CN114565250A - Ordered electricity utilization intelligent monitoring method and system based on big data - Google Patents
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Abstract
The invention provides an ordered power utilization intelligent monitoring method based on big data, and belongs to the technical field of communication. The method comprises the following steps: configuring a unit reference daily load curve and a user reference daily load curve according to the issued ordered power utilization tasks; on a specified day, calculating a user execution effect and a user execution deviation of the user for executing the ordered power utilization task in real time according to the user reference daily load curve and the real-time daily load curve of the user; when the user execution effect is lower than a first preset effect threshold and the user execution deviation is lower than a first preset deviation threshold, generating a corresponding prompt message and sending the prompt message to a manager; calculating unit execution effects and unit execution deviations of the unit execution ordered power utilization tasks in real time according to the unit reference daily load curves and the real-time daily load curves of the users; and generating a corresponding prompt message and sending the prompt message to a manager when the unit execution effect is lower than a second preset effect threshold and the unit execution deviation is lower than a second preset deviation threshold.
Description
Technical Field
The invention relates to the technical field of communication, in particular to an ordered power utilization intelligent monitoring method and system based on big data.
Background
In order to realize the double-carbon target, the state formulates an ordered power utilization scheme according to the production execution condition and the power utilization requirements of users, develops demand-side management and correspondence, ensures the operation safety and stability of a power grid, and practically ensures the power utilization requirements of civilian and public services and important users. The traditional orderly power utilization management is rough, part of equipment does not support load curve data acquisition or data quality problems such as missing points and the like exist in acquired data, the traditional orderly power utilization monitoring is carried out in a mode of depending on manual investigation or after diagnosis, real-time analysis, monitoring and regulation of a power utilization process cannot be met, the execution of an orderly power utilization scheme is difficult to finely and effectively advance, and timeliness is poor.
Disclosure of Invention
In view of this, the invention provides an orderly power utilization intelligent monitoring method and system based on big data, which are used for solving the problem that the orderly power utilization scheme is difficult to be finely and effectively promoted to be executed.
The technical scheme adopted by the embodiment of the invention for solving the technical problem is as follows:
the ordered power utilization intelligent monitoring method based on big data comprises the following steps:
according to the issued ordered power utilization tasks, a unit reference daily load curve is configured for a unit participating in executing the ordered power utilization tasks, a user reference daily load curve is configured for a user participating in executing the ordered power utilization tasks in the unit, the ordered power utilization tasks are execution targets of each execution time interval of the unit on a specified day, and the number of the execution time intervals is at least one;
on the appointed day, calculating the user execution effect and the user execution deviation of the ordered power utilization task executed by the user in real time according to the user reference daily load curve and the real-time daily load curve of the user;
when the user execution effect is lower than a first preset effect threshold value, generating a user non-execution prompt message and sending the user non-execution prompt message to a manager of the user so as to prompt the user to execute the ordered power utilization task according to the user reference daily load curve;
when the user execution deviation is not lower than a first preset deviation threshold, generating a user execution in-place prompt message and reporting the message to the manager of the unit;
calculating unit execution effects and unit execution deviations of the unit for executing the ordered power utilization tasks in real time according to the unit reference daily load curves and the real-time daily load curves of the users;
when the unit execution effect is lower than a second preset effect threshold, generating a unit orderly power utilization non-execution prompt message and sending the message to a manager of the unit so as to prompt the unit to monitor the power utilization condition of the user in real time;
and when the unit execution deviation is not lower than a second preset deviation threshold, generating a unit execution in-place prompt message and reporting the unit execution in-place prompt message to a superior unit of the unit.
Preferably, the configuring, according to the issued ordered power utilization task, a unit reference daily load curve for a unit participating in executing the ordered power utilization task, and a user reference daily load curve for a user participating in executing the ordered power utilization task in the unit, includes:
configuring sub-execution targets of the single user in each execution period according to the execution targets of the execution periods and the trend characteristics of the historical daily load curve of the user, wherein the sub-execution targets are decomposed from the execution targets in the same period;
according to the execution time interval and the corresponding sub-execution targets, selecting a curve which is matched with the execution time interval and the sub-execution targets at the same time from the historical daily load curves of the user as a user reference daily load curve, wherein the user reference daily load curve is a complete 96-point load curve;
calculating the unit reference daily load curve according to the user reference daily load curves of all the users:
wherein the reference load of each point on the unit reference daily load curve is represented as PRiReference load of each point on the user reference daily load curve of each user k is represented as PikI is a time point sequence value of every 15 minutes starting from 0, and n is the number of users participating in the execution of the ordered electricity utilization task in the unit.
