Efficient sewage purification system for multi-source pollution treatment under flood and drought scenes

Abstract

This invention relates to the field of wastewater treatment technology and discloses a high-efficiency wastewater treatment system for multi-source pollution treatment under flood and drought scenarios. The method includes dividing the wastewater treatment process into continuous treatment stages, generating theoretically expected purification trajectories for each stage through simulation, and simultaneously reconstructing the actual operating trajectory from actual monitoring data. By comparing the two trajectories in real time at each stage, the system can identify specific treatment delays. Based on this, combined with the real-time status of pollution sources entering that stage, the system dynamically assesses its real-time purification potential at the current stage. When the assessed dynamic potential is lower than a preset capacity threshold for that stage, the system automatically triggers an embedded pollution purification enhancement program. This invention achieves dynamic diagnosis and early intervention throughout the entire multi-source pollution purification process, improving the adaptability and treatment reliability of wastewater treatment systems under extreme flood and drought scenarios.

Description

Technical Field

[0001] This invention relates to the field of wastewater purification technology, specifically a high-efficiency wastewater purification system for treating multi-source pollution in flood and drought scenarios. Background Technology

[0002] Currently, in the face of wastewater treatment impacted by multiple sources of pollution under extreme weather conditions such as floods and droughts, conventional technologies mainly rely on single-point or multi-point monitoring and control based on static thresholds. The system typically sets fixed pollutant concentration thresholds at key nodes; when monitoring data exceeds the threshold, pre-set adjustment or enhancement measures are triggered. This method treats the entire continuous, multi-stage purification process as several relatively independent control units, and its control logic is based on feedback from instantaneous exceedance signals.

[0003] Such static threshold feedback-based technical solutions have drawbacks. Their response exhibits significant lag, only taking action after the pollution load has already impacted the system and the treatment effect has deteriorated. The control method is crude, unable to identify the specific stage in the process where the efficiency decline originates, and difficult to quantify the degree of degradation in the treatment capacity at that stage relative to its design or theoretical potential. This results in a lack of precision in control measures, potentially leading to excessive reagent dosage, energy waste, or adverse effects on subsequent process units.

[0004] A technology is needed to proactively and dynamically assess and precisely pinpoint the operational efficiency of multi-stage wastewater treatment systems. This requires not only monitoring the absolute concentration of pollutants but also evaluating the dynamic trends and remaining capacity of the entire system in real time. This necessitates a method that can compare theoretical predictions with real-time performance, integrating pollution load characteristics with system treatment status, thereby accurately predicting and initiating minimum necessary enhancement measures before a substantial deficiency in purification capacity is identified. Summary of the Invention

[0005] The purpose of this invention is to provide a high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios, the method comprising:

[0007] The scenario definition module is used to define multiple characteristic parameters of a multi-source pollution treatment system under flood and drought scenarios, and to divide the sewage purification process into several continuous treatment stages based on the characteristic parameters.

[0008] The status analysis module is used to analyze the pollution source status of the multi-source pollution treatment system at each stage of the treatment process, and calculate the pollution source characteristic value of the corresponding treatment stage based on the pollution source status.

[0009] The trajectory simulation module is used to simulate and generate the expected purification trajectory of the multi-source pollution purification process over a continuous time period based on the pollution source characteristic values ​​of all processing stages.

[0010] The trajectory reconstruction module is used to synchronously acquire monitoring data generated by the multi-source pollution treatment system during actual operation, and reconstruct the actual purification trajectory of the multi-source pollution treatment system from the monitoring data.

[0011] The comparison and identification module is used to compare the actual purification trajectory with the expected purification trajectory in real time at the corresponding stage, and identify the purification process delay of the multi-source pollution treatment system at each treatment stage based on the comparison results.

[0012] The potential assessment module is used to assess the dynamic purification potential of the multi-source pollution treatment system at the current treatment stage by combining the state of the pollution source with the delay of the purification process.

[0013] The program execution module is used to activate the pollution purification enhancement program within the multi-source pollution treatment system when the dynamic purification potential is lower than the purification capacity threshold required by the current treatment stage.

