Control method and equipment of building safety detection system and storage medium

Abstract

The invention discloses a control method and device of a building safety detection system and a storage medium, and belongs to the technical field of building safety detection. The method comprises the following steps: after an abnormal environment event is detected, acquiring at least one building data acquisition sensor deployed in a to-be-detected building and a building data sequence acquired in an event occurrence time period of the abnormal environment event, receiving building detection data uploaded by a user, and acquiring a building life cycle file of the to-be-detected building, and the building detection data and the building data sequence are mapped to the digital twin model in the building life cycle file, and the influence information of the abnormal environment event on the to-be-detected building is identified according to the mapping result through the building state analysis model. According to multi-dimensional building detection data, internal damage and potential safety hazards of the building are verified, and the reliability of building safety detection is improved.

Description

Technical Field

[0001] This application relates to the field of building safety inspection technology, and in particular to a control method, equipment and storage medium for a building safety inspection system. Background Technology

[0002] In related technologies, the safety assessment of buildings after experiencing abnormal environmental events typically relies on on-site investigations and specialized inspections initiated after the event. This usually involves recording the visible damage patterns and deploying temporary monitoring instruments at key locations to obtain limited structural response data. This data is then combined with available basic building drawings and other relevant information for comprehensive comparison and analysis to form a safety assessment.

[0003] However, this assessment process relies excessively on surface-visible information and scattered local data, making it difficult to systematically and accurately identify hidden safety hazards inside or related to buildings. The lack of high-fidelity digital retrospective of the building's complete historical state, as well as the absence of a mechanism for deep fusion analysis of real-time sensor data, user-reported information, and building 3D models, results in insufficient ability to identify structural hazards that are not directly visible, are developing, or are in a critical state.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a control method, equipment and storage medium for a building safety inspection system, which aims to solve the technical problem of insufficient ability to identify structural hazards in buildings for safety inspection.

[0006] To achieve the above objectives, this application provides a control method for a building safety monitoring system, the method comprising the following steps: After detecting an abnormal environmental event, acquire the building data sequence collected by at least one building data acquisition sensor deployed in the building to be detected during the time period of the abnormal environmental event. Receive building inspection data uploaded by users and obtain the building lifecycle file of the building to be inspected; The building inspection data and the building data sequence are mapped to the digital twin model in the building lifecycle archive; By using a building status analysis model, the impact of abnormal environmental events on the building under test is identified based on the mapping results.

[0007] In one embodiment, before the steps of receiving building inspection data uploaded by the user and obtaining the building lifecycle file of the building to be inspected, the method further includes: Receive building performance indicators collected by the building data acquisition sensors and determine the acquisition time corresponding to the building performance indicators; Obtain the building information model of the building to be tested, and associate the building performance indicators with the building information model based on the acquisition time; Based on the building information model collected at different times and the building performance indicators, a building lifecycle archive is formed.

[0008] In one embodiment, prior to the step of acquiring building data sequences collected by at least one building data acquisition sensor deployed in the building to be detected during the time period of the abnormal environmental event after detecting an abnormal environmental event, the method further includes: The sensor data collected by the detection sensor is used to construct sequence information based on the sensor data; The discreteness of the sequence information within the preset sliding time window is calculated. If the dispersion is greater than or equal to the first dispersion threshold, the abnormal environmental event is determined. Alternatively, meteorological information corresponding to the building to be tested can be obtained, and abnormal environmental events can be determined based on the meteorological information.

[0009] In one embodiment, after the step of calculating the discreteness of the sequence information within a preset sliding time window, the method further includes: The dispersion is compared with the first dispersion threshold or the second dispersion threshold; If the dispersion is greater than the second dispersion threshold and less than the first dispersion threshold, the building data acquisition sensor is triggered to acquire building data. If the dispersion is greater than or equal to the first dispersion threshold, the abnormal environmental event is determined.

[0010] In one embodiment, the step of receiving building inspection data uploaded by the user and obtaining the building lifecycle file of the building to be inspected includes: Receive the building inspection data, and acquire the sensor data and multimedia data from the building inspection data; Obtain the sensor location information corresponding to the sensor data, and identify the building location information corresponding to the multimedia data; By matching the sensor location information with the building location information, a mapping relationship between the sensor data and the multimedia data is constructed to form the building detection data; Based on the building to be inspected, obtain the corresponding building lifecycle file.

[0011] In one embodiment, the step of identifying the impact information of the abnormal environmental event on the building to be detected based on the mapping result using a building status analysis model includes: Based on the mapping structure, the spatial location and damage mechanism of the building data sequence and the building detection data are compared; Based on the comparison results, valid evidence of damage to the building to be inspected was determined; Based on the spatial location, obtain the structural features of the corresponding part of the building to be inspected; Using a building condition analysis model, hierarchical reasoning is performed based on the structural features and the valid damage evidence, and internal damage information is determined based on the reasoning results. By integrating the internal damage information, the impact information of the building to be inspected is calculated.

