CN113533672B - Water quality on-line monitoring method and device - Google Patents
Water quality on-line monitoring method and device Download PDFInfo
- Publication number
- CN113533672B CN113533672B CN202111077131.6A CN202111077131A CN113533672B CN 113533672 B CN113533672 B CN 113533672B CN 202111077131 A CN202111077131 A CN 202111077131A CN 113533672 B CN113533672 B CN 113533672B
- Authority
- CN
- China
- Prior art keywords
- monitoring
- area
- sub
- water area
- water quality
- 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.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 461
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 249
- 238000000034 method Methods 0.000 title claims abstract description 94
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000004458 analytical method Methods 0.000 claims abstract description 29
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 238000009792 diffusion process Methods 0.000 claims description 91
- 230000002093 peripheral effect Effects 0.000 claims description 23
- 239000000126 substance Substances 0.000 claims description 17
- 230000008569 process Effects 0.000 claims description 15
- 238000012806 monitoring device Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 5
- 230000007704 transition Effects 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 4
- 238000007499 fusion processing Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 description 100
- 230000009286 beneficial effect Effects 0.000 description 9
- 239000003344 environmental pollutant Substances 0.000 description 7
- 231100000719 pollutant Toxicity 0.000 description 7
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 6
- 239000001301 oxygen Substances 0.000 description 6
- 229910052760 oxygen Inorganic materials 0.000 description 6
- 238000001514 detection method Methods 0.000 description 5
- 238000003911 water pollution Methods 0.000 description 5
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 4
- 239000002689 soil Substances 0.000 description 4
- 239000003643 water by type Substances 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
- 229910052757 nitrogen Inorganic materials 0.000 description 2
- JMANVNJQNLATNU-UHFFFAOYSA-N oxalonitrile Chemical compound N#CC#N JMANVNJQNLATNU-UHFFFAOYSA-N 0.000 description 2
- 238000012876 topography Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- VYZAMTAEIAYCRO-UHFFFAOYSA-N Chromium Chemical compound [Cr] VYZAMTAEIAYCRO-UHFFFAOYSA-N 0.000 description 1
- 241000192700 Cyanobacteria Species 0.000 description 1
- ISWSIDIOOBJBQZ-UHFFFAOYSA-N Phenol Chemical compound OC1=CC=CC=C1 ISWSIDIOOBJBQZ-UHFFFAOYSA-N 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 description 1
- 229910052785 arsenic Inorganic materials 0.000 description 1
- RQNWIZPPADIBDY-UHFFFAOYSA-N arsenic atom Chemical compound [As] RQNWIZPPADIBDY-UHFFFAOYSA-N 0.000 description 1
- 238000003321 atomic absorption spectrophotometry Methods 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
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 229930002875 chlorophyll Natural products 0.000 description 1
- 235000019804 chlorophyll Nutrition 0.000 description 1
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 description 1
- 229910052804 chromium Inorganic materials 0.000 description 1
- 239000011651 chromium Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000002848 electrochemical method Methods 0.000 description 1
- 238000004817 gas chromatography Methods 0.000 description 1
- 239000003673 groundwater Substances 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 238000004255 ion exchange chromatography Methods 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement 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
- 239000002957 persistent organic pollutant Substances 0.000 description 1
- 239000000575 pesticide Substances 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 238000001637 plasma atomic emission spectroscopy Methods 0.000 description 1
- 231100000614 poison Toxicity 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 239000002352 surface water Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 239000003440 toxic substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/152—Water filtration
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Computational Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Evolutionary Biology (AREA)
- Food Science & Technology (AREA)
- Probability & Statistics with Applications (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention provides a water quality on-line monitoring method and a device, wherein the method comprises the following steps: calling and constructing a monitoring set from a monitoring database according to the region attribute of the target water area and the monitoring requirement on the target water area; preprocessing the monitoring set according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes to obtain a comprehensive set; acquiring a water area processing flow related to the comprehensive set, and establishing a corresponding relation between the water area processing flow and each second monitoring mode in the comprehensive set; and acquiring a monitoring trigger condition based on the corresponding relation, constructing a corresponding result monitoring model for characteristic analysis, determining a water quality result of the target water area, and outputting an early warning display. By calling different monitoring modes, the corresponding relation between the water area processing flow and the monitoring modes is established to carry out corresponding monitoring, so that the monitoring accuracy is improved, the monitoring time is saved, and the monitoring efficiency is improved.
Description
Technical Field
The invention relates to the technical field of water quality monitoring, in particular to a water quality online monitoring method and a water quality online monitoring device.
Background
The water pollution can seriously damage the ecological environment and influence human survival, if the sustainable growth of human society is realized, the problem of water pollution is solved firstly, and the key is how to efficiently and accurately finish water quality detection, thereby providing a basis for subsequent water resource treatment and protection. Through water resource protection, water area shoreline management, water pollution control, water environment quality and water ecological function treatment, water quality protection has achieved remarkable results.
However, the traditional water quality detection method needs to bring the sample back to the laboratory, and the detection process is complicated; especially when rivers, lake area are great, need the researcher to gather the sample in different positions, the operation process is wasted time and energy, and required cost of labor is higher, moreover, because the uncertain factor in the target waters is more, when carrying out on-line monitoring, the accuracy of monitoring is unfavorable for according to a monitoring point and a monitoring mode in the target waters simply.
Therefore, the invention provides a water quality on-line monitoring method and a water quality on-line monitoring device.
Disclosure of Invention
The invention provides an online water quality monitoring method, which is used for carrying out corresponding monitoring by calling different monitoring modes and establishing a corresponding relation between a water area treatment process and the monitoring modes, thereby not only improving the monitoring accuracy, but also saving the monitoring time and further improving the monitoring efficiency.
The invention provides a water quality on-line monitoring method, which comprises the following steps:
step 1: determining a target water area to be monitored, and calling and constructing a monitoring set from a monitoring database according to the area attribute of the target water area and the monitoring requirement on the target water area;
step 2: preprocessing the monitoring set according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes to obtain a comprehensive set;
and step 3: acquiring a water area processing flow related to the comprehensive set, and establishing a corresponding relation between the water area processing flow and each second monitoring mode in the comprehensive set;
and 4, step 4: acquiring a monitoring trigger condition based on the corresponding relation, correspondingly monitoring the target water area when the monitoring trigger condition is met, and constructing a corresponding result monitoring model;
and 5: and performing characteristic analysis on all constructed result monitoring models, determining the water quality result of the target water area, and outputting and displaying the result in an early warning manner.
