Construction process monitoring method based on IOT technology digital visualization platform

Abstract

The invention relates to a construction process monitoring method based on an IOT technology digital visualization platform, and belongs to the technical field of construction monitoring, and the method comprises the following steps: 1, obtaining the temperature, humidity and noise data of a construction site, and calculating the average value and discrete value of the temperature, humidity and noise data; 2, after the average value and discrete value of the temperature, humidity and noise are obtained each time, the proportion and density of the constant monitoring point, the fixed temporary monitoring point and the movable temporary monitoring point are determined to serve as proportion data and density data; 3, according to the proportion and density data, determining the layout position of the monitoring point location; 4, a constant monitor, a fixed temporary monitor and a movable temporary monitor are arranged at the arrangement positions, and the construction process is monitored; and 5, the control module receives data of the frequent monitoring point, the fixed temporary monitoring point and the movable temporary monitoring point, and instructs the fixed temporary monitoring point and the movable temporary monitoring point to start, stop or move.

Description

Technical Field

[0001] This invention belongs to the field of construction monitoring technology, specifically relating to a construction process monitoring method based on an IoT technology digital visualization platform. Background Technology

[0002] With the continuous development and popularization of Internet of Things (IoT) technology, more and more industries are beginning to utilize IoT technology to achieve intelligent management and monitoring. In the construction industry, IoT technology can be used to monitor the construction process in real time, improving construction quality and safety. Various safety risks exist during construction, such as working at heights and operating machinery. Traditional manual monitoring methods are insufficient to provide comprehensive coverage, easily leading to safety accidents. A digital visualization platform based on IoT technology can achieve comprehensive, real-time monitoring, promptly identifying and resolving safety hazards.

[0003] A common monitoring solution, such as the construction process monitoring method based on an IoT technology digital visualization platform disclosed in Chinese patent CN117934212A, relates to the field of construction monitoring technology. The method includes: acquiring map information of the target construction area; determining monitoring deployment points based on the map information and the specifications of the data acquisition terminals; deploying data acquisition terminals in the target construction area using IoT technology; collecting real-time monitoring data through the data acquisition terminals; conducting early warning assessments based on the real-time monitoring data; obtaining the current monitoring index; and sending it to the visualization platform. This enables refined management of the construction process, real-time monitoring of construction quality, timely detection and handling of construction quality problems, and improved construction efficiency and quality.

[0004] However, the above solutions only consider the deployment of fixed monitoring points. To better reflect actual conditions, such as in some scenarios where more monitors need to be deployed in hotspot areas, and fixed monitors are difficult to deploy in a timely manner, mobile monitoring points need to be considered. However, mobile monitoring points are more expensive than fixed monitoring points, and their location and number need to be adjusted according to new parameters. The above solutions do not specifically acquire and calculate data for the deployment of mobile monitoring points, resulting in insufficient monitoring effectiveness. Therefore, a construction process monitoring method based on an IoT technology digital visualization platform that incorporates mobile monitoring points and provides good monitoring results is needed. Summary of the Invention

[0005] To address the aforementioned problems in existing technologies, this invention provides a construction process monitoring method based on an IoT technology digital visualization platform, which incorporates mobile monitoring points and offers excellent monitoring results.

[0006] The objective of this invention can be achieved through the following technical solutions: A construction process monitoring method based on an IoT technology digital visualization platform includes the following steps: Step 1: Obtain temperature, humidity, and noise data at the construction site, and calculate the average and discrete values ​​of the temperature, humidity, and noise data; Step 2: After obtaining the average and discrete values ​​of temperature, humidity and noise each time, determine the ratio and density of constant monitoring points, fixed temporary monitoring points and mobile temporary monitoring points, as proportional data density data; Step 3: Determine the locations of monitoring points based on the ratio and density data; Step 4: Deploy permanent monitoring devices, fixed temporary monitoring devices, and mobile temporary monitoring devices at the deployment location to monitor the construction process; Step 5: The control module receives data from permanent monitoring points, fixed temporary monitoring points, and mobile temporary monitoring points, and instructs the fixed temporary monitoring points and mobile temporary monitoring points to start, stop, or move.

[0007] As a preferred embodiment of the present invention, step two further includes: increasing the density of constant monitoring points, fixed temporary monitoring points and mobile temporary monitoring points when the average values ​​of temperature, humidity and noise exceed the threshold, and increasing the proportion of fixed temporary monitoring points and mobile temporary monitoring points when the discrete values ​​of temperature, humidity and noise exceed the threshold.

