An equipment system reliability evaluation method

Abstract

The application discloses a kind of equipment system reliability evaluation method, it is related to reliability evaluation technical field.The method includes: the running state data of each equipment in equipment system is collected, and is abstracted as the multilayer network including detection, scheduling, job and target object node;According to connection relationship, enumerate feasible job chain;Each job chain is regarded as super node, and the cooperation, competition, timing and backup relationship between job chain are identified and are represented by super edge;Determine the chain level efficiency of each job chain and correct with super edge, obtain the corrected chain level efficiency;According to it, system reliability is determined;Further determine key job chain or key equipment and configure operation and maintenance guarantee resource.The present application represents the high-order relationship between job chain with super edge, the influence of the relationship that will be missed when job chain is regarded as independent on system efficiency is counted, more accurate reliability evaluation result is obtained, can be used for the operation and maintenance decision of multi-equipment cooperative inspection and defect disposal system of submarine pipeline, submarine cable, pipe network and station.

Description

Technical Field

[0001] This invention relates to the field of reliability assessment technology, and in particular to a method for assessing the reliability of an equipment system. Background Technology

[0002] As the scale of infrastructure such as subsea oil and gas pipelines, offshore wind power cables, urban underground pipe networks, and large-scale facility equipment continues to expand, their inspection and defect handling increasingly rely on a collaborative operation mode involving multiple autonomous equipment units. In this mode, detection equipment is responsible for discovering defects or attachments on the objects to be inspected; scheduling equipment allocates and authorizes tasks; the operational equipment detects, cleans, or repairs the defects or attachments; and a verification process confirms the effectiveness of the repairs. Multiple units of equipment, sharing limited communication bandwidth, scheduling computing power, and operational tools, constitute an equipment system focused on the same target objects. Assessing the reliability of such an equipment system is crucial for planning maintenance schedules, configuring spare parts, and allocating support resources.

[0003] Existing reliability assessment methods typically decompose the system into several independent task paths or task chains. They first calculate the reliability of each path separately according to a series-parallel structure, and then synthesize the overall system reliability under the assumption that the paths are independent. This approach has shortcomings when dealing with equipment systems involving multiple pieces of equipment operating collaboratively: different task chains within an equipment system often share the same equipment or resource and are mutually constrained in their execution sequence. Therefore, there are higher-order relationships between task chains, such as collaboration, competition, timing, and backup. Treating task chains as independent fails to account for the efficiency gains from multiple task chains working together, the efficiency losses caused by multiple task chains competing for the same resource, and the timing dependency formed when the initiation of one task chain depends on the output of another. This leads to a discrepancy between the reliability assessment results and the actual performance of the equipment system.

[0004] Furthermore, existing methods primarily analyze physical failures of nodes and connections themselves, such as equipment failure or link interruption, neglecting another failure mode: even when equipment and connections remain physically available, disruptions in coordination and scheduling relationships between work chains can significantly reduce the effectiveness of the equipment system. Due to the lack of modeling tools for these higher-order relationships and relationship disruption failure modes, existing methods struggle to accurately identify the critical work chains and key equipment that have the greatest impact on overall reliability within the equipment system, making it difficult to allocate maintenance and support resources accordingly. Summary of the Invention

[0005] This invention proposes a reliability assessment method for equipment systems. For equipment systems with multiple pieces of equipment operating collaboratively, it addresses the technical problem that existing reliability assessment methods treat work chains as independent of each other, failing to take into account higher-order relationships such as collaboration, competition, timing, and backup between work chains sharing resources, as well as relationship breakage failure modes. This results in inaccurate assessment results and makes it difficult to identify critical work chains and critical equipment and configure operation and maintenance support resources accordingly.

