A method and system for displaying a bridge structure BIM model

Abstract

This invention relates to the field of dynamic modeling technology, specifically to a method and system for displaying a bridge structure BIM model, solving the technical problem of low model loading and rendering efficiency in existing technologies. The method includes: acquiring the state data of the bridge structure's BIM model within a display observation window; the state data includes model analysis pressure data, attribute query load data, and component dynamic association data; the component dynamic association data is used to characterize the association relationship between dynamic monitoring data and components; based on the state data, determining the attribute refresh frequency correction coefficient for each component in the BIM model; the attribute refresh frequency correction coefficient is used to characterize the correction magnitude of the component's attribute refresh frequency; correcting the attribute refresh frequency of the corresponding component based on the attribute refresh frequency correction coefficient, and loading and displaying the BIM model's attributes according to the corrected attribute refresh frequency.

Description

Technical Field

[0001] This invention relates to the field of dynamic modeling technology, specifically to a method and system for displaying BIM models of bridge structures. Background Technology

[0002] With the continuous expansion of transportation infrastructure construction, the structural forms of bridge engineering are becoming increasingly complex. Their entire lifecycle (design, construction, and operation and maintenance) involves complex needs such as multi-disciplinary collaboration, multi-source data interaction, and cross-cycle information transmission. Building Information Modeling (BIM) technology, with its advantages of digitalization, visualization, and informatization, has been widely applied in the field of bridge engineering. By constructing digital models that integrate multi-dimensional data such as geometric information, component attributes, material parameters, and construction procedures, it enables efficient sharing and management of engineering information. The presentation of the BIM model is a core link throughout the entire process of design review, construction coordination, and operation and maintenance management. Its presentation effect, real-time performance, and smooth interaction directly affect the decision-making efficiency and management quality at each stage of the project.

[0003] In existing BIM model display solutions for bridge structures, a combination of overall model loading and static rendering is commonly used. However, this approach often faces numerous performance bottlenecks during model display. For example, when dealing with complex models or scenarios involving dynamic data access, the system is prone to issues such as delayed attribute loading, delayed response of the attribute panel, and the inability to link geometric displays with component attribute information in real time. This results in insufficient model interaction smoothness, low model loading and rendering efficiency, and an inability to meet the real-time and accurate requirements of BIM model display in engineering practice, thus hindering the in-depth application of BIM technology in complex bridge engineering scenarios. Summary of the Invention

[0004] To address the technical problem of low efficiency in loading and rendering existing models, the present invention aims to provide a method and system for displaying bridge structure BIM models. The specific technical solution adopted is as follows:

[0005] This application provides a method for displaying a BIM model of a bridge structure, including:

[0006] Acquire the status data of the bridge structure's BIM model within the display observation window; the status data includes model-analyzed pressure data, attribute query load data, and component dynamic association data; the component dynamic association data is used to characterize the relationship between dynamic monitoring data and components.

[0007] Based on the status data, determine the attribute refresh frequency correction coefficient for each component in the BIM model; the attribute refresh frequency correction coefficient is used to characterize the correction magnitude of the attribute refresh frequency of the component.

[0008] The attribute refresh frequency of each component is corrected based on the attribute refresh frequency correction coefficient, and the attributes of the BIM model are loaded and displayed according to the corrected attribute refresh frequency.

[0009] In one possible implementation, the method includes:

[0010] Based on the model analysis of stress data and attribute query load data in the status data, the static binding delay index is determined; the static binding delay index is used to characterize the actual query delay intensity caused by the fixed data structure in the BIM model.

[0011] Based on the component dynamic association data in the status data, the dynamic update driving attribute overload pressure index is determined; the dynamic update driving attribute overload pressure index is used to characterize the system pressure intensity triggered by dynamic data to update component attributes.

[0012] The attribute refresh frequency correction coefficient for each component is determined based on the static binding delay index and the dynamic update-driven attribute overload pressure index.

[0013] In one possible implementation, the method includes:

[0014] Obtain the dynamic monitoring data update frequency and attribute loading count for each component in the component dynamic association data;

[0015] The dynamic update-driven attribute overload pressure index is calculated based on the dynamic monitoring data update frequency and attribute loading times of each component, as well as the average load level of all components.

[0016] In one possible implementation, the method includes:

[0017] Based on the model parsing pressure data, the model parsing complexity index is determined; the model parsing complexity index is used to characterize the volatility of model parsing and rendering load.

[0018] Based on the model's analytical complexity index and attribute query load data, the attribute loading response load intensity is determined; the attribute loading response load intensity is used to characterize the pressure on the system when processing attribute queries.

[0019] The static binding structure influence index is determined based on the load intensity of the attribute loading response; the static binding structure influence index is used to characterize the degree of redundancy caused by the fixed data structure in the BIM model to the query path.

[0020] The static binding delay index is determined based on the static binding structure impact index.

[0021] In one possible implementation, the model analytical stress data includes the total number of components and the total number of triangular faces in the BIM model, statistically analyzed at different times within the observation window; the method includes:

[0022] The analysis of the changing trends of the total number of components and the total number of triangular faces in the BIM model at different times within the observation window is performed, and the analytical complexity index of the model is calculated based on the changing trends of the total number of components and the total number of triangular faces at adjacent times.

[0023] In one possible implementation, the attribute query load data includes the current load data of the attribute query queue; the method includes:

[0024] Based on the comparison between the current load data and the preset load threshold, and combined with the model analysis complexity index, the attribute loading response load intensity is calculated.

[0025] In one possible implementation, the method includes:

[0026] Get the number of components accessed in each of the multiple attribute query events that occur within the display observation window;

[0027] The static binding structure influence index is calculated based on the deviation of the number of components accessed in each attribute query event from the average number of components accessed, and the attribute loading response load intensity.

[0028] In one possible implementation, the method includes:

[0029] Get the time interval between the request and response of each attribute query event in multiple attribute query events that occur within the display observation window;

[0030] The static binding latency index is calculated based on the time interval between the request and response of each attribute query event and the static binding structure impact index.

[0031] In one possible implementation, the method includes:

[0032] For each component, the attribute refresh frequency of the component is corrected according to the attribute refresh frequency correction coefficient corresponding to the component, so as to obtain the corrected attribute refresh frequency.

[0033] The attribute loading tasks of each component are scheduled according to the revised attribute refresh frequency in order to load and display the attributes of the BIM model.

[0034] This application provides a system for displaying BIM models of bridge structures, including:

[0035] The data acquisition unit is used to acquire the status data of the bridge structure's BIM model within the display observation window. The status data includes model analytical pressure data, attribute query load data, and component dynamic association data. The component dynamic association data is used to characterize the relationship between dynamic monitoring data and components.

[0036] The frequency correction coefficient determination unit is used to determine the attribute refresh frequency correction coefficient of each component in the BIM model based on the status data; the attribute refresh frequency correction coefficient is used to characterize the correction magnitude of the attribute refresh frequency of the component.

[0037] The display control unit is used to correct the attribute refresh frequency of the corresponding component based on the attribute refresh frequency correction coefficient of each component, and to load and display the attributes of the BIM model according to the corrected attribute refresh frequency.

[0038] The present invention has the following beneficial effects:

[0039] In view of the technical problem of low loading and rendering efficiency of existing models, this application provides a method and system for displaying bridge structure BIM models. By acquiring the status data of the bridge structure BIM model within the display observation window, this application comprehensively captures key factors affecting display performance, such as model analysis pressure, attribute query load, and dynamic data association, providing comprehensive and reliable data support for subsequent frequency correction. By determining the attribute refresh frequency correction coefficient through status data, the load pressure and correction requirements of each component are quantified. By dynamically correcting the attribute refresh frequency and realizing on-demand loading, the application effectively solves the problems of attribute loading lag and the inability to link geometry and attribute display in real time in existing methods, significantly improving loading and rendering efficiency, ensuring the smoothness and real-time performance of BIM model interaction, and meeting the high precision and high real-time performance requirements of BIM model display at all stages of bridge engineering. Attached Figure Description

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A system architecture diagram of a bridge structure BIM model display system provided in one embodiment of the present invention;

[0042] Figure 2 This is one of the flowcharts illustrating a method for displaying a bridge structure BIM model according to an embodiment of the present invention;

[0043] Figure 3 This is a second schematic flowchart illustrating a method for displaying a bridge structure BIM model, provided as an embodiment of the present invention.

[0044] Figure 4This is the third flowchart illustrating a method for displaying a bridge structure BIM model, as provided in one embodiment of the present invention. Detailed Implementation

[0045] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a bridge structure BIM model display method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] In view of the technical problem of low loading and rendering efficiency of existing models, this application provides a method and system for displaying bridge structure BIM models. By acquiring the status data of the bridge structure BIM model within the display observation window, this application comprehensively captures key factors affecting display performance, such as model analysis pressure, attribute query load, and dynamic data association, providing comprehensive and reliable data support for subsequent frequency correction. By determining the attribute refresh frequency correction coefficient through status data, the load pressure and correction requirements of each component are quantified. By dynamically correcting the attribute refresh frequency and realizing on-demand loading, the application effectively solves the problems of attribute loading lag and the inability to link geometry and attribute display in real time in existing methods, significantly improving loading and rendering efficiency, ensuring the smoothness and real-time performance of BIM model interaction, and meeting the high precision and high real-time performance requirements of BIM model display at all stages of bridge engineering.

[0048] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and system for displaying a bridge structure BIM model provided by this invention.

[0049] Please see Figure 1 This diagram illustrates a system architecture diagram of a bridge structure BIM model display system according to an embodiment of the present invention. The bridge structure BIM model display system 10 includes: a data acquisition unit 11, a frequency correction coefficient determination unit 12, and a display control unit 13. Data interaction between the units is achieved through communication links, which can adopt wired or wireless transmission methods to adapt to the deployment requirements of different engineering scenarios.

[0050] Data acquisition unit 11 is used to acquire the status data of the BIM model of the bridge structure within the display observation window.

[0051] The status data includes model parsing pressure data, attribute query load data, and component dynamic association data. The component dynamic association data is used to characterize the relationship between dynamic monitoring data and components.

[0052] In some embodiments, the data acquisition unit 11 can establish a communication connection with the BIM model file storage module, the sensor monitoring platform, and the system background task queue management module through a data interface to achieve synchronous acquisition of multi-source data.

[0053] The observation window is a preset time window used to limit the time range of data collection. Its value can be adjusted according to the needs of the engineering scenario. For example, it can be set to 10 minutes in daily display scenarios and 5 minutes in intensive monitoring scenarios during the construction phase, to ensure the timeliness and representativeness of data collection.

[0054] During the acquisition of pressure data from the model parsing, the data acquisition unit 11 can call the Industry Foundation Classes (IFC) import parser (such as IfcOpenShell) to read the BIM model tree and statistically display the total number of components (semantic / attribute dimension indicators) and the total number of triangular faces (geometric / rendering dimension indicators) at different times within the observation window. Attribute query load data can be read from the system's background task queue, including the number of pending attribute queries at each time within the observation window and preset load thresholds. Component dynamic correlation data can be obtained through integration with the sensor monitoring platform, including the update frequency of dynamic monitoring data (such as strain, displacement, temperature, etc.) corresponding to each component, as well as the number of times each component's attributes are loaded, extracted from the system attribute loading log.

[0055] For example, the aforementioned dynamic monitoring data can be collected through relevant sensors. Various sensors can be deployed at critical points and structurally weak areas of the bridge, such as strain gauges and stress gauges to obtain the stress on the main beam or pylon, displacement gauges to monitor beam deflection or support displacement, and temperature sensors to monitor changes in ambient and material temperatures. These sensing units are fixed to the surface or interior of the target component using mounting brackets, embedded parts, or adhesive methods.

[0056] For example, the data acquisition unit 11 can also preprocess the collected raw state data, including data deduplication, format conversion, time synchronization and other operations, to ensure the consistency and validity of the data and provide a reliable data foundation for subsequent calculations.

[0057] The frequency correction coefficient determination unit 12 is used to determine the attribute refresh frequency correction coefficient of each component in the BIM model based on the status data.

[0058] The attribute refresh frequency correction coefficient is used to characterize the magnitude of the correction to the attribute refresh frequency of the component.

[0059] The frequency correction coefficient determination unit 12 has a built-in data processing module that can perform multi-dimensional analysis of state data based on preset algorithm logic to achieve accurate calculation of the correction coefficient. The frequency correction coefficient determination unit 12 can integrate multiple factors such as model parsing pressure, attribute query load, and dynamic data association to quantify the load pressure of each component during the attribute refresh process, providing a quantitative basis for frequency correction.

[0060] In some embodiments, the frequency correction coefficient determination unit 12 may be deployed on a local server using an edge computing architecture to reduce data transmission latency and improve the calculation efficiency of the correction coefficient.

[0061] The display control unit 13 is used to correct the attribute refresh frequency of the corresponding component based on the attribute refresh frequency correction coefficient of each component, and to load and display the attributes of the BIM model according to the corrected attribute refresh frequency.

[0062] The display control unit 13 can be linked with the BIM model rendering engine. First, this application can assign an initial refresh frequency to each component (for example, the initial refresh frequency can be set to 1 time / second, which can be adjusted according to system performance and display accuracy requirements), and then dynamically correct the initial refresh frequency according to the frequency correction coefficient. At the same time, the display control unit 13 can have a built-in task scheduling module to schedule the attribute loading tasks of each component according to the corrected refresh frequency, so as to realize the on-demand loading and real-time updating of attribute data.

[0063] For example, the display control unit 13 can also monitor the system's background load status in real time. If the load exceeds a preset threshold, an emergency correction mechanism can be triggered to further optimize the refresh frequency of high-load components and ensure system stability. In addition, this unit also supports the synchronous output of attribute loading results and BIM model geometric rendering results to the display interface, realizing real-time linkage between geometric display and attribute information.

[0064] It should be noted that the various embodiments of this application can be referenced or learned from each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can be referenced from each other without limitation.

[0065] Please see Figure 2 The diagram illustrates a method flowchart for displaying a bridge structure BIM model according to an embodiment of the present invention. The method includes the following steps:

[0066] Step 201: Obtain the status data of the bridge structure's BIM model within the display observation window.

[0067] The status data includes model parsing pressure data, attribute query load data, and component dynamic association data. The component dynamic association data is used to characterize the relationship between dynamic monitoring data and components.

[0068] In some embodiments, the setting of the observation window needs to be combined with the actual needs of the engineering scenario. For example, in the design review scenario, since the model operation frequency is low, the observation window can be set to 15 minutes; in the construction coordination scenario, since it is necessary to frequently view component attributes and construction progress data, the observation window can be set to 5 minutes to ensure that the data can reflect the changes in system status in a timely manner.

[0069] Model resolution stress data is the core data characterizing the load of BIM model resolution and rendering. For example, model resolution stress data includes the total number of components and the total number of triangular faces of the BIM model at different times within the display observation window.

[0070] The total number of components can be obtained by parsing the component list of the BIM model file, and the total number of triangles can be obtained by reading the triangle count field in the model's "mesh" structure or by calculating based on the vertex index. The two reflect the model parsing pressure from the semantic / attribute dimension and the geometric / rendering dimension, respectively.

[0071] Attribute query load data reflects the system's workload in processing attribute queries. For example, attribute query load data includes the current load data of the attribute query queue, such as the number of pending attribute queries at each moment within the observation window, and a preset load threshold (the maximum number of pending queries the system can handle). The preset load threshold should be set in conjunction with system hardware performance (such as CPU processing power and memory size) and display requirements. For instance, for a medium-configuration server, the preset load threshold could be set to 50 queries to ensure system response efficiency under normal load.

[0072] The component dynamic correlation data reflects the degree of coupling between dynamic monitoring data and components. This includes the update frequency of dynamic monitoring data for each component (e.g., temperature data updated every 30 seconds, strain data updated every 10 seconds) and the number of attribute loadings (the total number of times the system loads the component's attributes within the observation window). The dynamic monitoring data includes strain, displacement, and temperature data from key stress-bearing parts and structurally weak areas of the bridge. This data is collected by deployed sensors, pre-processed, and then uploaded to the data center. The data acquisition unit can obtain this data through an interface with the data center.

[0073] Step 202: Based on the status data, determine the attribute refresh frequency correction coefficient for each component in the BIM model.

[0074] The attribute refresh frequency correction coefficient is used to characterize the magnitude of correction to the attribute refresh frequency of a component. A larger value of the attribute refresh frequency correction coefficient indicates a greater current load pressure on the component, requiring a more significant reduction in the attribute refresh frequency; a smaller value indicates a lower load pressure, allowing the refresh frequency to be maintained or appropriately increased.

[0075] In some embodiments, this application can comprehensively consider factors such as model parsing pressure, attribute query load, and dynamic data association, and through weighted analysis of multi-dimensional data, achieve accurate quantification of the attribute refresh frequency correction coefficient, ensuring the pertinence and effectiveness of the frequency correction strategy.

[0076] Step 203: Correct the attribute refresh frequency of the corresponding component based on the attribute refresh frequency correction coefficient of each component, and load and display the attributes of the BIM model according to the corrected attribute refresh frequency.

[0077] In one possible implementation, this application corrects the attribute refresh frequency of each component according to the attribute refresh frequency correction coefficient corresponding to the component, obtains the corrected attribute refresh frequency, and schedules the attribute loading tasks of each component according to the corrected attribute refresh frequency to load and display the attributes of the BIM model.

[0078] For example, during initialization, this application first assigns an initial refresh frequency to each component in the BIM model. The initial refresh frequency can be set based on the balance between display real-time performance and system load. For example, for core bridge components (such as main beams and cable towers), the initial refresh frequency can be set to 2 times / second to ensure timely updates of key attribute information; for secondary components (such as ancillary facilities), the initial refresh frequency can be set to 0.5 times / second to reduce unnecessary system load.

[0079] Subsequently, the initial refresh frequency is corrected according to the aforementioned attribute refresh frequency correction coefficient, and the attribute loading tasks of each component are scheduled according to the corrected attribute refresh frequency. The attribute data and model geometric rendering results are synchronously output to the display interface to achieve real-time linkage between geometric display and attribute information.

[0080] After each observation window ends, the above technical solution is re-executed to iteratively update the attribute refresh frequency, forming a dynamic adjustment closed loop.

[0081] For example, the corrected formula is shown below:

[0082]

[0083] in, For the corrected components The attribute refresh frequency, For components The attribute refresh rate before correction (such as the initial refresh rate or the corrected attribute refresh rate corresponding to the previous observation window). For components The attribute refresh rate correction coefficient, with a value ranging from 0 to 1. To ensure the coefficient (ranging from 0.1 to 0.2), it is used to prevent update interruptions caused by the attribute refresh rate dropping to zero. When Greater than When this happens, the attribute refresh frequency is reduced to alleviate system load. Less than or equal to At the same time, the refresh frequency of the corrected attributes remains unchanged or increases, thereby accelerating attribute updates. This guarantee coefficient is set. This application can adaptively increase, maintain, or decrease the refresh rate based on the real-time pressure status of the component, thereby achieving overall load balancing and optimization of real-time response.

[0084] Based on the above technical solution, this application comprehensively captures key factors affecting display performance, such as model analysis pressure, attribute query load, and dynamic data association, by acquiring the status data of the bridge structure's BIM model within the display observation window. This provides comprehensive and reliable data support for subsequent frequency correction. By determining the attribute refresh frequency correction coefficient through status data, the load pressure and correction requirements of each component are quantified. By dynamically correcting the attribute refresh frequency and achieving on-demand loading, the problem of attribute loading lag and the inability to link geometry and attribute display in real time in existing methods is effectively solved. This significantly improves loading and rendering efficiency, ensures the smoothness and real-time performance of BIM model interaction, and meets the high precision and high real-time performance requirements of BIM model display at all stages of bridge engineering.

[0085] As one possible embodiment of this application, combined with Figure 2 ,like Figure 3 As shown, step 202 above can be achieved through the following steps:

[0086] Step 301: Based on the model analysis of the status data and the attribute query load data, determine the static binding latency index.

[0087] Among them, the static binding delay index is used to characterize the actual query delay intensity caused by the fixed data structure in the BIM model, and can quantify the impact of data structure fixation on attribute query path and response time.

[0088] For example, this application can quantify the complexity of the data in the BIM model by parsing the pressure data and quantify the processing performance occupied by loading the data in the BIM model by querying the load data through the attribute query, thereby evaluating the static binding delay index based on the above two aspects.

[0089] Step 302: Determine the overload pressure index of the dynamic update driving attribute based on the component dynamic association data in the status data.

[0090] Among them, the dynamic update-driven attribute overload pressure index is used to characterize the system pressure intensity of dynamic data triggering component attribute updates, and can reflect the degree of coupling between the dynamic monitoring data update frequency and component attribute loading.

[0091] In one possible implementation, this application can obtain the dynamic monitoring data update frequency and attribute loading times of each component in the component dynamic association data, and calculate the dynamic update driving attribute overload pressure index based on the dynamic monitoring data update frequency and attribute loading times of each component, as well as the average load level of all components.

[0092] The dynamic monitoring data update frequency refers to the total number of times the dynamic monitoring data (such as strain, displacement, temperature, etc.) corresponding to the component is updated within the display observation window. It reflects the degree of coupling between the component and the dynamic monitoring data. The higher the update frequency, the more frequently the dynamic data triggers the component's attributes. The attribute loading count refers to the total number of times the system loads the component's attributes within the display observation window. It reflects the actual amount of operation the system performs on the component's attributes. The more loading counts, the more computational resources the system devotes to the component.

[0093] In some embodiments, the update frequency of dynamic monitoring data can be obtained from the update log of the sensor monitoring platform, and the number of attribute loadings can be obtained from the attribute loading log in the system background. During the data acquisition process, it is necessary to ensure that the time range is consistent with the display observation window to avoid data deviation.

[0094] For example, the dynamic update-driven attribute overload stress exponent satisfies the following formula:

[0095]

[0096] in, For components Dynamically updated driver attribute overload stress index For components The frequency of dynamic monitoring data updates For components The number of times the attribute is loaded. This represents the total number of components in the BIM model currently displayed within the observation window. This is a normalization function (e.g., maximum / minimum normalization) used to map the calculation results to the range of 0 to 1. It is a safety parameter used to correct fractions where the denominator is 0, such as .

[0097] The above It can characterize components Dynamic load, It can characterize the average load level of the overall dynamic load of the system, when When it is greater than 1, it indicates that the component The dynamic load is higher than the average load level, and the larger the value, the greater the dynamic load. This necessitates reducing the attribute refresh frequency to alleviate system pressure. When less than or equal to 1, it indicates that the component When the dynamic load is lower than or equal to the average load level, the dynamic overload pressure is small, and the attribute refresh frequency can be maintained or appropriately increased.

[0098] Step 303: Determine the attribute refresh frequency correction coefficient for each component based on the static binding delay index and the dynamic update driving attribute overload pressure index.

[0099] For example, the attribute refresh rate correction factor satisfies the following formula:

[0100]

[0101] in, For components The attribute refresh frequency correction coefficient, For static binding latency index, For components Dynamically updated driver attributes overload stress index. The hyperbolic tangent function is used to map the calculation result to the range of 0 to 1. This is a weighting coefficient used to adjust the influence of the static binding delay index and the dynamic update-driven attribute overload pressure index, and can be determined based on experimental data statistics. The larger the value, the greater the combined strength of the component's static structural delay and dynamic load pressure; the attribute refresh frequency correction coefficient. The closer the value is to 1, the greater the adjustment range of the corresponding attribute refresh frequency, thereby effectively reducing system load; conversely, The smaller the value, the higher the attribute refresh rate correction factor. The closer the value is to 0, the smaller the adjustment range of the attribute refresh frequency, ensuring timely updates of attribute information.

[0102] Based on the above technical solutions, this application can quantify the key factors affecting attribute refresh from both the static structural level and the dynamic load level, thereby achieving a comprehensive and accurate assessment of component load pressure. This enables the frequency adjustment strategy to simultaneously adapt to the impact of static structural delay and dynamic load pressure, further improving the real-time performance of BIM model display and the stability of system operation, and providing more reliable technical support for BIM model applications in complex scenarios.

[0103] As one possible embodiment of this application, combined with Figure 3 ,like Figure 4 As shown, step 301 above can be achieved through the following steps:

[0104] Step 401: Determine the model parsing complexity index based on the model parsing pressure data.

[0105] Among them, the model parsing complexity index is used to characterize the volatility of model parsing and rendering load.

[0106] In some embodiments, the model analysis stress data includes the total number of components and the total number of triangular faces of the BIM model, as statistically analyzed at different times within the display observation window.

[0107] This application can perform trend analysis on the total number of components and the total number of triangular faces of the BIM model at different times within the display observation window, and calculate the analytical complexity index of the model based on the trend of the total number of components and the total number of triangular faces at adjacent times.

[0108] For example, the model's analytical complexity index satisfies the following formula:

[0109]

[0110] in, To analyze the complexity index of the model, To display the number of times within the observation window, To display the time within the observation window The total number of components in the BIM model. To display the time within the observation window The total number of components in the BIM model. To display the time within the observation window The total number of triangular faces in the BIM model is counted. To display the time within the observation window The total number of triangular faces in the BIM model. This indicates the rate of change in the total number of components at adjacent time points. The ratio of the total number of triangles at adjacent time points represents the change rate. Both reflect the changing trend of the model's analytical load from the semantic (attribute) dimension and the geometric (rendering) dimension, respectively. By calculating the average of the change rates of multiple adjacent time points, it is possible to suppress misjudgments caused by single data jitter and highlight the continuous changing trend of the load.

[0111] Model analytical complexity index A larger value indicates that the model's analytical complexity continues to increase within the observation window. This suggests that the model may be refined or locally loaded with high-precision geometry in the short term, increasing the risk of coupling between analysis and rendering and potentially leading to attribute query delays. The model analytical complexity index... The smaller the value, the more gradual the change in model parsing load, and the lower the risk of coupling between parsing and rendering.

[0112] Step 402: Determine the attribute loading response load intensity based on the model parsing complexity index and attribute query load data.

[0113] Among them, the attribute loading response load intensity is used to characterize the pressure on the system to process attribute queries.

[0114] In some embodiments, the attribute query load data includes the current load data of the attribute query queue.

[0115] This application can calculate the attribute loading response load intensity based on the comparison between the current load data and the preset load threshold, and in conjunction with the model analysis complexity index.

[0116] For example, the current load data in the attribute query load data can be the number of pending attribute queries at each moment within the observation window, which is obtained by reading real-time records from the system's background task queue. The preset load threshold can be set based on system hardware performance and display requirements; for example, it can be set to the maximum number of pending queries the system can handle, ensuring the system can respond normally to attribute queries under this load.

[0117] For example, the property loading response load intensity satisfies the following formula:

[0118]

[0119] in, Load response load strength for the attribute. To analyze the complexity index of the model, To display the number of times within the observation window, To display the time within the observation window The number of pending attribute queries at that time. This is the preset load threshold.

[0120] The ratio represents the average saturation level of the attribute query queue. A larger ratio indicates a more congested queue and greater pressure on the system to process attribute queries. This ratio is then compared with the model analytical complexity index. This combination enables coupled analysis of model parsing load and query queue load, comprehensively reflecting the overall pressure of the system in processing attribute queries. Attribute loading response load intensity The larger the value, the more complex the model parsing and the more congested the query queue, resulting in greater pressure on the system to process attribute queries and a higher likelihood of attribute panel loading delays; attribute loading response load intensity The smaller the value, the less pressure the system has on processing attribute queries, and the higher the attribute loading response efficiency.

[0121] Step 403: Determine the static binding structure impact index based on the load intensity of the load load based on the attributes.

[0122] Among them, the static binding structure impact index is used to characterize the degree of redundancy caused by the fixed data structure in the BIM model to the query path.

[0123] In one possible implementation, this application can obtain the number of components accessed in each of the multiple attribute query events that occur within the display observation window. Then, based on the deviation of the number of components accessed in each attribute query event from the average number of components accessed, and the attribute loading response load intensity, the static binding structure influence index is calculated.

[0124] The number of components accessed in each attribute query event can be obtained from the access path log of each attribute query recorded in the system backend. The number of components reflects the depth of the query path. The larger the value, the deeper the query path and the stronger the restriction of the static binding structure.

[0125] For example, the influence index of a statically bound structure satisfies the following formula:

[0126]

[0127] in, The static binding structure affects the index. Load response load strength for the attribute. To display the number of attribute query events that occurred within the observation window, For attribute query events The number of components accessed The average number of accessed components for all attribute query events recorded in the system backend.

[0128] This represents the deviation of the number of components accessed in a single attribute query event from the average level. A larger ratio indicates a deeper query path and stronger constraints from the static binding structure. By averaging multiple attribute query events, the overall structural problems can be reflected, avoiding reliance on the randomness of a single query. Subsequently, by combining attribute loading response load intensity, a coupled analysis of system load pressure and structural redundancy is achieved, quantifying the impact of the static binding structure on the query path. Static Binding Structure Influence Index The larger the value, the more severe the query path redundancy caused by the statically bound data structure. This redundancy is amplified under high system load, leading to significant query latency. The statically bound structure impact index... The smaller the value, the less impact the statically bound data structure has on the query path, and the higher the query efficiency.

[0129] It should be noted that when no attribute query events occur within the observation window, the display shows the number of attribute query events that occur within the observation window. If the value is 0, then the static binding structure influence index can be directly set. A value of 0 indicates that there is currently no structured query pressure.

[0130] Step 404: Determine the static binding delay index based on the static binding structure influence index.

[0131] Among them, the static binding latency index is used to characterize the actual query latency intensity caused by the fixed data structure.

[0132] In one possible implementation, this application can obtain the time interval between the request and response of each attribute query event in multiple attribute query events occurring within the display observation window, and then calculate the static binding latency index based on the time interval between the request and response of each attribute query event and the static binding structure influence index.

[0133] For example, the static binding latency exponent satisfies the following formula:

[0134]

[0135] in, For static binding latency index, The static binding structure affects the index. To display the number of attribute query events that occurred within the observation window, For attribute query events The time interval between the request and the response. This is a normalization function (e.g., maximum / minimum normalization) used to map the calculation results to the range of 0 to 1.

[0136] Static binding latency index The larger the value, the greater the actual latency caused by the static binding data structure, and the lower the response efficiency of component attribute queries; static binding latency index The smaller the value, the less impact the statically bound data structure has on query latency, and the higher the query response efficiency.

[0137] It should be noted that when no attribute query events occur within the observation window, the display shows the number of attribute query events that occur within the observation window. If the value is 0, the static binding delay index can be set directly. A value of 0 indicates that the impact of the current statically bound data structure on query latency is minimized.

[0138] Based on the above technical solutions, this application comprehensively and systematically quantifies the query latency problem caused by statically bound data structures, from model parsing complexity, attribute query load, static structural redundancy to actual query latency. By observing the changing trends of the total number of components and the total number of triangles, it accurately captures the coupling risk between model parsing and rendering. By combining the query queue load, it comprehensively reflects the overall pressure of the system in processing attribute queries. By quantifying the redundancy impact of statically bound structures on the query path, it reveals structural bottlenecks. Finally, by combining the actual query response time, it obtains the static binding latency index, achieving a complete assessment from potential risks to actual consequences.

[0139] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0140] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for displaying a BIM model of a bridge structure, characterized in that, include: Acquire the status data of the bridge structure's BIM model within the display observation window; the status data includes model analytical pressure data, attribute query load data, and component dynamic association data; the component dynamic association data is used to characterize the relationship between dynamic monitoring data and components. Based on the status data, determine the attribute refresh frequency correction coefficient for each component in the BIM model; The attribute refresh frequency correction coefficient is used to characterize the magnitude of the correction to the attribute refresh frequency of the component. The attribute refresh frequency of the corresponding component is corrected based on the attribute refresh frequency correction coefficient of each component, and the attributes of the BIM model are loaded and displayed according to the corrected attribute refresh frequency. The step of determining the attribute refresh frequency correction coefficient for each component in the BIM model based on the status data includes: Based on the model parsing pressure data and attribute query load data in the state data, a static binding delay index is determined; the static binding delay index is used to characterize the actual query delay intensity caused by the fixed data structure in the BIM model. Based on the component dynamic association data in the status data, the dynamic update driving attribute overload pressure index is determined; the dynamic update driving attribute overload pressure index is used to characterize the system pressure intensity of component attribute updates triggered by dynamic data. Based on the static binding delay index and the dynamic update driving attribute overload pressure index, determine the attribute refresh frequency correction coefficient for each component; Specifically, determining the dynamically updated driving attribute overload stress index based on the component dynamic association data in the status data includes: Obtain the dynamic monitoring data update frequency and attribute loading count for each component in the dynamic association data of the components; The dynamic update-driven attribute overload pressure index is calculated based on the dynamic monitoring data update frequency and attribute loading times of each component, as well as the average load level of all components. The step of determining the static binding latency index by parsing the pressure data and querying the load data based on the attributes in the state data includes: Based on the model parsing pressure data, a model parsing complexity index is determined; the model parsing complexity index is used to characterize the volatility of model parsing and rendering load. Based on the model's analytical complexity index and the attribute query load data, the attribute loading response load intensity is determined; the attribute loading response load intensity is used to characterize the pressure on the system when processing attribute queries. Based on the load intensity of the attribute loading, the static binding structure influence index is determined; the static binding structure influence index is used to characterize the degree of redundancy impact of the fixed data structure in the BIM model on the query path. The static binding delay index is determined based on the static binding structure influence index; The determination of the static binding delay index based on the static binding structure influence index includes: Obtain the time interval between the request and response of each attribute query event in the multiple attribute query events that occur within the display observation window; The static binding latency index is calculated based on the time interval between the request and response of each attribute query event and the static binding structure impact index.

2. The method for displaying a bridge structure BIM model according to claim 1, characterized in that, The model analysis stress data includes the total number of components and the total number of triangular faces of the BIM model at different times within the display observation window; The step of determining the model parsing complexity index based on the model parsing pressure data includes: Based on the total number of components and the total number of triangular faces of the BIM model at different times within the display observation window, a trend analysis is performed, and the analytical complexity index of the model is calculated according to the trend of the total number of components and the total number of triangular faces at adjacent times.

3. The method for displaying a bridge structure BIM model according to claim 1, characterized in that, The attribute query load data includes the current load data of the attribute query queue; Based on the model parsing complexity index and the attribute query load data, the attribute loading response load intensity is determined, including: Based on the comparison between the current load data and the preset load threshold, and in conjunction with the model parsing complexity index, the load intensity of the attribute loading response is calculated.

4. The method for displaying a bridge structure BIM model according to claim 1, characterized in that, Based on the load intensity of the load on the attribute, determine the static binding structure impact index, including: Obtain the number of components accessed in each of the multiple attribute query events that occur within the display observation window; The static binding structure influence index is calculated based on the deviation of the number of components accessed in each attribute query event from the average number of components accessed, and the load intensity of the attribute loading response.

5. The method for displaying a bridge structure BIM model according to claim 1, characterized in that, The attribute refresh frequency of each component is adjusted based on the attribute refresh frequency correction coefficient, and the attributes of the BIM model are loaded and displayed according to the adjusted attribute refresh frequency, including: For each component, the attribute refresh frequency of the component is corrected according to the attribute refresh frequency correction coefficient corresponding to the component, so as to obtain the corrected attribute refresh frequency. The attribute loading tasks of each component are scheduled according to the revised attribute refresh frequency in order to load and display the attributes of the BIM model.

6. A display system for a bridge structure BIM model, which executes the display method as described in any one of claims 1-5, characterized in that, include: The data acquisition unit is used to acquire the status data of the BIM model of the bridge structure within the display observation window; the status data includes model analytical pressure data, attribute query load data, and component dynamic association data; the component dynamic association data is used to characterize the association relationship between dynamic monitoring data and components. The frequency correction coefficient determination unit is used to determine the attribute refresh frequency correction coefficient of each component in the BIM model based on the status data. The attribute refresh frequency correction coefficient is used to characterize the magnitude of the correction to the attribute refresh frequency of the component. The display control unit is used to correct the attribute refresh frequency of the corresponding component based on the attribute refresh frequency correction coefficient of each component, and to load and display the attributes of the BIM model according to the corrected attribute refresh frequency.

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