[0014] Preferably, the specific characteristic parameters defining a multi-source pollution treatment system in flood and drought scenarios include:

[0015] Collect hydrological and meteorological data of the environment where the multi-source pollution treatment system is located;

[0016] Core indicators characterizing alternating flood and drought scenarios were extracted from the hydrological and meteorological data.

[0017] By coupling the core indicators with the design and operation parameters of the multi-source pollution treatment system, multiple characteristic parameters of the multi-source pollution treatment system under flood and drought scenarios are obtained.

[0018] Preferably, calculating the pollution source characteristic values ​​for the corresponding treatment stage based on the pollution source status specifically includes:

[0019] Identify the main pollutant types and their concentration distribution in the pollution source state;

[0020] Analyze the migration and transformation pathways of the main pollutant types within the multi-source pollution treatment system;

[0021] Based on the concentration distribution and the migration and transformation pathway, the pollution source characteristic values ​​for the corresponding treatment stage are calculated using a weighted fusion method; specifically including:

[0022] The weighting coefficient for each pollutant is determined based on its degradation difficulty and toxicity impact along the migration and transformation pathway.

[0023] The concentration distribution data of each pollutant is multiplied by its corresponding weighting coefficient to obtain the weighted concentration value;

[0024] The initial fusion value is obtained by calculating the arithmetic mean of all weighted concentration values;

[0025] The initial fusion values ​​are logarithmically transformed to compress the data range, and the standardized pollution source characteristic values ​​are finally output.

[0026] Preferably, the simulation of the expected purification trajectory based on the pollution source characteristic values ​​of all treatment stages specifically includes:

[0027] Establish a chain model of the purification process with the treatment stages as sequential nodes;

[0028] Input the pollution source characteristic values ​​of each treatment stage into the corresponding node of the purification process chain model;

[0029] Run the chain model of the purification process and output the expected purification trajectory of the multi-source pollution purification process in a continuous time.

[0030] The establishment of the purification process chain model with processing stages as sequential nodes includes:

[0031] Define each processing stage as an independent node in the chain model, and assign a unique stage identifier to each node;

[0032] The sequential relationship between nodes is determined based on the wastewater purification process flow, forming a unidirectional chain connection structure;

[0033] A corresponding purification efficiency function is configured for each node. The purification efficiency function is trained based on the historical operating data of this stage and is used to simulate the removal rate of a specific pollutant in this stage.

[0034] The stage identifiers, connection structures, and purification efficiency functions are integrated into a unified mathematical framework to construct a complete chain model of the purification process.

[0035] Preferably, reconstructing the actual purification trajectory of the multi-source pollution treatment system from the monitoring data specifically includes:

[0036] The monitoring data is divided into several monitoring data segments according to time windows;

[0037] Instantaneous calculation of pollutant removal efficiency is performed for each monitoring data segment;

[0038] By connecting the instantaneous calculation results of all monitoring data segments in chronological order, the actual purification trajectory of the multi-source pollution treatment system is reconstructed; specifically:

[0039] Generate a corresponding timestamp for each monitoring data segment, and bind and store the instantaneous calculation results with the timestamps;

[0040] All instantaneous calculation results are sorted in ascending order of timestamp to form time series data;

[0041] Detect discontinuities in time series data and use the average of adjacent data points for interpolation to complete the data;

[0042] The completed time series data is fitted into a continuous curve, which serves as the actual purification trajectory of the multi-source pollution treatment system.

[0043] Preferably, based on the comparison results, the specific delays in the purification process of the multi-source pollution treatment system at each treatment stage include:

[0044] Calculate the difference in purification level between the expected purification trajectory and the actual purification trajectory at the end of the same treatment stage;

[0045] When the difference in cleanliness continuously exceeds the allowable fluctuation range, it is determined that there is a delay in the cleanliness process in this processing stage;

[0046] Record the stage and duration of the delay in the purification process;

[0047] The calculation of the difference in cleanliness between the expected cleanliness trajectory and the actual cleanliness trajectory at the end of the same treatment stage includes:

[0048] Extract the theoretical degree of purification at the end of a specified treatment stage from the expected purification trajectory;

[0049] Locate the measured purification level at the end point of the same treatment stage from the actual purification trajectory;

[0050] Calculate the absolute difference between the theoretical purification value and the measured purification value;

[0051] The absolute difference is standardized to eliminate the influence of dimensions, resulting in comparable purification differences.

