Component library application method supporting cross-system multiplexing

Abstract

The invention relates to the technical field of component multiplexing, and discloses a component library application method supporting cross-system multiplexing. The method comprises the following steps: identifying running context information of a target application system, and extracting a reusable component set from a component library according to the running context information; based on the performance constraint condition of the target application system, classifying the reusable component set into a core function component subset and a support function component subset, screening environment adaptive components from the remaining components of the component library, and jointly forming a to-be-processed component set; evaluating the interactive coupling degree of adjacent components in the to-be-processed component set, and constructing a component dependence network by taking the components as nodes and the interactive coupling degree as an edge weight; determining a key path through a heuristic search algorithm, and arranging an initialization sequence of each component according to the key path; and deploying the components according to the sequence to realize cross-system component multiplexing. The method can effectively cope with different system differences, improves the component multiplexing efficiency and success rate, and is suitable for a cross-system component multiplexing scene.

Description

Technical Field

[0001] The present invention relates to the technical field of component reuse, and in particular to a component library application method that supports cross-system reuse. Background Art

[0002] With the rapid development of information technology, enterprise-level application systems are becoming increasingly multi-platform and heterogeneous. Different systems are often built based on different technical architectures, development languages, and operating environments. In this context, component reuse, a key means of improving development efficiency and reducing maintenance costs, has become a key bottleneck restricting the industrialization of software production due to its cross-system applicability.

[0003] Traditional component libraries are often designed for a single technology stack or specific business scenario. Components are highly coupled to native systems and often rely on runtime environments, interface specifications, or data formats. For example, a payment component developed for an e-commerce platform may be deeply tied to that platform's user authentication system, making it difficult to directly reuse in payment scenarios within financial systems. Graphical components developed for Windows often experience rendering anomalies in Linux environments, requiring extensive secondary development to adapt. This "system-binding" component design results in a component reuse rate of less than 30% when enterprises integrate multiple systems. This not only results in a significant waste of R&D resources, but also introduces additional system vulnerability risks due to duplicate development. Existing cross-system component reuse solutions have significant limitations: on the one hand, most solutions only reduce coupling at the component call layer through standardized interfaces, but ignore the impact of the operating environment and performance constraints on component compatibility. For example, the order processing component of a logistics system can run stably in a stand-alone environment, but after migrating to a high-concurrency cloud environment, it encounters data consistency issues due to the lack of adaptation to the distributed locking mechanism. On the other hand, component deployment lacks dynamic adaptability. When the resource configuration of the target system changes, the components cannot automatically adjust the initialization order, which may lead to startup failures due to resource competition. According to industry research, over 65% of enterprises require significant manpower to adapt environments and optimize performance when reusing components across systems. The average migration cost for each component accounts for over 40% of its 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, building a cross-system reuse mechanism that can dynamically identify the target system context, accurately match performance constraints, and enable efficient component deployment has become a critical issue in software engineering. Summary of the Invention

[0004] The purpose of the present invention is to provide a component library application method that supports cross-system reuse, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides a component library application method that supports cross-system reuse, the method comprising: Identifying the running context information of the target application system, and extracting a set of reusable components from a component library according to the running 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, and at the same time, environmental adaptation components are screened 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 environmental adaptation component together forming a component set to be processed; Evaluate the interactive coupling degree between each pair of adjacent components in the set of components to be processed, regard the components in the set of components to be processed as nodes, and use the interactive coupling degree as the edge weight connecting the nodes, thereby constructing a 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 an 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 gradually deployed according to the initialization sequence to achieve cross-system component reuse.

[0006] Preferably, extracting a reusable component set from a component library based on the running context information includes: using the running context information to query a component usage history database to obtain relevant component records; clustering the queried relevant component records according to component interface compatibility standards to form multiple component clusters; selecting a component cluster that is compatible with the current running requirements of the target application system from the component clusters, and integrating all components in the selected component cluster into a reusable component set, wherein the clustering process involves component function label matching.

[0007] Preferably, the clustering processing of the queried related component records based on the component interface compatibility standard includes: defining pre-configured interface specification categories in the component library; for each queried related component record, detecting the interface specification category to which the component interface attribute belongs, and assigning the component to the component cluster of the corresponding interface specification category, and the interface attribute detection is based on component metadata parsing.

[0008] Preferably, the classification of 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 primary functional modules and secondary functional modules indicated by the performance constraints of the target application system; locating the component activity scope corresponding to the primary functional module in the reusable component set, and locating the component activity scope corresponding to the secondary functional module in the reusable component set; classifying the components covered by the component activity scope of the primary functional module as a core functional component subset, and classifying the components covered by the component activity scope of the secondary functional module as a supporting functional component subset.

