Intelligent water environment treatment system

Abstract

The invention provides an intelligent water environment treatment system, and relates to the technical field of water treatment, and the system comprises a data acquisition module which transmits water quality parameter data monitored in real time to a target processing center; the target processing center performs pollution analysis based on the water quality parameter data to obtain a pollution analysis result; the self-treatment module is used for automatically adjusting corresponding operation parameters of water treatment equipment to realize pollution treatment based on a target treatment suggestion generated according to the pollution analysis result; and the interaction module provides water quality data query, alarm and user interaction functions. Performing pollution analysis based on the water quality parameter data monitored in real time through the target processing center to obtain a pollution analysis result; according to a target treatment suggestion generated according to the pollution analysis result, automatically adjusting corresponding operation parameters of the water treatment equipment to realize pollution treatment; water quality data query, alarm and user interaction functions are provided, the pollution condition can be effectively and intelligently analyzed, and the water environment can be efficiently treated.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment, and in particular to an intelligent water environment treatment system. Background Art

[0002] In recent years, with the acceleration of urbanization and the expansion of industrial production, the impact of human activities on the water environment has become increasingly significant. Water pollution has become a major global environmental issue that demands urgent attention. This not only poses a severe challenge to the ecological environment, such as the destruction of aquatic habitats and the reduction of biodiversity, but also poses a significant threat to human health. In the face of this dire situation, existing water environment management methods are ineffective due to problems such as untimely monitoring and limited treatment effects. Therefore, how to achieve real-time water quality monitoring, intelligent pollution analysis, and efficient water environment management has become a major research focus.

[0003] Therefore, the present invention provides an intelligent water environment management system. Summary of the Invention

[0004] The present invention provides an intelligent water environment management system, which is used to perform pollution analysis based on real-time monitored water quality parameter data through a target treatment center to obtain pollution analysis results; according to the target treatment suggestions generated based on the pollution analysis results, the corresponding operating parameters of the water treatment equipment are automatically adjusted to achieve pollution treatment; and water quality data query, alarm and user interaction functions are provided, which can effectively and intelligently analyze the pollution status and efficiently manage the water environment.

[0005] The present invention provides an intelligent water environment management system, comprising: Data acquisition module: used to monitor water quality parameters in real time using the set monitoring equipment deployed in the target water area, and transmit the monitored water quality parameter data to the target processing center; Water quality analysis module: used by the target processing center to receive water quality parameter data and perform pollution analysis to obtain pollution analysis results; Self-treatment module: used to generate target treatment suggestions based on the pollution analysis results, and automatically adjust the corresponding operating parameters of the water treatment equipment based on the target treatment suggestions to achieve pollution treatment; Interactive module: used to provide water quality data query, alarm and user interaction functions.

[0006] Preferably, the water quality analysis module includes: Data processing unit: used for the target processing center to regularly regard the parameter data of each water quality parameter of the target water area within the first preset time period received as the first parameter data; Aggregating and preprocessing the first parameter data and key meteorological data corresponding to the same time period to obtain first target data, and marking the first target data with time characteristics; Taking the current target water area as a matching condition, extracting the key geographical features of the monitoring area corresponding to the target water area from a preset geographical data repository; The pollution analysis unit includes: an initial identification block, a related identification block and an output block; The initial identification block is used to input the first target data and key geographical features into a pre-established pollution identification model to obtain a first pollution identification result; Extracting pollutant types from the first pollution identification results and summarizing and arranging them to obtain a first pollutant set; Correlation identification block: used to select any preset correlation algorithm from the preset correlation algorithm list to calculate the correlation coefficient between the corresponding first parameter data of each water quality parameter to obtain the first coefficient; Present all the first coefficients obtained in the form of a matrix to obtain a parameter correlation matrix; According to the parameter correlation matrix, water quality parameter combinations with absolute values greater than a set correlation threshold are marked as strongly correlated parameter pairs; Using the strongly correlated parameter pair and the target water body area as matching conditions, extracting corresponding possible associated pollutants from a set associated pollution database; Summarize and organize all possible associated pollutants obtained to obtain a second pollutant set; The overlapping pollutants in the first pollutant set and the second pollutant set are regarded as key pollutants; Output block: used to extract the corresponding attribute information of the current key pollutant from the first pollution identification result and mark it as key attribute information; Outputting the key pollutants and corresponding key attribute information as pollutant analysis results; Prediction analysis and output unit: used to input the first target data and key geographical features into a pre-established water quality prediction model to obtain a water quality prediction result within a second preset time period; Extract the predicted values of each water quality parameter from the water quality prediction results and construct them according to the time series to obtain the parameter-prediction change curve of each water quality parameter; Extract the predicted change characteristics from the parameter-prediction change curve, and combine them with the predicted values of the corresponding water quality parameters and the correlation between the key pollutants and the corresponding strong parameter pairs to input them into the pre-established concentration prediction model to obtain the predicted concentration change data of the current key pollutants; The predicted concentration change data of all key pollutants are output as pollutant analysis results.

[0007] Preferably, the pollution analysis unit further includes: Quantity acquisition block: used to obtain the average quantity of pollutant types in the first pollutant set and the second pollutant set; Difference analysis block: used to compare the currently acquired number of key pollutants with the average number of pollutants to obtain the absolute difference of the first number; Correlation re-identification block: when the absolute difference of the first quantity is greater than the reference quantity difference, any preset correlation algorithm that has not been used is selected from the preset correlation algorithm list to perform water quality parameter correlation analysis to obtain a new second pollutant set and new key pollutants; Re-output block: used to output the new key pollutant as a key pollutant when the absolute difference between the average number of pollutant types in the first pollutant set and the new second pollutant set and the first number of the number of types of the new key pollutant is not greater than the benchmark number difference.

[0008] Preferably, the self-processing module includes: Prediction curve establishment unit: used to establish the predicted concentration change curve of each key pollutant using the predicted concentration change data in the pollutant analysis results; Prediction curve analysis unit: used to calculate the curve area and curve slope of the current predicted concentration change curve, and obtain a first area value and a first slope accordingly; A reference data acquisition unit is configured to extract historical concentration change data of the key pollutants in the current target water area from the pollution record database, and sequentially obtain corresponding historical concentration change data of the three historical screening time periods closest to the current time period using the second preset time period as a division unit, and mark the corresponding historical concentration change data as reference historical concentration change data; Reference curve establishing unit: used for establishing a reference concentration change curve of the current key pollutant using the reference concentration change data; Reference curve analysis unit: used to calculate the curve area and curve slope of the current reference concentration change curve, and obtain the corresponding reference area value and reference slope; A pollution assessment unit is configured to calculate a concentration change assessment coefficient of a current key pollutant using the first area value, the first slope, and all reference area values and reference slopes; Risk analysis unit: used to extract the current concentration of each key pollutant from the pollutant analysis results, and calculate the pollution risk score in combination with the concentration change assessment coefficient; Suggestion acquisition unit: used to match the pollution risk score and key pollutants as matching conditions and obtain corresponding pollution treatment suggestions from a set treatment suggestion library; Suggestion optimization unit: used to extract adjustable operating parameters of water treatment equipment from pollution treatment suggestions, optimize the adjustable operating parameters to obtain key operating parameter values, and generate target treatment suggestions based on the key operating parameter values; Adjustment unit: used to generate corresponding water treatment instructions based on the target treatment suggestions, and transmit the water treatment instructions to the corresponding water treatment equipment. The water treatment equipment automatically adjusts the corresponding operating parameters according to the received water treatment instructions to achieve pollution treatment.

