A method for dynamic evaluation and maintenance early warning of purification efficiency of agricultural non-point source pollution treatment facilities

Abstract

The application discloses a kind of dynamic evaluation and maintenance early warning method of agricultural non-point source pollution control facility purification efficiency, water quality online monitoring equipment is deployed on upstream and downstream of management facility, and water quality index is collected in real time and carried out effectiveness check;According to the concentration difference of upstream and downstream, instantaneous purification efficiency is calculated, and comprehensive instantaneous purification efficiency is obtained by dynamic weighting according to multiple indexes according to standard multiple;Eliminate accidental fluctuation by time window sliding average;Set reference efficiency threshold and low-efficiency threshold, and divide facility state into three levels of normal operation, primary early warning and senior early warning;The trend slope of historical efficiency is fitted, and when it is continuously decreased, trend early warning is triggered;For the multi-stage facilities arranged in series, the total purification efficiency and the contribution rate of each stage are calculated and the fluctuation weighted correction is made, and the short board facility is identified to generate a maintenance priority list;Finally, an evaluation report containing purification efficiency, early warning result and short board identification is generated.The application realizes real-time evaluation, hierarchical early warning and predictive proactive maintenance of management facility purification efficiency.

Description

Technical Field

[0001] This invention relates to the field of agricultural pollution control technology, and in particular to a method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities. Background Technology

[0002] In the current process of agricultural non-point source pollution control, ecological interception ditches, ecological buffer zones, and constructed wetlands are widely used treatment facilities. However, during long-term operation, these facilities are easily affected by factors such as sediment deposition, substrate adsorption saturation, and plant degradation, leading to changes in purification efficiency.

[0003] Numerous patent applications have been filed regarding technological improvements to pollution control facilities. For example, patent CN119285106A discloses a control system for non-point source pollution in contiguous farmland; CN121629894A discloses a water-retaining ecological cycle restoration ditch system and method for farmland in hilly areas. These patents primarily focus on the structural design, construction methods, and initial interception effects of the facilities, playing a positive role in the construction and operation phases of pollution control facilities.

[0004] However, most existing related technologies focus on the structural design of treatment facilities, improvement of fillers and verification of short-term purification effects, lacking a complete solution for dynamic assessment, graded prediction and proactive operation and maintenance during long-term operation. Field continuous monitoring experiments show that after 6 to 12 months of continuous operation of ecological interception ditches and multi-level artificial wetlands, problems such as suspended solids accumulation, vegetation aging and matrix adsorption saturation occur in a concentrated manner, and the comprehensive purification efficiency of nitrogen and phosphorus can decrease by 30% to 60%, as seen in Fu Chengkai et al., Research progress on the formation mechanism, effectiveness and prevention and control technology of artificial wetland blockage, Environmental Engineering, 2025, 43(3):178-190; The 2026 watershed engineering survey review further points out that the current domestic contiguous farmland interception projects generally do not have the supporting automated efficiency assessment and early warning methods. Managers can only carry out emergency maintenance after the downstream water quality exceeds the standard. The dilemma of the industry emphasizing construction and neglecting operation and maintenance is common, as seen in Duan Weili et al., Sediment reduction efficiency of ecological drainage ditches under different hydraulic conditions, Journal of Ecology and Environment, 2026, 35(4):529-539.

[0005] The closest prior art to this invention relates to a method for evaluating the purification effect of agricultural non-point source pollution control facilities. CN120875275A discloses a method and system for evaluating the purification effect of farmland drainage ditches based on the Internet of Things. This scheme obtains the purification effect evaluation by collecting surface temperature, micro-deformation, multispectral, and anthropogenic interference data and inputting them into a three-layer progressive inversion model. This method relies on multi-source remote sensing data, has a long data acquisition cycle, and requires a large number of measured samples for the construction and calibration of the inversion model, thus having limitations in terms of real-time performance and universality.

[0006] CN121457135A discloses a method and system for evaluating the ecological restoration benefits of ditches. This method calculates the purification capacity coefficient based on hydraulic retention time and pollutant degradation rate constant, and establishes a self-purification efficiency matrix. However, this method requires establishing a hydraulic model and calibrating degradation rate parameters, which is complex. It calculates the theoretical capacity under design conditions rather than actual operating efficiency, making it difficult to reflect changes in actual operating conditions such as blockage and vegetation degradation. The existing technology has the following defects and shortcomings: First, the assessment suffers from poor real-time performance. Existing technologies struggle to achieve continuous and dynamic assessments of the purification efficiency of treatment facilities. CN120875275A relies on indirect inversion using multi-source remote sensing data, resulting in long data acquisition and processing cycles. CN121457135A requires the establishment of a hydraulic model and parameter calibration, which is complex to obtain. The assessment results reflect the theoretical capacity under design conditions rather than actual operational efficiency. Furthermore, managers cannot monitor the operational status of treatment facilities in real time. When facility efficiency suddenly declines, it is difficult to detect in a timely manner, leading to the direct discharge of pollutants without effective interception.

[0007] Secondly, there is a lack of an automatic early warning mechanism for declining purification efficiency and maintenance needs. The existing technologies mentioned above are all at the assessment level, providing scores or grades for purification effectiveness, but they do not establish an automatic correlation mechanism to determine the extent to which a decline in purification efficiency should trigger a maintenance warning. When problems such as substrate blockage or plant degradation occur in the facility, managers cannot obtain timely maintenance alerts through existing assessment methods. They often only respond passively when the treatment effect has significantly deteriorated and downstream water quality exceeds standards. By this time, the facility may have already severely failed, resulting in high repair costs.

