CN112948524A - Intelligent electric meter operation area grouping method and system based on environment and geographic characteristics - Google Patents
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Abstract
The invention discloses a method, a system, a terminal and a storage medium for grouping operation areas of an intelligent electric meter based on environmental and geographic characteristics, wherein the method comprises the following steps: acquiring historical meteorological data of at least 1 typical position in each ground-level administrative district in an area to be grouped; acquiring geographic information of each ground-level administrative area in an area to be grouped; merging each ground level administrative region and the corresponding historical meteorological data and geographic information thereof into a grouped element table, and carrying out normalization processing on each element; selecting K geopolitical regions as initial clustering centers of K region groups according to the number K of the region groups to be divided, wherein K is a positive integer greater than or equal to 1; and grouping the operating areas of the intelligent electric energy meters according to the grouping element meter by adopting a K-means algorithm. The invention can take the meteorological data and the geographic characteristics related to the environmental stress type as the elements of the area grouping, and objectively and quickly group the target area according to the selected elements.
Description
Technical Field
The invention relates to the technical field of intelligent electric meter operation grouping, in particular to an intelligent electric meter operation area grouping method, system, terminal and storage medium based on environment and geographic characteristics.
Background
The intelligent electric energy meters installed and used in different regions may have greatly different failure rate levels due to different geographical features and environmental conditions, and the main failure modes and failure mechanisms may also be different. In order to facilitate the electric energy meter operation management unit to identify the influence that environment and geographic difference of different regions may bring on the electric energy meter operation reliability, the operation areas of the intelligent electric meter need to be grouped.
Currently, the existing grouping methods are generally grouped according to climate zones or according to a single environmental condition in combination with geographical location characteristics, such as according to frigid zones, temperate zones, subtropical zones, or according to temperature conditions, north-south locations, or altitudes. However, grouping according to climate zones or according to a single environmental condition in combination with geographic location characteristics is not fine enough to reflect climate differences in local-level areas. And grouping is carried out according to a single environmental condition, so that the difference of the influence of the comprehensive environmental conditions in different areas on the operation of the electric energy meter cannot be reflected.
Disclosure of Invention
The purpose of the invention is: provided are a smart meter operation region grouping method, system, terminal and storage medium based on environmental and geographic characteristics, which can take meteorological data and geographic characteristics related to environmental stress types as elements of region grouping. And objectively and quickly grouping the target areas according to the selected elements.
In order to achieve the above object, the present invention provides a method for grouping operation regions of a smart meter based on environmental and geographic characteristics, comprising:
s1, acquiring historical meteorological data of at least 1 typical position in each ground-level administrative district in the area to be grouped;
s2, acquiring geographic information of each ground-level administrative district in the area to be grouped;
s3, combining each geographical administrative district and the corresponding historical meteorological data and geographic information thereof into a grouped element table, and carrying out normalization processing on each element;
s4, selecting K geopolitical regions as initial clustering centers of K region groups according to the number K of the region groups to be divided, wherein K is a positive integer greater than or equal to 1;
and S5, grouping the intelligent electric energy meter operation areas according to the grouping element meter by adopting a K-means algorithm.
Further, the historical meteorological data includes: the annual average temperature, the annual average relative humidity, the annual average annual precipitation, the annual temperature annual difference and the annual average temperature annual difference.
Further, the obtaining of the geographic information of each ground-level administrative district in the area to be grouped specifically includes:
the geographical information of each ground-level administrative district in the to-be-grouped area is obtained, the ground-level administrative district with the coastline is marked as '1', and the ground-level administrative district without the coastline is marked as '0'.
Further, the normalization processing specifically adopts the following formula:
in the formula (I), the compound is shown in the specification,
is the original value of the element, xmaxj、xminjAre respectively j-th elementsMaximum and minimum values of, xijIs an index value after normalization processing.
Further, according to the number K of the area groups to be divided, K surface administrative areas are selected as initial clustering centers of the K area groups, and the specific selection rule is as follows:
selecting two types of land-level administrative regions including a coastal region and an inland region; selecting two geographical administrative areas comprising the south most end and the north most end; and selecting a land-level administrative district containing significant differences of topographic features.
