Safety evaluation method and system for high-rise building in operation period

Abstract

The application discloses a kind of safety evaluation method and system of operating period high-rise building, belong to civil engineering and intelligent construction technology cross field, this method includes according to the layout strategy and sampling strategy of sensor, obtains and high-rise building safety associated acquisition data;According to partition rule and acquisition data, obtain multiple groups of measured data;Based on multiple groups of measured data, determine the dynamic weight corresponding to each type of acquisition data in acquisition data;Build the risk score model of high-rise building, determine the preliminary score result of high-rise building;Establish the mechanical simulation model of high-rise building, determine the predicted deformation data of high-rise building;According to at least one of preliminary score result and predicted deformation data, determine the risk assessment result of high-rise building.Combination of risk assessment model model, mechanical simulation model, real-time monitoring data, this method solves the problem that existing technology is not timely, accuracy is not high and cannot carry out dynamic evaluation to operating period high-rise building risk assessment.

Description

Technical Field

[0001] This application belongs to the intersection of civil engineering and intelligent construction technology, and specifically relates to a safety assessment method and system for high-rise buildings during operation. Background Art

[0002] With the rapid advancement of urbanization in my country, high-rise buildings are becoming increasingly popular in modern urban construction. However, these buildings often face significant wind safety issues during actual operation, especially the dynamic response of high-rise building structures under wind loads due to factors such as material aging and component loss. Currently, wind-resistant design of high-rise buildings at home and abroad is mainly concentrated in the design stage, and wind tunnel experiments and mechanical modeling are widely used to ensure the structural safety of buildings. Existing wind resistance assessment methods are mainly limited to the static stage and lack the ability to capture the dynamic response of real-time wind load changes during the operation period, making it difficult to achieve comprehensive structural health dynamic assessment and efficient early warning. The lack of dynamic assessment methods for the wind resistance safety of high-rise buildings during the operation period makes it difficult to accurately reflect the status of the building under actual wind load changes, which can easily lead to inaccurate risk prediction and delayed management during operation.

[0003] In the existing technology, the wind resistance monitoring method during the operation period usually adopts regular inspections or passive monitoring methods, which makes it difficult to capture the dynamic response characteristics of the building under the action of wind in real time and accurately, resulting in insufficient visualization of wind-induced deformation and stress changes. In addition, the wind resistance design and wind-induced structural response assessment of buildings mostly rely on a single mechanical model or physical model, and cannot be dynamically adjusted in combination with actual monitoring data. With the development of the Internet of Things and BIM technology, digital twin technology has gradually been introduced into the full life cycle management of buildings. However, the application of existing building digital twin technology in wind resistance safety assessment is mainly limited to static information display and management, lacking a comprehensive simulation and evaluation of the wind-induced dynamic response of high-rise buildings, and focusing more on the visualization and information integration of BIM data. The degree of integration with mathematical models, building mechanics simulation models, and real-time monitoring data is not high, especially in the dynamic mechanical analysis under wind loads. There is a lack of integration capabilities. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a safety assessment method and system for high-rise buildings in operation, which can solve the problem that the existing wind resistance safety assessment is mainly limited to static information display and management, and the degree of integration with mathematical models, building mechanics simulation models, and real-time monitoring data is not high, especially in the dynamic analysis under wind loads. The lack of integration capability makes it impossible to realize the dynamic safety assessment of high-rise buildings under wind loads.

[0005] In order to solve the above technical problems, this application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a safety assessment method for a high-rise building during operation, the method comprising:

[0007] According to the sensor deployment strategy and sampling strategy, the collected data related to the safety of high-rise buildings is obtained, including wind load data;

[0008] According to the partitioning rules and the collected data, multiple groups of measured data are obtained, and the partitioning rules include time partitioning rules and location partitioning rules;

[0009] Based on multiple groups of measured data, determine the dynamic weight corresponding to each type of collected data;

[0010] Build a risk scoring model for high-rise buildings, input each type of collected data and the corresponding dynamic weight into the scoring model, and determine the preliminary scoring results for high-rise buildings;

[0011] Establish a mechanical simulation model of a high-rise building and determine the predicted deformation data of the high-rise building based on the mechanical simulation model and wind load data;

[0012] A risk assessment result of the high-rise building is determined based on at least one of the preliminary scoring result and the predicted deformation data.

[0013] In some possible embodiments, the steps of acquiring data associated with high-rise building safety according to the sensor deployment strategy and sampling frequency strategy specifically include:

[0014] Determine multiple sensor deployment locations in high-rise buildings based on the sensor deployment strategy;

[0015] The expression of the deployment strategy is: ;

[0016] in, is the wind sensitivity coefficient of the i-th part, is the importance weight of the i-th part, Multiple deployment locations for sensors to meet deployment strategies;

[0017] The sampling frequency is determined according to the sampling strategy of the sensor, and data is collected at multiple deployment locations. The sampling frequency is adjusted in combination with the wind speed data in the wind load data;

[0018] The expression of the sampling strategy is: ;

[0019] in, is the natural frequency of the high-rise building, and and satisfy ≥ 2 , is the wind speed-vibration response coefficient, is the wind speed data in the wind load data.

[0020] In some possible embodiments, multiple sets of measured data are obtained according to partitioning rules and collected data, where the partitioning rules include time partitioning rules and location partitioning rules, specifically including:

[0021] The sampling data is divided according to the time partitioning rule and the location partitioning rule to obtain multiple groups of measured data. The measured data are the collected data of multiple layout locations within each time unit.

[0022] The relationship between the time partition rules is: ,in, is the first time unit, is the second time unit, is the nth time unit;

[0023] The relationship between the location partitioning rules is: ,in, This is the first placement position of the sensor. This is the second placement of the sensor. is the mth placement position of the sensor;

[0024] Among them, the expression for obtaining multiple sets of measured data is: ,in, is the data collected at position j during the i-th unit time, is the time index, is the position index.

[0025] In some possible embodiments, the step of determining the dynamic weight corresponding to each type of collected data in the collected data based on multiple sets of measured data specifically includes:

[0026] The formula for determining the dynamic weight corresponding to each type of collected data is:

[0027] ;

[0028] in, is the dynamic weight of the i-th type of collected data at the t-th time unit, is the dynamic weight of the i-th type of collected data at the (t-1)th time unit;

[0029] Dynamic weights satisfy: , where α is the weight adjustment coefficient, is the average value of multiple groups of measured data of the i-th category, The value of the collected data for category i at the tth time unit.

