CN113434601A - Cloud data storage and local edge control system and method for power grid internet of things - Google Patents
Cloud data storage and local edge control system and method for power grid internet of things Download PDFInfo
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Abstract
The invention provides a cloud data storage and local edge control system and method for power grid internet of things, the local edge control system is used for processing the power grid data in the defined area and is in communication connection with the terminal server, the local edge control system comprises an Internet of things acquisition module, an Internet of things gateway, a cloud storage module and a local edge control module, the Internet of things acquisition module is in communication connection with the Internet of things gateway, the Internet of things gateway is in communication connection with the cloud storage module, the cloud storage module is connected with an external storage system, the invention can store the data of the power grid into the cloud storage module after processing, and can clear invalid data, improve the effectiveness of data storage, reduce the burden of data storage, the problem that the storage processing of the power grid data in the existing power system is insufficient is solved.
Description
Technical Field
The invention relates to the technical field of power grid data processing, in particular to a cloud data storage and local edge control system and method for power grid internet of things.
Background
The electric power system is an electric energy production and consumption system which consists of links such as a power plant, a power transmission and transformation line, a power supply and distribution station, power utilization and the like. The power grid data comprises power data of all links in a power system, including power grid data of all fields such as power transmission, power transformation and power utilization ends, the data of the power system belongs to key data, and much data needs to be stored.
In the existing power system, when the power grid data are stored, the data are directly acquired and then stored, the stored data occupy a large amount of storage space in a storage module in a terminal processor, a large amount of stored data are very burdensome on the operation of the terminal processor, a large amount of processing resources are consumed, meanwhile, the data are stored in a disordered mode, the complexity of the processing process is increased for subsequent extraction and search, the operating efficiency of data processing is reduced, meanwhile, the data storage module of the existing power control system can only store the data in a single mode, and the function is very single.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a cloud data storage and local edge control system and method for power grid internet of things, which can process and store the data of a power grid in a cloud storage module, can clean invalid data, improve the effectiveness of data storage, reduce the burden of data storage and solve the problem of defects in the storage and processing of the power grid data in the conventional power system.
In order to achieve the purpose, the invention is realized by the following technical scheme: the system comprises a cloud data storage and local edge control system facing the power grid internet of things, wherein the local edge control system is used for processing the power grid data in a defined area and is in communication connection with a terminal server, the local edge control system comprises an internet of things acquisition module, an internet of things gateway, a cloud storage module and a local edge control module, the internet of things acquisition module is in communication connection with the internet of things gateway, the internet of things gateway is in communication connection with the cloud storage module, the cloud storage module is connected with an external storage system, prestored data are stored in the cloud storage module, and the internet of things gateway is in communication connection with the local edge control module;
the system comprises an Internet of things acquisition module, a power grid data acquisition unit, a monitoring data acquisition unit, a user side data acquisition unit and a power distribution end data acquisition unit, wherein the power grid data acquisition unit is used for acquiring local power grid operation data, the monitoring data acquisition unit is used for acquiring monitoring data of local power grid equipment, the user side data acquisition unit is used for acquiring user power consumption data of the local power grid, and the power distribution end data acquisition unit is used for acquiring power distribution data of a power distribution system;
the Internet of things gateway is used for transmitting the data acquired in the Internet of things acquisition module to the local edge control module;
the local edge control module comprises a first demarcation unit, a second demarcation unit, a feature comparison unit and a storage unit;
the first planning unit is used for planning the category of pre-stored data in the cloud storage module, and the second planning unit is used for planning the category of data acquired by the Internet of things acquisition module;
the first dividing subunit is configured with a first dividing strategy; the second demarcation unit is configured with a second demarcation strategy;
the first planning strategy comprises: dividing the power grid data in the pre-stored data into first main point power grid data, marking the first main point power grid data as A1, and adding n to a plurality of branch data in the first main point power grid data in sequence at a rear digit marked by A1;
dividing monitoring data in the pre-stored data into first main point monitoring data, marking the first main point monitoring data as A2, and adding m to a plurality of branch data in the first main point monitoring data in sequence at a rear digit marked by A2;
dividing user side data in the pre-stored data into first main point user side data, marking the first main point user side data as A3, and adding i to a plurality of branch data in the first main point user side data in sequence at a back digit marked by A3;