[0008] Secondly, the inability to identify weak links in multi-level pollution control systems makes it difficult to determine the varying roles of different facilities in the overall pollution reduction process. In actual pollution control projects, multiple facilities are typically arranged in series along the water flow direction to form a multi-level interception system. However, the aforementioned existing technologies are all designed for assessing individual facilities and lack an assessment system that links upstream and downstream processes. When the overall pollution control effect fails to meet standards, managers cannot determine which level of facility is inefficient, leading to indiscriminate inspections or uniform maintenance of all facilities, resulting in a waste of human and material resources and making it difficult to accurately optimize the pollution control layout.

[0009] Finally, existing online water quality monitoring resources are not fully utilized. Furthermore, current technologies typically require additional data acquisition methods, increasing system complexity. Currently, the field of agricultural non-point source pollution monitoring has developed sky-ground-water collaborative detection technology, capable of acquiring real-time online water quality monitoring data for farmland areas. However, none of the aforementioned existing technologies utilize this existing monitoring data to dynamically assess the purification efficiency of treatment facilities. Instead, they require additional data collection on temperature, deformation, multispectral density, etc., or the establishment of complex models, increasing implementation costs.

[0010] Therefore, it is necessary to design a dynamic assessment and maintenance early warning method for the purification efficiency of agricultural non-point source pollution control facilities to solve the above problems. Summary of the Invention

[0011] The technical problem to be solved by this invention is to provide a dynamic assessment and maintenance early warning method for the purification efficiency of agricultural non-point source pollution control facilities, so as to realize real-time calculation, hierarchical early warning, trend prediction, and multi-level linkage of shortcomings in the purification efficiency of the control facilities. To achieve the above technical solution, the technical solution adopted by the present invention is as follows: A method for dynamic assessment and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities, comprising the following steps: Deploy online water quality monitoring equipment upstream and downstream of the treatment facilities to collect water quality index data in real time and check its effectiveness. The instantaneous purification efficiency at each sampling time is calculated based on the upstream influent concentration and downstream effluent concentration of the treatment facility. When monitoring multiple water quality indicators, the comprehensive instantaneous purification efficiency is obtained by weighting the pollution levels of each indicator. Set a time window, and perform a moving average of the instantaneous purification efficiency at each sampling time within the time window to obtain the moving average purification efficiency. Set a baseline efficiency threshold and an inefficiency threshold. Based on the comparison between the moving average purification efficiency and the two thresholds, classify the operating status of the treatment facilities into three levels: normal operation, primary warning, and advanced warning, and perform corresponding maintenance operations. The system obtains the moving average purification efficiency over multiple consecutive time windows, predicts trends based on the changing trends of the moving average purification efficiency, and triggers a trend warning when it determines that the purification efficiency is continuously declining. When multiple treatment facilities are arranged in series along the water flow direction, the moving average purification efficiency of each level of facility is calculated, the total purification efficiency of the entire treatment system and the contribution rate of each level of facility are calculated, the weakest facilities are identified based on the contribution rate, and a maintenance priority list is generated. Generate and output an assessment report, which includes the purification efficiency of each treatment facility, the results of graded early warning, the results of trend prediction, and the identification of facilities with shortcomings.

[0012] Preferably, the water quality index data includes one or more of total nitrogen, total phosphorus, ammonia nitrogen, and permanganate index, and the online water quality monitoring equipment collects water quality index data at a frequency of once every 1 hour to once every 6 hours.

[0013] Furthermore, the validity check includes: removing negative values, values ​​exceeding the sensor's range, and values ​​that remain unchanged; when upstream or downstream data is missing at a certain moment, that moment is not included in the calculation, and the calculation is performed again after the data is completed at the next moment.

[0014] Furthermore, a data validity threshold is set. Within a time window, if the proportion of valid data sampling times to the total number of sampling times is lower than the data validity threshold, the evaluation result of that time window is determined to be unreliable, a data insufficiency prompt is generated, and the warning result is not output temporarily until the data is recovered and then evaluated again.

[0015] Furthermore, the data validity threshold is 70%.

[0016] Preferably, the calculation of the instantaneous purification efficiency at each sampling time based on the upstream influent concentration and downstream effluent concentration of the treatment facility includes calculating the instantaneous purification efficiency: ; in, Indicates the sampling time The instantaneous purification efficiency of the treatment facilities for current water quality indicators. Indicates the sampling time The influent concentration obtained from monitoring points upstream of the treatment facility, Indicates the sampling time The effluent concentration obtained from monitoring points downstream of the treatment facility and The units are all milligrams per liter.

[0017] Preferably, the step of calculating the comprehensive instantaneous purification efficiency by weighting the pollution levels of each indicator when monitoring multiple water quality indicators includes: obtaining the water quality standard limits corresponding to each water quality indicator, and calculating the sampling time. The exceedance multiple of each indicator concentration in the influent, where the exceedance multiple is the sampling time. The ratio of the influent concentration to the corresponding water quality standard limit for this indicator is used. The weighting coefficient of each indicator is determined based on the exceedance multiple, with a higher weight for a larger exceedance multiple. The sum of the weighting coefficients for each indicator is 1. The overall instantaneous purification efficiency is then calculated. ; in, Indicates the sampling time The overall instantaneous purification efficiency, This indicates the total number of water quality indicators monitored. This refers to the water quality indicator number. Indicates the first The weighting coefficients of each water quality indicator. Indicates the sampling time No. The instantaneous purification efficiency of each water quality indicator; the method for calculating the exceedance multiple is as follows: ; in, Indicates the sampling time No. The number of times the influent concentration of each water quality indicator exceeded the standard. Indicates the sampling time No. The influent concentration of each water quality indicator obtained from the monitoring point upstream of the treatment facility. Indicates the first The water quality standard limits corresponding to each water quality indicator are determined according to the corresponding water function zone category in the "Surface Water Environmental Quality Standard" GB 3838-2002.

