CN116562690A - Method, device, equipment and readable storage medium for determining electricity consumption monitoring index - Google Patents
Method, device, equipment and readable storage medium for determining electricity consumption monitoring index Download PDFInfo
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- 230000005611 electricity Effects 0.000 title claims abstract description 209
- 238000012544 monitoring process Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 44
- 239000003344 environmental pollutant Substances 0.000 claims description 57
- 231100000719 pollutant Toxicity 0.000 claims description 57
- 230000015654 memory Effects 0.000 claims description 17
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- 239000011449 brick Substances 0.000 description 2
- 239000004566 building material Substances 0.000 description 2
- 229910052742 iron Inorganic materials 0.000 description 2
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/06—Electricity, gas or water supply
Abstract
The invention provides a method, a device, equipment and a readable storage medium for determining an electricity consumption monitoring index, which are applied to the technical field of data processing, and are used for determining a target industry, wherein the target industry belongs to an industry that the correlation between electricity consumption and discharge meets a first preset condition, obtaining the electricity consumption of a preset statistical dimension of the target industry and the maximum value of pollution discharge of the preset statistical dimension in a certain time period of the target industry, predicting the pollution discharge of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the correlation between the electricity consumption of the target industry and the discharge, determining the electricity consumption monitoring index based on the pollution discharge of the preset statistical dimension of the target industry and the maximum value of pollution discharge of the preset statistical dimension in a certain time period of the target industry, and judging the severity of pollution discharge by the size of the electricity consumption monitoring index, thereby being beneficial to accurately predicting environmental quality and better making pollution control countermeasures.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a method, a device and equipment for determining an electricity consumption monitoring index and a readable storage medium.
Background
The pollutant emission and the environmental quality have close causal relationship, and timely grasping the pollutant emission situation is an important basis and basis for formulating and implementing pollution control countermeasures.
In the prior art, environmental quality is considered from the dimension of single pollutant discharge amount or single electricity consumption, and the environmental quality cannot be accurately predicted, so that the pollution control countermeasure is not easy to formulate and implement.
Disclosure of Invention
In view of the above problems, the present application proposes a method, an apparatus, a device and a readable storage medium for determining an electricity consumption monitoring index, and in order to accurately predict environmental quality, the specific scheme is as follows:
a method of determining a power usage monitoring index, comprising:
determining a target industry, wherein the target industry belongs to an industry of which the correlation between the electricity consumption and the emission meets a first preset condition;
acquiring the electricity consumption of a preset statistical dimension of the target industry and the maximum value of pollution emission of the preset statistical dimension in a certain time period of the target industry;
predicting pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the association relation between the electricity consumption and emission of the target industry;
and determining an electricity consumption monitoring index based on the pollution emission amount of the preset statistical dimension of the target industry and the maximum value of the pollution emission amount of the preset statistical dimension in a certain time period of the target industry.
Optionally, the industry that the correlation between the electricity consumption and the discharge meets the first preset condition is determined by adopting the following manner:
determining a correlation coefficient between the electricity consumption and the pollution emission of each industry;
screening out the correlation coefficient between the electricity consumption and the pollution emission which meet a second preset condition from the correlation coefficients between the electricity consumption and the pollution emission in each industry;
and taking the industry corresponding to the correlation coefficient between the electricity consumption and the pollution discharge which meet the second preset condition as the industry of which the correlation between the electricity consumption and the pollution discharge meets the first preset condition.
Optionally, for industries in which the correlation between each of the electricity consumption and the pollutant emission amount satisfies the first preset condition, determining a correlation coefficient between the electricity consumption and the pollutant emission amount of the industries includes:
acquiring the historical power consumption of enterprises, the historical pollution emission of the enterprises and the enterprise quantity of the industries;
and determining a correlation coefficient between the electricity consumption of the industry and the pollution emission based on the historical electricity consumption of the industry, the historical pollution emission of the industry and the enterprise number of the industry.
