Power plant equipment alarm data linkage analysis method and system based on BIM

Abstract

The invention discloses a BIM-based power plant equipment alarm data linkage analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: exporting a power plant equipment model, and enabling the power plant equipment model to carry out the modeling obtaining of power plant equipment through a BIM modeling tool; equipment in the power plant equipment model is divided into a first type of equipment and a second type of equipment according to equipment functions, the first type of equipment is power plant equipment with independent functions, and the second type of equipment is multifunctional power plant equipment; acquiring a fault device which sends out an alarm signal, if the fault device belongs to a first type of device, performing associated fault influence analysis on the fault device according to a surface type alarm propagation analysis model, and outputting a fault influence index; and if the fault equipment belongs to the second type of equipment, the superposition correlation fault influence of the fault equipment in each functional line is analyzed according to the linear superposition alarm propagation analysis model, and a fault influence index is output. Therefore, the technical effects of improving the analysis precision and enhancing the universality and adaptability are achieved.

Description

Technical Field

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

[0002] The existing BIM-based power plant equipment alarm data linkage analysis system can usually achieve the establishment and simple analysis of equipment models, such as visual management of equipment through BIM models, and preliminary data correlation analysis when equipment fault alarms occur, to help maintenance personnel quickly locate the problem area, but there are certain limitations. When processing the fault alarm data of power plant equipment, a unified analysis model is usually used to perform correlation fault impact analysis on all equipment. This method is similar to a surface correlation analysis and does not fully consider the functional characteristics of different equipment and the differences in their functions in different functional lines. Especially for those power plant equipment with multiple functions, this unified surface analysis model cannot accurately evaluate the degree of fault impact on each functional line, resulting in inaccurate and incomplete analysis results, which makes it difficult to meet the needs of refined equipment management in complex power plant environments. Summary of the invention

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

[0004] In a first aspect, the present invention provides a method for linkage analysis of power plant equipment alarm data based on BIM, wherein the method for linkage analysis of power plant equipment alarm data based on BIM comprises:

[0005] The power plant equipment model is exported, and the power plant equipment model is obtained by modeling the power plant equipment through a BIM modeling tool.

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

[0007] The faulty device that sends out the alarm signal is obtained. If the faulty device belongs to the first type of device, an associated fault impact analysis is performed on the faulty device according to the surface alarm propagation analysis model, and a fault impact index is output.

[0008] If the faulty device belongs to the second type of device, the impact of the superimposed associated faults of the faulty device on each functional line is analyzed according to the linear superimposed alarm propagation analysis model, and a fault impact index is output.

[0009] In a feasible implementation, the influence of the superimposed associated faults of the faulty equipment on each functional line is analyzed according to the linear superimposed alarm propagation analysis model, and a fault influence index is outputted. The method includes:

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

[0011] A plurality of functional circuits of the faulty device are identified in the power plant device model according to the plurality of functions.

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

[0013] In a feasible implementation, a plurality of functional lines of the faulty equipment are identified in the power plant equipment model according to the plurality of functions, and the method includes:

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

[0015] According to the forward propagation circuit and the reverse propagation circuit, a functional circuit corresponding to any function is obtained, and so on, multiple functional circuits corresponding to the multiple functions are output.

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

[0017] Each of the multiple functional lines is weighted and identified according to the device topological distance and the device role, and multiple identified functional lines are output.

[0018] A propagation attenuation coefficient is set, and with the faulty device as the center, forward propagation attenuation and reverse propagation attenuation are performed on the weights of each functional line according to the propagation attenuation coefficient to obtain a plurality of updated identified functional lines.

[0019] Perform fault impact analysis using the updated multiple identification function lines and output multiple line fault impact indicators.

[0020] In a feasible implementation, the superposition influence of the multiple line fault influence indicators is integrated to output the fault influence indicator, and the method includes:

[0021] Based on the power plant equipment model, an importance coefficient of each functional line in the multiple functional lines is obtained.

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

[0023] In a feasible implementation, 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] in, is the dynamic attenuation coefficient, is the initial attenuation coefficient, is the alarm frequency of the faulty equipment in the historical period, is the current load rate of the faulty equipment 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, if the faulty device belongs to the first type of device, an associated fault impact analysis is performed on the faulty device according to a surface alarm propagation analysis model, and a fault impact index is output, the method comprising:

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

[0028] A propagation impact threshold is set, and the edge division of the power equipment model based on the propagation impact size identifier is performed based on the propagation impact threshold to obtain a propagation impact network based on the faulty equipment.

[0029] The propagation impact network is calculated to output the fault impact index.

[0030] In a feasible implementation, a surface fault propagation analysis is performed 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.

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

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

[0033] The power plant equipment model is exported as an IFC format file, and the IFC format file is parsed and stored in the target graph database.