[0018] Preferably, the weighting coefficients of each indicator are determined based on the exceedance multiple, including determining the first... Weighting coefficients for each water quality indicator: ; in, Indicates the first The weighting coefficients of each water quality indicator. Indicates the sampling time No. The number of times the influent concentration of each water quality indicator exceeded the standard. This represents the total number of monitored water quality indicators. When the sum of the exceedance multiples of the influent concentration of all indicators is 0, the weighting coefficient of each indicator is taken as... .

[0019] Preferably, the set time window, which involves averaging the instantaneous purification efficiency at each sampling moment within the time window to obtain the moving average purification efficiency, includes: Calculate the moving average purification efficiency: ; in, Indicates the sampling time The moving average purification efficiency Indicates time window The effective number of samples within, Indicates the first time within the time window Instantaneous purification efficiency at each sampling moment Indicates a time window. Indicates the current sampling time; the time window Valid sampling times within No less than 10, when the number of valid samples is reduced due to missing data. If the number is less than 10, the time window will be automatically extended until the effective number of samples is reached. Not less than 10; The standard deviation of the purification efficiency within the calculation time window: ; Calculate the standard deviation of the purification efficiency within the time window, where, Indicates the sampling time The standard deviation of purification efficiency Indicates time window The effective number of samples within, Indicates the first time within the time window Instantaneous purification efficiency at each sampling moment Indicates the sampling time The moving average purification efficiency Indicates a time window. This indicates the current sampling time; when the standard deviation exceeds a preset stability threshold, a prompt indicating unstable purification efficiency is generated.

[0020] Furthermore, the time window The value ranges from 24 hours to 72 hours, and is adaptively adjusted according to the response characteristics of the treatment facilities and the monitoring frequency.

[0021] Furthermore, the preset stability threshold is 15%.

[0022] Preferably, the step of classifying the operating status of the treatment facility into three levels—normal operation, primary warning, and advanced warning—based on the comparison results of the moving average purification efficiency and two thresholds includes: when the moving average purification efficiency is greater than or equal to the baseline efficiency threshold, the treatment facility is determined to be operating normally, and no warning is generated; when the moving average purification efficiency is greater than or equal to the inefficient threshold but less than the baseline efficiency threshold, a primary warning is issued, the treatment facility is automatically marked, and a prompt message for enhanced inspection is generated; when the moving average purification efficiency is less than the inefficient threshold, an advanced warning is issued, and a maintenance work order is automatically generated, the maintenance work order including the location of the treatment facility, efficiency data, and maintenance suggestions.

[0023] Preferably, the determination of the baseline efficiency threshold and the inefficiency threshold includes: determining the initial reference value of the baseline efficiency threshold based on the removal rate required by the engineering design specifications of the treatment facility or the environmental impact assessment report; taking the 85th percentile of the moving average purification efficiency during the normal operation period of the treatment facility as the baseline efficiency threshold, and taking 120% of the historical lowest efficiency of the treatment facility as the inefficiency threshold; adjusting the baseline efficiency threshold according to the season; and increasing the baseline efficiency threshold when the water quality at the downstream section of the treatment facility exceeds the standard.

[0024] Furthermore, the adjustment of the baseline efficiency threshold according to the season includes: taking 50% in spring, summer and autumn, and taking 35% in winter; the increase of the baseline efficiency threshold when the water quality at the downstream section of the treatment facility exceeds the standard includes: increasing the baseline efficiency threshold by 10 percentage points.

[0025] Furthermore, taking total nitrogen removal as an example, the baseline efficiency threshold for ecological interception ditches is set at 40% to 60%, and the inefficient threshold is set at 20% to 30%; the baseline efficiency threshold for ecological floating islands is set at 30% to 50%, and the inefficient threshold is set at 15% to 25%.

[0026] Preferably, the trend prediction based on the changing trend of the moving average purification efficiency includes: Take the past The slope of the fitted purification efficiency trend is calculated by taking the moving average purification efficiency over several consecutive time windows. ; in, This represents the slope of the fitted trend. Indicates the number of consecutive time windows. The time window number, Indicates the first The moving average purification efficiency over a continuous time window; when the trend slope When the value is less than 0, the purification efficiency is determined to be on a continuous downward trend; When a trend warning is triggered when the purification efficiency is determined to be continuously declining, the cumulative decline rate is calculated. ; in, This indicates the cumulative decrease. This represents the moving average purification efficiency over the first consecutive time window. Indicates the first The sliding average purification efficiency over a continuous time window; when the cumulative decrease exceeds the preset decrease threshold or trend slope. When the absolute value exceeds the preset slope threshold, a trend warning is triggered; The efficiency fluctuation characteristics within the sliding window are correlated with typical failure modes. When the purification efficiency decreases by more than 20% across windows, it is identified as a sudden failure mode; when the trend slope of 5 consecutive time windows is less than 0, it is identified as a gradual failure mode; and when the standard deviation of purification efficiency is greater than 15% and persists, it is identified as a fluctuating failure mode.

[0027] Furthermore, the preset descent threshold is 15%.

[0028] Furthermore, the preset slope threshold is determined based on the statistical distribution of the trend slope fitted during the normal operation of the treatment facility, or is set by the management personnel according to the type of treatment facility.

[0029] Furthermore, the number of continuous time windows Not less than 3, when the monitoring frequency is once every 2 hours. Take 5 to 6 samples; when the monitoring frequency is once every 6 hours, Take 3 to 4.

