An on-line multi-parameter water quality detection method

Abstract

The present invention discloses a multi-parameter on-line water quality detection method. The present invention relates to the technical field of water quality detection, and solves the problem that the original numerical evaluation method is not standard and cannot achieve a more accurate numerical detection effect. By using a variety of monitoring sensors in the water quality monitor to collect key water quality pollution data such as turbidity, acidity and alkalinity, and dissolved oxygen content in real time, the present invention can capture the dynamic changes of water quality frequently and comprehensively, and presents the evolution process of water quality data at different times in an intuitive way by generating a water quality change curve, providing a clear data visualization basis for subsequent in-depth analysis. Based on the curve, a series of steps for numerical feature and curve segment feature analysis, especially for locking the characteristic time period, fully excavate the water quality change law, ensure the overall accuracy of the detection data, can accurately identify the most representative time period of water quality change, and make the subsequent water quality evaluation more targeted and reliable.

Description

Technical Field

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

[0002] In modern society, water resources, as the basic resources for human survival and development, directly affect the stability of the ecosystem, human health and well-being, and the sustainable development of various industries. However, traditional water quality detection methods have exposed many limitations in dealing with the increasingly complex water environment monitoring requirements.

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

[0004] Regarding the online detection process of water quality, generally, the associated water quality detector needs to be placed in a specified detection area. However, the water flow is constantly in a flowing state, and the detected water quality data is also constantly fluctuating. Based on the constantly fluctuating water quality data to evaluate whether there is an abnormality in the water quality, the numerical evaluation is not standard, and it is impossible to achieve a more accurate numerical detection effect, and the numerical characteristics are not obvious. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an online multi-parameter water quality 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 object, the present invention is realized through the following technical solutions: an online multi-parameter water quality detection method, including the following steps:

[0007] Step 1: Set a monitoring period, place a water quality monitor at a specified monitoring position, and confirm different water quality data monitored within the monitoring period to generate a water quality change curve for different water quality data within this monitoring period. The specific method is as follows:

[0008] Based on the set monitoring period, confirm the different water quality data associated with different times within the monitoring period. The monitoring period is a preset period. Using the timeline of the corresponding monitoring period as the horizontal axis and the relevant parameters of the water quality data as the vertical axis, confirm the corresponding points within the constructed two-dimensional coordinate system based on the corresponding water quality data associated with the corresponding times, and connect several groups of points within the same two-dimensional coordinate system to confirm the water quality change curve associated with the corresponding water quality data;

[0009] Step 2: Based on the different water quality change curves associated with different water quality data, confirm the numerical characteristics of the corresponding water quality change curves, and then based on the numerical characteristics, confirm the curve segment characteristics associated with different curve segments within the water quality change curves. Conduct a unified analysis of multiple groups of water quality change curves to lock in the characteristic time periods. The specific method is as follows:

[0010] S21: According to the different water quality data associated with different times within the water quality change curve, perform a mean value processing on several groups of water quality data to confirm the mean curve belonging to this water quality change curve, and mark the confirmed mean curve within the two-dimensional coordinate system where this water quality change curve is located, and sequentially confirm and mark the mean curves associated with different water quality change curves;

[0011] S22: Based on the water quality change curve and the associated mean curve, confirm the curve segment characteristics of different curve segments within this water quality change curve. The time characteristics associated with different curve segments are not less than five groups of unit times, and each curve segment selected from the water quality change curve is different. For a single selected curve segment, mark the different water quality data associated with different times within this curve segment as S i , where i represents different times, and then mark the mean water quality data associated with the corresponding mean curve of this water quality change curve as J, and use: S i -J = C i to confirm the difference data C associated with the corresponding water quality data i , and then perform a mean value processing on the multiple groups of difference data C associated with multiple times within this curve segment i to lock in the curve segment characteristics regarding this curve segment;

[0012] Then confirm the associated time period of this curve segment, lock in the other curve segments associated with the associated time period from other water quality change curves, and confirm the curve segment characteristics associated with the other curve segments;

[0013] Record the multiple groups of curve segment characteristics associated with different curve segments belonging to the same group of associated time periods, and mark them as TZ j-q , where j represents different associated time periods, and q represents different curve segments within the corresponding associated time period;