[0012] In one embodiment, the step of mapping the building inspection data and the building data sequence to a digital twin model in the building lifecycle archive includes: Based on the sensor deployment information recorded in the building lifecycle file, determine the first spatial location of the building data acquisition sensor in the digital twin model; Identify the second spatial location corresponding to the building detection data; The building data sequence is associated with the first spatial location in the digital twin model, and the building detection data is associated with the second spatial location in the digital twin model.

[0013] In one embodiment, after the step of identifying the impact information of the abnormal environmental event on the building to be detected based on the mapping result using the building status analysis model, the method further includes: Based on the severity and spatial distribution of the impact information, the affected structural components are visually marked in the digital twin model; Based on the visual markers and the impact information, a safety assessment report for the building to be inspected is generated.

[0014] In addition, to achieve the above objectives, this application also provides a control device for a building safety inspection system, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the building safety inspection system as described above.

[0015] In addition, to achieve the above objectives, this application also provides a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the control method of the building safety detection system as described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: This application acquires building data sequences collected by building data acquisition sensors during the event period after detecting an abnormal environmental event, receives building detection data uploaded by users, and retrieves a complete building lifecycle archive. By mapping the sensor data sequences and user-reported information to a digital twin model in the archive, a multi-dimensional information space integrating real-time dynamic response, on-site intuitive observation, and historical baseline status is constructed. Subsequently, the building status analysis model identifies based on the mapping results, enabling the system to transcend reliance on surface-visible information and achieve in-depth correlation analysis and cross-validation of changes in the overall dynamic characteristics of the building structure to signs of local damage. This systematically identifies internal damage, performance degradation paths, and related safety hazards that are difficult to detect from a single data source or by experience, significantly improving the accuracy and comprehensiveness of identifying hidden safety risks and providing a reliable basis for subsequent precise decision-making. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the control method for the building safety inspection system of this application; Figure 2 This is a flowchart illustrating the second embodiment of the control method for the building safety inspection system of this application; Figure 3 This is a flowchart illustrating the third embodiment of the control method for the building safety inspection system of this application; Figure 4 This is a flowchart illustrating the fourth embodiment of the control method for the building safety inspection system of this application; Figure 5 This is a flowchart illustrating the fifth embodiment of the control method for the building safety inspection system of this application; Figure 6 This is a schematic diagram of the structure of the control equipment of the building safety detection system in the hardware operating environment involved in the embodiments of this application.

[0020] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0022] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: after detecting an abnormal environmental event, acquire the building data sequence collected by at least one building data acquisition sensor deployed in the building to be tested during the time period of the abnormal environmental event, receive the building detection data uploaded by the user, and acquire the building life cycle file of the building to be tested. Map the building detection data and the building data sequence to the digital twin model in the building life cycle file. Through the building status analysis model, identify the impact information of the abnormal environmental event on the building to be tested based on the mapping result.

[0024] In current technologies, safety assessments of buildings after abnormal environmental events typically rely on on-site investigations and specialized inspections initiated after the event. This usually involves recording visible damage patterns and deploying temporary monitoring instruments at key locations to obtain limited structural response data. This data is then combined with available basic building drawings and other documentation for comprehensive comparison and analysis to form a safety assessment. However, this assessment process over-relies on surface-visible information and scattered localized data, making it difficult to systematically and accurately identify hidden safety hazards within the building or related structures. The lack of high-fidelity digital retrospective analysis of the building's complete historical state, as well as the absence of mechanisms for deep fusion analysis of real-time sensor data, user-reported information, and 3D building models, results in insufficient ability to identify indirect, developing, or critical structural hazards.

[0025] This application acquires building data sequences collected by building data acquisition sensors during the event period after detecting an abnormal environmental event, receives building detection data uploaded by users, and retrieves a complete building lifecycle archive. By mapping the sensor data sequences and user-reported information to a digital twin model in the archive, a multi-dimensional information space integrating real-time dynamic response, on-site intuitive observation, and historical baseline status is constructed. Subsequently, the building status analysis model identifies based on the mapping results, enabling the system to transcend reliance on surface-visible information and achieve in-depth correlation analysis and cross-validation of changes in the overall dynamic characteristics of the building structure to signs of local damage. This systematically identifies internal damage, performance degradation paths, and related safety hazards that are difficult to detect from a single data source or by experience, significantly improving the accuracy and comprehensiveness of identifying hidden safety risks and providing a reliable basis for subsequent precise decision-making.

[0026] To better understand the above technical solutions, exemplary embodiments of this application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0027] It should be noted that the executing entity in this embodiment can be a building safety detection system, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or control device of a building safety detection system capable of the above functions. This embodiment does not specifically limit the specific implementation. The following uses a building safety detection system as an example to describe this embodiment and the following embodiments.

[0028] Based on this, embodiments of this application provide a control method for a building safety detection system, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the control method for the building safety inspection system of this application.

