CN108600345B - Self-learning method of power concentrator - Google Patents
Self-learning method of power concentrator Download PDFInfo
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Abstract
The invention relates to a self-learning method of a power concentrator, which is characterized in that the power concentrator stores an analysis strategy corresponding to a message request of a main station of a power office, which can be analyzed, into a message analysis strategy database of the power concentrator, and feeds back the message request of the main station of the power office, which cannot be analyzed, of the power concentrator to a background of a manufacturer of the power concentrator, so that a correct message analysis strategy fed back by the background of the manufacturer of the power concentrator is obtained and is fed back to the message analysis strategy database, and therefore the actual analysis requirement of the power concentrator on most of the message requests sent by the main station of the power office is met by continuously updating the message analysis strategy database of the power concentrator. According to the self-learning method of the power concentrator, the problem of complexity caused by the fact that technicians do field maintenance back and forth can be effectively solved only through remote data communication between the power concentrator and a power station master station and between the power concentrator and a power concentrator manufacturer background.
Description
Technical Field
The invention relates to the field of power concentrators, in particular to a self-learning method of a power concentrator.
Background
In recent years, there are many problems with power concentrators, and power station masters in provinces have made various demands on power concentrator manufacturers and also have made various problems with these power concentrators. And feeding back to the power concentrator manufacturer.
In general, after receiving a problem sent by a power office master station, a power concentrator manufacturer assigns a technician to the power concentrator with the problem in time to perform field inspection and maintenance; then, the technician can feed back the problem of the power concentrator found in the maintenance process to the developer of the power concentrator factory in time, at this time, the power concentrator factory needs to arrange the staff to modify the corresponding program of the power concentrator according to the problem, and the technician goes to the site of the power concentrator with the problem to perform upgrading treatment, thereby completing the whole solving process for the problem of the power concentrator.
However, the existing whole solving process for the problem of the power concentrator takes a long time, and wastes a lot of manpower and material resources, that is, a technician of a power concentrator manufacturer needs to continuously go and go between a power concentrator site and the power concentrator manufacturer, and the technician needs to timely feed back the problem of the power concentrator found in the maintenance process to a developer, so that the normal use of the power concentrator can be ensured. In addition, since the power concentrators are almost hung on higher poles or walls, it is inconvenient for technicians to perform field maintenance.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a self-learning method for a power concentrator in view of the above prior art. The self-learning method of the power concentrator can realize classification and analysis processing of the power concentrator aiming at the message request sent by the power station master station, and automatically replace the message analysis strategy database, thereby effectively avoiding the complex problem caused by field maintenance of technicians.
The technical scheme adopted by the invention for solving the technical problems is as follows: a self-learning method of a power concentrator is used for a system formed by a power concentrator, a power station main station and a power concentrator manufacturer background, wherein the power concentrator is respectively in communication connection with the power station main station and the power concentrator manufacturer background, and is characterized by sequentially comprising the following steps 1-8:
step 1, the power station master station sends a message request to a power concentrator;
step 2, the power concentrator receives and analyzes the message request sent by the power station main station:
when the power concentrator successfully analyzes the message request sent by the power station master station, the power concentrator sends feedback information corresponding to the message request to the power station master station; otherwise, the power concentrator forwards the message request sent by the master station of the power office to the background of the manufacturer of the power concentrator;
step 3, the power concentrator manufacturer background provides an analysis strategy aiming at a message request sent by the power concentrator to the power concentrator, and after the power concentrator successfully passes the test aiming at the message request by the power concentrator manufacturer background, the power concentrator stores the analysis strategy aiming at the message request into a message analysis strategy database of the power concentrator; otherwise, the power concentrator sends the unsuccessfully analyzed message request to the power concentrator manufacturer background again;
step 4, the power concentrator manufacturer background analyzes the message request which is not successfully analyzed by the power concentrator, and when the message request which is not successfully analyzed by the concentrator is successfully analyzed within the preset analysis times, the power concentrator manufacturer background sends the analysis strategy corresponding to the message request to the power concentrator, and the power concentrator updates the original message analysis strategy database; otherwise, the background assigned personnel of the power concentrator manufacturer perform field maintenance on the power concentrator;
step 5, the power concentrator detects abnormal information appearing in the running process of the power concentrator, and the power concentrator sends the detected abnormal information to a power concentrator manufacturer background; the background of the power concentrator manufacturer sends a solution strategy for solving the abnormal information of the power concentrator to the power concentrator, and the power concentrator solves the corresponding abnormal information by using the solution strategy;
step 6, the power bureau master station arranges the subsequent new message requests aiming at the power concentrator into a preset format request file and sends the arranged preset format request file to the power concentrator;
step 7, the power concentrator analyzes a preset format request file sent by the power station master station, and analyzes a new message request sent by the power station master station according to the preset format request file:
when the power concentrator successfully analyzes the new message request of the power station master station, the power concentrator feeds back a prompt of successful learning to the power station master station, the power concentrator stores an analysis strategy corresponding to the new message request to the updated message analysis strategy database in the step 4, and a self-learning updated message analysis strategy database is obtained; otherwise, the power concentrator feeds back a prompt of learning failure to the power station master station;
and 8, the power concentrator correspondingly analyzes the message request sent by the power station master station by using the analysis strategy in the self-learning updated message analysis strategy database.
