BIM-based power plant equipment disassembly simulation management system and method

Abstract

The application discloses a BIM-based power plant equipment disassembly and assembly simulation management system and method, relates to the related field of equipment disassembly and assembly simulation management, and comprises the following modules: a building module for building a BIM model after data collection on power plant equipment; a recording module for performing real-time operation recording, building an operation record dataset and a response dataset; a first detection module for disassembly and assembly credibility detection on the operation record dataset; a second detection module for response conflict detection on the response dataset by using a conflict channel; a fusion authentication module for time sequence correlation analysis on a first disassembly and assembly simulation detection result and a second disassembly and assembly simulation detection result, and for generating a fusion authentication result; and a management module for disassembly and assembly simulation management. The application solves the technical problem that the safety and accuracy of disassembly and assembly operations cannot be comprehensively and accurately evaluated in the prior art, and achieves the technical effect of comprehensively and accurately evaluating the safety and accuracy of disassembly and assembly operations.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of equipment disassembly simulation management, and particularly relates to a power plant equipment disassembly simulation management system and method based on BIM. BACKGROUND

[0002] In the maintenance and management of power plant equipment, it is crucial to ensure the accuracy and safety of the disassembly process. Incorrect disassembly operations can not only cause equipment damage, but also trigger safety accidents, posing a serious threat to the normal operation of the power plant and personnel safety. Currently, the main method to solve the problem of disassembly accuracy and safety of power plant equipment is to rely on traditional drawings and on-site experience for disassembly operations. Drawings are not detailed enough or updated in time, making it difficult for operators to accurately understand the equipment structure and disassembly steps. On-site experience is limited by the personal ability and experience level of the operator, lacking unified standards and standardized guidance. These problems can lead to misoperation in the disassembly process, increasing the risk of equipment damage and safety.

[0003] In the related art, there is a technical problem that the safety and accuracy of disassembly operations cannot be comprehensively and accurately evaluated in the management of power plant equipment disassembly. SUMMARY

[0004] The present application provides a power plant equipment disassembly simulation management system and method based on BIM, which collects data of power plant equipment, including equipment parameters and installation space data, establishes a BIM model based on these data, records the operation process in real time when the user starts to perform simulation disassembly operations, generates operation record data set, and synchronously updates the response data set of the BIM model, analyzes the operation record data set, detects the rationality and credibility of the disassembly operation, generates the first disassembly simulation detection result, sets a conflict channel based on the BIM model, detects conflicts in the response data set, identifies potential disassembly conflicts, generates the second disassembly simulation detection result, performs time sequence correlation analysis on the first and second disassembly simulation detection results, comprehensively considers the time sequence logic and conflict situation of the disassembly operation, generates a fusion authentication result, and manages the disassembly simulation process according to the fusion authentication result, etc. Technical means, achieving the technical effect of comprehensively and accurately evaluating the safety and accuracy of disassembly operations.

[0005] The application provides a BIM-based power plant equipment disassembly simulation management system, comprising: a building module, configured to build a BIM model after data collection on power plant equipment, the data collection comprising equipment parameter data collection and installation space data collection; a recording module, configured to perform real-time operation recording when a user starts to perform simulation disassembly, build an operation record data set and a response data set of the BIM model; a first detection module, configured to perform disassembly simulation detection on the operation record data set, and generate a first disassembly simulation detection result; a second detection module, configured to set a conflict channel based on the BIM model, perform response conflict detection on the response data set by using the conflict channel, and build a second disassembly simulation detection result; a fusion authentication module, configured to perform time sequence correlation analysis on the first disassembly simulation detection result and the second disassembly simulation detection result, and generate a fusion authentication result; and a management module, configured to perform disassembly simulation management according to the first disassembly simulation detection result, the second disassembly simulation detection result and the fusion authentication result.

[0006] In possible implementation manners, the first detection module comprises: a disassembly simulation module, configured to perform disassembly simulation on the power plant equipment by using the BIM model, and build a disassembly simulation set; a disassembly sequence constraint building module, configured to build disassembly sequence constraints according to the disassembly simulation set, wherein the disassembly sequence constraints comprise floating sequence constraints and fixed sequence constraints; a disassembly credible detection module, configured to perform disassembly credible detection on the operation record data set by using the disassembly sequence constraints, and generate a sequence simulation detection result; a disassembly time node analysis module, configured to perform disassembly time node analysis on the operation record data set, and build a component disassembly window; a window length distribution evaluation module, configured to perform window length distribution evaluation on the component disassembly window, and generate a window simulation detection result; and a first disassembly simulation detection result generation module, configured to generate the first disassembly simulation detection result according to the sequence simulation detection result and the window simulation detection result.

[0007] In possible implementation manners, the disassembly credible detection module comprises: a floating component positioning module, configured to position floating components according to the floating sequence constraints; a first credible detection standard generation module, configured to obtain component criticality of the floating components, and generate a first credible detection standard according to the component criticality; a second credible detection standard generation module, configured to obtain a component collision risk level of the floating components, and generate a second credible detection standard according to the collision risk level; and a floating component disassembly credible detection module, configured to perform disassembly credible detection on the floating components by using the first credible detection standard and the second credible detection standard, so as to build the sequence simulation detection result.

[0008] In a possible implementation, the window length distribution evaluation module comprises: a component calibration disassembly time establishing module, configured to establish a minimum disassembly granularity of a component, perform simulated disassembly of a BIM model according to the minimum disassembly granularity, and establish a component calibration disassembly time; a standard component time allocation proportion establishing module, configured to establish a standard component time allocation proportion according to the component calibration disassembly time; a proportion distribution evaluation module, configured to perform proportion distribution evaluation of a part disassembly window by using the standard component time allocation proportion, and generate a proportion distribution evaluation result; a component disassembly evaluation result establishing module, configured to perform window-by-window comparison of the part disassembly window by using the component calibration disassembly time, and establish a component disassembly evaluation result; and a window simulation detection result generating module, configured to generate a window simulation detection result according to the proportion distribution evaluation result and the component disassembly evaluation result.

[0009] In a possible implementation, the second detection module comprises: a first conflict sub-channel building module, configured to establish a fixed space constraint according to installation space data, and build a first conflict sub-channel by using the fixed space constraint; a second conflict sub-channel establishing module, configured to obtain a device component structure according to the BIM model, establish a fixed component constraint according to the device component structure, and establish a second conflict sub-channel by using the fixed component constraint; and a conflict channel establishing module, configured to set a dynamic conflict sub-channel, perform dynamic update of the dynamic conflict sub-channel based on the response data set, and establish a conflict channel by using the first conflict sub-channel, the second conflict sub-channel, and the dynamic conflict sub-channel, so as to complete response conflict detection.

[0010] In a possible implementation, the fusion authentication module comprises: a time sequence alignment module, configured to perform time sequence alignment on the first disassembly simulation detection result and the second disassembly simulation detection result; a delayed association search module, configured to extract a reference comparison node from the first disassembly simulation detection result, perform delayed association search on the second disassembly simulation detection result mapped by time sequence alignment, and establish a search matching result; and a detection cross-validation module, configured to perform detection cross-validation by using the search matching result, and establish the fusion authentication result.

