CN111311089A - Big data statistical method and system of power Internet of things - Google Patents
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Abstract
The invention provides a big data statistical method and system of an electric power Internet of things. The system comprises a data acquisition module, a data management module, a data summarization module, a database, a data query module, an identity verification module, a data conversion module, a data comparison module and a classification management module of the sensor. The invention can divide the city into a plurality of areas for management, improves the convenience of management, performs classified management on data so as to be convenient for later-stage workers to observe, analyzes the change of sensor data so as to be convenient for the workers to observe whether the sensor is normal or not, improves the stability during data transmission, improves the reliability, reduces the possibility of data loss, and enables the workers to visually observe the data change through data comparison.
Description
Technical Field
The invention relates to the field of urban power management, in particular to a big data statistical method and system of a power internet of things.
Background
Electric power is energy using electric energy as power, a large-scale electric power system appearing in the 20 th century is one of the most important achievements in the history of human engineering science, and is an electric power production and consumption system consisting of links of power generation, power transmission, power transformation, power distribution, power utilization and the like.
The big data statistics system of current digital electric power thing networking, when using, just carry out the statistics of universities with sensor data, the staff later stage of being not convenient for is put in order and is observed, analyzes the change of its power consumption condition and whether the sensor is normally working, and the practicality is lower, and stability when unable assurance data transmission has reduced its reliability.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a big data statistical method and system for an electric power internet of things, and solve the technical problems that a worker is inconvenient to tidy and observe in the later stage, the change of the power utilization condition of the worker and whether a sensor works normally or not are analyzed, the practicability is low, the stability during data transmission cannot be guaranteed, and the reliability is low.
In one aspect of the present invention, a big data statistics system of an electric power internet of things is provided, including:
the data acquisition module is used for acquiring power consumption data of different areas in a city through a sensor and comprises a first data acquisition unit, a second data acquisition unit and a third data acquisition unit;
the data management module is used for managing the sensor data collected in the urban area and comprises a first data management unit, a second data management unit and a third data management unit;
the data summarization module is used for summarizing the sensor data collected in the urban area;
the database is used for storing the data collected by the data collection module and summarized by the data summarization module;
the data query module is used for providing data query and data interaction for a user;
the identity authentication module is used for authenticating the identity of the user;
the data conversion module is used for carrying out format conversion or encryption and decryption processing on the transmission data and comprises a first data conversion unit, a second data conversion unit, a third data conversion unit and a fourth data conversion unit;
the data comparison module is used for comparing the collected and summarized data with standard data or estimated data one by one;
the classification management module of the sensor is used for performing classification management on the sensor according to types or using areas;
wherein, the output end of the first data acquisition unit is connected with the input end of the first data management unit through the first data conversion unit, the output end of the second data acquisition unit is connected with the input end of the second data management unit through the second data conversion unit, the output end of the third data acquisition unit is connected with the input end of the third data management unit through the third data conversion unit, the output ends of the first data management unit, the second data management unit and the third data management unit are all connected with the input end of the data summarization module, the output end of the data summarization module is connected with the input end of the fourth data conversion unit, the output end of the fourth data conversion unit is connected with the input end of the data comparison module, the output end of the data comparison module is connected with the input end of the database, and the output end of the data query module is connected with the input end of the identity verification module, and the output end of the identity authentication module is connected with the input end of the database.
Further, the first data management unit comprises a monthly data summarization unit, a quarterly data summarization unit, a annual data summarization unit and a data backup unit, and the monthly data summarization unit, the quarterly data summarization unit and the annual data summarization unit are used for summarizing and processing sensor data in different areas in a city within a limited time range.
Further, the second data management unit comprises a second february data summarizing unit, a second quarterly data summarizing unit, a second annual data summarizing unit and a second data backup unit, and the second february data summarizing unit, the second quarterly data summarizing unit and the second annual data summarizing unit are used for summarizing sensor data of different areas in a city within a limited time range.
Further, the third data management unit comprises a data summarization unit for the third month, a data summarization unit for the third quarter, a data summarization unit for the third year and a data backup unit, and the data summarization unit for the third month, the data summarization unit for the third quarter and the data summarization unit for the third year are used for summarizing and processing the sensor data of different areas in the city within a limited time range.
Furthermore, the identity verification module comprises an account password verification unit, an identity card verification unit and a fingerprint verification unit, and the account password verification unit, the identity card verification unit and the fingerprint verification unit are used for verifying the identity of the user.
