Component library application method supporting cross-system reuse

Abstract

The application relates to the technical field of component reuse, and discloses a component library application method supporting cross-system reuse. The method comprises the following steps: identifying the running context information of a target application system, and extracting a reusable component set from a component library according to the running context information; on the basis of the performance constraint conditions of the target application system, classifying the reusable component set into a core function component subset and a support function component subset, screening environment adaptation components from the remaining components in the component library, and jointly forming a to-be-processed component set; evaluating the interactive coupling degrees of adjacent components in the to-be-processed component set, constructing a component dependency network by taking the components as nodes and the interactive coupling degrees as edge weights; determining a critical path by using a heuristic search algorithm, arranging the initialization sequence of each component according to the critical path; and deploying the components according to the sequence, so that cross-system component reuse is realized. The method can effectively cope with the differences between different systems, improve the component reuse efficiency and success rate, and is suitable for cross-system component reuse scenarios.

Description

Technical Field

[0001] This invention relates to the field of component reuse technology, specifically to a method for applying a component library that supports cross-system reuse. Background Technology

[0002] With the rapid development of information technology, enterprise application systems are showing a trend towards multi-platform and heterogeneous development, with different systems often built on different technical architectures, development languages, and operating environments. Against this backdrop, component reuse, as a core means to improve development efficiency and reduce maintenance costs, has become a key bottleneck restricting the industrial production of software due to its cross-system applicability.

[0003] Traditional component libraries are often designed for a single technology stack or specific business scenarios, resulting in high coupling between components and native systems. They frequently depend on runtime environments, interface specifications, or data formats. For example, a payment component developed for an e-commerce platform might be deeply tied to that platform's user authentication system, making it difficult to directly reuse in a financial system's payment scenario. Graphical components developed for Windows systems often exhibit rendering anomalies in Linux environments, requiring extensive secondary development for adaptation. This "system-bound" component design leads to a component reuse rate of less than 30% when integrating multiple systems, resulting in a significant waste of development resources and introducing additional system vulnerability risks due to redundant development.

[0004] Existing cross-system component reuse solutions have significant limitations: On the one hand, most solutions only reduce the coupling of the component call layer through standardized interfaces, but ignore the impact of the runtime environment and performance constraints on component compatibility. For example, an order processing component of a logistics system can run stably in a single-machine environment, but after migrating to a high-concurrency cloud environment, it causes data consistency problems due to the lack of adaptation to distributed lock mechanisms. On the other hand, component deployment lacks dynamic adaptation capabilities. When the resource configuration of the target system changes, the component cannot automatically adjust its initialization order, which may lead to startup failure due to resource contention.

[0005] Industry research data shows that over 65% of enterprises invest significant manpower in environment adaptation and performance optimization when reusing components across systems, with migration costs for each component accounting for more than 40% of their development costs. Furthermore, due to a lack of scientific component dependency analysis methods, approximately 30% of system failures stem from dependency conflicts caused by improper component initialization order. Therefore, how to build a cross-system reuse mechanism that can dynamically identify the target system context, accurately match performance constraints, and achieve efficient component deployment has become a critical issue that urgently needs to be addressed in the field of software engineering. Summary of the Invention

[0006] The purpose of this invention is to provide a method for using a component library that supports cross-system reuse, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a method for using a component library that supports cross-system reuse, the method comprising:

[0008] Identify the runtime context information of the target application system, and extract a set of reusable components from the component library based on the runtime context information;

[0009] Based on the performance constraints of the target application system, the components in the reusable component set are classified into a core functional component subset and a supporting functional component subset. At the same time, environment-adaptive components are selected from the remaining components in the component library that are not covered by the reusable component set. The core functional component subset, the supporting functional component subset, and the environment-adaptive components together form a component set to be processed.

[0010] The interaction coupling degree between each pair of adjacent components in the set of components to be processed is evaluated, and the components in the set of components to be processed are regarded as nodes. The interaction coupling degree is used as the edge weight connecting the nodes, thereby constructing the component dependency network of the set of components to be processed.

[0011] The critical path of the component dependency network is determined by a heuristic search algorithm, and the initialization sequence of each component in the set of components to be processed is arranged according to the critical path. Each component in the set of components to be processed is deployed step by step according to the initialization sequence to achieve cross-system component reuse.

[0012] Preferably, the step of extracting a set of reusable components from the component library based on the runtime context information includes: querying a component usage history database using the runtime context information to obtain relevant component records; clustering the queried relevant component records according to the component interface compatibility standard to form multiple component clusters; selecting a component cluster that matches the current runtime requirements of the target application system from the component clusters, and integrating all components in the selected component clusters into a set of reusable components, wherein the clustering process involves component function tag matching.

[0013] Preferably, the clustering process of the relevant component records queried according to the component interface compatibility standard includes: defining pre-configured interface specification categories in the component library; for each relevant component record queried, detecting the interface specification category to which the component's interface attribute belongs, and assigning the component to the component cluster of the corresponding interface specification category, wherein the interface attribute detection is based on component metadata parsing.

[0014] Preferably, classifying the components in the reusable component set into a core functional component subset and a supporting functional component subset based on the performance constraints of the target application system includes: analyzing the main functional modules and secondary functional modules indicated by the performance constraints of the target application system; locating the activity range of the components corresponding to the main functional modules in the reusable component set, and locating the activity range of the components corresponding to the secondary functional modules in the reusable component set; classifying the components covered by the activity range of the main functional module components into the core functional component subset, and simultaneously classifying the components covered by the activity range of the secondary functional module components into the supporting functional component subset.

