BIM-based Power Plant Equipment Alarm Data Linkage Analysis Method and System

Abstract

The present invention discloses a method and system for linked analysis of power plant equipment alarm data based on BIM, which relates to the technical field of data processing. The method includes: exporting a power plant equipment model, which is obtained by modeling power plant equipment through a BIM modeling tool; dividing the equipment in the power plant equipment model into first-class equipment and second-class equipment according to equipment functions, where the first-class equipment is power plant equipment with independent functions, and the second-class equipment is power plant equipment with multiple functions; obtaining a faulty equipment that issues an alarm signal. If the faulty equipment belongs to the first-class equipment, perform an associated fault impact analysis on the faulty equipment according to the surface-type alarm propagation analysis model, and output a fault impact index; if the faulty equipment belongs to the second-class equipment, analyze the superimposed associated fault impacts of the faulty equipment on each functional line according to the line-type superimposed alarm propagation analysis model, and output a fault impact index. Furthermore, the technical effects of improving the analysis accuracy, enhancing the generality and adaptability are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method and system for linked analysis of power plant equipment alarm data based on BIM. Background Art

[0002] Existing systems for linked analysis of power plant equipment alarm data based on BIM can generally establish and perform simple analysis on equipment models. For example, they can perform visual management of equipment through BIM models and conduct preliminary data correlation analysis when equipment fails and alarms to help maintenance personnel quickly locate problem areas, but there are certain limitations. When processing fault alarm data of power plant equipment, a unified analysis model is usually used to perform associated fault impact analysis on all equipment. This method is similar to surface-type correlation analysis and does not fully consider the functional characteristics of different equipment and the differences in their roles in different functional lines. Especially for those power plant equipment with multiple functions, this unified surface-type analysis model cannot accurately evaluate the fault impact degree of each equipment on different functional lines, resulting in inaccurate and incomplete analysis results and being difficult to meet the requirements of refined management of equipment in a complex power plant environment. Summary of the Invention

[0003] The present invention provides a method and system for linked analysis of power plant equipment alarm data based on BIM to solve the technical problems of low adaptability, inaccurate and incomplete analysis results in the prior art, and achieve the technical effects of improving analysis accuracy, enhancing generality and adaptability.

[0004] In a first aspect, the present invention provides a method for linked analysis of power plant equipment alarm data based on BIM. Among them, the method for linked analysis of power plant equipment alarm data based on BIM includes:

[0005] Export a power plant equipment model, which is obtained by modeling power plant equipment through a BIM modeling tool.

[0006] Divide the equipment in the power plant equipment model into first-class equipment and second-class equipment according to equipment functions. The first-class equipment is power plant equipment with independent functions, and the second-class equipment is power plant equipment with multiple functions.

[0007] Obtain a faulty equipment that issues an alarm signal. If the faulty equipment belongs to the first-class equipment, perform associated fault impact analysis on the faulty equipment according to a surface-type alarm propagation analysis model and output a fault impact index.

[0008] If the faulty equipment belongs to the second-class equipment, analyze the superimposed associated fault impacts of the faulty equipment on each functional line according to a line-type superimposed alarm propagation analysis model and output a fault impact index.

[0009] In a feasible implementation manner, analyze the superimposed associated fault impacts of the faulty device on each functional line according to the linear superimposed alarm propagation analysis model, and output fault impact indicators. The method includes:

[0010] If the faulty device belongs to the second type of device, obtain multiple functions of the faulty device.

[0011] Identify multiple functional lines of the faulty device in the power plant equipment model according to the multiple functions.

[0012] The linear superimposed alarm propagation analysis mode respectively performs fault impact analysis on the multiple functional lines, outputs multiple line fault impact indicators, and fuses the superimposed impacts of the multiple line fault impact indicators to output the fault impact indicators.

[0013] In a feasible implementation manner, identify multiple functional lines of the faulty device in the power plant equipment model according to the multiple functions. The method includes:

[0014] Taking the faulty device as the starting point, select any one of the multiple functions to perform forward fault propagation and reverse fault propagation respectively, and output the corresponding forward propagation line and reverse propagation line under any one function.

[0015] According to the forward propagation line and the reverse propagation line, obtain the corresponding functional line under any one function, and so on, and output multiple functional lines corresponding to the multiple functions.

[0016] In a feasible implementation manner, the linear superimposed alarm propagation analysis mode respectively performs fault impact analysis on the multiple functional lines, and outputs multiple line fault impact indicators. The method includes:

[0017] Perform weight identification on each of the multiple functional lines according to the device topological distance and the device role, and output multiple identified functional lines.

[0018] Set a propagation attenuation coefficient, and take the faulty device as the center, and perform forward propagation attenuation and reverse propagation attenuation on the weights of each functional line according to the propagation attenuation coefficient to obtain multiple updated identified functional lines.

[0019] Perform fault impact analysis on the multiple updated identified functional lines, and output multiple line fault impact indicators.

[0020] In a feasible implementation manner, fuse the superimposed impacts of the multiple line fault impact indicators to output the fault impact indicators. The method includes:

[0021] Based on the power plant equipment model, obtain the importance coefficients of each functional line among the multiple functional lines.

[0022] Perform a superposition impact analysis on the multiple line fault impact indicators after fusion according to the importance coefficients of each functional line, and output the fault impact indicators.

[0023] In a feasible implementation manner, the propagation attenuation coefficient is dynamically adjusted according to real-time operation data. The alarm frequency and current load rate of the faulty device in the historical period are collected, and the dynamic attenuation coefficient is calculated. The expression is as follows:

[0024] ;

[0025] Wherein, is the dynamic attenuation coefficient, is the initial attenuation coefficient, is the alarm frequency of the faulty device in the historical period, is the current load rate of the faulty device in the historical period, is the maximum allowable alarm frequency of the faulty device in the historical period, is the rated load rate.