Preferably, the calculating, in real time, a user execution effect and a user execution deviation of the ordered power utilization task executed by the user according to the user reference daily load curve and the real-time daily load curve of the user includes:
calculating the user execution effect D of the user k in real time according to the real-time daily load curve of the user k and the user reference daily load curve of the user kk:
Dk=max(Pik-P′ik)
Wherein the user execution effect D is calculatedkAnd the value of i is required to be positioned in the execution time interval, and the real-time load of the user k is represented as P'ik;
Calculating the user execution deviation I in real timek:
Ik=Dk-Mk
Wherein, the maximum sub-execution target of the user k is represented as MkThe maximum sub-execution target is the maximum value in the sub-execution targets of the user k;
the real-time calculation of the unit execution effect and the unit execution deviation of the unit execution of the ordered power utilization task according to the unit reference daily load curve and the real-time daily load curve of the unit comprises the following steps:
according to the real-time daily load curve of each user and the unit reference daily load curve, calculating the unit execution effect PD of the unit in real time:
PD=max(PRi-PEi)
when calculating the unit execution effect PD, the value of i must be in the execution time interval, and the real-time load of the unit is expressed as PEi;
Calculating the unit execution deviation I in real timeD:
Ik=PD-MD
Wherein the maximum execution target of the unit is represented as MDThe maximum execution target is the maximum value among the respective execution targets of the unit.
Preferably, after the generating the prompt message that the orderly power utilization of the unit does not reach the standard and sending the prompt message to the manager of the unit, the method further comprises the following steps:
when the real-time daily load curve of the user has data abnormal points, performing abnormal point correction operation on the data abnormal points according to the data trend in the historical daily load curve of the user;
or,
and when the real-time daily load curve of the user has data missing points, carrying out missing point correction operation on the missing points according to the data trend in the historical daily load curve of the user.
Preferably, the values of the first preset effect threshold, the second preset effect threshold, the first preset deviation threshold and the second preset deviation threshold are all 0.
Further, the invention also provides an ordered power consumption intelligent monitoring system based on big data, which comprises a device layer, a network layer, a master station layer and an application layer, wherein:
the device layer provides an acquisition master station, an acquisition terminal and communication equipment;
the network layer is used for providing an uplink communication network between the acquisition terminal and the acquisition master station;
the application layer is used for providing a human-computer interaction platform and receiving ordered power utilization tasks;
the master station layer comprises a configuration module and a calculation module:
the configuration module is used for configuring a unit reference daily load curve for a unit participating in executing the ordered power utilization tasks according to the ordered power utilization tasks, and configuring a user reference daily load curve for a user participating in executing the ordered power utilization tasks in the unit, wherein the ordered power utilization tasks are execution targets of the unit in each execution time interval on a specified day, and the number of the execution time intervals is at least one;
the calculation module is used for calculating the user execution effect and the user execution deviation of the ordered power utilization tasks executed by the user in real time on the appointed day according to the user reference daily load curve and the real-time daily load curve of the user;
the application layer is used for generating a user non-execution prompting message and sending the user non-execution prompting message to a manager of the user when the user execution effect is lower than a first preset effect threshold value so as to prompt the user to execute the ordered power utilization task according to the user reference daily load curve; when the user execution deviation is not lower than a first preset deviation threshold, generating a user execution in-place prompt message and reporting the message to a manager of the unit;
the calculation module is used for calculating the unit execution effect and the unit execution deviation of the unit execution of the ordered power utilization task in real time according to the unit reference daily load curve and the real-time daily load curve of each user;
the application layer is used for generating a unit orderly power utilization unexecuted prompt message and sending the unit orderly power utilization unexecuted prompt message to a manager of the unit when the unit execution effect is lower than a second preset effect threshold value so as to prompt the unit to monitor the power utilization condition of the user in real time; when the unit execution deviation is not lower than a second preset deviation threshold, generating a unit execution in-place prompt message and reporting the unit execution in-place prompt message to a superior unit of the unit;
the application layer is further used for displaying data in the ordered power utilization tasks, displaying all data in the process of processing the ordered power utilization tasks by the network layer and displaying all data in the process of processing the ordered power utilization tasks by the application layer through a visual interface of a human-computer interaction terminal.
Preferably, the configuration module is configured to configure sub-execution targets of a single user in each execution period according to the execution targets of the respective execution periods and trend characteristics of historical daily load curves of the user, where the sub-execution targets are decomposed from the execution targets in the same period;
the configuration module is used for selecting a curve which is matched with the execution time interval and the sub-execution target at the same time from the historical daily load curves of the user as a user reference daily load curve according to the execution time interval and the corresponding sub-execution target, wherein the user reference daily load curve is a complete 96-point load curve;
the calculation module is configured to calculate the unit reference daily load curve according to the user reference daily load curves of all the users:
wherein the reference load of each point on the unit reference daily load curve is represented as PRiReference load of each point on the user reference daily load curve of each user k is represented as PikI is a time point sequence value of every 15 minutes starting from 0, and n is the number of users participating in the execution of the ordered electricity utilization task in the unit.
