Automatic treatment system for water pollution monitoring

Abstract

The invention discloses an automatic treatment system for water pollution monitoring, which relates to the technical field of water environment monitoring and comprises a water body data acquisition module, a water body data processing module, a self-purification influence analysis module, a pollutant sensitivity identification module, a treatment decision judgment module and a result feedback module. The water body data acquisition module acquires physical, chemical and biological characteristic data of a water area to be monitored; the water body data processing module is used for calculating a self-cleaning index, a pollution susceptibility index and a biological response index after removing abnormal and repeated data in the data; the self-cleaning influence analysis module determines the influence of the pollution type on the self-cleaning capacity of the water body by calculating a self-cleaning influence coefficient; the pollutant sensitivity identification module obtains the pollutant sensitivity through analysis, and reflects the sensitivity of the target water body to the pollutants; the treatment decision judgment module judges the manual intervention condition according to the self-cleaning influence coefficient and the pollution sensitivity; the result feedback module is used for judging the treatment standard reaching condition; the system can accurately quantify pollution and self-cleaning association, and the monitoring accuracy is improved.

Description

Technical Field

[0001] This invention belongs to the field of water environment monitoring technology, specifically, it relates to an automatic processing system for water pollution monitoring. Background Technology

[0002] Water pollution directly disrupts the balance of aquatic ecosystems and poses a threat to human drinking water safety and health. Therefore, accurate monitoring and efficient treatment of water pollution have become key technological requirements in the field of water environment.

[0003] However, there are significant shortcomings in existing water pollution monitoring: On the one hand, failing to consider the dynamic differences in the self-purification capacity of water bodies and formulating measures based solely on fixed pollution parameter thresholds can easily lead to insufficient treatment or waste of resources due to underestimating or overestimating the self-purification capacity. On the other hand, focusing only on the physicochemical parameters of water bodies without incorporating the status of biological communities into the assessment, while biological communities are more sensitive to changes in water quality, can easily lead to over-intervention or delayed treatment.

[0004] Therefore, it is urgent to solve the above problems in order to achieve more accurate and appropriate water pollution monitoring and treatment. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art; Therefore, the present invention provides an automatic treatment system for water pollution monitoring, which can be implemented through the following technical solution: An automated treatment system for water pollution monitoring includes: The water body data acquisition module is used to collect water body data of the water area to be monitored. The water body data includes one or more of the following: physical characteristic data, chemical characteristic data, and biological characteristic data. The water body data processing module, connected to the water body data acquisition module, is used to preprocess the collected water body data to remove outliers and duplicates, and calculates the self-purification index, which reflects the natural purification capacity of the water body, the pollution susceptibility index, which reflects the potential risk of pollution, and the biological response index, which reflects the adaptive state of the biological community, based on the preprocessed data. The self-purification impact analysis module, connected to the water body data processing module, receives the self-purification index, the pollution susceptibility index, and the biological response index. By analyzing the correlation deviation and dynamic coupling relationship between them, and combining the identified dominant pollution type, a self-purification impact coefficient is calculated to quantify the degree of influence of pollution type on self-purification capacity. The pollutant sensitivity identification module is connected to the water body data processing module. It receives the self-purification index and the pollution susceptibility index, calculates the contribution ratio of the concentration of different types of pollutants to the overall pollution risk, and performs correlation analysis with the self-purification index to identify the sensitivity of the target water body to specific types of pollutants and outputs the pollution sensitivity. The governance decision-making module is connected to the self-purification impact analysis module and the pollutant sensitivity identification module. It is used to determine whether the target water body needs to be purified naturally or needs to be artificially treated immediately based on the positive or negative value of the self-purification impact coefficient. When artificial treatment is required, it determines the key direction of treatment based on the dominant pollutant type indicated by the pollution sensitivity.

[0006] Furthermore, in the water body data processing module, the factors used to calculate the self-purification index include dissolved oxygen content, redox potential, and flow velocity, and these are compared with their respective baseline values ​​during the ecologically stable period.

