A biological remediation analysis system based on sewage algal data

Abstract

This invention discloses a biological treatment analysis system based on wastewater algae data. This invention relates to the field of intelligent wastewater treatment technology, constructing a quantitative mapping relationship between algal physiology and wastewater pollution indicators, and building a full-link closed-loop analysis and control system to achieve proactive, precise, and intelligent control of wastewater algae treatment. The advantages of this invention are: by constructing a dedicated quantitative mapping analysis logic between core algal physiological indicators and core wastewater pollution indicators, it abandons the conventional processing method of existing technologies that only perform simple post-event statistical display of collected data and cannot establish the intrinsic correlation between the two types of indicators. This quantitative mapping relationship is used as the core judgment basis for system operation, directly achieving precise matching between algal physiological state and wastewater treatment needs. It fundamentally changes the passive response mode of existing algae treatment technologies, which can only provide post-event remediation, and solves the long-standing problems in this field of unstable treatment efficiency and the tendency of algal activity imbalance to cause secondary pollution.

Description

Technical Field

[0001] This invention relates to the field of intelligent wastewater treatment technology, specifically to a biological treatment analysis system based on wastewater algae data. Background Technology

[0002] With the continuous advancement of ecological civilization construction in my country, comprehensive wastewater treatment and aquatic ecological restoration have become core tasks in the field of ecological environmental protection. Currently, the demand for treatment of various types of water pollution in my country, including urban domestic sewage, industrial wastewater, eutrophication of landscape water bodies, and agricultural and aquaculture wastewater, continues to grow. The industry's demand for research and application of green, low-carbon, and sustainable wastewater treatment technologies is also increasing. Algae, as a type of photosynthetic autotrophic microorganism, possess the ability to efficiently assimilate and absorb nitrogen and phosphorus nutrients, adsorb and enrich heavy metal ions, and degrade recalcitrant organic pollutants extracellularly. Compared with traditional wastewater treatment technologies such as physical sedimentation, chemical oxidation, and activated sludge processes, algal biological treatment technology has significant advantages such as low treatment cost, strong environmental friendliness, no secondary pollution, and simultaneous resource utilization of pollutants. It has been widely studied and applied in various scenarios, including deep treatment of urban wastewater, purification of industrial organic wastewater, ecological restoration of eutrophic water bodies, and harmless treatment of aquaculture wastewater, becoming a core technology direction with great development potential in the field of green wastewater treatment.

[0003] Existing wastewater algae biological treatment and control systems mainly achieve biological purification of wastewater through algae cultivation and treatment devices with fixed operating parameters by monitoring conventional water quality indicators. This approach has certain shortcomings. First, the system can only perform simple post-event statistical displays of the collected basic data, failing to establish an intrinsic correlation between core algal physiological indicators and wastewater pollution indicators. It remains in a passive response state after pollution exceeds standards, easily leading to unstable treatment efficiency and secondary pollution caused by algal activity imbalance. Second, the system's data collection, analysis, and control processes are fragmented, lacking a complete closed-loop operating logic and exhibiting poor scenario adaptability. It cannot ensure that treatment parameters continuously reflect actual changes in on-site water quality and algal growth status. Therefore, we propose a wastewater algae-based data-driven biological treatment analysis system. Summary of the Invention

[0004] The purpose of this invention is to provide a biological treatment and analysis system based on wastewater algae data.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a biological treatment and analysis system based on wastewater algae data, the biological treatment and analysis system comprising the following modules:

[0006] Algal physiological data acquisition module: used to collect real-time data on core physiological indicators of algae in wastewater, such as photosynthetic activity and community structure;

[0007] Wastewater pollutant data acquisition module: used to collect real-time data on core pollution indicators such as pollutant concentration and pollutant degradation rate in wastewater;

[0008] Data preprocessing module: Used to receive raw data transmitted from algae physiological data acquisition module and sewage pollutant data acquisition module, and to screen and denoise the raw data to obtain standardized data;

[0009] Quantitative mapping analysis module: This module receives standardized data transmitted from the data preprocessing module, establishes a quantitative mapping relationship between core physiological indicators of algae and core pollution indicators of wastewater, and analyzes the matching results between the physiological state of algae and the needs of wastewater treatment based on this mapping relationship.

[0010] Governance Parameter Decision Module: This module receives the matching results transmitted from the quantitative mapping analysis module and determines specific control schemes for governance parameters such as light intensity, nutrient dosage, and hydraulic retention time based on the matching results.

