Intelligent monitoring and early warning method, system and equipment for building construction support structure

Abstract

The present invention provides an intelligent monitoring and early warning method, system and equipment for a construction support structure, which belongs to the field of data processing. A plurality of sensors arranged at the construction site of the construction support structure are used to collect physical data related to the construction support structure in real time, and the physical data include inclination data, axial force data, displacement data, strain data and environmental data; the safety state of the construction support structure is judged according to the physical data; by judging the safety state, it is judged whether to automatically issue an early warning signal, and / or whether to automatically trigger the operation of a processing program for controlling the addition of support structures, adjusting load distribution and / or suspending construction operations. The method can intelligently judge the risk trend, and adjust the early warning threshold in real time through an adaptive algorithm, so that the system can maintain efficient and accurate risk judgment under different construction stages and environmental conditions.

Description

Technical Field

[0001] The present invention belongs to the field of data processing, and in particular relates to an intelligent monitoring and early warning method, system and equipment for a building construction support structure. Background Art

[0002] The construction of building construction support structure is a technology widely used in construction projects, especially in the construction of complex structures such as bridges, large buildings, and super high-rise buildings. The core of the construction technology of building construction support structure is to use the support structure to bear the deadweight of the formwork and concrete as well as the construction load, so as to ensure the stability and safety of the engineering structure during the construction process. However, since the structures involved in the construction of building construction support structure are usually highly complex and the stress state is changeable, its safety has always been the focus of industry attention. Traditional safety monitoring of building construction support structure construction relies on regular inspections and experience judgments by construction personnel. This method is not only labor-intensive, but also limited by the subjectivity and lag of manual judgment, making it difficult to detect potential structural risks in a timely manner. Some construction sites use static monitoring equipment, such as fixed sensors and monitoring instruments, to regularly record parameters such as the inclination, stress, and strain of the formwork structure. However, these devices usually lack real-time data processing capabilities, and the monitoring results are often lagging, and cannot reflect the dynamic changes of the structure in real time. Some projects use an early warning system based on simple threshold judgment. When a monitoring parameter exceeds the set safety threshold, the system will issue an alarm. However, such systems usually lack comprehensive analysis capabilities for complex multi-dimensional data, are prone to false alarms or missed alarms, and are difficult to cope with complex and changing construction environments. Although the above-mentioned existing technologies have improved the safety monitoring capabilities of building construction support structures to a certain extent, there are still many shortcomings. Existing static monitoring equipment and threshold-based early warning systems cannot provide real-time and continuous monitoring, and cannot timely capture sudden risks that occur in the formwork structure during construction. This lag may lead to serious safety hazards. Traditional systems are usually based on threshold judgments based on a single parameter (such as stress or inclination), and lack the ability to comprehensively analyze multi-source data. This makes the system prone to false alarms or missed alarms when facing complex stress states, affecting the accuracy of early warnings. Most existing systems rely on manual observation and set fixed thresholds, lack adaptability, and cannot automatically adjust the early warning standards according to changes in site conditions. In addition, these systems usually lack intelligent analysis of data change trends, making it difficult to predict potential structural instability risks. Traditional early warning systems usually only provide simple alarm signals, but cannot provide construction managers with clear risk sources and analysis results, making the decision-making process more difficult. Summary of the invention

[0003] The purpose of the present invention is to propose an intelligent monitoring and early warning method, system and equipment for a building construction support structure to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0004] In order to achieve the above object, according to one aspect of the present invention, a method for intelligent monitoring and early warning of a building construction support structure is provided, the method comprising the following steps:

[0005] Multiple sensors, including inclination sensors, axial force sensors, displacement sensors, strain sensors and environmental monitoring sensors, are arranged at the construction site of the building construction support structure to collect physical data related to the construction support structure in real time, wherein the physical data includes inclination data, axial force data, displacement data and strain data;

[0006] According to the physical data, the safety status of the building construction support structure is judged.

[0007] By judging the safety status, it is determined whether to automatically issue an early warning signal and / or whether to automatically trigger the operation of a processing program for controlling the addition of a supporting structure, adjusting load distribution and / or suspending construction operations.

[0008] Further, each of the physical data has a plurality of different measurement points: the measurement points of the inclination data are the key nodes in the support system of the building construction support structure, including the joints of the steel pipes, the top and bottom of the scaffolding;

[0009] The measurement points of the axial force data are located on the supporting rods and steel pipe columns of the building construction support structure;

[0010] The measurement points of displacement data are located at the bottom of the building construction support structure, key support points and the deformation area of ​​the overall structure;

[0011] The strain data are measured at the connection points of the structure in the building construction support structure.

[0012] Furthermore, according to the physical data, the method for determining the safety status of the building construction support structure may be:

[0013] After the physical data is preprocessed, the preprocessed data is serialized and the serialized data is input into a long short-term memory network, wherein the long short-term memory network is a recurrent neural network structure that introduces an attention mechanism, and the long short-term memory network is used as a multi-task learning model, and the multi-task learning model is used to predict the risk parameters of various types of physical data respectively, and then the risk parameters of various types of physical data are integrated to generate an overall construction risk index as a measure of the safety status of the building construction support structure;

[0014] Among them, in the multi-task output layer, it is necessary to design a multivariate loss function, which includes both the deviation risk of a single physical data and the error of the overall risk index. The optimization goal of the multivariate loss function is to enable the multi-task learning model to improve the accuracy of predicting the risk level of each construction stage.

[0015] Furthermore, the method for judging the safety status of the building construction support structure according to the physical data may also be:

[0016] Calculate the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure, and combine the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure to determine the safety status of the building construction support structure;

[0017] Setting a sampling time, obtaining values ​​for judging the safety status of the building construction support structure multiple times within the sampling time, comparing the increase of the values ​​for judging the safety status of the building construction support structure at different sampling times over time, and judging the safety status according to the increase of the values ​​over time;

[0018] Among them, the centroid of the building construction support structure represents the center of mass in the building construction support structure.

