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

Abstract

The invention discloses a safety assessment method and system for a high-rise building in an operation period, and belongs to the crossing field of civil engineering and intelligent construction technology, and the method comprises the steps: obtaining collection data related to the safety of the high-rise building according to a layout strategy and a sampling strategy of a sensor; obtaining a plurality of groups of measured data according to a partitioning rule and the collected data; determining a dynamic weight corresponding to each type of acquisition data in the acquisition data based on the multiple groups of actually measured data; building a risk scoring model of the high-rise building, and determining a preliminary scoring result of the high-rise building; establishing a mechanical simulation model of the high-rise building, and determining predicted deformation data of the high-rise building; and determining a risk assessment result of the high-rise building according to at least one of the preliminary scoring result and the predicted deformation data. Through the combination of the risk assessment model, the mechanical simulation model and real-time monitoring data, the method solves the problems that the risk assessment of the high-rise building in the operation period is not timely, the accuracy is not high and dynamic assessment cannot be performed in the prior art.

Description

Technical Field

[0001] The present 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 in operation. Background Art

[0002] With the rapid advancement of urbanization in my country, high-rise buildings are becoming more and more popular in modern urban construction. However, these buildings often face wind safety issues that cannot be ignored 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. At present, the wind resistance 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. The existing wind resistance assessment methods are mainly limited to the static stage, lacking the dynamic response capture 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 dynamic assessment means of wind resistance safety of high-rise buildings during the operation period are insufficient, making it difficult to accurately reflect the state of the building in actual wind load changes, which easily leads 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 comprehensive simulation and evaluation of 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. The 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 the lack of integration capability in dynamic analysis under wind loads, and the problem of being unable to realize 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: In a first aspect, an embodiment of the present application provides a safety assessment method for a high-rise building in operation, the method comprising: According to the layout strategy and sampling strategy of sensors, the collected data related to the safety of high-rise buildings are obtained, including wind load data; 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; Based on multiple groups of measured data, determine the dynamic weight corresponding to each type of collected data; 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 high-rise buildings; 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; 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.

[0006] In some possible embodiments, according to the sensor deployment strategy and sampling frequency strategy, the step of acquiring the collected data associated with the safety of the high-rise building specifically includes: According to the sensor deployment strategy, determine multiple deployment locations of sensors in high-rise buildings; The expression of the deployment strategy is: ; in, is the wind sensitivity coefficient of the ith part, is the importance weight of the ith part, Provide multiple deployment locations for sensors to meet deployment strategies; The sampling frequency is determined according to the sampling strategy of the sensor, and the data collected at multiple deployment locations are collected. The sampling frequency is adjusted 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, It is the wind speed data in the wind load data.

[0007] In some possible embodiments, multiple groups of measured data are obtained according to the partitioning rules and the collected data, and the partitioning rules include the steps of time partitioning rules and location partitioning rules, specifically including: The sampling data is divided according to the time partitioning rule and the location partitioning rule to obtain multiple groups of measured data, where the measured data are the collected data of multiple layout locations within each time unit; The relationship between the time partition rules is: ,in, is the first time unit, is the second time unit, is the nth time unit; 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; Among them, the expression for obtaining multiple sets of measured data is: ,in, is the data collected at position j in the ith unit time, is the time index, Is the position index.

[0008] In some possible embodiments, based on multiple groups of measured data, the step of determining the dynamic weight corresponding to each type of collected data in the collected data specifically includes: The formula for determining the dynamic weight corresponding to each type of 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; 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 i-th type of collected data at the t-th time unit.

[0009] In some possible embodiments, the steps of building a risk scoring model for high-rise buildings, inputting each type of collected data and the corresponding dynamic weight into the scoring model, and determining a preliminary scoring result of the high-rise buildings specifically include: Construct a high-rise building operation period risk scoring model based on the weighted method; Based on the risk scoring model, the dynamic weight corresponding to each type of collected data is weighted to obtain a preliminary scoring result; The expression of the preliminary scoring result is: ; in, The preliminary scoring results for high-rise buildings are: is the weight of the i-th type of collected data in the t-th time unit, is the value of the data collected in the i-th category, It is the weighted value of the i-th type of collected data in the t-th time unit.

