Pipeline split charging management platform and method

Abstract

The invention relates to the technical field of system engineering, and discloses a pipeline split charging management platform and method.The system comprises a real-time state monitoring module, a knowledge base instantiation module, a scheme derivation planning module, a vulnerability analysis module, an interference analogue simulation module and a stress resistance strengthening strategy module; acquiring a real-time constraint; loading a historical knowledge base instance, and obtaining a path planning and interference simulation operation instance; deriving a path planning instance according to the real-time constraint, and generating an initial split charging scheme; analyzing a scheme node load and topological relation, and identifying key weak links; based on weak link simulation disturbance, a targeted interference scheme is generated; strengthening the initial scheme according to the interference scheme, and forming a pipeline split charging execution strategy; according to the invention, the efficiency of the pipeline subpackage execution strategy of the pipeline subpackage management platform can be improved.

Description

Technical Field

[0001] This invention relates to the field of systems engineering technology, and in particular to a pipeline distribution management platform and method. Background Technology

[0002] Pipeline sub-packing is a crucial link in process industries and logistics transportation systems, and its management efficiency directly impacts the overall system's operational stability and resource utilization efficiency. Existing pipeline sub-packing management methods typically rely on pre-defined path planning rules and static resource allocation strategies. They involve collecting historical operational data, establishing a sub-packing path database, and executing sub-packing tasks based on fixed scheduling logic. Some systems have also attempted to introduce interference mitigation mechanisms, simulating typical abnormal scenarios to verify the robustness of sub-packing schemes and improve the system's adaptability under common failure conditions. Such methods can meet basic sub-packing management needs in certain scenarios and constitute the mainstream technical foundation of current pipeline sub-packing management.

[0003] However, existing methods struggle to achieve deep adaptation between path planning and real-time operating conditions when dealing with complex and ever-changing dispensing requirements and dynamic resource constraints. Furthermore, in terms of interference simulation and resilience enhancement, existing methods largely rely on simulation of isolated scenarios and local strategy optimization, lacking a systematic identification and targeted reinforcement mechanism for the overall vulnerable structure of the dispensing system. This makes it difficult for them to maintain high efficiency and reliability in actual operation. Therefore, how to improve the efficiency of pipeline dispensing execution strategies has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a pipeline distribution management platform and method to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a pipeline assembly management platform, characterized in that the system includes a real-time status monitoring module, a knowledge base instantiation module, a scheme derivation and planning module, a vulnerability analysis module, an interference simulation module, and a stress resistance enhancement strategy module, wherein: The real-time status monitoring module is used to monitor and analyze the demand and resource status of the pipeline assembly, and obtain the real-time constraints of the pipeline assembly. The knowledge base instantiation module is used to instantiate and load the historical knowledge base of the pipeline distribution management platform to obtain the path planning operation instance and interference simulation operation instance of the management platform. The scheme derivation and planning module is used to deduce the scheme for the path planning operation instance based on the real-time constraints, and obtain the initial packaging scheme of the path planning operation instance. The vulnerability analysis module is used to perform vulnerability aggregation analysis on the node load data and topology connection relationship of the initial packaging scheme to obtain the key weak links of the initial packaging scheme. The interference simulation module is used to perform disturbance simulation on the interference simulation operation instance based on the key weak links, so as to obtain a targeted interference scheme for the interference simulation operation instance. The anti-reverse reinforcement strategy module is used to reinforce the initial disassembly scheme based on the targeted interference scheme, and obtain the pipeline disassembly execution strategy of the initial disassembly scheme.

[0006] In a preferred embodiment, when the real-time status monitoring module performs monitoring and parsing of the pipeline assembly requirements and resource status to obtain the real-time constraints of the pipeline assembly, it is specifically used for: Dynamically sense the demand for pipeline sub-assembly to obtain the demand flow for the pipeline sub-assembly; A completeness check is performed on the resources of the pipeline sub-packing to obtain the resource spectrum of the pipeline sub-packing; By matching and comparing the demand flow with the resource spectrum in the spatiotemporal dimensions, the imbalance points of supply and demand in the pipeline packaging are obtained; Feature extraction is performed on the imbalance points to obtain the bottleneck description of the pipeline assembly; The bottleneck description is translated into the limiting rules for pipeline sub-assembly, and the limiting rules are logically aggregated to obtain the constraint benchmark for pipeline sub-assembly. The timeliness of the constraint benchmark is corrected to obtain the real-time constraint conditions for the pipeline assembly.

[0007] In a preferred embodiment, when the knowledge base instantiation module instantiates and loads the historical knowledge base of the pipeline loading management platform to obtain the path planning operation instance and interference simulation operation instance of the management platform, it is specifically used for: Extract multi-dimensional data from the historical knowledge base of the pipeline distribution management platform to obtain the original operation log of the historical knowledge base; The original operation logs are subjected to semantic normalization processing to obtain standardized semantic data of the historical knowledge base; A topological structure is constructed on the logical relationships in the standardized semantic data to obtain the entity relationship network of the standardized semantic data. By mining the flow rules of the entity relationship network, the path configuration logic of the management platform is obtained; Based on the disturbance response logic in the standardized semantic unit, the path configuration logic and the disturbance response logic are co-encapsulated to obtain the path planning operation instance and disturbance simulation operation instance of the management platform.

[0008] In a preferred embodiment, when the scheme derivation and planning module performs scheme derivation on the path planning runtime instance based on the real-time constraints to obtain the initial packaging scheme of the path planning runtime instance, it is specifically used for: The real-time constraints are semantically deconstructed to obtain the structural constraint vector of the real-time constraints. Based on the structural constraint vector, the path planning operation instance is identified to obtain candidate instances of the path planning operation instance; The adaptation instance is enumerated with strategies to obtain the alternative strategy space for the adaptation instance; The situation evolution of the candidate strategy space is performed to obtain the evolution trajectory cluster of the candidate strategy space; The convergence of the evolutionary trajectory cluster is evaluated to obtain the initial packaging scheme of the path planning running instance.

[0009] In a preferred embodiment, when the vulnerability analysis module performs vulnerability aggregation analysis on the node load data and topology connectivity of the initial assembly scheme to identify the key weaknesses of the initial assembly scheme, it is specifically used for: Perform topological association mapping on the initial packaging scheme to obtain the topological connection map of the initial packaging scheme; The node load of the initial packaging scheme is analyzed to obtain the node pressure mode of the initial packaging scheme. By analyzing the structural relationships in the topology connection graph, a topological robustness description of the initial packaging scheme is obtained; Risk fusion is performed on the node stress pattern and the topology robustness profile to obtain the vulnerability aggregation result of the initial packaging scheme; The vulnerability aggregation results are quantitatively evaluated to obtain the vulnerability level of the initial packaging scheme; Based on the vulnerability level, the vulnerability aggregation results are critically identified to obtain the key weak links of the initial packaging scheme.

[0010] In a preferred embodiment, when the vulnerability analysis module performs risk fusion on the node stress patterns and the topology robustness profile to obtain the vulnerability aggregation result of the initial packaging scheme, it is specifically used for: By performing feature intersection on the node stress mode and the topology robustness description, a comprehensive feature expression of the initial packaging scheme is obtained; The feature density of the comprehensive feature representation is evaluated to obtain the node distribution characteristics of the initial packaging scheme; Based on the topological robustness description, the similarity measure of the node distribution characteristics is performed to obtain the node affinity matrix of the initial packaging scheme; Based on the node affinity matrix, structural clustering is performed on the node distribution characteristics to obtain the node community structure of the initial packaging scheme. The structural criticality of the node community structure is evaluated to obtain the key community identifiers of the initial packaging scheme. Vulnerability aggregation is performed on the key community identifiers to obtain the vulnerability aggregation result of the initial packaging scheme.

[0011] In a preferred embodiment, when the interference simulation module performs disturbance simulation on the interference simulation instance based on the key weak points to obtain a targeted interference scheme for the interference simulation instance, it is specifically used for: Based on the aforementioned key weaknesses, the topological relationships of the interference simulation operation instance are deconstructed to obtain the associated architecture of the interference simulation operation instance. Identify the key perturbation factors in the associated architecture to obtain the perturbation factor set of the interference simulation running instance; Based on the set of disturbance factors, disturbance parameters are configured for the associated architecture to obtain the parameterized disturbance strategy for the disturbance simulation instance. The parameterized perturbation strategy is enumerated to obtain the perturbation scenario set of the perturbation simulation running instance; The impact diffusion of the disturbance scenario set is simulated to obtain the global response data of the disturbance scenario set; Failure mode identification is performed on the global response data to obtain the abnormal event sequence of the global response data; Causal correlation mining is performed on the abnormal event sequence to obtain a preliminary causal chain set of the abnormal event sequence; The preliminary causal chain set is networked and integrated to obtain a causal relationship graph of the preliminary causal chain set; By optimizing the countermeasure strategies for key causal links in the causal relationship graph, a targeted interference scheme for the interference simulation instance is obtained.

