CN116389944A - Investigation strategy customization system based on remote meter reading data acquisition - Google Patents
Investigation strategy customization system based on remote meter reading data acquisition Download PDFInfo
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- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000013527 convolutional neural network Methods 0.000 claims description 30
- 238000013024 troubleshooting Methods 0.000 claims description 11
- 230000000875 corresponding effect Effects 0.000 claims description 6
- 230000001186 cumulative effect Effects 0.000 claims description 6
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- 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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention relates to an investigation strategy customization system based on remote meter reading data acquisition, which comprises: the content prediction device adopts an intelligent prediction mechanism to predict the total power consumption of the set block in the future time section according to the total power consumption of each time section corresponding to the set number of each time section before the future time section and the block information of the set block; and the checking and processing device sends out a field checking request when the absolute value of the difference value between the predicted total power consumption and the total power consumption in the future time section of the set neighborhood acquired by adopting the remote meter reading mode exceeds the set electric quantity difference value. The investigation strategy customization system based on remote meter reading data acquisition is intelligent in design and simple and convenient to operate. The method and the system can predict the total electricity consumption in the future time section of the set block, and judge whether the future time section of the set block needs to be checked and marked based on the predicted total electricity consumption, so that the labor cost and the time cost of electricity consumption supervision are reduced.
Description
Technical Field
The invention relates to the field of remote meter reading, in particular to an investigation strategy customization system based on remote meter reading data acquisition.
Background
In general, remote meter reading is performed for a specific purpose, one is for a house and the other is for a business or a factory, and mixed management of the house, the business or the factory exists, and each house, the business or the factory in the managed area is an object of remote meter reading. The remote ammeter reading system also has a property management function, and can be provided with a plurality of charges.
The remote ammeter reading system also has the functions of recording all operations of a user, switch displacement, overrun parameters and actual demand time of other users, such as overload automatic power-off, automatic protection of power-off of large current and automatic power-off of normal load. The remote ammeter meter reading management system of the Internet of things is an intelligent management system for electric charge collection, electric energy metering and statistical analysis in places such as property communities, shopping malls, apartments, office buildings, industrial parks, dormitories, factories and the like.
However, in the actual use of the remote meter reading system, the system manager needs to pay attention to fault detection and fault investigation that each electricity consumption block is kept for 24 hours, and this general management mode does not fully utilize the existing meter reading data on one hand, and easily wastes a large amount of manpower and material resources due to lack of emphasis on the other hand.
Disclosure of Invention
In order to solve the technical problems in the related art, the invention provides an investigation strategy customization system based on remote meter reading data acquisition, which can predict the total electricity consumption in future time segments of a set block based on the past remote meter reading electricity quantity data of the set block and each block information of the set block, and carry out investigation marking on the future time segments of the set block when the absolute value of the difference between the predicted total electricity consumption and the total electricity consumption in the future time segments of the set block, which are acquired by an electricity meter reading mechanism in a remote meter reading mode, exceeds the set electricity quantity difference, so that the attention of fault detection and fault investigation for keeping 24 hours of uninterrupted for each electricity block is avoided.
According to an aspect of the present invention, there is provided an investigation policy customization system based on remote meter reading data acquisition, the system comprising:
the electricity meter reading mechanism is used for collecting the total electricity consumption in each time section of the set block by adopting a remote meter reading mode, wherein the total electricity consumption in each time section of the set block is a cumulative value of the electricity consumption of each electricity consumption object in the set block in the time section;
the timing operation mechanism is connected with the electric quantity meter reading mechanism and is used for providing timing signals for the acquisition action of a remote meter reading mode adopted by the electric quantity meter reading mechanism;
the data input mechanism is connected with the electric quantity meter reading mechanism and is used for acquiring the total power consumption of each time segment of a preset number of preset blocks before a future time segment, wherein the future time segment and the preset number of each time segment before the future time segment jointly occupy a whole time interval on a time axis;
the system comprises a block analysis mechanism, a power generation mechanism and a power generation mechanism, wherein the block analysis mechanism is used for acquiring various block information of a set block, and the various block information of the set block comprises the occupied area of the set block, the people flow of the set block, the total number of power utilization objects of the set block and the historical power utilization peak value of the set block;
the content prediction device is respectively connected with the data input mechanism and the neighborhood analysis mechanism and is used for predicting the total electricity consumption of the set neighborhood in the future time section as the predicted total electricity consumption according to the total electricity consumption of the set neighborhood in each time section corresponding to the set number of time sections before the future time section and each piece of neighborhood information of the set neighborhood by adopting a convolutional neural network model;
the checking processing device is respectively connected with the electric quantity meter reading mechanism and the content predicting device and is used for sending out a field checking request when the absolute value of the difference value between the received predicted total power consumption and the total power consumption in the future time section of the set neighborhood acquired by the electric quantity meter reading mechanism in a remote meter reading mode exceeds the set electric quantity difference value;
the step of predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block, by adopting a convolutional neural network model, comprises the following steps: the value of the set number is positively correlated with the total number of the electric objects of the set neighborhood.
The investigation strategy customization system based on remote meter reading data acquisition is intelligent in design and simple and convenient to operate. The method and the system can predict the total electricity consumption in the future time section of the set block, and judge whether the future time section of the set block needs to be checked and marked based on the predicted total electricity consumption, so that the labor cost and the time cost of electricity consumption supervision are reduced.