[0006] To address the aforementioned technical problems, this invention provides a method for reliability assessment of an equipment system, wherein the equipment system includes multiple pieces of equipment for detecting, scheduling, and operating target objects, and includes the following steps: Step S1: Collect the operating status data of each piece of equipment in the equipment system, and abstract the equipment system into a multi-layer network. The multi-layer network includes detection nodes, scheduling nodes, operation nodes and target object nodes, as well as the connection relationships between nodes that respectively represent detection coverage, communication reachability, scheduling authorization and operation function. Step S2: Enumerate feasible job chains according to the connection relationship. The job chain is an ordered sequence of nodes from the probe node through the scheduling node to the job node to perform a job on the target object. Step S3: Treat each job chain as a super node, identify the collaborative relationship, competitive relationship, temporal relationship and backup relationship between job chains based on the nodes or resources shared between job chains and the execution constraints between job chains, and represent the collaborative relationship, competitive relationship, temporal relationship and backup relationship with a super edge; Step S4: Determine the chain-level efficiency of each work chain based on the operating status data of each piece of equipment, and correct the chain-level efficiency based on the super-edge to obtain the corrected chain-level efficiency; Step S5: Determine the system reliability of the equipment system based on the corrected chain-level performance; Step S6: Determine the critical work chain or critical equipment in the equipment system based on the system reliability, and configure the operation and maintenance support resources of the equipment system according to the critical work chain or critical equipment.

[0007] Preferably, the method for identifying the relationship between job chains in step S3 includes: when two job chains share detection information or scheduling information and jointly improve job efficiency, the relationship between the two job chains is identified as a cooperative relationship; when two job chains share the same resource node and interfere with each other or cause a decrease in efficiency, the relationship between the two job chains is identified as a competitive relationship; when the start of one job chain is conditional on the output of another job chain, the relationship between the two job chains is identified as a timing relationship; when the two job chains are functionally equivalent and can be substituted for each other, the relationship between the two job chains is identified as a backup relationship.

[0008] Preferably, the chain-level efficiency of each work chain in step S4 is determined based on the product of structural availability, stage success rate, and timeliness impact, wherein the expression for structural availability is: ; In the formula For the work chain Structural availability, and respectively, work chain The set of nodes and the set of edges contained therein. For nodes Availability For the edge Availability; The expression for the success rate of the aforementioned stage is: ; In the formula For the work chain The success rate of the stage, For the work chain The set of stages included For the work chain In the stage The probability of success; The expression for the timeliness impact is: ; ; In the formula For timeliness impact, For the work chain The overall execution time This represents the urgency level coefficient. For nodes The node operation time, For the edge Communication latency.

[0009] Preferably, the method for correcting the chain-level performance based on the hyperedge in step S4 includes: for a hyperedge composed of cooperative relationships, multiplying the sum of the chain-level performance of each cooperative work chain by a cooperative gain coefficient to obtain the corrected chain-level performance, expressed as: ; In the formula For the work chain and work chain The combined effectiveness after collaboration The cooperative gain coefficient is not less than 1. and These are the work chains that participate in the collaboration. and work chain Chain-level efficiency; For a superedge formed by competing relationships, the chain-level efficiency of the work chain is multiplied by the competition loss factor to obtain the corrected chain-level efficiency, expressed as: ; In the formula For the work chain Corrected chain-level performance The competition loss coefficient has a value between 0 and 1.

[0010] Preferably, the method further includes a step of determining a chain group coupling metric for the target object, wherein the chain group coupling metric includes a hyperedge conflict index and a hyperedge redundancy, and the expression for the hyperedge conflict index is: ; In the formula This is a hyperborder conflict index. For a hyperedge consisting of competitive relationships, For the work chain The time spent waiting due to resources being occupied. For task window time, To compete for the number of job chains contained in a superedge; The expression for the hyperedge redundancy is: ; In the formula For the hyperedge redundancy of the target object, This represents the total number of all feasible operation chains for the target object. The number of job chains that are targeted at the target object and belong to the backup superedge. For the work chain Chain-level efficiency.

[0011] Preferably, the system reliability in step S5 refers to the task reliability of the equipment system in completing all target object operations within the task window, and the task reliability is determined by the following formula: ; In the formula For mission reliability, For the set of all target objects, For the target object The set of all feasible operation chains, For the work chain The revised chain-level performance.

[0012] Preferably, in step S6, the critical task chain is determined based on the hyperedge connectivity of the task chain in the multi-layer network, wherein the hyperedge connectivity is determined by the following formula: ; In the formula For the work chain The hyperedge connectivity, For the work chain With the work chain The total number of shortest paths between information points. The total number of shortest paths for information that pass through the job chain The number of paths.

