CN109614570B - Method and device for predicting section water quality parameter data - Google Patents
Method and device for predicting section water quality parameter data Download PDFInfo
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Abstract
The invention provides a method for predicting section water quality parameter data, which comprises the following steps: acquiring first and second water quality parameter information in real time; calculating a first mean value of each water quality parameter data of each discharge port and a second mean value of each water quality parameter data of each section; calculating the Pearson correlation coefficient and the maximum mutual information coefficient of each water quality parameter at the discharge port and the section according to the first mean value and the second mean value; when the Pearson correlation coefficient is larger than a preset first threshold and the maximum mutual information coefficient is smaller than a preset second threshold, determining the linear correlation of the Pearson correlation coefficient and predicting first current water quality parameter data of a first section; predicting second current water quality parameter data of the first section by using a preset model according to the current water quality parameter data of the first discharge port; and fitting according to the first current water quality parameter data and the second current water quality parameter data to obtain target water quality parameter data of the first section. Therefore, timeliness and accuracy of the prediction result are improved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a device for predicting section water quality parameter data.
Background
Under the current situation, the water quality condition in the region is known quantitatively and qualitatively, and the method has obvious practical significance for reasonably formulating a regional water quality optimization scheme, improving the quality and efficiency of water quality treatment and effectively treating water pollution. At the present stage, most of water quality pollution analysis and data sources are obtained from a discharge port and a section, if section equipment breaks down, some equipment cannot monitor data within a period of time, and the analysis and supervision of water quality are very troublesome.
In the prior art, the numerical value of the point (the point where the cross-section equipment fails) is directly estimated by an interpolation method, or the change condition of the missing parameter in the period is estimated by the same time sequence. However, the results inferred from the two methods have large deviation from the actual data.
Therefore, an efficient and accurate method for filling up the missing data is needed.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for predicting a water quality parameter of a cross section aiming at the defects in the prior art so as to solve the problems in the prior art.
In a first aspect, the present invention provides a method for predicting cross-section water quality parameter data, comprising:
acquiring first water quality parameter information in real time, wherein the first water quality parameter information comprises water quality parameters of a plurality of discharge ports in a first area and water quality parameter data of the water quality parameters;
acquiring second water quality parameter information in real time, wherein the second water quality parameter information comprises water quality parameters of each section and water quality parameter data of the water quality parameters in a plurality of sections in the first area;
calculating a first average value of each water quality parameter data of each discharge port within a preset time according to the water quality parameter data of each discharge port;
calculating a second average value of each water quality parameter data of each section within a preset time according to the water quality parameter data of each section;
calculating the Pearson correlation coefficient and the maximum mutual information coefficient of each water quality parameter at the discharge port and the section according to the first mean value and the second mean value;
when the Pearson correlation coefficient is larger than a preset first threshold value and the maximum mutual information coefficient is smaller than a preset second threshold value, determining that the water quality parameter has linear correlation between the discharge port and the section;
predicting first current water quality parameter data of the first section by utilizing the linear correlation according to the current water quality parameter data of the first discharge port; the first row of ports is any one of the plurality of rows of ports, and the first section is a section corresponding to the first row of ports;
predicting second current water quality parameter data of the first section by using a preset model according to the current water quality parameter data of the first discharge port;
and fitting according to the first current water quality parameter data and the second current water quality parameter data to obtain target water quality parameter data of a first cross section.
In a possible implementation manner, before predicting second current water quality parameter data of the first cross section by using a preset model according to current water quality parameter data of the first discharge port, the method further includes:
establishing an initial preset model according to the river flow velocity of a first area, the distance from the first discharge port to a first section and the pollutant concentration at the first discharge port;
and correcting the initial preset model according to the known water quality parameter data of the discharge port and the section to obtain a preset model.
In a possible implementation manner, when the pearson correlation coefficient is greater than a preset first threshold and the maximum mutual information coefficient is smaller than a preset second threshold, after determining that there is a linear correlation between the discharge port and the section, the method further includes:
and calculating the correlation coefficient of the water quality parameter at the discharge port and the section.
In a possible implementation manner, the preset model is specifically a hydrodynamic water quality model.