[0009] Preferably, the calculation formula for the contamination risk score is as follows: Where, Expressed as the pollution risk score of the current target water area; It is expressed as the maximum number of key pollutants appearing in the current target water area; It is expressed as the concentration value of the key pollutant of type i at the current moment; Indicates the concentration value of the key pollutant of type i at the last moment, where i=1, 2, 3, , n; n represents the total number of key pollutant types; Expressed as the impact weight of real-time concentration changes of key pollutants on the analyzed pollution risk level; Expressed as the concentration change assessment coefficient of the i-th key pollutant; It is expressed as the impact weight of the predicted concentration change of key pollutants on the analyzed pollution risk level.

[0010] Preferably, the suggestion optimization unit includes: Summary and marking block: used to summarize the adjustable operating parameters in the current pollution treatment suggestion to obtain an adjustable parameter combination; Identify the key pollutants that should be treated according to the current pollution treatment proposals and mark them as target treatment pollutants; Range expansion block: used to expand the current concentration of the target pollutant based on the concentration change assessment coefficient to obtain a reference concentration range; Record filtering block: used to extract the first historical processing record of the current pollution treatment suggestion from the preset pollution treatment database; From the first historical processing records, select historical processing records in which the historical concentration of the target processing pollutant in the past falls within the corresponding reference concentration range, and mark them as first records; A benchmark value and concentration difference acquisition block is configured to determine, based on the historical pollutant treatment record data and the historical operating cost record data in the first record, a first historical adjustable parameter combination set whose historical pollutant treatment efficiency is higher than the average pollutant treatment efficiency, and a second historical adjustable parameter combination set whose historical operating cost is lower than the average operating cost; If there is an overlapping parameter combination in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set, marking the overlapping parameter combination as a reference group; When there is a single reference group, the current reference group is considered the baseline parameter group; When there are multiple reference groups, the reference possibility coefficient of each reference group is obtained by taking a weighted average of the historical pollutant treatment efficiency and the historical operating cost, and the reference group with the largest reference possibility coefficient is regarded as the benchmark parameter group; Taking the historical value of each adjustable operating parameter in the benchmark parameter group as the benchmark parameter value; Determine, based on the first record, a first historical concentration of the target pollutant corresponding to the current baseline parameter group, obtain a concentration difference between the first historical concentration and the current concentration of the target pollutant, and output the difference as a key concentration difference; If there is no overlapping parameter combination in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set, averaging the historical values of the same adjustable operating parameter with the parameter combination with the lowest historical pollutant treatment efficiency selected from the first historical adjustable parameter combination set and the parameter combination with the highest historical operating cost selected from the second historical adjustable parameter combination set to obtain a historical average value of each adjustable operating parameter, and outputting it as the benchmark parameter value; Determining, based on the first record, a second historical concentration of a target pollutant treated corresponding to a parameter combination with the lowest historical pollutant treatment efficiency selected from the first set of historical adjustable parameter combinations, and obtaining a first concentration difference between the second historical concentration of the target pollutant and a current concentration; Determine a third historical concentration of the target pollutant treated corresponding to the parameter combination with the highest historical operating cost selected from the second historical adjustable parameter combination set, and obtain a second concentration difference between the third historical concentration and the current concentration of the target pollutant; Calculating an average of the first concentration difference and the second concentration difference and outputting the average as the key concentration difference; Parameter adjustment block: used to adjust the baseline parameter value of each adjustable operating parameter by analyzing the correlation between the adjustable operating parameters and the pollutant treatment efficiency and operating costs in combination with the key concentration difference to obtain the key operating parameter value; Suggestion generation block: combines the current pollution treatment suggestion with the key operating parameter value to generate the target treatment suggestion.

[0011] Preferably, the parameter adjustment block is used to: Taking the historical values of the currently adjustable operating parameters within the third preset time period as independent variables, and the corresponding historical pollutant treatment efficiency and historical operating costs as dependent variables, respectively construct a parameter-treatment efficiency relationship graph and a parameter-cost relationship graph; Extracting corresponding slopes of the parameter-processing efficiency relationship graph and the parameter-cost relationship graph, and outputting them as efficiency impact values and cost impact values, respectively; Based on the efficiency impact value and the cost impact value, and in combination with the key concentration difference, the baseline parameter value of the currently adjustable operating parameter is adjusted to obtain the key operating parameter value; The calculation formula for the key operating parameter values is as follows: Where, The key operating parameter values represented as currently adjustable operating parameters; Indicates the baseline parameter value of the currently adjustable operating parameter; Expressed as the corresponding efficiency impact value of the currently adjustable operating parameters; It is expressed as the contribution weight of the correlation between the adjustable operating parameters and the pollutant treatment efficiency to the parameter adjustment; It is expressed as the corresponding cost impact value of the currently adjustable operating parameters; It is expressed as the contribution weight of the correlation between the adjustable operating parameters and the operating cost to the parameter adjustment; It represents the corresponding key concentration difference when the current adjustable operating parameter is the benchmark parameter; e is represented as a constant with a value of 2.7.

[0012] Preferably, the interaction module includes: Visualization unit: used to provide a water treatment monitoring interface, where administrators can quickly query various water quality parameter data, corresponding operating parameters of water treatment equipment, pollution treatment progress status, and parameter abnormality alarm information; Interactive unit: used to provide remote control and data export functions. Administrators can manually adjust system parameters, issue processing instructions, export water quality data, and perform operational management operations.

[0013] Compared with the prior art, the present invention has the following advantages: The target treatment center conducts pollution analysis based on real-time monitored water quality parameter data to obtain pollution analysis results; according to the target treatment suggestions generated based on the pollution analysis results, the corresponding operating parameters of the water treatment equipment are automatically adjusted to achieve pollution treatment; water quality data query, alarm and user interaction functions are provided, which can effectively and intelligently analyze the pollution status and efficiently manage the water environment.