[0029] In this embodiment, the control method of the building safety detection system includes steps S10 to S40: Step S10: After detecting an abnormal environmental event, acquire the building data sequence collected by at least one building data acquisition sensor deployed in the building to be detected during the time period of the abnormal environmental event. In this embodiment, abnormal environmental events refer to external forces that may pose a significant threat to the structural safety of a building, such as earthquakes, strong winds, and explosive impacts. Their core characteristic is that they can cause the building as a whole or in parts to exhibit dynamic responses exceeding normal ranges. Building data acquisition sensors are sensing devices permanently or semi-permanently installed on key parts of the building structure to continuously monitor changes in its physical state. Examples include vibration sensors for measuring acceleration, strain sensors for measuring micro-deformation, and tilt sensors for measuring angular changes. The building data sequence is an ordered set of data continuously collected and digitized by these sensors at a fixed sampling frequency within a specific time period, comprehensively recording the structure's response history under the influence of events.

[0030] Specifically, the building safety monitoring system implements this step through its built-in event detection and data acquisition module. This module continuously monitors real-time data streams from all deployed sensors and uses algorithms to analyze the data streams in real time to determine whether any abnormal environmental events have occurred. When the detection algorithm determines that an abnormal event has occurred, the system immediately determines the start and end times of the event, thus defining the event's time period. Subsequently, according to a pre-configured sensor list and communication protocol, the system sends instructions to the corresponding data acquisition terminal or data storage unit to retrieve all raw data recorded by each specified sensor within that time period. This raw data is arranged chronologically and packaged in a standardized format, ultimately forming a set of building data sequences that can be used for subsequent analysis.

[0031] As an optional implementation, the system employs an intelligent event detection mechanism based on adaptive thresholds and pattern matching. This mechanism first utilizes historical sensor data from the building during its quiescent period to establish a dynamically changing normal response baseline model for each monitoring point. This model encompasses the data fluctuation range under different time periods and environmental conditions. During real-time monitoring, the system compares the current data with the baseline model. When data from multiple sensors simultaneously exhibits continuous deviations exceeding a preset threshold, it is preliminarily identified as an abnormal event. Simultaneously, the system has a built-in database of typical abnormal event patterns, such as the vibration waveform characteristics unique to earthquake events. When the matching degree between real-time data and a certain pattern in the database exceeds a certain standard, the event type is further confirmed. After confirming the event, the system uses the starting point of the event's characteristic waveform as the zero point of time, retrospectively counting backwards for a preset period to include event precursors, and extending backwards until the vibration significantly attenuates, thereby accurately defining the time period in which the event occurred.

[0032] As an alternative implementation, the system integrates external authoritative early warning information as an auxiliary or triggering condition for event detection. The system accesses public service data in real time, such as earthquake monitoring networks and typhoon or severe convective weather warnings issued by meteorological departments, via a network interface. When it receives an official early warning covering the area of ​​the building to be monitored, the system immediately enters alert mode. At this time, the system does not passively wait but initiates a pre-triggered data acquisition mode. On the one hand, it instructs all sensors to enter high-frequency sampling mode to obtain more detailed data; on the other hand, it uses the estimated start time of the official early warning as a reference point, combined with the actual changes in local sensor data, to comprehensively determine the precise boundaries of the event's time period.

[0033] For example, when a magnitude 4.5 earthquake occurs, its seismic waves propagate to a building equipped with vibration sensors within tens of seconds. Before the event, the acceleration readings of each sensor are in a state of normal, small fluctuations. At the moment the seismic waves arrive, multiple vibration sensors located at the bottom and middle of the building almost simultaneously record a sharp increase in acceleration amplitude, with waveforms exhibiting typical seismic wave characteristics. The system's event detection algorithm captures this abrupt change in real time, confirms that the spatial correlation and temporal synchronization of the data from multiple sensors meet the conditions for an abnormal event by comparing them, and identifies it as an earthquake event based on the waveform characteristics. The algorithm then automatically marks the time point when the acceleration first increases significantly as the start point of the event and the time point when the vibration basically returns to stability as the end point of the event; the 30 seconds in between are defined as the event period. Immediately afterwards, the system automatically extracts all sampling point data from the three vibration sensors within these 30 seconds from the data storage server. Each sensor collects 100 points per second, forming three acceleration time series with a length of 3000 data points, thus completing the building data sequence acquisition step in this step.

[0034] Step S20: Receive building inspection data uploaded by the user and obtain the building lifecycle file of the building to be inspected; In this embodiment, the building inspection data uploaded by users specifically refers to first-hand information about the building's appearance submitted by on-site surveyors, building maintenance personnel, or authorized residents through dedicated mobile applications, web portals, or other channels after an abnormal environmental event occurs. This building inspection data includes sensor data collected by handheld sensors, or on-site photos and short videos with geolocation tags, as well as textual records describing the location, type, and initial observations of damage. The building lifecycle archive is a digital archive system specifically established for the building, covering its entire process from design, construction, operation and maintenance to the present. It is a structured database storing all key static attributes and dynamic process records, including original design parameters, material certificates, construction records, previous inspection and maintenance reports, and performance baselines formed by long-term monitoring data.