[0014] S23. Select the minimum and maximum values from different water quality change curves, confirm the parameter intervals associated with the corresponding water quality change curves, equally divide the parameter intervals associated with different water quality change curves into ten equal parts synchronously, record the equally divided points, quantify the corresponding first group of equally divided points as 0, the second group of equally divided points as 1, ……, and the last group of equally divided points as 10;

[0015] S24. According to the TZ recorded during the corresponding associated period j-q , confirm the parameter interval associated with the corresponding value TZ j-q , confirm this TZ j-q , the location within the parameter interval, and based on the confirmed location, confirm the quantization value LH j-q associated with TZ j-q . Perform mean processing on multiple groups of quantization values LH j-q associated with the associated period, confirm the quantization mean value, and record the confirmed quantization mean value as the period feature of this associated period;

[0016] S25. Among the different period features corresponding to different associated periods, select the maximum value, and record the associated period corresponding to the maximum value as the feature period;

[0017] Step 3. Based on the confirmed feature period, confirm the associated curve segments from each water quality change curve, then lock the data to be detected for the corresponding detection items based on the curve segments, and conduct a unified analysis of the water quality abnormality degrees of multiple groups of detection items according to the standard intervals associated with different detection items, and determine the abnormal parameters. The specific sub-steps are as follows:

[0018] S31. Based on the feature period, confirm the associated curve segments from each water quality change curve, perform mean processing on multiple groups of water quality data associated with the corresponding curve segments, and confirm the data to be detected associated with the corresponding detection items;

[0019] S32. Confirm the standard interval associated with the corresponding detection item, and the standard interval is a preset interval. Randomly select a point in a plane and use this point as the reference point. Then, based on the specific number G of detection items, generate G equally spaced straight lines around the reference point. The starting points of the G straight lines are all the reference point, and different straight lines correspond to different detection items. Generate relevant scales for different detection items on different straight lines. Based on the generated scales, confirm the points where the endpoint values of the preset interval are located and record them as feature points. Connect the feature points on several adjacent straight lines to generate a group of polygons, and record this polygon as the standard polygon;

[0020] S33. Then, based on the data to be detected associated with different detection items, find the corresponding scales on the straight lines associated with the corresponding detection items and mark the data points, and then connect the data points on several adjacent straight lines to confirm a group of polygons to be checked;

[0021] S34. Identify whether the overall contour of the polygon to be verified exceeds the radiation range of the standard polygon. If so, directly generate a water quality anomaly signal and display it. If not, it means that the water quality data monitored during this monitoring period is normal.

[0022] S35. After the water quality anomaly signal is generated, confirm the overall area M1 of the polygon to be verified, and then confirm the overall area M2 of the standard polygon. If M1 > M2, then use: (M1 ÷ M2) × 100% = YC to confirm the anomaly parameter YC, and synchronously display the confirmed anomaly parameter YC. If M1 ≤ M2, keep the original water quality anomaly signal unchanged.

[0023] Preferably, it further includes:

[0024] Step Four: Based on the monitoring characteristics associated with different monitoring periods, lock the monitoring periods where anomaly parameters are generated, sort the generated anomaly parameters based on the chronological relationship, and based on the change characteristics of the sorted anomaly parameters, confirm whether a trend anomaly signal is generated. The specific method is as follows:

[0025] Sort the anomaly parameters associated with different monitoring periods according to the chronological order from front to back to confirm the anomaly parameter sorting sequence.

[0026] Confirm the change situation of adjacent parameters from the anomaly parameter sorting sequence. Among adjacent parameters, if the latter set of anomaly parameters is higher than the former set, record the trend anomaly parameter column. If the recorded trend anomaly parameter column exceeds three groups, directly generate a trend anomaly signal for display. If it does not exceed three groups, no trend anomaly signal is generated.

