A method, system, medium and program product for generating an operation and maintenance mode analysis model

Abstract

A method, system, medium, and program product for generating an operation and maintenance mode analysis model relate to the field of electrical digital data processing. In this method, equipment operation data is obtained; a basic equipment life model is established based on equipment failure time data and non-failure working time data; an environmental correction coefficient is calculated based on equipment operation environment parameter data and preset environmental impact factors, and the environmental correction coefficient is combined with the basic life model to obtain a revised life prediction model; the correlation between each equipment node is calculated based on the equipment topology structure, and an equipment importance evaluation model is constructed; equipment maintenance cost, replacement cost, and downtime loss are calculated based on the prediction results of the revised life prediction model to obtain a cost loss model; the output results of the equipment importance evaluation model are weighted and fused with the calculation results of the cost loss model to generate an operation and maintenance mode analysis model. This application improves the scientific nature and accuracy of operation and maintenance mode analysis.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to a method, system, medium and program product for generating an operation and maintenance mode analysis model. Background Art

[0002] During the operation and maintenance process, equipment system O&M management faces significant challenges due to the multi-source, heterogeneous, massive, and complex nature of O&M data. Traditional O&M management models consume significant manpower and financial resources, resulting in energy waste and direct economic losses. Furthermore, they are chaotic and lack security, failing to implement comprehensive analysis of energy stations, energy efficiency assessment, and coordinated O&M management.

[0003] In related technologies, it is possible to collect equipment failure time data and actual working time data of non-faulty parts, calculate Weibull life distribution parameters using maximum estimation and failure rate functions, and combine component failure cost model analysis to determine operation and maintenance costs, parts replacement costs, and indirect cost losses caused by downtime, thereby realizing the prediction and management of equipment operation and maintenance costs.

[0004] However, due to the complexity and uncertainty of equipment operating environments, relying solely on equipment lifespan and cost factors to make O&M decisions has limitations. On the one hand, equipment operating conditions are affected by a variety of external factors, such as ambient temperature and load variations, which can lead to deviations in equipment lifespan predictions. On the other hand, different equipment differs in their importance within the system and their impact on life safety. O&M decisions based solely on lifespan and cost can neglect the timely maintenance of critical equipment, increasing system operational risks and reducing the scientific nature and accuracy of O&M model analysis. Summary of the Invention

[0005] This application provides a method, system, medium and program product for generating an operation and maintenance mode analysis model, which is used to address the limitations of related technologies when making operation and maintenance decisions and improve the scientificity and accuracy of operation and maintenance mode analysis.

[0006] In a first aspect, the present application provides a method for generating an operation and maintenance mode analysis model to obtain equipment operation data, the operation data including equipment failure time data, equipment non-failure working time data, and equipment operation environment parameter data;

[0007] Based on the equipment failure time data and non-failure working time data, the equipment basic life model is established using Weibull distribution;

[0008] Calculate the environmental correction coefficient based on the equipment operating environment parameter data and the preset environmental impact factors, and combine the environmental correction coefficient with the basic life model to obtain a revised life prediction model;

[0009] Calculate the correlation of each device node based on the device topology structure, and build an equipment importance evaluation model based on the impact of equipment failure on system operation and personnel safety;

[0010] Calculate equipment maintenance cost, replacement cost and downtime loss based on the prediction results of the revised life prediction model to obtain a cost loss model;

[0011] The output results of the equipment importance evaluation model are weighted and fused with the calculation results of the cost loss model to generate an operation and maintenance mode analysis model.

[0012] By adopting the above technical solution, by obtaining equipment operation data and establishing a basic life model of Weibull distribution, combined with the environmental correction coefficient, a more accurate life prediction model is obtained, which can reflect the impact of the actual operating environment on the equipment life. The importance evaluation model constructed based on the correlation calculated based on the equipment topology structure and considering the impact of equipment failure can comprehensively evaluate the importance of the equipment in the system. The cost loss model obtained by calculating maintenance costs, replacement costs and downtime losses can quantify the economic impact of equipment failure. The operation and maintenance mode analysis model generated by weighted fusion of the importance evaluation model and the cost loss model takes into account the system importance of the equipment, making operation and maintenance decisions more scientific and reasonable. It can achieve optimal cost configuration while ensuring system reliability, and improve the scientificity and accuracy of operation and maintenance mode analysis.

[0013] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the environmental correction coefficient based on the device operating environment parameter data and the preset environmental impact factor specifically includes:

[0014] Segmenting the equipment operating environment parameter data according to the time series to obtain multiple environment parameter data segments;

[0015] Calculating the deviation value from the preset standard environmental parameter for each environmental parameter data segment;

[0016] Calculate the correction value corresponding to each environmental parameter based on the deviation value and the preset environmental impact factor;

[0017] The environmental correction coefficient is obtained by weighted summing the correction values ​​corresponding to each environmental parameter.

[0018] By adopting the above technical solution, the equipment operating environment parameter data is segmented and processed according to time series, which can reflect the dynamic changes of environmental parameters over time. By calculating the deviation between the environmental parameters and the preset standard environmental parameters, the degree of difference between the actual operating environment and the standard environment can be quantified. Based on the deviation value and the preset environmental impact factor, the corresponding correction value of each environmental parameter is calculated to reflect the weight of the impact of different environmental parameters on the equipment life. The environmental correction coefficient, obtained by weighted summation of the correction values ​​corresponding to each environmental parameter, comprehensively considers the coupling effects of multiple environmental factors, making the life prediction model closer to actual operating conditions, improving prediction accuracy, and providing a more reliable basis for equipment management and maintenance decisions.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the association degree of each device node based on the device topology structure specifically includes:

[0020] Construct a topological diagram of the device system. The nodes in the topological diagram represent devices, and the edges represent the connection relationships between devices.