Optionally, the correlation between the electricity consumption and the emission of the target industry is obtained by adopting the following modes:
and determining a regression equation between the electricity consumption and the pollution emission of the target industry, wherein the regression equation characterizes the association relationship between the electricity consumption and the pollution emission of the target industry.
Optionally, the determining a regression equation between the electricity consumption and the pollutant emission of the target industry includes:
acquiring the historical enterprise electricity consumption and the historical enterprise pollution emission of the target industry;
determining slope regression parameters and constant regression parameters of the target industry based on the enterprise historical electricity consumption of the target industry and the enterprise historical pollution emission of the target industry;
and determining a regression equation between the electricity consumption and the pollutant discharge of the target industry based on the slope regression parameter of the target industry and the constant regression parameter of the target industry.
A power usage monitoring index determination device, comprising:
the first determining unit is used for determining a target industry, and the target industry belongs to an industry in which the correlation between the electricity consumption and the emission meets a first preset condition;
the acquisition unit is used for acquiring the electricity consumption of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension in a certain time period of the target industry;
the prediction unit is used for predicting the pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the association relation between the electricity consumption and the emission of the target industry;
and the second determining unit is used for determining an electricity consumption monitoring index based on the pollution discharge amount of the preset statistical dimension of the target industry and the maximum value of the pollution discharge amount of the preset statistical dimension in a certain time period of the target industry.
Optionally, the industry that the correlation between the electricity consumption and the discharge meets the first preset condition is determined by adopting the following manner:
a first determining subunit, configured to determine a correlation coefficient between the electricity consumption and the pollutant emission of each industry;
a screening unit, configured to screen out a correlation coefficient between the electricity consumption and the pollutant discharge amount that meets a second preset condition from the correlation coefficients between the electricity consumption and the pollutant discharge amounts in the industries;
and the second determination subunit is used for taking the industry corresponding to the correlation coefficient between the electricity consumption and the pollution discharge meeting the second preset condition as the industry of which the correlation between the electricity consumption and the pollution discharge meets the first preset condition.
Optionally, for industries in which the correlation between each of the electricity consumption and the pollutant emission amount satisfies the first preset condition, determining a correlation coefficient between the electricity consumption and the pollutant emission amount of the industries includes:
the acquisition subunit is used for acquiring the enterprise historical electricity consumption, the enterprise historical pollution emission and the enterprise number of the industry;
and a third determining subunit, configured to determine a correlation coefficient between the electricity consumption of the industry and the pollutant discharge amount based on the historical electricity consumption of the industry, the historical pollutant discharge amount of the industry and the number of enterprises of the industry.
A power usage monitoring index determining device comprising a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement each step of the method for determining an electricity consumption monitoring index according to any one of the above.
A readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, performs the steps of the method of determining an electricity usage monitoring index as described in any of the preceding claims.
Based on the technical scheme, the method, the device, the equipment and the readable storage medium for determining the electricity consumption monitoring index provided by the invention have the advantages that firstly, the target industry is determined, the correlation between the electricity consumption and the emission is satisfied, the electricity consumption of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension in a certain time period of the target industry are obtained, the pollution emission of the preset statistical dimension of the target industry is predicted based on the electricity consumption of the preset statistical dimension of the target industry and the correlation between the electricity consumption and the emission of the target industry, the electricity consumption and the pollutant emission are correlated, the correlation between the electricity consumption and the pollutant emission is determined, the electricity consumption monitoring index is determined based on the pollution emission of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension in a certain time period of the target industry, the electricity consumption monitoring index is obtained through multi-dimensional sample data, the severity of the pollution emission can be judged through the size of the electricity consumption monitoring index, the environment quality can be predicted accurately, and pollution control countermeasures can be better and implemented.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a schematic flow chart of a method for determining an electricity consumption monitoring index according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for determining industries in which a correlation between electricity consumption and emissions meets a first preset condition according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for determining a correlation coefficient between electricity consumption and pollutant emissions for industries for which the correlation between electricity consumption and pollutant emissions meets a first preset condition, according to an embodiment of the present invention;
FIG. 4 is a process schematic diagram of a method of determining a regression equation between electricity usage and pollutant emissions for a target industry according to an embodiment of the present invention;
FIG. 5 is a script screenshot of determining slope regression parameters and constant regression parameters for a target industry according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a device for determining electricity consumption monitoring index according to an embodiment of the present invention;
fig. 7 is a block diagram of a hardware structure of a power consumption monitoring index determining device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely illustrative of the manner in which embodiments of the invention have been described in connection with the description of the objects having the same attributes. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
In order to accurately predict the environmental quality, the invention provides a method for determining the electricity consumption monitoring index, and the method for determining the electricity consumption monitoring index provided by the invention is further described in detail below with reference to the accompanying drawings and the detailed description.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for determining an electricity consumption monitoring index according to an embodiment of the present invention. The method may comprise the steps of:
step S101: and determining a target industry, wherein the target industry belongs to an industry of which the correlation between the electricity consumption and the emission meets a first preset condition.
