CN113449789A - Quality control method for monitoring water quality by full-spectrum water quality monitoring equipment based on big data - Google Patents
Quality control method for monitoring water quality by full-spectrum water quality monitoring equipment based on big data Download PDFInfo
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Abstract
The invention provides a quality control method for monitoring water quality by full-spectrum water quality monitoring equipment based on big data, which comprises the following steps: collecting original monitoring data and standard monitoring data of a water body to be detected; acquiring original monitoring data and standard monitoring data of a water body to be detected in real time by a big data technology; establishing a Support Vector Regression (SVR) algorithm initial model according to the to-be-detected water body monitoring original data and the standard monitoring data; and calling a support vector regression algorithm initial model SVR to perform fitting training on the to-be-detected water body monitoring original data and the standard monitoring data, and dynamically adjusting and optimizing parameters of the support vector regression algorithm initial model SVR by controlling a data set of the to-be-detected water body monitoring original data to obtain a support vector regression algorithm optimization model SVR and training parameter data. Calling original monitoring data and standard monitoring data of the water body to be monitored by a big data technology, establishing a Support Vector Regression (SVR) algorithm initial model, and performing dynamic optimization to ensure the accuracy and precision of the monitoring data.
Description
Technical Field
The invention relates to the technical field of water quality monitoring, in particular to a quality control method for monitoring water quality by full-spectrum water quality monitoring equipment based on big data.
Background
The water resource is one of the key resources of human production and life, with the development and utilization of the water resource by human beings, a large amount of pollutants flow into the water body, so that the water bodies of rivers, lakes, oceans and the like are polluted in different degrees, the ecological environment is seriously damaged, the sustainable development of the country and the health of human beings are seriously threatened, and the protection of the water resource and the treatment of water pollution become the most concerned problems of the modern society at present.
In recent years, with the rapid development of the spectroscopic technology, the ultraviolet-visible full-spectrum automatic monitoring technology is widely applied to multi-parameter detection of water quality, and not only can directly or indirectly measure the concentrations of most metal ions, non-metal ions and organic pollutants in a water body, but also can simultaneously measure two or more water quality parameters by utilizing the additivity of absorbance. The full-spectrum water quality monitoring equipment also has the advantages of rapid detection process, no pollution and the like, so that the full-spectrum water quality monitoring equipment has remarkable advantages in real-time online monitoring of surface water, domestic sewage and industrial wastewater.
At present, can set up quality of water automatic monitoring station according to the water of difference usually and monitor quality of water in the position of difference, but the quality of water automatic monitoring station that comprises full gloss register for easy reference water quality testing equipment often appears the quality of water index of the water of monitoring and the quality of water standard index have great deviation scheduling problem in the monitoring process, and then has influenced the normal judgement to the pollution degree of quality of water, lead to monitoring result degree of accuracy not enough and the precision scheduling problem not high.
Disclosure of Invention
The invention aims to solve the problems of insufficient accuracy, low precision and the like of the monitoring result of the automatic monitoring system of the existing full-spectrum water quality monitoring equipment, and provides a quality control method for monitoring water quality by the full-spectrum water quality monitoring equipment based on big data. The quality control method for monitoring water quality by the full-spectrum water quality monitoring equipment is to improve and optimize the existing full-spectrum automatic water quality monitoring system so as to overcome adverse influence factors and improve the monitoring result of the full-spectrum automatic water quality monitoring system.
The technical scheme for realizing the purpose of the invention is as follows: the quality control method for monitoring water quality by using the full-spectrum water quality monitoring equipment based on big data comprises the following steps of:
collecting original monitoring data and standard monitoring data of a water body to be detected;
the full-spectrum water quality monitoring system acquires original monitoring data and standard monitoring data of the water body to be detected in real time through a big data technology;
establishing a Support Vector Regression (SVR) algorithm initial model according to the to-be-detected water body monitoring original data and the standard monitoring data;
calling a support vector regression algorithm initial model SVR to perform fitting training on the monitoring original data and the standard monitoring data of the water body to be detected;
and dynamically adjusting and optimizing parameters of the support vector regression algorithm initial model SVR model by controlling a data set of the water body monitoring original data to be detected, so as to obtain the support vector regression algorithm optimization model SVR and training parameter data.
According to the method, the original data of the water body to be monitored and the standard monitoring data are obtained, stored and called through a big data technology, the support vector regression algorithm initial model SVR is established, the dynamic optimization of the parameters of the original data set of the water body to be monitored and the support vector regression algorithm initial model SVR is carried out through the support vector regression algorithm initial model SVR, the precision of the monitoring data of the full-spectrum water quality monitoring equipment meets the requirement, the accuracy of the monitoring data of the full-spectrum water quality monitoring equipment is ensured, and the precision level of the monitoring data of the full-spectrum automatic monitoring technology is improved.
