CN112926751A - Power transmission and transformation auxiliary equipment service system based on big data - Google Patents
Power transmission and transformation auxiliary equipment service system based on big data Download PDFInfo
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Abstract
The invention discloses a big data-based power transmission and transformation auxiliary equipment service system, which comprises: the system comprises a data gateway module, a data acquisition module, a big data analysis module and a data display module; the data gateway module is used for connecting each terminal device to enable the hardware device to have communication capacity; the data acquisition module is used for acquiring and storing the real-time information of the terminal equipment from the data gateway module according to a preset time interval; the big data analysis module is used for carrying out early warning analysis on the real-time information of the terminal equipment; the data display module is used for displaying the early warning analysis result of the big data analysis module and the real-time information of the terminal equipment. According to the invention, through a big data analysis method, a daily information base and an alarm base are established, the real-time condition of the terminal equipment is mastered, timely and active intelligent service is provided for customers, and the problem that the power supply is unstable due to untimely equipment maintenance caused by untimely damage and maintenance of the terminal equipment is avoided.
Description
Technical Field
The invention relates to a power transmission and transformation auxiliary equipment service system based on big data, and belongs to the technical field of power transmission and transformation system monitoring.
Background
The power transmission and transformation system consists of a series of electrical equipment, and auxiliary equipment such as a terminal box, a switch cabinet, a lightning arrester, a bird repeller and the like are arranged in addition. The traditional electric transmission and transformation auxiliary equipment is actively maintained after manual timing inspection or failure. In recent years, with the development of information technology, the conventional auxiliary device service is also changing. The service data generated by various auxiliary devices and the big data are fused, and the storage and the processing of the terminal device data are realized. By a big data analysis method, a daily information base and an alarm base are established, the real-time condition of the terminal equipment is mastered, and timely and active intelligent service is provided for customers. In the prior art, the situations that related personnel cannot timely and clearly master real-time information of all equipment and damage and maintenance of terminal equipment are not timely exist generally. The problem of unstable power supply due to untimely maintenance of the equipment sometimes occurs.
Disclosure of Invention
The invention aims to provide a big data-based power transmission and transformation auxiliary equipment service system, which is used for establishing a daily information base and an alarm base, mastering the real-time condition of terminal equipment and providing timely and active intelligent service for customers.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention provides a big data-based power transmission and transformation auxiliary equipment service system, which comprises: the system comprises a data gateway module, a data acquisition module, a big data analysis module and a data display module;
the data gateway module is used for connecting each terminal device; the terminal equipment comprises a terminal box, a switch cabinet, a lightning arrester and a bird repeller;
the data acquisition module is used for acquiring and storing the real-time information of the terminal equipment from the data gateway module according to a preset time interval;
the big data analysis module is used for carrying out early warning analysis on the real-time information of the terminal equipment;
and the data display module is used for displaying the early warning analysis result of the big data analysis module and the real-time information of the terminal equipment.
Further, the terminal device communicates with the respective data receiving box or the main case in a wireless manner, and stores the terminal data in the respective receiving box or the main case;
and the data gateway module is connected with a data receiving box or a mainframe box of the terminal equipment.
Further, the data acquisition module is specifically configured to,
communicating with a data gateway module according to the address and the port of the terminal equipment to acquire corresponding terminal data;
the data acquisition module reads terminal data from the data gateway module in the following format:
1 byte header-fixed value 0x 68;
frame length of 2 bytes-data length of the entire frame;
1 byte equipment type-lightning arrester 0x 01/terminal box 0x 02/switch cabinet 0x 03/bird repeller 0x 05;
1 byte instruction type-0 x 11;
1 byte check code — calculated using CRC 8;
1 byte end of frame-fixed value 0x 68;
the data gateway module responds terminal data to the data acquisition module in the following format:
1 byte header-fixed value 0x 16;
frame length of 2 bytes-data length of the entire frame;
1 byte equipment type-lightning arrester 0x 01/terminal box 0x 02/switch cabinet 0x 03/bird repeller 0x 05;
1 byte instruction type-0 x 91;
n bytes of device data, namely lightning arrester device data/terminal box device data/switch cabinet device data/bird repeller device data, where N is the length of a single device data and the number of data;
1 byte check code — calculated using CRC 8;
1 byte end of frame-fixed value 0x 16.