In a possible implementation manner, the obtaining, by fitting according to the first current water quality parameter data and the second current water quality parameter data, target water quality parameter data of a first cross section specifically includes:
setting a first weight for the first current water quality parameter data;
setting a second weight for the second current water quality parameter data;
and calculating target water quality parameter data of the first section according to the first weight, the first current water quality parameter data, the second weight and the second current water quality parameter data.
In a second aspect, the present invention provides an apparatus for predicting water quality parameter data of a cross-section, the apparatus comprising:
the device comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring first water quality parameter information in real time, and the first water quality parameter information comprises water quality parameters of each discharge outlet and water quality parameter data of the water quality parameters in a plurality of discharge outlets in a first area;
the acquisition unit is further used for acquiring second water quality parameter information in real time, wherein the second water quality parameter information comprises water quality parameters of each section and water quality parameter data of the water quality parameters in a plurality of sections in the first area;
the calculating unit is used for calculating a first mean value of each water quality parameter data of each discharge port within a preset time period according to the water quality parameter data of each discharge port;
the calculation unit is further used for calculating a second average value of each water quality parameter data of each section within a preset time according to the water quality parameter data of each section;
the calculating unit is further used for calculating the Pearson correlation coefficient and the maximum mutual information coefficient of each water quality parameter at the discharge port and the section according to the first average value and the second average value;
the determining unit is used for determining that the water quality parameters have linear correlation between the discharge port and the cross section when the Pearson correlation coefficient is greater than a preset first threshold and the maximum mutual information coefficient is less than a preset second threshold;
the prediction unit is used for predicting first current water quality parameter data of a first cross section by utilizing the linear correlation according to the current water quality parameter data of the first discharge port; the first row of ports is any one of a plurality of rows of ports, and the first section is a section corresponding to the first row of ports;
the prediction unit is further used for predicting second current water quality parameter data of the first section by using a preset model according to the current water quality parameter data of the first discharge port;
and the fitting unit is used for fitting to obtain target water quality parameter data of a first section according to the first current water quality parameter data and the second current water quality parameter data.
In one possible implementation, the apparatus further includes:
the system comprises an establishing unit, a calculating unit and a calculating unit, wherein the establishing unit is used for establishing an initial preset model according to the river flow velocity of a first area, the distance from a first discharge port to a first section and the pollutant concentration at the first discharge port;
and the correction module is used for correcting the initial preset model according to the known water quality parameter data of the discharge port and the section to obtain the preset model.
In a possible implementation manner, the calculating unit is further configured to calculate a correlation coefficient of the water quality parameter at the discharge port and the section.
In a possible implementation manner, the preset model is specifically a hydrodynamic water quality model.
In a possible implementation manner, the fitting unit is specifically configured to:
setting a first weight for the first current water quality parameter data;
setting a second weight for the second current water quality parameter data;
and calculating target water quality parameter data of the first section according to the first weight, the first current water quality parameter data, the second weight and the second current water quality parameter data.
In a third aspect, the present invention provides an apparatus for predicting cross-sectional water quality parameter data, comprising a memory for storing a program and a processor for performing the method of any of the first aspect.
In a fourth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of any one of the first aspects.
In a fifth aspect, the invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of the first aspects.
By applying the method and the device for predicting the water quality parameter data of the section, the accuracy and the timeliness of the prediction result are improved.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting water quality parameter data of a cross section according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus for predicting water quality parameter data of a cross section according to a second embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be further noted that, for the convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic flow chart of a method for predicting water quality parameter data of a cross section according to an embodiment of the present invention. The application scenario of the method is the condition that the reading of the section monitoring equipment is abnormal, and the abnormality comprises data missing or obvious data errors and the like. As shown in fig. 1, the method comprises the following steps:
Wherein, the water quality parameter is a characteristic index of various substances in water for representing the quality degree and the change trend of the water environment (water body). Water quality parameters include, but are not limited to: PH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, total phosphorus, permanganate. The water quality parameter data is a specific numerical value of the water quality parameter, and corresponds to the water quality parameter, and the water quality parameter data includes but is not limited to: specific values of PH, dissolved oxygen content, chemical oxygen demand content, ammonia nitrogen content, total phosphorus content, and permanganate content. The discharge opening is the first opening of each enterprise for discharging sewage. At the discharge port, a monitoring device is generally arranged to acquire water quality parameter data at the discharge port. The first zone may be the stream of river a.