In a possible implementation manner, step 1, retrieving and constructing a monitoring set from a monitoring database according to the area attribute of the target water area and the monitoring requirement for the target water area, includes:
analyzing the monitoring requirement to obtain a plurality of analysis subcommands;
judging whether a water quality interference object exists at the edge of the target water area;
if the water quality interference object exists, acquiring topographic information where the water quality interference object is located and attribute information of the water quality interference object, and analyzing the interference probability of the water quality interference object to a first monitoring area based on a probability analysis model;
when the interference probability is larger than or equal to a preset probability, dividing the target water area according to the current position of the water quality interference object and the allowed maximum interference range to obtain an interfereable area and a non-interfereable area;
acquiring a first area attribute of the interference area and a second area attribute of the non-interference area;
matching a first sub-command of the plurality of parsing sub-commands to the disturbable region based on a first region attribute, and simultaneously matching a second sub-command of the plurality of parsing sub-commands to the non-disturbable region based on a second region attribute;
calling a first feasible mode from the monitoring database according to the first sub-command, calling a second feasible mode from the monitoring database according to the second sub-command, and calling a third feasible mode from the monitoring database according to the remaining sub-commands in the plurality of analysis sub-commands;
and constructing a monitoring set based on the first feasible way, the second feasible way and the third feasible way.
In a possible implementation manner, step 1, before determining the target water area needing to be monitored, includes:
acquiring a monitoring instruction input by a quality inspector;
extracting a first monitoring intention from the monitoring instruction, and determining a first monitoring area according to the first monitoring intention;
the first monitoring area is a target water area.
In one possible implementation manner, dividing the target water area according to the current position of the water quality interference object and the allowed maximum interference range includes:
acquiring the attribute information of the water quality interference object and the position terrain of the current position, and acquiring the diffusion rule of the water quality interference object based on the target water area from a diffusion database;
acquiring a diffusion direction and a diffusion rate based on the diffusion rule, and constructing a diffusion distribution map;
determining the position distribution of the diffusion substances in the outermost diffusion region based on the diffusion distribution map, and obtaining a distribution value according to the position distribution and the position weight of the corresponding position based on the current position;
when the distribution value is smaller than a preset value, regarding the outermost diffusion area as an allowable maximum interference range;
otherwise, based on the corresponding historical diffusion rule, continuing to expand the peripheral area of the diffusion distribution diagram until an allowable maximum interference range is obtained;
planning a corresponding allowable maximum interference range based on the current position of the water quality interference object;
acquiring all planning results, judging whether an overlapping range related to the allowed maximum interference range exists in the target water area, and if so, determining the number of water quality interference objects existing in the overlapping range;
determining the division level of the overlapping range according to the number, and carrying out same-class calibration on the overlapping range of the same-class division level;
and dividing the target water area according to the calibration result.
In a possible implementation manner, step 2, preprocessing the monitoring set according to the monitoring attribute of each first monitoring manner in the monitoring set and the monitoring dependency between all the first monitoring manners to obtain a comprehensive set, including:
determining a first area in the target water area correspondingly monitored by each first monitoring mode;
when two or more than two first monitoring modes exist in the first area, determining the monitoring matching degree of every two monitoring modes corresponding to the first area according to the monitoring attributes;
when all the monitoring matching degrees are greater than or equal to the corresponding preset matching degrees, reserving all the monitoring modes corresponding to the first area;
otherwise, acquiring a mismatch monitoring mode of which the monitoring matching degree is smaller than the corresponding preset matching degree;
acquiring independent monitoring indexes between the unmatched monitoring modes and monitoring weights of the independent monitoring indexes to determine monitoring dependence between the corresponding unmatched monitoring modes;
meanwhile, acquiring a mismatch index corresponding to the mismatch monitoring mode;
acquiring a replacement monitoring mode meeting the monitoring dependency and the mismatching index to replace the mismatching monitoring mode;
and constructing to obtain a comprehensive set according to the reserved monitoring mode and the replaced monitoring mode.
In a possible implementation manner, step 3, acquiring a water area processing flow related to the integrated set, and establishing a corresponding relationship between the water area processing flow and each second monitoring manner in the integrated set, includes:
acquiring the pre-monitoring work of each monitoring mode in the comprehensive set;
determining a monitoring operation set of the pre-monitoring work, and obtaining a water area processing flow based on the monitoring operation set;
determining the flow state of each sub-flow in the water area processing flow, and matching a corresponding second monitoring mode to the flow state;
and establishing a connection relation between each sub-process and the corresponding second monitoring mode, wherein the connection relation is a corresponding relation.
In a possible implementation manner, step 4, obtaining a monitoring trigger condition based on the corresponding relationship, performing corresponding monitoring on the target water area when the monitoring trigger condition is met, and constructing a corresponding result monitoring model, including:
acquiring matched sub-processes and monitoring modes according to the corresponding relation, and acquiring execution events corresponding to the matched sub-processes and monitoring modes;
determining the initial time of the execution event and the transition stage of the execution event and the adjacent time, and establishing a first time index;
acquiring a trigger condition related to the execution event and setting the trigger condition on the first time index;
when the triggering condition and the time condition corresponding to the first time index are met, correspondingly monitoring a target water area corresponding to the sub-process;
according to the obtained monitoring result of each sub-process, a first sub-model of each sub-process in the same sub-region in the target water area is constructed, and meanwhile, a second sub-model of the corresponding sub-region in the target water area, which is obtained by the sub-process in the execution process at the same monitoring time point, is obtained;
according to the first sub-model, constructing an integral sub-model of the same sub-area, and further obtaining a first integral model of the target water area;
according to the second submodel, constructing a complete submodel of the target water area at the same monitoring time point, and further obtaining a second integral model of the target water area;
if the first integral model is completely consistent with the second integral model, all the obtained complete sub-models are used as result monitoring models;
otherwise, carrying out model analysis on the first integral model and the second integral model to determine the reliability of the first integral model and the reliability of the second integral model;
when the reliability of the second integral model is greater than that of the first integral model, all the obtained second sub models are used as result monitoring models;
otherwise, carrying out model fusion processing on the first overall model and the second overall model to obtain a third overall model, and taking the third overall model as a result monitoring model.
In a possible implementation manner, step 5, performing feature analysis on all constructed result monitoring models, determining a water quality result of the target water area, and outputting to perform early warning display, includes:
respectively carrying out characteristic analysis on each result monitoring model to obtain corresponding water quality parameters;
determining the parameter types of the water quality parameters corresponding to the same result monitoring model, and setting the parameter weights corresponding to the same result monitoring model according to the monitoring requirements;
sequencing the parameter weights to obtain a parameter list, comparing each parameter in the parameter list with a threshold corresponding to a preset parameter to obtain an unqualified parameter, and performing annotation display on the unqualified grade of the unqualified parameter in the parameter list;
and acquiring a water quality result of the target water area according to the annotation result, and outputting and early warning for display.
