Water quality index annual standard reaching early warning method based on target margin and trend line

Abstract

The invention provides a water quality index annual standard reaching early warning method based on a target margin and a trend line. The method comprises the following steps: firstly, collecting historical month-by-month water quality monitoring data of a section, calculating upper and lower limit extreme value lines of the same month, and drawing a multi-year monthly mean value trend line after removing outliers; calculating a target margin according to a water quality target, predicting a month-by-month index concentration early warning value after the ith month by using a trend line, and introducing parameters to calculate a comprehensive pressure index of the section. And finally, risk early warning is carried out through standard reaching condition three-level research and judgment. The method overcomes the defects that a traditional method does not consider seasonal fluctuation of water quality, lacks a dynamic correction mechanism and depends on experience judgment, comprehensively evaluates the risk that the water quality reaches the standard from multiple dimensions, enables early warning to be more scientific and accurate, can effectively assist surface water quality management decision, and improves reliability and effectiveness of water quality early warning.

Description

Technical Field

[0001] The present invention relates to the field of surface water monitoring, and in particular to an annual water quality indicator compliance early warning method based on target margins and trend lines. Background Art

[0002] The present invention is directed to the technical difficulties in annual water quality target management of surface water monitoring sections. Generally, surface water quality monitoring sections all have annual target assessment requirements. Traditional methods simply divide the annual target into 12 months, completely ignoring the seasonal fluctuation pattern of water quality indicators (such as the naturally high concentration during the flood season). This results in the flood season threshold being set too tightly (such as when the target is 20 mg / L, the allowed value in July is still 20 mg / L), and the actual concentration is very likely to break through the annual mean upper limit; while the non-flood season threshold is too loose (such as the January requirement of ≤20 mg / L, but the historical mean is only 12 mg / L), and an effective early warning cannot be made in time. Currently, only the concentration of the current month is compared with the target concentration, and a dynamic correction mechanism for the threshold of subsequent months based on the monitored data has not been established. When an abnormally high value appears in a certain month (such as the measured 24.7 mg / L in February> the target 20 mg / L), the impact of the high value in February on the threshold of the subsequent months is not reflected, resulting in an exponential increase in the cumulative risk of exceeding the standard without timely detection. At present, we only rely on experience to judge the concentration of indicators in the remaining months and then judge the compliance with the standards for the whole year. This is highly subjective. Therefore, it is urgent to develop an early warning method that integrates real-time data feedback and trend correction. Summary of the Invention

[0003] The purpose of the present invention is to provide an annual water quality indicator compliance early warning method based on target margin and trend line to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a water quality indicator annual compliance early warning method based on target margin and trend line, comprising the following steps:

[0005] Step 1: Collect and organize the historical monitoring data of the section: According to the preset time, collect the monitoring data of the water quality index j of the section on a monthly basis within the preset time, calculate the maximum and minimum values ​​of the water quality index j on a monthly basis, and obtain the upper extreme value line L1 and the lower extreme value line L2 of the same month;

[0006] It should be noted that indicator j includes 23 items (except water temperature) in Table 1 of the "Surface Water Environmental Quality Standard" (GB 3838-2002).

[0007] Step 2: Draw a multi-year monthly average trend line: Eliminate outlier data from the historical data of each indicator collected in step 1, and then calculate the multi-year monthly average concentration data of each indicator j based on the remaining data. The multi-year monthly average historical trend curve L of each indicator is obtained from the monthly average concentration data;

[0008] Step 3. Calculate the target margin: Based on the water quality index j in step 1 corresponding to the cross-section water quality target, obtain the annual target concentration c for each indicator. The annual target capacity is 12*c, so the target margin after the i-th month is equal to Q. Accordingly, calculate the sum of the corresponding multi-year monthly average concentrations, Qm.

[0009] Step 4. Calculate the monthly indicator concentration warning value after the oth month: Using the multi-year monthly average trend line L, under the premise of ensuring that the annual average reaches the target, predict the upper limit of the monthly indicator concentration after the ith month. If the curve formed by the upper limit of the monthly indicator concentration after the ith month is the prediction warning trend line L0, when L0 is higher than the multi-year monthly average curve L, then the indicator has no pressure to meet the standard for this warning value; if L0 is lower than the multi-year monthly average curve L, then the warning value shows that the indicator has met the standard. The greater the gap, the greater the pressure, that is, the pressure value K = Q / Qm. If it is greater than or equal to 1, there is no pressure, and K is automatically assigned to 0. If it is less than 1, there is pressure to meet the standard. The smaller the value, the greater the pressure.

