CN117783471A - Water quality on-line monitoring system - Google Patents
Water quality on-line monitoring system Download PDFInfo
- Publication number
- CN117783471A CN117783471A CN202410212981.XA CN202410212981A CN117783471A CN 117783471 A CN117783471 A CN 117783471A CN 202410212981 A CN202410212981 A CN 202410212981A CN 117783471 A CN117783471 A CN 117783471A
- Authority
- CN
- China
- Prior art keywords
- water quality
- data
- critical
- flow
- characteristic factors
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 234
- 238000012544 monitoring process Methods 0.000 title claims abstract description 105
- 239000010865 sewage Substances 0.000 claims abstract description 85
- 238000013500 data storage Methods 0.000 claims abstract description 33
- 238000007405 data analysis Methods 0.000 claims abstract description 11
- 230000002265 prevention Effects 0.000 claims abstract description 10
- 230000000007 visual effect Effects 0.000 claims abstract description 10
- 238000004891 communication Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 69
- 239000003550 marker Substances 0.000 claims description 44
- 238000012216 screening Methods 0.000 claims description 7
- 238000013079 data visualisation Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 230000003449 preventive effect Effects 0.000 claims description 6
- 230000004931 aggregating effect Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 5
- 239000001301 oxygen Substances 0.000 description 5
- 229910052760 oxygen Inorganic materials 0.000 description 5
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 239000003344 environmental pollutant Substances 0.000 description 3
- 231100000719 pollutant Toxicity 0.000 description 3
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 229910001385 heavy metal Inorganic materials 0.000 description 2
- 150000002500 ions Chemical class 0.000 description 2
- 244000005700 microbiome Species 0.000 description 2
- 229910052757 nitrogen Inorganic materials 0.000 description 2
- 229910052698 phosphorus Inorganic materials 0.000 description 2
- 239000011574 phosphorus Substances 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 239000010802 sludge Substances 0.000 description 2
- 238000004065 wastewater treatment Methods 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 description 1
- JVMRPSJZNHXORP-UHFFFAOYSA-N ON=O.ON=O.ON=O.N Chemical compound ON=O.ON=O.ON=O.N JVMRPSJZNHXORP-UHFFFAOYSA-N 0.000 description 1
- 239000005708 Sodium hypochlorite Substances 0.000 description 1
- MMDJDBSEMBIJBB-UHFFFAOYSA-N [O-][N+]([O-])=O.[O-][N+]([O-])=O.[O-][N+]([O-])=O.[NH6+3] Chemical compound [O-][N+]([O-])=O.[O-][N+]([O-])=O.[O-][N+]([O-])=O.[NH6+3] MMDJDBSEMBIJBB-UHFFFAOYSA-N 0.000 description 1
- 239000005422 algal bloom Substances 0.000 description 1
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000010170 biological method Methods 0.000 description 1
- 230000031018 biological processes and functions Effects 0.000 description 1
- 229910052793 cadmium Inorganic materials 0.000 description 1
- BDOSMKKIYDKNTQ-UHFFFAOYSA-N cadmium atom Chemical compound [Cd] BDOSMKKIYDKNTQ-UHFFFAOYSA-N 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000000460 chlorine Substances 0.000 description 1
- 229910052801 chlorine Inorganic materials 0.000 description 1
- 239000000084 colloidal system Substances 0.000 description 1
- 230000000593 degrading effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 239000003651 drinking water Substances 0.000 description 1
- 235000020188 drinking water Nutrition 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012851 eutrophication Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000003673 groundwater Substances 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 description 1
- 229910052753 mercury Inorganic materials 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- QJGQUHMNIGDVPM-UHFFFAOYSA-N nitrogen group Chemical group [N] QJGQUHMNIGDVPM-UHFFFAOYSA-N 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 244000052769 pathogen Species 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 239000013535 sea water Substances 0.000 description 1
- SUKJFIGYRHOWBL-UHFFFAOYSA-N sodium hypochlorite Chemical compound [Na+].Cl[O-] SUKJFIGYRHOWBL-UHFFFAOYSA-N 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001954 sterilising effect Effects 0.000 description 1
- 238000004659 sterilization and disinfection Methods 0.000 description 1
- 239000002352 surface water Substances 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 239000002351 wastewater Substances 0.000 description 1
Abstract
The invention relates to the technical field of water quality monitoring, in particular to a water quality online monitoring system which comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data storage module, a data visual module, a critical state selection module, a critical matching module, a data analysis module and a monitoring prevention module; the data acquisition module is used for acquiring water quality data; the data storage module is used for storing historical data information; the data visual module is used for generating a digital twin model and constructing a multi-characteristic factor topological network; the critical state selection module is used for determining a critical state; the critical matching module is used for constructing critical markers of each flow subsequence; the data analysis module is used for generating suspicious markers according to the real-time water quality data; the monitoring and preventing module generates preventing measures according to the suspicious markers, so that the early failure prediction before the sewage treatment equipment is converted from the normal state to the failure state is realized, and the preventing measures are taken before the failure of the sewage treatment equipment is mature.