[0021] Calculate the degree centrality, betweenness centrality, and closeness centrality of each node;

[0022] Construct node importance evaluation indicators based on degree centrality, betweenness centrality and closeness centrality;

[0023] The correlation degree of each device node is calculated based on the node importance evaluation index.

[0024] By employing the above technical solution, a topological diagram of the device system is constructed, visually displaying the connectivity between devices and the system structure. By calculating the degree centrality, betweenness centrality, and closeness centrality of nodes, the position and role of devices in the network are quantified from different perspectives. A node importance evaluation index constructed based on multiple centrality metrics comprehensively reflects the structural importance of devices in the system. The device node correlation calculated using the node importance evaluation index accurately reflects the propagation path and impact range of device failures, helping to identify key devices and weak links in the system.

[0025] In conjunction with some embodiments of the first aspect, in some embodiments, after weighted fusion of the output results of the equipment importance evaluation model and the calculation results of the cost loss model to generate the operation and maintenance mode analysis model, the method further includes:

[0026] Based on the equipment life cycle, the operation phase is divided into the following stages: the running-in period, the stabilization period, and the aging period.

[0027] Calculate the equipment state transfer matrix in each operation stage. The state transfer matrix represents the transition probability between different states.

[0028] Identify the dominant failure mode in each operation stage based on the state transition matrix;

[0029] The operation and maintenance mode analysis model is modified in sections based on the dominant failure mode to obtain a modified operation and maintenance mode analysis model.

[0030] By adopting the above technical solution, the operating stages are divided according to the equipment lifecycle, and the dividing points between the running-in period, stabilization period, and aging period are identified, reflecting the evolution of equipment performance over time. The state transition matrix within each operating stage is calculated, and the probability of the equipment transitioning between different states is quantified, reflecting the dynamic change characteristics of the equipment state. The dominant failure mode in each operating stage is identified through the state transition matrix, revealing the typical failure forms in different stages. Based on the dominant failure mode, the operation and maintenance mode analysis model is segmented and revised to make the model better adapt to the characteristics of the equipment at different lifecycle stages, improve the pertinence and effectiveness of operation and maintenance decisions, and achieve the rational allocation of operation and maintenance resources.

[0031] In conjunction with some embodiments of the first aspect, in some embodiments, dividing the operation phases based on the device life cycle specifically includes:

[0032] Obtain historical fault data and operating parameter data of the equipment;

[0033] Construct the phase space of equipment operation trajectory, the dimensions of which include the time between failures, operating parameter deviation, and maintenance response time;

[0034] Calculate the trajectory attractor in the phase space, which represents the stable state set of the device operation;

[0035] The stage dividing points are identified based on the morphological changes of trajectory attractors, and the operation stages are divided according to the stage dividing points.

[0036] By adopting the above technical solution, which constructs a phase space of equipment operation trajectories and calculates trajectory attractors to divide the operation phases, it is possible to mathematically characterize the dynamic evolution of the equipment's operating state. Trajectory attractors in the phase space reflect the equipment's stable operating mode at different stages. By analyzing the morphological changes of trajectory attractors, the location of the stage demarcation points can be accurately identified. Incorporating time between failures, operating parameter deviations, and maintenance response time as phase space dimensions improves the accuracy and reliability of stage division. This provides a more efficient time scale for state transition analysis and failure mode identification, allowing the entire operation and maintenance mode analysis to more closely reflect the actual operating characteristics of the equipment.

[0037] In conjunction with some embodiments of the first aspect, in some embodiments, after performing segmented correction on the operation and maintenance mode analysis model based on the dominant failure mode to obtain the corrected operation and maintenance mode analysis model, the method further includes:

[0038] Construct a high-dimensional feature space based on equipment operation data;

[0039] Calculate the distribution of singular points in high-dimensional feature space. Singular points represent the mutation locations of the system state.

[0040] Analyze the topological structure of singular points and identify critical paths;

[0041] Construct a mutation warning mechanism based on the critical path.

[0042] By adopting the above technical solution, which constructs a mutation warning mechanism by analyzing the distribution characteristics of singular points in a high-dimensional feature space, early warning of potential equipment failures is achieved. This high-dimensional feature space contains multidimensional information about equipment operation and can more comprehensively characterize the system state. Singular points, as locations where system states suddenly change, have a distribution pattern that reflects the critical state where equipment failure may occur. By analyzing the topological structure of these singular points and identifying critical paths, the main patterns of system state evolution can be understood. This mutation warning mechanism, based on critical paths, has a strong theoretical foundation and can promptly issue warning signals when the system approaches a critical state, thereby reducing the probability of unexpected equipment failures.

[0043] In conjunction with some embodiments of the first aspect, in some embodiments, analyzing the topological structure of the singularity points and identifying the critical path specifically includes:

[0044] Construct the homotopy group of singular points, which describes the connectivity relationship between singular points;

[0045] Calculate the fundamental group based on the homotopy group, which represents the topological invariant of the connectivity relationship;

[0046] Mapping the fundamental group to an algebraic torus, which preserves the topological invariant properties;

[0047] Calculate the genus of an algebraic torus, which represents the complexity of the topological structure;

[0048] The path with the minimum complexity is selected as the critical path based on the genus number.