[0026] In a feasible implementation manner, if the faulty device belongs to the first type of device, perform an associated fault impact analysis on the faulty device according to the surface alarm propagation analysis model, and output the fault impact indicators. The method includes:

[0027] If the faulty device belongs to the first type of device, perform a surface fault propagation analysis in the power plant equipment model with the first type of device as the center, and output a power equipment model marked based on the propagation impact size.

[0028] Set a propagation impact threshold, and perform edge division on the power equipment model marked based on the propagation impact size with the propagation impact threshold to obtain a propagation impact network based on the faulty device.

[0029] Perform calculations on the propagation impact network and output the fault impact indicators.

[0030] In a feasible implementation manner, performing a surface fault propagation analysis in the power plant equipment model with the first type of device as the center includes propagation characteristic vectors of propagation range, impact degree, and duration.

[0031] Perform a high-dimensional vector mapping on the propagation characteristic vectors, output high-dimensional propagation characteristic vectors, and calculate the propagation impact size according to the high-dimensional propagation characteristic vectors.

[0032] In a feasible implementation manner, the method for exporting the power plant equipment model includes:

[0033] Export the power plant equipment model as an IFC format file, and after parsing the IFC format file, store it in the target graph database.

[0034] Call the target graph database for power plant equipment correlation analysis, and output the power plant equipment models with correlations.

[0035] In a second aspect, the present invention also provides a BIM-based power plant equipment alarm data linkage analysis system. Among them, the BIM-based power plant equipment alarm data linkage analysis system includes:

[0036] A model export unit for exporting a power plant equipment model, which is obtained by modeling power plant equipment through a BIM modeling tool.

[0037] An equipment classification unit for classifying the equipment in the power plant equipment model into first-class equipment and second-class equipment according to equipment functions. The first-class equipment is power plant equipment with independent functions, and the second-class equipment is power plant equipment with multiple functions.

[0038] A surface alarm analysis unit for obtaining a faulty device that issues an alarm signal. If the faulty device belongs to the first-class equipment, perform associated fault impact analysis on the faulty device according to the surface alarm propagation analysis model, and output a fault impact index.

[0039] A line alarm analysis unit for, if the faulty device belongs to the second-class equipment, analyzing the superimposed associated fault impacts of the faulty device on each functional line according to the line superimposed alarm propagation analysis model, and outputting a fault impact index.

[0040] In a feasible implementation manner, the line alarm analysis unit includes:

[0041] A function identification unit for, if the faulty device belongs to the second-class equipment, obtaining multiple functions of the faulty device.

[0042] A functional line identification unit for identifying multiple functional lines of the faulty device in the power plant equipment model according to the multiple functions.

[0043] A line fault impact analysis unit for performing fault impact analysis on the multiple functional lines respectively according to the line superimposed alarm propagation analysis mode, outputting multiple line fault impact indexes, and fusing the superimposed impacts of the multiple line fault impact indexes to output the fault impact index.

[0044] The present invention discloses a method and system for linkage analysis of power plant equipment alarm data based on BIM, including: exporting a power plant equipment model, which is obtained by modeling power plant equipment through a BIM modeling tool; classifying according to equipment functions, and dividing the equipment in the power plant equipment model into first-class equipment and second-class equipment, where the first-class equipment refers to power plant equipment with independent functions, and the second-class equipment refers to power plant equipment with multiple functions; obtaining a faulty equipment that issues an alarm signal. If the faulty equipment belongs to the first-class equipment, then based on a surface-type alarm propagation analysis model, an associated fault impact analysis is carried out, and a fault impact index is output; if the faulty equipment belongs to the second-class equipment, then based on a line-type superimposed alarm propagation analysis model, the superimposed associated fault impacts of the equipment on each functional line are analyzed, and a fault impact index is output. The method and system for linkage analysis of power plant equipment alarm data based on BIM disclosed by the present invention solve the technical problems of low adaptability, inaccurate and incomplete analysis results, and achieve the technical effects of improving analysis accuracy, enhancing generality and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a schematic flowchart of the method for linkage analysis of power plant equipment alarm data based on BIM of the present invention;

[0046] Figure 2 is a schematic structural diagram of the system for linkage analysis of power plant equipment alarm data based on BIM of the present invention.

[0047] Description of reference numerals: model export unit 11, equipment classification unit 12, surface alarm analysis unit 13, line alarm analysis unit 14. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The following will combine the accompanying drawings of the specification and specific embodiments to elaborate on the above technical solutions in detail to better understand the above technical solutions. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments for explaining the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. In addition, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings rather than all.

[0049] Embodiment 1, as Figure 1 is a schematic flowchart of the method for linkage analysis of power plant equipment alarm data based on BIM of the present invention, where the method for linkage analysis of power plant equipment alarm data based on BIM includes:

[0050] S100: Export a power plant equipment model, and the power plant equipment model is obtained by modeling power plant equipment through a BIM modeling tool.

[0051] Specifically, through BIM modeling tools such as Revit and ArchiCAD, precise 3D modeling of power plant equipment can be carried out, and various attribute information such as equipment type, function parameters, connection relationships, etc. can be assigned to the equipment; exporting the power plant equipment model means saving and extracting the equipment model created by the BIM modeling tool in a specific format (such as IFC format) for further analysis and application in other systems or platforms.

[0052] In some embodiments, the method for exporting the power plant equipment model includes:

[0053] Export the power plant equipment model as an IFC format file, parse the IFC format file, and store it in the target graph database; call the target graph database to perform power plant equipment correlation analysis and output the power plant equipment model with correlation.