[0052] Preferably, assessing the dynamic purification potential by combining the state of the pollution source with the delay in the purification process specifically includes:

[0053] The theoretical maximum purification load for the current treatment stage is determined based on the state of the pollution source.

[0054] The theoretical maximum purification load is reduced and corrected based on the purification process delay.

[0055] The reduced and corrected purification load value is taken as the dynamic purification potential of the multi-source pollution treatment system at the current treatment stage.

[0056] Preferably, the reduction and correction of the theoretical maximum purification load based on the purification process delay specifically includes:

[0057] The duration and intensity of the delay in the purification process are obtained;

[0058] Construct a reduction function with delay duration and occurrence intensity as input variables;

[0059] The reduction correction factor for the theoretical maximum purification load is calculated using the reduction function.

[0060] Preferably, the construction of the reduction function with delay duration and occurrence intensity as input variables includes:

[0061] The delay duration is defined as a continuous input variable, and the occurrence intensity is defined as a discrete input variable.

[0062] A basic expression for the reduction function is established using a multiple regression method. This expression includes an interaction term between the delay duration and the occurrence intensity.

[0063] Least squares estimation of the coefficients of the reduction function is performed using a historical delayed event dataset;

[0064] The goodness of fit of the reduction function was verified by residual analysis, and the function form was adjusted until the preset accuracy requirements were met.

[0065] Preferably, the pollution purification enhancement procedure includes increasing the reagent dosing rate and enhancing the physical stirring intensity of specific reaction units within the multi-source pollution treatment system.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] By simulating and generating the expected purification trajectory throughout all treatment stages, and comparing it in real-time with the actual purification trajectory reconstructed from actual monitoring data, a refined diagnosis of the system's operational status is achieved. This approach goes beyond simple monitoring of the final effluent indicators, accurately pinpointing the specific treatment stage where performance degradation occurs and quantifying its delay relative to the expected path. This allows system operators to identify bottlenecks in advance, recognizing weak links in the process chain before the overall effluent quality deteriorates, and shifting management decisions from passively responding to exceeding performance limits to proactively intervening in process anomalies.

[0068] By coupling the characteristics of the pollution source status at the current treatment stage with the purification process delay identified through trajectory comparison, the dynamic purification potential of the system is assessed, and this serves as the sole decision-making basis for whether to initiate enhanced procedures. This mechanism avoids the blindness of triggering thresholds based solely on pollutant concentration. It comprehensively considers the system's input load and internal health to determine whether the system can adequately handle the current load and achieve the stage objectives using existing processes. Intervention is only triggered when the assessed potential is indeed lower than required, ensuring that any enhanced measures are initiated precisely and on demand, avoiding ineffective consumption of energy and reagents, and achieving precise and economical control. Attached Figure Description

[0069] Figure 1 This is a schematic diagram illustrating the working principle of the high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios as described in this invention.

[0070] Figure 2 A flowchart defining the feature parameters;

[0071] Figure 3 A flowchart reconstructing the actual purification trajectory;

[0072] Figure 4 A comparison chart of the theoretical maximum purification load and dynamic purification potential for each stage of wastewater treatment;

[0073] Figure 5 This is a scatter plot showing the relationship between delay duration and reduction correction factor. Detailed Implementation

[0074] 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.

[0075] Please see Figure 1This invention provides a high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios. The method includes: after system startup, a scenario definition module operates first, which is responsible for defining multiple characteristic parameters of the multi-source pollution treatment system under flood and drought scenarios and dividing the entire wastewater purification process into several continuous treatment stages based on these characteristic parameters. Subsequently, a state analysis module works for each divided treatment stage, analyzing the pollution source state of the multi-source pollution treatment system at the corresponding stage, and calculating the pollution source characteristic value of the corresponding treatment stage based on the analyzed pollution source state. A trajectory simulation module simulates and generates the expected purification trajectory of the multi-source pollution purification process within a continuous time period based on the pollution source characteristic values ​​calculated for all treatment stages. At the same time, a trajectory reconstruction module synchronously acquires the monitoring data generated by the multi-source pollution treatment system during actual operation and reconstructs the actual purification trajectory of the system from this monitoring data. A comparison and identification module compares the reconstructed actual purification trajectory with the simulated expected purification trajectory in real time for the corresponding stage, and identifies the purification process delay of the multi-source pollution treatment system at each treatment stage based on the comparison results. The potential assessment module combines the pollution source status provided by the status analysis module with the purification process delay identified by the comparison and identification module to evaluate the dynamic purification potential of the multi-source pollution treatment system at the current treatment stage. Finally, the program execution module continuously monitors the evaluated dynamic purification potential. When the dynamic purification potential is lower than the preset purification capacity threshold for the current treatment stage, the preset pollution purification enhancement program within the multi-source pollution treatment system is automatically activated.

