Automatic scheme generation system and method for MBR (Membrane Bio-Reactor) membrane module type selection and server

Abstract

The invention provides an automatic scheme generation system, method and server for MBR (membrane bioreactor) model selection, and relates to the technical field of industrial wastewater treatment.The system comprises a user interaction module, a knowledge base module, an intelligent reasoning module and a scheme generation module; wherein the user interaction module is used for acquiring user demand information and converting the user demand information into structured demand parameters; the knowledge base module is used for storing professional knowledge and rules of MBR membrane module type selection; the intelligent reasoning module is used for calculating key design parameters according to the structured demand parameters, calling the knowledge base module, and performing membrane module type and configuration matching processing on the key design parameters to obtain a target membrane module type selection scheme; and the scheme generation module is used for receiving the target membrane module type selection scheme sent by the intelligent reasoning module and carrying out economic analysis processing and document generation processing on the target membrane module type selection scheme to obtain a target pre-sales scheme document. According to the invention, the model selection efficiency of the MBR membrane module can be obviously improved.

Description

An automated solution generation system, method, and server for MBR membrane module selection. Technical Field

[0001] This invention relates to the technical field of industrial wastewater treatment, and in particular to an automated solution generation system, method, and server for MBR membrane module selection. Background Technology

[0002] Membrane bioreactors (MBRs), as a core technology in modern wastewater treatment, have been widely used in municipal wastewater and industrial wastewater treatment. Currently, the selection of MBR membrane modules mainly relies on manual experience, but this approach is costly, time-consuming, and lacks standardization and consistency. Current research suggests using wastewater treatment equipment selection software for MBR membrane module selection; however, existing selection software has limited functionality, only capable of simple parameter calculations, and cannot continuously optimize selection strategies based on market changes, technological iterations, and user feedback, nor can it quickly provide complete selection solutions. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide an automated solution generation system, method and server for MBR membrane module selection, which can significantly improve the selection efficiency of MBR membrane modules.

[0004] In a first aspect, embodiments of the present invention provide an automated solution generation system for MBR membrane module selection. The system includes: a user interaction module, a knowledge base module, an intelligent reasoning module, and a solution generation module. The user interaction module acquires user requirement information, converts it into structured requirement parameters, and sends it to the intelligent reasoning module. The knowledge base module stores professional knowledge and rules related to MBR membrane module selection. The intelligent reasoning module calculates key design parameters based on the structured requirement parameters and calls the knowledge base module to perform membrane module model and configuration matching processing on the key design parameters to obtain a target membrane module selection solution. The solution generation module receives the target membrane module selection solution sent by the intelligent reasoning module and performs economic analysis and document generation processing on the target membrane module selection solution to obtain a target pre-sales solution document.

[0005] In one implementation, the user interaction module includes a requirement collection unit and a requirement parsing unit; wherein, the requirement collection unit is used to collect user requirement information through a structured form and perform natural language processing on the user requirement information to obtain a natural language description of the user requirement information; the requirement parsing unit is used to receive the natural language description of the user requirement information sent by the requirement collection unit and perform structured parsing processing on the natural language description to obtain structured requirement parameters.

[0006] In one implementation, the knowledge base module includes: a product knowledge base, a selection rule base, a historical case base, and an industry standard base; wherein, the product knowledge base is used to store the technical parameters, performance characteristics, and applicable scenarios of various types of MBR membrane modules; the selection rule base is used to store the selection rules and constraints of MBR membrane modules; the historical case base is used to store the feedback information corresponding to historical selection cases of MBR membrane modules; and the industry standard base is used to store industry standards and industry specifications for MBR membrane modules.

[0007] In one implementation, the intelligent reasoning module includes a reference calculation unit, a scheme matching unit, and an optimization evaluation unit. The reference calculation unit performs parameter calculations on structured requirement parameters using a deep model to obtain key design parameters, which are then sent to the scheme matching unit. These key design parameters include design flux, required membrane area, and aeration rate. The scheme matching unit calls a knowledge base module to match the key design parameters with membrane module models and configurations, obtaining a set of membrane module selection schemes, which is then sent to the optimization evaluation unit. The optimization evaluation unit performs multi-objective optimization evaluation on the set of membrane module selection schemes to obtain a target membrane module selection scheme.