[0030] Preferably, the step of calculating the moving average purification efficiency of each level of facility, calculating the total purification efficiency of the entire treatment system, and the contribution rate of each level of facility includes: calculating the moving average purification efficiency of each level of treatment facility within the current time window. Calculate the overall purification efficiency of the entire treatment system: ; in, This indicates the overall purification efficiency of the entire treatment system. This indicates the influent concentration at the very beginning of the entire multi-stage treatment system. This indicates the effluent concentration downstream of the final stage of treatment; calculate the contribution rate of each stage of treatment: ; in, Indicates the first The contribution rate of level-one governance facilities Indicates the first The moving average purification efficiency of the treatment facilities within the current time window. This indicates the total number of treatment facilities arranged in series. For the treatment facility serial number; The contribution rate of each level of governance facility is adjusted for volatility-weighted reasons: ; in, Indicates the first The adjusted contribution rate of the level-one governance facilities Indicates the first Contribution rate of the first-level governance facilities before modification. This represents the fluctuation penalty coefficient. Indicates the first The standard deviation of the purification efficiency of the treatment facilities within the current time window. Indicates the first The moving average purification efficiency of the treatment facility within the current time window; the fluctuation penalty coefficient. The value range is from 0.2 to 0.5.

[0031] Furthermore, when the corrected contribution rate is less than 0, the corrected contribution rate is set to 0.

[0032] Furthermore, the step of identifying the weak facilities based on their contribution rate and generating a maintenance priority list includes: marking the first-level or several-level governance facilities with the lowest contribution rate as weak facilities, sorting all governance facilities from low to high according to their contribution rate, with the lower the contribution rate, the higher the maintenance priority, generating a maintenance priority list, and highlighting the weak facilities and their contribution rates in the evaluation report.

[0033] Furthermore, the first-level or several-level governance facilities with the lowest contribution rate are the first preset number of governance facilities sorted from low to high according to their contribution rate, or governance facilities with a contribution rate lower than the average contribution rate of all governance facilities.

[0034] Furthermore, the assessment report includes: the name, location, and type of each treatment facility; the moving average purification efficiency value and its change curve for the current time window; the graded early warning results and trigger times; trend prediction results, including whether a trend early warning has been triggered, the trend slope, and the cumulative decrease; multi-level linkage assessment results, including the purification efficiency, contribution rate, weakness identification, and maintenance priority list of each level of facility; and maintenance recommendations, which are specific action plans for different early warning levels.

[0035] The beneficial effects of this invention are as follows: 1. This invention directly calculates purification efficiency using upstream and downstream water quality monitoring concentration data, eliminating the need for hydraulic models or remote sensing inversion. A single calculation requires only one arithmetic operation, and with the aid of moving averages to eliminate random sampling errors, the assessment can be completed within seconds of data acquisition. Therefore, managers can monitor the facility's operational status in real time and immediately detect any decline in efficiency, solving the problem of delayed assessment in existing technologies.

[0036] 2. To address the problem that existing technologies cannot promptly indicate maintenance needs based on changes in purification efficiency, this invention sets a baseline efficiency threshold and an inefficiency threshold. Based on the relationship between the moving average purification efficiency and the threshold, the operating status of the treatment facility is divided into normal state, primary warning state, and advanced warning state, and corresponding inspection prompts or maintenance suggestions are generated. This realizes the correlation between purification efficiency evaluation results and facility maintenance needs, and improves the initiative of facility operation and maintenance management.

[0037] 3. Addressing the issue that existing technologies cannot identify key weak links in multi-level treatment facilities, this invention calculates the purification efficiency of each level of a series-connected treatment facility. It then performs a comprehensive analysis combining contribution rate and efficiency fluctuation to identify weak facilities that significantly impact the overall treatment effect and generate a maintenance priority list. This avoids indiscriminate maintenance of all facilities and improves the targeting of maintenance measures. Existing multi-level assessments simply calculate contribution rates based on efficiency percentages without considering differences in facility operational stability. This invention constructs a fluctuation penalty coefficient using standard deviation, simultaneously considering both the facility's pollutant removal capacity and long-term operational stability, resulting in assessment results that better align with engineering operation and maintenance needs.

[0038] 4. This invention can reuse the monitoring data of existing online water quality monitoring systems to conduct purification efficiency assessments. Generally, there is no need to modify the original monitoring equipment hardware structure. The entire assessment process can be realized simply by expanding the corresponding data processing and performance assessment software modules on the existing data management platform. This helps to reduce the difficulty of system modification and improve the applicability of the method.

[0039] 5. In the process of calculating the comprehensive purification efficiency, this invention dynamically adjusts the weight of indicators by using a weighting method based on the degree of pollution of different water quality indicators, thereby enabling the comprehensive evaluation results to be adaptively adjusted according to the changes of different pollution factors. Compared with the evaluation method using fixed weights, this invention is better able to reflect the impact of different pollution indicators on the evaluation of the treatment effect during actual operation.

[0040] 6. This invention establishes a diagnostic mechanism that correlates efficiency fluctuation characteristics with typical failure modes. The system can automatically identify abrupt, gradual, and fluctuating failures and output maintenance suggestions, providing auxiliary reference for managers to formulate maintenance plans. Attached Figure Description

[0041] Figure 1 This is an overall flowchart of the multi-level performance evaluation and early warning method for governance facilities in this embodiment of the invention; Figure 2 This is a logic diagram for hierarchical early warning judgment in an embodiment of the present invention; Figure 3 This is a trend prediction logic diagram in an embodiment of the present invention; Figure 4 This is a flowchart of the multi-level governance facility linkage assessment in an embodiment of the present invention; Figure 5 is a schematic diagram of the water quality monitoring point layout of the multi-stage series treatment facility in an embodiment of the present invention; Figure 6 is a block diagram of the evaluation software module architecture in an embodiment of the present invention. Detailed Implementation