[0013] Preferably, the method further includes the step of determining a performance collapse threshold for the equipment system, and outputting an early warning when the failure rate of the equipment system exceeds the performance collapse threshold, wherein the performance collapse threshold is determined by the following formula: ; In the formula The performance crash threshold is a value between 0 and 1. For the job chain to act as a supernode, the expiration is performed; For average transcendence; The second moment of the hypermarginality reflects the degree of dispersion of the hypermarginality distribution; This represents the number of job chains contained in a single hyperedge; The average cardinality of the superedges reflects the average size of the relationships between chains; The second moment of the hyperedge cardinality reflects the degree of existence of large-scale cooperative groups.

[0014] Preferably, the method further includes a relationship failure assessment step: under the premise that the nodes and the connection relationships remain physically available, the relationship between the operation chains represented by a portion of the super-edges is made to fail according to a predetermined ratio, and the corrected chain-level performance and the system reliability are re-determined based on the failed super-edges to obtain the reliability of the equipment system under the relationship failure mode.

[0015] Preferably, in step S3, for a superedge formed by temporal relationships, the executable probability of the next task chain is determined based on the success probability of the previous task chain and the conditional probability of the next task chain succeeding given the success of the previous task chain, expressed as follows: In the formula This represents the probability of execution for the next task chain. and These are random variables representing the task results of the previous and next task chains, respectively. A value of 1 indicates that the corresponding task chain is successful, and a value of 0 indicates that the corresponding task chain is unsuccessful. For a hyperedge formed by backup relationships, the probability of successfully handling the target object is determined by the product of the failure probabilities of each backup job chain, denoted as: In the formula This represents the coverage probability that the target object is successfully processed. Let A be the set of job chains contained within a hyperedge formed by backup relationships. For the work chain The revised chain-level performance.

[0016] The beneficial effects of the present invention include at least the following: This invention abstracts the equipment system into a multi-layered network and enumerates feasible work chains. Each work chain is then treated as a super node, and super edges represent four types of relationships between work chains: collaboration, competition, timing, and backup. Based on this, the chain-level performance of the work chains is corrected, and the system reliability is calculated. This ensures that the evaluation process incorporates the impact of higher-order relationships on system performance that were missed when work chains were treated as independent, thus obtaining reliability evaluation results that more closely match the actual performance of the equipment system. Furthermore, this invention outputs key work chains or key equipment in the equipment system based on system reliability, and allocates operation and maintenance support resources accordingly, providing quantifiable technical basis for operation and maintenance decisions. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the equipment system reliability assessment method of the present invention; Figure 2 This is a schematic diagram illustrating the structure of the equipment system as a multi-layered network in this invention; Figure 3 This is a schematic diagram illustrating the four types of hyperedge relationships among job chains in this invention: collaboration, competition, timing, and backup. Figure 4 This is a schematic curve illustrating how the system effectiveness changes with the failure rate of the equipment system in this invention, and how it drops sharply when the failure rate exceeds the effectiveness collapse threshold. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0019] like Figure 1As shown, this invention provides a method for reliability assessment of an equipment system. The equipment system consists of multiple pieces of equipment that perform detection, identification, operation, and verification of a target object. Each piece of equipment is a physical device with corresponding detection, communication, scheduling, or operation functions, capable of outputting operational status data characterizing its own state during operation. The target object is the object for which the equipment system needs to perform operations. This invention does not limit the specific scenarios in which the equipment system is applied; it can be used for reliability assessment of equipment systems in multi-equipment collaborative inspection and defect handling systems, such as subsea oil and gas pipelines, offshore wind power cables, urban underground pipe networks, and station equipment. It includes the following steps.

[0020] Step S1: Collect the operational status data of each piece of equipment in the equipment system, and abstract the equipment system into a multi-layer network. The multi-layer network includes detection nodes, scheduling nodes, operation nodes and target object nodes, as well as the connection relationships between nodes that respectively represent detection coverage, communication reachability, scheduling authorization and operation role.