[0008] As a preferred technical solution of the present invention, step one further includes: obtaining the temperature T, humidity S and noise Z at the construction site, and calculating the average values ​​TJ, SJ, ZJ and discrete values ​​TL, SL, ZL of the temperature, humidity and noise; step two further includes: setting the densities of constant monitoring points, fixed temporary monitoring points and mobile temporary monitoring points to M1, M2 and M3 respectively, where M1=TJ / TJ0+SJ / SJ0+ZJ / ZJ0, M2=a×M1, M3=a×c×M1, c is a pre-input correction coefficient, a=(TL / TL0+SL / SL0+ZL / ZL0) / 3, TJ0, SJ0 and ZJ0 are mean thresholds, and TL0, SL0 and ZL0 are discrete thresholds.

[0009] As a preferred technical solution of the present invention, after obtaining the temperature, humidity and noise data of the construction site in step one, the method further includes: storing the data in a database; the control module arranges the currently obtained data and the previously obtained data in reverse order of acquisition time, and calculates the difference between the rate of change of the average value and the rate of change of the dispersion; when the difference is greater than a set value, it determines whether the dispersion growth trend is positive or negative; when the dispersion is positively increasing, it lowers the dispersion threshold; when the dispersion is negatively increasing, it lowers the average threshold.

[0010] As a preferred technical solution of the present invention, in step one, the difference e between the rate of change of the average value and the rate of change of the dispersion is calculated. When e is greater than e0, the positive or negative trend of the dispersion growth is determined. When the dispersion is positively increasing, the dispersion threshold is reduced by A1 times. When the dispersion is negatively increasing, the average threshold is reduced by A2 times, where A1 = e / e0 × f1 and A2 = e / e0 × f2.

[0011] As a preferred technical solution of the present invention, step five further includes: the control module acquires the concentration of temperature, humidity and noise data in space, and when the concentration of a certain place in the construction site exceeds the threshold, it is marked as a hot spot area, and then the mobile temporary monitor is concentrated in the hot spot area.

[0012] As a preferred technical solution of the present invention, step five further includes: the control module acquires the magnitude and coordinates of temperature, humidity and noise data, and for each data coordinate point, determines whether the sum of data values ​​within a fixed range around it exceeds a threshold; if so, it is determined to be a hotspot area.

[0013] As a preferred technical solution of the present invention, step one further includes: after calculating the average change rate and the dispersion change rate, when the sum of the absolute values ​​of the average change rate and the dispersion change rate exceeds the period threshold, increasing the frequency of acquiring temperature, humidity and noise data at the construction site.

[0014] The beneficial effects of this invention are as follows: (1) By increasing the density of constant monitoring points, fixed temporary monitoring points and mobile temporary monitoring points when the average values ​​of temperature, humidity and noise exceed the threshold, and increasing the proportion of fixed temporary monitoring points and mobile temporary monitoring points when the discrete values ​​of temperature, humidity and noise exceed the threshold, the density of monitoring points is increased when the average values ​​of various data are high and more monitoring points are needed. When the dispersion is high, the data will be concentrated at one point and the monitoring points need to be flexibly called, the proportion of fixed temporary monitoring points and mobile temporary monitoring points is increased. When there is no need for excessive density or too many fixed temporary monitoring points and mobile temporary monitoring points, the cost is reduced. (2) When the rate of change of the average value and the rate of change of the dispersion are less than the set value, the positive or negative trend of the dispersion growth is determined. When the dispersion is positive, the dispersion threshold is reduced. When the dispersion is negative, the mean threshold is reduced. When the dispersion decreases and the mean increases, and the data tends to become more uniform and larger, the density of monitoring points is increased and the proportion of mobile monitoring points under the same conditions is reduced. When the dispersion increases and the mean decreases, and the data tends to become smaller and more dispersed, the density of monitoring points is reduced and the proportion of mobile monitoring points under the same conditions is increased. (3) When the concentration of a certain area at the construction site exceeds the threshold, it is marked as a hot spot. Then, the mobile temporary monitors are concentrated in the hot spot area to complete the flexible deployment of the mobile monitors. (4) By increasing the frequency of acquiring temperature, humidity and noise data at the construction site when the sum of the absolute values ​​of the average change rate and the dispersion change rate exceeds the threshold, the accuracy of data acquisition when the data fluctuates rapidly is improved, and the potential impact of a fixed collection frequency not being able to adapt to the dynamic construction environment is reduced. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a block diagram of the control loop of the present invention; Detailed Implementation

