CN-121999895-A - Wastewater treatment process design and optimization intelligent body and construction method thereof

CN121999895ACN 121999895 ACN121999895 ACN 121999895ACN-121999895-A

Abstract

The invention belongs to the technical field of water treatment technology and artificial intelligence, in particular to an intelligent agent for designing and optimizing a wastewater treatment process and a construction method thereof, wherein the construction method comprises collecting multiple multi-mode data, carrying out characteristic extraction and fusion on the multi-mode data, and obtaining fusion data, constructing a water treatment vertical domain knowledge graph based on professional knowledge, historical operation data and fault cases of the water treatment industry, and generating a wastewater treatment process and a flow control strategy through a preset decision model based on the fusion data and the water treatment vertical domain knowledge graph. The intelligent agent constructed by the method can quickly generate an energy-saving low-carbon high-efficiency water treatment process according to multi-mode data, and the AI artificial intelligence technology is used for intelligently optimizing the wastewater treatment process, so that the design efficiency of the wastewater treatment process is effectively improved, the research and development period is greatly shortened, the treatment energy consumption and the operation and maintenance cost are reduced, and the stability of the water quality of the effluent is improved.

Inventors

REN SHUKUI
MA HONGLU
ZHOU CHAO
Yue Huanlin
YE QIANLING

Assignees

中国轻工业长沙工程有限公司

Dates

Publication Date: 20260508
Application Date: 20260214

Claims (10)