[0019] Further, preferably, the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure is calculated, and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is combined to obtain a method for judging the safety state of the building construction support structure:

[0020] Calculate the absolute value of the difference between the inclination centroid distance ratio and the inclination value ratio of each inclination data measurement point as the value of the centroid of the inclination associated distance building construction support structure, and add up the sum as the value distribution of the centroid of the inclination associated distance building construction support structure;

[0021] The absolute value of the difference between the axial force centroid distance ratio and the axial force value ratio of each axial force data measurement point is calculated as the value of the centroid of the axial force associated distance building construction support structure, and the accumulated sum is used as the numerical distribution of the centroid of the axial force associated distance building construction support structure;

[0022] The absolute value of the difference between the displacement centroid distance ratio and the displacement value ratio of each displacement data measurement point is calculated as the value of the centroid of the displacement associated distance building construction support structure, and the accumulated sum is used as the numerical distribution of the centroid of the displacement associated distance building construction support structure;

[0023] The absolute value of the difference between the strain centroid distance ratio and the strain value ratio of each strain data measurement point is calculated as the value of the centroid of the strain associated distance building construction support structure, and the accumulated sum is used as the numerical distribution of the strain associated distance centroid of the building construction support structure;

[0024] The sum obtained by adding the numerical distribution of the correlation distance between the inclination angle and the axial force and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is used as a basis for judging the safety status of the building construction support structure.

[0025] Further, the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure is calculated, and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is combined to obtain a method for judging the safety state of the building construction support structure. Alternatively, the method may be:

[0026] Calculate the correlation coefficient between the inclination centroid distance and the inclination value of each inclination data measurement point as the numerical distribution of the centroid of the inclination correlation distance building construction support structure;

[0027] Calculate the correlation coefficient between the axial force centroid distance and the axial force value of each axial force data measurement point as the numerical distribution of the axial force correlation distance to the centroid of the building construction support structure;

[0028] Calculate the correlation coefficient between the displacement centroid distance and the displacement value of each displacement data measurement point as the numerical distribution of the centroid of the displacement correlation distance building construction support structure;

[0029] Calculate the correlation coefficient between the strain centroid distance and the strain value of each strain data measurement point as the value distribution of the centroid of the strain correlation distance building construction support structure;

[0030] The absolute value of the difference between the numerical distribution of the correlation distance between the inclination angle and the axial force and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is added together, and the sum obtained is used as a basis for judging the safety status of the building construction support structure.

[0031] Furthermore, a sampling time is set, and values ​​for judging the safety status of the building construction support structure are obtained multiple times within the sampling time, and the growth rates of the values ​​of the safety status at different sampling times over time are compared, and the safety status is judged by the growth rate of the values ​​of the safety status over time. The specific method is:

[0032] Set the sampling time and sampling frequency, obtain the safety status value multiple times, and calculate the growth rate of the safety status value between each adjacent sampling time point;

[0033] The safety status is judged by analyzing the changing trend of the growth rate, and / or by setting a growth rate threshold. By setting the sampling time, calculating and analyzing the growth rate of the safety status value of the building construction support structure, the safety status of the structure can be dynamically judged. This method can provide real-time and accurate risk assessment by analyzing the changing trend of the growth rate and setting a reasonable growth rate threshold, thereby ensuring the safety of the construction process.

[0034] The present invention also provides an intelligent monitoring and early warning system for a building construction support structure, the intelligent monitoring and early warning system for a building construction support structure comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implements the steps in the intelligent monitoring and early warning method for a building construction support structure when executing the computer program, the intelligent monitoring and early warning system for a building construction support structure can be run in computing devices such as desktop computers, laptop computers, PDAs, and cloud data centers, and the executable system may include, but is not limited to, a processor, a memory, and a server cluster, and the processor executes the computer program to run in the following system units:

[0035] A monitoring unit, used for real-time collection of physical data related to the construction support structure, wherein the physical data includes inclination data, axial force data, displacement data, strain data and environmental data;

[0036] A calculation unit, for determining the safety status of the building construction support structure according to the physical data;

[0037] The early warning unit is used to determine whether to automatically issue an early warning signal by judging the safety status, and / or determine whether to automatically trigger the operation of a processing program for controlling the addition of a supporting structure, adjusting the load distribution and / or suspending the construction operation.

[0038] Correspondingly, the present invention also provides an electronic device, a readable storage medium and a computer program product:

[0039] An electronic device comprises: 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 so that the at least one processor can execute the intelligent monitoring and early warning method for a building construction support structure and the methods of each step therein.

[0040] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the intelligent monitoring and early warning method for a building construction support structure and the methods of the various steps therein.

[0041] A computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the intelligent monitoring and early warning method of a building construction support structure and the methods of each step therein.

[0042] The beneficial effects of the present invention are as follows: the present invention provides an intelligent monitoring and early warning method, system and equipment for a construction support structure, which utilizes multi-source sensor data, through advanced computing models and real-time data analysis, to achieve comprehensive and accurate safety monitoring of the formwork structure. By arranging multiple sensors (including inclination sensors, axial force sensors, displacement sensors, strain sensors and environmental monitoring sensors) at the construction site, real-time collection of physical data related to the formwork structure is achieved, and combined with a dynamically adjusted early warning threshold, real-time monitoring of the safety status of the formwork structure during the construction process is achieved to ensure timely discovery of potential risks. The method of the present invention calculates the correlation distance between the inclination, axial force, displacement and strain data and the centroid of the formwork structure, and uses a multivariate joint analysis method to comprehensively analyze the data of different sensors, thereby improving the accuracy and reliability of risk assessment and reducing the possibility of false alarms and missed reports. In addition, combined with the analysis of the real-time data growth rate, the method can intelligently judge the risk trend, and adjust the early warning threshold in real time through an adaptive algorithm, so that the system can maintain efficient and accurate risk judgment under different construction stages and environmental conditions. The method described in the present invention not only provides clear risk judgment results, but also provides construction management personnel with detailed analysis basis and explanatory information through the visual display of data such as growth rate and correlation distance, thereby helping to make more accurate decisions and enhancing the practicality and credibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings:

[0044] Figure 1 Shown is a flow chart of an intelligent monitoring and early warning method for a building construction support structure;

[0045] Figure 2 Shown is a system structure diagram of an intelligent monitoring and early warning system for a building construction support structure. DETAILED DESCRIPTION

[0046] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0047] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0048] like Figure 1 The flowchart of the intelligent monitoring and early warning method of the building construction support structure according to the present invention is shown below. Figure 1 The following describes an intelligent monitoring and early warning method, system and device for a building construction support structure according to an embodiment of the present invention.

[0049] The present invention provides an intelligent monitoring and early warning method for a building construction support structure, wherein the building construction support structure may be an engineering rigid structure, and the engineering rigid structure may specifically include a structure such as a high formwork, etc., as an example, and the method specifically includes the following steps:

[0050] Multiple sensors, including inclination sensors, axial force sensors, displacement sensors, strain sensors and environmental monitoring sensors, are arranged at the construction site of the building construction support structure to collect physical data related to the construction support structure in real time, wherein the physical data includes inclination data, axial force data, displacement data, strain data and environmental data;

[0051] Determining the safety status of the building construction support structure based on the physical data;

[0052] By judging the safety status, it is judged whether to automatically issue an early warning signal and whether to automatically trigger the operation of a processing program for controlling the addition of a supporting structure, adjusting load distribution and / or suspending construction operations.