[0009] Preferably, the screening of environment adaptation 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 allocating 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 components have a dependency relationship with the core functional component subset, then selecting the remaining components with the priority association number as the environment adaptation components; if the remaining components only have a dependency relationship with the supporting functional component subset, then selecting the remaining components with the secondary priority association number as the environment adaptation components.

[0010] Preferably, the evaluation of the interactive 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 respectively, the component type including one of a core functional component subset, a supporting functional component subset and an environmental adaptation component; based on the identified component type, calculating the dependency influence coefficient of the first component, and calculating 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 interactive coupling degree according to the correlation deviation.

[0011] Preferably, the quantification of the interactive coupling degree based on the association deviation includes: scanning the dependency influence coefficients of all components in the set of components to be processed, and determining the distribution median of these dependency influence coefficients; dividing a plurality of association deviation intervals according to the distribution median, and locating the target association deviation interval to which the calculated association deviation belongs; and using the preset coupling degree value corresponding to the target association deviation interval as the interactive coupling degree.

[0012] Preferably, 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 on the initialization sequence, and generating a parameter difference sequence based on the detected configuration parameters, wherein the ordering of the 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 deployed component through the deep reinforcement learning model; deploying the corresponding component according to the output resource allocation amount to complete the component application.

[0013] Preferably, determining the critical path of the component dependency network by a heuristic search algorithm includes: initializing the starting node and the 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 interactive coupling degree.

[0014] Preferably, during the deployment process, the runtime performance indicators of the current components are monitored, and the fitness difference between the current performance indicators and the predefined performance benchmark is calculated; when the fitness difference exceeds the allowed threshold, the subsequent component deployment strategy is adjusted based on the current system load status to optimize the initialization sequence.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This component library application method, which supports cross-system reuse, extracts a collection of reusable components by identifying the target application system's operating context. This ensures that the extracted components are more closely aligned with the target system's actual operating environment, reducing component incompatibilities caused by environmental differences. By classifying components based on the target application system's performance constraints and screening for environmentally compatible components to form a collection of pending components, the selection and combination of components can better meet the target system's performance requirements, enabling them to function better within the target system and preventing performance mismatches from impacting overall system performance. Evaluating the degree of interaction coupling between adjacent components in the set of components to be processed and constructing a component dependency network clearly demonstrates the relationships between components, providing a strong reference for subsequent component initialization and deployment. By using a heuristic search algorithm to determine the critical path of the component dependency network and arranging the component initialization sequence based on this critical path, the system ensures that components are initialized in a reasonable order, reducing interaction issues caused by improper initialization order and ensuring smooth collaboration between components. Deploying components step-by-step according to the initialization sequence streamlines the deployment process, reduces the likelihood of errors, and increases the success rate of cross-system component reuse. This approach forms a comprehensive process, from component extraction and classification to dependency analysis, initialization sequence arrangement, and deployment. This effectively addresses differences between systems, expands the scope of component reuse, improves its efficiency, and simplifies component reuse during system development and maintenance, enabling developers to more easily leverage existing components to build new applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a working principle diagram of the component library application method supporting cross-system reuse according to the present invention; Figure 2 Flowchart for component interface compatibility clustering process; Figure 3 Flowchart for selecting components suitable for the environment; Figure 4 Flowchart for interaction coupling metric. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] See also Figure 1 The present invention provides a component library application method that supports cross-system reuse, the method comprising: Through intelligent component screening, classification, and deployment mechanisms, efficient component reuse across system environments is achieved. This approach first identifies the target system's operational context and, based on this information, extracts a set of reusable components from the component library. Subsequently, based on performance constraints, the components are divided into a subset of core and supporting functional components, supplemented by environmental adaptation components to form a set of pending components. By constructing a component dependency network and calculating the degree of interaction coupling, a heuristic algorithm is employed to determine the critical path and schedule the initialization sequence, ultimately achieving orderly component deployment.

[0019] Example 1: See Figure 2 , a reusable component extraction process driven by running context information. The process starts with a structured query of the component usage history database. The database contains historical data such as component call records, performance logs, and environmental parameters in different system environments. After obtaining the running context information of the target application system, the system performs feature extraction operations to capture the system architecture characteristics, runtime environment parameters, and business requirement characteristics. Based on these characteristics, multi-dimensional query conditions are generated, including key dimensions such as operating system version, middleware configuration, and resource constraints. The query engine will retrieve historical component call records with similar context features and return a result set containing metadata fields such as component identifier, interface specification version, dependent component list, and historical deployment success rate.

[0020] Interface compatibility processing uses 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 has several specific specification standards. For example, the communication protocol layer includes protocol standards such as HTTP / 2, gRPC, and AMQP, and the data exchange layer covers serialization specifications such as JSONSchema, ProtocolBuffers, and Avro. The component metadata parser performs syntax analysis on the interface description files in the query results, extracting their communication mode, serialization method, and interface method signature features to form a structured interface feature vector. The vector space model is used to calculate the similarity index between this feature vector and the preset specification category, 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 to ensure that the components within the cluster have communication interoperability.