[0012] In a preferred embodiment, when the anti-reverse reinforcement strategy module executes the anti-reverse reinforcement of the initial assembly scheme based on the targeted interference scheme to obtain the pipeline assembly execution strategy of the initial assembly scheme, it is specifically used for: The perturbation mode deconstruction of the targeted interference scheme yields the perturbation mode sequence of the targeted interference scheme; Based on the perturbation pattern sequence, robust defect diagnosis is performed on the initial packaging scheme to obtain defect identifiers of the initial packaging scheme; The defect identifiers are integrated with compensation rules to obtain the compensation mechanism for the initial packaging scheme; Based on the compensation mechanism, the initial packaging scheme is reconstructed using multiple strategies to obtain candidate strategies for the initial packaging scheme. The candidate strategies are quantitatively evaluated to obtain the resilience enhancement index of the candidate strategies; Based on the stress resistance enhancement index, the optimal strategy is selected from the candidate strategies to obtain the pipeline dispensing execution strategy.

[0013] In a preferred embodiment, when the resilience enhancement strategy module performs a quantitative evaluation of the candidate strategy to obtain the resilience enhancement index of the candidate strategy, the formula for calculating the resilience enhancement index is as follows: ; In the formula, The stress resistance enhancement index is mentioned above. The total number of key perturbation patterns identified in the perturbation pattern sequence. To be in key disturbance modes Below, the performance degradation of the initial packaging scheme, In key disturbance modes The performance degradation of the candidate strategy is as follows. For key disturbance modes Severity weighting The incremental overall execution cost of the candidate strategy compared to the initial packaging scheme is... The cost benchmark is the historical knowledge base mentioned above. The total number of compensation mechanisms activated for the candidate strategy; As a stability regulator, To maintain a weighted contribution to performance, Weighting for cost efficiency It contributes weight to the simplicity of the solution.

[0014] To address the above problems, the present invention also provides a pipeline distribution management method, the method comprising: S1. Monitor and analyze the demand and resource status of pipeline sub-assembly to obtain the real-time constraints of the pipeline sub-assembly. S2. Instantiate and load the historical knowledge base of the pipeline distribution management platform to obtain the path planning operation instance and interference simulation operation instance of the management platform; S3. Based on the real-time constraints, perform scheme derivation on the path planning operation instance to obtain the initial packaging scheme of the path planning operation instance; S4. Perform vulnerability aggregation analysis on the node load data and topology connection relationship of the initial packaging scheme to obtain the key weak links of the initial packaging scheme; S5. Based on the key weak links, perform disturbance simulation on the interference simulation operation instance to obtain a targeted interference scheme for the interference simulation operation instance. S6. Based on the targeted interference scheme, the initial packaging scheme is reinforced to obtain the pipeline packaging execution strategy of the initial packaging scheme.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses a real-time status monitoring module to dynamically perceive the demand flow and resource spectrum of pipeline assembly, and performs spatiotemporal matching and comparison to obtain real-time constraints. The scheme derivation and planning module then uses these constraints to identify instances and enumerate strategies for path planning execution instances, generating an initial assembly scheme. This process ensures a high degree of fit between path planning and the current operating environment, avoiding the disconnect caused by static planning in traditional methods.

[0016] 2. This invention uses a vulnerability analysis module to aggregate and analyze the node load and topology connections of the initial assembly scheme, identifying key weak points. An interference simulation module performs disturbance simulations based on these weak points, generating targeted interference schemes. A resilience enhancement strategy module then utilizes these schemes for resilience enhancement, resulting in a pipeline assembly execution strategy. This systematic mechanism achieves the identification and targeted enhancement of the overall vulnerable structure, overcoming the shortcomings of traditional methods that rely on isolated optimization. Attached Figure Description

[0017] Figure 1 This is a system architecture diagram of a pipeline distribution management platform provided in an embodiment of the present invention; Figure 2 This is a schematic flowchart of a pipeline sub-packaging management method provided in an embodiment of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 belong to some, but not all, embodiments of the present invention. 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.

[0020] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0021] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0022] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0023] In practice, the server-side equipment deployed by the pipeline repackaging management platform may consist of one or more devices. The aforementioned pipeline repackaging management platform can be implemented as: a business instance, a virtual machine, or a hardware device. For example, the pipeline repackaging management platform can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, the pipeline repackaging management platform can be understood as software deployed on a cloud node, used to provide pipeline repackaging management to various user terminals. Alternatively, the pipeline repackaging management platform can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Or, the pipeline repackaging management platform can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide pipeline repackaging management to various user terminals.

[0024] In terms of implementation, the pipeline distribution management platform and the user client are mutually compatible. That is, if the pipeline distribution management platform is an application installed on a cloud service platform, then the user client is a client that establishes a communication connection with the application; or if the pipeline distribution management platform is implemented as a website, then the user client is implemented as a webpage; or if the pipeline distribution management platform is implemented as a cloud service platform, then the user client is implemented as a mini-program in an instant messaging application.

[0025] like Figure 1 The diagram shown is a system architecture diagram of a pipeline distribution management platform provided in an embodiment of the present invention.

[0026] The pipeline distribution management platform 100 of this invention can be located on a cloud server. In terms of implementation, it can function as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the pipeline distribution management platform 100 may include a real-time status monitoring module 101, a knowledge base instantiation module 102, a scheme derivation and planning module 103, a vulnerability analysis module 104, an interference simulation module 105, and a resilience enhancement strategy 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0027] In this embodiment of the invention, each of the above-mentioned modules in the pipeline assembly management platform can be implemented independently and can call other modules. This "calling" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the pipeline assembly management platform provided by this embodiment of the invention, the applicability of the pipeline assembly management platform architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the pipeline assembly management platform. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0028] The following describes the various components and specific workflows of the pipeline distribution management platform, using specific embodiments as examples: The real-time status monitoring module 101 is used to monitor and analyze the demand and resource status of pipeline sub-assembly, and obtain the real-time constraints of pipeline sub-assembly.

[0029] In this embodiment of the invention, when the real-time status monitoring module performs monitoring and parsing of the pipeline assembly requirements and resource status to obtain the real-time constraints of the pipeline assembly, it is specifically used for: Dynamically sense the demand for pipeline sub-assembly to obtain the demand flow for the pipeline sub-assembly; A completeness check is performed on the resources of the pipeline sub-packing to obtain the resource spectrum of the pipeline sub-packing; By matching and comparing the demand flow with the resource spectrum in the spatiotemporal dimensions, the imbalance points of supply and demand in the pipeline packaging are obtained; Feature extraction is performed on the imbalance points to obtain the bottleneck description of the pipeline assembly; The bottleneck description is translated into the limiting rules for pipeline sub-assembly, and the limiting rules are logically aggregated to obtain the constraint benchmark for pipeline sub-assembly. The timeliness of the constraint benchmark is corrected to obtain the real-time constraint conditions for the pipeline assembly.

[0030] The real-time status monitoring module continuously captures various demand-related information generated during the pipeline dispensing process. This process is known as dynamic sensing, rather than simply acquiring demand at a single point in time or for a single type. The captured demand information covers multiple aspects, including the different forms of materials to be dispensed, such as liquids, gases, and solid particles; the specific required quantity for each material; the time requirements for completing the dispensing; the accuracy standards for the dispensing process; and the target delivery location of the materials. By continuously recording and organizing this captured demand information, a coherent and complete demand data sequence is formed, which is the demand flow for pipeline dispensing.

[0031] Completeness verification involves a comprehensive check to ensure that all resources required for repackaging are complete and can meet actual needs. It also involves detailed verification of the availability, quantity, and core performance parameters of each resource. Required resources include multiple categories: pipe sections of varying diameters, lengths, and materials; power equipment such as pumps and compressors; control components such as valves and flow regulators; metering devices including flow meters and weighing equipment; power supply equipment such as motors and power modules; control modules such as PLC controllers; and routine maintenance tools. During the verification, the quantity of each resource is confirmed to meet requirements, its operational status is verified to be normal, performance parameters are confirmed to meet standards, and issues such as resource damage, missing resources, or performance degradation are investigated. The system then compiles all resource information to form a comprehensive resource profile.

[0032] From the two core perspectives of time and space, each specific demand in the demand flow is meticulously compared with the corresponding resources in the resource spectrum to analyze whether the two are synchronized and coordinated in time and adapted in spatial configuration. In the time dimension, the required packaging time for each material is compared with the available time of the corresponding resources. For example, if a material needs to be packaged between 9:00 AM and 11:00 AM, it is necessary to confirm whether the corresponding conveying pumps, pipelines, control modules, etc., are available and not occupied by other tasks during this time period. In the spatial dimension, the target material delivery location is compared with the spatial distribution of resources, and the quantity and accuracy requirements in the demand are compared with the resource supply capacity. Through comprehensive comparison, the mismatches between demand and resources in terms of time connection, spatial configuration, and capacity matching are accurately identified—that is, the points of imbalance in the supply-demand relationship.

[0033] For each imbalance point, a thorough analysis is conducted, examining its root causes, specific manifestations, scope of impact, and severity. First, the causes of the imbalance are identified, such as insufficient resources, substandard resource performance, scheduling conflicts, or unreasonable spatial allocation. Next, the manifestations are outlined, such as insufficient pumps preventing timely material loading, incompatible pipe materials leading to leakage risks due to corrosive materials, or insufficient metering device accuracy causing excessive loading errors. Then, the scope of impact is determined, identifying whether it affects only a single material loading stage or impacts multiple stages, resulting in overall efficiency decline or the inability to complete specific tasks. Finally, the severity is assessed, determining whether it constitutes a critical imbalance, whether it will disrupt the overall loading process, or only cause minor efficiency losses. This information is then compiled to create a bottleneck description restricting the progress of pipeline loading.

