CN114236085A - Water quality sampling device and method for industrial sewage monitoring - Google Patents

Abstract

The utility model provides a water quality sampling device and method for industrial sewage monitoring can not only utilize the sewage evaluation result data of target sewage monitoring sample data in the process of generating depolarization sewage monitoring sample data, the sewage monitoring result that is confirmed based on the sewage description data of target sewage monitoring sample data, under the effect of sewage monitoring result, the degree of accuracy of the depolarization sewage monitoring sample data that obtains is improved through the mode of finishing target sewage monitoring sample data, the shortcoming that a large amount of target sewage monitoring sample data are required to obtain accurate data sample in the traditional technology has been improved, this application can be based on a small amount of target sewage monitoring sample data, still can generate accurate sewage monitoring result.

Description

Water quality sampling device and method for industrial sewage monitoring

Technical Field

The application relates to the technical field of sewage monitoring, in particular to a water quality sampling device and method for industrial sewage monitoring.

Background

With the rapid progress of the industry, the problem of polluted water discharged by the industry comes along, and therefore, a technical scheme is needed to improve the technical problem.

Disclosure of Invention

In view of this, the application provides a water quality sampling device and method for industrial sewage monitoring.

In a first aspect, a water quality sampling method for industrial sewage monitoring is provided, the method comprising:

determining at least one target sewage monitoring sample data; identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data;

clustering each target sewage monitoring sample data to obtain sewage description data bound with the target sewage monitoring sample data; identifying the depolarization processing result of the sewage description data bound by each target sewage monitoring sample data to obtain a sewage monitoring result;

performing the clustering treatment on the target sewage monitoring sample data to obtain sewage description data of a target part sewage label;

and trimming the target sewage monitoring sample data based on the difference condition between the sewage description data of the target part sewage label and the sewage monitoring result to obtain depolarized sewage monitoring sample data.

In an independently implemented embodiment, the clustering each target sewage monitoring sample data to obtain sewage description data bound to the target sewage monitoring sample data includes: loading each target sewage monitoring sample data to a clustering thread for clustering processing to obtain sewage description data bound with the target sewage monitoring sample data;

the clustering processing is carried out on the target sewage monitoring sample data to obtain sewage description data of the target part sewage label, and the clustering processing comprises the following steps: loading the target sewage monitoring sample data to the clustering thread for clustering processing to obtain sewage description data of the target part sewage label;

the step of finishing the target sewage monitoring sample data based on the difference between the sewage description data of the target partial sewage label and the sewage monitoring result to obtain depolarized sewage monitoring sample data comprises the following steps: determining the missing condition of the sewage data according to the difference condition of the sewage description data of the target part sewage label and the sewage monitoring result; and compensating the vector of the target sewage monitoring sample data based on the sewage data missing condition until a preset compensation termination requirement is met.

In an independently implemented embodiment, the remedying the vector of the target sewage monitoring sample data based on the sewage data missing condition until reaching a preset remedying termination requirement includes:

identifying the missing condition of the sewage data along the clustering thread to obtain the sewage pollution level of the target sewage monitoring sample data;

correcting the vector of the target sewage monitoring sample data based on the sewage pollution level; and if the make-up termination requirement is not met, loading the modified target sewage monitoring sample data to the clustering thread again for clustering treatment, and continuously modifying the vector of the target sewage monitoring sample data based on the obtained sewage description data of the target part sewage label.

In a separately implemented embodiment, the method further comprises:

determining the binding percentage of each target sewage monitoring sample data; the identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring item bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data includes: according to the binding percentage of each target sewage monitoring sample data, identifying a sewage evaluation result weighted depolarization processing result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain the target sewage monitoring sample data;

the identifying the depolarization processing result of the sewage description data bound to each target sewage monitoring sample data to obtain a sewage monitoring result includes: and identifying a weighted depolarization processing result of the sewage description data bound by each target sewage monitoring sample data according to the binding percentage of each target sewage monitoring sample data to obtain the sewage monitoring result.