[0021] Multiple filtering mechanisms are implemented during the clustering process. After completing the initial grouping, the system performs internal compatibility verification for each component cluster. This verification involves method signature compatibility testing, analyzing the input and output parameter structures of the interface methods through the abstract syntax tree to identify conflicting data type definitions. At the same time, the consistency of the exception handling mechanism is tested to confirm whether the error code system and exception propagation mode between different components are compatible. For conflicting components, the system activates the automatic adaptation mechanism, generates interface adapter template suggestions, or migrates the conflicting components to a new component cluster. The final output component clusters form mutually isolated compatibility units, laying a technical foundation for subsequent selection.

[0022] Function label matching is based on the semantic understanding technology architecture. The system maintains a multi-layered functional ontology library, with the top layer divided into two branches: business function domain and technical function domain. The business function domain adopts the industry standard classification system. For example, in the financial field, it is subdivided into subcategories such as payment settlement, risk management, and customer service; the technical function domain includes technical areas such as data processing, transaction management, and security control. The natural language processor analyzes the functional description text in the component document and performs keyword extraction, entity recognition, and dependency syntax analysis. The text features are mapped to the semantic vector space through the word embedding model, and similarity matching is performed with the vector representation of the standardized function labels in the ontology library. Each component is assigned a primary function label and an auxiliary function label, and the confidence score of each label is recorded. After the label assignment is completed, the system constructs a component-function matrix to quantify the contribution of each component to specific functional requirements.

[0023] 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 labels. During the component cluster preselection phase, the functional label coverage index of each component cluster is calculated, and component clusters below the set threshold are filtered out. Fine-grained matching is performed on candidate component clusters, analyzing the weight values ​​of each unit in the component-function matrix to select the minimum component cluster combination that can fully cover the core functional requirements. Finally, a resource requirement compatibility check is performed, comparing the component operating resource requirements with the target system environment constraints, and eliminating components with significant resource conflicts. Finally, all component entries of the selected component clusters are merged to generate the output result of a reusable component set.

[0024] This collection construction mechanism includes a feedback optimization loop. Runtime data generated after component deployment is automatically fed back to the historical database to update the component's environmental adaptation record. When the same context environment appears multiple times, the system optimizes the weight distribution of query conditions, adjusts the evaluation threshold for interface compatibility, and continuously improves the screening accuracy of the component collection. The composition structure of the component cluster is also dynamically reconstructed based on the actual deployment results, merging component clusters with high interoperability and splitting component groups with compatibility risks. The functional ontology library is regularly updated through industry specification updates and technology evolution analysis to keep the functional classification system synchronized with current technological developments.

[0025] Example 2: Reference Figure 3 , focusing on component classification and environmental adaptation under performance constraints. The performance constraint parsing engine uses a semantic rule-based parsing framework to process the input performance constraint text. The framework has a built-in constraint expression syntax analyzer that can recognize typical performance indicator descriptions including response time upper limit, throughput lower limit, CPU occupancy threshold, etc. The parsing process first converts the constraints described in natural language into an intermediate representation, and identifies the key performance parameters and constraint relationship operators through pattern matching. The constraint template library maintained by the system stores standard expression patterns for various performance constraints, and each template is associated with a specific constraint type and importance identifier. The parser performs multiple rounds of matching on the input constraints with the template library, and labels the successfully matched constraints as mandatory or recommended to form a structured set of constraints.

[0026] The algorithm for identifying primary functional modules is based on the topological sorting of constraints. The system constructs a constraint dependency graph, where nodes represent performance constraints and edges represent the logical dependencies between constraints. A depth-first search is used to traverse the graph, identifying the set of constraints on the critical path. The functional modules corresponding to these constraints are labeled as primary functional modules. Secondary functional modules correspond to the set of constraints on the branch paths in the graph. Module importance scores are calculated using the following formula: , in, Representation Module The importance rating of is the set of constraints associated with the module, It is a constraint The base weight of A quantitative value representing the strictness of the constraint, is the strictness threshold, is the adjustment coefficient. This formula comprehensively considers the number, weight, and strictness of the constraints, and the scoring result is used to verify the rationality of the primary / secondary module division.

[0027] A bidirectional indexing mechanism is implemented for locating the activity scope of components. The component function mapping table constructed by the system is stored in a graph database, which includes component nodes, functional module nodes, and the relationship 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 mark the component's support coefficient for the functional module. During positioning queries, the system simultaneously performs forward traversal from functional modules to components and reverse verification from components to functional modules to ensure that each selected component is doubly confirmed. The determination of the core functional component subset additionally introduces the dimension of historical stability assessment. The system accesses the component quality knowledge base to obtain indicators such as the average failure interval time and abnormal recovery success rate of candidate components in past deployments, and comprehensively calculates the reliability score of the component.