[0007] Furthermore, in the water body data processing module, the factors used to calculate the pollution susceptibility index include total nitrogen concentration, total phosphorus concentration, heavy metal ion equivalent concentration and water level, and these are compared with their respective environmental carrying capacity thresholds or water level critical thresholds.

[0008] Furthermore, in the water data processing module, the factors used to calculate the biological response index include the number of plankton, the activity of the microbial community, and the aquatic biodiversity index, and these are compared with their respective baseline values ​​during the ecologically stable period.

[0009] Furthermore, in the self-cleaning impact analysis module, the identification of the dominant pollution type is based on whether the ratio of total nitrogen to total phosphorus concentration or the ratio of heavy metal ion equivalent concentration to the sum of total nitrogen and total phosphorus concentrations exceeds a preset critical value.

[0010] Furthermore, in the pollutant sensitivity identification module, the contribution ratio of each type of pollutant is calculated based on the proportion of the ratio of the real-time concentration of each type of pollutant to its environmental carrying capacity threshold in the total ratio.

[0011] Furthermore, the pollutant sensitivity identification module determines the pollution sensitivity by comparing the sensitivity correlation values ​​of various pollutants, where the sensitivity correlation value is the ratio of the contribution percentage of each pollutant to the self-cleaning index.

[0012] Furthermore, it also includes a results feedback module, which is used to collect updated data of the target water body after the artificial treatment is carried out, and to evaluate the treatment effect and identify weak links in the treatment by calculating the compliance rate of the updated data with the treatment standard and comparing the difference between the updated data and the original data.

[0013] Furthermore, the governance standards are a threshold system that covers the acceptable range of physical, chemical, and biological characteristic parameters, formulated based on the functional positioning of the water area, water environment quality targets, and ecological protection requirements.

[0014] Furthermore, the results feedback module determines whether the governance has met the standards based on whether the calculated compliance rate is greater than or equal to a preset threshold.

[0015] The beneficial effects of this invention are: (1) It can accurately quantify the relationship between pollution and self-purification and key pollution factors. By calculating the self-purification index, pollution susceptibility index, self-purification impact coefficient and pollution sensitivity, it can clarify the degree of influence of different types of pollution on the self-purification capacity of water bodies, and locate the core pollutants that are more likely to cause pollution, thus avoiding the inadequate treatment and waste of resources caused by the existing technology due to neglecting the dynamic differences of self-purification or lack of identification of key factors. (2) To achieve coordinated monitoring of physicochemical parameters and biological responses and closed-loop optimization of governance effects, the biological community status is included in the assessment through the biological response index to detect ecological anomalies earlier. Combined with the result feedback module, the governance compliance status and weak links are analyzed. This avoids excessive intervention or governance lag by relying solely on physicochemical parameters, and provides a basis for dynamically adjusting governance strategies, thereby improving the adaptability and efficiency of governance. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This application provides an automated treatment system for water pollution monitoring; As an embodiment of this application, the system specifically includes: The water body data acquisition module is used to collect water body data of the water area to be monitored. The water body data includes one or more of the following: physical characteristic data, chemical characteristic data, and biological characteristic data. The water body data processing module, connected to the water body data acquisition module, is used to preprocess the collected water body data to remove outliers and duplicates, and calculates the self-purification index, which reflects the natural purification capacity of the water body, the pollution susceptibility index, which reflects the potential risk of pollution, and the biological response index, which reflects the adaptive state of the biological community, based on the preprocessed data. The self-purification impact analysis module, connected to the water body data processing module, receives the self-purification index, the pollution susceptibility index, and the biological response index. By analyzing the correlation deviation and dynamic coupling relationship between them, and combining the identified dominant pollution type, a self-purification impact coefficient is calculated to quantify the degree of influence of pollution type on self-purification capacity. The pollutant sensitivity identification module is connected to the water body data processing module. It receives the self-purification index and the pollution susceptibility index, calculates the contribution ratio of the concentration of different types of pollutants to the overall pollution risk, and performs correlation analysis with the self-purification index to identify the sensitivity of the target water body to specific types of pollutants and outputs the pollution sensitivity. The governance decision-making module is connected to the self-purification impact analysis module and the pollutant sensitivity identification module. It is used to determine whether the target water body needs to be purified naturally or needs to be artificially treated immediately based on the positive or negative value of the self-purification impact coefficient. When artificial treatment is required, it determines the key direction of treatment based on the dominant pollutant type indicated by the pollution sensitivity.