And 102, acquiring second water quality parameter information in real time, wherein the second water quality parameter information comprises water quality parameters of each cross section in a plurality of cross sections in the first area and water quality parameter data of the water quality parameters.
Wherein, the section is the section in the river, and at the section department, be equipped with monitoring facilities for the quality of water parameter data of section department is obtained.
In the last example, the monitoring equipment arranged at the discharge port can acquire water quality parameter data in real time, for example, 60 groups of water quality parameters within 1 hour are acquired, and the 60 groups of water quality parameters include 60 PH value data, 60 dissolved oxygen content values, 60 chemical oxygen demand content values, 60 ammonia nitrogen content values, 60 total phosphorus content values and 60 permanganate content values. The preset time is 24 hours, and the specific content numerical value of each water quality parameter in one day can be calculated.
And 104, calculating a second average value of each water quality parameter data of each section within a preset time according to the water quality parameter data of each section.
According to the above discussion of step 103, the same method can be used to obtain the specific content value of each water quality parameter within one day at the section.
And 105, calculating the Pearson correlation coefficient and the maximum mutual information coefficient of each water quality parameter at the discharge port and the section according to the first average value and the second average value.
Wherein, the Pearson coefficient is used for counting the correlation degree between the two parameters. The maximum mutual information coefficient is used to estimate the correlation between the variables.
And 106, when the Pearson correlation coefficient is greater than a preset first threshold value and the maximum mutual information coefficient is less than a preset second threshold value, determining that the water quality parameter has linear correlation between the discharge port and the section.
Specifically, in the same time dimension, a pearson correlation coefficient and a maximum mutual information coefficient between the water quality parameters are calculated according to a first average value of each row of ports and a second average value of the cross section, for example, if a relation between dissolved oxygen between the first row of ports and the first cross section satisfies that the pearson correlation coefficient is greater than a preset first threshold, and the maximum mutual information coefficient is smaller than a preset second threshold, it can be determined that linear correlation exists between the dissolved oxygen of the first row of ports and the dissolved oxygen of the first cross section. For another example, if the cod of the first discharge port and the first cross section also satisfy that the pearson correlation coefficient is greater than the preset first threshold and the maximum mutual information coefficient is smaller than the preset second threshold, it may be determined that a linear correlation exists between the cod of the first discharge port and the cod of the first cross section, and a correlation coefficient between the two may be calculated.
Specifically, as shown in the example 106, if the dissolved oxygen and chemical oxygen demand measurements of the first row of ports are normal and the two data are missing in the first section due to equipment failure or some other reason, the missing data in the first section can be predicted according to the known data of the first row of ports. The corresponding here means that geographically adjacent, e.g. downstream of the a row, is section B.
And step 108, predicting second current water quality parameter data of the first section by using a preset model according to the current water quality parameter data of the first discharge port.
The preset model can be established by the following method: the method comprises the steps of firstly establishing an initial preset model according to the river flow velocity of a first area, the distance from a first discharge port to a first section and the pollutant concentration at the first discharge port, and finally correcting the initial preset model according to the known water quality parameter data of the discharge port and the section to obtain the preset model. The preset model may be a one-dimensional hydrodynamic water quality model. According to the model, the value of the parameter of the first section can be obtained by inputting the value of a certain water quality parameter of the first discharge port.
It will be appreciated that the model may be trained multiple times in building the model to improve the accuracy of the model.
And step 109, fitting according to the first current water quality parameter data and the second current water quality parameter data to obtain target water quality parameter data of the first section.
Specifically, the result prediction results of step 107 and step 108 are fitted to obtain target water quality parameter data of the first cross section.
In one example, a first weight may be set for a first current water quality parameter data; then setting a second weight for the second current water quality parameter data; and finally, calculating target water quality parameter data of the first section according to the first weight, the first current water quality parameter data, the second weight and the second current water quality parameter data.
Therefore, by applying the method for predicting the water quality parameter data of the cross section provided by the embodiment of the invention, the timeliness and the accuracy of the prediction result are improved.
Fig. 2 is a schematic structural diagram of a device for predicting water quality parameter data of a cross section according to a second embodiment of the present invention. As shown in fig. 2, the apparatus for predicting the cross-sectional water quality parameter data may be applied to a method for predicting the cross-sectional water quality parameter data. The device includes: an acquisition unit 201, a calculation unit 202, a determination unit 203, a prediction unit 204, a fitting unit 205, a creation unit 206, and a correction unit 207.
