CN111049923A - Water quality detection method for goose raising pool - Google Patents
Water quality detection method for goose raising pool Download PDFInfo
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- CN111049923A CN111049923A CN201911323120.4A CN201911323120A CN111049923A CN 111049923 A CN111049923 A CN 111049923A CN 201911323120 A CN201911323120 A CN 201911323120A CN 111049923 A CN111049923 A CN 111049923A
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- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
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- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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Abstract
The invention discloses a water quality detection method for a goose raising pond, which comprises the following steps of S1, arranging dispersed monitoring points for collecting water quality information; s2, performing cross-region acquisition and networking communication on the dispersed monitoring points, judging whether the received water quality information is the information of the corresponding monitoring point position, and if not, feeding back the data acquisition abnormality of the dispersed monitoring points; if the collected data is normal, the step S3 is executed; the intelligent water quality monitoring system is convenient for users to master key water quality information in time, has high intelligent degree, is more scientific and environment-friendly compared with the traditional culture, and is more convenient for culture monitoring management; and because the scattered monitoring points are arranged and data fusion is carried out at the rear end, the front end is easy to maintain, the labor cost and the energy loss are reduced, and the step S2 is directly utilized to filter abnormal data.
Description
Technical Field
The invention relates to the technical field of water quality detection, in particular to a water quality detection method for a goose raising pond.
Background
In the prior art, the water quality information acquired by the fixed monitoring nodes can only reflect the water quality in a reasonable area of the monitoring points, and if a plurality of fixed monitoring nodes are required to be arranged in a large-area lake or pool, the maintenance is difficult, and the labor cost and the loss are large.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the water quality detection method of the goose raising pool, which utilizes the technology of Internet of things, is convenient for users to master key water quality information in time, has high intelligent degree, is more scientific and environment-friendly compared with the traditional breeding and is more convenient for breeding monitoring and management; and because the scattered monitoring points are arranged and data fusion is carried out at the rear end, the front end is easy to maintain, the labor cost and the energy loss are reduced, and the step S2 is directly utilized to filter abnormal data.
The purpose of the invention is realized by the following technical scheme:
a water quality detection method for a goose raising pond comprises the following steps:
s1, arranging scattered monitoring points for collecting water quality information;
s2, performing cross-region acquisition and networking communication on the dispersed monitoring points, judging whether the received water quality information is the information of the corresponding monitoring point position, and if not, feeding back the data acquisition abnormity of the dispersed monitoring points; if the collected data is normal, the step S3 is carried out;
s3, analyzing the received normal water quality information data, extracting data of different monitoring points, identifying and judging data of the same type sensor and data of the different type sensor, respectively carrying out data fusion processing on the data of the same type sensor and the data of the different type sensor, and respectively storing the data in a database divided storage area;
and S4, the water quality detection result obtained after the data fusion processing is up to the server side, and remote detection is realized.
Further, in step S1, geographical coordinates are set in the aquaculture water area, and static water quality monitoring nodes are set.
Further, the static water quality monitoring node comprises a water quality sensor.
Further, the water quality sensor is used for detecting any one of temperature, pH value, conductivity, total phosphorus and dissolved oxygen.
Further, in step S1, an electronic tag is set, a positioning device and a communication device are built in the electronic tag, and the monitoring information is sent to the background receiving server in real time through the positioning device and the communication device.
Further, in step S3, the weight of the parameter value is directly calculated for the data collected by the data calculation of the same type of sensor, and the weighting and fusion are performed; and performing feature fusion on the heterogeneous sensor data by adopting a data classifier.
Furthermore, the weight coefficient of the data of the same type of sensor is adjusted in real time according to variance change, so that the mean square error of the fusion model is minimum.
Further, the data classifier comprises any one of a support vector machine and a neural network module.
