CN112948524B - Intelligent ammeter operation area grouping method and system based on environment and geographic features - Google Patents
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Abstract
The invention discloses a method, a system, a terminal and a storage medium for grouping intelligent ammeter operation areas based on environment and geographic characteristics, wherein the method comprises the following steps: acquiring historical meteorological data of at least 1 typical position in each ground administrative area in an area to be grouped; obtaining geographic information of each ground administrative area in an area to be grouped; combining each ground administrative area and the historical meteorological data and geographic information corresponding to each ground administrative area into a group element list, and carrying out normalization processing on each element; selecting K ground administrative areas as initial clustering centers of K area groups according to the number K of area groups to be divided, wherein K is a positive integer greater than or equal to 1; and adopting a K-means algorithm to group the intelligent electric energy meter operation areas according to the grouping element table. The invention can group the meteorological data and the geographic features related to the environmental stress type as the factors of the regional grouping, and objectively and rapidly group the target regions according to the selected factors.
Description
Technical Field
The invention relates to the technical field of intelligent ammeter operation grouping, in particular to an intelligent ammeter operation region grouping method, system, terminal and storage medium based on environment and geographic characteristics.
Background
The intelligent electric energy meter installed and used in different areas can show great difference in fault rate level due to different geographic characteristics and environmental conditions, and the main fault modes and failure mechanisms can also be different. In order to facilitate the electric energy meter operation management unit to identify the influence possibly caused by the environmental and geographic differences of different regions on the operation reliability of the electric energy meter, the operation regions of the intelligent electric energy meter need to be grouped.
Currently, existing grouping methods typically group by weather zone or by a combination of geographical location characteristics according to a single environmental condition, such as grouping by cold zone, warm zone, subtropical zone, etc., or grouping by temperature condition, north-south location, or altitude. However, grouping is performed according to climate zones or according to single environmental conditions and geographic position characteristics, and the grouping fineness is insufficient, so that the climate difference of the urban area cannot be reflected. And the electric energy meters are grouped according to single environmental conditions, so that the difference of the influence of the comprehensive environmental conditions of different areas on the operation of the electric energy meters can not be reflected.
Disclosure of Invention
The purpose of the invention is that: provided are a method, a system, a terminal and a storage medium for grouping operation areas of a smart meter based on environment and geographic features, wherein meteorological data and geographic features related to environment stress types can be used as elements of area grouping. And objective and rapid grouping of the target areas is performed according to the selected elements.
In order to achieve the above object, the present invention provides a method for grouping operation areas of smart meters based on environmental and geographic features, comprising:
S1, acquiring historical meteorological data of at least 1 typical position in each ground administrative area in an area to be grouped;
S2, obtaining geographic information of each ground administrative area in the area to be grouped;
S3, merging each ground administrative area and the corresponding historical meteorological data and geographic information into a grouping element list, and carrying out normalization processing on each element;
S4, selecting K ground administrative areas as initial clustering centers of K regional groups according to the number K of regional groups required to be divided, wherein K is a positive integer greater than or equal to 1;
S5, adopting a K-means algorithm, and grouping the intelligent electric energy meter operation areas according to the grouping element table.
Further, the historical meteorological data includes: year after year years of average air temperature, year after year years of average relative humidity, year after year years of average precipitation, year after year years of air temperature and year after year years of average air temperature.
Further, the obtaining the geographic information of each ground administrative area in the area to be grouped specifically includes:
and obtaining the geographic information of each ground administrative area in the area to be grouped, and marking the ground administrative area with the coastline as '1', and marking the ground administrative area without the coastline as '0'.
Further, the normalization process specifically adopts the following formula:
In the method, in the process of the invention, As element original values, x maxj、xminj is a maximum value and a minimum value of the j-th element, and x ij is an index value after normalization processing.
Further, the K land administrative areas are selected as initial clustering centers of the K area groups according to the number K of area groups to be divided, and the specific selection rules are as follows:
Selecting two land-level administrative areas including the land and the sea; selecting two ground administrative areas including a southwest end and a northest end; and selecting a ground administrative area containing obvious differences in topographic features.