[0014] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a structural diagram of an intelligent water environment management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0018] The embodiment of the present invention provides an intelligent water environment management system, such as Figure 1 Shown, including: Data acquisition module: used to monitor water quality parameters in real time using the set monitoring equipment deployed in the target water area, and transmit the monitored water quality parameter data to the target processing center; Water quality analysis module: used by the target processing center to receive water quality parameter data and perform pollution analysis to obtain pollution analysis results; Self-treatment module: used to generate target treatment suggestions based on the pollution analysis results, and automatically adjust the corresponding operating parameters of the water treatment equipment based on the target treatment suggestions to achieve pollution treatment; Interactive module: used to provide water quality data query, alarm and user interaction functions.

[0019] In this embodiment, the target water area refers to a specific water area that requires water quality monitoring and pollution treatment, such as rivers, lakes, and reservoirs; the set monitoring equipment refers to a variety of sensors used to monitor water quality parameters in real time, such as pH sensors, dissolved oxygen sensors, turbidity sensors, etc.; water quality parameters include pH value, dissolved oxygen content, turbidity and other parameters.

[0020] In this embodiment, the target processing center refers to a data processing and analysis center, which is used to receive water quality parameter data from set monitoring equipment in real time and run a water quality analysis module to perform pollution analysis; the pollution analysis result refers to the conclusion obtained by the target processing center based on the received water quality parameter data after processing by the water quality analysis module, including the type, concentration, source of pollution, etc. of pollutants; the target treatment suggestion refers to the strategy for treating pollutants in the water body area, such as adjusting the operating parameters of the water treatment equipment, increasing or decreasing the dosage of the reagent, etc.; the water treatment equipment refers to mechanical equipment used to purify water quality, including various filters, reactors, aeration devices, sedimentation tanks, etc.; the operating parameters include filtration rate, aeration volume, dosage of the reagent, reaction time, etc.

[0021] The beneficial effects of the above technical solution are: pollution analysis is carried out by the target treatment center based on real-time monitored water quality parameter data to obtain pollution analysis results; according to the target treatment suggestions generated based on the pollution analysis results, the corresponding operating parameters of the water treatment equipment are automatically adjusted to achieve pollution treatment; water quality data query, alarm and user interaction functions are provided, which can effectively and intelligently analyze the pollution status and efficiently manage the water environment.

[0022] An embodiment of the present invention provides an intelligent water environment management system, wherein the water quality analysis module includes: Data processing unit: used for the target processing center to regularly regard the parameter data of each water quality parameter of the target water area within the first preset time period received as the first parameter data; Aggregating and preprocessing the first parameter data and key meteorological data corresponding to the same time period to obtain first target data, and marking the first target data with time characteristics; Taking the current target water area as a matching condition, extracting the key geographical features of the monitoring area corresponding to the target water area from a preset geographical data repository; The pollution analysis unit includes: an initial identification block, a related identification block and an output block; The initial identification block is used to input the first target data and key geographical features into a pre-established pollution identification model to obtain a first pollution identification result; Extracting pollutant types from the first pollution identification results and summarizing and arranging them to obtain a first pollutant set; Correlation identification block: used to select any preset correlation algorithm from the preset correlation algorithm list to calculate the correlation coefficient between the corresponding first parameter data of each water quality parameter to obtain the first coefficient; Present all the first coefficients obtained in the form of a matrix to obtain a parameter correlation matrix; According to the parameter correlation matrix, water quality parameter combinations with absolute values greater than a set correlation threshold are marked as strongly correlated parameter pairs; Using the strongly correlated parameter pair and the target water body area as matching conditions, extracting corresponding possible associated pollutants from a set associated pollution database; Summarize and organize all possible associated pollutants obtained to obtain a second pollutant set; The overlapping pollutants in the first pollutant set and the second pollutant set are regarded as key pollutants; Output block: used to extract the corresponding attribute information of the current key pollutant from the first pollution identification result and mark it as key attribute information; Outputting the key pollutants and corresponding key attribute information as pollutant analysis results; Prediction analysis and output unit: used to input the first target data and key geographical features into a pre-established water quality prediction model to obtain a water quality prediction result within a second preset time period; Extract the predicted values of each water quality parameter from the water quality prediction results and construct them according to the time series to obtain the parameter-prediction change curve of each water quality parameter; Extract the predicted change characteristics from the parameter-prediction change curve, and combine them with the predicted values of the corresponding water quality parameters and the correlation between the key pollutants and the corresponding strong parameter pairs to input them into the pre-established concentration prediction model to obtain the predicted concentration change data of the current key pollutants; The predicted concentration change data of all key pollutants are output as pollutant analysis results.

[0023] In this embodiment, the first preset time period refers to a specific time range for collecting and analyzing water quality parameter data; the first parameter data refers to the monitoring data of various water quality parameters (such as pH value, dissolved oxygen content, turbidity, etc.) of the target water body area within the first preset time period; the key meteorological data refers to the specific meteorological information related to the target water body area within the same time period, which is monitored by preset meteorological monitoring equipment (such as temperature sensors, wind direction monitors) extracted from the target processing center; the first target data is a data set obtained after data aggregation and preprocessing (referring to data cleaning and data formatting) of the first parameter data and the key meteorological data corresponding to the same time period; the time feature refers to the feature related to the time attribute of the first parameter data.

[0024] In this embodiment, the preset geographic data repository refers to a database that stores geographic information related to the target water body area, including topography, landform, soil type, hydrogeological conditions, and the geographical distribution of treatment stations, monitoring stations, and cameras; key geographic features refer to geographic information directly related to the target water body area extracted from the preset geographic data repository.

[0025] In this embodiment, the pollution identification model is used to identify pollutants based on input water quality parameter data and key geographical features. It is obtained by collecting a large amount of water quality monitoring data (including monitoring values of multiple water quality parameters (such as temperature, pH value, dissolved oxygen, turbidity, ammonia nitrogen, total phosphorus, etc.)) and corresponding key geographical features (such as the location, topography, climate, etc. of the water area), and then preprocessing the collected data (including data cleaning, outlier processing and missing value filling) and annotating the corresponding pollutant identification results to train the neural network as training data; the first pollution identification result refers to the preliminary pollutant identification result obtained after the pollution identification model analyzes the input data, including the type, concentration and possible pollution source of the pollutant; the first pollutant set is a set of pollutant types extracted and summarized from the first pollution identification result.