[0049] By employing the above technical solution, which constructs homotopy groups of singular points and utilizes algebraic topology to identify critical paths, the optimality of the identified paths is ensured. The homotopy group describes the connectivity between singular points, while the fundamental group extracts the topological invariants of this connectivity. Mapping the fundamental group to an algebraic torus preserves the topological invariants and simplifies computational complexity. The complexity of the topological structure is characterized by calculating the genus of the algebraic torus, and the path with the minimum complexity is selected as the critical path. This ensures that the selected path has the simplest topological structure, improving the accuracy and interpretability of mutation warnings.

[0050] In the second aspect, an embodiment of the present application provides an operation and maintenance mode analysis model generation system, which operation and maintenance mode analysis model generation system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0051] In a third aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which, when executed on a system, enables the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0052] In a fourth aspect, an embodiment of the present application provides a computer program product, which, when executed on a system, enables the system to execute the method described in any possible implementation manner in the first aspect.

[0053] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0054] 1. The present application provides a method for generating an operation and maintenance mode analysis model. By obtaining equipment operation data and establishing a basic life model of the Weibull distribution, a more accurate life prediction model is obtained by combining the environmental correction coefficient, which can reflect the impact of the actual operating environment on the equipment life. The importance evaluation model constructed based on the equipment topology calculation correlation and the degree of impact of equipment failure can comprehensively evaluate the importance of the equipment in the system. The cost loss model obtained by calculating the maintenance cost, replacement cost and downtime loss can quantify the economic impact of equipment failure. The operation and maintenance mode analysis model generated by weighted fusion of the importance evaluation model and the cost loss model takes into account the system importance of the equipment, making the operation and maintenance decision more scientific and reasonable, and can achieve optimal cost configuration while ensuring system reliability, thereby improving the scientificity and accuracy of the operation and maintenance mode analysis.

[0055] 2. This application provides a method for generating an operation and maintenance mode analysis model, which divides the operation stages based on the equipment life cycle, identifies the dividing points of the running-in period, the stabilization period, and the aging period, and reflects the evolution of equipment performance over time. The state transition matrix in each operation stage is calculated, the probability of the equipment transitioning between different states is quantified, and the dynamic change characteristics of the equipment state are reflected. The dominant failure mode of each operation stage is identified through the state transition matrix, and the typical failure forms of different stages are revealed. The operation and maintenance mode analysis model is segmented and corrected based on the dominant failure mode, so that the model can better adapt to the characteristics of the equipment at different life cycle stages, improve the pertinence and effectiveness of operation and maintenance decisions, and realize the rational allocation of operation and maintenance resources.

[0056] 3. The present application provides a method for generating an operation and maintenance mode analysis model, which constructs a technical solution for a mutation warning mechanism by analyzing the distribution characteristics of singular points in a high-dimensional feature space, thereby realizing early warning of potential equipment failures. The high-dimensional feature space contains multi-dimensional information of equipment operation and can more comprehensively characterize the system state. Singular points are the locations where system states mutate, and their distribution patterns reflect the critical state where equipment failures may occur. By analyzing the topological structure of these singular points and identifying critical paths, the main laws of system state evolution can be mastered. The mutation warning mechanism constructed based on critical paths has a strong theoretical basis and can issue warning signals in a timely manner when the system approaches a critical state, thereby reducing the probability of unexpected equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a method for generating an operation and maintenance mode analysis model in an embodiment of the present application.

[0058] Figure 2 It is a flow chart of a method for improving an operation and maintenance mode analysis model in an embodiment of the present application.

[0059] Figure 3 This is a schematic diagram of the physical device structure of an operation and maintenance mode analysis model generation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0061] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0062] The following uses an embodiment and combines Figure 1 , a method for generating an operation and maintenance mode analysis model in an embodiment of the present application is described:

[0063] See also Figure 1, which is a flow chart of a method for generating an operation and maintenance mode analysis model in an embodiment of the present application.

[0064] S101, obtaining equipment operation data;

[0065] The system obtains equipment operation data, which includes equipment failure time data, equipment non-failure working time data, and equipment operation environment parameter data.

[0066] In this step, the system uses various data collection methods to obtain device operating data. This data includes device failure time data, device non-fault operating time data, and device operating environment parameter data. Device failure time data records the specific time when the device failure occurred and can be used to analyze device failure patterns and lifespan characteristics. Device non-fault operating time data records the length of time the device operates under normal operating conditions and can be used to assess device reliability and stability. Device operating environment parameter data records various environmental parameters of the device, such as temperature, humidity, and vibration, which may affect the device's operating status.

[0067] The system can obtain equipment operating data in a variety of ways. One method is to install various sensors and monitoring devices on the equipment to collect real-time operating parameters and status information, and transmit this data to a data center for storage and analysis. Another method is to obtain operating data through the equipment's own diagnostic and logging systems. Many modern equipment have built-in specialized diagnostic and logging modules that can record detailed equipment operating conditions and fault information. Furthermore, the system can obtain equipment operating data through manual inspection and recording. For example, maintenance personnel can regularly inspect the equipment and record its operating conditions and the time when faults occur.

[0068] S102. Based on the equipment failure time data and non-failure working time data, establish a basic equipment life model using Weibull distribution;

[0069] In this step, the system uses the equipment's failure time data and non-failure operating time data to establish a basic life model for the equipment using the Weibull distribution. The Weibull distribution is a commonly used life distribution model that effectively describes the failure patterns and life characteristics of equipment. By analyzing the equipment's failure time data and non-failure operating time data, the system estimates the parameters of the Weibull distribution, including the shape parameter and scale parameter. The shape parameter determines the trend of the equipment's failure rate, while the scale parameter determines the average life of the equipment. Based on these estimated parameters, the system establishes a basic life model for the equipment, which is used to predict the equipment's remaining life and failure probability.

