state pair containing the intervention trajectory and the counterfactual trajectory. S2.2 Extract the load change rate and acceleration features, calculate the load change rate using the exponentially weighted moving average algorithm, and calculate the load acceleration using the second-order difference; combine the comparison state to calculate the individual treatment effect estimate. When the counterfactual trajectory shows that the natural load drop exceeds the set threshold and lasts for M cycles under no-intervention conditions, it is determined to be a false overload and an inhibition instruction is generated. When the counterfactual trajectory shows that the number of connections is growing exponentially, the optimal intervention lead time is calculated and a pre-intervention instruction is generated.

4. The basic database management method for a heterogeneous cloud environment according to claim 3, characterized in that, In step S3, the specific method for topological feature extraction is as follows: S3.1 Receive time-series data stream, map the CPU utilization, query rate per second and active connection rate into three-dimensional state vectors and embed them into a high-dimensional state space, construct a dynamic point cloud set that evolves over time, establish adjacency relationships based on the Euclidean distance metric of each state vector within the sliding time window, set a distance threshold ε to expand layer by layer to form a simple complex structure, and output the simple complex as a multi-scale topological skeleton to the coherence calculation stage. S3.2 Receive simple complex structure, calculate zero-dimensional homology group to track the evolution of the number of connected components over time, calculate one-dimensional homology group to capture the birth and death times of loops caused by resource competition, construct a persistence graph to record the birth and death times of each homology class, calculate the duration and set a noise filtering threshold, screen stable topological features with duration greater than the threshold, extract persistent topological invariants corresponding to deadlock precursor modes and resource competition modes, quantify and generate a topological health index and output it to the hysteresis decision-making stage.

5. The basic database management method for a heterogeneous cloud environment according to claim 4, characterized in that, The specific method for step S3.1 is as follows: S3.11 Receive the high-confidence causal dataset after decontamination, extract the CPU utilization, query rate per second and cross-domain network latency indicators to construct a three-dimensional state vector, embed it into a high-dimensional Euclidean space to form a temporal state snapshot, and construct a dynamic point cloud set that evolves over time. S3.

12. Set the width of the sliding time pane to W time periods, extract a local state subset of the dynamic point cloud set, calculate the Euclidean distance between each state vector to establish adjacency relationships, set the distance threshold ε=0.5σ, where σ is the standard deviation of the state vector, connect neighboring points with a distance less than ε to expand layer by layer to form a Vietoris-Rips complex structure, and terminate the expansion when the complex dimension reaches the preset upper limit or the coverage reaches the threshold, construct a multi-scale topological skeleton representing the evolution trajectory of the system's healthy state and output it to the coherence calculation stage.

6. The basic database management method for a heterogeneous cloud environment according to claim 5, characterized in that, The specific method for step S3.2 is as follows; S3.

21. Receive the multi-scale topological skeleton, perform orientation processing on each simplification in the simplex and assign integer coefficients, construct a linear combination to generate a chain group, establish the incidence matrix representation of the boundary operator, calculate the surface relations of the simplification to obtain the boundary matrix, calculate the rank of the matrix by performing Smith canonical decomposition on the boundary matrix, obtain the zero-dimensional homology group to track the generation and dissolution time of connected components, obtain the one-dimensional homology group to capture the birth and death time of loop structures, and construct a set of algebraic invariants characterizing the topological defects of the state space. S3.

22. Construct a persistent graph based on the set of algebraic invariants. The horizontal axis records the birth time of topological features, and the vertical axis records the extinction time. Calculate the duration of each homology class, set a noise filtering threshold, and select stable homology classes with a duration greater than the threshold. Extract persistent loop features corresponding to deadlock precursors and connected component splitting features corresponding to resource contention. Map persistent topological invariants to normalized values, generate a topological health index, and output it to the hysteresis decision-making process.