In one possible way of realisation,
in the process of obtaining the distribution value according to the position distribution and the position weight of the corresponding position of the diffusion substance in the outermost peripheral diffusion region based on the current position, the method further includes:
calculating the distribution density of the diffusion substance in the outermost diffusion region according to the following formula
；
Wherein n represents the number of distribution points of the positional distribution of the diffusion substance in the outermost peripheral diffusion region; s represents a region area of the outermost peripheral diffusion region; Δ S represents a corrected area for the outermost peripheral diffusion region; alpha represents an influence factor on the number of distribution points, and the value range is [0,0.2 ]]；
Representing the historical diffusion accuracy of the obtained corresponding diffusion substance, and the value range is [0.80, 0.98 ]]；
Based on distribution density
Matching corresponding preset values Z in a density-attribute-position mapping table according to the attribute information of the water quality interference object and the position terrain of the current position;
calculating a distribution value F corresponding to the position distribution and the position weight of the corresponding position based on the current position according to the following formula;
wherein,
indicating that the ith distribution point is based on the positional weight of the outermost peripheral diffusion region,
a location weight indicating that the ith distribution point is based on the current location,
represents the maximum positional weight based on the outermost peripheral diffusion area among all distribution points,
represents a maximum location weight based on the current location among all distribution points,
represents the minimum positional weight based on the outermost peripheral diffusion region among all distribution points,
representing a minimum location weight based on the current location among all distribution points;
and comparing the distribution value F with a preset value Z, and executing corresponding subsequent operation.
The invention provides a water quality on-line monitoring device, which comprises:
the determining module is used for determining a target water area to be monitored, and calling and constructing a monitoring set from a monitoring database according to the area attribute of the target water area and the monitoring requirement on the target water area;
the processing module is used for preprocessing the monitoring set according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes to obtain a comprehensive set;
the establishing module is used for acquiring a water area processing flow related to the comprehensive set and establishing a corresponding relation between the water area processing flow and each second monitoring mode in the comprehensive set;
the construction module is used for acquiring a monitoring trigger condition based on the corresponding relation, correspondingly monitoring the target water area when the monitoring trigger condition is met, and constructing a corresponding result monitoring model;
and the early warning module is used for carrying out characteristic analysis on all constructed result monitoring models, determining the water quality result of the target water area, and outputting and carrying out early warning display.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a water quality on-line monitoring method in an embodiment of the invention;
FIG. 2 is a diagram of the water quality disturbing objects corresponding to the disturbing objects and the non-disturbing objects according to the embodiment of the present invention;
FIG. 3 is a graph showing a diffusion profile according to an embodiment of the present invention;
fig. 4 is a structural diagram of an online water quality monitoring device in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The invention provides an online water quality monitoring method, as shown in figure 1, comprising the following steps:
step 1: determining a target water area to be monitored, and calling and constructing a monitoring set from a monitoring database according to the area attribute of the target water area and the monitoring requirement on the target water area;
step 2: preprocessing the monitoring set according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes to obtain a comprehensive set;
and step 3: acquiring a water area processing flow related to the comprehensive set, and establishing a corresponding relation between the water area processing flow and each second monitoring mode in the comprehensive set;
and 4, step 4: acquiring a monitoring trigger condition based on the corresponding relation, correspondingly monitoring the target water area when the monitoring trigger condition is met, and constructing a corresponding result monitoring model;
and 5: and performing characteristic analysis on all constructed result monitoring models, determining the water quality result of the target water area, and outputting and displaying the result in an early warning manner.
In this embodiment, the monitoring method may include: chemical method, electrochemical method, atomic absorption spectrophotometry, ion selective electrode method, ion chromatography, gas chromatography, plasma emission spectroscopy (ICP-AES) method, biological monitoring, remote sensing monitoring technology (hyperspectral monitoring technology), etc., and the monitoring attributes corresponding to the monitoring mode are, for example, related to comprehensive indicators reflecting the water quality conditions, such as temperature, chroma, turbidity, pH, conductivity, suspended matter, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, etc., whether toxic substances, such as phenol, cyanogen, arsenic, lead, chromium, cadmium, mercury, organic pesticides, etc., exist in the reaction water quality, or the measurement of flow rate and flow rate, etc.
In this embodiment, the region attributes, such as: frequent surface and groundwater monitoring, surveillance production and life process monitoring, and emergency accident monitoring to determine regional attributes.
In this embodiment, the monitoring requirement refers to a monitoring index for the target water area, such as a requirement for acquiring a pollution component, a pollution density, and the like of the water area.
In this embodiment, the integrated set includes several monitoring modes.
In this embodiment, the monitoring dependency is, for example, the monitoring method adopted for the target water area is the turbidity method 1 and the flow method 2, in this case, the turbidity detection and the flow detection need to be performed for the target water area, and in this case, the determination method 1 and the determination method 2 have dependency.
In this embodiment, the preprocessing refers to a process of screening, replacing, adjusting, and the like, for a monitoring mode in the monitoring set, and a water area processing procedure, for example, divides a water area into a plurality of areas, at this time, performs turbidity monitoring as one sub-procedure, performs chromaticity monitoring as the next sub-procedure after performing turbidity monitoring, and so on.
In this embodiment, for example, the sub-process of turbidity monitoring is completed by using the monitoring method 1 and the monitoring method 2, and at this time, a correspondence relationship between turbidity monitoring, a monitoring region, and a monitoring method may be established.
In this embodiment, a condition exists between monitoring trigger conditions, such as turbidity monitoring, and when this condition is satisfied, turbidity monitoring is performed and a monitoring model related to turbidity is obtained.
In this embodiment, the monitoring model is analyzed, and the reminding is also performed in order to compare the water quality parameter related to the monitoring model with a preset parameter.
The beneficial effects of the above technical scheme are: corresponding monitoring is carried out by calling different monitoring modes and establishing the corresponding relation between the water area processing flow and the monitoring modes, so that the monitoring accuracy is improved, the monitoring time is saved, and the monitoring efficiency is improved.
