A water quality monitoring device state identification method based on online monitoring data

Abstract

This invention relates to the field of water quality monitoring technology, specifically to a method for identifying the status of water quality testing equipment based on online monitoring data. This solution mainly includes three parts: data verification, data cleaning, and equipment status classification. By acquiring and analyzing pollutant values, it effectively identifies the operating status of online water quality monitoring equipment, helping staff to more accurately assess the equipment's operational status.

Description

Technical Field

[0001] This invention relates to the field of water quality monitoring technology, and specifically to a method for identifying the status of water quality testing equipment based on online monitoring data. Background Technology

[0002] Environmental online monitoring equipment refers to devices that utilize modern communication and computer technologies to monitor, detect, and control the environmental quality of the monitored object in real time, continuously, and automatically. It boasts advantages such as fast information acquisition, convenient data storage, simple operation, strong anti-interference capabilities, and ease of remote monitoring, meeting the needs of environmental monitoring and management. It is a widely used monitoring instrument in current environmental protection work. In the specifications for online water quality monitoring equipment for pollution sources, some online monitoring devices are required to take samples every two hours and transmit a value every ten minutes. This means that under normal conditions, before the next sample is taken and a value is obtained, the transmitted value is the value from the previous sample, and this period is a constant value.

[0003] However, due to the complexity of factors affecting the operating status of online monitoring equipment, the data is often not constant before the next sampling. Therefore, it is necessary to analyze these non-constant values ​​and then specifically investigate the causes of this behavior. The status of online monitoring equipment can be broadly categorized as data acquisition instrument transmission problems, human interference with the analyzer, analyzer malfunction, abnormal analyzer operation, and normal analyzer operation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a water quality monitoring equipment status identification method based on online monitoring data, which can identify the operating status of online water quality monitoring equipment based on pollutant values, and help staff accurately assess the equipment's operating condition.

[0005] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: a method for identifying the status of water quality monitoring equipment based on online monitoring data, the method comprising the following steps:

[0006] (1) Obtain minute data within a preset number of days from the online monitoring equipment, and filter the monitoring factor data in the data;

[0007] (2) Obtain data of one of the monitoring factors to get dataset S1. The length of dataset S1 is denoted as L. Calculate the maximum number of decimal places in dataset S1. Perform first-order difference on dataset S1 to obtain the difference set diff1.

[0008] When the number of non-zero values ​​in diff1 is greater than L / 2, remove the maximum number of bits in the smallest data point from all data in dataset S1. The remaining data is denoted as dataset S2. Difference is performed on dataset S2, denoted as diff2. When the number of non-zero values ​​in diff2 is greater than L / 2, dataset S2 is marked as having a data acquisition instrument transmission problem and does not proceed to the next stage. When the number of non-zero values ​​in diff2 is less than 22, dataset S2 is marked as having an abnormal analyzer operation and does not proceed to the next stage.

[0009] (3) Extract the time points in diff2 that are not zero at the current time and are zero at the next time, and record them as the data output point time series K1, with the duration recorded as LK1. Calculate the output point of the device based on the data output point time series K1.

[0010] (3.1) Obtain the hour values ​​of the K1 time series, calculate the number of even hour values ​​EVEN, and obtain the minute values ​​MIN of the K1 time series;

[0011] (3.2) If EVEN > LK1 / 2 and MIN < 30, and the factor is not ammonia nitrogen, then the factor is sampled in odd numbers, and the sampling time is 60 min + MIN, that is, the equipment output points are 1: MIN, 3: MIN, 5: MIN... 23: MIN; if the factor is ammonia nitrogen or MIN > 30, then the factor is sampled in even numbers, and the sampling time is MIN, that is, the equipment output points are 0: MIN, 2: MIN, 4: MIN... 22: MIN;

[0012] (3.3) If EVEN≤LK1 / 2, MIN<30, and the factor is not ammonia nitrogen, then the factor is sampled in an even number of times, and the sampling time is 60min+MIN, that is, the equipment output points are 0:MIN, 2:MIN, 4:MIN……22:MIN. If the factor is ammonia nitrogen or MIN>30, then the factor is sampled in an odd number of times, and the sampling time is MIN, that is, the equipment output points are 1:MIN, 3:MIN, 5:MIN……23:MIN.