In a second aspect, a water quality sampling device for industrial sewage monitoring is provided, which comprises:

the data processing module is used for determining at least one target sewage monitoring sample data; identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data;

the result detection module is used for carrying out clustering treatment on each target sewage monitoring sample data to obtain sewage description data bound with the target sewage monitoring sample data; identifying the depolarization processing result of the sewage description data bound by each target sewage monitoring sample data to obtain a sewage monitoring result;

the sewage description module is used for carrying out the clustering treatment on the target sewage monitoring sample data to obtain sewage description data of a target part sewage label;

and the sample detection module is used for finishing the target sewage monitoring sample data based on the difference condition between the sewage description data of the target part sewage label and the sewage monitoring result to obtain depolarized sewage monitoring sample data.

In an independently implemented embodiment, the clustering processing is performed on each target sewage monitoring sample data by the result detection module to obtain the sewage description data bound to the target sewage monitoring sample data, and the clustering processing includes: loading each target sewage monitoring sample data to a clustering thread for clustering processing to obtain sewage description data bound with the target sewage monitoring sample data;

the clustering processing is carried out on the target sewage monitoring sample data to obtain sewage description data of the target part sewage label, and the clustering processing comprises the following steps: loading the target sewage monitoring sample data to the clustering thread for clustering processing to obtain sewage description data of the target part sewage label;

the step of finishing the target sewage monitoring sample data based on the difference between the sewage description data of the target partial sewage label and the sewage monitoring result to obtain depolarized sewage monitoring sample data comprises the following steps: determining the missing condition of the sewage data according to the difference condition of the sewage description data of the target part sewage label and the sewage monitoring result; and compensating the vector of the target sewage monitoring sample data based on the sewage data missing condition until a preset compensation termination requirement is met.

In an independently implemented embodiment, the result detecting module is configured to compensate the vector of the target sewage monitoring sample data based on the sewage data missing condition until a compensation termination requirement set in advance is met, and includes:

identifying the missing condition of the sewage data along the clustering thread to obtain the sewage pollution level of the target sewage monitoring sample data;

correcting the vector of the target sewage monitoring sample data based on the sewage pollution level; and if the make-up termination requirement is not met, loading the modified target sewage monitoring sample data to the clustering thread again for clustering treatment, and continuously modifying the vector of the target sewage monitoring sample data based on the obtained sewage description data of the target part sewage label.

In a separately implemented embodiment, the method further comprises:

determining the binding percentage of each target sewage monitoring sample data; the identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring item bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data includes: according to the binding percentage of each target sewage monitoring sample data, identifying a sewage evaluation result weighted depolarization processing result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain the target sewage monitoring sample data;

the identifying the depolarization processing result of the sewage description data bound to each target sewage monitoring sample data to obtain a sewage monitoring result includes: and identifying a weighted depolarization processing result of the sewage description data bound by each target sewage monitoring sample data according to the binding percentage of each target sewage monitoring sample data to obtain the sewage monitoring result.

The embodiment of the application provides a water quality sampling device and method for industrial sewage monitoring, which can utilize the sewage evaluation result data of target sewage monitoring sample data and the sewage monitoring result determined based on the sewage description data of the target sewage monitoring sample data in the process of generating the depolarization sewage monitoring sample data, improve the accuracy of the obtained depolarization sewage monitoring sample data in a mode of finishing the target sewage monitoring sample data under the action of the sewage monitoring result, and overcome the defect that a large amount of target sewage monitoring sample data is needed to obtain accurate data samples in the prior art.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a flowchart of a water quality sampling method for industrial sewage monitoring according to an embodiment of the present application.

Fig. 2 is a block diagram of a water sampling device for industrial sewage monitoring according to an embodiment of the present application.

Fig. 3 is an architecture diagram of a water sampling system for industrial sewage monitoring according to an embodiment of the present application.

Detailed Description

In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

Referring to fig. 1, a method for sampling water quality for industrial wastewater monitoring is shown, which may include the following steps 100-400.

Step 100, determining at least one target sewage monitoring sample data; and identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain the target sewage monitoring sample data.

200, clustering each target sewage monitoring sample data to obtain sewage description data bound with the target sewage monitoring sample data; and identifying the depolarization processing result of the sewage description data bound by each target sewage monitoring sample data to obtain a sewage monitoring result.