[0010] Step 5. Calculate the cross-section comprehensive pressure index: Introduce f1 and F2, where F1 is the ratio n / N of the number of indicators whose forecast and warning trend line L0 is lower than L to the number of indicators participating in all forecasts and warnings; F2 is the sum of the pressure values ​​K1+K2+…Kn, where Kn is a value less than 1. The cross-section comprehensive pressure index can be expressed as The value of M is [0, 1);

[0011] Step 6. Three-level assessment of compliance: When the monitoring data for the i-th month is obtained, the first step is to determine whether the early warning forecast trend line L0 of indicator j after the i-th month is lower than the multi-year monthly average curve L; the second step is to determine whether the concentration of indicator j exceeds the upper limit extreme line L1 of the same month; the third step is to calculate the comprehensive pressure index of the section, and make a comprehensive judgment based on the data in the second and third steps to conduct risk warning.

[0012] Preferably, the specific implementation process for eliminating outlier data in the historical data of each indicator collected in step 1 is as follows:

[0013] Step 21: First, receive the historical data of the cross-section index j collected in step 1, and then calculate the data mean Cm and standard deviation σ of the historical data of the index j;

[0014] Step 22: Determine whether each data in the historical data of the cross-section index j is within the range of (Cm-2σ, Cm+2σ);

[0015] Step 23: When the judgment result is yes, the current data is retained; if the judgment result is no, the current data is cleared; and then the above steps are repeated to obtain the outlier data.

[0016] Preferably, the target margin after the i-th month in step 3 is Q=12*c-c1-c2-…-ci, where c1, c2…ci are the indicator concentrations monitored in the first i months of the year, 1≤i≤12, and the purpose is to provide an early warning of the concentration reaching the standard in the next few months; the sum of the multi-year monthly average concentrations is Qm=12*c-cm1-cm2-…-cmi.

[0017] Preferably, the warning value is calculated as: Connect each predicted value to obtain the prediction and warning trend line L0.

[0018] Preferably, the specific implementation steps of the three-level assessment of compliance in step 6 are as follows:

[0019] Step 61: First, determine the early warning forecast for each indicator j after the i-th month, that is, whether the early warning trend line L0 is lower than the multi-year monthly average curve L; secondly, calculate the cross-section comprehensive pressure index and the number of indicators lower than L;

[0020] Step 62: The second step is to determine whether the concentration of indicator j exceeds the upper limit extreme value line L1 of the same month, and record the number z of times that indicator j exceeds the upper limit extreme value line L1 of the same month. The risk is divided according to the range of the number of indicators and whether it is exceeded, specifically divided into: z∈[1,3)&No, it is judged as low risk; z∈[1,3)&Yes, it is judged as lower risk; z∈[3,5)&Yes or No, it is judged as medium risk; z∈[5,24)&No, it is judged as higher risk; z∈[5,24)&Yes, it is judged as high risk;

[0021] Step 63, the third step is to calculate the cross-section comprehensive pressure index M and classify M into risk categories, specifically: when M∈[0,0.1), it is judged as low risk; when M∈[0.1,0.2), it is judged as relatively low risk; when M∈[0.2,0.3), it is judged as medium risk; when M∈[0.3,0.4), it is judged as relatively high risk; when M∈[0.5,1), it is judged as high risk;

[0022] Step 64: Based on the results of the judgments in step 62 and step 63, the one with the highest risk judgment is taken as the final risk warning.

[0023] Compared with the existing technology, the present invention has the following advantages: it takes into account seasonal fluctuations and optimizes threshold settings: the traditional method simply divides the annual target into 12 months, ignoring the seasonal fluctuations of water quality indicators, resulting in excessively tight thresholds during the flood season and excessively loose thresholds during the non-flood season. By collecting and collating historical monitoring data from sections and plotting multi-year monthly average trend lines, the present invention can fully account for seasonal variations in water quality indicators. When calculating target margins and warning values, dynamic adjustments are made based on actual historical data and trends, making the threshold settings for different months more reasonable and avoiding concentration breakthroughs and untimely warnings caused by seasonal factors.