[0034] The target graph database is called to perform power plant equipment correlation analysis, and a power plant equipment model with correlation is output.

[0035] In a second aspect, the present invention further provides a BIM-based power plant equipment alarm data linkage analysis system, wherein the BIM-based power plant equipment alarm data linkage analysis system comprises:

[0036] The model exporting unit is used to export the power plant equipment model, wherein the power plant equipment model is obtained by modeling the power plant equipment using a BIM modeling tool.

[0037] The equipment classification unit is used to classify the equipment in the power plant equipment model into first-category equipment and second-category equipment according to equipment functions, wherein the first-category equipment is power plant equipment with independent functions, and the second-category equipment is power plant equipment with multiple functions.

[0038] The surface alarm analysis unit is used to obtain the faulty device that sends out the alarm signal. If the faulty device belongs to the first type of device, the faulty device is subjected to associated fault impact analysis according to the surface alarm propagation analysis model, and a fault impact index is output.

[0039] The line alarm analysis unit is used to analyze the impact of the superimposed associated faults of the faulty device on each functional line according to the linear superimposed alarm propagation analysis model if the faulty device belongs to the second type of device, and output a fault impact index.

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

[0041] A function identification unit is used to obtain multiple functions of the faulty device if the faulty device belongs to the second type of device.

[0042] A functional circuit identification unit is used to identify multiple functional circuits of the faulty equipment in the power plant equipment model according to the multiple functions.

[0043] The line fault impact analysis unit is used to perform fault impact analysis on the multiple functional lines respectively in the linear superposition alarm propagation analysis mode, output multiple line fault impact indicators, and fuse the superposition impact of the multiple line fault impact indicators to output the fault impact indicator.

[0044] The present invention discloses a power plant equipment alarm data linkage analysis method and system based on BIM, comprising: exporting a power plant equipment model, the model is modeled and acquired by a BIM modeling tool for the power plant equipment; classifying the equipment in the power plant equipment model into first-category equipment and second-category equipment according to equipment function, wherein the first-category equipment refers to power plant equipment with independent functions, and the second-category equipment refers to power plant equipment with multiple functions; acquiring a faulty device that sends an alarm signal, and if the faulty device belongs to the first-category equipment, performing an associated fault impact analysis based on a surface-type alarm propagation analysis model, and outputting a fault impact index; if the faulty device belongs to the second-category equipment, analyzing the superimposed associated fault impact of the device on each functional line based on a line-type superimposed alarm propagation analysis model, and outputting a fault impact index. The power plant equipment alarm data linkage analysis method and system based on BIM disclosed by the present invention solve the technical problems of low adaptability and inaccurate and incomplete analysis results, and achieve the technical effects of improving analysis accuracy, enhancing versatility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0048] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0049] Embodiment 1, as Figure 1 The present invention is a flow chart of a method for linking and analyzing power plant equipment alarm data based on BIM, wherein the method for linking and analyzing power plant equipment alarm data based on BIM includes:

[0050] S100: Exporting a power plant equipment model, wherein the power plant equipment model is obtained by modeling the power plant equipment using a BIM modeling tool.

[0051] Specifically, through BIM modeling tools such as Revit and ArchiCAD, accurate three-dimensional modeling of power plant equipment can be performed, and various attribute information of the equipment can be given, such as equipment type, functional parameters, connection relationships, etc.; exporting the power plant equipment model is to save and extract 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, a method of deriving a power plant equipment model includes:

[0053] The power plant equipment model is exported as an IFC format file, and the IFC format file is parsed and stored in a target graph database; the target graph database is called to perform power plant equipment correlation analysis, and a power plant equipment model with correlation is output.

[0054] For example, first, use BIM modeling tools to model power plant equipment, including drawing the geometric shape of the equipment, defining the properties of the equipment (such as equipment name, model, function, etc.), and establishing topological relationships between equipment (such as pipeline connection, electrical connection, etc.); then, export the built power plant equipment model to an IFC format file, because the IFC format is an open, 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, which has good associative data storage and query performance, and is suitable for storing and managing power plant equipment model data with complex associative relationships; finally, call the power plant equipment model in the target graph database for correlation analysis, such as analyzing the physical connection relationship and functional dependency relationship between equipment, and output the associative power plant equipment model to provide 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, so that the model data can be seamlessly shared and exchanged between different systems, avoiding the errors and time costs caused by repeated data entry and format conversion. By storing the model in a graph database, it is possible to efficiently perform equipment correlation analysis and quickly extract the complex relationship between 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 rapid and accurate response to power plant equipment failures.

[0056] S200: Dividing the equipment in the power plant equipment model into first-category equipment and second-category equipment according to equipment functions, wherein the first-category equipment is power plant equipment with independent functions, and the second-category equipment is power plant equipment with multiple functions.