[0013] (3.4) Based on the results of steps (3.2) and (3.3), determine whether the device output point is -10min≤data output point≤device output point+10min. If not, and the number of true values ​​is ≥3, then the dataset S2 is marked as the analyzer is not operating normally; if 0 < the number of true values ​​< 3, then the dataset S2 is marked as the analyzer is faulty; if the number of true values ​​is 0, then the dataset S2 is marked as the analyzer is operating normally; the remaining states are recorded as human interference with the analyzer.

[0014] Compared with existing technologies, this solution has the following significant advantages: This solution mainly includes three parts: data verification, data cleaning, and equipment status classification. By acquiring and analyzing pollutant values, it effectively identifies the operating status of online water quality monitoring equipment, helping staff to more accurately assess the equipment's operating condition. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0016] Figure 1 This is a schematic diagram of the steps of the present invention;

[0017] Figure 2 This is a schematic diagram of dataset S2 in Example 1 of the present invention;

[0018] Figure 3 This is a schematic diagram of diff2 in Embodiment 1 of the present invention;

[0019] Figure 4 This is a schematic diagram of the sample preparation results in step (3.2) of Example 1 of the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] Example 1:

[0022] The water quality monitoring equipment status identification method based on online monitoring data according to the present invention includes the following steps:

[0023] (1) Obtain 7 days of minute data from online monitoring equipment and screen the data for monitoring factors such as chemical oxygen demand, total nitrogen, ammonia nitrogen, and total phosphorus;

[0024] (2) Obtain data of one of the monitoring factors to get dataset S1. The length of dataset S1 is denoted as L. Calculate the maximum number of decimal places in dataset S1. Perform first-order difference on dataset S1 to obtain the difference set diff1.

[0025] When the number of non-zero values ​​of diff1 is greater than L / 2, the maximum number of bits of the smallest data point in dataset S1 is removed, and the remaining data is denoted as dataset S2. In this embodiment, dataset S2 is as follows: Figure 2 As shown.

[0026] Difference the dataset S2, denoted as diff2, as follows: Figure 3 As shown.

[0027] When the number of non-zero values ​​in diff2 is greater than L / 2, the dataset S2 is marked as having a data acquisition instrument transmission problem and will not proceed to the next stage; when the number of non-zero values ​​in diff2 is less than 22, the dataset S2 is marked as having an abnormal analyzer operation and will not proceed to the next stage.

[0028] (3) Extract the time points in diff2 that are not zero at the current time and are zero at the next time, and record them as the data output point time series K1, with the duration recorded as LK1. Calculate the output point of the device based on the data output point time series K1.

[0029] (3.1) Obtain the hour values ​​of the K1 time series, calculate the number of even hour values ​​EVEN, and obtain the minute values ​​MIN of the K1 time series;

[0030] (3.2) If EVEN > LK1 / 2 and MIN < 30, and the factor is not ammonia nitrogen, then the factor is sampled in odd numbers, with a sampling time of 60 min + MIN, i.e., the equipment output points are 1: MIN, 3: MIN, 5: MIN... 23: MIN; if the factor is ammonia nitrogen or MIN > 30, then the factor is sampled in even numbers, with a sampling time of MIN, i.e., the equipment output points are 0: MIN, 2: MIN, 4: MIN... 22: MIN. In this embodiment, the sampling results are as follows: Figure 4 As shown;

[0031] If EVEN≤LK1 / 2 and MIN<30, and the factor is not ammonia nitrogen, then the factor is sampled in an even number of times, with a sampling time of 60min+MIN, i.e., the equipment output points are 0:MIN, 2:MIN, 4:MIN...22:MIN. If the factor is ammonia nitrogen or MIN>30, then the factor is sampled in an odd number of times, with a sampling time of MIN, i.e., the equipment output points are 1:MIN, 3:MIN, 5:MIN...23:MIN.