In the present application, the correlation between the amount of electricity used and the amount of discharged electricity can be obtained by the pearson coefficient method, and the specific implementation will be described in detail by examples later, which will not be described here.
Step S102: and acquiring the electricity consumption of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension in a certain time period of the target industry.
In the present application, the preset statistical dimension may be one day or one week, and the period of time may be in the last year.
Step S103: and predicting the pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the association relation between the electricity consumption and the emission of the target industry.
In the present application, the pollution emission amount of the preset statistical dimension of the target industry can be predicted based on the electricity consumption of the preset statistical dimension of the target industry by a linear regression method, and specific implementation manner will be described in detail by the following examples, which will not be described here.
Step S104: and determining the electricity consumption monitoring index based on the pollution emission amount of the preset statistical dimension of the target industry and the maximum value of the pollution emission amount of the preset statistical dimension in a certain time period of the target industry.
In the application, the pollution emission of the preset statistical dimension of the target industry can be converted into the electricity consumption monitoring index in the interval of 0 to 100 points through the intelligent index conversion model based on the pollution emission of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension of the target industry in a certain time period, wherein the larger the electricity consumption monitoring index is, the higher the potential pollution risk is represented.
In summary, the method for determining the electricity consumption monitoring index provided by the invention includes the steps of firstly determining a target industry, wherein the target industry belongs to an industry in which the correlation between electricity consumption and emission meets a first preset condition, obtaining the maximum value of the electricity consumption of a preset statistical dimension of the target industry and the pollution emission of the preset statistical dimension in a certain time period of the target industry, predicting the pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the correlation between the electricity consumption and the emission of the target industry, correlating the electricity consumption with the pollutant emission, determining the correlation between the electricity consumption and the pollutant emission, determining the electricity consumption monitoring index based on the pollution emission of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension in a certain time period of the target industry, wherein the electricity consumption monitoring index is obtained through multi-dimensional sample data, and judging the severity of the pollution emission through the size of the electricity consumption monitoring index, thereby being beneficial to accurately predicting the environmental quality, and making and implementing pollution control countermeasures better.
On the basis of the above-described embodiments of the present invention, in still another embodiment of the present invention, a specific implementation of an industry in which the correlation between the amount of electricity consumption and the amount of discharge is determined to satisfy the first preset condition is described in detail.
As an embodiment, please refer to fig. 2, which is a schematic flow chart of a method for determining an industry in which a correlation between a power consumption and an emission meets a first preset condition. The method may comprise the steps of:
step S201: and determining a correlation coefficient between the electricity consumption and the pollution emission of each industry.
It should be noted that the correlation coefficient may indicate whether there is an obvious linear correlation between the amount of electricity used and the amount of pollutant emissions.
For easy understanding, please refer to tables 1 and 2, table 1 is the electricity consumption and SO for each industry 2 Correlation coefficient between emissions, table 2 shows electricity consumption and NO for each industry 2 Correlation coefficient between the discharge amounts.