[0021] In this embodiment, operational status data refers to data obtained by each piece of equipment during operation through self-monitoring, used to characterize its ability to perform corresponding functions, such as equipment availability, communication link quality, and the time required to perform operations. Based on the collected operational status data, the equipment system is abstracted into a multi-layered network including detection nodes, scheduling nodes, operation nodes, and target object nodes. Detection nodes correspond to the equipment performing detection, scheduling nodes correspond to the equipment performing scheduling, operation nodes correspond to the equipment performing operations, and target object nodes correspond to the target object to which the operation is performed. Figure 2 As shown, Figure 2 The structure of the multi-layer network is illustrated. The connections between nodes represent detection coverage, communication reachability, scheduling authorization, and job action, respectively: the connections representing detection coverage and job action are represented in binary form as whether a detection node can cover a target object and whether a job node can perform a job on a target object; the connections representing communication reachability use symmetric weights between 0 and 1 to represent the quality of the bidirectional communication link between nodes; the connections representing scheduling authorization use directed weights between 0 and 1 to represent the reachability of scheduling nodes to job nodes.

[0022] Step S2: Enumerate feasible job chains based on the connection relationships. In this embodiment, a job chain is an ordered sequence of nodes from the probe node through the scheduling node to the job node, performing a job on the target object. For each target object, by traversing the combinations of probe nodes, scheduling nodes, and job nodes that satisfy the connection relationships, all feasible job chains for that target object can be enumerated.

[0023] Step S3: Treat each job chain as a super node. Based on the nodes or resources shared between job chains and the execution constraints between job chains, identify the collaborative relationships, competitive relationships, temporal relationships and backup relationships between job chains, and use super edges to represent the collaborative relationships, competitive relationships, temporal relationships and backup relationships.

[0024] Specifically, when two job chains share detection or scheduling information and jointly improve operational efficiency, the relationship between the two job chains is identified as a cooperative relationship; when two job chains share the same resource node but interfere with each other or cause a decrease in efficiency, the relationship between the two job chains is identified as a competitive relationship; when the start of one job chain is conditional on the output of another job chain, the relationship between the two job chains is identified as a temporal relationship; when two job chains are functionally equivalent and can be substituted for each other, the relationship between the two job chains is identified as a backup relationship. After identifying the above relationships, cooperative superedges, competitive superedges, temporal superedges, and backup superedges are used to represent the corresponding relationships, and each superedge connects the supernodes corresponding to several job chains participating in the relationship; for example... Figure 3 As shown, Figure 3 The diagram illustrates four types of hyperedge relationships between job chains: collaboration, competition, timing, and backup. Circles represent job chains that act as supernodes, and dashed ellipses and arrows represent the hyperedge relationships between job chains.

[0025] Step S4: Determine the chain-level efficiency of each work chain based on the operating status data of each piece of equipment, and correct the chain-level efficiency based on the super-edge to obtain the corrected chain-level efficiency.

[0026] Specifically, in this embodiment, the chain-level efficiency of the work chain is determined based on the product of structural availability, stage success rate, and timeliness impact. The expression for structural availability is: ; In the formula For the work chain Structural availability, and respectively, work chain The set of nodes and the set of edges contained therein. For nodes Availability For the edge The availability of nodes and edges is determined by the running status data collected in step S1.

[0027] The expression for the success rate of a stage is: ; In the formula For the work chain The success rate of the stage, For the work chain The set of stages included For the work chain In the stage The probability of success.

[0028] The expression for the timeliness impact is: ; Among them, the overall execution time In the formula For timeliness impact, For the work chain The overall execution time This represents the urgency level coefficient. For nodes The node operation time, For the edge Communication latency.

[0029] Chain-level efficiency of the work chain That is, take the product of the above three factors: structural availability, phase success rate, and timeliness impact. .

[0030] After obtaining the chain-level performance of each task chain, the chain-level performance is corrected based on the hyperedge. In this embodiment, for a hyperedge formed by cooperative relationships, the sum of the chain-level performance of each participating task chain is multiplied by a cooperative gain coefficient to obtain the joint performance after cooperation, expressed as: In the formula For the work chain and work chain The combined effectiveness after collaboration The cooperative gain coefficient is not less than 1. and These are the work chains that participate in the collaboration. and work chain Chain-level efficiency.

[0031] For a superedge consisting of competing relationships, the chain-level efficiency of the work chain is multiplied by the competition loss factor to obtain the corrected chain-level efficiency, expressed as: In the formula For the work chain Corrected chain-level performance The competition loss coefficient has a value between 0 and 1.

[0032] Synergistic gain coefficient With competition loss coefficient It can be calibrated based on the historical operating data or simulation results of the equipment system. It reflects the efficiency gain ratio of multiple work chains working together compared to a single work chain. It reflects the proportion of efficiency loss when two work chains compete for the same resource.