Further, the grouping of the intelligent electric energy meter operation areas according to the grouping element table by adopting a K-means algorithm specifically comprises the following steps:
s51, after the initial clustering centers are selected, calculating the distance between the remaining geographical administrative regions and each initial clustering center, and regulating the distance to the nearest region group;
s52, calculating the mass center of each zone group, and calculating a clustering quality objective function;
s53, replacing the initial clustering center by the centroid of each region group, and repeatedly executing the steps S51-S53 until the clustering quality objective function converges.
Further, the centroid of each region group is calculated, and the clustering quality objective function is calculated, specifically using the following calculation formula:
in the formula, miThe sample mean value of the ith area cluster is taken as the sample mean value of the ith area cluster; ciA set of samples for the ith cluster; n is a radical ofiTotal number of samples for the ith cluster; e is a clustering quality objective function; and x is a sample point of the administrative district in the ground level.
The invention also provides an intelligent electric meter operation area grouping system based on environmental and geographic characteristics, which comprises the following steps: a meteorological data module, a geographic information module, a normalization processing module, a selection module and a grouping module, wherein,
the meteorological data module is used for acquiring historical meteorological data of at least 1 typical position in each ground-level administrative district in the area to be grouped;
the geographic information module is used for acquiring geographic information of each ground-level administrative area in the area to be grouped;
the normalization processing module is used for combining each ground-level administrative region and the corresponding historical meteorological data and geographic information thereof into a grouping element table and carrying out normalization processing on each element;
the selection module is used for selecting K geopolitical regions as initial clustering centers of K region groups according to the number K of the region groups to be divided, wherein K is a positive integer greater than or equal to 1;
and the grouping module is used for grouping the operating areas of the intelligent electric energy meters according to the grouping element meter by adopting a K-means algorithm.
The present invention also provides a computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, the one or more programs cause the one or more processors to implement the environment and geographic characteristic-based smart meter operation region grouping method as any one of the above.
The invention also provides a computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for grouping the operating areas of the smart meters based on the environmental and geographic characteristics as described in any one of the above.
Compared with the prior art, the intelligent electric meter operation area grouping method, the intelligent electric meter operation area grouping system, the intelligent electric meter operation area grouping terminal and the intelligent electric meter operation area grouping storage medium based on the environment and the geographic characteristics have the beneficial effects that:
according to the method, on the basis of the environmental stress types (temperature, humidity, temperature change, thunder and lightning and salt fog) to which the intelligent electric energy meter is sensitive, meteorological data and geographic characteristics related to the environmental stress types are selected to serve as the elements of area grouping, and the target areas can be objectively and quickly grouped according to the selected elements.
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Fig. 1 is a schematic flowchart of a method for grouping operation areas of a smart meter based on environmental and geographic characteristics according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a smart meter operation area grouping system based on environmental and geographic characteristics according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, the method for grouping the operating areas of the smart meters based on the environmental and geographic characteristics according to the present invention includes:
s1, acquiring historical meteorological data of at least 1 typical position in each ground-level administrative district in the area to be grouped;
specifically, historical meteorological data of at least 1 typical position (a basic meteorological station in each regional administrative district can be selected preferentially) in each regional administrative district in the area to be grouped is collected for not less than the last ten years;
it should be noted that the historical meteorological data includes: the annual average temperature, the annual average relative humidity, the annual average annual precipitation, the annual temperature annual difference and the annual average temperature annual difference.
Specifically, the annual average air temperature refers to: average value of average temperature of each year in the collected historical meteorological data.
The year of the year average relative humidity refers to: average value of relative humidity in each year in the collected historical meteorological data.
The annual average annual precipitation is as follows: average annual precipitation per year of the historical meteorological data collected.
The annual temperature is worse and means that: average value of annual temperature difference in collected historical meteorological data. The annual poor temperature refers to the difference between the average temperature of the highest month and the average temperature of the lowest month in a year.