Description
Technical Field
The invention relates to the technical field of water quality monitoring, in particular to an online water quality monitoring system.
Background
In the prior art CN116609500A, a water quality data monitoring method based on the Internet of things is used for acquiring water quality data information and environmental parameters of a designated area through a sensor terminal in a data acquisition module, marking acquisition time and setting a monitoring period; establishing a water quality pollution prediction model based on the BP neural network, and acquiring water quality pollution prediction information of different time stamps of the current monitoring period of a designated area; acquiring a deviation value of water quality data information and water quality pollution prediction information of a designated area, and compensating and correcting the water quality pollution prediction information; the geographic features of the appointed area are obtained through GIS geographic data, and the average concentration, the pollutant diffusion value and the main pollutant transfer direction of each pollutant of the appointed area are obtained; establishing a space-time feature integrated visual view about a target area, and visually displaying pollution early-warning information based on the space-time feature integrated visual view, so that timeliness, accuracy and continuity of water quality monitoring are ensured;
the prior art CN107782869A 'an online water quality monitoring system' comprises a water quality monitoring unit, a data acquisition and transmission unit and a data storage and processing unit; the data storage and processing unit comprises software and/or hardware for storing and processing data measured by the water quality on-line monitoring equipment transmitted by the data acquisition and transmission equipment; the data acquisition and transmission unit connects the water quality monitoring unit with the data storage and processing unit in a wireless and/or wired communication mode. The water quality on-line monitoring system can be connected with various on-line monitoring devices, can be continuously expanded, and can be widely applied to various water quality on-line monitoring fields, including drinking water, polluted water, surface water such as groundwater, rivers, lakes and reservoirs, seawater, pipe network water supply and water for scientific research institutions. The online water quality monitoring system provided by the invention has the advantages that the fidelity and confidentiality of water quality data are enhanced, the collected monitoring data are more abundant than those of other platforms, and the working efficiency of later-stage on-site operation and maintenance and pollution treatment is effectively improved;
sewage treatment process control faces multiple difficulties and has a certain challenge, and early failures before the sewage treatment equipment is converted from a normal state to a failure state are often not easy to detect at present, because symptoms of the failure of the sewage treatment equipment may be weak or difficult to identify, which may lead to failure to take preventive measures before the failure is mature.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a water quality online monitoring system which comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data storage module, a data visual module, a critical state selection module, a critical matching module, a data analysis module and a monitoring prevention module;
the data acquisition module is used for acquiring current sewage treatment flow information, setting water quality monitoring points according to the flow information and acquiring water quality data of each point;
the data storage module is used for storing historical data information of each flow subsequence;
the data visual module is used for generating a digital twin model according to the assembly sequence and the assembly structure of each sewage treatment device in the current sewage treatment process, and constructing a multi-characteristic factor topology network of each flow subsequence according to twin data in the digital twin model;
the critical state selection module is used for marking the operation state of the sewage treatment equipment in each flow subsequence in a plurality of history monitoring periods in a period of time before the normal operation state is converted into the fault state as a critical state;
the critical matching module is used for obtaining key characteristic factors of all flow subsequences according to the average standard deviation of the concentrations of all types of water quality characteristic factors when all flow subsequences are in a critical state, and constructing critical markers of all flow subsequences according to the key characteristic factors and the concentration correlation coefficient time sequence of other types of water quality characteristic factors and the key characteristic factors;
the data analysis module is used for judging whether to generate suspicious markers according to the real-time water quality data of each flow subsequence of the current monitoring period;
and the monitoring and preventing module carries out consistency matching on suspicious markers generated by the subsequences of each flow and a critical marker database in the data storage module, and generates preventing measures according to the matching results.
Further, the data acquisition module acquires current sewage treatment flow information, sets water quality monitoring points according to the flow information, and the process of acquiring water quality data of each point comprises the following steps:
acquiring process flow characteristics of current sewage treatment equipment, extracting flow information according to the process flow characteristics, splitting a sewage treatment flow according to sewage treatment, and dividing the sewage treatment flow into a plurality of flow subsequences;
setting water quality monitoring points in each flow subsequence, and acquiring various water quality characteristic factors to be monitored of each water quality monitoring point by utilizing data retrieval according to the functional characteristics in the process unit characteristics of the corresponding flow subsequence;
and the water quality data monitoring point location acquires water quality data in real time according to various water quality characteristic factors to be monitored, marks the monitoring time and sets a monitoring period.
Further, the process of generating the digital twin model by the data visual module according to the assembly sequence and the assembly structure of each sewage treatment device in the current sewage treatment process comprises the following steps:
constructing a digital space, acquiring physical entities of sewage treatment equipment and water quality data of all water quality monitoring points in the physical space in the current sewage treatment process, performing three-dimensional modeling treatment on the physical entities of the sewage treatment equipment to generate a three-dimensional model, and performing data format preprocessing on the water quality data to generate twin data;
the method comprises the steps of obtaining the assembly sequence and the assembly structure of each sewage treatment device in the current sewage treatment process, constructing a three-dimensional model topological directed graph, taking a three-dimensional model of each flow subsequence as a node of the topological directed graph, taking the assembly sequence and the assembly structure of each sewage treatment device as a connection relation between the nodes, and matching twin data with the three-dimensional model to generate a digital twin model.