[0028] Environmental adaptation component screening uses a mechanism that combines a dependency propagation tree with priority scheduling. The system analyzes the call relationships between the remaining components and the selected components and constructs 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 in proportion to their weights. When the accumulated energy value of the remaining components exceeds the activation threshold, they are marked as candidate environmental adaptation components. Supporting functional components use an attenuated propagation model, where the 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 competition is detected.

[0029] Priority scheduling implements a multi-queue management scheme. Candidate environmental adaptation components are placed into preparation queues of varying priorities based on their energy values. The high-priority queue corresponds to adaptation components related to core functions and employs an immediate preloading strategy; the medium-priority queue handles components related to support functions and implements an on-demand loading mechanism; and the low-priority queue houses general-purpose adaptation components and employs a lazy loading approach. Each queue implements a cache replacement algorithm based on recent usage 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 within each queue.

[0030] The component classification results are verified using a cross-check process. The core functional component subsets and supporting functional component subsets generated by the system are subject to architectural rationality review. The reviewer checks the component coupling indicators within each subset to ensure that they do not exceed the preset coupling limit. At the same time, the degree of interface isolation between subsets is verified, and the number and complexity of cross-subset call relationships are measured. The set of environmental adaptation components must pass the dependency completeness test to confirm that it can cover the key external dependencies of all selected components. When classification defects are found, the system initiates a classification adjustment loop, re-evaluates the component importance scores and dependencies, and iteratively optimizes the classification results.

[0031] This implementation method includes an adaptive adjustment mechanism. During the actual operation of the components, the performance monitoring module continuously collects the resource consumption data and function implementation effects of each component. This runtime information is fed back to the classification decision engine to calibrate the initial classification parameters. The identification criteria of core functional components will be dynamically adjusted according to the actual business impact, and the selection strategy of environmental adaptation components will change elastically with changes in system load. Historical classification decisions and their effect data are recorded in the case library to provide a reference for the classification of subsequent similar scenarios. The system regularly performs offline optimization of the classification strategy, and updates the component importance scoring algorithm and dependency evaluation model by analyzing the success patterns and failure lessons in historical cases.

[0032] The entire component classification and environment adaptation process utilizes a layered processing architecture. The bottom layer comprises the original component library and performance constraint input interface. The middle layer includes processing modules such as constraint parsing, functional module identification, and component mapping. The upper layer houses the classification decision-making and adaptation component selection logic. Information is exchanged between these layers using standardized data formats, ensuring modularity and scalability. The system supports parallel processing of classification requests for multiple application scenarios, allocating an independent processing context to each request to avoid decision interference between different scenarios. Classification output utilizes versioning, maintaining a complete record of all classifications and their change history, enabling backtracking and comparative analysis of results.

[0033] Example 3: Reference Figure 4, focusing on the evaluation and quantification mechanism of component interaction coupling. The process starts with the initialization configuration of the component type identification system. The system maintains a type feature knowledge base, which stores the discrimination rule sets of three types of components. The identification features of core functional components include directly implementing business requirements, processing key data flows, etc.; the features of supporting functional components involve auxiliary operations, logging, etc.; and the environmental adaptation components are manifested as 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, analyzes its metadata description, interface definition and historical usage records, and determines its specific type through multi-level filtering logic. The identification process adopts a progressive verification strategy, first checking the explicit type annotation, and if there is no annotation, analyzing the function description text, and finally examining the actual call relationship pattern.

[0034] The calculation of the dependency impact coefficient is based on the type weight matrix. The system defines basic impact factors for each type of component, and these factors are dynamically adjusted according to the characteristics of the system architecture. When evaluating the interaction between two adjacent components, the system first determines their type combination pattern, with a total of six possible permutations. For each type combination, a conversion function is preset to map the basic impact factor to the dependency impact coefficient in a specific scenario. This conversion process takes into account factors such as the call direction, data traffic, and abnormal propagation path between components. The dynamic adjustment of the dependency impact coefficient uses the following calculation formula: ,

[0035] in, Presentation Component For components The dependence influence coefficient of It is a component The active impact factor, For components The passive response factor, Reflects the abnormal transmission intensity between the two, is the distance adjustment parameter, Represents the logical distance on the call chain. This formula balances the inherent characteristics of components and the actual interaction features, making the calculation result reflect the type nature while adapting to the specific context.