[0057] Specifically, equipment function refers to the specific tasks or roles that power plant equipment performs 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. Power plant equipment with independent functions refers to equipment that only performs a single function. Such equipment usually performs a single, clear function, and its operation and maintenance are relatively independent, such as ordinary transmission lines, single cooling equipment, etc. Multifunctional power plant equipment refers to equipment that can perform multiple different functions at the same time. Such equipment can perform multiple functions and may involve multiple systems or processes. Its management and maintenance need to comprehensively consider its multiple functions. For example, some large generator sets may have multiple functions such as power generation, frequency regulation, and voltage regulation at the same time.

[0058] Through the above classification method, the equipment is divided into two categories, and the 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 operating efficiency and equipment reliability of the power plant.

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

[0060] Specifically, the surface alarm propagation analysis model regards the equipment and their relationships as a whole planar network, and evaluates the impact of the fault on the entire system by analyzing the propagation path and impact range of the faulty equipment in the network. The fault impact index is a quantitative parameter to measure the degree of fault impact, such as the size of the impact range, the number of affected devices, the duration of the impact, etc.

[0061] In some embodiments, if the faulty device belongs to the first type of device, performing a correlation fault impact analysis on the faulty device according to a surface alarm propagation analysis model and outputting a fault impact index, the method includes:

[0062] If the faulty equipment belongs to the first category of equipment, a surface fault propagation analysis is performed in the power plant equipment model with the first category of equipment as the center, and an electric power equipment model based on the propagation impact size identifier is output; a propagation impact threshold is set, and the electric power equipment model based on the propagation impact size identifier is edge-divided using the propagation impact threshold to obtain a propagation impact network based on the faulty equipment; the propagation impact network is calculated, and the fault impact index is output.

[0063] Specifically, the operating status of the power plant equipment is monitored in real time, and when it is detected that the equipment sends an alarm signal, the faulty equipment information that sends the alarm signal is immediately obtained. Then, according to the previous equipment classification results, it is determined whether the faulty equipment belongs to the first category equipment or the second category equipment.

[0064] Specifically, if the faulty equipment belongs to the first category of equipment, that is, power plant equipment with independent functions, a surface alarm propagation analysis model is used for analysis. For example, the equipment is used as the central node of the analysis, and a graph structure model is constructed based on the physical connection and business logic relationship of the equipment in the power plant, in which each node represents each type of equipment in the power plant, and the edge represents the physical, logical or information connection between the equipment; with the faulty equipment as the center, fault propagation analysis is performed along each connection edge in the power plant equipment model, including calculating the degree of influence of the fault propagation from the central node to the surrounding equipment through a graph attention mechanism, a transfer probability model or a propagation function, and marking the size of the propagation impact received by each node, such as expressed by a numerical value or level (for example, "high", "medium", "low"), so as to quickly and comprehensively evaluate the potential impact of the failure of independent functional equipment on the surrounding equipment and the entire system, and provide maintenance personnel with quantitative information on the impact of the fault so that timely measures can be taken.

[0065] Furthermore, based on actual experience and historical data, a propagation impact threshold is preset to distinguish between equipment that is more severely affected and equipment that is less affected. For example, an impact degree of 0.5 is set as the propagation impact threshold, and nodes exceeding this value can be considered to be significantly affected. Then, the propagation impact threshold is used to screen the propagation impact size of each node in the power plant equipment model obtained after the surface fault propagation analysis, and the edges or nodes with an impact degree lower than the threshold are removed. The remaining parts constitute a propagation impact network based on the faulty equipment, which only contains equipment that is strongly associated with the fault. Optionally, this network can be represented by a graph, in which the weight of the node reflects the size of its propagation impact, and the weight of the edge can represent the probability or intensity of fault transmission between adjacent equipment.

[0066] Furthermore, the propagation impact network is statistically analyzed and comprehensively calculated to output an overall fault impact index, wherein methods such as weighted average of node weights and cumulative propagation impact sum can be used as fault impact indicators. For example, the sum of the propagation impact weights of all nodes is calculated and normalized as the overall impact value of the fault, which is used to reflect the degree of impact that the fault may cause in the power plant system, and to provide reference for subsequent scheduling, early warning and maintenance decisions.

[0067] Example description: Assume that in a power plant equipment model, faulty equipment A (first-class equipment) fails. After surface propagation analysis, the following propagation impact score is obtained (value range 0-1):

[0068] Table 1 Example of communication impact scores

[0069] If the propagation impact threshold is set to 0.5, only nodes with scores greater than or equal to 0.5 (A, B, and C) will be retained. The propagation impact scores of these three nodes are cumulatively calculated based on the average value, and the output fault impact index is 0.78, indicating that the fault has a high propagation risk in the power plant system.