Further, the device data format is as follows:
terminal box device data: 4 bytes of device ID, 5 bytes of data time, 1 byte of temperature data, 1 byte of humidity data, 2 bytes of fan current, 2 bytes of heater current;
switchgear equipment data: 4 bytes of device ID, 1 byte of temperature data, 1 byte of humidity data, 1 byte of device status;
bird repeller equipment data: 4 bytes of equipment ID, 1 byte of reserved field 1, 5 bytes of data time, 1 byte of voltage value, 2 bytes of reserved field 2, 1 byte of audible sound health, 1 byte of red light health, 1 byte of blue light health, 2 bytes of ultrasonic bird repelling accumulated time within 15 minutes, 2 bytes of audible sound bird repelling accumulated time within 15 minutes, and 2 bytes of 15-minute implosion flashing light bird repelling accumulated time;
lightning arrester equipment data: a device ID of 3 bytes, a data time of 6 bytes, waveform data of 16 bytes, a current peak of 2 bytes, a lightning strike number of 2 bytes.
Further, the data acquisition module is also used for,
judging whether the frame header of the received response data frame is a frame header fixed value or not; if yes, carrying out the next step;
intercepting the complete data according to the frame length;
calculating a check code according to the complete data, and if the check code is correct, carrying out the next step;
checking whether the frame tail is a frame tail fixed value; if yes, carrying out the next step;
according to the data type of the terminal equipment, dividing the equipment data length in the response data by the single equipment data length corresponding to the type of the terminal equipment to obtain the total data record number;
equally dividing the equipment data in the response data according to the collected total data record number to obtain single terminal data;
and analyzing each piece of terminal data independently to obtain and store specific terminal information.
Further, the big data analysis module is specifically configured to,
carrying out data cleaning on the acquired data of the online terminal equipment;
comparing the cleaned data with an alarm threshold value of the terminal equipment;
if the alarm is not in the alarm threshold range, giving an alarm;
if the alarm threshold value is within the range, data prediction is carried out;
and if the predicted data is not within the alarm threshold range, giving an early warning.
Further, the big data analysis module is also used for,
setting alarm threshold values of different terminal devices, specifically as follows:
alarm threshold of terminal box:
maximum humidity threshold: 66-85;
minimum humidity threshold: 55-65 parts;
maximum threshold of temperature: 15-25;
minimum threshold for temperature: 5-10;
the switch cabinet has no alarm threshold value setting;
the bird repeller has no alarm threshold value setting;
the lightning arrester has no alarm threshold value.
Further, the big data analysis module is specifically configured to,
acquiring a first data record for the same terminal device, generating a hash value by using the device ID and the data time, and temporarily storing the generated hash value and the terminal data;
acquiring the next data record of the terminal equipment, generating a hash value by the equipment ID and the data time, checking whether the generated hash value exists in temporary storage, and if not, temporarily storing the generated hash value and the terminal data; if yes, not storing;
repeating the above process to clean all data records;
for the switch cabinet equipment, the data time adopts the server acquisition time as the data time.
Further, the big data analysis module is specifically configured to,
the latest 6 pieces of cleaned data are taken from the same terminal equipment and are arranged according to the time sequence; if the data does not have 6 pieces of data, the terminal equipment does not give an early warning;
subtracting the last second data value from the last data value to obtain a difference value; subtracting the last third data value from the last second data value to obtain a difference value; and so on, 5 difference values are obtained in total;
summing the 5 difference values to obtain an average value;
adding the average value to the latest data of the terminal equipment to obtain a predicted value;
and if the predicted value is not within the alarm threshold range, early warning is carried out.
Further, if the single terminal device does not generate any collected data within 30 minutes, the terminal device is offline; otherwise online.
The invention achieves the following beneficial effects:
according to the invention, through a big data analysis method, a daily information base and an alarm base are established, the real-time condition of the terminal equipment is mastered, timely and active intelligent service is provided for customers, and the problem that the power supply is unstable due to untimely equipment maintenance caused by untimely damage and maintenance of the terminal equipment is avoided.
Drawings
Fig. 1 is a big data-based service system architecture of electric transmission and transformation auxiliary equipment according to the present invention.
Detailed Description
The invention is further described below. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention provides a big data-based power transmission and transformation auxiliary equipment service system which comprises a data gateway module, a data acquisition module, a big data analysis module and a data display module.
In particular, the method comprises the following steps of,
and the data gateway module is used for connecting each terminal device so that the terminal devices have uniform network communication capacity.
And the data acquisition module is used for acquiring and storing the real-time information of the terminal equipment from the data gateway module according to the preset time interval.
And the big data analysis module is used for carrying out early warning analysis on the real-time information.
And the data display module is used for displaying the result of the big data analysis and the real-time information.