[0015] Preferably, the step of selecting environment-adaptive components from the remaining components in the component library that are not covered by the reusable component set includes: allocating a priority association number to the core functional component subset and a secondary priority association number to the supporting functional component subset; for the remaining components in the component library that are not covered by the reusable component set, if the remaining component has a dependency relationship with the core functional component subset, then the remaining components with the priority association number are selected as environment-adaptive components; if the remaining component only has a dependency relationship with the supporting functional component subset, then the remaining components with the secondary priority association number are selected as environment-adaptive components.

[0016] Preferably, the evaluation of the interaction coupling degree between each pair of adjacent components in the set of components to be processed includes: for any two adjacent components in the set of components to be processed, identifying the component type of each component, wherein the component type includes one of a subset of core functional components, a subset of supporting functional components, and an environment adaptation component; based on the identified component type, calculating the dependency influence coefficient of the first component and the dependency influence coefficient of the second component; measuring the correlation deviation between the dependency influence coefficient of the first component and the dependency influence coefficient of the second component, and quantifying the interaction coupling degree based on the correlation deviation.

[0017] Preferably, the step of quantifying the interaction coupling degree based on the correlation deviation includes: scanning the dependency influence coefficients of all components in the set of components to be processed, and determining the median of the distribution of these dependency influence coefficients; dividing multiple correlation deviation intervals according to the median of the distribution, and locating the target correlation deviation interval to which the calculated correlation deviation belongs; and using the preset coupling degree value corresponding to the target correlation deviation interval as the interaction coupling degree.

[0018] Preferably, the step-by-step deployment of each component in the set of components to be processed following the initialization sequence includes: detecting the configuration parameters of each deployment node in the initialization sequence, and generating a parameter difference sequence based on the detected configuration parameters, wherein the order of parameters in the parameter difference sequence is consistent with the initialization sequence; inputting the parameter difference sequence into a deep reinforcement learning model, and outputting the resource allocation amount of each deployment component through the deep reinforcement learning model; and deploying the corresponding component according to the output resource allocation amount to complete the component application.

[0019] Preferably, determining the critical path of the component dependency network using a heuristic search algorithm includes: initializing the start node and end node of the component dependency network, applying the A* search strategy to traverse the network edge weights, and generating the minimum cost path as the critical path, wherein the edge weights are dynamically updated based on the interaction coupling degree.

[0020] Preferably, during deployment, the runtime performance metrics of the current component are monitored, and the fit difference between the current performance metrics and the predefined performance benchmark is calculated. When the fit difference exceeds the allowable threshold, the deployment strategy of subsequent components is adjusted based on the current system load status to optimize the initialization sequence.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] This cross-system reuse-enabled component library application method extracts a set of reusable components by identifying the runtime context information of the target application system. This ensures that the extracted components are more closely aligned with the actual operating environment of the target system, reducing component incompatibility caused by environmental differences. By classifying components based on the performance constraints of the target application system and filtering for environment-adaptive components, a set of components to be processed is formed. This allows the selection and combination of components to better meet the performance requirements of the target system, enabling components to function more effectively within the target system and avoiding impacts on the overall system performance due to performance mismatches.

[0023] Assessing the interaction coupling between adjacent components in the set of components to be processed and constructing a component dependency network can clearly present the relationships between components, providing a strong reference for subsequent component initialization and deployment. By determining the critical path of the component dependency network through a heuristic search algorithm and arranging the component initialization sequence according to the critical path, it is possible to ensure that components are initialized in a reasonable order, reducing interaction problems caused by improper initialization order and guaranteeing smooth collaboration between components.

[0024] Deploying components step-by-step according to the initialization sequence ensures an orderly deployment process, reduces the likelihood of errors, and increases the success rate of cross-system component reuse. This method forms a complete workflow from component extraction, classification, and dependency analysis to initialization sequence arrangement and deployment. It effectively addresses differences between systems, expands the scope of component reuse, improves the efficiency of component reuse, simplifies component reuse operations during system development and maintenance, and enables developers to more easily leverage existing components to build new application systems. Attached Figure Description

[0025] Figure 1 This is a schematic diagram illustrating the working principle of the component library application method supporting cross-system reuse as described in this invention.

[0026] Figure 2 A flowchart for clustering processing for component interface compatibility;

[0027] Figure 3 A flowchart for selecting components for environment adaptation;

[0028] Figure 4 A flowchart for measuring interactive coupling. Detailed Implementation

[0029] 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.

[0030] Please see Figure 1 This invention provides a method for using a component library that supports cross-system reuse, the method comprising:

[0031] This method achieves efficient component reuse across system environments through intelligent component selection, classification, and deployment mechanisms. First, it identifies the target system's runtime context information and extracts a set of reusable components from a component library based on this information. Then, based on performance constraints, the components are divided into a subset of core functional components and a subset of supporting functional components, and environment-adaptive components are added to form a set of components to be processed. By constructing a component dependency network and calculating the interaction coupling degree, a heuristic algorithm is used to determine the critical path and arrange the initialization sequence, ultimately achieving orderly component deployment.

[0032] Example 1: See Figure 2 The process involves extracting reusable components driven by runtime context information. This process begins with a structured query of a historical database containing component usage records, performance logs, and environmental parameters from different system environments. Once the runtime context information of the target application system is obtained, the system performs feature extraction to capture system architecture features, runtime environment parameters, and business requirement features. Based on these features, multi-dimensional query conditions are generated, including key dimensions such as operating system version, middleware configuration, and resource constraints. The query engine retrieves historical component call records with similar context characteristics, returning a result set containing metadata fields such as component identifier, interface specification version, list of dependent components, and historical deployment success rate.