[0033] Step S5: Determine the system reliability of the equipment system based on the corrected chain-level performance.

[0034] In this embodiment, the system reliability is taken as the task reliability of the equipment system in completing all target object operations within the task window, which is determined by the following formula: ; In the formula For mission reliability, For the set of all target objects, For the target object The set of all feasible operation chains, For the work chain The revised chain-level performance.

[0035] The above formula shows that for each target object, as long as at least one of the feasible operation chains for that target object is successful, the target object is considered to have completed the operation; the probability that the equipment system completes all target object operations within the task window is the product of the probabilities that each target object has completed the operation.

[0036] To further characterize the coupling degree between work chains, in another embodiment of the present invention, a chain group coupling metric is also determined for the target object. The chain group coupling metric includes a hyperedge conflict index and a hyperedge redundancy. The expression for the hyperedge conflict index is: ; In the formula This is a hyperborder conflict index. For a hyperedge consisting of competitive relationships, For the work chain The time spent waiting due to resources being occupied. For task window time, The number of job chains contained in a competing superedge.

[0037] The expression for hyperedge redundancy is: ; In the formula To address the hyperedge redundancy of the target object, This represents the total number of feasible operation chains for this target object. The number of job chains that are targeted at this object and belong to the backup superedge. For the work chain The chain-level efficiency is as follows. The larger the hyperedge conflict index, the higher the degree of resource contention and mutual waiting among the operation chains within the competing hyperedge; the larger the hyperedge redundancy, the higher the degree to which the target object is protected by an equivalent backup operation chain.

[0038] For hyperedges formed by temporal relationships, this implementation also determines the executable probability of the next task chain. This probability is determined based on the success probability of the previous task chain and the conditional probability of the next task chain succeeding given the success of the previous task chain, expressed as follows: In the formula This represents the probability of execution for the next task chain. and These are random variables representing the task results of the previous and next task chains, respectively. A value of 1 indicates that the corresponding task chain was successful, and a value of 0 indicates that the corresponding task chain failed.

[0039] Among them, the probability of success of the previous operation chain The chain-level efficiency can be obtained by adjusting the previous operation chain, with conditional probability. The probability of success of the next work chain, which is based on the premise that the previous work chain has successfully delivered its output, can be obtained from the historical operation records of the equipment system, or estimated by simulation tests when historical data is lacking.

[0040] For the hyperedge formed by backup relationships, this implementation also determines the coverage probability of the target object being successfully handled, which is determined based on the product of the failure probabilities of each backup job chain, and is expressed as: ; In the formula This represents the coverage probability that the target object is successfully processed. Let A be the set of job chains contained within a hyperedge formed by backup relationships. For the work chain The revised chain-level performance. The closer the coverage probability is to 1, the higher the guarantee that the target object can still be successfully handled when part of the backup job chain fails.

[0041] Step S6: Determine the critical work chains or critical equipment in the equipment system based on the system reliability, and configure the operation and maintenance support resources of the equipment system according to the critical work chains or critical equipment.

[0042] In this embodiment of the invention, the critical task chain is determined based on the hyperedge connectivity of the task chain in the multi-layer network. The hyperedge connectivity is determined by the following formula: ; In the formula For the work chain The hyperedge connectivity, For the work chain With the work chain The total number of shortest paths between information points. The total number of shortest paths for information that pass through the job chain The number of paths. The greater the hyperedge connectivity, the more pivotal the operation chain is in the information transmission between operation chains, and the greater its impact on system reliability. Based on this, operation chains with larger hyperedge connectivity can be identified as critical operation chains, and the equipment on which critical operation chains depend can be identified as critical equipment.

[0043] In this embodiment of the invention, a performance collapse threshold for the equipment system is also determined, and an early warning is output when the failure rate of the equipment system exceeds the performance collapse threshold. The performance collapse threshold is determined by the following formula: ; In the formula The performance collapse threshold is a value between 0 and 1. When the failure rate of the equipment system is lower than... The system maintains its function and the failure rate exceeds [a certain percentage]. The system's efficiency collapsed. The extent of a task chain as a supernode is the number of superedges participating in a task chain. For average transcendence, The second moment of the hypermarginality reflects the degree of dispersion of the hypermarginality distribution. This represents the number of job chains contained in a single hyperedge. The average cardinality of the superedges. The second moment is the hyperedge basis. For example... Figure 4 As shown, as the failure rate of the equipment system increases, the system efficiency decreases slowly when the failure rate is below the efficiency collapse threshold, and decreases sharply when the failure rate exceeds the efficiency collapse threshold. Accordingly, when the failure rate of the equipment system is detected to be close to or exceed the efficiency collapse threshold, an early warning is issued to prompt maintenance personnel to take timely measures to ensure its operation.