[0035] Specifically, the system collaborates on this step through its data access and archive management services. For user-uploaded information, the system provides a secure and standardized data receiving interface. When a user submits a mixed data packet containing images, videos, and text, the interface service initiates an automated preprocessing process, including format conversion, compression, and virus scanning of media files, and key information extraction and standardization of text. The processed information is assigned a unique identifier and stored in a temporary database along with metadata such as the submitting user and submission time. Simultaneously, the system initiates a query request to the central archive based on the unique identifier of the building to be evaluated. The central archive does not simply return a list of files, but intelligently retrieves and dynamically assembles the most relevant subset of archives based on the context of the current evaluation task. For example, it will focus on extracting the building's structural design drawings, recent structural inspection reports, and historical maintenance records for areas potentially affected by the event. The preprocessed building inspection data and the dynamically assembled building lifecycle archive are then linked together and delivered to the subsequent analysis module.

[0036] In one implementation, the system employs AI-based enhancement processing for the building inspection data uploaded by users. Upon receiving the original image uploaded by the user, the system extracts the embedded GPS coordinates and uses a pre-trained computer vision model to analyze the image, identifying typical damage features such as cracks, spalling, and deformation, and attempting to define the feature regions to preliminarily determine the damage type. For the text description submitted by the user, the system uses natural language processing technology to extract key entities and attributes such as "third floor," "east exterior wall," and "45-degree diagonal crack," structuring them. The system then attempts to fuse the visual analysis results with the text parsing results; if they match, the confidence level is increased; if contradictions exist, manual review is prompted. In this way, the originally unstructured user report is transformed into a high-value inspection information entry containing structured fields such as precise spatial location and quantitative estimation of damage type.

[0037] Optionally, the building safety inspection system can receive building inspection data, acquire sensor data and multimedia data from the building inspection data, acquire sensor location information corresponding to the sensor data, and identify building location information corresponding to the multimedia data. By matching the sensor location information with the building location information, a mapping relationship between the sensor data and the multimedia data is constructed to form the building inspection data. Based on the building to be inspected, the corresponding building lifecycle file is obtained.

[0038] For example, after an earthquake, a property engineer used a mobile app to photograph several horizontal cracks on the side of a column on the second floor of the building. Upon submission, the engineer selected "load-bearing column" and "horizontal cracks" through the application interface and marked their locations on a map. After receiving this information packet, the system's computer vision module automatically identified the crack features in the photo and confirmed the crack type. Simultaneously, based on the phone's GPS and the photo's perspective, combined with the building information model (BIM), the system precisely located the damage to the "2F-A-3 column" component in the model. The system then retrieved the building's archive from the database based on the building number "BLD-2022-001". The archive contained the column's original design reinforcement drawings, concrete strength report, and a curve showing the column's slowly decreasing vibration frequency over the past three years, as well as a grouting record from a minor repair conducted in the vicinity six months prior. This constituted the building's lifecycle archive required for this assessment.

[0039] Step S30: Map the building inspection data and the building data sequence to the digital twin model in the building lifecycle archive; In this embodiment, the building safety inspection system aligns and fuses discrete data from different data sources and with different structures—namely, building inspection data and building data sequences—into the corresponding positions in the digital twin model maintained by the building lifecycle archive. Based on the sensor deployment information recorded in the building lifecycle archive, the system determines the first spatial position of the building data acquisition sensors in the digital twin model, identifies the second spatial position corresponding to the building inspection data, associates the building data sequence with the corresponding first spatial position in the digital twin model, and associates the building inspection data with the corresponding second spatial position in the digital twin model.

[0040] Specifically, the building safety inspection system invokes its spatial data fusion engine to perform mapping operations. For building data sequences, the spatial data fusion engine uses sensor deployment information recorded in the building lifecycle archive, which clarifies the one-to-one correspondence between the model and serial number of each physical sensor and its installation location in the building information model. The engine queries this correspondence table, treating each data sequence as a set of time-series attributes, and binds it to a specified component node in the digital twin model, i.e., the first spatial location. For building inspection data, the engine utilizes the parsed structured location descriptions or coordinate information to perform spatial queries and topological analysis in the three-dimensional space of the digital twin model, finding the matching or nearest component or surface, i.e., the second spatial location. The engine associates user-described damage details and related media file links as status annotation attributes with the model elements at this second spatial location. After mapping is complete, the engine generates a global data-location association index, providing fast query services for subsequent analysis.

[0041] As an optional implementation, the mapping process employs a multi-level matching and conflict resolution strategy. For sensor data, since the installation information is clear, precise matching and direct mapping are used. For the location in the user-reported information, the system first attempts to directly locate it using the high-precision coordinates it carries. When the coordinate accuracy is insufficient or missing, multi-level matching is initiated: first, the search range is narrowed based on the building floor and area described by the user; then, using image content and a feature matching algorithm, the uploaded photo is compared with a multi-angle view pre-rendered by the digital twin model in that area to determine the specific component or wall.

[0042] Optionally, if there are slight differences in the location inferences obtained by different methods, the system will calculate an optimal location estimate; if the differences are huge, the information will be marked as requiring manual confirmation and possible contradictions will be indicated.

[0043] As an alternative implementation, the system maintains a timeline for each component in the digital twin model. When mapping building data sequences, the precise timestamps of the sequences are aligned with the model's timeline, marking them as response data during the event. Simultaneously, the system attempts to extract or infer observation times from user-uploaded information, also aligning these observation times with the model's timeline.