[0026] In this embodiment, the preset correlation algorithm list refers to a list containing multiple algorithms for calculating correlation coefficients and performing correlation analysis; the preset correlation algorithm refers to a specific algorithm selected from the preset correlation algorithm list for calculating the correlation coefficient between water quality parameters, such as the Pearson correlation coefficient, the Spearman rank correlation coefficient, etc.; the first coefficient refers to the correlation coefficient between water quality parameters calculated using the preset correlation algorithm, which is used to evaluate the degree of correlation between two water quality parameters; the parameter correlation matrix is a set of correlation coefficients between water quality parameters presented in matrix form, which is used to intuitively display the correlation between parameters; a water quality parameter combination refers to a combination of two water quality parameters based on the first parameter data and using a preset correlation algorithm for correlation analysis; the set correlation threshold is a pre-set threshold for judging the strength of the correlation between water quality parameters, such as 0.7; a strongly correlated parameter pair refers to a water quality parameter combination whose absolute value of the correlation coefficient is greater than the set correlation threshold.

[0027] In this embodiment, the set associated pollution database refers to a database that stores the association between known pollutants and water quality parameters; possible associated pollutants refer to pollutants that may be related to current water quality parameter changes extracted from the set associated pollution database based on strongly correlated parameter pairs and target water body areas; the second pollutant set is a set of pollutant types obtained by summarizing and arranging possible associated pollutants; key pollutants refer to overlapping pollutants in the first pollutant set and the second pollutant set; key attribute information refers to attribute information related to key pollutants, that is, pollutant concentrations and possible pollution sources.

[0028] In this embodiment, the water quality prediction model is used to predict the changes in water quality parameters in the future period based on the input water quality parameter data and key geographical features. It is obtained by collecting a large amount of water quality monitoring data and meteorological data and geographical feature data for the corresponding time period, and then extracting features useful for water quality prediction (including historical values of water quality parameters, change trends, meteorological conditions and geographical features, etc.) from all the collected data to train the neural network as a training method; the second preset time period refers to a pre-set time range for water quality prediction.

[0029] In this embodiment, the water quality prediction result is the water quality prediction value for a period of time in the future obtained after the water quality prediction model analyzes the input data; the parameter-prediction change curve refers to a curve graph of the changes of various water quality parameters over time constructed based on the water quality prediction result; the prediction change characteristics refer to the slope of the curve, the peak value of the curve and the amplitude; the concentration prediction model is used to predict the concentration of key pollutants in a period of time in the future. It is obtained by collecting historical concentration data of various pollutants, as well as corresponding water quality parameter data, meteorological data and geographical feature data, and then performing data preprocessing and extracting pollutant concentration prediction-related features (such as the value of water quality parameters, change trends, meteorological conditions, geographical features and correlations between pollutants) of all collected data as training parties to train the neural network; the predicted concentration change data refers to the data set obtained after the concentration prediction model predicts the concentration of key pollutants, including the predicted concentration values of key pollutants in the future and their change trends.

[0030] The beneficial effects of the above technical solution are: by receiving water quality parameter data in real time through the target processing center and conducting pollution analysis, the pollution analysis results are obtained, which can improve the efficiency and accuracy of water quality monitoring and analysis, and provide data support for subsequent pollutant treatment.

[0031] An embodiment of the present invention provides an intelligent water environment management system, wherein the pollution analysis unit further includes: Quantity acquisition block: used to obtain the average quantity of pollutant types in the first pollutant set and the second pollutant set; Difference analysis block: used to compare the currently acquired number of key pollutants with the average number of pollutants to obtain the absolute difference of the first number; Correlation re-identification block: when the absolute difference of the first quantity is greater than the reference quantity difference, any preset correlation algorithm that has not been used is selected from the preset correlation algorithm list to perform water quality parameter correlation analysis to obtain a new second pollutant set and new key pollutants; Re-output block: used to output the new key pollutant as a key pollutant when the absolute difference between the average number of pollutant types in the first pollutant set and the new second pollutant set and the first number of the number of types of the new key pollutant is not greater than the benchmark number difference.

[0032] In this embodiment, the average number of pollutant types refers to the average number of pollutant types in the first pollutant set and the second pollutant set; the first absolute difference in number refers to the absolute difference between the number of key pollutant types currently obtained and the average number of pollutant types; the formula for the benchmark number difference is expressed as ,in, Expressed as a baseline quantity difference; Expressed as the total number of pollutant types in the first pollutant set; Expressed as the total number of pollutant types in the second pollutant set.

[0033] In this embodiment, the new second pollutant set refers to a new set of possible related pollutants obtained after using a new correlation algorithm to perform water quality parameter correlation analysis; the new key pollutants refer to overlapping pollutants screened out from the new second pollutant set and the initial first pollutant set.

[0034] The beneficial effect of the above technical solution is that by comparing the difference between the number of types of key pollutants and the average number of pollutant types, and setting the benchmark number difference as the basis for judgment, key pollutants can be screened out more scientifically and reasonably to avoid omissions or misjudgments.

[0035] An embodiment of the present invention provides an intelligent water environment management system, wherein the self-processing module includes: Prediction curve establishment unit: used to establish the predicted concentration change curve of each key pollutant using the predicted concentration change data in the pollutant analysis results; Prediction curve analysis unit: used to calculate the curve area and curve slope of the current predicted concentration change curve, and obtain a first area value and a first slope accordingly; A reference data acquisition unit is configured to extract historical concentration change data of the key pollutants in the current target water area from the pollution record database, and sequentially obtain corresponding historical concentration change data of the three historical screening time periods closest to the current time period using the second preset time period as a division unit, and mark the corresponding historical concentration change data as reference historical concentration change data; Reference curve establishing unit: used for establishing a reference concentration change curve of the current key pollutant using the reference concentration change data; Reference curve analysis unit: used to calculate the curve area and curve slope of the current reference concentration change curve, and obtain the corresponding reference area value and reference slope; A pollution assessment unit is configured to calculate a concentration change assessment coefficient of a current key pollutant using the first area value, the first slope, and all reference area values and reference slopes; Risk analysis unit: used to extract the current concentration of each key pollutant from the pollutant analysis results, and calculate the pollution risk score in combination with the concentration change assessment coefficient; Suggestion acquisition unit: used to match the pollution risk score and key pollutants as matching conditions and obtain corresponding pollution treatment suggestions from a set treatment suggestion library; Suggestion optimization unit: used to extract adjustable operating parameters of water treatment equipment from pollution treatment suggestions, optimize the adjustable operating parameters to obtain key operating parameter values, and generate target treatment suggestions based on the key operating parameter values; Adjustment unit: used to generate corresponding water treatment instructions based on the target treatment suggestions, and transmit the water treatment instructions to the corresponding water treatment equipment. The water treatment equipment automatically adjusts the corresponding operating parameters according to the received water treatment instructions to achieve pollution treatment.