[0070] Through the above-mentioned surface fault propagation analysis, the propagation impact range of the faulty equipment on the surrounding equipment can be fully and intuitively displayed, 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 the inclusion of equipment with less impact into the fault handling scope, and improving the pertinence and efficiency of fault handling. The final output fault impact index provides maintenance personnel with a quantitative and intuitive fault impact assessment result, which helps maintenance personnel quickly judge the severity of the fault and reasonably arrange maintenance resources, thereby improving the scientificity and effectiveness of the entire power plant equipment management.

[0071] In some implementations, a surface fault propagation analysis is performed 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; the propagation feature vectors are mapped to high-dimensional vectors to output high-dimensional propagation feature vectors, and the propagation impact size is calculated based on the high-dimensional propagation feature vectors.

[0072] Specifically, the propagation feature vector is a set of parameters that describe 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 device to other devices; the impact degree refers to the intensity of the impact of the fault on other devices; and the duration refers to the duration of the fault impact.

[0073] Specifically, high-dimensional vector mapping is the process of converting the original propagation feature vector to a higher-dimensional space, and mathematical transformation methods such as kernel function mapping and multi-layer perceptron (MLP) mapping can be used; the high-dimensional propagation feature vector is the mapped vector, which represents the propagation characteristics in a higher-dimensional space and can more accurately describe the complex characteristics of fault propagation; finally, the propagation impact size is calculated based on the high-dimensional propagation feature vector. For example, an indicator for quantifying the propagation impact size can be obtained by calculating the modulus and norm of the high-dimensional vector, using Euclidean distance, cosine similarity or other similarity measurement methods to calculate the distance between the fault center and other nodes in the high-dimensional feature space, etc., so as to more accurately evaluate the scope and degree of the impact of fault propagation and provide a more accurate basis for subsequent fault handling and decision-making. Preferably, after normalization, the propagation impact size is mapped to a preset range (e.g., 0 to 1) to compare different fault scenarios.

[0074] S400: If the faulty device belongs to the second type of device, analyze the impact of the superimposed associated faults of the faulty device on each functional line according to the linear superimposed alarm propagation analysis model, and output a fault impact index.

[0075] Specifically, the linear superposition alarm propagation analysis model is an analysis method specifically for multifunctional equipment. The model assumes that the fault impact is propagated linearly on each functional line. The impact of each line can be quantified independently, and then the overall fault impact index is obtained through linear superposition to achieve a comprehensive fault impact assessment.

[0076] By analyzing the fault impact of each functional line separately and using the linear superposition method to obtain a comprehensive index, the quantification of the fault impact is more intuitive. At the same time, the weight of each line can be dynamically adjusted according to different equipment and actual on-site conditions to improve the accuracy of the evaluation.

[0077] In some embodiments, the influence of the superimposed associated faults of the faulty device on each functional line is analyzed according to the linear superimposed alarm propagation analysis model, and a fault influence index is outputted. The method includes:

[0078] If the faulty device belongs to the second type of equipment, multiple functions of the faulty device are obtained; multiple functional lines of the faulty device are identified in the power plant equipment model according to the multiple functions; the linear superposition alarm propagation analysis mode performs fault impact analysis on the multiple functional lines respectively, outputs multiple line fault impact indicators, and integrates the superposition impact of the multiple line fault impact indicators to output the fault impact indicator.

[0079] 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.

[0080] 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.

[0081] 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:

[0082] ;

[0083] 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.

[0084] 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.

[0085] 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.

[0086] In some implementations, a plurality of functional circuits of the faulty equipment are identified in the power plant equipment model according to the plurality of functions, and the method includes:

[0087] 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 circuit and reverse propagation circuit under any function; according to the forward propagation circuit and reverse propagation circuit, obtain the corresponding functional circuit under any function, and so on, output multiple functional circuits corresponding to the multiple functions.

[0088] Specifically, by accessing the preset propagation rule library, forward fault propagation and reverse fault propagation are performed on any of the multiple functions, wherein forward fault propagation refers to the process of propagating the fault from the faulty device to the downstream device along the connection relationship between the devices, for example, pump failure → flow drop → heat exchanger overheating → generator overheating; reverse fault propagation refers to the process of propagating the fault from the faulty device to the upstream device along the connection relationship between the devices.

[0089] Specifically, the forward propagation line and the reverse propagation line represent the paths that the fault passes through during the forward and reverse propagation processes, respectively. During the forward fault propagation process, starting from the faulty device, the fault is propagated to the downstream device along the connection relationship between the devices, and the path of fault propagation is recorded to obtain the forward propagation line; during the reverse fault propagation process, starting from the faulty device, the fault is propagated to the upstream device along the connection relationship between the devices, and the path of fault propagation is recorded to obtain the reverse propagation line.

