Collaborative correction method for representativeness error in automatic river water quality station monitoring data

Abstract

Provided in the present invention is a collaborative correction method for a representativeness error in automatic river water quality station monitoring data. The method comprises: performing correlation analysis on automatic water quality station monitoring data and full-cross-section sampling monitoring data; correcting a systematic representativeness error in an automatic water quality station monitoring data sequence; calculating a synchronization error between the automatic water quality station monitoring data and the full-cross-section sampling monitoring data; and correcting a random representativeness error in the automatic water quality station monitoring data. Therefore, the problem of cross-section representativeness of automatic water quality station monitoring data can be solved, and the influence of short-term random factors on the automatic water quality station monitoring data can be eliminated, thereby improving the accuracy and rationality of river cross-section water quality monitoring.

Description

A collaborative correction method for representativeness error of automatic river water quality monitoring data Technical Field

[0001] This invention relates to the field of water environment monitoring and analysis technology, and in particular to a collaborative correction method for representativeness error of automatic river water quality monitoring data. Background Technology

[0002] Currently, there are two main methods for river water quality monitoring. One method utilizes river crossing facilities such as cableways and boats to establish multiple vertical lines along the river cross-section, with multiple measuring points along these lines. Water quality parameters at each measuring point are manually sampled and analyzed to infer the distribution of water quality parameters across the river cross-section. This method offers high monitoring accuracy and good cross-sectional representativeness, but the sampling and analysis process is time-consuming and lacks timeliness. The other method involves establishing automatic water quality monitoring stations. Water quality parameters are automatically sampled at one or more points along the riverbank and analyzed on-site, assuming the samples can represent the distribution of water quality parameters across the river cross-section. This method offers better timeliness but has poor cross-sectional representativeness.

[0003] Currently, there are calculation methods that integrate data from two types of monitoring to obtain a distribution of water quality parameters that can represent the river cross section and meet the time requirements. However, most of these methods only convert the data from automatic water quality stations into full-section sampling data through correlation analysis between the automatic water quality station data and the full-section sampling data. They only correct systematic errors and lack effective correction techniques for random errors caused by changes in precipitation distribution, sampling process, and analysis methods. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a collaborative correction method for representativeness errors in automatic river water quality monitoring data.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides a method for collaborative correction of representativeness errors in automatic river water quality monitoring data, comprising the following steps:

[0007] S1. Correlation analysis between automatic water quality monitoring station data and full-section sampling monitoring data;

[0008] S2, Correction of representative systematic errors in water quality automatic monitoring station data sequences;

[0009] S3. Calculation of synchronization error between automatic water quality monitoring station and full-section sampling monitoring data;

[0010] S4. Correction of random errors in the representativeness of water quality automatic monitoring station data.

[0011] Furthermore, S1 specifically involves: extracting full-section sampling and monitoring data (x1, x2, ... x) from samples taken at the same time. n) and water quality automatic monitoring station data (y1, y2, ... y n A correlation model was established between full-section sampling monitoring data and automatic water quality station monitoring data.

[0012] Furthermore, S2 specifically involves: using full-section sampling monitoring data and automatic water quality monitoring data, and combining all monitoring data sequences (ρ1, ρ2, ... ρ) from the automatic water quality monitoring stations. k ) is converted into a full-section data sequence of automatic water quality monitoring stations (z1, z2, ... z k ), and record the corresponding time (t1, t2, ... t). k ).

[0013] Furthermore, S3 specifically involves: using the full-section data sequence (z1, z2, ... z) converted from automatic water quality monitoring stations. k Interpolation of two adjacent full-section samples x i x i+1 Corresponding time t i ′, t i+1 Full-section data after conversion of automatic water quality monitoring station z i ′,z i+1 ′;

[0014] Calculate the synchronization error between the full-section data and the full-section sampling and monitoring data after the automatic water quality monitoring station conversion: Δ i ′=x i -z i ′,Δ i+1 ′=x i+1 -z i+1 ′.

[0015] Furthermore, S4 specifically involves: calculating the synchronization error Δ between the converted full-section data from the automatic water quality monitoring station and the full-section sampling and monitoring data. i ′,Δ i+1 The time-segmented interpolation method was used to correct the full-section data sequence (z1, z2, ... z) after conversion from the automatic water quality monitoring station. k ) located at t i ′, t i+1 The sequence z between ' ij ′, (j=1,2,…m):

[0016] Among them, z ij Located at t i ′, t i+1 Full-section data sequence after conversion of automatic water quality monitoring stations between ′; t ij Located at t i ′, t i+1 The sampling time corresponding to the full-section data sequence after the automatic water quality monitoring station conversion between ′;

[0017] Calculate the sequence z of full-section sampling and monitoring time. ij That is, to obtain the full-section data sequence of automatic water quality monitoring stations (z1′, z2′, ... z′) after revision by systematic and random errors. k ′).