[0053] Further, each of the physical data has a plurality of different measurement points: the measurement points of the inclination data are the key nodes in the support system of the building construction support structure, including the joints of the steel pipes, the top and bottom of the scaffolding;

[0054] The measurement points of the axial force data are located on the supporting rods and steel pipe columns of the building construction support structure;

[0055] The measurement points of displacement data are located at the bottom of the building construction support structure, key support points and the deformation area of ​​the overall structure;

[0056] The strain data were measured at the connection points of the structure supporting the building construction.

[0057] In some embodiments, the measurement points of the inclination data are key nodes of the support system, such as steel pipe connections, the top and bottom of the scaffold, etc. The inclination data is used to detect the inclination of the support system and determine whether there are signs of inclination or instability.

[0058] The measurement points of the axial force data are located on the bearing structures such as the supporting rods, steel pipe columns, etc. The axial force data is used to monitor the stress changes on each supporting point and identify overload or uneven support.

[0059] The displacement data measurement points are at the bottom of the supporting structure, key supporting points, and the deformation area of ​​the overall structure.

[0060] The displacement data can be used to detect foundation settlement, lateral or longitudinal displacement of the support system, and determine whether there is a tendency of deformation or instability.

[0061] Environmental data can be measured at the locations of environmental monitoring sensors such as wind speed sensors, temperature sensors, humidity sensors, etc. at the construction site.

[0062] The environmental data may be used to evaluate the impact of environmental factors on the support structure, for example, to determine whether the support structure is in a safe state under severe weather conditions.

[0063] The measurement points of strain data are located in the weak areas, connection points and important load-bearing parts of the structure.

[0064] The strain data can be used to monitor the strain of the material, and the strain can be converted into a resistance change value using a resistance strain gauge to determine whether there is a risk of stress concentration or material fatigue.

[0065] Among them, the inclination data can identify the inclination trend and determine whether there is a risk of overall or local instability. A continuously increasing inclination change may indicate that the supporting structure is gradually becoming unstable. A sudden change in inclination usually means a sudden external force (such as strong wind) or a local failure of the structure.

[0066] The axial force data pattern can be used to evaluate the load distribution and identify whether there is overloading or uneven loading. If the axial force at some nodes is much higher than that at other nodes, it may indicate that the location is subjected to undue overloading and immediate action should be taken. Abnormal fluctuations in axial force may indicate uneven foundation settlement or local damage to the supporting structure.

[0067] Displacement data can be used to determine the foundation settlement or deformation trend of the support system and predict the stability of the structure. Continuous horizontal or vertical displacement usually indicates the instability of the foundation, which may lead to the instability of the entire structure. Accelerated changes in displacement may be associated with stress concentration or partial failure of the support system.

[0068] Environmental data can assess the impact of the environment on structural safety, especially the response measures under extreme weather conditions, including but not limited to the measured values ​​of wind speed, temperature, humidity, etc. High wind speed or drastic temperature changes may cause stress concentration or material degeneration of the supporting structure. Combine environmental data with structural data to analyze the immediate impact of external factors on the supporting system to ensure the accuracy of early warning.

[0069] Strain data can be used to identify areas of material fatigue or stress concentration, providing early warning of possible structural damage. If the strain continues to increase and exceeds a preset threshold, it may indicate that the material is about to reach its fatigue limit and needs to be strengthened or replaced in advance.

[0070] Locally high strain values ​​may mean the presence of stress concentrations, increasing the risk of structural failure.

[0071] From the perspective of multidimensional data correlation analysis, the correlation between inclination angle and axial force shows that an increase in inclination angle is usually accompanied by a change in axial force. Comprehensive analysis can detect potential instability points earlier. Usually, when the inclination angle changes, the axial force will also change accordingly. Analyzing the time series of the two can determine whether the support system is gradually becoming unstable.

[0072] The correlation between displacement and strain can be used to determine the concentration of strain when the structure is displaced. The correlation analysis between the two can help identify risk areas in advance. Foundation settlement and displacement often affect the stress distribution of the support system. Detecting the abnormal correlation between the two can identify risks in advance. The role of environmental factors is that environmental data can be used as an external variable to correlate and analyze the inclination, axial force, displacement and strain data to determine the comprehensive impact of external conditions on structural safety. For example, strong winds or sudden temperature changes may cause stress concentration or changes in material properties of the support structure, which requires additional analysis in the monitoring data.

[0073] Each physical data has multiple different measurement points, and each measurement point can be equipped with one or more sensors to measure one or more physical data.

[0074] Furthermore, the method for determining the safety status of the building construction support structure according to the physical data is:

[0075] After the physical data is preprocessed, the preprocessed data is serialized and the serialized data is input into a long short-term memory network, wherein the long short-term memory network is a recurrent neural network structure that introduces an attention mechanism, and the long short-term memory network is used as a multi-task learning model, and the multi-task learning model is used to predict the risk parameters of various types of physical data respectively, and then the risk parameters of various types of physical data are integrated to generate an overall construction risk index as a measure of the safety status of the building construction support structure;

[0076] Among them, in the multi-task output layer, it is necessary to design a multivariate loss function, which includes both the deviation risk of a single physical data and the error of the overall risk index. The optimization goal of the multivariate loss function is to enable the multi-task learning model to improve the accuracy of predicting the risk level of each construction stage.

[0077] Deep neural networks and other AI algorithms excel in processing complex pattern recognition and big data analysis, but in the construction of building support structures, real-time monitoring and early warning are the key to ensuring safety. Traditional artificial intelligence algorithms such as deep neural networks are often "black box" models, and it is difficult to explain the internal logic, which may lead to misjudgments in construction environments where decisions need to be made quickly. The above algorithms, due to their strong interpretability, enable construction personnel to understand the source of risks and take corresponding measures, avoiding unnecessary delays and misunderstandings.

[0078] The data of the construction site of the building support structure may have great uncertainty or incompleteness, especially in new projects or special conditions, where historical data may be insufficient. Traditional AI algorithms may face the problem of overfitting or insufficient generalization ability, while the above algorithm avoids the reliance on large data sets by directly processing and analyzing existing physical data, thus being able to provide accurate risk assessment even when the data is incomplete.