[0010] 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 includes: Determine the risk factor based on the preliminary scoring results and the dynamic risk threshold ; The expression of dynamic risk threshold is: ; in, is the dynamic risk threshold, This is the current preliminary rating result. is the initial risk threshold, is the threshold adjustment coefficient, It is the average of multiple preliminary scoring results; Based on risk factor Combined with the corresponding preset risk level, the risk assessment result is obtained; The expression for determining the risk factor is: ; in, is the risk factor, is the dynamic risk threshold.

[0011] 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: Based on the BIM model of the high-rise building, the linear components and cross-sectional components in the BIM model are meshed. The linear components are meshed using beam elements, and the cross-sectional components are meshed using shell elements. 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; The wind load data in the collected data is input into the mechanical simulation model to obtain the predicted deformation data.

[0012] 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 includes: 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; The predicted deformation data are compared with the safety threshold to determine the risk assessment results of the high-rise building.

[0013] In some possible embodiments, after 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, the step further includes: Generate early warning information based on risk assessment results; Generate periodic wind resistance performance assessment reports for high-rise buildings based on multiple sets of measured data; Send warning information and periodic wind resistance performance evaluation reports to the user end; In a second aspect, an embodiment of the present application provides a safety assessment system for high-rise buildings in operation, the system comprising: An acquisition module is configured to acquire collected data associated with high-rise building safety according to a sensor deployment strategy and a sampling frequency strategy; The first data processing module is configured to obtain 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; A first determination 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 second data processing module is configured to 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 obtain a preliminary scoring result of the high-rise building; establish a mechanical simulation model of 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; 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.

[0014] In an embodiment of the present application, the safety assessment method for high-rise buildings in operation obtains dynamic and more comprehensive risk assessment results for high-rise buildings 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, and the collected data include wind load data, which are used as input parameters of 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, and the partitioning rules include time partitioning rules and location partitioning 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 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, Continuously obtain risk scores in real time; establish a mechanical simulation model for high-rise buildings, determine the predicted deformation data of high-rise buildings based on the mechanical simulation model and wind load data, obtain mechanical simulation analysis by combining the collected data with the physical model, input the wind load data in the collected data for simulation to obtain the predicted stress and displacement data of the high-rise building and visualize the data; determine the risk assessment results of the high-rise building 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 the dynamic threshold, the predicted deformation data with the safety threshold, or combine the two to determine the respective weights to obtain the risk assessment results, and issue early warnings based on the assessment results and multiple sets of measured data, generate periodic assessment reports, and make the risk assessment results more accurate. This method can monitor in real time, realize the dynamic assessment of high-rise buildings under wind loads, make the risk assessment results more comprehensive and accurate, and facilitate timely management by managers. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of a safety assessment method for a high-rise building in operation period provided by some embodiments of the present application; Figure 2 is a schematic diagram of sensor deployment locations of high-rise buildings during operation provided by some embodiments of the present application; Figure 3 It is a structural diagram of a safety assessment system for high-rise buildings in operation period provided by some embodiments of the present application. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0017] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here. In addition, the "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally represents that the objects associated with each other are in an "or" relationship.

[0018] In conjunction with the accompanying drawings, a safety assessment method and system for a high-rise building in operation provided by an embodiment of the present application will be described in detail through specific embodiments and application scenarios.

[0019] 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: Step S101: acquiring collected data associated with high-rise building safety according to the sensor deployment strategy and sampling strategy, wherein the collected data includes wind load data; 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; Step S103: determining a dynamic weight corresponding to each type of collected data in the collected data based on multiple groups of measured data; Step S104: constructing a risk scoring model for high-rise buildings, inputting each type of collected data and the corresponding dynamic weight into the scoring model, and determining a preliminary scoring result for the high-rise buildings; 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; Step S106: 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.

[0020] In some embodiments of the present application, step S101: according to the sensor deployment strategy and sampling strategy, acquire the collected data associated with the safety of the high-rise building, and the collected data may include wind load data. By arranging multiple sensors such as wind load sensors, stress sensors, and displacement sensors based on satellite positioning at key locations of the high-rise building, multiple types of data affecting the safety of the high-rise building are collected in real time according to the sampling frequency, and the collected data may include but are not limited to: wind speed, wind pressure, wind direction, temperature, humidity, stress, and displacement data.