[0011] In a possible implementation, the management module comprises: a personal archive creating module, configured to create a personal archive of a user; a disassembly weakness data generating module, configured to generate disassembly weakness data according to the first disassembly simulation detection result, the second disassembly simulation detection result, and the fusion authentication result; and a personal archive updating module, configured to update the disassembly weakness data to the personal archive, so as to perform disassembly simulation management on the user.

[0012] In a possible implementation, the personal profile updating module comprises: a periodic reinforcement training scheme generation module configured to generate a periodic reinforcement training scheme according to the disassembly weakness data and the user data, and establish a timing feedback node; a periodic reinforcement training detection module configured to perform periodic reinforcement training detection of the user at the timing feedback node, and establish a detection data set; and a reinforcement compensation module configured to generate reinforcement compensation by using the detection data set, optimize the periodic reinforcement training scheme based on the reinforcement compensation, and perform user management according to the optimized periodic reinforcement training scheme.

[0013] In a possible implementation, the system further comprises a training module configured to activate voice guidance and operation guidance when the user starts to perform simulation training, and perform training prompt management of the user by using the voice guidance and the operation guidance.

[0014] In a possible implementation, the training module comprises a pre-warning correction module configured to identify operation deviation of the user from the operation guidance, establish correction guidance based on the operation deviation, and report operation abnormality pre-warning.

[0015] The application further provides a BIM-based power plant equipment disassembly simulation management method, comprising: after data collection of power plant equipment is performed, establishing a BIM model, the data collection comprising equipment parameter data collection and installation space data collection; when a user starts to perform simulation disassembly, performing real-time operation recording, establishing an operation record data set and a response data set of the BIM model; performing disassembly credibility detection on the operation record data set, and generating a first disassembly simulation detection result; setting a conflict channel based on the BIM model, performing response conflict detection of the response data set by using the conflict channel, and establishing a second disassembly simulation detection result; performing timing correlation analysis on the first disassembly simulation detection result and the second disassembly simulation detection result, and generating a fusion authentication result; and performing disassembly simulation management according to the first disassembly simulation detection result, the second disassembly simulation detection result and the fusion authentication result.

[0016] The BIM-based power plant equipment disassembly and assembly simulation management system and method proposed in this application is intended to establish a BIM model by establishing a module after data collection of the power plant equipment. The data collection includes equipment parameter data collection and installation space data collection. When the user starts to perform simulated disassembly and assembly, the recording module performs real-time operation recording, establishes an operation record data set and a response data set of the BIM model, and performs disassembly and assembly credibility detection on the operation record data set through the first detection module to generate a first disassembly and assembly simulation detection result. Based on the BIM model, a conflict channel is set through the second detection module, and the response conflict detection of the response data set is performed using the conflict channel to establish a second disassembly and assembly simulation detection result. The first and second disassembly and assembly simulation detection results are subjected to time series correlation analysis through the fusion authentication module to generate a fusion authentication result. The management module performs disassembly and assembly simulation management based on the first and second disassembly and assembly simulation detection results and the fusion authentication result. The technical effect of comprehensively and accurately evaluating the safety and accuracy of disassembly and assembly operations is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0018] Figure 1 A structural diagram of the BIM-based power plant equipment disassembly and assembly simulation management system provided in an embodiment of the present application.

[0019] Figure 2 A flow chart of a BIM-based power plant equipment disassembly and assembly simulation management method provided in an embodiment of the present application.

[0020] Explanation of the accompanying drawings: establishment module 10, recording module 20, first detection module 30, second detection module 40, fusion authentication module 50, management module 60. DETAILED DESCRIPTION

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0022] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings, and the described embodiments should not be regarded as limitations to the present application. All other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0023] In the following description, "some embodiments" are referred to, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The term "first\second" referred to only distinguishes similar objects, and does not represent a specific order of the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0024] The embodiments of the present application provide a BIM-based power plant equipment disassembly simulation management system, as shown in Figure 1 The system comprises:

[0025] The establishing module 10 is configured to establish a BIM model after data collection of the power plant equipment, and the data collection comprises equipment parameter data collection and installation space data collection.

[0026] Specifically, the size, weight, material, interface information and other physical and performance parameters of the power plant equipment are collected through field measurement, equipment manual review or data provided by the equipment manufacturer. These parameters are input into the system using professional data collection software or tools. The installation area of the equipment is accurately scanned using a three-dimensional laser scanner or a drone aerial photography technology to obtain three-dimensional point cloud data on site. The point cloud data is imported into the BIM software to generate a three-dimensional model of the installation space through software processing. In the BIM software, the three-dimensional model of the power plant equipment is created according to the collected equipment parameters and installation space data. The scale, position and direction of the model are adjusted to ensure that the model is consistent with the actual situation. The attribute information of the equipment, such as name, model, manufacturer, etc., is added.

[0027] The recording module 20 is configured to perform real-time operation recording when the user starts to perform simulation disassembly, and establish an operation record data set and a response data set of the BIM model.

[0028] Specifically, when the user performs the disassembly simulation through a virtual reality (VR) or augmented reality (AR) interface, the system captures the user's operation actions in real time. The operation actions are converted into data form, recording information such as the time, type (such as rotation, movement, connection, etc.), operation object, etc. of each step of operation. According to the user's operation, the system updates the state of the BIM model in real time, records the changes of the model in the disassembly process, such as the movement of parts, the change of connection state, etc.

[0029] The first detection module 30 is configured to perform disassembly simulation detection on the operation record data set to generate a first disassembly simulation detection result.

[0030] Specifically, the operation record data set is analyzed by an algorithm to check whether the disassembly sequence meets the requirements of the equipment manual (an official document containing information such as equipment operation, maintenance, disassembly, etc.), whether the disassembly time allocation is reasonable, whether there is excessive idle time or unnecessary repeated operation, and whether there is an operation that may cause damage or safety hazards. For example, a topological sorting algorithm based on graph theory is used to analyze the operation sequence in the operation record data set in combination with the disassembly sequence constraints in the equipment manual to determine whether it meets the preset disassembly sequence requirements. A sliding window algorithm is used to analyze the time sequence in the operation record data set to identify whether there is excessive idle time or unnecessary repeated operation. In combination with the safety specifications in the equipment manual, a rule engine algorithm is used to analyze the operation record data set to identify operations that may cause damage or safety hazards. According to the detection result, a report containing information such as disassembly sequence rationality, time allocation efficiency, weak point identification, etc. is generated.

[0031] Further, the present application provides a specific implementation, in which the first detection module 30 includes: a disassembly simulation module for simulating the disassembly of the power plant equipment using the BIM model to establish a disassembly simulation set; a disassembly sequence constraint establishment module for establishing disassembly sequence constraints according to the disassembly simulation set, the disassembly sequence constraints including floating sequence constraints and fixed sequence constraints; a disassembly simulation detection module for performing disassembly simulation detection on the operation record data set through the disassembly sequence constraints to generate a sequence simulation detection result; a disassembly time node analysis module for analyzing the operation record data set to establish a component disassembly window; a window length distribution evaluation module for performing window length distribution evaluation of the component disassembly window to generate a window simulation detection result; and a first disassembly simulation detection result generation module for generating a first disassembly simulation detection result according to the sequence simulation detection result and the window simulation detection result.

