Water quality purification process design multi-agent system

Abstract

The application provides a water quality purification process design multi-agent system, and belongs to the technical field of water quality purification.The multi-agent system can generate a process scheme of a target water quality purification process according to engineering design requirements of the target water quality purification process in cooperation with multiple agents, without human participation, so that time and labor are saved, new technologies can be effectively integrated, and the water quality purification effect of the target water quality purification process constructed can be ensured.

Description

Technical Field

[0001] This invention relates to the field of water purification technology, and in particular to a multi-agent system for water purification process design. Background Technology

[0002] Water purification processes are technical means of removing impurities from wastewater through physical, chemical, and biological methods to achieve water purification. With the rapid development of the water purification industry, equipment and technologies that can improve water purification efficiency (such as new technologies like anaerobic ammonia oxidation and electrocatalytic oxidation) are constantly emerging, and the design requirements for water purification processes are also becoming more diversified (such as design requirements for discharge standards, cost control, and resistance to water quality fluctuations).

[0003] However, existing water purification process designs rely heavily on personal experience and require repeated trial and error. Meanwhile, the pace of new technology iterations far exceeds the learning speed of engineers, resulting in time-consuming and labor-intensive existing water purification process designs that cannot be effectively integrated with new technologies. Consequently, the water purification effect of the constructed processes is poor. Summary of the Invention

[0004] This invention proposes a multi-agent system for water purification process design, which can coordinate multiple agents to generate process schemes for the target water purification process based on the engineering design requirements of the target water purification process. This eliminates the need for human intervention, saving time and effort, and can also be effectively integrated with new technologies, thereby ensuring the water purification effect of the constructed target water purification process.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides a multi-agent system for water purification process design, including a requirements analysis agent, a scheme design agent, a scheme evaluation agent, and a scheme optimization agent. The requirements analysis agent is used to determine a requirements analysis report for the target water purification process based on the engineering design requirements of the target water purification process; the requirements analysis report includes engineering design objectives and engineering constraints. The scheme design agent is used to determine the process scheme for the target water purification process based on the requirements analysis report. The scheme evaluation agent is used to evaluate the process scheme and determine an evaluation report for the process scheme; the evaluation report includes evaluation indicators for the process scheme and suggestions for scheme optimization. The scheme optimization agent is used to determine the final process scheme for the target water purification process based on the process scheme and the evaluation report.

[0007] In one implementation, determining the process scheme for the target water purification process includes: acquiring engineering knowledge related to the requirements analysis report; generating the process scheme for the target water purification process from the engineering knowledge based on the engineering knowledge, the requirements analysis report, and process scheme generation prompts using a large language model; the process scheme generation prompts are used to guide the process scheme generation model to generate the process scheme; the process scheme includes a process chain of pretreatment, biological treatment, and physicochemical treatment, as well as the corresponding facilities for the process chain.

[0008] In one implementation, determining the evaluation report of the process scheme includes: evaluating the process scheme using a scheme evaluation model based on a large language model based on the process scheme and scheme evaluation prompts, and obtaining the evaluation report of the process scheme; the scheme evaluation prompts are used to guide the scheme evaluation model to evaluate the process scheme, and the evaluation dimensions include technical dimensions, economic dimensions, risk dimensions, and operational dimensions.

[0009] In one implementation, determining the final process scheme for the target water purification process includes:

[0010] The evaluation indicators in the evaluation report are assessed: if an indicator exceeds a threshold, the process scheme is determined as the final process scheme for the target water purification process. Otherwise, based on the optimization suggestions, process scheme, and optimization prompts in the evaluation report, a scheme optimization model based on a large language model is used to optimize the process scheme and update the process scheme for the target water purification process; and based on the evaluated process scheme, the steps of the process scheme evaluation report are determined; wherein, the optimization prompts are used to guide the scheme optimization model to optimize the process scheme.

[0011] In one implementation, the system further includes a report-writing agent. This agent is used to determine the engineering design report for the target water purification process based on the final process plan.

[0012] In one implementation, determining the engineering design report for the target water purification process includes: analyzing the final process scheme using a report writing model based on a large language model, based on the final process scheme and report writing prompts, to generate the engineering design report for the target water purification process; the report writing prompts are used to guide the report writing model to generate the engineering design report.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] The multi-agent system for water purification process design provided by this invention comprises three agents: a requirements analysis agent, a scheme design agent, and a scheme optimization agent. The former analyzes the engineering design requirements of the target water purification process, extracting implicit design requirements to generate a requirements analysis report. The latter integrates relevant engineering information from the requirements analysis report to generate a process scheme for the target water purification process. The latter evaluates the scheme based on its efficient parallel simulation capabilities, generating an evaluation report. The former optimizes the scheme based on the evaluation report and its own efficient parallel simulation capabilities to determine the final process scheme. This entire process requires no human intervention, saving time and effort while effectively integrating with new technologies, thus ensuring the water purification effect of the constructed target water purification process. Attached Figure Description

[0015] Figure 1 This is one of the schematic diagrams of a multi-agent system for water purification process design provided in the embodiments of this application;

[0016] Figure 2 This is a flowchart illustrating the processing logic of the demand analysis agent in the multi-agent system provided in this application embodiment;

[0017] Figure 3 This is the second schematic diagram of the multi-agent system for water purification process design provided in the embodiments of this application;

[0018] Figure 4 This is a flowchart illustrating the processing logic of the intelligent agents in the multi-agent system provided in this application embodiment;

[0019] Figure 5 This is the third schematic diagram of the multi-agent system for water purification process design provided in the embodiments of this application;

[0020] Figure 6 This is the fourth schematic diagram of the multi-agent system for water purification process design provided in the embodiments of this application;

[0021] Figure 7 This is the fifth schematic diagram of the multi-agent system for water purification process design provided in the embodiments of this application;

[0022] Figure 8 This is a flowchart illustrating the processing logic of the scheme evaluation agent in the multi-agent system provided in this application embodiment;

[0023] Figure 9 This is the sixth schematic diagram of the multi-agent system for water purification process design provided in the embodiments of this application;

[0024] Figure 10This is a flowchart illustrating the processing logic of the intelligent agent in the multi-agent system provided in this application embodiment for scheme optimization;

[0025] Figure 11 This is the seventh schematic diagram of the multi-agent system for water purification process design provided in the embodiments of this application;

[0026] Figure 12 This is a flowchart illustrating the processing logic of the report writing agent in the multi-agent system provided in this application embodiment;

[0027] Figure 13 This is the eighth schematic diagram of the multi-agent system for water purification process design provided in the embodiments of this application. Detailed Implementation

[0028] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0029] The methods and apparatus provided in this application relate to the field of water purification technology and can be used to collaboratively generate process schemes for target water purification processes from multiple intelligent agents.

[0030] Understandably, traditional water purification process design methods heavily rely on personal experience. Designers often spend months repeatedly trying and failing, combining process units from scratch. For example, they might decide whether to adopt a "hydrolysis acidification pretreatment + anaerobic biological treatment + aerobic deep treatment" route based on experience and small-scale experiments, or to introduce membrane technology to replace the sedimentation stage. This experience-driven model is not only inefficient but also suffers from serious subjective limitations: engineers' insufficient understanding of new technologies may lead to conservative solutions. On the other hand, process design must simultaneously meet multiple objectives such as emission standards, cost control, and resistance to water quality fluctuations, but the existing toolchain is fragmented: process simulation software such as BioWin can only optimize single reactor parameters, economic analysis relies on manual spreadsheet calculations, and CAD drawings are often disconnected from the core process logic, resulting in fragmented solutions. Especially when new constraints need to be added midway through the design process, such as requiring an increase in total nitrogen removal rate from 60% to 80%, the entire design process almost needs to be restarted. This static design paradigm is no longer suitable for dynamic engineering needs.

[0031] At the same time, the contradiction between the explosion of industry knowledge and the lack of technological accumulation is becoming increasingly acute. In recent years, wastewater treatment process design specifications have been continuously updated, new technologies such as anaerobic ammonia oxidation and electrocatalytic oxidation have emerged, and equipment parameter databases have been dynamically changing. However, the knowledge scattered in papers, patents, and case reports lacks an effective integration mechanism, and this knowledge dilemma directly leads to low design efficiency.