[0021] In this embodiment, the sensors may be deployed according to the following sensor deployment strategy formula: , in this formula It is the wind sensitivity coefficient of each part of a high-rise building. The wind sensitivity coefficient can be determined in combination with the floors: if the top floor is higher, the edge sensitivity is higher; The importance weights of various structures in high-rise buildings, such as nodes and columns, are higher than beams. The importance weights can be determined in combination with expert advice and specific actual conditions. It can also be adjusted in combination with the characteristics of the building structure, and sensors can be deployed at key structural points that are easily affected by wind to ensure that the sensor data is representative. To ensure that the layout location is representative, the layout can be made based on comprehensive considerations of key stress-bearing parts. Top: The top of a high-rise building is susceptible to direct impact from wind loads and is one of the main sources of wind-induced vibration. The top sensor can directly measure wind load data such as wind speed and wind pressure, which is convenient for analyzing the dynamic response of the top of a high-rise building. Middle and bottom: The stress conditions of high-rise buildings at different heights may be different. The deployment of sensors in the middle and bottom can capture the distribution of wind loads with height changes and provide comprehensive wind load distribution data. Nodes and supporting components: The nodes and key supporting components of high-rise buildings, such as columns, beams, and shear walls, are key parts for force transmission and deformation. The deployment of sensors can monitor the stress and displacement changes at these locations and 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. Corners and edge locations: Wind pressure concentration is prone to occur at the edges and corners of high-rise buildings. Deploying sensors in these locations helps 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 structure 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 of general areas: For areas with less force, the layout of sensors can be appropriately reduced to save costs and resources while ensuring the effectiveness of data collection.

[0022] The frequency of data collection can be determined according to 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. The data collection frequency can also be adjusted in combination with the changes in wind load during the operation of the building. It is usually recommended to increase the sampling frequency under high wind speed or strong wind weather conditions. The basis for determining and adjusting the specific sampling frequency includes: determining the initial sampling according to the natural frequency of the structure. The natural frequency of high-rise buildings is generally low, generally between 0.1 Hz and 2 Hz. The sampling frequency should be at least twice the natural frequency of the building to meet the requirements of the Nyquist sampling theorem and ensure that the sampling can capture the complete vibration characteristics of the structure. Therefore, the initial sampling frequency should generally be set to 1Hz-10 Hz to capture the dynamic response of the building under wind load. According to the wind load change rate: The frequency characteristics of wind loads are generally higher, especially in typhoons or strong wind weather, which may produce higher fluctuations. The initial sampling frequency can be set according to the wind speed data records or historical wind load data at the location of the building. In the case of low wind speed, the sampling frequency can be set to 1 Hz -2 Hz; in strong wind conditions, the sampling frequency can be increased to 5 Hz -10 Hz to capture rapidly changing wind loads. Frequency adjustment through spectrum analysis of preliminary data: spectrum analysis is performed on the preliminary collected wind load and structural response data to observe the main frequency components contained in the signal. If the sampling frequency is too low, the high-frequency components may not be fully reflected in the spectrum, and the sampling frequency should be appropriately increased. Noise and data redundancy detection: If no high-frequency components are found in the spectrum analysis, but there is a lot of high-frequency noise or data redundancy in the collected data, the sampling frequency can be appropriately reduced to reduce the collection of invalid data. Dynamically adjust the sampling frequency based on wind speed and weather conditions. Low wind speed conditions: Under low wind speed conditions, such as wind speeds below 10 m / s, the vibration of the building structure caused by wind speed is small, and the sampling frequency can be set to 1 Hz -2 Hz. Medium wind speed conditions: When the wind speed is in the range of 10 m / s-20 m / s, the sampling frequency can be set to 3 Hz -5 Hz to capture the fluctuation characteristics of wind loads. High wind speed or strong wind weather: Under strong wind, typhoon and other weather conditions, the wind speed usually exceeds 20 m / s, and the sampling frequency can be set to 5 Hz -10 Hz to ensure that high-frequency wind load change information is obtained.

[0023] It is worth noting that the sensor data collected above is uploaded to the cloud server through the Internet of Things transmission technology, and the data is stored and managed in real time. The data transmission of the Internet of Things sensor generally adopts 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 transmission of real-time data such as wind speed and strain; HTTP is suitable for regular data upload, with large data volume but no need for real-time transmission; CoAP is suitable for ultra-low power devices. The protocol selection is mainly based on the real-time, reliability and power consumption requirements of data transmission. The sensor sends the collected data to the local gateway or cloud server through the protocol. The transmission process of the collected data is as follows: the sensor sends the collected data to the local gateway via wireless (Wi-Fi, LoRa) or wired connection. Among them, wireless connection (Wi-Fi, LoRa) needs to ensure wireless signal coverage, and wired connection (Ethernet or optical fiber) needs to arrange the path reasonably according to the structure of high-rise buildings to avoid interference and physical damage. The gateway encapsulates the data into a message format specified by the protocol (such as the publish-subscribe mode of MQTT) and transmits it to the cloud server through the network. The cloud server receives and decodes the data and stores it in the database for subsequent partition storage and visualization.