dividing user side data in the pre-stored data into first main point power distribution end data, marking the first main point power distribution end data as A4, and sequentially adding j to a plurality of branch data in the first main point power distribution end data at a rear digit marked by A4;
the second demarcation strategy includes: classifying data acquired by a power grid data acquisition unit, a monitoring data acquisition unit, a user side data acquisition unit and a power distribution end data acquisition unit into first main point power grid data, first main point monitoring data, first main point user end data and first main point power distribution end data, and sequentially adding 1 to the rear bits of branch data in the first main point power grid data, the first main point monitoring data, the first main point user end data and the first main point power distribution end data corresponding to branch data marks in a first division strategy;
the feature comparison unit is configured with a feature screening strategy, which comprises: extracting safety data in the pre-stored data, dividing a safety range of each group of data according to the range of the safety data, dividing a value range exceeding the safety range into danger ranges, and dividing the danger ranges into danger grades, wherein the danger grades comprise a high danger grade, a medium danger grade and a low danger grade;
respectively corresponding the first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data of the first main point power distribution end data to the safety range and the danger range of each group of data, re-marking the data in the safety range on the marks of the corresponding groups, and sequentially adding 1 to the back digits of the data marks in the danger range on the marks of the corresponding groups;
the storage unit is configured with a storage policy, which includes: and storing the marks of the data in the safety range and the data in the danger range to a cloud storage module through an internet of things gateway.
Further, the first planning strategy further comprises: adding 001 to the rear digit of the mark of the corresponding group of the data in the safety range; where a1 is set to 100, a2 is set to 200, A3 is set to 300, a4 is set to 400, and n, m, i, and j are positive integers less than 10, respectively.
Further, the local edge control module further includes an early warning unit, the early warning unit is configured with an early warning policy, and the early warning policy includes: and when the received first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data in the first main point power distribution end data belong to a dangerous range, sending a dangerous grade corresponding to the data to a terminal server through the Internet of things gateway.
Further, the local edge control module further includes a cache unit, where the cache unit is configured with a cache policy, and the cache policy includes: when branch data in the received first main point power grid data, the received first main point monitoring data, the received first main point user end data and the received first main point power distribution end data belong to a safety range, the data are stored in a cache unit, and a mark corresponding to the data is stored in a cloud storage module through an internet of things gateway.
Further, the local edge control module further includes a cleaning unit configured with a cleaning policy, where the cleaning policy includes: and deleting the data in the buffer unit at intervals of a first period.
The processing method of the cloud data storage and local edge control system further facing the power grid Internet of things comprises the following steps: step A, data in a local power grid are collected through an Internet of things collection module and then transmitted to a local edge control module through an Internet of things gateway for processing;
step B, classifying the data collected by the power grid data collection unit, the monitoring data collection unit, the user side data collection unit and the power distribution end data collection unit into first main point power grid data, first main point monitoring data, first main point user side data and first main point power distribution end data, wherein the data are respectively marked as 100, 200, 300 and 400;
step C, further classifying the first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data in the first main point power distribution end data, sequentially marking the branch data in the first main point power grid data as 1001 to 100n, sequentially marking the branch data in the first main point monitoring data as 2001 to 200m, sequentially marking the branch data in the first main point user end data as 3001 to 300i, and sequentially marking the branch data in the first main point power distribution end data as 4001 to 400 j;
d, classifying the power grid data in the cloud storage module according to the classification method in the step B and the classification method in the step C, extracting the safety data in each group of branch data, and defining the safety range and the danger range of each group according to the safety data of each group;
and E, respectively corresponding the data acquired by the internet of things acquisition module to the classified data in the cloud storage module, respectively comparing the data with the safety ranges of the corresponding groups, increasing the number of digits 001 of the data belonging to the safety ranges in the marks of the corresponding groups, sequentially adding 1 to the number of digits of the data marks in the danger ranges in the marks of the corresponding groups, and then storing the marked data to the remote storage module through the internet of things gateway.
Further, the step E further includes: and E1, storing the marks of the data belonging to the safety range to a remote storage module through the Internet of things gateway, storing the marked data belonging to the danger range to a cloud storage module through the Internet of things gateway, and storing the data belonging to the safety range to a cache unit.