[0090] Specifically, a functional line refers to the complete path formed by a 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.

[0091] Through the above method, multiple functions of the faulty equipment are analyzed in turn, and multiple functional lines corresponding to the multiple functions are output, which helps to accurately identify the specific impact paths of the multi-functional equipment in different functional scenarios, avoids omissions or misjudgments of the fault propagation scope, and provides a clear line basis for subsequent fault impact analysis.

[0092] In some implementations, the linear superposition alarm propagation analysis mode performs fault impact analysis on the multiple functional lines respectively and outputs multiple line fault impact indicators, and the method includes:

[0093] Weight identification is performed on each of the multiple functional lines according to the device topological distance and the device role, and multiple identified functional lines are output; a propagation attenuation coefficient is set, and with the faulty device as the center, forward propagation attenuation and reverse propagation attenuation are performed on the weight of each functional line according to the propagation attenuation coefficient to obtain updated multiple identified functional lines; fault impact analysis is performed on the updated multiple identified functional lines, and multiple line fault impact indicators are output.

[0094] Specifically, first, according to the physical position relationship (topological 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.), an initial weight is assigned to each functional line, and each functional line is output as multiple identified functional lines according to the weight label; for example, the closer the distance, the higher the possibility of fault propagation, and a larger weight can be assigned; the farther the distance, the lower the weight; the core equipment has a greater impact on fault propagation, and its corresponding functional line is assigned a higher weight.

[0095] Then, with the faulty device as the center, considering the attenuation effect of the fault signal on each functional line, the initial weights are processed with forward (fault propagation outward) and reverse (tracing back upstream from the faulty device) propagation attenuation, and the weights of each functional line are updated; among them, the preset attenuation coefficient is used to reflect the attenuation characteristics of the fault signal with distance and time.

[0096] Next, a fault impact analysis is performed on the updated multiple identified functional lines, and the weight value and the attenuated influence of each device are comprehensively considered to calculate the fault impact index of each functional line, and multiple line fault impact indexes are output. For example, for each functional line, the fault impact index of the line is calculated in combination with the updated weight and the fault propagation analysis result; the fault impact indicators of all functional lines are combined (for example, using weighted summation or weighted average) to generate an overall fault impact index, that is, the fault impact index of the faulty equipment superimposed on each functional line, reflecting the comprehensive risk of the fault.

[0097] The above steps, by weighting the equipment topological distance and role, can distinguish the differences in the role of different equipment in fault propagation, improving the refinement 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 multiple line fault impact indicators finally output provide accurate basic data for the subsequent fault impact superposition analysis, which helps to more accurately evaluate the overall impact of multi-function equipment failures on the system.

[0098] In some implementations, fusing the superimposed impact of the multiple line fault impact indicators to output the fault impact indicator, the method includes:

[0099] Based on the power plant equipment model, the importance coefficient of each functional line in the multiple functional lines is obtained; according to the importance coefficient of each functional line, a superposition impact analysis is performed on the fusion of the multiple line fault impact indicators, and the fault impact indicator is output.

[0100] Specifically, first, different importance coefficients are assigned to each functional line according to the physical connection between the equipment, the functional dependency and the role in the system (for example, control, monitoring, communication, etc.). The higher the value, the more critical the role of the functional line in fault propagation. For example, a main pump plays a key role in the cooling efficiency of the main power generation circuit in the system and participates in the regulation of the auxiliary circulation circuit. According to experience or model calculation, the influence weight of the main pump on the cooling efficiency of the "main power generation circuit" can be set to 0.7, and the weight of the influence on the "auxiliary circulation circuit" is 0.3.

[0101] Furthermore, based on the obtained fault impact indicators of multiple functional lines and their respective importance coefficients, a weighted superposition analysis is performed to calculate the overall fault impact indicator. For example, for the two parts of fault impact involved in the main pump, the power generation main circuit part: weighted value = 0.7 × fault impact indicator - main circuit; auxiliary circulation line part: weighted value = 0.3 × fault impact indicator - auxiliary circulation line; overall fault impact indicator = 0.7 × fault impact indicator - main circuit + 0.3 × fault impact indicator - auxiliary circulation line. The calculated overall fault impact indicator can be used to reflect the comprehensive impact of the failure of key equipment or lines of the power plant on the safety and economy of the entire system operation, and provide a quantitative basis for subsequent risk assessment, early warning and emergency response.

[0102] In some implementations, 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 a historical period are collected, and the dynamic attenuation coefficient is calculated, and the expression is as follows:

[0103] ;

[0104] in, is the dynamic attenuation coefficient, is the initial attenuation coefficient, is the alarm frequency of the faulty equipment in the historical period, is the current load rate of the faulty equipment in the historical period, is the maximum allowable alarm frequency of the faulty device in the historical period, is the rated load rate.