[0076] Example 1: See Figure 2 In practical implementation, the scenario definition module defines multiple characteristic parameters of the multi-source pollution treatment system under flood and drought scenarios. This module collects hydrological and meteorological data of the environment in which the multi-source pollution treatment system is located, including continuous precipitation, average temperature, relative humidity, and cross-sectional flow information. Core indicators characterizing the alternating flood and drought scenarios are extracted from the hydrological and meteorological data. These core indicators include flood return period, drought index, and runoff modulus. The core indicators are then coupled with the design and operating parameters of the multi-source pollution treatment system, which include maximum design flow and theoretical hydraulic retention time. The coupling process generates multiple characteristic parameters of the multi-source pollution treatment system under flood and drought scenarios by assigning specific weights to different indicators and parameters and summing them. In some embodiments, the weights are determined using the analytic hierarchy process (AHP).

[0077] In practical implementation, the state analysis module analyzes the pollution source state of the multi-source pollution treatment system for a specific treatment stage. The module identifies the main pollutant types and their concentration distributions within the pollution source state. The main pollutant types include total phosphorus, heavy metal ions, and organic pollutants. It analyzes the migration and transformation pathways of the main pollutant types within the multi-source pollution treatment system. These pathways describe the biodegradation and adsorption / precipitation processes of pollutants in anaerobic, aerobic, and advanced treatment units. Based on the concentration distribution and migration and transformation pathways, the state analysis module calculates the pollution source characteristic value for the corresponding treatment stage using a weighted fusion method. In practical implementation, the weighted fusion method determines the weight coefficient for each pollutant based on its degradation difficulty and toxicity impact along the migration and transformation pathways. The concentration of each pollutant is divided by its corresponding standard reference concentration to obtain the standardized concentration ratio for each pollutant. The standard reference concentration is taken from the national emission standard limit. The standardized concentration ratio of each pollutant is multiplied by its corresponding weight coefficient to obtain a weighted standardized value. A geometric mean is calculated for all weighted standardized values, ultimately outputting a dimensionless pollution source characteristic value. In some embodiments, the geometric mean calculation is implemented using logarithmic operations. It is understandable that the weighted fusion process is expressed through mathematical formulas, such as the characteristic values ​​of pollution sources. The calculation formula is:

[0078]

[0079] in: Indicates the characteristic value of the pollution source. Indicates the quantity of major pollutant types, Indicates the first The weighting coefficients of the pollutants and satisfying , Indicates the first The measured concentrations of the pollutants, Indicates the first The standard reference concentrations corresponding to the pollutants, Represents the natural logarithm. Represents an exponential function. Optional, standard reference concentration. Alternatively, a historical concentration baseline value can be selected.

[0080] Example 2: See Figure 3In practical implementation, the trajectory simulation module generates the expected purification trajectory based on the pollution source characteristic values ​​of all treatment stages. The module establishes a chain model of the purification process with treatment stages as sequential nodes. Establishing the chain model involves defining each treatment stage as an independent node and assigning a unique stage identifier to each node. The sequential relationship between nodes is determined according to the wastewater purification process flow, forming a unidirectional chain connection structure. A corresponding purification efficiency function is configured for each node. This function is trained based on historical operating data of the treatment stages and is used to simulate the removal rate of specific pollutants by each treatment stage. The stage identifiers, connection structure, and purification efficiency function are integrated into a unified mathematical framework to construct a complete chain model of the purification process. In some embodiments, the purification efficiency function uses a regression model based on machine learning. After the chain model is constructed, the trajectory simulation module inputs the pollution source characteristic values ​​of each treatment stage into the corresponding node of the chain model. The chain model is run, outputting the expected purification trajectory of the multi-source pollution purification process over continuous time. It can be understood that the expected purification trajectory is plotted with time on the horizontal axis and the overall system purification efficiency on the vertical axis.

