Urban road rainwater runoff grading treatment system and method

Abstract

The application discloses a kind of urban road rainwater runoff grading treatment system and method, it is related to urban rainwater pollution treatment technical field.For the problem that existing system is difficult to monitor multiple index water quality in real time, accurately identify pollution degree and lack of dynamic of shunt control, the system is equipped with multi-source spectral water quality monitor at pipe network end, real-time monitoring turbidity, COD, TOC and heavy metal concentration, control center accesses rainfall intensity data, calculates pollutant load instantaneous rate by weighted summation formula L , according to L Comparison result of threshold value, rainwater is distributed to enhanced treatment unit containing cyclone grit, electrochemical degradation of heavy metals and chemical flocculation unit, biological aerated filter system or horizontal flow sedimentation tank in controllable shunt module in shunt well.Mainly used to realize rainwater quality efficient treatment and resource recycling, guarantee the safety of receiving water body.

Description

Technical Field

[0001] This invention relates to the field of urban stormwater pollution control technology. More specifically, this invention relates to a graded treatment system and method for urban road stormwater runoff. Background Technology

[0002] In the field of urban stormwater pollution control technology, existing urban road stormwater runoff treatment systems face the following technical challenges: First, it is difficult to accurately identify the degree of rainwater runoff pollution. Existing systems lack the ability to monitor multiple indicators such as turbidity, COD, TOC, and heavy metal concentrations in real time, making it impossible to take targeted treatment measures based on different pollution levels. This is because traditional monitoring technologies are limited and cannot achieve simultaneous, real-time collection and analysis of multiple indicators, resulting in inaccurate judgments on the degree of rainwater pollution.

[0003] Secondly, rainwater diversion control strategies lack dynamism and scientific rigor. Traditional systems fail to fully integrate multiple factors such as real-time network load, reservoir water level, and wastewater treatment plant load, making diversion strategies ill-suited to complex actual conditions. This is due to a lack of comprehensive processing and analysis capabilities for multi-source data, hindering dynamic optimization and adjustment of diversion strategies. Consequently, they struggle to make scientifically sound diversion decisions when faced with varying rainfall intensities and network loads.

[0004] Third, the control of chemical dosing during the treatment process is not precise enough. The existing system cannot adjust the chemical dosage in real time according to the effluent quality of the treatment unit, resulting in low chemical utilization efficiency and potentially affecting the treatment effect. This is because there is a lack of a real-time monitoring and feedback mechanism for the treated water quality, making it difficult to establish a precise control relationship between water quality indicators and chemical dosage. As a result, it is impossible to adjust the chemical dosage in a timely manner according to changes in water quality during the treatment process. Summary of the Invention

[0005] One object of the present invention is to provide a graded treatment system for urban road stormwater runoff, comprising: A multi-source spectral water quality monitor is installed at the end of the rainwater collection pipe network to monitor the turbidity, COD, TOC and heavy metal concentration of rainwater runoff in real time. The control center receives rainwater monitoring data from the multi-source spectral water quality monitor, accesses rainfall intensity data from rainfall forecast information, and calls up the historical biotoxicity database. The control center uses a weighted summation formula to calculate the instantaneous rate of pollutant load. L The formula is: ,in, w i For the first i The weighting coefficients of each parameter, f i ( xi ) is the first i A standardized data mapping function with one parameter. n The parameters include real-time monitored turbidity, COD, TOC, heavy metal concentration, rainfall intensity, and historical biotoxicity data. Multiple diversion wells are set at the branch nodes of the rainwater collection pipe network. Each diversion well is equipped with a controllable diversion module. The controllable diversion module is connected to the command output terminal of the control center and performs rainwater distribution operations according to the hierarchical commands of the control center. The enhanced treatment unit has its inlet connected to the first outlet of the diversion well, and its interior is sequentially equipped with a vortex sedimentation treatment unit, an electrochemical heavy metal degradation treatment unit, and a chemical flocculation treatment unit. The aerated biological filter system has its inlet connected to the second outlet of the diversion well; The horizontal flow sedimentation tank has its inlet connected to the third outlet of the diversion well; Among them, when L When the first threshold is reached, the controllable diversion module distributes rainwater to the enhanced treatment unit; when L When the second threshold is reached, the controllable diversion module distributes rainwater to the aerated biological filter system. When the second threshold ≤ L When the water level is ≤ the first threshold, the controllable diversion module will distribute the rainwater to the horizontal sedimentation tank.

[0006] Preferably, the control center includes an adaptive weighting model. This model dynamically adjusts the weighting coefficients of rainfall monitoring, rainfall intensity, and historical biotoxicity parameters based on a Bayesian inference framework. The dynamic adjustment process includes: Set initial weighting coefficients for real-time rainfall monitoring, rainfall intensity, and historical biotoxicity parameters; Based on the current rainfall intensity R t Rainfall intensity similar to historical rainfall events R h The absolute difference Δ R and historical rainfall intensity standard deviation s Calculate the matching factor ; Using formula The initial weighting coefficients of historical biotoxicity parameters are adjusted, where, The weighting coefficients for historical biotoxicity parameters at the current moment. These are the weighting coefficients of historical biotoxicity parameters from the previous time step. α For the preset correction factor of historical biotoxicity parameters, | α |≤0.3, when Δ R ≤ s hour α>0, when ΔR> s hour α <0; The weighting coefficients of rainwater monitoring, rainfall intensity, and historical biotoxicity parameters are scaled proportionally to a sum of 1.

[0007] Preferably, it also includes a collaborative control component, which includes: The heavy metal concentration monitoring module is located at the effluent end of the electrochemical heavy metal degradation treatment unit to detect the residual heavy metal concentration in real time. The parameter coordination controller is connected to the heavy metal concentration monitoring module and the reagent dosing system in the chemical flocculation treatment unit. The parameter coordination controller receives residual heavy metal concentration data and executes a graded control strategy. When the concentration of residual heavy metals is ≤ the safety threshold, the dosage of chemical flocculant is maintained in the dosing system; When the concentration of residual heavy metals exceeds the safety threshold, the parameter coordination controller performs the following operations: Calculate the concentration Δ of residual heavy metals exceeding the safety threshold. C According to Δ C Within the preset concentration range, select the corresponding dosage increase ratio. m %, of which the preset concentration range and m The correspondence of % is as follows: i) If 0 μg / L < ΔC ≤ 5 μg / L, then m %=10%; ii) If 5 μg / L < ΔC ≤ 10 μg / L, then m %=20%; iii) If 10 μg / L < ΔC ≤ 20 μg / L, then m %=40%; iv) If ΔC > 20 μg / L, then m %=60%; Increase the flocculant dosage to 1+ of the original dosage. m % times.

[0008] Preferably, the parameter co-controller is simultaneously connected to the adaptive weight model. Before executing the hierarchical control strategy, the parameter co-controller adjusts the flocculant dosage based on the instantaneous pollutant load rate. The adaptive weight model is configured to execute: Instantaneous pollutant load rate based on real-time calculation L t The built-in time-series prediction algorithm generates instantaneous pollutant load rate predictions. : Will L t With history NThe instantaneous rate data of pollutant load at consecutive time steps constitute a time series; A time-series prediction algorithm is used to perform single-step prediction of the sequence, and the predicted value for the next acquisition time is output. The time series prediction algorithm can be any one of the following: moving average model, autoregressive integral moving average model, or long short-term memory network. Through the control center t The actual instantaneous pollutant load rate monitoring value is calculated at time +1. L t+1 : Comparison with predicted values Compared with actual monitoring values L t+1 The mean absolute error ε and μ are used to calculate the prediction confidence coefficient μ=e -λε λ is the error sensitivity coefficient, with a value ranging from 0.5 to 1.0; Multiply by the predicted instantaneous rate of pollutant load k The baseline advance compensation amount is calculated, where, k The preset compensation coefficient has a value range of 0.1 to 0.3. Multiply the baseline advance compensation amount by the prediction confidence coefficient to generate the effective advance compensation amount ΔE; Based on ΔE, the flocculant dosage is increased to 1+ΔE times the original dosage.

[0009] Preferably, the effluent from the aerated biological filter system is treated by an ultrafiltration membrane before entering the purified water storage tank. The urban road stormwater runoff classification and treatment system also includes: The global optimization module connects to the control center, the purified water storage tank, and the sewage treatment plant, and also integrates rainfall forecast information. It is configured as follows: Real-time acquisition of the current water level of the purified water storage tank, the real-time surplus treatment capacity of the sewage treatment plant, and the rainfall intensity; The first and second thresholds are dynamically adjusted based on the above parameters: When the real-time surplus treatment capacity of the wastewater treatment plant exceeds the preset capacity, a first threshold reduction instruction is generated, and the first threshold is multiplied by a reduction coefficient, where 0.7 ≤ reduction coefficient < 1.0. When the rainfall intensity exceeds the rainstorm threshold and the current water level of the purified water storage tank reaches or exceeds the water level warning value, a second threshold adjustment instruction is generated, and the second threshold is multiplied by the adjustment coefficient, where 1.0 < adjustment coefficient ≤ 1.5.

[0010] Preferably, it also includes: The performance monitoring module acquires the electrochemical degradation electrode loss rate and ultrafiltration membrane transmembrane pressure difference of the enhanced treatment unit in real time. The state constraint layer, connecting the performance monitoring module and the global optimization module, is configured to execute: Receive threshold adjustment instructions generated by the global optimization module; If a first threshold reduction instruction is received and the current electrode loss rate is greater than the upper limit of the loss rate, the reduction instruction is discarded and an electrode loss warning is triggered; otherwise, the reduction instruction is allowed to be executed. If a command to increase the second threshold is received, and the real-time transmembrane pressure difference of the ultrafiltration membrane is greater than the pressure critical value, the increase range is limited so that the adjusted second threshold does not exceed the initial threshold; otherwise, the command to be executed according to the increase coefficient is allowed.