[0033] Interface compatibility handling employs a layered protocol matching architecture. The system predefines a multi-level interface specification category system, with the top level divided into a communication protocol layer, a data exchange layer, and a service contract layer. Each layer contains several specific specification standards; for example, the communication protocol layer includes protocol standards such as HTTP / 2, gRPC, and AMQP, while the data exchange layer covers serialization specifications such as JSONSchema, Protocol Buffers, and Avro. The component metadata parser performs syntactic analysis on the interface description files in the query results, extracting their communication modes, serialization methods, and interface method signature features to form a structured interface feature vector. The similarity index between this feature vector and the preset specification category is calculated using a vector space model, and the system sets a dynamic matching threshold. When the similarity between a component's interface feature vector and a specific specification category exceeds the threshold, it is assigned to the corresponding component cluster. Each component cluster corresponds to a unique interface specification combination identifier, ensuring interoperability among components within the cluster.

[0034] Multiple filtering mechanisms are implemented during clustering. After initial grouping, the system performs internal compatibility verification for each component cluster. This verification involves method signature compatibility checking, analyzing the input and output parameter structure of interface methods through abstract syntax trees to identify conflicting data type definitions. Simultaneously, it checks the consistency of exception handling mechanisms, confirming the compatibility of error code systems and exception propagation patterns between different components. For conflicting components, the system initiates an automatic adaptation mechanism, generating interface adapter template suggestions, or migrating the conflicting component to a new component cluster. The final output component clusters form mutually isolated compatibility units, establishing a technical foundation for subsequent selection.

[0035] Functional label matching is built upon a semantic understanding technology architecture. The system maintains a multi-layered functional ontology, with the top layer divided into two main branches: business functional domains and technical functional domains. The business functional domains adopt industry-standard classification systems, such as subdividing them into payment settlement, risk management, and customer service in the financial field; the technical functional domains encompass technical categories such as data processing, transaction management, and security control. A natural language processor analyzes the functional description text in component documents, performing keyword extraction, entity recognition, and dependency parsing. A word embedding model maps text features to a semantic vector space, performing similarity matching with the vector representations of standardized functional labels in the ontology. Each component is assigned a primary functional label and auxiliary functional labels, while simultaneously recording the confidence score for each label. After label assignment, the system constructs a component-functional matrix to quantify the contribution of each component to specific functional requirements.

[0036] The target adaptation employs a multi-stage screening strategy. The system parses the target application system's requirements specification document, extracts functional requirement statements, and converts them into a standard set of functional tags. In the component cluster pre-selection stage, the functional tag coverage index for each component cluster is calculated, filtering out clusters below a set threshold. Fine-grained matching is performed on candidate component clusters, analyzing the weight values ​​of each unit in the component-function matrix to select the smallest combination of component clusters that fully covers the core functional requirements. Finally, a resource requirement compatibility check is conducted, comparing the component's runtime resource requirements with the target system's environmental constraints to eliminate components with significant resource conflicts. Finally, all component entries from the selected component clusters are merged, generating a set of reusable components as output.

[0037] This collection construction mechanism incorporates a feedback optimization loop. Runtime data generated after component deployment is automatically fed back to the historical database, updating the component's environment adaptation records. When similar contexts occur multiple times, the system optimizes the weighting of query conditions, adjusts the evaluation threshold for interface compatibility, and continuously improves the filtering accuracy of the component collection. The composition structure of component clusters is also dynamically restructured based on actual deployment results, merging highly interoperable component clusters and splitting component groups with compatibility vulnerabilities. The functional ontology library undergoes regular version iterations through industry standard updates and technology evolution analysis to keep the functional classification system in sync with current technological developments.

[0038] Example 2: Reference Figure 3 This paper focuses on component classification and environment adaptation under performance constraints. The performance constraint parsing engine uses a semantic rule-based parsing framework to process the input performance constraint text. This framework has a built-in constraint expression parser that can identify typical performance metric descriptions, including upper limits for response time, lower limits for throughput, and CPU utilization thresholds. The parsing process first converts the constraints described in natural language into an intermediate representation, and then identifies key performance parameters and constraint relation operators through pattern matching. The system maintains a constraint template library that stores standard expression patterns for various performance constraints, with each template associated with a specific constraint type and importance level. The parser performs multiple rounds of matching between the input constraints and the template library, labeling successfully matched constraints as mandatory or recommended, forming a structured set of constraints.

[0039] The main functional module identification algorithm is based on the topological sorting results of constraints. The system constructs a constraint dependency graph, where nodes represent performance constraints and edges represent logical dependencies between constraints. A depth-first search is used to traverse this graph, identifying the set of constraints on the critical path. The functional modules corresponding to these constraints are marked as main functional modules. Secondary functional modules correspond to the set of constraints on branch paths in the graph. The module importance score is calculated using the following formula:

[0040] ,

[0041] in, Representation module Importance rating It is the set of constraints associated with this module. Constraints The baseline weights, A quantitative value representing the strictness of the constraint. For the strictness threshold, This is the adjustment coefficient. The formula comprehensively considers the number, weight, and strictness of constraints, and the scoring results are used to verify the rationality of the primary / secondary module division.

[0042] The component activity scope positioning implements a two-way indexing mechanism. The component function mapping table constructed by the system is stored in a graph database, containing component nodes, functional module nodes, and the edges between them. Component node attributes record metadata such as interface specifications and resource requirements; functional module nodes store descriptive information such as performance constraints and business requirements; and relationship edges indicate the degree of support of the component for the functional module. During positioning queries, the system simultaneously performs forward traversal from functional module to component and reverse verification from component to functional module, ensuring that each selected component has undergone double confirmation. The determination of the subset of core functional components additionally introduces a historical stability assessment dimension. The system accesses the component quality knowledge base to obtain indicators such as the average time between failures and the success rate of anomaly recovery in past deployments of candidate components, and comprehensively calculates the reliability score of the components.

