Multi-parameter water quality online detection method

Abstract

The invention discloses a multi-parameter water quality online detection method, relates to the technical field of water quality detection, and solves the problem that an original numerical evaluation mode is not standard and cannot achieve a more accurate numerical detection effect. Key water quality pollution data such as turbidity, pH value and dissolved oxygen content are collected in real time by using multiple monitoring sensors in the water quality monitor, the water quality change dynamic state can be comprehensively captured at high frequency, and the evolution process of water quality data at different moments is intuitively presented in a manner of generating a water quality change curve; a clear data visualization basis is provided for subsequent deep analysis, numerical feature and curve segment feature analysis is performed based on a curve, especially a series of steps of locking feature time periods are performed, a water quality change rule is fully excavated, the overall accuracy of detection data is guaranteed, the most representative time period of water quality change can be accurately identified, and the accuracy of water quality detection is improved. And the subsequent water quality evaluation is more targeted and reliable.

Description

Technical Field

[0001] The invention relates to the technical field of water quality detection, and in particular to a multi-parameter water quality online detection method. Background Art

[0002] In today's society, water resources are basic resources for human survival and development, and their quality is directly related to the stability of the ecosystem, human health and well-being, and the sustainable development of various industries; however, traditional water quality testing methods have exposed many limitations when responding to increasingly complex water environment monitoring needs.

[0003] The application with publication number CN105974079A discloses an online water quality monitoring method and system, including: a water quality detection device obtains multiple water quality parameters of a water sample to be detected in the form of analog signals; the water quality detection device at least sends the obtained multiple water quality parameters to a server; the server converts the received multiple water quality parameters into digital signals; the server selects to send the water quality parameters converted into digital signals to a user terminal that requires the water quality parameters.