[0011] Preferably, it also includes: Redundant offloading protection components include: The fault diagnosis module monitors the operating status of the controllable shunt module in real time. The bypass switching unit connects the inlet of the horizontal flow sedimentation tank to the end of the rainwater collection network. When the fault diagnosis module detects that the controllable diversion module has failed, it automatically activates the bypass switching unit to force all rainwater runoff into the horizontal sedimentation tank, and at the same time sends an equipment fault alarm to the control center.

[0012] Preferably, the adaptive weight model includes a sudden pollution identification layer, which performs the following: Real-time calculation of abrupt gradients in turbidity, COD, TOC, and heavy metal concentration in rainwater runoff output by multi-source spectral water quality monitoring instrument; When any of the above mutation gradients exceeds the maximum fluctuation threshold in the historical biotoxicity-related parameters, a spectral anomaly index is generated. The maximum fluctuation threshold is based on the 95th percentile value of historical data statistics. The dynamic weight allocation layer response spectrum anomaly index forces the calculation weight of the instantaneous rate of pollutant load to be allocated to real-time rainwater monitoring data, while ignoring rainfall forecast information and historical biotoxicity correlation parameters; If the instantaneous rate of pollutant load exceeds 80% of the first threshold at this time, the control center determines that... L > First threshold.

[0013] Preferably, the global optimization module includes a deep processing allocation module, which executes: Real-time monitoring of the water level in the purified water storage tank and the transmembrane pressure difference of the ultrafiltration membrane; Constructing a water level-membrane pressure co-constraint function: When the water level in the regulating reservoir is greater than the warning water level and the transmembrane pressure difference of the ultrafiltration membrane is greater than 90% of the critical value, the control center dynamically raises the second threshold. The controllable diversion module distributes rainwater to the aerated biological filter system according to the formula. Q = β× ( Smax - S 当前 ) Traffic limiting will be implemented, among which, Q To allow traffic splitting, β The attenuation coefficient is... S max To the maximum safe capacity of the storage tank, S 当前 Real-time water level; Excess rainwater is diverted to a horizontal sedimentation tank via a controllable diversion module.

[0014] A method for treating urban road stormwater runoff classification and treatment system as described above is provided, comprising the following steps: S1. Real-time monitoring and data acquisition: The turbidity, COD, TOC and heavy metal concentration of rainwater runoff are monitored in real time by a multi-source spectral water quality monitor installed at the end of the rainwater collection pipe network. Synchronously access real-time rainfall intensity from rainfall forecast information; S2. Dynamically calculate the instantaneous rate of pollutant load L: The real-time rainfall monitoring data, rainfall intensity, and historical biotoxicity-related parameters were standardized and outliers were removed. Dynamically adjusting weight coefficients based on a Bayesian inference framework: Set initial weights for real-time monitoring data, rainfall intensity, and historical biotoxicity parameters; Based on the current rainfall intensity R t Rainfall intensity similar to historical rainfall events R h The absolute difference Δ R and historical rainfall intensity standard deviation s Calculate the matching factor ; Using formula The initial weighting coefficients of historical biotoxicity parameters are adjusted, where, The weighting coefficients for historical biotoxicity parameters at the current moment. These are the weighting coefficients of historical biotoxicity parameters from the previous time step. α For the preset correction factor of historical biotoxicity parameters, | α |≤0.3, when Δ R ≤ s hour α >0, when ΔR> s hour α <0; The weighting coefficients of rainwater monitoring, rainfall intensity, and historical biotoxicity parameters are scaled proportionally to a sum of 1. The instantaneous rate of pollutant load L is calculated using a weighted summation formula: ,in: w i The weight coefficient of the i-th data source is dynamically adjusted. f i ( x i Let be the normalized data mapping function for the i-th data source, and n be the total number of data sources; S3, Hierarchical diversion control: When L > the first threshold, the rainwater is allocated to the enhanced treatment unit, where it undergoes vortex sedimentation treatment, electrochemical degradation of heavy metals treatment, and chemical flocculation treatment in sequence. When L < the second threshold, the rainwater is distributed to the aerated biological filter system for treatment. The effluent from the aerated biological filter system is then treated by an ultrafiltration membrane and fed into the purified water storage tank. When the second threshold ≤ L ≤ the first threshold, the rainwater will be distributed to the horizontal sedimentation tank for treatment. S4. Dynamically optimized threshold: Real-time acquisition of the current water level of the purified water storage tank, the real-time surplus treatment capacity of the sewage treatment plant, and the rainfall intensity; The first and second thresholds are dynamically adjusted based on the following conditions: When the real-time surplus treatment capacity of the wastewater treatment plant exceeds the preset capacity, a first threshold reduction instruction is generated: the first threshold is multiplied by a reduction coefficient, where 0.7 ≤ reduction coefficient < 1.0; When the rainfall intensity exceeds the rainstorm threshold and the current water level of the purified water storage tank reaches or exceeds the water level warning value, a second threshold adjustment instruction is generated: the second threshold is multiplied by the adjustment coefficient, where 1.0 < adjustment coefficient ≤ 1.5; The updated first and second thresholds are applied in real time to the hierarchical diversion control in step S3.

[0015] The present invention has at least the following beneficial effects: This invention utilizes a multi-source spectral water quality monitor to achieve real-time synchronous monitoring of turbidity, COD, TOC, and heavy metal concentrations, providing multi-dimensional data support for quantifying pollution load. It is based on the instantaneous rate of pollutant load. L The hierarchical control strategy precisely divides rainwater into high-pollution, conventionally polluted, and clean stages, driving the controllable diversion module to allocate it to the enhanced treatment unit, aerated biological filter, or horizontal sedimentation tank according to the degree of pollution. This design breaks through the traditional "one-size-fits-all" treatment mode, ensuring that high-pollution rainwater undergoes three-stage enhanced treatment: vortex grit removal, electrochemical degradation, and chemical flocculation, ensuring the efficient removal of toxic substances such as heavy metals; clean rainwater is preferentially fed into the aerated biological filter for resource reuse, reducing energy and chemical consumption.

[0016] This invention employs a Bayesian inference framework through an adaptive weighting model to dynamically adjust the weights of historical biotoxicity parameters. It quantifies the similarity between current rainfall and historical events through a matching factor. This mechanism effectively solves the problem of historical data lag and makes the weight allocation more in line with the actual rainfall characteristics.

[0017] This invention utilizes a collaborative control component to provide real-time feedback on the effluent quality of the electrochemical unit through a heavy metal concentration monitoring module, and executes a four-level gradient compensation strategy: based on the range of residual heavy metal concentration ΔC, the flocculant dosage is increased by 10%-60%. This design breaks through the traditional experience-based dosing mode and achieves precise matching between the dosage of the agent and the degree of pollution residue.

[0018] This invention generates pollutant load prediction values ​​based on a time-series prediction algorithm, quantifies the prediction accuracy through a reliability coefficient, and calculates the advance compensation amount ΔE accordingly. This mechanism enables the flocculant dosage to be increased to 1+ΔE times in advance before the sudden increase in pollutant load, thus solving the control lag problem.

[0019] This invention utilizes a global optimization module to link three data sources: water level in the storage tank, wastewater treatment plant load, and rainfall intensity, to achieve dynamic adjustment of thresholds: when the wastewater treatment plant has high surplus treatment capacity, the first threshold is lowered to increase the frequency of enhanced treatment and make full use of idle treatment capacity; when there is heavy rain and the water level in the storage tank exceeds the warning level, the second threshold is raised to reduce the amount of rainwater entering the deep treatment process and alleviate the risk of overflow.

[0020] This invention utilizes a state constraint layer to obtain electrode loss rate and membrane pressure difference in real time through an efficiency monitoring module, and establishes a linkage mechanism between hardware status and optimization instructions: when the first threshold is lowered, if the electrode loss rate exceeds the limit, the instruction is discarded and an alarm is triggered to avoid overload damage to the electrochemical unit; when the second threshold is raised, if the membrane pressure difference is greater than 90% of the critical value, the upward adjustment is limited to protect the integrity of the ultrafiltration membrane.

[0021] This invention utilizes a redundant diversion protection component to forcibly divert all rainwater into a horizontal sedimentation tank within 0.5 seconds via a bypass switching unit in the event of a controllable diversion module failure, ensuring uninterrupted system operation. Simultaneously, it triggers an equipment fault alarm, locates the fault point, and prompts maintenance, thus reducing the risk of system failure.

[0022] This invention utilizes a sudden pollution identification layer to generate a spectral anomaly index through abrupt gradient detection. A dynamic weight allocation layer immediately forces the calculation weights to be allocated to real-time monitoring data, ignoring rainfall forecasts and historical parameters. When L > 80% of the first threshold, a high pollution stage is directly determined, with a response time of <3 seconds. This mechanism has a high accuracy rate in identifying sudden pollution events such as industrial leaks and traffic accidents.

[0023] This invention utilizes a deep treatment distribution module to limit the amount of rainwater entering the aerated biological filter through a water level-membrane pressure co-constraint function and flow rate formula. Excess rainwater is switched to a horizontal sedimentation tank to avoid overpressure operation of the ultrafiltration membrane under high water level conditions. This strategy reduces the membrane fouling rate.

[0024] The processing method provided by this invention achieves full-process adaptive control through three-stage coordination: dynamic weight calculation, emergency response to pipeline sediment disturbance, and real-time threshold optimization.