Optionally, in the self-learning method of the power concentrator, the solution policy includes a patch file for power concentrator anomaly information or/and an upgrade program file for power concentrator anomaly information.
Optionally, in the self-learning method of the power concentrator, the preset format request file conforms to the 376 protocol or/and the 698 protocol.
In an improved self-learning method of the power concentrator, the message request includes at least one of a read data request for data within the power concentrator, a parameter setting request for an operating parameter of the power concentrator, and a control operation request for the power concentrator.
In a further improvement, the self-learning method of the power concentrator further comprises: and the power concentrator manufacturer background arranges the upgrading file of the power concentrator manufacturer aiming at the power concentrator into a preset format upgrading file and sends the arranged preset format upgrading file to the power concentrator, and the power concentrator correspondingly analyzes the upgrading file subsequently sent by the power concentrator manufacturer background according to the received preset format upgrading file.
In a further improvement, the self-learning method of the power concentrator further comprises: in step 7, the power station master station makes a judgment process according to the prompt of the power concentrator feedback learning failure: when the power station master station judges that the reason of the learning failure of the power concentrator is a software fault, the power station master station sends a software file for correspondingly solving the software fault to the power concentrator; when the power station master station judges that the reason of the learning failure of the power concentrator is a hardware fault, the power station master station assigns maintenance personnel to replace corresponding hardware.
In step 2, when the power concentrator successfully analyzes the message request sent by the power station master station, the power concentrator sends feedback information corresponding to the message request to the power station master station according to a preset sending time interval.
Further, in the self-learning method of the power concentrator, the preset number of times of analysis in step 4 is three; or/and the self-learning method of the power concentrator further comprises the following steps a 1-a 3:
a1, the electric concentrator statistically analyzes the electricity usage habit and the electricity consumption of the electric energy meter user, and the electric concentrator automatically sets a personalized collection scheme and a personalized collection task which are matched with the electricity usage habit and the electricity consumption of the electric energy meter user;
step a2, the power concentrator acquires the electric quantity related data and the electric energy meter related event data aiming at the corresponding electric energy meter by utilizing the personalized acquisition scheme and the personalized acquisition task; the personalized acquisition scheme comprises a common electric energy acquisition scheme, an event acquisition scheme and a reporting scheme; the personalized task comprises a task execution frequency configured and personalized for each user group;
step a3, the power concentrator respectively establishes a normal power utilization range of the electric energy meter corresponding to each time period for each electric energy meter under jurisdiction, and the power concentrator detects power consumption data of users corresponding to each electric energy meter:
when the electric power concentrator judges that the power consumption of a user in a corresponding time period exceeds the normal power consumption range of the user in the time period, the electric power concentrator reports the electric energy meter data of the user to an electric power station main station for early warning processing; otherwise, the electric energy meter data of the user is not reported to the main station of the electric power station by the electric power concentrator.
In step 5, the power concentrator sends the detected abnormal information to a power concentrator manufacturer background according to a preset sending frequency.