Further, the process of constructing the multi-feature factor topology network of each flow sub-sequence by the data visualization module according to the twin data in the digital twin model comprises the following steps:
the method comprises the steps of obtaining water quality characteristic factors related to twin data of each flow sub-sequence in a digital twin model, constructing a multi-characteristic factor topology network of each flow sub-sequence, mapping each water quality characteristic factor related to twin data of each flow sub-sequence into a node in a corresponding multi-characteristic factor topology network, and connecting all nodes.
Further, the process of marking the operation state of the sewage treatment equipment in each flow subsequence in the plurality of history monitoring periods as the critical state in a period of time before the normal operation state is converted into the fault state by the critical state selection module comprises the following steps:
corresponding historical operation states of the sewage treatment equipment of the flow subsequence at different moments in a historical monitoring period are obtained from the data storage module, wherein the operation states comprise a normal operation state and a fault state;
setting critical time length, marking the moment of converting the sewage treatment equipment of the flow subsequences from the normal operation state to the fault state as critical moment, taking the critical moment as an endpoint, taking the other moment in the normal operation state, with the time span equal to the critical time length, as an endpoint, and marking the operation state of each flow subsequence in the time period between the two endpoints as the critical state.
Further, the process of obtaining key characteristic factors of each flow subsequence by the critical matching module according to the average standard deviation of the concentration of each type of water quality characteristic factors when each flow subsequence is in a critical state, and constructing critical markers of each flow subsequence according to the key characteristic factors and the concentration correlation coefficient time sequence of other types of water quality characteristic factors and the key characteristic factors comprises the following steps:
acquiring corresponding historical water quality data when the flow subsequence is in a normal operation state and a critical state in a historical monitoring period from a data storage module, acquiring average concentration of various types of water quality characteristic factors in the normal operation state according to the historical water quality data in the normal operation state, and acquiring concentration time sequence sequences of various types of water quality characteristic factors in the critical state according to the historical water quality data in the critical state;
acquiring the concentration average standard deviation of each type of water quality characteristic factors in a critical state according to the concentration time sequence of each type of water quality characteristic factors in the critical state and the average concentration of each type of water quality characteristic factors in a normal operation state, screening out the water quality characteristic factors corresponding to the highest average standard deviation, and marking the water quality characteristic factors as key characteristic factors;
then, according to the concentration time sequence of each type of water quality characteristic factors in the critical state, acquiring concentration correlation coefficient time sequence of other types of water quality characteristic factors and key characteristic factors in the critical state, screening out other types of water quality characteristic factors with continuously increased concentration correlation coefficients with the key characteristic factors in the critical state according to the concentration correlation coefficient time sequence, mapping the key characteristic factors and the other types of water quality characteristic factors into a multi-characteristic factor topological network, forming a topological sub-network taking the key characteristic factors and the other types of water quality characteristic factors as nodes, and marking the topological sub-network as a critical marker;
and acquiring historical water quality data when the critical state of the critical marker is converted into the fault state in the historical monitoring period, and correlating the critical dynamic marker with the historical water quality data in the fault state.
Further, the process of judging whether to generate the suspicious marker by the data analysis module according to the real-time water quality data of each flow subsequence in the current monitoring period comprises the following steps:
acquiring real-time water quality data of each flow sub-sequence in a current monitoring period and average concentration of various water quality characteristic factors when sewage treatment equipment of each flow sub-sequence in a historical monitoring period is in a normal operation state, acquiring key characteristic factors of each flow sub-sequence according to the real-time water quality data and the average concentration of various water quality characteristic factors, then acquiring concentration correlation coefficient real-time sequences of other types of water quality characteristic factors and the key characteristic factors, judging whether other types of water quality characteristic factors with continuously increased concentration correlation coefficients of the key characteristic factors exist or not, if so, constructing a topology sub-network of the current monitoring period according to the key characteristic factors and the other types of water quality characteristic factors, and marking the topology sub-network as a suspicious marker.
Further, the process of performing consistency matching on suspicious markers generated by the subsequences of each flow and a critical marker database in the data storage module by the monitoring and prevention module and generating prevention measures according to the matching result comprises the following steps:
acquiring critical markers in a plurality of historical monitoring periods from a data storage module, and aggregating the critical markers in the plurality of historical monitoring periods to construct a critical marker database;
when a suspicious marker is generated by a flow subsequence, matching the suspicious marker with a critical marker database in real time, and if the water quality characteristic factors of all nodes in a topological sub-network corresponding to the suspicious marker are consistent with the water quality characteristic factors of all nodes in a topological sub-network corresponding to a certain critical marker in the critical marker database, acquiring historical water quality data associated with the certain critical marker, searching in a data storage module according to the historical water quality data, and acquiring preventive measures of the historical water quality data from the data storage module.