[0004] For the online detection process of water quality, it is generally necessary to place the associated water quality detector in the designated detection area. However, the water flow is always in a flowing state, and the detected water quality data is also in a constantly fluctuating state. The numerical evaluation is not standard to determine whether there is any abnormality in the water quality based on the constantly fluctuating water quality data, and a more accurate numerical detection effect cannot be achieved, and its numerical characteristics are not obvious. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a multi-parameter water quality online detection method, which solves the problem that the original numerical evaluation method is not standard and cannot achieve a more accurate numerical detection effect.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-parameter water quality online detection method, comprising the following steps: Step 1: Set the monitoring period, place the water quality monitor at the designated monitoring location, and confirm the different water quality data monitored during the monitoring period to generate a water quality change curve for different water quality data during this monitoring period. The specific method is as follows: Based on the set monitoring period, different water quality data associated with different moments in the monitoring period are confirmed, wherein the monitoring period is a preset period, the time line of the corresponding monitoring period is used as the horizontal coordinate axis, and the relevant parameters of the water quality data are used as the vertical coordinate axis. Based on the corresponding water quality data associated with the corresponding moments, corresponding points are confirmed in the constructed two-dimensional coordinate system, and several groups of points in the same two-dimensional coordinate system are connected to confirm the water quality change curve associated with the corresponding water quality data; Step 2: Based on different water quality change curves associated with different water quality data, confirm the numerical characteristics of the corresponding water quality change curves, and then confirm the segment characteristics associated with different curve segments in the water quality change curves based on the numerical characteristics. Perform a unified analysis on multiple groups of water quality change curves and lock the characteristic time period. The specific method is as follows: S21. According to different water quality data associated with different moments in the water quality change curve, several groups of water quality data are averaged, and the average curve belonging to the water quality change curve is confirmed, and the confirmed average curve is calibrated in the two-dimensional coordinate system where the water quality change curve is located, and the average curves associated with different water quality change curves are confirmed and calibrated in turn; S22. Based on the water quality change curve and the associated mean curve, the curve segment characteristics of different curve segments in the water quality change curve are confirmed. The time characteristics associated with different curve segments are not less than five groups of unit moments. Each curve segment selected from the water quality change curve is different. For a single selected curve segment, the different water quality data associated with different moments in this curve segment are calibrated as S i , where i represents different moments, and then the mean water quality data associated with the mean curve corresponding to the water quality change curve is calibrated as J, using: S i -J=C i Confirm the difference data C associated with the corresponding water quality data i , and then the difference data C associated with multiple groups of time in this curve segment i Perform mean processing to lock the curve segment features of this curve segment; Then confirm the associated time period of this curve segment, lock other curve segments associated with the associated time period from other water quality change curves, and confirm the curve segment characteristics associated with other curve segments; The multiple groups of curve segment features associated with different curve segments belonging to the same group of associated time periods are recorded and marked as TZ j-q , where j represents different associated time periods, and q represents different curve segments in the corresponding associated time periods; S23, selecting the minimum value and the maximum value from different water quality change curves, confirming the parameter interval associated with the corresponding water quality change curve, synchronously dividing the parameter interval associated with the different water quality change curves into ten equal parts, and recording the equal division points, quantizing the corresponding first group of equal division points to 0, quantizing the second group of equal division points to 1, ..., quantizing the last group of equal division points to 10; S24, according to the TZ recorded in the corresponding associated time period j-q , confirm the corresponding value TZ j-q The associated parameter interval confirms this TZ j-q , located in the parameter interval, confirm TZ based on the confirmed location j-q The associated quantized value LH j-q, associate multiple groups of quantized values ​​LH associated with the time period j-q Performing mean processing, confirming the quantitative mean, and recording the confirmed quantitative mean as the time period feature of this associated time period; S25, selecting a maximum value from the different time period features corresponding to the different associated time periods, and recording the associated time period corresponding to the maximum value as the feature time period; Step 3: Based on the confirmed characteristic time period, confirm the associated curve segment from each water quality change curve, and then lock the data to be tested of the corresponding test item based on the curve segment. According to the standard intervals associated with different test items, conduct a unified analysis of the abnormal degree of water quality of multiple groups of test items and determine the abnormal parameters. The specific sub-steps are as follows: S31, based on the characteristic time period, confirm the associated curve segment from each water quality change curve, perform mean processing on multiple groups of water quality data associated with the corresponding curve segment, and confirm the to-be-tested data associated with the corresponding detection item; S32, confirm the standard interval associated with the corresponding detection item, the standard interval is a preset interval, randomly select a point in a plane, and use this point as a reference point, then based on the specific number G of detection items, generate G straight lines of equal distance around the reference point, the starting points of the G straight lines are all the reference points, and different straight lines correspond to different detection items, and generate relevant scales for different detection items on different straight lines, confirm the points where the endpoint values ​​of the preset interval are located based on the generated scales, record them as feature points, connect the feature points on several adjacent straight lines, generate a set of polygons, and record these polygons as standard polygons; S33, based on the to-be-checked data associated with different detection items, find the corresponding scale on the straight line associated with the corresponding detection item and calibrate the data points, then connect the data points on several adjacent straight lines to confirm a group of polygons to be checked; S34, identifying whether the overall outline of the polygon to be checked exceeds the radiation range of the standard polygon. If so, directly generating and displaying a water quality abnormality signal; if not, it means that the water quality data monitored during this monitoring period is normal; S35. After the abnormal water quality signal is generated, confirm the overall area M1 of the polygon to be checked, and then confirm the overall area M2 of the standard polygon. If M1>M2, use: (M1÷M2)×100%=YC to confirm the abnormal parameter YC, and display the confirmed abnormal parameter YC synchronously; if M1≤M2, keep the original abnormal water quality signal unchanged.

[0007] Preferably, it also includes: Step 4: Based on the monitoring characteristics associated with different monitoring cycles, lock the monitoring cycle where abnormal parameters are generated, and sort the abnormal parameters generated based on the time sequence, and based on the change characteristics of the sorted abnormal parameters, confirm whether a trend abnormal signal is generated. The specific method is as follows: According to the time-ordering method, the abnormal parameters associated with different monitoring periods are sorted to confirm the abnormal parameter sorting sequence; Confirm the changes of adjacent parameters in the abnormal parameter sorting sequence. Among the adjacent parameters, if the latter group of abnormal parameters is higher than the previous group of abnormal parameters, record the trend abnormal parameter column. If the recorded trend abnormal parameter column exceeds three groups, directly generate a trend abnormal signal for display. If it does not exceed three groups, no trend abnormal signal is generated.

