CN115367823A - Sewage purification treatment chemical quantity control system based on big data learning and image recognition - Google Patents
Sewage purification treatment chemical quantity control system based on big data learning and image recognition Download PDFInfo
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- CN115367823A CN115367823A CN202211119766.2A CN202211119766A CN115367823A CN 115367823 A CN115367823 A CN 115367823A CN 202211119766 A CN202211119766 A CN 202211119766A CN 115367823 A CN115367823 A CN 115367823A
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- water quality
- image recognition
- frequency information
- purification treatment
- big data
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/008—Control or steering systems not provided for elsewhere in subclass C02F
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/005—Processes using a programmable logic controller [PLC]
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/02—Temperature
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/06—Controlling or monitoring parameters in water treatment pH
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/10—Solids, e.g. total solids [TS], total suspended solids [TSS] or volatile solids [VS]
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/11—Turbidity
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/16—Total nitrogen (tkN-N)
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/40—Liquid flow rate
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- Life Sciences & Earth Sciences (AREA)
- Hydrology & Water Resources (AREA)
- Engineering & Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- Water Supply & Treatment (AREA)
- Chemical & Material Sciences (AREA)
- Organic Chemistry (AREA)
- Physical Water Treatments (AREA)
Abstract
The invention relates to the technical field of sewage purification treatment, and discloses a sewage purification treatment agent amount control system based on big data learning and image recognition, which comprises a camera module for collecting sewage images and an image recognition module for extracting characteristic values; the PLC is used for extracting water quality information and transmitting the water quality information and the characteristic value to the OPC equipment, and the OPC equipment transmits the water quality information and the characteristic value to the rear end for storage; and the robot learning model arranged at the rear end takes the water quality information and the characteristic value as input, predicts the frequency information of the control pump, and sends the frequency information to the PLC controller through the OPC equipment, and the PLC controller controls the control pump to work based on the frequency information. The system is used for purifying water quality in the sewage treatment process, can realize accurate control of the dosage of the medicament to be pushed accurately, and meanwhile, managers can dynamically adjust the dosage of the medicament through the system. Can save the medicament amount by more than 30 percent.
Description
Technical Field
The invention relates to the technical field of sewage purification treatment, and discloses a sewage purification treatment chemical quantity control system based on big data learning and image recognition.
Background
Sewage treatment (sewage treatment, water treatment): the sewage is purified to reach the water quality requirement of being discharged into a certain water body or being reused. Sewage treatment is widely applied to various fields such as buildings, agriculture, traffic, energy, petrifaction, environmental protection, urban landscape, medical treatment, catering and the like, and is increasingly used in daily life of common people.
In the prior art, in the sewage treatment process, the dosage control is mainly to control the dosage to be put in according to the experience of people, and is not flexible enough, especially for some large-scale sewage treatment plants, the dosage is put in, and the dosage is mainly to adjust the flow at regular time to achieve the dosage control, thus the dosage waste is caused.
Disclosure of Invention
The invention aims to provide a sewage purification treatment chemical quantity control system based on big data learning and image recognition, so as to solve the problems in the background art.
In order to achieve the above object, the basic scheme of the invention is as follows: a sewage purification treatment chemical quantity control system with big data learning and image recognition comprises,
the system comprises a camera module for collecting sewage images and an image identification module for extracting characteristic values;
the PLC is used for extracting water quality information and transmitting the water quality information and the characteristic value to the OPC equipment, and the OPC equipment transmits the water quality information and the characteristic value to the rear end for storage;
and the robot learning model arranged at the rear end takes the water quality information and the characteristic value as input, predicts the frequency information of the control pump, and sends the frequency information to the PLC through the OPC equipment, and the PLC controls the control pump to work based on the frequency information.
Further, the characteristic values include suspended particle size and suspended particle number.
Further, the water quality information comprises PH, temperature, turbidity, total nitrogen, total phosphorus, inflow and outflow.
The intelligent monitoring system further comprises a Smart Server module, wherein the Smart Server module compares actual running frequency information of the PLC to control the pump with predicted frequency information, if the deviation exceeds a certain threshold value, the Smart Server module pushes the predicted information to the front end, and the front end displays the predicted frequency information and sends the information to the PLC through OPC equipment.