[0036] In this embodiment, the predicted concentration change curve refers to a concentration change curve established in time series with the predicted concentration change data of key pollutants in the pollutant analysis results as the dependent variable, which helps to intuitively understand the future change trend of pollutant concentration; the first area value refers to the curve area of the current predicted concentration change curve; the first slope refers to the curve slope of the current predicted concentration change curve; the reference historical concentration change data refers to extracting the historical concentration change data of key pollutants from the pollution record database, and screening out the data of the three historical time periods closest to the current time period; the reference concentration change curve is a concentration change curve established based on the reference historical concentration change data of three different historical time periods; the reference area value refers to the curve area of the current reference concentration change curve; the reference slope refers to the curve slope of the current reference concentration change curve.

[0037] In this embodiment, the concentration change assessment coefficient is used to reflect the degree and speed of change of the current pollution status compared with the history. The calculation formula is: , where Expressed as the current concentration change assessment coefficient of key pollutants; It is expressed as a constant with a value of 2.7; It is expressed as the first slope of the corresponding predicted concentration change curve of the current key pollutants; Expressed as the reference slope of the j-th reference concentration change curve of the current key pollutant, where j = 1, 2, 3; It is expressed as the weight of the effect of the difference in the slope of the curve on the change in the analyzed concentration; It is expressed as the weight of the effect of the difference in the curve area on the change in the analyzed concentration; It is expressed as the first area value of the corresponding predicted concentration change curve of the current key pollutant; It is expressed as the reference area value of the j-th reference concentration change curve of the current key pollutant; the weights assigned to the curve slope differences and curve area differences are obtained by solving the matrix constructed after pairwise comparison and relative importance scoring using the hierarchical analysis method; the pollution risk score is used to provide a data basis for obtaining treatment recommendations; the current concentration refers to the concentration of each key pollutant determined from the currently obtained pollutant analysis results.

[0038] In this embodiment, a treatment suggestion library is set to store multiple pollution treatment suggestions, and the most appropriate treatment suggestion is matched according to the pollution risk score; adjustable operating parameters refer to adjustable equipment operating parameters extracted from the matched pollution treatment suggestions, such as filtration rate, aeration volume, agent dosage, reaction time, etc.; key operating parameter values are obtained after optimizing the adjustable operating parameters; water treatment instructions are used to guide water treatment equipment to automatically adjust parameters.

[0039] The beneficial effect of the above technical solution is: by generating target treatment suggestions according to the pollution analysis results, and based on the target treatment suggestions, automatically adjusting the key operating parameter values of the water treatment equipment to achieve pollution treatment, the pollution treatment efficiency can be effectively improved.

[0040] The embodiment of the present invention provides an intelligent water environment management system. The calculation formula of the pollution risk score is as follows: Where, Expressed as the pollution risk score of the current target water area; It is expressed as the maximum number of key pollutants appearing in the current target water area; It is expressed as the concentration value of the key pollutant of type i at the current moment; Indicates the concentration value of the key pollutant of type i at the last moment, where i=1, 2, 3, , n; n represents the total number of key pollutant types; Expressed as the impact weight of real-time concentration changes of key pollutants on the analyzed pollution risk level; Expressed as the concentration change assessment coefficient of the i-th key pollutant; It is expressed as the impact weight of the predicted concentration change of key pollutants on the analyzed pollution risk level.

[0041] In this embodiment, the weights assigned to the real-time concentration changes and predicted concentration changes of key pollutants are obtained by solving a matrix constructed by performing pairwise comparisons and relative importance scoring using the hierarchical analysis method.

[0042] The beneficial effect of the above technical solution is that by calculating the pollution risk score, a reliable data basis can be provided for screening pollution treatment suggestions, which in turn helps to determine accurate target treatment suggestions and effectively improve the pollution treatment efficiency.

[0043] An embodiment of the present invention provides an intelligent water environment management system, wherein the suggestion optimization unit includes: Summary and marking block: used to summarize the adjustable operating parameters in the current pollution treatment suggestion to obtain an adjustable parameter combination; Identify the key pollutants that should be treated according to the current pollution treatment proposals and mark them as target treatment pollutants; Range expansion block: used to expand the current concentration of the target pollutant based on the concentration change assessment coefficient to obtain a reference concentration range; Record filtering block: used to extract the first historical processing record of the current pollution treatment suggestion from the preset pollution treatment database; From the first historical processing records, select historical processing records in which the historical concentration of the target processing pollutant in the past falls within the corresponding reference concentration range, and mark them as first records; A benchmark value and concentration difference acquisition block is configured to determine, based on the historical pollutant treatment record data and the historical operating cost record data in the first record, a first historical adjustable parameter combination set whose historical pollutant treatment efficiency is higher than the average pollutant treatment efficiency, and a second historical adjustable parameter combination set whose historical operating cost is lower than the average operating cost; If there is an overlapping parameter combination in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set, marking the overlapping parameter combination as a reference group; When there is a single reference group, the current reference group is considered the baseline parameter group; When there are multiple reference groups, the reference possibility coefficient of each reference group is obtained by taking a weighted average of the historical pollutant treatment efficiency and the historical operating cost, and the reference group with the largest reference possibility coefficient is regarded as the benchmark parameter group; Taking the historical value of each adjustable operating parameter in the benchmark parameter group as the benchmark parameter value; Determine, based on the first record, a first historical concentration of the target pollutant corresponding to the current baseline parameter group, obtain a concentration difference between the first historical concentration and the current concentration of the target pollutant, and output the difference as a key concentration difference; If there is no overlapping parameter combination in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set, averaging the historical values of the same adjustable operating parameter with the parameter combination with the lowest historical pollutant treatment efficiency selected from the first historical adjustable parameter combination set and the parameter combination with the highest historical operating cost selected from the second historical adjustable parameter combination set to obtain a historical average value of each adjustable operating parameter, and outputting it as the benchmark parameter value; Determining, based on the first record, a second historical concentration of a target pollutant treated corresponding to a parameter combination with the lowest historical pollutant treatment efficiency selected from the first set of historical adjustable parameter combinations, and obtaining a first concentration difference between the second historical concentration of the target pollutant and a current concentration; Determine a third historical concentration of the target pollutant treated corresponding to the parameter combination with the highest historical operating cost selected from the second historical adjustable parameter combination set, and obtain a second concentration difference between the third historical concentration and the current concentration of the target pollutant; Calculating an average of the first concentration difference and the second concentration difference and outputting the average as the key concentration difference; Parameter adjustment block: used to adjust the baseline parameter value of each adjustable operating parameter by analyzing the correlation between the adjustable operating parameters and the pollutant treatment efficiency and operating costs in combination with the key concentration difference to obtain the key operating parameter value; Suggestion generation block: combines the current pollution treatment suggestion with the key operating parameter value to generate the target treatment suggestion.