[0036] The correlation deviation metric implements multi-dimensional difference analysis. The system defines a deviation feature space, encompassing dimensions such as interface compatibility, data format matching, and call frequency synchronization. Each dimension's measurement utilizes a comparison algorithm. For example, interface compatibility is calculated by comparing method signatures, while data format matching examines the consistency of schema definitions. The measurement results for each dimension are normalized and then fed into a weighted aggregation function to generate a comprehensive correlation deviation value. The deviation analysis process records a detailed comparison log, including fully matched feature items, partially matched items, and completely mismatched items, providing an explanation for subsequent coupling quantification.

[0037] Coupling quantification uses a dynamic interval mapping strategy. The system continuously monitors the distribution of dependency influence coefficients of all components and uses an adaptive clustering algorithm to identify data-dense and data-sparse areas. Based on the distribution characteristics, a non-uniformly divided correlation deviation interval system is constructed, with higher density interval boundaries set in key areas. Each interval is associated with a preset coupling value, which forms an ordered sequence and meets the monotonicity requirement. The quantification process implements a two-level verification mechanism, first checking whether the calculated correlation deviation falls within the valid range, and then verifying whether the coupling value corresponding to the selected interval conforms to the expected relationship pattern of the type combination. When an abnormal quantization result is detected, the system initiates a retrospective analysis process to check the intermediate results of the dependency influence coefficient calculation and the characteristic composition of the correlation deviation.

[0038] The interactive coupling evaluation system adopts a distributed architecture design. The evaluation task is decomposed into four sub-stages: type identification, coefficient calculation, deviation measurement, and coupling quantification. Each stage corresponds to an independent processing unit. The processing results are transmitted between units through message queues, forming a pipeline execution mode. The system implements a result caching mechanism, directly returning cached values ​​for repeated component combinations and adopting an incremental calculation strategy for similar combinations. All intermediate data generated during the evaluation process are attached with timestamps and context tags to support the traceability and reproduction of results. Lazy loading technology is used for performance optimization. Detailed deviation analysis is only carried out when necessary, and pre-calculated approximations are used in general scenarios.

[0039] The application of evaluation results is managed under version control. After each component set update, the system generates a version identifier for the new interaction coupling evaluation results. This version information includes the evaluation time, parameter configuration, and key statistical indicators, facilitating subsequent comparative analysis. The dependency network builder uses versioned coupling values ​​as edge weights, ensuring that the network representation is strictly synchronized with the evaluation results. The system maintains a historical record of evaluations, recording differences between versions and triggering notifications when significant changes in coupling patterns are detected.

[0040] This implementation includes a self-learning optimization loop. The system collects component interaction data during actual runtime, including metrics such as call latency, error transmission rate, and resource contention. This runtime feedback data is used to calibrate the calculation parameters of dependency influence coefficients, adjust the weight distribution of associated deviations, and optimize the division strategy of coupling intervals. The learning process adopts a small batch incremental update mode, and the accumulated runtime experience is regularly incorporated into the evaluation model. The system also implements a mechanism to detect abnormal interaction patterns, and automatically triggers the model retraining process when it finds that the actual observed component interaction characteristics are persistently different from the evaluation predictions.

[0041] Component interaction coupling assessment is deeply integrated with environmental awareness. During the assessment process, the system collects real-time data on the deployment environment's characteristics, including information such as the container orchestration model, service mesh configuration, and resource quota limits. 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 weighting of anomaly propagation intensity is increased accordingly. This integration ensures that coupling assessment results always reflect the actual constraints of the current environment.

[0042] The interpretability of the evaluation process is achieved through a diagnostic reporting mechanism. The system generates a structured diagnostic report for each set of significant interaction coupling evaluation results, detailing the basis for type identification, the calculation path for dependency influence coefficients, the characteristic decomposition of association deviations, and the process for determining the final coupling degree. The report adopts a hierarchical presentation strategy, presenting key conclusions at the summary level and technical analysis at the detail level to support the understanding needs of different roles. The diagnostic report is integrated with the component dependency network visualization tool, allowing for exploration of the distribution patterns and key influencing factors of interaction coupling through a graphical interface.

[0043] Example 4: Specific implementation of component deployment sequence optimization and execution. For example, consider the upgrade of an e-commerce platform's order processing system, which requires integrating three core components: a payment gateway, inventory services, and logistics scheduling. The configuration parameter detector first scans the environmental characteristics of each deployment node and generates the following parameter difference record, Table 1.

[0044] Table 1: Parameter difference record table Component Name Memory baseline (MB) CPU thread requirements Storage Type Network latency (ms) Dependent library version Payment Gateway 2048 4 SSD 35 OpenSSL 3.0 Inventory Services 4096 8 NVMe 18 JDK17 Logistics Scheduling 3072 6 SSD 42 Python 3.9 A parameter difference sequence generator converts these discrete parameters into time-series feature vectors. Positive deviations of the payment gateway node's memory configuration from the baseline are encoded as resource demand intensity signals, 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, which processes hardware resource configuration, software environment characteristics, and network topology characteristics, respectively. During the training phase, a virtual environment simulation is used to simulate component deployment scenarios under different parameter combinations. The model gradually learns the payment gateway's sensitivity to cryptographic library versions, the inventory service's dependence on memory bandwidth, and the logistics scheduling component's tolerance to network jitter.