Further, the method adopts a K-means algorithm to group the intelligent electric energy meter operation areas according to the grouping element table, specifically:
S51, after the initial clustering center is selected, calculating the distance between the rest ground administrative areas and each initial clustering center, and regulating the distance into an area group with the nearest distance;
s52, calculating the mass center of each region group, and calculating a clustering quality objective function;
S53, replacing the initial clustering center with the center of mass of each region group, and repeatedly executing the steps S51-S53 until the clustering quality objective function converges.
Further, the mass center of each region group is calculated, and a clustering quality objective function is calculated, specifically adopting the following calculation formula:
Wherein m i is the sample mean value of the ith regional cluster; c i is the sample set of the ith cluster; n i is the total number of samples for the ith cluster; e is a clustering quality objective function; and x is a sample point of a ground administrative area.
The invention also provides a smart electric meter operation area grouping system based on the environment and the geographic characteristics, which comprises: the system comprises 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 administrative area in the area to be grouped;
The geographic information module is used for acquiring geographic information of each ground administrative area in the area to be grouped;
The normalization processing module is used for merging each ground administrative area and the corresponding historical meteorological data and geographic information into a group element table, and performing normalization processing on each element;
The selecting module is used for selecting K ground administrative areas as initial clustering centers of K area groups according to the number K of area groups to be divided, wherein K is a positive integer greater than or equal to 1;
the grouping module is used for grouping the intelligent electric energy meter operation areas according to the grouping element list by adopting a K-means algorithm.
The invention also provides a computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of smartmeter operational area grouping based on environmental and geographic features as set forth in any one of the preceding claims.
The invention also provides a computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method for grouping operating areas of a smart meter based on environmental and geographical features as described in any one of the above.
Compared with the prior art, the intelligent ammeter operation area grouping method, system, terminal and storage medium based on the environment and the geographic characteristics have the beneficial effects that:
According to the intelligent electric energy meter, based on the sensitive environmental stress types (temperature, humidity, temperature change, thunder and salt fog) of the intelligent electric energy meter, meteorological data and geographic features related to the environmental stress types are selected to serve as elements for regional grouping, and target regions can be objectively and rapidly grouped according to the selected elements.
Drawings
FIG. 1 is a schematic flow chart of a method for grouping operating areas of a smart meter based on environmental and geographic features 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 features 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.
It should be understood that the step numbers used herein are for convenience of description only and are not limiting as to the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification 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 stated 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 any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As shown in fig. 1, the method for grouping the operation areas of the smart electric meter based on the environment and the geographic features of the invention comprises the following steps:
S1, acquiring historical meteorological data of at least 1 typical position in each ground administrative area in an area to be grouped;
Specifically, collecting historical meteorological data of not less than the last decade at least 1 typical position (the basic meteorological station in each district can be selected preferentially) in each district in the district to be grouped;
it should be noted that, the historical meteorological data includes: year after year years of average air temperature, year after year years of average relative humidity, year after year years of average precipitation, year after year years of air temperature and year after year years of average air temperature.
Specifically, the year after year years average air temperature refers to: in the collected historical weather data, average air temperature is averaged over each year.
The year after year year average relative humidity refers to: in the collected historical meteorological data, the average of the relative humidity is averaged over the years.
The year after year average annual precipitation refers to: in the collected historical meteorological data, the average annual precipitation amount is averaged each year.
The year after year air temperature year worse means that: the historical meteorological data collected is an average of the annual worse air temperatures. The poor air temperature year refers to the difference between the average air temperature of the highest month and the average air temperature of the lowest month in one year.
The average temperature daily worse in year after year years is as follows: among the collected historical weather data, the average temperature of each year is a worse average. The day-to-day air temperature difference, also called day-to-day air temperature amplitude, refers to the difference between the highest and lowest air temperatures in a day.
S2, obtaining geographic information of each ground administrative area in the area to be grouped;
Specifically, geographical information of each land administrative area in the area to be grouped is acquired, and the land administrative area having the coastline is marked as "1", and the land administrative area not having the coastline is marked as "0".