[0011] Control command issuance module: Used to receive the control plan transmitted by the governance parameter decision module, convert it into executable control commands and issue them;

[0012] Treatment execution module: Used to receive control commands transmitted by the control command sending module, execute corresponding control operations, and realize precise control of the biological treatment process of wastewater algae;

[0013] Data storage and feedback module: This module receives standardized data from the data preprocessing module and control result data from the governance execution module, stores the data, and feeds back the control results to the quantitative mapping analysis module to optimize the quantitative mapping relationship.

[0014] As a further aspect of the present invention: the algal physiological data acquisition module includes a photosynthetic activity sensor and a community structure monitor. The photosynthetic activity sensor is used to collect chlorophyll fluorescence parameters and photosynthetic rate data of algae. The community structure monitor is used to collect species composition ratio and abundance data of dominant species of algae. Both the photosynthetic activity sensor and the community structure monitor are deployed in the algal aggregation layer of the sewage treatment area, and the acquisition frequency is once every 5-10 minutes.

[0015] As a further aspect of the present invention: the wastewater pollutant data acquisition module includes a water quality sensor group and a degradation rate monitoring unit. The water quality sensor group is used to collect data on the concentrations of chemical oxygen demand, ammonia nitrogen, total phosphorus, and heavy metal ions in wastewater. The degradation rate monitoring unit is used to calculate the pollutant degradation rate data based on the following formula by continuously monitoring the concentration difference data of the same pollutant at different time points:

[0016] ;

[0017] in, The pollutant degradation rate is expressed in mg / (L·h). For pollutants in the initial time Concentration data at the time, in mg / L. For pollutants in subsequent time Concentration data at the time, in mg / L. This is the initial monitoring time point. For subsequent monitoring time points, and > The time interval is set to 1-2 hours, and the monitoring points of the water quality sensor group correspond one-to-one with the collection points of the algae physiological data acquisition module.

[0018] As a further aspect of the present invention: the data preprocessing module first uses the Raida criterion to remove outliers in the original data, then uses linear interpolation to supplement missing data values, and finally uses the Z-score standardization method to transform physiological index data and pollution index data of different dimensions into standardized data of the same dimension. The range of values ​​for the standardized data is [0,1].

[0019] As a further aspect of the present invention: the quantization mapping relationship established by the quantization mapping analysis module is calculated based on the following formula:

[0020] ;

[0021] in, This represents the comprehensive physiological activity value of algae, with a value range of [0,1]. For standardized photosynthetic activity index data, For standardized community structure index data, , represents the weighting coefficient for the photosynthetic activity index. is the weighting coefficient of the community structure index, and , The value range is 0.6-0.7. The value range is 0.3-0.4, and the weighting coefficient is calibrated by combining the analytic hierarchy process with the actual scenario of sewage treatment.

[0022] ;

[0023] in, The degree of matching between the physiological state of algae and the needs of wastewater treatment is represented by a value ranging from [0,1]. For standardized pollutant concentration data, For standardized pollutant degradation rate data, The correction coefficient for pollutant degradation rate ranges from 0.2 to 0.3. This correction coefficient is determined based on experimental data fitting according to the wastewater type (domestic sewage, industrial wastewater). The quantitative mapping analysis module calculates the comprehensive value of algal physiological activity. Then calculate the governance matching degree. ,Will This is a result of matching the physiological state of algae with the needs of wastewater treatment.

[0024] As a further aspect of the present invention: the logic of the governance parameter decision module in determining the control scheme is as follows: a preset governance matching degree threshold range is defined, and when the governance matching degree... When ≥0.8, the current governance parameters are maintained unchanged; when 0.5≤ When <0.8, according to The specific values ​​are to increase light intensity and nutrient dosage proportionally, with the light intensity increase ratio being (0.8-). ()×20%, the increase in nutrient salt dosage is (0.8-) )×15%, when When the light intensity is less than 0.5, in addition to increasing the light intensity and nutrient dosage according to the above proportions, the hydraulic retention time should be extended simultaneously, with an extension ratio of (0.5- The treatment parameters include light intensity, nutrient dosage, and hydraulic retention time, with the parameter adjustment priority being: light intensity > nutrient dosage > hydraulic retention time.

[0025] As a further aspect of the present invention: the control command issuing module uses the Modbus-RTU communication protocol to transmit control commands. During the command transmission process, the CRC32 check algorithm is used to verify the data to ensure the accuracy of the command transmission. The control command includes parameter type, target value, execution time, and check code field. The execution time is within 1-3 minutes after the command is issued, and the transmission status (success / failure) is fed back to the governance parameter decision module in real time after the command is issued. If the transmission fails, it is reissued within 30 seconds, and the number of reissues does not exceed 3 times.