[0044] In this embodiment, the adjustable parameter combination is obtained by summarizing all adjustable operating parameters in the current pollution treatment proposal; the target treatment pollutant refers to the pollutant targeted by the current pollution treatment proposal; the first historical treatment record refers to the historical treatment record related to the current pollution treatment proposal extracted from the preset pollution treatment database, such as the parameter combination, treatment efficiency, operating cost and other information used in the past to treat pollutants.

[0045] In this embodiment, the reference concentration range is a concentration interval calculated based on the current concentration of the target pollutant and the concentration change evaluation coefficient, which is expressed as ,in, Indicates the current concentration value of the pollutant being treated for the current target; It is expressed as the concentration change assessment coefficient of the current target treatment pollutant; ln is expressed as the natural logarithm; e is expressed as the friction constant, which is 2.7.

[0046] In this embodiment, the first historical adjustable parameter combination set refers to the set of all adjustable parameter combinations whose processing efficiency is higher than the average processing efficiency in the historical processing records; the second historical adjustable parameter combination set refers to the set of all adjustable parameter combinations whose operating costs are lower than the average operating costs in the historical processing records; the reference group refers to the parameter combination that appears simultaneously in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set; the reference possibility coefficient refers to an indicator used to evaluate the pros and cons of multiple reference groups, which is obtained by weighted average calculation of the historical pollutant treatment efficiency and historical operating cost of the current reference group. For example, if there is a historical pollutant treatment efficiency of reference group 1, and historical operating costs , at this time, the reference possibility coefficient of reference group 1 is Where, It is expressed as the weight given to the efficiency of historical pollutant treatment; It is expressed as the weight assigned to the historical operating cost; the weights assigned to the historical pollutant treatment efficiency and the historical operating cost are obtained by solving the matrix constructed after pairwise comparison and relative importance scoring using the hierarchical analysis method.

[0047] In this embodiment, the first concentration difference refers to the concentration difference between the historical concentration (i.e., the second historical concentration) of the target treated pollutant treated by the parameter combination with the lowest historical pollutant treatment efficiency selected from the first historical adjustable parameter combination set and the current concentration; the second concentration difference refers to the concentration difference between the historical concentration (i.e., the third historical concentration) of the target treated pollutant treated by the parameter combination with the highest historical operating cost selected from the second historical adjustable parameter combination set and the current concentration.

[0048] In this embodiment, the benchmark parameter group refers to an adjustable parameter combination selected from historical treatment records that simultaneously meets the requirements of high efficiency (higher than average pollutant treatment efficiency) and low cost (lower than average operating cost), and when there are multiple adjustable parameter combinations that meet the conditions, the reference possible coefficient of each combination is calculated by weighted average, and the combination with the largest coefficient is selected as the benchmark parameter group; the benchmark parameter value refers to the historical value of each adjustable operating parameter in the benchmark parameter group (there is a reference group), or the average value of the historical values of the same adjustable operating parameter in the parameter combination with the lowest historical pollutant treatment efficiency and the highest historical operating cost (there are no overlapping parameter combinations in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set); the critical concentration difference refers to the difference between the corresponding historical concentration of the benchmark parameter group and the current concentration, or the average value of the first concentration difference and the second concentration difference (there are no overlapping parameter combinations in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set).

[0049] In this embodiment, the key operating parameter value is obtained by adjusting the baseline parameter value of the adjustable operating parameter; the target treatment suggestion refers to a strategy for treating pollutants in the water body area.

[0050] The beneficial effect of the above technical solution is: by optimizing the adjustable operating parameters in the pollution treatment suggestions to obtain key operating parameter values, and then generating target treatment suggestions based on the key operating parameter values, effective suggestions can be provided for pollution treatment, thereby improving pollution treatment efficiency and the accuracy of cost control.

[0051] An embodiment of the present invention provides an intelligent water environment management system, wherein the parameter adjustment block is used to: Taking the historical values of the currently adjustable operating parameters within the third preset time period as independent variables, and the corresponding historical pollutant treatment efficiency and historical operating costs as dependent variables, respectively construct a parameter-treatment efficiency relationship graph and a parameter-cost relationship graph; Extracting corresponding slopes of the parameter-processing efficiency relationship graph and the parameter-cost relationship graph, and outputting them as efficiency impact values and cost impact values, respectively; Based on the efficiency impact value and the cost impact value, and in combination with the key concentration difference, the baseline parameter value of the currently adjustable operating parameter is adjusted to obtain the key operating parameter value; The calculation formula for the key operating parameter values is as follows: Where, The key operating parameter values represented as currently adjustable operating parameters; Indicates the baseline parameter value of the currently adjustable operating parameter; Expressed as the corresponding efficiency impact value of the currently adjustable operating parameters; It is expressed as the contribution weight of the correlation between the adjustable operating parameters and the pollutant treatment efficiency to the parameter adjustment; It is expressed as the corresponding cost impact value of the currently adjustable operating parameters; It is expressed as the contribution weight of the correlation between the adjustable operating parameters and the operating cost to the parameter adjustment; It represents the corresponding key concentration difference when the current adjustable operating parameter is the benchmark parameter; e is represented as a constant with a value of 2.7.

[0052] In this embodiment, the third preset time period refers to a predetermined time range for determining the historical values of the current adjustable operating parameters; the parameter-processing efficiency relationship diagram refers to a curve diagram for displaying the relationship between the historical values of the current adjustable operating parameters within the third preset time period and the corresponding historical pollutant treatment efficiency; the parameter-cost relationship diagram refers to a curve diagram for displaying the relationship between the historical values of the current adjustable operating parameters within the third preset time period and the corresponding historical operating costs; the efficiency impact value is a slope value extracted from the parameter-processing efficiency relationship diagram, which is used to quantify the degree of influence of the current adjustable operating parameters on the pollutant treatment efficiency; the cost impact value is a slope value extracted from the parameter-cost relationship diagram, which is used to quantify the degree of influence of the current adjustable operating parameters on the operating cost.

[0053] The beneficial effect of the above technical solution is: by combining the correlation between the adjustable operating parameters and the pollutant treatment efficiency and operating costs with the key concentration difference analysis, the baseline parameter value of each adjustable operating parameter is adjusted, which can help generate accurate and effective target treatment recommendations.

[0054] An embodiment of the present invention provides an intelligent water environment management system, wherein the interaction module includes: Visualization unit: used to provide a water treatment monitoring interface, where administrators can quickly query various water quality parameter data, corresponding operating parameters of water treatment equipment, pollution treatment progress status, and parameter abnormality alarm information; Interactive unit: used to provide remote control and data export functions. Administrators can manually adjust system parameters, issue processing instructions, export water quality data, and perform operational management operations.