The annual average daily temperature difference refers to: average air temperature of each year is a poor average value in the collected historical meteorological data. The difference of the temperature day is also called the amplitude of the temperature day, which is the difference between the highest value and the lowest value of the temperature in a day.
S2, acquiring geographic information of each ground-level administrative district in the area to be grouped;
specifically, geographic information of each ground-level administrative district in the area to be grouped is obtained, the ground-level administrative district with the coastline is marked as '1', and the ground-level administrative district without the coastline is marked as '0'.
S3, combining each geographical administrative district and the corresponding historical meteorological data and geographic information thereof into a grouped element table, and carrying out normalization processing on each element;
specifically, the normalization processing specifically adopts the following formula:
in the formula (I), the compound is shown in the specification,
is the original value of the element, xmaxj、xminjMaximum and minimum values, x, of the j-th class of elements, respectivelyijIs an index value after normalization processing.
S4, selecting K geopolitical regions as initial clustering centers of K region groups according to the number K of the region groups to be divided, wherein K is a positive integer greater than or equal to 1;
specifically, the selection principle of the initial clustering center is as follows:
selecting two types of land-level administrative regions including a coastal region and an inland region; selecting two geographical administrative areas comprising the south most end and the north most end; land-level administrative areas (e.g., mountains, hills, and plains) containing significant differences in topographical features are selected.
And S5, grouping the intelligent electric energy meter operation areas according to the grouping element meter by adopting a K-means algorithm.
Specifically, step S5 includes the steps of:
s51, after the initial clustering centers are selected, calculating the distance between the remaining geographical administrative regions and each initial clustering center, and regulating the distance to the nearest region group;
s52, calculating the mass center of each zone group, and calculating a clustering quality objective function;
s53, replacing the initial clustering center by the centroid of each region group, and repeatedly executing the steps S51-S53 until the clustering quality objective function converges.
In one embodiment of the present invention, the historical meteorological data includes: the annual average temperature, the annual average relative humidity, the annual average annual precipitation, the annual temperature annual difference and the annual average temperature annual difference.
In an embodiment of the present invention, the obtaining geographic information of each geo-administrative area in the area to be grouped specifically includes:
the geographical information of each ground-level administrative district in the to-be-grouped area is obtained, the ground-level administrative district with the coastline is marked as '1', and the ground-level administrative district without the coastline is marked as '0'.
In an embodiment of the present invention, the normalization processing specifically adopts the following formula:
in the formula (I), the compound is shown in the specification,
is the original value of the element, xmaxj、xminjMaximum and minimum values, x, of the j-th class of elements, respectivelyijIs an index value after normalization processing.
In an embodiment of the present invention, the K geodetic regions are selected as initial clustering centers of the K regional groups according to the number K of the regional groups to be divided, and the specific selection rule is as follows:
selecting two types of land-level administrative regions including a coastal region and an inland region; selecting two geographical administrative areas comprising the south most end and the north most end; and selecting a land-level administrative district containing significant differences of topographic features.
In an embodiment of the present invention, the grouping of the operation areas of the intelligent electric energy meter according to the grouping element table by using a K-means algorithm specifically includes:
s51, after the initial clustering centers are selected, calculating the distance between the remaining geographical administrative regions and each initial clustering center, and regulating the distance to the nearest region group;
s52, calculating the mass center of each zone group, and calculating a clustering quality objective function;
s53, replacing the initial clustering center by the centroid of each region group, and repeatedly executing the steps S51-S53 until the clustering quality objective function converges.
In one embodiment of the present invention, the centroid of each region group is calculated, and the clustering quality objective function is calculated by using the following calculation formula:
in the formula, miThe sample mean value of the ith area cluster is taken as the sample mean value of the ith area cluster; ciA set of samples for the ith cluster; n is a radical ofiTotal number of samples for the ith cluster; e is a clustering quality objective function; and x is a sample point of the administrative district in the ground level.