[0032] The rapid development of artificial intelligence technology offers new hope for resolving this contradiction. Knowledge graph technology can structurally integrate design specifications and equipment parameters, Large Language Models (LLM) can quickly analyze the technical points in scientific research papers, and Multi-Agent Systems (MAS) have demonstrated the potential for cross-domain collaboration. However, multi-agent technology cannot be directly applied to water purification process design scenarios: general agents lack water treatment expertise, and the generated solutions frequently violate basic design principles, such as setting excessive surface loads in sedimentation tanks or incorrectly selecting biological methods for wastewater containing heavy metals. More seriously, there is a failure in collaboration between agents. When the economic assessment agent requires cost control, the process design agent may still output expensive MBR membrane process solutions. This contradiction exposes the fundamental defects of traditional architectures in terms of embedding specialized rules and dynamic response.

[0033] To address the problems in the background technology where existing water purification process design heavily relies on personal experience and requires repeated trial and error, while the pace of new technology iteration far exceeds the learning speed of engineers, resulting in time-consuming and labor-intensive design processes that cannot be effectively integrated with new technologies, and consequently, compromised water purification process design, this application provides a multi-agent system for water purification process design. This system can coordinate multiple agents to generate process schemes for the target water purification process based on the engineering design requirements, eliminating the need for human intervention. This not only saves time and labor but also enables effective integration with new technologies, thereby ensuring the water purification effect of the constructed target water purification process.

[0034] To better understand the technical solutions of the embodiments of this application, the technical terms involved in the embodiments of this application will be explained below.

[0035] 1. Intelligent agent (or artificial intelligence agent)

[0036] Artificial intelligence (AI) is a technological system that simulates human intelligence through algorithms and data. The four core technologies of AI are perception, reasoning, learning, and action. An AI agent, as a specific application of AI, is an intelligent entity capable of perceiving its environment, making autonomous decisions, and executing actions. Its goal is to complete specific tasks through interaction with the outside world.

[0037] Specifically, an intelligent agent typically comprises four core modules: a perception module, a decision-making module (also known as a technology module), an action module, and a memory module. The perception module is used to acquire information through data input (such as APIs) and transform that information into a understandable format. The decision-making module (e.g., a large language model) performs logical reasoning, task planning, or strategy generation based on the information acquired by the perception module. The action module executes the decision results (e.g., generating text, calling APIs). The memory module (e.g., a database, a knowledge base) is used to store data, supporting long-term reasoning and contextual understanding.

[0038] It should be noted that, for the intelligent agent in the embodiments of this application, the perception module of the intelligent agent can be either a large language model or other deep learning models, and the embodiments of this application do not limit it.

[0039] 2. Prompt words

[0040] Prompts are instructions or questions that users input into large language models (such as GPT-4, Claude, LLaMA, etc.) to guide the large language model to output specific types of response results.

[0041] As the core commands for user-model interaction, prompts directly influence the output quality and direction of large language models. To ensure a satisfactory output, prompts need to include task instructions, contextual information, input data, output format, and examples. If the output of a large language model is unsatisfactory after prompts are input, adjustments to the prompts can improve its performance.

[0042] like Figure 1 As shown in the embodiments of this application, the multi-agent system for water purification process design includes a demand analysis agent, a scheme design agent, a scheme evaluation agent, and a scheme optimization agent.

[0043] The requirements analysis agent is used to determine the requirements analysis report of the target water purification process based on the engineering design requirements of the target water purification process.

[0044] In one implementation method, the aforementioned requirements analysis report for determining the target water purification process, such as... Figure 2 and Figure 3 As shown, it includes S101:

[0045] S101. Based on the engineering design requirements and requirements analysis prompts of the target water purification process, the requirements of the engineering design are analyzed using a requirements analysis model based on a large language model, and a requirements analysis report of the target water purification process is obtained.

[0046] Optionally, the engineering design requirements for the aforementioned target water purification process may include engineering design requirements such as basic project attribute requirements and existing facility reuse requirements;

[0047] For example, the following provides the specific contents of the above-mentioned basic attribute requirements for the project and the above-mentioned requirements for reuse of existing facilities;

[0048] 1. Basic Project Attribute Requirements: Project type (e.g., new wastewater treatment plant, expansion and renovation of existing wastewater treatment plant, construction or consulting project of reclaimed water reuse system, etc.), design scale (e.g., average daily treatment volume, such as 100,000 m³ / d, 50,000 m³ / d; peak hourly water volume and fluctuation coefficient, such as 1.2~1.5 times the average daily volume, etc.), and project geographical location and climate characteristics (e.g., dyeing and printing industrial park in East China, cold regions in North China, the lower limit of winter water temperature needs to be specified, such as ≥8℃).

[0049] 2. Existing facility reuse requirements (renovation projects): Identify reusable process units (such as existing AAO tanks and secondary sedimentation tanks), equipment models and parameters (such as existing MBR membrane module materials and flux, and secondary sedimentation tank surface load), and reuse ratio requirements (such as increasing treatment capacity by 60% using existing tank capacity).

[0050] Understandably, the aforementioned requirements analysis prompts are used to guide the requirements analysis model to obtain a requirements analysis report; the aforementioned requirements analysis report may include engineering design objectives and engineering constraints, etc.; the aforementioned engineering constraints may include technical constraints, economic constraints, and time constraints, etc.; the aforementioned engineering design objectives may include effluent compliance objectives and reuse function objectives, etc.

[0051] For example, the specific contents included in the above-mentioned effluent compliance targets and reuse function targets are given below;

[0052] 1. Effluent compliance target: The key indicators of the effluent meet the specified national standard limits (such as COD≤200mg / L, color≤80 times, and / or, coliform bacteria≤3 MPN / L, etc.), and when the influent water quality fluctuates (such as COD±20%, and / or, salinity±15%, etc.), the effluent compliance rate is ≥99.5%;

[0053] 2. Reuse Functional Objectives (if there is a need for reuse): A specific percentage of reuse rate (e.g., 50% of greywater reuse, 80% of greening irrigation reuse) and the quality of reused water meets the requirements of the scenario (e.g., reused water COD≤50mg / L, SS≤10mg / L, and / or, hardness≤100mg / L).

[0054] For example, the above requirements analysis prompts are shown below;

[0055]

Role Definition

[0056] You are a "Senior Water Purification Process Project Manager + Requirements Analyst". You need to have a full understanding of the wastewater treatment engineering design process, be familiar with current national standards such as "Outdoor Drainage Design Code" GB50014-2006 and "Pollutant Discharge Standard for Urban Wastewater Treatment Plants" GB18918-2002, and be able to accurately identify explicit information and implicit requirements in engineering needs.

[0057] [Task Instructions]

[0058] 1. Analyze the user's design requirements for the target water purification process and extract core information, including but not limited to: project type (new construction / renovation / reuse), design scale (water volume and fluctuation coefficient), influent water quality indicators (including special pollutants), effluent standards (national standard number and specific limits must be indicated), and existing facility status (required for renovation projects).

[0059] 2. Check the completeness of the requirements: If core information is missing (such as no effluent standard, or the renovation project does not mention existing facility parameters), it should be marked "to be supplemented" and the direction of supplementation should be clearly stated; if the information is vague (such as "the treatment effect is good"), it should be converted into quantitative indicators.

[0060] 3. Prioritize Requirements: Prioritize extracted requirements according to the logic of "Environmental compliance (effluent meets standards) > Technical feasibility (process adapts to water quality) > Project implementation (facility reuse / land use)";

[0061] Output Format

[0062] Generate a standardized requirements analysis report (JSON format).