[0045] 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 plan that includes AVX instruction set reservations. The base resource allocation is set to 1800MB of memory and 2 CPU threads, with elastic expansion reserving 300MB of memory and an additional 2 threads for cryptographic 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. The base memory allocation is 3500MB, including cache preload space, and elastic expansion is mainly for concurrent query buffers. The network compensation mechanism of the logistics scheduling component is activated. The model adds TCP window adjustment parameters to the base resource configuration and reserves bandwidth burst capacity.

[0046] The A* search strategy exhibits dynamic adaptability during path planning. Initial critical path analysis determined that the payment gateway should be deployed first because it provides an encrypted communication foundation for other components. When real-time monitoring reveals that network latency fluctuations exceed a threshold, the path weight calculator reevaluates 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 has shown a more stable deployment success rate in simulation tests. The path optimizer continuously tracks deployment progress. When the inventory service completes initialization and detects that there are sufficient remaining resources, it reintegrates the logistics scheduling component into the parallel deployment queue.

[0047] The deployment executor implements progressive resource injection. The payment gateway deployment first allocates the declared base resource volume, while the stability monitor continuously monitors its handshake success rate and transaction processing latency. After three consecutive detection cycle indicators meet the target, the executor gradually releases elastic expansion resources while monitoring resource utilization efficiency. The inventory service deployment process triggered the anomaly detection mechanism. The initial memory allocation failed to meet its cache warm-up requirements. Based on the model's recommendations, the executor dynamically adjusted the base resource volume to 3800MB and reduced the elastic expansion ratio. When the logistics scheduling component encountered network jitter during deployment, the executor automatically enabled the compensation strategy, extending the verification intervals at each stage and temporarily increasing the heartbeat detection frequency.

[0048] The version rollback mechanism is deeply integrated with the deployment process. When a certificate verification exception occurred after deploying the third version of the payment gateway, the system automatically compared the configuration differences between the current version and the previous version. The rollback decider analyzed the correlation between the exception pattern and the configuration parameters and chose to roll back to the second version while maintaining the existing resource allocation plan. The rollback process records detailed resource release and re-application logs to ensure that resource leaks caused by version switching are not caused. A similar mechanism is used for grayscale updates of the inventory service, where the new version is fully activated only when resource utilization falls below a threshold.

[0049] Resource reclamation strategies implement intelligent predictions. After deployment, the system analyzes the actual resource usage patterns of each component and identifies that the payment gateway's elastic scalability resources have been idle for extended periods during non-transaction periods. The resource regulator works in conjunction with the business cycle forecasting module to establish an on-demand reclamation mechanism, automatically shrinking excess allocations during daily low-volume periods. The inventory service uses a different resource reclamation strategy. Due to its cache nature, it must remain pre-warmed, triggering partial memory release only during periods of extreme idleness. The logistics scheduling component dynamically adjusts network bandwidth reservations, automatically scaling them up and down based on the real-time demands of the delivery route planning algorithm.

[0050] 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 log shows the complete evolution of resource allocation: from an initial conservative configuration, through three elastic expansion adjustments, to the final stabilization of the 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.

[0051] Exception handling utilizes a hierarchical response model. When a critical CPU instruction set incompatibility error is detected during payment gateway deployment, the system immediately pauses the entire deployment sequence and escalates the issue to a manual intervention channel. Insufficient memory allocation in the inventory service is identified as a moderate anomaly, triggering an automatic adjustment process without interrupting deployment. Fluctuations in network latency in logistics scheduling are considered tolerable anomalies; the system simply logs a warning and continues execution. Corresponding recovery strategies and resource reallocation logic for each anomaly type ensure that problem handling is coordinated with system status.

[0052] Dynamic optimization of deployment strategies is reflected in multiple dimensions. In terms of time, the system learned that payment gateway deployments during business hours require higher success criteria, and therefore automatically adjusted the verification threshold. In terms of space, it was found that inventory services deployed on physical machines are more stable than in container environments, and physical nodes are subsequently preferred. In terms of version, records show that version 2.3 of the logistics scheduling component is most compatible with the current system, and the model will prioritize this version. These optimization experiences are structured and stored in a knowledge graph, forming a reusable deployment strategy library.

[0053] Dynamic management of inter-component dependencies is another key feature. When the payment gateway is deployed, the inventory service detects the new encrypted channel characteristics and automatically adjusts its request retry mechanism. The logistics scheduling component dynamically optimizes its timeout settings based on the actual available payment interface response time. These runtime adaptive changes are fed back into the deployment knowledge base to refine the dependency modeling of subsequent deployment sequences. The resulting deployment sequence not only considers the static component dependency graph but also incorporates the implicit constraints generated by these dynamic adjustments.