[0025] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to embodiments, so that those skilled in the art can implement it based on the description.

[0027] It should be noted that, unless otherwise specified, the experimental methods described in the following implementation plan are all conventional methods, and the reagents and materials described are all commercially available unless otherwise specified.

[0028] According to one implementation of the present invention, it includes: A multi-source spectral water quality monitor is installed at the end of the rainwater collection pipe network to monitor the turbidity, COD, TOC and heavy metal concentration of rainwater runoff in real time. The control center receives rainwater monitoring data from the multi-source spectral water quality monitor, accesses rainfall intensity data from rainfall forecast information, and calls up the historical biotoxicity database. The control center uses a weighted summation formula to calculate the instantaneous rate of pollutant load. L The formula is: ,in, w i For the first i The weighting coefficients of each parameter, f i ( x i ) is the first i A standardized data mapping function with one parameter. n The parameters include real-time monitored turbidity, COD, TOC, heavy metal concentration, rainfall intensity, and historical biotoxicity data. Multiple diversion wells are set at the branch nodes of the rainwater collection pipe network. Each diversion well is equipped with a controllable diversion module. The controllable diversion module is connected to the command output terminal of the control center and performs rainwater distribution operations according to the hierarchical commands of the control center. The enhanced treatment unit has its inlet connected to the first outlet of the diversion well, and its interior is sequentially equipped with a vortex sedimentation treatment unit, an electrochemical degradation heavy metal treatment unit, and a chemical flocculation treatment unit. The aerated biological filter system has its inlet connected to the second outlet of the diversion well; The horizontal flow sedimentation tank has its inlet connected to the third outlet of the diversion well; Among them, when L When the first threshold is reached, the controllable diversion module distributes rainwater to the enhanced treatment unit; when L When the second threshold is reached, the controllable diversion module distributes rainwater to the aerated biological filter system. When the second threshold ≤ L When the water level is ≤ the first threshold, the controllable diversion module will distribute the rainwater to the horizontal sedimentation tank; The multi-source spectral water quality monitor, installed at the end of the rainwater collection network, can monitor the turbidity, COD, TOC, and heavy metal concentration of rainwater runoff in real time. This monitor can be a commercially available integrated spectrometer, such as one containing an optical probe to measure turbidity, an ultraviolet absorption module to measure COD and TOC, and an electrochemical sensor to detect heavy metals. The casing material can be 316L stainless steel or IP67 waterproof engineering plastic to ensure corrosion resistance and waterproof performance. During operation, the multi-source spectral water quality monitor continuously collects rainwater samples and outputs various data in real time through built-in spectral analysis technology, transmitting the data to the control center via wired or wireless communication. The calculation of the instantaneous pollutant load rate L involves parameter weights. The initial weights can be set as follows: real-time rainwater monitoring data weight 0.5, rainfall intensity weight 0.3, and historical biotoxicity parameter weight 0.2. Parameter standardization can be performed using the minimum-maximum normalization method, as shown in the formula: ,in x min and x max Determined based on historical datasets; The control center receives rainfall monitoring data and integrates rainfall intensity data from rainfall forecast information, using a weighted summation formula. Calculate the instantaneous rate L of pollutant load, where, w i For the first i The weighting coefficients of each parameter, f i ( x i ) is the first i A standardized data mapping function with one parameter. nThe parameters include rainfall monitoring, rainfall intensity, and historical biotoxicity. The control center can use an industrial programmable logic controller or an embedded microprocessor system. Its data processing module runs an adaptive weighting algorithm. During operation, the control center accesses rainfall forecast information through a public meteorological service API, integrates real-time monitoring data, rainfall intensity, and historical biotoxicity parameters, and dynamically adjusts the weighting coefficients based on a Bayesian inference framework. The calculated L value is used for classification judgment: the first threshold is set to 50 kg / h, the second threshold is set to 10 kg / h, when L > 50 kg / h it is a high pollution stage, when L < 10 kg / h it is a clean stage, and when 10 kg / h ≤ L ≤ 50 kg / h it is a conventional pollution stage. Multiple diversion wells are equipped with controllable diversion modules, which are connected to a control center. These modules perform tiered treatment based on flow rate (L). The diversion wells are installed at branch nodes or key confluence points of the stormwater network. The controllable diversion modules can use electric ball valves or pneumatic butterfly valves and are connected to the control center via narrowband IoT. When L > 50 kg / h, the controllable diversion modules distribute stormwater to the enhanced treatment unit, which includes a sequentially arranged vortex sedimentation treatment unit, an electrochemical heavy metal degradation treatment unit, and a chemical flocculation treatment unit. When L < 10 kg / h, the flow is distributed to… Aerated biological filter system; when 10kg / h≤L≤50kg / h, the water is distributed to the horizontal flow sedimentation tank. The material of the diversion well can be concrete or stainless steel to ensure structural strength and durability. During operation, the controllable diversion module receives instructions from the control center and switches valves according to the L value to guide rainwater to the corresponding treatment unit: highly polluted rainwater first undergoes cyclone sedimentation to remove particulate impurities, then electrochemical degradation of heavy metals, and finally chemical flocculation sedimentation; clean rainwater enters the aerated biological filter for treatment and is reused; conventionally polluted rainwater undergoes preliminary purification in the horizontal flow sedimentation tank. By using a multi-source spectral water quality monitor to achieve real-time monitoring of multiple indicators, combined with dynamic calculations at the control center and tiered control of diversion wells, the system can accurately identify the degree of rainwater pollution and implement differentiated treatment. This solution effectively solves the problems of traditional systems struggling to monitor multiple water quality indicators in real time and lacking dynamism in diversion control. It enables efficient rainwater treatment and resource reuse, ensuring the safety of receiving water bodies, while optimizing the allocation of treatment resources and reducing energy and chemical consumption. According to another implementation of the present invention, the control center includes an adaptive weight model, which dynamically adjusts the weight coefficients of rainfall monitoring, rainfall intensity, and historical biotoxicity parameters based on a Bayesian inference framework. The dynamic adjustment process includes: Set initial weighting coefficients for real-time rainfall monitoring, rainfall intensity, and historical biotoxicity parameters; Based on the current rainfall intensity R t Rainfall intensity similar to historical rainfall events R h The absolute difference ΔR and historical rainfall intensity standard deviation s Calculate the matching factor ; Using formula The initial weighting coefficients of historical biotoxicity parameters are adjusted, where, The weighting coefficients for historical biotoxicity parameters at the current moment. These are the weighting coefficients of historical biotoxicity parameters from the previous time step. α For the preset correction factor of historical biotoxicity parameters, | α |≤0.3, when Δ R ≤ s hour α >0, when ΔR> s hour α <0; The weighting coefficients of rainwater monitoring, rainfall intensity, and historical biotoxicity parameters are scaled proportionally to a sum of 1. For the initial weight setting, the adaptive weight model of the control center needs to set the initial weight coefficients for real-time rainwater monitoring, rainfall intensity, and historical biotoxicity parameters. The initial weights can be set with reference to historical data experience values. For example, the weight of real-time rainwater monitoring data can be set to 0.5, the weight of rainfall intensity can be set to 0.3, and the weight of historical biotoxicity parameters can be set to 0.2. The control center can use an industrial programmable logic controller or an embedded microprocessor system. Its data processing module has a built-in adaptive weight algorithm. The hardware carrier of the model can be an aluminum alloy chassis or a flame-retardant plastic shell to ensure heat dissipation and safety. It is installed in the central processing unit of the control center. During operation, the initial weight parameters are stored in the register of the control center as a reference value for dynamic adjustment. The parameter setting method is based on the statistical analysis of local historical rainfall and water quality monitoring data. For example, water quality data of similar rainfall events in the past 5 years can be collected for weight calibration. Regarding the calculation of the matching factor, the model currently reflects the rainfall intensity. R t Rainfall intensity similar to historical rainfall events R h The absolute difference Δ R and historical rainfall intensity standard deviation s Calculate the matching factor Historical rainfall intensity data is stored in the control center's database and can be obtained by querying rainfall data from the same period over the past three years. The calculation module can use the floating-point arithmetic unit within the control center to receive rainfall intensity data in real time and perform exponential calculations. During operation, the system continuously monitors the current rainfall intensity. R t The intensity of similar rainfall in historical data R h Compare and calculate Δ R=| R t - R h | and call the standard deviation of historical rainfall intensity s The matching factor is calculated using an exponential function. or This factor is used to quantify the similarity between current rainfall and historical events, providing a basis for weight adjustment; In the dynamic correction and scaling mechanism of weighting coefficients, the formula is used. The initial weighting coefficients of historical biotoxicity parameters are adjusted, where, The weighting coefficients for historical biotoxicity parameters at the current moment. These are the weighting coefficients of historical biotoxicity parameters from the previous time step. α For the preset correction factor of historical biotoxicity parameters, | α |≤0.3, when Δ R ≤ s hour α >0, when ΔR> s hour α <0, after correction, the weight coefficients of the three types of parameters are scaled proportionally to a sum of 1. The value of the correction coefficient α can be set according to the local rainfall characteristics. For example, α can be set to 0.2 in rainy areas and 0.15 in dry areas. The scaling algorithm can use a normalization formula to ensure that the sum of the weights is 1. During operation, the system selects the sign of α based on the comparison result of ΔR and σ, substitutes it into the formula to calculate the weight of the current historical biotoxicity parameter, and then normalizes it with the weights of other parameters to generate a dynamically adjusted weight vector for calculating the instantaneous rate L of pollutant load. By dynamically adjusting the weight coefficients using a Bayesian inference framework, the weight allocation can be optimized in real time based on the similarity between current rainfall and historical events. This effectively solves the problem of historical data lag and makes the calculation of the instantaneous rate of pollutant load more consistent with the actual rainfall characteristics. This mechanism improves the accuracy of pollution degree identification, provides a more scientific basis for diversion control, and thus optimizes the targeting and effectiveness of rainwater classification treatment, ensuring the stable operation of the treatment system under different rainfall conditions.

