Analysis method and system for monitoring accompanying change time difference of liquid level of drainage pipeline based on Internet of Things

Abstract

The invention discloses an analysis method and system for monitoring the accompanying change time difference of the liquid level of a drainage pipeline based on the Internet of Things, and the method comprises the following steps: S1, selecting a plurality of water level monitoring points according to the mutual connectivity of pipelines; s2, defining an analysis period, and obtaining Internet of Things monitoring original data of the selected monitoring points; s3, data cleaning: abnormal mutation data are cleared; s4, calculating average interval time t according to the time interval frequency of the original data; s5, obtaining the latest start t0 and the earliest end time te of all monitoring point data according to the original data; and S6, according to t, t0 and te, calculating a standard equal difference time axis array t [], and through comprehensive acquisition, cleaning and analysis of drainage pipeline liquid level data, realizing judgment of drainage pipeline liquid level accompanying change trend correlation and accurate calculation of accompanying change time difference, thereby solving the problem that underground drainage water level change is difficult to predict, and improving the prediction accuracy of the underground drainage water level change. And the capability of managing and coping with disasters of the urban drainage system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drainage pipeline monitoring, and particularly relates to an analysis method and system for the time difference of the accompanying change of the drainage pipeline liquid level based on Internet of Things monitoring. Background Art

[0002] With the rapid development of cities, the efficient operation of the drainage system is crucial for the normal operation of cities. The change of the drainage pipeline liquid level not only reflects the real-time state of the drainage system, but is also closely related to urban flood control and drainage, sewage discharge, etc. However, there are many problems in the current monitoring and analysis of the drainage pipeline liquid level.

[0003] Traditional means of monitoring the drainage pipeline liquid level are limited, and it is difficult to achieve real-time and accurate monitoring of a large range of drainage pipelines. Due to the lack of effective data collection and analysis methods, it is impossible to accurately judge the correlation between the liquid levels of drainage pipelines in different regions, resulting in difficulties in predicting the change of the underground drainage water level. In the face of situations such as heavy rainfall or peak water use, it is impossible to timely and accurately grasp the change trend of the drainage pipeline liquid level, which is likely to cause disasters such as urban waterlogging, posing a serious threat to the lives and property safety of urban residents and urban infrastructure. Therefore, it is urgent to develop a method and system that can effectively analyze the time difference of the accompanying change of the drainage pipeline liquid level and accurately predict the change of the underground drainage water level. Summary of the Invention

[0004] In view of the problems in the related art, the present invention proposes an analysis method and system for the time difference of the accompanying change of the drainage pipeline liquid level based on Internet of Things monitoring. By comprehensively collecting, cleaning, and analyzing the drainage pipeline liquid level data, it realizes the judgment of the correlation of the accompanying change trend of the drainage pipeline liquid level and the accurate calculation of the accompanying change time difference, thereby solving the problem of difficult prediction of the change of the underground drainage water level and improving the management of the urban drainage system and the ability to respond to disasters.

[0005] For this purpose, the specific technical solutions adopted by the present invention are as follows: Including the following steps: S1: Select multiple water level monitoring points according to the pipe connectivity; S2: Define the analysis period and obtain the original Internet of Things monitoring data of the selected monitoring points; S3: Data cleaning, removing abnormal mutation data; S4: Calculate the average interval time t according to the time interval frequency of the original data; S5: Obtain the latest start time t0 and the earliest end time te of all monitoring point data according to the original data; S6: Calculate the standard arithmetic time axis array t[] according to t, t0, and te; S7: Package the standard data, and package the data of each monitoring point with the standard time axis; S8: Calculate the slope of each set of points in the standard data; S9: For every two sets of standard data, judge the correlation based on the slope. After determining the correlation, calculate the time difference of the concomitant change.

[0006] The selection of multiple water level monitoring points based on the mutual connectivity of pipelines includes the following steps: S101: Select the first pipeline liquid level monitoring point; S102: When selecting another pipeline liquid level monitoring point, it is necessary to judge whether the monitoring point to be selected is connected to any of the already selected monitoring points. If it is not connected to any of them, then this monitoring point is not selected.