Compared with the prior art, the invention has the beneficial effects that: obtaining key characteristic factors of each flow subsequence according to the average standard deviation of the concentration of each type of water quality characteristic factor when each flow subsequence is in a critical state, constructing critical marks of each flow subsequence according to the key characteristic factors and the time sequence of the concentration correlation coefficient of other types of water quality characteristic factors and the key characteristic factors, judging whether suspicious marks are generated according to the real-time water quality data of each flow subsequence in the current monitoring period, carrying out consistency matching on the suspicious marks generated by each flow subsequence and a critical mark database in a data storage module, generating preventive measures according to the matching results, and realizing the prediction of early faults before the sewage treatment equipment is converted from a normal state to a fault state, thereby taking preventive measures before the sewage treatment equipment is mature.
Drawings
Fig. 1 is a schematic diagram of an online water quality monitoring system according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application, taken in conjunction with the accompanying drawings, clearly and completely describes the technical solutions of the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
As shown in fig. 1, the water quality online monitoring system comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data storage module, a data visualization module, a critical state selection module, a critical matching module, a data analysis module and a monitoring prevention module;
the data acquisition module is used for acquiring current sewage treatment flow information, setting water quality monitoring points according to the flow information and acquiring water quality data of each point;
the data storage module is used for storing historical data information of each flow subsequence;
the data visual module is used for generating a digital twin model according to the assembly sequence and the assembly structure of each sewage treatment device in the current sewage treatment process, and constructing a multi-characteristic factor topology network of each flow subsequence according to twin data in the digital twin model;
the critical state selection module is used for marking the operation state of the sewage treatment equipment in each flow subsequence in a plurality of history monitoring periods in a period of time before the normal operation state is converted into the fault state as a critical state;
the critical matching module is used for obtaining key characteristic factors of all flow subsequences according to the average standard deviation of the concentrations of all types of water quality characteristic factors when all flow subsequences are in a critical state, and constructing critical markers of all flow subsequences according to the key characteristic factors and the concentration correlation coefficient time sequence of other types of water quality characteristic factors and the key characteristic factors;
the data analysis module is used for judging whether to generate suspicious markers according to the real-time water quality data of each flow subsequence of the current monitoring period;
and the monitoring and preventing module carries out consistency matching on suspicious markers generated by the subsequences of each flow and a critical marker database in the data storage module, and generates preventing measures according to the matching results.
It should be further noted that, in the specific implementation process, the data acquisition module acquires current sewage treatment flow information, sets water quality monitoring points according to the flow information, and the process of acquiring water quality data of each point comprises:
acquiring process flow characteristics of current sewage treatment equipment, extracting flow information according to the process flow characteristics, splitting a sewage treatment flow according to sewage treatment, and dividing the sewage treatment flow into a plurality of flow subsequences;
it should be further noted that in practice, the process characteristics of the wastewater treatment facility generally depend on the type of wastewater to be treated by the wastewater treatment facility and the target discharge objectives, and include, but are not limited to:
pretreatment: primarily filtering and removing large-particle impurities such as sand and leaves in the sewage;
and (3) biochemical treatment: degrading organic matters by utilizing microorganisms through a biological method, an activated sludge method and other methods, and converting organic matters in sewage into microorganisms and stable inorganic matters;
and (3) precipitation treatment: the treated sewage is precipitated, so that suspended matters are deposited at the bottom to form sludge, and the precipitation treatment is beneficial to removing residual suspended matters and colloid matters;
denitrification dephosphorization: aiming at sewage with more substances containing nitrogen and phosphorus, special denitrification and dephosphorization treatment is needed to meet the emission standard;
and (3) disinfection: sterilizing the treated sewage by using chlorine, sodium hypochlorite, ultraviolet rays and the like to kill bacteria and pathogens in the sewage;
setting water quality monitoring points in each flow subsequence, and acquiring various water quality characteristic factors to be monitored of each water quality monitoring point by utilizing data retrieval according to the functional characteristics in the process unit characteristics of the corresponding flow subsequence;
it should be further noted that, in the implementation process, various types of water quality characteristic factors to be monitored at each water quality monitoring point include, but are not limited to:
pH value: the pH value is an important index of the pH value of the water body, and has great influence on a plurality of chemical reactions and biological processes;
dissolved Oxygen (DO): dissolved oxygen is the concentration of oxygen dissolved in water and has important influence on biological growth and oxidation of organic matters in water;
turbidity: the turbidity represents the quantity of suspended substances in water and is an important index for representing the clarity of the water body;
conductivity: conductivity is a measure of the electrolyte content in water, and can indirectly reflect the total dissolved solids in water;
ammonia nitrogen, nitrite nitrogen and nitrate nitrogen: these indexes are important parameters for representing nitrogenous substances in the water body, and have important influence on the ecological environment and biological community of the water body;
chemical Oxygen Demand (COD) and Biochemical Oxygen Demand (BOD): these two parameters are important indexes for representing the pollution degree of organic matters in the water body, BOD reflects the biodegradability of the organic matters in the water, and COD can represent the total amount of the organic matters in the water body;
total phosphorus and total nitrogen: the two parameters are also important indexes for representing the content of nutrient salts in the water body, and have important influence on eutrophication of the water body and algal bloom;
heavy metal ions: the monitoring of heavy metal ions such as lead, cadmium, mercury and the like has important significance for the pollution condition of water bodies.