[0020] As a second embodiment of the present invention, it is implemented based on the first embodiment; Specifically, the water body data acquisition module is used to collect water body data of the water area to be monitored. The water area to be monitored is a water unit with a clear hydrological boundary and covering a certain catchment area. It can be a naturally formed lake or river, or a man-made reservoir. For the sake of contextual description, the water bodies in the water area to be monitored are marked as target water bodies. The water body data includes one or more of the following: physical property data, chemical property data, and biological property data. The physical property data includes at least one of water temperature, turbidity, transparency, flow rate, and water level; The chemical property data include at least one of dissolved oxygen content, redox potential, total nitrogen concentration, total phosphorus concentration, total organic carbon content, and heavy metal ion equivalent concentration. The biological characteristic data includes at least one of the following: phytoplankton species and quantity, microbial community activity, and aquatic biodiversity index. The water body data collected for the first time is marked as raw data.

[0021] Specifically, the water body data processing module is used to acquire the collected water body data and process it. The specific processing procedure is as follows: First, the collected water data is preprocessed. The specific processing method is as follows: Water body data that exceeds the normal fluctuation range of historical data for the same type of water body in the monitored water area are marked as abnormal data. The normal fluctuation range is determined according to the 3σ principle, which is existing technology and will not be elaborated here. After removing abnormal data, duplicate data are removed. The preprocessed water body data is obtained. The self-purification index of the pretreated water body is analyzed based on the following method: In the pretreated water data, dissolved oxygen content is denoted as DO, oxidation-reduction potential is denoted as ORP, and flow velocity is denoted as V. Furthermore, the dissolved oxygen content, oxidation-reduction potential, and flow velocity corresponding to the ecological stability period of the water body to be monitored are recorded as DO0, ORP0, and V0, respectively. The ecological stability period is the period when the water body's self-purification capacity is in a naturally stable state. Through the formula: SC=(DO / DO0)×(ORP / ORP0)×(V / V0), The calculated result is labeled as the self-netting index SC.

[0022] The self-purification index SC calculated above is used to reflect the comprehensive ability of a target water body to reduce pollutant concentrations through natural processes such as physical mixing, chemical transformation and biodegradation. The higher the index value, the stronger the self-purification efficiency. The pollution susceptibility index of the pretreated water body is analyzed based on the pretreated water body data, specifically as follows: The total nitrogen concentration in the pretreated water data is denoted as TN, the total phosphorus concentration as TP, the heavy metal ion equivalent concentration as M, and the water level as H. Furthermore, the total nitrogen concentration is denoted as TN0, the total phosphorus concentration as TP0, and the heavy metal ion equivalent concentration as M0 under the environmental carrying capacity threshold of the water body to be monitored. The environmental carrying capacity threshold is the maximum limit of pollutants that the water body can withstand to maintain ecological balance. The water level obtained below the critical water level threshold is denoted as H0. The critical water level threshold is the minimum water level value to avoid pollutant retention. Through the formula: PR=(TN / TN0)×(TP / TP0)×(M / M0)×(H0 / H), The calculated result is labeled as the Pollution Susceptibility Index (PR).