[0081] In practical implementation, the trajectory reconstruction module reconstructs the actual purification trajectory of the multi-source pollution treatment system from the monitoring data. The module segments the monitoring data into several data segments based on time windows. For each data segment, the instantaneous pollutant removal efficiency is calculated. This instantaneous calculation is obtained by dividing the difference between the influent and effluent pollutant concentrations within the time window by the ratio of the influent concentration. The instantaneous calculation results of all monitoring data segments are connected in chronological order to reconstruct the actual purification trajectory of the multi-source pollution treatment system. Specifically, a timestamp is generated for each monitoring data segment, and the instantaneous calculation results are bound to the timestamps for storage. All instantaneous calculation results are sorted in ascending order of timestamps to form time-series data. Discontinuities in the time-series data are detected, and interpolation is performed using the average value of adjacent data points. The completed time-series data is fitted into a continuous curve, which serves as the actual purification trajectory of the multi-source pollution treatment system. Optionally, linear interpolation is used for interpolation. In some embodiments, the functional expression of the actual purification trajectory... This can be expressed as:

[0082]

[0083] in: The actual purification efficiency is represented by a dimensionless number ranging from 0 to 1. This represents standardized time, which is the actual time. With a characteristic time constant The ratio, i.e. , is a dimensionless number; This represents the dimensionless polynomial coefficients obtained through fitting.

[0084] Example 3: In specific implementation, the comparison and identification module identifies the purification process delay of the multi-source pollution treatment system at each treatment stage based on the comparison results. The comparison and identification module calculates the purification degree difference between the expected purification trajectory and the actual purification trajectory at the end of the same treatment stage. Calculating the purification degree difference includes extracting the theoretical purification degree value at the end of a specified treatment stage from the expected purification trajectory, locating the measured purification degree value at the end of the same treatment stage from the actual purification trajectory, calculating the absolute difference between the theoretical and measured purification degree values, and standardizing the absolute difference to eliminate the influence of dimensions, thus obtaining a purification degree difference that can be used for comparison. In some embodiments, the standardization of the purification degree difference adopts the minimum-maximum normalization method. The calculation formula is:

[0085]

[0086] in: This represents the difference in purification level after standardization. This represents the theoretical purification level extracted from the expected purification trajectory. This represents the measured purification level value located from the actual purification trajectory. This represents the scaling factor used for normalization. The scaling factor is a fixed percentage value of the system's designed purification capacity.

[0087] In practical implementation, when the purification difference continuously exceeds the allowable fluctuation range, the comparison and identification module determines that there is a purification process delay in the corresponding processing stage. The allowable fluctuation range is a pre-set threshold interval. The comparison and identification module records the occurrence stage and duration of the purification process delay. The occurrence stage is determined by the identifier of the processing stage, and the delay duration is calculated from the moment the purification difference first exceeds the allowable fluctuation range until the difference falls back into the allowable range. In some embodiments, the allowable fluctuation range is determined based on statistical analysis of historical operating data of the multi-source pollution treatment system. Optionally, the comparison and identification module performs a sliding window average calculation on the purification difference to smooth instantaneous fluctuations before comparing it with the allowable fluctuation range.

[0088] Example 4: In specific implementation, the potential assessment module combines the pollution source status and the purification process delay to assess the dynamic purification potential of the multi-source pollution treatment system at the current treatment stage. The potential assessment module determines the theoretical maximum purification load at the current treatment stage based on the pollution source status. The theoretical maximum purification load is then reduced and corrected according to the purification process delay, and the reduced and corrected purification load value is used as the dynamic purification potential of the multi-source pollution treatment system at the current treatment stage. In some embodiments, the theoretical maximum purification load is obtained by querying a preset pollution source status-load correspondence table, which is established based on the design parameters and historical operating data of the multi-source pollution treatment system.

