CN110308705A - A kind of apparatus control method based on big data and artificial intelligence water quality prediction - Google Patents
A kind of apparatus control method based on big data and artificial intelligence water quality prediction Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 16
- 238000007637 random forest analysis Methods 0.000 claims abstract description 19
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 238000012549 training Methods 0.000 claims abstract description 7
- 238000012360 testing method Methods 0.000 claims abstract description 5
- 238000010606 normalization Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000003066 decision tree Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 2
- 239000013589 supplement Substances 0.000 claims description 2
- 239000000203 mixture Substances 0.000 claims 2
- 239000010865 sewage Substances 0.000 abstract description 13
- 238000005265 energy consumption Methods 0.000 abstract description 5
- 230000008569 process Effects 0.000 description 6
- 238000005070 sampling Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 5
- 238000005273 aeration Methods 0.000 description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 3
- 229910052760 oxygen Inorganic materials 0.000 description 3
- 239000001301 oxygen Substances 0.000 description 3
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 2
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
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- 230000007613 environmental effect Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 229910052698 phosphorus Inorganic materials 0.000 description 2
- 239000011574 phosphorus Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000007596 consolidation process Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000010802 sludge Substances 0.000 description 1
- 238000013456 study Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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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
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F3/00—Biological treatment of water, waste water, or sewage
- C02F3/02—Aerobic processes
- C02F3/12—Activated sludge processes
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
-
- 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
- C02F2209/001—Upstream control, i.e. monitoring for predictive control
-
- 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
-
- 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
- C02F2209/08—Chemical Oxygen Demand [COD]; Biological Oxygen Demand [BOD]
-
- 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
- C02F2209/14—NH3-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
- C02F2209/16—Total nitrogen (tkN-N)
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W10/00—Technologies for wastewater treatment
- Y02W10/10—Biological treatment of water, waste water, or sewage
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- Databases & Information Systems (AREA)
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- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Organic Chemistry (AREA)
- Environmental & Geological Engineering (AREA)
- Data Mining & Analysis (AREA)
- Hydrology & Water Resources (AREA)
- Chemical & Material Sciences (AREA)
- Water Supply & Treatment (AREA)
- Quality & Reliability (AREA)
- Microbiology (AREA)
- Feedback Control In General (AREA)
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- Automation & Control Theory (AREA)
- Activated Sludge Processes (AREA)
Abstract
The present invention relates to a kind of apparatus control method based on big data and artificial intelligence water quality prediction, comprising the following steps: historical data is pre-processed；Pretreated historical data is trained to obtain random forest using random forest sorting algorithm；Classified the random forest that test sample is put into after training to obtain prediction result；Each equipment is controlled according to prediction result.The present invention can be improved the stable effluent quality of sewage treatment plant, reduce manual intervention degree, reduce energy consumption.
Description
Technical field
The present invention relates to technical field of sewage, are based on big data and artificial intelligence water quality prediction more particularly to one kind
Apparatus control method.
Background technique
Slow, most of sewage treatment is reformed in bio sewage treating process last 100 years technology development based on activated sludge
Factory has only reached the automatic control of sewage disposal device, i.e., controls sewage disposal device by craft or semi-hand in Central Control Room,
In this case, experience is leaned on for the control of effluent quality substantially, while equipment energy consumption and dosage can not be effectively reduced, is discharged
The problem of overproof water quality and equipment high energy consumption, can not be effectively improved.
The water quality monitoring equipment of environmental protection administration is generally placed in the unattended monitoring station of water factory, or by Chinese Ministry of Environmental Protection
Door periodically does sampling monitoring to the water quality of sewage plant, is easy to produce and monitors situation not in place, and qualified discharge can be not raw to periphery
State environment impacts.
Existing sewage treatment plant's operation mode is the operating parameter of equipment to be controlled by manually, for example manual control exposes
Tolerance, manual control dosage need operation maintenance personnel to have O&M experience abundant in this way, and have the people of abundant O&M experience
Member, it is more rare in sewage treatment industry.And the manual control device of operation maintenance personnel, can not also it accomplish timely, frequently
Equipment operating parameter is modified, therefore will cause that effluent quality is unstable and the high problem of operation cost.