In a preferred embodiment of the present invention, the support vector regression algorithm initial model SVR established as above is:
wherein, yiStandard monitoring data acquired in real time for i consecutive days;
ximonitoring original data of the water body to be detected, which are acquired in real time in i days continuously;
As an improvement of the quality control method for monitoring the water quality by the full-spectrum water quality monitoring equipment based on the big data, the quality control method also comprises a verification step of a support vector regression algorithm optimization model SVR, namely, training parameter data of 1-M days are compared with standard monitoring data according to the time sequence of 1-M days to obtain the time i ' with the maximum fitting degree with the standard monitoring data and the support vector regression algorithm optimization model SVR, wherein M, i ' is an integer, and i is more than or equal to M and more than or equal to i ' and more than or equal to 1.
Further, after verification, the support vector regression algorithm optimization model SVR is obtained as follows:
yi`standard monitoring data obtained for consecutive days i';
xi`original data of water body to be detected for full-spectrum water quality monitoring acquired continuously at i 'day, wherein i' is more than or equal to 1;
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the original data of the water body to be monitored and the standard monitoring data are obtained, stored and called through a big data technology, the support vector regression algorithm initial model SVR is established, the dynamic optimization of the parameters of the original data set of the water body to be monitored and the support vector regression algorithm initial model SVR is carried out through the support vector regression algorithm initial model SVR, the precision of the monitoring data of the full-spectrum water quality monitoring equipment meets the requirement, the accuracy of the monitoring data of the full-spectrum water quality monitoring equipment is ensured, and the precision level of the monitoring data of the full-spectrum automatic monitoring technology is improved.
2. By adopting the quality control method, the best fitting degree of the monitored original data of the water body to be monitored and the standard monitoring data on the 14 th day can be obtained after comparison and verification. When the monitoring precision error meets the technical specification of operation and maintenance of an automatic monitoring station for surface water quality, namely A is more than BIV, the relative error is less than or equal to +/-20 percent. Meanwhile, the invention can realize the following quality control targets of monitoring water quality by full-spectrum water quality monitoring equipment: when BII is more than A and less than or equal to BIV, the relative error is less than or equal to plus or minus 30 percent.
Detailed Description
The invention will be further described with reference to specific embodiments, and the advantages and features of the invention will become apparent as the description proceeds. These examples are illustrative only and do not limit the scope of the present invention in any way. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention, and that such changes and modifications may be made without departing from the spirit and scope of the invention.
In the description of the present embodiments, it is to be understood that the terms "central," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, indicate orientations or positional relationships that are created or described simply to facilitate the description of the invention, but do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the creation of the present invention.
Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicit to a number of indicated technical features. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the invention, the meaning of "a plurality" is two or more unless otherwise specified.
Example 1:
the embodiment provides a quality control method for monitoring water quality by full-spectrum water quality monitoring equipment based on big data, and in the embodiment, the quality control method comprises the following steps:
and S1, acquiring the monitoring original data and the standard monitoring data of the water body to be detected.
The acquisition of the original data of the water body to be detected can be the original data obtained by detecting a water sample in a laboratory through full-spectrum water quality monitoring equipment, and can also be the original data of the real-time detection of the water bodies of rivers, lakes, oceans and the like through the full-spectrum water quality monitoring equipment in the detection base station. The original data of the water body to be detected has the characteristics of initial data, and can be called into the following support vector regression algorithm initial model SVR as an initial value X through a big data technology.
The standard monitoring data is acquired from a standard automatic station, is obtained by a chemical method, a spectroscopic method and the like, has standard property, retrievability, availability and comparability, and can be called into the support vector regression algorithm initial model SVR as a target value Y by a big data technology.
And S2, acquiring the original monitoring data and the standard monitoring data of the water body to be detected in real time by the full-spectrum water quality monitoring system through a big data technology. The full-spectrum water quality monitoring system stores the acquired original monitoring data and standard monitoring data of the water body to be detected in a database.
The big data technology is used for acquiring, transmitting, storing and analyzing a large amount of complex data with various types and sources at high speed, extracting valuable data by processing mass data and obtaining high-value products and services. The water quality monitoring technology based on big data is continuously accumulated and developed on the basis of combining a computer information technology and a traditional water quality monitoring technology, the informatization and intelligentization requirements of water quality monitoring are met, the water quality monitoring technology is a product combining water quality monitoring and a new technology, the appearance of the big data comprehensively expands the time scale and the space scale of water quality monitoring, and the water quality monitoring data presents the characteristics of rapid, massive and multisource big data.