[0007] The definition of the analysis period and the acquisition of the original IoT monitoring data of the selected monitoring points include the following steps: S201: Define the period, and in principle, the period range is greater than one hour; S202: Select the data time period, which is set during rainfall or peak water usage periods; S203: According to the period and the selected time period, obtain the original monitoring data of the liquid level monitoring points and encapsulate them independently.

[0008] The data cleaning to remove abnormal mutation data includes the following steps: S301: Remove the data beyond the liquid level measurement range according to the liquid level measurement range; S302: Select the data time period, which is set during rainfall or peak water usage periods; S303: According to the period and the selected time period, obtain the original monitoring data of the liquid level monitoring points and encapsulate them independently.

[0009] An analysis system for the time difference of the concomitant change of the liquid level of a drainage pipeline based on IoT monitoring includes: A monitoring point selection module, which is used to select multiple water level monitoring points based on the mutual connectivity of pipelines; A data acquisition module, which is used to define the analysis period and obtain the original IoT monitoring data of the selected monitoring points; A data cleaning module, which is used to remove abnormal mutation data; A time calculation module, which is used to calculate the average interval time t based on the time interval frequency of the original data, and obtain the latest start time t0 and the earliest end time te of all monitoring point data, and then calculate the standard arithmetic time axis array t[]; A data processing module, which is used to encapsulate the standard data and calculate the slope of each set of points in the standard data; An analysis module, which is used to judge the correlation of every two sets of standard data based on the slope, and calculate the time difference of the concomitant change after determining the correlation.

[0010] The monitoring point selection module is specifically used to: first select the first pipeline liquid level monitoring point, and then when selecting the pipeline liquid level monitoring point, determine whether the monitoring point to be selected is connected to any of the selected monitoring points. If neither is connected, the monitoring point will not be selected.

[0011] The data acquisition module is specifically used to: define the cycle, the cycle range is generally greater than one hour; select the data period during rainfall or peak water usage; obtain the original monitoring data of the liquid level monitoring point according to the cycle and selected time period and encapsulate it independently.

[0012] The data cleaning module is specifically used to: remove over-range data according to the liquid level monitoring range, select the data period during rainfall or peak water use, obtain the original monitoring data of the liquid level monitoring point according to the cycle and selected period, and encapsulate it independently Monitoring point selection: Select multiple water level monitoring points based on the interconnectivity of the pipelines. First, select the first pipeline liquid level monitoring point; when selecting monitoring points, determine whether the monitoring point to be selected is connected to the selected monitoring point. If neither is connected, the monitoring point will not be selected. In this way, it is ensured that the selected monitoring points can effectively reflect the changes in the liquid levels of the interrelated pipelines.

[0013] Data acquisition and processing: Define the analysis cycle, which should be greater than one hour in principle. Select the data period during rainfall or peak water usage, obtain the original monitoring data of the liquid level monitoring point according to the set cycle and selected period, and package it independently. Then perform data cleaning and remove the over-range data according to the liquid level monitoring range to further ensure the accuracy and reliability of the data.

[0014] Time axis and data processing: Calculate the average interval time t based on the original data time interval frequency, and obtain the latest start time t0 and the earliest end time te of all monitoring point data. Calculate the standard arithmetic time axis array t[] based on t, t0, and te, and use this standard time axis to encapsulate the data of each monitoring point, so that the data of different monitoring points can be easily analyzed under the same time scale.

[0015] Correlation and time difference calculation: Calculate the slope of each point of the standard data, and determine the correlation between each two sets of standard data based on the slope. When the two sets of data are determined to be related, calculate the time difference of the accompanying changes to quantify the time relationship of the liquid level changes at different monitoring points. Beneficial effects of the present invention The present invention can accurately determine the correlation of the accompanying change trend of the drainage pipe liquid level through a comprehensive and systematic data processing flow, providing a strong basis for in-depth understanding of the law of liquid level changes inside the drainage system.