Summary of the invention
The equipment based on big data and artificial intelligence water quality prediction that technical problem to be solved by the invention is to provide a kind of
Control method improves the stable effluent quality of sewage treatment plant, reduces manual intervention degree, reduces energy consumption.
The technical solution adopted by the present invention to solve the technical problems is: providing a kind of based on big data and artificial intelligence water
The apparatus control method of matter prediction, comprising the following steps:
(1) historical data is pre-processed；
(2) pretreated historical data is trained to obtain random forest using random forest sorting algorithm；
(3) classified the random forest that test sample is put into after training to obtain prediction result；
(4) each equipment is controlled according to prediction result.
In the step (1) to historical data carry out pretreatment include: data cleansing, data pick-up, data merge, number
According to grouping and data normalization.
The data cleansing specifically: duplicate data extra in historical data are subjected to screening removing, by the number of missing
It is complete according to supplement, the data of mistake are corrected or deleted.
The data pick-up specifically: the partial information for extracting field and record in former tables of data forms a newer field
And new record.
The data merge specifically, the information of the several fields of certain in tables of data or different record data are combined into one
A newer field and new recorded data specifically include field and merge and record merging, and the field merging is to close certain several field
It and is a newer field；The record merging is that will have common data field, structure, and different tables of data record information is closed
And into a new tables of data.
The data grouping specifically: numeric type data is carried out by equidistant or non-equidistant according to the purpose of analysis and is grouped.
The data normalization specifically: be allowed to data bi-directional scaling to fall in a specific sections.
Random forest sorting algorithm in the step (2) specifically: be concentrated with from equipment and water quality data sample and put back to ground
Repetition randomly selects k sample and generates new training sample set, then according to self-service sample set generate k classification tree form with
Machine forest, the classification results of new data are voted by classification tree depending on the score how much formed.
K classification tree, which is generated, according to self-service sample set forms random forest specifically: k decision tree is merged,
The foundation of each tree depends on the sample of an independent draws, each tree distribution having the same in forest, and error in classification takes
Certainly in the classification capacity of every one tree and the correlation between them；It goes to divide each node using random method, then
Compare the error generated under different situations, determines that selection is special according to the inherent evaluated error, classification capacity and the correlation that detect
The number of sign.
Beneficial effect
Due to the adoption of the above technical solution, compared with prior art, the present invention having the following advantages that and actively imitating
Fruit: the present invention using big data and intelligent algorithm by a large amount of O&M empirical data, through over cleaning, study, analysis, in conjunction with
The collected data of the sensor of each process section, such as influent quality, water, dissolved oxygen content, allow computer to carry out automatic control equipment
Operating parameter and dosage, manual intervention in water factory's operation can be effectively reduced causes that effluent quality is unstable, energy consumption medicine consumption is high
The problem of, reduce the dependence during sewage plant is runed to the expert teacher talent.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
Present invention will be further explained below with reference to specific examples.It should be understood that these embodiments are merely to illustrate the present invention
Rather than it limits the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, those skilled in the art
Member can make various changes or modifications the present invention, and such equivalent forms equally fall within the application the appended claims and limited
Range.
Embodiments of the present invention are related to a kind of apparatus control method based on big data and artificial intelligence water quality prediction, such as
Shown in Fig. 1, comprising the following steps:
(1) historical data is pre-processed；Wherein, carrying out pretreatment to historical data includes: data cleansing, data pumping
It takes, data merge, data grouping and data normalization.
Data cleansing is exactly to remove extra duplicate data screening, the data of missing is supplemented completely, by the number of mistake
According to correction or delete.
Data pick-up is also referred to as the part letter that data split, refer to reservation, extract certain fields in former tables of data, record
Breath, forms newer field, a new record.Main method has field fractionation and random sampling.Arbitrary sampling method mainly has simply
Random sampling, stratified sampling, systematic sampling etc..
Data merge the information or different record data for referring to the several fields of certain in integrated data table, are combined into one newly
Field, new recorded data, there are mainly two types of operations: field merges, record merges.Field merges, and is to merge certain several field
For a newer field.Record merges, and also referred to as vertical consolidation is that will have common data field, structure, different tables of data
Information is recorded, is merged into a new tables of data.