[0079] Data from construction sites needs to be processed in real time, and overly complex AI algorithms may cause computational delays, which is unacceptable during the construction of building support structures. Relatively speaking, each step of the algorithm based on interpretable mathematical formulas and physical models can be clearly explained, providing construction managers with a more intuitive understanding and basis for risk judgment.

[0080] Further, the method for judging the safety status of the building construction support structure according to the physical data is either:

[0081] Calculate the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure, and combine the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure to determine the safety status of the building construction support structure;

[0082] Setting a sampling time, obtaining values ​​for judging the safety status of the building construction support structure multiple times within the sampling time, comparing the increase of the values ​​for judging the safety status of the building construction support structure at different sampling times over time, and judging the safety status according to the increase of the values ​​over time;

[0083] Among them, the centroid of the building construction support structure represents the mass center of the building construction support structure.

[0084] Further, the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure is calculated, and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is combined to obtain the calculation method of the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure for judging the safety state of the building construction support structure. The calculation method can be:

[0085] The measuring point used to measure the inclination data is the inclination data measuring point, the coordinates of each inclination data measuring point are obtained, the distance between the coordinates of each inclination data measuring point and the coordinates of the center of mass of the building construction support structure is obtained as the inclination center of mass distance, and the inclination value measured by each inclination data measuring point is obtained;

[0086] Calculate the ratio of the inclination centroid distance of each inclination data measurement point to the maximum value of the inclination centroid distances of all inclination data measurement points as the inclination centroid distance ratio of the inclination data measurement point;

[0087] Calculate the ratio of the inclination value of each inclination data measurement point to the maximum value of the inclination values ​​of the inclination data measurement point, as the inclination value ratio of the inclination data measurement point;

[0088] The absolute value of the difference between the ratio of the inclination centroid distance of each inclination data measurement point and its corresponding inclination value ratio is used as the value of the centroid of the inclination associated distance building construction support structure corresponding to the inclination data measurement point, and then the sum of the values ​​of the centroid of the inclination associated distance building construction support structure corresponding to each inclination data measurement point is used as the numerical distribution of the centroid of the inclination associated distance building construction support structure.

[0089] The measuring point used to measure the axial force data is the axial force data measurement point, the coordinates of each axial force data measurement point are obtained, the distance between the coordinates of each axial force data measurement point and the coordinates of the center of mass of the building construction support structure is obtained as the axial force center of mass distance, and the axial force value measured at each axial force data measurement point is obtained;

[0090] Calculate the ratio of the axial force centroid distance of each axial force data measurement point to the maximum value of the axial force centroid distances of the axial force data measurement points, as the axial force centroid distance ratio of the axial force data measurement point;

[0091] Calculate the ratio of the axial force value of each axial force data measurement point to the maximum value of the axial force values ​​of the axial force data measurement point, as the axial force value ratio of the axial force data measurement point;

[0092] The absolute value of the difference between the ratio of the axial force centroid distance of each axial force data measurement point and its corresponding axial force value ratio is used as the value of the centroid of the axial force associated distance building construction support structure corresponding to the axial force data measurement point, and then the sum of the values ​​of the centroid of the axial force associated distance building construction support structure corresponding to each axial force data measurement point is used as the numerical distribution of the centroid of the axial force associated distance building construction support structure.

[0093] The absolute value of the difference between the numerical distribution of the inclination angle associated distance from the center of mass of the building construction support structure and the numerical distribution of the axial force associated distance from the center of mass of the building construction support structure is used to represent the numerical distribution of the inclination angle and the axial force associated distance from the center of mass of the building construction support structure.

[0094] The calculation method of the numerical distribution of the displacement and strain correlation distance from the center of mass of the building construction support structure is:

[0095] The measuring point used to measure the displacement data is the displacement data measurement point, the coordinates of each displacement data measurement point are obtained, the distance between the coordinates of each displacement data measurement point and the coordinates of the center of mass of the building construction support structure is obtained as the displacement center of mass distance, and the displacement value measured by each displacement data measurement point is obtained;

[0096] Calculate the ratio of the displacement centroid distance of each displacement data measurement point to the maximum value of the displacement centroid distances of the displacement data measurement points as the displacement centroid distance ratio of the displacement data measurement point;

[0097] Calculate the ratio of the displacement value of each displacement data measurement point to the maximum value of the displacement values ​​of the displacement data measurement point as the displacement value ratio of the displacement data measurement point;

[0098] The absolute value of the difference between the displacement centroid distance ratio of each displacement data measurement point and its corresponding displacement numerical ratio is taken as the value of the centroid of the displacement associated distance building construction support structure corresponding to the displacement data measurement point, and then the sum of the values ​​of the centroid of the displacement associated distance building construction support structure corresponding to each displacement data measurement point is taken as the numerical distribution of the centroid of the displacement associated distance building construction support structure.

[0099] The measuring point used to measure the strain data is the strain data measuring point, the coordinates of each strain data measuring point are obtained, the distance between the coordinates of each strain data measuring point and the coordinates of the centroid of the building construction support structure is obtained as the strain centroid distance, and the strain value measured at each strain data measuring point is obtained;

[0100] Calculate the ratio of the strain centroid distance of each strain data measurement point to the maximum value of the strain centroid distances of the strain data measurement points as the strain centroid distance ratio of the strain data measurement point;

[0101] Calculate the ratio of the strain value of each strain data measurement point to the maximum value of the strain values ​​of the strain data measurement point as the strain value ratio of the strain data measurement point;

[0102] The absolute value of the difference between the strain centroid distance ratio of each strain data measurement point and its corresponding strain value ratio is taken as the value of the centroid of the strain correlation distance building construction support structure corresponding to the strain data measurement point, and then the sum of the values ​​of the strain correlation distance to the centroid of the building construction support structure corresponding to each strain data measurement point is taken as the numerical distribution of the strain correlation distance to the centroid of the building construction support structure.

[0103] Then, the absolute value of the difference between the numerical distribution of the displacement-related distance from the mass center of the building construction support structure and the numerical distribution of the strain-related distance from the mass center of the building construction support structure is used to represent the numerical distribution of the displacement-strain related distance from the mass center of the building construction support structure.

[0104] The numerical distribution of the correlation distance between the inclination angle and the axial force and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure are added together, and the sum is used to judge the safety status of the building construction support structure.