[0024] Establish a dynamic correction mechanism to effectively reduce risks: The current method only compares the concentration of the current month with the target concentration, and does not establish a dynamic correction mechanism for the threshold values ​​of subsequent months based on the monitored data. The present invention calculates the target margin and issues a target-reaching warning for the concentrations of the following months based on the indicator concentrations monitored in the first i months of the year. When calculating the monthly indicator concentration warning values ​​after the i-th month, the multi-year monthly average trend line is used to make predictions on the premise that the annual average reaches the target. This dynamic correction mechanism can promptly reflect the impact of previous abnormal data on the threshold values ​​of subsequent months, effectively reduce the cumulative risk of exceeding the standard, and avoid the situation where the risk increases exponentially without being detected.

[0025] Multi-dimensional comprehensive analysis and judgment to improve the accuracy of early warning: The present invention adopts a three-level analysis and judgment method for compliance with standards to evaluate the risk of water quality compliance from multiple dimensions. The first step is to judge the relationship between the early warning trend line L0 and the multi-year monthly average curve L to determine whether the indicator has compliance pressure; the second step is to judge whether the indicator concentration breaks through the upper limit extreme line L1 of the same month; the third step is to calculate the cross-section comprehensive pressure index M, and divide the number of indicators that break through the upper limit extreme line and the comprehensive pressure index into risks, and finally take the one with the highest risk judgment as the final risk warning. Compared with relying solely on experience, this multi-dimensional comprehensive analysis and judgment method is more scientific and accurate, and can provide a more reliable decision-making basis for water quality management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Schematic diagram of the method flow of the present invention;

[0027] Figure 2 Schematic diagram of the method for eliminating outlier data of the present invention;

[0028] Figure 3 This is a schematic diagram of the three-level evaluation process of the present invention;

[0029] Figure 4 The structural diagram of L, L1 and L2 for selecting permanganate index, ammonia nitrogen and total phosphorus in the embodiment;

[0030] Figure 5This is a schematic diagram of the three-level analysis and judgment process of the compliance status in the embodiment. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0032] See also Figure 1-3 The present invention provides a technical solution: an annual water quality indicator compliance early warning method based on target margin and trend line, comprising the following steps:

[0033] Step 1: Collect and organize the historical monitoring data of the section: According to the preset time, collect the monitoring data of the water quality index j of the section on a monthly basis within the preset time, calculate the maximum and minimum values ​​of the water quality index j on a monthly basis, and obtain the upper extreme value line L1 and the lower extreme value line L2 of the same month;

[0034] Step 2: Draw a multi-year monthly average trend line: Eliminate outlier data from the historical data of each indicator collected in step 1, and then calculate the multi-year monthly average concentration data of each indicator j based on the remaining data. The multi-year monthly average historical trend curve L of each indicator is obtained from the monthly average concentration data;

[0035] The specific implementation process for removing outlier data from the historical data of each indicator collected in step 1 is as follows:

[0036] Step 21: First, receive the historical data of the cross-section index j collected in step 1, and then calculate the data mean Cm and standard deviation σ of the historical data of the index j;

[0037] Step 22: Determine whether each data in the historical data of the cross-section index j is within the range of (Cm-2σ, Cm+2σ);

[0038] Step 23: When the judgment result is yes, the current data is retained; if the judgment result is no, the current data is cleared; and then the above steps are repeated to obtain the outlier data.

[0039] Step 3. Calculate the target margin: Based on the water quality index j in step 1 corresponding to the cross-section water quality target, obtain the annual target concentration c for each indicator. The annual target capacity is 12*c, so the target margin after the i-th month is equal to Q. Accordingly, calculate the sum of the corresponding multi-year monthly average concentrations, Qm.

[0040] The target margin after the i-th month is Q = 12*c-c1-c2-…-ci, where c1, c2…ci are the indicator concentrations monitored in the first i months of the year, 1≤i≤12. The purpose is to provide an early warning of the concentrations in the next few months. The sum of the multi-year monthly average concentrations is Qm = 12*c-cm1-cm2-…-cmi.

[0041] Step 4. Calculate the monthly indicator concentration warning value after the i-th month: Using the multi-year monthly average trend line L, under the premise of ensuring that the annual average reaches the target, predict the upper limit of the monthly indicator concentration after the i-th month. If the curve formed by the upper limit of the monthly indicator concentration after the i-th month is the prediction warning trend line L0, when L0 is higher than the multi-year monthly average curve L, then the indicator has no pressure to meet the standard for this warning value; if L0 is lower than the multi-year monthly average curve L, then the warning value shows that the indicator has met the standard. The greater the gap, the greater the pressure, that is, the pressure value K = Q / Qm. If it is greater than or equal to 1, there is no pressure, and K is automatically assigned to 0. If it is less than 1, there is pressure to meet the standard. The smaller the value, the greater the pressure.