Specifically, referring to fig. 1, the terminal device includes a terminal box, a switch cabinet, a lightning arrester, and a bird repeller. Each terminal device communicates with the respective data receiving box or the main case in a wireless mode, and stores the terminal data in the respective receiving box or the main case.
The data receiving box or the main case is connected with the data gateway module, so that the data receiving box and the main case have the capability of communicating with the outside.
Specifically, the data acquisition module communicates with the data gateway module according to the address and the port of the terminal device to acquire corresponding terminal data.
Specifically, the reading and response format of the terminal data is as follows:
table 1 data acquisition module read data instruction
Table 2 data gateway module response data
Table 3 terminal box response data-device data reply
Table 4 switchgear response data-device data reply
| Device ID | 4 bytes |
| Temperature data | 1 byte |
| Humidity data | 1 byte |
| Device status | 1 byte |
TABLE 5 bird repeller response data-device data reply
| Device ID | 4 bytes |
| Reserved field 1 | 1 byte |
| Time of data | 5 bytes |
| Value of voltage | 1 byte |
| Reserved field 2 | 2 bytes |
| Audible sound health | 1 byte |
| Red light health | 1 byte |
| Blue light health | 1 byte |
| Ultrasonic bird repelling cumulative time within 15 minutes | 2 bytes |
| Bird repelling cumulative time capable of hearing within 15 minutes | 2 bytes |
| Cumulative bird repelling time of 15 minutes implosion flashing lamp | 2 bytes |
Table 6 arrester response data-device data reply
| Device ID | 3 bytes |
| Time of data | 6 bytes |
| Waveform data | 16 bytes |
| Peak value of current | 2 bytes |
| Number of lightning strokes | 2 bytes |
Specifically, the data acquisition module is further configured to calculate a check code for information exchange between the data acquisition module and the data gateway module:
the check code is used for checking the correctness of the frame header, the frame length, the equipment type, the instruction type and the equipment data, the CRC8 is adopted to calculate the check code in the invention, and the calculation process is as follows:
a: defining a byte array with the length of 128 and a default checking result;
b: acquiring a byte array and a length of a data instruction or response data before a check code;
c: traversing the byte array in the step B, checking each byte data, carrying out XOR operation on each byte data and the check result, setting the XOR operation result as the byte array index defined in the step 1, and acquiring the array content as a new check code according to the array index; the new check code is used as the check code for the next calculation;
d: and D, the check code obtained in the step C is the final check code.
Specifically, the data acquisition module accesses the data gateway module at regular time. And the data gateway module returns the terminal data to the data acquisition module, and deletes the historical terminal records stored locally.
Specifically, the data acquisition module is further configured to analyze the acquired terminal data, where the analysis process is as follows:
a: and judging whether the frame header of the whole data is a frame header fixed value or not. If so, the next step is performed.
B: and acquiring the frame length, and intercepting the complete data according to the frame length.
C: and calculating a check code according to the complete data, and judging whether the data is correct or not. If it is correct to proceed to the next step.
D: and checking whether the frame tail is a fixed value of the frame tail. If it is correct, proceed to the next step.
E: intercepting the device data from the complete data:
the device data lengths of different terminal device types are fixed, and the total device data length in the response data is divided by the corresponding single device data length defined in tables 3-6 to obtain the total data record number;
and equally dividing the equipment data in the response data according to the collected total data record number to obtain single terminal data.
F: and according to the definitions of the terminal data formats in the tables 3 to 6, each piece of terminal data is independently analyzed to obtain specific terminal information.
Specifically, the data acquisition module is further configured to store the analyzed terminal data.
In particular, the big data analysis module is specifically configured to,
setting alarm thresholds of different terminal devices, wherein the specific format is defined as follows:
TABLE 7 terminal Box alarm threshold settings
| Maximum humidity threshold | The threshold range is 66-85 |
| Minimum humidity threshold | The threshold range is 55-65 |
| Maximum threshold value of temperature | The threshold range is 15-25 |
| Minimum threshold value of temperature | The threshold range is 5-10 |
The switch cabinet has no alarm threshold value setting;
the bird repeller has no alarm threshold value setting;
the lightning arrester has no alarm threshold value.
The big data analysis module is also used for carrying out alarm analysis according to the terminal data:
a: the single terminal device does not receive any collected data within 30 minutes and is considered to be offline; otherwise online.
B: carrying out alarm analysis on the online terminal equipment:
b1: the data is cleaned up by the data cleaning device,
the data acquisition frequency is high, and real-time data of the terminal equipment at the same moment can be acquired for multiple times. For repeated data, it needs to be cleaned, and only one valid data is kept.