[0044] Step S40: Using the building status analysis model, identify the impact information of the abnormal environmental event on the building to be detected based on the mapping results.

[0045] In this embodiment, the building safety inspection system uses a building condition analysis model to perform in-depth calculations and inferences on impact information based on the mapping relationship between building inspection data and building data sequences and structural parts in a digital twin model. The building condition analysis model integrates expertise in solid mechanics, structural dynamics, and materials science, and incorporates a machine learning algorithm-based computation and inference engine to simulate expert thinking for analysis. The impact information may include a list of visible damage, inferences about hidden damage, quantitative assessments of the remaining performance of components and the overall structure, scientific classification of risk levels, and targeted remedial recommendations.

[0046] Specifically, based on the mapping structure, the spatial location and damage mechanism of the building data sequence and the building detection data are compared. Based on the comparison results, valid damage evidence of the building to be tested is determined. Based on the spatial location, the structural characteristics of the corresponding parts of the building to be tested are obtained. Through the building state analysis model, hierarchical reasoning is performed based on the structural characteristics and valid damage evidence. Based on the reasoning results, internal damage information is determined. The internal damage information is integrated, and the impact information of the building to be tested is calculated.

[0047] Once the building condition analysis model is triggered, it identifies spatially adjacent areas or areas belonging to the same structural unit that are simultaneously mapped by abnormal sensor data and user-reported damage. It extracts features from the sensor data, such as maximum response, residual deformation, and frequency variation, and compares these with the damage types reported by users for mechanistic consistency. Verified areas are marked as high-confidence damage evidence. The model infers the depth and concealment of damage impact. Based on the confirmed location and extent of damage, combined with detailed design parameters of the component obtained from the lifecycle archive, it uses simplified mechanical models or empirical formulas to calculate the possible states of its internal invisible parts, such as the level of steel reinforcement stress and the height of the concrete compression zone. The model performs a comprehensive assessment of the overall structural performance, employing a performance-based evaluation method. It substitutes the stiffness reduction of the component caused by local damage into the simplified structural model to calculate the building's response under subsequent possible loads, assessing whether it meets performance objectives such as life safety and collapse prevention. The model integrates all analysis results to form an impact information report including a damage distribution map, component safety level, overall safety factor, risk warning, and preliminary repair priority.

[0048] Optionally, the building condition analysis model includes a constructed condition analysis knowledge graph. Based on the tree structure of the knowledge graph, child nodes of each level are matched sequentially to form a search path for the knowledge graph. Based on this search path, the inference result can be obtained, and internal damage information can be inferred.

[0049] Optionally, the model employs three inference threads: a physical rule-based inference thread, a case-matching inference thread, and a data-driven machine learning thread. The physical rule-based thread, based on structural mechanics principles, infers the possible internal force distribution and weak points of the structure from sensor data. The case-matching thread searches a historical disaster case database for historical cases most similar to the current event in terms of intensity, building type, observed data patterns, and damage manifestations, and uses their final damage assessment results as a reference. The machine learning thread utilizes a trained deep learning model to perform end-to-end analysis of the multimodal data fused in the digital twin model, outputting damage predictions. The results from these three threads are fused through a confidence-weighted fusion module to derive impact information.

[0050] Optionally, different specialized analysis sub-models can be invoked based on different structural parts and damage types, such as a sub-model for analyzing the compression-bending damage of frame columns and a sub-model for analyzing the shear resistance of infill walls. During the overall integration phase, the model employs system reliability theory or the analytic hierarchy process (AHP) to weightedly synthesize the impact of each localized damage on the overall performance and calculate the overall safety index.

[0051] For example, based on the mapping results of an earthquake event, the building condition analysis model detected crack information in the east wall of room 301 (3F-Room-301) near beam 5 (3F-B-5) mapped by sensor "ACC-203," as reported by a user. Through spatial topology analysis, the model determined that this wall was an infill wall of the beam, classifying it as belonging to the same structural unit. The model extracted the beam's acceleration data, showing an abnormal concentration of vibration energy in a certain frequency range, while the user-reported wall cracks were intersecting diagonal cracks. The model consulted its knowledge base and determined that a certain vibration mode of the beam might cause in-plane shear failure of the infill wall; the mechanisms were consistent, thus confirming the presence of beam-wall interaction damage caused by the earthquake in this area. Based on the beam's design reinforcement and observed vibration characteristics, the model deduced that the beam itself might have experienced slight yielding. Then, the model incorporated the impact of this damage on floor stiffness into a simplified three-dimensional frame model of the building for calculation, determining that the inter-story drift angle under subsequent aftershocks might approach the code limit. The model output impact information identified moderate damage in a localized area on the 3rd floor, and the overall building safety level was rated as "Yellow - Restricted Use". It was recommended to conduct a detailed inspection of the relevant areas on the 3rd floor and to consider adding temporary supports. A risk assessment report was also provided.