Further, the method further comprises: and F, dividing the danger range into danger levels, marking the danger levels on the data in the danger range, and sending the data marked with the danger levels to the terminal server through the Internet of things gateway.
Further, the method further comprises: and G, deleting the data in the cache unit every first period.
The invention has the beneficial effects that: the data collected by the power grid data collection unit, the monitoring data collection unit, the user side data collection unit and the power distribution end data collection unit can be classified into first main point power grid data, first main point monitoring data, first main point user side data and first main point power distribution end data through the characteristic defining unit, and branch data of the data are classified and marked, so that the data classification delicacy is improved, and the received data can be classified into a safety range and a danger range through the characteristic comparison unit, so that the data are monitored; the invalid safety data are marked and independently stored, so that the reasonability of safety data processing is improved while the data can be referred.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic block diagram of a first embodiment of the present invention;
fig. 2 is a schematic block diagram of a second embodiment of the present invention.
In the figure: 1. a local edge control system; 11. an Internet of things acquisition module; 111. a power grid data acquisition unit; 112. a monitoring data acquisition unit; 113. a user data acquisition unit; 114. a distribution terminal data acquisition unit; 12. an Internet of things gateway; 13. a cloud storage module; 14. a local edge control module; 141. a first delimiting unit; 142. a second delimiting unit; 143. a feature comparison unit; 144. a storage unit; 145. an early warning unit; 146. a buffer unit; 147. and a cleaning unit.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
In the first embodiment, please refer to fig. 1, a cloud data storage and local edge control system for a power grid internet of things is provided, where the local edge control system 1 is configured to process power grid data in a defined area, the local edge control system 1 is in communication connection with a terminal server, the local edge control system 1 includes an internet of things acquisition module 11, an internet of things gateway 12, a cloud storage module 13, and a local edge control module 14, the internet of things acquisition module 11 is in communication connection with the internet of things gateway 12, the internet of things gateway 12 is in communication connection with the cloud storage module 13, the cloud storage module 13 is connected with an external storage system, the cloud storage module 13 stores pre-stored data, and the internet of things gateway 12 is in communication connection with the local edge control module 14.
The local edge control module 14 comprises an edge controller, the edge controller is a physical interface between IT and OT, and on the basis of completing the control function of a workstation or a production line, the interface capability and the computing capability of the industrial equipment are improved, the applicability of the industrial equipment is improved, and the data acquired by the internet of things acquisition module 11 can be transmitted to the edge controller in time for processing through connection between the edge controller and the internet of things gateway 12.
Thing allies oneself with collection module 11 includes electric wire netting data acquisition unit 111, control data acquisition unit 112, user side data acquisition unit 113 and distribution end data acquisition unit 114, electric wire netting data acquisition unit 111 is used for gathering local electric wire netting operating data, control data acquisition unit 112 is used for gathering local electric wire netting equipment's monitoring data, user side data acquisition unit 113 is used for gathering local electric wire netting's user power consumption data, distribution end data acquisition unit 114 is used for gathering distribution system's distribution data, and above-mentioned four data contain the most important component part in the electric wire netting data among the electric power system, through the collection to above-mentioned data, can carry out comprehensive data monitoring to electric power system.
The internet of things gateway 12 is configured to transmit the data collected in the internet of things collection module 11 to the local edge control module 14.