Further, the data acquisition module comprises a power consumption sensor, a current sensor, an abnormal voltage amplitude sensor and an instantaneous current change induction device; the current sensor and the current sensor of the electricity consumption sensor are in direct proportion configuration with the use type and the region range, the abnormal voltage amplitude sensor and the instantaneous current change induction and result are in direct proportion configuration with the use change, all the sensor collected data are stored in a time dimension and are counted through nodes of hours, days, weeks and months, and all the sensor collected data are classified into first data collection, second data collection and third data collection in different combinations.
Correspondingly, the invention also provides a big data statistical method of the power internet of things, which is realized by the system and comprises the following steps;
step S1, dividing the whole management area into four dimensional areas according to different electricity utilization attributes;
step S2, carrying out normalization operation according to dimension big data analysis;
step S3, setting different degrees of grades of 5 grades for each data according to 12 data indexes and a 5-grade litters table;
step S4, checking the validity of the data scale by a factor analysis method;
step S5, standardizing and naming the factors according to the use types, the use changes and the region ranges;
step S6, setting the coverage area of the transformer substation as an independent variable, and analyzing the use characteristics represented by the extracted factors by using a multivariate variance analysis method;
and step S7, sorting according to the multivariate variance results of the coverage area of the transformer substation, and dividing to obtain the urban area division result.
Further, in the step S1, the specific process of dividing the entire management area into four dimensional areas is as follows:
dividing the region into a first dimension according to the attributes of the industrial electricity, the commercial electricity and the residential electricity of the region; dividing the power consumption into a second dimension according to the power consumption, the power consumption increase rate and the daily period power consumption attribute of the power consumption time; dividing the power into third dimensions according to the power utilization attributes of the district, the town and the village; and dividing into a fourth dimension according to the power utilization attributes of the power grid main line coverage area, the branch line coverage area and the substation coverage area.
Further, in the step S2, the normalization operation specifically includes:
the data for 4 dimensions is set as a 12-dimensional vector P, i.e.:
P＝{p1，p2，p3，…p12}
p is normalized according to the following equation:
further, in step S4, the validity test of the data table is performed by extracting factors according to the maximum variance method, selecting factors with characteristic values greater than 1, and setting the factor load greater than 0.5 as a standard, where a test statistic KMO value closer to 1 means that the variables are more strongly correlated with each other and more suitable for factor analysis.
In summary, the embodiment of the invention has the following beneficial effects:
according to the big data statistical method and the system of the power Internet of things, a city can be divided into a plurality of areas for management through the first data acquisition, the second data acquisition and the third data acquisition, the management convenience is improved, the data are classified and managed through monthly data collection and quarterly data collection and annual data collection so as to be convenient for later-stage workers to observe and analyze the change of the sensor data so as to be convenient for the workers to observe whether the sensor is normal, the safety of a database can be improved through account password verification, identity card verification and fingerprint verification, non-workers are prevented from watching the data, the signals are converted into digital signals through the first data conversion, the second data conversion and the third data conversion, the stability during data transmission is improved, the reliability of the data is improved, and the possibility of data loss is reduced, the data comparison can enable the staff to visually observe the data change.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a structural diagram of a big data statistical system of an electric power internet of things provided by the invention;
fig. 2 is a schematic structural diagram of a first data management unit of an embodiment of a big data statistics system of an electric power internet of things provided by the invention;
fig. 3 is a schematic structural diagram of a second data management unit of an embodiment of the big data statistics system of the power internet of things provided by the invention.
Fig. 4 is a schematic structural diagram of a third data management unit of an embodiment of a big data statistics system of an electric power internet of things provided by the invention.
Fig. 5 is a schematic structural diagram of an authentication module of an embodiment of a big data statistics system of an electric power internet of things provided by the invention.
Fig. 6 is a schematic diagram of a database structure of an embodiment of a big data statistics system of an electric power internet of things provided by the invention.
Fig. 7 is a schematic data acquisition diagram of an embodiment of a big data statistical system of the power internet of things provided by the invention.
