Integrated construction platform safety monitoring method and system for urban renewal housing

Abstract

The application discloses an integrated construction platform safety monitoring method and system for urban renewal housing, relates to the technical field of safety monitoring, and comprises the following steps: laying a sensing network to collect stress and strain, displacement, inclination, load and vibration data in real time; removing abnormal values based on space-time consistency; constructing a cooperative monitoring index system comprising a stress stability index, a load balance index, a posture deviation index and a wind vibration response index; establishing a safety evaluation model for multi-parameter fusion, obtaining a fusion safety evaluation value by dynamically allocating index weights, identifying stress abnormal areas and introducing an environmental correction coefficient; setting a graded warning range based on the evaluation value, triggering corresponding grade warnings and synchronously executing safety control actions. The application improves the intelligent level of building machine construction safety monitoring through multi-parameter cooperative monitoring, intelligent identification of abnormal data, reconstruction of full-field stress distribution and quantitative correction of environmental factors.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology, specifically to a safety monitoring method and system for an integrated construction platform for urban renewal residential buildings. Background Technology

[0002] As urbanization enters the stock renewal phase, urban renewal has become a key measure to optimize urban spatial layout and improve functional quality. In some urban village renovation projects, residential construction, as the core content of urban renewal, faces unprecedented challenges. Traditional construction methods suffer from poor construction environments, high labor intensity, low levels of automation, and low construction efficiency. Residential building construction machines transform traditional open-air high-altitude operations into a factory-like enclosed work space, significantly improving working conditions and enhancing construction safety. Through the deployment of automated monitoring equipment and a digital management platform, real-time monitoring of parameters such as equipment stress and strain, load, displacement deviation, and verticality is achieved.

[0003] However, existing safety monitoring technologies for building construction machinery still have the following shortcomings: monitoring parameters are relatively independent, mostly based on single threshold alarms, lacking a collaborative assessment mechanism under the coupling effect of multiple parameters, making it difficult to comprehensively reflect the structural safety status; data processing methods are relatively simple, and the identification of sensor anomalies relies on human experience; safety assessments are mainly based on point monitoring data; the special characteristics of high-altitude operations in urban renewal projects are not fully considered, and there is a lack of quantitative correction for the impact of environmental factors such as construction height and real-time wind force, limiting the accuracy of early warnings.

[0004] Therefore, there is an urgent need for an integrated construction platform safety monitoring method and system for urban renewal residential buildings, which can realize multi-parameter collaborative monitoring, intelligent identification of abnormal data, reconstruction of stress distribution across the entire field, and quantitative correction of environmental factors, thereby improving the intelligence level and early warning accuracy of building construction safety monitoring. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing a safety monitoring method and system for an integrated construction platform for urban renewal housing.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A safety monitoring method for an integrated construction platform for urban renewal residential buildings includes the following steps:

[0008] Step S1: Deploy a sensing network at key nodes of the steel platform of the residential building machine, supporting columns, wall mounts, hydraulic lifting cylinders and formwork suspension points to collect stress and strain data, displacement data, tilt angle data, load data and vibration data in real time.

[0009] Step S2: Perform outlier removal processing on the collected data based on spatiotemporal consistency to obtain the monitoring data sequence;

[0010] Step S3: Based on the monitoring data sequence, construct a collaborative monitoring index system that includes stress stability index, load balance index, attitude deviation index and wind vibration response index;

[0011] Step S4: Establish a multi-parameter fusion safety assessment model. Dynamically assign weights to monitoring indicators under different working conditions using an adaptive weighting algorithm. Combine this with spatial interpolation to obtain the stress distribution across the entire steel platform, identify stress anomaly areas, and calculate the stress anomaly index. Simultaneously, introduce an environmental correction coefficient to correct for the influence of wind force and height, and finally obtain the fusion safety assessment value.

[0012] Step S5: Based on the fusion safety assessment value, set a graded early warning range. When the fusion safety assessment value is within the graded early warning range, the corresponding level of early warning is triggered, and the hydraulic synchronous control system is linked to perform safety control actions.

[0013] Furthermore, in step S2, the outlier removal based on spatiotemporal consistency adopts a spatiotemporal consistency discrimination function. The spatiotemporal consistency discrimination function includes a single-point temporal deviation term and a spatial neighborhood cooperative deviation term. When the value of the spatiotemporal consistency discrimination function exceeds a preset outlier threshold, it is determined to be an outlier and replaced by spatial interpolation of adjacent sensor data.