[0008] The present invention provides a multi-parameter water quality online detection method. Compared with the prior art, it has the following beneficial effects: The present invention utilizes multiple monitoring sensors in the water quality monitor to collect key water pollution data such as turbidity, pH, dissolved oxygen content, etc. in real time, and can capture the dynamics of water quality changes at a high frequency and in a comprehensive manner. The water quality change curve generated intuitively presents the evolution process of water quality data at different times, providing a clear data visualization basis for subsequent in-depth analysis. The numerical characteristics and segment characteristics are analyzed based on the curve, especially a series of steps to lock the characteristic time period, which fully explores the law of water quality changes, ensures the overall accuracy of the detection data, and can accurately identify the most representative time period of water quality changes, making the subsequent water quality assessment more targeted and reliable; By constructing standard polygons and polygons to be checked, and comparing the data to be tested based on the test items with the standard interval, it is possible to intuitively and efficiently identify whether the water quality is abnormal. It can not only quickly generate water quality abnormality signals, but also calculate abnormal parameters by comparing the polygon areas when an abnormality occurs, accurately quantify the degree of water quality abnormality, and comprehensively display multiple water quality characteristics during the monitoring period, greatly improving the accuracy and comprehensiveness of water quality abnormality judgments. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram of determining the polygon to be checked in the present invention. DETAILED DESCRIPTION

[0010] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] First embodiment See also Figure 1 , the present application provides a multi-parameter water quality online detection method, comprising the following steps: Step 1: Set the monitoring cycle, place the water quality monitor at the designated monitoring location, and confirm the different water quality data monitored during the monitoring cycle to generate a water quality change curve for different water quality data during this monitoring cycle. Specifically, the water quality monitor is equipped with a variety of monitoring sensors, which can monitor the monitoring parameters associated with the water quality in real time. The monitored data include turbidity, pH value, dissolved oxygen content and other water pollution data. The specific number of monitoring items is determined by the built-in sensor. The specific method of generating water quality change curves for different water quality data is: Based on the set monitoring period, different water quality data associated with different moments in the monitoring period are confirmed. The monitoring period is a preset period, which is formulated by the operator based on experience, and is generally 1 hour or even shorter. The timeline of the corresponding monitoring period is used as the horizontal coordinate axis, and the relevant parameters of the water quality data are used as the vertical coordinate axis. Based on the corresponding water quality data associated with the corresponding moments, the corresponding points are confirmed in the constructed two-dimensional coordinate system, and several groups of points in the same two-dimensional coordinate system are connected to confirm the water quality change curve associated with the corresponding water quality data.