[0052] This application embodiment acquires building data sequences collected by building data acquisition sensors during the event's time period after detecting an abnormal environmental event, and receives building detection data uploaded by the user. Simultaneously, it retrieves a complete building lifecycle archive. By mapping the sensor data sequences and user-reported information to a digital twin model within the archive, a multi-dimensional information space integrating real-time dynamic response, on-site observation, and historical baseline states is constructed. Then, a building state analysis model identifies issues based on the mapping results. This allows the system to transcend reliance on surface-visible information, achieving in-depth correlation analysis and cross-validation of changes in overall dynamic characteristics and signs of localized damage in building structures. This systematically identifies internal damage, performance degradation paths, and associated safety hazards that are difficult to detect from a single data source or through experience, significantly improving the accuracy and comprehensiveness of hidden safety risk identification and providing a reliable basis for subsequent precise decision-making.

[0053] Based on the same inventive concept, this application also provides a second embodiment, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the control method for the building safety inspection system of this application.

[0054] In this embodiment, the control method of the building safety detection system further includes steps S21 to S23: Step S21: Receive the building performance indicators collected by the building data acquisition sensor, and determine the acquisition time corresponding to the building performance indicators; Step S22: Obtain the building information model of the building to be tested, and associate the building performance indicators with the building information model based on the acquisition time; Step S23: Based on the building information model at different collection times and the building performance indicators, form the building life cycle archive.

[0055] In this embodiment, the building safety monitoring system executes an automated data integration and model update process. The system establishes data channels with all sensors, continuously receives and parses data packets reported by the sensors, extracts performance index values ​​and their precise acquisition timestamps, and completes data time-stamping. Based on a preset sensor deployment mapping relationship, the system determines the specific component object corresponding to each performance index data point in the building information model (BIM). Using the acquisition time as an index, the system writes the performance index as the dynamic state attribute of the component at a specific moment into the time-series attribute database associated with that component. By continuously accumulating state snapshots and data sequences of all components at different times, a complete building lifecycle archive is constructed, with the BIM model as the spatial framework and the time-series database as the state filler.

[0056] Optionally, to ensure the accuracy and reliability of building safety inspections, different types of sensors are deployed in the building, such as hydrostatic sensors, weather instruments, vibration sensors, and tilt angle sensors.

[0057] This application embodiment improves the reliability of building safety inspection by deploying building sensors and collecting sensor data, combined with building information modeling, to form a building life cycle file based on time evolution of the building to be inspected.

[0058] Since the system described in Embodiment 2 of this application is a system used to implement the method of Embodiment 1 of this application, those skilled in the art can understand the specific structure and variations of the system based on the method described in Embodiment 1 of this application, and therefore will not be described again here. All systems used in the method of Embodiment 1 of this application fall within the scope of protection of this application.

[0059] Based on the same inventive concept, this application also provides a third embodiment, referring to... Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the control method for the building safety inspection system of this application.

[0060] In this embodiment, the control method of the building safety detection system further includes steps S11 to S13: Step S11: Detect sensor data collected by the sensor and construct sequence information based on the sensor data; Step S12: Calculate the discreteness of the sequence information within the preset sliding time window; Step S13: If the dispersion is greater than or equal to the first dispersion threshold, determine the abnormal environmental event; or, obtain the meteorological information corresponding to the building to be detected, and determine the abnormal environmental event based on the meteorological information.

[0061] In this embodiment, sequence information refers to a continuous dataset formed by segmenting raw sensor data into fixed time intervals. Dispersion is a statistical characteristic that measures the intensity of fluctuations in this dataset, typically calculated using standard deviation or variance. Meteorological information refers to disaster warning data such as typhoons and rainstorms issued for the target area from official monitoring networks. By analyzing the changes in the statistical characteristics of the sensor data stream or integrating external authoritative disaster warning information, automatic identification of abnormal events can be achieved.

[0062] Specifically, the system employs a dual-path detection mechanism that combines internally driven data and externally triggered information. In the internal detection path, the system acquires sensor data streams in real time and organizes them into continuous time-series segments. The system uses a fixed-duration analysis window that slides along the time axis to continuously calculate the dispersion of the data sequence within the window. This dispersion is compared to a first dispersion threshold derived from long-term historical data statistics. If it consistently exceeds this threshold, it is determined that the building structure response has experienced a severe disturbance exceeding the normal range, thus confirming the occurrence of an abnormal environmental event. In the externally triggered path, the system accesses public service networks such as meteorological and seismic data in real time via an application programming interface (API), analyzes their issued warning information, and directly determines the abnormal environmental event based on external authoritative information when the information level reaches a preset standard.

[0063] This application embodiment combines internal data anomaly analysis with direct confirmation of external information to detect various potential threats, thereby automatically triggering subsequent assessment processes. This effectively overcomes the delays and omissions in the traditional manual reporting mode, ensuring the proactiveness and timeliness of the system response.

[0064] Since the system described in Embodiment 3 of this application is a system used to implement the method of Embodiment 1 of this application, those skilled in the art can understand the specific structure and variations of the system based on the method described in Embodiment 1 of this application, and therefore will not be described again here. All systems used in the method of Embodiment 1 of this application fall within the scope of protection of this application.