[0014] Furthermore, in step S3, the stress stability index is calculated by summing the squares of the relative deviations between the measured stress values ​​and the design stress values ​​of each stress sensor and taking a negative exponential function.

[0015] The load balance index is calculated by subtracting the average value of the relative deviation between the measured load and the average load of each lifting cylinder from 1.

[0016] The attitude deviation index is calculated by subtracting the weighted average of the above ratios from the ratio of the measured tilt angle of the steel platform to the tilt angle limit, and the ratio of the vertical displacement standard deviation to the displacement standard deviation limit.

[0017] The wind vibration response index is calculated based on the ratio of the root mean square value of acceleration collected by the vibration sensor to the acceleration limit, and then the average of the summation of the ratios is taken as a negative exponential function.

[0018] Further, step S4 includes the following steps:

[0019] Step S4.1: Automatically identify the current working condition based on the change rate of the lifting cylinder stroke and the change rate of the platform tilt angle, and calculate the real-time weight of each index based on the sensitivity factor of each index. The sensitivity factor is obtained by normalizing the change rate of each index per unit time.

[0020] Step S4.2: Based on the ratio of the current construction height to the maximum design height and the ratio of the real-time wind speed to the design allowable wind speed, the negative exponential function of the product of the two ratios is used as the environmental correction coefficient.

[0021] Step S4.3: Use Kriging interpolation to obtain the stress distribution across the entire steel platform, identify stress anomaly regions, and calculate the stress anomaly index.

[0022] Step S4.4: Sum the adaptive weights and the exponent, and then multiply by the negative exponential function of the environmental correction coefficient and the stress anomaly index to obtain the fusion safety assessment value.

[0023] Further, in step S4.3, the specific calculation process of the stress anomaly index is as follows: calculate the stress concentration factor at each location, that is, the ratio of local stress to average stress; identify the region where the stress concentration factor exceeds the preset concentration factor threshold as the stress over-limit region; within the stress over-limit region, perform area integration on the extent to which the local stress at each location exceeds the allowable stress limit at that location, and the integration result is the stress anomaly index.

[0024] Further, in step S5, the graded warning includes four levels: blue warning, yellow warning, orange warning, and red warning. The range of blue warning is [0.6, 0.8), the range of yellow warning is [0.4, 0.6), the range of orange warning is [0.2, 0.4), and the range of red warning is [0.0, 0.2].

[0025] Furthermore, in step S5, when a yellow or higher warning is triggered, the system automatically adjusts the stroke of the lifting cylinder to improve the platform's attitude; when an orange warning is triggered, the system automatically reduces the lifting speed or suspends the operation and notifies technicians to detect the identified stress anomaly area; when a red warning is triggered, the system performs an emergency stop and issues an audible and visual alarm, while simultaneously locating the stress anomaly area.

[0026] A safety monitoring system for an integrated construction platform for urban renewal residential buildings, used to implement any one of the safety monitoring methods for an integrated construction platform for urban renewal residential buildings, including:

[0027] The data acquisition module includes stress and strain sensors, displacement sensors, tilt sensors, load sensors and vibration sensors deployed at key parts of the building machine, used to collect real-time operating status data of the building machine;

[0028] The edge computing module, located on the industrial control computer of the building machine platform, is used to remove outliers from the collected data based on spatiotemporal consistency, perform real-time calculations of stress stability index, load balance index, attitude deviation index and wind vibration response index, and obtain preliminary fusion values ​​based on an adaptive weighting algorithm.

[0029] The IoT transmission module uses wireless communication technology to transmit the processing results of the edge computing module to the cloud server in real time.

[0030] The cloud-based analysis module, deployed on a cloud server, is used to receive and store monitoring data, perform Kriging space interpolation to obtain the stress distribution across the entire field, identify stress anomaly areas and calculate the stress anomaly index, combine real-time wind data and construction height to calculate the environmental correction coefficient, and fuse the environmental correction coefficient, stress anomaly index and preliminary fusion value to obtain the fused safety assessment value.

[0031] The graded early warning and control module triggers graded early warnings according to the preset early warning range based on the fused safety assessment value output by the cloud analysis module, and links the hydraulic synchronous control system to perform corresponding safety control actions. At the same time, it pushes early warning information and the location of abnormal areas to the mobile terminal.

[0032] Furthermore, the cloud-based analysis module also includes a digital twin sub-module, which constructs a three-dimensional visual digital twin of the building machine based on the BIM model and real-time monitoring data, and dynamically renders a stress field cloud map, using different colors to mark areas of stress anomalies.