And 300, performing the clustering treatment on the target sewage monitoring sample data to obtain sewage description data of the target part sewage label.

Step 400, based on the difference between the sewage description data of the target part sewage label and the sewage monitoring result, finishing the target sewage monitoring sample data to obtain depolarized sewage monitoring sample data.

It can be understood that, when the contents described in the above steps 100 to 400 are executed, the accuracy of the obtained depolarized wastewater monitoring sample data can be improved by modifying the target wastewater monitoring sample data under the effect of the wastewater monitoring result by using not only the wastewater evaluation result data of the target wastewater monitoring sample data but also the wastewater monitoring result determined based on the wastewater description data of the target wastewater monitoring sample data, so that the defect that a large amount of target wastewater monitoring sample data is required to obtain an accurate data sample in the conventional art is overcome.

In this embodiment, when performing clustering processing on each target sewage monitoring sample data, there is a problem of inaccurate sewage description, so that it is difficult to accurately obtain sewage description data bound to the target sewage monitoring sample data, and in order to improve the above technical problem, the step of performing clustering processing on each target sewage monitoring sample data to obtain sewage description data bound to the target sewage monitoring sample data described in step 200 may specifically include the content described in the following step a 1.

Step a1, the clustering process is performed on the target sewage monitoring sample data to obtain sewage description data of a target part sewage label, and the process comprises the following steps: and loading the target sewage monitoring sample data to the clustering thread for clustering treatment to obtain the sewage description data of the target part sewage label.

It can be understood that when the content described in the step a1 is executed, when the clustering process is performed on each target sewage monitoring sample data, the problem of inaccurate sewage description is solved, so that it is difficult to accurately obtain the sewage description data bound by the target sewage monitoring sample data.

In this embodiment, when the target sewage monitoring sample data is modified based on the difference between the sewage description data of the target partial sewage label and the sewage monitoring result, there is a problem of data missing, so that it is difficult to accurately obtain the depolarized sewage monitoring sample data, and in order to improve the above problem, the step of modifying the target sewage monitoring sample data based on the difference between the sewage description data of the target partial sewage label and the sewage monitoring result, so as to obtain the depolarized sewage monitoring sample data may specifically include the content described in the following step s 1.

S1, determining the missing condition of sewage data according to the difference condition of the sewage description data of the target part sewage label and the sewage monitoring result; and compensating the vector of the target sewage monitoring sample data based on the sewage data missing condition until a preset compensation termination requirement is met.

It can be understood that, when the target sewage monitoring sample data is modified based on the difference between the sewage description data of the target partial sewage tag and the sewage monitoring result in the execution of the above-described content of step s1, the problem of data missing is improved, so that the depolarized sewage monitoring sample data can be accurately obtained.

In this embodiment, the step of compensating the vector of the target sewage monitoring sample data based on the sewage data missing condition until the preset compensation termination requirement is met has a problem of inaccurate identification, so that it is difficult to accurately meet the preset compensation termination requirement, and in order to improve the above technical problem, the step of compensating the vector of the target sewage monitoring sample data based on the sewage data missing condition described in the step s1 until the preset compensation termination requirement is met may specifically include the following steps s11 and s 12.

And step s11, identifying the missing condition of the sewage data along the clustering thread to obtain the sewage pollution level of the target sewage monitoring sample data.

Step s12, correcting the vector of the target sewage monitoring sample data based on the sewage pollution level; and if the make-up termination requirement is not met, loading the modified target sewage monitoring sample data to the clustering thread again for clustering treatment, and continuously modifying the vector of the target sewage monitoring sample data based on the obtained sewage description data of the target part sewage label.

It can be understood that, when the contents described in the above steps s11 and s12 are executed, the vector of the target sewage monitoring sample data is compensated based on the sewage data missing condition, and until the compensation termination requirement set in advance is met, the problem of inaccurate identification is solved, so that the compensation termination requirement set in advance can be accurately met.

Based on the above, the following descriptions of step d1 and step d2 may be included.