[0012] Step 2: Based on different water quality change curves associated with different water quality data, confirm the numerical characteristics of the corresponding water quality change curves, and then confirm the segment characteristics associated with different curve segments in the water quality change curves based on the numerical characteristics, and analyze multiple groups of water quality change curves in a unified manner to lock the characteristic time period. Specifically, because the water is in a flowing state, the different water quality characteristics collected in each stage are different. In order to confirm the most characteristic time period, it is necessary to make specific confirmation based on the data characteristics of the curve, and lock the time period characteristics from the confirmed data characteristics to ensure the overall accuracy of subsequent test data; The specific sub-steps of locking the characteristic period are as follows: S21. According to different water quality data associated with different moments in the water quality change curve, several groups of water quality data are averaged, and the average curve belonging to the water quality change curve is confirmed, and the confirmed average curve is calibrated in the two-dimensional coordinate system where the water quality change curve is located, and the average curves associated with different water quality change curves are confirmed and calibrated in turn; S22. Based on the water quality change curve and the associated mean curve, the curve segment characteristics of different curve segments in the water quality change curve are confirmed. The time characteristics associated with different curve segments are not less than five groups of unit moments. The unit moment is generally 1 second, that is, the time length involved in the curve segment shall not be less than five seconds. The maximum value of the time length is not limited. The curve segments selected from the water quality change curve are different each time. For example, assuming that the time length of the corresponding water quality change curve is 10 seconds, the water quality data change lines corresponding to 0-10 are respectively, then When selecting curve segments, the following may be included: a curve segment of 0-5 seconds, a curve segment of 0-6 seconds, a curve segment of 0-7 seconds, a curve segment of 0-8 seconds, a curve segment of 0-9 seconds, a curve segment of 1-6 seconds, a curve segment of 1-7 seconds, a curve segment of 1-8 seconds, a curve segment of 1-9 seconds, a curve segment of 2-7 seconds, a curve segment of 2-8 seconds, a curve segment of 2-9 seconds, ... and so on. Multiple groups of different curve segments may be selected and divided. For a single selected curve segment, different water quality data associated with different moments in the curve segment are calibrated as S i , where i represents different moments, and then the mean water quality data associated with the mean curve corresponding to the water quality change curve is calibrated as J, using: S i -J=C i Confirm the difference data C associated with the corresponding water quality data i , and then the difference data C associated with multiple groups of time in this curve segment i Perform mean processing to lock the curve segment features of this curve segment; Then confirm the associated time period of this curve segment, lock other curve segments associated with the associated time period from other water quality change curves, and confirm the curve segment characteristics associated with other curve segments; The multiple groups of curve segment features associated with different curve segments belonging to the same group of associated time periods are recorded and marked as TZ j-q , where j represents different associated time periods, and q represents different curve segments in the corresponding associated time periods; S23, selecting the minimum value and the maximum value from different water quality change curves, confirming the parameter interval associated with the corresponding water quality change curve, synchronously dividing the parameter interval associated with the different water quality change curves into ten equal parts, and recording the equal division points (the equal division points include the minimum value point and the maximum value point of the parameter interval), quantizing the corresponding first group of equal division points to 0, quantizing the second group of equal division points to 1, ..., quantizing the last group of equal division points to 10; S24, according to the TZ recorded in the corresponding associated time period j-q , confirm the corresponding value TZ j-q The associated parameter interval confirms this TZ j-q , located in the parameter interval, confirm TZ based on the confirmed location j-q The associated quantized value LH j-q , associate multiple groups of quantized values ​​LH associated with the time period j-q Performing mean processing, confirming the quantitative mean, and recording the confirmed quantitative mean as the time period feature of this associated time period; S25. Based on the different time period characteristics corresponding to different associated time periods, the maximum value is selected, and the associated time period corresponding to the maximum value is recorded as the characteristic time period. Specifically, the multiple groups of curve characteristics associated with this characteristic time period have the strongest characteristic expression in terms of comprehensive performance. Based on the location of the corresponding value in the interval, numerical quantization is performed, and numerical evaluation can be effectively performed to specifically confirm the quantitative mean according to the location of the corresponding value in the interval, and by selecting the time period for the confirmed quantitative mean, the most characteristic characteristic time period is selected, and water quality evaluation is performed on the multiple groups of data associated with the selected characteristic time period to ensure the specific accuracy of the corresponding water quality evaluation; Step 3: Based on the confirmed characteristic time period, the associated curve segment is confirmed from each water quality change curve, and then the to-be-tested data of the corresponding test item is locked based on the curve segment. According to the standard intervals associated with different test items, the abnormal degree of water quality of multiple groups of test items is uniformly analyzed, and the abnormal parameter output is determined. The specific sub-steps for determining the abnormal parameters are: S31, based on the characteristic time period, confirm the associated curve segment from each water quality change curve, perform mean processing on multiple groups of water quality data associated with the corresponding curve segment, and confirm the to-be-tested data associated with the corresponding detection item; S32, confirm the standard interval associated with the corresponding detection item, the standard interval is a preset interval, the endpoint values ​​of the preset interval are all preset values, which are formulated by the operator based on experience, randomly select a point in a plane, and use this point as a reference point, and then based on the specific number G of detection items, generate G equidistant straight lines around the reference point (the G straight lines are in an equidistant arrangement around the reference point), the starting points of the G straight lines are all the reference points, and different straight lines correspond to different detection items, and related scales for different detection items are generated on different straight lines. The points where the endpoint values ​​of the preset interval are located are confirmed based on the generated scales and recorded as feature points, and the feature points on several adjacent straight lines are connected to generate a set of polygons, which are recorded as standard polygons; S33, based on the to-be-checked data associated with different detection items, find the corresponding scale on the straight line associated with the corresponding detection item and calibrate the data points, then connect the data points on several adjacent straight lines to confirm a group of polygons to be checked; S34, identifying whether the overall outline of the polygon to be checked exceeds the radiation range of the standard polygon. If so, directly generating and displaying a water quality abnormality signal; if not, it means that the water quality data monitored during this monitoring period is normal; S35, after the abnormal water quality signal is generated, confirm the overall area M1 of the polygon to be checked, and then confirm the overall area M2 of the standard polygon. If M1≤M2, keep the original abnormal water quality signal unchanged. If M1>M2, use: (M1÷M2)×100%=YC to confirm the abnormal parameter YC, and display the confirmed abnormal parameter YC synchronously; Specific, combined Figure 2 , first select a group of reference points, and then generate the corresponding number of straight lines according to the number of detection items. Different detection items correspond to different straight lines. There are relevant scales on the straight lines corresponding to the detection item data. Different detection items have different standard intervals. According to the endpoint values ​​of the standard interval, the corresponding feature points are confirmed and the corresponding standard polygons are generated. Then, according to the data to be detected associated with different detection items, the associated specific scales are found on the corresponding straight lines, and the data points are calibrated based on the corresponding scales. Then, the data points on adjacent straight lines are connected to generate a group of polygons to be checked. Figure 2 If the polygon to be checked far exceeds the standard polygon, an abnormal water quality signal will be generated, and specific confirmation of the abnormal parameters is required. The abnormal parameters are the associated percentage parameters, which can fully display the multiple water quality characteristics associated with the corresponding monitoring period and ensure the comprehensiveness of this monitoring process.