[0008] In one implementation, the solution generation module includes: a technical solution generation unit, an economic analysis unit, and a solution document generation unit; wherein, the technical solution generation unit is used to perform automated data filling and knowledge retrieval processing on the target membrane module selection scheme to generate a target technical solution, wherein the target technical solution includes: technical parameters and configuration scheme; the economic analysis unit is used to perform economic analysis processing on the target membrane module selection scheme to obtain economic analysis results; the solution document generation unit is used to receive the target technical solution sent by the technical solution generation unit and the economic analysis results sent by the economic analysis unit, and generate a target pre-sales solution document based on the target technical solution and the economic analysis results.

[0009] In one embodiment, the system further includes a database module; wherein the database module is used to store user demand information, target pre-sales solution documents, and feedback information corresponding to the target pre-sales solution documents, so as to update the knowledge base module using the user demand information, target pre-sales solution documents, and feedback information, and to perform model optimization processing on the deep model based on the feedback information.

[0010] Secondly, embodiments of the present invention also provide an automated solution generation method for MBR membrane module selection. The method is applied to an automated solution generation system for MBR membrane module selection. The method includes: acquiring user requirement information and performing structured parsing processing on the user requirement information to convert it into structured requirement parameters; performing parameter calculation processing on the structured requirement parameters using a deep model to obtain key design parameters; and matching the key design parameters with membrane module models and configurations according to a preset knowledge base to obtain a set of membrane module selection solutions. The key design parameters include: design flux, required membrane area, and aeration rate; performing multi-objective optimization evaluation processing on the set of membrane module selection solutions to obtain a target membrane module selection solution; and performing economic analysis and document generation processing on the target membrane module selection solution to obtain a target pre-sales solution document.

[0011] In one implementation, after obtaining the target pre-sales solution document, the process includes: obtaining user feedback on the target pre-sales solution document, and optimizing the deep model based on the feedback and historical selection cases, so as to use the optimized deep model to perform the next round of membrane module selection operation.

[0012] Thirdly, embodiments of the present invention also provide a server, including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.

[0013] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects: The embodiments of the present invention provide an automated solution generation system, method and server for MBR membrane module selection. The system includes: a user interaction module, a knowledge base module, an intelligent reasoning module and a solution generation module. The user interaction module obtains user demand information, converts the user demand information into structured demand parameters and sends them to the intelligent reasoning module. The knowledge base module stores professional knowledge and rules for MBR membrane module selection. Then, the intelligent reasoning module calculates key design parameters based on the structured demand parameters and calls the knowledge base module to perform membrane module model and configuration matching processing on the key design parameters to obtain the target membrane module selection solution. Finally, the solution generation module receives the target membrane module selection solution sent by the intelligent reasoning module, performs economic analysis processing and document generation processing on the target membrane module selection solution, and obtains the target pre-sales solution document. The embodiments of the present invention can significantly improve the selection efficiency of MBR membrane modules.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 is a schematic diagram of an automated scheme generation system for MBR membrane module selection provided in an embodiment of the present invention; Figure 2 is a detailed schematic diagram of an automated scheme generation system for MBR membrane module selection provided in an embodiment of the present invention; Figure 3 is a flowchart of an automated scheme generation method for MBR membrane module selection provided in an embodiment of the present invention; Figure 4 is a schematic diagram of a server provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Currently, MBR (Membrane Bioreactor) technology, as a core technology in modern wastewater treatment, has been widely applied in municipal wastewater and industrial wastewater treatment. With increasing environmental protection requirements and growing wastewater treatment demands, the selection of MBR membrane modules has become increasingly complex. The selection process needs to consider multiple factors, including treatment volume, water quality characteristics, project type, application scenario, membrane material characteristics, and aeration method. Currently, MBR membrane module selection mainly relies on manual experience, which presents the following problems: 1. The selection process is complex, requiring the participation of professional technicians, resulting in high labor costs; 2. Selection results are influenced by individual experience, lacking standardization and consistency; 3. The selection cycle is long, affecting project progress; 4. It is difficult to quickly respond to diverse customer needs, reducing customer satisfaction; 5. Key parameters may be overlooked during the selection process, leading to solutions that do not match actual needs; 6. There is a lack of historical data accumulation and knowledge transfer mechanisms. Furthermore, while some wastewater treatment equipment selection software exists on the market, most are limited in function, only capable of simple parameter calculations and unable to provide complete selection solutions. Some manufacturers offer online selection tools, but do not provide MBR products.

