Catalytic ozonation wastewater treatment method and system based on micro-nano aeration

Abstract

The invention relates to the technical field of catalytic ozonation wastewater treatment systems based on micro-nano aeration, in particular to a catalytic ozonation wastewater treatment method and system based on micro-nano aeration. The system comprises a treatment investigation unit which is used for carrying out data acquisition on wastewater to be treated at different wastewater treatment links and carrying out classified storage and analysis; the treatment scheme unit is used for formulating graded wastewater treatment schemes according to the analysis result, constructing corresponding simulation training models for different grades of schemes, collecting operation problems through simulation training, and optimizing the wastewater treatment schemes based on the operation problems; and the problem collection unit is used for establishing a process design decision matrix based on problems in the implementation process after the optimized wastewater treatment scheme is implemented, and adjusting the wastewater treatment scheme. Dynamic calibration and optimization adjustment of the wastewater treatment scheme are realized, and the stability and accuracy of the treatment system are improved.

Description

Technical Field

[0001] This invention relates to the technical field of ozone catalytic oxidation wastewater treatment systems based on micro-nano aeration, and particularly to a method, system, and computer-readable storage medium for ozone catalytic oxidation wastewater treatment based on micro-nano aeration. Background Technology

[0002] Currently, oxygen itself is a strong oxidant that can directly oxidize and decompose some organic matter in water. Under the action of a catalyst, ozone can be efficiently converted into hydroxyl radicals with stronger oxidizing power. Ozone catalytic oxidation technology is a very effective and popular technology in the field of deep wastewater treatment. Existing wastewater treatment methods cannot investigate and treat each stage of the wastewater treatment process in advance. Therefore, it is impossible to formulate a reasonable and appropriate wastewater treatment plan based on the actual situation of the wastewater treatment. Even if a corresponding treatment plan is formulated, it cannot be dynamically calibrated, so the wastewater treatment plan cannot be kept in an accurate state. Summary of the Invention

[0003] The present invention aims to at least partially solve one of the technical problems in the related art.

[0004] Therefore, the first objective of this invention is to provide an ozone catalytic oxidation wastewater treatment system based on micro-nano aeration, comprising: The processing and investigation unit is used to collect data on the wastewater to be treated at different stages of wastewater treatment, and to classify, store and analyze the collected data according to different stages. The treatment scheme unit is used to formulate graded wastewater treatment schemes based on the analysis results, construct corresponding simulation training models for different grade schemes, collect operational problems through simulation training, and optimize the wastewater treatment schemes based on the operational problems. The problem collection unit establishes a process design decision matrix based on the problems encountered during the implementation of the optimized wastewater treatment plan. It conducts a comprehensive evaluation of the plan by combining the weights of treatment effect, economy, feasibility and safety, and adjusts the wastewater treatment plan according to the evaluation results.

[0005] In one embodiment of the present invention, the processing and investigation unit includes: The survey module is used to collect data on untreated wastewater; the data includes basic physicochemical indicators, pollutant composition characteristics, water quality fluctuations, and engineering conditions. The data collection and analysis module is used to preprocess the collected data and classify, store, and analyze its features according to the collection time and data type. The preprocessing method includes outlier removal.

[0006] In one embodiment of the present invention, the processing unit includes: The treatment plan formulation module is used to formulate corresponding wastewater treatment plans based on the data results processed by the data processing and investigation unit. Depending on the degree of pollution of the wastewater, the wastewater treatment plan includes a first treatment plan, a second treatment plan, and a third treatment plan. The simulation module is used to simulate and train wastewater treatment schemes and output the problems that arise during the training. The scheme adjustment module collects the problems generated during training from the scheme simulation module, analyzes the collected problems, adjusts the corresponding wastewater treatment scheme based on the analysis results, and sends the adjusted wastewater treatment scheme to the scheme simulation module for re-simulation.

[0007] In one embodiment of the present invention, the problem collection unit includes: The problem collection and processing module is used to collect problems encountered during the implementation of wastewater treatment solutions and to conduct a comprehensive evaluation of the wastewater treatment solutions based on their implementation effects. The optimization module is used to process the collected problems based on expert analysis and to optimize and adjust the wastewater treatment plan.