[0023] The pollution susceptibility index (PR) calculated above is used to reflect the potential probability of typical pollution (such as eutrophication and heavy metal contamination) occurring in the target water body under the current conditions. The higher the index value, the greater the pollution risk. The biological response index (BR) of the pretreated water body was analyzed using the following method: In the pre-processed water data, the number of plankton is denoted as P, the activity of the microbial community is denoted as B, and the aquatic biodiversity index is denoted as A. Furthermore, the number of plankton, the activity of microbial communities, and the aquatic biodiversity index in the water body data corresponding to the ecological stability period are recorded as P0, B0, and A0, respectively. Through the formula: BR = (P / P0) × (B / B0) × (A / A0), The calculated result is labeled as the biological response index BR; The biological response index (BR) calculated above is used to reflect the adaptation status of the biological community in the target water body to changes in water quality.

[0024] Specifically, the self-purification impact analysis module is used to obtain the self-purification index, pollution susceptibility index, and biological responsiveness. It analyzes these three types of data to obtain the self-purification impact coefficient. The specific analysis process is as follows: First, the self-purification index SC, pollution susceptibility index PR, and biological response index BR output by the water body data processing module are obtained. The self-purification index benchmark value is denoted as SC0, and the self-purification index benchmark value is the self-purification index corresponding to the target water body during the ecological stability period. Then, according to the formula: D1=(PR-1)×(SC0 / SC), The correlation deviation between the pollution susceptibility index and the self-cleaning index was calculated and denoted as D1. Then, according to the formula: D2=(BR-1)×(SC / SC0), The dynamic coupling value between the biological response index and the self-purification index was calculated and denoted as D2. Then, calculate the ratio of total nitrogen concentration to total phosphorus concentration, TN / TP, denoted as R1, and calculate the ratio of heavy metal ion equivalent concentration to the sum of total nitrogen concentration and total phosphorus concentration, M / (TN+TP), denoted as R2. When R1 is greater than the preset ratio, the pollution type is determined to be eutrophication, and the corresponding value is recorded as F1; when R2 exceeds the preset threshold, the pollution type is determined to be heavy metal pollution, and the corresponding value is recorded as F2; ​​either F1 or F2 is marked as the pollution type coefficient FT. Through the formula: K=(D1+D2)×FT, The calculated result is labeled as the self-cleaning impact coefficient, denoted as K; The self-purification impact coefficient K calculated above is used to measure the effect of pollution susceptibility index and biological responsiveness on the self-purification index, thereby further analyzing the impact of water pollution type on the self-purification capacity of the target water body. When the self-purification influence coefficient K is positive and the larger the absolute value, it indicates that the corresponding water pollution type has a stronger inhibitory effect on the self-purification capacity of the target water body; when the self-purification influence coefficient K is negative and the larger the absolute value, it indicates that the self-purification capacity of the target water body is promoted more significantly under the corresponding water pollution type.