[0033] Furthermore, the system also includes a mobile terminal APP, which is communicatively connected to the cloud analysis module to receive graded early warning information, view real-time monitoring data and historical data curves, and receive precise location information of stress anomaly areas.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] 1. This invention uses a spatiotemporal consistency-based outlier removal method to identify abnormal data by utilizing the spatial correlation of sensors. This method is more accurate and reliable than the traditional single-point threshold method, effectively ensuring the quality of input data and avoiding misjudgments caused by single sensor failures.

[0036] 2. This invention constructs a collaborative monitoring index system that includes stress stability index, load balance index, attitude deviation index and wind vibration response index. It comprehensively characterizes the safety status of building construction machines from four dimensions: stress state, load distribution, geometric attitude and dynamic response, overcoming the limitations of traditional single-parameter monitoring.

[0037] 3. This invention uses an adaptive weighting algorithm to automatically identify static or dynamic climbing conditions based on the change rate of the lifting cylinder stroke and the change rate of the platform tilt angle, and dynamically adjusts the weights of each index to make the safety assessment more consistent with the actual operating state of the building machine.

[0038] 4. This invention introduces an environmental correction coefficient, which quantifies the current construction height and real-time wind speed as correction factors, enabling the safety assessment to reflect the actual impact of high-altitude operations and wind loads on structural safety. It is particularly suitable for high-rise residential construction scenarios in urban renewal projects.

[0039] 5. This invention uses the Kriging space interpolation method to reconstruct the stress distribution of the entire steel platform, identify stress anomaly regions and calculate stress anomaly indices, and quantifies local stress concentrations into indicators that can participate in the fusion evaluation, thus realizing the upgrade from point monitoring to surface evaluation. Attached Figure Description

[0040] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0041] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0042] Figure 2 This is a system schematic diagram according to an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] like Figure 1 As shown, the safety monitoring method for an integrated construction platform for urban renewal residential buildings includes the following steps:

[0045] Step S1: Deploy a sensing network at key nodes of the steel platform of the residential building machine, supporting columns, wall mounts, hydraulic lifting cylinders and formwork suspension points to collect stress and strain data, displacement data, tilt angle data, load data and vibration data in real time.

[0046] Step S2: Perform outlier removal processing on the collected data based on spatiotemporal consistency to obtain the monitoring data sequence;

[0047] Step S3: Based on the monitoring data sequence, construct a collaborative monitoring index system that includes stress stability index, load balance index, attitude deviation index and wind vibration response index;

[0048] Step S4: Establish a multi-parameter fusion safety assessment model. Dynamically assign weights to monitoring indicators under different working conditions using an adaptive weighting algorithm. Combine this with spatial interpolation to obtain the stress distribution across the entire steel platform, identify stress anomaly areas, and calculate the stress anomaly index. Simultaneously, introduce an environmental correction coefficient to correct for the influence of wind force and height, and finally obtain the fusion safety assessment value.

[0049] Step S5: Based on the fusion safety assessment value, set a graded early warning range. When the fusion safety assessment value is within the graded early warning range, the corresponding level of early warning is triggered, and the hydraulic synchronous control system is linked to perform safety control actions.

[0050] Stress and strain sensors, displacement sensors, tilt sensors, load sensors, and vibration sensors are respectively installed at key nodes of the steel platform of the residential building machine, supporting columns, wall mounts, hydraulic lifting cylinders, and formwork suspension points to form a multi-dimensional sensing network.

[0051] Stress-strain sensors are used to monitor stress changes in key stress-bearing parts and are deployed at locations such as the main beam nodes of the steel platform and the base of the supporting columns.

[0052] Displacement sensors are used to monitor the relative displacement of various parts of the platform and are installed at the connection between the support columns and the wall mounts, as well as at the expansion joints of the steel platform.

[0053] Tilt sensors are used to monitor the overall tilt angle of the steel platform and are installed at the four corners and center of the platform.

[0054] Load sensors are used to monitor the real-time load of the hydraulic jacking cylinder and the load of the formwork suspension system. They are integrated inside the cylinder or at the suspension point.

[0055] Vibration sensors are used to monitor the vibration response of the platform under dynamic loads such as wind loads, and are deployed at the edges and center of the platform.

[0056] In step S2, the outlier removal based on spatiotemporal consistency adopts a spatiotemporal consistency discrimination function. The spatiotemporal consistency discrimination function includes a single-point temporal deviation term and a spatial neighborhood cooperative deviation term. When the value of the spatiotemporal consistency discrimination function exceeds a preset outlier threshold, it is determined to be an outlier and replaced by spatial interpolation of adjacent sensor data.