[0055] In this embodiment, the water treatment monitoring interface refers to a comprehensive display platform that integrates various water quality parameter data, corresponding operating parameters of water treatment equipment, and pollution treatment progress status; the pollution treatment progress status is used to reflect the completion status of the current pollution treatment task, such as treatment progress, treatment success / failure, etc.; processing instructions refer to water treatment instructions; water quality data refers to various parameter information about water quality, including pH value, dissolved oxygen content, ammonia nitrogen content, and heavy metal ion concentration, etc.: the remote control function refers to the administrator issuing processing instructions or performing other management operations (such as starting or stopping a specific water treatment process) on the water treatment equipment through the water treatment monitoring interface; the data export function means that the administrator can export data (such as water quality data, treatment records, pollution analysis results, etc.) in a specific format (such as CSV, Excel, PDF, etc.) to a local or designated storage location.

[0056] In this embodiment, the parameter abnormality alarm information includes alarm type, device type, device number, alarm signal, alarm content, alarm status, alarm time, alarm operation and alarm analysis; Among them, alarm analysis includes: Water quality analysis: Select a site to display statistics for the total number of signal alarms, unhandled alarms, and alarms for the day, as well as a signal legend table and a histogram of signal percentages. Click the signal legend to toggle the display of corresponding signal statistics. Select the time granularity (year / month) and date. You can also toggle the display of the histogram of signal percentages for the selected time period.

[0057] Overall analysis: The total cumulative number of all signals, the number of unhandled alarms, and the number of alarms for the day are counted, and a bar chart showing the percentages is displayed. Select the time granularity (year / month) and date. You can switch to displaying the bar chart showing the percentages of signals for the selected time period.

[0058] In this embodiment, the operational management operations include: the system provides an entry for adding maintenance, water maintenance and equipment maintenance work orders, provides an entry for adding production reports, forms a weekly production report and consumption analysis through daily production reports, and displays the inventory statistics list information. The list displays the serial number, project, time, inventory, outbound, balance, and operation of each agent, and provides an entry for adding inventory records, and automatically generates agent outbound records based on production reports.

[0059] The beneficial effects of the above technical solution are: by providing intuitive information display and convenient remote control functions, it significantly improves the practicality and operational convenience of the water management monitoring system, provides administrators with comprehensive decision-making support, and helps to achieve more efficient and scientific water management.

[0060] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An intelligent water environment management system, characterized in that: include: Data acquisition module: used to monitor water quality parameters in real time using the set monitoring equipment deployed in the target water area, and transmit the monitored water quality parameter data to the target processing center; Water quality analysis module: used by the target processing center to receive water quality parameter data and perform pollution analysis to obtain pollution analysis results; Self-treatment module: used to generate target treatment suggestions based on the pollution analysis results, and automatically adjust the corresponding operating parameters of the water treatment equipment based on the target treatment suggestions to achieve pollution treatment; Interactive module: used to provide water quality data query, alarm and user interaction functions.

2. The intelligent water environment management system according to claim 1 is characterized in that: The water quality analysis module includes: Data processing unit: used for the target processing center to regularly regard the parameter data of each water quality parameter of the target water area within the first preset time period received as the first parameter data; Aggregating and preprocessing the first parameter data and key meteorological data corresponding to the same time period to obtain first target data, and marking the first target data with time characteristics; Taking the current target water area as a matching condition, extracting the key geographical features of the monitoring area corresponding to the target water area from a preset geographical data repository; The pollution analysis unit includes: an initial identification block, a related identification block and an output block; The initial identification block is used to input the first target data and key geographical features into a pre-established pollution identification model to obtain a first pollution identification result; Extracting pollutant types from the first pollution identification results and summarizing and arranging them to obtain a first pollutant set; Correlation identification block: used to select any preset correlation algorithm from the preset correlation algorithm list to calculate the correlation coefficient between the corresponding first parameter data of each water quality parameter to obtain the first coefficient; Present all the first coefficients obtained in the form of a matrix to obtain a parameter correlation matrix; According to the parameter correlation matrix, water quality parameter combinations with absolute values greater than a set correlation threshold are marked as strongly correlated parameter pairs; Using the strongly correlated parameter pair and the target water body area as matching conditions, extracting corresponding possible associated pollutants from a set associated pollution database; Summarize and organize all possible associated pollutants obtained to obtain a second pollutant set; The overlapping pollutants in the first pollutant set and the second pollutant set are regarded as key pollutants; Output block: used to extract the corresponding attribute information of the current key pollutant from the first pollution identification result and mark it as key attribute information; Outputting the key pollutants and corresponding key attribute information as pollutant analysis results; Prediction analysis and output unit: used to input the first target data and key geographical features into a pre-established water quality prediction model to obtain a water quality prediction result within a second preset time period; Extract the predicted values of each water quality parameter from the water quality prediction results and construct them according to the time series to obtain the parameter-prediction change curve of each water quality parameter; Extract the predicted change characteristics from the parameter-prediction change curve, and combine them with the predicted values of the corresponding water quality parameters and the correlation between the key pollutants and the corresponding strong parameter pairs to input them into the pre-established concentration prediction model to obtain the predicted concentration change data of the current key pollutants; The predicted concentration change data of all key pollutants are output as pollutant analysis results.

3. The intelligent water environment management system according to claim 2, characterized in that: The pollution analysis unit also includes: Quantity acquisition block: used to obtain the average quantity of pollutant types in the first pollutant set and the second pollutant set; Difference analysis block: used to compare the currently acquired number of key pollutants with the average number of pollutants to obtain the absolute difference of the first number; Correlation re-identification block: when the absolute difference of the first quantity is greater than the reference quantity difference, any preset correlation algorithm that has not been used is selected from the preset correlation algorithm list to perform water quality parameter correlation analysis to obtain a new second pollutant set and new key pollutants; Re-output block: used to output the new key pollutant as a key pollutant when the absolute difference between the average number of pollutant types in the first pollutant set and the new second pollutant set and the first number of the number of types of the new key pollutant is not greater than the benchmark number difference.