[0025] Specifically, the pollutant sensitivity identification module is used to obtain the self-cleaning index and the pollution susceptibility index. The pollution sensitivity is obtained by analyzing the above two types of data. The specific analysis process is as follows: First, the self-purification index SC and pollution susceptibility index PR output by the water body data processing module are obtained, as well as the total nitrogen concentration TN, total phosphorus concentration TP, and heavy metal ion equivalent concentration M in the pre-processed water body data. At the same time, the total nitrogen concentration TN0, total phosphorus concentration TP0, and heavy metal ion equivalent concentration M0 under the environmental carrying capacity threshold of the water body to be monitored are obtained. Then, according to the formula: R_TN=(TN / TN0)÷[(TN / TN0)+(TP / TP0)+(M / M0)], The contribution percentage of total nitrogen concentration to the pollution susceptibility index is calculated and denoted as R_TN. The contribution percentage refers to the proportion of this type of pollution in the pollution susceptibility index. According to the formula: R_TP=(TP / TP0)÷[(TN / TN0)+(TP / TP0)+(M / M0)], The contribution of total phosphorus concentration to the pollution susceptibility index is calculated and denoted as R_TP; According to the formula: R_M=(M / M0)÷[(TN / TN0)+(TP / TP0)+(M / M0)], The contribution of heavy metal ion equivalent concentration to the pollution susceptibility index was calculated and denoted as R_M; Next, the correlation between the self-purification index and the contribution ratio of each pollutant was calculated: Calculate the total nitrogen sensitivity correlation value, denoted as S_TN, calculated as R_TN÷SC; Calculate the total phosphorus sensitivity correlation value, denoted as S_TP, calculated as R_TP÷SC; Calculate the correlation value of heavy metal ion sensitivity, denoted as S_M, and the calculation method is R_M÷SC; Subsequently, the values ​​of S_TN, S_TP, and S_M were compared, and the sensitivity correlation value with the largest value was selected and marked as the pollution sensitivity. The calculated pollution sensitivity is used to reflect the sensitivity of the target water body to water pollution caused by different types of pollutants, that is, to determine which pollutant is more likely to cause water pollution. When the pollution sensitivity is dominated by S_TN, it indicates that total nitrogen is the pollutant that is more likely to cause pollution of the target water body, and the larger the S_TN value, the stronger the sensitivity of the target water body to total nitrogen pollution. When the pollution sensitivity is dominated by S_TP, it indicates that total phosphorus is a pollutant that is more likely to cause pollution of the target water body, and the larger the S_TP value, the stronger the sensitivity of the target water body to total phosphorus pollution. When the pollution sensitivity is dominated by S_M, it indicates that heavy metal ions are more likely to cause pollution to the target water body, and the larger the S_M value, the stronger the sensitivity of the target water body to heavy metal ion pollution.

[0026] Specifically, the governance decision-making module is used to determine whether the target water body requires artificial intervention or relies on natural self-purification. The specific process is as follows: Obtain the self-cleaning impact coefficient K and pollution sensitivity output by the self-cleaning impact analysis module; When the self-purification influence coefficient K is negative, it is determined that the target water body can rely on natural self-purification. When the self-purification impact coefficient K is positive, it is determined that immediate human intervention is required. At this time, when the pollution sensitivity is dominated by S_TN and S_TP, the main focus is on eutrophication; when the pollution sensitivity is dominated by S_M, the main focus is on heavy metal ion pollution.

[0027] As a third embodiment of this application, the difference from the above embodiments is that it further includes: The results feedback module is used to obtain secondary water body data of the target water body several days after the water pollution treatment is completed, mark it as updated data, analyze the updated data, and determine whether the target water body has met the treatment standards. The specific process is as follows: The governance standard is a water body parameter threshold system formulated based on the functional positioning, water environment quality objectives and ecological protection requirements of the water area, covering the qualified range of physical, chemical and biological characteristic data; The updated data will be compared with the governance standards to calculate the compliance rate of each parameter; Compliance rate = (Number of parameters meeting the governance standards / Total number of parameters) × 100%; The number of parameter items that meet the governance standards refers to the number of parameter items in the updated data whose values ​​are within the acceptable range specified by the governance standards. The total number of parameter items refers to the total number of parameter items in all physical, chemical, and biological characteristic data that are involved in the evaluation of the governance effect; When the compliance rate is greater than or equal to the preset threshold, it is determined that the governance standard has been met; When the compliance rate is less than the preset threshold, it is determined that the governance standard has not been met; For water bodies that do not meet the standards, analyze the difference between the changes in the non-compliant parameters and the original data; Change difference = Parameter value in updated data - Parameter value in original data; Weak links in governance are identified by the absolute value of the difference in change; the smaller the absolute value, the worse the governance effect.

[0028] As a fourth embodiment of this application, it includes all the implementation processes of the above embodiments.