[0042] The warning value calculation is: Connect each predicted value to obtain the prediction and warning trend line L0.

[0043] Step 5. Calculate the cross-section comprehensive pressure index: Introduce F1 and F2, where F1 is the ratio n / N of the number of indicators whose forecast and warning trend line L0 is lower than L to the number of indicators participating in all forecasts and warnings; F2 is the sum of the pressure values ​​K1+K2+…Kn, where Kn is a value less than 1. The cross-section comprehensive pressure index can be expressed as The value of M is [0, 1);

[0044] Step 6. Three-level assessment of compliance: When the monitoring data for the i-th month is obtained, the first step is to determine whether the early warning forecast trend line L0 of indicator j after the i-th month is lower than the multi-year monthly average curve L; the second step is to determine whether the concentration of indicator j exceeds the upper limit extreme line L1 of the same month; the third step is to calculate the comprehensive pressure index of the section, and make a comprehensive judgment based on the data in the second and third steps to conduct risk warning.

[0045] The specific implementation steps for the three-level assessment of compliance are as follows:

[0046] Step 61: First, determine the early warning forecast for each indicator j after the i-th month, that is, whether the early warning trend line L0 is lower than the multi-year monthly average curve L; secondly, calculate the cross-section comprehensive pressure index and the number of indicators lower than L;

[0047] Step 62: The second step is to determine whether the concentration of indicator j exceeds the upper limit extreme value line L1 of the same month, and record the number z of times that indicator j exceeds the upper limit extreme value line L1 of the same month. The risk is divided according to the range of the number of indicators and whether it is exceeded, specifically divided into: z∈[1,3)&No, it is judged as low risk; z∈[1,3)&Yes, it is judged as lower risk; z∈[3,5)&Yes or No, it is judged as medium risk; z∈[5,24)&No, it is judged as higher risk; z∈[5,24)&Yes, it is judged as high risk;

[0048] Step 63, the third step is to calculate the cross-section comprehensive pressure index M and classify M into risk categories, specifically: when M∈[0,0.1), it is judged as low risk; when M∈[0.1,0.2), it is judged as relatively low risk; when M∈[0.2,0.3), it is judged as medium risk; when M∈[0.3,0.4), it is judged as relatively high risk; when M∈[0.5,1), it is judged as high risk;

[0049] Step 64: Based on the results of the judgments in step 62 and step 63, the one with the highest risk judgment is taken as the final risk warning.

[0050] The following is explained with reference to specific embodiments:

[0051] Based on this method, a forecast and early warning of water quality compliance in 2025 is conducted for a certain section.

[0052] 1) Collect and organize historical monitoring data of sections

[0053] Collect the historical monthly water quality monitoring data of Section A from 2021 to 2024. The indicators include 23 items (except water temperature) in Table 1 of the "Surface Water Environmental Quality Standard" (GB 3838-2002). In order to make the case of this invention concise and easy to understand, this case takes the permanganate index, ammonia nitrogen, and total phosphorus as examples. Calculate the maximum and minimum values ​​of each indicator every month for many years, and obtain the upper limit extreme value line L1 and the lower limit extreme value line L2 of the same month, as shown in the figure below. Figure 4 .

[0054] 2) Draw a multi-year monthly average trend line

[0055] According to the process, outliers in the historical data of each indicator are eliminated, and the multi-year monthly average concentration of each indicator is calculated based on the remaining data. Thus, the multi-year monthly average historical trend curve L of each indicator is obtained, as shown in the following example: Figure 4 .

[0056] 3) Calculate target margin

[0057] According to Table 1 of GB 3838-2002, the target for Section A is Class X. The target concentrations for the permanganate index, ammonia nitrogen, and total phosphorus are to be below 6 mg / L, 0.25 mg / L, and 0.1 mg / L, respectively. The annual target capacities are 72 mg / L, 2 g / L, and 1.2 mg / L, respectively. Knowing that the monitored concentrations for these three indicators in January were 3.8 mg / L, 0.62 mg / L, and 0.13 mg / L, and in February were 3.5 mg / L, 0.63 mg / L, and 0.12 mg / L, the target margins after February are 64.7 mg / L, 1.75 mg / L, and 0.95 mg / L, respectively. Accordingly, the sum of the corresponding multi-year monthly average concentrations, i.e., the sums for March through December, are calculated to be 50.4 mg / L, 2.74 mg / L, and 1.21 mg / L, respectively.