[0070] To establish a Weibull distribution life model, the system needs to process and analyze the equipment's failure time data and non-failure operating time data. First, the system screens and cleans the data based on its completeness and reliability, eliminating incomplete or erroneous data records. Then, the system performs statistical analysis on the data, calculating statistics such as the number of equipment failures, the time between failures, and the failure rate. Based on these statistics, the system can estimate the shape and scale parameters of the Weibull distribution using methods such as maximum likelihood estimation or least squares estimation. Once the parameter estimates are obtained, the system can establish a Weibull distribution life model for equipment life prediction and reliability analysis.

[0071] In the process of establishing a Weibull distribution life model, some technical problems may be encountered, such as the accuracy of parameter estimation and model applicability. To address these problems, the system can use a variety of estimation methods, such as Bayesian estimation and Bootstrap estimation, to improve the accuracy and robustness of parameter estimation. When selecting a model, the system can select an appropriate life distribution model, such as exponential distribution and lognormal distribution, based on the failure mechanism and data characteristics of the equipment. In addition, the system can also use a hierarchical modeling method for life data to establish a hierarchical life model based on the different operating conditions and environmental conditions of the equipment, thereby improving the model's applicability and prediction accuracy. By optimizing the parameter estimation method and rationalizing the model selection, the system can establish a more accurate and reliable basic life model.

[0072] S103, calculating an environmental correction coefficient based on the equipment operating environment parameter data and preset environmental impact factors, and combining the environmental correction coefficient with the basic life model to obtain a corrected life prediction model;

[0073] The system calculates the environmental correction coefficient based on the equipment operating environment parameter data and the preset environmental impact factors, specifically including: segmenting the equipment operating environment parameter data according to the time series to obtain multiple environmental parameter data segments; calculating the deviation value of each environmental parameter data segment from the preset standard environmental parameter; calculating the correction value corresponding to each environmental parameter based on the deviation value and the preset environmental impact factor; performing weighted summation on the correction value corresponding to each environmental parameter to obtain the environmental correction coefficient; and combining the environmental correction coefficient with the basic life model to obtain a revised life prediction model.

[0074] In this step, the system takes into account the impact of the equipment operating environment on the equipment life, and corrects the basic life model by introducing an environmental correction coefficient to obtain a more accurate life prediction model. The equipment operating environment parameter data reflects the various characteristics of the environment in which the equipment is located, such as temperature, humidity, vibration, etc. These environmental factors may accelerate or slow down the aging and failure process of the equipment, thereby affecting the actual life of the equipment. The preset environmental impact factor is a pre-set weight of the impact of various environmental factors on the equipment life based on prior knowledge and historical experience. Based on the equipment operating environment parameter data and the preset environmental impact factor, the system calculates the environmental correction coefficient to quantify the degree of impact of environmental factors on the equipment life. Combining the environmental correction coefficient with the basic life model can obtain a revised life prediction model, which not only takes into account the reliability characteristics of the equipment itself, but also takes into account the impact of environmental factors, and can more accurately predict the actual life of the equipment.

[0075] When calculating the environmental correction coefficient, the system first segments the equipment operating environment parameter data according to the time series to obtain multiple environmental parameter data segments. Each segment corresponds to a time window, reflecting the average state of the environment in which the equipment is located within the time window. Then, the system calculates the deviation value of each environmental parameter data segment from the preset standard environmental parameter. The larger the deviation value, the greater the difference between the environment in which the equipment is located and the standard environment. Based on the deviation value and the preset environmental impact factor, the system calculates the correction value of each environmental parameter for the equipment life. Finally, the correction value of each environmental parameter is weighted and summed to obtain a comprehensive environmental correction coefficient. If the environmental correction coefficient is greater than 1, it means that the environmental factors have accelerated the aging and failure of the equipment, and if it is less than 1, it means that the aging and failure have been slowed down. By multiplying the environmental correction coefficient with the basic life model, a corrected life prediction model can be obtained.

[0076] When introducing environmental correction factors, technical challenges may arise, such as the rationality of environmental parameter selection and the accuracy of environmental impact factor settings. To address these issues, the system can employ feature selection and dimensionality reduction when selecting environmental parameters, identifying the subset of environmental parameters that most significantly impact equipment lifespan. When setting environmental impact factors, the system can employ a data-driven approach, using machine learning algorithms to automatically learn and optimize the influence weights of each environmental parameter from historical data, thereby improving the accuracy of pre-set environmental impact factors.

[0077] S104. Calculate the correlation of each device node based on the device topology, and construct a device importance evaluation model based on the impact of device failure on system operation and on personnel safety;

[0078] The system calculates the correlation of each device node based on the device topology structure, specifically including: constructing a topological relationship diagram of the device system, where nodes in the topological relationship diagram represent devices and edges represent the connection relationship between devices; calculating the degree centrality, betweenness centrality and closeness centrality of each node; constructing node importance evaluation indicators based on degree centrality, betweenness centrality and closeness centrality; calculating the correlation of each device node based on the node importance evaluation indicators; and constructing an equipment importance evaluation model based on the impact of equipment failure on system operation and the impact on personnel safety.

[0079] In this step, the system analyzes the device topology and the impact of failures to construct an equipment criticality evaluation model. This model is used to assess the importance of each device node to system operation and personnel safety. The device topology reflects the connectivity and dependencies between device nodes within the system. By calculating the correlation between device nodes, the importance of each node in the system can be quantitatively assessed. A node with a higher correlation indicates a more critical role in the system and a greater impact on system operation. The system also considers the impact of a device failure on system operation and personnel safety. Device failures can cause system malfunctions, performance degradation, or even complete system failure, impacting normal system operation. Failures in critical equipment can also endanger personnel safety. Therefore, the severity of the impact of a device failure is also a key factor in evaluating device criticality. The system constructs the device criticality evaluation model by comprehensively considering the correlation between device nodes, the impact of a failure on system operation, and the impact on personnel safety.