[0026] As a further embodiment of the present invention: the treatment execution module includes a light adjustment unit, a nutrient salt dosing unit, and a hydraulic control unit. The light adjustment unit is an adjustable power LED light source group with a wavelength range of 450-660nm. The light intensity is adjusted by changing the working power of the LED light source, and the light intensity adjustment range is 2000-8000 lux. The nutrient salt dosing unit is a quantitative dosing pump with a dosing accuracy of ≤±1%. It is used to accurately add nitrogen source (urea) and phosphorus source (potassium dihydrogen phosphate) nutrients according to the control instructions, and the dosing amount adjustment range is 0.1-5L / h. The hydraulic control unit is a variable frequency water pump with a frequency range of 5-50Hz. It is used to adjust the sewage circulation rate according to the control instructions, thereby changing the hydraulic retention time, and the hydraulic retention time adjustment range is 4-24h.

[0027] As a further aspect of the present invention: the data storage and feedback module uses a distributed database to store data, including raw collected data, standardized data, governance matching degree calculation results, control schemes, and control result data, with a storage time of no less than one year. The feedback mechanism is as follows: when the control results of the governance execution module show that the pollutant degradation rate increases by less than 10%, the data storage and feedback module triggers a feedback signal, and the quantitative mapping analysis module recalibrates the weight coefficients based on the feedback signal. , and correction factor The recalibration method is as follows: ×(1+0.05× ), , ,in, To adjust the difference between the degradation rates of pollutants before and after regulation and the ratio of the degradation rate before regulation, the quantitative mapping relationship is continuously optimized.

[0028] Compared with the prior art, the beneficial effects of the present invention by adopting the above technical solution are as follows:

[0029] 1. This invention constructs a dedicated quantitative mapping analysis logic between core physiological indicators of algae and core pollution indicators of wastewater. It abandons the conventional processing method of existing technologies that only perform simple post-event statistical display of collected data and cannot establish the intrinsic relationship between the two types of indicators. This quantitative mapping relationship is used as the core judgment basis for system operation, directly realizing the precise matching between the physiological state of algae and the needs of wastewater treatment. It fundamentally changes the passive response mode of existing algae treatment technologies that can only be remedied after the fact. It solves the long-standing problems in the field of unstable treatment efficiency, algal activity imbalance that easily leads to secondary pollution, and the disconnect between treatment strategies and actual needs. It enables the biological treatment of wastewater algae from passive monitoring to active and precise regulation. The entire treatment process is controllable and traceable, and the operational risks brought about by human experience intervention are greatly reduced.

[0030] 2. This invention breaks through the limitations of existing technologies where data collection, analysis, and control are fragmented and lack a complete closed-loop operating logic by establishing a full-link collaborative system architecture encompassing data acquisition, quantitative modeling, intelligent decision-making, closed-loop control, and feedback optimization. Relying on a real-time feedback mechanism of treatment results, it continuously optimizes the core quantitative mapping analysis logic, enabling the system to dynamically adapt to changes in the operating environment for different wastewater types and algae growth stages. This solves the industry problem of poor adaptability and inability of treatment parameters to continuously match actual changes in the field when applying different scenarios, achieving intelligent closed-loop management of the entire wastewater algae biological treatment process. While ensuring stable pollutant treatment effects, it significantly improves the system's environmental adaptability and long-term operational stability, while simultaneously optimizing treatment efficiency and operating costs. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the system flow in an embodiment of the present invention. Detailed Implementation

[0032] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0033] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0034] Please see the appendix Figure 1 This invention discloses a biological treatment and analysis system based on wastewater algae data. The biological treatment and analysis system includes the following modules:

[0035] Algal physiological data acquisition module: used to collect real-time data on core physiological indicators of algae in wastewater, such as photosynthetic activity and community structure;

[0036] Wastewater pollutant data acquisition module: used to collect real-time data on core pollution indicators such as pollutant concentration and pollutant degradation rate in wastewater;

[0037] Data preprocessing module: Used to receive raw data transmitted from algae physiological data acquisition module and sewage pollutant data acquisition module, and to screen and denoise the raw data to obtain standardized data;

[0038] Quantitative mapping analysis module: This module receives standardized data transmitted from the data preprocessing module, establishes a quantitative mapping relationship between core physiological indicators of algae and core pollution indicators of wastewater, and analyzes the matching results between the physiological state of algae and the needs of wastewater treatment based on this mapping relationship.