[0058] 4) Calculate the indicator concentration warning value for each month after February

[0059] Using the multi-year monthly average trend line L, under the premise of ensuring that the annual average reaches the target, the upper limit of the index concentration allowed to be reached each month after February is predicted. The upper limit of the permanganate index in March is (4.5 is the historical multi-year average concentration in March), that is, c3 = 5.77 mg / L. The upper limit value from April to December is calculated based on this. The same is true for ammonia nitrogen and total phosphorus. The calculation results are as follows: Figure 4 .

[0060] If the curve formed by the upper limit of the indicator concentration allowed in each month after February (the forecast warning trend line L0) is higher than the multi-year monthly average curve L, then the warning result shows that the indicator is under no pressure to meet the standard. If it is lower than the multi-year monthly average curve L, then the warning result shows that the indicator is under standard. The greater the difference, the greater the pressure, that is, the pressure value K = Q / Qm. If it is greater than or equal to 1, there is no pressure, and if it is less than 1, there is pressure to meet the standard. The smaller the value, the greater the pressure. The pressure values ​​for the permanganate index, ammonia nitrogen, and total phosphorus are 64.7 / 50.4 = 1.28, 1.75 / 2.74 = 0.64, and 0.95 / 1.21 = 0.78, respectively.

[0061] 5) Calculate the comprehensive pressure index of the section

[0062] Introducing F1 and F2, F1 is the ratio n / N of the number of indicators whose indicator prediction and warning trend line L0 is lower than L to the number of indicators participating in all predictions and warnings; in this case, N is 3 and n is 2, that is, F1 = 0.67. F2 is the sum of the pressure values ​​K1 + K2 + ... Kn (Kn is a value less than 1), that is, F2 = 1.42. The cross-section comprehensive pressure index can be expressed as That is, M=0.7, and the value of M is [0, 1).

[0063] 6) Three-level assessment of compliance

[0064] When the monitoring data for the i-th month is obtained (i.e. the monitoring data for February is obtained), the first step is to determine whether the early warning forecast trend line L0 after February is lower than the multi-year monthly average curve L (the permanganate index is lower, and the ammonia nitrogen and total phosphorus are higher); the second step is to determine whether the concentration of indicator j exceeds the upper limit line L1 of the same month (none of the three indicators exceeded the upper limit value in February), and the third step is to calculate the comprehensive pressure index of the section (0.7), then the compliance risk level of section A is high. The compliance risk level judgment process is as follows: Figure 5 .

[0065] The present invention discloses an annual water quality indicator compliance warning method based on target margin and trend line, which aims to solve the problem of annual water quality target management of surface water monitoring sections. The method first collects and organizes the monitoring data of the section in historical years, calculates the upper and lower extreme value lines of the same month; then eliminates the outlier data and draws the multi-year monthly average trend line; then calculates the target margin, predicts the monthly indicator concentration warning value after the i-th month based on the trend line, introduces relevant parameters to calculate the comprehensive pressure index of the section; finally, conducts risk warning through three-level assessment of compliance status. Compared with traditional methods, it fully considers the seasonal fluctuation law of water quality indicators, establishes a dynamic correction mechanism for the threshold value of subsequent months based on the monitored data, avoids subjective judgment, can more accurately assess the risk of water quality compliance, provides a reliable basis for surface water quality management, and effectively improves the scientific nature and effectiveness of water quality warning.