4. The intelligent water environment management system according to claim 1, characterized in that: The self-processing module includes: Prediction curve establishment unit: used to establish the predicted concentration change curve of each key pollutant using the predicted concentration change data in the pollutant analysis results; Prediction curve analysis unit: used to calculate the curve area and curve slope of the current predicted concentration change curve, and obtain a first area value and a first slope accordingly; A reference data acquisition unit is configured to extract historical concentration change data of the key pollutants in the current target water area from the pollution record database, and sequentially obtain corresponding historical concentration change data of the three historical screening time periods closest to the current time period using the second preset time period as a division unit, and mark the corresponding historical concentration change data as reference historical concentration change data; Reference curve establishing unit: used for establishing a reference concentration change curve of the current key pollutant using the reference concentration change data; Reference curve analysis unit: used to calculate the curve area and curve slope of the current reference concentration change curve, and obtain the corresponding reference area value and reference slope; A pollution assessment unit is configured to calculate a concentration change assessment coefficient of a current key pollutant using the first area value, the first slope, and all reference area values and reference slopes; Risk analysis unit: used to extract the current concentration of each key pollutant from the pollutant analysis results, and calculate the pollution risk score in combination with the concentration change assessment coefficient; Suggestion acquisition unit: used to match the pollution risk score and key pollutants as matching conditions and obtain corresponding pollution treatment suggestions from a set treatment suggestion library; Suggestion optimization unit: used to extract adjustable operating parameters of water treatment equipment from pollution treatment suggestions, optimize the adjustable operating parameters to obtain key operating parameter values, and generate target treatment suggestions based on the key operating parameter values; Adjustment unit: used to generate corresponding water treatment instructions based on the target treatment suggestions, and transmit the water treatment instructions to the corresponding water treatment equipment. The water treatment equipment automatically adjusts the corresponding operating parameters according to the received water treatment instructions to achieve pollution treatment.

5. The intelligent water environment management system according to claim 4 is characterized in that: The calculation formula for the contamination risk score is as follows: Where, Expressed as the pollution risk score of the current target water area; It is expressed as the maximum number of key pollutants appearing in the current target water area; It is expressed as the concentration value of the key pollutant of type i at the current moment; Indicates the concentration value of the key pollutant of type i at the last moment, where i=1, 2, 3, , n; n represents the total number of key pollutant types; Expressed as the impact weight of real-time concentration changes of key pollutants on the analyzed pollution risk level; Expressed as the concentration change assessment coefficient of the i-th key pollutant; It is expressed as the impact weight of the predicted concentration change of key pollutants on the analyzed pollution risk level.

6. The intelligent water environment management system according to claim 4, characterized in that: The suggested optimization unit includes: Summary and marking block: used to summarize the adjustable operating parameters in the current pollution treatment suggestion to obtain an adjustable parameter combination; Identify the key pollutants that should be treated according to the current pollution treatment proposals and mark them as target treatment pollutants; Range expansion block: used to expand the current concentration of the target pollutant based on the concentration change assessment coefficient to obtain a reference concentration range; Record filtering block: used to extract the first historical processing record of the current pollution treatment suggestion from the preset pollution treatment database; From the first historical processing records, select historical processing records in which the historical concentration of the target processing pollutant in the past falls within the corresponding reference concentration range, and mark them as first records; A benchmark value and concentration difference acquisition block is configured to determine, based on the historical pollutant treatment record data and the historical operating cost record data in the first record, a first historical adjustable parameter combination set whose historical pollutant treatment efficiency is higher than the average pollutant treatment efficiency, and a second historical adjustable parameter combination set whose historical operating cost is lower than the average operating cost; If there is an overlapping parameter combination in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set, marking the overlapping parameter combination as a reference group; When there is a single reference group, the current reference group is considered the baseline parameter group; When there are multiple reference groups, the reference possibility coefficient of each reference group is obtained by taking a weighted average of the historical pollutant treatment efficiency and the historical operating cost, and the reference group with the largest reference possibility coefficient is regarded as the benchmark parameter group; Taking the historical value of each adjustable operating parameter in the benchmark parameter group as the benchmark parameter value; Determine, based on the first record, a first historical concentration of the target pollutant corresponding to the current baseline parameter group, obtain a concentration difference between the first historical concentration and the current concentration of the target pollutant, and output the difference as a key concentration difference; If there is no overlapping parameter combination in the first historical adjustable parameter combination set and the second historical adjustable parameter combination set, averaging the historical values of the same adjustable operating parameter with the parameter combination with the lowest historical pollutant treatment efficiency selected from the first historical adjustable parameter combination set and the parameter combination with the highest historical operating cost selected from the second historical adjustable parameter combination set to obtain a historical average value of each adjustable operating parameter, and outputting it as the benchmark parameter value; Determining, based on the first record, a second historical concentration of a target pollutant treated corresponding to a parameter combination with the lowest historical pollutant treatment efficiency selected from the first set of historical adjustable parameter combinations, and obtaining a first concentration difference between the second historical concentration of the target pollutant and a current concentration; Determine a third historical concentration of the target pollutant treated corresponding to the parameter combination with the highest historical operating cost selected from the second historical adjustable parameter combination set, and obtain a second concentration difference between the third historical concentration and the current concentration of the target pollutant; Calculating an average of the first concentration difference and the second concentration difference and outputting the average as the key concentration difference; Parameter adjustment block: used to adjust the baseline parameter value of each adjustable operating parameter by analyzing the correlation between the adjustable operating parameters and the pollutant treatment efficiency and operating costs in combination with the key concentration difference to obtain the key operating parameter value; Suggestion generation block: combines the current pollution treatment suggestion with the key operating parameter value to generate the target treatment suggestion.

7. The intelligent water environment management system according to claim 6, characterized in that: The parameter adjustment block is used to: Taking the historical values of the currently adjustable operating parameters within the third preset time period as independent variables, and the corresponding historical pollutant treatment efficiency and historical operating costs as dependent variables, respectively construct a parameter-treatment efficiency relationship graph and a parameter-cost relationship graph; Extracting corresponding slopes of the parameter-processing efficiency relationship graph and the parameter-cost relationship graph, and outputting them as efficiency impact values and cost impact values, respectively; Based on the efficiency impact value and the cost impact value, and in combination with the key concentration difference, the baseline parameter value of the currently adjustable operating parameter is adjusted to obtain the key operating parameter value; The calculation formula for the key operating parameter values is as follows: Where, The key operating parameter values represented as currently adjustable operating parameters; Indicates the baseline parameter value of the currently adjustable operating parameter; Expressed as the corresponding efficiency impact value of the currently adjustable operating parameters; It is expressed as the contribution weight of the correlation between the adjustable operating parameters and the pollutant treatment efficiency to the parameter adjustment; It is expressed as the corresponding cost impact value of the currently adjustable operating parameters; It is expressed as the contribution weight of the correlation between the adjustable operating parameters and the operating cost to the parameter adjustment; It represents the corresponding key concentration difference when the current adjustable operating parameter is the benchmark parameter; e is represented as a constant with a value of 2.

7.

8. The intelligent water environment management system according to claim 1 is characterized in that: The interaction module includes: Visualization unit: used to provide a water treatment monitoring interface, where administrators can quickly query various water quality parameter data, corresponding operating parameters of water treatment equipment, pollution treatment progress status, and parameter abnormality alarm information; Interactive unit: used to provide remote control and data export functions. Administrators can manually adjust system parameters, issue processing instructions, export water quality data, and perform operational management operations.

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