[0032] Specifically, using the established BIM model, the virtual simulation technology is used to simulate the disassembly process of the equipment, and the virtual disassembly operation of the power plant equipment is performed. The components, tools, operation sequence and related physical parameters (such as force, displacement, time, etc.) involved in each disassembly operation are recorded. A disassembly simulation set is established, which contains all possible disassembly schemes and their corresponding operation sequences and physical parameters.

[0033] Each disassembly scheme in the disassembly simulation set is analyzed to extract the operation sequence. The operation sequence refers to the sequence of disassembly of components, for example, first disassemble component A, then disassemble component B, and finally disassemble component C. The disassembly sequence constraints are divided into floating sequence constraints and fixed sequence constraints. Floating sequence constraints allow the disassembly sequence of components to be adjusted within a certain range, that is, for some components, their disassembly sequence can be adjusted within a certain range, but cannot violate other fixed sequence constraints. For example, components A and B can be disassembled in any order, but they must be disassembled before component C. Fixed sequence constraints require that disassembly must be performed in a specific order. Fixed sequence constraints are based on equipment manuals or industry standards to determine that certain components must be disassembled in a specific order. For example, the equipment manual specifies that component B must be disassembled before component A, and this sequence is fixed and cannot be changed.

[0034] For example, the disassembly simulation set of a certain power plant equipment contains the following two disassembly schemes: Scheme 1: A→B→C→D, Scheme 2: B→A→C→D. By analyzing the operation sequence, the following disassembly sequence constraints can be extracted: Fixed sequence constraint: C must be disassembled before D. Floating sequence constraint: The disassembly sequence of A and B can be interchanged, but must be completed before C.

[0035] The actual disassembly operation record (operation record data set) performed by the user is compared with the disassembly sequence constraints. It is checked whether each operation in the operation record data set meets the requirements of the disassembly sequence constraints. The sequence simulation detection result is generated, indicating which operations violate the constraints and which operations are compliant.

[0036] For each component, the start disassembly time and the completion disassembly time are extracted from the operation record data set. For example, the disassembly start time of component A is recorded as T1, and the completion time is recorded as T2. According to the extracted time nodes, a disassembly window is established for each component, indicating the time range required for the disassembly of the component. The disassembly windows of all components are recorded to form a component disassembly window set.

[0037] For example, the operation record dataset is shown in Table 1. By analyzing the operation record dataset, the disassembly time nodes of each component can be extracted: the disassembly time nodes of component A: start time T1, completion time T2. The disassembly time nodes of component B: start time T3, completion time T4. Further, the disassembly window is established: A: [T1, T2]; B: [T3, T4].

[0038] Table 1: Operation record dataset

[0039] Operation time Operation type Operation object T1 Start disassembly A T2 Complete disassembly A T3 Start disassembly B T4 Complete disassembly B

[0040] The length of each component disassembly window, i.e. the difference between the completion time and the start time, is calculated. For example, the window length of component A is T2-T1. Statistical analysis is performed on the window lengths of all components to calculate statistical indicators such as the average, maximum, and minimum values. By comparing the window lengths of the components, components with excessively long disassembly times can be identified, which may be bottlenecks or lengthy links in the disassembly process, and a window simulation detection result is generated to indicate which components have excessively long disassembly times and which components have relatively reasonable disassembly times.

[0041] For example, the disassembly window is shown in Table 2. Through statistical analysis: the average window length is (5+10+3+8) / 4=6.5 minutes, the maximum window length is 10 minutes (component B), and the minimum window length is 3 minutes (component C). According to the analysis result, it can be identified that the disassembly time of component B is the longest, which may be a bottleneck or a lengthy link in the disassembly process.

[0042] Table 2: Disassembly window of components

[0043] Component Disassembly window Window length A [T1, T2] T2-T1 = 5 minutes B [T3, T4] T4-T3 = 10 minutes C [T5, T6] T6-T5 = 3 minutes D [T7, T8] T8-T7 = 8 minutes

[0044] The sequence simulation detection result and the window simulation detection result are combined to generate a first disassembly simulation detection result. The result includes an evaluation of the disassembly sequence compliance, a rationality analysis of the disassembly time, and possible improvement suggestions. This implementation method, through the disassembly sequence constraint establishment module and the disassembly credible detection module, ensures that the actual disassembly operation performed by the user meets the requirements of the equipment manual or industry specifications, avoiding incorrect or illegal operations. Through the disassembly time node analysis module and the window length distribution evaluation module, bottlenecks or lengthy links in the disassembly process are identified, providing data support for optimizing disassembly time allocation. These steps and modules work together to enable the BIM-based power plant equipment disassembly simulation management system to provide more accurate and reliable disassembly simulation results for users, thereby providing strong technical support for the maintenance, repair, and upgrade of power plant equipment.

[0045] In a possible implementation, the disassembly and assembly trusted detection module comprises: a floating component positioning module configured to position floating components according to the floating sequence constraint; a first trusted detection standard generation module configured to obtain component criticality of the floating components, and generate a first trusted detection standard according to the component criticality; a second trusted detection standard generation module configured to obtain a component collision risk level of the floating components, and generate a second trusted detection standard according to the collision risk level; and a floating component disassembly and assembly trusted detection module configured to perform disassembly and assembly trusted detection on the floating components by using the first trusted detection standard and the second trusted detection standard, so as to establish the sequence simulation detection result.

[0046] Specifically, the defined floating sequence constraints are obtained from the disassembly and assembly sequence constraint establishment module, which describe which components can be adjusted in a certain range during the disassembly and assembly process. By using the component information in the BIM model, all components that meet the floating sequence constraint, i.e., floating components, are identified. Each floating component is assigned a unique identifier for reference in subsequent steps.

[0047] The attribute information of the components, including the importance, functional complexity, and maintenance frequency of the components, is extracted from the equipment manual, historical maintenance records, data provided by the equipment manufacturer, and the BIM model. According to the functional importance of the components in the equipment, a score is given (high score for critical components and low score for non-critical components). According to the functional complexity of the components, a score is given (high score for components with complex functions and low score for simple components). According to the historical maintenance frequency of the components, a score is given (high score for components with high maintenance frequency and low score for components with low maintenance frequency). The above indicators are weighted and summed to obtain the comprehensive criticality score of each component. According to the score result, a criticality threshold is set to judge the trustworthiness of the components during the disassembly and assembly process. For example, components with a score higher than the threshold are considered as high-criticality components, which need to strictly follow the disassembly and assembly sequence and operation specifications.

[0048] For example, there are three floating components A, B, and C in a certain power plant equipment, and their attribute information is shown in Table 3. The weights are set as follows: importance weight 0.4, functional complexity weight 0.3, and maintenance frequency weight 0.3. The comprehensive criticality score is calculated as follows: component A: 8×0.4+7×0.3+6×0.3=7.1; component B: 5×0.4+4×0.3+3×0.3=4.1; component C: 9×0.4+8×0.3+7×0.3=8.1. The criticality threshold is set to 6.0, so that: the criticality scores of components A and C are higher than the threshold, and they belong to high-criticality components. The criticality score of component B is lower than the threshold, and it belongs to a low-criticality component.