S3, merging each ground administrative area and the corresponding historical meteorological data and geographic information into a grouping element list, and carrying out normalization processing on each element;
specifically, the normalization process specifically adopts the following formula:
In the method, in the process of the invention, As element original values, x maxj、xminj is a maximum value and a minimum value of the j-th element, and x ij is an index value after normalization processing.
S4, selecting K ground administrative areas as initial clustering centers of K regional groups according to the number K of regional groups required 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 land-level administrative areas including the land and the sea; selecting two ground administrative areas including a southwest end and a northest end; land-level administrative areas (e.g., mountains, hills, and plains) containing significant differences in topographical features are selected.
S5, adopting a K-means algorithm, and grouping the intelligent electric energy meter operation areas according to the grouping element table.
Specifically, step S5 includes the steps of:
S51, after the initial clustering center is selected, calculating the distance between the rest ground administrative areas and each initial clustering center, and regulating the distance into an area group with the nearest distance;
s52, calculating the mass center of each region group, and calculating a clustering quality objective function;
S53, replacing the initial clustering center with the center of mass 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 comprises: year after year years of average air temperature, year after year years of average relative humidity, year after year years of average precipitation, year after year years of air temperature and year after year years of average air temperature.
In one embodiment of the present invention, the obtaining the geographic information of each ground administrative area in the to-be-grouped area specifically includes:
and obtaining the geographic information of each ground administrative area in the area to be grouped, and marking the ground administrative area with the coastline as '1', and marking the ground administrative area without the coastline as '0'.
In one embodiment of the present invention, the normalization process specifically uses the following formula:
In the method, in the process of the invention, As element original values, x maxj、xminj is a maximum value and a minimum value of the j-th element, and x ij is an index value after normalization processing.
In a certain embodiment of the present invention, the number K of regional groups divided according to the need selects K ground administrative areas as an initial clustering center of K regional groups, and a specific selection rule is as follows:
Selecting two land-level administrative areas including the land and the sea; selecting two ground administrative areas including a southwest end and a northest end; and selecting a ground administrative area containing obvious differences in topographic features.
In one embodiment of the present invention, the method adopts a K-means algorithm to group the operation areas of the intelligent electric energy meter according to the grouping element table, specifically:
S51, after the initial clustering center is selected, calculating the distance between the rest ground administrative areas and each initial clustering center, and regulating the distance into an area group with the nearest distance;
s52, calculating the mass center of each region group, and calculating a clustering quality objective function;
S53, replacing the initial clustering center with the center of mass 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, specifically using the following calculation formula:
Wherein m i is the sample mean value of the ith regional cluster; c i is the sample set of the ith cluster; n i is the total number of samples for the ith cluster; e is a clustering quality objective function; and x is a sample point of a ground administrative area.
For a better understanding of the present invention, it can be understood by the following specific examples:
for example: the intelligent ammeter operation area in Guangdong province is grouped, and the specific steps are as follows:
(1) Collecting meteorological observation data of the last ten years of a basic meteorological station at 1 in each district of the Guangdong province;
(2) Collecting geographic information of each ground administrative area;
(3) Combining the meteorological data and the geographic information into a grouping element table (shown in table 1), carrying out normalization processing, and obtaining a normalized result shown in table 2;
Table 1 grouping element table
Table 2 normalized group element table
(4) Assuming that Guangdong province needs to be divided into 3 regional groups, selecting 3 ground-level administrative areas as initial clustering centers according to an initial clustering center selection principle, wherein the selection result is shown in a table 3;
(5) And (3) grouping the intelligent electric energy meter operation areas based on the grouping element table by adopting a K-means algorithm, wherein the grouping result is shown in a table 3.
TABLE 3 area grouping results based on initial cluster centers
Compared with the prior art, the intelligent ammeter operation area grouping method based on the environment and the geographic characteristics has the beneficial effects that:
According to the intelligent electric energy meter, based on the sensitive environmental stress types (temperature, humidity, temperature change, thunder and salt fog) of the intelligent electric energy meter, meteorological data and geographic features related to the environmental stress types are selected to serve as elements for regional grouping, and target regions can be objectively and rapidly grouped according to the selected elements.