[0027] The present invention provides a multi-parameter on-line water quality detection method. Compared with the prior art, it has the following beneficial effects:

[0028] The present invention uses a variety of monitoring sensors in the water quality monitor to collect key water quality pollution data such as turbidity, acidity and alkalinity, and dissolved oxygen content in real time. It can capture the dynamic changes of water quality frequently and comprehensively, and intuitively presents the evolution process of water quality data at different times by generating a water quality change curve, providing a clear data visualization basis for subsequent in-depth analysis. Based on the curve, numerical feature and curve segment feature analysis, especially a series of steps to lock the characteristic time period, fully excavate the water quality change law, ensure the overall accuracy of the detection data, can accurately identify the most representative time period of water quality change, and make the subsequent water quality assessment more targeted and reliable.

[0029] By constructing a standard polygon and a polygon to be verified, and comparing the data to be detected of the detection item with the standard interval, it is possible to intuitively and efficiently identify whether the water quality is abnormal. It can not only quickly generate a water quality abnormality signal, but also calculate the abnormality parameter by comparing the polygon areas when an abnormality occurs, accurately quantify the degree of water quality abnormality, comprehensively display multiple water quality characteristics within the monitoring period, and greatly improve the accuracy and comprehensiveness of water quality abnormality judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic flowchart of the method of the present invention;

[0031] Figure 2 It is a schematic diagram for determining the polygon to be verified of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] The First Embodiment

[0034] Please refer to Figure 1 , the present application provides a multi-parameter on-line water quality detection method, including the following steps:

[0035] Step 1. Set a monitoring period, place a water quality monitor at a designated monitoring location, and confirm different water quality data monitored within the monitoring period to generate a water quality change curve for different water quality data within this monitoring period. Specifically, a variety of monitoring sensors are set in the water quality monitor to perform real-time monitoring on the monitoring parameters associated with the water quality. The monitored data includes water quality pollution data such as turbidity, pH value, dissolved oxygen content, etc. The specific number of monitoring items is determined by the built-in sensors;

[0036] The specific method for generating the water quality change curve of different water quality data is as follows:

[0037] Based on the set monitoring period, confirm the different water quality data associated with different times within the monitoring period. The monitoring period is a preset period, which is determined by the operator according to experience and generally takes a value of 1 h or even shorter. Taking the time line of the monitoring period as the horizontal coordinate axis and the relevant parameters of the water quality data as the vertical coordinate axis, confirm the corresponding points within the constructed two-dimensional coordinate system based on the corresponding water quality data associated with the corresponding time, and connect several groups of points within the same two-dimensional coordinate system to confirm the water quality change curve associated with the corresponding water quality data.

[0038] Step 2: Based on the different water quality change curves associated with different water quality data, confirm the numerical characteristics of the corresponding water quality change curves. Then, based on the numerical characteristics, confirm the curve segment characteristics associated with different curve segments within the water quality change curves, and conduct a unified analysis of multiple groups of water quality change curves to lock in the characteristic time periods. Specifically, since the water is in a flowing state, the different water quality characteristics collected at each stage are different. To confirm the most characteristic time periods, it is necessary to specifically confirm based on the data characteristics of the curves, and lock in the time period characteristics from the confirmed data characteristics to ensure the overall accuracy of the subsequent detection data;

[0039] Among them, the specific sub-steps for locking in the characteristic time periods are as follows:

[0040] S21: According to the different water quality data associated with different moments within the water quality change curve, perform a mean value processing on several groups of water quality data, confirm the mean curve belonging to this water quality change curve, and calibrate the confirmed mean curve within the two-dimensional coordinate system where this water quality change curve is located, and sequentially confirm and calibrate the mean curves associated with different water quality change curves;

[0041] S22: Based on the water quality change curve and the associated mean curve, confirm the curve segment characteristics of different curve segments within this water quality change curve. The time characteristics associated with different curve segments are not less than five groups of unit moments. The unit moment generally takes a value of 1 second, that is, the time length involved in its curve segment shall not be less than five seconds, and the maximum value of the time length is not limited in any way. Each curve segment selected from the water quality change curve is different. For example, assuming that the time length of the corresponding water quality change curve is 10 seconds, which is the water quality data change line corresponding to 0 - 10, then when selecting curve segments, it can include: curve segments of 0 - 5 seconds, 0 - 6 seconds, 0 - 7 seconds, 0 - 8 seconds, 0 - 9 seconds, 1 - 6 seconds, 1 - 7 seconds, 1 - 8 seconds, 1 - 9 seconds, 2 - 7 seconds, 2 - 8 seconds, 2 - 9 seconds, and so on. Multiple groups of different curve segments can be selected and divided. For a single selected curve segment, calibrate the different water quality data associated with different moments within this curve segment as S i , where i represents different moments, and then calibrate the mean water quality data associated with the corresponding mean curve of this water quality change curve as J, and use: S i -J = C i to confirm the difference data C associated with the corresponding water quality data i , and then perform a mean value processing on the multiple groups of difference data C associated with multiple moments within this curve segment i to lock in the curve segment characteristics regarding this curve segment;