[0008] In one embodiment of the present invention, the problem collection and processing module includes a process design decision assistance matrix, which is used for: Collect data on the implementation effect of wastewater treatment solutions, classify the implementation effect data, and assign weights to the implementation effect data of different categories; Radar charts are generated based on problems encountered during implementation and the implementation effect data after weighting, in order to assist in decision-making.

[0009] In one embodiment of the present invention, the optimization processing module is further configured to: The number of times the solution is optimized and adjusted within a set period is accumulated. When the accumulated number exceeds a set threshold, the corresponding wastewater treatment solution is adjusted in all aspects.

[0010] To achieve the above objectives, a second aspect of the present invention proposes a method for treating wastewater by ozone catalytic oxidation based on micro-nano aeration, comprising: S1. Obtain multi-dimensional data before wastewater treatment and classify and store the data. The multi-dimensional data includes basic physicochemical indicators, pollutant composition characteristics, water quality fluctuations and engineering conditions. S2, Based on the multi-dimensional data, a comprehensive analysis is performed to generate a basis for the formulation of the treatment plan. The comprehensive analysis includes pre-processing abnormal data, assessing water quality fluctuation trends, calculating pollutant removal target values, and analyzing engineering adaptability and cost feasibility. S3. Based on the analysis results, formulate a graded wastewater treatment plan, and build corresponding simulation training models for different grade plans. Collect operational problems through simulation training, and optimize the wastewater treatment plan based on the operational problems. S4. After the optimized wastewater treatment scheme is implemented, a process design decision matrix is ​​established based on the problems encountered during the implementation process. The scheme is comprehensively evaluated by combining the weights of treatment effect, economy, feasibility and safety, and the wastewater treatment scheme is adjusted according to the evaluation results.

[0011] In one embodiment of the present invention, S3 includes: S31. Based on the analysis results, a graded wastewater treatment plan is formulated. According to the degree of pollution of the wastewater, the wastewater treatment plan includes a first treatment plan, a second treatment plan and a third treatment plan. S32, simulates and trains the wastewater treatment scheme and outputs the problems that arise during the training. S33: Collect the problems generated during training from the simulation module, analyze the collected problems, adjust the corresponding wastewater treatment scheme based on the analysis results, and simulate the adjusted wastewater treatment scheme again.

[0012] In one embodiment of the present invention, S4 includes: S41, Collect data on the implementation effect of wastewater treatment solutions, classify the implementation effect data, and assign weights to the implementation effect data of different categories; S42, generate a radar chart based on problems encountered during implementation and the implementation effect data after empowerment; S43. Based on expert analysis, the collected problems are addressed, and the wastewater treatment plan is optimized and adjusted.

[0013] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0014] The methods, systems, and storage media of this invention enable dynamic calibration and optimization of wastewater treatment solutions, improve the stability and accuracy of the treatment system, and effectively address the treatment challenges posed by water quality fluctuations and complex pollutant compositions.

[0015] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an ozone catalytic oxidation wastewater treatment method based on micro-nano aeration according to an embodiment of the present invention; Figure 2 This is a structural diagram of an ozone catalytic oxidation wastewater treatment system based on micro-nano aeration according to an embodiment of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

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

[0019] The following description, with reference to the accompanying drawings, describes an ozone catalytic oxidation wastewater treatment method and system based on micro-nano aeration, according to an embodiment of the present invention.

[0020] Example 1 Figure 1 This is a flowchart of an embodiment of the present invention of an ozone catalytic oxidation wastewater treatment method based on micro-nano aeration.

[0021] like Figure 1 As shown, the ozone catalytic oxidation wastewater treatment method based on micro-nano aeration includes the following steps: S1. Obtain multi-dimensional data before wastewater treatment and classify and store the data. The multi-dimensional data includes basic physicochemical indicators, pollutant composition characteristics, water quality fluctuations, and engineering conditions.

[0022] Specifically, acquiring multi-dimensional data on wastewater before starting the wastewater treatment system is fundamental to building an efficient and stable treatment solution. This step involves systematically collecting data on the wastewater treatment area through a comprehensive survey module, covering four main categories: basic physicochemical indicators, pollutant composition characteristics, water quality fluctuations, and engineering conditions. Basic physicochemical indicators include COD, pH, suspended solids (SS), salinity, reducing substances, oils, and heavy metal ions. COD is detected using the potassium dichromate method or ultraviolet-visible spectroscopy, pH is measured using a pH meter, and suspended solids are quantified by filtration and weighing. The recommended testing frequency is once per hour to ensure the timeliness and representativeness of the data.