[0080] To calculate the relevance of device nodes, the system first constructs a topological diagram of the device system. Nodes in the diagram represent devices, and edges represent connections between devices. Based on this topological diagram, the system calculates centrality metrics for each node, such as degree centrality, betweenness centrality, and closeness centrality. Degree centrality reflects the number of direct connections a node has. A higher degree centrality indicates a node's local importance. Betweenness centrality reflects a node's intermediary role in the network. A higher betweenness centrality indicates a node's global importance. Closeness centrality reflects the distance between a node and other nodes. A higher closeness centrality indicates a node's accessibility within the network. Based on these centrality metrics, the system constructs a node importance evaluation index and calculates the comprehensive relevance of each device node using a weighted combination. To quantify the impact of device failures, the system categorizes and quantifies the severity of device failures based on historical data and expert experience, taking into account both the impact on system operation and the threat to personnel safety. The node relevance, failure impact score, and safety score are weighted and summed to produce a comprehensive device importance evaluation value.

[0081] In the process of building a device importance evaluation model, some technical problems may be encountered, such as the problem of dynamic changes in topology structure and the subjectivity of fault impact assessment. To address the problem of dynamic changes in topology structure, the system can introduce an incremental calculation method. When the device topology changes, only the association of the affected nodes is updated, rather than recalculating the association of the entire network, thereby improving calculation efficiency. To address the subjectivity of fault impact assessment, the system can use methods such as hierarchical analysis and the Delphi method to integrate the opinions of multiple experts and reduce the subjective bias of the evaluation results. In addition, the system can also introduce a dynamic weight adjustment mechanism. According to the real-time information of the equipment operating status and the occurrence of faults, the weight of the fault impact score and the node association can be dynamically adjusted, so that the importance evaluation results can adapt to the dynamic changes of the equipment system.

[0082] S105, calculating equipment maintenance cost, replacement cost, and downtime loss based on the prediction results of the revised life prediction model to obtain a cost loss model;

[0083] In this step, the system uses the revised lifespan prediction model developed earlier to predict the remaining lifespan of the equipment. Based on the predictions, the system calculates the maintenance and replacement costs under different maintenance scenarios, as well as the losses caused by equipment downtime. Ultimately, it develops a cost-loss model that comprehensively considers various cost factors. This model can quantify the economic benefits of different O&M strategies and provide a basis for subsequent O&M optimization.

[0084] The system can implement this step in a variety of ways. First, based on historical equipment maintenance and downtime data, the system can use machine learning algorithms (such as neural networks and support vector machines) to establish equipment maintenance cost models and downtime loss models. This model can then be combined with lifespan prediction results to develop a complete cost loss model. Second, the system can also incorporate market price information for equipment maintenance and replacement, combined with factors such as equipment criticality and failure rate, and use methods such as Monte Carlo simulation to estimate expected cost losses under different scenarios, ultimately generating a cost loss model.

[0085] A new technical challenge that may arise during this step is how to properly balance the relationship between short-term maintenance costs and long-term replacement costs and downtime losses in the cost-loss model. To address this, the system can design a multi-objective optimization model, introducing a discount factor to balance short-term and long-term costs while also considering factors such as equipment importance and the impact of failures on production. By solving this optimization model, the optimal cost-loss weight distribution scheme is obtained, which is used to guide the generation of the cost-loss model.

[0086] S106: Perform weighted fusion on the output result of the equipment importance evaluation model and the calculation result of the cost loss model to generate an operation and maintenance mode analysis model.

[0087] The goal of this step is to comprehensively consider equipment criticality and economic cost-effectiveness, generating an analytical model to guide equipment operation and maintenance decisions. The system first normalizes the output of the equipment criticality evaluation model and the calculation results of the cost loss model, ensuring that their dimensions are consistent and within a similar numerical range. The system then linearly weights the two together using a specific weight ratio to generate a comprehensive score for each device or device combination. The weighting parameters can be adjusted based on the company's maintenance strategy and risk appetite, optimizing resource allocation by balancing criticality with cost-benefit.

[0088] The key to this step is designing a reasonable weighted fusion mechanism. The system can employ common multi-attribute decision-making methods, such as the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation, to determine the weight coefficients of equipment importance and cost impact in decision-making. Furthermore, the system can incorporate game theory, viewing equipment O&M decisions as a multi-stakeholder game. By solving Nash equilibrium or minimum-maximum regret equations, a balanced and robust weight distribution scheme can be determined that balances equipment reliability and maximizes cost-effectiveness.

[0089] One technical challenge that may arise during the fusion process is how to handle inconsistent or even contradictory assessments of importance and cost. For example, a device might have a high importance but also a high maintenance cost, or a device might have a low importance but a significant maintenance benefit. To address this, the system can introduce a nonlinear function during fusion. This weighting can be assigned to devices with significant discrepancies between importance and cost-benefit, highlighting their special role in maintenance decisions. Devices with moderately high importance and cost-benefit scores can be assigned a moderate weighting to prevent extreme values ​​from dominating their scores.