[0042] Re-confirm the associated time period of this curve segment, lock the other curve segments associated with the associated time period from other water quality change curves, and confirm the curve segment characteristics associated with the other curve segments;

[0043] Record the multiple sets of curve segment characteristics associated with different curve segments belonging to the same group of associated time periods, and label them as TZ j-q , where j represents different associated time periods, and q represents different curve segments within the corresponding associated time period;

[0044] S23. Select the minimum value and the maximum value from different water quality change curves, confirm the parameter intervals associated with the corresponding water quality change curves, equally divide the parameter intervals associated with different water quality change curves into ten equal parts synchronously, and record the equal division points (the equal division points include the minimum value point and the maximum value point of the parameter interval). Quantify the corresponding first set of equal division points as 0, the second set of equal division points as 1,..., and the last set of equal division points as 10;

[0045] S24. According to the TZ recorded for the corresponding associated time period j-q , confirm the parameter interval associated with the corresponding value TZ j-q , confirm this TZ j-q , the location within the parameter interval, and based on the confirmed location, confirm the quantization value LH j-q associated with TZ j-q . Perform a mean process on the multiple sets of quantization values LH j-q associated with the associated time period, confirm the quantization mean value, and record the confirmed quantization mean value as the time period characteristic of this associated time period;

[0046] S25. Among the different time period characteristics corresponding to different associated time periods, select the maximum value, and record the associated time period corresponding to the maximum value as the characteristic time period. Specifically, in terms of the comprehensive performance of the multiple sets of curve characteristics associated with this characteristic time period, its characteristic expressiveness is the strongest. Based on the location of the corresponding value within the interval, perform numerical quantization, which can effectively perform numerical evaluation, to specifically confirm the quantization mean value according to the location of the corresponding value within the interval, and select the most characteristic characteristic time period by selecting the quantization mean value, and perform water quality evaluation on the multiple sets of data associated with the selected characteristic time period to ensure the specific accuracy of the corresponding water quality evaluation;

[0047] Step 3. Based on the confirmed characteristic time period, confirm the associated curve segments from each water quality change curve, then lock the data to be detected for the corresponding detection items based on the curve segments, and uniformly analyze the water quality abnormality degrees of multiple sets of detection items according to the standard intervals associated with different detection items, and determine the output of abnormal parameters. The specific sub-steps for determining abnormal parameters are as follows:

[0048] S31. Based on the characteristic period, confirm the associated curve segments from each water quality change curve, perform mean processing on the multiple sets of water quality data associated with the corresponding curve segments, and confirm the data to be detected associated with the corresponding detection item;

[0049] S32. Confirm the standard interval associated with the corresponding detection item. The standard interval is a preset interval, and the endpoint values of the preset interval are all preset values, which are determined by the operator based on experience. Randomly select a point in a plane and use this point as the reference point. Then, based on the specific number G of detection items, generate G equally spaced straight lines around the reference point (the G straight lines are in an equally spaced arrangement around the reference point). The starting points of the G straight lines are all the reference point, and different straight lines correspond to different detection items. Generate relevant scales for different detection items on different straight lines. Based on the generated scales, confirm the points where the endpoint values of the preset interval are located and mark them as characteristic points. Connect the characteristic points on several adjacent straight lines to generate a set of polygons, and denote this polygon as the standard polygon;

[0050] S33. Then, based on the data to be detected associated with different detection items, find the corresponding scales on the straight lines associated with the corresponding detection items and mark the data points. Then, connect the data points on several adjacent straight lines to confirm a set of polygons to be checked;