The invention provides a water quality on-line monitoring method, step 1, according to the regional attribute of the said goal waters and monitoring requirement to the said goal waters, call and construct and monitor the set from the monitoring database, including:
analyzing the monitoring requirement to obtain a plurality of analysis subcommands;
judging whether a water quality interference object exists at the edge of the target water area;
if the water quality interference object exists, acquiring topographic information where the water quality interference object is located and attribute information of the water quality interference object, and analyzing the interference probability of the water quality interference object to a first monitoring area based on a probability analysis model;
when the interference probability is larger than or equal to a preset probability, dividing the target water area according to the current position of the water quality interference object and the allowed maximum interference range to obtain an interfereable area and a non-interfereable area;
acquiring a first area attribute of the interference area and a second area attribute of the non-interference area;
matching a first sub-command of the plurality of parsing sub-commands to the disturbable region based on a first region attribute, and simultaneously matching a second sub-command of the plurality of parsing sub-commands to the non-disturbable region based on a second region attribute;
calling a first feasible mode from the monitoring database according to the first sub-command, calling a second feasible mode from the monitoring database according to the second sub-command, and calling a third feasible mode from the monitoring database according to the remaining sub-commands in the plurality of analysis sub-commands;
and constructing a monitoring set based on the first feasible way, the second feasible way and the third feasible way.
In this embodiment, the water quality interfering object, for example, the edge of the area of the first monitoring area is constructed by soil, and the soil may collapse, at this time, the water quality of the collapsed area may be greatly affected, for example, the water quality is seriously polluted, and therefore, the area is divided into areas with maximum allowable pollution, so as to ensure the effectiveness and accuracy of subsequent monitoring.
In this embodiment, the first region attribute and the second region attribute are identical to the above-described region attribute.
In this embodiment, the monitoring requirements are, for example, requirements related to turbidity, chromaticity, pollution density, and the like.
In this embodiment, the property information refers to the degree of influence of the water quality interfering object on the water pollution, for example, the influence of an object with a fast diffusion speed on the water pollution is smaller than that of an object with a slow diffusion speed.
In this embodiment, the probability analysis model is preset and related to the terrain information and the property information of the model, and the preset probability is also preset.
In this embodiment, as shown in fig. 2, the current position is a1, the corresponding a2 is the target water area, A3 is the water area edge line of the target water area, a1 is located inside the edge of A3, a4 is the allowable maximum interference range corresponding to the water quality interference object at the current position, a5 is the allowable maximum interference range corresponding to other objects, the remaining area is the non-interference area, and the area formed by a4 and a5 is the interference area.
In this embodiment, for example: the method comprises the steps that sub-commands 1, 2, 3, 4 and 5 exist, the sub-commands matched with an interference-capable area based on a first area attribute are 1 and 2, the sub-commands matched with a non-interference-capable area based on a second area attribute are 3, the rest sub-commands are 4 and 5, and at the moment, monitoring modes are called from a monitoring database, wherein the monitoring database is preset and comprises various command combinations and monitoring modes corresponding to the command combinations, and the corresponding monitoring modes are convenient to obtain.
In this embodiment, the monitoring set includes at least one monitoring mode.
The beneficial effects of the above technical scheme are: the interference probability is determined by determining the current position of the water quality interference object and the attribute information of the water quality interference object, the corresponding allowed maximum interference range is convenient to determine, the region is accurately monitored by region division, the attribute is further determined, the subcommand is distributed, the monitoring mode is obtained, the accuracy and the effectiveness of constructing a monitoring set are guaranteed, and the accuracy of follow-up water quality monitoring and the effectiveness of obtaining related data are guaranteed.
The invention provides a water quality on-line monitoring method, which comprises the following steps of 1, before determining a target water area needing to be monitored:
acquiring a monitoring instruction input by a quality inspector;
extracting a first monitoring intention from the monitoring instruction, and determining a first monitoring area according to the first monitoring intention;
the first monitoring area is a target water area.
The beneficial effects of the above technical scheme are: by acquiring the monitoring instruction, the intention is convenient to acquire, the water area is convenient to effectively determine, and a foundation is provided for subsequent monitoring.
The invention provides a water quality on-line monitoring method, which divides a target water area according to the current position of a water quality interference object and an allowable maximum interference range, and comprises the following steps:
acquiring the attribute information of the water quality interference object and the position terrain of the current position, and acquiring the diffusion rule of the water quality interference object based on the target water area from a diffusion database;
acquiring a diffusion direction and a diffusion rate based on the diffusion rule, and constructing a diffusion distribution map;
determining the position distribution of the diffusion substances in the outermost diffusion region based on the diffusion distribution map, and obtaining a distribution value according to the position distribution and the position weight of the corresponding position based on the current position;
when the distribution value is smaller than a preset value, regarding the outermost diffusion area as an allowable maximum interference range;
otherwise, based on the corresponding historical diffusion rule, continuing to expand the peripheral area of the diffusion distribution diagram until an allowable maximum interference range is obtained;
planning a corresponding allowable maximum interference range based on the current position of the water quality interference object;
acquiring all planning results, judging whether an overlapping range related to the allowed maximum interference range exists in the target water area, and if so, determining the number of water quality interference objects existing in the overlapping range;
determining the division level of the overlapping range according to the number, and carrying out same-class calibration on the overlapping range of the same-class division level;
and dividing the target water area according to the calibration result.
In this embodiment, the water quality disturbing object, such as soil, is easy to diffuse if the soil is soft, and the topography of the current location is an accelerated diffusion topography, and at this time, the corresponding diffusion rule is obtained from the diffusion database.
In this embodiment, the diffusion database includes the attribute information and the terrain, and the diffusion rule includes the diffusion direction and the diffusion efficiency.
In this embodiment, for example, fig. 3, B1 is a diffusion profile, B2 is an outermost diffusion region, and B3 is a diffusing species.
In the process of obtaining the distribution value according to the position distribution and the position weight of the corresponding position of the diffusion substance in the outermost peripheral diffusion region based on the current position, the method further includes:
calculating the distribution density of the diffusion substance in the outermost diffusion region according to the following formula
；
Wherein n represents a diffusion in the outermost diffusion regionThe number of distribution points of the position distribution of the matter; s represents a region area of the outermost peripheral diffusion region; Δ S represents a corrected area for the outermost peripheral diffusion region; alpha represents an influence factor on the number of distribution points, and the value range is [0,0.2 ]]；
Representing the historical diffusion accuracy of the obtained corresponding diffusion substance, and the value range is [0.80, 0.98 ]]；
Based on distribution density
Matching corresponding preset values Z in a density-attribute-position mapping table according to the attribute information of the water quality interference object and the position terrain of the current position;
calculating a distribution value F corresponding to the position distribution and the position weight of the corresponding position based on the current position according to the following formula;
wherein,
indicating that the ith distribution point is based on the positional weight of the outermost peripheral diffusion region,
a location weight indicating that the ith distribution point is based on the current location,
represents the maximum positional weight based on the outermost peripheral diffusion area among all distribution points,
represents a maximum location weight based on the current location among all distribution points,
represents the minimum positional weight based on the outermost peripheral diffusion region among all distribution points,
representing a minimum location weight based on the current location among all distribution points;
and comparing the distribution value F with a preset value Z, and executing corresponding subsequent operation.