[0024] In some embodiments of the present application, step S102: obtaining multiple groups of measured data according to the partitioning rules and the collected data, the partitioning rules include time partitioning rules and location partitioning rules. Before partitioning the collected data, the collected data needs to be preprocessed and the storage format needs to be set.

[0025] 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 divided by hours or minutes 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 partition rule for partitioning by 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 stored in partitions according to the building structure position (such as top, middle, bottom) or sensor number, which is convenient for monitoring the structural response data of different floors or parts of high-rise buildings. Among them, the expression for obtaining multiple groups of measured data is: ,in, is the data collected at position j in the ith 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.

[0026] In this embodiment, before partitioning the collected data, the collected data is preprocessed and the storage format is set. Specifically, the methods that can be used include but are not limited to: setting a reasonable range boundary, presetting a reasonable range of values ​​according to the climatic conditions of the monitoring site and the structural characteristics of the high-rise building. Once the collected data exceeds this range, it can be marked as abnormal. For example, wind speed range: set a reasonable upper limit of wind speed (such as 20 m / s) according to local climatic conditions, and the wind speed exceeding this value can be marked as abnormal. Displacement or stress range: set the maximum allowable value of stress and strain according to structural characteristics, and the monitoring data exceeding this value is marked as abnormal. Time series smoothing detection: moving average method, calculate the average value within a period of time near the current data point, and compare it with the current value. If the current value deviates too much from the average value, it is considered abnormal. For example, set a point that deviates more than 2 times the mean as abnormal. Exponential smoothing method: perform exponential smoothing on time series data and calculate the difference between the expected value and the actual value. If the difference exceeds a preset threshold (such as 3 times the standard deviation), it is marked as abnormal. In addition, it also includes but is not limited to linear regression or time series model detection, mutation detection, box plot method, standard score (Z-score) and other methods that use certain mathematical or machine learning methods to find outliers to mark abnormal sensor data that may appear during the collection process to ensure the integrity and reliability of the data. Among them, the data storage formats set include but are not limited to the following categories: InfluxDB and TimescaleDB time series data formats, which are designed for storing time series data, use timestamps as indexes, support fast query of data change trends, and are suitable for high-frequency monitoring data. JSON and Parquet data formats: JSON format is suitable for storing semi-structured data, which is convenient for parsing and expansion; while Parquet format is suitable for big data storage, providing compression and columnar storage, which is convenient for storing large-scale data and fast query. Relational database formats, such as Structured Query Language (SQL): suitable for structured data storage, data storage in tables, and convenient for complex queries and data association analysis.

[0027] In some embodiments of the present application, step S103: based on multiple groups of measured data, determine the dynamic weight corresponding to each type of collected data in the collected data. The dynamic weight is used to determine the preliminary scoring result of the high-rise building.

[0028] In this embodiment, the average value of each type of data in the multiple groups 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 i-th type of collected data at the t-th time unit.

[0029] In some embodiments of the present application, step S104: construct 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 result of the high-rise building. The risk scoring model obtains a mathematical expression through a weighted method, and each type of collected data and the corresponding dynamic weight are input into the expression to obtain a preliminary evaluation score.

[0030] In this embodiment, the expression for obtaining the preliminary scoring result by inputting each type of collected data and the corresponding dynamic weight into the risk scoring model is: ,in, The preliminary scoring results for high-rise buildings are: is the weight of the i-th type of collected data in the t-th time unit, is the value of the data collected in the i-th category, is the weighted value of the i-th category of collected data at the t-th time unit. The dynamic weight of each category in a certain time unit in the past and each category of collected data collected in real time are input into this expression to obtain the preliminary scoring result.

[0031] In some embodiments of the present application, step S105: establish a mechanical simulation model of a high-rise building, and determine predicted deformation data of the high-rise building based on the mechanical simulation model and wind load data. The mechanical simulation model combines the physical model of the high-rise building, the numerical analysis method and BIM visualization, calculates and analyzes the force deformation of the high-rise building under the action of wind force according to the real-time collected data, and obtains predicted deformation data, which includes stress and displacement, etc.