[0090] In the above embodiment, by obtaining equipment operation data and establishing a basic life model of Weibull distribution, a more accurate life prediction model is obtained by combining the environmental correction coefficient, which can reflect the impact of the actual operating environment on the equipment life. The importance evaluation model constructed based on the equipment topology structure calculates the correlation and considers the impact of equipment failure, which can comprehensively evaluate the importance of the equipment in the system. The cost loss model obtained by calculating the maintenance cost, replacement cost and downtime loss can quantify the economic impact of equipment failure. The operation and maintenance mode analysis model generated by weighted fusion of the importance evaluation model and the cost loss model takes into account the system importance of the equipment, making the operation and maintenance decision more scientific and reasonable, and can achieve optimal cost configuration while ensuring system reliability, thereby improving the scientificity and accuracy of the operation and maintenance mode analysis.

[0091] In the above embodiment, after the operation and maintenance mode analysis model is generated, the equipment often exhibits different performance characteristics and failure patterns in different operation stages. In order to more accurately characterize the dynamic characteristics of the equipment at different life cycle stages and further improve the accuracy of the operation and maintenance mode analysis, the following is combined with Figure 2 , describes an improved method for an operation and maintenance mode analysis model in an embodiment of the present application:

[0092] See also Figure 2 , which is a flow chart of a method for improving an operation and maintenance mode analysis model in an embodiment of the present application.

[0093] S201, dividing the operation phases based on the equipment life cycle to obtain the demarcation points of the three phases: the running-in phase, the stabilization phase, and the aging phase;

[0094] The system divides the operation phases based on the equipment life cycle and obtains the demarcation points of the three phases: the running-in period, the stabilization period, and the aging period. Specifically: historical fault data and operating parameter data of the equipment are obtained;

[0095] Construct the phase space of equipment operation trajectory, the dimensions of which include the time between failures, operating parameter deviation, and maintenance response time;

[0096] Calculate the trajectory attractor in the phase space, which represents the stable state set of the device operation;

[0097] The stage dividing points are identified based on the morphological changes of trajectory attractors, and the operation stages are divided according to the stage dividing points.

[0098] In this step, the system analyzes the operational characteristics of the equipment throughout its lifecycle and divides it into three typical phases: the run-in period, the stabilization period, and the aging period. The run-in period refers to the initial period of operation when the equipment's performance and reliability are unstable and the failure rate is relatively high. The stabilization period is when the equipment's performance stabilizes and the failure rate remains relatively low. The aging period is when the equipment has been in operation for a long time, its components begin to age and degrade, and the failure rate gradually increases. By identifying the dividing points between these three phases, the system can more precisely characterize the dynamic behavior of the equipment and optimize the operation and maintenance strategy in a targeted manner.

[0099] To implement this step, the system first obtains the equipment's historical fault data and operating parameter data, such as vibration, temperature, and current. The system then constructs a multidimensional phase space of the equipment's operating trajectory, using key indicators such as fault interval time, operating parameter deviation, and maintenance response time as the coordinate axes of the phase space. Within this phase space, the equipment's operating trajectory forms an attractor, reflecting its dynamic characteristics. By analyzing the attractor's morphological changes, such as the size and distribution density of the attractor's domain, the system can identify the time points when the equipment's state undergoes significant changes, thereby determining the dividing points between the running-in period, the stabilization period, and the aging period. When dividing the phases, the system can also utilize data mining techniques such as cluster analysis and principal component analysis to comprehensively consider the influence of multiple indicators and improve the accuracy of the division.

[0100] A potential technical issue in this step is how to deal with the differences in operating characteristics between different devices. To address this, when constructing the phase space, the system can select appropriate indicator systems and data normalization methods for different types and models of equipment to ensure that the phase space objectively reflects the actual status of the equipment. In addition, the system can also introduce adaptive thresholds and dynamic update mechanisms to timely adjust the stage division model based on the historical performance and current operating parameters of the equipment, and use incremental learning algorithms to optimize the identification rules of the demarcation points, thereby realizing a continuously evolving and self-improving stage division framework to meet the needs of equipment lifecycle management.

[0101] S202, calculating the device state transfer matrix in each operation stage;

[0102] The system calculates the state transfer matrix of the equipment in each operation stage. The state transfer matrix represents the transition probability between different states.

[0103] The purpose of this step is to characterize the dynamic reliability of the equipment by analyzing its state transition patterns within different operating phases. The system first discretizes the equipment's operating states into typical states, such as normal, deceleration, shutdown, and maintenance. The system then statistically analyzes the transition frequencies and probabilities between each state to generate a state transition matrix. Each element in the matrix represents the probability of transitioning from one state to another. This calculation allows the system to understand the distribution patterns and evolution trends of the equipment's states at different stages, providing valuable guidance for optimizing operation and maintenance decisions and formulating maintenance plans.

[0104] To implement this step, the system can use a Markov chain model to describe the device state transition process. A Markov chain is a typical stochastic process model that assumes that the state at the next moment is related only to the current state, and not to past states. This memoryless nature aligns well with the actual operation of the device. The system can use methods such as maximum likelihood estimation and Bayesian estimation to learn state transition probabilities based on the device's historical state sequence. When estimating parameters, the system can also consider introducing smoothing techniques to mitigate the impact of data sparsity. Furthermore, to characterize the temporal dynamics of state transitions, the system can use more complex sequence models such as hidden Markov models (HMMs) to capture the underlying mechanisms of device degradation and failure through hidden states.

[0105] S203, identifying the dominant failure mode of each operation stage according to the state transition matrix;

[0106] In this step, the system uses the state transition matrix obtained in the previous step to further analyze the dominant failure modes of the equipment during each operational phase. A dominant failure mode is the primary cause of the equipment's ultimate failure. By identifying the dominant failure modes at different stages, the system can develop more targeted operation and maintenance strategies, preventing or delaying failures, thereby improving equipment reliability and availability.