[0049] Table 3: Component criticality attribute information

[0050] Assembly Importance score Functional complexity score Maintenance frequency score A 8 7 6 B 5 4 3 C 9 8 7

[0051] The information of geometry, material property, functional importance of components is extracted from the BIM model. The components are scored according to their shape complexity (components with complex shape are more likely to collide, higher score). The components are scored according to their material brittleness property (components with brittle material are more sensitive to collision, higher score). The components are scored according to their functional importance (components with critical function are more sensitive to collision, higher score). The scores of the above indicators are weighted and summed up to get the collision sensitivity score of each component. According to the score results, the components are divided into different collision risk levels, such as low risk, medium risk and high risk. The level division here is based on the tolerance of components to collision, that is, the more sensitive the components are to collision, the higher the risk level. For each collision risk level, a risk threshold is set, which represents the maximum collision risk degree allowed under this level, used to judge whether the disassembly operation is safe. This threshold is set according to industry experience, safety specifications and specific requirements of components, to ensure that the disassembly operation does not exceed the bearing range of components. The collision sensitivity refers to the sensitivity of components to collision during disassembly, that is, the degree of damage that components may suffer after being collided.

[0052] For example, the attribute information of floating components A, B, C is shown in Table 4. The weights are set as follows: geometry weight 0.3, material property weight 0.3, functional importance weight 0.4. Calculate the collision sensitivity score: component A: 7x0.3+6x0.3+8x0.4=7.1; component B: 4x0.3+5x0.3+5x0.4=4.7; component C: 9x0.3+7x0.3+9x0.4=8.4. According to the score results, the collision risk levels are divided: high risk: score greater than or equal to 8.0 (component C); medium risk: score between 6.0 and 7.9 (component A); low risk: score less than 6.0 (component B).

[0053] Table 4: Component collision risk attribute information

[0054]

[0055]

[0056] According to the operation record data set, analyze the operation sequence, operation time and whether there is collision risk of each floating component by the user. Apply the first trusted detection standard to check whether the operation exceeds the criticality threshold of the component (for example, whether the high criticality component is operated correctly). Apply the second trusted detection standard to check whether the operation exceeds the collision risk threshold of the component (for example, whether the high risk component collides). According to the above standards, judge whether the disassembly operation is trusted, and generate sequence simulation detection results to indicate which disassembly operations are trusted and which are not, and give corresponding explanations and suggestions.

[0057] For example, the operation record dataset is shown in Table 5. According to the previous evaluation results: components A and C are high criticality components, and component C is a high collision risk component. Apply the first credible detection standard to check if the operation sequence meets the criticality requirements. Components A and C are high criticality components, and the disassembly sequence needs to be strictly followed. The user operation sequence is A→B→C, which meets the requirements. Apply the second credible detection standard to check if the operation exceeds the collision risk threshold. Component C collides during disassembly, which is a high-risk operation and does not meet the requirements. The sequence simulation detection result is generated: component A: operation is credible. Component B: operation is credible. Component C: operation is not credible (collision occurs, exceeds collision risk threshold). The final detection result indicates that the user has an operation that is not credible when disassembling component C, and it is recommended to re-plan the disassembly steps to avoid collision risks.

[0058] Table 5: Operation record dataset

[0059] Operation time Operation type Operation object Operation result [T1, T2] Disassembly A Success [T3, T4] Disassembly B Success [T5, T6] Disassembly C Collision

[0060] This implementation ensures that the disassembly operation not only meets the requirements of the equipment manual or industry specifications, but also avoids potential safety risks and performance problems through the application of the first credible detection standard and the second credible detection standard. These steps and modules work together to enable the BIM-based power plant equipment disassembly simulation management system to provide more accurate and reliable disassembly simulation results for users, helping users develop more reasonable and efficient disassembly strategies and reduce risks and costs during the disassembly process.

[0061] In one possible implementation, the window length distribution evaluation module includes: a component calibration disassembly time establishment module for establishing the minimum disassembly granularity of a component, simulating the disassembly of a BIM model according to the minimum disassembly granularity, and establishing a component calibration disassembly time; a standard component time allocation proportion establishment module for establishing a standard component time allocation proportion according to the component calibration disassembly time; a proportion distribution evaluation module for performing proportion distribution evaluation of the part disassembly window using the standard component time allocation proportion to generate a proportion distribution evaluation result; a component disassembly evaluation result establishment module for performing individual window comparison of the part disassembly window using the component calibration disassembly time to establish a component disassembly evaluation result; and a window simulation detection result generation module for generating a window simulation detection result according to the proportion distribution evaluation result and the component disassembly evaluation result.

[0062] Specifically, based on the physical characteristics of the components, functional independence, and the feasibility of disassembly operations, the minimum disassembly granularity of each component is determined, i.e., the smallest unit that the component cannot be further subdivided in the disassembly process. For example, check the physical connection method of the component (such as bolt connection, welding, riveting, etc.), if the component is combined with other parts through irreversible or complex connection method, it needs to be regarded as a whole. Then, use the BIM model to simulate disassembly, record the disassembly time of each component according to the minimum disassembly granularity. Based on the component calibration disassembly time, calculate the proportion of the disassembly time of each component in the total disassembly time, establish the standard component time allocation ratio, which is used to evaluate the balance of the disassembly process. Compare the difference between the disassembly time proportion of each component in the actual disassembly process and the standard component time allocation ratio, evaluate the proportion distribution of the disassembly window of the parts, and assess whether the disassembly time proportion of each component in the actual disassembly process conforms to the expected standard component time allocation ratio.

[0063] Using component calibration disassembly time, compare each component of the disassembly window, evaluate the disassembly efficiency and quality of each component, including checking whether the disassembly time exceeds the expectation, whether the disassembly process is smooth, etc. Combine the proportion distribution evaluation result and the component disassembly evaluation result to generate the window simulation detection result, which is a comprehensive score or rating, reflecting the overall efficiency and quality of the disassembly process. This implementation can identify bottlenecks and potential problems in the disassembly process through proportion distribution evaluation and component disassembly evaluation results, and more accurately reflect the efficiency and quality of the disassembly process.

[0064] The second detection module 40 is configured to set a conflict channel based on the BIM model, perform response conflict detection on the response data set using the conflict channel, and establish a second disassembly simulation detection result.

[0065] Specifically, possible conflict areas or paths are preset in the BIM model, such as component interference caused by narrow space, overlapping motion trajectories, etc. Use the conflict channel to analyze the response data set, check whether physical conflicts or space limitations will occur during disassembly. Identify and record the location, cause and potential consequences of the conflict. According to the detection result, generate a report containing information such as conflict location, cause, consequence, etc.

[0066] In a possible implementation, the second detection module 40 comprises: a first conflict sub-channel building module, configured to build a fixed space constraint according to installation space data, and build a first conflict sub-channel according to the fixed space constraint; a second conflict sub-channel building module, configured to obtain a device component structure according to the BIM model, build a fixed component constraint according to the device component structure, and build a second conflict sub-channel according to the fixed component constraint; and a conflict channel building module, configured to set a dynamic conflict sub-channel, dynamically update the dynamic conflict sub-channel based on the response data set, build a conflict channel according to the first conflict sub-channel, the second conflict sub-channel, and the dynamic conflict sub-channel, and complete response conflict detection.