Numeric type data is carried out equidistant or non-equidistant according to the purpose of analysis and is grouped by data grouping, this process is also referred to as
Data Discretization is generally used for checking distribution, such as temperature, pressure, speed etc..Wherein, become for drawing the grouping of distribution map X-axis
Amount, cannot change its sequence, generally be arranged from small to large by grouping section, could observe the distribution rule of data in this way
Rule.
Operation for Unequal distance can recompile as different variables.Recompiling can be the numerical value of a variable
It requires to assign new numerical value according to specified, continuous variable can also be re-encoded as discrete variable, such as the age is recompiled
For age bracket.
Data normalization is to be allowed to data bi-directional scaling to fall in a specific sections.Data normalization be exactly in order to
The influence for eliminating dimension (unit), is conveniently compared analysis.Common data normalization method has 0-1 standardization and Z standard
Change.
0-1 standardization is also referred to as deviation standardization, it is to carry out linear transformation to initial data, and result is made to fall on [0,1] area
Between.0-1 standardization is exactly the conversion for easily doing the decimal system, hundred-mark system there are also a benefit, only need to be multiplied by 10 or 100,
He divides system similarly.
Z standardization is also referred to as standard deviation standardization, it is that the observed value (former data) in variable is subtracted being averaged for the variable
Value, then divided by the standard deviation of the variable.Treated data fit standardized normal distribution, i.e. mean value are 0, standard deviation 1
(2) pretreated historical data is trained to obtain random forest using random forest sorting algorithm；
Random forest sorting algorithm specifically, repeat to randomly select k with putting back to from equipment and water quality data sample set N
A sample generates new training sample set, then generates k classification tree according to self-service sample set and forms random forest, new data
Classification results by the score that how much is formed of classification tree ballot depending on.Its essence is a kind of improvement to decision Tree algorithms, will be more
A decision tree merges, and the foundation of each tree depends on the sample of an independent draws, and each tree in forest has phase
Same distribution, the classification capacity and the correlation between them that error in classification depends on every one tree.Feature selecting is using random
Method go to divide each node, then compare the error generated under different situations.The inherent evaluated error that is able to detect that,
Classification capacity and correlation determine the number of selection feature.The possible very little of the classification capacity of single tree, but it is a large amount of being randomly generated
Decision tree after, a test sample can select most probable classification by the classification results of every one tree after counting.
Random forest has fabulous accuracy rate, can effectively operate on large data sets, be capable of handling with higher-dimension
The input sample of feature, and dimensionality reduction is not needed, importance of each feature in classification problem can be assessed, in generating process
In, a kind of internal unbiased esti-mator for generating error can be got, fine result can be also obtained for default value problem.
(3) classified the random forest that test sample is put into after training to obtain prediction result；
(4) each equipment is controlled according to prediction result.Such as water quality is installed additional in the water inlet of sewage disposal system
Sensor, monitoring COD, SS, ammonia nitrogen, total phosphorus, pH etc.；Dissolved oxygen sensor is installed additional respectively in biochemistry pool leading portion, middle section, rear end,
Monitor dissolved oxygen content；Water quality sensor, monitoring COD, total phosphorus, ammonia nitrogen, pH etc. are installed additional in water outlet.Influent quality is by random gloomy
Prediction result is obtained after woods prediction, computer will provide the device parameters such as the dosage estimated, aeration quantity according to prediction result, and
And each process section residence time in the sewage plant obtained according to big data analysis, it calculates when advancing water passes through to each technique
The time of section, the device parameters such as the aeration quantity estimated, dosage are handed down to equipment at this time point, while molten according to biochemistry pool
Solve the real time data of lambda sensor, the aeration quantity of dynamic regulation air blower.Under normal operation, whole process is not necessarily to manual intervention,
And effluent quality is more more stable than before intelligent control, and reaches discharge standard.
Claims (9)
1. a kind of apparatus control method based on big data and artificial intelligence water quality prediction, which comprises the following steps:
(1) historical data is pre-processed；
(2) pretreated historical data is trained to obtain random forest using random forest sorting algorithm；
(3) classified the random forest that test sample is put into after training to obtain prediction result；
(4) each equipment is controlled according to prediction result.
2. the apparatus control method according to claim 1 based on big data and artificial intelligence water quality prediction, feature exist
In, in the step (1) to historical data carry out pretreatment include: data cleansing, data pick-up, data merge, data point
Group and data normalization.