[0034] Translation transforms the specific problems described by bottlenecks into actionable and clearly defined rules, ensuring that the constraints corresponding to the bottlenecks can be accurately identified and followed by the pipeline distribution management platform. Logical aggregation integrates and categorizes rules according to their inherent logical relationships, forming a unified set of rules for the system. During translation, rules are formulated for each bottleneck. For example, "Insufficient number of delivery pumps during a certain time period, unable to meet the simultaneous distribution of three materials" is translated as "A maximum of two materials are allowed to be distributed simultaneously during this time period, prioritizing materials with high demand"; "Pipeline material is not suitable for corrosive materials" is translated as "Corrosive materials are prohibited from passing through this pipeline section"; "Insufficient accuracy of metering devices, unable to meet high-precision distribution" is translated as "High-precision materials require a designated high-precision metering device." During aggregation, rules for the same type of resource are grouped together, causal relationship rules are associated, primary and secondary rules are distinguished, and ultimately, an orderly constraint benchmark for the system is formed.

[0035] Based on real-time changes in pipeline assembly, the effective time range of each constraint rule in the constraint benchmark is adjusted promptly, expired and invalid rules are removed, and new time-sensitive constraints are added to ensure that the constraint benchmark is consistent with the current assembly situation. During the correction process, factors affecting the validity of the rules are monitored in real time, including changes in resource status, demand, and the external environment. Rules are verified and adjusted one by one for each of these changes. For example, after the delivery pump is repaired, the rule "insufficient delivery pumps during a certain period" is removed; after a new emergency task is added, a rule "prioritize resources for this task, and postpone other non-emergency tasks" is added; after power is restored, the limit on the number of delivery pumps in operation is relaxed; and the effective start and end times of each rule are clearly defined to ensure that it only takes effect at the corresponding time. Through targeted corrections, real-time constraints that perfectly match the current pipeline assembly situation are ultimately formed.

[0036] The beneficial effects include: continuously capturing various demand information to ensure comprehensive and consistent demand data, providing a reliable foundation for supply and demand matching, avoiding discrepancies between packaging plans and actual needs, comprehensively verifying resource status and parameters, identifying potential resource problems in advance, preventing packaging interruptions, inefficiencies, or substandard quality due to resource issues, providing accurate resource basis for precise supply and demand matching, comparing supply and demand in both time and space to ensure accurate comparison, locating supply and demand contradictions, avoiding vague judgments that fail to pinpoint core issues, providing clear direction for bottleneck analysis and rule formulation, uncovering the core characteristics of imbalance points, clearly defining bottleneck situations, eliminating vague understanding, providing a basis for converting bottlenecks into restrictive rules, ensuring rules accurately target bottlenecks, transforming bottleneck descriptions into clear and executable rules, facilitating platform identification; aggregating rules to form a coherent constraint benchmark, avoiding rule chaos, providing a standardized constraint basis for solution derivation, correcting constraint benchmarks according to real-time changes in packaging, eliminating outdated rules and supplementing new content, ensuring constraints fit the current situation, avoiding unreasonable solutions, and ensuring accurate and feasible solution derivation.

[0037] The knowledge base instantiation module 102 is used to instantiate and load the historical knowledge base of the pipeline distribution management platform to obtain the path planning operation instance and interference simulation operation instance of the management platform.

[0038] In this embodiment of the invention, when the knowledge base instantiation module instantiates and loads the historical knowledge base of the pipeline sub-packaging management platform to obtain the path planning operation instance and interference simulation operation instance of the management platform, it is specifically used for: Extract multi-dimensional data from the historical knowledge base of the pipeline distribution management platform to obtain the original operation log of the historical knowledge base; The original operation logs are subjected to semantic normalization processing to obtain standardized semantic data of the historical knowledge base; A topological structure is constructed on the logical relationships in the standardized semantic data to obtain the entity relationship network of the standardized semantic data. By mining the flow rules of the entity relationship network, the path configuration logic of the management platform is obtained; Based on the disturbance response logic in the standardized semantic unit, the path configuration logic and the disturbance response logic are co-encapsulated to obtain the path planning operation instance and disturbance simulation operation instance of the management platform.

[0039] The historical knowledge base stores various relevant data accumulated during pipeline sub-assembly management, covering details of past sub-assembly route planning, resource allocation records, interference event handling processes, and feedback on the effectiveness of solution implementation. To obtain the raw operational logs, it is necessary to define the collection scope for this multi-dimensional data, limiting it to data related to pipeline sub-assembly route planning and interference response. Simultaneously, data type filtering criteria must be established to ensure that the collected data meets subsequent processing requirements. Following this scope and criteria, all relevant data is comprehensively collected, and this raw, unprocessed data is integrated and summarized to ultimately form the raw operational logs of the historical knowledge base.

[0040] The original operation logs contained inconsistent semantic information, with varying formats. For example, different descriptive terms might exist for the same type of packaging path, and the data record formats could also differ. To address this issue, a comprehensive review of all semantic information in the original operation logs was conducted to clarify the different representation methods and data format types. Based on the review results, a unified semantic representation specification was established, clarifying the standard representation methods for various types of information, and a unified data format standard was determined to standardize the data recording format. Subsequently, each piece of semantic information in the original operation logs was adjusted and corrected according to the established specifications and standards, unifying different representations into standard representations and different formats into standard formats, ultimately resulting in standardized semantic data.

[0041] The standardized semantic data contains multiple entities related to pipeline assembly, such as assembly nodes, path segments, resource types, and interference types. These entities have various logical relationships, including associations, dependencies, and triggers. First, a comprehensive analysis of the standardized semantic data is performed to identify all entities and clarify the attribute characteristics of each entity, such as the location of assembly nodes and the quantity limits of resource types. Next, the logical relationships between each entity are analyzed one by one to determine the type and strength of the associations. Then, following the rules for constructing a topology, each entity is treated as a node, and the logical relationships between entities are treated as edges connecting the nodes, thus building an entity relationship network that clearly presents the relationships between entities.

[0042] The entity relationship network already reveals the static associations between entities. Building upon this, we further analyze the dynamic interaction processes between entities. Specifically, we analyze the data flow sequence between nodes in the distribution path, clarifying the order in which data is transmitted from one node to another; we streamline the resource allocation process across different path segments, understanding the specific steps and methods of resource allocation; and we clarify the sequence of triggers and responses between different entities, understanding how changes in one entity trigger reactions in other entities. Through a comprehensive review and in-depth analysis of these dynamic interaction processes, we summarize and extract universally applicable rules and constraints that must be followed in pipeline distribution management. These rules and constraints together constitute the path configuration logic of the management platform.

[0043] First, response logic for various disturbance events is extracted from standardized semantic data. This includes identifying different types of disturbance events, clarifying key characteristics and judgment criteria for disturbance event identification, determining the selection criteria for response measures, and selecting appropriate response measures based on factors such as the type and severity of the disturbance event. The execution steps of the response process are then outlined, clarifying the implementation sequence and operational requirements of each response measure. Next, the compatibility between path configuration logic and disturbance response logic is analyzed to find the connection points for their collaborative work, determining under what circumstances the path configuration logic needs to call the disturbance response logic, and the interaction methods between the two. Finally, according to a pre-defined encapsulation specification, the path configuration logic and disturbance response logic are integrated into a unified functional unit. This functional unit contains both the core logic of path planning, enabling normal pipeline loading path planning, and the ability to respond to disturbances, allowing for timely and effective response measures when encountering disturbance events. Ultimately, this results in path planning operation instances and disturbance simulation operation instances.

[0044] The beneficial effects include ensuring the integrity and reliability of the original operation logs, providing a rich and reliable basic data source for subsequent semantic processing, instance generation, and other operations, effectively avoiding deviations in subsequent processing results due to missing data or irrelevant interference, eliminating differences in semantic expression and data format in the original operation logs, enabling subsequent logical relationship analysis, topology construction, and other operations to be carried out based on a unified standard, significantly improving the accuracy and efficiency of subsequent processing, presenting the logical relationships of each entity in the standardized semantic data as an intuitive and systematic entity relationship network, facilitating subsequent mining of flow rules and in-depth analysis of entity relationships, providing clear structural support for obtaining path configuration logic, extracting the general rules and constraints of pipeline dispensing path planning, forming the path configuration logic of the management platform, providing the core basis for the generation of subsequent path planning operation instances, ensuring that the instances meet actual dispensing requirements and have high feasibility, enabling the generated operation instances to have dual functions of path planning and interference response and to work collaboratively, providing a fully functional basic unit for subsequent scheme derivation and interference simulation, and effectively improving the practicality and applicability of the instances.

[0045] The scheme derivation and planning module 103 is used to deduce the scheme for the path planning operation instance based on the real-time constraints, and obtain the initial packaging scheme of the path planning operation instance.

[0046] In this embodiment of the invention, when the scheme derivation and planning module performs scheme derivation on the path planning running instance based on the real-time constraints to obtain the initial packaging scheme of the path planning running instance, it is specifically used for: The real-time constraints are semantically deconstructed to obtain the structural constraint vector of the real-time constraints. Based on the structural constraint vector, the path planning operation instance is identified to obtain candidate instances of the path planning operation instance; The adaptation instance is enumerated with strategies to obtain the alternative strategy space for the adaptation instance; The situation evolution of the candidate strategy space is performed to obtain the evolution trajectory cluster of the candidate strategy space; The convergence of the evolutionary trajectory cluster is evaluated to obtain the initial packaging scheme of the path planning running instance.