[0042] Example 1: like Figure 1As shown, a method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities includes the following steps: Step 1: Deploy online water quality monitoring equipment upstream and downstream of the treatment facilities to collect water quality index data in real time and check its effectiveness; Step 2: Calculate the instantaneous purification efficiency at each sampling time based on the upstream influent concentration and downstream effluent concentration of the treatment facility. When monitoring multiple water quality indicators, calculate the comprehensive instantaneous purification efficiency by weighting the pollution levels of each indicator. Step 3: Set a time window and perform a moving average on the instantaneous purification efficiency at each sampling time within the time window to obtain the moving average purification efficiency. Step 4: Set the baseline efficiency threshold and the inefficiency threshold. Based on the comparison between the moving average purification efficiency and the two thresholds, classify the operating status of the treatment facility into three levels: normal operation, primary warning, and advanced warning, and perform corresponding maintenance operations. Step 5: Obtain the moving average purification efficiency over multiple consecutive time windows in the past, predict the trend based on the changing trend of the moving average purification efficiency, and trigger a trend warning when it is determined that the purification efficiency is continuously declining. Step 6: When multiple treatment facilities are arranged in series along the water flow direction, calculate the moving average purification efficiency of each level of facility, calculate the total purification efficiency of the entire treatment system and the contribution rate of each level of facility, identify the weakest facilities based on the contribution rate, and generate a maintenance priority list. Step 7: Generate and output an assessment report, which includes the purification efficiency of each treatment facility, the results of graded early warning, the results of trend prediction, and the identification of facilities with shortcomings.

[0043] Preferably, the water quality index data includes one or more of total nitrogen, total phosphorus, ammonia nitrogen, and permanganate index, and the online water quality monitoring equipment collects water quality index data at a frequency of once every 1 hour to once every 6 hours.

[0044] Furthermore, the validity check includes: removing negative values, values ​​exceeding the sensor's range, and values ​​that remain unchanged; when upstream or downstream data is missing at a certain moment, that moment is not included in the calculation, and the calculation is performed again after the data is completed at the next moment.

[0045] Furthermore, a data validity threshold is set. Within a time window, if the proportion of valid data sampling times to the total number of sampling times is lower than the data validity threshold, the evaluation result of that time window is determined to be unreliable, a data insufficiency prompt is generated, and the warning result is not output temporarily until the data is recovered and then evaluated again.

[0046] Furthermore, the data validity threshold is 70%.

[0047] Preferably, the calculation of the instantaneous purification efficiency at each sampling time based on the upstream influent concentration and downstream effluent concentration of the treatment facility includes calculating the instantaneous purification efficiency: ; in, Indicates the sampling time The instantaneous purification efficiency of the treatment facilities for current water quality indicators. Indicates the sampling time The influent concentration obtained from monitoring points upstream of the treatment facility, Indicates the sampling time The effluent concentration obtained from monitoring points downstream of the treatment facility and The units are all milligrams per liter.

[0048] Preferably, the step of calculating the comprehensive instantaneous purification efficiency by weighting the pollution levels of each indicator when monitoring multiple water quality indicators includes: obtaining the water quality standard limits corresponding to each water quality indicator, and calculating the sampling time. The exceedance multiple of each indicator concentration in the influent, where the exceedance multiple is the sampling time. The ratio of the influent concentration to the corresponding water quality standard limit for this indicator is used. The weighting coefficient of each indicator is determined based on the exceedance multiple, with a higher weight for a larger exceedance multiple. The sum of the weighting coefficients for each indicator is 1. The overall instantaneous purification efficiency is then calculated. ; in, Indicates the sampling time The overall instantaneous purification efficiency, This indicates the total number of water quality indicators monitored. This refers to the water quality indicator number. Indicates the first The weighting coefficients of each water quality indicator. Indicates the sampling time No. The instantaneous purification efficiency of each water quality indicator; the method for calculating the exceedance multiple is as follows: ; in, Indicates the sampling time No. The number of times the influent concentration of each water quality indicator exceeded the standard. Indicates the sampling time No. The influent concentration of each water quality indicator obtained from the monitoring point upstream of the treatment facility. Indicates the first The water quality standard limits corresponding to each water quality indicator are determined according to the corresponding water function zone category in the "Surface Water Environmental Quality Standard" GB 3838-2002.

[0049] Preferably, the weighting coefficients of each indicator are determined based on the exceedance multiple, including determining the first... Weighting coefficients for each water quality indicator: ; in, Indicates the first The weighting coefficients of each water quality indicator. Indicates the sampling time No. The number of times the influent concentration of each water quality indicator exceeded the standard. This represents the total number of monitored water quality indicators. When the sum of the exceedance multiples of the influent concentration of all indicators is 0, the weighting coefficient of each indicator is taken as... .

[0050] like Figure 2 As shown, preferably, the set time window, the instantaneous purification efficiency at each sampling moment within the time window is averaged to obtain the average purification efficiency, including: Calculate the moving average purification efficiency: ; in, Indicates the sampling time The moving average purification efficiency Indicates time window The effective number of samples within, Indicates the first time within the time window Instantaneous purification efficiency at each sampling moment Indicates a time window. Indicates the current sampling time; the time window Valid sampling times within No less than 10, when the number of valid samples is reduced due to missing data. If the number is less than 10, the time window will be automatically extended until the effective number of samples is reached. Not less than 10; The standard deviation of the purification efficiency within the calculation time window: ; Calculate the standard deviation of the purification efficiency within the time window, where, Indicates the sampling time The standard deviation of purification efficiency Indicates time window The effective number of samples within, Indicates the first time within the time window Instantaneous purification efficiency at each sampling moment Indicates the sampling time The moving average purification efficiency Indicates a time window. This indicates the current sampling time; when the standard deviation exceeds a preset stability threshold, a prompt indicating unstable purification efficiency is generated.

[0051] Furthermore, the time window The value ranges from 24 hours to 72 hours, and is adaptively adjusted according to the response characteristics of the treatment facilities and the monitoring frequency.

[0052] Furthermore, the preset stability threshold is 15%.