[0032] (3.4) Based on the results of steps (3.2) and (3.3), determine whether the device output point is -10min≤data output point≤device output point+10min. If not, and the number of true values ​​is ≥3, then the dataset S2 is marked as the analyzer is not operating normally; if 0 < the number of true values ​​< 3, then the dataset S2 is marked as the analyzer is faulty; if the number of true values ​​is 0, then the dataset S2 is marked as the analyzer is operating normally; the remaining states are recorded as human interference with the analyzer.

[0033] Example 2:

[0034] The following are the results of the status identification and analysis of the online water quality monitoring equipment, taking minute-by-minute data from a certain region over the past week, from October 3, 2022 to October 8, 2022:

[0035]

[0036]

[0037] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying the status of water quality monitoring equipment based on online monitoring data, characterized in that, The method includes the following steps: (1) Obtain minute data within a preset number of days from the online monitoring equipment, and filter the monitoring factor data in the data; (2) Obtain data of one of the monitoring factors to get dataset S1. The length of dataset S1 is denoted as L. Calculate the maximum number of decimal places in dataset S1. Perform first-order difference on dataset S1 to obtain the difference set diff1. When the number of non-zero values ​​in diff1 is greater than L / 2, remove the maximum number of bits in the smallest data point from all data in dataset S1. The modified data is denoted as dataset S2. Difference is performed on dataset S2, denoted as diff2. When the number of non-zero values ​​in diff2 is greater than L / 2, dataset S2 is marked as having a data acquisition instrument transmission problem and will not proceed to the next stage. When the number of non-zero values ​​in diff2 is less than 22, dataset S2 is marked as having an abnormal analyzer operation and will not proceed to the next stage. (3) Extract the time points in diff2 that are not zero at the current time and are zero at the next time, and record them as the data output point time series K1, with the duration recorded as LK1. Calculate the output point of the device based on the data output point time series K1. (3.1) Obtain the hour values ​​of the K1 time series, calculate the number of even hour values ​​EVEN, and obtain the minute values ​​MIN of the K1 time series; (3.2) If EVEN > LK1 / 2 and MIN < 30, and the factor is not ammonia nitrogen, then the factor is sampled in odd numbers, and the sampling time is 60 min + MIN, that is, the equipment output points are 1: MIN, 3: MIN, 5: MIN... 23: MIN; if the factor is ammonia nitrogen or MIN > 30, then the factor is sampled in even numbers, and the sampling time is MIN, that is, the equipment output points are 0: MIN, 2: MIN, 4: MIN... 22: MIN; (3.3) If EVEN≤LK1 / 2, MIN<30, and the factor is not ammonia nitrogen, then the factor is sampled in an even number of times, and the sampling time is 60min+MIN, that is, the equipment output points are 0:MIN, 2:MIN, 4:MIN……22:MIN. If the factor is ammonia nitrogen or MIN>30, then the factor is sampled in an odd number of times, and the sampling time is MIN, that is, the equipment output points are 1:MIN, 3:MIN, 5:MIN……23:MIN. (3.4) Based on the results of steps (3.2) and (3.3), determine whether the device output point is -10min≤data output point≤device output point+10min. If not, and the number of true values ​​is ≥3, then the dataset S2 is marked as the analyzer is not operating normally; if 0 < the number of true values ​​< 3, then the dataset S2 is marked as the analyzer is faulty; if the number of true values ​​is 0, then the dataset S2 is marked as the analyzer is operating normally; the remaining states are recorded as human interference with the analyzer.

2. The method for identifying the status of water quality monitoring equipment based on online monitoring data according to claim 1, characterized in that, In step (1), the monitoring factors include chemical oxygen demand, total nitrogen, ammonia nitrogen, and total phosphorus.

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