[0047] Real-time constraints encompass various aspects, including supply-demand imbalance points in pipeline distribution, bottleneck descriptions, and limiting rules. Initially, this information is presented as scattered expressions with some semantic connections. Semantic deconstruction systematically organizes these scattered expressions, breaking them down into the core meaning of each constraint, clarifying the logical connections and hierarchical relationships between different constraints, eliminating redundant and repetitive content, and transforming vague constraint requirements into clear and quantifiable information units. Subsequently, these information units are categorized and integrated according to pre-defined classification standards to form an information set with a fixed structure and clear direction. This set constitutes the structural constraint vector, which fully encompasses all key limiting elements of the real-time constraints.

[0048] The structural constraint vector clarifies the core limitations of pipeline distribution in terms of resources, path, and time. Instance identification is based on these core constraints, comparing and analyzing each path planning instance one by one. First, the core attributes of each path planning instance are identified, including its applicable resource range, covered path types, and applicable time conditions. Then, these core attributes are matched one by one with the constraint dimensions in the structural constraint vector to determine if the instance's attributes conflict with the constraint dimensions. Instances with attributes that completely conflict with the constraint dimensions or have only partially conflicting key attributes are eliminated. Instances with attributes that highly match the constraint dimensions, have no key conflicts, and whose matching degree meets the preset standard are retained as candidate instances.

[0049] Strategy enumeration involves comprehensively exploring all feasible packaging strategies for each candidate instance. Each candidate instance corresponds to specific path configuration logic and resource allocation rules. Based on these logics and rules, different execution methods are derived one by one, including different combinations of specific path selection, resource allocation ratios, and the order of packaging steps. Each possible combination is meticulously analyzed and recorded to ensure that no feasible execution method is overlooked. All identified feasible execution methods are then integrated to form a set containing multiple potential packaging strategies; this set constitutes the candidate strategy space.

[0050] Situational evolution simulates the operation of alternative strategies in a real pipeline assembly scenario, analyzing the dynamic changes of these strategies under different environmental conditions. First, actual operating environment parameters for pipeline assembly are set, including resource consumption rates, path stability, and potential minor external disturbances. Each strategy in the alternative strategy space is then placed in this simulated environment for virtual execution. During execution, key operational data for each strategy is continuously recorded chronologically, including remaining resources, path accessibility, assembly completion progress, and any minor anomalies. This fully presents the development and changes of each strategy from initiation to stable operation. The complete evolutionary process of each strategy forms an independent evolutionary trajectory, and the integration of all strategy evolutionary trajectories constitutes an evolutionary trajectory cluster.

[0051] Convergence assessment analyzes the development trend of each evolutionary trajectory to determine whether the corresponding strategy can stably achieve the expected goals of pipeline loading. First, the core expected goals of pipeline loading are defined, including key evaluation indicators such as loading efficiency, resource utilization, path operational stability, and loading quality. Passing standards and optimal ranges are set for each indicator. For each trajectory in the evolutionary trajectory cluster, key operational data at different time points are extracted. The final achievement value of the strategy corresponding to each trajectory on each evaluation indicator and the fluctuation range during operation are calculated to determine whether the strategy can stably approach the expected goal within a preset time, and whether there are cases of excessive fluctuation, failure to achieve the core goal, or poor performance. Trajectories that meet the preset convergence requirements, have fluctuation ranges within the allowable range, and can stably achieve the expected goals are selected. From the strategies corresponding to these trajectories, the performance of each strategy on all evaluation indicators is comprehensively compared, and the strategy with the best overall performance is selected as the initial loading scheme.

[0052] The beneficial effects are as follows: It eliminates the ambiguity and vagueness of real-time constraint information, provides precise basis for identifying path planning operation instances, avoids deviations in subsequent scheme derivation, ensures the accuracy of scheme derivation, quickly eliminates instances that do not meet constraint requirements, reduces the scope of subsequent strategy analysis, and reduces ineffective workload to improve scheme derivation efficiency. Simultaneously, it ensures that candidate instances are adapted to real-time constraints, provides high-quality objects for subsequent strategy mining, comprehensively covers all feasible packaging strategies for candidate instances, avoids missing potentially effective strategies leading to suboptimal final solutions, provides rich samples for situational evolution analysis, improves the rationality and comprehensiveness of initial packaging schemes, intuitively presents the dynamic performance of each alternative strategy in the actual scenario, clearly reveals the strategy development law, provides detailed dynamic data for convergence evaluation, avoids the practical application risks of selecting strategies solely based on static attributes, accurately screens out strategies that can stably achieve the expected goals, avoids application risks, and ensures the reliability and effectiveness of initial packaging schemes. Furthermore, it selects the optimal strategy through comprehensive performance comparison, ensuring the comprehensive advantages of the scheme in terms of packaging efficiency and resource utilization.

[0053] The vulnerability analysis module 104 is used to perform vulnerability aggregation analysis on the node load data and topology connection relationship of the initial packaging scheme to obtain the key weak links of the initial packaging scheme.

[0054] In this embodiment of the invention, when the vulnerability analysis module performs vulnerability aggregation analysis on the node load data and topology connection relationship of the initial assembly scheme to obtain the key weak links of the initial assembly scheme, it is specifically used for: Perform topological association mapping on the initial packaging scheme to obtain the topological connection map of the initial packaging scheme; The node load of the initial packaging scheme is analyzed to obtain the node pressure mode of the initial packaging scheme. By analyzing the structural relationships in the topology connection graph, a topological robustness description of the initial packaging scheme is obtained; Risk fusion is performed on the node stress pattern and the topology robustness profile to obtain the vulnerability aggregation result of the initial packaging scheme; The vulnerability aggregation results are quantitatively evaluated to obtain the vulnerability level of the initial packaging scheme; Based on the vulnerability level, the vulnerability aggregation results are critically identified to obtain the key weak links of the initial packaging scheme.

[0055] When performing risk fusion on the node stress patterns and the topology robustness profile to obtain the vulnerability aggregation result of the initial packaging scheme, the vulnerability analysis module is specifically used for: By performing feature intersection on the node stress mode and the topology robustness description, a comprehensive feature expression of the initial packaging scheme is obtained; The feature density of the comprehensive feature representation is evaluated to obtain the node distribution characteristics of the initial packaging scheme; Based on the topological robustness description, the similarity measure of the node distribution characteristics is performed to obtain the node affinity matrix of the initial packaging scheme; Based on the node affinity matrix, structural clustering is performed on the node distribution characteristics to obtain the node community structure of the initial packaging scheme. The structural criticality of the node community structure is evaluated to obtain the key community identifiers of the initial packaging scheme. Vulnerability aggregation is performed on the key community identifiers to obtain the vulnerability aggregation result of the initial packaging scheme.

[0056] First, a topology mapping operation is performed on the initial packaging scheme. All nodes involved in the packaging process are comprehensively examined, clarifying the specific function of each node, such as data transmission, load balancing, and load distribution. Simultaneously, the relationships between nodes are systematically analyzed, including directly connected nodes and indirect connections via intermediate nodes. Then, according to the logical order of node connections and the actual physical or logical paths of the connections, all nodes and their relationships are presented visually, clearly showing the location distribution and connections of each node, thus forming a topology connection map.

[0057] Load data for each node at different operating stages is collected through data acquisition components, including load magnitude, load change frequency, and peak load time points under different scenarios such as normal operation, peak operation, and low load periods. This collected load data is categorized and organized, and in-depth analysis is conducted to identify the load variation patterns of different nodes, pinpointing which nodes are prone to excessively high or low loads, and the impact of load fluctuations on node operation. This process clarifies the intrinsic load state and characteristics of each node, ultimately forming a node stress model that reflects the load variation patterns and state characteristics of each node.

[0058] The key focus is on the connection strength between nodes to determine if there are issues such as connection fragility or low transmission efficiency; analyzing the diversity of connection paths to check for single path dependence, i.e., whether there are backup paths to ensure transmission after a failure of one path; identifying critical nodes to determine which nodes are the core support of the entire topology and whether their failure would lead to widespread connection interruption. Through these analyses, the underlying logic behind the structural relationships is uncovered, and the topology's ability to resist failures and maintain normal operation is comprehensively assessed, ultimately forming a topology robustness profile that fully reflects this capability.

[0059] The load risks reflected in the node stress model, such as high-load nodes being prone to overload and low-load nodes wasting resources, are comprehensively integrated with the structural risks reflected in the topology robustness description, such as weak connections being prone to breakage and critical node failures having a wide impact. During the integration process, the superimposed effects of high-load nodes and weak connections are fully considered; for example, weak paths connected to high-load nodes are more prone to transmission failures. Simultaneously, the dynamic effects of node stress changes on topology robustness are analyzed; for example, a continuous increase in node load may lead to a decrease in the robustness of its connection paths. By comprehensively considering the interaction and superposition effects of various risks, a vulnerability aggregation result that comprehensively reflects the overall vulnerability of the initial packaging scheme is obtained.