[0039] Governance Parameter Decision Module: This module receives the matching results transmitted from the quantitative mapping analysis module and determines specific control schemes for governance parameters such as light intensity, nutrient dosage, and hydraulic retention time based on the matching results.

[0040] Control command issuance module: Used to receive the control plan transmitted by the governance parameter decision module, convert it into executable control commands and issue them;

[0041] Treatment execution module: Used to receive control commands transmitted by the control command sending module, execute corresponding control operations, and realize precise control of the biological treatment process of wastewater algae;

[0042] Data storage and feedback module: This module receives standardized data from the data preprocessing module and control result data from the governance execution module, stores the data, and feeds back the control results to the quantitative mapping analysis module to optimize the quantitative mapping relationship.

[0043] Example 1

[0044] This embodiment is designed for urban domestic sewage treatment, with a treatment capacity of 500m³. 3 / d, the influent water quality is: COD 200-250mg / L, ammonia nitrogen 25-30mg / L, total phosphorus 3-4mg / L, water temperature 15-25℃, and the selected algae is a composite species of Chlorella and Scenedesmus, with an initial inoculation density of 5×10 6 cells / mL.

[0045] This embodiment uses the wastewater algae-based data biological treatment and analysis system described in this invention. The parameter settings for each module are as follows:

[0046] Algal physiological data acquisition module: A photosynthetic activity sensor and a community structure monitor are deployed in the algal aggregation layer of the wastewater treatment pond (0.5m underwater), with a data acquisition frequency of once every 5 minutes. The acquired indicators include chlorophyll fluorescence parameters. (Photosynthetic activity index), species abundance ratio of Chlorella and Scenedesmus (community structure index). This represents the maximum photochemical quantum yield of photosystem II, also known as the maximum photochemical efficiency. It is variable fluorescence, representing the maximum fluorescence output of algae after dark adaptation. Compared with initial fluorescence yield The difference between the values ​​is a core non-destructive testing indicator characterizing algal photosynthetic activity. It directly reflects the health status and photosynthetic capacity of the algal photosystem. The value typically ranges from 0 to 1; a higher value indicates stronger photosynthetic activity and better growth. Healthy algae exhibit better photosynthetic activity and growth. It remained stable within the 0.65-0.85 range;

[0047] Wastewater pollutant data acquisition module: The monitoring points of the water quality sensor group correspond one-to-one with the collection points of the algae physiological data acquisition module. The monitoring indicators include COD, ammonia nitrogen, and total phosphorus concentration. The monitoring time interval of the degradation rate monitoring unit is set to 1 hour, and the degradation rate of ammonia nitrogen is calculated using a formula. ;

[0048] Data preprocessing module: Outliers are removed using the Laida criterion, missing values ​​are filled using linear interpolation, and all data are standardized to the [0,1] interval using the Z-score standardization method;

[0049] Quantitative mapping analysis module: weighting coefficients =0.65, =0.35, correction factor =0.25, the comprehensive value of algal physiological activity was calculated using the formula. The governance matching degree is calculated using a formula. ;

[0050] Governance parameter decision module: Preset threshold range is ≥0.8 keep the parameter unchanged, 0.5≤ <0.8 Adjust light and nutrients, <0.5 Synchronously adjust hydraulic residence time;

[0051] Control command issuance module: The module uses the Modbus-RTU protocol to transmit commands, with CRC32 checksum. The execution time is within 2 minutes after the command is issued. If the transmission fails, it will be retransmitted within 30 seconds, and the number of retransmissions will not exceed 3.

[0052] Treatment execution module: LED light source group wavelength 450-660nm, initial light intensity 3000lux; quantitative dosing pump adds urea and potassium dihydrogen phosphate, initial dosing rate 0.5L / h; variable frequency water pump initial hydraulic retention time 12h;

[0053] Data storage and feedback module: It adopts a distributed database with a data storage time of 1 year. When the degradation rate increases by less than 10% after regulation, feedback optimization is triggered.