[0105] Specifically, the propagation attenuation coefficient is a parameter used to describe the attenuation of the fault impact as the distance propagates, reflecting the attenuation law of the fault impact during the propagation process. The propagation attenuation coefficient is determined based on real-time operation data to better match the current equipment operation status. Among them, real-time operation data refers to the data generated by the equipment during actual operation, such as alarm frequency, load rate, etc.

[0106] Specifically, the formula The attenuation factor is dynamically adjusted according to the alarm frequency of the device: When the historical alarm frequency of the device is high (f is large), the value of this item will increase, thereby increasing , reflecting that the fault impact decays faster during propagation, indicating that the risk of equipment failure is higher; conversely, when the alarm frequency is low, this item is close to 1, and the attenuation coefficient is mainly determined by the basic value.

[0107] Specifically, The other part is used to reflect the ratio of the current load rate of the equipment to the rated load rate: when the equipment load rate is high (close to or exceeding the rated load rate), the ratio tends to 1 or higher, indicating that the equipment is in a high load state, which may cause the impact of the failure to be more significant, and the attenuation coefficient is adjusted to reflect this; when the load rate is low, the ratio is smaller, and the dynamic attenuation coefficient is reduced accordingly.

[0108] Through the above-mentioned dynamic adjustment method based on real-time data, the fault propagation analysis can be made more in line with the actual operating status of the equipment, the accuracy and timeliness of the analysis results can be improved, and more refined control of the propagation of fault impacts can be achieved, providing a scientific basis for power plant equipment scheduling, fault warning and maintenance decisions.

[0109] In summary, the BIM-based power plant equipment alarm data linkage analysis method provided by the present invention has the following technical effects:

[0110] By exporting the power plant equipment model, the model is modeled and obtained by the BIM modeling tool; the equipment is classified according to the function, and the equipment in the power plant equipment model is divided into the first category of equipment and the second category of equipment, wherein the first category of equipment refers to power plant equipment with independent functions, and the second category of equipment refers to power plant equipment with multiple functions; the faulty equipment that sends out the alarm signal is obtained, and if the faulty equipment belongs to the first category of equipment, the associated fault impact analysis is performed based on the surface alarm propagation analysis model, and the fault impact index is output; if the faulty equipment belongs to the second category of equipment, the superimposed associated fault impact of the equipment on each functional line is analyzed based on the linear superimposed alarm propagation analysis model, and the fault impact index is output, thereby achieving the technical effect of improving the analysis accuracy, enhancing the versatility and adaptability.

[0111] Embodiment 2, as Figure 2It is a structural diagram of the power plant equipment alarm data linkage analysis system based on BIM of the present invention. For example, Figure 1 The flow chart of the power plant equipment alarm data linkage analysis method based on BIM in the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0112] Based on the same concept as the BIM-based power plant equipment alarm data linkage analysis method in the above embodiment, the present invention also provides a BIM-based power plant equipment alarm data linkage analysis system, which includes:

[0113] The model exporting unit 11 is used to export a power plant equipment model, wherein the power plant equipment model is obtained by modeling the power plant equipment using a BIM modeling tool.

[0114] The equipment classification unit 12 is used to classify the equipment in the power plant equipment model into first-category equipment and second-category equipment according to equipment functions. The first-category equipment is power plant equipment with independent functions, and the second-category equipment is power plant equipment with multiple functions.

[0115] The surface alarm analysis unit 13 is used to obtain the faulty device that sends out the alarm signal. If the faulty device belongs to the first type of device, the faulty device is subjected to associated fault impact analysis according to the surface alarm propagation analysis model, and a fault impact index is output.

[0116] The line alarm analysis unit 14 is used to analyze the impact of the superimposed associated faults of the faulty device on each functional line according to the linear superimposed alarm propagation analysis model if the faulty device belongs to the second type of device, and output a fault impact index.

[0117] Wherein, the line alarm analysis unit 14 includes:

[0118] A function identification unit is used to obtain multiple functions of the faulty device if the faulty device belongs to the second type of device.

[0119] A functional circuit identification unit is used to identify multiple functional circuits of the faulty equipment in the power plant equipment model according to the multiple functions.

[0120] The line fault impact analysis unit is used to perform fault impact analysis on the multiple functional lines respectively in the linear superposition alarm propagation analysis mode, output multiple line fault impact indicators, and fuse the superposition impact of the multiple line fault impact indicators to output the fault impact indicator.

[0121] In some implementations, the functional line identification unit in the line alarm analysis unit 14 includes:

[0122] The fault propagation line analysis unit is used to take the faulty device as a 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 function.

[0123] The function circuit output unit is used to obtain the function circuit corresponding to any function according to the forward propagation circuit and the reverse propagation circuit, and so on, to output multiple function circuits corresponding to the multiple functions.