[0013] Second embodiment Step 4: Based on the monitoring characteristics associated with different monitoring cycles, lock the monitoring cycle where abnormal parameters are generated, and sort the abnormal parameters generated based on the time sequence, and based on the change characteristics of the sorted abnormal parameters, confirm whether a trend abnormal signal is generated. The specific method for confirmation is: According to the time-ordering method, the abnormal parameters associated with different monitoring periods are sorted to confirm the abnormal parameter sorting sequence; Confirm the changes of adjacent parameters in the abnormal parameter sorting sequence. Among the adjacent parameters, if the latter group of abnormal parameters is higher than the former group of abnormal parameters, record the trend abnormal parameter column. If the recorded trend abnormal parameter column exceeds three groups, directly generate the trend abnormal signal for display. If it does not exceed three groups, no trend abnormal signal is generated. Specifically, the proposed abnormal parameter sorting sequence is {10, 11, 13, 15, 16, ...}, that is, the trend abnormal parameter column is: 10-11, 11-13, 13-15, 15-16. There are four groups. If three groups exceed the set values, the corresponding trend abnormal signal will be directly generated for display.

[0014] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0015] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-parameter water quality online detection method, characterized in that: The following steps are involved: Step 1: Set the monitoring period, place the water quality monitor at the designated monitoring location, confirm the different water quality data monitored during the monitoring period, and generate a water quality change curve for different water quality data during this monitoring period; Step 2: based on different water quality change curves associated with different water quality data, confirm the numerical characteristics of the corresponding water quality change curves, and then confirm the segment characteristics associated with different curve segments in the water quality change curves based on the numerical characteristics, and analyze multiple groups of water quality change curves in a unified manner to lock the characteristic time period; Step 3: Based on the confirmed characteristic time period, confirm the associated curve segment from each water quality change curve, and then lock the data to be tested of the corresponding test item based on the curve segment. According to the standard intervals associated with different test items, conduct a unified analysis of the degree of water quality abnormality of multiple groups of test items and determine the abnormal parameters.

2. A multi-parameter water quality online detection method according to claim 1, characterized in that: In step 1, the specific method of generating the water quality change curve of different water quality data is: Based on the set monitoring period, different water quality data associated with different times within the monitoring period are confirmed. The monitoring period is a preset period, with the timeline of the corresponding monitoring period as the horizontal coordinate axis, and the relevant parameters of the water quality data as the vertical coordinate axis. Based on the corresponding water quality data associated with the corresponding time, the corresponding points are confirmed in the constructed two-dimensional coordinate system, and several groups of points in the same two-dimensional coordinate system are connected to confirm the water quality change curve associated with the corresponding water quality data.

3. A multi-parameter water quality online detection method according to claim 1, characterized in that: In the step 2, the specific method of confirming the segment characteristics associated with different curve segments in the water quality change curve based on the numerical characteristics is: S21. According to different water quality data associated with different moments in the water quality change curve, several groups of water quality data are averaged, and the average curve belonging to the water quality change curve is confirmed, and the confirmed average curve is calibrated in the two-dimensional coordinate system where the water quality change curve is located, and the average curves associated with different water quality change curves are confirmed and calibrated in turn; S22. Based on the water quality change curve and the associated mean curve, the curve segment characteristics of different curve segments in the water quality change curve are confirmed. The time characteristics associated with different curve segments are not less than five groups of unit moments. Each curve segment selected from the water quality change curve is different. For a single selected curve segment, the different water quality data associated with different moments in this curve segment are calibrated as S i , where i represents different moments, and then the mean water quality data associated with the mean curve corresponding to the water quality change curve is calibrated as J, using: S i -J=C i Confirm the difference data C associated with the corresponding water quality data i , and then the difference data C associated with multiple groups of time in this curve segment i Perform mean processing to lock the curve segment features of this curve segment; Then confirm the associated time period of this curve segment, lock other curve segments associated with the associated time period from other water quality change curves, and confirm the curve segment characteristics associated with other curve segments; The multiple groups of curve segment features associated with different curve segments belonging to the same group of associated time periods are recorded and marked as TZ j-q , where j represents different associated time periods, and q represents different curve segments within the corresponding associated time periods.