[0105] Further, the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure is calculated, and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is combined to obtain a specific method for judging the safety state of the building construction support structure, or it can also be:

[0106] The calculation method of the numerical distribution of the correlation between the inclination angle and the axial force from the center of mass of the building construction support structure is:

[0107] The measuring point used to measure the inclination data is the inclination data measuring point, the coordinates of each inclination data measuring point are obtained, the distance between the coordinates of each inclination data measuring point and the coordinates of the center of mass of the building construction support structure is obtained as the inclination center of mass distance, and the inclination value measured by each inclination data measuring point is obtained;

[0108] The correlation coefficient between the inclination centroid distance of each inclination data measurement point and the inclination value of each inclination data measurement point is calculated as the numerical distribution of the centroid of the inclination correlation distance building construction support structure.

[0109] The measuring point used to measure the axial force data is the axial force data measurement point, the coordinates of each axial force data measurement point are obtained, the distance between the coordinates of each axial force data measurement point and the coordinates of the center of mass of the building construction support structure is obtained as the axial force center of mass distance, and the axial force value measured at each axial force data measurement point is obtained;

[0110] The correlation coefficient between the axial force centroid distance of each axial force data measurement point and the axial force value of each axial force data measurement point is calculated as the numerical distribution of the centroid of the axial force correlation distance building construction support structure.

[0111] The absolute value of the difference between the numerical distribution of the inclination angle associated distance from the center of mass of the building construction support structure and the numerical distribution of the axial force associated distance from the center of mass of the building construction support structure is used to represent the numerical distribution of the inclination angle and the axial force associated distance from the center of mass of the building construction support structure.

[0112] The calculation method of the numerical distribution of the displacement and strain correlation distance from the center of mass of the building construction support structure is:

[0113] The measuring point used to measure the displacement data is the displacement data measurement point, the coordinates of each displacement data measurement point are obtained, the distance between the coordinates of each displacement data measurement point and the coordinates of the center of mass of the building construction support structure is obtained as the displacement center of mass distance, and the displacement value measured by each displacement data measurement point is obtained;

[0114] The displacement centroid distance of each displacement data measurement point and the correlation coefficient between the displacement values ​​of each displacement data measurement point are calculated as the numerical distribution of the centroid of the displacement correlation distance building construction support structure.

[0115] The measuring point used to measure the strain data is the strain data measuring point, the coordinates of each strain data measuring point are obtained, the distance between the coordinates of each strain data measuring point and the coordinates of the centroid of the building construction support structure is obtained as the strain centroid distance, and the strain value measured at each strain data measuring point is obtained;

[0116] The strain centroid distance of each strain data measurement point and the correlation coefficient between the strain values ​​of each strain data measurement point are calculated as the numerical distribution of the centroid of the strain correlation distance building construction support structure.

[0117] Then, the absolute value of the difference between the numerical distribution of the displacement-related distance from the mass center of the building construction support structure and the numerical distribution of the strain-related distance from the mass center of the building construction support structure is used to represent the numerical distribution of the displacement-strain related distance from the mass center of the building construction support structure.

[0118] The numerical distribution of the correlation distance between the inclination angle and the axial force and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure are added together, and the sum is used to judge the safety status of the building construction support structure.

[0119] Furthermore, a sampling time is set, and values ​​for judging the safety status of the building construction support structure are obtained multiple times within the sampling time, and the growth rates of the values ​​of the safety status at different sampling times over time are compared, and the safety status is judged by the growth rate of the values ​​of the safety status over time. The specific method is:

[0120] Set the sampling time and sampling frequency, obtain the safety status value multiple times, and calculate the growth rate of the safety status value between each adjacent sampling time point;

[0121] The safety status is determined by analyzing the changing trend of the growth rate and / or by setting a growth rate threshold.

[0122] In some embodiments, a fixed sampling time interval T is set (e.g., every 5 seconds, 10 seconds, or 30 seconds), and the safety status value of the support structure is collected multiple times during this time. The collection frequency f (e.g., 1 Hz, 2 Hz) within each sampling time T is set, that is, the number of times data is collected within T.

[0123] In each sampling time T, the safety status value S(t) of the building construction support structure is calculated and recorded through sensor data, and the safety status value at each sampling time point is recorded. The growth rate of the safety status value between each adjacent sampling time point is calculated, and the growth rate = increment / original total amount * 100%. The growth rate in multiple sampling time periods is analyzed to observe its change trend over time. The average value, standard deviation, maximum value, minimum value and other statistics of the growth rate can be calculated as a baseline, or a growth rate change curve can be drawn.

[0124] The safety status can be judged by analyzing the changing trend of the growth rate. For example, if the growth rate gradually increases over time, it may indicate that the risk of the supporting structure is increasing; if the growth rate remains stable or decreases, it means that the supporting structure may be in a relatively safe state.

[0125] The growth rate of the value of the environmental data in the sampling time interval may also be recorded, and the Baseline may be adjusted using the growth rate of the value of the environmental data in the sampling time interval as a weight.

[0126] A growth rate threshold can be set to distinguish different risk levels. If the growth rate in a certain period exceeds the threshold, it indicates that there is a potential risk. According to changes in the construction environment or the needs of different stages, the growth rate threshold can be adjusted in real time to ensure the accuracy of risk judgment.

[0127] In one implementation example, the mathematical expectation of the growth rate of the safety status value between each adjacent sampling time point is calculated, or the predicted value of the next moment in the future is predicted according to the sequence law. The larger the Status, the closer the support system is to an unstable state, and immediate measures need to be taken. Different thresholds can be set to divide the risk level. For example:

[0128] 0≤Status<0.5*Baseline: Low risk, the system is in a safe state.

[0129] 0.5*Baseline≤Status≤1.0*Baseline: Medium risk, requiring monitoring and attention.

[0130] Status>1.0*Baseline: High risk, safety measures must be taken immediately, and it is judged that an early warning signal is automatically issued and / or the operation of the processing program for controlling the components for adding support structures, adjusting load distribution and / or suspending construction operations is automatically triggered.

[0131] Among them, the functional components that the handler can control may include:

[0132] Add support structure: Based on real-time monitoring data, automatically recommend or execute adding support structure at specific locations to improve overall stability.

[0133] Adjust load distribution: By changing the load distribution, stress concentration in specific areas can be reduced, thereby reducing risks.

[0134] Suspend construction operations: When necessary, the system will automatically suspend construction and wait for the next safety assessment and manual intervention.

[0135] In an embodiment of the method of the present invention, the method for determining the safety status of the building construction support structure according to the physical data may include:

[0136] Solution 1: Use artificial intelligence algorithms;

[0137] Solution 2: Calculate the numerical distribution of the correlation between the inclination angle and the axial force from the center of mass of the building construction support structure, combined with the numerical distribution of the correlation between the displacement and the strain from the center of mass of the building construction support structure;

[0138] Solution 3: Calculate the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the construction support structure, and perform suboptimal processing based on the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the construction support structure.