[0030] In some possible embodiments, the step of building the risk scoring model of the high-rise building, inputting each type of collected data and the corresponding dynamic weight into the scoring model, and determining the preliminary scoring result of the high-rise building, specifically comprises:

[0031] building the risk scoring model of the high-rise building in operation based on the weight weighting method;

[0032] based on the risk scoring model, weighting the dynamic weight corresponding to each type of collected data to obtain the preliminary scoring result;

[0033] The expression of the preliminary scoring result is:

[0034] ;

[0035] wherein, is the preliminary scoring result of the high-rise building, is the weight of the i-th type of collected data in the t-th time unit, is the value of the i-th type of collected data, is the weighted value of the i-th type of collected data in the t-th time unit.

[0036] In some possible embodiments, the step of determining the risk assessment result of the high-rise building according to at least one of the preliminary scoring result and the predicted deformation data, specifically comprises:

[0037] determining the risk coefficient according to the preliminary scoring result and the dynamic risk threshold ;

[0038] The expression of the dynamic risk threshold is: ;

[0039] wherein, is the dynamic risk threshold, is the current preliminary scoring result, is the initial risk threshold, is the threshold adjustment coefficient, is the average value of a plurality of preliminary scoring results.

[0040] based on the risk coefficient combining the corresponding preset risk level, to obtain the risk assessment result;

[0041] The expression of the risk coefficient is: ;

[0042] wherein, is the risk coefficient, is the dynamic risk threshold.

[0043] In some possible embodiments, the steps of establishing a mechanical simulation model of a high-rise building and determining predicted deformation data of the high-rise building based on the mechanical simulation model and wind load data specifically include:

[0044] Based on the BIM model of the high-rise building, meshing is performed on the linear components and cross-sectional components in the BIM model. The linear components are meshed using beam elements, while the cross-sectional components are meshed using shell elements.

[0045] Customize the basic mechanical properties parameters of the materials in the meshed BIM model to obtain a mechanical simulation model of the high-rise building;

[0046] The wind load data in the collected data is input into the mechanical simulation model to obtain the predicted deformation data.

[0047] In some possible embodiments, the step of determining a risk assessment result of a high-rise building based on at least one of the preliminary scoring result and the predicted deformation data specifically includes:

[0048] Based on the code limits corresponding to high-rise buildings and the physical working parameters of high-rise buildings, the safety threshold corresponding to the predicted deformation data is preset;

[0049] The predicted deformation data are compared with the safety threshold to determine the risk assessment results of the high-rise building.

[0050] In some possible embodiments, after determining the risk assessment result of the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data, the method further includes:

[0051] Generate early warning information based on risk assessment results;

[0052] Generate periodic wind resistance performance assessment reports for high-rise buildings based on multiple sets of measured data;

[0053] Send warning information and periodic wind resistance performance assessment reports to the user end;

[0054] In a second aspect, an embodiment of the present application provides a safety assessment system for high-rise buildings in operation, the system comprising:

[0055] An acquisition module is configured to acquire collected data related to high-rise building safety according to a sensor deployment strategy and a sampling frequency strategy;

[0056] The first data processing module is configured to obtain multiple groups of measured data according to a partitioning rule and collected data, wherein the partitioning rule includes a time partitioning rule and a location partitioning rule;

[0057] The first determination module is configured to determine the dynamic weight corresponding to each type of collected data in the collected data based on multiple groups of measured data;

[0058] The second data processing module is configured to construct a risk scoring model for high-rise buildings, input each type of collected data and the corresponding dynamic weight into the scoring model to obtain a preliminary scoring result for the high-rise building; establish a mechanical simulation model for the high-rise building, and determine the predicted deformation data of the high-rise building based on the mechanical simulation model and wind load data;

[0059] The second determination module is configured to determine a risk assessment result of the high-rise building according to at least one of the preliminary scoring result and the predicted deformation data.

[0060] In an embodiment of the present application, the safety assessment method for high-rise buildings in operation obtains dynamic and more comprehensive high-rise building risk assessment results by combining IoT sensors, risk scoring models, and mechanical simulation models. According to the sensor deployment strategy and sampling strategy, different types of collected data related to the safety of high-rise buildings are obtained. The collected data include wind load data, which are used as input parameters for the risk scoring model and the mechanical simulation model for subsequent preliminary scoring and mechanical simulation analysis; according to the partitioning rules and collected data, multiple groups of measured data are obtained. The partitioning rules include time partitioning rules and location partitioning rules. The multiple groups of measured data are used to determine the dynamic weights of various types of data, which is convenient for establishing a risk scoring model and is also convenient for obtaining subsequent wind resistance performance evaluation reports as data support; a risk scoring model for the operation period of high-rise buildings is built, and each type of collected data and the corresponding dynamic weight are input into the scoring model to determine the preliminary scoring results of the high-rise building. A mathematical model is used to build a risk scoring model, which is convenient for rapid and Continuously derive risk scores in real time; establish a mechanical simulation model for high-rise buildings, determine predicted deformation data for the high-rise buildings based on the mechanical simulation model and wind load data, obtain mechanical simulation analysis by combining collected data with physical models, input wind load data from the collected data for simulation to obtain predicted stress and displacement data for the high-rise buildings, and visualize the data; determine risk assessment results for the high-rise buildings based on at least one of the preliminary scoring results and the predicted deformation data, determine the risk assessment results by comparing the scoring results with dynamic thresholds, comparing the predicted deformation data with safety thresholds, or combining the two to determine their respective weights to obtain risk assessment results, and issue warnings based on the assessment results and multiple sets of measured data, generating periodic assessment reports, and making risk assessment results more accurate. This method can monitor in real time, realize dynamic assessment of high-rise buildings under wind loads, make risk assessment results more comprehensive and accurate, and facilitate timely management by management personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of a safety assessment method for a high-rise building in operation provided by some embodiments of the present application;

[0062] Figure 2 is a schematic diagram of sensor deployment locations for high-rise buildings during operation, provided by some embodiments of the present application;

[0063] Figure 3 This is a structural diagram of a safety assessment system for high-rise buildings in operation provided by some embodiments of the present application. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0065] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and are not used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of this application can be implemented in an order other than those illustrated or described herein. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0066] In the following, in conjunction with the accompanying drawings, a safety assessment method and system for high-rise buildings in operation provided by the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0067] In some embodiments of this application, please refer to Figure 1 , provides a safety assessment method for high-rise buildings in operation, the method provided includes:

[0068] Step S101: acquiring data related to high-rise building safety according to the sensor deployment strategy and sampling strategy, the collected data including wind load data;

[0069] Step S102: obtaining multiple groups of measured data according to the partitioning rules and the collected data, wherein the partitioning rules include a time partitioning rule and a location partitioning rule;

[0070] Step S103: determining a dynamic weight corresponding to each type of collected data based on multiple groups of measured data;

[0071] Step S104: Build a risk scoring model for high-rise buildings, input each type of collected data and the corresponding dynamic weight into the scoring model, and determine the preliminary scoring results of the high-rise buildings;

[0072] Step S105: establishing a mechanical simulation model of the high-rise building, and determining predicted deformation data of the high-rise building based on the mechanical simulation model and wind load data;

[0073] Step S106: Determine a risk assessment result of the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data.