S3, establishing a Support Vector Regression (SVR) algorithm initial model according to the to-be-detected water body monitoring original data and the standard monitoring data;
the artificial intelligence technology is a method technology for researching and developing intelligence for simulating, extending and expanding people, the operation principle of the artificial intelligence technology is complex, a big data technology is needed to be used as a support to maintain the operation of the artificial intelligence technology, the machine learning is highly dependent on experience mainly according to a machine learning algorithm, a computer continuously learns a strategy from experience and knowledge for solving the same kind of problems, so that the best answer is obtained, and when similar problems are met, the problem is solved by using experience knowledge and new experience is accumulated, so that the result is optimized.
The support vector regression algorithm initial model SVR established above is:
wherein, yiStandard monitoring data acquired in real time for i consecutive days;
ximonitoring original data of the water body to be detected, which are acquired in real time in i days continuously;
S4, calling a Support Vector Regression (SVR) algorithm initial model to perform fitting training on the monitoring original data and the standard monitoring data of the water body to be detected;
and S5, dynamically adjusting and optimizing parameters of the support vector regression algorithm initial model SVR model by controlling the data set of the water body monitoring original data to be tested to obtain the support vector regression algorithm optimization model SVR and training parameter data.
Specifically, when a Support Vector Regression (SVR) initial model is called for training, firstly, a big data technology is used for calling standard monitoring data stored in a database as a target value Yi of an algorithm model, meanwhile, raw data of the water body to be tested are called from the database for sample training, and an artificial intelligence (SVR) algorithm is used for achieving the effect of training the non-standard monitoring data (namely the raw data of the water body to be tested) by the standard monitoring data.
After training, the support vector regression algorithm optimization model SVR and the training data set are stored in a model parameter base, in the process of training the support vector regression algorithm initial model SVR, the model, the optimization algorithm and the update weight and deviation parameters are adopted to ensure higher operation efficiency and better effect, meanwhile, through the optimization and adjustment of the algorithm model parameters, the nonstandard monitoring data (namely the monitoring original data of the water body to be detected) are closer and closer to the standard monitoring data according to the artificial intelligence calculation result of the support vector regression algorithm optimization model SVR in the process of machine learning to the standard monitoring data, thereby ensuring that the synchronous monitoring deviation minimization of the monitoring data of the full-spectrum water quality monitoring equipment and the standard monitoring data is realized, and through the method (quality control method) for controlling the quality of the original monitoring data, finally, the requirements of the full-spectrum water quality monitoring equipment on the accuracy and precision of the monitoring data are met.
Meanwhile, in the same full-spectrum water quality monitoring system, the functions of data storage, data calling, data transmission, data analysis and the like of a big data technology are utilized to realize the transfer of a machine learning algorithm model (support vector regression algorithm initial model SVR) and parameters, realize that full-spectrum water quality monitoring equipment at different points loads the support vector regression algorithm initial model SVR and training parameter data, the model structure is an optimized and adjusted support vector regression algorithm optimized model SVR, the training parameter data is obtained after training by a support vector regression algorithm, so that the full spectrum water quality monitoring equipment at different point locations in the system can finally meet the requirements on the accuracy and precision of the monitoring data of the full spectrum water quality monitoring equipment at all the point locations by the method (quality control method) for carrying out quality control on the original monitoring data.
As an improvement of the quality control method for monitoring water quality by the full-spectrum water quality monitoring device based on the big data, the quality control method further comprises step S6.
S6, and a step of verifying the support vector regression algorithm optimization model SVR, namely, comparing the training parameter data of 1-M days with the standard monitoring data according to the time sequence of 1-M days to obtain the time i ' with the maximum fitting degree with the standard monitoring data and the support vector regression algorithm optimization model SVR, wherein M, i ' is an integer, and i is more than or equal to M and more than or equal to i ' is more than or equal to 1.
After verification, the support vector regression algorithm optimization model SVR is obtained as follows:
yi`standard monitoring data obtained for consecutive days i';
xi`original data of water body to be detected for full-spectrum water quality monitoring acquired continuously at i 'day, wherein i' is more than or equal to 1;
According to the invention, the original data of the water body to be monitored and the standard monitoring data are acquired, stored and called through a big data technology, the support vector regression algorithm initial model SVR is established, the original data of the water body to be monitored is dynamically optimized through the support vector regression algorithm initial model SVR, the accuracy of the monitoring data of the full-spectrum water quality monitoring equipment is ensured, and the accuracy level of the monitoring data of the full-spectrum automatic monitoring technology is improved.
The quality control method for monitoring water quality by the full-spectrum water quality monitoring device based on big data is described below by specific embodiments.