[0054] Exemplarily, first, use the BIM modeling tool to model the power plant equipment, including drawing the geometric shape of the equipment, defining the attributes of the equipment (such as equipment name, model, function, etc.), and establishing the topological relationship between the equipment (such as pipeline connection, electrical connection, etc.); then, export the built power plant equipment model as an IFC format file because the IFC format is an open and standardized building information exchange format that can be recognized and parsed by different software systems, which helps to ensure the integrity and consistency of the model data. Next, parse the IFC format file and store it in the target graph database. The graph database has good performance in storing and querying associated data and is suitable for storing and managing the power plant equipment model data with complex associated relationships; finally, call the power plant equipment model in the target graph database to perform correlation analysis, such as analyzing the physical connection relationship and functional dependency relationship between the equipment, and output the power plant equipment model with correlation, providing basic data support for subsequent fault alarm analysis.

[0055] Through the above process, the accuracy and standardization of the power plant equipment model are ensured, enabling seamless sharing and exchange of model data between different systems, and avoiding errors and time costs caused by duplicate data entry and format conversion. By storing the model in the graph database, efficient equipment correlation analysis can be carried out, quickly extracting the complex relationships between the equipment, providing an accurate data basis for subsequent fault propagation analysis, thereby improving the data quality and analysis efficiency of the entire alarm data linkage analysis system, and helping to achieve a fast and accurate response to power plant equipment failures.

[0056] S200: Divide the equipment in the power plant equipment model into first-class equipment and second-class equipment according to the equipment function. The first-class equipment is the power plant equipment with independent functions, and the second-class equipment is the power plant equipment with multiple functions.

[0057] Specifically, the device function refers to the specific tasks or roles that power plant equipment undertakes during operation. For example, the main function of a generator is to generate electricity, and the main function of a transformer is to change the voltage level. Independent-function power plant equipment refers to those that undertake only a single function. Such equipment usually performs a single, well-defined function, and its operation and maintenance are relatively independent, such as ordinary transmission lines, single cooling equipment, etc. Multi-functional power plant equipment refers to those that can undertake multiple different functions simultaneously. Such equipment can perform multiple functions, which may involve multiple systems or processes, and its management and maintenance need to comprehensively consider its multiple roles. For example, some large generator sets may have multiple functions such as power generation, frequency modulation, and voltage regulation at the same time.

[0058] Through the above classification method, the equipment is divided into two categories, and an appropriate alarm propagation analysis model can be selected according to the functional characteristics of the equipment, avoiding the problem of inaccurate analysis results caused by using a single analysis model, which helps to improve the operation efficiency and equipment reliability of the power plant.

[0059] S300: Obtain the faulty device that issues the alarm signal. If the faulty device belongs to the first type of device, perform an associated fault impact analysis on the faulty device according to the surface alarm propagation analysis model, and output a fault impact index.

[0060] Specifically, the surface alarm propagation analysis model is a fault analysis model that regards the device and its mutual relationships as an overall planar network, and evaluates the impact of the fault on the entire system by analyzing the propagation path and influence range of the faulty device in this network; the fault impact index is a quantitative parameter that measures the degree of fault impact, such as the size of the influence range, the number of affected devices, the duration of the influence, etc.

[0061] In some embodiments, if the faulty device belongs to the first type of device, perform an associated fault impact analysis on the faulty device according to the surface alarm propagation analysis model, and output a fault impact index. The method includes:

[0062] If the faulty device belongs to the first type of device, perform a surface fault propagation analysis in the power plant equipment model with the first type of device as the center, and output a power equipment model marked based on the size of the propagation impact; set a propagation impact threshold, and perform edge division on the power equipment model marked based on the size of the propagation impact with the propagation impact threshold to obtain a propagation impact network based on the faulty device; calculate the propagation impact network, and output the fault impact index.

[0063] Specifically, the operating status of power plant equipment is monitored in real time. When an alarm signal is detected from the equipment, the information of the faulty equipment that emits the alarm signal is immediately obtained. Then, based on the previous equipment classification results, it is determined whether the faulty equipment belongs to the first type of equipment or the second type of equipment.

[0064] Specifically, if the faulty equipment belongs to the first type of equipment, that is, the power plant equipment with independent functions, the surface alarm propagation analysis model is used for analysis. Exemplarily, the equipment is used as the central node of the analysis, and a graph structure model is constructed according to the physical connection and business logic relationship of the equipment in the power plant, where each node represents various types of equipment in the power plant, and the edges represent the physical, logical or information connections between the equipment; with the faulty equipment as the center, fault propagation analysis is carried out along each connection edge in the power plant equipment model, including calculating the influence degree of the fault propagation from the central node to the surrounding equipment through the graph attention mechanism, transfer probability model or propagation function, and for each node, marking the size of the received propagation influence, such as representing it with a numerical value or level (for example, "high", "medium", "low"), so as to quickly and comprehensively evaluate the potential impact of the independent function equipment fault on the surrounding equipment and the entire system, and provide quantitative information on the fault impact for maintenance personnel to take timely measures.

[0065] Furthermore, according to practical experience and historical data, a propagation influence threshold is preset to distinguish the equipment with greater influence and less influence. For example, the influence degree of 0.5 is set as the propagation influence threshold, and the nodes exceeding this value can be considered to be significantly affected; then, using the propagation influence threshold, the sizes of the propagation influences of each node in the power plant equipment model obtained after the surface fault propagation analysis are screened, and the edges or nodes with an influence degree lower than the threshold are removed, and the remaining part constitutes the propagation influence network based on the faulty equipment. This network only includes the equipment with a strong correlation with the fault. Optionally, this network can be represented by a graph, where the weight of the node reflects the size of its propagation influence, and the weight of the edge can represent the probability or intensity of the fault transfer between adjacent equipment.