[0074] In some embodiments of the present application, step S101: acquiring data related to high-rise building safety based on a sensor deployment and sampling strategy, which may include wind load data. By deploying multiple sensors, such as wind load sensors, stress sensors, and satellite-based displacement sensors, at key locations within the high-rise building and sampling them at a frequency, multiple types of data affecting high-rise building safety are collected in real time. This data may include, but is not limited to, wind speed, wind pressure, wind direction, temperature, humidity, stress, and displacement data.

[0075] In this embodiment, the sensors may be deployed according to the following sensor deployment strategy formula: , in this formula The wind sensitivity coefficient of each part of a high-rise building can be determined based on the number of floors: for example, if the top floor is higher, the edge sensitivity is higher; The importance weights of various structures in a high-rise building are assigned, such as nodes and columns, which are weighted higher than beams. This weighting can be determined based on expert advice and specific practical circumstances. Sensors can also be adjusted based on the building's structural characteristics, deploying sensors at key structural points susceptible to wind impact to ensure representative sensor data. To ensure representativeness, placement can be determined based on comprehensive considerations of key stress-bearing locations. Top: The top of a high-rise building is susceptible to direct wind loads and is a major source of wind-induced vibration. Top sensors can directly measure wind load data such as wind speed and pressure, facilitating analysis of the dynamic response of the top of a high-rise building. Middle and bottom: The stresses on a high-rise building may vary at different heights. Placing sensors at the middle and bottom can capture the distribution of wind loads as they change with height, providing comprehensive wind load distribution data. Nodes and supporting components: Nodes and key supporting components of a high-rise building, such as columns, beams, and shear walls, are critical locations for force transmission and deformation. Deploying sensors can monitor stress and displacement changes at these locations to identify structural weaknesses. Multi-directional deployment: The wind load distribution in different directions of high-rise buildings is different. Especially in typhoon or strong wind environment, the wind load in a specific direction is larger. Deploy sensors on the facades in multiple directions to ensure that comprehensive data such as wind direction and wind speed are collected. Corner and edge locations: Wind pressure concentration is prone to occur at the edges and corners of high-rise buildings. Deploying sensors in these locations will help monitor extreme wind load effects and capture the impact of edge effects on buildings. Sensor layout density: Increase the density of sensor layout in key areas where stress is concentrated or deformation is large to obtain high-precision data. Especially in the supporting structures at the top and middle of the building, by encrypting the layout of sensors, stress changes in key areas can be captured more carefully. Reduce the layout in general areas: For areas with less force impact, the layout of sensors can be appropriately reduced to save costs and resources while ensuring the effectiveness of data collection.

[0076] The frequency of data collection can be determined based on the sampling strategy: ,in, is the natural frequency of each structure of a high-rise building, based on the structural mechanics calculation of high-rise buildings, is the wind speed-vibration response coefficient, The real-time wind speed data collected above can also be adjusted based on changes in wind loads during building operation. It is generally recommended to increase the sampling frequency during high wind speeds or strong winds. The specific sampling frequency is determined and adjusted based on the following: Initial sampling is based on the natural frequency of the structure. The natural frequency of high-rise buildings is generally low, typically between 0.1 Hz and 2 Hz. The sampling frequency should be at least twice the building's natural frequency to meet the requirements of the Nyquist sampling theorem and ensure that the sampling captures the complete vibration characteristics of the structure. Therefore, the initial sampling frequency should generally be set between 1 Hz and 10 Hz to capture the dynamic response of the building under wind loads. Based on the rate of change of wind loads: Wind loads typically have higher frequency characteristics, especially during typhoons or strong winds, which can produce higher fluctuations. The initial sampling frequency can be set based on recorded wind speed data or historical wind load data at the building's location. In low wind speeds, the sampling frequency can be set between 1 Hz and 2 Hz; in strong winds, the sampling frequency can be increased to 5 Hz to 10 Hz to capture rapidly changing wind loads. Frequency adjustment through preliminary data spectrum analysis: Spectral analysis is performed on the initially collected wind load and structural response data to observe the main frequency components in the signal. If the sampling frequency is too low, the high-frequency components may not be fully reflected in the spectrum. In this case, the sampling frequency should be appropriately increased. Noise and data redundancy detection: If no high-frequency components are found in the spectrum analysis, but the collected data contains significant high-frequency noise or data redundancy, the sampling frequency can be appropriately reduced to reduce the collection of invalid data. The sampling frequency is dynamically adjusted based on wind speed and weather conditions. Low wind speed conditions: In low wind speeds, such as those below 10 m / s, the vibration of the building structure caused by wind speed is minimal, so the sampling frequency can be set to 1 Hz-2 Hz. Medium wind speed conditions: When the wind speed is between 10 m / s and 20 m / s, the sampling frequency can be set to 3 Hz-5 Hz to capture the fluctuating characteristics of the wind load. High wind speed or strong wind conditions: In strong winds and typhoons, wind speeds typically exceed 20 m / s. The sampling frequency can be set to 5 Hz-10 Hz to ensure the capture of high-frequency wind load fluctuations.

[0077] It's worth noting that the collected sensor data is uploaded to a cloud server via IoT transmission technology for real-time storage and management. IoT sensor data transmission generally uses the following communication protocols: Message Queuing Telemetry Transport (MQTT), Hypertext Transfer Protocol (HTTP), or Constrained Application Protocol (CoAP). MQTT is suitable for high-frequency, low-bandwidth sensor data transmission, such as real-time data like wind speed and strain. HTTP is suitable for periodic data uploads with large data volumes but no real-time transmission required. CoAP is suitable for ultra-low-power devices. The protocol selection is primarily based on the real-time, reliability, and power requirements of data transmission. Sensors use the protocol to send collected data to a local gateway or cloud server. The data transmission process is as follows: Sensors send collected data to a local gateway via wireless (Wi-Fi, LoRa) or wired connections. Wireless connections (Wi-Fi, LoRa) must ensure wireless signal coverage, while wired connections (Ethernet or fiber) must be routed appropriately within the structure of high-rise buildings to avoid interference and physical damage. The gateway encapsulates the data into a protocol-specific message format (such as MQTT's publish-subscribe model) and transmits it over the network to the cloud server. The cloud server receives and decodes the data and stores it in a database for subsequent partitioned storage and visualization.

