A method and device for determining a comprehensive risk index of a wind smoke system

Abstract

This invention relates to the field of equipment management technology in thermal power plants, and provides a method and apparatus for determining the comprehensive risk index of a flue gas system. The method includes: acquiring a system structure diagram of the flue gas system, which includes a primary air fan, a forced draft fan, an induced draft fan, an air preheater, and an electrostatic precipitator; eliminating electrostatic precipitators that have no substantial impact on power generation reliability based on prior knowledge; automatically identifying the series and parallel logical relationships between equipment based on the eliminated system structure diagram; and calculating the comprehensive risk index by applying series and parallel risk formulas in a hierarchical manner based on the identification results. This invention can automatically eliminate invalid equipment, avoiding its interference with the evaluation results, automatically identify series and parallel logical relationships, avoid subjective biases caused by manual pre-setting, achieve accurate quantification of the risk index, and provide a scientific basis for graded maintenance of equipment.

Description

Technical Field

[0001] This invention relates to the field of equipment management technology for thermal power plants, specifically to a method and apparatus for determining the comprehensive risk index of a flue gas system. Background Technology

[0002] Currently, the flue gas system of thermal power plants, as a key component of the boiler combustion system, directly affects the unit's power generation capacity and safety level. Existing risk assessment methods for flue gas systems mostly employ fault tree analysis or pre-defined series and parallel structures for risk index calculation. However, these methods have significant technical shortcomings in practical applications: First, existing identification of series and parallel relationships relies heavily on manual pre-setting, lacking the ability to automatically analyze system structure diagrams. When the system structure changes or multi-level hybrid connections exist, manual judgment is prone to omissions or errors, leading to inaccurate risk assessment results. Second, some equipment, such as electrostatic precipitators, primarily affect environmental indicators but have a relatively small actual impact on the unit's power generation capacity. Including them in reliability calculations using traditional methods causes the assessment results to deviate from the actual risk level. Finally, existing methods lack an emergency coefficient calculation system based on the integration of equipment health status and importance, and lack an automatic identification mechanism for redundant configurations of flue gas system equipment, making it difficult to accurately quantify risk indices and scientifically prioritize maintenance. Summary of the Invention

[0003] To address the problems of inaccurate risk assessment results, deviation from actual risk levels, and difficulty in accurately quantifying risk indices in existing technologies, this application proposes a method and device for determining the comprehensive risk index of a flue gas system. By integrating system diagram analysis with prior knowledge, invalid equipment is automatically eliminated, series and parallel logic is identified, and an improved risk calculation formula is introduced to achieve accurate calculation of the risk index, providing a quantitative basis for graded maintenance of equipment.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for determining the comprehensive risk index of a flue gas system includes: obtaining a system structure diagram of the flue gas system, wherein the flue gas system includes a primary air fan, a forced draft fan, an induced draft fan, an air preheater, and an electrostatic precipitator; eliminating electrostatic precipitators that have no substantial impact on power generation reliability based on prior knowledge; automatically identifying the series and parallel logical relationships between the equipment according to the system structure diagram after elimination; and calculating the comprehensive risk index by calling the series risk formula and the parallel risk formula in a hierarchical manner according to the identification results.

[0005] The above scheme eliminates the interference of invalid equipment on the evaluation results by automatically removing equipment that has no substantial impact on power generation reliability. It also avoids subjective bias caused by manual pre-setting by automatically identifying series and parallel logical relationships and combining them with hierarchical calculation, thus achieving accurate quantification of the risk index.

[0006] In some possible implementations, the equipment of the flue gas system includes two primary air fans, two supply air fans, two induced draft fans, and two air preheaters, with each type of equipment using a redundant configuration on both sides (A / B).

[0007] This solution provides fundamental data support for accurately identifying parallel structures and assessing the reliability of the computing system by clearly defining the redundant configuration of the equipment.

[0008] In some possible implementations, the automatic identification of the series and parallel logical relationships between devices includes: constructing a device topology diagram; if multiple devices must work simultaneously to maintain the operation of the subsystem, they are identified as a series structure; if any one of the multiple devices can independently maintain the operation of the subsystem, it is identified as a parallel structure; for a mixed structure, it is decomposed into series and parallel sub-modules layer by layer.