[0107] The system can implement this step using a variety of techniques. First, based on the state transition matrix, the system can calculate the transition probability from the normal state to the failed state, sort the transition paths, identify the state transition sequence most likely to lead to failure, and then infer the dominant failure mode. Second, the system can incorporate Markov Chain Monte Carlo (MCMC) simulation technology to simulate the random process of equipment state evolution, statistically analyze the triggering factors and transition paths of the failure state, and use this to determine the dominant failure mode. Third, the system can also utilize reliability modeling methods such as fault tree analysis (FTA) and event tree analysis (ETA). By comprehensively considering the quantitative information provided by the state transition matrix and the qualitative knowledge provided by expert experience, the system can infer the failure propagation mechanism and influencing factors, thereby comprehensively characterizing the failure behavior of the equipment.

[0108] When identifying dominant failure modes, the system may face the challenges of complex failure mechanisms and diverse influencing factors. Some failure modes may be caused by the combination of multiple states, exhibiting certain correlations and temporal sequences. To address this issue, the system can employ data-driven methods such as association rule mining and temporal pattern recognition to discover frequent patterns and dependencies in the state transition matrix. These methods can then be combined with knowledge of physical mechanisms to construct a multi-state failure mechanism map. Furthermore, the system can apply sensitivity analysis techniques to calculate the weight of the impact of state transition probabilities on failures, identifying the weak links that contribute most to failure. The system can also introduce a competitive failure model to consider the competition and coupling effects between different failure mechanisms, improving the accuracy of failure prediction. By employing these strategies, the system can summarize and extract key failure modes at each stage from massive amounts of operational data, and use this to guide subsequent optimization of operational decisions.

[0109] S204: Modify the operation and maintenance mode analysis model in sections based on the dominant failure mode to obtain a modified operation and maintenance mode analysis model.

[0110] The core of this step is to locally adjust and optimize the O&M analysis model based on the dominant failure modes at different stages, enabling it to more accurately guide phased O&M decisions. Because failure patterns and influencing factors vary across equipment lifecycle stages, using a unified analysis model is difficult to achieve satisfactory results. However, through segmented corrections, the system can fully leverage the unique prior knowledge of each stage, improving the accuracy of local predictions while ensuring the consistency and continuity of the global model.

[0111] To achieve segmented correction, the system first divides the original operation and maintenance mode analysis model into several sub-models, each of which corresponds to an operation stage. Then, the system makes targeted corrections to the corresponding sub-models based on the dominant failure modes identified in each stage. This correction can be reflected in many aspects: first, adjusting the structure and parameters of the model so that it can more sensitively capture the characteristics of the dominant failure mode; second, introducing new influencing factors and constraints to embed failure mechanism knowledge into the model; third, optimizing the model's objective function and solution algorithm to improve the efficiency and accuracy of local search. During the correction process, the system can also use technologies such as transfer learning and incremental learning to draw on experience and knowledge between different stages to achieve collaborative optimization between sub-models.

[0112] In the above embodiment, the operation phases are divided based on the equipment life cycle, and the dividing points of the running-in period, stabilization period, and aging period are identified, reflecting the evolution of equipment performance over time. The state transition matrix within each operation phase is calculated, and the probability of the equipment transitioning between different states is quantified, reflecting the dynamic change characteristics of the equipment state. The dominant failure mode of each operation phase is identified through the state transition matrix, revealing the typical failure forms of different stages. Based on the dominant failure mode, the operation and maintenance mode analysis model is segmented and corrected, so that the model can better adapt to the characteristics of the equipment at different life cycle stages, improve the pertinence and effectiveness of operation and maintenance decisions, and achieve the rational allocation of operation and maintenance resources.

[0113] Furthermore, in another embodiment, after the system performs segmented correction on the operation and maintenance mode analysis model based on the dominant failure mode to obtain the corrected operation and maintenance mode analysis model, it can also construct a high-dimensional feature space based on the equipment operation data;

[0114] Calculate the distribution of singular points in high-dimensional feature space. Singular points represent the mutation locations of the system state.

[0115] Analyze the topological structure of singular points and identify key paths. Specifically, construct the homotopy group of singular points, which describes the connectivity between singular points.

[0116] Calculate the fundamental group based on the homotopy group, which represents the topological invariant of the connectivity relationship;

[0117] Mapping the fundamental group to an algebraic torus, which preserves the topological invariant properties;

[0118] Calculate the genus of an algebraic torus, which represents the complexity of the topological structure;

[0119] Select the path with the minimum complexity as the critical path based on the genus number;

[0120] Construct a mutation warning mechanism based on the critical path.

[0121] In this embodiment, after obtaining the revised O&M analysis model, the system further mines the underlying information contained in the equipment operation data to construct a high-dimensional feature space that characterizes the complex dynamic behavior of the equipment state. Within this feature space, the system focuses on the distribution pattern of singular points. Singular points are locations where system states experience dramatic or sudden changes, typically corresponding to critical events such as equipment failure and performance degradation.

[0122] To perform topological analysis of singular points, the system first constructs homotopy groups between singular points. A homotopy group is an algebraic structure that describes connectivity, characterizing the equivalence relations between singular points that can be continuously transformed. Using the homotopy group, the system can classify scattered singular points into several connected branches. Then, based on the homotopy group, the system calculates the fundamental group. The fundamental group is an invariant that reflects the topological relationships between connected branches and reveals the skeletal structure of the distribution of singular points. To facilitate the analysis and comparison of the complexity of different topological structures, the system further maps the fundamental group onto an algebraic torus. An algebraic torus is a planar expansion that preserves topological characteristics. By calculating the genus of an algebraic torus, the system can quantitatively assess the complexity of the topological structure. Finally, the system selects the connected path with the smallest genus as the critical path and constructs a mutation warning mechanism based on this path. The critical path corresponds to the most likely trajectory of the device state evolution. Singular points occurring along this path are most likely to trigger system mutations and are therefore the focus of early warning.

