A safety monitoring method and system for building construction

Abstract

The present invention relates to the field of data processing technology, and in particular to a safety monitoring method and system for construction. The method comprises the following steps: collecting a plurality of wind speed data, obtaining local data of each wind speed data, obtaining the noise performance level of each wind speed data based on the local data, correcting the noise performance level of each wind speed data, and obtaining the corrected noise performance level of each wind speed data; obtaining a final filtering window for each wind speed data based on the corrected noise performance level of each wind speed data, filtering each wind speed data based on the final filtering window of each wind speed data, and obtaining all filtered wind speed data; predicting the wind speed data at the next sampling moment based on all filtered wind speed data, and monitoring the safety of construction based on the wind speed of the wind speed data at the next sampling moment. The present invention provides more accurate prediction results.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a safety monitoring method and system for building construction. Background Art

[0002] In construction safety monitoring, monitoring wind speed is extremely important, because strong winds may cause construction workers at high altitudes to lose their balance, increasing the risk of falling, and may also cause unsecured building materials or construction equipment to fall, posing a threat to ground personnel and equipment; some large construction equipment such as tower cranes and construction elevators may be damaged under strong winds, so real-time monitoring of wind speed at construction sites plays a significant role in construction safety; HTFE (Time Series Adaptive Forecasting Method Based on Historical Trends and Forecast Errors) is a method for continuous prediction of continuous states by determining the trend type of recent time series. The present invention uses this algorithm to predict wind speed data at construction sites in real time, which facilitates timely prediction of risks and improves construction safety.

[0003] Zheng Junbao and Zhang Xu, Research on Time Series Prediction Methods Based on Historical Trends and Prediction Errors, Computer Age, Issue 9, 2023. In this paper, the principle and process of the HTFE algorithm are proposed.

[0004] By inputting the collected wind speed data into the HTFE algorithm and predicting the wind speed data at the next sampling moment, the safety of the current construction can be judged. However, construction sites are usually accompanied by various mechanical equipment and construction operations, that is, environmental noise, which will cause noise in the collected wind speed data. If the collected wind speed data is directly input into the HTFE algorithm to predict the wind speed data at the next sampling moment, the noisy data will lead to inaccurate prediction results. Summary of the Invention

[0005] In order to solve the technical problem that when the collected wind speed data is directly input into the HTFE algorithm to predict the wind speed data at the next sampling moment, the noise data in the collected wind speed data leads to inaccurate prediction results, the present invention provides a safety monitoring method and system for construction.

[0006] In a first aspect, the present invention provides a safety monitoring method for construction, which adopts the following technical solution:

[0007] A safety monitoring method for building construction, comprising the steps of:

[0008] Collect a number of wind speed data; obtain the noise performance level of each wind speed data; obtain the corrected noise performance level of each wind speed data: , Represents the degree of corrected noise performance of the i-th wind speed data; Represents the noise performance of the i-th wind speed data; Reference wind speed data representing wind speed data; Represents the variance of the wind speed difference sequence between the i-th wind speed data and its j-th reference wind speed data; Represents the variance of the wind speed direction difference sequence between the i-th wind speed data and its j-th reference wind speed data;

[0009] Get the final filter window for each wind speed data , represents the final filtering window of the i-th wind speed data; Represents the preset mean filter window; Represents the floor symbol;

[0010] Using a mean filtering algorithm, each wind speed data is filtered according to the final filtering window to obtain all filtered wind speed data; based on all filtered wind speed data, the wind speed data at the next sampling moment is predicted, and the safety of the building construction is monitored.