Preferably, the calculating module is configured to calculate the user execution effect D of the user k in real time according to the real-time daily load curve of the user k and the user reference daily load curve of the user kk:
Dk=max(Pik-P′ik)
Wherein the user execution effect D is calculatedkAnd the value of i is required to be positioned in the execution time interval, and the real-time load of the user k is represented as P'ik;
The calculation module is used for calculating the user execution deviation I in real timek:
Ik=Dk-Mk
Wherein the maximum sub-execution target of the user k is represented as MkThe maximum sub-execution target is the maximum value in the sub-execution targets of the user k;
the calculating module is configured to calculate the unit execution effect PD of the unit in real time according to the real-time daily load curve of each user and the unit reference daily load curve:
PD=max(PRi-PEi)
when calculating the unit execution effect PD, the value of i must be in the execution time interval, and the real-time load of the unit is expressed as PEi;
The calculation module is used for calculating the unit execution deviation I in real timeD:
Ik=PD-MD
Wherein the maximum execution target of the unit is represented as MDThe maximum execution target is the maximum value among the respective execution targets of the unit.
Preferably, the master station layer further comprises:
the curve correction module is used for performing abnormal point correction operation on the data abnormal point according to the data trend in the historical daily load curve of the user when the real-time daily load curve of the user has the data abnormal point; and when the real-time daily load curve of the user has data missing points, carrying out missing point correction operation on the missing points according to the data trend in the historical daily load curve of the user.
Preferably, the values of the first preset effect threshold, the second preset effect threshold, the first preset deviation threshold and the second preset deviation threshold are all 0.
According to the technical scheme, the ordered power utilization intelligent monitoring method based on the big data, provided by the embodiment of the invention, configures a unit reference daily load curve for a unit participating in executing the ordered power utilization task according to the issued ordered power utilization task, configures a user reference daily load curve for a user participating in executing the ordered power utilization task in the unit, calculates the user execution effect and the user execution deviation of the user executing the ordered power utilization task in real time according to the user reference daily load curve and the real-time daily load curve of the user on a specified day, and generates a user non-execution prompt message and sends the user non-execution prompt message to a manager of the user when the user execution effect is lower than a first preset effect threshold so as to prompt the user to execute the ordered power utilization task according to the user reference daily load curve; when the execution deviation of the user is not lower than a first preset deviation threshold value, generating a prompt message for the user to execute in place and reporting the prompt message to a manager of a unit; calculating unit execution effects and unit execution deviations of the unit execution ordered power utilization tasks in real time according to the unit reference daily load curves and the real-time daily load curves of the users; when the unit execution effect is lower than a second preset effect threshold, generating a unit orderly power utilization unexecuted prompt message and sending the message to a manager of a unit so as to prompt the unit to monitor the power utilization condition of the user in real time; when the unit execution deviation is not lower than a second preset deviation threshold, generating a unit execution in-place prompt message and reporting the message to a superior unit of the unit; by the scheme with timeliness, the execution of the orderly power utilization scheme is finely and effectively promoted.
Drawings
FIG. 1 is a flow chart of the ordered power consumption intelligent monitoring method based on big data.
FIG. 2 is a diagram of the present invention based on big data for orderly power consumption intelligent monitoring system architecture.
FIG. 3 is a flow chart of the orderly power utilization monitoring method and completion of the present invention.
FIG. 4 is a diagram of an orderly power consumption monitoring model according to the present invention.
Detailed Description
The technical scheme and the technical effect of the invention are further elaborated in the following by combining the drawings of the invention.