For a better understanding of the present invention, it can be understood by the following specific examples:
for example: grouping the operation areas of the intelligent electric meters in Guangdong province, and specifically comprising the following steps:
(1) collecting meteorological observation data of the last decade of 1 basic meteorological station in each local administrative district in Guangdong province;
(2) collecting geographic information of each level administrative region;
(3) the meteorological data and the geographic information are combined into a grouping element table (shown in table 1), normalization processing is carried out, and the result after normalization is shown in table 2;
table 1 grouping elements table
TABLE 2 normalized grouping elements Table
(4) Assuming that Guangdong province needs to be divided into 3 regional groups, selecting 3 geographical administrative regions as initial clustering centers according to an initial clustering center selection principle, wherein the selection results are shown in Table 3;
(5) and grouping the operating areas of the intelligent electric energy meter by adopting a K-means algorithm based on the grouping element table, wherein the grouping result is shown in a table 3.
TABLE 3 region grouping results based on initial cluster centers
Compared with the prior art, the intelligent electric meter operation area grouping method based on the environment and the geographic characteristics has the beneficial effects that:
according to the method, on the basis of the environmental stress types (temperature, humidity, temperature change, thunder and lightning and salt fog) to which the intelligent electric energy meter is sensitive, meteorological data and geographic characteristics related to the environmental stress types are selected to serve as the elements of area grouping, and the target areas can be objectively and quickly grouped according to the selected elements.
The invention also provides an intelligent electric meter operation area grouping system 200 based on environmental and geographic characteristics, which comprises: a meteorological data module 201, a geographic information module 202, a normalization processing module 203, a selection module 204, and a grouping module 205, wherein,
the meteorological data module 201 is configured to obtain historical meteorological data of at least 1 typical position in each geographical administrative district in the area to be grouped;
the geographic information module 202 is configured to obtain geographic information of each ground-level administrative area in an area to be grouped;
the normalization processing module 203 is configured to combine each of the geographical administrative areas and the corresponding historical meteorological data and geographic information thereof into a grouped element table, and perform normalization processing on each element;
the selecting module 204 is configured to select K geopolitical regions as initial clustering centers of K region groups according to the number K of the region groups to be divided, where K is a positive integer greater than or equal to 1;
the grouping module 205 is configured to group the operating areas of the intelligent electric energy meters according to the grouping element table by using a K-means algorithm.
Compared with the prior art, the intelligent electric meter operation area grouping system based on the environment and the geographic characteristics has the beneficial effects that:
according to the method, on the basis of the environmental stress types (temperature, humidity, temperature change, thunder and lightning and salt fog) to which the intelligent electric energy meter is sensitive, meteorological data and geographic characteristics related to the environmental stress types are selected to serve as the elements of area grouping, and the target areas can be objectively and quickly grouped according to the selected elements.
The present invention also provides a computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, the one or more programs cause the one or more processors to implement the environment and geographic characteristic-based smart meter operation region grouping method as any one of the above.
It should be noted that the processor may be a Central Processing Unit (CPU), other general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an application-specific programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., the general-purpose processor may be a microprocessor, or the processor may be any conventional processor, the processor is a control center of the terminal device, and various interfaces and lines are used to connect various parts of the terminal device.
The memory mainly includes a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the data storage area may store related data and the like. In addition, the memory may be a high speed random access memory, may also be a non-volatile memory, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (FlashCard), and the like, or may also be other volatile solid state memory devices.
It should be noted that the terminal device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the terminal device is only an example and does not constitute a limitation of the terminal device, and may include more or less components, or combine some components, or different components.
The invention also provides a computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for grouping the operating areas of the smart meters based on the environmental and geographic characteristics as described in any one of the above.
It should be noted that the computer program may be divided into one or more modules/units (e.g., computer program), and the one or more modules/units are stored in the memory and executed by the processor to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.