[0011] The innovation of the present invention lies in analyzing the numerical variation characteristics of wind speed data, obtaining the noise performance degree of each wind speed data, and subsequently correcting the noise performance degree of the wind speed data according to the relationship between the wind speed data collected by different anemometers to obtain the corrected noise performance degree of each wind speed data. Based on the corrected noise performance degree, the preset mean filter window is corrected and filtered. For wind speed data with a greater noise performance degree, a larger filter window is used to improve the filtering effect, and for wind speed data with a smaller noise performance degree, a smaller filter window is used to improve the filtering efficiency. This can effectively reduce the impact of noise on wind speed data caused by equipment failure, environmental factors, etc., and finally use the filtered wind speed data to predict the wind speed data at the next sampling moment, which can significantly improve the accuracy of the algorithm prediction, and then the safety of building construction can be monitored based on accurate prediction results.

[0012] Preferably, obtaining the noise performance level of each wind speed data includes:

[0013] Obtaining the fitting value of the wind speed direction and the fitting value of the wind speed magnitude of the local data of each wind speed data;

[0014] ;

[0015] Where, Represents the noise performance of the i-th wind speed data; Represents the number of local data of the i-th wind speed data; Represents the fitted value of the wind speed magnitude of the jth local data of the i-th wind speed data; represents the actual value of the wind speed of the jth local data of the i-th wind speed data; || represents the absolute value symbol; tanh() represents the normalization function; Represents the fitted value of the wind speed direction of the jth local data of the i-th wind speed data; Represents the actual value of the wind speed direction of the jth local data of the i-th wind speed data.

[0016] It is convenient to adaptively obtain the filtering window of each wind speed data according to the noise performance level of each wind speed data.

[0017] Preferably, the step of obtaining the fitting value of the wind speed direction and the fitting value of the wind speed magnitude of the local data of each wind speed data includes:

[0018] The number of data is preset m, and each wind speed data and the m wind speed data before and after each wind speed data in the wind speed data sequence to which it belongs are recorded as local data of each wind speed data; the wind speed magnitude of the local data of each wind speed data is curve fitted using the least squares method to obtain the fitting value of the wind speed magnitude of each local data of each wind speed data; the sine value of the wind speed direction of all local data of each wind speed data is curve fitted using the least squares method to obtain the fitting value of the wind speed direction of each local data of each wind speed data.

[0019] This facilitates the subsequent reflection of the noise situation of the wind speed data based on the difference between the fitted value and the true value of the wind speed data.

[0020] Preferably, obtaining the reference wind speed data of the wind speed data includes:

[0021] The other wind speed data at the sampling moment corresponding to each wind speed data is recorded as the reference wind speed data of each wind speed data.

[0022] This facilitates subsequent analysis of the wind speed data and its reference wind speed data, and corrects the degree of noise impact on the wind speed data.

[0023] Preferably, obtaining the wind speed difference sequence between the i-th wind speed data and the j-th reference wind speed data includes:

[0024] For the jth reference wind speed data of the i-th wind speed data, the absolute value of the difference in wind speed between the k-th local data of the i-th wind speed data and the k-th local data of the j-th reference wind speed data is recorded as the wind speed difference of the k-th local data; the sequence composed of the wind speed differences of all local data of the i-th wind speed data is recorded as the wind speed difference sequence between the local data of the i-th wind speed data and its j-th reference wind speed data.

[0025] Preferably, obtaining the wind speed direction difference sequence between the i-th wind speed data and its j-th reference wind speed data includes:

[0026] The absolute value of the difference between the sine values of the wind speed direction between the kth local data of the i-th wind speed data and the kth local data of the j-th reference wind speed data is recorded as the wind speed direction difference of the k-th local data; the sequence composed of the wind speed direction differences of all local data of the i-th wind speed data is recorded as the wind speed direction difference sequence between the local data of the i-th wind speed data and its j-th reference wind speed data.

[0027] Preferably, predicting the wind speed data at the next sampling moment based on all filtered wind speed data and monitoring the safety of the construction includes:

[0028] The error factor of the preset prediction interval is T, the number of historical trend observation steps is h, and the prediction error factor is T1. All filtered wind speed data are input into the HTFE algorithm to obtain the predicted value of the wind speed data at the next sampling moment. If the wind speed of the wind speed data at the next sampling moment is less than 5m / s, all construction projects can proceed normally; when the wind speed is between 5-7.5m / s, the building materials on the site need to be covered, and other projects can continue; when the wind speed is between 7.5-10m / s, high-risk operations such as high-altitude operations should be suspended; when the wind speed is greater than 10m / s, all construction activities should be stopped.

[0029] The accuracy of algorithm predictions is improved, and the safety of building construction can be monitored based on accurate prediction results.

[0030] In a second aspect, the present invention provides a safety monitoring system for construction, which adopts the following technical solution:

[0031] A safety monitoring system for building construction includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned safety monitoring method for building construction is implemented.

[0032] By adopting the above technical solution, the above-mentioned safety monitoring method for construction is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0033] The present invention has the following technical effects: the purpose of the present invention is to analyze the numerical change characteristics of wind speed data, obtain the noise performance level of each wind speed data, and subsequently correct the noise performance level of the wind speed data according to the relationship between the wind speed data collected by different anemometers to obtain the corrected noise performance level of each wind speed data, and perform post-filtering on the preset mean filter window based on the corrected noise performance level. For wind speed data with a greater noise performance level, a larger filter window is used to improve the filtering effect, and for wind speed data with a smaller noise performance level, a smaller filter window is used to improve the filtering efficiency, which can effectively reduce the impact of noise on wind speed data caused by equipment failure, environmental factors, etc., and finally use the filtered wind speed data to predict the wind speed data at the next sampling moment, which can significantly improve the accuracy of the algorithm prediction, and then the safety of the construction can be monitored based on the accurate prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding numbers represent the same or corresponding parts.

[0035] Figure 1 This is a flow chart of a method for safety monitoring of building construction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0037] It should be understood that when the terms "first," "second," and the like are used in the claims, description, and drawings of the present invention, they are merely used to distinguish between different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the description and claims of the present invention indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0038] The embodiment of the present invention discloses a safety monitoring method for building construction, referring to Figure 1 , including steps S1 to S4:

[0039] S1: Collect some wind speed data.

[0040] In an embodiment of the present invention, the preset sampling time is 2 seconds / time, and the data is collected for a total of two hours. Three anemometers are installed at the height positions of 10m, 20m, and 30m on the tower crane body, and three wind speed data are collected each time. Each wind speed data includes the wind speed magnitude and wind speed direction. The wind speed data collected under each anemometer are sorted to obtain each wind speed data sequence.

[0041] S2: Obtain local data of each wind speed data, and obtain the noise performance level of each wind speed data based on the local data.

[0042] It should be noted that the present invention judges the safety of the current construction by inputting the collected wind speed data into the HTFE algorithm and predicting the wind speed data at the next sampling moment. However, the construction site is usually accompanied by various mechanical equipment and construction operations, that is, environmental noise, which will cause noise in the collected wind speed data. If the collected wind speed data is directly input into the HTFE algorithm for prediction, the noise data will lead to inaccurate prediction results. Therefore, to address the above problem, the present invention needs to filter the collected wind speed data.

[0043] It should be noted that it is known that when an anemometer collects wind speed data at a construction site, it will be affected by various surrounding mechanical equipment and construction operations, such as the roar of an excavator, the stirring sound of a mixer, and the knocking sound of workers during construction, resulting in some data in the wind speed data deviating from the normal wind speed data change trend. Therefore, the degree of deviation of the wind speed data can be used to preliminarily obtain the noise performance degree of the wind speed data. The greater the degree of deviation, the stronger the noise performance degree; the wind speed data contains two indicators, namely wind speed magnitude and wind speed direction. Therefore, the deviation of the wind speed data is divided into wind speed magnitude deviation and wind speed direction deviation. The deviation of the wind speed magnitude of the wind speed data can directly perform curve fitting on the wind speed magnitude of all local data of the wind speed data, and reflect whether the wind speed magnitude of the wind speed data has deviated based on the fitting error of the wind speed magnitude; the deviation of the wind speed direction of the wind speed data can directly perform curve fitting on the sine value of the wind speed direction of all local data of the wind speed data, and reflect whether the wind speed direction of the wind speed data has deviated based on the fitting error of the wind speed direction.