[0067] Specifically, according to installation space data (space information required for installation of power plant equipment, including size, position, layout, etc.), space limitations and constraint conditions of the power plant equipment in the installation process are determined. These constraint conditions include equipment size, installation position, surrounding obstacles, etc. A first conflict sub-channel is built using these constraint conditions, which represents the conflict area that the equipment may encounter in the fixed space.

[0068] The structure information of the device components is obtained through the BIM model, including component size, shape, connection relationship, etc. According to this information, a fixed component constraint is built, which is a limitation condition formed by components that cannot be moved or changed in position in the equipment. A second conflict sub-channel is built using these fixed component constraints, which represents the conflict area that may exist between the internal components of the equipment.

[0069] A dynamic conflict sub-channel is set, which is dynamically updated based on the response data set. The response data set contains the operations performed by the user in the simulation disassembly process and the real-time feedback of the BIM model. The dynamic conflict sub-channel can reflect the conflict situation in the disassembly process in real time. The first conflict sub-channel, the second conflict sub-channel, and the dynamic conflict sub-channel are combined to build a complete conflict channel. This conflict channel is used for comprehensive response conflict detection. This implementation builds a comprehensive and accurate conflict detection mechanism by combining fixed space constraints, fixed component constraints, and dynamic response data, which helps to discover and solve potential conflict problems in time during the disassembly simulation process.

[0070] The fusion authentication module 50 is configured to perform time sequence correlation analysis on the first disassembly simulation detection result and the second disassembly simulation detection result, and generate a fusion authentication result.

[0071] Specifically, the first disassembly simulation detection result and the second disassembly simulation detection result are associated in chronological order to check the logical coherence and consistency in the disassembly process, and to ensure the rationality and feasibility of the disassembly steps. By integrating the two detection results, a fusion authentication result containing information such as disassembly sequence rationality, time allocation efficiency, weak point identification, conflict detection, and suggested improvement measures is generated.

[0072] In one possible implementation, the fusion authentication module 50 includes: a time sequence alignment module for time sequence alignment of the first disassembly simulation detection result and the second disassembly simulation detection result; a delayed association search module for extracting reference comparison nodes from the first disassembly simulation detection result and performing delayed association search on the second disassembly simulation detection result mapped by time sequence alignment to establish a search matching result; a detection cross-validation module for detection cross-validation using the search matching result to establish the fusion authentication result.

[0073] Specifically, by steps such as synchronization of timestamps, adjustment of time intervals, etc., the timestamps in the first disassembly simulation detection result and the second disassembly simulation detection result are compared and calibrated to ensure consistency in the time dimension.

[0074] In the first disassembly simulation detection result, key reference comparison nodes are extracted, which are important events or turning points in the disassembly process, such as disassembly of core components or high-risk components, critical steps or dependencies in the disassembly sequence, etc. By time sequence alignment mapping into the second disassembly simulation detection result, delayed association search is performed, i.e. finding events corresponding or associated with the reference comparison nodes in the second disassembly simulation detection result, and recording the matching conditions of these events. Taking the disassembly of component C as an example, the first detection result shows that its disassembly time is too long, and the second detection result finds that component C has a spatial conflict with component D during disassembly, which indicates that the long disassembly time of component C is not an operation problem, but is caused by the spatial conflict. This inconsistent detection result reveals a potential operation path problem, which requires optimizing the disassembly path of component C or adjusting its spatial relationship with other components, thereby improving the disassembly efficiency and avoiding conflicts.

[0075] The search matching result obtained by the delayed correlation search module is used to cross-verify the first disassembly simulation detection result and the second disassembly simulation detection result, including evaluating the accuracy, consistency, integrity, etc. of the matching result. Through cross-verification, the correlation and reliability between the two detection results are further confirmed. This implementation manner can ensure that the first disassembly simulation detection result and the second disassembly simulation detection result are consistent in the time dimension, and the correlation and reliability between the two are further confirmed, which helps to improve the accuracy and reliability of the entire disassembly simulation management system, thereby guiding the subsequent disassembly process optimization and improving the disassembly efficiency and quality.

[0076] The management module 60 is configured to perform disassembly simulation management according to the first disassembly simulation detection result, the second disassembly simulation detection result, and the fusion authentication result.

[0077] Specifically, the disassembly scheme is optimized and adjusted according to the disassembly simulation detection result and the fusion authentication result. A detailed disassembly plan is formulated, including time, personnel, tools, steps, etc. All data in the disassembly simulation process (including operation records, detection results, authentication reports, etc.) are archived and saved. A convenient query function is provided to facilitate users to check relevant data and information at any time. The embodiment of the present application adopts data collection on power plant equipment, including equipment parameters and installation space data, and establishes a BIM model based on these data. When a user starts to perform simulation disassembly operation, the operation process is recorded in real time to generate an operation record data set, and the response data set of the BIM model is updated synchronously. The operation record data set is analyzed to detect the rationality and credibility of the disassembly operation, and a first disassembly simulation detection result is generated. A conflict channel is set based on the BIM model to detect conflicts in the response data set, identify potential disassembly conflicts, and generate a second disassembly simulation detection result. The first and second disassembly simulation detection results are analyzed in time sequence, the time sequence logic and conflict situation of the disassembly operation are considered comprehensively, a fusion authentication result is generated, and the disassembly simulation process is managed according to the fusion authentication result, etc. Technical means are adopted to achieve the technical effect of comprehensively and accurately evaluating the safety and accuracy of the disassembly operation.

[0078] In a possible implementation manner, the management module 60 includes: a personal archive creation module configured to create a personal archive of a user; a disassembly weakness data generation module configured to generate disassembly weakness data according to the first disassembly simulation detection result, the second disassembly simulation detection result, and the fusion authentication result; and a personal archive updating module configured to update the disassembly weakness data to the personal archive for disassembly simulation management of the user.

[0079] Specifically, when the user first uses the system, the system creates an independent personal profile through the user registration information (such as name, ID, authority, etc.). This profile is a database record that stores the user's personal information, learning progress, disassembly simulation scores, etc., for tracking the user's disassembly simulation learning and management.