The local edge control module 14 includes a first defining unit 141, a second defining unit 142, a feature comparing unit 143, and a storage unit 144;
the first defining unit 141 is configured to define a category of pre-stored data in the cloud storage module 13, and the second defining unit 142 is configured to define a category of data acquired by the internet of things acquisition module 11;
the first dividing subunit is configured with a first dividing strategy; the second delimiting unit 142 is configured with a second delimiting strategy;
the first planning strategy comprises: dividing the power grid data in the pre-stored data into first main point power grid data, marking the first main point power grid data as A1, and adding n to a plurality of branch data in the first main point power grid data in sequence at a rear digit marked by A1;
dividing monitoring data in the pre-stored data into first main point monitoring data, marking the first main point monitoring data as A2, and adding m to a plurality of branch data in the first main point monitoring data in sequence at a rear digit marked by A2;
dividing user side data in the pre-stored data into first main point user side data, marking the first main point user side data as A3, and adding i to a plurality of branch data in the first main point user side data in sequence at a back digit marked by A3;
dividing user side data in the pre-stored data into first main point power distribution end data, marking the first main point power distribution end data as A4, and sequentially adding j to a plurality of branch data in the first main point power distribution end data at a rear digit marked by A4;
the second demarcation strategy includes: classifying data acquired by the power grid data acquisition unit 111, the monitoring data acquisition unit 112, the user side data acquisition unit 113 and the power distribution end data acquisition unit 114 into first main point power grid data, first main point monitoring data, first main point user side data and first main point power distribution end data, and sequentially adding 1 to back digits of branch data marks in a first division strategy corresponding to branch data in the first main point power grid data, the first main point monitoring data, the first main point user side data and the first main point power distribution end data;
the feature comparing unit 143 is configured with a feature screening policy, which includes: extracting safety data in the pre-stored data, dividing a safety range of each group of data according to the range of the safety data, dividing a value range exceeding the safety range into danger ranges, and dividing the danger ranges into danger grades, wherein the danger grades comprise a high danger grade, a medium danger grade and a low danger grade;
respectively corresponding the first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data of the first main point power distribution end data to the safety range and the danger range of each group of data, re-marking the data in the safety range on the marks of the corresponding groups, and sequentially adding 1 to the back digits of the data marks in the danger range on the marks of the corresponding groups;
the storage unit 144 is configured with storage policies, which include: and storing the marks of the data in the safety range and the data in the danger range to a cloud storage module 13 through the internet of things gateway 12.
The first planning strategy further comprises: adding 001 to the rear digit of the mark of the corresponding group of the data in the safety range; where a1 is set to 100, a2 is set to 200, A3 is set to 300, a4 is set to 400, and n, m, i, and j are positive integers less than 10, respectively.
For example, one branch data of the grid data in the pre-stored data may be labeled as 1001, one branch data corresponding to one of the newly collected grid data belonging to the safety range may be labeled as 1001001, and one branch data corresponding to one of the newly collected grid data belonging to the hazard range may be labeled as 10011.
In a second embodiment, referring to fig. 2, the local edge control module 14 further includes an early warning unit 145, where the early warning unit 145 is configured with an early warning policy, where the early warning policy includes: when the received first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data in the first main point power distribution end data belong to a dangerous range, the dangerous grade corresponding to the data is sent to a terminal server through the internet of things gateway 12, and the early warning module is set to perform early warning in time when the power grid is monitored to be abnormal in data.
The local edge control module 14 further includes a caching unit 146, where the caching unit 146 is configured with caching policies, where the caching policies include: when received branch data in the received first main point power grid data, the first main point monitoring data, the first main point user end data and the first main point power distribution end data belong to a safety range, the data are stored in the cache unit 146, a mark corresponding to the data is stored in the cloud storage module 13 through the internet of things gateway 12, the cache unit 146 is set to cache the power grid data in the safety range, the data are marked, and the mark of the power grid data in the safety range is stored in the cloud storage module 13, so that the storage space of the data can be greatly reduced.
The local edge control module 14 further comprises a cleaning unit 147, the cleaning unit 147 being configured with a cleaning strategy comprising: the data in the cache unit 146 is deleted every first period, and the effective data storage amount of the cloud storage space can be increased by periodically deleting the data in the secure range occupying the largest storage space.
The processing method of the cloud data storage and local edge control system 1 facing the power grid internet of things comprises the following steps: step A, data in a local power grid are collected through an Internet of things collection module 11 and then transmitted to a local edge control module 14 through an Internet of things gateway 12 for processing;
step B, classifying the data collected by the grid data collection unit 111, the monitoring data collection unit 112, the user side data collection unit 113 and the distribution end data collection unit 114 into first main point grid data, first main point monitoring data, first main point user side data and first main point distribution end data, and respectively marking the data as 100, 200, 300 and 400;
step C, further classifying the first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data in the first main point power distribution end data, sequentially marking the branch data in the first main point power grid data as 1001 to 100n, sequentially marking the branch data in the first main point monitoring data as 2001 to 200m, sequentially marking the branch data in the first main point user end data as 3001 to 300i, and sequentially marking the branch data in the first main point power distribution end data as 4001 to 400 j;
d, classifying the power grid data in the cloud storage module 13 according to the classification method in the step B and the classification method in the step C, extracting the safety data in each group of branch data, and defining the safety range and the danger range of each group according to the safety data of each group;
and E, respectively corresponding the data acquired by the internet of things acquisition module 11 to the classified data in the cloud storage module 13, respectively comparing the data with the safety range of the corresponding group, increasing the number of digits 001 of the data belonging to the safety range in the mark of the corresponding group, sequentially adding 1 to the number of digits of the data mark in the danger range in the mark of the corresponding group, and then storing the marked data to the remote storage module through the internet of things gateway 12.