[0078] In some embodiments of the present application, step S102: obtaining multiple sets of measured data according to partitioning rules and collected data, wherein the partitioning rules include time partitioning rules and location partitioning rules. Before partitioning the collected data, the collected data needs to be pre-processed and a storage format needs to be set.

[0079] In this embodiment, the expression of the time partition rule is: ,in, is the first time unit, is the second time unit, The nth time unit is in hours or minutes. Specifically, data is partitioned and stored by time period (such as day, week, month). High-frequency data can be partitioned by hour or minute to quickly locate data in a specific time period. For example, wind load data can be partitioned by day to easily query data on a specific date. The expression of the partitioning rule based on sensor location is: ,in, This is the first placement position of the sensor. This is the second placement of the sensor. is the mth placement position of the sensor. Specifically, it can be partitioned and stored according to the building structure position (such as top, middle, bottom) or sensor number to facilitate monitoring the structural response data of different floors or parts of high-rise buildings. Among them, the expression for obtaining multiple sets of measured data is: ,in, is the data collected at position j during the i-th unit time, is the time index, is the position index, which can also be considered The data collected at a certain time unit at a certain location is finally obtained. That is, multiple sets of measured data.

[0080] In this embodiment, before partitioning the collected data, the data is preprocessed and its storage format is configured. Specifically, methods that can be used include, but are not limited to, setting reasonable range boundaries. Based on the climatic conditions of the monitoring site and the structural characteristics of high-rise buildings, a reasonable range of values ​​is preset. Any collected data outside this range is marked as abnormal. For example, a wind speed range: A reasonable upper limit for wind speed (e.g., 20 m / s) is set based on local climatic conditions. Wind speeds exceeding this value are marked as abnormal. A displacement or stress range: Maximum allowable stress or strain values ​​are set based on structural characteristics. Monitoring data exceeding this value is marked as abnormal. Time series smoothing detection: The moving average method calculates the average value of a period of time near the current data point and compares it with the current value. If the current value deviates significantly from the average, it is considered an anomaly. For example, a point that deviates more than twice the mean is considered an anomaly. Exponential smoothing: Exponentially smooth time series data and calculates the difference between the expected and actual values. If the difference exceeds a preset threshold (e.g., three standard deviations), it is marked as an anomaly. Other methods include, but are not limited to, linear regression or time series model testing, mutation detection, boxplots, and Z-scores, which use mathematical or machine learning techniques to identify outliers and flag sensor data anomalies that may occur during the collection process, ensuring data integrity and reliability. Data storage formats include, but are not limited to, the following: InfluxDB and TimescaleDB time series data formats, specifically designed for storing time series data. They use timestamps as indexes, support fast queries on data trends, and are suitable for high-frequency monitoring data. JSON and Parquet data formats: JSON is suitable for storing semi-structured data, facilitating parsing and expansion; Parquet is ideal for big data storage, providing compression and columnar storage for large-scale data storage and fast queries. Relational database formats, such as Structured Query Language (SQL), are suitable for structured data storage, storing data in separate tables, and facilitating complex queries and data correlation analysis.

[0081] In some embodiments of the present application, step S103: based on multiple sets of measured data, determining a dynamic weight corresponding to each type of collected data. The dynamic weight is used to determine a preliminary scoring result for the high-rise building.

[0082] In this embodiment, the average value of each type of data in multiple sets of measured data and each type of data collected by the sensor in real time are input into the dynamic weight formula to obtain the dynamic weight corresponding to each type of collected data. The formula for determining the dynamic weight corresponding to each type of collected data in the collected data is: ,in, is the dynamic weight of the i-th type of collected data at the t-th time unit, is the dynamic weight of the i-th type of collected data at the (t-1)th time unit. The dynamic weights of multiple collected data satisfy: , α is the weight adjustment coefficient, is the average value of multiple groups of measured data of the i-th category, The value of the collected data for category i at the tth time unit.

[0083] In some embodiments of the present application, step S104: constructing a high-rise building risk scoring model, inputting each type of collected data and its corresponding dynamic weight into the scoring model, and determining a preliminary scoring result for the high-rise building. The risk scoring model uses a weighted method to generate a mathematical expression, and each type of collected data and its corresponding dynamic weight are input into the expression to obtain a preliminary assessment score.

[0084] In this embodiment, the expression for the preliminary scoring result obtained by inputting each type of collected data and the corresponding dynamic weight into the risk scoring model is: ,in, This is the preliminary scoring result for high-rise buildings. is the weight of the i-th type of collected data in the t-th time unit, is the value of the data collected for category i, is the weighted value of the i-th category of collected data at the t-th time unit. Input the dynamic weight of each category in a certain time unit in the past and the real-time collected data of each category into this expression to obtain the preliminary scoring result.

[0085] In some embodiments of the present application, step S105: establishing a mechanical simulation model of the high-rise building and determining predicted deformation data for the high-rise building based on the mechanical simulation model and wind load data. This mechanical simulation model combines the physical model of the high-rise building with numerical analysis methods and BIM visualization. Based on real-time collected data, it calculates and analyzes the force and deformation of the high-rise building under wind loads to obtain predicted deformation data, including stress and displacement.

[0086] In this embodiment, for the physical model, a mechanical simulation model can be constructed based on the BIM model information of the high-rise building. The types of BIM models include but are not limited to Revit models, 3DM models, and obj models. Parameters such as the component materials, component dimensions, and node connection methods of the high-rise building are input into the BIM model. The mechanical simulation model includes all parameters and information used for building mechanics simulation, including but not limited to: determining the type and accuracy of the component model, such as a two-dimensional plane model or a three-dimensional solid model of the theoretical calculation model, and the degree of model simplification, such as beam elements, shell elements, and solid elements; defining material properties; geometric modeling; loading boundary conditions; dividing the finite element mesh; and selecting analysis methods and solution parameters. The solution can be obtained using specifications, textbooks, or the latest theoretical calculation models, or finite element simulation software, which includes but is not limited to SAP2000, Abaqus, and ANSYS.