[0066] Furthermore, statistical analysis and comprehensive calculation are carried out on the propagation influence network, and an overall fault influence index is output. Among them, methods such as weighted average of node weights and total cumulative propagation influence can be used as the fault influence index. For example, calculate the total sum of the propagation influence weights of all nodes and normalize it as the overall influence value of this fault, which is used to reflect the possible influence degree of this fault in the power plant system and provide a reference for subsequent scheduling, early warning and maintenance decisions.

[0067] Example illustration: Assume that in a power plant equipment model, the faulty equipment A (the first type of equipment) fails. After surface propagation analysis, the following propagation influence scores (ranging from 0 to 1) are obtained:

[0068] Table 1 Exemplary propagation influence scores

[0069]

[0070] Set the propagation impact threshold to 0.5, then only retain the nodes (A, B, C) with a score greater than or equal to 0.5; perform cumulative calculation based on the average value of the propagation impact scores of these three nodes, and the output fault impact index is 0.78, indicating that this fault has a high propagation risk in the power plant system.

[0071] Through the above-mentioned surface-type fault propagation analysis, it can comprehensively and intuitively display the propagation impact range of the faulty equipment on the surrounding equipment, avoiding one-sided estimation of the fault impact; setting the propagation impact threshold and dividing the propagation impact network makes the definition of the fault impact range more scientific and reasonable, avoiding including equipment with less impact in the fault handling scope, improving the pertinence and efficiency of fault handling, and the finally output fault impact index provides a quantitative and intuitive fault impact assessment result for maintenance personnel, helping maintenance personnel quickly judge the severity of the fault and reasonably arrange maintenance resources, thereby improving the scientificity and effectiveness of the overall power plant equipment management.

[0072] In some implementation manners, perform surface-type fault propagation analysis in the power plant equipment model with the first type of equipment as the center, including propagation feature vectors of propagation range, impact degree, and duration; perform high-dimensional vector mapping on the propagation feature vectors, output high-dimensional propagation feature vectors, and calculate the propagation impact magnitude according to the high-dimensional propagation feature vectors.

[0073] Specifically, the propagation feature vector is a parameter set describing the fault propagation characteristics, including propagation range, impact degree, duration, etc.; among them, the propagation range refers to the coverage area where the fault propagates from the faulty equipment to other equipment; the impact degree refers to the impact intensity of the fault on other equipment; the duration refers to the duration of the fault impact.

[0074] Specifically, high-dimensional vector mapping is the process of transforming the original propagation feature vector into a higher-dimensional space, which can use mathematical transformation methods such as kernel function mapping and multi-layer perceptron (MLP) mapping; the high-dimensional propagation feature vector is the vector after mapping, which represents the propagation feature in the higher-dimensional space and can more accurately describe the complex characteristics of fault propagation; finally, calculate the propagation impact size according to the high-dimensional propagation feature vector. Exemplarily, the distance between the fault center and other nodes in the high-dimensional feature space can be calculated by calculating the modulus length, norm of the high-dimensional vector, using Euclidean distance, cosine similarity or other similarity measurement methods, etc., to obtain an index quantifying the propagation impact size, so as to more accurately evaluate the influence range and degree of fault propagation, and provide a more accurate basis for subsequent fault handling and decision-making. Preferably, after normalization processing, the propagation impact size is mapped to a preset range (such as 0 to 1) for comparing different fault scenarios.

[0075] S400: If the faulty device belongs to the second type of device, analyze the superimposed associated fault impact of the faulty device on each functional line according to the linear superposition alarm propagation analysis model, and output a fault impact index.

[0076] Specifically, the linear superposition alarm propagation analysis model is an analysis method specifically for multi-functional devices. This model assumes that the fault impact propagates linearly on each functional line, the impacts of each line can be independently quantified, and then the overall fault impact index is obtained through linear superposition to achieve comprehensive fault impact assessment.

[0077] By analyzing the fault impacts of each functional line separately and using the linear superposition method to obtain a comprehensive index, the quantification of the fault impact is made more intuitive. At the same time, the weights of each line can be dynamically adjusted according to different devices and actual on-site situations to improve the accuracy of the assessment.

[0078] In some embodiments, analyzing the superimposed associated fault impact of the faulty device on each functional line according to the linear superposition alarm propagation analysis model and outputting a fault impact index, the method includes:

[0079] If the faulty device belongs to the second type of device, obtain multiple functions of the faulty device; identify multiple functional lines of the faulty device in the power plant device model; the linear superposition alarm propagation analysis mode separately analyzes the fault impacts of the multiple functional lines, outputs multiple line fault impact indexes, and fuses the superimposed impacts of the multiple line fault impact indexes to output the fault impact index.

[0080] Specifically, when the faulty device belongs to the second category of devices (i.e., multi-function devices), firstly, the function list of the faulty device is extracted from the device management system or the device configuration file to identify the multiple functions of the device. For example, a multi-function device may simultaneously assume monitoring, control and communication functions. Then, according to the function of the faulty device, each function is mapped to the corresponding physical or logical line using the topological structure of the power plant equipment, thereby identifying the functional lines associated with each function in the power plant equipment model. For example, the control function corresponds to the control line, the monitoring function corresponds to the monitoring line, and the communication function corresponds to the communication line.

[0081] Specifically, for each functional line, a linear superposition alarm propagation analysis model is used to perform an independent fault impact assessment, such as the scope of fault propagation on the line, the degree of impact and the duration, to generate a fault impact index for each functional line. For example, the fault impact index of the control line may focus on the risk of interruption of signal transmission; the monitoring line focuses on data collection and feedback delay; and the communication line reflects the reliability and delay of data transmission.

[0082] Furthermore, a mathematical model (such as a weighted model based on propagation probability) is used to calculate the impact of each functional line, which can be expressed as:

[0083] ;

[0084] Among them, E i represents the fault impact index of the ith line, R i is the propagation range, I i is the degree of influence, T i is the duration, w i is the importance weight of the corresponding line.