[0009] This solution, by constructing a topology diagram and identifying the structure based on the device's operating logic, can accurately resolve complex system connection relationships and improve the automation level of risk assessment.

[0010] In some possible implementations, the construction of the device topology graph specifically includes: establishing a directed or undirected graph with each device in the flue gas system as a node and the functional dependencies between devices as edges, to represent the logical connection relationships between devices.

[0011] This scheme uses graph theory to formally describe the relationships between devices, providing a standardized data structure for subsequent algorithm processing.

[0012] In some possible implementations, the automatic identification of the series and parallel logical relationships between devices based on the eliminated system structure diagram specifically includes: identifying primary air fan A and primary air fan B as a parallel structure; identifying forced draft fan A, induced draft fan A, and air preheater A as a series subsystem on side A; identifying forced draft fan B, induced draft fan B, and air preheater B as a series subsystem on side B; and identifying the series subsystem on side A and the series subsystem on side B as a parallel structure.

[0013] This solution accurately divides the series and parallel logic for the specific process flow of the flue gas system, ensuring the consistency between the risk calculation model and the actual physical system.

[0014] In some possible implementations, the hierarchical invocation of the series risk formula and the parallel risk formula to calculate the comprehensive risk index includes: starting from the bottom-level equipment, calculating the emergency coefficient and comprehensive risk index of each subsystem layer by layer upwards until the comprehensive risk index of the entire smoke and gas system is obtained.

[0015] This scheme employs a hierarchical computation strategy, which decomposes complex system risks into subsystem risks for solution, reducing computational complexity and improving the accuracy of results.

[0016] In some possible implementations, the urgency coefficient is calculated based on the device's health status score and importance score, wherein the health status score depends on online or offline status monitoring results, and devices with missing status monitoring results are supplemented as if they were in normal status.

[0017] This solution integrates two dimensions: the health status and importance of the equipment, making the calculated urgency coefficient more reflective of the true urgency of the equipment's risks.

[0018] In some possible implementations, the method also includes: determining the equipment maintenance priority based on the calculated comprehensive risk index, specifically by sorting the equipment from largest to smallest according to the influence coefficient of the equipment on the comprehensive risk index of the system, with the larger the influence coefficient, the higher the maintenance priority.

[0019] This scheme establishes a correlation mechanism between risk index and maintenance decision-making, providing a scientific and quantitative basis for the graded maintenance of equipment.

[0020] In some possible implementations, the impact coefficient is the reduction in the overall comprehensive risk index of the flue gas system when the emergency coefficient of a certain equipment drops to zero after maintenance, and this impact coefficient serves as a quantitative basis for the hierarchical maintenance sequence of equipment within the flue gas system.

[0021] This solution quantifies the contribution of individual devices to the overall system risk, enabling precise prioritization of maintenance and optimizing the allocation of maintenance resources.

[0022] Furthermore, the present invention also provides a device for determining the comprehensive risk index of a flue gas system, comprising: an acquisition module for acquiring a system structure diagram of the flue gas system, wherein the flue gas system includes a primary air fan, a forced draft fan, an induced draft fan, an air preheater, and an electrostatic precipitator; an elimination module for eliminating electrostatic precipitators that have no substantial impact on power generation reliability based on prior knowledge; an identification module for automatically identifying the series and parallel logical relationships between devices according to the eliminated system structure diagram; and a determination model for calculating the comprehensive risk index by hierarchically calling the series risk formula and the parallel risk formula based on the identification results.

[0023] The aforementioned device, through the coordinated operation of its various modules, enables the automatic and accurate calculation of the risk index of the flue gas system.