[0139] The method for determining the safety status of the building construction support structure in the scheme 1 includes:

[0140] After preprocessing the physical data, serializing the preprocessed data;

[0141] The serialized data is input into the long short-term memory network, which is a recurrent neural network structure with an attention mechanism.

[0142] The LSTM network is used as a multi-task learning model to predict risk parameters of various types of physical data respectively;

[0143] The risk parameters of various physical data are integrated to generate an overall construction risk index as a basis for judging the safety status of the building construction support structure;

[0144] Among them, in the multi-task output layer, a multivariate loss function is designed, which includes the deviation risk of single physical data and the error of the overall risk index. The optimization goal of the multivariate loss function is to improve the risk prediction accuracy of each construction stage.

[0145] The method for determining the safety status of the building construction support structure in the second solution includes:

[0146] Calculate the absolute value of the difference between the inclination centroid distance ratio and the inclination value ratio of each inclination data measurement point as the value of the centroid of the inclination associated distance building construction support structure, and add up the sum as the value distribution of the centroid of the inclination associated distance building construction support structure;

[0147] The absolute value of the difference between the axial force centroid distance ratio and the axial force value ratio of each axial force data measurement point is calculated as the value of the centroid of the axial force associated distance building construction support structure, and the accumulated sum is used as the numerical distribution of the centroid of the axial force associated distance building construction support structure;

[0148] The absolute value of the difference between the displacement centroid distance ratio and the displacement value ratio of each displacement data measurement point is calculated as the value of the centroid of the displacement associated distance building construction support structure, and the accumulated sum is used as the numerical distribution of the centroid of the displacement associated distance building construction support structure;

[0149] The absolute value of the difference between the strain centroid distance ratio and the strain value ratio of each strain data measurement point is calculated as the value of the centroid of the strain-related distance building construction support structure, and the accumulated sum is used as the value distribution of the centroid of the strain-related distance building construction support structure;

[0150] The sum obtained by adding the numerical distribution of the correlation distance between the inclination angle and the axial force and the numerical distribution of the correlation distance between the displacement and the strain to the centroid of the building construction support structure is used as a basis for judging the safety status of the building construction support structure.

[0151] The method for determining the safety status of the building construction support structure in the scheme 3 includes:

[0152] Calculate the correlation coefficient between the inclination centroid distance and the inclination value of each inclination data measurement point as the numerical distribution of the centroid of the inclination correlation distance building construction support structure;

[0153] Calculate the correlation coefficient between the axial force centroid distance and the axial force value of each axial force data measurement point as the numerical distribution of the axial force correlation distance to the centroid of the building construction support structure;

[0154] Calculate the correlation coefficient between the displacement centroid distance and the displacement value of each displacement data measurement point as the numerical distribution of the centroid of the displacement correlation distance building construction support structure;

[0155] Calculate the correlation coefficient between the strain centroid distance and the strain value of each strain data measurement point as the value distribution of the centroid of the strain correlation distance building construction support structure;

[0156] The absolute value of the difference between the numerical distribution of the correlation distance between the inclination angle and the axial force and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is added together, and the sum obtained is used as a basis for judging the safety status of the building construction support structure.

[0157] In the application site of building construction support structure detection, a series of test items were conducted to comprehensively test and compare the actual effects of Scheme 1, Scheme 2 and Scheme 3:

[0158] In terms of accuracy test items, since Solution 1 uses deep learning algorithms (such as LSTM and attention mechanism), it has a good fitting ability for complex nonlinear relationships. However, in the highly specific and data-limited scenario of building support structure construction, model training may be insufficient, resulting in lower accuracy than expected. Solution 2 can more directly reflect the physical state of the support structure through the precise calculation of the correlation distance and center of mass of the inclination angle, axial force, displacement and strain, so it has higher accuracy. Solution 3 also relies on physical calculations, but compared with the optimal processing of Solution 2, Solution 3 uses suboptimal processing methods such as correlation coefficients, and its accuracy is slightly lower than that of Solution 2.

[0159] In terms of real-time performance, the deep learning model requires a lot of computing resources and a long inference time, which leads to poor real-time performance of Solution 1. Solution 2 can quickly process sensor data through relatively simple mathematical calculations and has better real-time performance. The calculation method of Solution 3 is simpler than that of Solution 2, so it performs better in real-time performance.

[0160] In terms of reliability, the deep learning model of Solution 1 may be unstable when facing new construction environments or abnormal sensor data, which affects reliability. Solution 2 performed stably in multiple test environments, and the calculation method had a high tolerance to data fluctuations and good reliability. The correlation coefficient calculation method of Solution 3 maintained a certain consistency in different environments, and its reliability was slightly lower than that of Solution 2.

[0161] In the adaptability test, the AI ​​algorithm of Solution 1 has a certain adaptability and can adapt to different construction environments through retraining, but it requires a lot of data support. Solution 2 performs stably in different construction scenarios and can adapt well to different conditions by adjusting weights and thresholds. Solution 3 is slightly more adaptable than Solution 2 because its calculation process is more general.

[0162] In terms of interpretability, the deep learning model of Solution 1 is often a "black box" model, which is difficult to provide intuitive explanations for users. Solution 2 is based on physical model calculations, and the results are easy to understand and have strong interpretability. The physical calculation method of Solution 3 also has good interpretability, but its complexity is slightly higher than that of Solution 2.

[0163] In terms of resource consumption, Solution 1 has more, and the deep learning model requires high computing resources, especially in real-time reasoning, which places greater demands on CPU / GPU. Solution 2 has a simple calculation method and consumes less computing resources. Solution 3 has a simpler calculation process and consumes the least resources.

[0164] In terms of scalability, the deep learning model of Solution 1 can handle larger data sets and sensors, but the training and inference time will increase accordingly. The calculation method of Solution 2 can be easily expanded to more sensor data and larger construction projects. The simplified calculation method of Solution 3 is slightly better than Solution 2 in scalability.

[0165] In terms of fault handling capabilities, the deep learning model in Solution 1 may be sensitive to sensor failures or data loss, and its processing capabilities are limited. Solution 2 can maintain good performance in the case of data loss or abnormalities by calculating the correlation distance.

[0166] The correlation coefficient calculation method of Solution 3 has a higher tolerance for abnormal data and a slightly better fault handling capability.