[0021] Therefore, the existing technical solutions mentioned above cannot meet the needs of modern MBR projects for efficient, accurate, and intelligent pre-sales support. Specifically: 1. Inefficiency and high labor costs: The manual consultation model has a long communication cycle. The formulation of a solution for a complex project may require repeated communication over several days or even weeks. The time cost of senior experts is extremely high, and they cannot respond 24 / 7, which restricts the speed of business expansion; 2. Strong knowledge dependence and difficulty in replication and inheritance: The quality of selection depends entirely on the level of experts, which is subjective and uncertain. Moreover, the experience and knowledge of experts are difficult to quantify, accumulate, and pass on. Once core personnel are lost, the company's technical competitiveness will be severely affected. The internal Excel tool also faces the same problem, with only a few people proficient in its use and maintenance; 3. Functional limitations. Unable to handle complex decisions: 3-1. Poor compatibility with replacement projects: Replacement projects are a market necessity, but their compatibility constraints (such as navigation frame center distance error ≤5mm, water inlet / air inlet size matching) are extremely complex. Existing tools are almost unable to effectively handle these physical constraints, leading to recommended solutions failing to adapt to actual installations, requiring secondary modifications and increasing costs; 3-2. Lack of solution generation capabilities: Existing tools can only output a product model and basic parameters at most, unable to automatically generate complete pre-sales solution documents including technical solutions, economic analysis (investment, operating costs), assembly drawings, and layout drawings; 4. Low level of intelligence and lack of self-learning ability: Both manual and existing tools remain at the calculation level, rather than intelligent decision-making. They cannot learn from massive amounts of historical project data and cannot continuously optimize selection models and recommendation strategies based on market changes, technological iterations, and user feedback; 5. Poor user experience and unfriendly interaction: Manual mode is limited by communication efficiency; internal software tools have complex interfaces and steep learning curves; manufacturer online tools are too simplified and cannot meet the in-depth customization needs of professional users.

[0022] Based on this, the automated solution generation system, method and server for MBR membrane module selection provided by this invention can transform the pre-sales selection of MBR membrane modules from a manual workshop-style service that relies on personal experience, is inefficient and costly, into a data-driven, intelligent, efficient and accurate industrial standard process, thereby comprehensively improving service level and selection efficiency.

[0023] To facilitate understanding of this embodiment, a detailed description of an automated solution generation method for MBR membrane module selection disclosed in this embodiment of the invention is provided first. This method is applied to an automated solution generation system for MBR membrane module selection. To facilitate understanding of the automated solution generation system for MBR membrane module selection, this embodiment of the invention provides a structural schematic diagram of the automated solution generation system for MBR membrane module selection, as shown in Figure 1. The system includes: a user interaction module, a knowledge base module, an intelligent reasoning module, and a solution generation module. The user interaction module is used to acquire user demand information, convert the user demand information into structured demand parameters, and then send it to the intelligent reasoning module. In one implementation, the user interaction module can enable the system to be built as an intelligent consultation system, shortening the manual consultation process that previously required several days to several minutes or even seconds, achieving 24 / 7 uninterrupted service, greatly improving pre-sales response speed, and significantly reducing reliance on senior experts, thereby significantly reducing labor costs and solving efficiency and cost issues.

[0024] The knowledge base module is used to store professional knowledge and rules for MBR membrane module selection. In one implementation, fragmented knowledge scattered in experts' minds, Excel spreadsheets, and product manuals is systematically constructed into a dynamically updated, reasonable, and learnable structured knowledge base, thereby realizing the digitization, assetization, and transferability of technical knowledge and solving the problem of knowledge accumulation and inheritance.

[0025] The intelligent reasoning module calculates key design parameters based on structured requirement parameters and calls the knowledge base module to match membrane module models and configurations to obtain a target membrane module selection scheme. In one implementation, by introducing artificial intelligence algorithms (such as natural language processing, knowledge graphs, and multi-objective optimization), the system can understand user natural language requirements, perform intelligent reasoning, and weigh multiple objectives, thereby solving the problem of intelligentization. Furthermore, it can integrate full product data from multiple mainstream brands to achieve objective and fair cross-brand comparisons and recommendations, breaking down brand barriers. A compatibility verification engine accurately handles complex physical dimensions and interface constraints in replacement projects, ensuring that recommended solutions are ready to use out of the box, thus overcoming replacement challenges.

[0026] To address the aforementioned artificial intelligence algorithms, the system employs deep learning-based natural language processing technology to understand the natural language descriptions input by users and extract key information. In addition, it constructs a knowledge graph for MBR membrane module selection, organizing product information, selection rules, historical cases, and other knowledge in graph form to improve the efficiency of knowledge representation and reasoning. Furthermore, it uses a multi-objective optimization algorithm to balance multiple objectives such as technical performance, economic cost, and environmental impact to find the optimal selection solution.

[0027] The solution generation module receives the target membrane module selection scheme sent by the intelligent inference module, performs economic analysis and document generation on the target membrane module selection scheme, and obtains the target pre-sales solution document. In one implementation, the solution generation module can not only recommend product models, but also generate a complete and professional pre-sales solution document containing technical parameters, configuration list, economic analysis, and key drawings with one click, thereby greatly improving the quality and professionalism of the solution and realizing the automated generation of the solution.

[0028] Furthermore, referring to Figure 2, which shows a schematic diagram of an automated solution generation system for MBR membrane module selection, the user interaction module includes a requirement acquisition unit and a requirement parsing unit. The requirement acquisition unit collects user requirement information through a structured form and performs natural language processing on the information to obtain a natural language description of the user requirements. The requirement parsing unit receives the natural language description of the user requirements sent by the requirement acquisition unit and performs structured parsing on the description to obtain structured requirement parameters. In one embodiment, the user interaction module also includes a result display unit, which can display the selection results and solution details in the form of charts, text, etc. Furthermore, VR / AR technology can be used to provide a more intuitive display of selection results and a solution preview.

[0029] The knowledge base module includes: a product knowledge base, a selection rule base, a historical case base, and an industry standard base. The product knowledge base stores the technical parameters, performance characteristics, and applicable scenarios for various types of MBR membrane modules. The selection rule base stores the selection rules and constraints for MBR membrane modules. The historical case base stores feedback information (i.e., historical selection cases and their effect evaluations) corresponding to historical selection cases of MBR membrane modules. The industry standard base stores industry standards and specifications for MBR membrane modules.

[0030] The intelligent reasoning module includes a reference calculation unit, a scheme matching unit, and an optimization evaluation unit. The reference calculation unit performs parameter calculations on structured requirement parameters using a deep model to obtain key design parameters, which are then sent to the scheme matching unit. These key design parameters include design flux, required membrane area, and aeration rate. The scheme matching unit calls the knowledge base module to match membrane module models and configurations with the key design parameters, resulting in a set of membrane module selection schemes. This set of schemes is then sent to the optimization evaluation unit. The optimization evaluation unit performs multi-objective optimization evaluation on the set of membrane module selection schemes to obtain the target membrane module selection scheme.

[0031] The solution generation module includes a technical solution generation unit, an economic analysis unit, and a solution document generation unit. The technical solution generation unit performs automated data filling and knowledge retrieval processing on the target membrane module selection scheme to generate a target technical solution, which includes technical parameters and configuration schemes. The economic analysis unit performs economic analysis on the target membrane module selection scheme to obtain economic analysis results. The solution document generation unit receives the target technical solution from the technical solution generation unit and the economic analysis results from the economic analysis unit, and generates a target pre-sales solution document based on the target technical solution and economic analysis results. The target pre-sales solution document includes the target technical solution, economic analysis results, and drawings.

[0032] The system also includes a database module; this module stores user requirement information, target pre-sales solution documents, and corresponding feedback information. It uses this information to update the knowledge base module and optimizes the deep learning model based on the feedback. In one implementation, the database module can achieve self-learning and continuous evolution of the model through a closed-loop user feedback mechanism, thereby addressing the issue of continuous optimization.

[0033] Based on the structural diagram of the automated scheme generation system for MBR membrane module selection shown in Figure 1, this embodiment of the invention provides a detailed description of the automated scheme generation method for MBR membrane module selection. Referring to the flowchart of an automated scheme generation method for MBR membrane module selection shown in Figure 3, the method mainly includes the following steps S302 to S306: Step S302: Obtain user requirement information and perform structured parsing processing on the user requirement information to convert the user requirement information into structured requirement parameters.

[0034] In one implementation, the system collects user demand information through a user interaction module. This user demand information includes: basic project information, water quality details of the influent to the membrane tank, and scenario-specific parameters. The basic project information includes: project name, project location, treated water volume, project type (new / replacement), application scenario, and water quality type. The water quality details of the influent to the membrane tank include: COD, water temperature, MLSS sludge concentration (i.e., mixed liquor suspended solids concentration (g / L)), and chloride ion concentration (Cl). - ( ), hardness, alkalinity, pH, TDS, etc.; Scenario-specific parameters: dynamically display the corresponding parameter input items according to different project types and application scenarios.

[0035] Furthermore, natural language processing technology can be used to convert the user's input natural language description into structured parameters. For example, if the user inputs "treat municipal sewage, water volume 8000 tons / day", the system will automatically parse it as: water quality type = municipal sewage, treatment volume = 8000 m³ / d.

[0036] Step S304: The structured requirement parameters are calculated and processed using a deep model to obtain key design parameters. Based on a preset knowledge base, the key design parameters are matched with membrane module models and configurations to obtain a set of membrane module selection schemes. The key design parameters include: design flux, required membrane area, and aeration rate.

[0037] In one implementation, the structured demand parameters are processed by a deep model to obtain key design parameters, as detailed in (1) to (3) below: (1) Design flux calculation: First, the baseline flux table is consulted according to the water quality type, and then the temperature correction factor is applied: And MLSS correction factor: , Calculate the design flux: J_design = baseline flux × f_temp × f_msss × 0.9 (safety factor) (2) Calculate the total membrane area required: A_total = (Q × 1000) / (J_design × H) where Q is the water treatment volume (m³ / d) and H is the operating time (usually taken as 22-23h / d) (3) Calculate the aeration volume: Q_air_total = A_total × SADm where SADm is the air-water ratio per unit membrane area, that is, the aeration volume required per unit membrane area (Nm³ air / m² membrane / h), which takes different values ​​according to the aeration method (pulse aeration, perforated pipe aeration, trough aeration).

[0038] Furthermore, deep learning models can continuously optimize selection results based on historical selection cases and feedback data, improving selection accuracy. Specifically, they can obtain user feedback on target pre-sales solution documents and optimize the deep model based on this feedback and historical selection cases. The optimized deep model can then be used to perform the next round of membrane module selection, thereby updating the knowledge base and optimizing the selection model based on user feedback, achieving self-learning. Alternatively, expert systems can be used to replace deep learning models for reasoning. Expert systems implement selection logic through rule bases and inference engines, or traditional machine learning algorithms (such as decision trees and support vector machines) can be used instead of deep learning models for selection.

[0039] In another implementation, the key design parameters are matched with the membrane module model and configuration to obtain a set of membrane module selection schemes, as detailed in (1) to (2) below: (1) Brand and model selection: 1. Compatibility priority (replacement items): the size / interface of the new module matches the original tank; 2. User preference: the brand specified by the user is preferred; 3. Performance matching: ensure that the recommended flux of the selected model is not less than the design flux; 4. Aeration mode matching: ensure that the selected model supports the aeration mode selected by the user.

[0040] (2) Quantity and configuration determination: 1. Area requirement: Ensure that the total membrane area is not less than the required total membrane area; 2. Uniform distribution: The units are evenly distributed in the corridor; 3. Space limitation: Ensure that the number / size of the units is compatible with the corridor or CWT space, where CWT is a containerized sewage treatment device; 4. Installation point matching (replacement item): Ensure that the size of the water outlet / air inlet is compatible with the original pipeline system.

[0041] Step S306: Perform multi-objective optimization evaluation on the set of membrane module selection schemes to obtain the target membrane module selection scheme. Then, perform economic analysis and document generation on the target membrane module selection scheme to obtain the target pre-sales scheme document. The target pre-sales scheme document includes: technical solution, economic analysis and drawings. The technical solution includes: recommended membrane module information, configuration scheme, key design parameters, etc.; the economic analysis includes: investment cost, operating cost, investment payback period, etc.; the drawings include: module assembly drawing, plan layout drawing, etc.

[0042] In one implementation, a multi-objective optimization evaluation process can be performed on a set of membrane module selection schemes based on technical indicators, economic indicators, and environmental indicators to obtain a target membrane module selection scheme. The technical indicators include flux, TMP (i.e., transmembrane pressure) and cleaning frequency, the economic indicators include investment cost and operating cost, and the environmental indicators include energy consumption and chemical consumption. The target pre-sales scheme document can be stored using blockchain technology to ensure that the data is tamper-proof and traceable.

[0043] In summary, compared with existing MBR membrane module selection methods, the present invention has the following advantages: 1. High degree of intelligence: The present invention adopts artificial intelligence technology, which can automatically complete complex tasks such as demand analysis, parameter calculation, scheme matching, and optimization evaluation, thereby improving selection efficiency and reducing manual intervention.

[0044] 2. Accurate and reliable selection results: Based on a professional knowledge base and scientific calculation methods, the present invention provides more accurate and reliable selection results, avoiding the experience bias that may occur in manual selection.

[0045] 3. Complete and comprehensive solution: This invention can generate a complete solution that includes technical solutions, economic analysis, drawings, etc., rather than just simple parameter calculations.

[0046] 4. High versatility: This invention is not limited to specific brand products, but can objectively compare products from different brands and provide users with the most suitable solution.

[0047] 5. Strong self-learning ability: This invention can collect user feedback, continuously optimize the selection model, and improve the accuracy of selection.

[0048] 6. Excellent user experience: This invention provides a user-friendly interface, supports natural language interaction, and significantly reduces the barrier to entry for users.

[0049] 7. Fast response speed: This invention can complete the selection process in a short time, greatly shortening the selection cycle and improving project progress.

[0050] 8. Knowledge accumulation and inheritance: This invention can accumulate and inherit selection experience, avoiding knowledge loss due to personnel turnover.

[0051] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0052] This invention provides a server, specifically, the server includes a processor and a storage device; the storage device stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.

[0053] Figure 4 is a schematic diagram of the structure of a server provided in an embodiment of the present invention. The server 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43. The processor 40, the communication interface 43 and the memory 41 are connected through the bus 42. The processor 40 is used to execute executable modules, such as computer programs, stored in the memory 41.

[0054] The memory 41 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0055] Bus 42 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only a single bidirectional arrow is used in Figure 4, but this does not indicate that there is only one bus or one type of bus.

[0056] The memory 41 is used to store programs. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0057] Processor 40 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 40 or by instructions in software form. Processor 40 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 41. The processor 40 reads the information in memory 41 and, in conjunction with its hardware, completes the steps of the above method.

[0058] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0059] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, 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 the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An automated solution generation system for MBR membrane module selection, characterized in that, The system includes a user interaction module, a knowledge base module, an intelligent reasoning module, and a solution generation module. The user interaction module acquires user requirement information, converts it into structured requirement parameters, and sends it to the intelligent reasoning module. The knowledge base module stores professional knowledge and rules for MBR membrane module selection. The intelligent reasoning module calculates key design parameters based on the structured requirement parameters and calls the knowledge base module to match the key design parameters with membrane module models and configurations to obtain a target membrane module selection solution. The solution generation module receives the target membrane module selection solution sent by the intelligent reasoning module and performs economic analysis and document generation processing on the target membrane module selection solution to obtain a target pre-sales solution document.

2. The automated solution generation system for MBR membrane module selection according to claim 1, characterized in that, The user interaction module includes a requirement collection unit and a requirement parsing unit; wherein, the requirement collection unit is used to collect user requirement information through a structured form, and perform natural language processing on the user requirement information to obtain a natural language description of the user requirement information; the requirement parsing unit is used to receive the natural language description of the user requirement information sent by the requirement collection unit, and perform structured parsing processing on the natural language description to obtain the structured requirement parameters.

3. The automated solution generation system for MBR membrane module selection according to claim 1, characterized in that, The knowledge base module includes: a product knowledge base, a selection rule base, a historical case base, and an industry standard base. The product knowledge base stores the technical parameters, performance characteristics, and applicable scenarios of various types of MBR membrane modules. The selection rule base stores the selection rules and constraints for MBR membrane modules. The historical case base stores feedback information corresponding to historical selection cases of MBR membrane modules. The industry standard base stores industry standards and specifications for MBR membrane modules.

4. The automated solution generation system for MBR membrane module selection according to claim 1, characterized in that, The intelligent reasoning module includes a reference calculation unit, a scheme matching unit, and an optimization evaluation unit. The reference calculation unit performs parameter calculations on the structured requirement parameters using a deep model to obtain the key design parameters, and sends these key design parameters to the scheme matching unit. The key design parameters include: design flux, required membrane area, and aeration rate. The scheme matching unit calls the knowledge base module to match the key design parameters with membrane module models and configurations, obtaining a set of membrane module selection schemes, and sends this set to the optimization evaluation unit. The optimization evaluation unit performs multi-objective optimization evaluation on the set of membrane module selection schemes to obtain the target membrane module selection scheme.

5. The automated solution generation system for MBR membrane module selection according to claim 1, characterized in that, The solution generation module includes: a technical solution generation unit, an economic analysis unit, and a solution document generation unit; wherein, the technical solution generation unit is used to perform automated data filling and knowledge retrieval processing on the target membrane module selection scheme to generate a target technical solution, wherein the target technical solution includes: technical parameters and configuration scheme; the economic analysis unit is used to perform economic analysis processing on the target membrane module selection scheme to obtain economic analysis results; the solution document generation unit is used to receive the target technical solution sent by the technical solution generation unit and the economic analysis results sent by the economic analysis unit, and generate the target pre-sales solution document based on the target technical solution and the economic analysis results.

6. The automated solution generation system for MBR membrane module selection according to claim 1, characterized in that, The system further includes a database module; wherein the database module is used to store the user demand information, the target pre-sales solution document, and the feedback information corresponding to the target pre-sales solution document, so as to update the knowledge base module using the user demand information, the target pre-sales solution document, and the feedback information, and to perform model optimization processing on the deep model based on the feedback information.

7. An automated method for generating MBR membrane module selection schemes, characterized in that, The method is applied to an automated solution generation system for MBR membrane module selection. The method includes: acquiring user requirement information and performing structured parsing processing on the user requirement information to convert it into structured requirement parameters; performing parameter calculation processing on the structured requirement parameters using a deep model to obtain key design parameters; and matching the key design parameters with membrane module models and configurations according to a preset knowledge base to obtain a set of membrane module selection solutions. The key design parameters include: design flux, required membrane area, and aeration rate. The set of membrane module selection solutions is then subjected to multi-objective optimization evaluation processing to obtain a target membrane module selection solution. Finally, the target membrane module selection solution is subjected to economic analysis and document generation processing to obtain a target pre-sales solution document.

8. The automated scheme generation method for MBR membrane module selection according to claim 7, characterized in that, After obtaining the target pre-sales solution document, the process includes: obtaining user feedback on the target pre-sales solution document, and optimizing the deep model based on the feedback and historical selection cases, so as to use the optimized deep model to perform the next round of membrane module selection.

9. A server, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 7-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method described in any one of claims 7-8.

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