[0080] Based on the first disassembly simulation detection results (such as disassembly sequence error, time node problem), the second disassembly simulation detection results (such as spatial conflict, component collision), and the fusion authentication results (such as time sequence mismatch, cross verification failure), the system identifies the weak links of the user in the disassembly simulation through algorithm analysis and generates disassembly weak data (weak points or error points of the user in the disassembly process). For example, a rule-based expert system algorithm is used to analyze the user's operation record data set in combination with the disassembly specifications in the equipment manual and historical maintenance data. According to the pre-set rule base, the algorithm identifies common error patterns in the user's operation (such as disassembly sequence error, long operation time, collision risk, etc.). The specific implementation is as follows: Extract the disassembly sequence, operation specification, safety requirements, etc. from the equipment manual, combine with historical maintenance data, and form a rule base. For example, the rule base includes fixed sequence rules such as "component A must be disassembled before component B" and time constraint rules such as "the disassembly time of component C should not exceed 10 minutes". Match the user's operation record data set with the rule base to identify operations that violate the rules. For example, if the user does not disassemble component B before disassembling component A, the rule matching is triggered and the operation is recorded as a disassembly sequence error. According to the rule matching result, generate disassembly weak data, including error type (such as sequence error, time overrun, collision risk, etc.), frequency, specific operation steps, etc. At the same time, use clustering analysis algorithm to classify user operation data and identify operation weak links with similar characteristics to generate detailed disassembly weak data. The specific implementation is as follows: Normalize the user's operation record data set and extract key features such as operation time, operation sequence, operation object, etc. Use K-Means clustering algorithm to divide the user operation data into multiple clusters. Each cluster represents an operation mode, for example, one cluster may contain all operations with long disassembly time, and another cluster may contain all operations with disassembly sequence errors. Analyze the characteristics of each cluster to identify operation weak links with similar characteristics. For example, if the disassembly time of most operations in a cluster exceeds 10 minutes, it is considered that the operation represented by the cluster has a weak link of time overrun. Combine the clustering results with the user's learning progress, historical performance, etc. to further refine the weak data. For example, for users with slow learning progress, focus on their weak links of time overrun; for users who make frequent mistakes, focus on their weak links of disassembly sequence errors. Combine the preliminary weak data generated by the rule-based expert system algorithm with the results of the clustering analysis algorithm to generate the final disassembly weak data. For example, if the rule matching finds that the user has a sequence error when disassembling component A, and the clustering analysis finds that the user's operation time when disassembling component A is generally long, the final weak data will record both the sequence error and the time overrun problem.The disassembly and assembly weak data (such as error types, frequency of occurrence, severity, etc.) is updated into the personal profile of the user for subsequent tracking and evaluation by adding new records or updating existing records in the database. This implementation realizes comprehensive tracking and management of the user's disassembly and assembly simulation learning by creating a personal profile, generating disassembly and assembly weak data, and updating the personal profile, thereby providing personalized learning paths and training recommendations for the user and helping the user better master the disassembly and assembly skills of power plant equipment.

[0081] In a possible implementation, the personal profile updating module includes: a periodic reinforcement training scheme generation module configured to generate a periodic reinforcement training scheme based on the disassembly and assembly weak data and user data, and establish a timing feedback node; a periodic reinforcement training detection module configured to perform periodic reinforcement training detection of the user at the timing feedback node, and establish a detection data set; and a reinforcement compensation module configured to generate reinforcement compensation using the detection data set, optimize the periodic reinforcement training scheme based on the reinforcement compensation, and manage the user according to the optimized periodic reinforcement training scheme.

[0082] Specifically, the disassembly and assembly weak data (such as common disassembly and assembly errors, inaccurate time node grasping, etc.) and user data (such as learning progress, historical performance, etc.) of the user are collected and analyzed. Based on these data, an algorithm is used to generate a personalized periodic reinforcement training scheme, including the content, difficulty, frequency of training, and the goal of each training phase. For example, a genetic algorithm is used to optimize the training scheme, which iteratively generates a better training scheme by simulating the natural selection process. The specific implementation is as follows: an initial training scheme population is generated based on the user data and disassembly and assembly weak data. Each training scheme includes training content, difficulty, frequency, and goal parameters. A fitness function is defined to evaluate the pros and cons of each training scheme. The fitness function considers the user's learning progress, historical performance, and weak link factors. For example, if a training scheme can effectively improve the user's performance in disassembly and assembly sequence errors, its fitness is higher. Through genetic operations such as selection, crossover, and mutation, a better training scheme is iteratively generated. The selection operation selects excellent training schemes according to the fitness function; the crossover operation exchanges part of the parameters of two training schemes; the mutation operation randomly changes part of the parameters to introduce new mutations. After multiple iterations, an optimized periodic reinforcement training scheme is finally generated. This scheme can dynamically adjust the training content and difficulty according to the user's weak links and learning progress.

[0083] Meanwhile, the user data is analyzed by combining the Bayesian network algorithm to predict the performance of the user in subsequent training, thereby customizing the training content and progress for each user. The specific implementation is as follows: a Bayesian network model is constructed according to the user data and the disassembly and assembly weak data. The model includes nodes such as the learning progress, historical performance, and weak links of the user, and the probability relationship between these nodes. The user data is analyzed by using the Bayesian network to predict the performance of the user in subsequent training. For example, the probability of improvement of the user in the disassembly and assembly sequence error is predicted according to the current learning progress and historical performance of the user. According to the analysis result of the inference, the training content and progress for each user are customized. For example, if the probability of improvement of the user in the disassembly and assembly sequence error is low, the difficulty and frequency of the related training content are increased; if the user performs well in the time overrun, the related training content is appropriately reduced. The optimized scheme generated by the genetic algorithm is combined with the inference analysis result of the Bayesian network to generate the final periodic reinforcement training scheme. For example, if the genetic algorithm suggests increasing the difficulty of a certain training content, and the Bayesian network predicts that the user has a high probability of improvement in this content, both suggestions will be considered in the final training scheme. Meanwhile, time feedback nodes are established, which are used to monitor and evaluate the progress of the user in each training stage.

[0084] In the time feedback nodes, periodic reinforcement training detection is performed on the user, including a series of tests or simulated disassembly and assembly tasks, for evaluating the progress and remaining problems of the user in the training process. The detection results of the user are collected to generate a detection data set. Based on the detection data set, the performance and problems of the user in the training are analyzed to generate reinforcement compensation, which includes additional training content, targeted guidance or suggestions, and adjustment of the training scheme. The periodic reinforcement training scheme is optimized according to the reinforcement compensation to ensure that the user can more effectively improve the disassembly and assembly skills in the subsequent training, and the optimized training scheme is used to guide the subsequent learning and management of the user. This implementation generates a personalized training scheme that meets the needs of the user by collecting and analyzing the disassembly and assembly weak data and the user data, ensuring the effectiveness and pertinence of the training. By setting the time feedback nodes, the progress of the user in the training process is tracked and evaluated in real time, and the reinforcement compensation and the optimized training scheme are generated based on the detection data set, which can compensate and improve the user's deficiencies in the training, thereby further improving the disassembly and assembly skills of the user.

[0085] In a possible implementation, the system further includes a training module configured to activate voice guidance and operation guidance when the user starts to perform the simulation training, and use the voice guidance and the operation guidance to manage the training prompt of the user.

[0086] Specifically, when the user starts the simulation training mode, the training module automatically activates the built-in voice guidance system. This system provides real-time voice prompts and guidance based on the user's training progress and current operation. Among them, the voice guidance is generated by voice synthesis technology, and the audio information for guiding the user's operation. In addition to voice guidance, the training module also generates detailed operation instructions based on the user's training content and current step. These instructions are displayed in the form of text, pictures or animations on the user interface, helping users understand the operation steps and precautions more intuitively. This implementation provides real-time voice guidance and operation instructions, which can help users master the disassembly and assembly skills faster, reduce errors and repeated operations, and improve training efficiency.

[0087] In a possible implementation, the training module comprises a pre-warning correction module for identifying the operation deviation of the user's actual operation from the operation instruction, and establishing a correction instruction based on the operation deviation, and issuing an operation abnormality pre-warning.