[0043] The environment adaptation component selection employs a mechanism combining dependency propagation trees and priority scheduling. The system analyzes the call relationships between remaining components and selected components, constructing a weighted dependency propagation tree structure. Nodes in the tree represent components, edges represent call relationships, and edge weights reflect call frequency and importance. Core functional components are assigned an initial propagation energy value, which is distributed to downstream components along the dependency edges according to their weights. When the accumulated energy value of remaining components exceeds the activation threshold, they are marked as candidate environment adaptation components. Supporting functional components use a decaying propagation model, where their allocated energy value decreases by a fixed coefficient with each hop. The system sets a dynamically adjusted energy allocation strategy, automatically reducing the energy propagation intensity on non-critical paths when resource contention is detected.

[0044] A priority-based scheduling system employs a multi-queue management approach. Candidate environment adaptation components are placed into preparation queues of varying priorities based on their energy levels. High-priority queues contain adaptation components related to core functionalities and employ an immediate pre-loading strategy; medium-priority queues handle components related to support functions and implement an on-demand loading mechanism; low-priority queues store general-purpose adaptation components and use lazy loading. Within each queue, a cache replacement algorithm based on recent usage frequency is implemented to ensure that frequently used adaptation components remain readily available. The queue manager monitors system resource utilization in real time and dynamically adjusts the component loading ratio and order for each queue.

[0045] The component classification results verification employs a cross-checking process. The core functional component subsets and supporting functional component subsets generated by the system undergo architectural rationality review. The reviewer checks the component coupling index within each subset to ensure it does not exceed the preset coupling limit. Simultaneously, it verifies the interface isolation between subsets and measures the number and complexity of cross-subset call relationships. The environment-adaptive component set must pass dependency completeness testing to confirm its ability to cover all critical external dependencies of the selected components. When classification defects are found, the system initiates a classification adjustment loop, re-evaluating the component importance scores and dependencies, and iteratively optimizing the classification results.

[0046] This implementation includes an adaptive adjustment mechanism. During actual component operation, the performance monitoring module continuously collects resource consumption data and functional performance data for each component. This runtime information is fed back to the classification decision engine to calibrate the initial classification parameters. The identification criteria for core functional components are dynamically adjusted based on the actual business impact, while the selection strategy for environment-adaptive components changes flexibly with system load. Historical classification decisions and their effects are recorded in a case library, providing a reference for classifying similar scenarios in the future. The system periodically performs offline optimization of the classification strategy, updating the component importance scoring algorithm and dependency evaluation model by analyzing successful patterns and lessons learned from historical cases.

[0047] The entire process of component classification and environment adaptation adopts a layered processing architecture. The bottom layer consists of the original component library and performance constraint input interface; the middle layer contains processing modules such as constraint parsing, functional module identification, and component mapping; and the top layer contains the classification decision and component selection logic. Each layer exchanges information through a standardized data format to ensure the modularity and scalability of the processing flow. The system supports parallel processing of classification requests from multiple application scenarios, with each request assigned an independent processing context to avoid decision interference between different scenarios. Classification results are version-managed, retaining a complete record of all classifications and change paths, supporting result backtracking and comparative analysis.

[0048] Example 3: Reference Figure 4This study focuses on the evaluation and quantification mechanism of component interaction coupling. The process begins with the initial configuration of the component type identification system. The system maintains a type feature knowledge base, storing the discrimination rule sets for three types of components. The identification features of core functional components include directly implementing business requirements and processing key data flows; features of supporting functional components involve auxiliary operations and logging; and environment-adaptive components exhibit behavioral patterns such as system-level interface encapsulation and runtime environment bridging. The type identifier scans each component in the set of components to be processed, analyzing its metadata description, interface definition, and historical usage records, and determines its specific type through multi-level filtering logic. The identification process employs a progressive verification strategy: first checking explicit type annotations; if no annotation is found, analyzing the functional description text; and finally examining the actual call relationship patterns.

[0049] The dependency impact coefficient is calculated based on the type weight matrix. The system defines basic impact factors for each component type, which are dynamically adjusted according to system architecture characteristics. When evaluating the interaction between two adjacent components, the system first determines their type combination patterns, with six possible permutations. For each type combination, a pre-defined transformation function maps the basic impact factors to a dependency impact coefficient for a specific scenario. This transformation process considers factors such as the call direction between components, data flow, and anomaly propagation paths. The dynamic adjustment of the dependency impact coefficient uses the following calculation formula:

[0050] ,

[0051] in, Representation Component For components Dependence influence coefficient, It is a component Active influencing factors For components Passive response factor, Reflecting the intensity of abnormal propagation between the two, It is a distance adjustment parameter. This represents the logical distance in the call chain. This formula balances the inherent characteristics of a component with the features of actual interaction, ensuring that the calculation results reflect both the nature of the type and the specific context.

[0052] The correlation deviation metric employs multi-dimensional difference analysis. The system defines a deviation feature space, including dimensions such as interface compatibility, data format matching, and call frequency synchronization rate. Each dimension's metric uses a specific comparison algorithm; for example, interface compatibility is calculated through method signature comparison, and data format matching examines the consistency of the schema definition. The measurement results for each dimension are normalized and then input into a weighted aggregation function to generate a comprehensive correlation deviation value. The deviation analysis process records detailed comparison logs, including fully matched features, partially matched features, and completely mismatched features, providing an interpretive basis for subsequent coupling metric measurement.