The invention has the beneficial effects that:
(1) the intelligent water quality monitoring system is convenient for users to master key water quality information in time, has high intelligent degree, is more scientific and environment-friendly compared with the traditional culture, and is more convenient for culture monitoring management; and because the scattered monitoring points are arranged and data fusion is carried out at the rear end, the front end is easy to maintain, the labor cost and the energy loss are reduced, and the step S2 is directly utilized to filter abnormal data.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of the steps of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following. All of the features disclosed in this specification, or all of the steps of a method or process so disclosed, may be combined in any combination, except combinations where mutually exclusive features and/or steps are used.
Any feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Specific embodiments of the present invention will be described in detail below, and it should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: it is not necessary to employ these specific details to practice the present invention. In other instances, well-known circuits, software, or methods have not been described in detail so as not to obscure the present invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Before describing the embodiments, some necessary terms need to be explained. For example:
if the terms "first," "second," etc. are used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. Thus, a "first" element discussed below could also be termed a "second" element without departing from the teachings of the present invention. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present.
The various terms appearing in this application are used for the purpose of describing particular embodiments only and are not intended as limitations of the invention, with the singular being intended to include the plural unless the context clearly dictates otherwise.
When the terms "comprises" and/or "comprising" are used in this specification, these terms are intended to specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence and/or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
As shown in fig. 1, a water quality detection method for a goose raising pond comprises the following steps:
s1, arranging scattered monitoring points for collecting water quality information;
s2, performing cross-region acquisition and networking communication on the dispersed monitoring points, judging whether the received water quality information is the information of the corresponding monitoring point position, and if not, feeding back the data acquisition abnormity of the dispersed monitoring points; if the collected data is normal, the step S3 is carried out;
s3, analyzing the received normal water quality information data, extracting data of different monitoring points, identifying and judging data of the same type sensor and data of the different type sensor, respectively carrying out data fusion processing on the data of the same type sensor and the data of the different type sensor, and respectively storing the data in a database divided storage area;
and S4, the water quality detection result obtained after the data fusion processing is up to the server side, and remote detection is realized.
Further, in step S1, geographical coordinates are set in the aquaculture water area, and static water quality monitoring nodes are set.
Further, the static water quality monitoring node comprises a water quality sensor.
Further, the water quality sensor is used for detecting any one of temperature, pH value, conductivity, total phosphorus and dissolved oxygen.
Further, in step S1, an electronic tag is set, a positioning device and a communication device are built in the electronic tag, and the monitoring information is sent to the background receiving server in real time through the positioning device and the communication device.
Further, in step S3, the weight of the parameter value is directly calculated for the data collected by the data calculation of the same type of sensor, and the weighting and fusion are performed; and performing feature fusion on the heterogeneous sensor data by adopting a data classifier.
Furthermore, the weight coefficient of the data of the same type of sensor is adjusted in real time according to variance change, so that the mean square error of the fusion model is minimum.
Further, the data classifier comprises any one of a support vector machine and a neural network module.
Example one
As shown in fig. 1, a water quality detection method for a goose raising pond comprises the following steps:
s1, arranging scattered monitoring points for collecting water quality information;
s2, performing cross-region acquisition and networking communication on the dispersed monitoring points, judging whether the received water quality information is the information of the corresponding monitoring point position, and if not, feeding back the data acquisition abnormity of the dispersed monitoring points; if the collected data is normal, the step S3 is carried out;
s3, analyzing the received normal water quality information data, extracting data of different monitoring points, identifying and judging data of the same type sensor and data of the different type sensor, respectively carrying out data fusion processing on the data of the same type sensor and the data of the different type sensor, and respectively storing the data in a database divided storage area;
and S4, the water quality detection result obtained after the data fusion processing is up to the server side, and remote detection is realized.
In other technical features of the embodiment, those skilled in the art can flexibly select and use the features according to actual situations to meet different specific actual requirements. However, it will be apparent to one of ordinary skill in the art that: it is not necessary to employ these specific details to practice the present invention. In other instances, well-known algorithms, methods or systems have not been described in detail so as not to obscure the present invention, and are within the scope of the present invention as defined by the claims.