S1, real-time monitoring is carried out by adopting full-spectrum water quality monitoring equipment, the original monitoring data of the water body to be detected are collected, standard monitoring data are called, and the original monitoring data of the water body to be detected and the data of the standard monitoring data within 0-23 hours of 1-30 days are obtained.
And S2, acquiring the original monitoring data and the standard monitoring data of the water body to be detected in real time by the full-spectrum water quality monitoring system through a big data technology. The full-spectrum water quality monitoring system stores the acquired original monitoring data and standard monitoring data of the water body to be detected in a database. The standard monitoring data are as follows in sequence: y is1[0-23]、y2[0-23]、……、y29[0-23]、y30[0-23](ii) a The water body monitoring original data to be detected sequentially comprise: x is the number of1[0-23]、x2[0-23]、……、x29[0-23]、x30[0-23]。
S3, establishing an initial Support Vector Regression (SVR) algorithm initial model by using an artificial intelligence technology, namely
Wherein, [ 0-23 ] is 0-23;
y30[0-23]: for 30 consecutive days [ 0-23 ]]Standard monitoring data acquired in real time;
x30[0-23]: for 30 consecutive days [ 0-23 ]]Real-time acquiring original data of the water body to be detected;
n represents the number of the characteristics; z represents the target vector.
S4, calling a Support Vector Regression (SVR) algorithm initial model to perform fitting training on the monitoring original data and the standard monitoring data of the water body to be detected;
and S5, dynamically adjusting and optimizing parameters of the support vector regression algorithm initial model SVR model by controlling the data set of the water body monitoring original data to be tested to obtain the support vector regression algorithm optimization model SVR and training parameter data.
And S6, a step of verifying the support vector regression algorithm optimization model SVR, namely, comparing the training parameter data of 1-30 days with the standard monitoring data according to the time sequence of 1-30 (M is 30) days to obtain the maximum time i ' of fitting with the standard monitoring data and the support vector regression algorithm optimization model SVR, wherein i ' is an integer, and 30 is more than or equal to i ' and more than or equal to 1.
Performing a comparison experiment on data Yi calculated by using an algorithm model and standard data Y, respectively adopting time data of 7 days, 14 days and 21 days, and performing comparison analysis on the experiment calculation result and the standard data to obtain data of a time series of 14 days (i ') which are best fit with the standard data, wherein the monitoring precision error accords with ' A ' in technical Specification for operation and maintenance of automatic surface Water quality monitoring station>BIVMeanwhile, the invention can realize the following quality control targets of monitoring water quality by full spectrum water quality monitoring equipment: when B is presentII<A≤BIVWhen the error is larger than or equal to +/-30 percent; when the automatic monitoring result and the laboratory analysis result are both lower than the class II standard limit value, the comparison experiment result is qualified; wherein, A, the water quality monitoring equipment measures the concentration; B-GB 3831 watch1 corresponding water quality class standard limit, BII、BIVRepresents the standard limit values of II-class water quality and IV-class water quality.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (4)
1. The quality control method for monitoring water quality by using the full-spectrum water quality monitoring equipment based on big data is characterized by comprising the following steps of:
collecting original monitoring data and standard monitoring data of a water body to be detected;
the full-spectrum water quality monitoring system acquires original monitoring data and standard monitoring data of the water body to be detected in real time through a big data technology;
establishing a Support Vector Regression (SVR) algorithm initial model according to the to-be-detected water body monitoring original data and the standard monitoring data;
calling a support vector regression algorithm initial model SVR to perform fitting training on the monitoring original data and the standard monitoring data of the water body to be detected;
and dynamically adjusting and optimizing parameters of the support vector regression algorithm initial model SVR model by controlling a data set of the water body monitoring original data to be detected, so as to obtain the support vector regression algorithm optimization model SVR and training parameter data.
2. The quality control method according to claim 1, wherein the support vector regression algorithm initial model SVR is:
wherein, yiStandard monitoring data acquired in real time for i consecutive days;
ximonitoring original data of the water body to be detected, which are acquired in real time in i days continuously;
3. The quality control method according to claim 2, further comprising a verification step of the support vector regression algorithm optimization model SVR by comparing training parameter data of 1 to M days with standard monitoring data in a time sequence of 1 to M days to obtain a time i 'with the maximum degree of fitting to the standard monitoring data and the support vector regression algorithm optimization model SVR, wherein M, i' is an integer, and i.gtoreq.M ≧ i ≧ 1.
4. The quality control method according to claim 3, characterized in that: the support vector regression algorithm optimization model SVR is as follows:
yi`standard monitoring data obtained for consecutive days i';
xi`original data of water body to be detected for full-spectrum water quality monitoring acquired continuously at i 'day, wherein i' is more than or equal to 1;
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