[0029] According to another implementation of the present invention, a cooperative control component is further included, comprising: The heavy metal concentration monitoring module is located at the effluent end of the electrochemical heavy metal degradation treatment unit to detect the residual heavy metal concentration in real time. The parameter coordination controller is connected to the heavy metal concentration monitoring module and the reagent dosing system in the chemical flocculation treatment unit. The parameter coordination controller receives residual heavy metal concentration data and executes a graded control strategy. When the concentration of residual heavy metals is ≤ the safety threshold, the dosage of chemical flocculant is maintained in the dosing system. When the concentration of residual heavy metals exceeds the safety threshold, the parameter coordination controller performs the following operations: Calculate the concentration Δ of residual heavy metals exceeding the safety threshold. C According to Δ C Within the preset concentration range, select the corresponding dosage increase ratio. m %, of which the preset concentration range and m The correspondence of % is as follows: i) If 0 μg / L < ΔC ≤ 5 μg / L, then m %=10%; ii) If 5 μg / L < ΔC ≤ 10 μg / L, then m %=20%; iii) If 10 μg / L < ΔC ≤ 20 μg / L, then m %=40%; iv) If ΔC > 20 μg / L, then m %=60%; Increase the flocculant dosage to 1+ of the original dosage. m % times; The collaborative control components include a heavy metal concentration monitoring module (located at the effluent end of the electrochemical heavy metal degradation treatment unit) and a parameter collaborative controller. The parameter collaborative controller receives residual heavy metal concentration data and executes a graded control strategy: when the residual heavy metal concentration is ≤ the safety threshold (0.5 mg / L), the flocculant dosage is maintained; when it exceeds the threshold, the dosage is increased (10%-60%) according to the ΔC range. The heavy metal concentration monitoring module, located at the effluent end of the electrochemical heavy metal degradation treatment unit, monitors the residual heavy metal concentration in real time. This heavy metal concentration monitoring module can be a commercially available product. Online heavy metal analyzers, such as those equipped with electrochemical sensors or spectral analysis probes, can measure the concentration of heavy metal ions such as lead and cadmium. The housing of the monitor can be made of 316L stainless steel to ensure corrosion resistance. It is installed on a pipe 1 meter away from the outlet of the electrochemical degradation heavy metal treatment unit. The probe is inserted to a depth of 1 / 3 of the pipe diameter to ensure measurement accuracy. During operation, the heavy metal concentration monitoring module continuously collects water samples and transmits the data to the parameter co-controller via a 4~20mA current signal or RS485 communication. The sampling frequency is once per minute. The parameter coordination controller is connected to the heavy metal concentration monitoring module and the reagent dosing system in the chemical flocculation treatment unit to receive residual heavy metal concentration data. The parameter coordination controller can be an industrial programmable logic controller or an embedded control unit, which is installed in the cabinet of the control center. It communicates with the heavy metal concentration monitoring module and the reagent dosing system through shielded cables. The controller has a built-in hierarchical control strategy program. When it receives the monitoring data, it immediately compares it with the preset safety threshold. The safety threshold can be set to 0.5 mg / L. This value is set based on the local environmental emission standards or the water quality requirements of the receiving water body and is manually entered in the controller through the human-machine interface. When the graded control strategy is implemented, if the residual heavy metal concentration is ≤0.5mg / L, the chemical flocculant dosage is maintained. If the residual concentration is >0.5mg / L, the excess value ΔC is calculated, and the corresponding dosage increase percentage m% is selected according to the preset concentration range where ΔC is located. Specifically: when 0μg / L < ΔC ≤ 5μg / L, m% = 10%; when 5μg / L < ΔC ≤ 10μg / L, m% = 20%; when 10μg / L < ΔC ≤ 20μg / L, m% = 4%. 0%; When ΔC > 20μg / L, m% = 60%. The reagent dosing system can use a precision metering pump, paired with a polyethylene storage tank, installed next to the chemical flocculation treatment unit. The pump flow rate adjustment range is 10~100L / h. During operation, the controller determines m% based on ΔC and increases the flocculant dosage to 1+m% of the original dosage. For example, if the original dosage is 20L / h, when ΔC = 8μg / L, m% = 20%, the new dosage is 20×(1+20%) = 24L / h. By linking real-time monitoring of heavy metal concentration with graded dosage of reagents, precise control of chemical flocculation treatment is achieved. This mechanism breaks through the limitations of traditional experience-based dosing methods, allowing the dosage of reagents to be directly matched with the degree of pollution residue. While ensuring the removal effect of heavy metals, it reduces reagent waste, lowers treatment costs, improves the economy and reliability of system operation, and ensures that the effluent quality meets the requirements of discharge standards.

[0030] According to another implementation of the present invention, the parameter coordination controller is simultaneously connected to the adaptive weight model. Before executing the hierarchical control strategy, the parameter coordination controller adjusts the flocculant dosage based on the instantaneous pollutant load rate. The adaptive weight model is configured to execute: Instantaneous pollutant load rate based on real-time calculation L t The built-in time-series prediction algorithm generates instantaneous pollutant load rate predictions. : Will L t With history NThe instantaneous rate data of pollutant load at consecutive time steps constitute a time series; A time-series prediction algorithm is used to perform single-step prediction of the sequence, and the predicted value for the next acquisition time is output. The time series prediction algorithm can be any one of the following: moving average model, autoregressive integral moving average model, or long short-term memory network. Through the control center t The actual instantaneous pollutant load rate monitoring value is calculated at time +1. L t+1 : Comparison with predicted values Compared with actual monitoring values L t+1 The mean absolute error ε and μ are used to calculate the prediction confidence coefficient μ=e -λε λ is the error sensitivity coefficient, with a value ranging from 0.5 to 1.0; Multiply by the predicted instantaneous rate of pollutant load k The baseline advance compensation amount is calculated, where, k The preset compensation coefficient ranges from 0.1 to 0.3. Multiply the baseline advance compensation amount by the prediction confidence coefficient to generate the effective advance compensation amount ΔE; Based on ΔE, the flocculant dosage is increased to 1 + ΔE times the original dosage; In the application of time-series prediction algorithms, the parameter collaborative controller is integrated with an adaptive weight model, based on the real-time calculated instantaneous rate of pollutant load. L t Predicted values ​​are generated through a built-in time series prediction algorithm. The time series is composed of L t The system consists of L data points from N consecutive historical time steps, where N can be set to 10-20, adjusted according to data stability. The prediction algorithm can be a moving average model, an autoregressive integral moving average model (ARIMA), or a long short-term memory network (LSTM). The window size for the moving average model can be set to 5-10 time steps. The controller hardware can be an industrial-grade programmable logic controller or an embedded computing unit, mounted in a cabinet in the control center. It connects to the adaptive weight model via a data interface. During operation, the system stores historical L data in the controller database, constructs an input matrix according to the time series, performs single-step prediction using the selected algorithm, and outputs the predicted value for the next acquisition time. The sampling time interval can be set to 10-15 minutes; In calculating the prediction confidence coefficient, the predicted values ​​are compared. Compared with actual monitoring values L t+1 The mean absolute error ε is calculated using the formula μ=e-λε The reliability coefficient μ is calculated, where λ is the error sensitivity coefficient, with a value ranging from 0.5 to 1.0. The specific value can be set according to the local water quality fluctuation characteristics. For example, in areas with large water quality fluctuations, λ is set to 0.8. The error calculation module can be integrated into the data processing unit of the controller to receive the predicted value and the actual monitoring value in real time. ε and μ are generated through arithmetic operations. During the operation, after the system obtains the actual monitoring value at time t+1, it calculates the absolute error between the two and takes the average. Substituting the average into the exponential function, μ is obtained. This coefficient is used to quantify the reliability of the prediction result and provide a weighting basis for the calculation of the compensation amount. In the mechanism for generating advance compensation and adjusting pesticide dosing, the benchmark advance compensation amount is generated from the predicted value. Multiply by the preset compensation coefficient k get, k The value range is 0.1 to 0.3, for example, taking... k =0.2, the effective advance compensation amount ΔE is the base advance compensation amount multiplied by μ, ultimately increasing the flocculant dosage to 1 + ΔE times the original dosage. The reagent dosing system can use a precision metering pump, paired with a polyethylene storage tank, installed next to the chemical flocculation treatment unit. The pump's flow rate adjustment range is 10-100 L / h. During operation, the controller generates a control signal based on ΔE to drive the metering pump to adjust the dosing rate. For example, if the original dosage is 30 L / h, when ΔE = 0.15, the new dosage is 30 × (1 + 0.15) = 34.5 L / h, compensation coefficient. k The setting method is based on historical data calibration, by comparing different k Considering the drug dosage effect and treatment cost under different values, select the optimal value; By combining time-series prediction algorithms with reliability coefficients, the system achieves advanced prediction of pollutant load changes, enabling the flocculant dosage to be adjusted in advance before a sudden increase in pollutant load. This mechanism effectively solves the problem of control lag in traditional systems, reduces fluctuations in treatment effect caused by load fluctuations, improves the accuracy of reagent dosing, reduces reagent consumption while ensuring treatment effect, and optimizes the economy and stability of system operation.