[0060] Multi-dimensional vulnerability assessment criteria are pre-defined, including the scope of risk impact (the number of nodes and the length of the packaging process that may be affected after a risk occurs); the probability of risk occurrence (the likelihood of various vulnerability factors actually occurring); and the severity of risk (the degree of damage to packaging efficiency, security, etc., after a risk occurs). Each risk indicator in the vulnerability aggregation results is compared and analyzed against the pre-defined criteria. Each risk indicator is scored according to established quantitative rules, and the overall score is calculated based on the weights of each indicator. Finally, the vulnerability of the initial packaging scheme is classified into different levels based on the final score, such as low vulnerability, medium vulnerability, and high vulnerability.

[0061] For different vulnerability levels, corresponding critical criteria are preset. For example, the critical criteria for high vulnerability levels are more stringent, while those for low vulnerability levels are relatively lenient. Based on these preset critical criteria, risk points exceeding the critical range in the vulnerability aggregation results are screened out. Special attention is paid to risk-related components that significantly impact the operational stability of the initial packaging scheme, and whose failure could lead to packaging process interruption, significant efficiency reduction, or security risks. The specific nodes or connections corresponding to these risk-related components are identified, ultimately determining the critical weak points in the initial packaging scheme.

[0062] Key features are extracted from node stress patterns, including the distribution areas of high-load nodes, node types with high load fluctuation frequencies, and the specific numerical range of load peaks. Simultaneously, key features are extracted from topology robustness descriptions, such as the specific locations of weak connections, the number and distribution of critical nodes, and the number of redundant connections in the topology. Then, the two types of features are cross-compared according to their inherent correlations; for example, the distribution of high-load nodes is correlated with the locations of weak connections. This integration forms a comprehensive feature representation that fully reflects both node stress attributes and topology attributes.

[0063] In statistical comprehensive feature representation, the distribution quantity of various features across different node regions and different connection paths is calculated. The feature distribution density within a unit area is calculated, for example, the ratio of the total number of high-load and weak-connection features within a node cluster region to the total number of nodes in that region. The distribution patterns of feature-dense and sparse regions are analyzed to identify the common attributes of node clusters corresponding to feature-dense regions and the common functional or structural characteristics of nodes in feature-sparse regions. This allows for the acquisition of node distribution features that reflect the node feature distribution status in the initial packaging scheme.

[0064] Referring to the connection relationships and structural positions of nodes in the topology robustness description, the similarities of different node distribution characteristics in terms of feature type, feature strength, and distribution patterns are compared. For example, the similarity between two node clusters in terms of the proportion of high-load features and the number of weak connections is compared. The similarity between nodes is scored according to a predetermined metric; higher similarity results in a higher score, and lower similarity results in a lower score. All similarity scores are systematically compiled to form a node affinity matrix that clearly reflects the degree of connection between nodes.

[0065] Based on the degree of association between nodes reflected in the node affinity matrix, nodes with high similarity and close association are grouped together to ensure that nodes within the same group have similar distribution characteristics, such as similar load characteristics and similar structural risk characteristics, and that the connections between nodes are close. Meanwhile, nodes with low similarity and loose association are divided into different groups to ensure that each group maintains relatively independent characteristic attributes. Through this grouping and classification, a node community with a clear structure and well-defined attributes is ultimately formed, i.e., a node community structure.

[0066] Considering the packaging function of each node cluster, determine whether it is involved in the core packaging process; analyze the cluster's position in the topology to see if it is on a critical transmission path; assess the cluster's correlation with other clusters to determine if it has a significant impact on the normal operation of other clusters; simultaneously, assess the cluster's impact on the overall packaging process, i.e., whether a cluster failure would cause the overall packaging process to stall or significantly reduce efficiency. Based on preset importance evaluation criteria, comprehensively score each node cluster, and select the node clusters that are crucial to the operation of the initial packaging scheme based on the scores, and clearly identify these critical clusters.

[0067] The vulnerability factors inherent in each critical cluster are collected, including the load risk of nodes within the cluster (e.g., some nodes are under high load for extended periods); structural risks in the internal connections of the cluster (e.g., weak links in internal connections); and potential risks in the connections between clusters (e.g., single data transmission paths and insufficient connection strength). These vulnerability factors are comprehensively integrated and summarized, taking into account the cumulative effects and mutual influences between various vulnerability factors. For example, the superposition of high-load nodes within a critical cluster and weak connections between clusters may lead to severe transmission failures. Ultimately, a vulnerability aggregation result that comprehensively reflects the overall vulnerability of the initial packaging scheme is obtained.

[0068] The beneficial effects are as follows: the topology connection map intuitively presents the initial packaging scheme structure, helping to quickly grasp the node connection status and laying the foundation for subsequent node pressure and topology robustness analysis; it breaks down node load data by scenario, accurately grasps the load status and change patterns, provides accurate data support, and avoids risk misjudgment due to data problems; it deeply analyzes the topology structure relationships, clarifies the topology robustness and advantages and disadvantages, and provides a solid structural basis for risk assessment; it integrates node load and topology structure risks, avoids the one-sidedness of single-dimensional analysis, comprehensively grasps the overall vulnerability of the scheme, provides a system risk basis for quantitative assessment, quantitatively converts the vulnerability aggregation results into clear levels, intuitively presents the vulnerability degree, helps to quickly judge the risk level, provides a level basis for critical identification, and conducts critical identification based on vulnerability levels to accurately screen key weak links, avoiding omissions or misjudgments of risks. This approach provides targeted support for subsequent scheme development and resilience enhancement. By analyzing feature density to understand the distribution patterns of node features, it identifies areas of feature concentration and scarcity, improves the relevance of similarity analysis, and provides a clear object for similarity measurement. Based on topological robustness description, a node affinity matrix is ​​formed to quantify the degree of node association, providing an accurate basis for structural clustering and ensuring reasonable clustering results. Nodes are grouped according to the affinity matrix, making the community structure more logical. Nodes within a community have similar features and are closely associated, improving the relevance and efficiency of subsequent community criticality assessment. A comprehensive analysis of node community functions, locations, and other factors accurately identifies key communities, avoiding misjudgment of secondary communities. This provides a core object for vulnerability aggregation, focusing on core risks and key communities to summarize vulnerabilities, avoiding redundant and insufficiently targeted results, ensuring accurate and effective results, and providing reliable core data support for subsequent quantitative assessment and criticality identification.

[0069] The interference simulation module 105 is used to perform disturbance simulation on the interference simulation operation instance based on the key weak link, so as to obtain a targeted interference scheme for the interference simulation operation instance.

[0070] In this embodiment of the invention, when the interference simulation module performs disturbance simulation on the interference simulation instance based on the key weak link to obtain a targeted interference scheme for the interference simulation instance, it is specifically used for: Based on the aforementioned key weaknesses, the topological relationships of the interference simulation operation instance are deconstructed to obtain the associated architecture of the interference simulation operation instance. Identify the key perturbation factors in the associated architecture to obtain the perturbation factor set of the interference simulation running instance; Based on the set of disturbance factors, disturbance parameters are configured for the associated architecture to obtain the parameterized disturbance strategy for the disturbance simulation instance. The parameterized perturbation strategy is enumerated to obtain the perturbation scenario set of the perturbation simulation running instance; The impact diffusion of the disturbance scenario set is simulated to obtain the global response data of the disturbance scenario set; Failure mode identification is performed on the global response data to obtain the abnormal event sequence of the global response data; Causal correlation mining is performed on the abnormal event sequence to obtain a preliminary causal chain set of the abnormal event sequence; The preliminary causal chain set is networked and integrated to obtain a causal relationship graph of the preliminary causal chain set; By optimizing the countermeasure strategies for key causal links in the causal relationship graph, a targeted interference scheme for the interference simulation instance is obtained.

[0071] Based on key weaknesses, this study analyzes the connections, hierarchical relationships, and interaction logic among the components of the interference simulation instance, breaking down the complex whole into analyzable structural units. First, the system components corresponding to the key weaknesses are identified. Then, for these components and their related components, their connection methods, data transmission paths, and functional dependencies are analyzed one by one, ultimately forming a relational architecture. This architecture clearly includes core elements such as component nodes, connection paths, and interaction rules, comprehensively presenting the structural relational characteristics of the interference simulation instance.

[0072] By combining the structural characteristics of the interconnected architecture and the features of its key weaknesses, and analyzing the functional boundaries, interaction thresholds, and environmental adaptability of each component node, various factors that may affect the key weaknesses and lead to system performance degradation or functional failure are identified. For each component node and connection path in the interconnected architecture, the parameter range and functional capacity under normal operating conditions are analyzed. Considering the vulnerability characteristics of the key weaknesses, potential disturbance factors are systematically investigated, including internal component aging, parameter fluctuations, external environmental changes, and data transmission interference. All identified factors are categorized and organized to form a complete set of disturbance factors, clarifying the affected objects and potential impact manifestations of each factor.

[0073] Based on the characteristics and impact of each factor in the disturbance factor set, as well as the operating parameters of the corresponding components in the associated architecture, specific quantifiable configuration items such as disturbance intensity, timing of action, duration, and scope of impact are set for each disturbance factor. For each factor in the disturbance factor set, its impact mechanism on the corresponding components in the associated architecture is first analyzed. Then, the timing of the disturbance factor's action is determined by combining the runtime sequence of the components in the associated architecture. The duration of the disturbance is set according to the system operating cycle and the potential duration of the disturbance's impact. The scope of the disturbance factor's impact is clarified based on the connection relationship of the associated architecture. These configuration items are bound to the corresponding disturbance factors to form a parameterized disturbance strategy, ensuring that each strategy has a clear configuration standard.