[0063] [Quality Requirements]

[0064] 1. The national standard number and indicator limits must be accurate. If relevant national standards are referenced, the sub-levels must be distinguished...;

[0065] 2. Water quality parameters must be labeled with units (such as mg / L, times, MPN / L) to avoid vague descriptions;

[0066] 3. Renovation projects need to clearly define the reuse requirements of existing facilities to avoid a disconnect between subsequent plans and actual working conditions;

[0067] Understandably, guided by the aforementioned requirements analysis prompts, the requirements analysis model first deeply understands the engineering design requirements of the target water purification process, identifying the project types (e.g., new construction / renovation / consulting), key design parameters (e.g., water quality, quantity, or discharge standards), and constraints (e.g., technical constraints, economic constraints, environmental constraints, time constraints), thus obtaining key information from the engineering design requirements. Next, it performs semantic parsing of these requirements, extracting explicit and implicit design requirements from the key information, identifying key performance indicators, and prioritizing them. Then, it assesses the sufficiency of the key information. If the assessment indicates insufficient sufficiency (e.g., missing project types, key design parameters, or constraints), it infers or prompts to supplement the missing information. After obtaining the supplemented key information, it generates the aforementioned requirements analysis report based on the priority of the key performance indicators within the key information.

[0068] It is understandable that the above-mentioned key performance indicators can cover the core concerns of water purification process design; the above-mentioned key performance indicators may include water treatment effect indicators (such as COD removal rate, ammonia nitrogen removal rate, SS removal rate, etc.), process design parameter indicators (such as hydraulic retention time, sludge concentration, surface loading, etc.), economic cost indicators (such as investment cost per ton of water, operating costs, etc.), and environmental impact indicators (such as carbon footprint, energy intensity, etc.). The embodiments of this application do not limit the specific content included in the above-mentioned key performance indicators.

[0069] It should be noted that the aforementioned requirement analysis model based on a large language model refers to a requirement analysis model that is trained based on a large language model. Therefore, the aforementioned requirement analysis model can be considered as a type of large language model. Specifically, the aforementioned requirement analysis model is obtained by fine-tuning a pre-trained large language model using knowledge from this technical field. In this embodiment of the application, the aforementioned large language model is a transformer-based large language model. The aforementioned large language model can be the Tongyi Qianwen-Omni-Turbo model, the GPT series of large language models (GPT-3, GPT-4, and GPT-5, etc.), or the LLaMA series of large language models (LLaMA-1 and LLaMA-2, etc.). This embodiment of the application does not limit the specific type of the aforementioned large language model.

[0070] The intelligent agent for solution design is used to determine the process scheme for the target water purification process based on the requirements analysis report.

[0071] Optionally, such as Figure 4 and Figure 5As shown, the process scheme for determining the target water quality purification process includes S201-S202:

[0072] S201. Obtain engineering knowledge related to the requirements analysis report;

[0073] The engineering knowledge related to the requirements analysis report includes standard and specification knowledge, engineering case knowledge, equipment parameter knowledge, and process unit knowledge related to the target water purification process. The engineering knowledge related to the requirements analysis report covers the design basis, technical parameters, historical cases, and latest research results of the water purification process related to the target water purification process. It has the effect of providing theoretical basis, technical reference, case reference, and innovative inspiration for the intelligent agent of solution design.

[0074] In one implementation, such as Figure 6 As shown, the above engineering knowledge can be retrieved from existing databases (such as existing cost databases, equipment selection databases, engineering case databases, patent databases, and literature databases such as Elsevier Scopus);

[0075] In another implementation, such as Figure 7 As shown, the above engineering knowledge can be retrieved from the requirements understanding knowledge base;

[0076] The aforementioned requirement understanding knowledge base can be obtained by integrating existing academic literature, patented technologies, engineering cases, standards and specifications, equipment parameters, and economic costs. Alternatively, it can be obtained by integrating data from existing cost databases, equipment selection databases, engineering case databases, patent databases, literature databases, standards and specifications databases, and process knowledge graphs related to process design. This application embodiment does not limit the data source of the aforementioned requirement understanding knowledge base. The type of the aforementioned requirement understanding knowledge base can be a PostgreSQL vector database or a Neo4j graph database. This application embodiment does not limit the type of the aforementioned requirement understanding knowledge base.

[0077] The following is an example of the standard and specification knowledge in the above-mentioned requirements understanding knowledge base;

[0078] {

[0079] "Entity Type": "Standard Specification"

[0080] Standard Number: GB 18918-2002

[0081] Standard Name: "Pollutant Discharge Standard for Urban Wastewater Treatment Plants"

[0082] Scope of application: "Urban wastewater treatment plants",

[0083] "Emission Level": {……},

[0084] Release date: 2002

[0085] "Revision Status": "Latest Revised 2019 Draft for Public Comment",

[0086] "Related Cases": ……,

[0087] Source: ……

[0088] }

[0089] The following is an example of engineering case knowledge from the above-mentioned requirements understanding knowledge base;

[0090] {

[0091] "Entity Type": "Project Case"

[0092] Project Name: Wastewater Treatment Plant Renovation Project in a Printing and Dyeing Industrial Park in East China

[0093] Project Type: Renovation Project

[0094] Design scale: 100,000 m³ / d

[0095] "Influent water quality": {……},

[0096] "Outlet water standard": {……},

[0097] "Process Used": ["Coagulation and Sedimentation", "Hydrolysis and Acidification", "A² / O", "MBR"],

[0098] "Investment Costs": {……},

[0099] "Result of running": {……},

[0100] Related literature: ……,

[0101] Implementing Unit: ……

[0102] }

[0103] The following is an example of the device parameter knowledge in the above-mentioned requirements understanding knowledge base;

[0104] {

[0105] Entity Type: Device

[0106] Name: Hollow Fiber Membrane Module

[0107] Model Number: MBR-2020

[0108] Manufacturer: "……"

[0109] Technical parameters: {……},

[0110] Applicable scenarios: {……},

[0111] Maintenance requirements: {……},

[0112] "Related Cases": ……,

[0113] Source: ……

[0114] }

[0115] The following is an example of the process unit knowledge in the above-mentioned requirements understanding knowledge base;

[0116] {

[0117] "Entity Type": "Process Unit"

[0118] Name: A² / O Process

[0119] "Aliases": ["AAO process", "Anaerobic-Anoxic-Aerobic process"],

[0120] "Applicable water quality": {

[0121] COD concentration range: 100-1000 mg / L

[0122] "BOD5 / COD ratio": ">0.3",

[0123] TN Removal Requirements: High

[0124] },

[0125] Typical parameters: {……},

[0126] "Advantages and disadvantages": {……},

[0127] Related literature: ……,

[0128] Source: ……

[0129] }

[0130] S202. Based on engineering knowledge, requirements analysis report and process scheme, generate prompt words, and use a scheme generation model based on large language model to generate a process scheme for the target water purification process from engineering knowledge.

[0131] The above-mentioned process scheme generation prompts are used to guide the scheme generation model in generating process schemes. The process scheme includes a process chain of pretreatment, biological treatment, and physiochemical treatment, as well as the corresponding facilities. The aforementioned process chain of pretreatment, biological treatment, and physiochemical treatment refers to a process chain that includes pretreatment, biological treatment, and physiochemical treatment. The facilities corresponding to the aforementioned process chain refer to the equipment and structures (such as various treatment tanks and buildings) used in pretreatment, biological treatment, and physiochemical treatment. Specifically, the aforementioned pretreatment can be treatment methods such as bar screens, grit removal, or equalization; the aforementioned biological treatment methods include A² / O, SBR, or... The above-mentioned physical and chemical treatments can be coagulation, filtration, or membrane separation. The facilities corresponding to the above process chain may include facilities used in the pretreatment stage, such as screen wells, grit chambers, and equalization tanks, for removing large particulate impurities and adjusting water quality and quantity; facilities used in the biological treatment stage, such as anaerobic tanks, anoxic tanks, and aerobic tanks, for degrading organic matter and removing nitrogen and phosphorus; and fine treatment facilities used in the physical and chemical treatment stage, such as filters, activated carbon adsorption tanks, and membrane treatment facilities, for further removing recalcitrant pollutants and achieving high-quality effluent.

[0132] For example, the prompt words generated by the above process scheme are shown below;

[0133]

Role Definition

[0134] You are a "water purification process design expert + system integration engineer". You need to be proficient in the "Outdoor Drainage Design Code" (GB50014-2006) and wastewater treatment engineering cases (such as industrial park dyeing wastewater treatment, municipal sewage upgrading and renovation, and reclaimed water reuse system). You need to have the ability to design the entire process chain of "pretreatment + biological treatment + physicochemical treatment", and be able to accurately match the process with the corresponding facilities to ensure that the solution meets the water quality requirements, discharge standards and engineering feasibility.