[0053] Coupling quantification employs a dynamic interval mapping strategy. The system continuously monitors the distribution of dependency influence coefficients for all components, using an adaptive clustering algorithm to identify data-dense and sparse regions. Based on distribution characteristics, a non-uniformly partitioned association deviation interval system is constructed, with higher-density interval boundaries set in key areas. Each interval is associated with a preset coupling degree value, which forms an ordered sequence and satisfies monotonicity requirements. The quantization process implements a two-level verification mechanism: first, it checks whether the calculated association deviation falls within the valid range; then, it verifies whether the coupling degree value corresponding to the selected interval conforms to the expected relationship pattern of the type combination. When abnormal quantization results are detected, the system initiates a backtracking analysis process to examine the intermediate results of dependency influence coefficient calculation and the characteristic composition of association deviations.

[0054] The interaction coupling evaluation system adopts a distributed architecture. The evaluation task is decomposed into four sub-stages: type identification, coefficient calculation, deviation measurement, and coupling measurement, each corresponding to an independent processing unit. Processing results are passed between units via message queues, forming a pipelined execution mode. The system implements a result caching mechanism, directly returning cached values ​​for recurring component combinations and employing an incremental calculation strategy for similar combinations. All intermediate data generated during the evaluation process is appended with timestamps and context markers, supporting result traceability and reproducibility. For performance optimization, lazy loading technology is used, performing detailed deviation analysis only when necessary, and using pre-calculated approximations in normal scenarios.

[0055] The evaluation results are implemented with version control management. Each time the component set is updated, the system generates a version identifier for the new interaction coupling degree evaluation result. Version information includes the evaluation time, parameter configuration, and key statistical indicators, facilitating subsequent comparative analysis. The dependency network builder uses versioned coupling degree values ​​as edge weights to ensure that the network representation is strictly synchronized with the evaluation results. The system maintains an evaluation history, recording differences between versions, and triggers a notification mechanism when a significant change in coupling degree pattern is detected.

[0056] This implementation includes a self-learning optimization loop. The system collects component interaction data during actual runtime, including metrics such as call latency, error propagation rate, and resource contention. This runtime feedback data is used to calibrate the calculation parameters of the dependency influence coefficient, adjust the weight allocation of correlation bias, and optimize the coupling degree interval partitioning strategy. The learning process adopts a mini-batch incremental update mode, periodically merging the accumulated runtime experience into the evaluation model. The system also implements an abnormal interaction pattern detection mechanism; when a persistent deviation is detected between the observed component interaction characteristics and the evaluation prediction, the model retraining process is automatically triggered.

[0057] The system deeply integrates component interaction coupling assessment with environment awareness. During the assessment process, it collects real-time characteristic data of the deployment environment, including container orchestration patterns, service mesh configurations, and resource quota limitations. These environmental factors are converted into adjustment parameters that dynamically influence the calculation of dependency impact coefficients. For example, in high-latency environments, the logical distance parameter on the call chain is appropriately amplified; in resource-constrained scenarios, the weight of anomaly propagation strength is correspondingly increased. This integration ensures that the coupling assessment results always reflect the actual constraints of the current environment.

[0058] The interpretability of the evaluation process is achieved through a diagnostic report mechanism. The system generates a structured diagnostic report for each set of important interaction coupling evaluation results, detailing the basis for type identification, the calculation path of the dependency influence coefficient, the characteristic decomposition of the association bias, and the process for determining the final coupling degree. The report adopts a hierarchical presentation strategy, with a summary layer presenting key conclusions and a detailed layer providing technical analysis, supporting the understanding needs of different roles. The diagnostic report is integrated with a component dependency network visualization tool, allowing exploration of the distribution patterns and key influencing factors of interaction coupling degree through a graphical interface.

[0059] Example 4: Specific practices focusing on the optimization and execution of component deployment sequences. Taking the upgrade of an e-commerce platform's order processing system as an example, this system needs to integrate three core components: payment gateway, inventory service, and logistics scheduling. The configuration parameter detector first scans the environmental characteristics of each deployment node, generating the following parameter difference record table 1.

[0060] Table 1: Record of Parameter Differences

[0061] Component Name Memory baseline (MB) CPU thread requirements Storage type Network latency (ms) Dependency library version Payment gateway 2048 4 SSD 35 OpenSSL 3.0 Inventory services 4096 8 NVMe 18 JDK 17 Logistics scheduling 3072 6 SSD 42 Python 3.9

[0062] A parameter difference sequence generator transforms these discrete parameters into temporal feature vectors. The positive deviation of the payment gateway node's memory configuration from the baseline is encoded as a resource demand intensity signal, while network latency parameters are converted into time compensation coefficients. The policy network of the deep reinforcement learning model comprises a three-layer convolutional structure, handling hardware resource configuration, software environment characteristics, and network topology features respectively. During the training phase, a virtual environment simulation is used to model component deployment scenarios under different parameter combinations. The model gradually learns the payment gateway's sensitivity to the version of the encryption library, the inventory service's dependence on memory bandwidth, and the logistics scheduling component's tolerance to network jitter.

[0063] Resource allocation decisions are implemented using a phased execution strategy. When deploying the payment gateway component, the model identifies the CPU instruction set support required by its OpenSSL dependency and generates a core allocation scheme that includes AVX instruction set reservations. The basic resource allocation is set to 1800MB of memory and 2 CPU threads, with 300MB of memory and 2 additional threads reserved for encryption operations during peak transaction periods. The inventory service deployment adopts a different strategy. Based on the characteristics of NVMe storage, the model recommends enabling direct memory access mode, with a basic memory allocation of 3500MB including cache preloading space, and elastic expansion primarily targeting concurrent query buffers. The network compensation mechanism of the logistics scheduling component is activated, and the model adds TCP window adjustment parameters to the basic resource configuration and reserves bandwidth burst capacity.