TABLE 1
TABLE 2
Step S202: and screening out the correlation coefficient between the electricity consumption and the pollutant discharge amount meeting the second preset condition from the correlation coefficients between the electricity consumption and the pollutant discharge amount of various industries.
In this application, the second preset condition may be that the correlation coefficient is greater than a preset threshold, where the preset threshold may be 0.5.
From tables 1 and 2 above, the electricity consumption and SO can be obtained 2 Industries with the related coefficient of the discharge amount larger than 0.5 are iron making, special chemical product manufacturing, building material manufacturing such as bricks, tiles and stones, and chemical drug manufacturing; electricity consumption and NO x Industries with the related coefficient of the discharge amount larger than 0.5 are manufacturing building materials such as iron making, bricks, tiles, stones and the like.
Step S203: and taking the industry corresponding to the correlation coefficient between the electricity consumption and the pollution discharge amount meeting the second preset condition as the industry of which the correlation between the electricity consumption and the pollution discharge amount meets the first preset condition.
In the present application, the industry corresponding to the correlation coefficient between the amount of electricity used and the amount of pollutant discharge that satisfies the second preset condition is the industry of strong linear correlation.
In summary, the method for determining industries in which the correlation between the electricity consumption and the emission meets the first preset condition has strong linear correlation, and is favorable for further linear regression analysis.
On the basis of the above-described embodiments of the present invention, in still another embodiment of the present invention, a specific implementation manner of determining a correlation coefficient between electricity consumption and pollutant discharge amount of an industry is described in detail for industries in which the correlation between electricity consumption and pollutant discharge amount of each industry satisfies a first preset condition.
As an embodiment, referring to fig. 3, a flow chart of a method for determining a correlation coefficient between electricity consumption and pollutant emission of an industry for each industry for which the correlation between electricity consumption and pollutant emission meets a first preset condition is disclosed. The method may comprise the steps of:
step S301: the method comprises the steps of obtaining enterprise historical electricity consumption, enterprise historical pollution emission and enterprise quantity of industries.
In the application, the historical electricity consumption of enterprises in the industry is represented by x, the historical pollutant discharge amount of the enterprises is represented by y, and the number of the enterprises is represented by N.
Step S302: and determining a correlation coefficient between the electricity consumption of the industry and the pollution emission based on the historical electricity consumption of the industry, the historical pollution emission of the industry and the number of the industry enterprises.
In the present application, by
And determining a correlation coefficient r between the electricity consumption and the pollution emission of the industry.
On the basis of the above-described embodiments of the present invention, in still another embodiment of the present invention, a specific implementation manner of determining the association between the electricity consumption and the discharge amount of the target industry is described in detail.
As an implementation manner, a regression equation between the electricity consumption and the pollutant discharge amount of the target industry can be determined, and the regression equation characterizes the association relationship between the electricity consumption and the pollutant discharge amount of the target industry.
In this application, the form of the regression equation is as follows:
Y＝βX+C
wherein X represents the electricity consumption of the target industry, Y represents the emission of the target industry, a beta slope regression parameter and a C constant regression parameter.
On the basis of the above disclosed embodiment of the present invention, in still another embodiment of the present invention, a specific implementation of a regression equation between the electricity consumption and the pollutant discharge amount of the determined target industry is described in detail.
Referring to fig. 4, a flow chart of a method for determining a regression equation between electricity consumption and pollutant emissions in a target industry according to the present disclosure is shown. The method may comprise the steps of:
step S401: and acquiring the historical power consumption and the historical pollution emission of the enterprise in the target industry.
Step S402: and determining slope regression parameters and constant regression parameters of the target industry based on the enterprise historical electricity consumption of the target industry and the enterprise historical pollution discharge of the target industry.
As an embodiment, taking the iron-making industry as an example, the electricity consumption and SO of the industry 2 Referring to fig. 5, a script screenshot for determining a slope regression parameter and a constant regression parameter of a target industry is shown, where the slope of the regression coefficient is 0.000789632 and the constant term is 141.190779 calculated by the historical power consumption of the enterprise and the historical pollutant emission of the enterprise in the iron-making industry.