In another improvement, in the self-learning method of the power concentrator, the parsing strategy in the message parsing strategy database corresponds to the message request sent by the power station master station one by one.
Compared with the prior art, the invention has the advantages that:
firstly, the self-learning method of the power concentrator can realize the classification and analysis processing of the power concentrator aiming at the message request sent by the master station of the power station, and automatically replace the message analysis strategy database of the power concentrator, thereby effectively avoiding the complex problem caused by the back and forth on-site maintenance of technicians;
secondly, in the invention, new message requests which are sent to the power concentrator by the power station master station subsequently are arranged into the preset format request file, so that the preset format request file can be compatible with the 376 protocol and the 698 protocol simultaneously, and the power station master station sends the preset format request messages to the power concentrator, therefore, even if the subsequent power station master station sends the message request of the 376 protocol or the message request of the 698 protocol, the power concentrator in the invention can accurately analyze the message requests, thereby effectively improving the compatibility of the power concentrator for analyzing different protocol message requests sent by the power station master station.
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Fig. 1 is a schematic flow chart of a self-learning method of a power concentrator according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
As shown in fig. 1, the self-learning method of the power concentrator in this embodiment is used for a system formed by a power concentrator, a power office master station, and a power concentrator vendor background, the power concentrator is respectively in communication connection with the power office master station and the power concentrator vendor background, and the self-learning method of the power concentrator sequentially includes the following steps 1 to 8:
step 1, a master station of a power station sends a message request to a power concentrator;
for example, the message request in the present embodiment may include at least one of a read data request for data in the power concentrator, a parameter setting request for an operating parameter of the power concentrator, and a control operation request for the power concentrator;
step 2, the power concentrator receives and analyzes the message request sent by the power station main station:
when the power concentrator successfully analyzes the message request sent by the power station master station, the power concentrator sends feedback information corresponding to the message request to the power station master station; otherwise, the power concentrator forwards the message request sent by the master station of the power office to the background of the manufacturer of the power concentrator;
that is, in general, an analysis policy for a power station master station to send a message request is pre-stored in a power concentrator; for example, for a message request a sent by a master station of a power station, an analysis policy a for analyzing the message request a is stored in the power concentrator in advance; once the power concentrator determines that the message request A is received, the power concentrator analyzes the message request A by using a prestored analysis strategy a; once the message request B is sent by the power station master station received by the power concentrator, since the power concentrator does not have an analysis strategy for analyzing the message request B stored in advance, the power concentrator cannot successfully analyze the message request B at this time, and therefore, the power concentrator sends the message request B sent by the power station master station to a power concentrator manufacturer for background processing;
of course, in the step 2, when the power concentrator successfully analyzes the message request sent by the power station master station, the power concentrator may also send feedback information corresponding to the message request to the power station master station according to a preset sending time interval as required;
step 3, the power concentrator manufacturer background provides an analysis strategy aiming at a message request sent by the power concentrator to the power concentrator, and after the power concentrator successfully passes the test aiming at the message request by the power concentrator manufacturer background, the power concentrator stores the analysis strategy aiming at the message request into a message analysis strategy database of the power concentrator; otherwise, the power concentrator sends the unsuccessfully analyzed message request to the power concentrator manufacturer background again;
in the embodiment, the analysis strategies in the message analysis strategy database correspond to the message requests sent by the power office master station one by one;
in the step 3, the power concentrator manufacturer background simulates the power concentrator manufacturer into a power office master station, and then sends a message request of the power office master station which is fed back by the power concentrator and is not successfully analyzed to the power concentrator again, so that the power concentrator omits the message request sent by the power office master station identity from the power concentrator manufacturer background by utilizing an analysis strategy fed back by the power concentrator manufacturer background; once the power concentrator can successfully analyze the current message request at this time, it indicates that the analysis strategy of the current message request is correct, so that the power concentrator stores the analysis strategy aiming at the message request into a message analysis strategy database of the power concentrator; of course, if the power concentrator cannot successfully analyze the current message request, the power concentrator will send the unresolved message request to the power concentrator manufacturer for background processing again;