[0124] In some implementations, the line fault impact analysis unit in the line alarm analysis unit 14 includes:

[0125] The function line weight identification unit is used to weight each of the multiple function lines according to the device topology distance and the device role, and output multiple identified function lines.

[0126] The propagation attenuation coefficient setting and weight updating unit is used to set the propagation attenuation coefficient, take the faulty device as the center, perform forward propagation attenuation and reverse propagation attenuation on the weights of each functional line according to the propagation attenuation coefficient, and obtain multiple updated identified functional lines.

[0127] The line fault impact analysis unit is used to perform fault impact analysis using the updated multiple identification function lines and output multiple line fault impact indicators.

[0128] In some implementations, the line fault impact analysis unit in the line alarm analysis unit 14 further includes:

[0129] The function line importance coefficient acquisition unit is used to acquire the importance coefficient of each function line in the multiple function lines based on the power plant equipment model.

[0130] The fault impact index output unit is used to perform a superposition impact analysis on the fused multiple line fault impact indicators according to the importance coefficient of each functional line, and output the fault impact index.

[0131] The propagation attenuation coefficient is dynamically adjusted according to the real-time operation data, the alarm frequency and current load rate of the faulty equipment in the historical period are collected, and the dynamic attenuation coefficient is calculated. The expression is as follows:

[0132] ;

[0133] in, is the dynamic attenuation coefficient, is the initial attenuation coefficient, is the alarm frequency of the faulty equipment in the historical period, is the current load rate of the faulty equipment in the historical period, is the maximum allowable alarm frequency of the faulty device in the historical period, is the rated load rate.

[0134] In some embodiments, the surface alarm analysis unit 13 includes:

[0135] A surface fault propagation analysis unit 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 based on the propagation impact size identifier.

[0136] The propagation influence network generation unit is used to set a propagation influence threshold, and use the propagation influence threshold to perform edge division on the power equipment model based on the propagation influence size identifier to obtain a propagation influence network based on the faulty equipment.

[0137] The fault impact index calculation unit is used to calculate the propagation impact network and output the fault impact index.

[0138] In some implementations, the surface fault propagation analysis unit performs surface 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. The propagation feature vector is mapped to a high-dimensional vector, outputs a high-dimensional propagation feature vector, and the propagation impact magnitude is calculated based on the high-dimensional propagation feature vector.

[0139] In some embodiments, the model deriving unit 11 includes:

[0140] The file export and storage unit is used to export the power plant equipment model into an IFC format file, and store the IFC format file into a target graph database after parsing the IFC format file.

[0141] The correlation analysis and model output unit is used to call the target graph database to perform correlation analysis on power plant equipment and output a power plant equipment model with correlation.

[0142] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the BIM-based power plant equipment alarm data linkage analysis system described in embodiment two. For the sake of brevity of the specification, they will not be further expanded here.

[0143] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A BIM-based power plant equipment alarm data linkage analysis method, characterized in that: include: Exporting a power plant equipment model, wherein the power plant equipment model is obtained by modeling the power plant equipment using a BIM modeling tool; Dividing the equipment in the power plant equipment model into first-category equipment and second-category equipment according to equipment functions, wherein the first-category equipment is power plant equipment with independent functions, and the second-category equipment is power plant equipment with multiple functions; Obtaining a faulty device that sends an alarm signal, and if the faulty device belongs to the first type of device, performing a correlation fault impact analysis on the faulty device according to a surface alarm propagation analysis model, and outputting a fault impact index; If the faulty device belongs to the second type of device, the impact of the superimposed associated faults of the faulty device on each functional line is analyzed according to the linear superimposed alarm propagation analysis model, and a fault impact index is output.

2. The BIM-based power plant equipment alarm data linkage analysis method according to claim 1, characterized in that: The influence of the superimposed associated faults of the faulty equipment on each functional line is analyzed according to the linear superimposed alarm propagation analysis model, and the fault impact indicators are output, including: If the faulty device belongs to the second type of device, obtaining multiple functions of the faulty device; identifying a plurality of functional circuits of the faulty equipment in the power plant equipment model according to the plurality of functions; The linear superposition alarm propagation analysis mode performs fault impact analysis on the multiple functional lines respectively, outputs multiple line fault impact indicators, and integrates the superposition impact of the multiple line fault impact indicators to output the fault impact indicator.

3. The BIM-based power plant equipment alarm data linkage analysis method according to claim 2, characterized in that: Identifying multiple functional circuits of the faulty equipment in the power plant equipment model according to the multiple functions includes: Taking the faulty device as a starting point, selecting any one of the multiple functions to perform forward fault propagation and reverse fault propagation respectively, and outputting a forward propagation line and a reverse propagation line corresponding to any one of the functions; According to the forward propagation circuit and the reverse propagation circuit, a functional circuit corresponding to any function is obtained, and so on, multiple functional circuits corresponding to the multiple functions are output.