[0018] Furthermore, the aforementioned automatic water quality station refers to a water quality monitoring station that achieves real-time and continuous monitoring of water quality through single-point or multi-point automatic sampling or automatic analysis.

[0019] The beneficial effects of this invention are: it can solve the problem of cross-sectional representativeness of water quality automatic monitoring station data; it can eliminate the influence of short-term accidental factors on water quality automatic monitoring station data; and it can improve the accuracy and rationality of river cross-section water quality monitoring. Attached Figure Description

[0020] Figure 1 is a flowchart of a collaborative correction method for representative errors in river water quality automatic monitoring station data;

[0021] Figure 2 shows the correlation analysis between the automatic water quality monitoring station and the total nitrogen concentration in the full cross-section sampling in the embodiment;

[0022] Figure 3 is a daily total nitrogen concentration curve of the automatic water quality station after revision for systematic and random errors in the embodiment. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] Please refer to Figure 1. A collaborative correction method for representativeness errors in automatic river water quality monitoring data includes the following steps:

[0025] S1. Correlation analysis between automatic water quality monitoring station data and full-section sampling monitoring data;

[0026] S2, Correction of representative systematic errors in water quality automatic monitoring station data sequences;

[0027] S3. Calculation of synchronization error between automatic water quality monitoring station and full-section sampling monitoring data;

[0028] S4. Correction of random errors in the representativeness of water quality automatic monitoring station data.

[0029] In one specific implementation, an automatic water quality station refers to a water quality monitoring station that achieves real-time and continuous monitoring of water quality through automatic sampling and analysis at single or multiple points.

[0030] Full-section sampling involves arranging multiple vertical lines and measuring points along the control section to reflect the water quality distribution characteristics of the river section.

[0031] Automatic water quality monitoring data refers to the concentration of various substances in existing rivers, including physical, chemical, and biological indicator substances.

[0032] Specifically, S1 involves extracting full-section sampling and monitoring data (x1, x2, ... x) from samples taken at the same time. n ) and water quality automatic monitoring station data (y1, y2, ... y n A correlation model was established between full-section sampling monitoring data and automatic water quality station monitoring data.

[0033] The same sampling time means that the sampling time of the full section is the same as that of the automatic water quality monitoring station. If the sampling time of the full section is not much different from that of the automatic water quality monitoring station, the sampling data of the automatic water quality monitoring station corresponding to the full section sampling time can be derived by using the two sampling data of the automatic water quality monitoring station before and after the full section sampling time and the process line linear interpolation method, so as to achieve the same sampling time.

[0034] The data includes full-section sampling monitoring data and automatic water quality monitoring data, including linear, nonlinear and intelligent correlation models determined through mathematical statistics and AI big data analysis.

[0035] Specifically, S2 involves using full-section sampling monitoring data and automatic water quality monitoring data from water quality stations to analyze the entire monitoring data sequence (ρ1, ρ2, ... ρ) from the automatic water quality stations. k ) is converted into a full-section data sequence of automatic water quality monitoring stations (z1, z2, ... z k ), and record the corresponding time (t1, t2, ... t). k ).

[0036] Among them, representative systematic error refers to the error that exists between automatic water quality stations and full-section sampling due to insufficient vertical lines and measurement point layout.

[0037] Specifically, S3 refers to: using the full-section data sequence (z1, z2, ... z) after conversion from automatic water quality monitoring stations. k Interpolation of two adjacent full-section samples x i x i+1 Corresponding time t i ′, t i+1 Full-section data after conversion of automatic water quality monitoring station z i ′,z i+1 ′;

[0038] Calculate the synchronization error between the full-section data and the full-section sampling and monitoring data after the automatic water quality monitoring station conversion: Δ i ′=x i -z i ′,Δ i+1 ′=x i+1 -zi+1 ′.

[0039] Specifically, S4 involves: calculating the synchronization error Δ between the converted full-section data from the automatic water quality monitoring station and the full-section sampling and monitoring data. i ′,Δ i+1 The time-segmented interpolation method was used to correct the full-section data sequence (z1, z2, ... z) after conversion from the automatic water quality monitoring station. k ) located at t i ′, t i+1 The sequence z between ' ij ′, (j=1,2,…m):

[0040] Among them, z ij Located at t i ′, t i+1 Full-section data sequence after conversion of automatic water quality monitoring stations between ′; t ij Located at t i ′, t i+1 The sampling time corresponding to the full-section data sequence after the automatic water quality monitoring station conversion between ′;

[0041] Calculate the sequence z of full-section sampling and monitoring time. ij That is, to obtain the full-section data sequence of automatic water quality monitoring stations (z1′, z2′, ... z′) after revision by systematic and random errors. k ′).