[0135] [Task Instructions]

[0136] 1. First, analyze the core information in the input "Demand Analysis Report": including design scale (e.g., 100,000 m³ / d), influent water quality (e.g., COD 1500 mg / L, salinity 2500 mg / L, ammonia nitrogen 45 mg / L), effluent standard, and project type (new construction / renovation; for renovation projects, reusable facilities must be clearly specified).

[0137] 2. Design a complete process chain in the order of "pretreatment → biological treatment → physicochemical treatment": Select appropriate processes based on water quality characteristics (e.g., salt-tolerant biological processes should be preferred for high-salinity wastewater, and membrane separation processes should be included for reuse requirements), and use graph neural network (GNN) logic to screen the optimal process combination;

[0138] 3. Match facilities to the process chain: Clarify the calculation basis for the structure size, equipment model and core parameters of each link to ensure that the function of the facilities is consistent with the process requirements (e.g., if biological treatment requires nitrogen and phosphorus removal, then an anaerobic tank + anoxic tank + aerobic tank should be provided).

[0139] [Process and Facility Design Rules]

[0140] 1. Pretreatment process and corresponding facility design:

[0141] If the influent SS ≥ the set threshold...: The process selection is "bar + grit chamber + equalization tank", and the facilities must include "mechanical bar well (bar spacing, flow velocity through the bar meets engineering specifications), horizontal flow grit chamber (retention time, horizontal flow velocity meets engineering specifications), equalization tank (retention time, mixing configuration, effective volume calculation meets engineering specifications)". The core function is to remove large particulate impurities and silt, and to homogenize the quality and quantity.

[0142] If the influent water quality fluctuates greatly: a separate "water quality regulation unit" is added, and the facility is a "baffle-type regulation tank (effective water depth and hydraulic retention time meet engineering specifications)" to avoid impacting the subsequent biological treatment unit;

[0143] 2. Design of biological treatment processes and corresponding facilities:

[0144] If nitrogen and phosphorus removal is required: the "A² / O process" should be given priority. The facilities include "anaerobic tank (retention time and volumetric load meet engineering specifications), anoxic tank (retention time and DO concentration meet engineering specifications), and aerobic tank (retention time and DO concentration; for renovation projects, suitable filler can be added as needed and the filling rate can be determined."

[0145] If the influent has high salinity: Select the "salt-tolerant MBBR process", and the facility is an "MBBR biological tank (retention time, volumetric loading, packing type and parameters comply with engineering specifications)";

[0146] If the water volume is small and the discharge is intermittent: the "SBR process" can be selected, and the facility is an "SBR reaction tank (cycle, duration of each stage, effective water depth, and equipment that meets engineering specifications)".

[0147] 3. Physical and chemical treatment processes and corresponding facility design:

[0148] For deep removal of COD / color: Select "coagulation sedimentation + O3-BAC process", the facility includes "high-efficiency sedimentation tank (surface load, reagent dosage meets engineering specifications), ozone contact tank (retention time, ozone dosage meets engineering specifications), biological activated carbon filter (filtration rate, activated carbon parameters, layer height meet engineering specifications)".

[0149] If there is a need for reuse: "Membrane separation process" must be selected, and the facility must be "MBR membrane module (material, flux, and operating pressure meet engineering specifications) or ultrafiltration + reverse osmosis system (ultrafiltration membrane pore size and reverse osmosis membrane desalination rate meet engineering specifications)" to ensure that the quality of the reused water meets the standards.

[0150] Output Format

[0151] 1. Process flow diagram: drawn using Mermaid syntax, with the process sequence and core parameters marked;

[0152] Process chain description table: ...;

[0153] Facilities list: ...;

[0154] [Quality Requirements]

[0155] Process selection must match water quality characteristics: for example, ordinary activated sludge process cannot be selected for high salinity wastewater, and membrane separation process must be included for reuse requirements;

[0156] Facility parameters must comply with national standards: for example, the retention time in the equalization tank should not be less than 1.5 hours (as required by GB50014-2006), and the surface load of the secondary sedimentation tank should not exceed 1.5 m³. 3 / m 2 ·h;

[0157] Upgrade projects must reflect facility reuse: for example, existing AAO tanks can have their treatment capacity increased by adding MBBR packing material, and the upgrade method and parameter adjustment basis for reuse facilities must be clearly defined;

[0158] Guided by the process scheme generation prompts, the above-mentioned scheme generation model designs the process flow of pretreatment, biological treatment, and physicochemical treatment in parallel based on engineering knowledge and requirements analysis reports. During this process, the scheme generation model uses graph neural networks (GNN) to search for the optimal topology path to intelligently combine each unit process to form a complete process chain of "pretreatment + biological treatment + deep treatment". Subsequently, the dimensions and parameters of the structures used in the above process chain are calculated and the equipment used in the above process chain is matched and configured through search. Thus, the process scheme of the target water purification process is obtained and output.

[0159] It is understandable that the above-mentioned solution can obtain device information by means of network search or by searching the device selection library during the process of matching and configuring the device. This application embodiment does not limit the above-mentioned device matching and configuration method.

[0160] It should be noted that the above-mentioned scheme generation model based on a large language model refers to the scheme generation model that is trained based on a large language model. Therefore, the above-mentioned scheme generation model can be considered as a kind of large language model. Specifically, the above-mentioned scheme generation model is obtained by fine-tuning the pre-trained large language model using knowledge from this technical field.

[0161] The solution evaluation agent is used to determine the evaluation report of the process solution based on the process solution.

[0162] For example, such as Figure 8 and Figure 9 As shown, the evaluation report for determining the above-mentioned process scheme includes S301:

[0163] S301. Based on the process scheme and scheme evaluation prompts, the process scheme is evaluated using a scheme evaluation model based on a large language model, and an evaluation report of the process scheme is obtained.

[0164] The aforementioned scheme evaluation prompts are used to guide the scheme evaluation model in evaluating the process scheme. The evaluation dimensions include technical, economic, risk, and operational dimensions. The aforementioned evaluation report includes the process scheme evaluation indicators and scheme optimization suggestions. The aforementioned process scheme evaluation indicators include technical evaluation indicators, economic evaluation indicators, risk evaluation indicators, and operational evaluation indicators.

[0165] For example, the prompts for evaluating the above solutions are shown below;

[0166]

Role Definition

[0167] You are a "Water Purification Engineering Assessor + Multi-Objective Decision-Making Expert". You need to be proficient in the "Outdoor Drainage Design Code" (GB50014-2006), the "Construction Engineering Quantity List Pricing Specification" (GB50500-2013), and the assessment theory of wastewater treatment engineering. You need to have the ability to comprehensively evaluate process solutions from four dimensions: technology, economy, risk, and operation. You also need to be able to combine similar engineering cases (such as municipal wastewater upgrading and renovation, industrial park wastewater treatment, and reclaimed water reuse systems) to output accurate assessment indicators and feasible optimization suggestions, ensuring that the assessment results meet the actual needs and compliance requirements of the project.

[0168] [Task Instructions]

[0169] 1. Input Analysis: First, extract the core information of the "process scheme", including the process combination of pretreatment / biological treatment / physicochemical treatment, key structure parameters, equipment configuration, design scale and effluent target;

[0170] 2. Multi-dimensional evaluation: Following the order of "technical dimension → economic dimension → risk dimension → operation dimension", fuzzy comprehensive evaluation (technical dimension), full life cycle cost analysis (economic dimension), Monte Carlo simulation (risk dimension), fault tree analysis (operation dimension) and other methods are used to quantitatively evaluate the merits of the process scheme;

[0171] 3. Report Output: Generate an assessment report that includes "Overview of Assessment, Dimensional Assessment Indicators, and Suggestions for Solution Optimization". The assessment indicators must clearly state the achievement status, and the optimization suggestions must be related to specific processes / equipment and indicate the expected effects after the improvement.

[0172] [Multi-dimensional evaluation rules]

[0173] 1. Technical Dimension Assessment (Weight 0.3, Full Marks 100, ≥80 marks is considered passing):

[0174] Evaluation indicators and judgment criteria:

[0175] Process feasibility (30 points): Determine whether the process combination is suitable for water quality characteristics and whether the structural parameters meet national standards;

[0176] Treatment effect stability (25 points): Based on data from similar cases, assess the fluctuation of influent;

[0177] Technology maturity (25 points): The score corresponds to the number of engineering application cases of the core process in the past 5 years;

[0178] Operational complexity (20 points): The number of professional operators required corresponds to the score;

[0179] 2. Economic Dimension Assessment (Weight 0.3, Full Marks 100, ≥75 points is considered passing):

[0180] Evaluation indicators and calculation methods:

[0181] Construction investment (40 points): Calculate the construction cost per ton of water (total investment / design scale), and score according to the industry benchmark value;

[0182] Operating Costs (30 points): Calculate the operating cost per ton of water (energy consumption + chemicals + labor + maintenance), and score according to the industry benchmark value;

[0183] Investment recovery period (30 points): Calculated based on the set discount rate and scored according to the industry benchmark value;

[0184] 3. Risk Dimension Assessment (Weight 0.2, Full Score 100, ≥80 points is considered passing):

[0185] Evaluation indicators and analysis methods:

[0186] Risk of effluent quality exceeding standards (40 points): The probability of exceeding standards under fluctuations in influent water quality is simulated using Monte Carlo simulation.

[0187] Equipment failure risk (30 points): Use fault tree analysis to determine the mean time between failures (MTBF) of core equipment;

[0188] Environmental compliance risk (30 points): Determine whether it complies with current environmental policies (e.g., sludge disposal must comply with relevant national standards);

[0189] 4. Operational Dimension Assessment (Weight 0.2, Full Score 100, ≥75 points is considered passing):

[0190] Evaluation indicators and judgment criteria:

[0191] Energy consumption level (30 points): The electricity consumption per ton of water is scored according to the industry benchmark value;

[0192] Chemical consumption (25 points): The amount of chemical used per ton of water is scored according to the industry benchmark value;

[0193] Operation and maintenance difficulty (25 points): The maintenance cycle of core equipment is scored according to the industry benchmark value;

[0194] Sludge production (20 points): The sludge production per ton of water treated is scored according to the industry benchmark value;

[0195] Output Format

[0196] 1. Assessment Overview: Briefly describe the core of the plan, the assessment method, and the overall compliance status.

[0197] 2. Dimensional Evaluation Indicator Table: Lists the name of each indicator, calculation results, target threshold, and achievement status.

[0198] 3. Solution Optimization Suggestion Form: List the issues to be optimized, optimization measures, and expected improvement effects;

[0199] [Quality Requirements]

[0200] 1. Compliance: All national standards referenced must be accurate, and the evaluation indicators must conform to industry norms. Conclusions that violate common sense in engineering are not allowed.

[0201] 2. Data accuracy: Cost calculations and energy consumption statistics must be derived from process parameters and must not be subjectively estimated;

[0202] 3. Feasibility Recommendations: Optimization measures must be tailored to the characteristics of the process (e.g., the suggestion to "cancel the membrane process" should not be made regarding MBR membrane maintenance issues), and the improvement effect must be quantifiable (e.g., "cost reduction of 20%" rather than "cost decrease").

[0203] 4. Case Relevance: The evaluation process must be linked to at least one similar engineering case to ensure that the evaluation results are supported by engineering practice;

[0204] Guided by the aforementioned evaluation prompts, the above-mentioned scheme evaluation model conducts a multi-dimensional parallel evaluation of the process scheme. In the technical dimension, it intelligently evaluates process feasibility, treatment effect stability, technological maturity, and operational management complexity, yielding technical evaluation indicators. In the economic dimension, it automatically calculates construction costs through intelligent investment estimation capabilities, uses professional cost analysis capabilities to evaluate operating costs and economic indicators, calculates return on investment and cost-effectiveness, and obtains economic evaluation indicators. In the risk dimension, it verifies emission compliance and the acceptability of environmental impact, obtaining risk evaluation indicators. In the operational dimension, it evaluates reliability, operational complexity, maintenance requirements, and personnel allocation, obtaining operational evaluation indicators. Simultaneously, it deeply analyzes the advantages, disadvantages, and improvement potential of the process scheme, generating detailed and targeted scheme optimization suggestions, and outputting a process scheme evaluation report.

[0205] In one implementation, during the technical dimension assessment, the scheme evaluation model is based on the fuzzy comprehensive evaluation algorithm embedded in the scheme evaluation prompts. It constructs a set of evaluation factors U={process applicability, processing efficiency, and shock resistance} and a set of comments V={very good, good, average, and poor}. The analytic hierarchy process is used to determine the weight vector W=(0.4, 0.35, 0.25) of each factor. The membership matrix R is calculated based on the historical operating data of similar projects, and finally, the technical feasibility score B=W×R is obtained. The technical feasibility score is used as the technical evaluation index.

[0206] In the economic dimension assessment, guided by the parameter estimation method in the scheme assessment prompts, the direct cost method is used to calculate the civil engineering cost C1=Σ(structure volume × unit cost) and the equipment cost C2=Σ(equipment quantity × equipment unit price). Through the energy consumption factors and reagent prices in the cost database, the life cycle cost formula LCC=CI+Σ(annual operating cost × discount factor) is used to calculate the total life cycle cost, and the total life cycle cost is used as the economic assessment indicator.

[0207] In the risk dimension assessment, the agent uses the Monte Carlo simulation algorithm in the scheme assessment prompt to set the influent water quality fluctuation range to ±20% and the coefficient of variation of the operating parameters to 0.15. It calculates the probability P (effluent ≤ standard value) of effluent compliance through 10,000 random sampling simulations and uses the probability of effluent compliance as the risk assessment indicator.

[0208] In the operational dimension assessment, the agent uses the fault tree analysis method in the scheme assessment prompts, with "system failure" as the top event, to identify 12 basic events such as equipment failure, operational error, and improper maintenance. The system reliability index R(t)=exp(-λt) is calculated through the minimum cut set, and the system reliability index is used as the operational assessment index.

[0209] It should be noted that the above-mentioned scheme evaluation model based on a large language model refers to the scheme evaluation model that is trained based on a large language model. Therefore, the above-mentioned scheme evaluation model can also be considered as a large language model. Specifically, the above-mentioned scheme evaluation model is obtained by fine-tuning the pre-trained large language model using knowledge from this technical field.

[0210] The solution optimization agent is used to determine the final process scheme for the target water purification process based on the process scheme and evaluation report.

[0211] In some embodiments, such as Figure 10 and Figure 11 As shown, the final process scheme for determining the target water quality purification process includes S401-S403:

[0212] S401. Judge the evaluation indicators in the evaluation report;

[0213] S402. When the evaluation index is greater than the index threshold, the process scheme shall be determined as the final process scheme of the target water quality purification process.

[0214] In one implementation, thresholds for technical evaluation indicators, economic evaluation indicators, risk evaluation indicators, and operational evaluation indicators are set. The technical evaluation indicators, economic evaluation indicators, risk evaluation indicators, and operational evaluation indicators in the evaluation report are judged respectively. Only when the technical evaluation indicator is greater than the technical evaluation indicator threshold, the economic evaluation indicator is greater than the economic evaluation indicator threshold, the risk evaluation indicator is greater than the risk evaluation indicator threshold, and the operational evaluation indicator is greater than the operational evaluation indicator threshold, is the process scheme determined as the final process scheme of the target water purification process.

[0215] In one application scenario of the above implementation method, the threshold values ​​for technical evaluation indicators are set to 90 points, economic evaluation indicators to 95%, risk evaluation indicators to 99.5%, and operational evaluation indicators to 99%. When the economic evaluation indicator reaches 90 points or above (out of 100), the cost-effectiveness ratio reaches 95% or above the industry benchmark, the risk evaluation indicator (probability of effluent compliance) reaches 99.5% or above, and the operational evaluation indicator (system reliability) reaches 99% or above, the final process solution is output. Alternatively, when the number of iterations reaches the preset maximum number of rounds (e.g., 5 rounds), the process is forcibly terminated, and the current process solution is taken as the final process solution.

[0216] In another implementation, a comprehensive index threshold is set, and the technical evaluation index, economic evaluation index, risk evaluation index and operation evaluation index are weighted and summed according to their importance to obtain a comprehensive evaluation index. When the comprehensive evaluation index is greater than the comprehensive index threshold, the process scheme is determined as the final process scheme of the target water purification process.

[0217] S403. Otherwise, based on the optimization suggestions, process schemes, and scheme optimization prompts in the evaluation report, the process scheme is optimized using a scheme optimization model based on a large language model, the process scheme of the target water purification process is updated, and then the process returns to S301.

[0218] Among them, the above-mentioned optimization prompts are used to guide the optimization model to optimize the process scheme;

[0219] For example, the optimized prompt words for the above solution are shown below;

[0220]

Role Definition

[0221] You are a "Water Purification Process Optimization Engineer + System Commissioning Expert". You need to be proficient in the "Outdoor Drainage Design Code" (GB50014-2006), wastewater treatment engineering optimization theory (such as load redistribution, parameter tuning, system coordination methods), and experience in similar engineering renovations (such as adding MBBR packing to the AAO tank of the North China Wastewater Treatment Plant and optimizing the MBR membrane of the Southern Reclaimed Water Plant). You need to have the ability to accurately locate the defects of the solution based on the assessment report, formulate targeted optimization measures, and ensure that the optimized solution meets the requirements of technical compliance, economic rationality and operational reliability. You need to form a closed-loop collaboration with the solution design intelligent agent and the multi-dimensional assessment intelligent agent.

[0222] [Task Instructions]

[0223] 1. Assessment Report Analysis: Prioritize extracting the core information from the "non-compliant indicators" and "optimization suggestions" in the assessment report, clarify the type of defect (such as "insufficient shock resistance" in the technical dimension, "overspending per ton of water" in the economic dimension, "low probability of effluent meeting standards" in the risk dimension, and "system reliability not meeting requirements" in the operational dimension), and associate it with the corresponding process / equipment parameters (such as insufficient shock resistance corresponding to short residence time in the equalization tank, and overspending corresponding to high MBR membrane costs).

[0224] 2. Targeted optimization: Based on the defect type, combined with water quality characteristics (such as high salinity, high COD), project type (new construction / renovation), and algorithm logic in the assessment prompts (fuzzy comprehensive evaluation, Monte Carlo simulation, fault tree analysis), formulate three types of optimization measures: "parameter adjustment, equipment replacement, and process supplementation" to ensure that the measures are quantifiable and implementable;

[0225] 3. Effect Verification and Iteration: After optimization, it is necessary to predict the extent of improvement in technical, economic, risk, and operational indicators (e.g., technical score from 85 to 92, water quality compliance probability from 98% to 99.6%). If it is predicted that all indicators will meet the standards, the optimized solution will be output. If there are still indicators that do not meet the standards, it is necessary to clarify the "further optimization direction" and trigger the iteration instruction to return to S301 (solution evaluation stage).

[0226]

Dimensional Optimization Strategy

[0227] 1. Technical optimization (addressing issues such as "inadequate process feasibility, unstable treatment results, and weak impact resistance"):

[0228] Process adaptability optimization: If the assessment shows "High-salt wastewater is treated with ordinary activated sludge process (technical score 70 points)," then replace it with salt-resistant MBBR process and adjust the parameters: add HDPE material MBBR suspended packing (specific surface area ≥500m² / m³), increase the filling rate from 30% to 40%, and extend the biological tank retention time from 6h to 7h. Refer to the wastewater treatment case of East China Printing and Dyeing Industrial Park to ensure technical score ≥90 points;

[0229] Shock resistance optimization: If the COD of the influent fluctuates by ±25%, resulting in unstable treatment effect (compliance rate of 95%), an "emergency adjustment unit" will be added. Optimization measures include: extending the retention time of the original equalization tank from 1.5h to 2.5h, adding a submersible mixer (power 0.1kW / m³), and verifying through fuzzy comprehensive evaluation to ensure that the shock resistance index is improved from "average" to "very good".

[0230] Technology maturity optimization: If there are fewer than 20 cases of core processes (such as novel electrocatalytic oxidation) (technology maturity score of 12 points), they will be replaced with mature processes (such as O3-BAC). Refer to the case of deep treatment in the Southern Reclaimed Water Plant to ensure that the technology maturity score is ≥20 points.

[0231] 2. Economic Dimension Optimization (addressing issues such as "excessive investment per ton of water, high operating costs, and long investment payback period"):

[0232] Construction cost optimization: If the cost of MBBR membrane modules accounts for 40% of the total equipment investment (3450 yuan per ton of water, exceeding the benchmark by 15%), the optimization measures are as follows: replace 30% of the MBBR membranes with ultrafiltration membranes (PVDF material, flux 18 LMH, cost 20% lower than MBBR membranes), and adjust the equipment list: 60 ultrafiltration membrane modules (50 m³ / d flux per module) and 140 MBR membrane modules. Through parameter estimation, ensure that the investment per ton of water is reduced to 3100 yuan (≤ 105% of the industry benchmark of 3000 yuan / m³), with an economic score ≥ 95%.

[0233] Operating cost optimization: If the operating cost per ton of water is 1.4 yuan (exceeding the threshold of 1.2 yuan), with aeration energy consumption accounting for 60%, then the optimization measures are: replace the blower aeration system with a jet aeration system, reducing aeration energy consumption from 0.6 kWh / m³ to 0.4 kWh / m³. Calculated using the life cycle cost formula (LCC=CI+Σ annual operating cost×discount factor), the operating cost per ton of water is reduced to 1.15 yuan, and the investment payback period is shortened from 9 years to 8 years.

[0234] Consumable cost optimization: If the PAC dosage is 60mg / L (chemical cost per ton of water is 0.18 yuan), the optimization measures are as follows: adjust the PAC dosage to 45mg / L, add PAM co-dosing (0.8mg / L), refer to the coagulation and sedimentation optimization case of the North China wastewater treatment plant, ensure that the SS removal rate is still ≥95%, and reduce the chemical cost per ton of water to 0.12 yuan;

[0235] 3. Risk dimension optimization (addressing issues such as "low probability of effluent meeting standards, high risk of equipment failure, and environmental compliance risks"):

[0236] Optimization of effluent compliance risk: If the Monte Carlo simulation shows that the effluent compliance probability is 98.5% (below the threshold of 99.5%) when the influent ammonia nitrogen fluctuation is ±20%, then the optimization measures are as follows: Add an "emergency aeration module" to the aerobic section of the biological tank, increase the DO concentration from 2-4 mg / L to 3-5 mg / L, set it to automatically start when ammonia nitrogen ≥50 mg / L, and re-simulate 10,000 times to ensure that the effluent compliance probability is ≥99.5%;

[0237] Equipment failure risk optimization: If the MBR membrane MTBF = 7000h (below the threshold of 8000h), the optimization measures are as follows: add an online chemical cleaning system (add 0.5% citric acid cleaning agent, extend the cleaning cycle from 30 days to 45 days), replace the membrane module seals (change the material from nitrile rubber to fluororubber), and verify through fault tree analysis to ensure that the MTBF ≥ 8500h;

[0238] Environmental compliance risk optimization: If there is no clear plan for sludge disposal (compliance risk score 18 points), the optimization measures are as follows: add a sludge deep dewatering unit (plate and frame filter press, processing capacity 10tDS / d), reduce the sludge moisture content from 80% to 60%, which complies with the "Sludge Quality for Mixed Landfill Disposal of Sludge from Urban Wastewater Treatment Plants" (GB / T 23485-2009), and ensure that the compliance risk score is ≥30 points;

[0239] 4. Operational optimization (addressing issues such as "low system reliability, high maintenance difficulty, and large staffing requirements"):

[0240] System reliability optimization: If the fault tree analysis shows that the minimum cut set of "system failure" includes "aeration fan failure" (reliability index R(t) = 97%, lower than the threshold of 99%), then the optimization measures are as follows: add one backup aeration fan (the same model as the main fan, with an air volume of 5000 m³ / h), adopt "one in use and one in standby" automatic switching control, recalculate R(t) = exp(-λt), and ensure that the reliability index is ≥99%;

[0241] Optimization of Operation and Maintenance Difficulty: If the MBR membrane maintenance cycle is 2 months (Operation and Maintenance Score 15 points), the optimization measures are as follows: Add a "membrane fouling monitoring sensor" to the MBR tank (real-time monitoring of transmembrane pressure difference, threshold 0.2MPa). When the pressure difference exceeds the threshold, online cleaning is automatically triggered. Referring to the operation and maintenance case of the Southern Reclaimed Water Plant, ensure that the maintenance cycle is extended to 3 months and the operation and maintenance score is ≥24 points.

[0242] Staffing optimization: If 8 operators are required per shift (operation complexity score of 8 points), the optimization measures are as follows: Add an "intelligent control system" (remotely monitor influent water quality and equipment operating parameters, and automatically alarm), simplify the operation process (such as automatic chemical dosing and one-click cleaning), and ensure that the number of operators is reduced to 5 per shift, with an operation complexity score of ≥20 points;

[0243] Output Format

[0244] 1. Overview of the optimized process scheme: ...;

[0245] 2. Parameter comparison table before and after optimization: ...;

[0246] 3. Iteration suggestions: ...;

[0247] [Quality Requirements]

[0248] 1. Compliance: All optimization parameters must comply with national standards (e.g., the MBBR biological tank retention time of 7h complies with GB50014-2006, and the membrane flux of 18LMH is within the reasonable range of the industry), and there must be no illegal parameters such as "biological tank DO concentration of 10mg / L" or "secondary sedimentation tank surface loading of 2.0m³ / m²·h".

[0249] 2. Feasibility of the measures: The optimization measures need to match the project type (the renovation project should prioritize the reuse of existing facilities, such as adding MBBR packing to the existing AAO tank without adding new structures), and have engineering case studies to support them (such as the North China wastewater treatment plant case for jet aeration optimization).

[0250] 3. Quantifiable results: The predicted improvement results must be linked to evaluation indicators (such as "the maintenance cycle is extended by 1 month → the maintenance score increases by 9 points"), and vague statements such as "the maintenance difficulty is reduced" or "the cost is reduced" are not allowed.

[0251] 4. Iteration Logic: Strictly follow the “S401-S403” process. If the optimization still fails to meet the standards, the “next optimization direction” must be clearly defined to ensure a smooth transition with S301 (solution evaluation) and no iteration gaps.

[0252] Guided by the optimization prompts in the evaluation report, the above-mentioned optimization model deeply analyzes the technical defects, economic deficiencies, environmental risks, and operational problems of the process scheme based on the optimization suggestions in the evaluation report. It optimizes the process scheme by redistributing load and adjusting process parameters, reduces the investment cost of the process scheme by optimizing equipment selection and improving system integration, improves the operational stability of the process scheme by coordinating operating parameters and optimizing control strategies, and enhances the system reliability of the process scheme by fine-tuning the process flow and hydraulic optimization, thus obtaining and outputting an updated process scheme.

[0253] Specifically, when the technical evaluation results indicate that the nitrogen and phosphorus removal efficiency of the biological treatment unit is low and the process stability is insufficient, key parameters in the design scheme, such as the volume ratio of the biological tank, dissolved oxygen control strategy, and sludge return ratio, are optimized and adjusted. Specifically, this includes extending the anaerobic tank retention time from 1.5 hours to 2.0 hours, increasing the volume of the anoxic tank by 20%, and optimizing the aeration system configuration to improve oxygen transfer efficiency.

[0254] The above-mentioned load redistribution and process parameter adjustment process includes: rationally allocating and optimizing the pollutant load of each treatment unit in the entire process system according to the characteristics of the influent water quality and quantity and the treatment requirements; for example, allocating the organic load mainly to the front-end enhanced pretreatment unit, and rationally allocating the nitrogen load to the biochemical reaction tanks in different functional areas, to ensure that each treatment unit operates within the optimal load range and to avoid the situation where some units are overloaded while other units are underloaded.

[0255] The aforementioned system integration and improvement process includes: overall optimization of the connection relationship between each independent unit in the process flow, material flow path, energy transfer method and control system; including optimizing sludge return path, adjusting the hydraulic connection method between different treatment units, integrating automated control system, unifying equipment configuration standards, etc., to ensure the coordination and unity of the entire treatment system in terms of technology, economy, operation and overall performance optimization;

[0256] It should be noted that the above-mentioned scheme optimization model based on the large language model refers to the scheme optimization model that is trained based on the large language model. Therefore, the above-mentioned scheme optimization model can also be considered as a large language model. Specifically, the above-mentioned scheme optimization model is obtained by fine-tuning the pre-trained large language model using knowledge in this technical field.

[0257] In some embodiments, the system also includes a report writing agent.

[0258] The report writing agent is used to determine the engineering design report of the target water purification process based on the final process scheme.

[0259] Optionally, such as Figure 12 and Figure 13 As shown, the process of determining the target water purification process in the engineering design report includes S501:

[0260] S501. Based on the final process scheme and report writing prompts, the final process scheme is analyzed using a report writing model based on a large language model to generate an engineering design report for the target water purification process.

[0261] The above report writing prompts are used to guide the report writing model in generating engineering design reports;

[0262] For example, the above report writing prompts are shown below;

[0263]

Role Definition

[0264] You are a "Water Purification Engineering Report Compilation Specialist + Data Visualization Expert". You need to be proficient in the "Outdoor Drainage Design Code" (GB50014-2006), the "Regulations on the Depth of Design Documents for Municipal Public Works", and the standards for compiling wastewater treatment engineering reports. You need to have a deep understanding of the core information of the "final process scheme" output by the multi-agent system (including process chain, structure parameters, equipment list, and economic data). You need to have the ability to transform technical parameters into standardized and professional engineering reports, and be able to present the scheme through visual forms such as Mermaid flowcharts and data charts to ensure that the report meets the actual needs of project implementation, approval and filing, and operation and maintenance reference.

[0265] [Task Instructions]

[0266] 1. Scheme Analysis: Extract key information from the "final process scheme", including basic project information (design scale, water quality indicators, effluent standards, project type), the entire process chain (process combination and logical relationship of pretreatment + biological treatment + physicochemical treatment), structure parameters (size, residence time, core design values), equipment configuration (model, quantity, technical parameters), economic data (construction investment, operating costs, investment payback period), and optimization iteration conclusions (e.g., achieving standards after 3 iterations, key improvement measures).

[0267] 2. Report Structure: The report structure is built according to the engineering logic of "design basis → detailed scheme description → economic analysis → environmental protection and safety → conclusions and recommendations" to ensure that the content of each chapter is closely connected and covers the core modules required by the "Regulations on the Depth of Preparation of Design Documents for Municipal Public Works".

[0268] 3. Visual presentation: Standardized process flow diagrams are drawn using Mermaid syntax (with process units and key parameters labeled), and data charts (bar charts, line charts, and tables) are used to present economic costs and technical parameters, ensuring that the information is intuitive and easy to understand;

[0269] 4. Compliance Verification: All national standards (such as GB50014-2006, GB18918-2002), engineering cases (such as wastewater treatment in East China Printing and Dyeing Industrial Park, and expansion and renovation of wastewater treatment plants in North China), and data sources (cost database, equipment selection database) cited in the report must be clearly marked, and parameter descriptions must conform to engineering specifications (such as units being uniformly "mg / L", "m³ / d", "h").

[0270] [Report Structure and Content Guidelines]

[0271] 1. Cover and Table of Contents (essential module for the opening of a report): ...;

[0272] 2. Core chapter content requirements: ...;

[0273] 2. Design basis: ...;

[0274] 3. Detailed description of the process scheme: Describe the process scheme in units according to the process chain. Each unit should include "the basis for process selection, structural parameters, equipment configuration, design logic", etc.

[0275] 4. Economic Analysis: ...;

[0276] 5. Environmental protection and safety measures: ...;

[0277] 6. Conclusions and Recommendations: ...;

[0278] 3. Visualization chart guidelines: ...;

[0279] Output Format

[0280] Summary version (≤10 pages): includes "design summary, process flow diagram, economic analysis conclusions, and recommendations", for project decision-making and rapid approval;

[0281] Detailed version (≥30 pages): Includes complete chapters + appendices (structure calculation sheets, equipment parameter tables, iteration record tables), for use in engineering construction and operation and maintenance filing;

[0282] File format: Output both Word (editable, charts can be modified) and PDF (standardized, uneditable) formats. All tables and charts in the Word version must be saved separately as editable objects (such as Excel spreadsheets or Mermaid source files).

[0283] Citations: All national standards, case studies, and data sources must be cited in the corresponding positions in the text (e.g., "Retention time in equalization tank 2.5h (GB50014-2006, Clause 5.3.2)"). A complete "List of National Standards" and "List of Case Studies" must be included in the appendix.

[0284] [Quality Requirements]

[0285] Compliance: National standard numbers, parameter units, and report structure must strictly comply with engineering specifications. Non-standard expressions such as "COD unit mg" and "retention time unit min (not marked)" are not allowed. Sludge treatment and environmental protection measures must comply with current environmental protection policies.

[0286] Accuracy: All parameters in the report (such as structure dimensions, equipment quantity, cost data) must be completely consistent with the "final process plan" and must not contain errors such as writing "200 sets of MBR membrane modules" as "180 sets" or "investment of 3100 yuan per ton of water" as "3000 yuan".

[0287] Readability: Technical terms should be marked with their full names when they appear for the first time (e.g., "MBR" or "MBBR" for moving bed biofilm reactors), complex calculation processes (e.g., volume calculation of structures) should be simplified, and core conclusions should be highlighted in bold.

[0288] Completeness: Key modules such as "sludge disposal," "safe operation and maintenance," and "iterative conclusions" must not be omitted. The appendix must include "structure calculation sheets" (e.g., MBBR biological tank volume = water volume × retention time = 100,000 m³ / d × 7h / 24h ≈ 29,167 m³, which matches the dimensions of 60m × 30m × 6m (effective volume 10,800 m³ / unit × 3 units = 32,400 m³)). Ensure that the report can be directly used for project implementation.

[0289] Guided by the aforementioned report writing prompts, the report writing model first determines the optimal report organization through document structure optimization capabilities, ensuring logical clarity and content completeness. It then utilizes the Mermaid flowchart engine to generate standardized process flow diagrams, employing color coding and standard layouts to ensure the professionalism and readability of the charts. Furthermore, it leverages professional data visualization capabilities to generate investment analysis charts, cost structure diagrams, and technical parameter tables, ensuring the accuracy and intuitiveness of data presentation. Simultaneously, it utilizes academic writing standardization tools to ensure professional expression and employs a citation format standardization engine to handle the unified format of literature citations. Finally, it generates an engineering design report that includes process plans, design basis, economic analysis, chart visualizations, and professional recommendations.

[0290] In one implementation, the design basis mainly comes from the process scheme output by the scheme design model and the final process scheme output by the scheme optimization agent, providing core technical content such as process flow selection, structure design parameters, and equipment selection; the economic analysis content comes from the economic dimension evaluation indicators (including detailed economic indicator data such as construction cost calculation, operating cost analysis, return on investment, and cost-benefit ratio) in the evaluation report output by the scheme evaluation model; the professional advice comes from the optimization suggestions output by the scheme evaluation model, as well as the optimization experience and targeted improvement measures accumulated by the scheme optimization model in multiple iterations.

[0291] It should be noted that the above-mentioned report writing model based on a large language model refers to the report writing model that is trained based on a large language model. Therefore, the above-mentioned report writing model can be considered as a kind of large language model. Specifically, the above-mentioned report writing model is obtained by fine-tuning a pre-trained large language model using knowledge from this technical field.

[0292] In summary, the multi-agent system for water purification process design provided in this application embodiment allows the following intelligent agents: the requirements analysis agent can extract implicit design requirements from the engineering design needs of the target water purification process, thereby obtaining a requirements analysis report; the scheme design agent can integrate relevant engineering information based on the requirements analysis report to generate a process scheme for the target water purification process; the scheme evaluation agent can effectively evaluate the process scheme based on its efficient parallel simulation capabilities, obtaining an evaluation report; and the scheme optimization agent, based on the evaluation report and its efficient parallel simulation capabilities, optimizes the process scheme of the target water purification process to determine the final process scheme. This entire process requires no human intervention, saving time and effort, and can be effectively integrated with new technologies, thus ensuring the water purification effect of the constructed target water purification process.

[0293] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0294] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-agent system for water quality purification process design, characterized in that, The demand analysis agent, the scheme design agent, the scheme evaluation agent, and the scheme optimization agent are included. The demand analysis agent is configured to determine a demand analysis report of the target water purification process based on engineering design requirements of the target water purification process. The engineering design requirements of the target water purification process include project basic attribute requirements and existing facility reuse requirements. According to the engineering design requirements of the target water purification process and demand analysis prompt words, a demand analysis model based on a large language model is used to analyze the engineering design requirements to obtain the demand analysis report of the target water purification process. The demand analysis prompt words are used to guide the demand analysis model to analyze the demand analysis report. The demand analysis report includes engineering design targets and engineering constraint conditions. Under the guidance of the demand analysis prompt words, the demand analysis model first deeply understands the engineering design requirements of the target water purification process, identifies the project type, key design parameters, and constraint conditions contained in the engineering design requirements, and obtains key information in the engineering design requirements. The demand analysis model then performs semantic analysis on the engineering design requirements, extracts design requirements in the key information, identifies key performance indicators in the key information, and prioritizes the key performance indicators. The demand analysis model then evaluates the sufficiency of the key information. When the evaluation result indicates that the sufficiency of the key information is insufficient, the demand analysis model reasons and completes the missing information in the key information or prompts for supplement. After obtaining the completed key information, the demand analysis model generates the demand analysis report of the target water purification process based on the priority of the key performance indicators in the key information. The key performance indicators include water treatment effect indicators, process design parameter indicators, economic cost indicators, and environmental impact indicators. The scheme design agent is configured to determine a process scheme of the target water purification process according to the demand analysis report. The scheme design agent is configured to determine a process scheme of the target water purification process according to the demand analysis report. The scheme design agent is configured to determine a process scheme of the target water purification process according to the demand analysis report. The scheme design agent is configured to determine a process scheme of the target water purification process according to the demand analysis report. The scheme design agent is configured to determine a process scheme of the target water purification process according to the demand analysis report. The scheme design agent is configured to determine a process scheme of the target water purification process according to the demand analysis report. The scheme evaluation agent is configured to determine an evaluation report of the process scheme based on the process scheme; the evaluation report includes evaluation indexes and scheme optimization suggestions of the process scheme. The scheme optimization agent is configured to determine a final process scheme of the target water quality purification process based on the process scheme and the evaluation report; including: judging the evaluation indexes in the evaluation report: when the evaluation index is greater than the index threshold, the process scheme is determined as the final process scheme of the target water quality purification process; otherwise, according to the optimization suggestions in the evaluation report, the process scheme and scheme optimization prompt words, a scheme optimization model based on a large language model is used to optimize the process scheme, update the process scheme of the target water quality purification process, and return to the step of determining the evaluation report of the process scheme based on the process scheme; wherein the scheme optimization prompt words are used to guide the scheme optimization model to optimize the process scheme.

2. The system of claim 1, wherein, The determination of the evaluation report of the process scheme includes: based on the process scheme and the scheme evaluation prompt words, a scheme evaluation model based on a large language model is used to evaluate the process scheme to obtain the evaluation report of the process scheme; the scheme evaluation prompt words are used to guide the scheme evaluation model to evaluate the process scheme, and the evaluation dimensions include technical dimension, economic dimension, risk dimension and operation dimension.

3. The system of claim 1, wherein The system further comprises a report writing agent; The report writing agent is configured to determine an engineering design report of the target water quality purification process according to the final process scheme.

4. The system of claim 3, wherein, The determination of the engineering design report of the target water quality purification process includes: according to the final process scheme and the report writing prompt words, a report writing model based on a large language model is used to analyze the final process scheme to generate the engineering design report of the target water quality purification process; the report writing prompt words are used to guide the report writing model to generate the engineering design report.

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