And the water quality data monitoring point location acquires water quality data in real time according to various water quality characteristic factors to be monitored, marks the monitoring time and sets a monitoring period.
It should be further noted that, in the implementation process, the process of generating the digital twin model by the data visual module according to the assembly sequence and the assembly structure of each sewage treatment device in the current sewage treatment process includes:
constructing a digital space, acquiring physical entities of sewage treatment equipment and water quality data of all water quality monitoring points in the physical space in the current sewage treatment process, performing three-dimensional modeling treatment on the physical entities of the sewage treatment equipment to generate a three-dimensional model, and performing data format preprocessing on the water quality data to generate twin data;
the method comprises the steps of obtaining the assembly sequence and the assembly structure of each sewage treatment device in the current sewage treatment process, constructing a three-dimensional model topological directed graph, taking a three-dimensional model of each flow subsequence as a node of the topological directed graph, taking the assembly sequence and the assembly structure of each sewage treatment device as a connection relation between the nodes, and matching twin data with the three-dimensional model to generate a digital twin model.
It should be further noted that, in the implementation process, the process of constructing the multi-feature factor topology network of each flow sub-sequence by the data visualization module according to the twin data in the digital twin model includes:
the method comprises the steps of obtaining water quality characteristic factors related to twin data of each flow sub-sequence in a digital twin model, constructing a multi-characteristic factor topology network of each flow sub-sequence, mapping each water quality characteristic factor related to twin data of each flow sub-sequence into a node in a corresponding multi-characteristic factor topology network, and connecting all nodes.
It should be further noted that, in the implementation process, the process of marking the operation state of the sewage treatment device in a period of time before the normal operation state is converted into the fault state by the critical state selection module in each flow subsequence in the plurality of history monitoring periods as the critical state includes:
corresponding historical operation states of the sewage treatment equipment of the flow subsequence at different moments in a historical monitoring period are obtained from the data storage module, wherein the operation states comprise a normal operation state and a fault state;
setting critical time length, marking the moment of converting the sewage treatment equipment of the flow subsequences from the normal operation state to the fault state as critical moment, taking the critical moment as an endpoint, taking the other moment in the normal operation state, with the time span equal to the critical time length, as an endpoint, and marking the operation state of each flow subsequence in the time period between the two endpoints as the critical state.
It should be further noted that, in the implementation process, the process of determining the operation state of the sewage treatment apparatus in the flow subsequence includes:
acquiring water quality data and monitoring time of each water quality monitoring point, acquiring the type of sewage to be treated and a target discharge target of each sewage treatment device according to the technological process characteristics of each sewage treatment device, and acquiring a plurality of water quality characteristic factors to be treated and threshold ranges of the water quality characteristic factors of each sewage treatment device according to the type of sewage to be treated and the target discharge target;
screening out a plurality of water quality characteristic factors to be processed in the water quality data of each water quality monitoring point, comparing the water quality characteristic factors to be processed in the water quality data of each water quality monitoring point with the threshold ranges of the water quality characteristic factors, judging whether the water quality characteristic factors are located in the threshold ranges, if the water quality characteristic factors which are not located in the threshold ranges are not present, marking the sewage treatment equipment where the water quality monitoring point is located as a normal state, if the water quality characteristic factors which are not located in the threshold ranges are present, marking the sewage treatment equipment where the water quality monitoring point is located as a fault state, and determining the critical moment of converting the sewage treatment equipment of the flow subsequence from the normal operation state to the fault state according to the monitoring time of the water quality data of the water quality monitoring point.