[0023] Regarding the composition and characteristics of pollutants, the focus is on analyzing the molecular structure of organic pollutants, the types of toxic substances, and their bioinhibitory properties. For example, organic fingerprinting is performed using GC-MS or HPLC, combined with biotoxicity testing (such as the luminescent bacteria method) to assess their impact on subsequent biological treatment units. The investigation of water quality fluctuations uses time-series analysis to collect daily / monthly concentration fluctuation data of wastewater and calculate the coefficient of variation. ,in Standard deviation, The value is the mean, used to determine whether the water quality exhibits shock load characteristics. If the coefficient of variation exceeds 0.3, it is recommended to add a buffer tank to stabilize the influent water quality.

[0024] The engineering conditions survey includes site space, energy supply (such as ozone generator power and gas source pressure), and compatibility with existing treatment facilities to ensure the system is feasible at the physical and resource levels. After data collection, the data collection and analysis module stores the data in a structured database according to categories and adds a timestamp to each data entry to support subsequent dynamic analysis and scheme optimization. This step provides data support for the subsequent tiered development of treatment schemes and simulation training, and is a crucial preliminary step for realizing the intelligent and adaptive adjustment of the wastewater treatment system.

[0025] S2, based on the multi-dimensional data, a comprehensive analysis is performed, including preprocessing abnormal data, assessing water quality fluctuation trends, calculating pollutant removal target values, analyzing engineering adaptability and cost feasibility, and generating a basis for formulating treatment plans.

[0026] Specifically, this step, "comprehensive analysis based on the multi-dimensional data, including preprocessing abnormal data, assessing water quality fluctuation trends, calculating pollutant removal target values, analyzing engineering adaptability and cost feasibility, and generating a basis for formulating treatment plans," is the core data processing link of the treatment plan unit in this invention. Its technical implementation is based on the systematic integration and quantitative evaluation of multi-source heterogeneous wastewater data, aiming to provide a scientific and executable decision-making basis for the subsequent formulation of wastewater treatment plans.

[0027] At the technical implementation level, this step first preprocesses the raw data using outlier removal methods to eliminate abnormal data points caused by sensor malfunctions, transient interference, or human error. Common methods include the 3σ principle, box plot method (IQR), or time-series-based sliding window detection algorithms to ensure the statistical validity of the dataset. Subsequently, the system performs trend analysis on water quality fluctuations, visualizing daily / monthly concentration changes using time-series line charts, and assessing the degree of fluctuation using the coefficient of variation (CV). The formula is as follows: ,in Standard deviation, The value is the average. If the CV value exceeds the set threshold (e.g., 0.25), the impact load assessment mechanism is triggered to determine whether a buffer pool needs to be added.

[0028] The target value for pollutant removal is calculated based on the difference between the influent mean and the effluent limit. For example, the target value for COD removal is... ,in The average COD of the influent is... This is the limit for COD in the effluent. This indicator is used to guide the optimal design of ozone dosage and catalyst ratio.

[0029] In the engineering compatibility analysis, the system assesses site space, energy supply, compatibility with existing facilities, and gas source compatibility to ensure the physical and operational feasibility of the ozone catalytic oxidation system. The cost feasibility analysis, through cost per ton of water calculation (unit: yuan / m³), sensitivity analysis (such as the impact of ozone price fluctuations on total cost), and operation and maintenance capability matching, determines the sustainability of the solution from an economic and management perspective.

[0030] This step is widely used in the design phase of industrial wastewater treatment, especially in scenarios with high pollution loads and frequent water quality fluctuations. Through quantitative analysis of multi-dimensional data, the system can output treatment schemes (first to third treatment schemes) corresponding to different pollution levels, and combined with an expert adjustment mechanism, achieve dynamic optimization and precise decision-making. Its technical value lies in improving the stability and economy of the wastewater treatment system, providing a reliable basis for subsequent simulation training and implementation adjustments.

[0031] S3. Based on the analysis results, formulate a graded wastewater treatment plan, and build corresponding simulation training models for different grade plans. Collect operational problems through simulation training, and optimize the wastewater treatment plan based on the operational problems.