[0054] The specific calculation process in this embodiment is as follows:

[0055] At a certain monitoring time, the raw data collected included: photosynthetic activity indicators. =0.65, community structure index: dominant algal species abundance ratio 90%; initial ammonia nitrogen concentration =28mg / L, concentration after 1 hour =24.5 mg / L, first calculate the ammonia nitrogen degradation rate using the formula: After data preprocessing, the standardized photosynthetic activity index was 3.5 mg / (L·h). =0.72, standardized community structure index =0.85, standardized ammonia nitrogen concentration =0.68, normalized degradation rate =0.62, the comprehensive value of algal physiological activity was calculated using the formula: =0.65×0.72+0.35×0.85=0.468+0.2975=0.7655;

[0056] Calculate the governance fit using the formula:

[0057] =0.7655×(1−0.68)+0.25×0.62=0.7655×0.32+0.155=0.24496+0.155=0.39996≈0.40

[0058] at this time ≈0.40 < 0.5, the governance parameter decision module generates a control plan:

[0059] The light intensity is increased by (0.8-0.4)×20%=8%, that is, the light intensity is adjusted to 3000×(1+8%)=3240 lux; the nutrient salt dosage is increased by (0.8-0.4)×15%=6%, that is, the dosage is adjusted to 0.5×(1+6%)=0.53L / h; the hydraulic retention time is extended by (0.5-0.4)×30%=3%, that is, the hydraulic retention time is adjusted to 12×(1+3%)=12.36h.

[0060] After the control command was issued to the treatment execution module, monitoring was conducted again after 1 hour. The ammonia nitrogen degradation rate increased to 4.2 mg / (L·h), an increase of 20% > 10%, without triggering feedback optimization. The treatment matching degree increased to 0.62, and the treatment effect was significantly improved.

[0061] This embodiment operates continuously for 30 days, recording system operation data, effluent water quality data, and algae growth status data throughout the process.

[0062] Example 2

[0063] This embodiment addresses the treatment of wastewater from the dyeing and printing industry, with a treatment capacity of 200m³. 3 / d, the influent water quality is: COD 800-1000mg / L, ammonia nitrogen 40-50mg / L, total phosphorus 5-6mg / L, color 300-400 times, water temperature 20-28℃, the selected algae is pollution-tolerant Scenedesmus obliquus, and the initial inoculation density is 8×10 6 cells / mL.

[0064] This embodiment uses the wastewater algae-based data biological treatment and analysis system described in this invention. The parameter settings for each module are as follows:

[0065] Algal physiological data acquisition module: The acquisition frequency is once every 10 minutes, and the acquired indicators include chlorophyll fluorescence parameters. Species abundance ratio (community structure index) of Scenedesmus obliquus, weighting coefficient =0.7, =0.3, correction factor =0.3;

[0066] Wastewater pollutant data acquisition module: The monitoring time interval of the degradation rate monitoring unit is set to 2 hours, and the monitoring indicators include COD, ammonia nitrogen, and total phosphorus concentration;

[0067] Treatment execution module: initial light intensity 5000 lux, initial nutrient salt dosage 1.2 L / h, initial hydraulic retention time 18h;

[0068] The settings for the remaining modules are the same as in Example 1.

[0069] The specific calculation process in this embodiment is as follows: At a certain monitoring time, the raw data collected is: photosynthetic activity index =0.45, community structure index, dominant algal species abundance ratio 75%; initial COD concentration =920mg / L, concentration after 2h =810 mg / L. First, calculate the COD degradation rate using the formula:

[0070] =55mg / (L·h);

[0071] After data preprocessing, standardized photosynthetic activity indicators =0.52, standardized community structure index =0.68, standardized COD concentration =0.82, normalized degradation rate =0.58. The comprehensive value of algal physiological activity was calculated using the formula:

[0072] =0.7×0.52+0.3×0.68=0.364+0.204=0.568;

[0073] Calculate the governance fit using the formula:

[0074] =0.568×(1−0.82)+0.3×0.58=0.568×0.18+0.174=0.10224+0.174=0.27624≈0.28;

[0075] at this time =0.28 < 0.5, the governance parameter decision module generates a control plan:

[0076] The light intensity is increased by (0.8-0.28)×20%=10.4%, that is, the light intensity is adjusted to 5000×(1+10.4%)=5520 lux; the nutrient dosage is increased by (0.8-0.28)×15%=7.8%, that is, the dosage is adjusted to 1.2×(1+7.8%)=1.2936L / h; the hydraulic retention time is extended by (0.5-0.28)×30%=6.6%, that is, the hydraulic retention time is adjusted to 18×(1+6.6%)=19.188h.

[0077] After the control measures were implemented, monitoring was conducted again 2 hours later. The COD degradation rate increased to 68 mg / (L·h), an increase of 23.6% > 10%, without triggering feedback optimization. The treatment matching degree increased to 0.55, and the treatment effect was significantly improved.