The invention provides an ordered power utilization intelligent monitoring method based on big data, which can realize full-flow intelligent monitoring, and the specific ordered power utilization task flow is as follows: provincial task issuing- > city (unit) is decomposed to the user- > participates in enterprise (user) execution- > real-time monitoring of execution condition- > short message early warning- > optimization of execution scheme, and a closed-loop online management and control mechanism is formed. As shown in fig. 1, the method of the present invention comprises the steps of:
step S1, according to the issued orderly power utilization tasks, configuring a unit reference daily load curve for units participating in executing the orderly power utilization tasks and configuring a user reference daily load curve for users participating in executing the orderly power utilization tasks in the units, wherein the orderly power utilization tasks are execution targets of the ordered power utilization tasks in each execution time interval in the appointed daily unit, the number of the execution time intervals is at least one, and for example, two time intervals of 0:00-6:00 and 17:00-20:00 in one day can be selected as the execution time intervals for monitoring the power utilization condition;
step S2, calculating the user execution effect and the user execution deviation of the ordered power utilization task executed by the user in real time on the appointed day according to the user reference daily load curve and the real-time daily load curve of the user;
step S3, when the execution effect of the user is lower than a first preset effect threshold, generating a user non-execution prompt message and sending the message to a manager of the user so as to prompt the user to execute an ordered power utilization task according to the user reference daily load curve, and through the step, the user can be timely reminded to avoid excessive power utilization of the user;
step S4, when the user execution deviation is not lower than the first preset deviation threshold, generating a user execution in-place prompt message and reporting the message to the manager of the unit;
step S5, calculating unit execution effect and unit execution deviation of the unit execution ordered power utilization task in real time according to the unit reference daily load curve and the real-time daily load curve of each user;
step S6, when the unit execution effect is lower than a second preset effect threshold, generating a unit orderly power utilization unexecuted prompt message and sending the message to a manager of a unit to prompt the unit to monitor the power utilization condition of a user in real time, and through the step, the unit can be timely reminded to take measures as early as possible to avoid the unit from being unsufficiently monitored;
and step S7, when the unit execution deviation is not lower than a second preset deviation threshold, generating a unit execution in-place prompt message and reporting the message to the upper unit of the unit.
Specifically, the specific implementation manner of step S1, according to the issued orderly power utilization task, configuring a unit reference daily load curve for a unit participating in executing the orderly power utilization task, and configuring a user reference daily load curve for a user participating in executing the orderly power utilization task in the unit, is as follows:
step S11, configuring sub-execution targets of a single user in each execution period according to the execution targets of each execution period and the trend characteristics of the historical daily load curve of the user, that is, decomposing the execution targets into sub-execution targets according to the historical electricity load of each user in the same execution period, and giving the sub-execution targets to each user, specifically, distributing the sub-execution targets according to the proportion of the electricity load;
step S12, according to each execution time interval and the corresponding sub-execution target, selecting a curve which simultaneously matches the execution time interval and the sub-execution target from the historical daily load curves of the user as a user reference daily load curve, wherein the user reference daily load curve is a complete 96-point load curve;
step S13, calculating a unit reference daily load curve from the user reference daily load curves of all users:
wherein, the reference load of each point on the unit reference daily load curve is represented as PRiThe reference load of each point on the user reference daily load curve of user k is represented as PikAnd i is a time point sequence value starting from 0 and every 15 minutes, each i point corresponds to one time from 0 to 24 hours, and n is the number of users participating in the execution of the ordered electricity utilization tasks in unit.
Step S2, calculating in real time a user execution effect and a user execution deviation of the user for executing the ordered power consumption task according to the user reference daily load curve and the real-time daily load curve of the user, includes:
step S21, calculating user execution effect D of user k in real time according to the real-time daily load curve of user k and the user reference daily load curve of user kk:
Dk=max(Pik-P′ik) (2)
Wherein the user execution effect D is calculatedkIn time, the value of i must be located in the execution period, and the real-time load of the user k is represented as P'ik;
Step S22, calculating the user execution deviation I in real timek:
Ik=Dk-Mk (3)
Wherein the maximum sub-execution target of user k is denoted as MkThe maximum sub-execution target is the maximum value of the sub-execution targets of the user k;
based on the calculation step and the definition of the step S2, the first preset effect threshold mentioned in the step S3 is set to 0, and when the first preset effect threshold is lower, the system gives an early warning prompt, which represents that the user does not execute the ordered power utilization task; the first preset deviation threshold mentioned in step S4 is set to 0, and the occurrence of the condition greater than or equal to the first preset deviation threshold represents that the user performs the ordered power utilization task in place.
Step S5 includes calculating, in real time, a unit execution effect and a unit execution deviation of the unit execution ordered power utilization task according to the unit reference daily load curve and the real-time daily load curve of the unit:
step S51, calculating a unit execution effect PD of the unit in real time according to the real-time daily load curve of each user and the unit reference daily load curve:
PD=max(PRi-PEi) (4)
when calculating unit execution effect PD, the value of i must be in execution time interval, and the real-time load of unit is expressed as PEi;
Step S52, calculating the unit execution deviation I in real timeD:
Ik=PD-MD (6)
Wherein the maximum execution target of a unit is represented as MDThe maximum execution target is the maximum value among the respective execution targets in units.
Based on the calculation step and the definition of the step S5, the second preset effect threshold mentioned in the step S6 is set to 0, and when the second preset effect threshold is lower than the second preset effect threshold, the system gives an early warning prompt to the unit that the ordered power utilization task is not executed; the second preset deviation threshold mentioned in step S7 is set to 0, and the occurrence of the condition greater than or equal to the second preset deviation threshold represents that the unit execution of the ordered power utilization task is in place.