For ease of understanding, please refer to tables 3 and 4, table 3 is the electricity consumption and SO for the target industry 2 Correlation coefficient between emissions, table 4 shows electricity consumption and NO for the target industry 2 Correlation coefficient between the discharge amounts.
TABLE 3 Table 3
TABLE 4 Table 4
Step S403: and determining a regression equation between the electricity consumption and the pollution emission of the target industry based on the slope regression parameter of the target industry and the constant regression parameter of the target industry.
In the present application, the regression equation between the electricity consumption and the pollutant emissions of the target industry is: predicted pollutant emission = slope regression parameter x power usage + constant regression parameter.
As an embodiment, taking the iron-making industry as an example, the electricity consumption and SO of the industry 2 Discharge ofThe correlation coefficient of the quantity is 0.844785561, the gradient of the regression coefficient is 0.000789632, and the constant term is 141.190779, so the regression equation between the electricity consumption and the pollutant discharge amount of the target industry is: predicting SO 2 Emission = 0.000789632 x electricity usage +141.190779.
The method is described in detail in the embodiments disclosed in the invention, and the method can be implemented by adopting various devices, so that the invention also discloses a device for determining the electricity consumption monitoring index, and specific embodiments are given below for details.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a determining device for electricity consumption monitoring index according to an embodiment of the present application, where the determining device includes:
a first determining unit 11 for determining a target industry belonging to an industry in which a correlation between the amount of electricity consumption and the amount of discharge satisfies a first preset condition.
And the obtaining unit 12 is used for obtaining the electricity consumption of the preset statistical dimension of the target industry and the maximum value of the pollutant discharge of the preset statistical dimension in a certain time period of the target industry.
And the prediction unit 13 is used for predicting the pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the association relation between the electricity consumption and the emission of the target industry.
A second determining unit 14, configured to determine an electricity consumption monitoring index based on the pollution emission amount of the preset statistical dimension of the target industry and the maximum value of the pollution emission amount of the preset statistical dimension in a certain period of time of the target industry.
As an embodiment, the industry in which the correlation between the electricity consumption amount and the discharge amount satisfies the first preset condition is determined in the following manner:
a first determination subunit for determining a correlation coefficient between the electricity consumption and the pollutant discharge amount of each industry.
And the screening unit is used for screening out the correlation coefficient between the electricity consumption and the pollution emission which meet the second preset condition from the correlation coefficient between the electricity consumption and the pollution emission in each industry.
And the second determination subunit is used for taking the industry corresponding to the correlation coefficient between the electricity consumption and the pollution discharge meeting the second preset condition as the industry of which the correlation between the electricity consumption and the pollution discharge meets the first preset condition.
As one embodiment, for industries for which the correlation between each of the electricity consumption amount and the pollutant discharge amount satisfies a first preset condition, determining a correlation coefficient between the electricity consumption amount and the pollutant discharge amount of the industries includes:
and the acquisition subunit is used for acquiring the enterprise historical electricity consumption, the enterprise historical pollution emission and the enterprise number of the industry.
And a third determining subunit, configured to determine a correlation coefficient between the electricity consumption of the industry and the pollutant discharge amount based on the historical electricity consumption of the industry, the historical pollutant discharge amount of the industry and the number of enterprises of the industry.
As an implementation manner, the association relationship between the electricity consumption and the emission of the target industry is obtained by adopting the following manner:
and determining a regression equation between the electricity consumption and the pollution emission of the target industry, wherein the regression equation characterizes the association relationship between the electricity consumption and the pollution emission of the target industry.
As an embodiment, the determining a regression equation between the electricity consumption and the pollutant emissions of the target industry includes:
and acquiring the historical enterprise electricity consumption and the historical enterprise pollution emission of the target industry.