In this embodiment, the distribution density is calculated to obtain a preset value from the relevant mapping table according to the distribution density, and the distribution value is calculated to compare with the preset value, so as to provide a data judgment basis for determining the allowable maximum interference range.
In this embodiment, for example, the allowable maximum interference ranges of 3 corresponding objects exist in the target water area, at this time, the allowable maximum interference ranges 1 and 2 exist, and at this time, the obtained water quality interference object is an object corresponding to the allowable maximum interference ranges 1 and 2.
In this embodiment, for example, the overlapping range includes 3 numbers for performing the same-class calibration, and the overlapping range includes 2 numbers for performing the same-class calibration.
The beneficial effects of the above technical scheme are: the diffusion rule is determined according to the attribute information and the position landform, a diffusion graph is further obtained, a foundation is provided for subsequent determination of related data in the outermost periphery, the number of allowable maximum interference ranges and overlapping ranges is determined, the same type of division is facilitated to be effectively achieved, and a foundation is provided for subsequent accurate monitoring.
The invention provides a water quality on-line monitoring method, step 2, according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes, the monitoring set is preprocessed to obtain a comprehensive set, which comprises the following steps:
determining a first area in the target water area correspondingly monitored by each first monitoring mode;
when two or more than two first monitoring modes exist in the first area, determining the monitoring matching degree of every two monitoring modes corresponding to the first area according to the monitoring attributes;
when all the monitoring matching degrees are greater than or equal to the corresponding preset matching degrees, reserving all the monitoring modes corresponding to the first area;
otherwise, acquiring a mismatch monitoring mode of which the monitoring matching degree is smaller than the corresponding preset matching degree;
acquiring independent monitoring indexes between the unmatched monitoring modes and monitoring weights of the independent monitoring indexes to determine monitoring dependence between the corresponding unmatched monitoring modes;
meanwhile, acquiring a mismatch index corresponding to the mismatch monitoring mode;
acquiring a replacement monitoring mode meeting the monitoring dependency and the mismatching index to replace the mismatching monitoring mode;
and constructing to obtain a comprehensive set according to the reserved monitoring mode and the replaced monitoring mode.
In this embodiment, for example, there are monitoring modes 1, 2, and 3, at this time, the monitoring mode 1 monitors the area 1, the monitoring mode 2 monitors the area 2, the monitoring mode 3 monitors the area 2, the monitoring matching degrees of the monitoring modes 2 and 3 in the area 2 are determined, and the monitoring matching degrees are related to the monitoring requirements.
In this embodiment, if the monitoring matching degrees of the monitoring modes 2 and 3 are less than the corresponding preset matching degrees, the monitoring modes 2 and 3 may be regarded as a non-matching monitoring mode.
In this embodiment, mismatch monitoring mode 2 and 3, there is independent monitoring index respectively, for example, mismatch monitoring mode 2's independent monitoring index is b1, b2, mismatch monitoring mode's independent monitoring index is c1, c2, this moment, obtain the weight of independent monitoring index, and confirm the monitoring dependence, for example, need the monitoring of turbidity and colourity, this moment, mismatch monitoring mode 2 can monitor the turbidity, mismatch monitoring mode 3 can monitor the colourity, this moment, can be according to monitoring weight and relevant monitoring index, confirm the monitoring dependence, the weight is bigger, and the monitoring mode that corresponds is more relevant with the monitoring demand, the monitoring dependence that corresponds is bigger.
In this embodiment, the mismatch indicator, such as the concentration monitoring mismatch indicator, is used.
The beneficial effects of the above technical scheme are: through judging two kinds of modes more than in the same region, can effectually replace the monitoring mode that mismatches, perhaps keep, guarantee to synthesize the validity of monitoring mode in the set, the reasonable monitoring of being convenient for.
The invention provides a water quality on-line monitoring method, step 3, obtain the water area treatment process correlated to said comprehensive set, and set up the corresponding relation of every second monitoring mode in said comprehensive set and said water area treatment process, including:
acquiring the pre-monitoring work of each monitoring mode in the comprehensive set;
determining a monitoring operation set of the pre-monitoring work, and obtaining a water area processing flow based on the monitoring operation set;
determining the flow state of each sub-flow in the water area processing flow, and matching a corresponding second monitoring mode to the flow state;
and establishing a connection relation between each sub-process and the corresponding second monitoring mode, wherein the connection relation is a corresponding relation.
In this embodiment, the pre-monitoring operation, such as dividing the area, performing different monitoring on the area, and the like, may be regarded as one of the pre-monitoring operations, and may obtain the corresponding operation set, so as to obtain the water area processing flow.
In this embodiment, the water area processing procedure includes: the method comprises the steps of carrying out first area division to monitor chromaticity, carrying out second area division to monitor turbidity, carrying out third area division, carrying out concentration monitoring and the like, wherein each division monitoring can be regarded as a sub-process.
In this embodiment, the sub-process of the chromaticity monitoring is monitored by the chromaticity monitoring methods 1 and 2, and at this time, a connection relationship between the two can be established.
The beneficial effects of the above technical scheme are: the monitoring mode is matched and obtained by determining the flow state, so that the corresponding relation is obtained, and convenience is provided for subsequent monitoring.
The invention provides a water quality on-line monitoring method, step 4, based on the corresponding relation, obtaining a monitoring trigger condition, when the monitoring trigger condition is satisfied, correspondingly monitoring the target water area, and constructing a corresponding result monitoring model, comprising:
acquiring matched sub-processes and monitoring modes according to the corresponding relation, and acquiring execution events corresponding to the matched sub-processes and monitoring modes;
determining the initial time of the execution event and the transition stage of the execution event and the adjacent time, and establishing a first time index;
acquiring a trigger condition related to the execution event and setting the trigger condition on the first time index;
when the triggering condition and the time condition corresponding to the first time index are met, correspondingly monitoring a target water area corresponding to the sub-process;
according to the obtained monitoring result of each sub-process, a first sub-model of each sub-process in the same sub-region in the target water area is constructed, and meanwhile, a second sub-model of the corresponding sub-region in the target water area, which is obtained by the sub-process in the execution process at the same monitoring time point, is obtained;
according to the first sub-model, constructing an integral sub-model of the same sub-area, and further obtaining a first integral model of the target water area;
according to the second submodel, constructing a complete submodel of the target water area at the same monitoring time point, and further obtaining a second integral model of the target water area;
if the first integral model is completely consistent with the second integral model, all the obtained complete sub-models are used as result monitoring models;
otherwise, carrying out model analysis on the first integral model and the second integral model to determine the reliability of the first integral model and the reliability of the second integral model;
when the reliability of the second integral model is greater than that of the first integral model, all the obtained second sub models are used as result monitoring models;
otherwise, carrying out model fusion processing on the first overall model and the second overall model to obtain a third overall model, and taking the third overall model as a result monitoring model.