D1, determining the binding percentage of each target sewage monitoring sample data; the identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring item bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data includes: and identifying the sewage evaluation result weighted depolarization processing result of the sewage monitoring items bound in each target sewage monitoring sample data according to the binding percentage of each target sewage monitoring sample data to obtain the target sewage monitoring sample data.

Step d2, the identifying the depolarization processing result of the sewage description data bound to each target sewage monitoring sample data to obtain a sewage monitoring result includes: and identifying a weighted depolarization processing result of the sewage description data bound by each target sewage monitoring sample data according to the binding percentage of each target sewage monitoring sample data to obtain the sewage monitoring result.

It can be understood that, when the contents described in the above steps d1 and d2 are performed, the sewage monitoring result can be accurately obtained through the precise depolarization processing.

On the basis, please refer to fig. 2 in combination, which provides an industrial sewage quality sampling device 200 for industrial sewage monitoring, which is applied to an industrial sewage quality sampling system, and the device includes:

a data processing module 210, configured to determine that at least one target sewage monitoring sample data is available; identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data;

the result detection module 220 is configured to perform clustering processing on each target sewage monitoring sample data to obtain sewage description data bound to the target sewage monitoring sample data; identifying the depolarization processing result of the sewage description data bound by each target sewage monitoring sample data to obtain a sewage monitoring result;

the sewage description module 230 is configured to perform the clustering processing on the target sewage monitoring sample data to obtain sewage description data of a target part sewage label;

and the sample detection module 240 is configured to modify the target sewage monitoring sample data based on the difference between the sewage description data of the target partial sewage tag and the sewage monitoring result, so as to obtain depolarized sewage monitoring sample data.

On the basis of the above, please refer to fig. 3, which shows a water sampling system 300 for industrial wastewater monitoring, comprising a processor 310 and a memory 320, which are communicated with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.

On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.

In summary, based on the above scheme, in the process of generating the depolarization sewage monitoring sample data, not only the sewage evaluation result data of the target sewage monitoring sample data but also the sewage monitoring result determined based on the sewage description data of the target sewage monitoring sample data are utilized, and the accuracy of the obtained depolarization sewage monitoring sample data is improved by trimming the target sewage monitoring sample data under the action of the sewage monitoring result, so that the defect that a large amount of target sewage monitoring sample data is needed to obtain an accurate data sample in the conventional technology is overcome.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (8)

1. A water quality sampling method for industrial sewage monitoring is characterized by comprising the following steps:

determining at least one target sewage monitoring sample data; identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data;

clustering each target sewage monitoring sample data to obtain sewage description data bound with the target sewage monitoring sample data; identifying the depolarization processing result of the sewage description data bound by each target sewage monitoring sample data to obtain a sewage monitoring result;

performing the clustering treatment on the target sewage monitoring sample data to obtain sewage description data of a target part sewage label;

and trimming the target sewage monitoring sample data based on the difference condition between the sewage description data of the target part sewage label and the sewage monitoring result to obtain depolarized sewage monitoring sample data.

2. The method according to claim 1, wherein the clustering each target sewage monitoring sample data to obtain the sewage description data bound to the target sewage monitoring sample data comprises: loading each target sewage monitoring sample data to a clustering thread for clustering processing to obtain sewage description data bound with the target sewage monitoring sample data;

the clustering processing is carried out on the target sewage monitoring sample data to obtain sewage description data of the target part sewage label, and the clustering processing comprises the following steps: loading the target sewage monitoring sample data to the clustering thread for clustering processing to obtain sewage description data of the target part sewage label;

the step of finishing the target sewage monitoring sample data based on the difference between the sewage description data of the target partial sewage label and the sewage monitoring result to obtain depolarized sewage monitoring sample data comprises the following steps: determining the missing condition of the sewage data according to the difference condition of the sewage description data of the target part sewage label and the sewage monitoring result; and compensating the vector of the target sewage monitoring sample data based on the sewage data missing condition until a preset compensation termination requirement is met.