4. The BIM-based power plant equipment alarm data linkage analysis method according to claim 2, characterized in that: The linear superposition alarm propagation analysis mode performs fault impact analysis on the multiple functional lines respectively, and outputs multiple line fault impact indicators, including: Weight identification is performed on each of the multiple functional lines according to the device topological distance and the device role, and multiple identified functional lines are output; Setting a propagation attenuation coefficient, taking the faulty device as the center, performing forward propagation attenuation and reverse propagation attenuation on the weights of each functional line according to the propagation attenuation coefficient, and obtaining a plurality of updated identified functional lines; Perform fault impact analysis using the updated multiple identification function lines and output multiple line fault impact indicators.

5. The BIM-based power plant equipment alarm data linkage analysis method according to claim 2, characterized in that: Fusion of the superimposed influence of the multiple line fault influence indicators to output the fault influence indicator includes: Based on the power plant equipment model, obtaining an importance coefficient of each functional line in the multiple functional lines; Perform a superposition impact analysis on the fused multiple line fault impact indicators according to the importance coefficient of each functional line, and output the fault impact indicator.

6. The BIM-based power plant equipment alarm data linkage analysis method according to claim 4, characterized in that: The propagation attenuation coefficient is dynamically adjusted according to the real-time operation data, the alarm frequency and current load rate of the faulty equipment in the historical period are collected, and the dynamic attenuation coefficient is calculated. The expression is as follows: ; in, is the dynamic attenuation coefficient, is the initial attenuation coefficient, is the alarm frequency of the faulty equipment in the historical period, is the current load rate of the faulty equipment in the historical period, is the maximum allowable alarm frequency of the faulty device in the historical period, is the rated load rate.

7. The BIM-based power plant equipment alarm data linkage analysis method according to claim 1, characterized in that: If the faulty device belongs to the first type of device, an associated fault impact analysis is performed on the faulty device according to the surface alarm propagation analysis model, and a fault impact index is output, including: If the faulty device belongs to the first type of device, a surface fault propagation analysis is performed in the power plant equipment model with the first type of device as the center, and a power equipment model based on the propagation impact size identifier is output; Setting a propagation impact threshold, using the propagation impact threshold to perform edge division on the power equipment model based on the propagation impact size identifier, and obtaining a propagation impact network based on the faulty equipment; The propagation impact network is calculated to output the fault impact index.

8. The BIM-based power plant equipment alarm data linkage analysis method according to claim 7, characterized in that: Conducting a surface 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 vector, output the high-dimensional propagation feature vector, and calculate the propagation impact size according to the high-dimensional propagation feature vector.

9. The BIM-based power plant equipment alarm data linkage analysis method according to claim 1, characterized in that: The exported power plant equipment models include: Exporting the power plant equipment model into an IFC format file, parsing the IFC format file and storing it in a target graph database; The target graph database is called to perform power plant equipment correlation analysis, and a power plant equipment model with correlation is output.

10. The BIM-based power plant equipment alarm data linkage analysis system is characterized by: The method for linking and analyzing alarm data of power plant equipment based on BIM according to any one of claims 1 to 9 comprises: A model export unit, used to export a power plant equipment model, wherein the power plant equipment model is obtained by modeling the power plant equipment using a BIM modeling tool; an equipment classification unit, for classifying the equipment in the power plant equipment model into first-category equipment and second-category equipment according to equipment functions, wherein the first-category equipment is power plant equipment with independent functions, and the second-category equipment is power plant equipment with multiple functions; A surface alarm analysis unit, used to obtain a faulty device that sends an alarm signal, and if the faulty device belongs to the first type of device, perform an associated fault impact analysis on the faulty device according to a surface alarm propagation analysis model, and output a fault impact index; The line alarm analysis unit is used to analyze the impact of the superimposed associated faults of the faulty device on each functional line according to the linear superimposed alarm propagation analysis model if the faulty device belongs to the second type of device, and output a fault impact index.

11. The BIM-based power plant equipment alarm data linkage analysis system according to claim 10, characterized in that: The line alarm analysis unit comprises: a function identification unit, configured to obtain a plurality of functions of the faulty device if the faulty device belongs to the second type of device; a functional circuit identification unit, configured to identify a plurality of functional circuits of the faulty equipment in the power plant equipment model according to the plurality of functions; The line fault impact analysis unit is used to perform fault impact analysis on the multiple functional lines respectively in the linear superposition alarm propagation analysis mode, output multiple line fault impact indicators, and fuse the superposition impact of the multiple line fault impact indicators to output the fault impact indicator.

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