[0057] Define the measurement value of the i-th sensor at time t as... Based on historical data, a prediction was made for this measuring point, and the predicted value was obtained. The standard deviation of the historical data for this measurement point is: Let the set of sensors spatially adjacent to the i-th sensor be denoted as . The measurement values ​​of adjacent sensors at time t are The standard deviation of the cooperation between the two sensors is Based on the statistical differences in historical data from the two sensors, the specific formula for the spatiotemporal consistency discriminant function is as follows:

[0058]

[0059] in, This represents the spatiotemporal consistency discriminant function. The spatial consistency weight coefficient is used to adjust the importance of the spatial term in the discriminant function. Its value range is usually 0.5 to 1.5. An anomaly threshold is set (determined based on engineering experience or statistical methods). When the spatial consistency discriminant function is greater than the anomaly threshold, the data point is determined to be an anomaly and replaced by spatial interpolation of adjacent sensor data, thereby obtaining a reliable monitoring data sequence.

[0060] In practical engineering, sensor data typically exhibits a slow changing trend and is superimposed with random noise, making it difficult to obtain reasonable prediction values. The exponentially weighted moving average method can be used; the anomaly threshold is usually set at 2.5. Based on the 3σ principle in statistics, the spatiotemporal consistency discriminant function approximately follows a standard normal distribution when the data is normal, and its value exceeds 3 with a very small probability of about 0.3%. Therefore, the reasonable threshold for the anomaly threshold can be set to 2~3.

[0061] In step S3, the stress stability index is calculated by summing the squares of the relative deviations between the measured stress values ​​and the design stress values ​​of each stress sensor and taking the negative exponential function.

[0062] The load balance index is calculated by subtracting the average value of the relative deviation between the measured load and the average load of each lifting cylinder from 1.

[0063] The attitude deviation index is calculated by subtracting the weighted average of the above ratios from the ratio of the measured tilt angle of the steel platform to the tilt angle limit, and the ratio of the vertical displacement standard deviation to the displacement standard deviation limit.

[0064] The wind vibration response index is calculated based on the ratio of the root mean square value of acceleration collected by the vibration sensor to the acceleration limit, and then the average of the summation of the ratios is taken as a negative exponential function.

[0065] The stress stability index is used to characterize the stability of stress levels in critical load-bearing components of a building machine relative to design values. The specific formula is as follows:

[0066]

[0067] in, Indicates the stress stability index, Indicates the total number of stress sensors. This represents the measured stress value of the i-th stress sensor at time t. This represents the design stress value at the location corresponding to the i-th stress sensor. This represents the allowable deviation limit of stress at the location corresponding to the i-th stress sensor, which is determined according to the structural design specifications. The closer the stress stability index value is to 1, the more stable the stress state is, and there is no risk of exceeding the limit.

[0068] The load balance index is used to characterize the uniformity of load distribution among the hydraulic lifting cylinders. The specific formula is as follows:

[0069]

[0070] in, Indicates the load balance index. Indicates the number of lifting cylinders. Indicates the first The measured load of the hydraulic cylinder at time t. This represents the average load of all cylinders at time t. The closer the load balance index is to 1, the more balanced the load of each cylinder is, and the smoother the lifting process is.

[0071] The attitude deviation index is used to characterize the overall tilt, settlement, and torsional deformation of a steel platform. The specific formula is as follows:

[0072]

[0073] in, Indicates the attitude deviation index. and Let represent the measured tilt angles of the platform about the x-axis and y-axis at time t, respectively. and These represent the permissible limits for tilt angles about the x-axis and y-axis, respectively. This represents the standard deviation of the vertical displacement of each measuring point on the platform relative to the reference plane at time t, reflecting the degree of uneven settlement of the platform as a whole. This represents the allowable limit of the standard deviation of vertical displacement. The closer the attitude deviation index is to 1, the closer the platform attitude is to the ideal state.

[0074] The wind vibration response index is used to characterize the dynamic response of a platform under wind loads. The specific calculation formula is as follows:

[0075]

[0076] in, Indicates the wind vibration response index. Indicates the number of vibration sensors. This represents the root mean square value of the acceleration of the k-th vibration sensor at time t. This represents the acceleration limit at the location corresponding to the kth vibration sensor, which is determined based on the wind-resistant design of the structure. The closer the wind vibration response index is to 1, the smaller the wind vibration response and the more stable the structure.

[0077] Step S4 includes the following steps:

[0078] Step S4.1: Automatically identify the current working condition based on the change rate of the lifting cylinder stroke and the change rate of the platform tilt angle, and calculate the real-time weight of each index based on the sensitivity factor of each index. The sensitivity factor is obtained by normalizing the change rate of each index per unit time.