[0032] Further, step S3 includes: S31. Based on the analysis results, a graded wastewater treatment plan is formulated. According to the degree of pollution of the wastewater, the wastewater treatment plan includes a first treatment plan, a second treatment plan and a third treatment plan. S32, simulates and trains the wastewater treatment scheme and outputs the problems that arise during the training. S33: Collect the problems generated during training from the simulation module, analyze the collected problems, adjust the corresponding wastewater treatment scheme based on the analysis results, and simulate the adjusted wastewater treatment scheme again.

[0033] Specifically, after the wastewater treatment plan is formulated, the system verifies the operational feasibility of the plan by constructing simulation training models corresponding to different levels of treatment, and collects potential operational problems during the simulation process. This step, based on the dynamic response characteristics of the ozone catalytic oxidation process and combined with the mass transfer efficiency and reaction kinetics model of the micro-nano aeration system, realizes the pre-simulation and optimization of the wastewater treatment process.

[0034] In some implementations, the construction of simulation training models relies on the classification standards of wastewater pollution levels, typically based on key indicators such as COD (Chemical Oxygen Demand) concentration, pH value, pollutant type, and toxicity. For example, the first treatment scheme is suitable for highly polluted wastewater with an average COD greater than 300 mg / L, the second treatment scheme is suitable for moderately polluted wastewater with an average COD between 100 and 300 mg / L, and the third treatment scheme is suitable for low-pollution wastewater with an average COD less than 100 mg / L. Each scheme is configured with corresponding operating parameters such as ozone dosage, catalyst type and dosage ratio, aeration time, and air-to-water ratio. For example, the ozone dosage can be set to... air-to-water ratio Catalyst dosage .

[0035] Optionally, during the simulation training process, the system sets up multi-dimensional monitoring indicators, including pollutant removal rate. Ozone consumption Energy consumption ratio These are used to quantitatively evaluate simulation results. When simulation results do not reach a preset threshold (such as COD removal rate),... Ozone utilization rate The system will trigger the problem collection mechanism and feed it back to the solution adjustment module for parameter optimization.

[0036] This step is widely applicable in practical scenarios such as industrial wastewater treatment, municipal sewage treatment, and reclaimed water quality control. Through simulation training, the system can identify process bottlenecks before actual operation, reduce commissioning costs, and improve the stability and adaptability of the wastewater treatment system, demonstrating significant engineering practical value.

[0037] S4. After the optimized wastewater treatment scheme is implemented, a process design decision matrix is ​​established based on the problems encountered during the implementation process. The scheme is comprehensively evaluated by combining the weights of treatment effect, economy, feasibility and safety, and the wastewater treatment scheme is adjusted according to the evaluation results.

[0038] Further, step S4 includes: S41, Collect data on the implementation effect of wastewater treatment solutions, classify the implementation effect data, and assign weights to the implementation effect data of different categories; S42, generate a radar chart based on problems encountered during implementation and the implementation effect data after empowerment; S43. Based on expert analysis, the collected problems are addressed, and the wastewater treatment plan is optimized and adjusted.

[0039] Specifically, this step is the core link in achieving dynamic optimization and stable implementation of wastewater treatment solutions. This step involves constructing a multi-dimensional decision-making model to systematically analyze and quantitatively evaluate the problems exposed during simulation training, thereby guiding the iterative optimization of the wastewater treatment solution.

[0040] At the technical implementation level, this step first involves classifying and recording various issues generated during the simulation training through a problem collection and processing module. These issues include, but are not limited to, insufficient ozone dosage, decreased catalyst activity, substandard mass transfer efficiency, and high equipment energy consumption. Subsequently, these issues are integrated with water quality analysis data, target requirements, engineering conditions, and economic indicators to construct a process design decision matrix. This matrix uses four key dimensions as evaluation indicators: treatment effect, economy, feasibility, and safety, assigned weights of 0.3, 0.25, 0.25, and 0.2 respectively, conforming to commonly used multi-objective decision analysis methods in the industry.