[0089] In practical implementation, the specific steps for reducing and correcting the theoretical maximum purification load based on the purification process delay include obtaining the delay duration and occurrence intensity. A reduction function is constructed with the delay duration and occurrence intensity as input variables. The reduction correction coefficient for the theoretical maximum purification load is then calculated using this function. Optionally, the occurrence intensity is graded based on the extent to which the purification difference exceeds the allowable fluctuation range. It can be understood that the reduction correction coefficient is a value between 0 and 1, used to multiply the theoretical maximum purification load. The calculation formula is:

[0090]

[0091] in: This indicates the reduction correction factor. This represents the basic efficiency coefficient, which is usually taken as 1. This represents the attenuation factor related to the delay duration. The delay duration indicates the duration of the purification process. This represents the reduction factor related to the intensity of occurrence. This indicates the severity level of the delay in the purification process. In some embodiments, refer to Table 1 for the severity level. Assign values ​​according to Table 1.

[0092] Table 1: Classification of Occurrence Intensity Levels

[0093]

[0094] In practice, after the reduction correction factor is calculated, the potential assessment module multiplies the theoretical maximum purification load by the reduction correction factor to obtain the reduced purification load value, which is then output as the dynamic purification potential. Optional, attenuation factor. and reduction factor The value is obtained by calibrating performance degradation records in historical data.

[0095] See Figure 4This chart is a core visualization of dynamic purification potential assessment: the horizontal axis represents the six continuous treatment stages of wastewater purification, and the vertical axis represents the purification load. The two bars represent the theoretical maximum purification load and the dynamic purification potential, respectively. The theoretical load and dynamic potential of the pretreatment and disinfection stages show relatively small differences, indicating that the purification delay has a weak impact in these stages. However, the aerobic treatment and sedimentation stages show significant differences, reflecting a noticeable purification process delay in these stages, requiring a logical assessment of whether to initiate enhanced procedures. This chart intuitively quantifies the gap between the actual purification capacity and the design capacity at each stage, providing crucial data support for accurately identifying weak links in the process and initiating enhanced procedures as needed.

[0096] Example 5: In specific implementation, the process of constructing a reduction function with delay duration and occurrence intensity as input variables is described. Specifically, the delay duration is defined as a continuous input variable, and the occurrence intensity is defined as a discrete input variable. The basic expression of the reduction function is established using a multiple regression method, specifically ordinary least squares. In its implementation, when establishing the basic expression of the reduction function using multiple regression, the system first treats delay duration as a continuous input variable and occurrence intensity as a discrete input variable, constructing a multinomial regression model that includes these two variables and their interaction term. The system then calls upon a historical delay event dataset, which integrates recorded delay duration, occurrence intensity, and corresponding actual purification efficiency loss data from past operations, as training samples. Subsequently, the system applies ordinary least squares regression analysis, iteratively calculating and minimizing the sum of squared residuals to estimate the coefficients in the model. After coefficient estimation, the system executes a residual analysis program, evaluating the model's goodness of fit by checking the distribution characteristics and randomness of the residuals. If systematic bias is found in the residuals or the preset accuracy threshold is not reached, the function form is automatically adjusted, such as by adding or deleting variables or changing the model structure, and the estimation and verification cycle is repeated until the reduction function stably reflects the impact of delay factors on purification potential. The basic expression includes an interaction term between delay duration and occurrence intensity. The coefficients of the reduction function are estimated using least-squares estimation based on a historical delay event dataset. This dataset contains recorded delay durations, intensity levels, and corresponding actual purification efficiency losses from past operations. Residual analysis is used to verify the goodness of fit of the reduction function, and the function form is adjusted until a preset accuracy requirement is met. This preset accuracy requirement is typically set to a coefficient of determination greater than 0.9. The final form of the reduction function is used by the potential assessment module to calculate the reduction correction coefficient. An optional, dimensionlessly consistent reduction function expression is provided. for:

[0097]

[0098] in: This represents the intermediate reduction value calculated from the delay duration and occurrence intensity. Indicates the delay duration. This represents a constant, a unit conversion and standardization factor, with a value equal to 1 hour. Indicates the intensity of occurrence. This represents the dimensionless regression coefficient obtained through least squares estimation.