[0074] Based on the configuration items of each perturbation factor in the parameterized perturbation strategy, different combinations of perturbation factors, perturbation intensity, and timing of action are combined to generate multiple specific application scenarios with different perturbation characteristics. First, the coverage dimensions of the scenario enumeration are determined, including scenarios with a single perturbation factor, scenarios with multiple perturbation factors acting synergistically, scenarios with different perturbation intensity gradients, and scenarios with different timing of action. For each dimension, the configuration items in the parameterized perturbation strategy are combined, and the scenarios generated by each combination are clearly defined, specifying the perturbation factors included in the scenario, the configuration parameters of each factor, the target of action, and the overall characteristics of the scenario. All generated scenarios are then compiled and summarized to form a perturbation scenario set, ensuring that the scenario set can cover all possible perturbation situations.

[0075] In each disturbance scenario, the simulation depicts the process by which the disturbance factor, acting according to predefined configuration parameters, gradually spreads from the point of impact to related components and the entire system. It records various response information, including the operating status, performance metrics, and data transmission status of each component during this process. For each scenario in the disturbance scenario set, the simulation is initiated based on the connection relationships and component interaction logic of the related architecture. Starting from the point of impact of the disturbance factor, the simulation tracks the initial impact of the disturbance on that component in real time. Then, following the data transmission paths and functional dependencies in the related architecture, it simulates the diffusion process of the impact to adjacent components and the related system. It records changes in operating parameters, functional execution, and fault occurrence status of each component during each diffusion stage. Simultaneously, it continuously collects overall system performance metrics and aggregates all component response information and overall system performance data for each scenario to form global response data.

[0076] This process analyzes the differences between the operating status and performance indicators of each component in the global response data and the normal operating standards. It identifies various failure scenarios, such as functional anomalies, performance degradation, and system downtime. These anomalies are then arranged chronologically to form an ordered event sequence. First, the normal operating standards for each system component are defined, including performance indicator thresholds, functional execution requirements, and data transmission specifications. For each disturbance scenario, the global response data is compared one by one with the differences between the operating status and performance indicators of each component and the normal standards. Failure scenarios are identified and relevant information is recorded. Then, all failure scenarios within the same scenario are sorted according to their chronological order of occurrence, forming an abnormal event sequence for that scenario. Finally, the abnormal event sequences for all disturbance scenarios are compiled.

[0077] This analysis examines the inherent connections between anomalous events in a sequence, determining whether a preceding anomalous event is the trigger for a subsequent one, or whether multiple anomalous events stem from the same root cause, thereby establishing a causal chain between the events. For each anomalous event sequence, starting with the first anomalous event, the analysis examines its relationship with subsequent adjacent events, combining the component interaction logic of the relational architecture and the mechanism of perturbation factors to determine the causal correlation between events and eliminate unrelated, accidental events. For multiple anomalous events triggered by the same perturbation factor, the perturbation factor is identified as the root cause, and the causal path that sequentially triggers each anomalous event is traced. Each identified causal relationship is logically organized into a causal chain, and all causal chains are aggregated to form a preliminary causal chain set.

[0078] The causal chains in the initial causal chain set are linked and integrated according to common event nodes, root causes, or affected objects to construct a network structure graph that can intuitively present the interconnections and mutual influences of all causal relationships. First, key nodes of all causal chains in the initial causal chain set are extracted, including root disturbance factors, intermediate abnormal events, and final failure results. Common key nodes in different causal chains are identified and used as connection points to connect related causal chains. The causal flow between nodes is then analyzed and integrated, clarifying the input and output causal relationships of each node. Based on these relationships and causal flows, a visual causal relationship graph is constructed, clearly marking the type of each node, the causal connections between nodes, and the flow direction.

[0079] Based on causal correlation graphs, key causal links with the greatest impact and highest probability of occurrence are identified. Strategies to suppress disturbance propagation, compensate for system defects, and enhance anti-interference capabilities are designed for the root causes and weak points in these links. These strategies are then adjusted and optimized to ensure their effectiveness and relevance. First, the impact range, probability of occurrence, and degree of damage to the core functions of each causal link in the causal correlation graph are analyzed to identify key causal links. For each key causal link, its root causes and intermediate weak points are traced, the propagation mechanism of the disturbance and the cause of system failure are analyzed, and countermeasure strategies are designed. The feasibility of the designed strategies is assessed, considering implementation costs, technical difficulty, and compatibility with existing systems. Based on the assessment results, the strategies are adjusted and optimized. All optimized strategies are integrated to form a targeted interference scheme, clarifying the implementation targets, implementation steps, and expected effects of each strategy in the scheme.

[0080] The beneficial effects are as follows: focusing on key weak links enhances the pertinence analysis's relevance; the correlation architecture provides a clear framework for identifying pertinence factors, effectively improving the accuracy and efficiency of structural analysis; combining the correlation architecture with key weak link identification factors avoids omissions and misjudgments of pertinence factors; the resulting pertinence factor set is comprehensive and accurate, laying a solid foundation for subsequent pertinence parameter configuration; parameterized configuration makes pertinence strategies more operable and controllable; it can simulate different pertinence scenarios, providing standardized input for scenario enumeration and impact diffusion simulation, improving the scientific rigor of simulation; multi-dimensional scenario enumeration covers pertinence combinations, avoiding the one-sidedness of single-scenario simulation; the generated pertinence scenario set can comprehensively verify the performance of the initial scheme, providing a reference for subsequent optimization; it simulates the pertinence impact diffusion process, reconstructs the propagation path, and records the entire... The bureau's response data is detailed, providing a solid data source for subsequent failure mode identification, accurately locating system problems. The time-generated abnormal event sequences clearly present the failure logic, providing a structured foundation for causal correlation mining, improving the efficiency of causal analysis, uncovering connections and root causes between abnormal events, avoiding fragmented analysis, and forming a preliminary causal chain set that clearly presents the failure propagation path, pointing the way for subsequent work. Integrating scattered causal chains to construct a graph, intuitively presenting the global causal network, helps to quickly identify critical paths and core nodes, providing a global perspective for countermeasure strategy optimization, focusing on key causal link optimization strategies, ensuring the relevance and effectiveness of the solution, and the formed targeted interference scheme provides a basis for strengthening the initial sub-assembly scheme's resilience, improving its anti-interference capability, and ensuring the stable operation of pipeline sub-assembly.

[0081] The anti-reverse reinforcement strategy module 106 is used to reinforce the initial disassembly scheme based on the targeted interference scheme, so as to obtain the pipeline disassembly execution strategy of the initial disassembly scheme.

[0082] In this embodiment of the invention, when the anti-reverse reinforcement strategy module executes the anti-reverse reinforcement of the initial assembly scheme based on the targeted interference scheme to obtain the pipeline assembly execution strategy of the initial assembly scheme, it is specifically used for: The perturbation mode deconstruction of the targeted interference scheme yields the perturbation mode sequence of the targeted interference scheme; Based on the perturbation pattern sequence, robust defect diagnosis is performed on the initial packaging scheme to obtain defect identifiers of the initial packaging scheme; The defect identifiers are integrated with compensation rules to obtain the compensation mechanism for the initial packaging scheme; Based on the compensation mechanism, the initial packaging scheme is reconstructed using multiple strategies to obtain candidate strategies for the initial packaging scheme. The candidate strategies are quantitatively evaluated to obtain the resilience enhancement index of the candidate strategies; Based on the stress resistance enhancement index, the optimal strategy is selected from the candidate strategies to obtain the pipeline dispensing execution strategy.

[0083] When the resilience enhancement strategy module performs a quantitative evaluation of the candidate strategy to obtain the resilience enhancement index of the candidate strategy, the formula for calculating the resilience enhancement index is as follows: ; In the formula, The stress resistance enhancement index is mentioned above. The total number of key perturbation patterns identified in the perturbation pattern sequence. To be in key disturbance modes Below, the performance degradation of the initial packaging scheme, In key disturbance modes The performance degradation of the candidate strategy is as follows. For key disturbance modes Severity weighting The incremental overall execution cost of the candidate strategy compared to the initial packaging scheme is... The cost benchmark is the historical knowledge base mentioned above. The total number of compensation mechanisms activated for the candidate strategy; As a stability regulator, To maintain a weighted contribution to performance, Weighting for cost efficiency It contributes weight to the simplicity of the solution.

[0084] The resilience enhancement strategy module first collects all relevant information about the targeted interference scheme, comprehensively analyzes its constituent elements, operational logic, and manifestations, and breaks down each type of disturbance into its constituent parts, clarifying the triggering conditions, target objects, scope of influence, and characteristics of each disturbance. Then, according to the chronological order of occurrence and the correlation of their impact, this decomposed disturbance information is systematically organized to form a coherent and complete sequence of disturbance patterns.