[0044] In another embodiment of the present invention, a relationship-break failure assessment step is further included to evaluate the reliability of the equipment system when both equipment and connections remain physically available, but the relationships between work chains are broken. Under the premise that both nodes and connections remain physically available, the relationships between work chains represented by a portion of the hyperedges are made invalid at a predetermined ratio. The corrected chain-level performance and system reliability are then re-determined based on the invalidated hyperedges, thus obtaining the reliability of the equipment system under the relationship-break failure mode. The predetermined ratio can take multiple different values ​​to examine the reliability changes of the equipment system under different degrees of relationship breakage, such as 10%, 20%, and 30%.

[0045] Based on the assessment results of the critical work chains, critical equipment, performance collapse thresholds, and relationship breakdown failures, the operation and maintenance support resources for the equipment system are configured. Specifically, backup equipment and maintenance personnel are prioritized for the critical equipment on which the critical work chains depend; additional work chains or backup work equipment are added for targets with low super-edge redundancy; communication bandwidth or scheduling computing power is expanded for competing super-edges with high super-edge conflict indices; and the operational failure rate of the equipment system is kept below the performance collapse threshold, thereby improving the overall reliability of the equipment system under given support resource constraints.

[0046] As a specific embodiment, the following uses a multi-autonomous underwater vehicle (AUV) collaborative inspection and defect handling system for subsea oil and gas pipelines as an example to further explain steps S1 to S6. In this embodiment, the equipment system includes several detection-type AUVs, a dispatch center located on a mother ship or shore-based, and several operational AUVs. The detection-type AUVs are equipped with side-scan sonar and underwater cameras to detect defects or deposits on the pipeline, and the operational AUVs are equipped with cleaning and repair equipment to handle the defects or deposits. The target objects are defects or deposits on the pipeline such as coating damage, localized corrosion, marine organism attachment, or suspended sections.

[0047] In step S1, each independent underwater vehicle and the dispatch center report their own operational status data in real time during the operation, including remaining battery power, the availability of thrusters and equipment, the signal-to-noise ratio of the underwater acoustic communication link, and positioning accuracy; based on this, the system is abstracted as follows: Figure 2 The multi-layered network shown is as follows: each detection-type autonomous underwater vehicle (AUV) is a detection node, the dispatch center is a dispatch node, each operational AUV is an operational node, and each defect or attachment is a target node. Detection nodes and target nodes establish detection coverage connections based on the coverage range of sonar and cameras. Dispatch nodes and operational nodes establish dispatch authorization connections based on authorization relationships. All nodes establish communication reachability connections based on underwater acoustic communication links. Operational nodes and target nodes establish operational function connections based on operational capabilities.

[0048] In step S2, for each defect, all feasible combinations are enumerated, such that the defect is discovered by a certain detection-type autonomous underwater vehicle, authorized by the dispatch center, and then handled by a certain operational-type autonomous underwater vehicle. Each combination is a work chain.

[0049] In step S3, each operation chain is treated as a super node and the relationships between them are identified. When two detection-type autonomous underwater vehicles jointly detect the same pipe section to improve the defect detection rate, the two corresponding operation chains form a cooperative relationship and are characterized by a cooperative superedge. When two operation chains simultaneously request to occupy the same operational autonomous underwater vehicle or the same underwater acoustic communication channel, they form a competitive relationship and are characterized by a competitive superedge. When the cleaning operation chain can only be started after the detection operation chain confirms the location and type of the defect, the two form a temporal relationship and are characterized by a temporal superedge. When both operational autonomous underwater vehicles can perform equivalent treatment on the same defect, the two corresponding operation chains form a backup relationship and are characterized by a backup superedge.