[0079] Step S4.2: Based on the ratio of the current construction height to the maximum design height and the ratio of the real-time wind speed to the design allowable wind speed, the negative exponential function of the product of the two ratios is used as the environmental correction coefficient.

[0080] Step S4.3: Use Kriging interpolation to obtain the stress distribution across the entire steel platform, identify stress anomaly regions, and calculate the stress anomaly index.

[0081] Step S4.4: Sum the adaptive weights and the exponent, and then multiply by the negative exponential function of the environmental correction coefficient and the stress anomaly index to obtain the fusion safety assessment value.

[0082] In step S4.3, the specific calculation process of the stress anomaly index is as follows: calculate the stress concentration factor at each location, that is, the ratio of local stress to average stress; identify the region where the stress concentration factor exceeds the preset concentration factor threshold as the stress over-limit region; within the stress over-limit region, perform area integration on the extent to which the local stress at each location exceeds the allowable stress limit at that location, and the integration result is the stress anomaly index.

[0083] Based on the stroke change rate of the lifting cylinder and the platform tilt angle change rate, the machine can be automatically identified as either in a static operation or a dynamic climbing operation. Based on this, the weights of each index are dynamically adjusted according to their sensitivity factors.

[0084] Define the normalized sensitivity factor of the m-th exponent at time t. Where m = 1, 2, 3, and 4 correspond to the stress stability index, load equilibrium index, attitude deviation index, and wind vibration response index, respectively. The sensitivity factor can be obtained by normalizing the rate of change of this index at the current moment. Indices with greater changes should be assigned higher weights to reflect their current significant impact on safety. The adaptive weights are calculated using the softmax function, with the specific formula as follows:

[0085]

[0086] in, This indicates the real-time weight of the index. This represents the adjustment coefficient for the m-th index, used to control the strength of the weight's response to sensitivity; it is determined empirically. An index representing an exponent;

[0087] Wherein, the adjustment coefficient Specific value examples include:

[0088] Stress is a direct reflection of structural safety. When the stress at a certain point fluctuates abnormally, it often means local damage or sudden load changes, which requires immediate attention. Therefore, the highest sensitivity is set so that the weight of this index rises rapidly when the stress is abnormal, and it dominates the safety assessment.

[0089] Slight fluctuations in cylinder load during the lifting process are normal. The hydraulic system has a certain self-balancing ability. When the load is severely uneven, the system can improve it by adjusting the cylinder stroke. Therefore, the sensitivity is set to medium, which can increase the weight in abnormal situations without having to adjust it frequently due to instantaneous fluctuations.

[0090] Platform attitude is a key monitoring indicator for climbing operations. When stationary, attitude changes slowly, but deviations may occur during climbing, requiring timely response. Therefore, it is given high sensitivity to ensure that it can be quickly adjusted when the attitude is abnormal during climbing.

[0091] The wind vibration response is greatly affected by instantaneous wind speed and has a certain degree of randomness. If the sensitivity is too high, gusts may cause the weight to fluctuate drastically, affecting the stability of the assessment. Therefore, a lower sensitivity is set to make the weight of the wind vibration index relatively stable and avoid false alarms.

[0092] The above values ​​are baseline values ​​and can be fine-tuned in practical applications. If the reliability of a certain type of sensor is poor, it can be appropriately reduced to reduce false alarms. If a certain indicator is of particular concern under a certain working condition, such as attitude during climbing, the corresponding adjustment coefficient can be temporarily increased.

[0093] Considering the impact of construction height and real-time wind force on the safety of the building construction machine, an environmental correction factor is introduced, the specific formula of which is:

[0094]

[0095] in, This represents the environmental correction factor. This represents the environmental sensitivity coefficient, used to adjust the correction strength, and is typically taken as 0.5 to 2. This indicates the current construction height, specifically the floor height where the building construction machine is located. Indicates the maximum design height. This indicates real-time wind speed, measured by meteorological sensors or on-site anemometers. This indicates the design-permitted wind speed; work should cease if this wind speed is exceeded.

[0096] Due to the limited number of stress sensors, spatial interpolation is required to obtain the stress distribution across the entire steel platform. Kriging interpolation is used to interpolate the measured values ​​at discrete stress sensor points, yielding an estimated stress value at any location on the entire steel platform surface at time t. Based on the interpolated stress field, a stress concentration factor is defined, with the following formula:

[0097]

[0098] in, This represents the stress concentration factor at any position (x, y) on the surface of the steel platform at time t. Let represent the estimated stress at any position (x, y) on the surface of the steel platform at time t, obtained using Kriging interpolation. The average stress on the platform at time t is obtained by averaging the measured values ​​from each stress sensor.