[0054] Example 5: Focus on runtime component performance monitoring and dynamic adjustment mechanism. The performance monitoring framework established during the system deployment phase includes a multi-level indicator collection system, capturing basic indicators such as CPU utilization and memory usage at the infrastructure layer, collecting operational data such as 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 delay at the application layer. These indicators are collected in real time through distributed probes, cleaned and standardized through streaming pipelines, and finally form a structured performance data stream. The monitoring agent adopts an adaptive sampling strategy, using high-frequency fine-grained sampling when the system load is low, and automatically switches to a sparse sampling mode that prioritizes key indicators when resource constraints are detected, balancing monitoring accuracy and system overhead.

[0055] The performance benchmark management system maintains a multi-dimensional reference standard library. Static benchmarks are derived from specifications and technical white papers provided by component vendors, defining expected performance values ​​under ideal conditions. Dynamic benchmarks are calculated based on historical operational data and include reference lines such as the median performance of similar systems and the system's historical best performance. The environmental awareness module analyzes the current hardware configuration, network conditions, and external dependency status in real time to generate environmental adjustment coefficients, which are used to dynamically adjust benchmark values. When payment gateway components are 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.

[0056] The fitness variance calculation utilizes multi-dimensional fusion analysis technology. Each performance metric is independently calculated to determine the degree of deviation between its current value and the baseline value, and dimensional differences are eliminated through normalization. The system establishes an indicator correlation graph model to identify groups of metrics with causal relationships, such as the correlation between database connection pool size and query response time. The variance analyzer considers both directly observed metric deviations and the coordinated variation patterns of related metric groups to generate a comprehensive fitness variance score. This score reflects the overall degree of deviation of the current component state from expected performance, rather than the abnormal fluctuations of a single metric.

[0057] The threshold management system implements a dynamic boundary adjustment strategy. Base threshold ranges are derived from the service level agreement requirements during component deployment, and the system continuously calibrates these thresholds based on actual operational stability data. When a component consistently outperforms 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 are gradual, with each change not exceeding the standard deviation of the historical fluctuation range. This prevents drastic changes in monitoring strategies from causing system instability.

[0058] The load status analyzer builds a profile of system resources. By continuously tracking utilization curves for resources such as CPU, memory, storage IO, and network bandwidth, it identifies system load patterns and bottlenecks. The analyzer distinguishes between steady-state loads and transient peaks, establishing a time-series prediction model for resource consumption. When periodic fluctuations in the memory usage of logistics scheduling components 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 and identifies possible points of resource contention, such as groups of components sharing hosts or network links.

[0059] Deployment strategy adjustments implement a graded response mechanism. When the discrepancy score enters the early warning range, the system initiates a resource fine-tuning process, adjusting the resource quota of individual components without changing the component deployment sequence. When the score enters the warning range, a local sequence reconstruction is triggered, rearranging the initialization order of the affected components and their direct dependencies. In the case of severe discrepancies, the system performs a safe rollback, restoring critical components to known stable versions while maintaining the current state of non-critical components. Each adjustment decision undergoes an impact assessment to predict the potential chain reactions of the change and select the optimization solution with the least disruption.

[0060] The initialization sequence optimizer uses an incremental update algorithm. When the deployment strategy needs to be adjusted, the system freezes the state of the already deployed components and treats it as the new system baseline environment. The dependency graph of the remaining components is reanalyzed to calculate the optimal initialization path under the new environmental constraints. The optimizer retains the remaining valid portions of the original sequence and replans only the affected sections. This conservative optimization strategy avoids resource waste and system turbulence caused by a full redeployment. The sequence adjustment process maintains transactional properties, ensuring that any intermediate states meet system integrity constraints.

[0061] The component interaction coordinator manages transition states during changes. When the payment gateway component needs to be downgraded due to performance issues, the coordinator first notifies all dependent components to enter compatibility mode, temporarily disabling advanced features. Upon receiving this notification, the inventory service adjusts its transaction verification logic and adopts a simplified encryption scheme. This coordination mechanism prevents functional anomalies caused by version mismatches between components. The coordinator maintains a state transition log, recording the time points of each component's mode switch, providing a complete timeline for post-event analysis.

[0062] The runtime knowledge base accumulates system behavior patterns. The handling process of each performance anomaly is structured and recorded, including the triggering conditions, measures taken, and the final results. These cases are converted into searchable knowledge items through feature extraction, forming a resource library for the system's self-learning. When patterns similar to historical cases are detected, the system prioritizes proven effective adjustment strategies, shortening response time. The knowledge base implements a case effectiveness evaluation mechanism, regularly eliminating ineffective or outdated solutions to ensure the timeliness of recommended strategies.