[0031] According to another implementation of the present invention, the effluent from the aerated biological filter system is treated by an ultrafiltration membrane and then enters a purified water storage tank. The urban road stormwater runoff classification and treatment system also includes: The global optimization module connects to the control center, the purified water storage tank, and the sewage treatment plant, and also integrates rainfall forecast information. It is configured as follows: Real-time acquisition of the current water level of the purified water storage tank, the real-time surplus treatment capacity of the sewage treatment plant, and the rainfall intensity; The first and second thresholds are dynamically adjusted based on the above parameters: When the real-time surplus treatment capacity of the wastewater treatment plant exceeds the preset capacity, a first threshold reduction instruction is generated, and the first threshold is multiplied by a reduction coefficient, where 0.7 ≤ reduction coefficient < 1.0. When the rainfall intensity exceeds the rainstorm threshold and the current water level of the purified water storage tank reaches or exceeds the water level warning value, a second threshold adjustment instruction is generated, and the second threshold is multiplied by the adjustment coefficient, where 1.0 < adjustment coefficient ≤ 1.5. For the effluent treatment of the aerated biological filter system, the effluent enters the purified water storage tank after being treated by an ultrafiltration membrane. The ultrafiltration membrane module can be a commercially available hollow fiber membrane system made of polyvinylidene fluoride (PVDF) or polyethersulfone (PES) with a pore size range of 0.01~0.1μm. It is installed on the effluent pipe of the aerated biological filter, 1~2 meters away from the filter outlet, and connected by a flange. The purified water storage tank can be made of reinforced concrete or stainless steel. A liquid level sensor is installed in the tank to monitor the real-time water level. The sensor is installed on the tank wall 0.5 meters from the bottom. During operation, the effluent from the aerated biological filter enters the ultrafiltration membrane module through the pipe and completes filtration under pressure. The permeate flows into the storage tank, and the concentrate is returned to the front-end treatment unit. The global optimization module connects to the control center, the purified water storage tank, and the sewage treatment plant, and also accesses rainfall forecast information. This module can use an industrial computer system, installed in the central control room, and communicates with each unit through a data interface. The hardware uses standard servers or edge computing devices, with an aluminum alloy chassis to ensure heat dissipation. The global optimization module connects to the sewage treatment plant's real-time surplus capacity interface, the storage tank's water level sensor, and the meteorological API. The global optimization module obtains the current water level of the storage tank (through the set liquid level sensor), the sewage treatment plant's real-time surplus treatment capacity (through the set sewage treatment plant data interface), and the rainfall intensity (through the meteorological API) in real time. The data is updated every 15 minutes to ensure timeliness. During operation, the module integrates the three data sources, generates threshold adjustment instructions through a built-in algorithm, and transmits them to the control center for execution. The conditions for dynamically adjusting the thresholds are as follows: When the real-time surplus treatment capacity of the sewage treatment plant is higher than the preset capacity, the first threshold is multiplied by the downward adjustment coefficient K1 (0.7 ≤ K1 < 1.0, for example, K1 = 0.8); when the rainfall intensity exceeds the heavy rain threshold (such as 50 mm / h) and the water level of the storage tank reaches the warning value (such as 80% of the design water level), the second threshold is multiplied by the upward adjustment coefficient K2 (1.0 < K2 ≤ 1.5, for example, K2 = 1.2). The preset capacity can be set to 30% of the design treatment capacity of the sewage treatment plant and is set in the module through the human-machine interface. The heavy rain threshold is determined based on local meteorological standards, and the warning value of the water level of the storage tank is 80% of the tank height. During operation, the module continuously judges the conditions and generates an instruction when the conditions are met. For example, when the surplus treatment capacity of the sewage treatment plant reaches 40%, the first threshold is lowered from 50 kg / h to 50 × 0.8 = 40 kg / h; when the rainfall intensity is 60 mm / h and the water level of the storage tank reaches 80%, the second threshold is raised from 10 kg / h to 10 × 1.2 = 12 kg / h. The adjusted thresholds are fed back to the shunt control module in real time; Through the global optimization module, the water level of the storage tank, the load of the sewage treatment plant, and the rainfall intensity are linked to achieve dynamic adjustment of the thresholds. When the surplus treatment capacity of the sewage treatment plant is high, the first threshold is lowered to increase the frequency of enhanced treatment and make full use of idle resources; when there is heavy rain and the water level of the storage tank exceeds the warning level, the second threshold is raised to reduce the amount of deep treatment water and relieve the overflow risk. This mechanism improves the adaptability of the system to external environmental changes, optimizes the allocation of treatment resources, ensures the stable operation of the system under different working conditions, and at the same time improves the efficiency of rainwater resource reuse.

[0032] According to another implementation manner of the present invention, it further includes: [[ID=​​​​​​​​​The performance monitoring module collects electrode loss rate and ultrafiltration membrane pressure difference in real time, and sets constraint threshold adjustment commands. It acquires the electrochemical degradation electrode loss rate and ultrafiltration membrane transmembrane pressure difference of the enhanced treatment unit in real time. The monitoring of electrochemical degradation electrode loss rate can use a resistive sensor to calculate the degree of loss by measuring the resistance change of the electrode material. The sensor is mounted on the electrode frame of the electrochemical degradation heavy metal treatment unit, 5-10 mm away from the electrode surface. The monitoring of ultrafiltration membrane transmembrane pressure difference can use a high-precision pressure sensor, which is installed on the inlet and outlet pipes of the ultrafiltration membrane module, within 1 meter of the membrane module to ensure measurement accuracy. The monitoring range of electrode loss rate can be set to 0-100%, and the pressure bearing threshold of ultrafiltration membrane transmembrane pressure difference can be set to 0.3 MPa. The sensor shell material can be made of 316L stainless steel, and the cable sheath can be made of waterproof fluororubber to ensure corrosion resistance and waterproof performance. During operation, the performance monitoring module collects data at a frequency of once per minute and transmits it to the state constraint layer through a 4-20mA current signal. The state constraint layer connects the performance monitoring module and the global optimization module, executing threshold adjustment commands. This layer can utilize an industrial-grade programmable logic controller (PLC), housed in a control center cabinet. It communicates with the performance monitoring and global optimization modules via a data interface. Upon receiving a first threshold reduction command from the global optimization module, the state constraint layer reads the current electrode wear rate. If the wear rate is greater than 30% (the upper limit), the reduction command is discarded, and an electrode wear warning is triggered. If the wear rate is less than or equal to 30%, the reduction command is allowed. Upon receiving a second threshold increase command, the state constraint layer reads the real-time transmembrane pressure difference of the ultrafiltration membrane. If the pressure difference is greater than 0.3 MPa (the critical pressure threshold), the increase is limited to ensure the adjusted second threshold does not exceed the initial threshold. If the pressure difference is less than or equal to 0.3 MPa, the command is allowed to be executed according to the increase coefficient. The 30% upper limit for electrode wear rate is determined based on the electrode material's lifespan curve through accelerated aging experiments. The transmembrane pressure difference critical value of 0.3 MPa is the recommended safe operating pressure limit by the ultrafiltration membrane manufacturer. By linking the performance monitoring module with the state constraint layer, a real-time feedback mechanism for hardware status and optimization commands is established. When the electrode loss rate exceeds the limit or the transmembrane pressure difference of the ultrafiltration membrane is too high, the state constraint layer intervenes in the threshold adjustment command in a timely manner to avoid overload damage to the electrochemical unit or integrity damage to the ultrafiltration membrane caused by overpressure. This mechanism ensures the safe operation of the equipment, guarantees the feasibility of the global optimization strategy, extends the service life of key equipment, and improves the reliability and stability of the system operation.

[0033] According to another implementation of the present invention, it further includes: Redundant offloading protection components include: The fault diagnosis module monitors the operating status of the controllable shunt module in real time. The bypass switching unit connects the inlet of the horizontal flow sedimentation tank to the end of the rainwater collection network. When the fault diagnosis module detects that the controllable diversion module has failed, it automatically activates the bypass switching unit to force all rainwater runoff into the horizontal sedimentation tank, and at the same time sends an equipment fault alarm to the control center. The redundant diversion protection component includes a fault diagnosis module (monitoring valve current / displacement) and a bypass switching unit (connecting the pipeline network and the horizontal sedimentation tank). The bypass switching valve uses a DN300-DN500 pneumatic gate valve with an action time ≤0.5 seconds. When a valve failure is detected, it forcibly switches to the horizontal sedimentation tank within 0.5 seconds and sends an alarm signal. The fault diagnosis module monitors the operating status of the controllable diversion module in real time. This module can use a current sensor or a displacement sensor to monitor the current signal or valve opening of the controllable diversion module (such as an electric ball valve). The sensor housing can be made of 316L stainless steel to ensure corrosion resistance. It is installed near the controllable diversion module in the diversion well, 10-20 cm away from the valve actuator, and connected to the control center via a shielded cable. The sampling frequency for fault diagnosis can be set to 10 times / second. When an abnormal current is detected (such as exceeding 120% of the rated current) or the valve opening feedback does not match the command (deviation exceeds 5%), it is determined that the controllable diversion module has failed. The specific value of the rated current is determined according to the model parameters of the selected valve. The valve opening deviation threshold of 5% is determined through debugging. The bypass switching unit connects the inlet of the horizontal flow sedimentation tank to the end of the rainwater collection network. When the fault diagnosis module detects a failure, it automatically activates and diverts all rainwater runoff into the horizontal flow sedimentation tank, while simultaneously sending an alarm signal. The bypass switching unit can be an electric butterfly valve or a pneumatic gate valve. The valve body material can be ductile iron, and the sealing ring can be made of EPDM rubber. It is installed on the connecting pipe between the inlet pipe of the horizontal flow sedimentation tank and the end of the rainwater network, 1 to 2 meters away from the inlet of the horizontal flow sedimentation tank. The valve diameter is determined according to the network flow rate, such as DN300 to DN500, and the action time is ≤0.5 seconds. During operation, the fault diagnosis module transmits the failure signal to the controller of the bypass switching unit. The controller drives the valve to fully open within 0.5 seconds and simultaneously sends an equipment fault alarm to the control center via wired or wireless communication. The alarm information includes the fault type and location. By linking the fault diagnosis module with the bypass switching unit, emergency diversion is achieved when the controllable diversion module fails. This mechanism ensures that the system can continue to operate when the equipment fails, avoids the direct discharge of rainwater without treatment, and triggers alarms to remind maintenance, reducing the risk of system failure, improving the overall reliability and safety of operation, and ensuring the continuity of urban road rainwater runoff treatment.