[0078] This embodiment runs continuously for 30 days, recording system operation data, effluent water quality data, and algae growth status data throughout the process.

[0079] Example 3

[0080] This embodiment focuses on the eutrophication remediation of urban landscape water bodies, with a water volume of 1000m³. 3 The initial water quality was: COD 50-60 mg / L, ammonia nitrogen 3-5 mg / L, total phosphorus 0.8-1.0 mg / L, chlorophyll a 40-50 μg / L, and water temperature 20-30℃. The selected algae were native compound algae species (Anabaena and Chlorella) to competitively inhibit the growth of algal blooms and purify the water.

[0081] This embodiment uses the wastewater algae-based data biological treatment and analysis system described in this invention. The parameter settings for each module are as follows:

[0082] Algal physiological data acquisition module: The acquisition frequency is once every 8 minutes, and the acquired indicators include chlorophyll fluorescence parameters. Species abundance ratio of native complex algal species (community structure index), weighting coefficient =0.6, =0.4, correction factor =0.2;

[0083] Wastewater pollutant data acquisition module: The monitoring time interval of the degradation rate monitoring unit is set to 1.5h, and the monitoring indicators include ammonia nitrogen, total phosphorus, and chlorophyll a concentration;

[0084] Treatment execution module: initial light intensity 2000 lux, initial nutrient salt dosage 0.2 L / h, initial hydraulic retention time 24h;

[0085] The settings for the remaining modules are the same as in Example 1.

[0086] This embodiment runs continuously for 30 days, recording system operation data, water quality data, and algal community structure change data throughout the process.

[0087] Comparative Example

[0088] This comparative example uses a conventional algae-based wastewater treatment system from the prior art, targeting the exact same urban domestic wastewater treatment scenario as Example 1, with a treatment capacity of 500m³. 3 / d, the influent water quality, algae species, and initial operating parameters are all consistent with those in Example 1.

[0089] The system in this comparative example only has a conventional water quality monitoring module, an algal biomass monitoring module, and a treatment execution module with fixed parameters. It lacks a quantitative mapping analysis module and a closed-loop control and feedback optimization module. It can only realize real-time monitoring and display of water quality data. The operating parameters remain fixed and are only manually adjusted when the effluent water quality exceeds the standard.

[0090] This comparative example was run continuously for 30 days, recording system operation data, effluent water quality data, and algae growth status data throughout the process. The recording dimensions were completely consistent with those of Example 1.

[0091] Result comparison and verification

[0092] This invention compares and verifies the 30-day continuous operation data of Examples 1-3 and Comparative Example 1 from three dimensions: pollutant treatment effect, operational stability, and economic performance. The core verification data are shown in the table below:

[0093] Average removal rate of core pollutants COD: 90.2%; Ammonia Nitrogen: 86.5%; Total Phosphorus: 88.3% COD: 91.5%; Ammonia Nitrogen: 84.2%; Total Phosphorus: 87.1%; Color: 86.7% COD: 78.4%; Ammonia Nitrogen: 90.1%; Total Phosphorus: 82.5% COD: 68.7%; Ammonia Nitrogen: 42.3%; Total Phosphorus: 51.6% effluent water quality compliance rate 100% 100% 100% 46.7% Number of abnormal algal growth events (algal bloom / decay) 0 times 0 times 0 times 3 times Pollutant degradation rate fluctuation ≤8.2% ≤9.5% ≤7.8% ≤32.6% Unit water treatment energy consumption 0.28kW·h / m³ 0.35kW·h / m³ 0.12kW·h / m³ 0.34kW·h / m³ Manual maintenance time 2 hours / week 3 hours / week 1 hour / week 5 hours / day The system adaptively adjusts the response time. ≤3min ≤3min ≤3min ≥24h (human intervention)

[0094] Supplementary verification data:

[0095] Validation of feedback optimization mechanism: During the operation of Example 1, a total of 8 feedback optimization signals were triggered. After each optimization, the pollutant degradation rate increased by an average of 12.6%, and the matching accuracy of the quantitative mapping model increased from the initial 82.3% to 96.8%.

[0096] Scene adaptability verification: Examples 1-3 cover three typical scenarios: domestic sewage, industrial wastewater, and landscape water bodies. The system can operate stably in all scenarios, with pollutant removal rate fluctuations of ≤5%, and there are no scene adaptability issues.