[0099] In practice, the pollution purification enhancement procedure includes increasing the reagent dosing rate and strengthening physical agitation in specific reaction units within the multi-source pollution treatment system. Upon receiving a start command, the program execution module sends a control signal to the dosing control unit to increase the reagent dosing rate in the specific reaction unit, and simultaneously sends a control signal to the agitator control unit to strengthen physical agitation. In some embodiments, the increase in the reagent dosing rate is proportionally adjusted based on the difference between the dynamic purification potential and the purification capacity threshold.

[0100] See Figure 5 This figure is a visualization of the dynamic purification potential reduction correction and the construction of the reduction function: the horizontal axis represents the delay duration of the purification process, and the vertical axis represents the reduction correction coefficient. Data points in the figure are categorized by intensity level, and the dashed line represents the trend fitting line. The longer the delay, the lower the overall reduction correction coefficient tends to be, corresponding to the logic that the longer the delay, the greater the reduction in theoretical purification load. The distribution of points at different intensity levels also reflects the rule that the higher the intensity of occurrence, the lower the reduction coefficient. The trend fitting line is a visualization of the multiple regression reduction function, clearly showing the attenuation effect of the delay factor on the dynamic purification potential. The core value of this figure is to quantify the relationship between delay and purification potential, providing an intuitive basis for parameter changes to accurately initiate pollution purification enhancement procedures.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios, characterized in that, The system includes: The scenario definition module is used to define multiple characteristic parameters of multi-source pollution treatment systems under flood and drought scenarios, specifically including: Collect hydrological and meteorological data of the environment in which the multi-source pollution treatment system is located. The hydrological and meteorological data includes continuous precipitation, average temperature, relative humidity and cross-sectional flow information. Core indicators characterizing the alternating flood and drought scenarios are extracted from the hydrological and meteorological data. These core indicators include flood recurrence interval, drought index, and runoff modulus. The core indicators are coupled with the design and operation parameters of the multi-source pollution treatment system. The design and operation parameters include the maximum design flow rate and the theoretical hydraulic retention time. The coupling process is carried out by assigning specific weights to different indicators and parameters and summing them to obtain multiple characteristic parameters of the multi-source pollution treatment system under flood and drought scenarios. Based on the characteristic parameters, the sewage purification process is divided into several continuous treatment stages. The state analysis module is used to analyze the pollution source state of the multi-source pollution treatment system at each processing stage, and calculate the pollution source characteristic value of the corresponding processing stage based on the pollution source state. Specifically, this includes: Identify the main pollutant types and their concentration distribution in the pollution source state; Analyze the migration and transformation pathways of the main pollutant types within the multi-source pollution treatment system; Based on the concentration distribution and the migration and transformation path, the pollution source characteristic values ​​of the corresponding treatment stage are calculated by weighted fusion. The trajectory simulation module is used to simulate and generate the expected purification trajectory of the multi-source pollution purification process over a continuous time period based on the pollution source characteristic values ​​of all processing stages. The trajectory reconstruction module is used to synchronously acquire monitoring data generated by the multi-source pollution treatment system during actual operation, and reconstruct the actual purification trajectory of the multi-source pollution treatment system from the monitoring data. The comparison and identification module is used to compare the actual purification trajectory with the expected purification trajectory in real time at the corresponding stage, and identify the purification process delay of the multi-source pollution treatment system at each treatment stage based on the comparison results. The potential assessment module is used to assess the dynamic purification potential of the multi-source pollution treatment system at the current treatment stage by combining the state of the pollution source with the delay of the purification process. The program execution module is used to activate the pollution purification enhancement program within the multi-source pollution treatment system when the dynamic purification potential is lower than the purification capacity threshold required by the current treatment stage.

2. The high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios as described in claim 1, characterized in that, The calculation of pollution source characteristic values ​​for the corresponding treatment stage based on the concentration distribution and the migration and transformation pathway using a weighted fusion method specifically includes: The weighting coefficient for each pollutant is determined based on its degradation difficulty and toxicity impact along the migration and transformation pathway. The concentration distribution data of each pollutant is multiplied by its corresponding weighting coefficient to obtain the weighted concentration value; The initial fusion value is obtained by calculating the arithmetic mean of all weighted concentration values; The initial fusion values ​​are logarithmically transformed to compress the data range, and the standardized pollution source characteristic values ​​are finally output.