Fig. 8 is a main flow diagram of an embodiment of a big data statistical method of an electric power internet of things provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an embodiment of a big data statistics system of an electric power internet of things provided by the present invention. In this embodiment, the system includes:
the data acquisition module 16 is used for acquiring power consumption data of different areas in a city through a sensor, and comprises a first data acquisition unit 1, a second data acquisition unit 2 and a third data acquisition unit 3;
the data management module 17 is used for managing the sensor data collected in the urban area and comprises a first data management unit 4, a second data management unit 5 and a third data management unit 6;
the data summarizing module 7 is used for summarizing the sensor data collected in the urban area;
the database 8 is used for storing the data collected by the data collection module and collected and processed by the data collection module;
the data query module 9 is used for providing data query and data interaction for a user;
an identity authentication module 10 for authenticating the identity of the user;
the data conversion module 18 is used for performing format conversion processing on transmission data and converting signals into digital signals, and comprises a first data conversion unit 11, a second data conversion unit 12, a third data conversion unit 13 and a fourth data conversion unit 14;
the data comparison module 15 is used for comparing the collected and summarized data with standard data or estimated data one by one;
a classification management module 18 of the sensor, which is used for performing classification management on the sensor according to types or using areas;
wherein, the output end of the first data acquisition unit 1 is connected with the input end of the first data management unit 4 through the first data conversion unit 11, the output end of the second data acquisition unit 2 is connected with the input end of the second data management unit 5 through the second data conversion unit 12, the output end of the third data acquisition unit 3 is connected with the input end of the third data management unit 6 through the third data conversion unit 13, the output ends of the first data management unit 4, the second data management unit 5 and the third data management unit 6 are all connected with the input end of the data summarization module 7, the output end of the data summarization module 7 is connected with the input end of the fourth data conversion unit 14, the output end of the fourth data conversion unit 14 is connected with the input end of the data comparison module 15 formula, the output end of the data comparison module 15 is connected with the input end of the database 8, the output end of the data query module 9 is connected with the input end of an identity verification module 10, and the output end of the identity verification module 10 is connected with the input end of the database 8; the first data conversion unit 11, the second data conversion unit 12, and the third data conversion unit 13 all use a/D converters for conversion, and the fourth data conversion unit 14 uses a D/a converter for conversion.
In a specific embodiment, as shown in fig. 2, the first data management unit 4 includes a first month data summarization unit 401, a first quarter data summarization unit 402, a first year data summarization unit 403, and a first data backup unit 404, where the first month data summarization unit 401, the first quarter data summarization unit 402, and the first year data summarization unit 403 are used to summarize sensor data of different areas in a city within a limited time range; as shown in fig. 3, the second data management unit 5 includes a second february data summarization unit 501, a second quarterly data summarization unit 502, a second annual data summarization unit 503 and a second data backup unit 504, and the second february data summarization unit 501, the second quarterly data summarization unit 502 and the second annual data summarization unit 503 are used for summarizing sensor data of different areas in a city within a defined time range; as shown in fig. 4, the third data management unit 6 includes a data summarization unit 601, a data summarization unit 602, a data summarization unit 603, and a data backup unit 604 for a third month, and the data summarization unit 601, the data summarization unit 602, and the data summarization unit 603 are configured to summarize sensor data of different areas in a city within a limited time range.
In a specific embodiment, as shown in fig. 5, the authentication module 10 includes an account password authentication unit 1001, an identity card authentication unit 1002, and a fingerprint authentication unit 1003, where the account password authentication unit 1001, the identity card authentication unit 1002, and the fingerprint authentication unit 1003 are used to authenticate the identity of the user, and the security of the database 8 can be improved through the account password authentication unit 1001, the identity card authentication unit 1002, and the fingerprint authentication unit 1003, so as to prevent a non-worker from viewing data.
In a specific embodiment, as shown in fig. 7, the data acquisition module 16 includes a power consumption sensor, a current sensor, an abnormal voltage amplitude sensor, and an instantaneous current change sensing device; the electricity consumption sensor current sensor and the current sensor are in direct proportion configuration with the use type and the region range, the abnormal voltage amplitude sensor and the instantaneous current change induction and the result are in direct proportion configuration with the use change, all the sensor acquisition data are stored in a time dimension and are counted by nodes of hours, days, weeks and months, and all the sensor acquisition data are classified into the first data acquisition unit 1, the second data acquisition unit 2 and the third data acquisition unit 3 in different combinations.
In a specific embodiment, as shown in fig. 6, the database 8 includes a data material 801, a bar data statistical graph 802, and a fourth data backup 803, the data material 801 is used to record power consumption conditions in a city, the bar data statistical graph 802 is used to display sensor data of the city in months, quarters, and years so as to facilitate observation and analysis by workers, facilitate power consumption scheduling, and the fourth data backup 803 is used to perform backup processing on total data to avoid data loss.