[0016] The accurately calculated adjoint change time difference helps to predict the change of the underground drainage water level in advance, enabling the urban drainage management department to take timely measures, such as adjusting the operation of drainage pumps and optimizing the drainage scheduling plan, etc., to effectively prevent disasters such as urban waterlogging.

[0017] The implementation of data collection based on Internet of Things technology improves the real-time and comprehensiveness of data acquisition, overcomes the limitations of traditional monitoring methods, and enhances the intelligent management level of the urban drainage system. Brief Description of the Drawings

[0018] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings: Figure 1 It is a schematic flow chart of a method for analyzing the adjoint change time difference of the liquid level in a drainage pipeline based on Internet of Things monitoring according to the present invention; Detailed Embodiments

[0019] According to an embodiment of the present invention, a method for analyzing the adjoint change time difference of the liquid level in a drainage pipeline based on Internet of Things monitoring is provided.

[0020] Arrangement of monitoring points: In the urban drainage pipeline system, according to the distribution and connectivity of the pipelines, a suitable monitoring area is determined. First, select the first pipeline liquid level monitoring point at a representative location, such as at the key node of the main drainage pipe. Then, according to the connectivity principle, gradually select other monitoring points. During the selection process, use a geographic information system (GIS) or drainage pipeline layout drawings to judge the connectivity relationship between the newly selected monitoring points and the already selected monitoring points, ensuring that the selected monitoring points can cover different areas and are interrelated.

[0021] Data collection and preparation: Set the analysis period to 3 hours, and select the concentrated rainfall period in the city as the data collection period. Use Internet of Things sensors, such as pressure liquid level sensors, ultrasonic liquid level sensors, etc., to collect the liquid level data of each monitoring point in real time. The sensors send the collected data to the data collection terminal through a wireless transmission module. The data collection terminal independently packages the data and stores it in the local database for subsequent processing.

[0022] Data cleaning and sorting: Read the original monitoring data from the local database, and remove the data that exceeds the range according to the range of the liquid level sensor. For example, if the range of a certain sensor is 0 - 10 meters, the data less than 0 meters and greater than 10 meters are regarded as abnormal data and deleted. Re - sort the cleaned data according to the analysis period and collection period to ensure the consistency and accuracy of the data.

[0023] Timeline Construction and Data Encapsulation: Analyze the time intervals of the original data and calculate the average interval time t. Suppose after calculation, the average interval time t is 5 minutes. At the same time, determine the latest start time t0 and the earliest end time te of the data at all monitoring points. Construct a standard arithmetic sequence timeline array t[] based on t, t0, and te. Using this timeline as a reference, repackage the data of each monitoring point so that the data of each monitoring point is comparable in terms of time.

[0024] Correlation and Time Difference Calculation: For the encapsulated standard data, calculate the slope of each group of points in chronological order. The slope reflects the rate of change of the liquid level over time. Compare the standard data of different monitoring points pairwise and determine their correlation based on the slope. If the slope change trends of two sets of data are similar and the change amplitudes are close within a certain time range, then these two sets of data are determined to be correlated. After determining that two sets of data are correlated, calculate the co-variation time difference by comparing the time nodes of their liquid level changes. For example, if the liquid level of monitoring point A rises 5 minutes earlier than that of monitoring point B, then the co-variation time difference is 5 minutes.

[0025] Result Application and Feedback: Feed back the calculated co-variation time difference of the liquid level and the correlation results to the urban drainage management department. Based on these results and combined with the actual operation of the drainage system, the management department formulates corresponding drainage scheduling strategies. When it is predicted that the liquid level in a certain area is about to rise, start the drainage pumps near this area in advance to improve the drainage capacity and ensure the normal operation of the urban drainage system. At the same time, optimize and adjust the layout of the monitoring points, data collection frequency, etc. according to the actual application effect, and continuously improve the accuracy and practicality of the analysis method.