[0050] In step S4, based on the operational status data reported by each autonomous underwater vehicle, the chain-level efficiency of each operational chain is calculated as the product of structural availability, phase success rate, and timeliness impact. The chain-level efficiency is then corrected according to cooperative and competitive hyperedges. For example, if a detection-response operational chain has a structural availability of 0.95, a phase success rate of 0.90, and a timeliness impact of 0.92, its chain-level efficiency is approximately 0.79. If this operational chain competes with another operational chain for the same operational autonomous underwater vehicle, and the competition loss coefficient is 0.1, then the corrected chain-level efficiency of this operational chain is approximately 0.71.

[0051] In step S5, the feasible operation chains corresponding to each defect are synthesized according to the task reliability expression to obtain the system reliability of the system in completing all defect handling within one inspection task window. Furthermore, the super-edge conflict index of the competing super-edge, the super-edge redundancy of the backup super-edge, and the executable probability of the next operation chain under the time-series super-edge and the coverage probability of the defect being successfully handled under the backup super-edge are calculated.

[0052] In step S6, the critical operational chains that have the greatest impact on system reliability and the critical equipment they depend on are identified based on hyperedge connectivity. For example, a certain operational autonomous underwater vehicle or dispatch center shared by multiple operational chains is identified, and the system's performance collapse threshold is calculated. Based on this, operation and maintenance support resources are configured, such as equipping critical operational autonomous underwater vehicles with backups, adding operational autonomous underwater vehicles that can be used as backups for pipe sections with low hyperedge redundancy, expanding the bandwidth of underwater acoustic channels with high hyperedge conflict indices, or staggering the scheduling sequence, so that the failure rate of the system during the inspection process remains below the performance collapse threshold.

[0053] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; only preferred embodiments of the present invention are illustrated. The descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. As long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0054] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for reliability assessment of an equipment system, wherein the equipment system comprises multiple pieces of equipment for detecting, scheduling, and operating target objects, characterized in that, Includes the following steps: Step S1: Collect the operating status data of each piece of equipment in the equipment system, and abstract the equipment system into a multi-layer network. The multi-layer network includes detection nodes, scheduling nodes, operation nodes and target object nodes, as well as the connection relationships between nodes that respectively represent detection coverage, communication reachability, scheduling authorization and operation function. Step S2: Enumerate feasible job chains according to the connection relationship. The job chain is an ordered sequence of nodes from the probe node through the scheduling node to the job node to perform a job on the target object. Step S3: Treat each job chain as a super node, identify the collaborative relationship, competitive relationship, temporal relationship and backup relationship between job chains based on the nodes or resources shared between job chains and the execution constraints between job chains, and represent the collaborative relationship, competitive relationship, temporal relationship and backup relationship with a super edge; Step S4: Determine the chain-level efficiency of each work chain based on the operating status data of each piece of equipment, and correct the chain-level efficiency based on the super-edge to obtain the corrected chain-level efficiency; Step S5: Determine the system reliability of the equipment system based on the corrected chain-level performance; Step S6: Determine the critical work chain or critical equipment in the equipment system based on the system reliability, and configure the operation and maintenance support resources of the equipment system according to the critical work chain or critical equipment.

2. The equipment system reliability assessment method according to claim 1, characterized in that, The methods for identifying the relationships between job chains in step S3 include: when two job chains share detection information or scheduling information and jointly improve job efficiency, the relationship between the two job chains is identified as a cooperative relationship; when two job chains share the same resource node and interfere with each other or cause a decrease in efficiency, the relationship between the two job chains is identified as a competitive relationship; when the start of one job chain is conditional on the output of another job chain, the relationship between the two job chains is identified as a timing relationship; when two job chains are functionally equivalent and can be substituted for each other, the relationship between the two job chains is identified as a backup relationship.

3. The equipment system reliability assessment method according to claim 1, characterized in that, In step S4, the chain-level efficiency of each work chain is determined based on the product of structural availability, stage success rate, and timeliness impact, wherein the expression for structural availability is: ； In the formula For the work chain Structural availability, and respectively, work chain The set of nodes and the set of edges contained therein. For nodes Availability For the edge Availability; The expression for the success rate of the aforementioned stage is: ； In the formula For the work chain The success rate of the stage, For the work chain The set of stages included For the work chain In the stage The probability of success; The expression for the timeliness impact is: ； ； In the formula For timeliness impact, For the work chain The overall execution time This is the urgency level coefficient. For nodes The node operation time, For the edge Communication latency.