It should be further noted that, in the implementation process, the process that the critical matching module obtains the critical characteristic factors of each flow subsequence according to the average standard deviation of the concentrations of the water quality characteristic factors of each type when each flow subsequence is in the critical state, and constructs the critical markers of each flow subsequence according to the critical characteristic factors and the concentration correlation coefficient time sequence of the other types of water quality characteristic factors and the critical characteristic factors includes:
acquiring corresponding historical water quality data when the flow subsequence is in a normal operation state and a critical state in a historical monitoring period from a data storage module, acquiring average concentration of various types of water quality characteristic factors in the normal operation state according to the historical water quality data in the normal operation state, and acquiring concentration time sequence sequences of various types of water quality characteristic factors in the critical state according to the historical water quality data in the critical state;
acquiring the concentration average standard deviation of each type of water quality characteristic factors in a critical state according to the concentration time sequence of each type of water quality characteristic factors in the critical state and the average concentration of each type of water quality characteristic factors in a normal operation state, screening out the water quality characteristic factors corresponding to the highest average standard deviation, and marking the water quality characteristic factors as key characteristic factors;
then, according to the concentration time sequence of each type of water quality characteristic factors in the critical state, acquiring concentration correlation coefficient time sequence of other types of water quality characteristic factors and key characteristic factors in the critical state, screening out other types of water quality characteristic factors with continuously increased concentration correlation coefficients with the key characteristic factors in the critical state according to the concentration correlation coefficient time sequence, mapping the key characteristic factors and the other types of water quality characteristic factors into a multi-characteristic factor topological network, forming a topological sub-network taking the key characteristic factors and the other types of water quality characteristic factors as nodes, and marking the topological sub-network as a critical marker;
and acquiring historical water quality data when the critical state of the critical marker is converted into the fault state in the historical monitoring period, and correlating the critical dynamic marker with the historical water quality data in the fault state.
It should be further described that, in the specific implementation process, the calculation formula of the average standard deviation of the concentration of each type of water quality characteristic factor in the critical state is as follows, according to the concentration time sequence of each type of water quality characteristic factor in the critical state and the average concentration of each type of water quality characteristic factor in the normal operation state:
；
wherein,representing the average standard deviation of the concentration of the i-th water quality characteristic factors; />Representing the concentration of the i-th water quality characteristic factor at the t moment; />Representing the average concentration of the i-th water quality characteristic factors; n represents the time span length of the critical state.
It should be further described that, in the specific implementation process, the calculation formula of the concentration correlation coefficient between the other types of water quality characteristic factors and the key characteristic factors in the critical state is obtained according to the concentration time sequence of the various types of water quality characteristic factors in the critical state:
；
wherein qr (ij) represents a concentration correlation coefficient of the i-th water quality characteristic factor and the j-th water quality characteristic factor;the concentration of the ith water quality characteristic factor at the t moment; />Representing the average concentration of the i-th water quality characteristic factors; />The concentration of the j-th water quality characteristic factors at the t moment; />Represents the average concentration of the j-th water quality characteristic factorsThe method comprises the steps of carrying out a first treatment on the surface of the n represents the time span length of the critical state.
It should be further noted that, in the implementation process, the process that the data analysis module judges whether to generate the suspicious marker according to the real-time water quality data of each flow subsequence in the current monitoring period includes:
acquiring real-time water quality data of each flow sub-sequence in a current monitoring period and average concentration of various water quality characteristic factors when sewage treatment equipment of each flow sub-sequence in a historical monitoring period is in a normal operation state, acquiring key characteristic factors of each flow sub-sequence according to the real-time water quality data and the average concentration of various water quality characteristic factors, then acquiring concentration correlation coefficient real-time sequences of other types of water quality characteristic factors and the key characteristic factors, judging whether other types of water quality characteristic factors with continuously increased concentration correlation coefficients of the key characteristic factors exist or not, if so, constructing a topology sub-network of the current monitoring period according to the key characteristic factors and the other types of water quality characteristic factors, and marking the topology sub-network as a suspicious marker.
It should be further noted that, in the implementation process, the process of performing consistency matching between the suspicious markers generated by the subsequences of each flow and the critical marker database in the data storage module by the monitoring and prevention module, and generating the prevention measures according to the matching result includes:
acquiring critical markers in a plurality of historical monitoring periods from a data storage module, and aggregating the critical markers in the plurality of historical monitoring periods to construct a critical marker database;
when a suspicious marker is generated by a flow subsequence, matching the suspicious marker with a critical marker database in real time, and if the water quality characteristic factors of all nodes in a topological sub-network corresponding to the suspicious marker are consistent with the water quality characteristic factors of all nodes in a topological sub-network corresponding to a certain critical marker in the critical marker database, acquiring historical water quality data associated with the certain critical marker, searching in a data storage module according to the historical water quality data, and acquiring preventive measures of the historical water quality data from the data storage module.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The water quality on-line monitoring system comprises a monitoring center and is characterized in that the monitoring center is in communication connection with a data acquisition module, a data storage module, a data visualization module, a critical state selection module, a critical matching module, a data analysis module and a monitoring prevention module;
the data acquisition module is used for acquiring current sewage treatment flow information, setting water quality monitoring points according to the flow information and acquiring water quality data of each point;
the data storage module is used for storing historical data information of each flow subsequence;
the data visual module is used for generating a digital twin model according to the assembly sequence and the assembly structure of each sewage treatment device in the current sewage treatment process, and constructing a multi-characteristic factor topology network of each flow subsequence according to twin data in the digital twin model;
the critical state selection module is used for marking the operation state of the sewage treatment equipment in each flow subsequence in a plurality of history monitoring periods in a period of time before the normal operation state is converted into the fault state as a critical state;
the critical matching module is used for obtaining key characteristic factors of all flow subsequences according to the average standard deviation of the concentrations of all types of water quality characteristic factors when all flow subsequences are in a critical state, and constructing critical markers of all flow subsequences according to the key characteristic factors and the concentration correlation coefficient time sequence of other types of water quality characteristic factors and the key characteristic factors;
the data analysis module is used for judging whether to generate suspicious markers according to the real-time water quality data of each flow subsequence of the current monitoring period;
and the monitoring and preventing module carries out consistency matching on suspicious markers generated by the subsequences of each flow and a critical marker database in the data storage module, and generates preventing measures according to the matching results.