And determining slope regression parameters and constant regression parameters of the target industry based on the enterprise historical electricity consumption of the target industry and the enterprise historical pollution emission of the target industry.
And determining a regression equation between the electricity consumption and the pollutant discharge of the target industry based on the slope regression parameter of the target industry and the constant regression parameter of the target industry.
Referring to fig. 6, fig. 6 is a hardware structure block diagram of an apparatus for determining an electricity consumption monitoring index according to an embodiment of the present application, and the hardware structure of the apparatus for determining an electricity consumption monitoring index may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.
In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 complete communication with each other through the communication bus 4.
The processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention, etc.
The memory 3 may comprise a high-speed RAM memory, and may also comprise a non-volatile memory (non-volatile memory) or the like, such as at least one disk memory.
Wherein the memory stores a program, the processor is operable to invoke the program stored in the memory, the program operable to:
and determining a target industry, wherein the target industry belongs to an industry in which the correlation between the electricity consumption and the emission meets a first preset condition.
And acquiring the electricity consumption of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension in a certain time period of the target industry.
And predicting the pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the association relation between the electricity consumption and the emission of the target industry.
And determining an electricity consumption monitoring index based on the pollution emission amount of the preset statistical dimension of the target industry and the maximum value of the pollution emission amount of the preset statistical dimension in a certain time period of the target industry.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
The embodiment of the application also provides a readable storage medium, which can store a program suitable for being executed by a processor, the program being configured to:
and determining a target industry, wherein the target industry belongs to an industry in which the correlation between the electricity consumption and the emission meets a first preset condition.
And acquiring the electricity consumption of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension in a certain time period of the target industry.
And predicting the pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the association relation between the electricity consumption and the emission of the target industry.
And determining an electricity consumption monitoring index based on the pollution emission amount of the preset statistical dimension of the target industry and the maximum value of the pollution emission amount of the preset statistical dimension in a certain time period of the target industry.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
It should be further noted that the above-described apparatus embodiments are merely illustrative, and that the units described as separate units may or may not be physically separate, and that units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present invention may be implemented by means of software plus necessary general purpose hardware, or of course by means of special purpose hardware including application specific integrated circuits, special purpose CPUs, special purpose memories, special purpose components, etc. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions can be varied, such as analog circuits, digital circuits, or dedicated circuits. However, a software program implementation is a preferred embodiment for many more of the cases of the present invention. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random-access Memory (RAM, random Access Memory), a magnetic disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method according to the embodiments of the present invention.
In summary, the above embodiments are only for illustrating the technical solution of the present invention, and are not limited thereto. Although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the above embodiments can be modified or some of the technical features can be replaced equivalently. Such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of determining an electricity usage monitoring index, comprising:
determining a target industry, wherein the target industry belongs to an industry of which the correlation between the electricity consumption and the emission meets a first preset condition;
acquiring the electricity consumption of a preset statistical dimension of the target industry and the maximum value of pollution emission of the preset statistical dimension in a certain time period of the target industry;
predicting pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the association relation between the electricity consumption and emission of the target industry;
and determining an electricity consumption monitoring index based on the pollution emission amount of the preset statistical dimension of the target industry and the maximum value of the pollution emission amount of the preset statistical dimension in a certain time period of the target industry.
2. The method according to claim 1, wherein the industry in which the correlation between the amount of electricity used and the amount of discharged electricity satisfies the first preset condition is determined by:
determining a correlation coefficient between the electricity consumption and the pollution emission of each industry;
screening out the correlation coefficient between the electricity consumption and the pollution emission which meet a second preset condition from the correlation coefficients between the electricity consumption and the pollution emission in each industry;
and taking the industry corresponding to the correlation coefficient between the electricity consumption and the pollution discharge which meet the second preset condition as the industry of which the correlation between the electricity consumption and the pollution discharge meets the first preset condition.