Further, in the processes of collecting 96-point power consumption data by the collection terminal, sending real-time data to the collection master station by the collection terminal, receiving the real-time data by the collection master station and the like, data abnormal conditions caused by equipment faults, network faults and the like can exist, the data abnormal points or leakage points generally appear on a real-time daily load curve, and the scheme optimization mode for the problems is as follows: when the data abnormal point appears on the real-time daily load curve of the user k, performing abnormal point correction operation on the data abnormal point according to the data trend in the historical daily load curve of the user; and when the real-time daily load curve of the user k has data missing points, carrying out missing point correction operation on the missing points according to the data trend in the historical daily load curve of the user.
Further, the user execution deviation I calculated according to step S2kAnd the reduced electric quantity E of the user k can be calculated in real timekFor the user offset load summation in the execution period 15/60, i, take the value in the execution period:
reducing the amount of electricity EkThe effect of executing the ordered power utilization tasks can be visually embodied.
Further, the present invention also provides an ordered power consumption intelligent monitoring system based on big data, as shown in fig. 2, the system architecture includes a device layer, a network layer, a master station layer and an application layer, wherein:
the device layer is used for providing an acquisition master station, a terminal and communication equipment; the device layer integrates multiple communication modes such as HPLC, RS485, Ethernet, broadband and narrowband carrier waves, wifi and the like, has the characteristics of high acquisition efficiency, ductility, flexibility, compatibility, interoperability and the like, meets the application requirements of minute-level power utilization curve data acquisition, and the terminal can be an intelligent ammeter, a concentrator, a load control terminal, an energy source controller, a loop state inspection instrument, an intelligent monitoring terminal, a photovoltaic inverter and the like;
the network layer is used for providing an uplink communication network between the acquisition terminal and the acquisition master station; the network layer mainly surrounds an uplink communication network between the terminal and the master station, and the terminal reports the collected data to the master station system through the means of a wireless public network, an optical fiber, a 5G power private network and the like, so that the communication rate and the data quality are effectively promoted;
the main station layer mainly acquires data and efficiently processes the data (load curve data are acquired by acquiring preposed high frequency, and the distributed big data platform realizes quick data query, real-time calculation and abnormal data fitting through kafka synchronous data);
the application layer is used for providing a human-computer interaction platform and providing visual interfaces such as ordered power utilization task issuing, decomposition, real-time monitoring of execution conditions, load curve display and the like, and information sharing and demand response of management personnel, business operating personnel and system operation and maintenance personnel at all levels are met; firstly, orderly power utilization tasks can be received through a computer;
specifically, the master station layer comprises a configuration module, a calculation module and a curve correction module:
the configuration module is used for configuring a unit reference daily load curve for units participating in executing the ordered power utilization tasks and configuring a user reference daily load curve for a user participating in executing the ordered power utilization tasks in the units, wherein the ordered power utilization tasks are execution targets in each execution time interval in a specified daily unit, and the number of the execution time intervals is at least one;
the calculation module is used for calculating the user execution effect and the user execution deviation of the ordered power utilization tasks executed by the user in real time on the appointed day according to the user reference daily load curve and the real-time daily load curve of the user;
the application layer is used for generating a user non-execution prompt message and sending the user non-execution prompt message to a manager of the user when the user execution effect is lower than a first preset effect threshold value so as to prompt the user to execute the ordered power utilization task according to the user reference daily load curve; when the execution deviation of the user is not lower than a first preset deviation threshold, generating a prompt message for the user to execute in place and reporting the prompt message to a manager of a unit;
the calculation module is used for calculating the unit execution effect and the unit execution deviation of the unit execution ordered power utilization task in real time according to the unit reference daily load curve and the real-time daily load curve of each user;
the application layer is used for generating a unit orderly power utilization non-execution prompt message and sending the message to a manager of a unit when the unit execution effect is lower than a second preset effect threshold value so as to prompt the unit to monitor the power utilization condition of the user in real time; when the unit execution deviation is not lower than a second preset deviation threshold, generating a unit execution in-place prompt message and reporting the unit execution in-place prompt message to a superior unit of the unit;
and the application layer is also used for displaying data in the ordered power utilization tasks, displaying all data in the process of processing the ordered power utilization tasks by the network layer and displaying all data in the process of processing the ordered power utilization tasks by the application layer through a visual interface of the human-computer interaction terminal.
The configuration module is used for configuring sub-execution targets of a single user in each execution period according to the execution targets of each execution period and the trend characteristics of the historical daily load curve of the user, wherein the sub-execution targets are decomposed from the execution targets in the same period; selecting a curve which is matched with the execution time interval and the sub-execution target at the same time from the historical daily load curves of the user as a user reference daily load curve according to the execution time interval and the corresponding sub-execution target, wherein the user reference daily load curve is a complete 96-point load curve;
and the calculation module is used for calculating a unit reference daily load curve according to the user reference daily load curves of all the users and referring to the calculation scheme of the formula (1).