3. The high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios as described in claim 1, characterized in that, The simulation of the expected purification trajectory based on the pollution source characteristic values ​​of all treatment stages specifically includes: Establish a chain model of the purification process with the treatment stages as sequential nodes; Input the pollution source characteristic values ​​of each treatment stage into the corresponding node of the purification process chain model; Run the chain model of the purification process and output the expected purification trajectory of the multi-source pollution purification process in a continuous time. The establishment of the purification process chain model with processing stages as sequential nodes includes: Define each processing stage as an independent node in the chain model, and assign a unique stage identifier to each node; The sequential relationship between nodes is determined based on the wastewater purification process flow, forming a unidirectional chain connection structure; A corresponding purification efficiency function is configured for each node. The purification efficiency function is trained based on the historical operating data of this stage and is used to simulate the removal rate of a specific pollutant in this stage. The stage identifiers, connection structures, and purification efficiency functions are integrated into a unified mathematical framework to construct a complete chain model of the purification process.

4. The high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios as described in claim 1, characterized in that, The actual purification trajectory of the multi-source pollution treatment system reconstructed from the monitoring data specifically includes: The monitoring data is divided into several monitoring data segments according to time windows; Instantaneous calculation of pollutant removal efficiency is performed for each monitoring data segment; By connecting the instantaneous calculation results of all monitoring data segments in chronological order, the actual purification trajectory of the multi-source pollution treatment system is reconstructed; specifically: Generate a corresponding timestamp for each monitoring data segment, and bind and store the instantaneous calculation results with the timestamps; All instantaneous calculation results are sorted in ascending order of timestamp to form time series data; Detect discontinuities in time series data and use the average of adjacent data points for interpolation to complete the data; The completed time series data is fitted into a continuous curve, which serves as the actual purification trajectory of the multi-source pollution treatment system.

5. The high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios as described in claim 1, characterized in that, Based on the comparison results, the specific delays in the purification process at each stage of the multi-source pollution treatment system were identified as follows: Calculate the difference in purification level between the expected purification trajectory and the actual purification trajectory at the end of the same treatment stage; When the difference in cleanliness continuously exceeds the allowable fluctuation range, it is determined that there is a delay in the cleanliness process in this processing stage; Record the stage and duration of the delay in the purification process; The calculation of the difference in cleanliness between the expected cleanliness trajectory and the actual cleanliness trajectory at the end of the same treatment stage includes: Extract the theoretical degree of purification at the end of a specified treatment stage from the expected purification trajectory; Locate the measured purification level at the end point of the same treatment stage from the actual purification trajectory; Calculate the absolute difference between the theoretical purification value and the measured purification value; The absolute difference is standardized to eliminate the influence of dimensions, resulting in comparable purification differences.

6. The high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios as described in claim 1, characterized in that, The dynamic purification potential assessment, which combines the state of the pollution source with the delay in the purification process, specifically includes: The theoretical maximum purification load for the current treatment stage is determined based on the state of the pollution source. The theoretical maximum purification load is reduced and corrected based on the purification process delay. The reduced and corrected purification load value is taken as the dynamic purification potential of the multi-source pollution treatment system at the current treatment stage.

7. The high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios as described in claim 6, characterized in that, The reduction and correction of the theoretical maximum purification load based on the purification process delay specifically includes: The duration and intensity of the delay in the purification process are obtained; Construct a reduction function with delay duration and occurrence intensity as input variables; The reduction correction factor for the theoretical maximum purification load is calculated using the reduction function.

8. A high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios according to claim 7, characterized in that, The construction of the reduction function with delay duration and occurrence intensity as input variables includes: The delay duration is defined as a continuous input variable, and the occurrence intensity is defined as a discrete input variable. A basic expression for the reduction function is established using a multiple regression method. This expression includes an interaction term between the delay duration and the occurrence intensity. Least squares estimation of the coefficients of the reduction function is performed using a historical delayed event dataset; The goodness of fit of the reduction function was verified by residual analysis, and the function form was adjusted until the preset accuracy requirements were met.

9. A high-efficiency wastewater purification system for multi-source pollution treatment in flood and drought scenarios as described in claim 1, characterized in that, The pollution purification enhancement program includes increasing the reagent dosing rate and strengthening the physical stirring intensity in specific reaction units within the multi-source pollution treatment system.

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