7. The basic database management method for a heterogeneous cloud environment according to claim 6, characterized in that, In step S4, the specific method for smoothing hysteresis decision-making is as follows: S4.1 Receive pre-intervention instructions and topology health index, construct an asymmetric dual-threshold hysteresis band with expansion threshold T_up and reduction threshold T_down, set the asymmetric difference Δ=T_up-T_down=15% and T_up=75% and T_down=30%, dynamically adjust the center position μ and boundary width δ of the hysteresis band according to the load change trend, and use the Sigmoid continuous interpolation function to perform smooth and gradual processing on the control quantity within the critical interval [μ-δ, μ+δ] to generate a control decision to be verified that satisfies the continuity constraint; S4.2 Receive the control decision to be verified, define the state space as normal, expanding, shrinking, network partition risk, and dual-master write risk, construct a state transition system model to characterize the transition relationship between each state, use the timing logic TLA+ to perform formal verification on the state switching action, check whether there are abnormal migration paths that violate split-brain avoidance and data consistency maintenance, and only generate incremental resource adjustment instructions and output them to the feedback optimization stage after the verification is passed.

8. The basic database management method for a heterogeneous cloud environment according to claim 7, characterized in that, The specific method for step S4.1 is as follows: S4.

11. Integrate pre-intervention instructions and topological health index to construct an asymmetric dual-threshold hysteresis band, configure the asymmetric difference Δ=15% between the expansion boundary T_up and the shrinkage boundary T_down and the initial boundary width δ0=5%, set the initial center position μ0 of the hysteresis band according to the historical load fluctuation variance, and establish a hysteresis control base for the load characteristics of heterogeneous cloud databases. S4.

12. Based on the load change trend v, perform an upward offset of the hysteresis band center position, with an offset amount Δμ=-k·v, where k is the system response coefficient. Dynamically adjust the boundary width δ=δ0·(1+|v| / v_max). Within the critical interval [μ-δ, μ+δ], use the Sigmoid function σ(x)=1 / (1+e^{-x}) to perform a smooth and gradual change on the control quantity, eliminating the step jump caused by hard threshold switching, and generating a control decision to be verified that satisfies the continuity constraint.

9. The basic database management method for a heterogeneous cloud environment according to claim 7, characterized in that, The specific method for step S4.2 is as follows: S4.21 Receive control decisions to be verified, define state variables including current master node identifier, replica synchronization delay, network partition flag, and data consistency status, and construct a state transition system model covering network partition and dual-master write anomaly scenarios; establish a security specification based on time-series logic TLA+, define the atomic proposition Safe≡(master node unique∧replica delay<threshold), characterize the critical constraints for split-brain avoidance and data consistency maintenance, and construct a computable and verifiable logical specification base; S4.

22. Based on the state transition system model, perform formal verification of the state switching actions in the control decision to be verified. Use the symbolic model detection algorithm to traverse the state space and check whether there is a reachable path from the initial state to the Safe state. Only when the verification is passed and no violation risk is confirmed, generate the incremental resource adjustment instruction and output it to the feedback optimization stage.

10. The basic database management method for a heterogeneous cloud environment according to claim 7, characterized in that, In step S5, the specific method for performing feedback optimization is as follows: S5.1 Collect the execution response data of resource adjustment instructions, construct a decision effect evaluation vector containing response time, resource utilization rate and switching frequency, construct a reward signal R=α·utilization rate+β·(1 / response time)-γ·switching frequency based on the evaluation vector, and use the strategy gradient algorithm to perform online collaborative optimization on the counterfactual inference threshold, topology filter parameters and hysteresis boundary coefficient to realize adaptive dynamic adjustment of control parameters. S5.2 Set the retraining cycle to T time units, extract the newly identified bottleneck topology patterns and update them to the feature knowledge base. Based on incremental observation data, use the online Bayesian update algorithm to periodically retrain the causal graph node relationships and causal link weights, continuously optimize the causal structure to adapt to the business evolution trend, and realize the global closed-loop self-evolution of the management method.

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