[0051] S34. Identify whether the overall contour of the polygon to be checked exceeds the radiation range of the standard polygon. If so, directly generate a water quality anomaly signal and display it. If not, it means that the water quality data monitored during this monitoring period is normal;

[0052] S35. After the water quality anomaly 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 water quality anomaly signal unchanged. If M1 > M2, use: (M1 ÷ M2) × 100% = YC to confirm the anomaly parameter YC, and synchronously display the confirmed anomaly parameter YC;

[0053] Specifically, combined with Figure 2 , first select a set 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 for the data of the corresponding detection items on the straight lines. Different detection items have different standard intervals. Confirm the corresponding characteristic points according to the endpoint values of the standard intervals, generate the corresponding standard polygons, and then, based on the data to be detected associated with different detection items, find the specific scales associated on the corresponding straight lines, and mark the data points based on the corresponding scales. Then, connect the data points on adjacent straight lines to generate a set of polygons to be checked. In Figure 2If the polygon to be verified far exceeds the standard polygon, a water quality anomaly signal will be generated, and specific confirmation of the anomaly parameters is required. The anomaly parameters are the associated percentage parameters, which can fully display multiple water quality characteristics associated during the corresponding monitoring period, ensuring comprehensiveness during this monitoring process.

[0054] Second Embodiment

[0055] Step 4: Based on the monitoring characteristics associated during different monitoring periods, lock the monitoring periods where anomaly parameters are generated, sort the generated anomaly parameters based on the chronological relationship, and based on the change characteristics of the sorted anomaly parameters, confirm whether a trend anomaly signal is generated. The specific method for confirmation is as follows:

[0056] Sort the anomaly parameters associated with different monitoring periods according to the sorting method from the earliest to the latest time to confirm the anomaly parameter sorting sequence;

[0057] Confirm the change situation of adjacent parameters from the anomaly parameter sorting sequence. Among adjacent parameters, if the latter set of anomaly parameters is higher than the former set, record the trend anomaly parameter column. If the recorded trend anomaly parameter column exceeds three groups, directly generate a trend anomaly signal for display. If it does not exceed three groups, no trend anomaly signal is generated;

[0058] Specifically, assume the generated anomaly parameter sorting sequence is {10, 11, 13, 15, 16,...}, that is, the trend anomaly parameter columns are: 10 - 11, 11 - 13, 13 - 15, 15 - 16. There are four groups, exceeding the set value of three groups, so directly generate the corresponding trend anomaly signal for display.

[0059] Some of the data in the above formula are numerically calculated after removing the dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0060] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An on-line multi-parameter water quality detection method, characterized in that, It includes 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 within this monitoring period; Step 2: Based on the 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 curve segment characteristics associated with different curve segments within the water quality change curves based on the numerical characteristics. The specific method is as follows: S21: According to the different water quality data associated with different times within the water quality change curve, perform mean processing on several groups of water quality data to confirm the mean curve belonging to this water quality change curve, and calibrate the confirmed mean curve within the two-dimensional coordinate system where this water quality change curve is located, and successively confirm and calibrate the mean curves associated with different water quality change curves; S22. Based on the water quality change curve and the associated mean curve, confirm the curve segment characteristics of different curve segments within this water quality change curve. The time characteristics associated with different curve segments are not less than five groups of unit time points, and each curve segment selected from the water quality change curve is different. For a single selected curve segment, calibrate the different water quality data associated with different time points within this curve segment as S i , where i represents different time points, and then calibrate the mean water quality data associated with the mean curve corresponding to this water quality change curve as J. Use: S i - J = C i to confirm the difference data C associated with the corresponding water quality data i , and then perform mean processing on the difference data C associated with multiple groups of time points within this curve segment i to lock the curve segment characteristics regarding 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; Record the multiple sets of curve segment features associated with different curve segments belonging to the same group of associated time periods, and label them as TZ j-q , where j represents different associated time periods, and q represents different curve segments within the corresponding associated time period; Then perform unified analysis on multiple groups of water quality change curves to lock the characteristic time period. The specific method is as follows: S23: Select the minimum value and the maximum value from different water quality change curves, confirm the parameter interval associated with the corresponding water quality change curve, equally divide the parameter intervals associated with different water quality change curves into ten equal parts synchronously, and record the equal division points. Quantify the corresponding first group of equal division points as 0, the second group of equal division points as 1,..., and the last group of equal division points as 10; S24. Confirm the corresponding numerical value TZ according to the TZ recorded during the corresponding associated period j-q , and confirm the parameter range associated with the corresponding numerical value TZ j-q , confirm the location of this TZ within the parameter range j-q , and confirm the quantization value LH associated with TZ based on the confirmed location j-q . Perform mean processing on multiple groups of quantization values LH associated with the associated period j-q , confirm the quantization mean value, and record the confirmed quantization mean value as the period feature of this associated period j-q ​ S25: Based on the different time period characteristics corresponding to different associated time periods, select the maximum value, and record the associated time period corresponding to the maximum value as the characteristic time period; Step 3: Based on the confirmed characteristic time period, confirm the associated curve segment from each water quality change curve, then lock the data to be detected for the corresponding detection item based on the curve segment, and perform unified analysis on the water quality abnormality degree of multiple groups of detection items according to the standard interval associated with different detection items, and determine the abnormal parameters.