3. the apparatus control method according to claim 2 based on big data and artificial intelligence water quality prediction, feature exist
In the data cleansing specifically: duplicate data extra in historical data are carried out screening removing, the data of missing are carried out
Supplement is complete, and the data of mistake are corrected or deleted.
4. the apparatus control method according to claim 2 based on big data and artificial intelligence water quality prediction, feature exist
In the data pick-up specifically: the partial information for extracting field and record in former tables of data forms a newer field and new note
Record.
5. the apparatus control method according to claim 2 based on big data and artificial intelligence water quality prediction, feature exist
In the data merge specifically, the information of the several fields of certain in tables of data or different record data are combined into one newly
Field and new recorded data specifically include field and merge and record merging, and the field merging is to merge into certain several field
One newer field；The record merging is that will have common data field, structure, and different tables of data record information is merged into
In one new tables of data.
6. the apparatus control method according to claim 2 based on big data and artificial intelligence water quality prediction, feature exist
In the data grouping specifically: numeric type data is carried out equidistant or non-equidistant according to the purpose of analysis and is grouped.
7. the apparatus control method according to claim 2 based on big data and artificial intelligence water quality prediction, feature exist
In the data normalization specifically: be allowed to data bi-directional scaling to fall in a specific sections.
8. the apparatus control method according to claim 1 based on big data and artificial intelligence water quality prediction, feature exist
In random forest sorting algorithm in the step (2) specifically: be concentrated with from equipment and water quality data sample put back to repeat with
Machine extracts k sample and generates new training sample set, and it is random gloomy then to generate k classification tree composition according to self-service sample set
Woods, the classification results of new data are voted by classification tree depending on the score how much formed.
9. the apparatus control method according to claim 8 based on big data and artificial intelligence water quality prediction, feature exist
According to k classification tree composition random forest of self-service sample set generation specifically: merge k decision tree, each tree
Foundation depend on the samples of an independent draws, each tree distribution having the same in forest, error in classification depends on every
The classification capacity of one tree and the correlation between them；It goes to divide each node using random method, then than less
With the error generated in situation, the number of selection feature is determined according to the inherent evaluated error, classification capacity and the correlation that detect
Mesh.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111221306A (en) * | 2020-01-17 | 2020-06-02 | 石化盈科信息技术有限责任公司 | Method for predicting key indexes of sewage system |
| CN111762934A (en) * | 2020-05-12 | 2020-10-13 | 中铁第四勘察设计院集团有限公司 | Full-automatic control system and method for modular micro-polluted water purification device |
| CN112819244A (en) * | 2021-02-23 | 2021-05-18 | 浙江大学 | Meteorological factor-based RF-HW water quality index hybrid prediction method |
| CN114397867A (en) * | 2022-03-18 | 2022-04-26 | 山西正合天科技股份有限公司 | Industrial personal computer control method and system based on Internet of things |
| CN114563988A (en) * | 2022-01-26 | 2022-05-31 | 浙江中控信息产业股份有限公司 | Water plant intelligent PAC adding method and system based on random forest algorithm |
| CN117388457A (en) * | 2023-10-16 | 2024-01-12 | 中山大学 | Method for improving prediction accuracy of effluent of sewage plant by coupling hydraulic retention time |
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| CN112819244B (en) * | 2021-02-23 | 2022-06-21 | 浙江大学 | Meteorological factor-based RF-HW water quality index hybrid prediction method |
| CN114563988A (en) * | 2022-01-26 | 2022-05-31 | 浙江中控信息产业股份有限公司 | Water plant intelligent PAC adding method and system based on random forest algorithm |
| CN114397867A (en) * | 2022-03-18 | 2022-04-26 | 山西正合天科技股份有限公司 | Industrial personal computer control method and system based on Internet of things |
| CN114397867B (en) * | 2022-03-18 | 2022-06-10 | 山西正合天科技股份有限公司 | Industrial personal computer control method and system based on Internet of things |
| CN117388457A (en) * | 2023-10-16 | 2024-01-12 | 中山大学 | Method for improving prediction accuracy of effluent of sewage plant by coupling hydraulic retention time |
| CN117388457B (en) * | 2023-10-16 | 2024-05-31 | 中山大学 | Method for improving prediction accuracy of effluent of sewage plant by coupling hydraulic retention time |
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