3. The method of claim 2, wherein the remedying the vector of the target sewage monitoring sample data based on the sewage data missing condition until reaching a preset remedying termination requirement comprises:

identifying the missing condition of the sewage data along the clustering thread to obtain the sewage pollution level of the target sewage monitoring sample data;

correcting the vector of the target sewage monitoring sample data based on the sewage pollution level; and if the make-up termination requirement is not met, loading the modified target sewage monitoring sample data to the clustering thread again for clustering treatment, and continuously modifying the vector of the target sewage monitoring sample data based on the obtained sewage description data of the target part sewage label.

4. The method of claim 3, further comprising:

determining the binding percentage of each target sewage monitoring sample data; the identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring item bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data includes: according to the binding percentage of each target sewage monitoring sample data, identifying a sewage evaluation result weighted depolarization processing result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain the target sewage monitoring sample data;

the identifying the depolarization processing result of the sewage description data bound to each target sewage monitoring sample data to obtain a sewage monitoring result includes: and identifying a weighted depolarization processing result of the sewage description data bound by each target sewage monitoring sample data according to the binding percentage of each target sewage monitoring sample data to obtain the sewage monitoring result.

5. The utility model provides a water sampling device for industrial sewage monitoring which characterized in that includes:

the data processing module is used for determining at least one target sewage monitoring sample data; identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data;

the result detection module is used for carrying out clustering treatment on each target sewage monitoring sample data to obtain sewage description data bound with the target sewage monitoring sample data; identifying the depolarization processing result of the sewage description data bound by each target sewage monitoring sample data to obtain a sewage monitoring result;

the sewage description module is used for carrying out the clustering treatment on the target sewage monitoring sample data to obtain sewage description data of a target part sewage label;

and the sample detection module is used for finishing the target sewage monitoring sample data based on the difference condition between the sewage description data of the target part sewage label and the sewage monitoring result to obtain depolarized sewage monitoring sample data.

6. The apparatus according to claim 5, wherein the result detection module is configured to perform clustering processing on each target sewage monitoring sample data to obtain the sewage description data bound to the target sewage monitoring sample data, and includes: loading each target sewage monitoring sample data to a clustering thread for clustering processing to obtain sewage description data bound with the target sewage monitoring sample data;

the clustering processing is carried out on the target sewage monitoring sample data to obtain sewage description data of the target part sewage label, and the clustering processing comprises the following steps: loading the target sewage monitoring sample data to the clustering thread for clustering processing to obtain sewage description data of the target part sewage label;

the step of finishing the target sewage monitoring sample data based on the difference between the sewage description data of the target partial sewage label and the sewage monitoring result to obtain depolarized sewage monitoring sample data comprises the following steps: determining the missing condition of the sewage data according to the difference condition of the sewage description data of the target part sewage label and the sewage monitoring result; and compensating the vector of the target sewage monitoring sample data based on the sewage data missing condition until a preset compensation termination requirement is met.

7. The apparatus of claim 6, wherein the result detection module is configured to compensate the vector of the target sewage monitoring sample data based on the sewage data missing condition until a preset compensation termination requirement is met, and the method comprises:

identifying the missing condition of the sewage data along the clustering thread to obtain the sewage pollution level of the target sewage monitoring sample data;

correcting the vector of the target sewage monitoring sample data based on the sewage pollution level; and if the make-up termination requirement is not met, loading the modified target sewage monitoring sample data to the clustering thread again for clustering treatment, and continuously modifying the vector of the target sewage monitoring sample data based on the obtained sewage description data of the target part sewage label.

8. The apparatus of claim 7, further comprising:

determining the binding percentage of each target sewage monitoring sample data; the identifying the depolarization processing result of the sewage evaluation result of the sewage monitoring item bound in each target sewage monitoring sample data to obtain target sewage monitoring sample data includes: according to the binding percentage of each target sewage monitoring sample data, identifying a sewage evaluation result weighted depolarization processing result of the sewage monitoring items bound in each target sewage monitoring sample data to obtain the target sewage monitoring sample data;

the identifying the depolarization processing result of the sewage description data bound to each target sewage monitoring sample data to obtain a sewage monitoring result includes: and identifying a weighted depolarization processing result of the sewage description data bound by each target sewage monitoring sample data according to the binding percentage of each target sewage monitoring sample data to obtain the sewage monitoring result.

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