The terminal data has the equipment ID and the data time, the first storage record is obtained, the equipment ID and the data time are generated into a hash value, and the generated hash value and the terminal data are temporarily stored.
And acquiring the next record of the terminal equipment, generating a hash value by the equipment ID and the data time, checking whether the generated hash value exists in the temporary storage, and if not, temporarily storing the generated hash value and the terminal data. If the record exists, the record is repeated, and no processing is performed. Repeating this step cleans up all data records.
And after the cleaning is finished, storing the cleaned terminal data.
Note that, according to table 4, the time when the switch cabinet has no data time, and the time of the server is used as the data time by default when the hash value is calculated.
B2: the alarm is analyzed, and the alarm is analyzed,
and comparing the cleaning data with an alarm threshold value of the terminal equipment, and regarding the cleaning data as an alarm if the cleaning data is not within the threshold value range.
B3: the early-warning analysis is carried out,
and predicting the data within the threshold range, and regarding the data with the prediction result not within the threshold range as early warning. The early warning can be preprocessed before a fault occurs, and the method comprises the following specific steps:
the same terminal device takes the latest 6 pieces of cleaned data, and the data are arranged according to the time sequence. If there are no 6 pieces of data, the device does not warn.
And subtracting the last second data value from the last data value to obtain a difference value. And subtracting the last third data value from the last second data value to obtain a difference value. By analogy, a total of 5 difference values can be obtained.
The 5 differences are summed and averaged.
If the average value is greater than 0, the data is indicated to have a rising trend. If the average value is less than 0, the data is indicated to have a downward trend.
The latest terminal data of the equipment plus the average value is the predicted value. And if the predicted value is not within the preset threshold range, the early warning is regarded as early warning.
And the big data analysis module is also used for recording and storing all alarms.
The big data analysis module is also used for sending all alarm/early warning information to the data display module, so that related personnel can know the situation in time.
And the big data analysis module is also used for carrying out cluster statistics on the terminal equipment data of the previous day and storing the terminal equipment data.
Specifically, the data display module is used for displaying an alarm processing result and a data processing result of the big data analysis module.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. The utility model provides a power transmission and transformation auxiliary equipment service system based on big data which characterized in that includes: the system comprises a data gateway module, a data acquisition module, a big data analysis module and a data display module;
the data gateway module is used for connecting each terminal device; the terminal equipment comprises a terminal box, a switch cabinet, a lightning arrester and a bird repeller;
the data acquisition module is used for acquiring and storing the real-time information of the terminal equipment from the data gateway module according to a preset time interval;
the big data analysis module is used for carrying out early warning analysis on the real-time information of the terminal equipment;
and the data display module is used for displaying the early warning analysis result of the big data analysis module and the real-time information of the terminal equipment.
2. A big data based transmission and transformation auxiliary equipment service system according to claim 1, wherein the terminal equipment wirelessly communicates with the respective data receiving box or main chassis and stores the terminal data in the respective receiving box or main chassis;
and the data gateway module is connected with a data receiving box or a mainframe box of the terminal equipment.
3. The big data based power transmission and transformation auxiliary equipment service system according to claim 1, wherein the data collection module is specifically configured to,
communicating with a data gateway module according to the address and the port of the terminal equipment to acquire corresponding terminal data;
the data acquisition module reads terminal data from the data gateway module in the following format:
1 byte header-fixed value 0x 68;
frame length of 2 bytes-data length of the entire frame;
1 byte equipment type-lightning arrester 0x 01/terminal box 0x 02/switch cabinet 0x 03/bird repeller 0x 05;
1 byte instruction type-0 x 11;
1 byte check code — calculated using CRC 8;
1 byte end of frame-fixed value 0x 68;
the data gateway module responds terminal data to the data acquisition module in the following format:
1 byte header-fixed value 0x 16;
frame length of 2 bytes-data length of the entire frame;
1 byte equipment type-lightning arrester 0x 01/terminal box 0x 02/switch cabinet 0x 03/bird repeller 0x 05;
1 byte instruction type-0 x 91;
n bytes of device data — arrester device data/terminal box device data/switch cabinet device data/bird repeller device data, N = length of single device data × number of data;
1 byte check code — calculated using CRC 8;
1 byte end of frame-fixed value 0x 16.