[0085] Based on the generated disturbance pattern sequence, the initial assembly scheme's adaptability and resistance to these disturbances are checked one by one, comparing the triggering conditions, target objects, scope of influence, and characteristics of each disturbance. A detailed analysis is conducted to determine whether the initial assembly scheme can maintain normal operation in the face of various disturbances, and whether there are any situations where it cannot withstand disturbances, its operating efficiency drops significantly, or its functions fail. Specific links in the initial assembly scheme that are incompatible with various disturbances or unable to cope with them are accurately located and clearly marked, ultimately forming a defect identification system for the initial assembly scheme.

[0086] For the identified defects, a comprehensive collection of rules applicable to remediating those defects was conducted. These rules were derived from proven solutions from historical knowledge bases, industry-recognized optimization principles, and reasonable approaches derived from real-world scenarios. The collected rules were categorized and filtered according to defect type, severity, and scope of impact. Conflicting or incompatible rules were eliminated, and the remaining rules were integrated and optimized. Specific compensation measures, implementation steps, and priority sequences for each type of defect were clarified, ultimately forming a coordinated, unified, and directly applicable compensation mechanism.

[0087] Guided by the established compensation mechanism, and targeting the identified defects in the initial packaging scheme, adjustments, replacements, or supplements are made to these defective links based on the compensation measures, execution steps, and priority order specified in the compensation mechanism. Multiple optimization schemes are designed. During the design process, different resource allocation methods, process adjustment paths, and combinations of coping strategies are fully considered to ensure that each optimization scheme can specifically address the defects, while also taking into account the feasibility and diversity of the schemes. These completed optimization schemes are then compiled into candidate strategies for the initial packaging scheme.

[0088] Each candidate strategy is comprehensively evaluated according to a pre-set unified standard. The evaluation focuses on the candidate strategy's performance retention under various disturbances, cost changes during execution, and the simplicity of the solution itself. By collecting relevant data from the candidate strategies' simulation operations, combined with cost benchmarks and various weighting standards accumulated in the historical knowledge base, and using a specific calculation method, the performance of these consideration dimensions is transformed into concrete values, ultimately forming an evaluation result that comprehensively reflects the resilience enhancement effect of the candidate strategies—the resilience enhancement index.

[0089] The resilience enhancement indices of all candidate strategies were compared to analyze the overall performance of each strategy in terms of performance retention, cost efficiency, and solution simplicity. Furthermore, considering the actual application requirements of pipeline sub-packaging, including resource supply, sub-packaging efficiency requirements, and operational stability standards, the strategy with the highest resilience enhancement index and best meeting the actual application needs was selected as the pipeline sub-packaging execution strategy.

[0090] The formula for calculating the resilience enhancement index is derived from practical experience in resilience optimization in pipeline sub-packing scenarios and operational data in the historical knowledge base. It is specifically used to quantitatively calculate the resilience enhancement index of candidate strategies, so as to comprehensively evaluate the resilience of candidate strategies in response to various disturbances and provide a unified quantitative standard for the selection of pipeline sub-packing execution strategies.

[0091] in The stress-resilience enhancement index is a core indicator for measuring the stress-resilience enhancement effect of candidate strategies. Its trend is positively correlated with the overall score of the numerator and negatively correlated with the value of the denominator; the higher the numerator score and the smaller the denominator value, the better. The higher the value, the better the resilience enhancement effect of the candidate strategy. The performance retention contribution weight is derived from the requirements for operational performance stability in pipeline assembly scenarios, and it primarily reflects the importance of the performance retention dimension in resilience assessment; the higher the value, the greater the contribution of performance retention-related factors. The stronger the influence, the better the performance of the candidate strategy remains. It will rise more significantly. This represents the total number of key disturbance modes. The specific value is determined by the number of key disturbance types actually identified in the disturbance mode sequence, covering all disturbance forms that significantly affect pipeline assembly. The overall score is maintained by the performance of the candidate strategy: The initial packaging scheme under critical disturbance modes The performance degradation is derived from the monitoring data of the initial solution under simulated disturbance scenarios; It is the performance degradation of the candidate strategy under this perturbation, which comes from the simulated running data of the candidate strategy in the same scenario; This represents the performance retention rate under the perturbation. The larger the value, the less the candidate strategy's performance decays and the better the retention effect. This is the severity weight of the perturbation, derived by analyzing the degree of impact and probability of occurrence of the perturbation. A larger value indicates a more severe impact, and the stronger the contribution of the performance retention rate to the overall score, thus driving up the numerator value and consequently increasing R. This is the cost efficiency contribution weight, derived from the cost control requirements of pipeline assembly scenarios. It primarily reflects the importance of cost efficiency in resilience assessment; the higher the value, the stronger the impact of cost efficiency-related factors on R, and the better the cost control performance of candidate strategies. It will rise more significantly. It is the cost efficiency coefficient of the candidate strategy: where It represents the increase in the overall execution cost of the candidate strategy compared to the initial plan, derived from a comparison of the resource and time costs of the two. It is a cost benchmark in the historical knowledge base, derived from proven standard operating costs for conventional pipeline assembly. The smaller the value, the closer it is to 0, and the closer this value is to 1, which will drive the molecular value to rise and increase R. The larger the value, the closer this part is to 0, which will cause the molecule value to decrease and drop. , This is the contribution weight of solution simplicity, derived from the requirement for ease of execution in pipeline assembly scenarios. It primarily reflects the importance of solution simplicity in resilience assessment; the larger the value, the stronger the impact of solution simplicity-related factors on the denominator, the easier it is for the denominator to increase, and consequently, the lower R. It is the complexity coefficient of the candidate strategy, where This represents the total number of compensation mechanisms activated by the candidate strategy, derived from the number of compensation mechanisms actually invoked by the candidate strategy. The more [amount], the larger this part of the value, the higher the denominator value, and the more [it will cause] [the problem]. reduce, The less, the smaller this part of the value, the lower the denominator value, and the more likely it is to cause... promote, It is a stability adjustment factor, derived from the parameterized fitting process of the resilience assessment model. Its value is small and remains stable, primarily used to correct fluctuations in the denominator and avoid bias due to [variable factors]. Small changes can cause excessive fluctuations in the denominator, ensuring The calculation results are stable and reliable.

[0092] The beneficial effects include: comprehensively identifying disturbance characteristics, providing a systematic basis for robustness defect diagnosis, avoiding information omissions that lead to bias, ensuring the smooth progress of subsequent processes, accurately locating the anti-disturbance defects of the initial solution, providing a clear direction for the construction of the compensation mechanism, avoiding blind compensation, improving the targeting and efficiency of optimization, ensuring the comprehensiveness and applicability of the compensation mechanism, making the optimization of the initial solution systematic, improving the efficiency and accuracy of compensation, providing support for solution reconstruction, allowing multiple candidate strategies to specifically compensate for defects, providing sufficient choices for the selection of the optimal strategy, avoiding the limitations of a single solution, improving the flexibility of optimization, quantifying the performance of candidate strategies, achieving objective evaluation, avoiding subjective bias, providing accurate basis for the selection of the optimal strategy, ensuring reliable results, combining screening with actual scenarios, ensuring that the execution strategy achieves the optimal balance in terms of resilience, cost, and convenience, effectively responding to disturbances, ensuring stable and efficient packaging, integrating performance, cost, and simplicity evaluations, comprehensively reflecting the resilience of candidate strategies, taking into account multi-dimensional needs, and providing a scientific standard for the selection of the optimal strategy.

[0093] Reference Figure 2 The diagram shown is a flowchart illustrating a pipeline sub-assembly management method according to an embodiment of the present invention. In this embodiment, the pipeline sub-assembly management method includes: S1. Monitor and analyze the demand and resource status of pipeline sub-assembly to obtain the real-time constraints of the pipeline sub-assembly. S2. Instantiate and load the historical knowledge base of the pipeline distribution management platform to obtain the path planning operation instance and interference simulation operation instance of the management platform; S3. Based on the real-time constraints, perform scheme derivation on the path planning operation instance to obtain the initial packaging scheme of the path planning operation instance; S4. Perform vulnerability aggregation analysis on the node load data and topology connection relationship of the initial packaging scheme to obtain the key weak links of the initial packaging scheme; S5. Based on the key weak links, perform disturbance simulation on the interference simulation operation instance to obtain a targeted interference scheme for the interference simulation operation instance. S6. Based on the targeted interference scheme, the initial packaging scheme is reinforced to obtain the pipeline packaging execution strategy of the initial packaging scheme.

[0094] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0095] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A pipeline dispensing management platform, characterized in that, The system comprises a real-time state monitoring module, a knowledge base instantiation module, a scheme derivation planning module, a vulnerability analysis module, an interference simulation module and an anti-reversal reinforcement strategy module, wherein: The real-time state monitoring module is configured to monitor the demand and resource state of the pipeline subpackaging to obtain real-time constraint conditions of the pipeline subpackaging. The knowledge base instantiation module is configured to instantiate and load a historical knowledge base of the pipeline subpackaging management platform to obtain a path planning running instance and an interference simulation running instance of the management platform. The scheme derivation planning module is configured to derive a scheme based on the real-time constraint conditions to obtain an initial subpackaging scheme of the path planning running instance. The vulnerability analysis module is configured to perform vulnerability aggregation analysis on node load data and topological connection relationships of the initial subpackaging scheme to obtain a key weak link of the initial subpackaging scheme. The interference simulation module is configured to perform disturbance simulation on the interference simulation running instance based on the key weak link to obtain a targeted interference scheme of the interference simulation running instance. The anti-reversal reinforcement strategy module is configured to perform anti-reversal reinforcement on the initial subpackaging scheme based on the targeted interference scheme to obtain a pipeline subpackaging execution strategy of the initial subpackaging scheme.