A calculating module, configured to calculate a user execution effect D of the user k in real time according to the real-time daily load curve of the user k and the user reference daily load curve of the user kkReferring to formula (2), wherein the user execution effect D is calculatedkThen, the value of i must be within the execution time interval;
a calculation module for calculating the user execution deviation I in real timekReferring to equation (3), where the maximum child execution target for user k is denoted as Mk;
The calculation module is used for calculating the unit execution effect PD of the unit in real time according to the real-time daily load curve and the unit reference daily load curve of each user, and referring to a formula (4) and a formula (5), wherein when the unit execution effect PD is calculated, the value of i is required to be positioned in an execution time interval;
a calculation module for calculating the unit execution deviation I in real timeDRefer to the calculation scheme of equation (6).
According to the user execution deviation I calculated by the calculation modulekAnd the reduced electric quantity E of the user k can be calculated in real timekReducing the electric quantity E through the interface display of the computerkThe effect of executing the ordered power utilization tasks can be visually embodied.
The curve correction module is used for performing abnormal point correction operation on the abnormal data points according to the data trend in the historical daily load curve of the user when the abnormal data points appear in the real-time daily load curve of the user; and when the data leakage point occurs in the real-time daily load curve of the user, performing leakage point correction operation on the leakage point according to the data trend in the historical daily load curve of the user.
As shown in fig. 3, in the implementation process of the ordered power utilization task, the provincial electric power company issues the ordered power utilization task, the local company (i.e. unit) selects the enterprise (user) participating in the ordered power utilization and configures a user reference daily load curve for the enterprise (user), on the specified day of executing the ordered power utilization task, the system monitors in the execution time period, analyzes the analysis content from the time 0 to the time in real time, and the mentioned user execution effect DkUser execution deviation IkUnit execution effect PD, unit execution deviation IDThe data are obtained by real-time analysis based on the existing data on the day, and the data change every moment, and when the conditions mentioned in the step 3, the step 4, the step 6 and the step 7 occur, the system carries out corresponding reminding so as to realize the timeliness and the effectiveness of the scheme.
As shown in fig. 4, the data flow direction of data acquisition, data call and data analysis for the high energy consuming users is embodied in the monitoring model diagram, the users and the service file relationship of the power consumption information acquisition system are utilized to perform real-time and historical data monitoring analysis, statistics and establishment of missing and abnormal data completion models for the high energy consuming monitoring users, areas and influence ranges, the method of distributed storage (HBASE) and memory parallel computing technology (SPARK) is used to collect and preprocess the missing and abnormal data, completion and marking are performed according to preset rules, accurate, effective and complete data storage of the monitoring load curve is guaranteed, and data reference is provided for subsequent equipment abnormal analysis.
In the system of the invention, through a high energy consumption user load data high concurrent access processing technology: in order to ensure that monitoring load data can be obtained quickly and accurately, historical data and batch data are accessed to a big data platform by adopting an Sqoop tool, and real-time data are collected to the big data platform by adopting a real-time data collection tool (Kafka + Storm/Flank); the stream processing technology realizes the quasi-real-time acquisition of high-energy consumption monitoring load data: the large data platform extracts monitoring load curve data in batch processing every 15 minutes, and adopts a stream processing technology to analyze whether the load curve data is complete, whether the load values before and after time are logical, whether the values are abnormal and the like in real time, so as to filter and mark unreasonable load data; the real-time monitoring load data analysis is realized based on a big data technology: through a large data platform Flink + Hbase technology, the load data is monitored, the load data is extracted in a streaming type calculation mode in a quasi-real-time mode, data storage is carried out according to the digital-analog requirement of a monitoring system, and the requirements of ordered power utilization large-screen monitoring and dimension real-time monitoring of single users and management units are met.
According to the embodiment of the invention, service monitoring models such as peak staggering and avoiding, ordered power utilization, round-trip stop and the like can be constructed based on high-energy-consumption monitoring load data, high-energy-consumption monitoring users, regions and influence ranges, core service functions such as high-energy-consumption user task issuing, task decomposition, peak staggering and avoiding ordered power utilization monitoring large-screen statistical display, high-energy-consumption user load real-time monitoring, management unit power utilization load real-time monitoring, real-time monitoring according to each dimension of industry and voltage grade and the like are realized, and reliability index monitoring and auxiliary support are provided for the whole-region high-energy-consumption power supply and utilization.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (10)
1. An ordered electricity utilization intelligent monitoring method based on big data is characterized by comprising the following steps:
according to the issued ordered power utilization tasks, a unit reference daily load curve is configured for a unit participating in executing the ordered power utilization tasks, a user reference daily load curve is configured for a user participating in executing the ordered power utilization tasks in the unit, the ordered power utilization tasks are execution targets of the unit in each execution time interval on a specified day, and the number of the execution time intervals is at least one;
on the appointed day, calculating the user execution effect and the user execution deviation of the ordered power utilization task executed by the user in real time according to the user reference daily load curve and the real-time daily load curve of the user;
when the user execution effect is lower than a first preset effect threshold value, generating a user non-execution prompt message and sending the user non-execution prompt message to a manager of the user so as to prompt the user to execute the ordered power utilization task according to the user reference daily load curve;
when the user execution deviation is not lower than a first preset deviation threshold, generating a user execution in-place prompt message and reporting the message to the manager of the unit;
calculating unit execution effects and unit execution deviations of the unit for executing the ordered power utilization tasks in real time according to the unit reference daily load curves and the real-time daily load curves of the users;
when the unit execution effect is lower than a second preset effect threshold value, generating a unit orderly power utilization unexecuted prompt message and sending the message to a manager of the unit to prompt the unit to monitor the power utilization condition of the user in real time;
and when the unit execution deviation is not lower than a second preset deviation threshold, generating a unit execution in-place prompt message and reporting the message to a superior unit of the unit.
2. The intelligent ordered power consumption monitoring method based on big data as claimed in claim 1, wherein the configuring a unit reference daily load curve for a unit participating in executing the ordered power consumption task and a user reference daily load curve for a user participating in executing the ordered power consumption task in the unit according to the issued ordered power consumption task comprises:
configuring sub-execution targets of the single user in each execution period according to the execution targets of the execution periods and the trend characteristics of the historical daily load curve of the user, wherein the sub-execution targets are decomposed from the execution targets in the same period;
according to the execution time interval and the corresponding sub-execution targets, selecting a curve which is matched with the execution time interval and the sub-execution targets at the same time from the historical daily load curves of the user as a user reference daily load curve, wherein the user reference daily load curve is a complete 96-point load curve;
calculating the unit reference daily load curve according to the user reference daily load curves of all the users:
wherein the reference load of each point on the unit reference daily load curve is represented as PRiThe reference load of each point on the user reference daily load curve of the user k is represented as PikI is a time point sequence value of every 15 minutes starting from 0, and n is the number of users participating in the execution of the ordered electricity utilization task in the unit.
3. The big-data-based orderly power utilization intelligent monitoring method according to claim 2,
the step of calculating the user execution effect and the user execution deviation of the ordered power utilization task executed by the user in real time according to the user reference daily load curve and the real-time daily load curve of the user comprises the following steps:
calculating the user execution effect D of the user k in real time according to the real-time daily load curve of the user k and the user reference daily load curve of the user kk:
Dk=max(Pik-P′ik)
Wherein the user execution effect D is calculatedkAnd the value of i is required to be positioned in the execution time interval, and the real-time load of the user k is represented as P'ik;
Calculating the user execution deviation I in real timek:
Ik=Dk-Mk
Wherein the userThe maximum sub-execution target of k is denoted as MkThe maximum sub-execution target is the maximum value in the sub-execution targets of the user k;
the real-time calculation of the unit execution effect and the unit execution deviation of the unit execution of the ordered power utilization task according to the unit reference daily load curve and the real-time daily load curve of the unit comprises the following steps:
according to the real-time daily load curve of each user and the unit reference daily load curve, calculating the unit execution effect PD of the unit in real time:
PD=max(PRi-PEi)
when calculating the unit execution effect PD, the value of i must be in the execution time interval, and the real-time load of the unit is expressed as PEi;
Calculating the unit execution deviation I in real timeD:
Ik=PD-MD
Wherein the maximum execution target of the unit is represented as MDThe maximum execution target is the maximum value among the respective execution targets of the unit.
4. The intelligent ordered power consumption monitoring method based on big data as claimed in claim 3, wherein after generating and sending the prompt message that the ordered power consumption of the unit does not reach the standard to the manager of the unit, the method further comprises:
when the real-time daily load curve of the user has data abnormal points, performing abnormal point correction operation on the data abnormal points according to the data trend in the historical daily load curve of the user;
or,
and when the real-time daily load curve of the user has data missing points, carrying out missing point correction operation on the missing points according to the data trend in the historical daily load curve of the user.
5. The intelligent ordered power consumption monitoring method based on big data as claimed in claim 4, wherein the values of the first preset effect threshold, the second preset effect threshold, the first preset deviation threshold and the second preset deviation threshold are all 0.
6. The utility model provides an orderly power consumption intelligent monitoring system based on big data which characterized in that, includes equipment layer, network layer, main website layer and application layer, wherein:
the device layer provides an acquisition master station, an acquisition terminal and communication equipment;
the network layer is used for providing an uplink communication network between the acquisition terminal and the acquisition master station;
the application layer is used for providing a human-computer interaction platform and receiving ordered power utilization tasks;
the master station layer comprises a configuration module and a calculation module:
the configuration module is used for configuring a unit reference daily load curve for a unit participating in executing the ordered power utilization tasks according to the ordered power utilization tasks, and configuring a user reference daily load curve for a user participating in executing the ordered power utilization tasks in the unit, wherein the ordered power utilization tasks are execution targets of the unit in each execution time interval on a specified day, and the number of the execution time intervals is at least one;
the calculation module is used for calculating the user execution effect and the user execution deviation of the ordered power utilization task executed by the user in real time on the appointed day according to the user reference daily load curve and the real-time daily load curve of the user;
the application layer is used for generating a user non-execution prompting message and sending the user non-execution prompting message to a manager of the user when the user execution effect is lower than a first preset effect threshold value so as to prompt the user to execute the ordered power utilization task according to the user reference daily load curve; when the user execution deviation is not lower than a first preset deviation threshold, generating a user execution in-place prompt message and reporting the message to the management personnel of the unit;
the calculation module is used for calculating the unit execution effect and the unit execution deviation of the unit for executing the ordered power utilization task in real time according to the unit reference daily load curve and the real-time daily load curve of each user;
the application layer is used for generating a unit orderly power utilization non-execution prompt message and sending the message to a manager of the unit when the unit execution effect is lower than a second preset effect threshold value so as to prompt the unit to monitor the power utilization condition of the user in real time; when the unit execution deviation is not lower than a second preset deviation threshold, generating a unit execution in-place prompt message and reporting the unit execution in-place prompt message to a superior unit of the unit;
the application layer is further used for displaying data in the ordered power utilization tasks, displaying all data in the process of processing the ordered power utilization tasks by the network layer and displaying all data in the process of processing the ordered power utilization tasks by the application layer through a visual interface of a human-computer interaction terminal.
7. The big-data-based orderly power consumption intelligent monitoring system according to claim 6,
the configuration module is used for configuring sub-execution targets of the single user in each execution period according to the execution targets of the execution periods and the trend characteristics of the historical daily load curve of the user, wherein the sub-execution targets are decomposed from the execution targets in the same period;
the configuration module is used for selecting a curve which is matched with the execution time interval and the sub-execution target at the same time from the historical daily load curves of the user as a user reference daily load curve according to the execution time interval and the corresponding sub-execution target, wherein the user reference daily load curve is a complete 96-point load curve;
the calculation module is configured to calculate the unit reference daily load curve according to the user reference daily load curves of all the users:
wherein the reference load of each point on the unit reference daily load curve is represented as PRiThe reference load of each point on the user reference daily load curve of the user k is represented as PikI is a time point sequence value of every 15 minutes starting from 0, and n is the number of users participating in the execution of the ordered electricity utilization task in the unit.
8. The big-data-based orderly power consumption intelligent monitoring system according to claim 7,
the calculation module is used for calculating the user execution effect D of the user k in real time according to the real-time daily load curve of the user k and the user reference daily load curve of the user kk:
Dk=max(Pik-P′ik)
Wherein the user execution effect D is calculatedkAnd the value of i is required to be positioned in the execution time interval, and the real-time load of the user k is represented as P'ik;
The calculation module is used for calculating the user execution deviation I in real timek:
Ik=Dk-Mk
Wherein the maximum sub-execution target of the user k is represented as MkThe maximum sub-execution target is the maximum value in the sub-execution targets of the user k;
the calculating module is configured to calculate the unit execution effect PD of the unit in real time according to the real-time daily load curve of each user and the unit reference daily load curve:
PD=max(PRi-PEi)
when calculating the unit execution effect PD, the value of i must be in the execution time interval, and the real-time load of the unit is expressed as PEi;
The calculation module is used for calculating the unit execution deviation I in real timeD:
Ik=PD-MD
Wherein the maximum execution target of the unit is represented as MDThe maximum execution target is the maximum value among the respective execution targets of the unit.
9. The big-data-based intelligent orderly power consumption monitoring system as claimed in claim 8, wherein said master station layer further comprises:
the curve correction module is used for performing abnormal point correction operation on the data abnormal point according to the data trend in the historical daily load curve of the user when the real-time daily load curve of the user has the data abnormal point; and when the real-time daily load curve of the user has data missing points, carrying out missing point correction operation on the missing points according to the data trend in the historical daily load curve of the user.
10. The intelligent big-data-based orderly power utilization monitoring system according to claim 9, wherein the first preset effect threshold, the second preset effect threshold, the first preset deviation threshold, and the second preset deviation threshold all take values of 0.
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