[0097] Long-term operational stability verification: Examples 1-3 were run for 30 days, and algae... The average value remained above 0.65, and the abundance of dominant algal species remained above 85%, with no issues of algal species degradation or reduced treatment efficiency.

[0098] Results Analysis

[0099] In terms of pollutant treatment effectiveness, the core pollutant removal rate and effluent quality compliance rate of the embodiments of the present invention are significantly improved compared with the comparative example. Through the quantitative mapping model of algal physiology and pollution indicators, the algal treatment capacity and sewage pollution load can be accurately matched, so that algae are always in the best degradation state. This solves the problem of unstable treatment effect and easy secondary pollution of existing technologies from the root, and achieves a comprehensive improvement in sewage treatment effect.

[0100] In terms of system intelligence and stability, the adaptive response speed and operational stability of the system of this invention are significantly improved compared with the existing technology. The full-link closed-loop architecture breaks the limitation of the fragmentation of each link in the existing technology. It can quickly adapt to the dynamic changes of water quality and environment without human intervention. At the same time, the system analysis accuracy is continuously improved through feedback optimization mechanism, which solves the pain point of the treatment efficiency decay that easily occurs in the long-term operation of the existing technology and ensures the long-term stability of the system operation.

[0101] In terms of economic performance, the unit water treatment energy consumption and manual operation and maintenance cost of the system of this invention are significantly reduced compared with the existing technologies. Precise parameter control avoids ineffective energy consumption, and the fully automated operation mode greatly reduces the investment in manual operation and maintenance. It achieves simultaneous optimization of treatment efficiency and operation economy, and provides reliable support for the large-scale promotion of algae wastewater treatment technology.

[0102] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.

Claims

1. A biological treatment and analysis system based on wastewater algae data, characterized in that, The biological treatment analysis system includes the following modules: Algal physiological data acquisition module: used to collect real-time data on photosynthetic activity, community structure and other core physiological indicators of algae in wastewater; Wastewater pollutant data acquisition module: used to collect real-time data on pollutant concentrations and degradation rates in wastewater, as well as other key pollution indicators. Data preprocessing module: filters and removes noise from the raw data to obtain standardized data; Quantitative mapping analysis module: Establishes a quantitative mapping relationship between core physiological indicators of algae and core pollution indicators of wastewater, and analyzes the matching results between the physiological state of algae and the needs of wastewater treatment based on this mapping relationship; Treatment parameter decision module: Determines specific control schemes for treatment parameters such as light intensity, nutrient dosage, and hydraulic retention time based on the matching results; Control command issuance module: Used to receive the control plan transmitted by the governance parameter decision module, convert it into executable control commands and issue them; Treatment execution module: Used to receive control commands transmitted by the control command sending module, execute corresponding control operations, and realize precise control of the biological treatment process of wastewater algae; Data storage and feedback module: This module receives standardized data transmitted from the data preprocessing module and control result data transmitted from the governance execution module, stores the data, and feeds back the control results to the quantitative mapping analysis module.

2. The biological treatment and analysis system for wastewater algae based on data according to claim 1, characterized in that: The algal physiological data acquisition module includes a photosynthetic activity sensor and a community structure monitor. The photosynthetic activity sensor is used to collect algal chlorophyll fluorescence parameters and photosynthetic rate data, and the community structure monitor is used to collect algal species composition ratio and dominant species abundance data. Both the photosynthetic activity sensor and the community structure monitor are deployed in the algal aggregation layer of the wastewater treatment area, and the acquisition frequency is once every 5-10 minutes.

3. The biological treatment and analysis system for wastewater algae based on data according to claim 1, characterized in that: The wastewater pollutant data acquisition module includes a water quality sensor group and a degradation rate monitoring unit. The water quality sensor group is used to collect data on the concentrations of chemical oxygen demand, ammonia nitrogen, total phosphorus, and heavy metal ions in wastewater. The degradation rate monitoring unit is used to calculate the pollutant degradation rate data based on the following formula by continuously monitoring the concentration difference data of the same pollutant at different time points: ； in, The pollutant degradation rate is expressed in mg / (L·h). For pollutants in the initial time Concentration data at the time, in mg / L. For pollutants in subsequent time Concentration data at the time, in mg / L. This is the initial monitoring time point. For subsequent monitoring time points, and > The time interval is set to 1-2 hours, and the monitoring points of the water quality sensor group correspond one-to-one with the collection points of the algae physiological data acquisition module.