[0123] In the above-described embodiment, a technical solution for constructing a mutation warning mechanism by analyzing the distribution characteristics of singular points in a high-dimensional feature space achieves early warning of potential equipment failures. The high-dimensional feature space contains multidimensional information about equipment operation and can more comprehensively characterize the system state. Singular points, as locations where system states suddenly mutate, have distribution patterns that reflect the critical state at which equipment failures may occur. By analyzing the topological structure of these singular points and identifying critical paths, the main patterns of system state evolution can be grasped. This mutation warning mechanism, constructed based on critical paths, has a strong theoretical foundation and can promptly issue warning signals when the system approaches a critical state, reducing the probability of unexpected equipment failures.

[0124] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of an operation and maintenance mode analysis model generation system provided in an embodiment of the present application.

[0125] It should be noted that Figure 3 The structure of the system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0126] like Figure 3As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage unit 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0127] The following components are connected to the I / O interface 305: an input section 306 including a camera, infrared sensor, and the like; an output section 307 including a liquid crystal display (LCD) and speakers; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the media can be installed in the storage section 308 as needed.

[0128] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0129] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take any of a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0131] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiments, or may exist independently and not incorporated into the system. The storage medium carries one or more computer programs, and when executed by a processor of a system, the system implements the methods provided in the above embodiments.

[0132] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0133] As used in the above embodiments, the term “when…” may be interpreted as “if…” or “after…” or “in response to determining…” or “in response to detecting…”, depending on the context. Similarly, the phrases “upon determining…” or “if (stated condition or event) is detected” may be interpreted as “if determining…” or “in response to determining…” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0134] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0135] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for generating an operation and maintenance mode analysis model, characterized in that: include: Acquiring equipment operation data, including equipment failure time data, equipment non-failure working time data, and equipment operation environment parameter data; Based on the equipment failure time data and the non-failure working time data, a basic equipment life model is established using Weibull distribution; Calculating an environmental correction coefficient based on the equipment operating environment parameter data and a preset environmental impact factor, and combining the environmental correction coefficient with the basic life model to obtain a corrected life prediction model; Calculate the correlation of each device node based on the device topology structure, and build an equipment importance evaluation model based on the impact of equipment failure on system operation and personnel safety; Calculating equipment maintenance cost, replacement cost, and downtime loss based on the prediction results of the revised life prediction model to obtain a cost loss model; Performing weighted fusion on the output result of the equipment importance evaluation model and the calculation result of the cost loss model to generate an operation and maintenance mode analysis model; Based on the equipment life cycle, the operation phases are divided into the following points: the running-in phase, the stabilization phase, and the aging phase. Specifically, the following points are obtained: Obtain historical fault data and operating parameter data of the equipment; Constructing a phase space of equipment operation trajectories, wherein the dimensions of the phase space include time between failures, operating parameter deviations, and maintenance response time; calculating a trajectory attractor in the phase space, wherein the trajectory attractor represents a set of stable states of operation of the device; Identifying phase demarcation points based on the morphological changes of the trajectory attractor, and dividing the operation phases according to the phase demarcation points; Calculating a state transition matrix of the device in each of the operating stages, wherein the state transition matrix represents a transition probability between different states; identifying the dominant failure mode of each of the operation stages according to the state transition matrix; Based on the dominant failure mode, the operation and maintenance mode analysis model is modified in sections to obtain a modified operation and maintenance mode analysis model; Construct a high-dimensional feature space based on equipment operation data; Calculating the distribution of singular points in the high-dimensional feature space, wherein the singular points represent the locations of sudden changes in the system state; Analyze the topological structure of the singular point and identify the critical path, specifically including: Constructing a homotopy group of the singular points, wherein the homotopy group describes a connectivity relationship between the singular points; Calculating a fundamental group based on the homotopy group, where the fundamental group represents a topological invariant of the connectivity relationship; mapping the fundamental group to an algebraic torus, the algebraic torus preserving the topological invariant property; Calculating the genus of the algebraic torus, where the genus represents the complexity of the topological structure; Selecting a path with the minimum complexity as a critical path according to the genus number; A mutation early warning mechanism is constructed based on the critical path.

2. The method according to claim 1, characterized in that Calculating the environmental correction coefficient based on the equipment operating environment parameter data and the preset environmental impact factor specifically includes: Segmenting the equipment operating environment parameter data according to a time series to obtain a plurality of environment parameter data segments; calculating a deviation value from a preset standard environment parameter for each of the environment parameter data segments; Calculate the correction value corresponding to each environmental parameter based on the deviation value and the preset environmental impact factor; The correction values ​​corresponding to the environmental parameters are weighted and summed to obtain the environmental correction coefficient.

3. The method according to claim 1, characterized in that The calculation of the association degree of each device node based on the device topology structure specifically includes: Constructing a topological relationship diagram of the device system, wherein nodes in the topological relationship diagram represent devices and edges represent connection relationships between the devices; Calculating the degree centrality, betweenness centrality, and closeness centrality of each of the nodes; Constructing a node importance evaluation index based on the degree centrality, the betweenness centrality, and the closeness centrality; The relevance of each device node is calculated according to the node importance evaluation index.

4. A system for generating an operation and maintenance mode analysis model, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method according to any one of claims 1 to 3.

5. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to perform the method according to any one of claims 1 to 3.

6. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to perform the method according to any one of claims 1 to 3.

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