Claims (10)
1. A method for grouping operation areas of smart meters based on environmental and geographic characteristics is characterized by comprising the following steps:
s1, acquiring historical meteorological data of at least 1 typical position in each ground-level administrative district in the area to be grouped;
s2, acquiring geographic information of each ground-level administrative district in the area to be grouped;
s3, combining each geographical administrative district and the corresponding historical meteorological data and geographic information thereof into a grouped element table, and carrying out normalization processing on each element;
s4, selecting K geopolitical regions as initial clustering centers of K region groups according to the number K of the region groups to be divided, wherein K is a positive integer greater than or equal to 1;
and S5, grouping the intelligent electric energy meter operation areas according to the grouping element meter by adopting a K-means algorithm.
2. The environmental and geographic feature based smart meter operation area grouping method according to claim 1, wherein the historical meteorological data comprises: the annual average temperature, the annual average relative humidity, the annual average annual precipitation, the annual temperature annual difference and the annual average temperature annual difference.
3. The environment and geographic feature based grouping method for smart meter operation areas according to claim 1, wherein the obtaining geographic information of each ground-level administrative district in the area to be grouped specifically comprises:
the geographical information of each ground-level administrative district in the to-be-grouped area is obtained, the ground-level administrative district with the coastline is marked as '1', and the ground-level administrative district without the coastline is marked as '0'.
4. The environment and geographic feature based grouping method for smart meter operation areas as claimed in claim 1, wherein the normalization process specifically employs the following formula:
in the formula (I), the compound is shown in the specification,
is the original value of the element, xmaxj、xminjMaximum and minimum values, x, of the j-th class of elements, respectivelyijIs an index value after normalization processing.
5. The environment and geographic feature based grouping method for smart meter operation areas according to claim 1, wherein K geodetic administrative areas are selected as initial clustering centers of K area groups according to the number K of area groups to be divided, and the specific selection rules are as follows:
selecting two types of land-level administrative regions including a coastal region and an inland region; selecting two geographical administrative areas comprising the south most end and the north most end; and selecting a land-level administrative district containing significant differences of topographic features.
6. The environment and geographic feature based grouping method for the operating areas of the intelligent electric meters according to claim 1, wherein the K-means algorithm is adopted to group the operating areas of the intelligent electric meters according to the grouping element table, and specifically comprises the following steps:
s51, after the initial clustering centers are selected, calculating the distance between the remaining geographical administrative regions and each initial clustering center, and regulating the distance to the nearest region group;
s52, calculating the mass center of each zone group, and calculating a clustering quality objective function;
s53, replacing the initial clustering center by the centroid of each region group, and repeatedly executing the steps S51-S53 until the clustering quality objective function converges.
7. The environment and geographic feature based grouping method for smart meter operation regions as claimed in claim 6, wherein the centroid of each region group is calculated, and the clustering quality objective function is calculated, specifically using the following calculation formula:
in the formula, miThe sample mean value of the ith area cluster is taken as the sample mean value of the ith area cluster; ciA set of samples for the ith cluster; n is a radical ofiTotal number of samples for the ith cluster; e is a clustering quality objective function; and x is a sample point of the administrative district in the ground level.
8. A smart meter operation area grouping system based on environmental and geographic characteristics, comprising: a meteorological data module, a geographic information module, a normalization processing module, a selection module and a grouping module, wherein,
the meteorological data module is used for acquiring historical meteorological data of at least 1 typical position in each ground-level administrative district in the area to be grouped;
the geographic information module is used for acquiring geographic information of each ground-level administrative area in the area to be grouped;
the normalization processing module is used for combining each ground-level administrative region and the corresponding historical meteorological data and geographic information thereof into a grouping element table and carrying out normalization processing on each element;
the selection module is used for selecting K geopolitical regions as initial clustering centers of K region groups according to the number K of the region groups to be divided, wherein K is a positive integer greater than or equal to 1;
and the grouping module is used for grouping the operating areas of the intelligent electric energy meters according to the grouping element meter by adopting a K-means algorithm.
9. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method for grouping smart meter operation regions based on environmental and geographic characteristics of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for grouping operation regions of a smart meter based on environmental and geographic characteristics according to any one of claims 1 to 7.
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