[0029] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. An automated treatment system for water pollution monitoring, characterized in that, include: The water body data acquisition module is used to collect water body data of the water area to be monitored. The water body data includes one or more of the following: physical characteristic data, chemical characteristic data, and biological characteristic data. The water body data processing module, connected to the water body data acquisition module, is used to preprocess the collected water body data to remove outliers and duplicates, and calculates the self-purification index, which reflects the natural purification capacity of the water body, the pollution susceptibility index, which reflects the potential risk of pollution, and the biological response index, which reflects the adaptive state of the biological community, based on the preprocessed data. The self-purification impact analysis module, connected to the water body data processing module, receives the self-purification index, the pollution susceptibility index, and the biological response index. By analyzing the correlation deviation and dynamic coupling relationship between them, and combining the identified dominant pollution type, a self-purification impact coefficient is calculated to quantify the degree of influence of pollution type on self-purification capacity. The pollutant sensitivity identification module is connected to the water body data processing module. It receives the self-purification index and the pollution susceptibility index, calculates the contribution ratio of the concentration of different types of pollutants to the overall pollution risk, and performs correlation analysis with the self-purification index to identify the sensitivity of the target water body to specific types of pollutants and outputs the pollution sensitivity. The governance decision-making module is connected to the self-purification impact analysis module and the pollutant sensitivity identification module. It is used to determine whether the target water body needs to be purified naturally or needs to be artificially treated immediately based on the positive or negative value of the self-purification impact coefficient. When artificial treatment is required, it determines the key direction of treatment based on the dominant pollutant type indicated by the pollution sensitivity.

2. The automatic treatment system for water pollution monitoring according to claim 1, characterized in that, In the water body data processing module, the factors used to calculate the self-purification index include dissolved oxygen content, redox potential, and flow velocity, and these are compared with their respective baseline values ​​during the ecologically stable period.

3. An automatic treatment system for water pollution monitoring according to claim 1, characterized in that, In the water body data processing module, the factors used to calculate the pollution susceptibility index include total nitrogen concentration, total phosphorus concentration, heavy metal ion equivalent concentration and water level, and these are compared with their respective environmental carrying capacity thresholds or water level critical thresholds.

4. An automatic treatment system for water pollution monitoring according to claim 1, characterized in that, In the water body data processing module, the factors used to calculate the biological response index include the number of plankton, the activity of microbial communities, and the aquatic biodiversity index, and these are compared with their respective baseline values ​​during the ecologically stable period.

5. An automatic treatment system for water pollution monitoring according to claim 1, characterized in that, In the self-cleaning impact analysis module, the identification of the dominant pollution type is based on whether the ratio of total nitrogen to total phosphorus concentration or the ratio of heavy metal ion equivalent concentration to the sum of total nitrogen and total phosphorus concentration exceeds a preset threshold.

6. An automatic treatment system for water pollution monitoring according to claim 1, characterized in that, In the pollutant sensitivity identification module, the contribution ratio of each type of pollutant is calculated based on the ratio of the real-time concentration of each type of pollutant to its environmental carrying capacity threshold in the total ratio.

7. An automatic treatment system for water pollution monitoring according to claim 6, characterized in that, The pollutant sensitivity identification module determines the pollution sensitivity by comparing the sensitivity correlation values ​​of various pollutants. The sensitivity correlation value is the ratio of the contribution percentage of each pollutant to the self-cleaning index.

8. An automatic treatment system for water pollution monitoring according to claim 6, characterized in that, It also includes a results feedback module, which is used to collect updated data of the target water body after the artificial treatment is carried out. By calculating the compliance rate of the updated data with the treatment standard and comparing the difference between the updated data and the original data, the treatment effect is evaluated and weak links in the treatment are identified.

9. An automatic treatment system for water pollution monitoring according to claim 8, characterized in that, The governance standards are a threshold system that covers the acceptable range of physical, chemical, and biological characteristic parameters, formulated based on the functional positioning of the water area, water environment quality targets, and ecological protection requirements.

10. An automatic treatment system for water pollution monitoring according to claim 8, characterized in that, The results feedback module determines whether the governance has met the standards based on whether the calculated compliance rate is greater than or equal to the preset threshold.

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