[0032] 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 component materials, component sizes, 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 the two-dimensional plane model and three-dimensional solid model of the theoretical calculation model, and the degree of simplification of the model, such as beam units, shell units, solid units, etc.; defining material properties; geometric modeling; loading boundary conditions; dividing finite element meshes; selecting analysis methods and solution parameters. Specifications, textbooks, or the latest theoretical calculation models, or finite element software simulation software can be used for solution. The finite element software simulation software includes but is not limited to SAP2000, Abaqus, and ANSYS.

[0033] Furthermore, for solving the problem according to the numerical analysis method, the wind load data collected in real time can be imported into the mechanical simulation model, and the monitored wind speed and wind direction can be converted into the load on the high-rise building through the prescribed conversion method according to the relevant national standards including but not limited to the "Technical Code for Concrete Structures of High-rise Buildings" (JGJ 3-2010), etc. The stress and displacement changes of the key parts of the structure are calculated through the concrete-related theoretical calculation method, finite element analysis or other suitable mechanical simulation methods, and the structural parts with large internal forces or deformations are identified to obtain the predicted deformation data of the high-rise building, which includes stress and displacement.

[0034] It is worth noting that if finite element software is used for simulation analysis, the linear components and cross-sectional components in the BIM model are meshed. The linear components are meshed using beam elements, which are used to simulate the main bending moments and shear forces of the structure; the cross-sectional components are meshed using shell elements, which are used to simulate the 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 quantity that varies with time. is the wind load factor, The real-time wind speed data collected can be adjusted to ensure the accuracy of the mechanical simulation model.

[0035] 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 is convenient 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.

[0036] In some embodiments of the present application, step S106: determining a risk assessment result of the high-rise building according to 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 of the high-rise building can be generated according to multiple sets of measured data, and the warning information and the periodic wind resistance performance assessment report can be sent to the user end.

[0037] In this embodiment, if the risk assessment result of the 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 rating result. is the preset initial risk threshold, is the threshold adjustment coefficient, is the average value of multiple preliminary scoring results, and the expression for determining the risk coefficient is: ,in, is the risk factor, is a dynamic risk threshold. Based on the risk factor Risk level Combined with the corresponding preset risk level, the risk assessment result is obtained.

[0038] In this embodiment, if the risk assessment result of a high-rise building is determined based on the predicted deformation data, the corresponding safety threshold can be preset based on the corresponding specification documents and actual working conditions. Specifically, the setting of the safety threshold is based on relevant building specifications, structural design standards and actual engineering experience to ensure the scientificity and rationality of the assessment system. The safety threshold of stress and displacement refers to the bearing capacity and displacement limit requirements in standards such as "Code for Loads on Building Structures" (GB 50009-2012) and "Technical Code for Concrete Structures of High-rise Buildings" (JGJ 3-2010), and is determined in combination with the actual working conditions of component characteristics, material properties and environmental conditions. For example, the safety threshold of stress and displacement of key components is set according to the specification limit, the natural frequency of the component and the wind speed to ensure that the stress and displacement of the structure are within the safe range under the action of wind load. The predicted deformation data is compared with the safety threshold to determine the risk assessment result of the high-rise building.

[0039] In this embodiment, if the risk assessment result of a high-rise building is determined by combining the preliminary scoring result and the predicted deformation data, the importance of the preliminary risk assessment score and the predicted deformation result in the risk assessment, as well as their mutual influence, can be comprehensively considered. Specifically, the predicted deformation data corresponds to a score mapping table, and each type of data in the predicted deformation data is converted into a corresponding score. The conversion process can be implemented using computer languages ​​such as Python and C++, and then the predicted risk score is obtained according to the 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. The weight ratio can be adjusted and optimized according to the actual situation or the advice of the building expert, and the preliminary risk assessment score and the predicted risk score are weighted and summed according to the determined ratio to obtain the risk assessment result of the high-rise building.