[0099] A concentration factor threshold is set and determined based on engineering experience. When the stress concentration factor exceeds the concentration factor threshold, the location is determined to be a stress anomaly area. Record the region formed by all anomalies, and further calculate the stress anomaly index to quantify the severity of the stress anomaly region. The specific formula is as follows:

[0100]

[0101] in, Indicates the stress anomaly index. This indicates the total area of ​​the steel platform. Indicates the area of ​​abnormal stress. This represents the allowable stress limit at location (x, y), determined according to design specifications. This represents the operator that takes a positive value, i.e., only if... Greater than The stress anomaly index is only included in the integral when the stress anomaly occurs; otherwise, the term is 0. The integral result is divided by the total area to obtain the normalized stress anomaly index. The larger the value, the more severe the stress anomaly.

[0102] The concentration factor threshold is typically set at 1.3. In general steel structure design, the allowable stress concentration factor is usually controlled between 1.2 and 1.5, depending on the material, connection type, and load type. For temporary structures like residential building machines, the safety factor can be appropriately relaxed, but considering the risks of working at heights, the value should not be too large. In similar building machine projects, when the local stress exceeds 30% of the average stress, it is considered abnormal and requires attention. Therefore, 1.3 is chosen as the threshold.

[0103] The adaptive weights are summed with each index to obtain a preliminary fusion value. This preliminary value is then multiplied by the environmental correction coefficient and the negative exponential penalty factor of the stress anomaly index to obtain the final fusion safety assessment value. The specific formula is as follows:

[0104]

[0105] in, Indicates the integrated security assessment value. This represents the m-th index, specifically: That is, the stress stability index. That is, the load balance index. That is, the attitude deviation index. That is, the wind vibration response index. This represents the stress anomaly penalty coefficient, used to adjust the intensity of the impact of stress anomalies on the overall safety assessment, and is usually taken as 1 to 5.

[0106] In step S5, the graded early warning includes four levels: blue warning, yellow warning, orange warning, and red warning. The range of blue warning is [0.6, 0.8), the range of yellow warning is [0.4, 0.6), the range of orange warning is [0.2, 0.4), and the range of red warning is [0.0, 0.2].

[0107] In step S5, when a yellow or higher warning is triggered, the system automatically adjusts the stroke of the lifting cylinder to improve the platform's attitude; when an orange warning is triggered, the system automatically reduces the lifting speed or suspends operation and notifies technicians to detect the identified stress anomaly area; when a red warning is triggered, the system performs an emergency stop and issues an audible and visual alarm, while simultaneously locating the stress anomaly area.

[0108] Based on the integrated safety assessment values, a graded early warning range is set, specifically divided into four levels: Blue warning [0.6, 0.8), indicating a state of concern, requiring enhanced on-site observation, with a focus on areas of abnormal stress and wind-induced vibration response; Yellow warning [0.4, 0.6), indicating a state of alert, where the system automatically adjusts the stroke of individual lifting cylinders to improve the platform's attitude and prompts management personnel to inspect; Orange warning [0.2, 0.4), indicating a state of danger, where the system automatically reduces the lifting speed or suspends operations and notifies technicians to conduct detailed inspections of areas of abnormal stress; Red warning [0.0, 0.2), indicating a state of emergency, where the system immediately executes an emergency shutdown, all cylinders self-lock, and an audible and visual alarm is issued, while simultaneously pinpointing the location of areas of abnormal stress for maintenance reference.

[0109] When the fusion safety assessment value falls within the corresponding range, a warning of the corresponding level is triggered, and the hydraulic synchronous control system is linked to perform the above-mentioned safety control actions; when the fusion safety assessment value is greater than or equal to 0.8, no warning is triggered.

[0110] A safety monitoring system for an integrated construction platform for urban renewal residential buildings, used to implement any one of the safety monitoring methods for an integrated construction platform for urban renewal residential buildings, including:

[0111] The data acquisition module includes stress and strain sensors, displacement sensors, tilt sensors, load sensors and vibration sensors deployed at key parts of the building machine, used to collect real-time operating status data of the building machine;

[0112] The edge computing module, located on the industrial control computer of the building machine platform, is used to remove outliers from the collected data based on spatiotemporal consistency, perform real-time calculations of stress stability index, load balance index, attitude deviation index and wind vibration response index, and obtain preliminary fusion values ​​based on an adaptive weighting algorithm.