1. An intelligent agent for designing and optimizing a wastewater treatment process, which is characterized by comprising the following components: The multi-mode data acquisition module is used for acquiring various multi-mode data; the multi-mode data fusion module is used for carrying out feature extraction and fusion on the multi-mode data to obtain fusion data; The vertical domain knowledge graph module is used for constructing a vertical domain knowledge graph of water treatment based on professional specifications, historical operation data and fault cases of the water treatment industry; the intelligent decision and control module is used for generating a wastewater treatment process and a flow control strategy through a preset decision model based on the fusion data output by the multi-mode data fusion module and the water treatment homeodomain knowledge spectrum constructed by the homeodomain knowledge spectrum module; And the self-adaptive learning module is used for self-learning based on the historical operation data and the real-time feedback information so as to iteratively optimize the decision model.
2. The wastewater treatment process design and optimization agent according to claim 1, wherein, The multi-mode data comprises water quality monitoring data, equipment operation data, process parameter data and environment data; the water quality monitoring data comprise pH value, COD, BOD, ammonia nitrogen content, turbidity, residual chlorine, dissolved oxygen, total phosphorus, total nitrogen and conductivity; the equipment operation data comprise the rotation speed of a water pump, the air quantity of a fan, the flow quantity of a dosing pump, the temperature of equipment and the vibration frequency of the equipment; the process parameter data comprise aeration time, sedimentation time, filtering speed and backwashing period; the environmental data includes water temperature, air temperature, and rainfall.
3. The process design and optimization agent for wastewater treatment according to claim 1 or 2, wherein, The multi-modal data fusion module firstly adopts a deep learning model to extract characteristics of multi-modal data, and then adopts an attention mechanism to distribute and fuse weights of characteristic representations extracted by different modal data, so as to obtain fusion data.
4. The process design and optimization agent for wastewater treatment according to claim 1 or 2, wherein, The vertical domain knowledge graph module comprises a knowledge acquisition module, a knowledge representation module, a knowledge fusion module and a knowledge storage module; the knowledge acquisition module is used for extracting knowledge from different sources of professional specifications, historical operation data and fault cases in the water treatment industry and supplementing the knowledge by combining expert experience; the knowledge representation module is used for representing the knowledge acquired by the knowledge acquisition module into a triplet form of entity-relation-entity or entity-attribute-value by adopting a resource description framework RDF; The knowledge fusion module is used for eliminating redundancy and contradiction of knowledge representation through entity alignment, attribute alignment and conflict resolution algorithm, and constructing and obtaining a water treatment vertical domain knowledge graph; The knowledge storage module is used for storing the water treatment vertical domain knowledge graph into a database.
5. The process design and optimization agent for wastewater treatment according to claim 1 or 2, wherein, The decision model comprises a water quality analysis and process matching sub-model, a device model selection and optimization sub-model and a process parameter optimization sub-model; the water quality analysis and process matching sub-model is constructed by adopting a long-short-term memory network LSTM model and is used for matching a process path based on historical water quality monitoring data and related wastewater treatment process influence factor data; the equipment model selection and optimization sub-model is constructed by adopting a combination model of a convolutional neural network CNN and a transducer and is used for carrying out feature extraction and fault analysis on historical equipment operation data so as to realize optimal configuration of equipment model selection; the process parameter optimization sub-model is constructed by adopting a reinforcement learning model and is used for dynamically optimizing the design parameters of the water treatment process based on the fusion data and aiming at reaching the standard of the water quality of the effluent and reducing the energy consumption and the cost.
6. A method for constructing an intelligent agent for designing and optimizing a wastewater treatment process according to any one of claims 1 to 5, comprising the steps of: S1, collecting various multi-mode data; S2, carrying out feature extraction and fusion on the multi-mode data to obtain fusion data; S3, constructing a water treatment vertical domain knowledge graph based on professional specifications, historical operation data and fault cases of the water treatment industry; S4, generating a wastewater treatment process and a flow control strategy through a preset decision model based on the fusion data and the water treatment vertical domain knowledge graph; S5, self-learning is conducted based on the historical operation data and the real-time feedback information so as to iteratively optimize the decision model.
7. The method for constructing an intelligent agent for designing and optimizing a wastewater treatment process according to claim 6, wherein, in step S1, The multi-mode data comprises water quality monitoring data, equipment operation data, process parameter data and environment data; the water quality monitoring data comprise pH value, COD, BOD, ammonia nitrogen content, turbidity, residual chlorine, dissolved oxygen, total phosphorus, total nitrogen and conductivity; the equipment operation data comprise the rotation speed of a water pump, the air quantity of a fan, the flow quantity of a dosing pump, the temperature of equipment and the vibration frequency of the equipment; the process parameter data comprise aeration time, sedimentation time, filtering speed and backwashing period; the environmental data includes water temperature, air temperature, and rainfall.
8. The method for constructing an intelligent agent for designing and optimizing a wastewater treatment process according to claim 6 or 7, wherein, in step S2, The feature extraction and fusion are carried out on the multi-mode data to obtain fusion data, which comprises the following steps: The method comprises the steps of firstly adopting a deep learning model to extract characteristics of multi-modal data, and then adopting an attention mechanism to distribute and fuse weight of characteristic representations extracted from different modal data to obtain fused data.
9. The method for constructing an intelligent agent for designing and optimizing a wastewater treatment process according to claim 6 or 7, wherein in step S3, The construction of the water treatment homeodomain knowledge graph based on the professional specifications, the historical operation data and the fault cases of the water treatment industry comprises the following steps: Extracting knowledge from different sources of professional specifications, historical operation data and fault cases in the water treatment industry, and simultaneously supplementing the knowledge by combining expert experience; The knowledge acquired by the knowledge acquisition module is expressed in a form of a triplet of entity-relation-entity or entity-attribute-value by adopting a resource description framework RDF; Through entity alignment, attribute alignment and conflict resolution algorithms, redundancy and contradiction of knowledge representation are eliminated, and a water treatment vertical domain knowledge graph is constructed; and storing the water treatment domain knowledge graph into a database.
10. The method for constructing an intelligent agent for designing and optimizing a wastewater treatment process according to claim 6 or 7, wherein, in step S4, The decision model comprises a water quality analysis and process matching sub-model, a device model selection and optimization sub-model and a process parameter optimization sub-model; The water quality analysis and process matching sub-model is constructed by adopting a long-short-term memory network LSTM model and is used for matching a process path based on historical water quality data and related wastewater treatment process influence factor data; the equipment model selection and optimization sub-model is constructed by adopting a combination model of a convolutional neural network CNN and a transducer and is used for carrying out feature extraction and fault analysis on historical equipment operation data so as to realize optimal configuration of equipment model selection; The process parameter optimization sub-model is constructed by adopting a reinforcement learning model and is used for dynamically optimizing the design parameters of the water treatment process with the aim of reaching the standard of the water quality of the effluent and reducing the energy consumption and the cost.