[0040] Furthermore, based on the risk assessment results, early warning information is generated, and a periodic wind resistance performance assessment report of a high-rise building 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 the safety threshold to determine whether there are long-term safety hazards in the components. The periodic wind resistance performance assessment report covers indicators such as stress, displacement, and deformation trends of the structure under wind loads, and provides detailed risk early warning records and quantitative analysis of safety status. Based on statistical models and trend predictions, the report also includes long-term change trends and potential hidden dangers of component performance, and the report supports export in multiple formats (including PDF and Excel), providing scientific decision support and document records for the operation and maintenance and management of high-rise buildings, and sending early warning information and periodic wind resistance performance assessment reports to the user end to ensure the accuracy and timeliness of information transmission.

[0041] In some embodiments of this application, please refer to Figure 2 This embodiment combines the method of the above embodiment with a high-rise building in operation as an example to further illustrate a safety assessment method for a high-rise building in operation: In this embodiment, according to the structural characteristics and wind load distribution characteristics of the high-rise building and the sensor deployment strategy in the safety assessment method for high-rise buildings in operation, 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 wind speed, wind direction, temperature, humidity, displacement and stress data of the building at different heights and locations are collected to obtain the collected data. The sensor deployment positions are marked in combination with the front facade drawing of the building.

[0042] Furthermore, according to the acquisition strategy, the initial frequency of the collected data is set to 2 Hz, and the sampling frequency is dynamically adjusted according to the natural frequency of the building and the wind speed. In windy weather, the sampling frequency can be automatically adjusted to 5 Hz -10 Hz. The collected data is filtered out of noise and abnormal data through smoothing and anomaly detection (such as moving average and Z-score detection), and the publish-subscribe mode of the MQTT protocol is used to ensure low latency and stable data transmission. The sensor is connected to the gateway via Wi-Fi or LoRa, and the data is encapsulated and uploaded to the cloud database via Ethernet or wireless network. The JSON data format is used for data exchange, which is convenient for transmission through the software interface.

[0043] Furthermore, when data is stored in the database, the reasonable range of data such as wind speed and stress will be judged, and data exceeding the reasonable value will be marked. In the database, wind load, displacement, stress and other data will be partitioned and stored according to the time partitioning rules and location partitioning rules in the above embodiment to obtain multiple groups of measured data. The time series database InfluxDB is used for management, which is convenient for fast query and trend analysis.

[0044] Furthermore, in combination with the dynamic weight formula of the above embodiment, this embodiment uses 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, dynamic weight of stress data Dynamic weight of 0.3 and displacement data is 0.1, and + + + =1.

[0045] Furthermore, the risk scoring model is obtained using the weighted method. The mathematical expression of the risk scoring model is: .

[0046] Based on the mathematical expression of the risk scoring model, the wind load data in the above multiple sets of measured data are , Temperature data , stress data , displacement data , Dynamic weight of wind load data , Dynamic weight of temperature data , Dynamic weight of stress data Dynamic weighting of displacement data Input them into the scoring model expression to obtain the preliminary scoring results of high-rise buildings is 64.5, see Table 1.

[0047] Table 1 Preliminary scoring table Furthermore, a mechanical simulation model of a high-rise building is established. This embodiment uses a two-dimensional space frame model according to the concrete structure design specification to simulate the mechanical response of a high-rise building under wind load. The specific steps are as follows: select the unit type, use beam units (Beam) to simulate linear components such as columns and beams, which are mainly used to bear bending moments and shear forces, and use shell units (Shell) to simulate surface components such as shear walls, floor slabs and core tubes, which mainly bear out-of-plane bending moments, shear forces and horizontal loads; then set boundary conditions, set the bottom node as a fixed node, constrain all degrees of freedom, prevent displacement and rotation of the foundation part, and for the boundary in the height direction, the top of the model is allowed to move freely to simulate the actual response under wind load; mesh division, mesh key components such as columns, beams, shear walls and floor slabs, the length-to-width ratio of the column and beam unit grids is 1:1, and the minimum side length of the shell unit grid does not exceed 0.5 meters, to ensure that the grid density in key parts is fine enough to improve the calculation accuracy.