The invention also provides a smart meter operation area grouping system 200 based on environmental and geographic features, comprising: 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 location in each ground administrative area in the area to be grouped;
the geographic information module 202 is configured to obtain geographic information of each ground administrative area in the to-be-grouped area;
The normalization processing module 203 is configured to combine the historical meteorological data and geographic information corresponding to each ground administrative area into a group element table, and perform normalization processing on each element;
The selecting module 204 is configured to select K ground administrative areas as an initial clustering center for K area groupings according to the number K of area 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 operation areas of the intelligent electric energy meter according to the grouping element table by adopting a K-means algorithm.
Compared with the prior art, the intelligent ammeter operation area grouping system based on the environment and the geographic characteristics has the beneficial effects that:
According to the intelligent electric energy meter, based on the sensitive environmental stress types (temperature, humidity, temperature change, thunder and salt fog) of the intelligent electric energy meter, meteorological data and geographic features related to the environmental stress types are selected to serve as elements for regional grouping, and target regions can be objectively and rapidly grouped according to the selected elements.
The invention also provides a computer terminal device, comprising: one or more processors; a memory coupled to the processor for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of smartmeter operational area grouping based on environmental and geographic features as set forth in any one of the preceding claims.
It should be noted that the processor may be a central processing unit (CentralProcessingUnit, CPU), or may be other general purpose processor, digital signal processor (DigitalSignalProcessor, DSP), application specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate array (Field-ProgrammableGateArray, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., and the general purpose processor may be a microprocessor, or the processor may be any conventional processor, which is a control center of the terminal device, and which connects the various parts of the terminal device using various interfaces and lines.
The memory mainly includes a program storage area, which may store an operating system, an application program required for at least one function, and the like, and a data storage area, which may store related data and the like. In addition, the memory may be a high-speed random access memory, a nonvolatile memory such as a plug-in hard disk, a smart memory card (SMARTMEDIACARD, SMC), a secure digital (SecureDigital, SD) card, a flash memory card (FLASHCARD), etc., or the memory may be other volatile solid-state memory devices.
It should be noted that the above-mentioned terminal device may include, but is not limited to, a processor, a memory, and those skilled in the art will understand that the above-mentioned terminal device is merely an example, and does not constitute limitation of the terminal device, and may include more or fewer components, or may combine some components, or different components.
The invention also provides a computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method for grouping operating areas of a smart meter based on environmental and geographical features 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), which are stored in the memory and executed by the processor to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (7)
1. An intelligent ammeter operation area grouping method based on environment and geographic characteristics is characterized by comprising the following steps:
S1, acquiring historical meteorological data of at least 1 typical position in each ground administrative area in an area to be grouped;
S2, obtaining geographic information of each ground administrative area in the area to be grouped;
S3, merging each ground administrative area and the corresponding historical meteorological data and geographic information into a grouping element list, and carrying out normalization processing on each element;
S4, selecting K ground administrative areas as initial clustering centers of K regional groups according to the number K of regional groups required to be divided, wherein K is a positive integer greater than or equal to 1;
S5, adopting a K-means algorithm, and grouping the intelligent electric energy meter operation areas according to the grouping element table;
Wherein the historical meteorological data comprises: year after year years of average air temperature, year after year years of average relative humidity, year after year years of average precipitation, year after year years of air temperature and year after year years of average air temperature; the year after year-year average air temperature is an average value of the average air temperatures of all years in the historical meteorological data; the year after year year average relative humidity is an average of the year average relative humidity in the historical meteorological data; the year after year average annual precipitation is the average value of the average annual precipitation in the historical meteorological data; the year after year air temperature year worse is the average value of the difference between the highest month average air temperature and the lowest month average air temperature of each year in the historical meteorological data; the year after year-year average air temperature daily difference is an average value of differences between a highest air temperature value and a lowest air temperature value in one day of each year in the historical meteorological data;
the obtaining of the geographic information of each ground administrative area in the area to be grouped specifically comprises the following steps:
the method comprises the steps of obtaining geographic information of each ground administrative area in an area to be grouped, marking the ground administrative area with a coastline as '1', and marking the ground administrative area without the coastline as '0';
the K regional groups are divided according to the requirement, K ground administrative areas are selected to serve as initial clustering centers of the K regional groups, and the specific selection rules are as follows:
Selecting two land-level administrative areas including the land and the sea; selecting two ground administrative areas including a southwest end and a northest end; and selecting a ground administrative area containing obvious differences in topographic features.