[0026] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An analysis method for monitoring the time difference of the liquid level in a drainage pipe based on the Internet of Things, characterized in that: The following steps are involved: S1: Select multiple water level monitoring points based on the interconnectivity of pipelines; S2: define the analysis cycle and obtain the raw data of IoT monitoring at the selected monitoring points; S3: Data cleaning, removing abnormal mutation data; S4: Calculate the average interval time t based on the time interval frequency of the original data; S5: Obtain the latest start time t0 and the earliest end time te of all monitoring point data based on the original data; S6: Calculate the standard arithmetic time axis array t[] based on t, t0, te; S7: Encapsulate standard data, and encapsulate the data of each monitoring point separately with a standard time axis; S8: Calculate the slope of each set of points in the standard data; S9: For every two sets of standard data, the correlation is determined based on the slope. After the correlation is determined, the time difference of the accompanying changes is calculated.

2. According to claim 1, a method for analyzing the time difference of the liquid level change in the drainage pipe based on the Internet of Things, characterized in that: The method of selecting a plurality of water level monitoring points according to the interconnectivity of the pipelines comprises the following steps: S101: Select the first pipeline liquid level monitoring point; S102: When selecting a pipeline liquid level monitoring point again, it is necessary to determine whether the monitoring point to be selected is connected to any of the selected monitoring points. If neither is connected, the monitoring point is not selected.

3. According to claim 1, a method for analyzing the time difference of the liquid level change in the drainage pipe based on the Internet of Things, characterized in that: Defining the analysis cycle and obtaining the raw data of IoT monitoring at the selected monitoring point includes the following steps: S201: Define the cycle, the cycle range should be greater than one hour in principle; S202: Selecting a data period, which is set at a rainfall or water consumption peak period; S203: According to the cycle and the selected time period, the original monitoring data of the liquid level monitoring point is obtained and packaged independently.

4. The method for analyzing the time difference of the liquid level change in the drainage pipe based on the Internet of Things according to claim 1 is characterized in that: The data cleaning and removal of abnormal mutation data includes the following steps: S301: removing out-of-range data according to the liquid level monitoring range; S302: Select a data period, set at a rainfall or water consumption peak period; S303: According to the cycle and the selected time period, the original monitoring data of the liquid level monitoring point is obtained and packaged independently.

5. An analysis system for monitoring the time difference of the liquid level change in drainage pipes based on the Internet of Things, characterized in that: include: A monitoring point selection module is used to select multiple water level monitoring points based on the interconnectivity of pipelines; Data collection module, used to define the analysis cycle and obtain the raw data of IoT monitoring at the selected monitoring points; Data cleaning module, used to remove abnormal mutation data; The time calculation module is used to calculate the average interval time t according to the time interval frequency of the original data, and obtain the latest start time t0 and the earliest end time te of all monitoring point data, and then calculate the standard arithmetic time axis array t[]; The data processing module is used to encapsulate the standard data and calculate the slope of each group of points of the standard data; The analysis module is used to determine the correlation between each two sets of standard data based on the slope, and calculate the accompanying change time difference after determining the correlation.

6. The analysis system for monitoring the time difference of the liquid level in the drainage pipe based on the Internet of Things according to claim 5 is characterized in that: The monitoring point selection module is specifically used to: first select the first pipeline liquid level monitoring point, and then when selecting the pipeline liquid level monitoring point, determine whether the monitoring point to be selected is connected to the selected monitoring point. If neither is connected, the monitoring point will not be selected.

7. The analysis system for monitoring the time difference of the liquid level in the drainage pipe based on the Internet of Things according to claim 5 is characterized in that: The data acquisition module is specifically used to: define a cycle, the cycle range is in principle greater than one hour; select a data period during rainfall or peak water use; and obtain the original monitoring data of the liquid level monitoring point according to the cycle and the selected period and encapsulate it independently.

8. The analysis system for monitoring the time difference of the liquid level in the drainage pipe based on the Internet of Things according to claim 5 is characterized in that: The data cleaning module is specifically used to: remove over-range data according to the liquid level monitoring range, select the data period during rainfall or peak water use, obtain the original monitoring data of the liquid level monitoring point according to the cycle and the selected period, and independently package it.

Images