[0167] In terms of economic efficiency, the development, training and maintenance costs of the model in Solution 1 are relatively high, especially when specialized hardware support is required, and the cost-effectiveness is relatively low. Solution 2 is based on physical computing methods, with lower development and maintenance costs and better economic efficiency. Solution 3 is slightly more economical than Solution 2 because the calculation process is simpler.

[0168] In summary, although Scheme 1 uses artificial intelligence algorithms and has potential advantages in processing complex data patterns, its high resource consumption, low real-time performance and poor interpretability limit its application in the construction of building support structures. Scheme 2 provides high-precision and high-reliability risk assessment through precise physical model calculations, and can adapt well to different construction scenarios, with low resource consumption and good user feedback, so it has the best overall effect. Scheme 3 simplifies some steps in the calculation method, resulting in slightly lower accuracy than Scheme 2, but it performs slightly better in terms of real-time performance and economy, so its comprehensive score is only slightly lower than Scheme 2.

[0169] The theoretical basis of Scheme 2 can be traced back to the combined application of classical mechanics, center of mass analysis and statistics: the center of mass represents the center point of the mass distribution of an object and reflects the stability of the structure. In the construction of building support structures, the center of mass position of the structure is closely related to its stress state and deformation degree. Scheme 2 can accurately evaluate the stability and risk of the structure by correlating the inclination, axial force, displacement and strain data with the center of mass. By calculating the correlation distance of the inclination, axial force, displacement and strain data relative to the center of mass, Scheme 2 can quantify the impact of each sensor data on the safety of the structure. This method combines geometric analysis with correlation analysis in statistics to ensure the scientificity and accuracy of the calculation. The ratio calculation (such as the ratio of the inclination center of mass distance, the ratio of the axial force center of mass distance, etc.) and the absolute value calculation in Scheme 2 help to eliminate the dimensional differences between different sensor data, unify the scale of risk assessment, and make the final assessment results more reasonable and reliable.

[0170] Solution 2 successfully solved several key technical problems in the construction of building support structures through its unique methodology:

[0171] First, the inclination angle, axial force, displacement and strain data in the construction of building support structures come from different sources and have different dimensions. How to uniformly process these multi-source data and conduct a comprehensive evaluation is a technical difficulty. Solution 2 successfully achieves the unified processing of multi-source data through the calculation of correlation distance and ratio, ensuring the consistency of the evaluation results.

[0172] Second, in the construction of building support structures, real-time performance and accuracy are usually mutually constrained. An overly complex algorithm may provide high accuracy but poor real-time performance, while a simple algorithm may have good real-time performance but insufficient accuracy. Solution 2 achieves an effective balance between real-time performance and accuracy by combining physical models and geometric calculations.

[0173] Third, decisions on construction sites often need to be based on an intuitive understanding of risks. Traditional black box algorithms (such as AI algorithms) lack interpretability and are difficult for construction workers to trust. The physical model calculation method of Solution 2 not only provides a high-precision risk assessment, but also clearly explains the source of risk, improving the operability of decision-making.

[0174] In some embodiments, the intelligent monitoring and early warning system for a building construction support structure runs on any computing device such as a desktop computer, a laptop computer, a PDA or a cloud data center. The computing device includes: a processor, a memory and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the intelligent monitoring and early warning method for a building construction support structure are implemented. The executable system may include, but is not limited to, a processor, a memory, and a server cluster.

[0175] An embodiment of the present invention provides an intelligent monitoring and early warning system for a building construction support structure, such as Figure 2 As shown, an intelligent monitoring and early warning system for a building construction support structure of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above-mentioned intelligent monitoring and early warning method embodiment for a building construction support structure when executing the computer program, and the processor executes the computer program to run in the monitoring unit of the following system, which is used to collect physical data related to the construction support structure in real time, wherein the physical data includes inclination data, axial force data, displacement data, strain data, and environmental data;

[0176] A calculation unit, for determining the safety status of the building construction support structure according to the physical data;

[0177] The early warning unit is used to determine whether to automatically issue an early warning signal by judging the safety status, and / or determine whether to automatically trigger the operation of a processing program for controlling the addition of a supporting structure, adjusting the load distribution and / or suspending the construction operation.

[0178] Among them, in order to better unify the numerical linear relationship and probabilistic connection between physical quantities of different units of measurement, different physical quantities can be dimensionally processed.

[0179] Among them, preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0180] The intelligent monitoring and early warning system for a construction support structure can be run on computing devices such as desktop computers, laptops, PDAs, and cloud data centers. The intelligent monitoring and early warning system for a construction support structure includes, but is not limited to, a processor and a memory. Those skilled in the art will understand that the example is merely an example of an intelligent monitoring and early warning method, system, and device for a construction support structure, and does not constitute a limitation on an intelligent monitoring and early warning method, system, and device for a construction support structure. It may include more or fewer components than the example, or a combination of certain components, or different components. For example, the intelligent monitoring and early warning system for a construction support structure may also include input and output devices, network access devices, buses, and the like.

[0181] The present invention also provides an electronic device, a readable storage medium and a computer program product:

[0182] An electronic device comprises: 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 so that the at least one processor can execute the intelligent monitoring and early warning method for a building construction support structure and the methods of each step therein.

[0183] A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the intelligent monitoring and early warning method for a building construction support structure and the methods of the various steps therein.

[0184] A computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the intelligent monitoring and early warning method of a building construction support structure and the methods of each step therein.

[0185] Wherein, electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0186] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0187] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0188] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0189] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0190] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0191] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0192] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete component gate circuits or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the intelligent monitoring and early warning system for a building construction support structure, and uses various interfaces and lines to connect the various sub-areas of the intelligent monitoring and early warning system for a building construction support structure.

[0193] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the intelligent monitoring and early warning method, system and device of the building construction support structure by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0194] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the present disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document is not limited here.

[0195] The present invention provides an intelligent monitoring and early warning method, system and equipment for a construction support structure, wherein multiple sensors arranged at the construction site of the construction support structure are used to collect physical data related to the construction support structure in real time, and the physical data include inclination data, axial force data, displacement data, strain data and environmental data; the safety state of the construction support structure is judged according to the physical data; by judging the safety state, it is judged whether to automatically issue an early warning signal and / or whether to automatically trigger the operation of a processing program for controlling the addition of support structures, adjusting load distribution and / or suspending construction operations. The method can intelligently judge the risk trend and adjust the early warning threshold in real time through an adaptive algorithm, so that the system can maintain efficient and accurate risk judgment under different construction stages and environmental conditions.