[0041] In application scenarios, this step is applicable to the formulation and optimization of ozone catalytic oxidation systems for treating wastewater of different pollution levels. For example, when treating high-concentration organic wastewater, if simulation training shows insufficient ozone utilization, the treatment effect can be improved by adjusting the type and dosage of catalyst to increase the efficiency of hydroxyl radical generation. If the simulation results after adjustment are still lower than the preset threshold (e.g., COD removal rate needs to reach over 90%), the system will automatically trigger a new round of adjustment until all indicators meet the design requirements.

[0042] The technical effect of this step is that, through quantitative evaluation and iterative optimization mechanisms, it significantly improves the scientificity and adaptability of wastewater treatment solutions, effectively reduces system operation risks and cost fluctuations, and enhances the robustness and implementation stability of ozone catalytic oxidation processes under complex water quality conditions, thus having significant engineering practical value.

[0043] The dynamic calibration method for wastewater treatment schemes in this invention enables dynamic calibration and optimization of wastewater treatment schemes, thereby improving system processing stability and scheme implementation accuracy.

[0044] Example 2 Figure 2 This is a structural diagram of a dynamic calibration system for a wastewater treatment scheme according to an embodiment of the present invention.

[0045] like Figure 2 As shown, an ozone catalytic oxidation wastewater treatment system based on micro-nano aeration is characterized by comprising: The processing and investigation unit is used to collect data on the wastewater to be treated at different stages of wastewater treatment, and to classify, store and analyze the collected data according to different stages. The treatment scheme unit is used to formulate graded wastewater treatment schemes based on the analysis results, construct corresponding simulation training models for different grade schemes, collect operational problems through simulation training, and optimize the wastewater treatment schemes based on the operational problems. The problem collection unit establishes a process design decision matrix based on the problems encountered during the implementation of the optimized wastewater treatment plan. It conducts a comprehensive evaluation of the plan by combining the weights of treatment effect, economy, feasibility and safety, and adjusts the wastewater treatment plan according to the evaluation results.

[0046] Furthermore, the processing investigation unit includes: The survey module is used to collect data on untreated wastewater; the data includes basic physicochemical indicators, pollutant composition characteristics, water quality fluctuations, and engineering conditions. The data collection and analysis module is used to preprocess the collected data and classify, store, and analyze its features according to the collection time and data type. The preprocessing methods include outlier removal. Furthermore, the processing unit includes: The treatment plan formulation module is used to formulate corresponding wastewater treatment plans based on the data results processed by the data processing and investigation unit. Depending on the degree of pollution of the wastewater, the wastewater treatment plan includes a first treatment plan, a second treatment plan, and a third treatment plan. The simulation module is used to simulate and train wastewater treatment schemes and output the problems that arise during the training. The scheme adjustment module collects the problems generated during training from the scheme simulation module, analyzes the collected problems, adjusts the corresponding wastewater treatment scheme based on the analysis results, and sends the adjusted wastewater treatment scheme to the scheme simulation module for re-simulation.

[0047] Furthermore, the issue collection unit includes: The problem collection and processing module is used to collect problems encountered during the implementation of wastewater treatment solutions and to conduct a comprehensive evaluation of the wastewater treatment solutions based on their implementation effects. The optimization module is used to process the collected problems based on expert analysis and to optimize and adjust the wastewater treatment plan.

[0048] Furthermore, the problem collection and processing module includes a process design decision support matrix, which is used for: Collect data on the implementation effect of wastewater treatment solutions, classify the implementation effect data, and assign weights to the implementation effect data of different categories; Radar charts are generated based on problems encountered during implementation and the implementation effect data after weighting, in order to assist in decision-making.

[0049] Furthermore, the optimization processing module is also used for: The number of times the solution is optimized and adjusted within a set period is accumulated. When the accumulated number exceeds a set threshold, the corresponding wastewater treatment solution is adjusted in all aspects.

[0050] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described ozone catalytic oxidation wastewater treatment method based on micro-nano aeration.

[0051] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0052] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A wastewater treatment system based on micro-nano aeration using ozone catalytic oxidation, characterized in that, include: The processing and investigation unit is used to collect data on the wastewater to be treated at different stages of wastewater treatment, and to classify, store and analyze the collected data according to different stages. The treatment scheme unit is used to formulate graded wastewater treatment schemes based on the analysis results, build corresponding simulation training models for different levels of schemes, collect operational problems through simulation training, and optimize the wastewater treatment schemes based on the operational problems. The problem collection unit establishes a process design decision matrix based on the problems encountered during the implementation of the optimized wastewater treatment plan. It conducts a comprehensive evaluation of the plan by combining the weights of treatment effect, economy, feasibility and safety, and adjusts the wastewater treatment plan according to the evaluation results.