[0065] Based on the same inventive concept, this application also provides a fourth embodiment, referring to... Figure 4 , Figure 4 This is a flowchart illustrating the fourth embodiment of the control method for the building safety inspection system of this application.

[0066] In this embodiment, the control method of the building safety detection system further includes steps S01 to S03: Step S01: Compare the dispersion with the first dispersion threshold or the second dispersion threshold; Step S02: If the dispersion is greater than the second dispersion threshold and less than the first dispersion threshold, trigger the building data acquisition action of the building data acquisition sensor; Step S03: If the dispersion is greater than or equal to the first dispersion threshold, determine the abnormal environmental event.

[0067] In this embodiment, the system uses a small number of sensors to collect building data in real time to determine the surrounding environmental information. When there is pedestrian traffic or other disturbances, the data collected by the sensors fluctuates. When the dispersion exceeds a first dispersion threshold, the system determines that the current building data is valuable for collection. Based on preset rules, the system will then activate more types of sensors to collect building fluctuation data. This building fluctuation data is used to support the system's learning and analysis of data such as building lifecycle records, verifying the building's state under external influences, and improving the accuracy of data analysis.

[0068] When the dispersion reaches or exceeds the first dispersion threshold, the system determines that the fluctuations in the current housing data are not due to normal environmental factors, and therefore determines that an abnormal environmental event such as a typhoon or earthquake has occurred.

[0069] Optionally, the system can also activate sensors to collect static state data of buildings based on a preset time period, such as from 0:00 to 4:00 AM, or a preset time interval. By combining static data with dynamic data of building fluctuations, the system can improve the accuracy of building data analysis.

[0070] This application determines the current state of a building by analyzing multiple dispersion thresholds, thereby determining whether data collection is needed to support data analysis and improving the reliability of abnormal environmental event verification.

[0071] Since the system described in Embodiment 4 of this application is a system used to implement the method of Embodiment 1 of this application, those skilled in the art can understand the specific structure and variations of the system based on the method described in Embodiment 1 of this application, and therefore will not be described again here. All systems used in the method of Embodiment 1 of this application fall within the scope of protection of this application.

[0072] Based on the same inventive concept, this application also provides a fifth embodiment, referring to... Figure 5 , Figure 5 This is a flowchart illustrating the fifth embodiment of the control method for the building safety inspection system of this application.

[0073] In this embodiment, the control method of the building safety detection system further includes steps S41-S42: Step S41: Based on the severity and spatial distribution of the impact information, visually mark the affected structural components in the digital twin model; Step S42: Based on the visual markers and the impact information, generate a safety assessment report for the building to be inspected.

[0074] In this embodiment, the system will also analyze the structured data output by the model, i.e., the impact information, and transform it into intuitive and visual graphical representations and engineering documents with direct guiding value.

[0075] Specifically, the building safety assessment system analyzes key conclusions such as the safety level, damage type, and extent of each component or area, and drives a digital twin model to visually mark corresponding components in the model according to preset rendering rules. For example, dangerous components are rendered in red, and warning components are rendered in yellow, with symbolic crack diagrams overlaid at the damage locations. After visualization is completed, the system calls the report generation engine to automatically arrange and integrate the text descriptions of the impact information, key data tables, and the generated visualization model views or screenshots according to standardized document templates to generate a safety assessment report.

[0076] Optionally, when the risk level in the impact information exceeds the risk threshold, the building safety assessment system can also generate a warning message based on the impact information and send it to the administrator terminal.

[0077] Since the system described in Embodiment 5 of this application is a system used to implement the method of Embodiment 1 of this application, those skilled in the art can understand the specific structure and variations of the system based on the method described in Embodiment 1 of this application, and therefore will not be described again here. All systems used in the method of Embodiment 1 of this application fall within the scope of protection of this application.

[0078] This application provides a control device for a building safety inspection system. The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the control method of the building safety inspection system in the first embodiment described above.

[0079] The following is for reference. Figure 6The diagram illustrates a structural schematic of a control device suitable for implementing the building safety detection system of the embodiments of this application. The control device of the building safety detection system in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The control equipment of the building safety detection system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0080] like Figure 6 As shown, the control device of the building safety detection system may include a processing unit 1001 (e.g., a core processor, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM) 1004. The random access memory 1004 also stores various programs and data required for the operation of the control device of the building safety detection system. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the control equipment of the building safety monitoring system to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows control equipment for a building safety monitoring system with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented or possessed alternatively.

[0081] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0082] The control device for the building safety inspection system provided in this application adopts the control method of the building safety inspection system in the above embodiments, which can solve the technical problem of insufficient ability to identify structural hazards in buildings. Compared with the prior art, the beneficial effects of the control device for the building safety inspection system provided in this application are the same as the beneficial effects of the control method for the building safety inspection system provided in the above embodiments, and other technical features in the control device for the building safety inspection system are the same as the features disclosed in the method of the previous embodiment, and will not be repeated here.

[0083] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0084] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0085] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the control method of the building safety detection system in the above embodiments.