[0024] This invention provides a method and apparatus for determining the comprehensive risk index of a flue gas system. By introducing prior knowledge, it automatically eliminates equipment (such as electrostatic precipitators) that has no substantial impact on power generation reliability, effectively avoiding interference from invalid equipment in the risk assessment results and making the assessment results closer to the actual risk level. By constructing an equipment topology diagram and using a recursive decomposition algorithm to automatically identify series and parallel logical relationships, it solves the problem of errors that are prone to occur in traditional manual preset methods under system structure changes or multi-level mixed connection conditions, improving the automation and accuracy of the assessment. By establishing an emergency coefficient calculation system based on the fusion of equipment health status and importance, and combining it with a calculation method that calls series and parallel risk formulas in a hierarchical manner, it achieves accurate quantification of the comprehensive risk index of the flue gas system. Furthermore, by calculating the impact coefficient of equipment on system risk, it provides a scientific and objective priority ranking basis for equipment hierarchical maintenance, significantly improving the intelligent level of flue gas system risk management. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a method for determining the comprehensive risk index of a flue gas system, provided in an embodiment of this application; Figure 2 This is a schematic diagram of a device for determining the comprehensive risk index of a flue gas system, provided in an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0029] Example 1: like Figure 1As shown in the figure, this embodiment provides a method for determining the comprehensive risk index of a flue gas system. This method achieves accurate quantification of the operational risk of the flue gas system by analyzing the system structure and integrating prior knowledge. The method specifically includes the following steps: Step S100: Obtain the system structure diagram of the flue gas system, which includes a primary air fan, a blower, an induced draft fan, an air preheater, and an electrostatic precipitator.

[0030] Specifically, the system structure diagram is a topological diagram describing the components of the flue gas system and their connections. It can be in the form of CAD drawings, P&ID diagrams (piping and instrumentation diagrams), or pre-stored adjacency list data structures. In this structure diagram, nodes represent specific physical devices, and edges represent material flow or functional dependencies between devices. The input for this step is the original system diagram data containing the aforementioned device list and connections. It should be understood that this data can be obtained manually or automatically retrieved from a power plant management information system (such as a MIS or SIS system) via an interface; this embodiment does not specifically limit this method.

[0031] Step S200: Based on prior knowledge, electrostatic precipitators that have no substantial impact on power generation reliability are eliminated.

[0032] Specifically, prior knowledge refers to a knowledge base summarized in advance by domain experts regarding the correlation between equipment functional attributes and system power generation reliability. In a flue gas system, the main function of an electrostatic precipitator is to remove dust from flue gas to meet environmental emission standards. Its operating status primarily affects environmental indicators, but does not substantially hinder the boiler's combustion stability or the unit's power output. Including it in the reliability calculation model would introduce irrelevant variables, causing the final risk index to deviate from the actual safety situation. Therefore, this step iterates through the equipment list, queries the prior knowledge base for the impact weight of each device on power generation reliability, and identifies and removes devices with impact weights below a preset threshold as invalid devices. This process simplifies the system model, eliminates the interference of redundant information on risk assessment, and lays the foundation for subsequent accurate calculations.

[0033] Step S300: Automatically identify the series and parallel logical relationships between devices based on the system structure diagram after elimination.

[0034] Specifically, after removing invalid devices, the system structure diagram is simplified to a logical block diagram containing only the core devices. This step aims to analyze the topology between these core devices, determining whether they are connected in series, parallel, or a hybrid configuration. The identification process is automatically executed based on graph theory algorithms, rather than relying on manually preset labels. For example, if a group of devices is identified as functionally redundant, it is determined to be in parallel; if a group of devices must operate simultaneously to maintain the process flow, it is determined to be in series. The output of this step is a structured logical relationship tree or relationship matrix, clearly defining the hierarchical position of each device in the system reliability model, thus solving the problems of error-prone manual judgment and difficulty in adapting to changes in system structure in traditional methods.

[0035] Step S400: Calculate the comprehensive risk index by calling the series risk formula and the parallel risk formula in layers according to the identification results.

[0036] Specifically, after clarifying the series and parallel logical relationships between devices, this step adopts a hierarchical calculation strategy, starting from the individual device units at the bottom and aggregating and calculating the risk values ​​of subsystems layer by layer upwards until the overall Comprehensive Risk Index (CRI) of the flue gas system is obtained. For series structures, a series risk formula is used, reflecting the logic that "failure of any device leads to subsystem failure"; for parallel structures, a parallel risk formula is used, reflecting the logic that "mutual backup reduces the probability of system failure". Through this hierarchical invocation and step-by-step aggregation, complex system risks are decomposed into calculable local risks. The final output comprehensive risk index can objectively reflect the current overall safe operation level of the flue gas system, providing a quantitative basis for the decision-making of operation and maintenance personnel.