[0034] According to another implementation of the present invention, the adaptive weight model includes a sudden pollution identification layer, which performs the following: Real-time calculation of abrupt gradients in turbidity, COD, TOC, and heavy metal concentration in rainwater runoff output by multi-source spectral water quality monitoring instrument; When any of the above mutation gradients exceeds the maximum fluctuation threshold in the historical biotoxicity-related parameters, a spectral anomaly index is generated. The maximum fluctuation threshold is based on the 95th percentile value of historical data statistics. The dynamic weight allocation layer response spectrum anomaly index forces the calculation weight of the instantaneous rate of pollutant load to be allocated to real-time rainwater monitoring data, while ignoring rainfall forecast information and historical biotoxicity correlation parameters; If the instantaneous rate of pollutant load exceeds 80% of the first threshold at this time, the control center determines that... L >First threshold; Specifically, for the abrupt change gradient calculation, the sudden pollution identification layer of the adaptive weighted model calculates the abrupt change gradients of turbidity, COD, TOC, and heavy metal concentrations output by the multi-source spectral water quality monitor in real time. The abrupt change gradient can be obtained by dividing the difference between adjacent time steps by the time interval. For example, using a 1-minute time interval, the calculation formula is: Abrupt change gradient = | X t - X t-1 | / Δt, where X t The parameter value at the current time. X t-1 The parameter value is the value at the previous moment, and Δt is the time interval (1 minute). The maximum fluctuation threshold in the historical biotoxicity-related parameters is based on the 95th percentile value of historical data statistics. For example, by collecting data from the past 3 years, the 95th percentile value of the abrupt change gradient of each parameter is calculated as the threshold. The turbidity threshold may be 20 NTU / min, and the COD threshold may be 15 mg / L / min. The multi-source spectral water quality monitor is installed at the end of the rainwater collection pipe network. Its data output interface is connected to the sudden pollution identification layer module of the control center. The module can adopt an industrial-grade data processing unit and be installed in the control center cabinet. Regarding the generation and weight redistribution of the spectral anomaly index, a spectral anomaly index is generated when any abrupt gradient exceeds the corresponding maximum fluctuation threshold. The dynamic weight allocation layer responds to this index by forcibly allocating the instantaneous pollutant load calculation weight to real-time rainwater monitoring data, ignoring rainfall forecasts and historical biotoxicity parameters. The generation logic of the spectral anomaly index is as follows: when any parameter abrupt gradient exceeds the historical 95th percentile threshold, the spectral anomaly index = 1; otherwise, the spectral anomaly index = 0. The spectral anomaly index triggers dynamic weight redistribution. During weight redistribution, the weight of real-time monitoring data is set to 1, and the weights of other parameters are set to 0. The dynamic weight allocation layer can be used as an algorithm module within the control center, running on an industrial programmable logic controller or embedded system. It is connected to the adaptive weight model via a data bus. During operation, the system continuously monitors the abrupt gradient of each parameter, and triggers weight redistribution when the threshold is exceeded, ensuring that real-time data dominates the pollution load calculation. In the mandatory pollution level determination mechanism, if the instantaneous rate of pollutant load exceeds 80% of the first threshold, the control center determines that L > the first threshold. The first threshold can be set to 50 kg / h, and 80% of it is 40 kg / h. After the weight is redistributed, the system recalculates the L value. If L > 40 kg / h, it is directly determined to be a high pollution stage. The determination logic is executed by the decision module of the control center. This module can use an industrial-grade computer, which is installed in the central control room. It receives the weight allocation results and the calculated L value through a network interface. During operation, the module compares the L value with 40 kg / h in real time. When the condition is met, it sends a high pollution stage command to the diversion system. The response time is ≤3 seconds. By using abrupt gradient detection and forced weight redistribution, the system can quickly identify sudden pollution events such as industrial leaks and traffic accidents. When pollutant indicators suddenly become abnormal, the system prioritizes judging the degree of pollution based on real-time monitoring data, shortens the response time, ensures that highly polluted rainwater enters the enhanced treatment unit in a timely manner, avoids the impact of sudden pollution on the receiving water body, and improves the safety and reliability of the system in responding to emergency pollution events.

[0035] According to another implementation of the present invention, the global optimization module includes a deep processing allocation module, which performs the following: Real-time monitoring of the water level in the purified water storage tank and the transmembrane pressure difference of the ultrafiltration membrane; Constructing a water level-membrane pressure co-constraint function: When the water level in the regulating reservoir is greater than the warning water level and the transmembrane pressure difference of the ultrafiltration membrane is greater than 90% of the critical value, the control center dynamically raises the second threshold. The controllable diversion module distributes rainwater to the aerated biological filter system according to the formula. Q = β× ( S max - S 当前 Traffic limiting will be implemented, among which,Q To allow traffic splitting, β The attenuation coefficient is... S max To the maximum safe capacity of the storage tank, S 当前 Real-time water level; Excess rainwater is diverted to a horizontal flow sedimentation tank via a controllable diversion module. For data acquisition, the deep processing distribution module acquires the water level of the purified water storage tank and the transmembrane pressure difference of the ultrafiltration membrane in real time. Based on the water level-membrane pressure coordination function, the influent flow of the flow-limited aeration biological filter is used. For water level monitoring in the storage tank, an ultrasonic level sensor can be used, installed on the inner wall of the storage tank 0.5 meters from the bottom, with a measurement range of 0~10 meters. For transmembrane pressure difference monitoring of the ultrafiltration membrane, a high-precision pressure sensor can be used, installed on the inlet and outlet pipes of the ultrafiltration membrane module, within 1 meter of the membrane module, with a measurement range of 0~0.6MPa. The sensor shell material can be made of 316L stainless steel, and the cable sheath can be made of waterproof fluororubber. The deep processing distribution module can use an industrial-grade data acquisition unit, which is installed in the control center cabinet and communicates with the sensor via a 485 bus, with a data update frequency of once per minute. In the construction and threshold adjustment of the water level-membrane pressure co-constraint function, when the water level in the storage tank is greater than the warning water level (such as the maximum safe capacity of the storage tank), S max When the water level is 80% and the transmembrane pressure difference of the ultrafiltration membrane is greater than 90% of the critical value (e.g., 0.3 MPa) (i.e., 0.27 MPa), the control center dynamically raises the second threshold. The initial value of the second threshold is set to 10 kg / h, and the increase is determined according to the deviation between the water level and the membrane pressure difference, for example, 10% each time. The logic of the constraint function is executed by the built-in algorithm of the deep processing allocation module. This module can use an industrial computer, which is installed in the central control room and connected to the global optimization module through a data interface. During operation, the module continuously compares the water level and membrane pressure difference data. When the conditions are met, it generates a threshold raising instruction and transmits it to the control center for execution. In the rainwater diversion and flow restriction calculation and switching mechanism, the controllable diversion module allocates the amount of rainwater to the aerated biological filter according to the formula. Q = β× ( S max - S 当前 Traffic limiting will be implemented, among which, Q To allow traffic splitting, β This is the attenuation coefficient (values ​​range from 0.5 to 0.8, for example, 0.6). S 当前For real-time water level monitoring, excess rainwater is diverted to the horizontal flow sedimentation tank via a controllable diversion module. This module can be an electric butterfly valve, installed on the connecting pipe between the diversion well and the aerated biological filter. The valve body is made of ductile iron, and the drive motor response time is ≤10 seconds. During operation, the module adjusts the valve opening based on the calculated Q value. For example... S max =1000m 3 , S 当前 =850m 3 When β=0.6, Q=0.6×(1000-850)=90m 3 / h, the excess portion is directed to the horizontal flow sedimentation tank. By using a water level-membrane pressure co-constraint function and a flow rate calculation formula, dynamic flow restriction of the influent to the aerated biological filter is achieved. When the water level in the regulating tank is too high and the transmembrane pressure difference of the ultrafiltration membrane approaches the critical value, the system automatically raises the second threshold and restricts the flow rate to prevent the ultrafiltration membrane from operating under overpressure conditions. At the same time, excess rainwater is switched to the horizontal flow sedimentation tank to balance the load of each treatment unit. This mechanism effectively reduces the membrane fouling rate, extends the service life of the ultrafiltration membrane, ensures the stable operation of the system under extreme conditions such as heavy rain, and improves the overall treatment efficiency and safety.