4. The big data based electric transmission and transformation auxiliary equipment service system according to claim 3, wherein the equipment data format is as follows:
terminal box device data: 4 bytes of device ID, 5 bytes of data time, 1 byte of temperature data, 1 byte of humidity data, 2 bytes of fan current, 2 bytes of heater current;
switchgear equipment data: 4 bytes of device ID, 1 byte of temperature data, 1 byte of humidity data, 1 byte of device status;
bird repeller equipment data: 4 bytes of equipment ID, 1 byte of reserved field 1, 5 bytes of data time, 1 byte of voltage value, 2 bytes of reserved field 2, 1 byte of audible sound health, 1 byte of red light health, 1 byte of blue light health, 2 bytes of ultrasonic bird repelling accumulated time within 15 minutes, 2 bytes of audible sound bird repelling accumulated time within 15 minutes, and 2 bytes of 15-minute implosion flashing light bird repelling accumulated time;
lightning arrester equipment data: a device ID of 3 bytes, a data time of 6 bytes, waveform data of 16 bytes, a current peak of 2 bytes, a lightning strike number of 2 bytes.
5. The big data based power transmission and transformation auxiliary equipment service system according to claim 3, wherein the data collection module is further configured to,
judging whether the frame header of the received response data frame is a frame header fixed value or not; if yes, carrying out the next step;
intercepting the complete data according to the frame length;
calculating a check code according to the complete data, and if the check code is correct, carrying out the next step;
checking whether the frame tail is a frame tail fixed value; if yes, carrying out the next step;
according to the data type of the terminal equipment, dividing the equipment data length in the response data by the single equipment data length corresponding to the type of the terminal equipment to obtain the total data record number;
equally dividing the equipment data in the response data according to the collected total data record number to obtain single terminal data;
and analyzing each piece of terminal data independently to obtain and store specific terminal information.
6. The big data based power transmission and transformation auxiliary equipment service system according to claim 1, wherein the big data analysis module is specifically configured to,
carrying out data cleaning on the acquired data of the online terminal equipment;
comparing the cleaned data with an alarm threshold value of the terminal equipment;
if the alarm is not in the alarm threshold range, giving an alarm;
if the alarm threshold value is within the range, data prediction is carried out;
and if the predicted data is not within the alarm threshold range, giving an early warning.
7. The big data based power transmission and transformation auxiliary equipment service system according to claim 6, wherein the big data analysis module is further configured to,
setting alarm threshold values of different terminal devices, specifically as follows:
alarm threshold of terminal box:
maximum humidity threshold: 66-85;
minimum humidity threshold: 55-65 parts;
maximum threshold of temperature: 15-25;
minimum threshold for temperature: 5-10;
the switch cabinet has no alarm threshold value setting;
the bird repeller has no alarm threshold value setting;
the lightning arrester has no alarm threshold value.
8. The big data based power transmission and transformation auxiliary equipment service system according to claim 6, wherein the big data analysis module is specifically configured to,
acquiring a first data record for the same terminal device, generating a hash value by using the device ID and the data time, and temporarily storing the generated hash value and the terminal data;
acquiring the next data record of the terminal equipment, generating a hash value by the equipment ID and the data time, checking whether the generated hash value exists in temporary storage, and if not, temporarily storing the generated hash value and the terminal data; if yes, not storing;
repeating the above process to clean all data records;
for the switch cabinet equipment, the data time adopts the server acquisition time as the data time.
9. The big data based power transmission and transformation auxiliary equipment service system according to claim 6, wherein the big data analysis module is specifically configured to,
the latest 6 pieces of cleaned data are taken from the same terminal equipment and are arranged according to the time sequence; if the data does not have 6 pieces of data, the terminal equipment does not give an early warning;
subtracting the last second data value from the last data value to obtain a difference value; subtracting the last third data value from the last second data value to obtain a difference value; and so on, 5 difference values are obtained in total;
summing the 5 difference values to obtain an average value;
adding the average value to the latest data of the terminal equipment to obtain a predicted value;
and if the predicted value is not within the alarm threshold range, early warning is carried out.
10. The big data based power transmission and transformation auxiliary equipment service system according to claim 6, wherein if a single terminal device does not generate any collected data within 30 minutes, it is offline; otherwise online.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109102690A (en) * | 2018-09-05 | 2018-12-28 | 西安西变组件有限公司 | Power station equipment remote patrol system based on Internet technology |
| CN112036610A (en) * | 2020-08-11 | 2020-12-04 | 广州番禺电缆集团有限公司 | Intelligent cable early warning platform and early warning method based on big data analysis |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109102690A (en) * | 2018-09-05 | 2018-12-28 | 西安西变组件有限公司 | Power station equipment remote patrol system based on Internet technology |
| CN112036610A (en) * | 2020-08-11 | 2020-12-04 | 广州番禺电缆集团有限公司 | Intelligent cable early warning platform and early warning method based on big data analysis |
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