[0088] Specifically, the pre-warning correction module monitors the user's actual operation in real time and compares it with the operation instruction provided by the training module. By comparing the differences between the two, the operation deviation of the user is identified. Once the operation deviation is identified, the pre-warning correction module generates corresponding correction instructions according to the type and degree of deviation. These instructions are presented in the form of text, pictures or voice to help users understand the deviation and guide them to perform the correct operation. In addition to generating correction instructions, the pre-warning correction module also issues an operation abnormality pre-warning to the user through the system interface or voice prompt, etc. to attract the user's attention and prompt them to correct the deviation in time. After the user provides the correction operation, the pre-warning correction module will again compare and evaluate the user's operation to ensure the correction effect. If the corrected operation still has problems, the module will repeat the above steps until the user fully masters the correct operation method. This implementation can timely discover and correct the user's operation deviation by monitoring and comparing the user's actual operation with the operation instruction in real time, thereby improving the quality and effect of simulation training.

[0089] In the foregoing, the BIM-based power plant equipment disassembly and assembly simulation management system according to the embodiments of the present application is described in detail. Next, the BIM-based power plant equipment disassembly and assembly simulation management method according to the embodiments of the present application will be described with reference to the accompanying drawings. Figure 1 The BIM-based power plant equipment disassembly and assembly simulation management method according to the embodiments of the present application is described in detail. Next, the BIM-based power plant equipment disassembly and assembly simulation management method according to the embodiments of the present application will be described with reference to the accompanying drawings. Figure 2 The BIM-based power plant equipment disassembly and assembly simulation management method according to the embodiments of the present application is described in detail. Next, the BIM-based power plant equipment disassembly and assembly simulation management method according to the embodiments of the present application will be described with reference to the accompanying drawings.

[0090] The BIM-based power plant equipment disassembly and assembly simulation management method according to the embodiments of the present application is described in detail. Next, the BIM-based power plant equipment disassembly and assembly simulation management method according to the embodiments of the present application will be described with reference to the accompanying drawings.

[0091] The BIM-based power plant equipment disassembly simulation management method comprises: after data collection of power plant equipment, a BIM model is established, the data collection comprising equipment parameter data collection and installation space data collection; when a user starts to perform simulation disassembly, real-time operation records are executed, an operation record data set and a response data set of the BIM model are established; disassembly credibility detection is performed on the operation record data set, and a first disassembly simulation detection result is generated; a conflict channel is set based on the BIM model, response conflict detection of the response data set is performed by using the conflict channel, and a second disassembly simulation detection result is established; time sequence correlation analysis is performed on the first disassembly simulation detection result and the second disassembly simulation detection result, and a fusion authentication result is generated; and disassembly simulation management is performed according to the first disassembly simulation detection result, the second disassembly simulation detection result and the fusion authentication result.

[0092] The disassembly credibility detection on the operation record data set to generate the first disassembly simulation detection result can further comprise: disassembly simulation of the power plant equipment is performed by using the BIM model, and a disassembly simulation set is established; disassembly sequence constraints are established according to the disassembly simulation set, the disassembly sequence constraints comprising floating sequence constraints and fixed sequence constraints; the disassembly credibility detection of the operation record data set is performed by using the disassembly sequence constraints, and a sequence simulation detection result is generated; the operation record data set is subjected to disassembly time node analysis, and a component disassembly window is established; window length distribution evaluation of the component disassembly window is performed, and a window simulation detection result is generated; and the first disassembly simulation detection result is generated according to the sequence simulation detection result and the window simulation detection result.

[0093] The disassembly credibility detection of the operation record data set by using the disassembly sequence constraints to generate the sequence simulation detection result can further comprise: floating components are positioned according to the floating sequence constraints; component criticality of the floating components is acquired, and a first credibility detection standard is generated according to the component criticality; component collision risk levels of the floating components are acquired, and a second credibility detection standard is generated according to the collision risk levels; the disassembly credibility detection of the floating components is performed by using the first credibility detection standard and the second credibility detection standard, so as to establish the sequence simulation detection result.

[0094] The window length distribution evaluation of the component disassembly window can further include: establishing a minimum disassembly granularity of the assembly, performing simulated disassembly of the BIM model according to the minimum disassembly granularity, and establishing a component calibration disassembly time; establishing a standard component time allocation ratio according to the component calibration disassembly time; performing proportional distribution evaluation of the component disassembly window using the standard component time allocation ratio to generate a proportional distribution evaluation result; performing individual window comparison of the component disassembly window using the component calibration disassembly time to establish a component disassembly evaluation result; and generating a window simulation detection result according to the proportional distribution evaluation result and the component disassembly evaluation result.

[0095] The establishment of the second disassembly simulation detection result based on the BIM model and the response conflict detection of the response data set using the conflict channel can further include: establishing a fixed space constraint according to installation space data, and building a first conflict sub-channel using the fixed space constraint; obtaining a device component structure according to the BIM model, establishing a fixed component constraint according to the device component structure, and establishing a second conflict sub-channel using the fixed component constraint; setting a dynamic conflict sub-channel, which is dynamically updated based on the response data set, and establishing a conflict channel using the first conflict sub-channel, the second conflict sub-channel, and the dynamic conflict sub-channel to complete the response conflict detection.

[0096] The time sequence correlation analysis of the first disassembly simulation detection result and the second disassembly simulation detection result to generate a fusion authentication result can further include: time sequence alignment of the first disassembly simulation detection result and the second disassembly simulation detection result; extracting a reference comparison node from the first disassembly simulation detection result and performing delayed correlation search on the second disassembly simulation detection result mapped by time sequence alignment to establish a search matching result; performing detection cross-validation using the search matching result to establish the fusion authentication result.

[0097] The disassembly simulation management according to the first disassembly simulation detection result, the second disassembly simulation detection result, and the fusion authentication result can further include: creating a personal archive of a user; generating disassembly weak data according to the first disassembly simulation detection result, the second disassembly simulation detection result, and the fusion authentication result; and updating the disassembly weak data to the personal archive to perform disassembly simulation management of the user.

[0098] The updating of the disassembly and assembly weak data to the personal file can further include: generating a periodic reinforcement training scheme according to the disassembly and assembly weak data and user data, and establishing a timing feedback node; performing periodic reinforcement training detection of the user at the timing feedback node, and establishing a detection data set; generating reinforcement compensation by using the detection data set, optimizing the periodic reinforcement training scheme based on the reinforcement compensation, and performing user management according to the optimized periodic reinforcement training scheme.

[0099] The method can further include: when the user starts to perform the simulation training, activating voice guidance and operation guidance, and performing training prompt management of the user by using the voice guidance and the operation guidance.

[0100] The training prompt management of the user by using the voice guidance and the operation guidance can further include: identifying operation deviation of the user from the operation guidance, establishing a correction guide based on the operation deviation, and issuing an operation abnormality warning.

[0101] The BIM-based power plant equipment disassembly and assembly simulation management system provided by the embodiment can execute the BIM-based power plant equipment disassembly and assembly simulation management method provided by any embodiment of the application, and has the corresponding function modules and beneficial effects of the execution method.

[0102] Although various references are made to certain modules in the system according to the embodiments of the application, however, any number of different modules can be used and run on the user terminal and / or the server, and the included modules are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for easy mutual differentiation, and are not used to limit the protection scope of the application.