[0063] Transparency in the dynamic adjustment process is ensured through a status reporting mechanism. The system generates a human-readable adjustment log, detailing the specific manifestations of performance deviations, the root cause analysis process, and the optimization measures taken. The report uses a hierarchical structure, allowing operations personnel to gradually drill down from general information to technical details. Visualization tools present the performance impact relationships between components as interactive graphs, intuitively demonstrating the path and expected effects of adjustment policies. All automated adjustment operations retain a manual review interface, and critical decisions require secondary confirmation before execution.

[0064] Fault-tolerance mechanisms are implemented throughout the entire monitoring and adjustment process. Redundant verification is implemented in performance data collection, and key metrics are obtained from at least two independent sources to avoid misjudgments caused by single-point monitoring failures. The variance analysis algorithm incorporates built-in outlier filtering logic to eliminate the impact of occasional noise on the overall assessment. The adjustment strategy generator considers multiple alternative plans and automatically switches to a backup plan if the primary plan is blocked. All automated operations have timeout protection. If the expected results are not achieved within the specified time, they will automatically fall back to a safe state to prevent the adjustment process from becoming stuck.

[0065] The system establishes a closed-loop feedback mechanism for performance adjustments. After each deployment strategy change, a dedicated tracking module records actual performance data and compares it with the expected improvement targets. Adjustment strategies that consistently deviate are flagged for review, triggering parameter optimization within the adjustment algorithm. Strategies with significant performance are then generalized to similar scenarios, creating a positive reinforcement cycle. Feedback data is also used to optimize the monitoring strategy itself, such as adjusting metric sampling frequency or redefining key thresholds, making system monitoring more accurate and efficient.

[0066] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0067] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A component library application method supporting cross-system reuse, characterized in that: The following steps are involved: Identifying the running context information of the target application system, and extracting a set of reusable components from a component library according to the running 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, and at the same time, environmental adaptation components are screened 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 environmental adaptation component together forming a component set to be processed; Evaluate the interactive coupling degree between each pair of adjacent components in the set of components to be processed, regard the components in the set of components to be processed as nodes, and use the interactive coupling degree as the edge weight connecting the nodes, thereby constructing a 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 an 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 gradually deployed according to the initialization sequence to achieve cross-system component reuse.

2. The component library application method supporting cross-system reuse according to claim 1, characterized in that: The method of extracting a reusable component set from a component library based on the running context information includes: using the running context information to query a component usage history database 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 from the component cluster that is compatible with the current running requirements of the target application system, and integrating all components in the selected component cluster into a reusable component set, wherein the clustering process involves component function label matching.

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

4. The component library application method supporting cross-system reuse according to claim 1, characterized in that: The classification of 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 the secondary functional modules indicated by the performance constraints of the target application system; locating the component activity scope corresponding to the main functional module in the reusable component set, and locating the component activity scope corresponding to the secondary functional module in the reusable component set; classifying the components covered by the component activity scope of the main functional module as the core functional component subset, and classifying the components covered by the component activity scope of the secondary functional module as the supporting functional component subset.

5. The component library application method supporting cross-system reuse according to claim 1, characterized in that: The screening of environment adaptation 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 allocating 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 components have a dependency relationship with the core functional component subset, then selecting the remaining components with the priority association number as the environment adaptation components; if the remaining components only have a dependency relationship with the supporting functional component subset, then selecting the remaining components with the secondary priority association number as the environment adaptation components.

6. The component library application method supporting cross-system reuse according to claim 1, characterized in that: The evaluation of the interactive 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 respectively, wherein the component type includes one of a core function component subset, a support function component subset, and an environment adaptation component; based on the identified component type, calculating the dependency influence coefficient of the first component and calculating 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 interactive coupling degree according to the correlation deviation.

7. The component library application method supporting cross-system reuse according to claim 6, characterized in that: The method of quantifying the interactive coupling degree according to the association 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 a plurality of association deviation intervals according to the median of the distribution, and locating the target association deviation interval to which the calculated association deviation belongs; and using a preset coupling degree value corresponding to the target association deviation interval as the interactive coupling degree.

8. The component library application method supporting 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 on the initialization sequence, and generating a parameter difference sequence based on the detected configuration parameters, wherein the ordering of the 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 deployed component through the deep reinforcement learning model; deploying the corresponding component according to the output resource allocation amount to complete the component application.

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

10. The component library application method supporting cross-system reuse according to claim 1, characterized in that: During the deployment process, the runtime performance indicators of the current components are monitored and the fitness difference between the current performance indicators and the predefined performance benchmark is calculated. When the fitness difference exceeds the allowable threshold, the subsequent component deployment strategy is adjusted based on the current system load status to optimize the initialization sequence.

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