2. The system according to claim 1, characterized in that, The processing and investigation unit includes: The survey module is used to collect data on untreated wastewater; the data includes basic physicochemical indicators, pollutant composition characteristics, water quality fluctuations, and engineering conditions. The data collection and analysis module is used to preprocess the collected data and classify, store, and analyze its features according to the collection time and data type. The preprocessing method includes outlier removal.

3. The system according to claim 1, characterized in that, The processing scheme unit includes: The treatment plan formulation module is used to formulate corresponding wastewater treatment plans based on the data results processed by the data processing and investigation unit. Depending on the degree of pollution of the wastewater, the wastewater treatment plan includes a first treatment plan, a second treatment plan, and a third treatment plan. The simulation module is used to simulate and train wastewater treatment schemes and output the problems that arise during the training. The scheme adjustment module collects the problems generated during training from the scheme simulation module, analyzes the collected problems, adjusts the corresponding wastewater treatment scheme based on the analysis results, and sends the adjusted wastewater treatment scheme to the scheme simulation module for re-simulation.

4. The system according to claim 1, characterized in that, The problem collection unit includes: The problem collection and processing module is used to collect problems encountered during the implementation of wastewater treatment solutions and to conduct a comprehensive evaluation of the wastewater treatment solutions based on their implementation effects. The optimization module is used to process the collected problems based on expert analysis and to optimize and adjust the wastewater treatment plan.

5. The system according to claim 4, characterized in that, The problem collection and processing module includes a process design decision support matrix, which is used for: Collect data on the implementation effect of wastewater treatment solutions, classify the implementation effect data, and assign weights to the implementation effect data of different categories; Radar charts are generated based on problems encountered during implementation and the implementation effect data after weighting, in order to assist in decision-making.

6. The system according to claim 4, characterized in that, The optimization processing module is also used for: The number of times the solution is optimized and adjusted within a set period is accumulated. When the accumulated number exceeds a set threshold, the corresponding wastewater treatment solution is adjusted in all aspects.

7. A method for treating wastewater using ozone catalytic oxidation based on micro-nano aeration, characterized in that, include: S1. Obtain multi-dimensional data before wastewater treatment and classify and store the data. The multi-dimensional data includes basic physicochemical indicators, pollutant composition characteristics, water quality fluctuations and engineering conditions. S2, Based on the multi-dimensional data, a comprehensive analysis is performed to generate a basis for the formulation of the treatment plan. The comprehensive analysis includes pre-processing abnormal data, assessing water quality fluctuation trends, calculating pollutant removal target values, and analyzing engineering adaptability and cost feasibility. S3. Based on the analysis results, formulate a graded wastewater treatment plan, and build corresponding simulation training models for different grade plans. Collect operational problems through simulation training, and optimize the wastewater treatment plan based on the operational problems. S4. After the optimized wastewater treatment scheme is implemented, a process design decision matrix is ​​established based on the problems encountered during the implementation process. The scheme is comprehensively evaluated by combining the weights of treatment effect, economy, feasibility and safety, and the wastewater treatment scheme is adjusted according to the evaluation results.

8. The method according to claim 7, characterized in that, S3 includes: S31. Based on the analysis results, a graded wastewater treatment plan is formulated. According to the degree of pollution of the wastewater, the wastewater treatment plan includes a first treatment plan, a second treatment plan, and a third treatment plan. S32, simulates and trains the wastewater treatment scheme and outputs the problems that arise during the training. S33: Collect the problems generated during training from the simulation module, analyze the collected problems, adjust the corresponding wastewater treatment scheme based on the analysis results, and simulate the adjusted wastewater treatment scheme again.

9. The method according to claim 7, characterized in that, S4 includes: S41, Collect data on the implementation effect of wastewater treatment solutions, classify the implementation effect data, and assign weights to the implementation effect data of different categories; S42, generate a radar chart based on problems encountered during implementation and the implementation effect data after weighting; S43. Based on expert analysis, the collected problems are addressed, and the wastewater treatment plan is optimized and adjusted.

10. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of any one of claims 7-9.

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