The obtaining unit 201 is configured to obtain first water quality parameter information in real time, where the first water quality parameter information includes a plurality of discharge ports in a first area, water quality parameters of each discharge outlet and water quality parameter data of the water quality parameters;
the obtaining unit 201 is further configured to obtain second water quality parameter information in real time, where the second water quality parameter information includes a water quality parameter of each section in the plurality of sections in the first area and water quality parameter data of the water quality parameter;
the calculating unit 202 is configured to calculate a first average value of each water quality parameter data of each discharge port within a preset time period according to the water quality parameter data of each discharge port;
the calculating unit 202 is further configured to calculate a second average value of each water quality parameter data of each section within a preset time according to the water quality parameter data of each section;
the calculating unit 202 is further configured to calculate a pearson correlation coefficient and a maximum mutual information coefficient of each water quality parameter at the discharge port and the section according to the first average value and the second average value;
the determining unit 203 is used for determining that the water quality parameter has linear correlation between the discharge port and the cross section when the pearson correlation coefficient is greater than a preset first threshold and the maximum mutual information coefficient is less than a preset second threshold;
the prediction unit 204 is configured to predict, according to the current water quality parameter data of the first discharge port, first current water quality parameter data of the first cross section by using linear correlation; the first row of ports is any one of the plurality of rows of ports, and the first section is a section corresponding to the first row of ports;
the prediction unit 204 is further configured to predict, according to the current water quality parameter data of the first discharge port, second current water quality parameter data of the first cross section by using a preset model;
the fitting unit 205 is configured to obtain target water quality parameter data of the first cross section by fitting according to the first current water quality parameter data and the second current water quality parameter data.
Further, the establishing unit 206 is configured to establish an initial preset model according to the river flow rate of the first area, the distance from the first discharge port to the first section, and the pollutant concentration at the first discharge port;
the correcting unit 207 is configured to correct the initial preset model according to the water quality parameter data with known discharge and section, so as to obtain a preset model.
Further, the calculating unit 202 is further configured to calculate a correlation coefficient of the water quality parameter at the discharge port and the section.
Further, the preset model is specifically a hydrodynamic water quality model.
Further, the fitting unit 205 is specifically configured to:
setting a first weight for the first current water quality parameter data;
setting a second weight for the second current water quality parameter data;
and calculating target water quality parameter data of the first section according to the first weight, the first current water quality parameter data, the second weight and the second current water quality parameter data.
Therefore, by applying the device for predicting the water quality parameter data of the cross section provided by the embodiment of the invention, the timeliness and the accuracy of the prediction result are improved.
The third embodiment of the invention provides equipment for predicting the water quality parameter data of the cross section, which comprises a memory and a processor, wherein the memory is used for storing programs, and the processor is used for executing the method provided by the first embodiment of the invention.
A fourth embodiment of the present invention provides a computer program product including instructions, which, when the computer program product runs on a computer, causes the computer to execute the method provided in the first embodiment of the present invention.
The fifth embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method provided in the first embodiment of the present invention is implemented.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. 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 steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (6)
1. A method for predicting water quality parameter data of a cross section is characterized by comprising the following steps: acquiring first water quality parameter information in real time, wherein the first water quality parameter information comprises water quality parameters of a plurality of discharge ports in a first area and water quality parameter data of the water quality parameters; acquiring second water quality parameter information in real time, wherein the second water quality parameter information comprises water quality parameters of each section and water quality parameter data of the water quality parameters in a plurality of sections in the first area; calculating a first average value of each water quality parameter data of each discharge port within a preset time according to the water quality parameter data of each discharge port; calculating a second average value of each water quality parameter data of each section within a preset time according to the water quality parameter data of each section; calculating the Pearson correlation coefficient and the maximum mutual information coefficient of each water quality parameter at the discharge port and the section according to the first mean value and the second mean value; when the Pearson correlation coefficient is larger than a preset first threshold value and the maximum mutual information coefficient is smaller than a preset second threshold value, determining that the water quality parameter has linear correlation between the discharge port and the section; predicting first current water quality parameter data of the first section by utilizing the linear correlation according to the current water quality parameter data of the first discharge port; the first row of ports is any one of a plurality of rows of ports, and the first section is a section corresponding to the first row of ports; predicting second current water quality parameter data of the first section by using a preset model according to the current water quality parameter data of the first discharge port; the method for establishing the preset model comprises the following steps: establishing an initial preset model according to the river flow velocity of a first area, the distance from the first discharge port to a first section and the pollutant concentration at the first discharge port; correcting the initial preset model according to the known water quality parameter data of the discharge port and the section to obtain a preset model; according to the first current water quality parameter data and the second current water quality parameter data, fitting to obtain target water quality parameter data of a first section, wherein the target water quality parameter data comprises: setting a first weight for the first current water quality parameter data; setting a second weight for the second current water quality parameter data; and calculating target water quality parameter data of the first section according to the first weight, the first current water quality parameter data, the second weight and the second current water quality parameter data.