Correspondingly, as shown in fig. 8, another aspect of the present invention further provides a big data statistics method for an electric power internet of things, which is implemented by means of any one of the systems described above, and the method includes the following steps:
step S1, dividing the whole management area into four dimensional areas according to different electricity utilization attributes;
step S2, carrying out normalization operation according to dimension big data analysis;
step S3, setting different degrees of grades of 5 grades for each data according to 12 data indexes and a 5-grade litters table;
step S4, checking the validity of the data scale by a factor analysis method;
step S5, carrying out standardized naming on the 3 factors according to the use types, the use changes and the region ranges;
step S6, setting the substation coverage area as an independent variable, analyzing the use characteristics represented by the extracted 3 factors by using a multivariate variance analysis method, wherein the analysis result is Wilks' Lambda is 0.95, F is 222.56, and p is less than 0.005, which indicates that the covered area p12 of the substation has significant differences in the use characteristics;
and step S7, sorting according to the multivariate variance results of the coverage area of the transformer substation, and dividing to obtain the urban area division result.
In a specific embodiment, in the step S1, the specific process of dividing the entire management area into four dimensional areas is: dividing the region into a first dimension according to the attributes of industrial power p1, commercial power p2 and residential power p3 of the region; dividing the power utilization attributes into a second dimension according to the power consumption p4, the power utilization increasing rate p5 and the power utilization time daily period p 6; dividing the power grid into a third dimension according to the electricity utilization attributes of a region p7, a village p8 and a village p 9; and dividing the power into a fourth dimension according to the power utilization attributes of the power grid main line coverage area p10, the branch line coverage area p11 and the substation coverage area p 12.
Specifically, in step S2, a normalization operation is performed according to the dimension big data analysis, and the normalized big data becomes a decimal number between (0, 1) to facilitate further big data analysis and region division, where the normalization operation specifically is:
the 4-dimensional P1-12 data is set as a 12-dimensional vector P, namely:
P＝{p1，p2，p3，…p12}
then, in order to adapt to the big data analysis and the need, P is normalized according to the following formula:
specifically, in step S4, the validity check of the data table is performed by extracting factors according to a maximum variance method, selecting factors with a characteristic value greater than 1, and setting the factor load greater than 0.5 as a standard, where the result indicates residential electricity consumption p3, electricity consumption increase rate p5, and village p 9; the factor accumulated interpretation rate is larger than 70%, the factor analysis requirement is met, the closer the test statistic KMO value is to 1, the stronger the correlation among variables is, the more suitable the factor analysis is, the KMO value of the invention is 0.822, the invention is suitable for the factor analysis, the significance of Bartlett sphericity test is 0.001, and the result of the factor analysis has better validity.
In summary, the embodiment of the invention has the following beneficial effects:
according to the big data statistical method and the system of the power Internet of things, a city can be divided into a plurality of areas for management through the first data acquisition, the second data acquisition and the third data acquisition, the management convenience is improved, the data are classified and managed through monthly data collection and quarterly data collection and annual data collection so as to be convenient for later-stage workers to observe and analyze the change of the sensor data so as to be convenient for the workers to observe whether the sensor is normal, the safety of a database can be improved through account password verification, identity card verification and fingerprint verification, non-workers are prevented from watching the data, the signals are converted into digital signals through the first data conversion, the second data conversion and the third data conversion, the stability during data transmission is improved, the reliability of the data is improved, and the possibility of data loss is reduced, the data comparison can enable the staff to visually observe the data change.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. The utility model provides a big data statistics system of electric power thing networking which characterized in that includes:
the data acquisition module is used for acquiring power consumption data of different areas in a city through a sensor and comprises a first data acquisition unit, a second data acquisition unit and a third data acquisition unit;
the data management module is used for managing the sensor data collected in the urban area and comprises a first data management unit, a second data management unit and a third data management unit;
the data summarization module is used for summarizing the sensor data collected in the urban area;
the database is used for storing the data collected by the data collection module and summarized by the data summarization module;
the data query module is used for providing data query and data interaction for a user;
the identity authentication module is used for authenticating the identity of the user;
the data conversion module is used for carrying out format conversion processing on the transmission data and converting the signals into digital signals and comprises a first data conversion unit, a second data conversion unit, a third data conversion unit and a fourth data conversion unit;
the data comparison module is used for comparing the collected and summarized data with standard data or estimated data one by one;
the classification management module of the sensor is used for performing classification management on the sensor according to types or using areas;
wherein, the output end of the first data acquisition unit is connected with the input end of the first data management unit through the first data conversion unit, the output end of the second data acquisition unit is connected with the input end of the second data management unit through the second data conversion unit, the output end of the third data acquisition unit is connected with the input end of the third data management unit through the third data conversion unit, the output ends of the first data management unit, the second data management unit and the third data management unit are all connected with the input end of the data summarization module, the output end of the data summarization module is connected with the input end of the fourth data conversion unit, the output end of the fourth data conversion unit is connected with the input end of the data comparison module, the output end of the data comparison module is connected with the input end of the database, and the output end of the data query module is connected with the input end of the identity verification module, and the output end of the identity authentication module is connected with the input end of the database.