[0036] A method for treating urban road stormwater runoff classification and treatment system is provided, comprising the following steps: S1. Real-time monitoring and data acquisition: The turbidity, COD, TOC and heavy metal concentration of rainwater runoff are monitored in real time by a multi-source spectral water quality monitor installed at the end of the rainwater collection pipe network. Synchronously access real-time rainfall intensity from rainfall forecast information; S2. Dynamically calculate the instantaneous rate of pollutant load L: The real-time rainfall monitoring data, rainfall intensity, and historical biotoxicity-related parameters were standardized and outliers were removed. Dynamically adjusting weight coefficients based on a Bayesian inference framework: Set initial weights for real-time monitoring data, rainfall intensity, and historical biotoxicity parameters; Based on the current rainfall intensity R t Rainfall intensity similar to historical rainfall events R h The absolute difference Δ R and historical rainfall intensity standard deviation s Calculate the matching factor ; Using formula The initial weighting coefficients of historical biotoxicity parameters are adjusted, where, The weighting coefficients for historical biotoxicity parameters at the current moment. These are the weighting coefficients of historical biotoxicity parameters from the previous time step. α For the preset correction factor of historical biotoxicity parameters, | α |≤0.3, when Δ R ≤ s hour α >0, when ΔR> s hour α <0; The weighting coefficients of rainwater monitoring, rainfall intensity, and historical biotoxicity parameters are scaled proportionally to a sum of 1. The instantaneous rate of pollutant load L is calculated using a weighted summation formula: ,in: w i The weight coefficient of the i-th data source is dynamically adjusted. f i ( x i Let be the normalized data mapping function for the i-th data source, and n be the total number of data sources; S3, Hierarchical diversion control: When L > the first threshold, the rainwater is allocated to the enhanced treatment unit, where it undergoes vortex sedimentation treatment, electrochemical degradation of heavy metals treatment, and chemical flocculation treatment in sequence. When L < the second threshold, the rainwater is distributed to the aerated biological filter system for treatment. The effluent from the aerated biological filter system is then treated by an ultrafiltration membrane and fed into the purified water storage tank. When the second threshold ≤ L ≤ the first threshold, the rainwater will be distributed to the horizontal sedimentation tank for treatment. S4. Dynamically optimized threshold: Real-time acquisition of the current water level of the purified water storage tank, the real-time surplus treatment capacity of the sewage treatment plant, and the rainfall intensity; The first and second thresholds are dynamically adjusted based on the following conditions: When the real-time surplus treatment capacity of the wastewater treatment plant exceeds the preset capacity, a first threshold reduction instruction is generated: the first threshold is multiplied by a reduction coefficient, where 0.7 ≤ reduction coefficient < 1.0; When the rainfall intensity exceeds the rainstorm threshold and the current water level of the purified water storage tank reaches or exceeds the water level warning value, a second threshold adjustment instruction is generated: the second threshold is multiplied by the adjustment coefficient, where 1.0 < adjustment coefficient ≤ 1.5; The updated first and second thresholds are applied in real time to the hierarchical diversion control in step S3; Among them, real-time monitoring and data acquisition: A multi-source spectral water quality monitor, installed 1 meter from the outlet at the end of the rainwater collection network, collects real-time data on turbidity, COD, TOC, and heavy metal concentrations of rainwater runoff. This monitor can be a commercially available device integrating an optical probe, ultraviolet absorption module, and electrochemical sensor. The casing is made of 316L stainless steel or IP67 waterproof engineering plastic to ensure corrosion resistance and waterproof performance. Monitoring data is transmitted to the control center in 2-minute intervals via a 4-20mA current signal or wireless communication. Simultaneously, the control center accesses rainfall forecast information in real-time through a public meteorological service API to obtain real-time rainfall intensity data, updating every 15 minutes. The control center can utilize an industrial programmable logic controller (PLC) or an embedded microprocessor system, installed in the central control room, to ensure the real-time performance and stability of data processing. Dynamically calculate the instantaneous rate L of pollutant load: First, the real-time rainfall monitoring data, rainfall intensity, and historical biotoxicity correlation parameters were standardized using a minimum-maximum normalization formula. ,in x min and x max Based on local historical datasets (e.g., turbidity range 0~1000 NTU), outliers were removed using the 3σ principle. Weighting coefficients were dynamically adjusted using a Bayesian inference framework: an initial weight of 0.5 for real-time monitoring data, 0.3 for rainfall intensity, and 0.2 for historical biotoxicity parameters; weighting was adjusted according to the current rainfall intensity. R t Rainfall intensity similar to historical levels R h The absolute difference ΔR and historical standard deviation σ are obtained through the formula Calculate the matching factor; when ΔR≤σ, the historical biotoxicity parameter correction coefficient α is taken as 0.2, and when ΔR>σ, α is taken as -0.15, using the formula... Adjust the weights, and finally scale them proportionally until the total weight sum is 1, using a weighted summation formula. Calculate the instantaneous rate L of pollutant load, where w i The weights are dynamically adjusted. f i ( x i ) is the standardized mapping function, n=3, Hierarchical flow control and dynamic threshold optimization: When L > 50 kg / h, the controllable diversion module distributes rainwater to the enhanced treatment unit, where it undergoes cyclone sedimentation, electrochemical degradation of heavy metals, and chemical flocculation treatment in sequence. When L < 10 kg / h, it is distributed to the aerated biological filter system, and the effluent is treated by ultrafiltration membrane before being sent to the purified water storage tank. When 10 kg / h ≤ L ≤ 50 kg / h, it is distributed to the horizontal sedimentation tank. The controllable diversion module in the diversion well can use an electric ball valve or a pneumatic butterfly valve, installed at the branch nodes of the rainwater pipe network, and connected to the control system via narrowband IoT. When the central connection dynamically optimizes the threshold, it obtains the water level of the regulating tank in real time (monitored by an ultrasonic level sensor installed on the tank wall 0.5 meters from the bottom), the real-time surplus treatment capacity of the sewage treatment plant, and the rainfall intensity: when the surplus treatment capacity of the sewage treatment plant is higher than 30% of the design capacity, the first threshold is lowered by multiplying by 0.8; when the rainfall intensity exceeds 50 mm / h and the water level of the regulating tank reaches 80% of the warning value, the second threshold is raised by multiplying by 1.2. The updated threshold is applied to the diversion control module in real time to ensure that the diversion strategy adapts to changes in operating conditions. This treatment method achieves precise control of rainwater classification treatment through real-time monitoring of multi-source data, dynamic weight calculation, and threshold optimization. It can effectively cope with rainwater runoff of different pollution levels, improve the efficiency of rainwater resource reuse, and optimize the energy consumption and reagent consumption of the treatment system while ensuring the safety of receiving water bodies.

[0037] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A graded treatment system for urban road stormwater runoff, characterized in that, include: A multi-source spectral water quality monitor is installed at the end of the rainwater collection pipe network to monitor the turbidity, COD, TOC and heavy metal concentration of rainwater runoff in real time. The control center receives rainwater monitoring data from the multi-source spectral water quality monitor, accesses rainfall intensity data from rainfall forecast information, and calls up the historical biotoxicity database. The control center uses a weighted summation formula to calculate the instantaneous rate of pollutant load. L The formula is: ,in, w i For the first i The weighting coefficients of each parameter, f i ( x i ) is the first i A standardized data mapping function with one parameter. n The parameters include real-time monitored turbidity, COD, TOC, heavy metal concentration, rainfall intensity, and historical biotoxicity data. Multiple diversion wells are set at the branch nodes of the rainwater collection pipe network. Each diversion well is equipped with a controllable diversion module. The controllable diversion module is connected to the command output terminal of the control center and performs rainwater distribution operations according to the hierarchical commands of the control center. The enhanced treatment unit has its inlet connected to the first outlet of the diversion well, and its interior is sequentially equipped with a vortex sedimentation treatment unit, an electrochemical heavy metal degradation treatment unit, and a chemical flocculation treatment unit. The aerated biological filter system has its inlet connected to the second outlet of the diversion well; The horizontal flow sedimentation tank has its inlet connected to the third outlet of the diversion well; Among them, when L When the first threshold is reached, the controllable diversion module distributes rainwater to the enhanced treatment unit; when L When the second threshold is reached, the controllable diversion module distributes rainwater to the aerated biological filter system. When the second threshold ≤ L When the water level is ≤ the first threshold, the controllable diversion module will distribute the rainwater to the horizontal sedimentation tank; The control center includes an adaptive weighting model, which dynamically adjusts the weighting coefficients of rainfall monitoring, rainfall intensity, and historical biotoxicity parameters based on a Bayesian inference framework. The dynamic adjustment process includes: Set initial weighting coefficients for real-time rainfall monitoring, rainfall intensity, and historical biotoxicity parameters; Based on the current rainfall intensity R t Rainfall intensity similar to historical rainfall events R h The absolute difference Δ R and historical rainfall intensity standard deviation σ Calculate the matching factor ; Using formula The initial weighting coefficients of historical biotoxicity parameters are adjusted, where, The weighting coefficients for historical biotoxicity parameters at the current moment. These are the weighting coefficients of historical biotoxicity parameters from the previous time step. α For the preset correction factor of historical biotoxicity parameters, | α |≤0.3, when Δ R ≤ σ hour α >0, when ΔR> σ hour α <0; The weighting coefficients of rainwater monitoring, rainfall intensity, and historical biotoxicity parameters are scaled proportionally to a sum of 1.