[0048] Furthermore, for the customization of the material parameters of the simulation model, this embodiment defines the basic mechanical performance parameters of the material according to the design specifications of concrete structures (such as "Specifications for Design of Concrete Structures" GB 50010-2010), so as to accurately reflect the strength and deformation capacity of concrete and steel bars in the calculation. The main parameters include: concrete material parameters as shown in 2, steel bar material parameters as shown in 3: Table 2 Concrete material parameters Table 3 Steel bar material parameters Furthermore, the wind load data in the real-time data collection is imported into the mechanical simulation model of the high-rise building for simulation to obtain the predicted stress data and displacement data. Specifically, the wind load is used as the horizontal load of the building structure and is applied according to the calculation method in the Code for Loads on Building Structures (GB 50009-2012). The calculation and application steps of the horizontal load are as follows: First, determine the design wind speed and wind pressure. The design wind speed V is determined according to the wind speed in the area where the high-rise building is located. The wind speed in the area where the high-rise building is located is 27.5m / s. Calculate the standard wind pressure (unit: Pa) according to the formula P = 0.613×V^2. Then calculate the wind pressure distribution, divide the wind load area according to the building height, and set a layer every 5 meters. Apply corresponding wind pressure to building areas of different heights. The wind load is converted into an equivalent surface load and applied to the facade and top components of the building. After the wind load data is applied, the model is solved using the horizontal load calculation method for buildings in 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 used as the predicted deformation data of the high-rise building. Please refer to Table 4.

[0049] Table 4 Predicted stress and displacement data In this embodiment, if the risk assessment result of the high-rise building is determined based on the preliminary scoring result, the risk coefficient is 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 rating result. The initial risk threshold is preset to 1.0. is the threshold adjustment coefficient, is the average value of multiple preliminary scoring results, and the expression for determining the risk coefficient is: , where the dynamic risk threshold The risk factor is 60. 64.5 / 60=1.08. Combined with the risk level table, refer to Table 5, according to the risk level corresponding to the risk factor 1.08, the risk assessment result is medium risk.

[0050] Table 5 Risk level table In this embodiment, if the risk assessment result of a high-rise building is determined based on the predicted deformation data, the corresponding safety threshold is preset based on the corresponding specification documents and actual working conditions. Specifically, the setting of the safety threshold is based on relevant building specifications, structural design standards and actual engineering experience to ensure the scientificity and rationality of the assessment system. The references to the safety thresholds of stress and displacement include but are not limited to the bearing capacity and displacement limit requirements in standards such as "Code for Loads on Building Structures" (GB 50009-2012) and "Technical Code for Concrete Structures of High-rise Buildings" (JGJ 3-2010), and the safety thresholds of stress and displacement of the components are determined in combination with the actual working conditions of the component characteristics, material properties 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 are connected to the model in real time in the form of data flow and displayed in real time on the BIM visualization interface. The interface is refreshed every 2 seconds. The components are digitally marked according to the current stress value for the components whose stress or displacement exceeds the red alarm limit. For key components, such as columns and shear walls, the current displacement (in mm) and stress (in kN) are directly displayed in the model in the form of digital annotations, which allows users to quickly identify and record the values ​​and obtain a risk assessment result of medium risk.

[0051] Table 6 Safety threshold table In some embodiments of the present application, corresponding warning information can also be generated according to the risk assessment results, please refer to Table 7, and a periodic wind resistance performance evaluation report is generated and sent to the user end. Specifically, the wind load and structural response data of the past month are first called from multiple groups of measured data stored in the partition, and the system automatically sorts out the displacement and stress change trends of each key component. Based on these data, the module identifies the peak and average values ​​of key components through time series analysis. For example, the average stress of a shear wall is 180 MPa and the peak stress is 220 MPa. The system will draw these data into a trend chart and compare it with the historical threshold to determine whether the component has long-term safety hazards. The analysis results are classified and summarized to generate a wind resistance performance evaluation report of the building. The report includes the risk level of key components (such as columns, beams and shear walls), the stress and displacement changes under wind load conditions, and the time period when the risk occurs. For example, the report records that the south column of the 15th floor has reached the orange warning state 5 times in the past 30 days. The system also generates a structural safety assessment score based on this data to quantify the wind resistance of the structure, ranging from 0 to 100, with higher scores indicating better wind resistance. Finally, the module automatically exports the complete report in PDF and Excel formats and sends it to the mailbox of the relevant manager. The assessment report includes four parts: "Overview of Key Components", "Historical Trend Chart", "Safety Score" and "Maintenance Recommendations", and is generated and archived monthly. Each report is marked with the generation date and assessment period, which makes it easier for managers to trace safety conditions in historical records and assist in formulating long-term maintenance and management plans for high-rise buildings.