The step E further comprises the following steps: step E1, the tag of the data belonging to the safe range is stored in the remote storage module through the internet of things gateway 12, the data belonging to the dangerous range is stored in the cloud storage module 13 through the internet of things gateway 12 after being tagged, and the data belonging to the safe range is stored in the cache unit 146.
The method further comprises the following steps: and F, dividing the danger range into danger levels, marking the danger levels on the data in the danger range, and sending the data marked with the danger levels to the terminal server through the Internet of things gateway 12.
The method further comprises the following steps: and G, deleting the data in the buffer unit 146 at intervals of a first period.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. A cloud data storage and local edge control system facing to power grid internet of things is characterized in that, the local edge control system (1) is used for processing the power grid data in the delimited area, the local edge control system (1) is in communication connection with the terminal server, the local edge control system (1) comprises an internet of things acquisition module (11), an internet of things gateway (12), a cloud storage module (13) and a local edge control module (14), the internet of things acquisition module (11) is in communication connection with the internet of things gateway (12), the internet of things gateway (12) is in communication connection with the cloud storage module (13), the cloud storage module (13) is connected with an external storage system, the cloud storage module (13) stores pre-stored data, the internet of things gateway (12) is in communication connection with a local edge control module (14).
2. The grid-tie oriented cloud data storage and local edge control system of claim 1,
the internet of things acquisition module (11) comprises a power grid data acquisition unit (111), a monitoring data acquisition unit (112), a user side data acquisition unit (113) and a power distribution side data acquisition unit (114), wherein the power grid data acquisition unit (111) is used for acquiring local power grid operation data, the monitoring data acquisition unit (112) is used for acquiring monitoring data of local power grid equipment, the user side data acquisition unit (113) is used for acquiring user power consumption data of a local power grid, and the power distribution side data acquisition unit (114) is used for acquiring power distribution data of a power distribution system;
the Internet of things gateway (12) is used for transmitting the data acquired in the Internet of things acquisition module (11) to the local edge control module (14);
the local edge control module (14) comprises a first defining unit (141), a second defining unit (142), a feature comparison unit (143) and a storage unit (144);
the first demarcation unit (141) is used for demarcating the category of pre-stored data in the cloud storage module (13), and the second demarcation unit (142) is used for demarcating the category of data acquired by the Internet of things acquisition module (11);
the first dividing subunit (1411) is configured with a first dividing strategy; the second delimiting unit (142) is configured with a second delimiting strategy;
the first planning strategy comprises: dividing the power grid data in the pre-stored data into first main point power grid data, marking the first main point power grid data as A1, and adding n to a plurality of branch data in the first main point power grid data in sequence at a rear digit marked by A1;
dividing monitoring data in the pre-stored data into first main point monitoring data, marking the first main point monitoring data as A2, and adding m to a plurality of branch data in the first main point monitoring data in sequence at a rear digit marked by A2;
dividing user side data in the pre-stored data into first main point user side data, marking the first main point user side data as A3, and adding i to a plurality of branch data in the first main point user side data in sequence at a back digit marked by A3;
dividing user side data in the pre-stored data into first main point power distribution end data, marking the first main point power distribution end data as A4, and sequentially adding j to a plurality of branch data in the first main point power distribution end data at a rear digit marked by A4;
the second demarcation strategy includes: classifying data acquired by a power grid data acquisition unit (111), a monitoring data acquisition unit (112), a user side data acquisition unit (113) and a power distribution end data acquisition unit (114) into first main point power grid data, first main point monitoring data, first main point user end data and first main point power distribution end data, and sequentially adding 1 to back digits of branch data marks in a first planning strategy, which correspond to branch data in the first main point power grid data, the first main point monitoring data, the first main point user end data and the first main point power distribution end data;
the feature alignment unit (143) is configured with a feature screening strategy comprising: extracting safety data in the pre-stored data, dividing a safety range of each group of data according to the range of the safety data, dividing a value range exceeding the safety range into danger ranges, and dividing the danger ranges into danger grades, wherein the danger grades comprise a high danger grade, a medium danger grade and a low danger grade;
respectively corresponding the first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data of the first main point power distribution end data to the safety range and the danger range of each group of data, re-marking the data in the safety range on the marks of the corresponding groups, and sequentially adding 1 to the back digits of the data marks in the danger range on the marks of the corresponding groups;
the storage unit (144) is configured with storage policies comprising: the method comprises the steps that the marks of data in a safety range and the data in a danger range are stored in a cloud storage module (13) through an internet of things gateway (12);
the first planning strategy further comprises: adding 001 to the rear digit of the mark of the corresponding group of the data in the safety range; where a1 is set to 100, a2 is set to 200, A3 is set to 300, a4 is set to 400, and n, m, i, and j are positive integers less than 10, respectively.