2. The urban road stormwater runoff classification and treatment system as described in claim 1, characterized in that, It also includes a collaborative control component, which includes: The heavy metal concentration monitoring module is located at the effluent end of the electrochemical heavy metal degradation treatment unit to detect the residual heavy metal concentration in real time. The parameter coordination controller is connected to the heavy metal concentration monitoring module and the reagent dosing system in the chemical flocculation treatment unit. The parameter coordination controller receives residual heavy metal concentration data and executes a graded control strategy. When the concentration of residual heavy metals is ≤ the safety threshold, the dosage of chemical flocculant is maintained in the dosing system; When the concentration of residual heavy metals exceeds the safety threshold, the parameter coordination controller performs the following operations: Calculate the concentration Δ of residual heavy metals exceeding the safety threshold. C According to Δ C Within the preset concentration range, select the corresponding dosage increase ratio. m %, of which the preset concentration range and m The correspondence of % is as follows: i) If 0 μg / L < ΔC ≤ 5 μg / L, then m %=10%; ii) If 5 μg / L < ΔC ≤ 10 μg / L, then m %=20%; iii) If 10 μg / L < ΔC ≤ 20 μg / L, then m %=40%; iv) If ΔC > 20 μg / L, then m %=60%; Increase the flocculant dosage to 1+ of the original dosage. m % times.

3. The urban road stormwater runoff classification and treatment system as described in claim 2, characterized in that, The parameter-coordinated controller is simultaneously connected to the adaptive weight model. Before executing the hierarchical control strategy, the parameter-coordinated controller adjusts the flocculant dosage based on the instantaneous pollutant load rate. The adaptive weight model is configured to execute: Instantaneous pollutant load rate based on real-time calculation L t The instantaneous rate prediction of pollutant load is generated through a built-in time-series prediction algorithm. : Will L t With history N The instantaneous rate data of pollutant load at consecutive time steps constitute a time series; A time-series prediction algorithm is used to perform single-step prediction of the sequence, and the predicted value for the next acquisition time is output. The time series prediction algorithm can be any one of the following: moving average model, autoregressive integral moving average model, or long short-term memory network. Through the control center t The actual instantaneous pollutant load rate monitoring value is calculated at time +1. L t+1 : Comparison with predicted values Compared with actual monitoring values L t+1 The mean absolute error ε and μ are used to calculate the prediction confidence coefficient μ=e -λε λ is the error sensitivity coefficient, with a value ranging from 0.5 to 1.0; Multiply by the predicted instantaneous rate of pollutant load k The baseline advance compensation amount is calculated, where, k The preset compensation coefficient ranges from 0.1 to 0.

3. Multiply the baseline advance compensation amount by the prediction confidence coefficient to generate the effective advance compensation amount ΔE; Based on ΔE, the flocculant dosage is increased to 1+ΔE times the original dosage.

4. The urban road stormwater runoff classification and treatment system as described in claim 1, characterized in that, The effluent from the aerated biological filter system is treated by an ultrafiltration membrane and then enters the purified water storage tank. The urban road stormwater runoff classification and treatment system also includes: The global optimization module connects to the control center, the purified water storage tank, and the sewage treatment plant, and also integrates rainfall forecast information. It is configured as follows: Real-time acquisition of the current water level of the purified water storage tank, the real-time surplus treatment capacity of the sewage treatment plant, and the rainfall intensity; The first and second thresholds are dynamically adjusted based on the above parameters: When the real-time surplus treatment capacity of the wastewater treatment plant exceeds the preset capacity, a first threshold reduction instruction is generated, and the first threshold is multiplied by a reduction coefficient, where 0.7 ≤ reduction coefficient < 1.

0. When the rainfall intensity exceeds the rainstorm threshold and the current water level of the purified water storage tank reaches or exceeds the water level warning value, a second threshold adjustment instruction is generated, and the second threshold is multiplied by the adjustment coefficient, where 1.0 < adjustment coefficient ≤ 1.

5.

5. The urban road stormwater runoff classification and treatment system as described in claim 4, characterized in that, Also includes: The performance monitoring module acquires the electrochemical degradation electrode loss rate and ultrafiltration membrane transmembrane pressure difference of the enhanced treatment unit in real time. The state constraint layer, connecting the performance monitoring module and the global optimization module, is configured to execute: Receive threshold adjustment instructions generated by the global optimization module; If a first threshold reduction instruction is received and the current electrode loss rate is greater than the upper limit of the loss rate, the reduction instruction will be discarded and an electrode loss warning will be triggered. Otherwise, the downward adjustment instruction can be executed; If a command to increase the second threshold is received, and the real-time transmembrane pressure difference of the ultrafiltration membrane is greater than the pressure critical value, the increase range is limited so that the adjusted second threshold does not exceed the initial threshold; otherwise, the command is allowed to be executed according to the increase coefficient.

6. The urban road stormwater runoff classification and treatment system as described in claim 1, characterized in that, Also includes: Redundant offloading protection components include: The fault diagnosis module monitors the operating status of the controllable shunt module in real time. The bypass switching unit connects the inlet of the horizontal flow sedimentation tank to the end of the rainwater collection network. When the fault diagnosis module detects that the controllable diversion module has failed, it automatically activates the bypass switching unit to force all rainwater runoff into the horizontal sedimentation tank, and at the same time sends an equipment fault alarm to the control center.

7. The urban road stormwater runoff classification and treatment system as described in claim 1, characterized in that, The adaptive weight model includes a sudden pollution identification layer, which performs the following: Real-time calculation of abrupt gradients in turbidity, COD, TOC, and heavy metal concentration in rainwater runoff output by multi-source spectral water quality monitoring instrument; When any of the above mutation gradients exceeds the maximum fluctuation threshold in the historical biotoxicity-related parameters, a spectral anomaly index is generated. The maximum fluctuation threshold is based on the 95th percentile value of historical data statistics. The dynamic weight allocation layer response spectrum anomaly index forces the calculation weight of the instantaneous rate of pollutant load to be allocated to real-time rainwater monitoring data, while ignoring rainfall forecast information and historical biotoxicity correlation parameters; If the instantaneous rate of pollutant load exceeds 80% of the first threshold at this time, the control center determines that... L > First threshold.

8. The urban road stormwater runoff classification and treatment system as described in claim 4, characterized in that, The global optimization module includes a depth processing allocation module, which executes as follows: Real-time monitoring of the water level in the purified water storage tank and the transmembrane pressure difference of the ultrafiltration membrane; Constructing a water level-membrane pressure co-constraint function: When the water level in the regulating reservoir is greater than the warning water level and the transmembrane pressure difference of the ultrafiltration membrane is greater than 90% of the critical value, the control center dynamically raises the second threshold. The controllable diversion module distributes rainwater to the aerated biological filter system according to the formula. Q = β× ( S max - S 当前 Traffic limiting will be implemented, among which, Q To allow traffic splitting, β The attenuation coefficient is... S max To the maximum safe capacity of the storage tank, S 当前 Real-time volume; Excess rainwater is diverted to a horizontal flow sedimentation tank via a controllable diversion module.

9. The treatment method based on the urban road stormwater runoff classification and treatment system according to claim 4, characterized in that, Includes the following steps: S1. Real-time monitoring and data acquisition: The turbidity, COD, TOC and heavy metal concentration of rainwater runoff are monitored in real time by a multi-source spectral water quality monitor installed at the end of the rainwater collection pipe network. Synchronously access real-time rainfall intensity from rainfall forecast information; S2. Dynamically calculate the instantaneous rate of pollutant load L: The real-time rainfall monitoring data, rainfall intensity, and historical biotoxicity-related parameters were standardized and outliers were removed. Dynamically adjusting weight coefficients based on a Bayesian inference framework: Set initial weights for real-time monitoring data, rainfall intensity, and historical biotoxicity parameters; Based on the current rainfall intensity R t Rainfall intensity similar to historical rainfall events R h The absolute difference Δ R and historical rainfall intensity standard deviation σ Calculate the matching factor ; Using formula The initial weighting coefficients of historical biotoxicity parameters are adjusted, where, The weighting coefficients for historical biotoxicity parameters at the current moment. These are the weighting coefficients of historical biotoxicity parameters from the previous time step. α For the preset correction factor of historical biotoxicity parameters, | α |≤0.3, when Δ R ≤ σ hour α >0, when ΔR> σ hour α <0; The weighting coefficients of rainwater monitoring, rainfall intensity, and historical biotoxicity parameters are scaled proportionally to a sum of 1. The instantaneous rate of pollutant load L is calculated using a weighted summation formula: ,in: w i The weight coefficient of the i-th data source is dynamically adjusted. f i ( x i Let be the normalized data mapping function for the i-th data source, and n be the total number of data sources; S3, Hierarchical diversion control: When L > the first threshold, the rainwater is allocated to the enhanced treatment unit, where it undergoes vortex sedimentation treatment, electrochemical degradation of heavy metals treatment, and chemical flocculation treatment in sequence. When L < the second threshold, the rainwater is distributed to the aerated biological filter system for treatment. The effluent from the aerated biological filter system is then treated by an ultrafiltration membrane and fed into the purified water storage tank. When the second threshold ≤ L ≤ the first threshold, the rainwater will be distributed to the horizontal sedimentation tank for treatment. S4. Dynamically optimized threshold: Real-time acquisition of the current water level of the purified water storage tank, the real-time surplus treatment capacity of the sewage treatment plant, and the rainfall intensity; The first and second thresholds are dynamically adjusted based on the following conditions: When the real-time surplus treatment capacity of the wastewater treatment plant exceeds the preset capacity, a first threshold reduction instruction is generated: the first threshold is multiplied by a reduction coefficient, where 0.7 ≤ reduction coefficient < 1.0; When the rainfall intensity exceeds the rainstorm threshold and the current water level of the purified water storage tank reaches or exceeds the water level warning value, a second threshold adjustment instruction is generated: the second threshold is multiplied by the adjustment coefficient, where 1.0 < adjustment coefficient ≤ 1.5; The updated first and second thresholds are applied in real time to the hierarchical diversion control in step S3.

Images