4. The biological treatment and analysis system for wastewater algae based on data according to claim 1, characterized in that: The data preprocessing module first uses the Raida criterion to remove outliers from the original data, then uses linear interpolation to supplement missing data values, and finally uses the Z-score standardization method to transform physiological and pollution index data of different dimensions into standardized data of the same dimension. The standardized data ranges from [0,1].

5. The biological treatment and analysis system for wastewater algae based on data according to claim 1, characterized in that, The quantization mapping relationship established by the quantization mapping analysis module is calculated based on the following formula: ； in, This represents the comprehensive physiological activity value of algae, with a value range of [0,1]. For standardized photosynthetic activity index data, For standardized community structure index data, The weighting coefficients for photosynthetic activity indicators are: is the weighting coefficient of the community structure index, and , The value range is 0.6-0.

7. The value range is 0.3-0.4, and the weighting coefficient is calibrated by combining the analytic hierarchy process with the actual scenario of sewage treatment. ； in, The degree of matching between the physiological state of algae and the needs of wastewater treatment is represented by a value ranging from [0,1]. For standardized pollutant concentration data, For standardized pollutant degradation rate data, The correction coefficient for pollutant degradation rate ranges from 0.2 to 0.

3. This correction coefficient is determined based on experimental data fitting according to the wastewater type (domestic sewage, industrial wastewater). The quantitative mapping analysis module calculates the comprehensive value of algal physiological activity. Then calculate the governance matching degree. ,Will This is a result of matching the physiological state of algae with the needs of wastewater treatment.

6. The biological treatment and analysis system for wastewater algae based on data according to claim 1, characterized in that, The logic of the governance parameter decision module in determining the control scheme is as follows: A preset governance matching degree threshold range is established; when the governance matching degree... When ≥0.8, the current governance parameters are maintained unchanged; when 0.5≤ When <0.8, according to The specific values ​​are to increase light intensity and nutrient dosage proportionally, with the light intensity increase ratio being (0.8-). ()×20%, the increase in nutrient salt dosage is (0.8-) )×15%, when When the light intensity is less than 0.5, in addition to increasing the light intensity and nutrient dosage according to the above proportions, the hydraulic retention time should be extended simultaneously, with an extension ratio of (0.5- The treatment parameters include light intensity, nutrient dosage, and hydraulic retention time, with the parameter adjustment priority being: light intensity > nutrient dosage > hydraulic retention time.

7. The biological treatment and analysis system for wastewater algae based on data according to claim 1, characterized in that: The control command issuing module uses the Modbus-RTU communication protocol to transmit control commands. During the command transmission process, the CRC32 check algorithm is used to verify the data to ensure the accuracy of the command transmission. The control command includes parameter type, target value, execution time, and check code field. The execution time is within 1-3 minutes after the command is issued, and the transmission status (success / failure) is fed back to the governance parameter decision module in real time after the command is issued. If the transmission fails, it will be reissued within 30 seconds, and the number of reissues will not exceed 3.

8. The biological treatment and analysis system for wastewater algae based on data according to claim 1, characterized in that: The treatment execution module includes a light adjustment unit, a nutrient salt dosing unit, and a hydraulic control unit. The light adjustment unit is an adjustable power LED light source group with a wavelength range of 450-660nm. The light intensity is adjusted by changing the working power of the LED light source, and the light intensity adjustment range is 2000-8000 lux. The nutrient salt dosing unit is a quantitative dosing pump with a dosing accuracy of ≤±1%. It is used to accurately add nitrogen source (urea) and phosphorus source (potassium dihydrogen phosphate) nutrients according to the control instructions, and the dosing rate adjustment range is 0.1-5L / h. The hydraulic control unit is a variable frequency water pump with a frequency range of 5-50Hz. It is used to adjust the sewage circulation rate according to the control instructions, thereby changing the hydraulic retention time, and the hydraulic retention time adjustment range is 4-24h.

9. The biological treatment and analysis system for wastewater algae based on data according to claim 1, characterized in that: The data storage and feedback module uses a distributed database to store data, including raw collected data, standardized data, governance matching degree calculation results, control schemes, and control result data. The storage time is no less than one year. The feedback mechanism is as follows: when the control results of the governance execution module show that the pollutant degradation rate increases by less than 10%, the data storage and feedback module triggers a feedback signal. The quantitative mapping analysis module then recalibrates the weighting coefficients based on the feedback signal. , and correction factor The recalibration method is as follows: ×(1+0.05× ), , ,in, To adjust the difference between the degradation rates of pollutants before and after regulation and the ratio of the degradation rate before regulation, the quantitative mapping relationship is continuously optimized.

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