Multi-agent system for water quality purification process design

Abstract

The invention provides a water quality purification process design multi-agent system, and belongs to the technical field of water quality purification. The multi-agent system provided by the invention can cooperate with a plurality of agents to generate the process scheme of the target water quality purification process according to the engineering design requirement of the target water quality purification process, manpower participation is not needed, time and labor are saved, effective integration with a new technology can be realized, and the efficiency is improved. And thus, the water quality purification effect of the constructed target water quality purification process 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: 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.

[0006] 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.

[0007] 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.

[0008] In one implementation, determining the final process scheme for the target water purification process includes: 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.

[0009] 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.

[0010] 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.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: 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

[0012] 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; 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; 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; Figure 4 This is a flowchart illustrating the processing logic of the intelligent agents in the multi-agent system provided in this application embodiment; 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; 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; 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; 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; 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; Figure 10 This is a flowchart illustrating the processing logic of the intelligent agent in the multi-agent system provided in this application embodiment for scheme optimization; 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; Figure 12This is a flowchart illustrating the processing logic of the report writing agent in the multi-agent system provided in this application embodiment; 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

[0013] 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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 1. Intelligent agent (or artificial intelligence agent) 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.

[0021] 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.

[0022] 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.

[0023] 2. Prompt words 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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: 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. 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; 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; 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℃). 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). 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. For example, the specific contents included in the above-mentioned effluent compliance targets and reuse function targets are given below; 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%; 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). For example, the above requirements analysis prompts are shown below;

Role Definition

[0028] 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.

[0029] Optionally, such as Figure 4 and Figure 5 As shown, the process scheme for determining the target water quality purification process includes S201-S202: S201. Obtain engineering knowledge related to the requirements analysis report; 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. 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); In another implementation, such as Figure 7 As shown, the above engineering knowledge can be retrieved from the requirements understanding knowledge base; 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. The following is an example of the standard and specification knowledge in the above-mentioned requirements understanding knowledge base; { "Entity Type": "Standard Specification" Standard Number: GB 18918-2002 Standard Name: "Pollutant Discharge Standard for Urban Wastewater Treatment Plants" Scope of application: "Urban wastewater treatment plants", "Emission Level": {……}, Release date: 2002 "Revision Status": "Latest Revised 2019 Draft for Public Comment", "Related Cases": ……, Source: …… } The following is an example of engineering case knowledge from the above-mentioned requirements understanding knowledge base; { "Entity Type": "Project Case" Project Name: Wastewater Treatment Plant Renovation Project in a Printing and Dyeing Industrial Park in East China Project Type: Renovation Project Design scale: 100,000 m³ / d "Influent water quality": {……}, "Outlet water standard": {……}, "Process Used": ["Coagulation and Sedimentation", "Hydrolysis and Acidification", "A² / O", "MBR"], "Investment Costs": {……}, "Result of running": {……}, Related literature: ……, Implementing Unit: …… } The following is an example of the device parameter knowledge in the above-mentioned requirements understanding knowledge base; { Entity Type: Device Name: Hollow Fiber Membrane Module Model Number: MBR-2020 Manufacturer: "……" Technical parameters: {……}, Applicable scenarios: {……}, Maintenance requirements: {……}, "Related Cases": ……, Source: …… } The following is an example of the process unit knowledge in the above-mentioned requirements understanding knowledge base; { "Entity Type": "Process Unit" Name: A² / O Process "Aliases": ["AAO process", "Anaerobic-Anoxic-Aerobic process"], "Applicable water quality": { COD concentration range: 100-1000 mg / L "BOD5 / COD ratio": ">0.3", TN Removal Requirements: High }, Typical parameters: {……}, "Advantages and disadvantages": {……}, Related literature: ……, Source: …… } 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. 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. For example, the prompt words generated by the above process scheme are shown below;

Role Definition

[0030] The solution evaluation agent is used to determine the evaluation report of the process solution based on the process solution.

[0031] For example, such as Figure 8 and Figure 9 As shown, the evaluation report for determining the above-mentioned process scheme includes S301: 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. 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. For example, the prompts for evaluating the above solutions are shown below;

Role Definition

[0032] 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.

[0033] 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: S401. Judge the evaluation indicators in the evaluation report; 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. 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. 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. 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. 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. Among them, the above-mentioned optimization prompts are used to guide the optimization model to optimize the process scheme; For example, the optimized prompt words for the above solution are shown below;

Role Definition

Dimensional Optimization Strategy

[0034] In some embodiments, the system also includes a report writing agent.

[0035] The report writing agent is used to determine the engineering design report of the target water purification process based on the final process scheme.

[0036] 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: 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. The above report writing prompts are used to guide the report writing model in generating engineering design reports; For example, the above report writing prompts are shown below;

Role Definition

[0037] 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.

[0038] 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.

[0039] 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 purification process design, characterized in that, This includes intelligent agents for demand analysis, solution design, solution evaluation, and solution optimization. The requirement analysis agent is used to determine a requirement analysis report for the target water purification process based on the engineering design requirements of the target water purification process; the requirement analysis report includes engineering design objectives and engineering constraints. The intelligent agent designed in the scheme is used to determine the process scheme of the target water purification process based on the requirements analysis report. The solution evaluation agent is used to determine an evaluation report of the process solution based on the process solution; the evaluation report includes the evaluation indicators of the process solution and suggestions for solution optimization; The scheme optimization agent is used to determine the final process scheme of the target water purification process based on the process scheme and the evaluation report.

2. The system as described in claim 1, characterized in that, The requirements analysis report for determining the target water purification process includes: Based on the engineering design requirements and requirement analysis prompts of the target water purification process, a requirement analysis model based on a large language model is used to analyze the engineering design requirements, resulting in a requirement analysis report for the target water purification process. The requirement analysis prompts are used to guide the requirement analysis model to obtain the requirement analysis report.

3. The system as described in claim 1, characterized in that, The process scheme for determining the target water quality purification process includes: Acquire engineering knowledge related to the requirements analysis report; Based on the engineering knowledge, the requirements analysis report, and the process scheme generation prompts, a scheme generation model based on a large language model is used to generate a process scheme for the target water purification process from the engineering knowledge. The process scheme generation prompts are used to guide the 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 facilities corresponding to the process chain.

4. The system as described in claim 1, characterized in that, The evaluation report determining the process scheme includes: Based on the aforementioned process scheme and scheme evaluation prompts, a scheme evaluation model based on a large language model is used to evaluate the process scheme, resulting in an evaluation report. The scheme evaluation prompts are used to guide the scheme evaluation model in evaluating the process scheme, and the evaluation dimensions include technical, economic, risk, and operational dimensions.

5. The system as described in claim 1, characterized in that, The final process scheme for determining the target water purification process includes: Judge the evaluation indicators 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 purification process; Otherwise, based on the optimization suggestions in the evaluation report, the process scheme, and the scheme optimization prompts, the process scheme is optimized using a scheme optimization model based on a large language model, and the process scheme of the target water purification process is updated; and the process returns to the step of determining the evaluation report of the process scheme based on the process scheme; wherein, the scheme optimization prompts are used to guide the scheme optimization model to optimize the process scheme.

6. The system as described in claim 1, characterized in that, The system also includes a report writing agent; The report-writing agent is used to determine the engineering design report of the target water purification process based on the final process scheme.

7. The system as described in claim 6, characterized in that, The engineering design report for determining the target water purification process includes: Based on the final process scheme and report writing prompts, a report writing model based on a large language model is used to analyze the final process scheme to generate an 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.

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