[0066] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The annual water quality indicator compliance early warning method based on target margin and trend line is characterized by: The following steps are involved: Step 1: Collect and organize the historical monitoring data of the section: According to the preset time, collect the monitoring data of the water quality index j of the section on a monthly basis within the preset time, calculate the maximum and minimum values ​​of the water quality index j on a monthly basis, and obtain the upper extreme value line L1 and the lower extreme value line L2 of the same month; Step 2: Draw a multi-year monthly average trend line: Eliminate outlier data from the historical data of each indicator collected in step 1, and then calculate the multi-year monthly average concentration data of each indicator j based on the remaining data. The multi-year monthly average historical trend curve L of each indicator is obtained from the monthly average concentration data; Step 3. Calculate the target margin: Based on the water quality index j in step 1 corresponding to the cross-section water quality target, obtain the annual target concentration c for each indicator. The annual target capacity is 12*c, so the target margin after the i-th month is equal to Q. Accordingly, calculate the sum of the corresponding multi-year monthly average concentrations, Qm. Step 4. Calculate the monthly indicator concentration warning value after the i-th month: Using the multi-year monthly average trend line L, under the premise of ensuring that the annual average reaches the target, predict the upper limit of the monthly indicator concentration after the i-th month. If the curve formed by the upper limit of the monthly indicator concentration after the i-th month is the prediction warning trend line L0, when L0 is higher than the multi-year monthly average curve L, then the indicator has no pressure to meet the standard for this warning value; if L0 is lower than the multi-year monthly average curve L, then the warning value shows that the indicator has met the standard. The greater the gap, the greater the pressure, that is, the pressure value K = Q / Qm. If it is greater than or equal to 1, there is no pressure, and K is automatically assigned to 0. If it is less than 1, there is pressure to meet the standard. The smaller the value, the greater the pressure. Step 5. Calculate the cross-section comprehensive pressure index: introduce F1 and F2, where F1 is the ratio n / N of the number of indicators whose indicator prediction and warning trend line L0 is lower than L to the number of indicators participating in all predictions and warnings; F2 is the sum of the pressure values ​​K1+K2+…Kn, where Kn is a value less than 1. The cross-sectional comprehensive pressure index can be expressed as The value of M is [0, 1); Step 6. Three-level assessment of compliance: When the monitoring data for the i-th month is obtained, the first step is to determine whether the early warning forecast trend line L0 of indicator j after the i-th month is lower than the multi-year monthly average curve L; the second step is to determine whether the concentration of indicator j exceeds the upper limit extreme line L1 of the same month; the third step is to calculate the comprehensive pressure index of the section, and make a comprehensive judgment based on the data in the second and third steps to conduct risk warning.

2. The annual water quality indicator compliance early warning method based on target margin and trend line according to claim 1 is characterized by: The specific implementation process for removing outlier data from the historical data of each indicator collected in step 1 is as follows: Step 21: First, receive the historical data of the cross-section index j collected in step 1, and then calculate the data mean Cm and standard deviation σ of the historical data of the index j; Step 22: Determine whether each data in the historical data of the cross-section index j is within the range of (Cm-2σ, Cm+2σ); Step 23: When the judgment result is yes, the current data is retained; if the judgment result is no, the current data is cleared; and then the above steps are repeated to obtain the outlier data.

3. The annual water quality indicator compliance early warning method based on target margin and trend line according to claim 1 is characterized by: In step 3, the target margin after the i-th month is Q = 12*c-c1-c2-…-ci, where c1, c2…ci are the indicator concentrations monitored in the first i months of the year, 1≤i≤12, and the purpose is to provide an early warning of the concentration reaching the standard in the next few months; the sum of the multi-year monthly average concentrations is Qm = 12*c-cm1-cm2-…-cmi.

4. The annual water quality indicator compliance early warning method based on target margin and trend line according to claim 1 is characterized by: Calculation of warning value: Connect each predicted value to obtain the prediction and warning trend line L0.

5. The annual water quality indicator compliance early warning method based on target margin and trend line according to claim 1 is characterized by: The specific implementation steps of the three-level assessment of compliance in step 6 are as follows: Step 61: First, determine the early warning forecast for each indicator j after the i-th month, that is, whether the early warning trend line L0 is lower than the multi-year monthly average curve K; secondly, calculate the cross-section comprehensive pressure index and the number of indicators lower than L; Step 62: The second step is to determine whether the concentration of indicator j exceeds the upper limit extreme value line L1 of the same month, and record the number z of times that indicator j exceeds the upper limit extreme value line K1 of the same month. The risk is divided according to the range of the number of indicators and whether it is exceeded, specifically divided into: z∈[1,3)&No, it is judged as low risk; z∈[1,3)&Yes, it is judged as lower risk; z∈[3,5)&Yes or No, it is judged as medium risk; z∈[5,24)&No, it is judged as higher risk; z∈[5,24)&Yes, it is judged as high risk; Step 63, the third step is to calculate the cross-section comprehensive pressure index M and classify M into risk categories, specifically: when M∈[0,0.1), it is judged as low risk; when M∈[0.1,0.2), it is judged as relatively low risk; when M∈[0.2,0.3), it is judged as medium risk; when M∈[0.3,0.4), it is judged as relatively high risk; when M∈[0.5,1), it is judged as high risk; Step 64: Based on the results of the judgments in step 62 and step 63, the one with the highest risk judgment is taken as the final risk warning.

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