2. The method for grouping the operating areas of the smart meter based on the environment and the geographic features according to claim 1, wherein the normalization process specifically adopts the following formula:
In the method, in the process of the invention, As element original values, x maxj、xminj is a maximum value and a minimum value of the j-th element, and x ij is an index value after normalization processing.
3. The method for grouping the operation areas of the intelligent ammeter based on the environment and the geographic characteristics according to claim 1, wherein the method for grouping the operation areas of the intelligent ammeter based on the grouping element table is characterized by comprising the following steps:
S51, after the initial clustering center is selected, calculating the distance between the rest ground administrative areas and each initial clustering center, and regulating the distance into an area group with the nearest distance;
s52, calculating the mass center of each region group, and calculating a clustering quality objective function;
S53, replacing the initial clustering center with the center of mass of each region group, and repeatedly executing the steps S51-S53 until the clustering quality objective function converges.
4. The method for grouping the operating areas of the smart meter based on the environment and the geographic features according to claim 3, wherein the calculating of the mass center of each area group and the calculating of the clustering quality objective function specifically adopts the following calculation formula:
Wherein m i is the sample mean value of the ith regional cluster; c i is the sample set of the ith cluster; n i is the total number of samples for the ith cluster; e is a clustering quality objective function; and x is a sample point of a ground administrative area.
5. An intelligent ammeter operating area grouping system based on environmental and geographic features, comprising: the system comprises 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 administrative area in the area to be grouped;
The geographic information module is used for acquiring geographic information of each ground administrative area in the area to be grouped;
The normalization processing module is used for merging each ground administrative area and the corresponding historical meteorological data and geographic information into a group element table, and performing normalization processing on each element;
The selecting module is used for selecting K ground administrative areas as initial clustering centers of K area groups according to the number K of area groups to be divided, wherein K is a positive integer greater than or equal to 1;
the grouping module is used for grouping the intelligent electric energy meter operation areas according to the grouping element table by adopting a K-means algorithm;
Wherein the historical meteorological data comprises: year after year years of average air temperature, year after year years of average relative humidity, year after year years of average precipitation, year after year years of air temperature and year after year years of average air temperature; the year after year-year average air temperature is an average value of the average air temperatures of all years in the historical meteorological data; the year after year year average relative humidity is an average of the year average relative humidity in the historical meteorological data; the year after year average annual precipitation is the average value of the average annual precipitation in the historical meteorological data; the year after year air temperature year worse is the average value of the difference between the highest month average air temperature and the lowest month average air temperature of each year in the historical meteorological data; the year after year-year average air temperature daily difference is an average value of differences between a highest air temperature value and a lowest air temperature value in one day of each year in the historical meteorological data;
the obtaining of the geographic information of each ground administrative area in the area to be grouped specifically comprises the following steps:
the method comprises the steps of obtaining geographic information of each ground administrative area in an area to be grouped, marking the ground administrative area with a coastline as '1', and marking the ground administrative area without the coastline as '0';
the K regional groups are divided according to the requirement, K ground administrative areas are selected to serve as initial clustering centers of the K regional groups, and the specific selection rules are as follows:
Selecting two land-level administrative areas including the land and the sea; selecting two ground administrative areas including a southwest end and a northest end; and selecting a ground administrative area containing obvious differences in topographic features.
6. A computer terminal device, comprising:
One or more processors;
A memory coupled to the processor for storing one or more programs;
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of smartmeter operation area grouping based on environmental and geographic features of any one of claims 1 to 4.
7. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method of grouping operating areas of a smart meter based on environmental and geographical features of any one of claims 1 to 4.
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