[0113] The IoT transmission module uses wireless communication technology to transmit the processing results of the edge computing module to the cloud server in real time.

[0114] The cloud-based analysis module, deployed on a cloud server, is used to receive and store monitoring data, perform Kriging space interpolation to obtain the stress distribution across the entire field, identify stress anomaly areas and calculate the stress anomaly index, combine real-time wind data and construction height to calculate the environmental correction coefficient, and fuse the environmental correction coefficient, stress anomaly index and preliminary fusion value to obtain the fused safety assessment value.

[0115] The graded early warning and control module triggers graded early warnings according to the preset early warning range based on the fused safety assessment value output by the cloud analysis module, and links the hydraulic synchronous control system to perform corresponding safety control actions. At the same time, it pushes early warning information and the location of abnormal areas to the mobile terminal.

[0116] The cloud-based analysis module also includes a digital twin sub-module, which constructs a three-dimensional visual digital twin of the building machine based on the BIM model and real-time monitoring data, and dynamically renders a stress field cloud map, using different colors to mark areas of stress anomalies.

[0117] The system also includes a mobile terminal APP, which is connected to the cloud analysis module to receive graded early warning information, view real-time monitoring data and historical data curves, and receive precise location information of stress anomaly areas.

[0118] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0119] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.

Claims

1. A safety monitoring method for an integrated construction platform for urban renewal residential buildings, characterized in that, Includes the following steps: Step S1: Deploy a sensing network at key nodes of the steel platform of the residential building machine, supporting columns, wall mounts, hydraulic lifting cylinders and formwork suspension points to collect stress and strain data, displacement data, tilt angle data, load data and vibration data in real time. Step S2: Perform outlier removal processing on the collected data based on spatiotemporal consistency to obtain the monitoring data sequence; Step S3: Based on the monitoring data sequence, construct a collaborative monitoring index system that includes stress stability index, load balance index, attitude deviation index and wind vibration response index; Step S4: Establish a multi-parameter fusion safety assessment model. Dynamically assign weights to monitoring indicators under different working conditions using an adaptive weighting algorithm. Combine this with spatial interpolation to obtain the stress distribution across the entire steel platform, identify stress anomaly areas, and calculate the stress anomaly index. Simultaneously, introduce an environmental correction coefficient to correct for the influence of wind force and height, and finally obtain the fusion safety assessment value. Step S5: Based on the fusion security assessment value, set a graded early warning range. When the fusion security assessment value is within the graded early warning range, trigger the corresponding level of early warning and link the hydraulic synchronous control system to perform safety control actions. In step S2, the outlier removal based on spatiotemporal consistency employs a spatiotemporal consistency discrimination function. This function includes a single-point temporal deviation term and a spatial neighborhood cooperative deviation term. When the value of the spatiotemporal consistency discrimination function exceeds a preset outlier threshold, it is determined to be an outlier and replaced with spatial interpolation of adjacent sensor data. Specifically, this includes: Define the measurement value of the p-th sensor at time t as... Based on the historical data of the p-th sensor, the value at the current moment is predicted to obtain the predicted value. ; Calculate the standard deviation of the historical data of the p-th sensor. Let the set of sensors spatially adjacent to the p-th sensor be denoted as . The measurement value of the adjacent sensor q at time t is The standard deviation of the cooperation between the two sensors is Based on the statistical differences in historical data from the two sensors, the specific formula for the spatiotemporal consistency discriminant function is as follows: in, This represents the spatiotemporal consistency discriminant function. This represents the spatial consistency weighting coefficient; Step S4 includes the following steps: Step S4.1: Automatically identify the current working condition based on the change rate of the lifting cylinder stroke and the change rate of the platform tilt angle, and calculate the real-time weight of each index based on the sensitivity factor of each index. The sensitivity factor is obtained by normalizing the change rate of each index per unit time. Step S4.2: Based on the ratio of the current construction height to the maximum design height and the ratio of the real-time wind speed to the design allowable wind speed, the negative exponential function of the product of the two ratios is used as the environmental correction coefficient. Step S4.3: Use Kriging interpolation to obtain the stress distribution across the entire steel platform, identify stress anomaly regions, and calculate the stress anomaly index. Step S4.4: Sum the adaptive weights and the exponential weights, then multiply by the negative exponential function of the environmental correction coefficient and the stress anomaly index to obtain the fused safety assessment value. The specific formula is as follows: in, Indicates the integrated security assessment value. Represents the environmental correction factor. This indicates the real-time weight of the index. This represents the m-th index, specifically: That is, the stress stability index. That is, the load balance index. That is, the attitude deviation index. That is, the wind vibration response index. This represents the stress anomaly penalty coefficient. Indicates the stress anomaly index; The specific formula for the environmental correction factor is as follows: in, Indicates the environmental sensitivity coefficient. This indicates the current construction height, specifically the floor height where the building machine is located. Indicates the maximum design height. Indicates real-time wind speed. This indicates the design-allowed wind speed.