4. The equipment system reliability assessment method according to claim 1, characterized in that, The method for correcting the chain-level performance based on the hyperedge in step S4 includes: for a hyperedge composed of cooperative relationships, multiplying the sum of the chain-level performance of each cooperative work chain by a cooperative gain coefficient to obtain the corrected chain-level performance, expressed as: ； In the formula For the work chain and work chain The combined effectiveness after collaboration The cooperative gain coefficient is not less than 1. and These are the work chains that participate in the collaboration. and work chain Chain-level efficiency; For a superedge formed by competing relationships, the chain-level efficiency of the work chain is multiplied by the competition loss factor to obtain the corrected chain-level efficiency, expressed as: ； In the formula For the work chain Corrected chain-level performance The competition loss coefficient has a value between 0 and 1.

5. The equipment system reliability assessment method according to claim 1, characterized in that, The method further includes a step of determining a chain group coupling metric for the target object. The chain group coupling metric includes a hyperedge conflict index and a hyperedge redundancy. The expression for the hyperedge conflict index is: ； In the formula This is a hyperborder conflict index. For a hyperedge consisting of competitive relationships, For the work chain The time spent waiting due to resources being occupied. For task window time, To determine the number of job chains contained within a competing superedge; The expression for the hyperedge redundancy is: ； In the formula For the hyperedge redundancy of the target object, This represents the total number of all feasible operation chains for the target object. The number of job chains that are targeted at the target object and belong to the backup superedge. For the work chain Chain-level efficiency.

6. The equipment system reliability assessment method according to claim 1, characterized in that, The system reliability mentioned in step S5 refers to the task reliability of the equipment system in completing all target object operations within the task window. The task reliability is determined by the following formula: ； In the formula For mission reliability, For the set of all target objects, For the target object The set of all feasible operation chains, For the work chain The revised chain-level performance.

7. The equipment system reliability assessment method according to claim 1, characterized in that, In step S6, the critical task chain is determined based on the hyperedge connectivity of the task chain in the multi-layer network. The hyperedge connectivity is determined by the following formula: ； In the formula For the work chain The hyperedge connectivity, For the work chain With the work chain The total number of shortest paths between information points. The total number of shortest paths for information that pass through the job chain The number of paths.

8. The equipment system reliability assessment method according to claim 1, characterized in that, The method further includes the step of determining the performance collapse threshold of the equipment system, and outputting an early warning when the failure rate of the equipment system exceeds the performance collapse threshold, wherein the performance collapse threshold is determined by the following formula: ； In the formula The performance crash threshold is a value between 0 and 1. For the job chain to act as a supernode, the expiration is performed; For average transcendence; The second moment of the hypermarginality reflects the degree of dispersion of the hypermarginality distribution; This represents the number of job chains contained in a single hyperedge; The average cardinality of the superedges reflects the average size of the relationships between chains; The second moment of the hyperedge cardinality reflects the degree of existence of large-scale cooperative groups.

9. The equipment system reliability assessment method according to claim 1, characterized in that, The method further includes a relationship failure assessment step: under the premise that the nodes and the connection relationships remain physically available, the relationship between the work chains represented by a portion of the super-edges is made to fail according to a predetermined ratio, and the corrected chain-level performance and the system reliability are re-determined based on the failed super-edges to obtain the reliability of the equipment system under the relationship failure mode.

10. The equipment system reliability assessment method according to claim 1, characterized in that, In step S3, for a hyperedge formed by temporal relationships, the executable probability of the next task chain is determined based on the success probability of the previous task chain and the conditional probability of the next task chain succeeding given the success of the previous task chain, expressed as follows: In the formula This represents the probability of execution for the next task chain. and These are random variables representing the task results of the previous and next task chains, respectively. A value of 1 indicates that the corresponding task chain is successful, and a value of 0 indicates that the corresponding task chain is unsuccessful. For a hyperedge formed by backup relationships, the probability of successfully handling the target object is determined by the product of the failure probabilities of each backup job chain, denoted as: In the formula This represents the coverage probability that the target object is successfully processed. Let A be the set of job chains contained within a hyperedge formed by backup relationships. For the work chain The revised chain-level performance.

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