[0042] The aforementioned automatic water quality station refers to a water quality monitoring station that achieves real-time and continuous monitoring of water quality through single-point or multi-point automatic sampling or automatic analysis.

[0043] Representative random error refers to the error between automatic water quality monitoring stations and full-section sampling within a certain period of time, caused by random factors such as sampling process and analysis methods.

[0044] Example:

[0045] Taking the total nitrogen index at water quality station A in a certain watershed as an example, manual monitoring at two points along two vertical lines on the left and right banks of the cross section was conducted monthly; the automatic monitoring station was set up on the left bank for shoreline monitoring, conducting four monitoring sessions daily. Statistical fitting was performed on the results of 36 manual monitoring sessions from 2021 to 2023 with the automatic monitoring results from the same sampling time. The automatic monitoring data was restored by referencing the full-section manual monitoring data, resulting in the corrected automatic monitoring data Y = 0.8774x + 0.2036, as shown in Figure 2.

[0046] Based on the conversion relationship, the total nitrogen concentration of the automatic water quality monitoring station is converted to the total nitrogen concentration of the entire cross-section. For example, if the monitoring data of the automatic station is 1.40 mg / L, it will be converted to 1.43 mg / L.

[0047] Taking the daily monitoring data of water quality station A in June as an example, calculate the synchronization error between the full-section data and the full-section sampling monitoring data after the automatic water quality station conversion according to S3;

[0048] The automatic station's correction concentration is corrected a second time according to the formula, as shown in Figure 3.

[0049] The embodiments described above are merely illustrative of implementation methods of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be defined by the appended claims.

Claims

1. A method for collaborative correction of representativeness errors in automatic river water quality monitoring data, characterized in that, Includes the following steps: S1. Correlation analysis between automatic water quality monitoring station data and full-section sampling monitoring data; S2, Correction of representative systematic errors in water quality automatic monitoring station data sequences; S3. Calculation of synchronization error between automatic water quality monitoring station and full-section sampling monitoring data; S4. Correction of random errors in the representativeness of water quality automatic monitoring station data; Specifically, S1 involves extracting full-section sampling and monitoring data (x1, x2, ... x) from samples taken at the same time. n ) and water quality automatic monitoring station data (y1, y2, ... y n A correlation model was established between full-section sampling monitoring data and automatic water quality station monitoring data; Specifically, S2 involves using full-section sampling monitoring data and automatic water quality monitoring data from water quality stations to analyze the entire monitoring data sequence (ρ1, ρ2, ... ρ) from the automatic water quality stations. k ) is converted into a full-section data sequence of automatic water quality monitoring stations (z1, z2, ... z k ), and record the corresponding time (t1, t2, ... t). k ); Specifically, S3 refers to: using the full-section data sequence (z1, z2, ... z) after conversion from automatic water quality monitoring stations. k Interpolation of two adjacent full-section samples x i x i+1 Corresponding time t i ′, t i+1 Full-section data after conversion of automatic water quality monitoring station z i ′,z i+1 ′; Calculate the synchronization error between the full-section data and the full-section sampling and monitoring data after the automatic water quality monitoring station conversion: Δ i ′=x i -z i ′,Δ i+1 ′=x i+1 -z i+1 ′; Specifically, S4 involves: calculating the synchronization error Δ between the converted full-section data from the automatic water quality monitoring station and the full-section sampling and monitoring data. i ′,Δ i+1 The automatic water quality monitoring station was corrected using a time-segmented interpolation method. Transformed full-section data sequence (z1, z2, ... z k ) located at t i ′, t i+1 The sequence z between ' ij ′, (j=1,2,…m): Among them, zij is located at t i ′, t i+1 The full-section data sequence after conversion of the automatic water quality monitoring stations between ti′ and ti+1′; the sampling time corresponding to the full-section data sequence after conversion of the automatic water quality monitoring stations between ti′ and ti+1′; Calculate the sequence z of full-section sampling and monitoring time. ij That is, to obtain the full-section data sequence of automatic water quality monitoring stations (z1′, z2′, ... z′) after revision by systematic and random errors. k ′).

2. The method for collaborative correction of representativeness error in automatic river water quality monitoring data according to claim 1, characterized in that, The aforementioned automatic water quality station refers to a water quality monitoring station that achieves real-time and continuous monitoring of water quality through single-point or multi-point automatic sampling or automatic analysis.

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