In this embodiment, the corresponding relationship includes a sub-process and a monitoring mode, and the execution event refers to an executed monitoring mode, and the initial time is a start time of the execution event, and the transition stage is a transition time between the current execution event and a previous execution event.
In this embodiment, the triggering condition is to establish a triggering time point of the next sub-process, so as to facilitate execution of the next sub-process, improve intelligence of the next sub-process, and facilitate intelligent monitoring.
In this embodiment, monitoring is achieved by adopting two ways: one is, for example, there are 3 sub-regions, each sub-region corresponds to a corresponding sub-process, a sub-process of the same sub-region may form a first sub-model, for example, each sub-region includes a plurality of sub-processes, and the same region executes all corresponding sub-regions, so that a sub-model of the region may be obtained, which is an integral sub-model of the same sub-region, and the other is, for example, there are three monitoring time points, a first monitoring time point, and different sub-regions, and as long as the sub-processes are executed, the monitoring data of the sub-regions are obtained, so that a sub-model of each sub-region of the same time point is constructed, which is a second sub-model, and a second integral model is obtained by construction.
The beneficial effects of the above technical scheme are: through two kinds of modes, establish the model, guarantee to acquire the precision and the accuracy of model, and through the judgement of reliability, obtain effectual whole model, come as result monitoring model, facilitate for water quality monitoring.
The invention provides a water quality on-line monitoring method, step 5, carry on the characteristic analysis to all result monitoring models constructed, confirm the water quality result of the said target water area, and output and carry on the early warning display, including:
respectively carrying out characteristic analysis on each result monitoring model to obtain corresponding water quality parameters;
determining the parameter types of the water quality parameters corresponding to the same result monitoring model, and setting the parameter weights corresponding to the same result monitoring model according to the monitoring requirements;
sequencing the parameter weights to obtain a parameter list, comparing each parameter in the parameter list with a threshold corresponding to a preset parameter to obtain an unqualified parameter, and performing annotation display on the unqualified grade of the unqualified parameter in the parameter list;
and acquiring a water quality result of the target water area according to the annotation result, and outputting and early warning for display.
In this embodiment, the water quality parameter specifically includes at least one of: oxygen demand, total phosphorus content, total nitrogen content, dissolved oxygen content, ammonia nitrogen content, suspended matter concentration, turbidity, total organic carbon content, heavy metal content, volatile organic pollutant content, chlorophyll, blue-green algae, etc.
In this embodiment, the model may be constructed by scanning the target water area according to the hyperspectral instrument to obtain the water quality index data.
In this embodiment, the result monitoring model is subjected to feature analysis, for example, the result features of the result monitoring model are analyzed, for example, the standard of the feature analysis is to analyze actual features involved in the model according to preset features of suspended matters, features of pollutants, oxygen content features and nitrogen content features of various proportions, and the like, as standard features of the feature analysis, so as to obtain water quality parameters involved in the model, such as: the preset standard features comprise: the characteristics of the suspended matters and the characteristics of the pollutants are taken as references to extract the two corresponding characteristics in the model to determine the actual parameters of the suspended matters and the actual parameters of the pollutants related to the model, and at the moment, the corresponding actual parameters are the obtained water quality parameters.
In this embodiment, the parameter type of the water quality parameter corresponding to the same result monitoring model is determined, and the parameter weight corresponding to the same result monitoring model is set according to the monitoring requirement, such as: the result monitoring model comprises: the suspended matter parameters and the pollutant parameters are included, and the monitoring requirement is mainly to measure the pollutant concentration, at this moment, the parameter weight of the set pollutant parameters can be 0.8, the parameter weight of the set suspended matter parameters can be 0.1, and at this moment, the parameter weight of the pollutant parameters is definitely greater than the parameter weight of the suspended matter parameters.
The beneficial effects of the above technical scheme are: through analyzing the model, the water quality parameters are obtained, and through comparison and annotation display, the water quality results are effectively output, unqualified parameters are clearly known, and early warning reminding and timely processing are facilitated.
The invention provides a water quality on-line monitoring device, as shown in figure 4, comprising:
the determining module is used for determining a target water area to be monitored, and calling and constructing a monitoring set from a monitoring database according to the area attribute of the target water area and the monitoring requirement on the target water area;
the processing module is used for preprocessing the monitoring set according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes to obtain a comprehensive set;
the establishing module is used for acquiring a water area processing flow related to the comprehensive set and establishing a corresponding relation between the water area processing flow and each second monitoring mode in the comprehensive set;
the construction module is used for acquiring a monitoring trigger condition based on the corresponding relation, correspondingly monitoring the target water area when the monitoring trigger condition is met, and constructing a corresponding result monitoring model;
and the early warning module is used for carrying out characteristic analysis on all constructed result monitoring models, determining the water quality result of the target water area, and outputting and carrying out early warning display.