[0085] Next, the fault impact indicators of each functional line are merged to generate a comprehensive fault impact indicator. For example, linear superposition or weighted average can be used; the final fault impact indicator reflects the overall impact of the faulty device superimposed on all its functional lines.

[0086] The use of a linear superposition alarm propagation analysis model to analyze the impact of multifunctional equipment failures can fully consider the differences in the role of equipment on different functional lines, avoiding the generality of traditional surface analysis models. By analyzing each line and conducting a comprehensive evaluation, the overall impact of the failure on the system can be accurately quantified, significantly improving the accuracy of the failure impact assessment. In addition, this analysis method can more comprehensively reflect the impact of the failure of multifunctional equipment, provide maintenance personnel with more accurate failure impact assessment results, help to reasonably arrange maintenance resources, reduce the impact of failures on the normal operation of the power plant, and thus improve the scientificity and effectiveness of the entire power plant equipment management.

[0087] In some implementations, to identify multiple functional lines of the faulty device in the power plant equipment model according to the multiple functions, the method includes:

[0088] Taking the faulty device as the starting point, select any one of the multiple functions to perform forward fault propagation and reverse fault propagation respectively, and output the corresponding forward propagation line and reverse propagation line under any one function; according to the forward propagation line and the reverse propagation line, obtain the corresponding functional line under any one function, and so on, and output the multiple functional lines corresponding to the multiple functions.

[0089] Specifically, by accessing a preset propagation rule library, perform forward fault propagation and reverse fault propagation on any one of the multiple functions respectively. Among them, forward fault propagation refers to the process of propagating faults from the faulty device downstream along the connection relationship between devices. For example, pump failure → flow rate decrease → heat exchanger overheat → generator overheat; reverse fault propagation refers to the process of propagating faults from the faulty device upstream along the connection relationship between devices.

[0090] Specifically, the forward propagation line and the reverse propagation line respectively represent the paths passed by the fault during the forward and reverse propagation processes. During the forward fault propagation process, starting from the faulty device, propagate the fault downstream along the connection relationship between devices, and record the path of the fault propagation to obtain the forward propagation line; during the reverse fault propagation process, starting from the faulty device, propagate the fault upstream along the connection relationship between devices, and record the path of the fault propagation to obtain the reverse propagation line.

[0091] Specifically, the functional line refers to the complete path formed by the faulty device and its affected upstream and downstream devices in a specific functional scenario. For example, for the control function, the forward propagation line and the reverse propagation line converge at the faulty device to form a complete control line.

[0092] By the above method, analyze the multiple functions of the faulty device in sequence and output the multiple functional lines corresponding to the multiple functions, which helps to accurately identify the specific influence paths of the multi-functional device in different functional scenarios, avoids missing or misjudging the fault propagation range, and provides a clear line basis for subsequent fault impact analysis.

[0093] In some implementations, the line-type superimposed alarm propagation analysis mode performs fault impact analysis on the multiple functional lines respectively and outputs multiple line fault impact indicators. The method includes:

[0094] Identify the weights of each of the multiple functional lines according to the device topology distance and device role, and output multiple identified functional lines; set the propagation attenuation coefficient, and centering on the faulty device, perform forward propagation attenuation and reverse propagation attenuation on the weights of each functional line according to the propagation attenuation coefficient to obtain the updated multiple identified functional lines; perform fault impact analysis on the updated multiple identified functional lines and output multiple line fault impact indicators.

[0095] Specifically, first, according to the physical location relationship (topology distance) of each functional line in the power plant equipment model and the role of each device in the system (such as core control, monitoring, communication, etc.), assign an initial weight to each functional line, and output each functional line as multiple identified functional lines according to the weight identification; for example, the closer the distance, the higher the probability of fault propagation, and a larger weight can be assigned; if the distance is farther, the weight is lower; the core device has a greater impact on fault propagation, and the corresponding functional line is assigned a higher weight.

[0096] Then, centering on the faulty device, considering the attenuation effect of the fault signal on each functional line, perform forward (fault propagating outward) and reverse (tracing upstream from the faulty device) propagation attenuation processing on the initial weight to update the weight of each functional line; among them, the preset attenuation coefficient is used to reflect the attenuation characteristics of the fault signal with distance and time.

[0097] Next, perform fault impact analysis on the updated multiple identified functional lines, comprehensively consider the weight value of each device and the attenuated influence, calculate the fault impact indicator of each functional line, and output multiple line fault impact indicators. Exemplarily, for each functional line, combine the updated weight and the fault propagation analysis result to calculate the fault impact indicator of this line; comprehensively process (for example, using weighted summation or weighted average) the fault impact indicators of all functional lines to generate an overall fault impact indicator, that is, the fault impact indicator superimposed by the faulty device on each functional line, reflecting the comprehensive risk of the fault.

[0098] The above steps can distinguish the differences in the roles of different devices in fault propagation by identifying weights according to the device topology distance and role, improving the refinement degree of fault impact analysis; setting the propagation attenuation coefficient and performing forward and reverse propagation attenuation simulates the weakening law of fault impact with distance propagation, making the fault impact assessment more in line with the actual situation. The finally output multiple line fault impact indicators provide accurate basic data for subsequent fault impact superposition analysis, helping to more accurately evaluate the overall impact of multi-functional device faults on the system.

[0099] In some implementation manners, the fault impact indicator is output by fusing the superimposed impacts of the multiple line fault impact indicators. The method includes:

[0100] Based on the power plant equipment model, obtain the importance coefficient of each functional line among the multiple functional lines; perform a superposition impact analysis on the integrated multiple line fault impact indicators according to the importance coefficients of each functional line, and output the fault impact indicators.