the solution strategy in the embodiment includes a patch file for the abnormal information of the power concentrator or/and an upgrade program file for the abnormal information of the power concentrator;
step 4, the background of the power concentrator manufacturer analyzes the message request which is not successfully analyzed by the power concentrator, and when the message request which is not successfully analyzed by the concentrator is successfully analyzed within the preset analysis times, the background of the power concentrator manufacturer sends the analysis strategy corresponding to the message request to the power concentrator, and the power concentrator updates the original message analysis strategy database; otherwise, background assigned personnel of a power concentrator manufacturer perform field maintenance on the power concentrator; for example, the preset number of times of parsing in this embodiment is set to three times;
step 5, the power concentrator detects abnormal information generated in the running process of the power concentrator, and the power concentrator sends the detected abnormal information to a power concentrator manufacturer background; a background of a power concentrator manufacturer sends a solution strategy for solving the abnormal information of the power concentrator to the power concentrator, and the power concentrator solves the corresponding abnormal information by using the solution strategy;
certainly, as an improvement, in step 5, the power concentrator may further send the detected abnormal information to a power concentrator manufacturer background according to a preset sending frequency;
step 6, the power bureau master station arranges the subsequent new message requests aiming at the power concentrator into a preset format request file and sends the arranged preset format request file to the power concentrator;
in this embodiment, the preset format request file for the new message request of the power office master station in step 6 may conform to a 376 protocol or a 698 protocol; for example:
when a preset format request file for the new message request of the power office master station conforms to the 376 specification, the power concentrator can analyze the message request of the power office master station conforming to the 376 specification according to the preset format request file;
when a preset format request file for the new message request of the power office master station conforms to a 698 protocol, the power concentrator can analyze the message request of the power office master station conforming to the 698 protocol according to the preset format request file;
certainly, as an optimal scheme, a preset format request file for a new message request of the power office master station is preferably compatible with a protocol conforming to 376 and a protocol conforming to 698, so that even when a subsequent power office master station sends a message request of the 376 protocol or a message request of the 698 protocol, the power concentrator in the embodiment can accurately analyze the message requests, and the compatibility of the power concentrator in analyzing different protocol message requests sent by the power office master station is effectively improved;
step 7, the power concentrator analyzes the preset format request file sent by the power station master station, and analyzes the new message request sent by the power station master station according to the preset format request file:
when the power concentrator successfully analyzes the new message request of the power station master station, the power concentrator feeds back a prompt of successful learning to the power station master station, the power concentrator stores an analysis strategy corresponding to the new message request to the updated message analysis strategy database in the step 4, and a self-learning updated message analysis strategy database is obtained; otherwise, the power concentrator feeds back a prompt of learning failure to the power station master station;
and 8, the power concentrator correspondingly analyzes the message request sent by the power station master station by using the analysis strategy in the self-learning updated message analysis strategy database.
That is to say, in the self-learning method of the power concentrator in this embodiment, the message analysis policy database of the power concentrator itself may be automatically replaced through the classification and analysis processing of the power concentrator on the message request sent by the power office master station, so as to store all the analysis policies capable of successfully analyzing the message request sent by the power office master station into the message analysis policy database of the power concentrator as much as possible; therefore, once the power station master station sends a new message request to the power concentrator, the power concentrator can directly call the corresponding message analysis strategy from the message analysis strategy database of the power concentrator to analyze the current message request sent by the received power station master station, a technician does not need to specially go to the power concentrator to perform maintenance and upgrade on site, and the problem of complexity caused by the fact that the technician performs maintenance on site back and forth is effectively avoided.
Of course, in order to better facilitate the power concentrator to successfully analyze the upgrade file subsequently sent by the power concentrator manufacturer background, the self-learning method of the power concentrator in this embodiment may further include: and the power concentrator manufacturer background arranges the upgrading file of the power concentrator manufacturer aiming at the power concentrator into a preset format upgrading file and sends the arranged preset format upgrading file to the power concentrator, and the power concentrator correspondingly analyzes the upgrading file subsequently sent by the power concentrator manufacturer background according to the received preset format upgrading file.