The beneficial effects of the above technical scheme are: corresponding monitoring is carried out by calling different monitoring modes and establishing the corresponding relation between the water area processing flow and the monitoring modes, so that the monitoring accuracy is improved, the monitoring time is saved, and the monitoring efficiency is improved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A water quality on-line monitoring method is characterized by comprising the following steps:
step 1: determining a target water area to be monitored, and calling and constructing a monitoring set from a monitoring database according to the area attribute of the target water area and the monitoring requirement on the target water area;
step 2: preprocessing the monitoring set according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes to obtain a comprehensive set;
and step 3: acquiring a water area processing flow related to the comprehensive set, and establishing a corresponding relation between the water area processing flow and each second monitoring mode in the comprehensive set;
and 4, step 4: acquiring a monitoring trigger condition based on the corresponding relation, correspondingly monitoring the target water area when the monitoring trigger condition is met, and constructing a corresponding result monitoring model;
and 5: performing characteristic analysis on all constructed result monitoring models, determining the water quality result of the target water area, and outputting and displaying the result in an early warning manner;
step 1, according to the area attribute of the target water area and the monitoring requirement of the target water area, calling and constructing a monitoring set from a monitoring database, wherein the monitoring set comprises the following steps:
analyzing the monitoring requirement to obtain a plurality of analysis subcommands;
judging whether a water quality interference object exists at the edge of the target water area;
if the water quality interference object exists, acquiring topographic information where the water quality interference object is located and attribute information of the water quality interference object, and analyzing the interference probability of the water quality interference object to a first monitoring area based on a probability analysis model;
when the interference probability is larger than or equal to a preset probability, dividing the target water area according to the current position of the water quality interference object and the allowed maximum interference range to obtain an interfereable area and a non-interfereable area;
acquiring a first area attribute of the interference area and a second area attribute of the non-interference area;
matching a first sub-command of the plurality of parsing sub-commands to the disturbable region based on a first region attribute, and simultaneously matching a second sub-command of the plurality of parsing sub-commands to the non-disturbable region based on a second region attribute;
calling a first feasible mode from the monitoring database according to the first sub-command, calling a second feasible mode from the monitoring database according to the second sub-command, and calling a third feasible mode from the monitoring database according to the remaining sub-commands in the plurality of analysis sub-commands;
constructing a monitoring set based on the first feasible mode, the second feasible mode and the third feasible mode;
dividing the target water area according to the current position of the water quality interference object and the allowed maximum interference range, wherein the dividing comprises the following steps:
acquiring the attribute information of the water quality interference object and the position terrain of the current position, and acquiring the diffusion rule of the water quality interference object based on the target water area from a diffusion database;
acquiring a diffusion direction and a diffusion rate based on the diffusion rule, and constructing a diffusion distribution map;
determining the position distribution of the diffusion substances in the outermost diffusion region based on the diffusion distribution map, and obtaining a distribution value according to the position distribution and the position weight of the corresponding positions of the diffusion substances in the outermost diffusion region based on the current position;
when the distribution value is smaller than a preset value, regarding the outermost diffusion area as an allowable maximum interference range;
otherwise, based on the corresponding historical diffusion rule, continuing to expand the peripheral area of the diffusion distribution diagram until an allowable maximum interference range is obtained;
planning a corresponding allowable maximum interference range based on the current position of the water quality interference object;
acquiring all planning results, judging whether an overlapping range related to the allowed maximum interference range exists in the target water area, and if so, determining the number of water quality interference objects existing in the overlapping range;
determining the division level of the overlapping range according to the number, and carrying out same-class calibration on the overlapping range of the same-class division level;
dividing the target water area according to the calibration result;
in the process of obtaining the distribution value according to the position distribution and the position weight of the corresponding position of the diffusion substance in the outermost peripheral diffusion region based on the current position, the method further includes:
calculating the distribution density of the diffusion substance in the outermost diffusion region according to the following formula
；
Wherein n represents the number of distribution points of the positional distribution of the diffusion substance in the outermost peripheral diffusion region; s represents a region area of the outermost peripheral diffusion region; Δ S represents a corrected area for the outermost peripheral diffusion region; alpha represents an influence factor on the number of distribution points, and the value range is [0,0.2 ]]；
Representing the historical diffusion accuracy of the obtained corresponding diffusion substance, and the value range is [0.80, 0.98 ]]；
Based on distribution density
Matching corresponding preset values Z in a density-attribute-position mapping table according to the attribute information of the water quality interference object and the position terrain of the current position;
calculating a distribution value F corresponding to the position distribution and the position weight of the corresponding position based on the current position according to the following formula;
wherein,
indicating that the ith distribution point is based on the positional weight of the outermost peripheral diffusion region,
a location weight indicating that the ith distribution point is based on the current location,
represents the maximum positional weight based on the outermost peripheral diffusion area among all distribution points,
represents a maximum location weight based on the current location among all distribution points,
represents the minimum positional weight based on the outermost peripheral diffusion region among all distribution points,
representing a minimum location weight based on the current location among all distribution points;
and comparing the distribution value F with a preset value Z, and executing corresponding subsequent operation.
2. The water quality on-line monitoring method according to claim 1, wherein the step 1, before determining the target water area to be monitored, comprises:
acquiring a monitoring instruction input by a quality inspector;
extracting a first monitoring intention from the monitoring instruction, and determining a first monitoring area according to the first monitoring intention;
the first monitoring area is a target water area.
3. The online water quality monitoring method according to claim 1, wherein step 2, according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes, preprocessing the monitoring set to obtain a comprehensive set, comprises:
determining a first area in the target water area correspondingly monitored by each first monitoring mode;
when two or more than two first monitoring modes exist in the first area, determining the monitoring matching degree of every two monitoring modes corresponding to the first area according to the monitoring attributes;
when all the monitoring matching degrees are greater than or equal to the corresponding preset matching degrees, reserving all the monitoring modes corresponding to the first area;
otherwise, acquiring a mismatch monitoring mode of which the monitoring matching degree is smaller than the corresponding preset matching degree;
acquiring independent monitoring indexes between the unmatched monitoring modes and monitoring weights of the independent monitoring indexes to determine monitoring dependence between the corresponding unmatched monitoring modes;
meanwhile, acquiring a mismatch index corresponding to the mismatch monitoring mode;
acquiring a replacement monitoring mode meeting the monitoring dependency and the mismatching index to replace the mismatching monitoring mode;
and constructing to obtain a comprehensive set according to the reserved monitoring mode and the replaced monitoring mode.
4. The water quality on-line monitoring method according to claim 1, wherein the step 3 of obtaining the water area treatment process related to the comprehensive set and establishing the corresponding relationship between the water area treatment process and each second monitoring mode in the comprehensive set comprises:
acquiring the pre-monitoring work of each monitoring mode in the comprehensive set;
determining a monitoring operation set of the pre-monitoring work, and obtaining a water area processing flow based on the monitoring operation set;
determining the flow state of each sub-flow in the water area processing flow, and matching a corresponding second monitoring mode to the flow state;
and establishing a connection relation between each sub-process and the corresponding second monitoring mode, wherein the connection relation is a corresponding relation.
5. The water quality on-line monitoring method according to claim 1, wherein step 4, based on the corresponding relationship, a monitoring trigger condition is obtained, when the monitoring trigger condition is satisfied, the target water area is correspondingly monitored, and a corresponding result monitoring model is constructed, including:
acquiring matched sub-processes and monitoring modes according to the corresponding relation, and acquiring execution events corresponding to the matched sub-processes and monitoring modes;
determining the initial time of the execution event and the transition stage of the execution event and the adjacent time, and establishing a first time index;
acquiring a trigger condition related to the execution event and setting the trigger condition on the first time index;
when the triggering condition and the time condition corresponding to the first time index are met, correspondingly monitoring a target water area corresponding to the sub-process;
according to the obtained monitoring result of each sub-process, a first sub-model of each sub-process in the same sub-region in the target water area is constructed, and meanwhile, a second sub-model of the corresponding sub-region in the target water area, which is obtained by the sub-process in the execution process at the same monitoring time point, is obtained;
according to the first sub-model, constructing an integral sub-model of the same sub-area, and further obtaining a first integral model of the target water area;
according to the second submodel, constructing a complete submodel of the target water area at the same monitoring time point, and further obtaining a second integral model of the target water area;
if the first integral model is completely consistent with the second integral model, all the obtained complete sub-models are used as result monitoring models;
otherwise, carrying out model analysis on the first integral model and the second integral model to determine the reliability of the first integral model and the reliability of the second integral model;
when the reliability of the second integral model is greater than that of the first integral model, all the obtained second sub models are used as result monitoring models;
otherwise, carrying out model fusion processing on the first overall model and the second overall model to obtain a third overall model, and taking the third overall model as a result monitoring model.