[0037] Example 2: This embodiment, based on Embodiment 1, provides a detailed explanation of the specific implementation process of automatically identifying the series and parallel logical relationships between devices in step S300. This process aims to transform physical connection relationships into a computer-understandable logical topology, providing a structural foundation for subsequent risk quantification calculations.

[0038] First, the identified objects possess specific physical configuration characteristics. The equipment in the flue gas system includes two primary air fans, two forced draft fans, two induced draft fans, and two air preheaters, with each type of equipment employing a redundant configuration on both sides (A / B). This configuration is a standard design in thermal power plants to ensure that the unit does not shut down due to a single equipment failure; sides A and B serve as backups for each other or share the load, forming the physical basis of the identification logic. It should be understood that although this embodiment uses a two-side (A / B) configuration as an example, in other embodiments, if the system employs a redundant configuration on three or more sides, the identification logic of this invention is equally applicable.

[0039] After obtaining the system structure diagram after removing invalid devices, the system first performs a topology construction step. This involves constructing a device topology graph, specifically: using each device in the flue gas system as nodes and the functional dependencies between devices as edges, creating a directed or undirected graph to represent the logical connections between devices. Specifically, nodes represent concrete physical device entities, such as "forced draft fan A" and "induced draft fan B"; edges represent the transmission paths of materials (such as air and flue gas) or the transmission paths of control signals between devices. If the system process flow has a clear directionality (such as flue gas flow direction), constructing a directed graph is more accurate and can express the impact of upstream devices on downstream devices; if only the existence of connections is considered, an undirected graph can be constructed. This step transforms complex physical connections into a standardized graph data structure, laying the data foundation for subsequent algorithmic identification.

[0040] Based on the constructed topology diagram, the system executes automatic identification logic. This automatic identification of series and parallel connections between devices includes: if multiple devices must work simultaneously to maintain the subsystem's operation, it is identified as a series structure; if any one of the devices can independently maintain the subsystem's operation, it is identified as a parallel structure; for hybrid structures, it is layer-by-layer decomposed into series and parallel sub-modules. This identification rule simulates the failure logic in reliability engineering: in a series system, the failure of any node will cause the entire link to be interrupted, logically representing an "AND" relationship; while in a parallel system, operation can be maintained as long as one path exists, logically representing an "OR" relationship. For complex hybrid structures, the algorithm adopts a recursive decomposition strategy, first identifying the local smallest unit, abstracting it into a virtual node, and then re-evaluating the connection relationships at a higher level, thereby avoiding omissions or errors that might result from manual pre-setting.

[0041] The specific identification results in this embodiment are as follows: Primary air fan A and primary air fan B are identified as a parallel structure; forced draft fan A, induced draft fan A, and air preheater A are identified as a series subsystem on side A; forced draft fan B, induced draft fan B, and air preheater B are identified as a series subsystem on side B; the series subsystems on side A and side B are identified as a parallel structure. This identification result accurately reflects the actual process flow of the flue gas system: primary air fans A and B are mutually redundant; the operation of either one is sufficient to maintain ventilation in the pulverizing system, hence they are identified as parallel; the equipment on side A (forced draft fan A, induced draft fan A, and air preheater A) must operate simultaneously to maintain complete airflow circulation in the flue gas duct on side A; the shutdown of any one device will cause the flue gas duct on that side to shut down, hence they are identified as series; the same applies to side B. Ultimately, the flue gas ducts on side A and side B are mutually redundant, exhibiting a parallel characteristic overall. Through the above automatic identification process, the system can accurately parse the logical topology hidden behind the complex physical connections, adapting to changes in the system structure without manual intervention.

[0042] Example 3: This embodiment, based on the above embodiments, provides a detailed explanation of the specific process of calculating the comprehensive risk index by hierarchically calling the risk formula in step S400. This process is one of the core inventive points of this invention. By introducing an urgency coefficient that integrates multi-dimensional parameters and combining it with the system topology for hierarchical recursive calculation, it achieves accurate quantification of system risk.

[0043] First, the foundation of risk calculation lies in the accurate description of the risk status of individual devices. In this embodiment, the urgency coefficient is calculated based on the device's health status score and importance score. The health status score depends on online or offline status monitoring results, and devices with missing status monitoring results are supplemented as if in normal condition. Specifically, the health status score (S_health) reflects the degree of degradation of the device's current operating condition, with a value ranging from 0 to 100. The lower the value, the worse the device's condition and the higher the risk of failure. This score data can be obtained in real time through sensors such as vibration, temperature, and pressure deployed on the device and then normalized, or it can be obtained through periodic manual inspections or offline diagnostic reports. For devices in the system that do not yet have monitoring devices installed or whose data transmission is interrupted, to ensure the continuity and conservatism of the calculation, the system defaults to supplementing the score as if in normal condition (e.g., assigning a score of 100) to avoid an abnormal increase in the system risk index due to missing data. The importance score (S_importance) reflects the functional position of the device in the system's process flow, and is usually preset based on design data or expert experience, with a value range of 0 to 100 as well. For example, the primary air fan, as the power source of the pulverizing system, is usually more important than general auxiliary equipment.

[0044] Based on the scoring of the above two dimensions, this embodiment uses the following formula to calculate the urgency factor of the equipment: UrgencyFactor_mach = 1 - (S_health × S_importance) / S_max. Where S_max is the normalized baseline value, usually taken as 10000 (i.e., a full score of 100 × 100). The physical meaning of this formula is: the better the health status of the equipment and the lower its importance, the closer its urgency factor is to 0, indicating that the current risk of the equipment is extremely low; conversely, if the health status of the equipment is extremely poor and its importance is extremely high, its urgency factor will approach 1, indicating that the equipment is in an extremely urgent state and requires immediate attention. Taking the blower A as an example, if its health status score is 75 and its importance score is 52, then its urgency factor is calculated as: 1 - (75 × 52) / 10000 = 1 - 0.39 = 0.61. Similarly, the urgency factors of all underlying equipment such as induced draft fan A and air preheater A can be calculated. After obtaining the urgency factors of each underlying equipment, the system executes a hierarchical calculation strategy. The hierarchical invocation of the series and parallel risk formulas to calculate the comprehensive risk index includes: starting from the bottom-level equipment, calculating the urgency coefficient and comprehensive risk index of each subsystem layer by layer upwards until the comprehensive risk index of the entire flue gas system is obtained. This process strictly follows the series and parallel logic structure identified in Example 2, and the specific steps are as follows: The first step is to calculate the risk of the series subsystem. For the series subsystem on side A (forced draft fan A, induced draft fan A, and air preheater A), since the failure of any device in the series structure will lead to the failure of the subsystem, according to the probability multiplication rule, the urgency coefficient of the subsystem is the product of the urgency coefficients of each device. Assuming the urgency coefficients of forced draft fan A, induced draft fan A, and air preheater A are 0.61, 0.695, and 0.785 respectively, then the urgency coefficient of the series subsystem on side A is: 0.61 × 0.695 × 0.785 = 0.3328. This value reflects the risk probability of the entire flue gas duct on this side failing. Similarly, the urgency coefficient of the series subsystem on side B can be calculated.

[0045] The second step is to calculate the risk of the parallel subsystem. For a parallel structure consisting of side A and side B, since the parallel system only fails when all branches fail simultaneously, its failure probability is the product of the failure probabilities of each branch. The urgency factor (i.e., system risk) is calculated using the probability complementarity relationship. The formula is: Urgency Factor_parallel = 1 - (1 - UF_A) × (1 - UF_B). Assuming the urgency factor of the B-side subsystem is 0.6581, then the urgency factor of the parallel subsystem is: 1 - (1 - 0.3328) × (1 - 0.6581) = 0.7719. Similarly, for a parallel structure consisting of primary air turbine A and primary air turbine B, the corresponding urgency factor of the parallel subsystem can also be calculated.

[0046] The third step is to calculate the overall system risk index. The emergency coefficients of each subsystem calculated above are aggregated again using the corresponding formula according to their top-level logical relationship (e.g., the primary fan parallel subsystem and the flue gas duct parallel subsystem are in series), ultimately obtaining the overall emergency coefficient of the flue gas system. Finally, the overall system emergency coefficient is converted into a comprehensive risk index (CRI), for example, using the formula: CRI = (1 - Urgency Factor_sys) × 10, mapping the risk value to an intuitive index from 0 to 10. Through this hierarchical recursive approach, this embodiment can not only output the overall system risk score but also trace the risk contribution of each subsystem and key equipment, providing maintenance personnel with a complete risk profile from macro to micro levels. Example 4: This embodiment, based on the above embodiments, provides a detailed explanation of the subsequent application steps of the method for determining the comprehensive risk index of the flue gas system. After obtaining the overall comprehensive risk index of the flue gas system, this invention further utilizes this index to provide a quantitative basis for equipment maintenance decisions, solving the problems of strong subjectivity and lack of scientific prioritization in traditional maintenance planning.

[0047] Specifically, this method also includes: determining equipment maintenance priorities based on the calculated comprehensive risk index. Specifically, equipment is ranked from largest to smallest based on its impact coefficient on the system's comprehensive risk index; the larger the impact coefficient, the higher the maintenance priority. This process transforms the macro-level system risk index into micro-level equipment-level action guidelines. The core logic is that even equipment with the same health status score will have vastly different contributions to the overall system risk due to their different positions in the system topology (e.g., whether in a series critical link or a parallel redundant branch). By quantifying this contribution, critical equipment that has a ripple effect can be identified, allowing for priority allocation of maintenance resources and maximizing operational efficiency.

[0048] To accurately measure the impact of a single device on the overall system risk, this embodiment introduces the concept of an "impact coefficient." The impact coefficient is the reduction in the overall comprehensive risk index of the flue gas system when the urgency coefficient of a device drops to zero after maintenance. This impact coefficient serves as a quantitative basis for the graded maintenance sequence of devices within the flue gas system. Reducing the urgency coefficient of a device to zero here is based on an idealized maintenance effect assumption, namely, that after perfect maintenance, the device recovers to its optimal health state and its importance score remains at the baseline, at which point its risk contribution is completely eliminated. By comparing the difference in the system's CRI before and after maintenance, the independent impact weight of that device can be extracted. It should be understood that in other embodiments, a more realistic urgency coefficient reduction ratio can be set based on historical maintenance data, but using a zero-based calculation provides a unified and comparable reference benchmark.

[0049] The calculation process will be explained in detail below with a specific example. Assuming the overall system CRI is 2.56 as calculated in Example 3, maintenance personnel need to develop a maintenance plan. Taking blower A as an example, the process of calculating its influence coefficient is as follows: First, assume that after maintenance, the emergency factor of blower A drops from the current 0.61 to 0. This means that in the risk calculation model, blower A no longer constitutes a risk source.

[0050] Secondly, based on this assumption, the emergency factor of the series subsystem on side A is recalculated. Since the blower A is located in the series link on side A, according to the series risk formula (product relationship), if one of the factors is 0, the emergency factor of the entire subsystem becomes 0. At this time, the failure risk of the series subsystem on side A is completely eliminated.

[0051] Next, the risk of the parallel subsystem consisting of sides A and B is recalculated. Since the risk on side A is zero, according to the parallel risk formula (probability complementarity), the urgency coefficient of this parallel subsystem will depend entirely on the state of side B. Assuming the urgency coefficient of the subsystem on side B is still 0.6581, the urgency coefficient of the parallel subsystem after maintenance is calculated as: 1 - (1 - 0) × (1 - 0.6581) = 0.6581. It can be seen that the risk value of the parallel subsystem at this time is lower than 0.7719 before maintenance.

[0052] Subsequently, the calculation continues upwards to recalculate the overall system emergency factor. The emergency factor of the primary fan parallel subsystem remains unchanged (assumed to be 0.9639), and it is connected in series with the overhauled flue gas duct parallel subsystem (emergency factor 0.6581). Therefore, the new overall system emergency factor is: 0.9639 × 0.6581 = 0.6345.

[0053] Finally, the new overall system CRI is calculated. According to the formula CRI=(1-Urgency Factor)×10, the CRI after maintenance is: (1-0.6345)×10=3.655. It should be noted that, in this embodiment, the urgency factor is defined as a measure of the probability of failure, and the larger its value, the higher the risk. CRI is defined as a risk index, and the larger the value, the higher the risk. The two must maintain logical consistency in their numerical trends. The impact coefficient of blower A after maintenance is calculated as the difference between the original CRI (2.56) and the new CRI. In this example, the maintenance of blower A caused a significant change in the system CRI, and the absolute value of this change is the impact coefficient of blower A.

[0054] Through the above steps, the system can traverse all equipment within the flue gas system and calculate its impact coefficient for each. The final ranking result may be as follows: Forced draft fan A (highest impact coefficient) > Induced draft fan A > Primary air fan A > Air preheater A > Other equipment. This ranking result clearly reveals that forced draft fan A is currently the weakest link restricting the safe operation of the system, and its maintenance will bring the greatest benefit in reducing system risks; therefore, it should be listed as the highest priority maintenance target. In this way, the present invention translates complex system risk assessment results into specific operational guidance suggestions, significantly improving the level of refinement in power plant operation and maintenance management.

[0055] Example 5: like Figure 2 As shown, this embodiment provides a device for determining the comprehensive risk index of a smoke and gas system. This device corresponds to the methods described in embodiments 1 to 4 above, and implements the above method steps in the form of hardware or software functional modules, thereby providing product carrier protection for the technical solution. Specifically, the device includes an acquisition module 210, a rejection module 220, an identification module 230, and a determination module 240.

[0056] The acquisition module 210 is used to acquire the system structure diagram of the flue gas system, which includes a primary air fan, a blower, an induced draft fan, an air preheater, and an electrostatic precipitator.

[0057] Specifically, the acquisition module, as the data input terminal of the device, can be implemented as a communication interface, data reading circuit, or human-machine interface. In practical applications, the acquisition module can connect to the power plant's distributed control system (DCS) or management information system (MIS) via wired or wireless networks to automatically capture the current flue gas system configuration screen or equipment connection relationship table; it can also receive manually imported CAD drawings or Excel spreadsheet files. The core function of this module is to transform unstructured or semi-structured raw system data into computer-recognizable structured data, such as generating an adjacency matrix containing a list of equipment nodes and a list of connection edges, providing a standardized data source for subsequent processing.

[0058] The rejection module 220 is used to reject electrostatic precipitators that have no substantial impact on power generation reliability based on prior knowledge.

[0059] Specifically, the elimination module has a built-in or connected prior knowledge base that stores the functional attributes of various devices and their impact weights on power generation reliability. The elimination module receives the system structure diagram data output by the acquisition module, traverses the device nodes, and matches the device identifiers with the invalid device list (such as electrostatic precipitators) in the prior knowledge base. If a match is found, the device node and its associated edges are removed from the topology data. This process cleanses and reduces the dimensionality of the system model; the data output by the elimination module is a simplified topology diagram containing only core devices, effectively reducing the complexity of subsequent calculations.

[0060] The identification module 230 is used to automatically identify the series and parallel logical relationships between devices based on the system structure diagram after elimination.

[0061] Specifically, the identification module is the core logic processing unit of the device, internally equipped with graph theory algorithms or a logic reasoning engine. The identification module receives a simplified topology diagram from the elimination module and automatically parses out series, parallel, or hybrid structures based on the connection relationships between devices and process flow rules. For example, the identification module can identify parallel nodes that are backups for each other and series links that must operate simultaneously by judging the connectivity and dependent paths between nodes. This module transforms abstract physical connections into a logical topology tree or relationship matrix, clarifying the hierarchical position of each device in the system reliability model, thus solving the problems of low efficiency and error-proneness in traditional manual identification.

[0062] The determination module 240 is used to calculate the comprehensive risk index by calling the series risk formula and the parallel risk formula in a hierarchical manner based on the identification results.

[0063] Specifically, the determination module is the calculation output unit of the device. It has pre-set emergency coefficient calculation formulas, series risk aggregation formulas, and parallel risk aggregation formulas as described in Example 3. The determination module receives the logical topology relationship output by the identification module and, combined with externally input or internally stored equipment health status data, recursively calculates by calling the corresponding formulas layer by layer, from the bottom-level equipment to the top-level system. Finally, the determination module outputs the overall Comprehensive Risk Index (CRI) of the ventilation and smoke system, which can be transmitted to the display unit for visualization or to the maintenance decision system as a basis for planning.

[0064] It should be understood that the above-described division of modules is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, such as the monitoring host or edge computing gateway of a power plant; or they can be deployed on different physical devices and work collaboratively through a network. For example, the acquisition module can be located at the data acquisition front end, while the determination module can be located on a cloud server. Furthermore, each module can be implemented in hardware, such as a Field-Programmable Gate Array (FPGA) or Application-Specific Integrated Circuit (ASIC); or it can be stored in memory as a software functional unit, implemented by a processor executing the corresponding software code. This flexibility in hardware and software implementation allows the device provided in this embodiment to adapt to the needs of power plant automation systems of different scales.

[0065] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention, such as replacing the flue gas system in this embodiment with other industrial fluid systems with series-parallel logic, or replacing the calculation model of the emergency coefficient with other equivalent risk quantification models, should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for determining the comprehensive risk index of a flue gas system, characterized in that, The method includes: Obtain the system structure diagram of the flue gas system, which includes a primary air fan, a blower, an induced draft fan, an air preheater, and an electrostatic precipitator. Electrostatic precipitators that have no substantial impact on power generation reliability are eliminated based on prior knowledge. Automatically identify the series and parallel logical relationships between devices based on the system structure diagram after elimination; Based on the identification results, the series risk formula and the parallel risk formula are called in a hierarchical manner to calculate the comprehensive risk index.

2. The method according to claim 1, characterized in that, The equipment in the flue gas system includes two primary air fans, two supply fans, two induced draft fans, and two air preheaters, with each type of equipment using a redundant configuration on both sides (A / B).

3. The method according to claim 1, characterized in that, The series and parallel logical relationships between the automatic identification devices include: Construct a device topology diagram; If multiple devices must work simultaneously to maintain the operation of a subsystem, it is identified as a series structure; If any one of the multiple devices can independently maintain the operation of the subsystem, it is identified as a parallel structure; For hybrid structures, they are broken down into series and parallel sub-modules layer by layer.

4. The method according to claim 3, characterized in that, The construction of the device topology graph specifically includes: using each device in the flue gas system as a node and the functional dependencies between devices as edges, establishing a directed or undirected graph to represent the logical connection relationships between devices.

5. The method according to claim 1, characterized in that, The automatic identification of series and parallel logical relationships between devices based on the eliminated system structure diagram specifically includes: Primary air fan A and primary air fan B are identified as a parallel structure; The forced draft fan A, induced draft fan A, and air preheater A are identified as a series subsystem on side A. The forced draft fan B, induced draft fan B, and air preheater B are identified as a series subsystem on the B side. The series subsystems on side A and side B are identified as a parallel structure.

6. The method according to claim 1, characterized in that, The hierarchical application of the series risk formula and the parallel risk formula to calculate the comprehensive risk index includes: Starting from the bottom-level equipment, the emergency coefficient and comprehensive risk index of each subsystem are calculated layer by layer upwards until the comprehensive risk index of the entire flue gas system is obtained.

7. The method according to claim 6, characterized in that, The urgency coefficient is calculated based on the device's health status score and importance score. The health status score depends on online or offline status monitoring results. For devices with missing status monitoring results, the results are supplemented as if they were in normal status.

8. The method according to claim 1, characterized in that, Also includes: The priority of equipment maintenance is determined based on the calculated comprehensive risk index. Specifically, the equipment is ranked from largest to smallest according to its impact coefficient on the comprehensive risk index of the system. The larger the impact coefficient, the higher the maintenance priority.

9. The method according to claim 8, characterized in that, The influence coefficient is the reduction value of the overall comprehensive risk index of the flue gas system when the emergency coefficient of a certain equipment drops to zero after maintenance. This influence coefficient serves as a quantitative basis for the hierarchical maintenance sequence of equipment within the flue gas system.

10. A device for determining the comprehensive risk index of a flue gas system, characterized in that, include: The acquisition module is used to acquire the system structure diagram of the flue gas system, which includes a primary air fan, a blower, an induced draft fan, an air preheater, and an electrostatic precipitator. The rejection module is used to reject electrostatic precipitators that have no substantial impact on power generation reliability based on prior knowledge. The identification module is used to automatically identify the series and parallel logical relationships between devices based on the system structure diagram after elimination; The determination module is used to calculate the comprehensive risk index by calling the series risk formula and the parallel risk formula in a hierarchical manner based on the identification results.

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