Further, the manager may modify the predicted frequency information through the front end.
Further, the robot learning model is trained every other time based on characteristic values and water quality information within 30 days of sewage.
The principle and the beneficial effects of the basic scheme are as follows: aiming at the existing sewage treatment dosage control mode, the system relies on advanced big data analysis and machine learning technology to form high-efficiency and accurate dosage control on the practical operation level, and replaces the current manual experience dosage control.
The system is mainly used for purifying water in the sewage treatment process, can realize accurate control of the medicament dosage and accurate pushing, and can dynamically adjust the medicament dosage of the medicament by managers through the system. Can save the medicament amount by more than 30 percent.
Drawings
FIG. 1 is a schematic flow chart of a sewage purification treatment chemical quantity control system based on big data learning and image recognition according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "vertical", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The embodiment is basically as shown in the attached figure 1:
the utility model provides a sewage purification treatment dosage control system of big data learning and image recognition, includes camera module, and camera module can be for camera or video camera etc. camera module is used for the image information at the sewage bottom to send image information to image recognition module, such as: the processor and the image recognition module are used for extracting characteristic values of the sewage, and the characteristic values are the size of suspended particles, the number of the suspended particles and the like.
The image recognition module sends the characteristic value to a Web server and a Redis server, the Web server sends the characteristic value to the front end, the Redis server sends the characteristic value information to a Smart Server module, the Smart Server module sends the characteristic value to OPC equipment, and the OPC equipment sends the characteristic value to the rear end and stores the characteristic value.
The PLC is used for extracting water quality information, and the water quality information includes PH, temperature, turbidity, total nitrogen, total phosphorus, inflow and outflow to send the eigenvalue to the rear end through OPC equipment, and store.
And the robot learning model arranged at the rear end takes the water quality information and the characteristic value as input and predicts the frequency information for controlling the work of the pump. The predicted frequency information is compared with frequency information for controlling the actual work of the pump through a Smart Server module, if the deviation exceeds a certain threshold value, the Smart Server module pushes the predicted information to the front end, the front end displays the predicted frequency information and sends the predicted frequency information to a PLC (programmable logic controller) through OPC (optical proximity correction) equipment, and the PLC controls the work frequency of the pump based on the predicted frequency information to dynamically adjust the dosage of the medicament.
In the embodiment, the robot learning model is trained every other based on the characteristic value and the water quality information of the sewage within 30 days, so that the learning model is continuously optimized according to the actual condition.
In this embodiment, the manager first determines the threshold value of the front end, and if the front end is in the automatic mode, the predicted frequency information is automatically issued to the PLC through the opc after being displayed at the front end for a period of time, and the PLC controls the pump with the medicament transmitted by the instruction, so as to control the medicament amount. If the mode is manual, the manager can also intervene manually when the manager feels that the prediction frequency information is not reasonable.
The foregoing is merely an example of the present invention and common general knowledge in the art of specific structures and/or features of the invention has not been set forth herein in any way. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be defined by the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (6)
1. Big data learning and image recognition's sewage purification treatment dosage control system, its characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the system comprises a camera module for collecting sewage images and an image identification module for extracting characteristic values;
the PLC is used for extracting water quality information and transmitting the water quality information and the characteristic value to the OPC equipment, and the OPC equipment transmits the water quality information and the characteristic value to the rear end for storage;
and the robot learning model arranged at the rear end takes the water quality information and the characteristic value as input, predicts the frequency information of the control pump, and sends the frequency information to the PLC controller through the OPC equipment, and the PLC controller controls the control pump to work based on the frequency information.
2. The big data learning and image recognition sewage purification treatment chemical quantity control system according to claim 1, wherein: the characteristic values include suspended particle size and suspended particle number.
3. The sewage purification treatment chemical quantity control system based on big data learning and image recognition as claimed in claim 2, wherein: the water quality information comprises PH, temperature, turbidity, total nitrogen, total phosphorus, inflow and outflow.