6. The method of on-line monitoring water quality as claimed in claim 1,
and 5, performing characteristic analysis on all constructed result monitoring models, determining the water quality result of the target water area, and outputting and displaying the result in an early warning manner, wherein the method comprises the following steps:
respectively carrying out characteristic analysis on each result monitoring model to obtain corresponding water quality parameters;
determining the parameter types of the water quality parameters corresponding to the same result monitoring model, and setting the parameter weights corresponding to the same result monitoring model according to the monitoring requirements;
sequencing the parameter weights to obtain a parameter list, comparing each parameter in the parameter list with a threshold corresponding to a preset parameter to obtain an unqualified parameter, and performing annotation display on the unqualified grade of the unqualified parameter in the parameter list;
and acquiring a water quality result of the target water area according to the annotation result, and outputting and early warning for display.
7. An on-line water quality monitoring device adopting the method of claim 1, which is characterized by comprising:
the determining module is used for determining a target water area to be monitored, and calling and constructing a monitoring set from a monitoring database according to the area attribute of the target water area and the monitoring requirement on the target water area;
the processing module is used for preprocessing the monitoring set according to the monitoring attribute of each first monitoring mode in the monitoring set and the monitoring dependency among all the first monitoring modes to obtain a comprehensive set;
the establishing module is used for acquiring a water area processing flow related to the comprehensive set and establishing a corresponding relation between the water area processing flow and each second monitoring mode in the comprehensive set;
the construction module is used for acquiring a monitoring trigger condition based on the corresponding relation, correspondingly monitoring the target water area when the monitoring trigger condition is met, and constructing a corresponding result monitoring model;
and the early warning module is used for carrying out characteristic analysis on all constructed result monitoring models, determining the water quality result of the target water area, and outputting and carrying out early warning display.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111077131.6A CN113533672B (en) | 2021-09-15 | 2021-09-15 | Water quality on-line monitoring method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111077131.6A CN113533672B (en) | 2021-09-15 | 2021-09-15 | Water quality on-line monitoring method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113533672A CN113533672A (en) | 2021-10-22 |
| CN113533672B true CN113533672B (en) | 2021-11-19 |
Family
ID=78092624
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111077131.6A Active CN113533672B (en) | 2021-09-15 | 2021-09-15 | Water quality on-line monitoring method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113533672B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114091280B (en) * | 2021-11-26 | 2022-07-05 | 江苏汉华热管理科技有限公司 | Method and device for detecting stability of heat preservation system of graphitization furnace |
| CN114965929A (en) * | 2022-06-08 | 2022-08-30 | 莱州明波水产有限公司 | Processing method for water quality detection on-line analysis |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6845336B2 (en) * | 2002-06-25 | 2005-01-18 | Prasad S. Kodukula | Water treatment monitoring system |
| US8918285B1 (en) * | 2014-02-25 | 2014-12-23 | Fractest Llc | Testing water contamination in geographic areas |
| CN107609742B (en) * | 2017-08-14 | 2020-10-30 | 环境保护部华南环境科学研究所 | Water environment early warning integrated management platform |
| CN109297968A (en) * | 2018-11-21 | 2019-02-01 | 河南工业职业技术学院 | A kind of method of generation face domain water quality monitoring result |
| CN113379261B (en) * | 2021-06-17 | 2023-06-06 | 上海市环境科学研究院 | Pollution site soil groundwater pollution risk management and control technical method |
-
2021
- 2021-09-15 CN CN202111077131.6A patent/CN113533672B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113533672A (en) | 2021-10-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113533672B (en) | Water quality on-line monitoring method and device | |
| Yan et al. | Research on the processing of coal mine water source data by optimizing BP neural network algorithm with sparrow search algorithm | |
| EP2920064B1 (en) | A method and system for monitoring quality of ballast water of a vessel | |
| CN109470667A (en) | A kind of combination water quality parameter and three-dimensional fluorescence spectrum carry out the method that pollutant is traced to the source | |
| Canter et al. | Handbook of variables for environmental impact assessment | |
| Phillips et al. | Best practice for establishing nutrient concentrations to support good ecological status | |
| CN104899419A (en) | Method for detecting nitrogen and/or phosphorus content in fresh water body | |
| Cullen | Biomonitoring and environmental management | |
| CN117974404B (en) | Land-land cooperative land-domain pollution source analysis method and system | |
| Simonet | Quality control in qualitative analysis | |
| CN115936192A (en) | Method and system for predicting risk of soil environmental pollutants | |
| CN113902249B (en) | Method and device for analyzing soil heavy metal influence factors | |
| CN108760664B (en) | Water quality multi-parameter automatic detection and analysis method | |
| CN113240006A (en) | Method and device for monitoring wastewater discharge, electronic equipment and storage medium | |
| CN109612985A (en) | A kind of detection method of Chloride Ion in Concrete content | |
| CN117368434A (en) | Water quality monitoring system based on big data | |
| CN116070931B (en) | Soil pollution ecological effect quantitative evaluation method based on field ecological data | |
| CN106885914B (en) | Multi-pollutant is into different water automatic detection analysis instrument and detection method | |
| CN115389730A (en) | Intelligent identification treatment method, system and storage medium for polluted water area | |
| CN115508322A (en) | Water pollution source multi-dimensional tracing method and device | |
| CN112966783A (en) | Method for identifying constant ion characteristics of mine water and distinguishing water source | |
| CN114624411A (en) | Water quality early warning method for water area near water intake of water source area | |
| CN118671294B (en) | Water environment monitoring system and method based on big data analysis | |
| Porro et al. | Knowledge-based and data-driven approaches for assessing greenhouse gas emissions from wastewater systems | |
| Kassim et al. | Design and Development of Handheld Soil Assessment by Using Ion-Selective Electrode for Site-Specific Available Potassium in Oil Palm Plantation |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |