CN113423082A - Method for efficiently acquiring terminal data of AMI system - Google Patents
Method for efficiently acquiring terminal data of AMI system Download PDFInfo
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- CN113423082A CN113423082A CN202110682151.XA CN202110682151A CN113423082A CN 113423082 A CN113423082 A CN 113423082A CN 202110682151 A CN202110682151 A CN 202110682151A CN 113423082 A CN113423082 A CN 113423082A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04Q2209/60—Arrangements in telecontrol or telemetry systems for transmitting utility meters data, i.e. transmission of data from the reader of the utility meter
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Abstract
The invention provides a method for efficiently acquiring terminal data of an AMI system, and relates to the technical field of AMI systems. The method for efficiently acquiring the AMI system terminal data comprises the following specific contents: the first stage is as follows: push mode: the system comprises three communication modes of 3G, G3-PLC and RS485, and the specific contents are as follows: a) reporting monthly data, daily data, load curve data and partial events to the HES in real time, wherein the 3G is a cellular network technology, so that the communication effect is very good, and the failure is caused in rare cases; b) G3-PLC, reporting monthly data, daily data, load curve data and partial events to the concentrator/gateway in real time, forwarding the reported data to the HES by the concentrator/gateway, and temporarily storing the data reported by the electric meter for a period of time. By utilizing different communication technologies and communication protocols to acquire data in different data acquisition time periods and scenes, how each unit in the system should be matched with each other in each stage is defined, and the integrity rate and the real-time rate of data acquisition are greatly improved.
Description
Technical Field
The invention relates to the technical field of AMI systems, in particular to a method for efficiently acquiring terminal data of an AMI system.
Background
The data acquisition integrity rate of the AMI system is always a key KPI index for evaluating the quality of the whole system, and with the popularization of intelligent electric meters, the electric power bureau can not read only one month of settlement electric energy data for settlement in one month any more, and more requirements are to read daily freezing data of 00:00, 15-minute load curve data and electric meter events. And it is required that these data can be read into the system in real time or near real time. The new requirements promote the development of the intelligent electric meter in the whole industry, various communication technologies are applied to the intelligent electric meter industry, but the 100% integrity rate and the real-time rate of data reading are always difficult in the industry and cannot be well solved.
The 15-minute curve data power bureau officially requires 99.5% in 24 hours, but how to realize the index, manufacturers in the industry can reach 500 terminals, the integrity rate of 99.5% of the 15-minute curve data is almost constant, the data is read in 24 hours for unlimited times, the real-time performance cannot be guaranteed, and the main factors influencing the integrity rate of data acquisition of the AMI system to 100% are as follows:
1. the load curve of 15 minutes, the data bulk is very large, under the condition that 500 pieces of tables are commonly available in each platform area, the concentrator is under 9600 baud rate in 24 hours a day, and it is difficult to read the data of the whole day through the Pull reading mode (request + response double-pass), because the time is insufficient;
2. G3-PLC or RF related technologies cannot guarantee 100% of the power-on;
3. due to low reading efficiency, the real-time performance of data acquisition is poor.
Therefore, the difficulty of acquiring load curve data in real time in advance by 100% is high, and therefore a new method for efficiently acquiring AMI system terminal data is developed.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a method for efficiently acquiring AMI system terminal data, and solves the problems that the data acquisition integrity rate of the AMI system designed by manufacturers in the industry at present cannot reach 100%, the reading efficiency is low, and the real-time performance of data acquisition is high.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for efficiently acquiring AMI system terminal data comprises the following specific contents:
the first stage is as follows: push mode
Push is the content in the DLMS/COSEM standard protocol, and the data reporting mode has the advantages that data can be reported to the master station in real time, so that the real-time performance greatly improves the user experience, the complexity of data acquisition is reduced, the occurrence of data complementary acquisition is avoided to the maximum extent, more than 99% of data can be reported in real time through the Push in the stage, and the Push mainly occurs in the period of 0-24 hours.
The Push mode comprises three communication modes of 3G, G3-PLC and RS485, and the specific contents are as follows:
a) reporting monthly data, daily data, load curve data and partial events to the HES in real time, wherein the 3G is a cellular network technology, so that the communication effect is very good, and the failure is caused in rare cases;
b) G3-PLC, reporting monthly data, daily data, load curve data and partial events to the concentrator/gateway in real time, forwarding the reported data to the HES by the concentrator/gateway, and temporarily storing the data reported by the electric meter for a period of time;
c) RS485, the monthly data, the daily data and the load curve data of the electric meters are collected by the concentrator at regular time and then are pushed to the HES, the events are reported in a following mode (the following reporting mode is adopted to avoid the mutual collision of data communication on the RS485, when the concentrator communicates with one electric meter through the MAC address of the HDLC communication protocol, other electric meters cannot respond, the events of the electric meter are reported to the concentrator firstly and then are the electric energy data, and the reporting mode of the events is not real-time, but is more real-time compared with the periodical query mode of the concentrator).
The meter periodically freezes and saves power data (monthly, daily, load curve, event) to EEPROM & Flash, for example, the load curve data (8 channels 15 minute intervals) can be saved for 45 days. The meter is the first level of historical data caching so that the concentrator and the HES can make data completions. The concentrator can store the collected electric meter data in Flash (the storage time is according to the Flash size of the concentrator), the concentrator is a second-level historical data cache, and the HES can directly read the historical data of the electric meter from the concentrator without directly reading the data in the electric meter. In the three stages, each stage carries out data integrity rate analysis in a specific time period, and only missing data is subjected to data complementary acquisition, so that the same ammeter data is prevented from being repeatedly read, the communication flow of the cellular network is prevented from being repeatedly consumed, the cost is reduced, and meanwhile, the pressure of the bandwidth can be effectively reduced.
Under the condition that the data of the electric meters are actively Push data, in order to avoid network congestion, a random time window is adopted at the data reporting time to avoid congestion (for example, power failure in the whole city is avoided, all the electric meters need to report power failure events, the electric meters adopt a peak staggering algorithm, and network congestion cannot be caused by random reporting), so that bandwidth exhaustion is avoided. If the data Push of the electric meter fails, the electric meter waits for a period of time, and reports the data again at another time until the data Push succeeds, so that the mechanism greatly ensures the integrity of the data.
The electric meter peak staggering algorithm reports definition at random:
t0: at the moment of freezing of the data of the load curve of the electric meter, for example: 00:00, 00:15,,01:00,,02:45,,
twin: reporting time window (0< Twin < X seconds, e.g.: X900 seconds)
Random (0-1): random decimal between 0 and 1, for example: 0.1,0.01,0.99,0.0678, … …
N: reporting times (if reporting fails, reporting again)
Tpush: reporting time randomly
Tpush＝T0+Twin*(N-1)+Twin*Random(0～1)
And a second stage: concentrator complementary data
After the electric meter data in the first stage are reported to the concentrator, the concentrator stores the reported data and pushes the data to the HES, and the days for storing the data depend on the flash storage size of the concentrator; the concentrator carries out complete rate analysis on the managed electric meter data, the time range is data in the period of the first 24 x 1 hour- >24 x 7 hours, if the data are incomplete, deep analysis is carried out to obtain the time point of missing data, the meter number and the data type, a data complementary collection task is generated to a data collection queue, after the collection tasks are executed, the collected data are pushed to the HES system again, a random window machine selection reporting mechanism is adopted during Push, network congestion is avoided, and in the period, about 1% of the data are subjected to complementary collection through the concentrator and then reported to the HES.
And a third stage: HES complementary data
And (3) running an analysis task inside the HES, analyzing the data missing situation in the period of 24 hours 7- >24 hours 15, if the data missing is found, generating a data supplementing and collecting task to a task collecting module of the HES, and retrying the task three times by default, wherein about 1% of data is supplemented and collected by the HES in the period.
Preferably, the storage time of the G3-PLC communication mode in the Push mode needs to be determined according to the internal storage capacity of the concentrator.
Preferably, the method for calculating the number of days for storing the storable load curve by the concentrator Flash is as follows:
assuming that the storage size of the concentrator Flash is 128MBytes, the concentrator uses the SQLite database to store load curve data, assuming that the load curve of each freezing time point of the electric meter occupies 100 Bytes of Flash, and the load curve cycle is 15 minutes, there will be 96 load curve data points in one day, if the concentrator manages 500 electric meters, the number of days that the concentrator can store the maximum load curve can be calculated as: 128000000/500/100/96 for about 27 days.
Preferably, the algorithm for judging data incompleteness by the concentrator is as follows:
the concentrator uses the SQLite database to store electric energy data of the electric meters, if the load curve period of the electric meters is 15 minutes, each electric meter should have 96 pieces of load curve data every day, the concentrator firstly calculates the number of data in one day by using SQL statement Count (x), judges whether the number is equal to 96, if the number is less than 96, the data is incomplete, then the deep data analysis process is further triggered: and inquiring the freezing time of all the existing data to find out the freezing time point of the missing data, and if the freezing time point of the missing data exists, generating an acquisition task for supplementing and acquiring the load curve data of the freezing time point of the ammeter into an acquisition task table of an SQLite database of the concentrator.
Preferably, the default retry number of the task in the HES data complementary acquisition stage can be configured as required.
When the electric meter is subjected to the three data acquisition modes and still has data loss, generally, the electric meter can have faults or damage and needs to be maintained.
(III) advantageous effects
The invention provides a method for efficiently acquiring terminal data of an AMI system. The method has the following beneficial effects:
1. according to the method for efficiently acquiring the AMI system terminal data, different communication technologies and communication protocols are utilized to acquire the data in different data acquisition time periods and scenes, how each unit (HES, concentrator, communication network and ammeter) in the system should be matched with each other in each stage is defined, the integrity rate and the real-time rate of data acquisition can be greatly improved, and the long-standing problem in the industry is solved.
2. According to the method for efficiently acquiring the terminal data of the AMI system, the scientific and systematic data acquisition method is adopted, the overall value of the AMI system is greatly improved, and the requirements of 100% data integrity and instantaneity required by a power bureau can be met, so that the overall efficiency is greatly improved.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
the embodiment of the invention provides a method for efficiently acquiring terminal data of an AMI system, which comprises the following specific contents:
the first stage is as follows: push mode
Push is the content in the DLMS/COSEM standard protocol, and the data reporting mode has the advantages that data can be reported to the master station in real time, so that the real-time performance greatly improves the user experience, the complexity of data acquisition is reduced, the occurrence of data complementary acquisition is avoided to the maximum extent, more than 99% of data can be reported in real time through the Push in the stage, and the Push mainly occurs in the period of 0-24 hours.
The Push mode comprises three communication modes of 3G, G3-PLC and RS485, and the specific contents are as follows:
a) reporting monthly data, daily data, load curve data and partial events to the HES in real time, wherein the 3G is a cellular network technology, so that the communication effect is very good, and the failure is caused in rare cases;
b) G3-PLC, reporting monthly data, daily data, load curve data and partial events to the concentrator/gateway in real time, forwarding the reported data to the HES by the concentrator/gateway, and temporarily storing the data reported by the electric meter for a period of time; the storage time of the G3-PLC communication mode in the Push mode is determined according to the internal storage capacity of the concentrator;
c) RS485, the monthly data, the daily data and the load curve data of the electric meters are collected by the concentrator at regular time and then are pushed to the HES, the events are reported by following (the following reporting mode is adopted to avoid the mutual collision of data communication on the RS485, when the concentrator communicates with one electric meter through the MAC address of the HDLC communication protocol, other electric meters cannot respond, the events of the electric meter are reported to the concentrator firstly and then are the electric energy data, and the reporting mode of the events is not real-time, but is more real-time than the regular query mode of the concentrator).
The method for calculating the number of days for storing the storable load curve by the concentrator Flash comprises the following steps:
assuming that the storage size of the concentrator Flash is 128MBytes, the concentrator uses the SQLite database to store load curve data, assuming that the load curve of each freezing time point of the electric meter occupies 100 Bytes of Flash, and the load curve cycle is 15 minutes, there will be 96 load curve data points in one day, if the concentrator manages 500 electric meters, the number of days that the concentrator can store the maximum load curve can be calculated as: 128000000/500/100/96 for about 27 days.
The meter periodically freezes and saves power data (monthly, daily, load curve, event) to EEPROM & Flash, for example, the load curve data (8 channels 15 minute intervals) can be saved for 45 days. The meter is the first level of historical data caching so that the concentrator and the HES can make data completions. The concentrator can store the collected electric meter data in Flash (the storage time is according to the Flash size of the concentrator), the concentrator is a second-level historical data cache, and the HES can directly read the historical data of the electric meter from the concentrator without directly reading the data in the electric meter. In the three stages, each stage carries out data integrity rate analysis in a specific time period, and only missing data is subjected to data complementary acquisition, so that the same ammeter data is prevented from being repeatedly read, the communication flow of the cellular network is prevented from being repeatedly consumed, the cost is reduced, and meanwhile, the pressure of the bandwidth can be effectively reduced.
Under the condition that the data of the electric meters are actively Push data, in order to avoid network congestion, a random time window is adopted at the data reporting time to avoid congestion (for example, power failure in the whole city is avoided, all the electric meters need to report power failure events, the electric meters adopt a peak staggering algorithm, and network congestion cannot be caused by random reporting), so that bandwidth exhaustion is avoided. If the data Push of the electric meter fails, the electric meter waits for a period of time, and reports the data again at another time until the data Push succeeds, so that the mechanism greatly ensures the integrity of the data.
The electric meter peak staggering algorithm reports definition at random:
t0: at the moment of freezing of the data of the load curve of the electric meter, for example: 00:00, 00:15,,01:00,,02:45,,
twin: reporting time window (0< Twin < X seconds, e.g.: X900 seconds)
Random (0-1): random decimal between 0 and 1, for example: 0.1,0.01,0.99,0.0678, … …
N: reporting times (if reporting fails, reporting again)
Tpush: reporting time randomly
Tpush＝T0+Twin*(N-1)+Twin*Random(0～1)
And a second stage: concentrator complementary data
After the electric meter data in the first stage are reported to the concentrator, the concentrator stores the reported data and pushes the data to the HES, and the days for storing the data depend on the flash storage size of the concentrator; the concentrator carries out complete rate analysis on the managed electric meter data, the time range is data in the period of the first 24 x 1 hour- >24 x 7 hours, if the data are incomplete, deep analysis is carried out to obtain the time point of missing data, the meter number and the data type, a data complementary collection task is generated to a data collection queue, after the collection tasks are executed, the collected data are pushed to the HES system again, a random window machine selection reporting mechanism is adopted during Push, network congestion is avoided, and in the period, about 1% of the data are subjected to complementary collection through the concentrator and then reported to the HES.
The algorithm for judging data incompleteness by the concentrator is as follows:
the concentrator uses the SQLite database to store electric energy data of the electric meters, if the load curve period of the electric meters is 15 minutes, each electric meter should have 96 pieces of load curve data every day, the concentrator firstly calculates the number of data in one day by using SQL statement Count (x), judges whether the number is equal to 96, if the number is less than 96, the data is incomplete, then the deep data analysis process is further triggered: and inquiring the freezing time of all the existing data to find out the freezing time point of the missing data, and if the freezing time point of the missing data exists, generating an acquisition task for supplementing and acquiring the load curve data of the freezing time point of the ammeter into an acquisition task table of an SQLite database of the concentrator.
And a third stage: HES complementary data
The internal analysis task of the HES is operated, the data missing condition in the period of 24 hours 7- >24 hours 15 is analyzed, if the data missing is found, a data supplementing and collecting task is generated to a task collecting module of the HES, the task is retried for three times by default, in the stage, about 1% of data is supplemented and collected by the HES, and the number of times of retries by default in the stage of data supplementing and collecting by the HES can be configured according to needs.
When the electric meter is subjected to the three data acquisition modes and still has data loss, generally, the electric meter can have faults or damage and needs to be maintained.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A method for efficiently acquiring AMI system terminal data is characterized by comprising the following steps: the method comprises the following specific contents:
the first stage is as follows: push mode
The system comprises three communication modes of 3G, G3-PLC and RS485, and the specific contents are as follows:
a) reporting monthly data, daily data, load curve data and partial events to the HES in real time, wherein the 3G is a cellular network technology, so that the communication effect is very good, and the failure is caused in rare cases;
b) G3-PLC, reporting monthly data, daily data, load curve data and partial events to the concentrator/gateway in real time, forwarding the reported data to the HES by the concentrator/gateway, and temporarily storing the data reported by the electric meter for a period of time;
c) RS485, the monthly data, the daily data and the load curve data of the electric meter are collected by the concentrator at regular time and then are pushed to the HES, and the event adopts a following reporting mode.
And a second stage: concentrator complementary data
In the first stage, after the electric meter data are reported to the concentrator, the concentrator stores the reported data and pushes the data to the HES; the concentrator carries out complete rate analysis on the managed electric meter data, the time range is data in the period of the first 24 x 1 hour- >24 x 7 hours, if the data are incomplete, deep analysis is carried out to obtain the time point of missing data, the meter number and the data type, a data complementary collection task is generated to a data collection queue, after the collection tasks are executed, the collected data are pushed to the HES system again, and a random window machine selection reporting mechanism is adopted during Push, so that network congestion is avoided.
And a third stage: HES complementary data
And (3) running an analysis task inside the HES, analyzing the data missing condition in the period of 24 hours 7- >24 hours 15, if the data missing condition is found, generating a data supplementing and acquiring task to a task acquisition module of the HES, and retrying the task by default three times.
2. The method for efficiently acquiring AMI system terminal data according to claim 1, wherein: the storage time of the G3-PLC communication mode in the Push mode is determined according to the internal storage capacity of the concentrator.
3. The method for efficiently acquiring AMI system terminal data according to claim 2, wherein the method for calculating the number of days of the storable load curve of the concentrator Flash storage is as follows:
assuming that the storage size of the concentrator Flash is 128MBytes, the concentrator uses the SQLite database to store load curve data, assuming that the load curve of each freezing time point of the electric meter occupies 100 Bytes of Flash, and the load curve cycle is 15 minutes, there will be 96 load curve data points in one day, if the concentrator manages 500 electric meters, the number of days that the concentrator can store the maximum load curve can be calculated as: 128000000/500/100/96 for about 27 days.
4. The method for efficiently acquiring AMI system terminal data according to claim 1, wherein: the algorithm for judging data incompleteness by the concentrator is as follows:
the concentrator uses the SQLite database to store electric energy data of the electric meters, if the load curve period of the electric meters is 15 minutes, each electric meter should have 96 pieces of load curve data every day, the concentrator firstly calculates the number of data in one day by using SQL statement Count (x), judges whether the number is equal to 96, if the number is less than 96, the data is incomplete, then the deep data analysis process is further triggered: and inquiring the freezing time of all the existing data to find out the freezing time point of the missing data, and if the freezing time point of the missing data exists, generating an acquisition task for supplementing and acquiring the load curve data of the freezing time point of the ammeter into an acquisition task table of an SQLite database of the concentrator.
5. The method for efficiently acquiring AMI system terminal data according to claim 1, wherein: the default retry times of the tasks in the HES data complementary acquisition stage can be configured as required.
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