[0064] The A* search strategy exhibits dynamic adaptability during path planning. Initial critical path analysis determines that the payment gateway should be deployed first, as it provides the encrypted communication foundation for other components. When real-time monitoring detects network latency fluctuations exceeding a threshold, the path weight calculator re-evaluates edge weights, and the logistics scheduling component is temporarily removed from the critical path. The search algorithm selects a new sequence from the candidate paths: Payment Gateway → Inventory Service → Logistics Scheduling. This path shows a more stable deployment success rate in simulation tests. The path optimizer continuously tracks deployment progress, and once the inventory service completes initialization and sufficient remaining resources are detected, the logistics scheduling component is reinstated into the parallel deployment queue.

[0065] The deployment executor implements a gradual resource injection. For the payment gateway deployment, the initial allocation of declared basic resources is followed by continuous monitoring of handshake success rate and transaction processing latency by the stability monitor. Once the metrics meet the targets for three consecutive monitoring cycles, the executor gradually releases elastically expanded resources while monitoring resource utilization efficiency. The inventory service deployment process triggers an anomaly detection mechanism; the initial memory allocation fails to meet its cache warm-up requirements. Based on model recommendations, the executor dynamically adjusts the basic resource allocation to 3800MB and reduces the elastic expansion ratio. When the logistics scheduling component deployment encounters network jitter, the executor automatically activates a compensation strategy, extending the verification interval between each stage and temporarily increasing the heartbeat detection frequency.

[0066] The version rollback mechanism is deeply integrated with the deployment process. When a certificate verification anomaly occurs after the third version of the payment gateway is deployed, the system automatically compares the configuration differences between the current version and historical versions. The rollback decision-maker analyzes the correlation between the anomaly pattern and configuration parameters, choosing to roll back to version two while maintaining the existing resource allocation scheme. The rollback process records detailed resource release and re-application logs to ensure that resource leaks do not occur due to version switching. A similar mechanism is used for canary updates to the inventory service; the new version is only allowed to be fully activated when resource utilization is below a threshold.

[0067] The resource recycling strategy employs intelligent prediction. After deployment, the system analyzes the actual resource usage patterns of each component, identifying that the payment gateway's elastic expansion resources are idle for extended periods during non-transactional hours. The resource regulator collaborates with the business cycle prediction module to establish an on-demand recycling mechanism, automatically reducing excess allocations during daily business downturns. A different strategy is used for inventory service resource recycling; due to its caching characteristics, it needs to be kept in a preheated state, triggering partial memory release only during extremely idle periods. The network bandwidth reservation of the logistics scheduling component is dynamically adjusted, automatically scaling up and down based on the real-time demands of the delivery route planning algorithm.

[0068] The entire deployment process generates a detailed audit trail. The system records parameter snapshots, model outputs, and execution results at each decision point, forming a traceable deployment chain. When the logistics scheduling component is finally deployed, the audit logs show the complete evolution of resource allocation: from an initial conservative configuration, through three elastic scaling adjustments, it finally stabilizes at an optimal state of 2950MB of memory and 5 CPU threads. This data is fed back into the model training phase to optimize the accuracy of subsequent deployment decisions.

[0069] Anomaly handling employs a tiered response model. If a critical CPU instruction set incompatibility error is detected during payment gateway deployment, the system immediately pauses the entire deployment sequence and reports the issue to the manual intervention channel. Insufficient memory allocation for the inventory service is identified as a moderate anomaly, triggering an automatic adjustment process without interrupting deployment. Network latency fluctuations in logistics scheduling are considered tolerable anomalies; the system only logs warning messages and continues execution. Recovery strategies and resource reallocation logic corresponding to each anomaly type ensure that problem handling remains coordinated with the system state.

[0070] The dynamic optimization of deployment strategies is reflected in multiple dimensions. In terms of time, the system learned that deploying the payment gateway during business hours requires higher success standards, so it automatically adjusts the verification threshold. In terms of space, it discovered that the inventory service is more stable when deployed on physical machines than in a container environment, so physical nodes will be prioritized going forward. In terms of version, records show that version 2.3 of the logistics scheduling component has the best compatibility with the current system, and the model will prioritize recommending this version. These optimization experiences are structured and stored using a knowledge graph, forming a reusable deployment strategy library.

[0071] Dynamic management of inter-component dependencies is another key feature. Once the payment gateway is deployed, the inventory service detects new encrypted channel features and automatically adjusts its request retry mechanism. The logistics scheduling component dynamically optimizes its timeout settings based on the actual available payment interface response times. These runtime adaptive changes are fed back to the deployment knowledge base to refine dependency modeling for subsequent deployment sequences. The final deployment sequence of the system considers not only the static component dependency graph but also the implicit constraints generated by these dynamic adjustments.

[0072] Example 5: Focusing on Runtime Component Performance Monitoring and Dynamic Adjustment Mechanisms. The performance monitoring framework established during system deployment includes a multi-level metric collection system. This ranges from capturing basic metrics such as CPU utilization and memory usage at the infrastructure layer, to collecting runtime data like message queue depth and thread pool status at the middleware layer, and monitoring key performance parameters such as business transaction success rate and request processing latency at the application layer. These metrics are collected in real-time through distributed probes, cleaned and standardized via a streaming pipeline, ultimately forming a structured performance data stream. The monitoring agent employs an adaptive sampling strategy, using high-frequency, fine-grained sampling when system load is low, and automatically switching to a sparse sampling mode prioritizing key metrics when resource constraints are detected, balancing monitoring accuracy with system overhead.