Description

Wastewater treatment process design and optimization intelligent body and construction method thereof Technical Field The invention belongs to the technical field of water treatment technology and artificial intelligence, and particularly relates to an intelligent agent for designing and optimizing a wastewater treatment process and a construction method thereof. Background The current wastewater treatment industry is facing a key transformation period for upgrading and enhancing the quality. The traditional wastewater treatment industry has the problems of long design period, low efficiency, large workload, high energy consumption of design results, and the like, and mainly depends on manual experience. With the popularization and the maturity improvement of the AI technology, a technical support is provided for the research and development of wastewater treatment process design intelligent bodies. Therefore, development of an intelligent wastewater treatment process design and optimization agent which can integrate multi-mode information, realize intelligent decision and self-adaptive learning, has low cost, high efficiency and stable performance, has technical innovation and commercial value, and has great strategic significance for realizing sustainable development is needed. Disclosure of Invention The invention aims to provide an intelligent agent for designing and optimizing a wastewater treatment process and a construction method thereof, which are used for solving the problems of long design period, low efficiency, large workload, high energy consumption of design results, and the like of the wastewater treatment process in the prior art, mainly relying on manual experience, improving the intelligent level and production efficiency of the wastewater treatment process design, and reducing the energy consumption and operation and maintenance cost of the wastewater treatment process. In order to achieve the above object, the technical scheme of the present invention is as follows: In a first aspect, the present invention also provides an agent for designing and optimizing a wastewater treatment process, comprising: The multi-mode data acquisition module is used for acquiring various multi-mode data; the multi-mode data fusion module is used for carrying out feature extraction and fusion on the multi-mode data to obtain fusion data; The vertical domain knowledge graph module is used for constructing a vertical domain knowledge graph of water treatment based on professional specifications, historical operation data and fault cases of the water treatment industry; the intelligent decision and control module is used for generating a wastewater treatment process and a flow control strategy through a preset decision model based on the fusion data output by the multi-mode data fusion module and the water treatment homeodomain knowledge spectrum constructed by the homeodomain knowledge spectrum module; And the self-adaptive learning module is used for self-learning based on the historical operation data and the real-time feedback information so as to iteratively optimize the decision model. Preferably, the multi-mode data comprises water quality monitoring data, equipment operation data, process parameter data and environment data; the water quality monitoring data comprise pH value, COD, BOD, ammonia nitrogen content, turbidity, residual chlorine, dissolved oxygen, total phosphorus, total nitrogen and conductivity; the equipment operation data comprise the rotation speed of a water pump, the air quantity of a fan, the flow quantity of a dosing pump, the temperature of equipment and the vibration frequency of the equipment; the process parameter data comprise aeration time, sedimentation time, filtering speed and backwashing period; the environmental data includes water temperature, air temperature, and rainfall. Preferably, the multi-modal data fusion module firstly adopts a deep learning model to extract features of the multi-modal data, and then adopts an attention mechanism to distribute and fuse weights of feature representations extracted by different modal data to obtain fusion data. Preferably, the vertical domain knowledge graph module comprises a knowledge acquisition module, a knowledge representation module, a knowledge fusion module and a knowledge storage module; the knowledge acquisition module is used for extracting knowledge from different sources of professional specifications, historical operation data and fault cases in the water treatment industry and supplementing the knowledge by combining expert experience; the knowledge representation module is used for representing the knowledge acquired by the knowledge acquisition module into a triplet form of entity-relation-entity or entity-attribute-value by adopting a resource description framework RDF; The knowledge fusion module is used for eliminating redundancy and contradiction of knowledge representation through entity alignment, attribut