[0052] Table 7 Warning information table See also Figure 3 , Figure 3 The safety assessment system 300 for a high-rise building in operation period of this embodiment includes: The acquisition module 310 is configured to acquire the collected data associated with the safety of the high-rise building according to the sensor deployment strategy and sampling frequency strategy; The first data processing module 320 is configured to obtain 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; 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 groups of measured data; The second data processing module 340 is configured to 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 obtain a preliminary scoring result of the high-rise building; establish a mechanical simulation model of 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; 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.

[0053] In summary, the safety assessment method for high-rise buildings in operation obtains dynamic and more comprehensive risk assessment results for 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 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. 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, and a mathematical model is used to build a risk scoring model. , which is convenient for obtaining risk scores quickly and in real time; establishing a mechanical simulation model of high-rise buildings, determining the predicted deformation data of high-rise buildings based on the mechanical simulation model and wind load data, obtaining mechanical simulation analysis by combining the collected data with the physical model, inputting the wind load data in the collected data for simulation to obtain the predicted stress and displacement data of high-rise buildings and visualize the data; determining the risk assessment results of high-rise buildings based on at least one of the preliminary scoring results and the predicted deformation data, determining the risk assessment results by comparing the scoring results with the dynamic threshold, comparing the predicted deformation data with the safety threshold, or combining the two to determine the respective weights to obtain the risk assessment results, and issuing early warnings based on the assessment results, generating periodic assessment reports, and making the risk assessment results more accurate. This method can monitor in real time, realize the dynamic assessment of high-rise buildings under wind loads, make the risk assessment results more comprehensive and accurate, and facilitate timely management by management personnel.

[0054] 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 repeated here.

[0055] 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, 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 repeated here.

[0056] 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), a random access memory (RAM), a magnetic disk or an optical disk.

[0057] It should be noted that, in this article, the term "comprises", "includes" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment 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.

[0058] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0059] 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 the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A safety assessment method for high-rise buildings in operation, characterized in that: The method comprises: According to the layout strategy and sampling strategy of the sensors, acquiring the collected data associated with the safety of the high-rise building, wherein the collected 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; Determine, based on the multiple groups of measured data, a dynamic weight corresponding to each type of collected data in the collected data; 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; 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; A risk assessment result of the high-rise building is determined according to at least one of the preliminary scoring result and the predicted deformation data.

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 ith 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 step 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 includes: Dividing the collected data according to the time partition rule and the location partition rule to obtain multiple groups of measured data, where the measured data is the collected data of multiple deployment locations 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 location partitioning rules is: ,in, is the first deployment position of the sensor, is the second deployment position of the sensor, is the mth deployment position of the sensor; The expressions for obtaining the multiple groups of measured data are as follows: ,in, is the collected data at the jth position 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 3 is characterized in that: The step of determining the dynamic weight corresponding to each type of the collected data in 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 the i-th category, is the value of the collected data of the i-th category at the t-th time unit.

5. The safety assessment method for high-rise buildings in operation according to claim 4 is characterized in that: 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 of 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, weighted processing is performed on 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 the i-th category in the t-th time unit, is the value of the collected data of the i-th category, is the weighted value of the collected data of the i-th category in the t-th time unit.

6. The safety assessment method for high-rise buildings in operation according to claim 5 is characterized in that: 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: 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, the risk assessment result is obtained; The expression for determining the risk coefficient is: ； in, is the risk factor, is the dynamic risk threshold.

7. 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 units and the cross-sectional components are meshed using shell units; Customize 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.

8. The safety assessment method for high-rise buildings in operation according to claim 7 is characterized in that: 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: 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.

9. 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 according to 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 according to the plurality of sets of measured data; The warning information and the periodic wind resistance performance evaluation report are sent to a user terminal.

10. A safety assessment system for high-rise buildings in operation, characterized in that: The system comprises: An acquisition module is configured to acquire collected data associated with the safety of the high-rise building according to a sensor deployment strategy and a sampling frequency strategy; A first data processing module is configured to obtain multiple groups of measured data according to a partition rule and the collected data, wherein the partition rule includes a time partition rule and a location partition rule; A first determination module is configured to determine a dynamic weight corresponding to each type of collected data in the collected data based on the multiple groups of measured data; The second data processing module is configured to build 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 of the high-rise building; establish a mechanical simulation model of the high-rise building, and determine the predicted deformation data of the high-rise building based on the mechanical simulation model and the wind load data; The second determination module is configured to determine the risk assessment result of the high-rise building according to at least one of the preliminary scoring result and the predicted deformation data.

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