2. The system of claim 1, wherein the data acquisition module acquires current sewage treatment flow information, sets water quality monitoring points according to the flow information, and the process of acquiring water quality data of each point comprises:
acquiring process flow characteristics of current sewage treatment equipment, extracting flow information according to the process flow characteristics, splitting a sewage treatment flow according to sewage treatment, and dividing the sewage treatment flow into a plurality of flow subsequences;
setting water quality monitoring points in each flow subsequence, and acquiring various water quality characteristic factors to be monitored of each water quality monitoring point by utilizing data retrieval according to the functional characteristics in the process unit characteristics of the corresponding flow subsequence;
and the water quality data monitoring point location acquires water quality data in real time according to various water quality characteristic factors to be monitored, marks the monitoring time and sets a monitoring period.
3. The system of claim 2, wherein the process of generating the digital twin model by the data visualization module according to the assembly sequence and assembly structure of each sewage treatment device in the current sewage treatment process comprises:
constructing a digital space, acquiring physical entities of sewage treatment equipment and water quality data of all water quality monitoring points in the physical space in the current sewage treatment process, performing three-dimensional modeling treatment on the physical entities of the sewage treatment equipment to generate a three-dimensional model, and performing data format preprocessing on the water quality data to generate twin data;
the method comprises the steps of obtaining the assembly sequence and the assembly structure of each sewage treatment device in the current sewage treatment process, constructing a three-dimensional model topological directed graph, taking a three-dimensional model of each flow subsequence as a node of the topological directed graph, taking the assembly sequence and the assembly structure of each sewage treatment device as a connection relation between the nodes, and matching twin data with the three-dimensional model to generate a digital twin model.
4. A water quality online monitoring system according to claim 3, wherein the process of constructing the multi-feature factor topology network of each flow sub-sequence by the data visualization module according to the twin data in the digital twin model comprises:
the method comprises the steps of obtaining water quality characteristic factors related to twin data of each flow sub-sequence in a digital twin model, constructing a multi-characteristic factor topology network of each flow sub-sequence, mapping each water quality characteristic factor related to twin data of each flow sub-sequence into a node in a corresponding multi-characteristic factor topology network, and connecting all nodes.
5. The system of claim 4, wherein the process of marking the operation state of the sewage treatment device in each flow sub-sequence in the plurality of history monitoring periods as the critical state for a period of time before the normal operation state is converted into the fault state by the critical state selection module comprises:
corresponding historical operation states of the sewage treatment equipment of the flow subsequence at different moments in a historical monitoring period are obtained from the data storage module, wherein the operation states comprise a normal operation state and a fault state;
setting critical time length, marking the moment of converting the sewage treatment equipment of the flow subsequences from the normal operation state to the fault state as critical moment, taking the critical moment as an endpoint, taking the other moment in the normal operation state, with the time span equal to the critical time length, as an endpoint, and marking the operation state of each flow subsequence in the time period between the two endpoints as the critical state.
6. The system of claim 5, wherein the process of the critical matching module obtaining key feature factors of each flow subsequence according to the average standard deviation of the concentration of each type of water quality feature factor when each flow subsequence is in a critical state, and constructing critical markers of each flow subsequence according to the key feature factors and the time sequence of concentration correlation coefficients of other types of water quality feature factors and the key feature factors comprises:
acquiring corresponding historical water quality data when the flow subsequence is in a normal operation state and a critical state in a historical monitoring period from a data storage module, acquiring average concentration of various types of water quality characteristic factors in the normal operation state according to the historical water quality data in the normal operation state, and acquiring concentration time sequence sequences of various types of water quality characteristic factors in the critical state according to the historical water quality data in the critical state;
acquiring the concentration average standard deviation of each type of water quality characteristic factors in a critical state according to the concentration time sequence of each type of water quality characteristic factors in the critical state and the average concentration of each type of water quality characteristic factors in a normal operation state, screening out the water quality characteristic factors corresponding to the highest average standard deviation, and marking the water quality characteristic factors as key characteristic factors;
then, according to the concentration time sequence of each type of water quality characteristic factors in the critical state, acquiring concentration correlation coefficient time sequence of other types of water quality characteristic factors and key characteristic factors in the critical state, screening out other types of water quality characteristic factors with continuously increased concentration correlation coefficients with the key characteristic factors in the critical state according to the concentration correlation coefficient time sequence, mapping the key characteristic factors and the other types of water quality characteristic factors into a multi-characteristic factor topological network, forming a topological sub-network taking the key characteristic factors and the other types of water quality characteristic factors as nodes, and marking the topological sub-network as a critical marker;
and acquiring historical water quality data when the critical state of the critical marker is converted into the fault state in the historical monitoring period, and correlating the critical dynamic marker with the historical water quality data in the fault state.