[0017] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0018] A construction process monitoring method based on an IoT technology digital visualization platform includes the following steps: Step 1: Obtain the temperature T, humidity S, and noise Z at the construction site, and calculate the average values ​​TJ, SJ, ZJ and discrete values ​​TL, SL, ZL for temperature, humidity, and noise. Step 2: After obtaining the average and discrete values ​​of temperature, humidity and noise each time, periodically determine the proportion and density of constant monitoring points, fixed temporary monitoring points and mobile temporary monitoring points as proportional data density data. For the first acquisition of temperature, humidity and noise data, a simple temporary sensor is used in combination with historical data from the construction site. As for the period, the period can be preset by the operators. When the temperature, humidity, and noise levels at the construction site are high, the upper and lower limits of the monitored data fluctuate significantly, increasing the probability of inaccurate data collection at individual monitoring points. Using fewer monitoring points increases the likelihood of inaccurate monitoring, necessitating an increase in monitoring point density to reduce instability. Conversely, when the temperature, humidity, and noise levels are low, fewer monitoring points are needed, saving energy, material costs, and computing power. Therefore, in step two: when the average values ​​of temperature, humidity, and noise exceed thresholds, increase the density of permanent monitoring points, fixed temporary monitoring points, and mobile temporary monitoring points; when the discrete values ​​of temperature, humidity, and noise exceed thresholds, increase the proportion of fixed temporary monitoring points and mobile temporary monitoring points.

[0019] Specifically, in step two: the densities of constant monitoring points, fixed temporary monitoring points, and mobile temporary monitoring points are set to M1, M2, and M3, respectively, where M1 = TJ / TJ0 + SJ / SJ0 + ZJ / ZJ0, M2 = a × M1, M3 = a × c × M1, c is a pre-input correction coefficient, a = (TL / TL0 + SL / SL0 + ZL / ZL0) / 3, TJ0, SJ0, and ZJ0 are mean thresholds, and TL0, SL0, and ZL0 are discrete thresholds; When TJ, SJ, and ZJ are large, it indicates that the upper and lower limits of the monitored data fluctuate greatly, and the probability of inaccurate data collection at a single monitoring point is greater. When monitoring with fewer monitoring points, there is a greater probability of inaccurate monitoring. In this case, it is necessary to increase the density of monitoring points to reduce the probability of monitoring instability. At this time, M1=TJ / TJ0+SJ / SJ0+ZJ / ZJ0, M2=a×M1, and M3=a×c×M1 all increase with the increase of TJ, SJ, and ZJ, thus completing the task of increasing the density of monitoring points when the data mean is large. When TJ, SJ, and ZJ are relatively small and there is no need for a high density of monitoring points, M1=TJ / TJ0+SJ / SJ0+ZJ / ZJ0, M2=a×M1, and M3=a×c×M1 all increase with the increase of TJ, SJ, and ZJ, thus reducing the density of monitoring points and lowering operating costs when the average data value is small. When the dispersion is high, it indicates that the data distribution exhibits significant regional differences. In this case, the upper and lower limits of the data to be monitored at some locations on the construction site fluctuate greatly, requiring an increase in the density of monitoring points at these locations. However, these locations are not always in one place, so more temporary monitoring points need to be deployed to capture and monitor data from various locations more flexibly. Therefore, when there are locations with large fluctuations in the upper and lower limits of the data to be monitored, several mobile temporary monitoring points are instructed to move to these locations. Conversely, when the dispersion is low, the above measures are not necessary, and instructing mobile temporary monitoring points would increase costs. Therefore, in this case, it is necessary to reduce the number of mobile temporary monitoring points to avoid additional cost increases. When the values ​​of TL, SL, and ZL are large, it is necessary to increase the ratio of fixed temporary monitoring points to mobile temporary monitoring points. In this case, in M2=a×M1 and M3=a×c×M1, M2 and M3 increase as a=(TL / TL0+SL / SL0+ZL / ZL0) / 3. When the dispersion is high, the ratio of fixed temporary monitoring points to mobile temporary monitoring points should be increased. When the values ​​of TL, SL, and ZL are small, there is no need to increase the ratio of fixed temporary monitoring points to mobile temporary monitoring points. In this case, in M2=a×M1 and M3=a×c×M1, M2 and M3 increase as a=(TL / TL0+SL / SL0+ZL / ZL0) / 3. When the dispersion is high, the ratio of fixed temporary monitoring points to mobile temporary monitoring points should be reduced. Then proceed to step three; based on the ratio and density data, determine the locations of the monitoring points. Step 4: Deploy permanent monitoring devices, fixed temporary monitoring devices, and mobile temporary monitoring devices at the deployment location to monitor the construction process; Step 5: The control module receives data from permanent monitoring points, fixed temporary monitoring points, and mobile temporary monitoring points, and instructs the fixed temporary monitoring points and mobile temporary monitoring points to start, stop, or move.

[0020] By increasing the density of permanent monitoring points, fixed temporary monitoring points, and mobile temporary monitoring points when the average values ​​of temperature, humidity, and noise exceed thresholds, and increasing the proportion of fixed temporary monitoring points and mobile temporary monitoring points when the discrete values ​​of temperature, humidity, and noise exceed thresholds, the system can increase the density of monitoring points when the average values ​​of various data are high and more monitoring points are needed, and increase the proportion of fixed temporary monitoring points and mobile temporary monitoring points when the dispersion is high, the data will be concentrated at one point, and flexible use of monitoring points is needed. This reduces costs when excessive density or too many fixed temporary monitoring points and mobile temporary monitoring points are not required. In step one, after acquiring the temperature, humidity, and noise data of the construction site, the following steps are also included: storing this data in the database; the control module sorts the currently acquired data and the previously acquired data in reverse order of acquisition time, and calculates the difference between the rate of change of the average value and the rate of change of the dispersion; when the difference is greater than the set value, it judges whether the dispersion growth trend is positive or negative; when the dispersion is positively increasing, it lowers the dispersion threshold; when the dispersion is negatively increasing, it lowers the average threshold. Specifically, in step one, the difference e between the rate of change of the average value and the rate of change of the dispersion is calculated. When e is greater than e0, the positive or negative trend of the dispersion growth is determined. When the dispersion is positively increasing, the dispersion threshold is reduced by A1 times. When the dispersion is negatively increasing, the average threshold is reduced by A2 times. Where A1 = e / e0 × f1, A2 = e / e0 × f2, and f1 and f2 are pre-input correction factor coefficients.

[0021] There are two possibilities at this point: In the first case, the dispersion increases relative to the mean. At this time, the data tends to be small and unevenly distributed. It is necessary to increase the proportion of temporary monitoring points under the same conditions, that is, to reduce the dispersion threshold. After reducing the dispersion threshold, the proportion of temporary monitoring points is greater under the premise of the same dispersion. In the second scenario, the relative dispersion of the mean increases. In this case, the data area is large and evenly distributed. There is no need to increase the proportion of temporary monitoring points. Instead, it is necessary to increase the density of all monitoring points under the same conditions to better monitor the data.

[0022] When the value of e is large, it means that the difference between the rate of change of the mean and the rate of change of the dispersion is large; If the dispersion decreases and the mean increases, and the data tends to become more uniform, increase the density of monitoring points and reduce the proportion of moving monitoring points under the same conditions. In this case, reduce the dispersion threshold by A1 times. When the dispersion increases negatively, the mean threshold is reduced by A2 times to complete the threshold reduction in the corresponding case.

[0023] By determining whether the growth trend of dispersion is positive or negative when the rate of change of the average value and the rate of change of dispersion are less than the set values, the dispersion threshold is reduced when dispersion is positive and the mean threshold is reduced when dispersion is negative. This allows for increasing the density of monitoring points and reducing the proportion of mobile monitoring points under the same conditions when dispersion decreases, the mean increases, and the data tends to become more uniform and larger. Conversely, it allows for decreasing the density of monitoring points and increasing the proportion of mobile monitoring points under the same conditions when dispersion increases, the mean decreases, and the data tends to become smaller and more dispersed.

[0024] Step five also includes: the control module acquires the concentration of temperature, humidity and noise data in space, and when the concentration in a certain area of ​​the construction site exceeds the threshold, it is marked as a hotspot area, and then the mobile temporary monitor is concentrated in the hotspot area.

[0025] Specifically, step five also includes the following steps for determining hotspot areas: the control module acquires the magnitude and coordinates of temperature, humidity, and noise data, and for each data coordinate point, it determines whether the sum of the data values ​​within a fixed range around it exceeds a threshold. If so, it is determined to be a hotspot area. The fixed range is preset by the operators as needed.

[0026] When the concentration of mobile temporary monitors at a certain location on the construction site exceeds a threshold, this location is marked as a hotspot area. Subsequently, the mobile temporary monitors are concentrated in the hotspot area to achieve flexible deployment of the mobile monitors. Step one also includes: after calculating the rate of change of the average value and the rate of change of the dispersion, when the sum of the absolute values ​​of the rate of change of the average value and the rate of change of the dispersion exceeds the period threshold, increasing the frequency of acquiring temperature, humidity and noise data at the construction site.

[0027] When the sum of the absolute values ​​of the average rate of change and the dispersion rate of change exceeds the period threshold, the construction environment, which represents dynamic changes, has an impact on monitoring. At this time, it is necessary to increase the data collection frequency to offset the environmental impact. By increasing the frequency of acquiring temperature, humidity, and noise data at the construction site when the sum of the absolute values ​​of the rate of change of the average value and the rate of change of the dispersion exceeds a threshold, the accuracy of data acquisition is improved when data fluctuates rapidly, and the potential impact of a fixed collection frequency not being suitable for the dynamic construction environment is reduced.

[0028] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A construction process monitoring method based on an IoT technology digital visualization platform, characterized in that: Includes the following steps: Step 1: Obtain temperature, humidity, and noise data at the construction site, and calculate the average and discrete values ​​of the temperature, humidity, and noise data; Step 2: After obtaining the average and discrete values ​​of temperature, humidity and noise each time, determine the ratio and density of constant monitoring points, fixed temporary monitoring points and mobile temporary monitoring points, as proportional data density data; Step 3: Determine the locations of monitoring points based on the ratio and density data; Step 4: Deploy permanent monitoring devices, fixed temporary monitoring devices, and mobile temporary monitoring devices at the deployment location to monitor the construction process; Step 5: The control module receives data from permanent monitoring points, fixed temporary monitoring points, and mobile temporary monitoring points, and instructs the fixed temporary monitoring points and mobile temporary monitoring points to start, stop, or move.

2. The construction process monitoring method based on an IoT technology digital visualization platform according to claim 1, characterized in that: Step two further includes: increasing the density of constant monitoring points, fixed temporary monitoring points, and mobile temporary monitoring points when the average values ​​of temperature, humidity, and noise exceed the threshold, and increasing the proportion of fixed temporary monitoring points and mobile temporary monitoring points when the discrete values ​​of temperature, humidity, and noise exceed the threshold.

3. The construction process monitoring method based on an IoT technology digital visualization platform according to claim 1, characterized in that: Step one further includes: obtaining the temperature T, humidity S, and noise Z at the construction site, and calculating the average values ​​TJ, SJ, ZJ and discrete values ​​TL, SL, ZL of the temperature, humidity, and noise; Step two further includes: setting the densities of constant monitoring points, fixed temporary monitoring points, and mobile temporary monitoring points to M1, M2, and M3, respectively, where M1 = TJ / TJ0 + SJ / SJ0 + ZJ / ZJ0, M2 = a × M1, M3 = a × c × M1, c is a pre-input correction coefficient, a = (TL / TL0 + SL / SL0 + ZL / ZL0) / 3, TJ0, SJ0, and ZJ0 are mean thresholds, and TL0, SL0, and ZL0 are discrete thresholds.

4. The construction process monitoring method based on an IoT technology digital visualization platform according to claim 3, characterized in that: In step one, after acquiring the temperature, humidity, and noise data of the construction site, the method further includes: storing this data in a database; the control module sorts the currently acquired data and the previously acquired data in reverse order of acquisition time; and calculates the difference between the rate of change of the average value and the rate of change of the dispersion. When the difference is greater than a set value, it determines whether the dispersion growth trend is positive or negative. When the dispersion is positive, it lowers the dispersion threshold; when the dispersion is negative, it lowers the average threshold.

5. The construction process monitoring method based on an IoT technology digital visualization platform according to claim 4, characterized in that: In step one, the difference e between the rate of change of the average value and the rate of change of the dispersion is calculated. When e is greater than e0, the positive or negative trend of the dispersion growth is determined. When the dispersion is positive, the dispersion threshold is reduced by A1 times. When the dispersion is negative, the average threshold is reduced by A2 times, where A1 = e / e0 × f1 and A2 = e / e0 × f2.

6. The construction process monitoring method based on an IoT technology digital visualization platform according to claim 1, characterized in that: Step five also includes: the control module acquires the concentration of temperature, humidity and noise data in space, and when the concentration in a certain area of ​​the construction site exceeds the threshold, it is marked as a hotspot area, and then the mobile temporary monitor is concentrated in the hotspot area.

7. The construction process monitoring method based on an IoT technology digital visualization platform according to claim 6, characterized in that: Step five also includes: the control module acquires the magnitude and coordinates of temperature, humidity and noise data, and for each data coordinate point, determines whether the sum of data values ​​within a fixed range around it exceeds a threshold. If so, it is determined to be a hotspot area.

8. The construction process monitoring method based on an IoT technology digital visualization platform according to claim 1, characterized in that: Step one further includes: after calculating the average change rate and the dispersion change rate, when the sum of the absolute values ​​of the average change rate and the dispersion change rate exceeds the period threshold, increasing the frequency of acquiring temperature, humidity and noise data at the construction site.

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