2. The method of claim 1, wherein when the pearson correlation coefficient is greater than a preset first threshold and the maximum mutual information coefficient is less than a preset second threshold, determining that the water quality parameter has a linear correlation between the discharge port and the section, further comprises: and calculating the correlation coefficient of the water quality parameters at the discharge port and the section.
3. The method according to claim 1, wherein the predetermined model is a hydrodynamic water quality model for predicting a second current water quality parameter data of the first cross section by inputting a current water quality parameter data of the first discharge.
4. An apparatus for predicting cross-sectional water quality parameter data, the apparatus comprising: the device comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring first water quality parameter information in real time, and the first water quality parameter information comprises water quality parameters of each discharge outlet and water quality parameter data of the water quality parameters in a plurality of discharge outlets in a first area; the acquisition unit is further used for acquiring second water quality parameter information in real time, wherein the second water quality parameter information comprises water quality parameters of each section and water quality parameter data of the water quality parameters in a plurality of sections in the first area; the calculating unit is used for calculating a first average value of each water quality parameter data of each row opening within a preset time length according to the water quality parameter data of each row opening; the calculating unit is further used for calculating a second average value of each water quality parameter data of each section within a preset time according to the water quality parameter data of each section; the calculation unit is also used for calculating the Pearson correlation coefficient and the maximum mutual information coefficient of each water quality parameter at the discharge port and the section according to the first average value and the second average value; the determining unit is used for determining that the water quality parameters have linear correlation at the discharge port and the section when the Pearson correlation coefficient is larger than a preset first threshold and the maximum mutual information coefficient is smaller than a preset second threshold; the prediction unit is used for predicting first current water quality parameter data of a first section by utilizing the linear correlation according to the current water quality parameter data of the first discharge port; the first row of ports is any one of the plurality of rows of ports, and the first section is a section corresponding to the first row of ports; the prediction unit is further used for predicting second current water quality parameter data of the first section by using a preset model according to the current water quality parameter data of the first discharge port; the method for establishing the preset model comprises the following steps: establishing an initial preset model according to the river flow velocity of a first area, the distance from the first discharge port to a first section and the pollutant concentration at the first discharge port; correcting the initial preset model according to the known water quality parameter data of the discharge port and the section to obtain a preset model; the fitting unit is used for fitting according to the first current water quality parameter data and the second current water quality parameter data to obtain target water quality parameter data of a first section; the step of fitting according to the first current water quality parameter data and the second current water quality parameter data to obtain target water quality parameter data of a first cross section comprises the following steps: setting a first weight for the first current water quality parameter data; setting a second weight for the second current water quality parameter data; and calculating target water quality parameter data of the first section according to the first weight, the first current water quality parameter data, the second weight and the second current water quality parameter data.
5. The apparatus of claim 4, wherein the computing unit is further configured to compute a correlation coefficient between the water quality parameter at the discharge port and the cross section.
6. The apparatus as claimed in claim 4, wherein the predetermined model is a hydrodynamic water quality model for predicting a second current water quality parameter data of the first section by inputting the current water quality parameter data of the first discharge.
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| CN112183676A (en) * | 2020-11-10 | 2021-01-05 | 浙江大学 | Water quality soft measurement method based on mixed dimensionality reduction and kernel function extreme learning machine |
| CN115728463B (en) * | 2022-12-01 | 2024-10-11 | 哈尔滨工业大学 | Interpretive water quality prediction method based on semi-embedded feature selection |
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