7. The system of claim 6, wherein the process of determining whether to generate suspicious markers based on real-time water quality data of each flow subsequence in the current monitoring period by the data analysis module comprises:
acquiring real-time water quality data of each flow sub-sequence in a current monitoring period and average concentration of various water quality characteristic factors when sewage treatment equipment of each flow sub-sequence in a historical monitoring period is in a normal operation state, acquiring key characteristic factors of each flow sub-sequence according to the real-time water quality data and the average concentration of various water quality characteristic factors, then acquiring concentration correlation coefficient real-time sequences of other types of water quality characteristic factors and the key characteristic factors, judging whether other types of water quality characteristic factors with continuously increased concentration correlation coefficients of the key characteristic factors exist or not, if so, constructing a topology sub-network of the current monitoring period according to the key characteristic factors and the other types of water quality characteristic factors, and marking the topology sub-network as a suspicious marker.
8. The system of claim 7, wherein the process of the monitoring and prevention module performing consistency matching on suspicious markers generated by each flow subsequence and a critical marker database in the data storage module, and generating the prevention measure according to the matching result comprises:
acquiring critical markers in a plurality of historical monitoring periods from a data storage module, and aggregating the critical markers in the plurality of historical monitoring periods to construct a critical marker database;
when a suspicious marker is generated by a flow subsequence, matching the suspicious marker with a critical marker database in real time, and if the water quality characteristic factors of all nodes in a topological sub-network corresponding to the suspicious marker are consistent with the water quality characteristic factors of all nodes in a topological sub-network corresponding to a certain critical marker in the critical marker database, acquiring historical water quality data associated with the certain critical marker, searching in a data storage module according to the historical water quality data, and acquiring preventive measures of the historical water quality data from the data storage module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410212981.XA CN117783471A (en) | 2024-02-27 | 2024-02-27 | Water quality on-line monitoring system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410212981.XA CN117783471A (en) | 2024-02-27 | 2024-02-27 | Water quality on-line monitoring system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117783471A true CN117783471A (en) | 2024-03-29 |
Family
ID=90393080
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410212981.XA Pending CN117783471A (en) | 2024-02-27 | 2024-02-27 | Water quality on-line monitoring system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117783471A (en) |
-
2024
- 2024-02-27 CN CN202410212981.XA patent/CN117783471A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models | |
| CN110297018A (en) | A kind of method and device that drainage pipeline networks pollutant emission is traced to the source | |
| CN103348237A (en) | Carbon nanotube sensor | |
| Nair et al. | Predictive models for river water quality using machine learning and big data techniques-a Survey | |
| Jana et al. | Optimization of effluents using artificial neural network and support vector regression in detergent industrial wastewater treatment | |
| Rustum et al. | Applying Kohonen self‐organizing map as a software sensor to predict biochemical oxygen demand | |
| CN115470702A (en) | Sewage treatment water quality prediction method and system based on machine learning | |
| CN115078667A (en) | Industrial sewage discharge treatment on-line monitoring analysis early warning system based on internet of things technology | |
| CN112786119B (en) | Method, device and medium for predicting TN (twisted nematic) treatment effect of multi-process type agricultural sewage facility | |
| Wang et al. | A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants | |
| Yu et al. | Using machine learning to reveal spatiotemporal complexity and driving forces of water quality changes in Hong Kong marine water | |
| CN112394695A (en) | Drainage basin water ecological environment monitoring system | |
| CN116298159A (en) | Industrial sewage plant pollutant tracing method and device, storage medium and computer equipment | |
| Ba-Alawi et al. | Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving | |
| CN208335253U (en) | One kind monitoring system in trade effluent based on improvement GA-BP | |
| CN117783471A (en) | Water quality on-line monitoring system | |
| JP2000107744A (en) | Client server system of water supply and drainage plant | |
| Gao et al. | Water potability analysis and prediction | |
| Kumar et al. | Analysis of an IoT based Water Quality Monitoring System | |
| Alfaleh et al. | Is the entropy-weighted water quality index a suitable index for evaluating the groundwater quality in Ha'il, Saudi Arabia? | |
| CN114693493A (en) | IoT-based polluted river water ecological restoration system | |
| CN114417622A (en) | Sewage treatment method, device, equipment and system | |
| CN113834528A (en) | Water environment monitoring system and method | |
| Bhardwaj et al. | Real time assessment of potable water quality in distribution network based on low cost multi-sensor array | |
| Senna et al. | Industry 4.0 as a strategy to contribute to the water supply universalization in developing countries |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |