CN111311089B - Big data statistics method and system for electric power Internet of things - Google Patents
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Abstract
The invention provides a big data statistics method and a big data statistics system for 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 manage the urban area in multiple areas, improves the convenience of management, and classifies and manages the data so as to facilitate the observation of later-stage staff and analyze the change of the sensor data, thereby facilitating the staff to observe whether the sensor is normal or not, improving the stability during data transmission, improving the reliability, reducing the possibility of data loss and enabling the staff to visually observe the change of the data through data comparison.
Description
Technical Field
The invention relates to the field of urban power management, in particular to a big data statistics method and system of an electric power Internet of things.
Background
The electric power is the energy source taking electric energy as power, the 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 consumption and the like.
The existing big data statistics system of the digital electric power Internet of things only carries out statistics on sensor data in a general way when the system is applied, is inconvenient for staff to carry out finishing and observation in the later period, analyzes the change of electricity consumption conditions and whether the sensor works normally or not, has low practicality, cannot guarantee stability during data transmission, and reduces reliability.
Disclosure of Invention
The technical problems to be solved by the embodiment of the invention are to provide a big data statistics method and a system of an electric power Internet of things, which solve the technical problems that the later-period arrangement observation of staff is inconvenient, the change of the electricity consumption condition of the staff is analyzed, whether a sensor works normally or not is analyzed, the practicability is low, the stability during data transmission cannot be ensured, 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 the city through the 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 acquired in the urban area and comprises a first data management unit, a second data management unit and a third data management unit;
the data summarizing module is used for summarizing the sensor data acquired in the urban area;
the database is used for storing the data acquired by the data acquisition 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 verification module is used for verifying the identity of a 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 carrying out classification management on the sensor according to the type or the use area;
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 summarizing module, the output end of the data summarizing 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 query module is connected with the input end of the identity verification module, and the output end of the identity verification module is connected with the input end of the database.
Further, the first data management unit comprises a first month data summarizing unit, a first quarter data summarizing unit, a first year data summarizing unit and a first data backup unit, wherein the first month data summarizing unit, the first quarter data summarizing unit and the first year data summarizing unit are used for summarizing sensor data of different areas in a city in a limited time range.
Further, the second data management unit comprises a second month data summarizing unit, a second quarter data summarizing unit, a second year data summarizing unit and a second data backup unit, wherein the second month data summarizing unit, the second quarter data summarizing unit and the second year data summarizing unit are used for summarizing sensor data of different areas in a city in a limited time range.
Further, the third data management unit comprises a third month data summarizing unit, a third quarter data summarizing unit, a third year data summarizing unit and a third data backup unit, wherein the third month data summarizing unit, the third quarter data summarizing unit and the third year data summarizing unit are used for summarizing sensor data of different areas in a city in a limited time range.
Further, the identity verification module comprises an account password verification unit, an identity card verification unit and a fingerprint verification unit, wherein the account password verification unit, the identity card verification unit and the fingerprint verification unit are used for verifying the identity of a user.
Further, the data acquisition module comprises a power consumption sensor, a current sensor, an abnormal voltage amplitude sensor and an instantaneous current change sensing device; the power consumption sensor current sensor and the current sensor are configured in proportion to the use type and the region range, the super-normal voltage amplitude sensor and the instantaneous current change induction are configured in proportion to the result and the use change, all sensor acquisition data are stored in a time dimension, statistics is carried out through nodes of hours, days, weeks and months, and all sensor acquisition data are classified into a first data acquisition, a second data acquisition and a third data acquisition in different combinations.
Correspondingly, the invention also provides a big data statistics method of the electric power Internet of things, which is realized by means of the system, and comprises the following steps of;
step S1, dividing the whole management area into four dimensional areas according to different electricity utilization attributes;
s2, performing normalization operation according to the dimensional big data analysis;
step S3, according to 12 data indexes and a 5-level Liket table, setting levels of different degrees of 5 levels for each data;
s4, performing validity check on the data table by adopting a factor analysis method;
step S5, carrying out standardized naming on the factors according to the use type, the use change and the region range;
s6, setting a transformer substation coverage area as an independent variable, and analyzing the use characteristics represented by the extracted factors by using a multivariate analysis of variance method;
and S7, sorting according to the multi-component variance results of the transformer substation coverage area, and dividing to obtain urban area division results.
Further, in the step S1, the specific process of dividing the whole management area into four dimension areas is as follows:
dividing the power consumption into a first dimension according to the industrial power consumption, commercial power consumption and residential power consumption attributes of the region; dividing the power consumption into a second dimension according to the power consumption, the power consumption increase rate and the power consumption time and day period power consumption attribute; dividing into a third dimension according to the electricity utilization attribute of the district, village and town; and dividing the power grid into a fourth dimension according to the power utilization attributes of the main line coverage area, the branch main line coverage area and the transformer substation coverage area of the power grid.
Further, in the step S2, the normalization operation specifically includes:
the data for 4 dimensions is set as a 12-dimensional vector P, namely:
P＝{p 1 ，p 2 ，p 3 ，…p 12 }
p is normalized according to the following formula:
further, in the step S4, the specific process of the validity test of the data table is to extract the factors according to the maximum variance method, select the factors with the characteristic value greater than 1, and set the factors with the factor load greater than 0.5 as the standard, wherein the test statistic KMO value closer to 1 means that the variables have stronger correlation with each other, and the method is more suitable for factor analysis.
In summary, the embodiment of the invention has the following beneficial effects:
according to the large data statistics method and system of the electric power Internet of things, the first data acquisition, the second data acquisition and the third data acquisition are adopted, the urban division can be managed in a plurality of areas, the management convenience is improved, the data are classified and managed through monthly data summarization and quarterly data summarization and annual data summarization, so that later workers can observe the data, the change of the sensor data is analyzed, the workers can observe whether the sensor is normal or not, 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 in data transmission is improved, the reliability is improved, the possibility of data loss is reduced, and the workers can observe the data change intuitively through data comparison.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that it is within the scope of the invention to one skilled in the art to obtain other drawings from these drawings without inventive faculty.
FIG. 1 is a block diagram of a big data statistics system of the electric power Internet of things, which is provided by the invention;
fig. 2 is a schematic diagram of a first data management unit of an embodiment of a big data statistics system of the electric power internet of things according to the present invention;
fig. 3 is a schematic diagram of a second data management unit of an embodiment of a big data statistics system of the electric 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 the electric power internet of things provided by the invention.
Fig. 5 is a schematic diagram of an authentication module structure of an embodiment of a big data statistics system of the 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 the electric power internet of things provided by the invention.
Fig. 7 is a schematic diagram of data collection of an embodiment of a big data statistics system of the electric power internet of things.
Fig. 8 is a schematic diagram of a main flow of an embodiment of a big data statistics method of the electric power internet of things provided by the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent.
Fig. 1 is a schematic diagram of an embodiment of a big data statistics system of an electric power internet of things. In this embodiment, the system comprises:
the data acquisition module 16 is used for acquiring power consumption data of different areas in the city through sensors 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 configured to manage sensor data collected in the urban area, and includes 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 acquired in the urban area;
the database 8 is used for storing the data collected by the data collection module and summarized by the data summarization module;
a data query module 9 for providing data query and data interaction for the user;
the identity verification module 10 is used for performing verification processing on the identity of a user;
a data conversion module 18 for performing format conversion processing on the transmission data, converting the signal into a digital signal, including 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;
the classification management module 18 of the sensor is used for classifying and managing the sensor according to types or use areas;
the output end of the first data acquisition unit 1 is connected with the input end of the first data management unit 4 through a 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 a 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 a 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, 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 the 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 D/a converters for conversion.
In a specific embodiment, as shown in fig. 2, the first data management unit 4 includes a first month data summarizing unit 401, a first quarter data summarizing unit 402, a first year data summarizing unit 403, and a first data backup unit 404, where the first month data summarizing unit 401, the first quarter data summarizing unit 402, and the first year data summarizing unit 403 are configured to summarize sensor data of different areas in a city within a defined time range; as shown in fig. 3, the second data management unit 5 includes a second month data summarizing unit 501, a second quarter data summarizing unit 502, a second year data summarizing unit 503, and a second data backup unit 504, where the second month data summarizing unit 501, the second quarter data summarizing unit 502, and the second year data summarizing unit 503 are configured to summarize 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 third month data summarizing unit 601, a third quarter data summarizing unit 602, a third year data summarizing unit 603, and a third data backup unit 604, where the third month data summarizing unit 601, the third quarter data summarizing unit 602, and the third year data summarizing unit 603 are configured to summarize sensor data of different areas in a city within a defined time range.
In a specific embodiment, as shown in fig. 5, the authentication module 10 includes an account password authentication unit 1001, an identification card authentication unit 1002 and a fingerprint authentication unit 1003, where the account password authentication unit 1001, the identification card authentication unit 1002 and the fingerprint authentication unit 1003 are used for authenticating the identity of the user, and the security of the database 8 can be improved through the account password authentication unit 1001, the identification card authentication unit 1002 and the fingerprint authentication unit 1003, so that non-staff people can avoid watching data.
In one 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 configured in proportion to the use type and the region range, the super-normal voltage amplitude sensor and the instantaneous current change induction are configured in proportion to the result and the use change, all the sensor acquired data are stored in a time dimension and counted through nodes of hours, days, weeks and months, and all the sensor acquired data are classified into a first data acquisition unit 1, a second data acquisition unit 2 and a 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 statistics diagram 802, and a fourth data backup 803, where the data material 801 is used to record the power consumption situation in the city, the bar data statistics diagram 802 is used to display the sensor data of the city in months, quarters and years, so that the staff can observe and analyze the sensor data conveniently, and convenience is provided for power consumption scheduling, and the fourth data backup 803 is used to perform backup processing on the total data, so as to avoid data loss.
Correspondingly, as shown in fig. 8, still another aspect of the present invention further provides a big data statistics method of the electric power internet of things, which is implemented by means of the system as any one of the 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;
s2, performing normalization operation according to the dimensional big data analysis;
step S3, according to 12 data indexes and a 5-level Liket table, setting levels of different degrees of 5 levels for each data;
s4, performing validity check on the data table by adopting a factor analysis method;
step S5, carrying out standardized naming on 3 factors according to the use type, the use variation and the region range;
step S6, setting the coverage area of the transformer substation as an independent variable, and analyzing the use characteristics represented by the extracted 3 factors by utilizing a multivariate analysis of variance method, wherein the analysis result is Wilks' lambda=0.95, F=222.56, and p is smaller than 0.005, which indicates that the use characteristics of the coverage area p12 of the transformer substation have obvious differences;
and S7, sorting according to the multi-component variance results of the transformer substation coverage area, and dividing to obtain urban area division results.
In a specific embodiment, in the step S1, the specific process of dividing the whole management area into four dimension areas is: dividing the power consumption into a first dimension according to the properties of industrial power consumption p1, commercial power consumption p2 and residential power consumption p3 of the region; dividing the power consumption into a second dimension according to the power consumption p4, the power consumption increase rate p5 and the power consumption time daily period p 6; dividing into a third dimension according to the electricity utilization attribute of the region p7, the village p8 and the village p9; and dividing the power utilization attribute into a fourth dimension according to the power utilization attribute of the main line coverage area p10, the branch main line coverage area p11 and the transformer substation coverage area p12 of the power grid.
Specifically, in the step S2, normalization calculation is performed according to the dimensional big data analysis, and the normalized decimal place becomes a decimal place between (0, 1) for facilitating the further big data analysis and region division, and the normalization calculation specifically includes:
the P1-12 data for 4 dimensions is set as a 12-dimensional vector P, namely:
P＝{p 1 ，p 2 ，p 3 ，…p 12 }
then to accommodate big data analysis and need, P is normalized according to the following formula:
specifically, in the step S4, the specific process of the validity test of the data table is to extract factors according to a maximum variance method, select factors with characteristic values greater than 1, and set factors with factor load greater than 0.5 as a standard, where the results indicate that the residential electricity consumption p3, the electricity consumption growth rate p5 and the village p9; the factor accumulation interpretation rate is larger than 70%, the factor analysis requirement is met, the test statistic KMO value is closer to 1, the correlation among variables is stronger, the factor analysis is more suitable, the KMO value is 0.822, the factor analysis is suitable, the Bartlett sphericity test significance is 0.001, and the factor analysis result has better effectiveness.
In summary, the embodiment of the invention has the following beneficial effects:
according to the large data statistics method and system of the electric power Internet of things, the first data acquisition, the second data acquisition and the third data acquisition are adopted, the urban division can be managed in a plurality of areas, the management convenience is improved, the data are classified and managed through monthly data summarization and quarterly data summarization and annual data summarization, so that later workers can observe the data, the change of the sensor data is analyzed, the workers can observe whether the sensor is normal or not, 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 in data transmission is improved, the reliability is improved, the possibility of data loss is reduced, and the workers can observe the data change intuitively through data comparison.
The above disclosure is only a preferred embodiment of the present invention, and it is needless to say that the scope of the invention is not limited thereto, and therefore, the equivalent changes according to the claims of the present invention still fall within the scope of the present invention.
Claims (8)
1. The big data statistics method of the electric power Internet of things is characterized by comprising the following steps of:
step S1, dividing the whole management area into four dimensional areas according to different electricity utilization attributes; the specific process of dividing the whole management area into four dimension areas is as follows: dividing the power consumption into a first dimension according to the industrial power consumption, commercial power consumption and residential power consumption attributes of the region; dividing the power consumption into a second dimension according to the power consumption, the power consumption increase rate and the power consumption time and day period power consumption attribute; dividing into a third dimension according to the electricity utilization attribute of the district, village and town; dividing the power grid into a fourth dimension according to the power utilization attributes of the main line coverage area, the branch main line coverage area and the transformer substation coverage area of the power grid;
s2, performing normalization operation according to the dimensional big data analysis; the normalization operation specifically comprises the following steps:
the data for four dimensions is set as a 12-dimensional vector P, namely:
P＝{p 1 ，p 2 ，p 3 ，…p 12 }
p is normalized according to the following formula:
wherein p is 1 ，p 2 ，p 3 ，…p 12 The electricity attribute i in four dimensions represents the serial number of the electricity attribute,
representing the normalized result of vector P,/>
A single normalized result representing each electrical attribute in four dimensions;
step S3, according to 12 data indexes and a 5-level Liket table, setting levels of different degrees of 5 levels for each data;
s4, performing validity check on the data table by adopting a factor analysis method;
step S5, carrying out standardized naming on the factors according to the use type, the use change and the region range;
s6, setting a transformer substation coverage area as an independent variable, and analyzing the use characteristics represented by the extracted factors by using a multivariate analysis of variance method;
and S7, sorting according to the multi-component variance results of the transformer substation coverage area, and dividing to obtain urban area division results.
2. The method according to claim 1, wherein in the step S4, the specific procedure of the validity test of the data table is to extract factors according to the maximum variance method, select factors with characteristic values greater than 1, and set the standard with factor load greater than 0.5, wherein the test statistic KMO value closer to 1 means that variables have stronger correlation with each other and are more suitable for factor analysis.
3. A big data statistics system of the internet of things for electric power, for implementing the method according to any of claims 1-2, comprising:
the data acquisition module is used for acquiring power consumption data of different areas in the city through the 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 acquired in the urban area and comprises a first data management unit, a second data management unit and a third data management unit;
the data summarizing module is used for summarizing the sensor data acquired in the urban area;
the database is used for storing the data acquired by the data acquisition 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 verification module is used for verifying the identity of a user;
the data conversion module is used for carrying out format conversion on the transmission data, converting the signal into a digital signal 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 carrying out classification management on the sensor according to the type or the use area;
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 summarizing module, the output end of the data summarizing 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 query module is connected with the input end of the identity verification module, and the output end of the identity verification module is connected with the input end of the database.
4. The system of claim 3, wherein the first data management unit includes a first month data summarization unit, a first quarter data summarization unit, a first year data summarization unit, and a first data backup unit, the first month data summarization unit, the first quarter data summarization unit, and the first year data summarization unit configured to summarize sensor data for different areas within a city over a defined time frame.
5. The system of claim 4, wherein the second data management unit includes a second month data summarization unit, a second quarter data summarization unit, a second year data summarization unit, and a second data backup unit, the second month data summarization unit, the second quarter data summarization unit, and the second year data summarization unit configured to summarize sensor data for different areas within a city over a defined time horizon.
6. The system of claim 5, wherein the third data management unit includes a third month data summary unit, a third quarter data summary unit, a third year data summary unit, and a third data backup unit, the third month data summary unit, the third quarter data summary unit, and the third year data summary unit configured to aggregate sensor data for different areas within a city over a defined time frame.
7. The system of claim 3, wherein the authentication module comprises an account password authentication unit, an identification card authentication unit, and a fingerprint authentication unit for authenticating an identity of a user.
8. The method of claim 3, wherein the data acquisition module comprises a power consumption sensor, a current sensor, an abnormal voltage amplitude sensor, and an instantaneous current change sensing device; the power consumption sensor current sensor and the current sensor are configured in proportion to the use type and the region range, the super-normal voltage amplitude sensor and the instantaneous current change induction are configured in proportion to the result and the use change, all sensor acquisition data are stored in a time dimension, statistics is carried out through nodes of hours, days, weeks and months, and all sensor acquisition data are classified into a first data acquisition, a second data acquisition and a third data acquisition in different combinations.
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