[0073] The performance benchmark management system maintains a multi-dimensional reference standard library. Static benchmarks are derived from component vendor specifications and technical white papers, defining expected performance values ​​under ideal conditions. Dynamic benchmarks are calculated based on historical operating data, including reference lines such as the performance median of similar systems and the system's historical best performance. The environment awareness module analyzes the current hardware configuration, network conditions, and external dependencies in real time, generating environment adjustment coefficients to dynamically correct benchmark values. When the payment gateway component is deployed in a cross-regional cloud environment, the system automatically adjusts its latency benchmark based on network topology data to avoid false alarms caused by physical distance.

[0074] The fit difference calculation employs a multi-dimensional fusion analysis technique. Each performance indicator independently calculates its deviation from the baseline value, eliminating dimensional differences through standardization. The system establishes an indicator correlation graph model to identify indicator groups with causal relationships, such as the correlation between database connection pool size and query response time. The difference analyzer comprehensively considers directly observed indicator deviations and the collaborative change patterns of related indicator groups to generate a comprehensive fit difference score. This score reflects the overall deviation between the current component state and expected performance, rather than the abnormal fluctuations of a single indicator.

[0075] The threshold management system implements a dynamic boundary adjustment strategy. The basic threshold range is derived from the service level agreement requirements at the time of component deployment, and the system continuously calibrates these thresholds based on actual operational stability data. When a component consistently performs better than the baseline, the system gradually tightens its threshold range to improve monitoring sensitivity; conversely, for components with poor stability, the threshold is appropriately relaxed to reduce false alarms. Threshold adjustments follow a gradual principle, with each change not exceeding the standard deviation of the historical fluctuation range, avoiding drastic changes in monitoring strategies that could lead to system instability.

[0076] The load status analyzer constructs a system resource profile. By continuously tracking the utilization curves of resources such as CPU, memory, storage I / O, and network bandwidth, it identifies system load patterns and bottlenecks. The analyzer distinguishes between steady-state loads and transient peaks, establishing a time-series predictive model for resource consumption. When periodic fluctuations in memory usage of the logistics scheduling component are detected, the system can predict the next peak period and prepare elastic resources in advance. The resource topology mapper records the physical deployment relationships between components, identifying potential resource contention points, such as component groups sharing hosts or network links.

[0077] Deployment strategy adjustments implement a tiered response mechanism. When the difference score enters the warning range, the system initiates a resource fine-tuning process, adjusting the resource quotas of individual components without changing the component deployment sequence. When the score enters the alert range, a local sequence reconstruction is triggered, rearranging the initialization order of affected components and their direct dependencies. In cases of severe differences, the system performs a safety rollback operation, restoring critical components to a known stable version while maintaining the current state of non-critical components. Each adjustment decision undergoes an impact assessment, predicting the potential chain reactions of the change and selecting the optimization scheme with the least disturbance.

[0078] The initialization sequence optimizer employs an incremental update algorithm. When a deployment strategy needs adjustment, the system freezes the state of deployed components, treating them as the new system baseline environment. The dependency graph of the remaining components is re-analyzed, and the optimal initialization path under the new environmental constraints is calculated. The optimizer retains the still valid parts of the original sequence, only replanning the affected sections. This conservative optimization strategy avoids the resource waste and system instability caused by a full redeployment. The sequence adjustment process maintains transactional characteristics, ensuring that any intermediate state satisfies system integrity constraints.

[0079] The component interaction coordinator manages the transition states during changes. When the payment gateway component needs to degrade due to performance issues, the coordinator first notifies all dependent components to enter compatibility mode, temporarily disabling advanced features. Upon receiving the notification, the inventory service adjusts its transaction verification logic, adopting a simplified encryption scheme. This coordination mechanism prevents functional anomalies caused by version incompatibility between components. The coordinator maintains a state transition log, recording the mode switch time of each component, providing a complete timeline for post-event analysis.

[0080] The runtime knowledge base accumulates system behavior patterns. The handling process of each performance anomaly event is structurally recorded, including triggering conditions, measures taken, and the final effect. These cases are converted into searchable knowledge entries through feature extraction, forming a resource library for the system's self-learning. When a pattern similar to historical cases is detected, the system prioritizes trying proven and effective adjustment strategies to shorten problem response time. The knowledge base implements a case effectiveness evaluation mechanism, regularly eliminating ineffective or outdated solutions to maintain the timeliness of recommended strategies.

[0081] The transparency of the dynamic tuning process is ensured through a status reporting mechanism. The system generates human-readable tuning logs, describing the specific manifestations of performance deviations, the root cause analysis process, and the optimization measures taken. The reports adopt a hierarchical structure, allowing operations personnel to gradually delve from summary information to technical details. Visualization tools present the performance impact relationships between components as interactive graphs, intuitively demonstrating the path of action and expected effects of tuning strategies. All automated tuning operations retain a manual review interface, and critical decisions require secondary confirmation before execution.

[0082] Fault tolerance mechanisms are implemented throughout the entire monitoring and adjustment process. Performance data acquisition undergoes redundant verification, and key indicators are obtained from at least two independent sources to avoid misjudgments caused by single-point monitoring failures. The difference analysis algorithm incorporates outlier filtering logic to eliminate the impact of occasional noise on the overall assessment. The adjustment strategy generator considers multiple alternatives and automatically switches to a backup plan when the primary plan is blocked. All automated operations have timeout protection; if the expected results are not achieved within the predetermined time, the system automatically reverts to a safe state to prevent the adjustment process from becoming stuck.

[0083] The system establishes a closed-loop feedback mechanism for performance tuning. After each deployment strategy change, a dedicated tracking module records the actual effect data and compares it with the expected improvement target. Tuning strategies that consistently deviate are marked for review, triggering the parameter optimization process of the tuning algorithm. Strategies with significant effects are promoted to similar scenarios, forming a positive reinforcement loop. Feedback data is also used to optimize the monitoring strategy itself, such as adjusting the sampling frequency of indicators or redefining key thresholds, making system monitoring more accurate and efficient.