2. The pipeline break management platform of claim 1, wherein, When the real-time state monitoring module performs monitoring on the demand and resource state of the pipeline subpackaging to obtain real-time constraint conditions of the pipeline subpackaging, it is specifically configured to: Dynamically perceive the demand of the pipeline subpackaging to obtain a demand flow of the pipeline subpackaging; Perform completeness verification on resources of the pipeline subpackaging to obtain a resource spectrum of the pipeline subpackaging; Match and compare the demand flow and the resource spectrum in time and space dimensions to obtain an imbalance point of the supply and demand relationship in the pipeline subpackaging; Extract features of the imbalance point to obtain a bottleneck description of the pipeline subpackaging; Translate the bottleneck description into a limitation rule of the pipeline subpackaging, perform logical aggregation on the limitation rule, and obtain a constraint benchmark of the pipeline subpackaging; Correct the timeliness of the constraint benchmark to obtain real-time constraint conditions of the pipeline subpackaging.

3. The pipeline break management platform of claim 1, wherein, When the knowledge base instantiation module performs instantiation and loading on the historical knowledge base of the pipeline subpackaging management platform to obtain a path planning running instance and an interference simulation running instance of the management platform, it is specifically configured to: Extract multi-dimensional data in the historical knowledge base of the pipeline subpackaging management platform to obtain original running logs of the historical knowledge base; Perform semantic normalization processing on the original running logs to obtain standardized semantic data of the historical knowledge base; Construct a topological structure based on logical relationships in the standardized semantic data to obtain an entity relationship network of the standardized semantic data; Perform flow conversion rule mining on the entity relationship network to obtain a path configuration logic of the management platform; According to disturbance coping logic in the standardized semantic unit, the path configuration logic and the disturbance coping logic are cooperatively encapsulated to obtain the path planning running instance and the interference simulation running instance of the management platform.

4. The pipeline break management platform of claim 1, wherein, The scheme derivation module is specifically configured to perform semantic deconstruction on the real-time constraint condition to obtain a structure constraint vector of the real-time constraint condition, perform instance discrimination on the path planning running instance based on the structure constraint vector to obtain a candidate instance of the path planning running instance, perform strategy enumeration on the adaptive instance to obtain an alternative strategy space of the adaptive instance, perform situation evolution on the alternative strategy space to obtain an evolution trajectory cluster of the alternative strategy space, and perform convergence evaluation on the evolution trajectory cluster to obtain an initial packaging scheme of the path planning running instance. The vulnerability analysis module is specifically configured to perform topological correlation mapping on the initial packaging scheme to obtain a topological connection graph of the initial packaging scheme, perform load state analysis on node load of the initial packaging scheme to obtain a node pressure mode of the initial packaging scheme, analyze a structure correlation relationship in the topological connection graph to obtain a topological robustness sketch of the initial packaging scheme, perform risk fusion on the node pressure mode and the topological robustness sketch to obtain a vulnerability aggregation result of the initial packaging scheme, perform quantitative evaluation on the vulnerability aggregation result to obtain a vulnerability level of the initial packaging scheme, and perform critical identification on the vulnerability aggregation result based on the vulnerability level to obtain a key weak link of the initial packaging scheme. The vulnerability analysis module is specifically configured to perform feature intersection on the node pressure mode and the topological robustness sketch to obtain a comprehensive feature expression of the initial packaging scheme, evaluate a feature density of the comprehensive feature expression to obtain a node distribution feature of the initial packaging scheme, perform similarity measurement on the node distribution feature based on the topological robustness sketch to obtain a node affinity-sympathy matrix of the initial packaging scheme, perform structure clustering on the node distribution feature based on the node affinity-sympathy matrix to obtain a node community structure of the initial packaging scheme, evaluate a structure criticality of the node community structure to obtain a key community identifier of the initial packaging scheme, and perform vulnerability aggregation on the key community identifier to obtain the vulnerability aggregation result of the initial packaging scheme. The interference simulation module is specifically configured to perform topological relationship deconstruction on the interference simulation running instance based on the key weak link to obtain an association architecture of the interference simulation running instance, perform strategy enumeration on the association architecture to obtain an alternative strategy space of the interference simulation running instance, perform situation evolution on the alternative strategy space to obtain an evolution trajectory cluster of the alternative strategy space, perform convergence evaluation on the evolution trajectory cluster to obtain a candidate interference scheme of the interference simulation running instance, and perform quantitative evaluation on the candidate interference scheme to obtain a targeted interference scheme of the interference simulation running instance. The interference simulation module is specifically configured to perform topological relationship deconstruction on the interference simulation running instance based on the key weak link to obtain an association architecture of the interference simulation running instance, perform strategy enumeration on the association architecture to obtain an alternative strategy space of the interference simulation running instance, perform situation evolution on the alternative strategy space to obtain an evolution trajectory cluster of the alternative strategy space, perform convergence evaluation on the evolution trajectory cluster to obtain a candidate interference scheme of the interference simulation running instance, and perform quantitative evaluation on the candidate interference scheme to obtain a targeted interference scheme of the interference simulation running instance. ​ 5. The pipeline break management platform of claim 1, wherein, ​ ​ ​ ​ ​ ​ ​ 6. The pipeline break management platform of claim 5, wherein, ​ ​ ​ ​ ​ ​ ​ 7. The pipeline break management platform of claim 1, wherein, ​ ​ Identify key disturbance factors in the associated architecture, obtain a disturbance factor set of the interference simulation running instance; Based on the disturbance factor set, the disturbance parameter configuration is carried out on the associated architecture, and a parameterized disturbance strategy of the interference simulation running instance is obtained; Scene enumeration is performed on the parameterized disturbance strategy, and a disturbance scene set of the interference simulation running instance is obtained; The influence diffusion simulation is carried out on the disturbance scene set, and the global response data of the disturbance scene set is obtained; The failure mode identification is carried out on the global response data, and an abnormal event sequence of the global response data is obtained; The causal association mining is carried out on the abnormal event sequence, and a preliminary causal chain set of the abnormal event sequence is obtained; The network integration is carried out on the preliminary causal chain set, and a causal association graph of the preliminary causal chain set is obtained; The key causal link in the causal association graph is subjected to countermeasure optimization, and a targeted interference scheme of the interference simulation running instance is obtained.

8. The pipeline break management platform of claim 1, wherein, When the anti-reversal reinforcement strategy module executes the anti-reversal reinforcement on the initial packaging scheme based on the targeted interference scheme to obtain the pipeline packaging execution strategy of the initial packaging scheme, it is specifically used for: The disturbance mode of the targeted interference scheme is deconstructed to obtain a disturbance mode sequence of the targeted interference scheme; Based on the disturbance mode sequence, the robustness defect diagnosis is carried out on the initial packaging scheme to obtain a defect identification of the initial packaging scheme; The defect identification is integrated with a compensation rule set to obtain a compensation mechanism of the initial packaging scheme; Based on the compensation mechanism, the multi-strategy reconstruction is carried out on the initial packaging scheme to obtain a candidate strategy of the initial packaging scheme; The candidate strategy is subjected to quantitative evaluation to obtain an anti-reversal reinforcement index of the candidate strategy; Based on the anti-reversal reinforcement index, the optimal strategy is selected from the candidate strategy to obtain the pipeline packaging execution strategy.

9. The pipeline break management platform of claim 1, wherein, When the anti-reversal reinforcement strategy module executes the quantitative evaluation on the candidate strategy to obtain the anti-reversal reinforcement index of the candidate strategy, the calculation formula of the anti-reversal reinforcement index is as follows: ； ； In the formula, The stress resistance enhancement index is mentioned above. The total number of key perturbation patterns identified in the perturbation pattern sequence. To be in key disturbance modes Below, the performance degradation of the initial packaging scheme, In key disturbance modes The performance degradation of the candidate strategy is as follows. For key disturbance modes Severity weighting The incremental overall execution cost of the candidate strategy compared to the initial packaging scheme is... The cost benchmark is the historical knowledge base mentioned above. The total number of compensation mechanisms activated for the candidate strategy; As a stability regulator, To maintain a weighted contribution to performance, Weighting for cost efficiency It contributes weight to the simplicity of the solution.

10. A pipe partial shipment management method characterized by, The method is used for a pipeline packaging management platform of claim 1, and the method comprises the following steps: S1, monitoring and analyzing the demand and resource state of pipeline packaging to obtain real-time constraint conditions of the pipeline packaging; S2, instantiating and loading a historical knowledge base of a pipeline packaging management platform to obtain a path planning running instance and an interference simulation running instance of the management platform; S3, based on the real-time constraint conditions, scheme derivation is carried out on the path planning running instance to obtain an initial packaging scheme of the path planning running instance; S4, vulnerability aggregation analysis is carried out on the node load data and topological connection relationship of the initial packaging scheme to obtain key weak links of the initial packaging scheme; S5, based on the key weak links, disturbance simulation is carried out on the interference simulation running instance to obtain a targeted interference scheme of the interference simulation running instance; S6, based on the targeted interference scheme, anti-reversal reinforcement is carried out on the initial packaging scheme to obtain a pipeline packaging execution strategy of the initial packaging scheme.

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