4. The big data learning and image recognition sewage purification treatment chemical quantity control system according to claim 3, wherein: the intelligent monitoring system further comprises a Smart Server module, the Smart Server module compares actual running frequency information and predicted frequency information of the PLC to control the pump, if the deviation exceeds a certain threshold value, the Smart Server module pushes the predicted information to the front end, and the front end displays the predicted frequency information and sends the predicted frequency information to the PLC through OPC equipment.
5. The sewage purification treatment chemical quantity control system based on big data learning and image recognition as claimed in claim 4, wherein: the manager may modify the predicted frequency information through the front end.
6. The big data learning and image recognition sewage purification treatment chemical quantity control system according to claim 5, wherein: every other time, the robot learning model is trained on the characteristic value and the water quality information within 30 days of sewage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211119766.2A CN115367823A (en) | 2022-09-15 | 2022-09-15 | Sewage purification treatment chemical quantity control system based on big data learning and image recognition |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211119766.2A CN115367823A (en) | 2022-09-15 | 2022-09-15 | Sewage purification treatment chemical quantity control system based on big data learning and image recognition |
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| CN115367823A true CN115367823A (en) | 2022-11-22 |
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| CN202211119766.2A Pending CN115367823A (en) | 2022-09-15 | 2022-09-15 | Sewage purification treatment chemical quantity control system based on big data learning and image recognition |
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Citations (8)
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|---|---|---|---|---|
| JPH05285308A (en) * | 1992-04-08 | 1993-11-02 | Mitsubishi Electric Corp | Controller for injecting flocculant |
| CN108681302A (en) * | 2018-05-15 | 2018-10-19 | 安徽天卓信息技术有限公司 | A kind of data visualization sewage treatment intelligent control system |
| CN110782190A (en) * | 2019-12-04 | 2020-02-11 | 江苏方天电力技术有限公司 | Phase modulator remote diagnosis system based on ubiquitous power internet of things technology |
| JP2020065964A (en) * | 2018-10-23 | 2020-04-30 | 水ｉｎｇ株式会社 | Wastewater treatment method and wastewater treatment system |
| CN111233118A (en) * | 2020-03-19 | 2020-06-05 | 中冶赛迪工程技术股份有限公司 | Intelligent control system and control method for high-density sedimentation tank |
| JP2020187770A (en) * | 2020-07-09 | 2020-11-19 | 水ｉｎｇ株式会社 | Operation control method of water or sludge treatment system |
| CN114790039A (en) * | 2022-05-27 | 2022-07-26 | 四川开泽环境科技有限公司 | Intelligent denitrification regulation and control method and system for aquaculture wastewater |
| CN114933339A (en) * | 2022-07-11 | 2022-08-23 | 大唐融合通信股份有限公司 | Sewage treatment control method, cloud server and edge side equipment |
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2022
- 2022-09-15 CN CN202211119766.2A patent/CN115367823A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05285308A (en) * | 1992-04-08 | 1993-11-02 | Mitsubishi Electric Corp | Controller for injecting flocculant |
| CN108681302A (en) * | 2018-05-15 | 2018-10-19 | 安徽天卓信息技术有限公司 | A kind of data visualization sewage treatment intelligent control system |
| JP2020065964A (en) * | 2018-10-23 | 2020-04-30 | 水ｉｎｇ株式会社 | Wastewater treatment method and wastewater treatment system |
| CN110782190A (en) * | 2019-12-04 | 2020-02-11 | 江苏方天电力技术有限公司 | Phase modulator remote diagnosis system based on ubiquitous power internet of things technology |
| CN111233118A (en) * | 2020-03-19 | 2020-06-05 | 中冶赛迪工程技术股份有限公司 | Intelligent control system and control method for high-density sedimentation tank |
| JP2020187770A (en) * | 2020-07-09 | 2020-11-19 | 水ｉｎｇ株式会社 | Operation control method of water or sludge treatment system |
| CN114790039A (en) * | 2022-05-27 | 2022-07-26 | 四川开泽环境科技有限公司 | Intelligent denitrification regulation and control method and system for aquaculture wastewater |
| CN114933339A (en) * | 2022-07-11 | 2022-08-23 | 大唐融合通信股份有限公司 | Sewage treatment control method, cloud server and edge side equipment |
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