4. A multi-parameter water quality online detection method according to claim 3, characterized in that: In step 3, multiple groups of water quality change curves are analyzed uniformly, and the specific method of locking the characteristic time period is as follows: S23, selecting the minimum value and the maximum value from different water quality change curves, confirming the parameter interval associated with the corresponding water quality change curve, synchronously dividing the parameter interval associated with the different water quality change curves into ten equal parts, and recording the equal division points, quantizing the corresponding first group of equal division points to 0, quantizing the second group of equal division points to 1, ..., quantizing the last group of equal division points to 10; S24, according to the TZ recorded in the corresponding associated time period j-q , confirm the corresponding value TZ j-q The associated parameter interval confirms this TZ j-q , located in the parameter interval, confirm TZ based on the confirmed location j-q The associated quantized value LH j-q , associate multiple groups of quantized values ​​LH associated with the time period j-q Performing mean processing to confirm the quantitative mean, and recording the confirmed quantitative mean as the time period feature of this associated time period; S25. Based on the different time period features corresponding to the different associated time periods, a maximum value is selected, and the associated time period corresponding to the maximum value is recorded as the characteristic time period.

5. A multi-parameter water quality online detection method according to claim 1, characterized in that: In step 3, the specific sub-steps for determining abnormal parameters are: S31, based on the characteristic time period, confirm the associated curve segment from each water quality change curve, perform mean processing on multiple groups of water quality data associated with the corresponding curve segment, and confirm the to-be-tested data associated with the corresponding detection item; S32, confirm the standard interval associated with the corresponding detection item, the standard interval is a preset interval, randomly select a point in a plane, and use this point as a reference point, then based on the specific number G of detection items, generate G straight lines of equal distance around the reference point, the starting points of the G straight lines are all the reference points, and different straight lines correspond to different detection items, and generate relevant scales for different detection items on different straight lines, confirm the points where the endpoint values ​​of the preset interval are located based on the generated scales, record them as feature points, connect the feature points on several adjacent straight lines, generate a set of polygons, and record these polygons as standard polygons; S33, based on the to-be-checked data associated with different detection items, find the corresponding scale on the straight line associated with the corresponding detection item and calibrate the data points, then connect the data points on several adjacent straight lines to confirm a group of polygons to be checked; S34, identifying whether the overall outline of the polygon to be checked exceeds the radiation range of the standard polygon. If so, directly generating and displaying a water quality abnormality signal; if not, it means that the water quality data monitored during this monitoring period is normal; S35. After the abnormal water quality signal is generated, confirm the overall area M1 of the polygon to be checked, and then confirm the overall area M2 of the standard polygon. If M1>M2, use: (M1÷M2)×100%=YC to confirm the abnormal parameter YC, and display the confirmed abnormal parameter YC synchronously.

6. A multi-parameter water quality online detection method according to claim 5, characterized in that: In step S35, if M1≤M2, the original abnormal water quality signal is kept unchanged.

7. A multi-parameter water quality online detection method according to claim 1, characterized in that: Also includes: Step 4: Based on the monitoring characteristics associated with different monitoring cycles, lock the monitoring cycle where abnormal parameters are generated, and sort the generated abnormal parameters based on the time sequence, and based on the change characteristics of the sorted abnormal parameters, confirm whether a trend abnormal signal is generated.

8. A multi-parameter water quality online detection method according to claim 7, characterized in that: In step 4, the specific method of confirming whether a trend abnormality signal is generated is: According to the time-ordering method, the abnormal parameters associated with different monitoring periods are sorted to confirm the abnormal parameter sorting sequence; Confirm the changes of adjacent parameters in the abnormal parameter sorting sequence. Among the adjacent parameters, if the latter group of abnormal parameters is higher than the previous group of abnormal parameters, record the trend abnormal parameter column. If the recorded trend abnormal parameter column exceeds three groups, directly generate a trend abnormal signal for display. If it does not exceed three groups, no trend abnormal signal is generated.

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