[0103] The above specific embodiments do not constitute a limitation on the protection scope of the application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent replacement and improvement made within the spirit and principles of the application should be included in the protection scope of the application. In some cases, the actions or steps described in the application can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

Claims

1. The BIM-based power plant equipment disassembly and assembly simulation management system is characterized by: The system comprises: Establish a module for building a BIM model after collecting data on power plant equipment. The data collection includes equipment parameter data collection and installation space data collection. The recording module is used to perform real-time operation recording when the user starts to perform simulated disassembly and assembly, and to establish an operation record data set and a response data set of the BIM model; a first detection module, configured to perform a disassembly and assembly credibility detection on the operation record data set to generate a first disassembly and assembly simulation detection result; A second detection module is configured to set a conflict channel based on the BIM model, perform response conflict detection on the response data set using the conflict channel, and establish a second assembly and disassembly simulation detection result; a fusion authentication module, configured to perform a time-series correlation analysis on the first assembly and disassembly simulation test result and the second assembly and disassembly simulation test result to generate a fusion authentication result; a management module, configured to perform disassembly and assembly simulation management according to the first disassembly and assembly simulation test result, the second disassembly and assembly simulation test result, and the fusion authentication result; The first detection module includes: A disassembly and assembly simulation module is used to simulate the disassembly and assembly of power plant equipment using the BIM model and establish a disassembly and assembly simulation set; An assembly and disassembly sequence constraint establishing module, configured to establish an assembly and disassembly sequence constraint according to the assembly and disassembly simulation set, wherein the assembly and disassembly sequence constraint includes a floating sequence constraint and a fixed sequence constraint; a disassembly and assembly trust detection module, configured to perform disassembly and assembly trust detection on the operation record data set according to the disassembly and assembly sequence constraint, and generate a sequence simulation detection result; A disassembly and assembly time node parsing module, configured to parse the operation record data set for disassembly and assembly time nodes and establish a parts disassembly and assembly window; a window length distribution evaluation module, configured to perform a window length distribution evaluation of the component disassembly and assembly window and generate a window simulation detection result; A first assembly and disassembly simulation test result generating module, configured to generate a first assembly and disassembly simulation test result according to the sequence simulation test result and the window simulation test result; The window length distribution evaluation module includes: A component calibration disassembly time establishment module is used to establish the minimum splitting granularity of the component, simulate the disassembly of the BIM model according to the minimum splitting granularity, and establish the component calibration disassembly time; A standard component time allocation ratio establishment module is used to establish a standard component time allocation ratio according to the component calibrated disassembly time; a proportion distribution evaluation module for evaluating the proportion distribution of component disassembly and assembly windows using the standard component time distribution ratio and generating a proportion distribution evaluation result; A component disassembly and assembly evaluation result establishment module is used to compare the component disassembly and assembly windows one by one using the component calibrated disassembly time to establish a component disassembly and assembly evaluation result; a window simulation test result generating module, configured to generate a window simulation test result based on the proportion distribution evaluation result and the component disassembly and assembly evaluation result; The second detection module includes: A first conflict sub-channel building module, configured to establish a fixed space constraint according to the installation space data, and build the first conflict sub-channel with the fixed space constraint; A second conflict sub-channel establishing module is configured to obtain an equipment component structure according to the BIM model, establish fixed component constraints according to the equipment component structure, and establish a second conflict sub-channel using the fixed component constraints; The conflict channel establishment module is used to set a dynamic conflict sub-channel, which is dynamically updated based on the response data set, and establish a conflict channel using the first conflict sub-channel, the second conflict sub-channel, and the dynamic conflict sub-channel to complete response conflict detection.

2. The BIM-based power plant equipment disassembly and assembly simulation management system according to claim 1, characterized in that: The disassembly and assembly trust detection module includes: a floating component positioning module, configured to position the floating component according to the floating sequence constraint; A first trusted detection standard generating module is configured to obtain a component criticality of a floating component and generate a first trusted detection standard according to the component criticality; A second trusted detection standard generating module, configured to obtain a component collision risk level of the floating component and generate a second trusted detection standard according to the collision risk level; The floating component assembly and disassembly trustworthy detection module is used to perform assembly and disassembly trustworthy detection of the floating component using the first trustworthy detection standard and the second trustworthy detection standard to establish the sequential simulation detection result.

3. The BIM-based power plant equipment disassembly and assembly simulation management system according to claim 1, characterized in that: The fusion authentication module includes: A time series alignment module, configured to perform time series alignment on the first assembly and disassembly simulation test result and the second assembly and disassembly simulation test result; a delayed association search module, configured to extract a reference comparison node from the first assembly and disassembly simulation detection result, and perform a delayed association search on the second assembly and disassembly simulation detection result mapped by time series alignment to establish a search matching result; The detection cross-validation module is used to perform detection cross-validation using the search matching results to establish the fusion authentication result.

4. The BIM-based power plant equipment disassembly and assembly simulation management system according to claim 1, characterized in that: The management module includes: Personal profile creation module, used to create a user's personal profile; a disassembly and assembly weakness data generating module, configured to generate disassembly and assembly weakness data based on the first disassembly and assembly simulation test result, the second disassembly and assembly simulation test result, and the fusion authentication result; The personal file updating module is used to update the disassembly and assembly weakness data to the personal file to perform disassembly and assembly simulation management of the user.

5. The BIM-based power plant equipment disassembly and assembly simulation management system according to claim 4, characterized in that: The personal profile update module includes: A periodic reinforcement training program generation module is used to generate a periodic reinforcement training program based on the disassembly and assembly weakness data and user data, and establish a time series feedback node; A periodic reinforcement training detection module is used to perform periodic reinforcement training detection of the user at the timing feedback node and establish a detection data set; The reinforcement compensation module is used to generate reinforcement compensation using the detection data set, optimize the periodic reinforcement training program based on the reinforcement compensation, and perform user management according to the optimized periodic reinforcement training program.

6. The BIM-based power plant equipment disassembly and assembly simulation management system according to claim 1, characterized in that: The system further comprises: The training module is used to activate voice guidance and operation guidance when the user starts to perform simulation training, and use the voice guidance and the operation guidance to manage the user's training prompts.

7. The BIM-based power plant equipment disassembly and assembly simulation management system according to claim 6, characterized in that: The training module includes: The early warning and correction module is used to identify the operation deviation between the user's actual operation and the operation guide, establish a correction guide based on the operation deviation, and report an abnormal operation warning.

8. The BIM-based power plant equipment disassembly and assembly simulation management method is characterized by: The method is implemented by executing the BIM-based power plant equipment disassembly and assembly simulation management system according to any one of claims 1 to 7, and the method includes: After collecting data on power plant equipment, a BIM model is established. Data collection includes equipment parameter data collection and installation space data collection; When the user starts to perform simulated disassembly and assembly, real-time operation records are performed to create an operation record dataset and a response dataset of the BIM model; Performing a disassembly and assembly credibility test on the operation record data set to generate a first disassembly and assembly simulation test result; Setting a conflict channel based on the BIM model, performing response conflict detection on the response data set using the conflict channel, and establishing a second assembly and disassembly simulation detection result; Performing a time series correlation analysis on the first assembly and disassembly simulation test result and the second assembly and disassembly simulation test result to generate a fusion authentication result; Disassembly and assembly simulation management is performed according to the first disassembly and assembly simulation detection result, the second disassembly and assembly simulation detection result, and the fusion authentication result.

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