3. The grid-tie-oriented cloud data storage and local edge control system of claim 2, the local edge control module (14) further comprising an early warning unit (145), the early warning unit (145) configured with an early warning policy, the early warning policy comprising: when the received first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data in the first main point power distribution end data belong to a danger range, a danger grade corresponding to the data is sent to a terminal server through an internet of things gateway (12).
4. The grid-tie-oriented cloud data storage and local edge control system of claim 3, the local edge control module (14) further comprising a caching unit (146), the caching unit (146) configured with caching policies comprising: when branch data in the received first main point power grid data, first main point monitoring data, first main point user end data and first main point power distribution end data belong to a safety range, the data are stored in a cache unit (146), and marks corresponding to the data are stored in a cloud storage module (13) through an internet of things gateway (12).
5. The grid-tie-oriented cloud data storage and local edge control system of claim 4, the local edge control module (14) further comprising a cleaning unit (147), the cleaning unit (147) configured with a cleaning policy comprising: the data in the buffer unit (146) is deleted every first cycle.
6. The processing method of the cloud data storage and local edge control system facing the power grid internet of things according to any one of claims 2 to 5, wherein the method comprises the following steps:
a, data in a local power grid are collected through an internet of things collection module (11), and then are transmitted to a local edge control module (14) through an internet of things gateway (12) for processing;
step B, classifying the data acquired by the power grid data acquisition unit (111), the monitoring data acquisition unit (112), the user side data acquisition unit (113) and the power distribution end data acquisition unit (114) into first main point power grid data, first main point monitoring data, first main point user side data and first main point power distribution end data, and respectively marking the data as 100, 200, 300 and 400;
step C, further classifying the first main point power grid data, the first main point monitoring data, the first main point user end data and the branch data in the first main point power distribution end data, sequentially marking the branch data in the first main point power grid data as 1001 to 100n, sequentially marking the branch data in the first main point monitoring data as 2001 to 200m, sequentially marking the branch data in the first main point user end data as 3001 to 300i, and sequentially marking the branch data in the first main point power distribution end data as 4001 to 400 j;
d, classifying the power grid data in the cloud storage module (13) according to the classification method in the step B and the classification method in the step C, extracting the safety data in each group of branch data, and defining the safety range and the danger range of each group according to the safety data of each group;
and E, respectively corresponding the data acquired by the internet of things acquisition module (11) to the classified data in the cloud storage module (13), respectively comparing the data with the safety range of the corresponding group, increasing the number of digits 001 of the data belonging to the safety range in the mark of the corresponding group, sequentially adding 1 to the number of digits of the data mark in the danger range in the mark of the corresponding group, and then storing the marked data to the remote storage module through the internet of things gateway (12).
7. The method of claim 6, the step E further comprising: and E1, storing the marks of the data in the safety range to a remote storage module through the Internet of things gateway (12), storing the marked data in the danger range to a cloud storage module (13) through the Internet of things gateway (12), and storing the data in the safety range to a cache unit (146).
8. The method of claim 7, further comprising: and F, dividing the danger range into danger levels, marking the danger levels on the data in the danger range, and sending the data marked with the danger levels to the terminal server through the Internet of things gateway (12).
9. The method of claim 8, further comprising: and G, deleting the data in the buffer unit (146) at intervals of a first period.
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