2. The system of claim 1, wherein the first data management unit comprises a first month data summarization unit, a first quarter data summarization unit, a first year data summarization unit, and a first data backup unit, and the first month data summarization unit, the first quarter data summarization unit, and the first year data summarization unit are configured to summarize sensor data for different regions within a city within a defined time frame.
3. The system of claim 2, wherein the second data management unit comprises a second february data summarization unit, a second quarterly data summarization unit, a second annual data summarization unit, and a second data backup unit, and wherein the second february data summarization unit, the second quarterly data summarization unit, and the second annual data summarization unit are configured to summarize sensor data for different regions within a city over a defined time frame.
4. The system of claim 3, wherein the third data management unit comprises a third month data summarization unit, a third quarter data summarization unit, a third year data summarization unit and a third data backup unit, and the third month data summarization unit, the third quarter data summarization unit and the third year data summarization unit are used for summarizing sensor data of different areas in a city within a defined time range.
5. The system of claim 1, wherein the authentication module comprises an account password authentication unit, an identity card authentication unit and a fingerprint authentication unit, and the account password authentication unit, the identity card authentication unit and the fingerprint authentication unit are used for authenticating the identity of the user.
6. The method of claim 1, wherein the data acquisition module comprises a power usage sensor, a current sensor, an abnormal voltage magnitude sensor, and an instantaneous current change sensing device; the current sensor and the current sensor of the electricity consumption sensor are in direct proportion configuration with the use type and the region range, the abnormal voltage amplitude sensor and the instantaneous current change induction and result are in direct proportion configuration with the use change, all the sensor collected data are stored in a time dimension and are counted through nodes of hours, days, weeks and months, and all the sensor collected data are classified into first data collection, second data collection and third data collection in different combinations.
7. A big data statistical method of an electric power Internet of things is realized by means of the system as claimed in any one of claims 1-6, and is characterized by comprising the following steps:
step S1, dividing the whole management area into four dimensional areas according to different electricity utilization attributes;
step S2, carrying out normalization operation according to dimension big data analysis;
step S3, setting different degrees of grades of 5 grades for each data according to 12 data indexes and a 5-grade litters table;
step S4, checking the validity of the data scale by a factor analysis method;
step S5, standardizing and naming the factors according to the use types, the use changes and the region ranges;
step S6, setting the coverage area of the transformer substation as an independent variable, and analyzing the use characteristics represented by the extracted factors by using a multivariate variance analysis method;
and step S7, sorting according to the multivariate variance results of the coverage area of the transformer substation, and dividing to obtain the urban area division result.
8. The method of claim 7, wherein in the step S1, the specific process of dividing the entire management area into four dimensional areas is:
dividing the region into a first dimension according to the attributes of the industrial electricity, the commercial electricity and the residential electricity of the region; dividing the power consumption into a second dimension according to the power consumption, the power consumption increase rate and the daily period power consumption attribute of the power consumption time; dividing the power into third dimensions according to the power utilization attributes of the district, the town and the village; and dividing into a fourth dimension according to the power utilization attributes of the power grid main line coverage area, the branch line coverage area and the substation coverage area.
9. The method according to claim 7, wherein in the step S2, the normalization operation is specifically:
the data for 4 dimensions is set as a 12-dimensional vector P, i.e.:
P＝{p1，p2，p3，…p12}
p is normalized according to the following equation:
10. the method of claim 7, wherein in step S4, the validity test of the data scale is performed by extracting factors according to the maximum variance method, selecting factors with a characteristic value greater than 1, and setting the factor load greater than 0.5 as a standard, wherein the closer the test statistic KMO value is to 1 means that the variables are more strongly correlated with each other and more suitable for the factor analysis.
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