[0053] Preferably, the step of classifying the operating status of the treatment facility into three levels—normal operation, primary warning, and advanced warning—based on the comparison results of the moving average purification efficiency and two thresholds includes: when the moving average purification efficiency is greater than or equal to the baseline efficiency threshold, the treatment facility is determined to be operating normally, and no warning is generated; when the moving average purification efficiency is greater than or equal to the inefficient threshold but less than the baseline efficiency threshold, a primary warning is issued, the treatment facility is automatically marked, and a prompt message for enhanced inspection is generated; when the moving average purification efficiency is less than the inefficient threshold, an advanced warning is issued, and a maintenance work order is automatically generated, the maintenance work order including the location of the treatment facility, efficiency data, and maintenance suggestions.

[0054] Preferably, the determination of the baseline efficiency threshold and the inefficiency threshold includes: determining the initial reference value of the baseline efficiency threshold based on the removal rate required by the engineering design specifications of the treatment facility or the environmental impact assessment report; taking the 85th percentile of the moving average purification efficiency during the normal operation period of the treatment facility as the baseline efficiency threshold, and taking 120% of the historical lowest efficiency of the treatment facility as the inefficiency threshold; adjusting the baseline efficiency threshold according to the season; and increasing the baseline efficiency threshold when the water quality at the downstream section of the treatment facility exceeds the standard.

[0055] Furthermore, the adjustment of the baseline efficiency threshold according to the season includes: taking 50% in spring, summer and autumn, and taking 35% in winter; the increase of the baseline efficiency threshold when the water quality at the downstream section of the treatment facility exceeds the standard includes: increasing the baseline efficiency threshold by 10 percentage points.

[0056] Furthermore, taking total nitrogen removal as an example, the baseline efficiency threshold for ecological interception ditches is set at 40% to 60%, and the inefficient threshold is set at 20% to 30%; the baseline efficiency threshold for ecological floating islands is set at 30% to 50%, and the inefficient threshold is set at 15% to 25%.

[0057] like Figure 3 As shown, preferably, the trend prediction based on the changing trend of the moving average purification efficiency includes: Take the past The slope of the fitted purification efficiency trend is calculated by taking the moving average purification efficiency over several consecutive time windows. ; in, This represents the slope of the fitted trend. Indicates the number of consecutive time windows. The time window number, Indicates the first The moving average purification efficiency over a continuous time window; when the trend slope When the value is less than 0, the purification efficiency is determined to be on a continuous downward trend; When a trend warning is triggered when the purification efficiency is determined to be continuously declining, the cumulative decline rate is calculated. ; in, This indicates the cumulative decrease. This represents the moving average purification efficiency over the first consecutive time window. Indicates the first The sliding average purification efficiency over a continuous time window; when the cumulative decrease exceeds the preset decrease threshold or trend slope. When the absolute value exceeds the preset slope threshold, a trend warning is triggered; The efficiency fluctuation characteristics within the sliding window are correlated with typical failure modes. When the purification efficiency decreases by more than 20% across windows, it is identified as a sudden failure mode; when the trend slope of 5 consecutive time windows is less than 0, it is identified as a gradual failure mode; and when the standard deviation of purification efficiency is greater than 15% and persists, it is identified as a fluctuating failure mode.

[0058] Furthermore, the preset descent threshold is 15%.

[0059] Furthermore, the preset slope threshold is determined based on the statistical distribution of the trend slope fitted during the normal operation of the treatment facility, or is set by the management personnel according to the type of treatment facility.

[0060] Furthermore, the number of continuous time windows Not less than 3, when the monitoring frequency is once every 2 hours. Take 5 to 6 samples; when the monitoring frequency is once every 6 hours, Take 3 to 4.

[0061] like Figure 4 As shown, preferably, the step of calculating the moving average purification efficiency of each level of facility, calculating the total purification efficiency of the entire treatment system, and the contribution rate of each level of facility includes: calculating the moving average purification efficiency of each level of treatment facility within the current time window. Calculate the overall purification efficiency of the entire treatment system: ; in, This indicates the overall purification efficiency of the entire treatment system. This indicates the influent concentration at the very beginning of the entire multi-stage treatment system. This indicates the effluent concentration downstream of the final stage of treatment; calculate the contribution rate of each stage of treatment: ; in, Indicates the first The contribution rate of level-one governance facilities Indicates the first The moving average purification efficiency of the treatment facilities within the current time window. This indicates the total number of treatment facilities arranged in series. For the treatment facility serial number; The contribution rate of each level of governance facility is adjusted for volatility-weighted reasons: ; in, Indicates the first The adjusted contribution rate of the level-one governance facilities Indicates the first Contribution rate of the first-level governance facilities before modification. This represents the fluctuation penalty coefficient. Indicates the first The standard deviation of the purification efficiency of the treatment facilities within the current time window. Indicates the first The moving average purification efficiency of the treatment facility within the current time window; the fluctuation penalty coefficient. The value range is from 0.2 to 0.5.

[0062] Furthermore, when the corrected contribution rate is less than 0, the corrected contribution rate is set to 0.

[0063] Furthermore, the step of identifying the weak facilities based on their contribution rate and generating a maintenance priority list includes: marking the first-level or several-level governance facilities with the lowest contribution rate as weak facilities, sorting all governance facilities from low to high according to their contribution rate, with the lower the contribution rate, the higher the maintenance priority, generating a maintenance priority list, and highlighting the weak facilities and their contribution rates in the evaluation report.

[0064] Furthermore, the first-level or several-level governance facilities with the lowest contribution rate are the first preset number of governance facilities sorted from low to high according to their contribution rate, or governance facilities with a contribution rate lower than the average contribution rate of all governance facilities.

[0065] Furthermore, the assessment report includes: the name, location, and type of each treatment facility; the moving average purification efficiency value and its change curve for the current time window; the graded early warning results and trigger times; trend prediction results, including whether a trend early warning has been triggered, the trend slope, and the cumulative decrease; multi-level linkage assessment results, including the purification efficiency, contribution rate, weakness identification, and maintenance priority list of each level of facility; and maintenance recommendations, which are specific action plans for different early warning levels.