In order to meet the collecting requirements of the electric energy meters of different electric energy meter users, the self-learning method of the electric power concentrator in the embodiment may further include the following steps a 1-a 3:
a1, the electric concentrator statistically analyzes the electricity usage habit and the electricity consumption of the electric energy meter user, and the electric concentrator automatically sets a personalized collection scheme and a personalized collection task which are matched with the electricity usage habit and the electricity consumption of the electric energy meter user;
step a2, the power concentrator acquires the electric quantity related data and the electric energy meter related event data aiming at the corresponding electric energy meter by utilizing the personalized acquisition scheme and the personalized acquisition task; the personalized acquisition scheme comprises a common electric energy acquisition scheme, an event acquisition scheme and a reporting scheme; the personalized task comprises a task execution frequency configured and personalized aiming at each user group;
step a3, the power concentrator establishes the normal power utilization range of the electric energy meter corresponding to each time period for each electric energy meter administered, and the power concentrator detects the power consumption data of the user corresponding to each electric energy meter:
when the electric power concentrator judges that the power consumption of a user in a corresponding time period exceeds the normal power consumption range of the user in the time period, the electric power concentrator reports the electric energy meter data of the user to an electric power station main station for early warning processing; otherwise, the power concentrator does not report the electric energy meter data of the user to the power station main station.
In addition, in step 7 in this embodiment, the power station master station may further perform a determination process according to the indication that the power concentrator feedback learning fails: when the power station master station judges that the reason of the learning failure of the power concentrator is a software fault, the power station master station sends a software file for correspondingly solving the software fault to the power concentrator; when the power station master station judges that the reason of the learning failure of the power concentrator is a hardware fault, the power station master station assigns maintenance personnel to replace corresponding hardware.
According to the self-learning method of the power concentrator in the embodiment, new message requests sent to the power concentrator by the power office master station subsequently are arranged into the preset format request file, so that the preset format request file can be compatible with 376 protocols and 698 protocols simultaneously, and the power office master station sends the preset format request files to the power concentrator, therefore, even if the subsequent power office master station sends message requests of the 376 protocols or message requests of the 698 protocols, the power concentrator can accurately analyze the message requests, and the compatibility of the power concentrator for analyzing different protocol message requests sent by the power office master station is effectively improved.
Although preferred embodiments of the present invention have been described in detail hereinabove, it should be clearly understood that modifications and variations of the present invention are possible to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A self-learning method of a power concentrator is used for a system formed by a power concentrator, a power station main station and a power concentrator manufacturer background, wherein the power concentrator is respectively in communication connection with the power station main station and the power concentrator manufacturer background, and is characterized by sequentially comprising the following steps 1-8:
step 1, the power station master station sends a message request to a power concentrator;
step 2, the power concentrator receives and analyzes the message request sent by the power station main station:
when the power concentrator successfully analyzes the message request sent by the power station master station, the power concentrator sends feedback information corresponding to the message request to the power station master station; otherwise, the power concentrator forwards the message request sent by the master station of the power office to the background of the manufacturer of the power concentrator;
step 3, the power concentrator manufacturer background provides an analysis strategy aiming at a message request sent by the power concentrator to the power concentrator, and after the power concentrator successfully passes the test aiming at the message request by the power concentrator manufacturer background, the power concentrator stores the analysis strategy aiming at the message request into a message analysis strategy database of the power concentrator; otherwise, the power concentrator sends the unsuccessfully analyzed message request to the power concentrator manufacturer background again;
step 4, the power concentrator manufacturer background analyzes the message request which is not successfully analyzed by the power concentrator, and when the message request which is not successfully analyzed by the concentrator is successfully analyzed within the preset analysis times, the power concentrator manufacturer background sends the analysis strategy corresponding to the message request to the power concentrator, and the power concentrator updates the original message analysis strategy database; otherwise, the background assigned personnel of the power concentrator manufacturer perform field maintenance on the power concentrator;
step 5, the power concentrator detects abnormal information appearing in the running process of the power concentrator, and the power concentrator sends the detected abnormal information to a power concentrator manufacturer background; the background of the power concentrator manufacturer sends a solution strategy for solving the abnormal information of the power concentrator to the power concentrator, and the power concentrator solves the corresponding abnormal information by using the solution strategy;
step 6, the power bureau master station arranges the subsequent new message requests aiming at the power concentrator into a preset format request file and sends the arranged preset format request file to the power concentrator;
step 7, the power concentrator analyzes a preset format request file sent by the power station master station, and analyzes a new message request sent by the power station master station according to the preset format request file:
when the power concentrator successfully analyzes the new message request of the power station master station, the power concentrator feeds back a prompt of successful learning to the power station master station, the power concentrator stores an analysis strategy corresponding to the new message request to the updated message analysis strategy database in the step 4, and a self-learning updated message analysis strategy database is obtained; otherwise, the power concentrator feeds back a prompt of learning failure to the power station master station;
and 8, the power concentrator correspondingly analyzes the message request sent by the power station master station by using the analysis strategy in the self-learning updated message analysis strategy database.