[0086] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0087] The aforementioned computer-readable storage medium may be included in the control equipment of the building safety inspection system; or it may exist independently and not be installed in the control equipment of the building safety inspection system.

[0088] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the control device of the building safety detection system, the control device of the building safety detection system: after detecting an abnormal environmental event, acquires building data sequences collected by at least one building data acquisition sensor deployed in the building to be detected during the time period of the abnormal environmental event; receives building detection data uploaded by the user; acquires the building lifecycle file of the building to be detected; maps the building detection data and the building data sequences to a digital twin model in the building lifecycle file; and identifies the impact information of the abnormal environmental event on the building to be detected based on the mapping result through a building status analysis model.

[0089] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0090] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0091] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0092] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the control method of the building safety detection system described above, which can solve the technical problem of insufficient ability to identify structural hazards in buildings for safety detection. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the control method of the building safety detection system provided in the above embodiments, and will not be repeated here.

[0093] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A control method for a building safety detection system, characterized in that, The method includes the following steps: After detecting an abnormal environmental event, acquire the building data sequence collected by at least one building data acquisition sensor deployed in the building to be detected during the time period of the abnormal environmental event. Receive building inspection data uploaded by users and obtain the building lifecycle file of the building to be inspected; The building inspection data and the building data sequence are mapped to the digital twin model in the building lifecycle archive; By using a building status analysis model and based on the mapping results, the impact of the abnormal environmental events on the building to be tested can be identified.

2. The control method for the building safety detection system as described in claim 1, characterized in that, Before the steps of receiving building inspection data uploaded by the user and obtaining the building lifecycle file of the building to be inspected, the method further includes: Receive building performance indicators collected by the building data acquisition sensors and determine the acquisition time corresponding to the building performance indicators; Obtain the building information model of the building to be tested, and associate the building performance indicators with the building information model based on the acquisition time; Based on the building information model collected at different times and the building performance indicators, a building lifecycle archive is formed.

3. The control method for the building safety detection system as described in claim 1, characterized in that, Before the step of acquiring the building data sequence collected by at least one building data acquisition sensor deployed in the building to be detected within the time period of the abnormal environmental event after detecting an abnormal environmental event, the method further includes: The sensor data collected by the detection sensor is used to construct sequence information based on the sensor data; The discreteness of the sequence information within the preset sliding time window is calculated. If the dispersion is greater than or equal to the first dispersion threshold, the abnormal environmental event is determined. Alternatively, meteorological information corresponding to the building to be tested can be obtained, and abnormal environmental events can be determined based on the meteorological information.

4. The control method for the building safety detection system as described in claim 3, characterized in that, After the step of calculating the discreteness of the sequence information within the preset sliding time window, the method further includes: The dispersion is compared with the first dispersion threshold or the second dispersion threshold; If the dispersion is greater than the second dispersion threshold and less than the first dispersion threshold, the building data acquisition sensor is triggered to acquire building data. If the dispersion is greater than or equal to the first dispersion threshold, the abnormal environmental event is determined.

5. The control method for the building safety detection system as described in claim 1, characterized in that, The steps of receiving building inspection data uploaded by the user and obtaining the building lifecycle file of the building to be inspected include: Receive the building inspection data, and acquire the sensor data and multimedia data from the building inspection data; Obtain the sensor location information corresponding to the sensor data, and identify the building location information corresponding to the multimedia data; By matching the sensor location information with the building location information, a mapping relationship between the sensor data and the multimedia data is constructed to form the building detection data; Based on the building to be inspected, obtain the corresponding building lifecycle file.

6. The control method for the building safety detection system as described in claim 1, characterized in that, The step of identifying the impact of abnormal environmental events on the building under test based on the mapping results using the building status analysis model includes: Based on the mapping structure, the spatial location and damage mechanism of the building data sequence and the building detection data are compared; Based on the comparison results, valid evidence of damage to the building to be inspected was determined; Based on the spatial location, obtain the structural features of the corresponding part of the building to be inspected; Using a building condition analysis model, hierarchical reasoning is performed based on the structural features and the valid damage evidence, and internal damage information is determined based on the reasoning results. By integrating the internal damage information, the impact information of the building to be inspected is calculated.

7. The control method for the building safety detection system as described in claim 1, characterized in that, The step of mapping the building inspection data and the building data sequence to the digital twin model in the building lifecycle archive includes: Based on the sensor deployment information recorded in the building lifecycle file, determine the first spatial location of the building data acquisition sensor in the digital twin model; Identify the second spatial location corresponding to the building detection data; The building data sequence is associated with the first spatial location in the digital twin model, and the building detection data is associated with the second spatial location in the digital twin model.

8. The control method for the building safety detection system as described in claim 1, characterized in that, After the step of identifying the impact of the abnormal environmental event on the building to be detected based on the mapping result using the building status analysis model, the method further includes: Based on the severity and spatial distribution of the impact information, the affected structural components are visually marked in the digital twin model; Based on the visual markers and the impact information, a safety assessment report for the building to be inspected is generated.

9. A control device for a building safety detection system, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the building safety detection system as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the control method of the building safety detection system as described in any one of claims 1 to 8.

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