[0066] Example 2: like Figure 5 As shown, the complete water flow path of farmland runoff is illustrated, passing sequentially through the main influent monitoring equipment, the three-stage series treatment facilities, and the main effluent monitoring equipment. Water quality monitoring points are independently set up at the inlet and outlet of each treatment facility, and all monitoring data are uniformly transmitted to the background evaluation platform for performance calculation. Assume that a farmland non-point source pollution control demonstration area is equipped with ecological interception ditch facility 1, permeable interception dam facility 2, and ecological floating island facility 3 along the drainage direction. Water quality online monitoring equipment is deployed upstream and downstream of each facility, and total nitrogen concentration data is collected every 2 hours.

[0067] The simulation period was set from 00:00 to 24:00 on April 1, 2026. The average total nitrogen concentration upstream of Facility 1 was 4.5 mg / L, and the average concentration downstream was 2.7 mg / L. The instantaneous purification efficiency was calculated to be approximately 40.0%. After 24-hour moving average processing, the Eavg was 39.3%. The baseline efficiency threshold was set at 50%, and the inefficiency threshold was set at 30%. Since 30% ≤ 39.3% < 50%, a primary warning was triggered, prompting increased inspections.

[0068] The sliding average efficiency of facility 1 over the past three time windows was 48%, 45%, and 42%, respectively, showing a continuous downward trend with a cumulative decrease of 12.5%. No trend warning has been triggered yet, and the system continues to monitor it.

[0069] The moving average efficiencies of facilities 2 and 3 are 35% and 55%, respectively. The contribution rates are calculated as follows: facility 1 approximately 30.4%, facility 2 approximately 27.1%, and facility 3 approximately 42.5%. Facility 2 is identified as the weakest link, and a maintenance priority list is generated.

[0070] The assessment software module automatically generates assessment reports, which are displayed through a web platform and pushed to management personnel. If the traditional manual quarterly inspection model is used, facility efficiency degradation is detected at least 15 days late, and the costs of dredging, seedling replacement, and repair increase by 40%. This invention can provide real-time early warnings and initiate small-scale inspections and maintenance in advance.

[0071] like Figure 6As shown, the assessment software module includes a data acquisition submodule, an efficiency calculation submodule, an early warning judgment submodule, a multi-level linkage submodule, and an output submodule. The data acquisition submodule collects raw water quality monitoring data and transmits it to the efficiency calculation submodule and the multi-level linkage submodule. The efficiency calculation submodule solves for the purification efficiency and sends the calculation results to the early warning judgment submodule and the multi-level linkage submodule. The early warning judgment submodule conducts tiered early warning and trend prediction, and the early warning results are input into the multi-level linkage submodule. The multi-level linkage submodule integrates various data to complete the multi-level facility effectiveness assessment, and finally, the output submodule outputs the assessment report, early warning information, and operation and maintenance suggestions.

Claims

1. A method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities, characterized in that, Includes the following steps: Deploy online water quality monitoring equipment upstream and downstream of the treatment facilities to collect water quality index data in real time and check its effectiveness. The instantaneous purification efficiency at each sampling time is calculated based on the upstream influent concentration and downstream effluent concentration of the treatment facility. When monitoring multiple water quality indicators, the comprehensive instantaneous purification efficiency is obtained by weighting the pollution levels of each indicator. Set a time window, and perform a moving average of the instantaneous purification efficiency at each sampling time within the time window to obtain the moving average purification efficiency. Set a baseline efficiency threshold and an inefficiency threshold. Based on the comparison between the moving average purification efficiency and the two thresholds, classify the operating status of the treatment facilities into three levels: normal operation, primary warning, and advanced warning, and perform corresponding maintenance operations. The system obtains the moving average purification efficiency over multiple consecutive time windows, predicts trends based on the changing trends of the moving average purification efficiency, and triggers a trend warning when it determines that the purification efficiency is continuously declining. When multiple treatment facilities are arranged in series along the water flow direction, the moving average purification efficiency of each level of facility is calculated, the total purification efficiency of the entire treatment system and the contribution rate of each level of facility are calculated, the weakest facilities are identified based on the contribution rate, and a maintenance priority list is generated. Generate and output an assessment report, which includes the purification efficiency of each treatment facility, the results of graded early warning, the results of trend prediction, and the identification of facilities with shortcomings.

2. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 1, characterized in that, The water quality index data includes one or more of total nitrogen, total phosphorus, ammonia nitrogen, and permanganate index, and the online water quality monitoring equipment collects water quality index data at a frequency of once every 1 hour to once every 6 hours.

3. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 1, characterized in that, The calculation of the instantaneous purification efficiency at each sampling time based on the upstream influent concentration and downstream effluent concentration of the treatment facility includes calculating the instantaneous purification efficiency: ； in, Indicates the sampling time The instantaneous purification efficiency of the treatment facilities for current water quality indicators. Indicates the sampling time The influent concentration obtained from monitoring points upstream of the treatment facility, Indicates the sampling time The effluent concentration obtained from monitoring points downstream of the treatment facility and The units are all milligrams per liter.

4. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 1, characterized in that, The method of calculating the comprehensive instantaneous purification efficiency by weighting the pollution levels of each indicator when monitoring multiple water quality indicators includes: obtaining the water quality standard limits corresponding to each water quality indicator, and calculating the sampling time. The exceedance multiple of each indicator concentration in the influent, where the exceedance multiple is the sampling time. The ratio of the influent concentration to the corresponding water quality standard limit for this indicator is used. The weighting coefficient of each indicator is determined based on the exceedance multiple, with a higher weight for a larger exceedance multiple. The sum of the weighting coefficients for each indicator is 1. The overall instantaneous purification efficiency is then calculated. ； in, Indicates the sampling time The overall instantaneous purification efficiency, This indicates the total number of water quality indicators monitored. This refers to the water quality indicator number. Indicates the first The weighting coefficients of each water quality indicator. Indicates the sampling time No. The instantaneous purification efficiency of each water quality indicator; the method for calculating the exceedance multiple is as follows: ； in, Indicates the sampling time No. The number of times the influent concentration of each water quality indicator exceeded the standard. Indicates the sampling time No. The influent concentration of each water quality indicator obtained from the monitoring point upstream of the treatment facility. Indicates the first The water quality standard limits corresponding to each water quality indicator.

5. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 4, characterized in that, The weighting coefficients of each indicator are determined based on the exceedance multiple, including determining the first... Weighting coefficients for each water quality indicator: ； in, Indicates the first The weighting coefficients of each water quality indicator. Indicates the sampling time No. The number of times the influent concentration of each water quality indicator exceeded the standard. This represents the total number of monitored water quality indicators. When the sum of the exceedance multiples of the influent concentration of all indicators is 0, the weighting coefficient of each indicator is taken as... .

6. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 1, characterized in that, The set time window is used to perform a moving average of the instantaneous purification efficiency at each sampling moment within the time window, resulting in a moving average purification efficiency including: Calculate the moving average purification efficiency: ； in, Indicates the sampling time The moving average purification efficiency Indicates time window The effective number of samples within, Indicates the first time within the time window Instantaneous purification efficiency at each sampling moment Indicates a time window. Indicates the current sampling time; the time window Valid sampling times within No less than 10, when the number of valid samples is reduced due to missing data. If the number is less than 10, the time window will be automatically extended until the effective number of samples is reached. Not less than 10; The standard deviation of the purification efficiency within the calculation time window: ； Calculate the standard deviation of the purification efficiency within the time window, where, Indicates the sampling time The standard deviation of purification efficiency Indicates time window The effective number of samples within, Indicates the first time within the time window Instantaneous purification efficiency at each sampling moment Indicates the sampling time The moving average purification efficiency Indicates a time window. This indicates the current sampling time; when the standard deviation exceeds a preset stability threshold, a prompt indicating unstable purification efficiency is generated.

7. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 1, characterized in that, The process of classifying the operating status of the treatment facility into three levels—normal operation, primary warning, and advanced warning—based on the comparison between the moving average purification efficiency and two thresholds includes: when the moving average purification efficiency is greater than or equal to the baseline efficiency threshold, the treatment facility is determined to be operating normally, and no warning is generated; when the moving average purification efficiency is greater than or equal to the inefficient threshold but less than the baseline efficiency threshold, a primary warning is issued, the treatment facility is automatically marked, and a prompt message for enhanced inspection is generated; when the moving average purification efficiency is less than the inefficient threshold, an advanced warning is issued, and a maintenance work order is automatically generated, which includes the location of the treatment facility, efficiency data, and maintenance recommendations.

8. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 1, characterized in that, The determination methods for the baseline efficiency threshold and the inefficiency threshold include: determining the initial reference value of the baseline efficiency threshold based on the removal rate required by the engineering design specifications of the treatment facility or the environmental impact assessment report; taking the 85th percentile of the moving average purification efficiency during the normal operation period of the treatment facility as the baseline efficiency threshold, and taking 120% of the historical lowest efficiency of the treatment facility as the inefficiency threshold; adjusting the baseline efficiency threshold according to the season; and increasing the baseline efficiency threshold when the water quality at the downstream section of the treatment facility exceeds the standard.

9. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 1, characterized in that, The trend prediction based on the changing trend of the moving average purification efficiency includes: Take the past The slope of the fitted purification efficiency trend is calculated by taking the moving average purification efficiency over several consecutive time windows. ； in, This represents the slope of the fitted trend. Indicates the number of consecutive time windows. The time window number, Indicates the first The moving average purification efficiency over a continuous time window; when the trend slope When the value is less than 0, the purification efficiency is determined to be on a continuous downward trend; When a trend warning is triggered when the purification efficiency is determined to be continuously declining, the cumulative decline rate is calculated. ； in, This indicates the cumulative decrease. This represents the moving average purification efficiency over the first consecutive time window. Indicates the first The sliding average purification efficiency over a continuous time window; when the cumulative decrease exceeds the preset decrease threshold or trend slope. When the absolute value exceeds the preset slope threshold, a trend warning is triggered; The efficiency fluctuation characteristics within the sliding window are correlated with typical failure modes. When the purification efficiency decreases by more than 20% across windows, it is identified as a sudden failure mode; when the trend slope of 5 consecutive time windows is less than 0, it is identified as a gradual failure mode; and when the standard deviation of purification efficiency is greater than 15% and persists, it is identified as a fluctuating failure mode.

10. The method for dynamic evaluation and maintenance early warning of the purification efficiency of agricultural non-point source pollution control facilities according to claim 1, characterized in that, The calculation of the moving average purification efficiency of each level of facility, the calculation of the total purification efficiency of the entire treatment system, and the contribution rate of each level of facility include: calculating the moving average purification efficiency of each level of treatment facility within the current time window. Calculate the overall purification efficiency of the entire treatment system: ； in, This indicates the overall purification efficiency of the entire treatment system. This indicates the influent concentration at the very beginning of the entire multi-stage treatment system. This indicates the effluent concentration downstream of the final stage of treatment; calculate the contribution rate of each stage of treatment: ； in, Indicates the first The contribution rate of level-one governance facilities Indicates the first The moving average purification efficiency of the treatment facilities within the current time window. This indicates the total number of treatment facilities arranged in series. For the treatment facility serial number; The contribution rate of each level of governance facility is adjusted for volatility-weighted reasons: ； in, Indicates the first The adjusted contribution rate of the level-one governance facilities Indicates the first Contribution rate of the first-level governance facilities before modification. This represents the fluctuation penalty coefficient. Indicates the first The standard deviation of the purification efficiency of the treatment facilities within the current time window. Indicates the first The moving average purification efficiency of the treatment facilities within the current time window.

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