2. The self-learning method for power concentrator as claimed in claim 1, wherein the solution policy comprises a patch file for power concentrator anomaly information or/and an upgrade program file for power concentrator anomaly information.
3. The self-learning method of the power concentrator as claimed in claim 1, wherein the preset format request file conforms to 376 or/and 698 specifications.
4. The power concentrator self-learning method of claim 1, wherein the message request comprises at least one of a read data request for data in the power concentrator, a parameter setting request for operating parameters of the power concentrator, and a control operation request for the power concentrator.
5. The self-learning method of the power concentrator as claimed in claim 1, further comprising: and the power concentrator manufacturer background arranges the upgrading file of the power concentrator manufacturer aiming at the power concentrator into a preset format upgrading file and sends the arranged preset format upgrading file to the power concentrator, and the power concentrator correspondingly analyzes the upgrading file subsequently sent by the power concentrator manufacturer background according to the received preset format upgrading file.
6. The self-learning method of the power concentrator as claimed in claim 1, further comprising: in step 7, the power station master station makes a judgment process according to the prompt of the power concentrator feedback learning failure:
when the power station master station judges that the reason of the learning failure of the power concentrator is a software fault, the power station master station sends a software file for correspondingly solving the software fault to the power concentrator;
when the power station master station judges that the reason of the learning failure of the power concentrator is a hardware fault, the power station master station assigns maintenance personnel to replace corresponding hardware.
7. The self-learning method of the power concentrator as claimed in any one of claims 1 to 6, wherein in step 2, when the power concentrator successfully parses the message request sent by the power office master station, the power concentrator sends feedback information corresponding to the message request to the power office master station according to a preset sending time interval.
8. The self-learning method for the power concentrator as claimed in claim 1, wherein the preset number of times of parsing in step 4 is three times; or/and the first and/or second light-emitting diodes are arranged in the light-emitting diode,
the self-learning method of the power concentrator further comprises the following steps a 1-a 3:
a1, the electric concentrator statistically analyzes the electricity usage habit and the electricity consumption of the electric energy meter user, and the electric concentrator automatically sets a personalized collection scheme and a personalized collection task which are matched with the electricity usage habit and the electricity consumption of the electric energy meter user;
step a2, the power concentrator acquires the electric quantity related data and the electric energy meter related event data aiming at the corresponding electric energy meter by utilizing the personalized acquisition scheme and the personalized acquisition task; the personalized acquisition scheme comprises a common electric energy acquisition scheme, an event acquisition scheme and a reporting scheme; the personalized task comprises a task execution frequency configured and personalized for each user group;
step a3, the power concentrator respectively establishes a normal power utilization range of the electric energy meter corresponding to each time period for each electric energy meter under jurisdiction, and the power concentrator detects power consumption data of users corresponding to each electric energy meter:
when the electric power concentrator judges that the power consumption of a user in a corresponding time period exceeds the normal power consumption range of the user in the time period, the electric power concentrator reports the electric energy meter data of the user to an electric power station main station for early warning processing; otherwise, the electric energy meter data of the user is not reported to the main station of the electric power station by the electric power concentrator.
9. The self-learning method for power concentrator as claimed in claim 7, wherein in step 5, the power concentrator transmits the detected abnormal information to the power concentrator manufacturer background according to the preset transmission frequency.
10. The self-learning method of the power concentrator as claimed in claim 7, wherein the parsing strategy in the message parsing strategy database corresponds to the message request sent by the power office master station.
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