[0196] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent monitoring and early warning method for a building construction support structure, characterized in that: The method comprises: By arranging a plurality of sensors on the construction support structure, including an inclination sensor, an axial force sensor, a displacement sensor, a strain sensor and an environmental monitoring sensor, physical data related to the construction support structure are collected in real time, and the physical data include inclination data, axial force data, displacement data and strain data; judging the safety state of the construction support structure according to the physical data, judging whether to automatically issue an early warning signal and / or judging whether to automatically trigger the operation of a processing program for controlling the addition of a support structure, adjusting load distribution and / or suspending construction operations by judging the safety state; The method for judging the safety status of the building construction support structure according to the physical data includes: Calculate the absolute value of the difference between the inclination centroid distance ratio and the inclination value ratio of each inclination data measurement point as the value of the centroid of the inclination associated distance construction support structure, and add up the accumulated sum as the numerical distribution of the centroid of the inclination associated distance construction support structure; calculate the ratio of the inclination centroid distance of each inclination data measurement point to the maximum value of the inclination centroid distances of all inclination data measurement points as the inclination centroid distance ratio of the inclination data measurement point; calculate the ratio of the inclination value of each inclination data measurement point to the maximum value of the inclination values ​​of the inclination data measurement point as the inclination value ratio of the inclination data measurement point; Calculate the absolute value of the difference between the axial force centroid distance ratio and the axial force value ratio of each axial force data measurement point as the value of the centroid of the axial force associated distance building construction support structure, and add up the accumulated sum as the numerical distribution of the centroid of the axial force associated distance building construction support structure; calculate the ratio of the axial force centroid distance of each axial force data measurement point to the maximum value of the axial force centroid distance of the axial force data measurement point as the axial force centroid distance ratio of the axial force data measurement point; calculate the ratio of the axial force value of each axial force data measurement point to the maximum value of the axial force value of the axial force data measurement point as the axial force value ratio of the axial force data measurement point; Calculate the absolute value of the difference between the displacement centroid distance ratio and the displacement value ratio of each displacement data measurement point as the value of the centroid of the displacement-related distance building construction support structure, and add up the accumulated sum as the numerical distribution of the centroid of the displacement-related distance building construction support structure; calculate the ratio of the displacement centroid distance of each displacement data measurement point to the maximum value of the displacement centroid distance of the displacement data measurement point as the displacement centroid distance ratio of the displacement data measurement point; calculate the ratio of the displacement value of each displacement data measurement point to the maximum value of the displacement value of the displacement data measurement point as the displacement value ratio of the displacement data measurement point; Calculate the absolute value of the difference between the strain centroid distance ratio and the strain value ratio of each strain data measurement point as the value of the strain-associated distance to the centroid of the building construction support structure, and add up the accumulated sum as the numerical distribution of the strain-associated distance to the centroid of the building construction support structure; calculate the ratio of the strain centroid distance of each strain data measurement point to the maximum value of the strain centroid distance of the strain data measurement point as the strain centroid distance ratio of the strain data measurement point; calculate the ratio of the strain value of each strain data measurement point to the maximum value of the strain value of the strain data measurement point as the strain value ratio of the strain data measurement point; The sum obtained by adding the numerical distribution of the correlation distance between the inclination angle and the axial force and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is used to judge the safety state of the building construction support structure.

2. The intelligent monitoring and early warning method for a building construction support structure according to claim 1 is characterized in that: in, Each of the physical data has multiple different measurement points: the measurement points of the inclination data are the key nodes in the support system of the building construction support structure, including the joints of the steel pipes, the top and bottom of the scaffolding; The measurement points of the axial force data are located on the supporting rods and steel pipe columns of the building construction support structure; The measurement points of displacement data are located at the bottom of the building construction support structure, key support points and the deformation area of ​​the overall structure; The strain data are measured at the connection points of the structure in the building construction support structure.

3. The intelligent monitoring and early warning method for a building construction support structure according to claim 1 is characterized in that: in, The method for judging the safety status of the building construction support structure according to the physical data is either: Calculate the numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure, and combine the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure to determine the safety status of the building construction support structure; Setting a sampling time, obtaining values ​​for judging the safety status of the building construction support structure multiple times within the sampling time, comparing the increase of the values ​​for judging the safety status of the building construction support structure at different sampling times over time, and judging the safety status according to the increase of the values ​​over time; Among them, the centroid of the building construction support structure represents the centroid of the building construction support structure.

4. An intelligent monitoring and early warning method for a building construction support structure according to any one of claims 1 or 3, characterized in that: in, The numerical distribution of the correlation distance between the inclination angle and the axial force and the centroid of the building construction support structure is calculated, and the numerical distribution of the correlation distance between the displacement and the strain and the centroid of the building construction support structure is combined to obtain a method for judging the safety state of the building construction support structure: Calculate the correlation coefficient between the inclination centroid distance and the inclination value of each inclination data measurement point as the numerical distribution of the centroid of the inclination-related distance construction support structure; calculate the correlation coefficient between the axial force centroid distance and the axial force value of each axial force data measurement point as the numerical distribution of the centroid of the axial force-related distance construction support structure; Calculate the correlation coefficient between the displacement centroid distance and the displacement value of each displacement data measurement point as the numerical distribution of the centroid of the displacement-related distance construction support structure; calculate the correlation coefficient between the strain centroid distance and the strain value of each strain data measurement point as the numerical distribution of the centroid of the strain-related distance construction support structure; add the absolute value of the difference between the numerical distribution of the centroid of the construction support structure of the inclination angle and axial force correlation distance and the numerical distribution of the centroid of the construction support structure of the displacement and strain correlation distance, and the sum is used to judge the safety status of the construction support structure.

5. The intelligent monitoring and early warning method for a building construction support structure according to claim 3 is characterized in that: in, The sampling time is set, and the values ​​for judging the safety status of the building construction support structure are obtained multiple times within the sampling time. The growth rates of the safety status values ​​at different sampling times over time are compared, and the safety status is judged by the growth rate of the safety status values ​​over time. The specific method is as follows: Set the sampling time and sampling frequency, obtain the safety status value multiple times, and calculate the growth rate of the safety status value between each adjacent sampling time point; The safety status is determined by analyzing the changing trend of the growth rate and / or by setting a growth rate threshold.

6. An intelligent monitoring and early warning system for a building construction support structure, characterized in that: The intelligent monitoring and early warning system for a building construction support structure runs in any computing device such as a desktop computer, a laptop computer or a cloud data center. The computing device includes: a processor, a memory and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the intelligent monitoring and early warning method for a building construction support structure as described in any one of claims 1 to 5 are implemented.

7. An electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed 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 method according to any one of claims 1 to 5.

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