For simplicity of explanation, the foregoing method embodiments are described as a series of acts or combinations, but those skilled in the art will appreciate that the present application is not limited by the order of acts, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and elements referred to are not necessarily required in this application.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The disclosed systems, modules, and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be referred to as an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may also be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It will be understood by those skilled in the art that all or part of the processes in the methods for implementing the embodiments described above can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a ROM, a RAM, etc.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A water quality detection method for a goose raising pond is characterized by comprising the following steps:
s1, arranging scattered monitoring points for collecting water quality information;
s2, performing cross-region acquisition and networking communication on the dispersed monitoring points, judging whether the received water quality information is the information of the corresponding monitoring point position, and if not, feeding back the data acquisition abnormity of the dispersed monitoring points; if the collected data is normal, the step S3 is carried out;
s3, analyzing the received normal water quality information data, extracting data of different monitoring points, identifying and judging data of the same type sensor and data of the different type sensor, respectively carrying out data fusion processing on the data of the same type sensor and the data of the different type sensor, and respectively storing the data in a database divided storage area;
and S4, the water quality detection result obtained after the data fusion processing is up to the server side, and remote detection is realized.
2. The method for detecting the water quality of the goose pond according to claim 1, wherein in step S1, geographical coordinates are set in the culture water area, and a static water quality monitoring node is set.
3. The method for detecting the water quality of the goose-raising pond according to claim 2, wherein the static water quality monitoring node comprises a water quality sensor.
4. The method for detecting the water quality of the goose-raising pond according to claim 1, wherein the water quality sensor is used for detecting any one of temperature, pH value, conductivity, total phosphorus and dissolved oxygen.
5. The water quality detection method for the goose pond according to claim 1, wherein in step S1, an electronic tag is arranged, a positioning device and a communication device are arranged in the electronic tag, and monitoring information is transmitted to a background receiving server in real time through the positioning device and the communication device.
6. The water quality detection method of the goose pond according to claim 1, wherein in step S3, the weight of parameter values is directly calculated for data collected by the data calculation of the same kind of sensors, and the weighting and fusion are performed; and performing feature fusion on the heterogeneous sensor data by adopting a data classifier.
7. The water quality detection method for the goose raising pond according to claim 6, wherein the weight coefficient is adjusted in real time according to variance variation for the data of the same type of sensors, so that the mean square error of the fusion model is minimized.
8. The method for detecting the water quality of the goose-raising pond according to claim 6, wherein the data classifier comprises any one of a support vector machine and a neural network module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201911323120.4A CN111049923A (en) | 2019-12-20 | 2019-12-20 | Water quality detection method for goose raising pool |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201911323120.4A CN111049923A (en) | 2019-12-20 | 2019-12-20 | Water quality detection method for goose raising pool |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113009097A (en) * | 2021-03-01 | 2021-06-22 | 中国联合网络通信集团有限公司 | Water quality detection method and device and electronic equipment |
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| US20130118239A1 (en) * | 2011-10-19 | 2013-05-16 | Siemens Aktiengeselleschaft | Remote Water Quality Monitoring |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102306231A (en) * | 2011-06-03 | 2012-01-04 | 中国科学院计算技术研究所 | Water environment parameter predicting device based on sea computation and method |
| US20130118239A1 (en) * | 2011-10-19 | 2013-05-16 | Siemens Aktiengeselleschaft | Remote Water Quality Monitoring |
Non-Patent Citations (1)
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| 管云霞: "基于数据融合和Web的水产养殖远程监控系统设计", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
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| CN113009097A (en) * | 2021-03-01 | 2021-06-22 | 中国联合网络通信集团有限公司 | Water quality detection method and device and electronic equipment |
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Application publication date: 20200421 |