[0087] Furthermore, for solutions based on numerical analysis methods, real-time wind load data can be imported into a mechanical simulation model. Based on relevant national standards, including but not limited to the "Technical Code for Concrete Structures of High-Rise Buildings" (JGJ 3-2010), the monitored wind speed and direction can be converted into loads on the high-rise building using prescribed conversion methods. Using concrete-related theoretical calculation methods, finite element analysis, or other appropriate mechanical simulation methods, stresses and displacements in key structural locations are calculated, identifying structural sections with significant internal forces or deformations. This results in predicted deformation data for the high-rise building, including stresses and displacements.

[0088] It is worth noting that when using finite element software for simulation analysis, the linear and cross-sectional components in the BIM model are meshed. Linear components are meshed using beam elements, which simulate the main bending moments and shear forces of the structure; cross-sectional components are meshed using shell elements, which simulate out-of-plane bending moments and shear forces. The parameter settings for different components can be adjusted by updating the material stiffness and wind load. Specifically, the material stiffness update expression is: ,in is the real-time stiffness of the material, is the initial stiffness, is the stiffness change caused by wind load. Wind load update expression: .in, The wind load is a time-varying quantity. is the wind load coefficient, The real-time wind speed data collected is used to adjust the material stiffness and wind load to ensure the accuracy of the mechanical simulation model.

[0089] Furthermore, for BIM visualization, the maximum stress and displacement values ​​of key parts can be marked by digital indicators, and the stress including axial force, bending moment, shear force value and displacement including deflection value can be directly displayed in digital form on the components of the model. These digital indicators are updated in real time, which makes it easy for users to quickly identify the extreme points of stress and deformation, and distinguish between safe and dangerous states through color gradients or warning signs. Specifically, a real-time refresh mechanism is set in the visualization interface of the BIM visualization platform, such as determining the refresh interval or refresh frequency, to ensure the dynamic display of the status of high-rise buildings. Users can interact with the model through the visualization interface to view the stress conditions and safety status of each component of the building. Real-time update formula: ,in, is the color of the component, is the stress or displacement value of the component, is a color gradient mapping function. When the user selects different building components in the model, detailed data can be displayed, as shown in the expression: ,in is the maximum normal stress, is the maximum bending moment, The maximum displacement is obtained, thereby providing intuitive and accurate component safety information.

[0090] In some embodiments of the present application, step S106: determining a risk assessment result for the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data. Warning information can be generated based on the risk assessment result, and a periodic wind resistance performance assessment report for the high-rise building can be generated based on multiple sets of measured data. The warning information and the periodic wind resistance performance assessment report can be sent to a user terminal.

[0091] In this embodiment, if the risk assessment result of a high-rise building is determined based on the preliminary scoring result, the risk coefficient can be determined based on the preliminary scoring result and the dynamic risk threshold. , the expression of the dynamic risk threshold is: ,in, is the dynamic risk threshold, This is the current preliminary scoring result. is the preset initial risk threshold, is the threshold adjustment coefficient, is the average value of multiple preliminary scoring results. The expression for determining the risk coefficient is: ,in, is the risk factor, is a dynamic risk threshold. Based on the risk coefficient Risk level Combined with the corresponding preset risk level, the risk assessment result is obtained.

[0092] In this embodiment, when determining the risk assessment results for a high-rise building based on predicted deformation data, corresponding safety thresholds can be preset based on relevant regulatory documents and actual operating conditions. Specifically, the safety thresholds are set based on relevant building codes, structural design standards, and actual engineering experience to ensure the scientific and rationality of the assessment system. Safety thresholds for stress and displacement are referenced, but not limited to, the bearing capacity and displacement limit requirements in standards such as the "Code for Loads on Building Structures" (GB 50009-2012) and the "Technical Code for Concrete Structures of High-Rise Buildings" (JGJ 3-2010), and are determined in combination with component characteristics, material properties, and actual operating conditions. For example, safety thresholds for stress and displacement of key components are set based on regulatory limits, component natural frequencies, and wind speeds to ensure that the stress and displacement of the structure remain within safe ranges under wind loads. The predicted deformation data is then compared with the safety thresholds to determine the risk assessment results for the high-rise building.

[0093] In this embodiment, when combining the preliminary scoring results and the predicted deformation data to determine the risk assessment results for a high-rise building, the importance of the preliminary risk assessment score and the predicted deformation results in the risk assessment, as well as their mutual influence, can be comprehensively considered. Specifically, a score mapping table is provided for the predicted deformation data, and each type of data in the predicted deformation data is converted into a corresponding score. This conversion process can be implemented using a computer language such as Python and C++. The predicted risk score is then obtained using a weighted average method, and the weight ratio of the preliminary risk assessment score and the predicted risk score in the final risk assessment is determined. This weight ratio can be adjusted and optimized based on actual conditions or the advice of building experts. The preliminary risk assessment score and the predicted risk score are weighted and summed according to the determined ratio to obtain the risk assessment results for the high-rise building.

[0094] Furthermore, based on the risk assessment results, early warning information is generated, and a periodic wind resistance performance assessment report for high-rise buildings can be generated based on multiple sets of measured data. Specifically, multiple sets of measured data are called, and the peak and average values ​​of each type of collected data in each set of measured data are identified through time series analysis. These data are plotted into trend charts and compared with safety thresholds to determine whether there are long-term safety hazards in the components. The periodic wind resistance performance assessment report covers indicators such as the stress, displacement, and deformation trend of the structure under wind loads, and provides detailed risk warning records and quantitative analysis of safety status. Based on statistical models and trend predictions, the report also includes long-term changes in component performance trends and potential hidden danger assessments. The report supports export in multiple formats (including PDF and Excel), providing scientific decision-making support and documentation for the operation, maintenance, and management of high-rise buildings. The early warning information and periodic wind resistance performance assessment reports are sent to the user end to ensure the accuracy and timeliness of information transmission.

[0095] In some embodiments of this application, please refer to Figure 2 This embodiment combines the methods of the above embodiments and takes a high-rise building in operation as an example to further illustrate a safety assessment method for a high-rise building in operation:

[0096] In this embodiment, based on the structural characteristics and wind load distribution characteristics of the high-rise building and the sensor deployment strategy in the above-mentioned safety assessment method for high-rise buildings in the operational period, wind load sensors, displacement sensors, and stress sensors are deployed at the top and structural nodes where the wind load is larger (for example, structural nodes at the top, middle, and bottom floors, facades, etc.) to increase the representativeness of the collected data. The sensors selected are wind sensors, stress sensors, and displacement sensors based on satellite positioning from a certain supplier. The collected data are collected, including wind speed, wind direction, temperature, humidity, displacement, and stress data at different heights and locations of the building. The sensor deployment positions are marked in combination with the front facade drawing of the building.

[0097] Furthermore, based on the acquisition strategy, the initial frequency of the collected data is set to 2 Hz, and the sampling frequency is dynamically adjusted based on the building's natural frequency and wind speed. In windy weather, the sampling frequency can be automatically adjusted to 5 Hz-10 Hz. The collected data is filtered for noise and abnormal data through smoothing and anomaly detection (such as moving average and Z-score detection). The MQTT protocol's publish-subscribe model ensures low-latency and stable data transmission. Sensors are connected to the gateway via Wi-Fi or LoRa. The data is encapsulated and uploaded to the cloud database via Ethernet or wireless network. Data exchange is performed in the JSON data format, which facilitates transmission through software interfaces.

[0098] Furthermore, when data is stored, the appropriate range for wind speed, stress, and other data is determined, and data exceeding the appropriate range is marked. Multiple sets of measured data are then partitioned and stored in the database according to the time and location partitioning rules described in the above embodiment. This data is managed using the time series database InfluxDB, facilitating fast query and trend analysis.

[0099] Furthermore, in combination with the dynamic weight formula of the above embodiment, this embodiment takes the wind load data, temperature data, stress data, and displacement data in the collected data as the main collected data, and converts the wind load data in multiple sets of measured data into , temperature data , stress data , displacement data The average values ​​of various measured data and various collected data are input into the dynamic weight formula , get the dynamic weight of wind load data 0.4, dynamic weight of temperature data 0.2, stress data dynamic weight 0.3 and displacement data dynamic weight 0.1, and + + + = 1.

[0100] Further, the risk score model is obtained using the weight weighting method, and the mathematical expression of the risk score model is: .

[0101] Based on the mathematical expression of the risk score model, the wind load data , temperature data , stress data , displacement data , wind load data dynamic weight , temperature data dynamic weight , stress data dynamic weight and displacement data dynamic weight in the above-mentioned multiple groups of measured data are respectively input into the score model expression to obtain the preliminary score result of the high-rise building is 64.5, please refer to Table 1.

[0102] Table 1 Preliminary score table

[0103]

[0104] Further, a mechanical simulation model of the high-rise building is established. In this embodiment, according to the Code for Design of Concrete Structures, a two-dimensional space frame model is used to simulate the mechanical response of the high-rise building under the action of wind load. The specific steps are as follows: selecting the unit type, using beam element (Beam) to simulate linear components such as columns and beams, which are mainly used to bear bending moment and shear force, and using shell element (Shell) to simulate surface components such as shear walls, floors and core tubes, which mainly bear out-of-plane bending moment, shear force and horizontal load; setting boundary conditions, setting the bottom node as a fixed node to constrain all degrees of freedom to prevent displacement and rotation of the foundation part, and allowing the top end of the model to move freely in the height direction to simulate the actual response under the action of wind load; meshing, meshing key components such as columns, beams, shear walls and floors, the column and beam element mesh length-width ratio is 1:1, the shell element mesh minimum side length is not more than 0.5 meters, and the mesh density in key parts is fine enough to improve the calculation accuracy.

[0105] Further, for the customization of the simulation model material parameters, the embodiment is according to the design specification of concrete structure (such as “Code of Design of Concrete Structures” GB 50010-2010) to define the basic mechanical property parameters of the material, so as to accurately reflect the strength and deformation capacity of concrete and steel in the calculation. The main parameters include: the concrete material parameters are shown in Table 2, and the steel material parameters are shown in Table 3:

[0106] Table 2 Concrete material parameter table

[0107]

[0108] Table 3 Steel material parameter table

[0109]

[0110] Further, the wind load data in the real-time collected data is imported into the mechanical simulation model of the high-rise building to obtain predicted stress data and displacement data. Specifically, the wind load is taken as the horizontal load of the building structure, and is applied according to the calculation method in “Code of Design of Concrete Structures” GB 50010-2010. The calculation and application steps of the horizontal load are as follows: first, the design wind speed and wind pressure are determined, the design wind speed V is determined according to the wind speed of the region where the high-rise building is located, and the wind speed of the region where the high-rise building is located is 27.5 m / s. The standard wind pressure (unit: Pa) is calculated according to the formula P = 0.613 × V^2. Then, the wind pressure distribution is calculated, the wind load area is divided according to the building height, and a layer is set every 5 meters. The corresponding wind pressure is applied to the building area of different heights. The wind load is converted into equivalent surface load and applied to the outer facade and top components of the building. After the wind load data is applied, the model is solved by the building horizontal load calculation method in the concrete structure design theory to obtain the stress data and displacement data of each floor. The maximum value of the stress data and displacement data of each floor is taken as the predicted deformation data of the high-rise building. Please refer to Table 4.

[0111] Table 4 Predicted stress and displacement data table

[0112]

[0113] In this embodiment, if the risk assessment result of the high-rise building is determined according to the preliminary score result, the risk coefficient is determined according to the preliminary score result and the dynamic risk threshold , and the expression of the dynamic risk threshold is: , wherein is the dynamic risk threshold, is the current preliminary score result, is the initial risk threshold preset as 1.0, is the threshold adjustment coefficient, The average of the plurality of preliminary scores is 60, and the expression for determining the risk coefficient is: wherein the dynamic risk threshold is 60, and the risk coefficient is 64.5 / 60 = 1.08. In combination with the risk level table, refer to Table 5, according to the risk level corresponding to the risk coefficient 1.08, the risk assessment result is medium risk.

[0114] Table 5 Risk Level Table

[0115]

[0116] In this embodiment, if the risk assessment result of the high-rise building is determined according to the predicted deformation data, the corresponding safety threshold is preset based on the corresponding specification file and the actual working condition. Specifically, the setting of the safety threshold is based on the relevant building specifications, structural design standards and actual engineering experience, to ensure the scientificity and rationality of the evaluation system. The safety threshold of stress and displacement refers to, but is not limited to, the bearing capacity and displacement limit requirements in standards such as “Building Structure Load Specification” (GB 50009-2012), “Technical Specification for High-rise Building Concrete Structure” (JGJ 3-2010), and determines the safety threshold of stress and displacement of the component based on the actual working condition of the component characteristics, material performance and environmental conditions. Please refer to Table 6. If it is found that the stress or displacement exceeds the set safety threshold, the predicted displacement and stress data of the mechanical simulation analysis are imported into the BIM model through the interface, and the data stream is connected with the model in real time and displayed on the BIM visualization interface in real time. The interface is refreshed every 2 seconds, and the components are marked according to the current stress value. For components whose stress or displacement exceeds the red alarm limit value, digital markers are used. For key components such as columns and shear walls, the current displacement (unit: mm) and stress (unit: kN) are directly displayed in the form of digital annotations in the model, which facilitates users to quickly identify and record the values, and the risk assessment result is medium risk.

[0117] Table 6 Safety Threshold Table

[0118]

[0119] In some embodiments of the present application, corresponding warning information can be generated based on the risk assessment results (see Table 7). Periodic wind resistance performance assessment reports are also generated and sent to the user. Specifically, the system first retrieves the past month's wind load and structural response data from multiple sets of measured data stored in partitions. The system then automatically analyzes the displacement and stress trends of each key component. Based on this data, the module uses time series analysis to identify the peak and average values ​​of key components. For example, a shear wall may have an average stress of 180 MPa and a peak stress of 220 MPa. The system plots this data into a trend chart and compares it with historical thresholds to determine whether the component presents long-term safety hazards. The analysis results are categorized and summarized to generate a wind resistance performance assessment report for the building. The report includes the risk level of key components (such as columns, beams, and shear walls), stress and displacement changes under wind load conditions, and the time period when the risk occurred. For example, the report records that the south column on the 15th floor has reached orange warning status five times in the past 30 days. Based on this data, the system also generates a structural safety assessment score to quantify the structure's wind resistance, with scores ranging from 0 to 100, with higher scores indicating greater wind resistance. Finally, the module automatically exports the complete report in PDF and Excel formats and sends it to the relevant manager's email address. The assessment report includes four sections: "Key Component Overview," "Historical Trend Chart," "Safety Score," and "Maintenance Recommendations," and is archived monthly. Each report is marked with the generation date and assessment period, allowing managers to easily trace safety status in historical records and assist in developing long-term high-rise building maintenance and management plans.

[0120] Table 7 Warning Information Table

[0121]

[0122] See also Figure 3 , Figure 3 The safety assessment system 300 for a high-rise building in operation in this embodiment includes:

[0123] The acquisition module 310 is configured to acquire collected data related to high-rise building safety according to the sensor deployment strategy and sampling frequency strategy;

[0124] The first data processing module 320 is configured to obtain multiple sets of measured data according to the partitioning rules and the collected data, wherein the partitioning rules include a time partitioning rule and a location partitioning rule;

[0125] The first determination module 330 is configured to determine the dynamic weight corresponding to each type of collected data in the collected data based on multiple sets of measured data;

[0126] The second data processing module 340 is configured to construct a risk scoring model for high-rise buildings, input each type of collected data and its corresponding dynamic weight into the scoring model to obtain a preliminary scoring result for the high-rise building; establish a mechanical simulation model for the high-rise building, and determine predicted deformation data for the high-rise building based on the mechanical simulation model and wind load data;

[0127] The second determination module 350 is configured to determine a risk assessment result of the high-rise building according to at least one of the preliminary scoring result and the predicted deformation data.

[0128] In summary, the safety assessment method for high-rise buildings in operation period obtains dynamic and more comprehensive risk assessment results of high-rise buildings by combining IoT sensors, risk scoring models and mechanical simulation models. According to the sensor deployment strategy and sampling strategy, the collected data related to the safety of high-rise buildings are obtained. The collected data include wind load data, stress data, and displacement data, which are used as input parameters of the risk scoring model and mechanical simulation model for subsequent preliminary scoring and mechanical simulation analysis; according to the zoning rules and collected data, multiple groups of measured data are obtained. The zoning rules include time zoning rules and location zoning rules. Multiple groups of measured data are used to determine the dynamic weights of various types of data, which is convenient for establishing a risk scoring model and is also convenient for obtaining subsequent wind resistance performance evaluation reports as data support; a risk scoring model for high-rise buildings in operation period is built, and each type of collected data and the corresponding dynamic weight are input into the scoring model to determine the preliminary scoring results of the high-rise building, and a mathematical model is used to build the risk scoring model. , facilitating the rapid and real-time continuous determination of risk scores; establishing a mechanical simulation model for high-rise buildings, determining predicted deformation data for high-rise buildings based on the mechanical simulation model and wind load data, obtaining mechanical simulation analysis by combining collected data with physical models, inputting wind load data from the collected data for simulation to obtain predicted stress and displacement data for high-rise buildings, and visualizing the data; determining risk assessment results for high-rise buildings based on at least one of the preliminary scoring results and predicted deformation data, determining risk assessment results by comparing the scoring results with dynamic thresholds, comparing predicted deformation data with safety thresholds, or combining the two to determine their respective weights to obtain risk assessment results, and issuing warnings based on the assessment results, generating periodic assessment reports, and making risk assessment results more accurate. This method can monitor in real time, achieving dynamic assessment of high-rise buildings under wind loads, making risk assessment results more comprehensive and accurate, and facilitating timely management by management personnel.

[0129] Optionally, an embodiment of the present application also provides an electronic device, including a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, each process of the above-mentioned embodiment of the safety assessment method for high-rise buildings in operation is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0130] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned embodiment of the safety assessment method for high-rise buildings in operation are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0131] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.

[0132] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited, and the functions are performed in the order shown or discussed, and may also include performing the functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0133] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of this application.

[0134] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A safety assessment method for high-rise buildings in operation, characterized in that: The method comprises: Acquiring data related to the safety of the high-rise building according to a sensor deployment strategy and a sampling strategy, wherein the data includes wind load data; According to the partitioning rules and the collected data, multiple groups of measured data are obtained, wherein the partitioning rules include time partitioning rules and location partitioning rules; Determining a dynamic weight corresponding to each type of collected data in the collected data based on the multiple groups of measured data; The step of determining the dynamic weight corresponding to each type of the collected data based on the multiple groups of measured data specifically includes: The formula for determining the dynamic weight corresponding to each type of collected data in the collected data is: ； in, is the dynamic weight of the collected data of the i-th category at the t-th time unit, is the dynamic weight of the collected data of the i-th category at the (t-1)th time unit; The dynamic weight satisfies: , where α is the weight adjustment coefficient, is the average value of multiple groups of measured data of category i, is the value of the collected data of the i-th category at the t-th time unit; Building a risk scoring model for the high-rise building, inputting each type of the collected data and the corresponding dynamic weight into the risk scoring model, and determining a preliminary scoring result for the high-rise building; The step of constructing a risk scoring model for the high-rise building, inputting each type of the collected data and the corresponding dynamic weight into the risk scoring model, and determining a preliminary scoring result for the high-rise building specifically includes: Building a risk scoring model for the high-rise building based on a weighted method; Based on the risk scoring model, weighting the dynamic weight corresponding to each type of the collected data to obtain the preliminary scoring result; The expression of the preliminary scoring result is: ； in, is the preliminary scoring result of the high-rise building. is the weight of the collected data of category i at the tth time unit, is the value of the collected data of category i, is the weighted value of the collected data of the i-th category at the t-th time unit; Establishing a mechanical simulation model of the high-rise building, and determining predicted deformation data of the high-rise building based on the mechanical simulation model and the wind load data; determining a risk assessment result of the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data; The step of determining the risk assessment result of the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data specifically includes: Determine the risk factor based on the preliminary scoring result and the dynamic risk threshold ; The expression of the dynamic risk threshold is: ； in, is the dynamic risk threshold, is the current preliminary scoring result, is the initial risk threshold, is the threshold adjustment coefficient, is the average value of multiple preliminary scoring results; Based on the risk factor In combination with the corresponding preset risk level, obtaining the risk assessment result; The expression for determining the risk coefficient is: ； in, is the risk factor, is the dynamic risk threshold.