[0044] In an embodiment of the present invention, each wind speed data and the m wind speed data before and after each wind speed data in the wind speed data sequence to which it belongs are recorded as local data of each wind speed data; in an embodiment of the present invention, the preset number of data m=7, in other embodiments, the implementer can preset the value of m according to the specific implementation method.

[0045] Use the least square method to perform curve fitting on the wind speed magnitude of the local data of each wind speed data to obtain the local wind speed magnitude curve of each wind speed data and obtain the fitting value of the wind speed magnitude of each local data of each wind speed data;

[0046] Using the least squares method to perform curve fitting on the sine values of the wind speed direction of all local data of each wind speed data, a local wind speed direction curve of each wind speed data is obtained, and a fitting value of the wind speed direction of each local data of each wind speed data is obtained;

[0047] Get the noise level of each wind speed data:

[0048] ;

[0049] Where, Represents the noise performance of the i-th wind speed data; Represents the number of local data of the i-th wind speed data; Represents the fitted value of the wind speed magnitude of the jth local data of the i-th wind speed data; represents the actual value of the wind speed of the jth local data of the i-th wind speed data; || represents the absolute value symbol; tanh() represents the normalization function; Represents the fitted value of the wind speed direction of the jth local data of the i-th wind speed data; The actual value of the wind speed direction of the jth local data representing the i-th wind speed data; The average difference between the actual value and the fitted value of the wind speed of all local data representing the wind speed data. The larger the value, the more the wind speed of the wind speed data has shifted, and the greater the noise level of the i-th wind speed data. The average difference between the actual value and the fitted value of the wind speed direction of all local data representing the wind speed data is larger, indicating that the wind speed direction of the wind speed data has shifted, and the noise performance of the i-th wind speed data is greater.

[0050] S3: Correct the noise performance level of each wind speed data to obtain the corrected noise performance level of each wind speed data; obtain the final filtering window of each wind speed data according to the corrected noise performance level of each wind speed data, filter each wind speed data based on the final filtering window of each wind speed data, and obtain all filtered wind speed data.

[0051] It should be noted that as the altitude increases, the air density decreases, the friction decreases, and the wind speed increases accordingly. Therefore, there is an error in the noise performance of each wind speed data. Since there are slight differences between the wind speed sizes of the wind speed data collected by anemometers installed at different altitudes, but the change trends of their wind speed sizes are consistent, and their wind speed directions are also consistent, the other wind speed data at the sampling moment corresponding to each wind speed data are used as the reference wind speed data of each wind speed data. According to the consistency of the wind speed size change of each wind speed data and its each reference wind speed data and the consistency of the wind speed direction change of each wind speed data and its each reference wind speed data, the noise performance of each wind speed data is corrected. The greater the consistency of the wind speed size change of the wind speed data and its each reference wind speed data and the consistency of the wind speed direction change, the smaller the noise performance of the wind speed data.

[0052] In the embodiment of the present invention, other wind speed data at the sampling moment corresponding to each wind speed data is recorded as reference wind speed data for each wind speed data.

[0053] For the jth reference wind speed data of the i-th wind speed data, the absolute value of the difference in wind speed between the k-th local data of the i-th wind speed data and the k-th local data of the j-th reference wind speed data is recorded as the wind speed difference of the k-th local data; the sequence composed of the wind speed differences of all local data of the i-th wind speed data is recorded as the wind speed difference sequence between the local data of the i-th wind speed data and its j-th reference wind speed data;

[0054] The absolute value of the difference between the sine values of the wind speed direction between the kth local data of the i-th wind speed data and the kth local data of the j-th reference wind speed data is recorded as the wind speed direction difference of the k-th local data; the sequence composed of the wind speed direction differences of all local data of the i-th wind speed data is recorded as the wind speed direction difference sequence between the local data of the i-th wind speed data and its j-th reference wind speed data.

[0055] Get the corrected noise representation for each wind speed data point:

[0056] ;

[0057] Where, Represents the degree of corrected noise performance of the i-th wind speed data; Represents the noise performance of the i-th wind speed data; Reference wind speed data representing wind speed data; Represents the variance of the wind speed difference sequence between the i-th wind speed data and its j-th reference wind speed data; Represents the variance of the wind speed direction difference sequence between the i-th wind speed data and its j-th reference wind speed data; The larger the value of , the smaller the consistency between the i-th wind speed data and the j-th reference wind speed data. At this time, the noise level of the i-th wind speed data is greater, and it is necessary to The value of is adjusted to be larger; The larger the value of , the smaller the consistency between the wind speed direction of the i-th wind speed data and its j-th reference wind speed data, indicating that the greater the noise performance of the i-th wind speed data, the more necessary it is to The value of is adjusted larger.

[0058] It should be further explained that when performing mean filtering on wind speed data, a larger filter window is required to improve the filtering effect for wind speed data with a greater degree of noise, and a smaller filter window is used to improve the filtering efficiency for wind speed data with a smaller degree of noise.

[0059] In this embodiment of the present invention, the final filtering window of each wind speed data is obtained:

[0060] ;

[0061] Where, represents the final filtering window of the i-th wind speed data; Represents the degree of corrected noise performance of the i-th wind speed data; Represents the preset mean filter window; Represents the floor symbol.

[0062] Use the mean filtering algorithm to filter each wind speed data according to the final filtering window of each wind speed data to obtain all filtered wind speed data.

[0063] S4: predicting the wind speed data at the next sampling moment based on all filtered wind speed data, and monitoring the safety of the building construction based on the wind speed of the wind speed data at the next sampling moment.

[0064] It should be noted that the wind speed data at the next sampling moment is predicted based on all filtered wind speed data.

[0065] In an embodiment of the present invention, the error factor of the preset prediction interval is T, the number of historical trend observation steps is h, and the prediction error factor is T1. According to the preset parameters of the HTFE algorithm, all filtered wind speed data are input into the HTFE algorithm to obtain the predicted value of the wind speed data at the next sampling moment. If the wind speed of the wind speed data at the next sampling moment is less than 5m / s, all construction projects can proceed normally; when the wind speed is between 5-7.5m / s, the building materials on the site need to be covered, and other projects can continue; when the wind speed is between 7.5-10m / s, high-risk operations such as high-altitude operations are suspended; when the wind speed is greater than 10m / s, all construction activities should be stopped.

[0066] In an embodiment of the present invention, the error factor of the preset prediction interval is T=0.5, the number of historical trend observation steps is h=3, and the prediction error factor is T1=0.1. In other embodiments, the implementer may preset the above parameter values according to the specific implementation situation.

[0067] An embodiment of the present invention further discloses a safety monitoring system for construction, comprising a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a safety monitoring method for construction according to the present invention is implemented.

[0068] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0069] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory, dynamic random access memory, static random access memory, enhanced dynamic random access memory, high bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of, accessible to, or connectable to the device.

[0070] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0071] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A safety monitoring method for building construction, characterized in that: Including steps: Collect a number of wind speed data; obtain the noise performance level of each wind speed data; Get the corrected noise performance level of each wind speed data: , Represents the degree of corrected noise performance of the i-th wind speed data; Represents the noise performance of the i-th wind speed data; Reference wind speed data representing wind speed data; Represents the variance of the wind speed difference sequence between the i-th wind speed data and its j-th reference wind speed data; Represents the variance of the wind speed direction difference sequence between the i-th wind speed data and its j-th reference wind speed data; Get the final filter window for each wind speed data , represents the final filtering window of the i-th wind speed data; Represents the preset mean filter window; Represents the floor symbol; Using a mean filtering algorithm, each wind speed data is filtered according to the final filtering window to obtain all filtered wind speed data; based on all filtered wind speed data, the wind speed data at the next sampling moment is predicted, and the safety of the building construction is monitored; The acquisition of the wind speed difference sequence between the i-th wind speed data and the j-th reference wind speed data includes: For the jth reference wind speed data of the i-th wind speed data, the absolute value of the difference in wind speed between the k-th local data of the i-th wind speed data and the k-th local data of the j-th reference wind speed data is recorded as the wind speed difference of the k-th local data; the sequence composed of the wind speed differences of all local data of the i-th wind speed data is recorded as the wind speed difference sequence between the local data of the i-th wind speed data and its j-th reference wind speed data; The acquisition of the wind speed direction difference sequence between the i-th wind speed data and the j-th reference wind speed data includes: The absolute value of the difference between the sine values of the wind speed direction between the kth local data of the i-th wind speed data and the kth local data of the j-th reference wind speed data is recorded as the wind speed direction difference of the k-th local data; the sequence composed of the wind speed direction differences of all local data of the i-th wind speed data is recorded as the wind speed direction difference sequence between the local data of the i-th wind speed data and its j-th reference wind speed data.

2. A safety monitoring method for construction according to claim 1, characterized in that: The noise performance level of each wind speed data is obtained, including: Obtaining the fitting value of the wind speed direction and the fitting value of the wind speed magnitude of the local data of each wind speed data; ； Where, Represents the noise performance of the i-th wind speed data; Represents the number of local data of the i-th wind speed data; Represents the fitted value of the wind speed magnitude of the jth local data of the i-th wind speed data; represents the actual value of the wind speed of the jth local data of the i-th wind speed data; || represents the absolute value symbol; tanh() represents the normalization function; Represents the fitted value of the wind speed direction of the jth local data of the i-th wind speed data; Represents the actual value of the wind speed direction of the jth local data of the i-th wind speed data.

3. A safety monitoring method for construction according to claim 2, characterized in that: The step of obtaining the fitting value of the wind speed direction and the fitting value of the wind speed magnitude of the local data of each wind speed data comprises: The number of data is preset m, and each wind speed data and the m wind speed data before and after each wind speed data in the wind speed data sequence to which it belongs are recorded as local data of each wind speed data; the wind speed magnitude of the local data of each wind speed data is curve fitted using the least squares method to obtain the fitting value of the wind speed magnitude of each local data of each wind speed data; the sine value of the wind speed direction of all local data of each wind speed data is curve fitted using the least squares method to obtain the fitting value of the wind speed direction of each local data of each wind speed data.

4. A safety monitoring method for construction according to claim 1, characterized in that: Acquisition of reference wind speed data of the wind speed data includes: The other wind speed data at the sampling moment corresponding to each wind speed data is recorded as the reference wind speed data of each wind speed data.

5. A safety monitoring method for construction according to claim 1, characterized in that: The wind speed data at the next sampling moment is predicted based on all filtered wind speed data, and the safety of the construction is monitored, including: The error factor of the preset prediction interval is T, the number of historical trend observation steps is h, and the prediction error factor is T1. All filtered wind speed data are input into the HTFE algorithm to obtain the predicted value of the wind speed data at the next sampling moment. If the wind speed of the wind speed data at the next sampling moment is less than 5m / s, all construction projects can proceed normally; when the wind speed is between 5-7.5m / s, the building materials on the site need to be covered, and other projects can continue; when the wind speed is between 7.5-10m / s, high-risk operations such as high-altitude operations should be suspended; when the wind speed is greater than 10m / s, all construction activities should be stopped.

6. A safety monitoring system for construction, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a safety monitoring method for building construction according to any one of claims 1 to 5 is implemented.

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