[0084] It should be noted that, in this document, relational terms such as "first" and "second" are used only 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 process, method, article, or apparatus.

[0085] 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 method for applying a component library that supports cross-system reuse, characterized in that, Includes the following steps: Identify the runtime context information of the target application system, and extract a set of reusable components from the component library based on the runtime context information; Based on the performance constraints of the target application system, the components in the reusable component set are classified into a core functional component subset and a supporting functional component subset. At the same time, environment-adaptive components are selected from the remaining components in the component library that are not covered by the reusable component set. The core functional component subset, the supporting functional component subset, and the environment-adaptive components together form a component set to be processed. The interaction coupling degree between each pair of adjacent components in the set of components to be processed is evaluated, and the components in the set of components to be processed are regarded as nodes. The interaction coupling degree is used as the edge weight connecting the nodes, thereby constructing the component dependency network of the set of components to be processed. The critical path of the component dependency network is determined by a heuristic search algorithm, and the initialization sequence of each component in the set of components to be processed is arranged according to the critical path. Each component in the set of components to be processed is deployed step by step according to the initialization sequence to achieve cross-system component reuse.

2. The method for applying a component library that supports cross-system reuse according to claim 1, characterized in that, The step of extracting a set of reusable components from the component library based on the runtime context information includes: querying the component usage history database using the runtime context information to obtain relevant component records; performing clustering processing on the queried relevant component records according to the component interface compatibility standard to form multiple component clusters; selecting a component cluster that matches the current runtime requirements of the target application system from the component clusters, and integrating all components in the selected component cluster into a set of reusable components, wherein the clustering process involves component function tag matching.

3. The method for applying a component library that supports cross-system reuse according to claim 2, characterized in that, The clustering process of the relevant component records queried according to the component interface compatibility standard includes: defining pre-configured interface specification categories in the component library; for each relevant component record queried, detecting the interface specification category to which the component's interface attribute belongs, and assigning the component to the component cluster of the corresponding interface specification category. The interface attribute detection is based on component metadata parsing.

4. The method for applying a component library that supports cross-system reuse according to claim 1, characterized in that, The step of classifying components in the reusable component set into a core functional component subset and a supporting functional component subset based on the performance constraints of the target application system includes: analyzing the main functional modules and secondary functional modules indicated by the performance constraints of the target application system; locating the activity range of components corresponding to the main functional modules and the activity range of components corresponding to the secondary functional modules in the reusable component set; classifying the components covered by the activity range of the main functional module components into the core functional component subset, and classifying the components covered by the activity range of the secondary functional module components into the supporting functional component subset.

5. The method for applying a component library that supports cross-system reuse according to claim 1, characterized in that, The process of selecting environment-adaptive components from the remaining components in the component library that are not covered by the reusable component set includes: allocating a priority association number to the core functional component subset and a secondary priority association number to the supporting functional component subset; for the remaining components in the component library that are not covered by the reusable component set, if the remaining component has a dependency relationship with the core functional component subset, then the remaining components with the priority association number are selected as environment-adaptive components; if the remaining component only has a dependency relationship with the supporting functional component subset, then the remaining components with the secondary priority association number are selected as environment-adaptive components.

6. The method for applying a component library that supports cross-system reuse according to claim 1, characterized in that, The evaluation of the interaction coupling degree between each pair of adjacent components in the set of components to be processed includes: for any two adjacent components in the set of components to be processed, identifying the component type of each component, wherein the component type includes one of the following: a subset of core functional components, a subset of supporting functional components, and an environment adaptation component; based on the identified component type, calculating the dependency influence coefficient of the first component and the dependency influence coefficient of the second component; measuring the correlation deviation between the dependency influence coefficient of the first component and the dependency influence coefficient of the second component, and quantifying the interaction coupling degree based on the correlation deviation.

7. The method for applying a component library that supports cross-system reuse according to claim 6, characterized in that, The step of quantifying the interaction coupling degree based on the correlation deviation includes: scanning the dependency influence coefficients of all components in the set of components to be processed, and determining the median of the distribution of these dependency influence coefficients; dividing multiple correlation deviation intervals according to the median of the distribution, and locating the target correlation deviation interval to which the calculated correlation deviation belongs; and using the preset coupling degree value corresponding to the target correlation deviation interval as the interaction coupling degree.

8. The method for applying a component library that supports cross-system reuse according to claim 1, characterized in that, The stepwise deployment of each component in the set of components to be processed following the initialization sequence includes: detecting the configuration parameters of each deployment node in the initialization sequence, and generating a parameter difference sequence based on the detected configuration parameters, wherein the order of parameters in the parameter difference sequence is consistent with the initialization sequence; inputting the parameter difference sequence into a deep reinforcement learning model, and outputting the resource allocation amount of each deployment component through the deep reinforcement learning model; and deploying the corresponding component according to the output resource allocation amount to complete the component application.

9. The method for applying a component library that supports cross-system reuse according to claim 1, characterized in that, The step of determining the critical path of the component dependency network using a heuristic search algorithm includes: initializing the start node and end node of the component dependency network, applying the A* search strategy to traverse the network edge weights, and generating the minimum cost path as the critical path, wherein the edge weights are dynamically updated based on the interaction coupling degree.

10. The method for applying a component library that supports cross-system reuse according to claim 1, characterized in that, During deployment, monitor the runtime performance metrics of the current components and calculate the fit difference between the current performance metrics and the predefined performance benchmark. When the fit difference exceeds the allowable threshold, adjust the deployment strategy of subsequent components based on the current system load status to optimize the initialization sequence.

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