2. The on-line multi-parameter water quality detection method according to claim 1, characterized in that In the above Step 1, the specific method for generating the water quality change curve of different water quality data is as follows: Based on the set monitoring period, confirm the different water quality data associated with different times within the monitoring period. The monitoring period is a preset period. Take the time line of the corresponding monitoring period as the horizontal coordinate axis, and the relevant parameters of the water quality data as the vertical coordinate axis. Confirm the corresponding points within the constructed two-dimensional coordinate system based on the corresponding water quality data associated with the corresponding time, and connect several groups of points within the same two-dimensional coordinate system to confirm the water quality change curve associated with the corresponding water quality data.

3. A multi-parameter water quality on-line detection method according to claim 1, characterized in that, In the above Step 3, the specific sub-steps for determining the abnormal parameters 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 the multiple groups of water quality data associated with the corresponding curve segment, and confirm the data to be detected 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 the reference point. Then, based on the specific number G of detection items, generate G equidistant lines around the reference point. The starting points of the G lines are all the reference point, and different lines correspond to different detection items. Generate relevant scales for different detection items on different lines. Based on the generated scales, confirm the points where the endpoint values of the preset interval are located and record them as characteristic points. Connect the characteristic points on several adjacent lines to generate a set of polygons, and record this polygon as the standard polygon; S33. Then, based on the data to be detected associated with different detection items, find the corresponding scales on the lines associated with the corresponding detection items and mark the data points. Then, connect the data points on several adjacent lines to confirm a set of polygons to be checked; S34. Identify whether the overall contour of the polygon to be checked exceeds the radiation range of the standard polygon. If it does, directly generate a water quality anomaly signal and display it. If it does not, it means that the water quality data monitored during this monitoring period is normal; S35. After the water quality anomaly 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, then use: (M1 ÷ M2) × 100% = YC to confirm the anomaly parameter YC, and synchronously display the confirmed anomaly parameter YC.

4. The on-line multi-parameter water quality detection method according to claim 3, characterized in that, In step S35, if M1 ≤ M2, keep the original water quality anomaly signal unchanged.

5. A multi-parameter water quality on-line detection method according to claim 1, characterized in that, It also includes: Step Four. Based on the monitoring characteristics associated with different monitoring periods, lock the monitoring periods in which anomaly parameters are generated, sort the generated anomaly parameters based on the chronological relationship, and based on the change characteristics of the sorted anomaly parameters, confirm whether a trend anomaly signal is generated.

6. The on-line multi-parameter water quality detection method according to claim 5, wherein, In step Four, the specific method for confirming whether a trend anomaly signal is generated is: Sort the anomaly parameters associated with different monitoring periods according to the chronological order from front to back to confirm the anomaly parameter sorting sequence; Confirm the change situation of adjacent parameters from the anomaly parameter sorting sequence. Among adjacent parameters, if the latter group of anomaly parameters is higher than the former group of anomaly parameters, record the trend anomaly parameter column. If the recorded trend anomaly parameter column exceeds three groups, directly generate a trend anomaly signal for display. If it does not exceed three groups, no trend anomaly signal is generated.

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