2. The method according to claim 1, characterized in that, In step S3, the stress stability index is calculated by summing the squares of the relative deviations between the measured stress values ​​and the design stress values ​​of each stress sensor and taking the negative exponential function. The load balance index is calculated by subtracting the average value of the relative deviation between the measured load and the average load of each lifting cylinder from 1. The attitude deviation index is calculated by subtracting the weighted average of the above ratios from the ratio of the measured tilt angle of the steel platform to the tilt angle limit, and the ratio of the vertical displacement standard deviation to the displacement standard deviation limit. The wind vibration response index is calculated based on the ratio of the root mean square value of acceleration collected by the vibration sensor to the acceleration limit, and then the average of the summation of the ratios is taken as a negative exponential function.

3. The method according to claim 2, characterized in that, In step S4.3, the specific calculation process of the stress anomaly index is as follows: calculate the stress concentration factor at each location, that is, the ratio of local stress to average stress; identify the region where the stress concentration factor exceeds the preset concentration factor threshold as the stress over-limit region; within the stress over-limit region, perform area integration on the extent to which the local stress at each location exceeds the allowable stress limit at that location, and the integration result is the stress anomaly index.

4. The method according to claim 3, characterized in that, In step S5, the graded early warning includes four levels: blue, yellow, orange, and red. The fusion security assessment value range corresponding to the blue warning is [0.6, 0.8), the fusion security assessment value range corresponding to the yellow warning is [0.4, 0.6), the fusion security assessment value range corresponding to the orange warning is [0.2, 0.4), and the fusion security assessment value range corresponding to the red warning is [0.0, 0.2].

5. The method according to claim 4, characterized in that, In step S5, when a yellow or higher warning is triggered, the system automatically adjusts the stroke of the lifting cylinder to improve the platform's attitude. When an orange alert is triggered, the system automatically reduces the jacking speed or suspends operations and notifies technicians to inspect the identified stress anomaly areas. When a red alert is triggered, the system performs an emergency shutdown and issues an audible and visual alarm, while simultaneously locating the stress anomaly area.

6. A safety monitoring system for an integrated construction platform for urban renewal residential buildings, used to implement the safety monitoring method for an integrated construction platform for urban renewal residential buildings as described in any one of claims 1-5, characterized in that, include: The data acquisition module includes stress and strain sensors, displacement sensors, tilt sensors, load sensors and vibration sensors deployed at key parts of the building machine, used to collect real-time operating status data of the building machine; The edge computing module, located on the industrial control computer of the building machine platform, is used to remove outliers from the collected data based on spatiotemporal consistency, perform real-time calculations of stress stability index, load balance index, attitude deviation index and wind vibration response index, and obtain preliminary fusion values ​​based on an adaptive weighting algorithm. The IoT transmission module uses wireless communication technology to transmit the processing results of the edge computing module to the cloud server in real time. The cloud-based analysis module, deployed on a cloud server, is used to receive and store monitoring data, perform Kriging space interpolation to obtain the stress distribution across the entire field, identify stress anomaly areas and calculate the stress anomaly index, combine real-time wind data and construction height to calculate the environmental correction coefficient, and fuse the environmental correction coefficient, stress anomaly index and preliminary fusion value to obtain the fused safety assessment value. The graded early warning and control module triggers graded early warnings according to the preset early warning range based on the fused safety assessment value output by the cloud analysis module, and links the hydraulic synchronous control system to perform corresponding safety control actions. At the same time, it pushes early warning information and the location of abnormal areas to the mobile terminal.

7. The system according to claim 6, characterized in that, The cloud-based analysis module also includes a digital twin sub-module, which constructs a three-dimensional visual digital twin of the building machine based on the BIM model and real-time monitoring data, and dynamically renders a stress field cloud map, using different colors to mark areas of stress anomalies.

8. The system according to claim 7, characterized in that, The system also includes a mobile terminal APP, which is connected to the cloud analysis module to receive graded early warning information, view real-time monitoring data and historical data curves, and receive precise location information of stress anomaly areas.

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