3. The method of claim 2, wherein determining a correlation coefficient between electricity usage and pollutant emissions for industries for which a correlation between electricity usage and pollutant emissions satisfies a first preset condition for each industry comprises:
acquiring the historical power consumption of enterprises, the historical pollution emission of the enterprises and the enterprise quantity of the industries;
and determining a correlation coefficient between the electricity consumption of the industry and the pollution emission based on the historical electricity consumption of the industry, the historical pollution emission of the industry and the enterprise number of the industry.
4. The method of claim 1, wherein the correlation between the electricity consumption and the emission of the target industry is obtained by:
and determining a regression equation between the electricity consumption and the pollution emission of the target industry, wherein the regression equation characterizes the association relationship between the electricity consumption and the pollution emission of the target industry.
5. The method of claim 4, wherein the determining a regression equation between electricity usage and pollutant emissions for the target industry comprises:
acquiring the historical enterprise electricity consumption and the historical enterprise pollution emission of the target industry;
determining slope regression parameters and constant regression parameters of the target industry based on the enterprise historical electricity consumption of the target industry and the enterprise historical pollution emission of the target industry;
and determining a regression equation between the electricity consumption and the pollutant discharge of the target industry based on the slope regression parameter of the target industry and the constant regression parameter of the target industry.
6. A device for determining an electricity usage monitoring index, comprising:
the first determining unit is used for determining a target industry, and the target industry belongs to an industry in which the correlation between the electricity consumption and the emission meets a first preset condition;
the acquisition unit is used for acquiring the electricity consumption of the preset statistical dimension of the target industry and the maximum value of the pollution emission of the preset statistical dimension in a certain time period of the target industry;
the prediction unit is used for predicting the pollution emission of the preset statistical dimension of the target industry based on the electricity consumption of the preset statistical dimension of the target industry and the association relation between the electricity consumption and the emission of the target industry;
and the second determining unit is used for determining an electricity consumption monitoring index based on the pollution discharge amount of the preset statistical dimension of the target industry and the maximum value of the pollution discharge amount of the preset statistical dimension in a certain time period of the target industry.
7. The apparatus of claim 6, wherein the industry in which the correlation between the amount of electricity used and the amount of discharged electricity satisfies the first preset condition is determined by:
a first determining subunit, configured to determine a correlation coefficient between the electricity consumption and the pollutant emission of each industry;
a screening unit, configured to screen out a correlation coefficient between the electricity consumption and the pollutant discharge amount that meets a second preset condition from the correlation coefficients between the electricity consumption and the pollutant discharge amounts in the industries;
and the second determination subunit is used for taking the industry corresponding to the correlation coefficient between the electricity consumption and the pollution discharge meeting the second preset condition as the industry of which the correlation between the electricity consumption and the pollution discharge meets the first preset condition.
8. The apparatus of claim 7, wherein determining a correlation coefficient between electricity consumption and pollutant emissions for industries for which a correlation between electricity consumption and pollutant emissions for each industry satisfies a first preset condition comprises:
the acquisition subunit is used for acquiring the enterprise historical electricity consumption, the enterprise historical pollution emission and the enterprise number of the industry;
and a third determining subunit, configured to determine a correlation coefficient between the electricity consumption of the industry and the pollutant discharge amount based on the historical electricity consumption of the industry, the historical pollutant discharge amount of the industry and the number of enterprises of the industry.
9. A power usage monitoring index determining device, comprising a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the respective steps of the electricity usage monitoring index determination method according to any one of claims 1 to 5.
10. A readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of the method of determining an electricity usage monitoring index according to any one of claims 1 to 5.
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| CN117609926A (en) * | 2024-01-23 | 2024-02-27 | 中科三清科技有限公司 | Pollution discharge mechanism production state determining method and device based on power data |
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| CN117609926A (en) * | 2024-01-23 | 2024-02-27 | 中科三清科技有限公司 | Pollution discharge mechanism production state determining method and device based on power data |
| CN117609926B (en) * | 2024-01-23 | 2024-04-16 | 中科三清科技有限公司 | Pollution discharge mechanism production state determining method and device based on power data |
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