[0101] Specifically, first, according to the physical connections, functional dependencies, and roles in the system among devices (such as control, monitoring, communication, etc.), different importance coefficients are assigned to each functional line. The higher the value, the more crucial the role of the functional line in fault propagation. For example, a certain main pump plays a key role in the cooling efficiency of the main power generation loop in the system and is also involved in the regulation of the auxiliary circulation line. According to experience or model calculation, the influence weight of the main pump on the cooling efficiency of the "main power generation loop" can be set to 0.7, and the weight of the influence on the "auxiliary circulation line" is 0.3.

[0102] Furthermore, based on the obtained fault impact indicators and respective importance coefficients of the multiple functional lines, a weighted superposition analysis is performed to calculate the overall fault impact indicator. For example, for the two parts of the fault impact involved by the main pump, for the main power generation loop part: weighted value = 0.7 × fault impact indicator - main loop; for the auxiliary circulation line part: weighted value = 0.3 × fault impact indicator - auxiliary circulation line; overall fault impact indicator = 0.7 × fault impact indicator - main loop + 0.3 × fault impact indicator - auxiliary circulation line. The calculated overall fault impact indicator can be used to reflect the comprehensive impact of faults in key equipment or lines of the power plant on the operation safety and economy of the entire system, providing a quantitative basis for subsequent risk assessment, early warning, and emergency response.

[0103] In some implementation manners, the propagation attenuation coefficient is dynamically adjusted according to real-time operation data. Collect the alarm frequency and current load rate of the faulty device in the historical period, and calculate the dynamic attenuation coefficient. The expression is as follows:

[0104] ;

[0105] Among them, is the dynamic attenuation coefficient, is the initial attenuation coefficient, is the alarm frequency of the faulty device in the historical period, is the current load rate of the faulty device in the historical period, is the maximum allowable alarm frequency of the faulty device in the historical period, is the rated load rate.

[0106] Specifically, the propagation attenuation coefficient is a parameter used to describe the weakening of the fault impact as it propagates over distance, reflecting the attenuation law of the fault impact during propagation. This propagation attenuation coefficient is determined based on real-time operation data to better conform to the current equipment operation status. Among them, real-time operation data refers to the data generated during the actual operation of the equipment, such as alarm frequency, load rate, etc.

[0107] Specifically, in the formula part, it is used to dynamically adjust the attenuation coefficient according to the alarm frequency of the equipment: when the historical alarm frequency of the equipment is high (f is large), the value of this item will increase, thereby increasing , reflecting that the fault impact attenuates faster during propagation, indicating a higher risk of equipment failure; conversely, when the alarm frequency is low, this item approaches 1, and the attenuation coefficient is mainly determined by the base value.

[0108] Specifically, the part is used to reflect the ratio of the current load rate of the equipment to the rated load rate: when the load rate of the equipment is high (close to or exceeding the rated load rate), this ratio tends to 1 or higher, indicating that the equipment is in a high-load state, which may cause the fault impact to be more significant, and thus adjust the attenuation coefficient to reflect this; when the load rate is low, this ratio is small, and the dynamic attenuation coefficient is correspondingly reduced.

[0109] Through the above dynamic adjustment method based on real-time data, the fault propagation analysis can be made more in line with the actual operation status of the equipment, improving the accuracy and timeliness of the analysis results, enabling more refined control of the fault impact propagation, and providing a scientific basis for power plant equipment scheduling, fault warning, and maintenance decision-making.

[0110] In summary, the BIM-based power plant equipment alarm data linkage analysis method provided by the present invention has the following technical effects:

[0111] By exporting the power plant equipment model, which is obtained by modeling the power plant equipment through BIM modeling tools; classifying according to equipment functions, the equipment in the power plant equipment model is divided into the first type of equipment and the second type of equipment. Among them, the first type of equipment refers to the power plant equipment with independent functions, and the second type of equipment refers to the power plant equipment with multiple functions; obtaining the faulty equipment that issues an alarm signal. If the faulty equipment belongs to the first type of equipment, the associated fault impact analysis is carried out based on the surface-type alarm propagation analysis model, and the fault impact index is output; if the faulty equipment belongs to the second type of equipment, based on the line-type superposition alarm propagation analysis model, the superposition associated fault impact of the equipment on each functional line is analyzed, and the fault impact index is output, thereby achieving the technical effects of improving the analysis accuracy, enhancing the generality and adaptability.

[0112] Embodiment 2, as Figure 2It is a schematic structural diagram of the BIM-based power plant equipment alarm data linkage analysis system of the present invention. For example, Figure 1 In the present invention, the schematic flow diagram of the BIM-based power plant equipment alarm data linkage analysis method can be implemented through a structure as shown in Figure 2 shown.

[0113] Based on the same concept as the BIM-based power plant equipment alarm data linkage analysis method in the above embodiment, the BIM-based power plant equipment alarm data linkage analysis system provided by the present invention further includes:

[0114] A model export unit 11, configured to export a power plant equipment model, where the power plant equipment model is obtained by modeling power plant equipment through a BIM modeling tool.

[0115] An equipment classification unit 12, configured to classify the equipment in the power plant equipment model into a first type of equipment and a second type of equipment according to equipment functions, where the first type of equipment is a power plant equipment with independent functions, and the second type of equipment is a power plant equipment with multiple functions.

[0116] A surface alarm analysis unit 13, configured to obtain a faulty equipment that issues an alarm signal. If the faulty equipment belongs to the first type of equipment, perform an associated fault impact analysis on the faulty equipment according to a surface alarm propagation analysis model, and output a fault impact index.

[0117] A line alarm analysis unit 14, configured to, if the faulty equipment belongs to the second type of equipment, analyze the superimposed associated fault impacts of the faulty equipment on each functional line according to a line superposition alarm propagation analysis model, and output a fault impact index.

[0118] Among them, the line alarm analysis unit 14 includes:

[0119] A function identification unit, configured to, if the faulty equipment belongs to the second type of equipment, obtain multiple functions of the faulty equipment.

[0120] A functional line identification unit, configured to identify multiple functional lines of the faulty equipment in the power plant equipment model according to the multiple functions.

[0121] A line fault impact analysis unit, configured to perform fault impact analysis on the multiple functional lines respectively according to the line superposition alarm propagation analysis model, output multiple line fault impact indexes, and fuse the superimposed impacts of the multiple line fault impact indexes to output the fault impact index.

[0122] In some implementation manners, the functional line identification unit in the line alarm analysis unit 14 includes:

[0123] A fault propagation line analysis unit, which is used to take the faulty device as the starting point, select any one of the multiple functions to perform forward fault propagation and reverse fault propagation respectively, and output the corresponding forward propagation line and reverse propagation line under any one function.

[0124] A function line output unit, which is used to obtain the corresponding function line under any one function according to the forward propagation line and the reverse propagation line, and so on, and output the multiple function lines corresponding to the multiple functions.

[0125] In some implementation manners, the line fault impact analysis unit in the line alarm analysis unit 14 includes:

[0126] A function line weight identification unit, which is used to identify the weights of each of the multiple function lines according to the device topological distance and the device role, and output multiple identified function lines.

[0127] A propagation attenuation coefficient setting and weight update unit, which is used to set the propagation attenuation coefficient, and take the faulty device as the center, perform forward propagation attenuation and reverse propagation attenuation on the weights of each function line according to the propagation attenuation coefficient, and obtain the updated multiple identified function lines.

[0128] A line fault impact analysis unit, which is used to perform fault impact analysis on the updated multiple identified function lines, and output multiple line fault impact indicators.

[0129] In some implementation manners, the line fault impact analysis unit in the line alarm analysis unit 14 further includes:

[0130] A function line importance coefficient acquisition unit, which is used to acquire the importance coefficient of each function line in the multiple function lines based on the power plant equipment model.

[0131] A fault impact indicator output unit, which is used to perform superposition impact analysis on the fused multiple line fault impact indicators according to the importance coefficient of each function line, and output the fault impact indicator.

[0132] Wherein, the propagation attenuation coefficient is dynamically adjusted according to the real-time operation data, the alarm frequency and the current load rate of the faulty device in the historical period are collected, and the dynamic attenuation coefficient is calculated. The expression is as follows:

[0133] ;

[0134] Wherein, is the dynamic attenuation coefficient, is the initial attenuation coefficient, is the alarm frequency of the faulty device in the historical period, is the current load rate of the faulty device in the historical period, is the maximum allowable alarm frequency of the faulty device within the historical period, is the rated load factor.

[0135] In some embodiments, the surface alarm analysis unit 13 includes:

[0136] A surface fault propagation analysis unit, configured to perform surface fault propagation analysis in the power plant equipment model with the first type of device as the center if the faulty device belongs to the first type of device, and output a power equipment model marked based on the propagation influence magnitude.

[0137] A propagation influence network generation unit, configured to set a propagation influence threshold, and perform edge division on the power equipment model marked based on the propagation influence magnitude with the propagation influence threshold to obtain a propagation influence network based on the faulty device.

[0138] A fault influence index calculation unit, configured to calculate the propagation influence network and output the fault influence index.

[0139] In some implementation manners, performing surface fault propagation analysis in the power plant equipment model with the first type of device as the center in the surface fault propagation analysis unit includes propagation feature vectors of propagation range, influence degree, and duration. Perform high-dimensional vector mapping on the propagation feature vectors, output high-dimensional propagation feature vectors, and calculate the propagation influence magnitude according to the high-dimensional propagation feature vectors.

[0140] In some embodiments, the model export unit 11 includes:

[0141] A file export and storage unit, configured to export the power plant equipment model as an IFC format file, and store it in the target graph database after parsing the IFC format file.

[0142] A relevance analysis and model output unit, configured to call the target graph database to perform power plant equipment relevance analysis and output a power plant equipment model with relevance.

[0143] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the foregoing Embodiment 1 are equally applicable to the BIM-based power plant equipment alarm data linkage analysis system described in Embodiment 2. For the sake of brevity of the specification, no further elaboration is made here.

[0144] It should be understood that the embodiments and the above descriptions disclosed in the present invention enable those skilled in the art to implement the present invention using the present invention. At the same time, the present invention is not limited to the above-mentioned part of the embodiments. It should be understood that those of ordinary skill in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for linkage analysis of power plant equipment alarm data based on BIM, characterized in that, Including: Exporting a power plant equipment model, which is obtained by modeling power plant equipment through a BIM modeling tool; Dividing the equipment in the power plant equipment model into first-class equipment and second-class equipment according to equipment functions, where the first-class equipment are power plant equipment with independent functions, and the second-class equipment are power plant equipment with multiple functions; Obtaining a faulty equipment that issues an alarm signal. If the faulty equipment belongs to the first-class equipment, performing an associated fault impact analysis on the faulty equipment according to a surface-type alarm propagation analysis model, and outputting a fault impact index; If the faulty equipment belongs to the second-class equipment, analyzing the superimposed associated fault impacts of the faulty equipment on each functional line according to a line-type superimposed alarm propagation analysis model, and outputting a fault impact index; Analyzing the superimposed associated fault impacts of the faulty equipment on each functional line according to a line-type superimposed alarm propagation analysis model, and outputting a fault impact index, including: If the faulty equipment belongs to the second-class equipment, obtaining multiple functions of the faulty equipment; Identifying multiple functional lines of the faulty equipment in the power plant equipment model according to the multiple functions; The line-type superimposed alarm propagation analysis mode performs fault impact analysis on the multiple functional lines respectively, outputs multiple line fault impact indexes, and fuses the superimposed impacts of the multiple line fault impact indexes to output the fault impact index; If the faulty equipment belongs to the first-class equipment, performing an associated fault impact analysis on the faulty equipment according to a surface-type alarm propagation analysis model, and outputting a fault impact index, including: If the faulty equipment belongs to the first-class equipment, performing a surface-type fault propagation analysis in the power plant equipment model with the first-class equipment as the center, and outputting a power equipment model marked based on the size of the propagation impact; Setting a propagation impact threshold, and performing edge division on the power equipment model marked based on the size of the propagation impact with the propagation impact threshold to obtain a propagation impact network based on the faulty equipment; Calculating the propagation impact network and outputting the fault impact index.