2. The safety assessment method for high-rise buildings in operation according to claim 1 is characterized in that: The step of acquiring the collected data associated with the safety of the high-rise building according to the sensor deployment strategy and sampling frequency strategy specifically includes: Determining a plurality of sensor deployment locations in the high-rise building according to the sensor deployment strategy; The expression of the deployment strategy is: ; in, is the wind sensitivity coefficient of the ith part, is the importance weight of the i-th part, A plurality of deployment positions for sensors that satisfy the deployment strategy; Determining a sampling frequency according to the sampling strategy of the sensor, collecting the collected data at the plurality of deployment positions, and adjusting the sampling frequency in combination with the wind speed data in the wind load data; The expression of the sampling strategy is: ; in, is the natural frequency of the high-rise building, and and satisfy ≥ 2 , is the wind speed-vibration response coefficient, is the wind speed data in the wind load data.

3. The safety assessment method for high-rise buildings in operation according to claim 2 is characterized in that: The steps of obtaining multiple groups of measured data according to the partitioning rules and the collected data, wherein the partitioning rules include time partitioning rules and location partitioning rules, specifically include: Dividing the collected data according to the time partitioning rule and the position partitioning rule to obtain multiple groups of measured data, where the measured data are the collected data of multiple deployment positions within each time unit; The relational expression of the time partition rule is: ,in, is the first time unit, is the second time unit, is the nth time unit; The relationship between the position partitioning rules is: ,in, is the first placement position of the sensor, is the second placement position of the sensor, is the mth placement position of the sensor; The expression for obtaining multiple groups of measured data is: ,in, is the collected data at position j in the i-th unit time, is the time index, is the position index.

4. The safety assessment method for high-rise buildings in operation according to claim 1 is characterized in that: The step of establishing a mechanical simulation model of the high-rise building and determining predicted deformation data of the high-rise building based on the mechanical simulation model and the wind load data specifically includes: Based on the BIM model of the high-rise building, meshing the linear components and cross-sectional components in the BIM model, wherein the linear components are meshed using beam elements and the cross-sectional components are meshed using shell elements; Customizing basic mechanical property parameters of materials in the meshed BIM model to obtain a mechanical simulation model of the high-rise building; The wind load data in the collected data is input into the mechanical simulation model to obtain the predicted deformation data.

5. The safety assessment method for high-rise buildings in operation according to claim 4 is characterized in that: The step of determining the risk assessment result of the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data specifically includes: Presetting a safety threshold corresponding to the predicted deformation data based on the code limit corresponding to the high-rise building and the physical working condition parameters of the high-rise building; The predicted deformation data is compared with the safety threshold to determine a risk assessment result of the high-rise building.

6. The safety assessment method for high-rise buildings in operation according to claim 1 is characterized in that: After the step of determining the risk assessment result of the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data, the method further includes: generating early warning information based on the risk assessment results; generating a periodic wind resistance performance evaluation report of the high-rise building based on the plurality of sets of measured data; The warning information and the periodic wind resistance performance evaluation report are sent to a user terminal.

7. A safety assessment system for high-rise buildings in operation, characterized in that: The system comprises: an acquisition module configured to acquire data associated with the safety of the high-rise building according to a sensor deployment strategy and a sampling frequency strategy, wherein the data includes wind load data; A first data processing module is configured to obtain multiple groups of measured data according to a partitioning rule and the collected data, wherein the partitioning rule includes a time partitioning rule and a location partitioning rule; A first determining module is configured to determine a dynamic weight corresponding to each type of collected data in the collected data based on multiple groups of measured data; The step of determining the dynamic weight corresponding to each type of the collected data based on the multiple groups of measured data specifically includes: The formula for determining the dynamic weight corresponding to each type of collected data in the collected data is: ； in, is the dynamic weight of the collected data of the i-th category at the t-th time unit, is the dynamic weight of the collected data of the i-th category at the (t-1)th time unit; The dynamic weight satisfies: , where α is the weight adjustment coefficient, is the average value of multiple groups of measured data of category i, is the value of the collected data of the i-th category at the t-th time unit; a second data processing module configured to construct a risk scoring model for the high-rise building, input each type of the collected data and the corresponding dynamic weight into the scoring model to obtain a preliminary scoring result for the high-rise building; establish a mechanical simulation model for the high-rise building, and determine predicted deformation data for the high-rise building based on the mechanical simulation model and the wind load data; The step of constructing a risk scoring model for the high-rise building, inputting each type of the collected data and the corresponding dynamic weight into the risk scoring model, and determining a preliminary scoring result for the high-rise building specifically includes: Building a risk scoring model for the high-rise building based on a weighted method; Based on the risk scoring model, weighting the dynamic weight corresponding to each type of the collected data to obtain the preliminary scoring result; The expression of the preliminary scoring result is: ； in, is the preliminary scoring result of the high-rise building. is the weight of the collected data of category i at the tth time unit, is the value of the collected data of category i, is the weighted value of the collected data of the i-th category at the t-th time unit; a second determining module configured to determine a risk assessment result of the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data; The step of determining the risk assessment result of the high-rise building based on at least one of the preliminary scoring result and the predicted deformation data specifically includes: Determine the risk factor based on the preliminary scoring result and the dynamic risk threshold ; The expression of the dynamic risk threshold is: ； in, is the dynamic risk threshold, is the current preliminary scoring result, is the initial risk threshold, is the threshold adjustment coefficient, is the average value of multiple preliminary scoring results; Based on the risk factor In combination with the corresponding preset risk level, obtaining the risk assessment result; The expression for determining the risk coefficient is: ； in, is the risk factor, is the dynamic risk threshold.

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