2. The BIM-based power plant equipment alarm data linkage analysis method according to claim 1, wherein Identifying multiple functional lines of the faulty equipment in the power plant equipment model according to the multiple functions, including: Taking the faulty equipment as the starting point, selecting any one of the multiple functions to perform forward fault propagation and reverse fault propagation respectively, and outputting the corresponding forward propagation line and reverse propagation line under any one function; According to the forward propagation line and the reverse propagation line, obtaining the corresponding functional line under any one function, and so on, outputting multiple functional lines corresponding to the multiple functions.

3. The BIM-based power plant equipment alarm data linkage analysis method according to claim 1, characterized in that The line-type superimposed alarm propagation analysis mode performs fault impact analysis on the multiple functional lines respectively, and outputs multiple line fault impact indexes, including: Performing weight marking on each of the multiple functional lines according to equipment topological distance and equipment role, and outputting multiple marked functional lines; Setting a propagation attenuation coefficient, and performing forward propagation attenuation and reverse propagation attenuation on the weights of each functional line with the faulty equipment as the center according to the propagation attenuation coefficient to obtain multiple updated marked functional lines; Perform fault impact analysis on multiple updated identification function circuits and output multiple circuit fault impact indicators.

4. The BIM-based power plant equipment alarm data linkage analysis method according to claim 1, wherein Fuse the superimposed impacts of the multiple circuit fault impact indicators to output the fault impact indicator, including: Based on the power plant equipment model, obtain the importance coefficient of each function circuit in the multiple function circuits; Perform a superimposed impact analysis on the fusion of the multiple circuit fault impact indicators according to the importance coefficient of each function circuit, and output the fault impact indicator.

5. The method for linked analysis of power plant equipment alarm data based on BIM according to claim 3, wherein The propagation attenuation coefficient is dynamically adjusted according to real-time operation data. Collect the alarm frequency and current load rate of the faulty equipment in the historical period, and calculate the dynamic attenuation coefficient. The expression is as follows: ; Among them, is the dynamic attenuation coefficient, is the initial attenuation coefficient, is the alarm frequency of the faulty device within the historical period, is the current load rate of the faulty device within the historical period, is the maximum allowable alarm frequency of the faulty device within the historical period, is the rated load rate.

6. The BIM-based power plant equipment alarm data linkage analysis method according to claim 1, wherein Perform surface fault propagation analysis in the power plant equipment model with the first type of equipment as the center, including propagation characteristic vectors of propagation range, impact degree, and duration; Perform high-dimensional vector mapping on the propagation characteristic vectors, output high-dimensional propagation characteristic vectors, and calculate the propagation impact size according to the high-dimensional propagation characteristic vectors.

7. The BIM-based power plant equipment alarm data linkage analysis method according to claim 1, characterized in that Export the power plant equipment model, including: Export the power plant equipment model as an IFC format file, and store it in the target graph database after parsing the IFC format file; Call the target graph database to perform power plant equipment correlation analysis and output a correlated power plant equipment model.

8. The BIM-based power plant equipment alarm data linkage analysis system is characterized in that For implementing the BIM-based power plant equipment alarm data linkage analysis method according to any one of claims 1-7, including: A model export unit for exporting a power plant equipment model, which is obtained by modeling power plant equipment through a BIM modeling tool; An equipment classification unit for classifying the equipment in the power plant equipment model into a first type of equipment and a second type of equipment according to equipment functions. The first type of equipment is a power plant equipment with independent functions, and the second type of equipment is a power plant equipment with multiple functions; A surface alarm analysis unit for obtaining a faulty equipment that issues an alarm signal. If the faulty equipment belongs to the first type of equipment, perform associated fault impact analysis on the faulty equipment according to the surface alarm propagation analysis model and output a fault impact indicator; A line alarm analysis unit for, if the faulty equipment belongs to the second type of equipment, analyzing the superimposed associated fault impacts of the faulty equipment on each function circuit according to the line-type superimposed alarm propagation analysis model and outputting a fault impact indicator; The line alarm analysis unit includes: A function identification unit for, if the faulty equipment belongs to the second type of equipment, obtaining multiple functions of the faulty equipment; A function circuit identification unit for identifying multiple function circuits of the faulty equipment in the power plant equipment model according to the multiple functions; A circuit fault impact analysis unit for performing fault impact analysis on each of the multiple function circuits respectively according to the line-type superimposed alarm propagation analysis mode, outputting multiple circuit fault impact indicators, and fusing the superimposed impacts of the multiple circuit fault impact indicators to output the fault impact indicator; The surface alarm analysis unit includes: A surface fault propagation analysis unit, which is used to perform surface fault propagation analysis in the power plant equipment model with the first type of equipment as the center if the faulty equipment belongs to the first type of equipment, and output a power equipment model marked based on the size of the propagation impact; A propagation impact network generation unit, which is used to set a propagation impact threshold, and perform edge division on the power equipment model marked based on the size of the propagation impact with the propagation impact threshold to obtain a propagation impact network based on the faulty equipment; A fault impact index calculation unit, which is used to calculate the propagation impact network and output the fault impact index.

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