Drawings
Embodiments of the present invention will be described below with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram of an internal structure of an investigation policy customization system based on remote meter reading data acquisition according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram of an internal structure of an investigation policy customization system based on remote meter reading data acquisition according to a second embodiment of the present invention.
Fig. 3 is a schematic diagram showing an internal structure of an investigation policy customization system based on remote meter reading data acquisition according to a third embodiment of the present invention.
Detailed Description
Embodiments of the remote meter reading data acquisition-based investigation strategy customization system of the present invention will be described in detail below with reference to the accompanying drawings.
Embodiment A
Fig. 1 is a schematic diagram of an internal structure of an investigation policy customization system based on remote meter reading data acquisition according to a first embodiment of the present invention, the system comprising:
the electricity meter reading mechanism is used for collecting the total electricity consumption in each time section of the set block by adopting a remote meter reading mode, wherein the total electricity consumption in each time section of the set block is a cumulative value of the electricity consumption of each electricity consumption object in the set block in the time section;
for example, setting the total amount of electricity used in each time segment of the neighborhood as a cumulative value of the amount of electricity used in the time segment by each electricity object in the neighborhood includes: the setting of the time length of each time segment can be manual mode setting or electronic mode setting;
the timing operation mechanism is connected with the electric quantity meter reading mechanism and is used for providing timing signals for the acquisition action of a remote meter reading mode adopted by the electric quantity meter reading mechanism;
the data input mechanism is connected with the electric quantity meter reading mechanism and is used for acquiring the total power consumption of each time segment of a preset number of preset blocks before a future time segment, wherein the future time segment and the preset number of each time segment before the future time segment jointly occupy a whole time interval on a time axis;
the system comprises a block analysis mechanism, a power generation mechanism and a power generation mechanism, wherein the block analysis mechanism is used for acquiring various block information of a set block, and the various block information of the set block comprises the occupied area of the set block, the people flow of the set block, the total number of power utilization objects of the set block and the historical power utilization peak value of the set block;
the content prediction device is respectively connected with the data input mechanism and the neighborhood analysis mechanism and is used for predicting the total electricity consumption of the set neighborhood in the future time section as the predicted total electricity consumption according to the total electricity consumption of the set neighborhood in each time section corresponding to the set number of time sections before the future time section and each piece of neighborhood information of the set neighborhood by adopting a convolutional neural network model;
the checking processing device is respectively connected with the electric quantity meter reading mechanism and the content predicting device and is used for sending out a field checking request when the absolute value of the difference value between the received predicted total power consumption and the total power consumption in the future time section of the set neighborhood acquired by the electric quantity meter reading mechanism in a remote meter reading mode exceeds the set electric quantity difference value;
the step of predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block, by adopting a convolutional neural network model, comprises the following steps: the value of the set number is positively correlated with the total number of the electric objects of the set neighborhood;
illustratively, positively associating the set number of values with the total number of electricity objects of the set neighborhood includes: the total number of the electricity consumption objects of the set neighborhood is 100, the selected set number is 20, the total number of the electricity consumption objects of the set neighborhood is 200, the selected set number is 50, the total number of the electricity consumption objects of the set neighborhood is 500, the selected set number is 100, the total number of the electricity consumption objects of the set neighborhood is 2000, and the selected set number is 200.
From the above, the invention has at least the following beneficial technical effects:
firstly, predicting the total electricity consumption in future time segments of a set block based on past remote meter reading electricity data of the set block and various block information of the set block, so as to provide key information for the establishment of a subsequent block investigation strategy, wherein the various block information of the set block comprises the occupied area, the flow of people, the total number of electricity consumption objects and the historical electricity consumption peak value of the set block;
secondly, introducing an investigation processing device for issuing a field investigation request when the absolute value of the difference value between the predicted total electricity consumption in the future time section of the set block and the total electricity consumption in the future time section of the set block acquired by the electricity meter reading mechanism in a remote meter reading mode exceeds the set electricity consumption difference value, so as to avoid performing investigation processing on all time sections of all blocks;
again, the predictive process is based on a customized convolutional neural network model, the number of learning of which is proportional to the footprint of a set block, and the number of each past time segment of the past remote meter reading to which the input content of the convolutional neural network model relates is positively correlated with the total number of electricity objects of the set block.
Embodiment B
Fig. 2 is a schematic diagram of an internal structure of an investigation policy customization system based on remote meter reading data acquisition according to a second embodiment of the present invention.
In fig. 2, unlike fig. 1, the remote meter reading data acquisition-based troubleshooting policy customization system in fig. 2 may further include the following components:
the cloud computing server is used for carrying out network data interaction with the checking processing device through a wireless network and carrying out on-site checking marking on a set block with an on-site checking request;
alternatively, the cloud computing server may be optionally replaced with a big data server or a blockchain server.
Embodiment C
Fig. 3 is a schematic diagram showing an internal structure of an investigation policy customization system based on remote meter reading data acquisition according to a third embodiment of the present invention.
In fig. 3, unlike fig. 1, the remote meter reading data acquisition-based troubleshooting policy customization system in fig. 3 may further include the following components:
the model building device is connected with the content prediction device and used for building a convolutional neural network model and sending the built convolutional neural network model to the content prediction device for use.
Next, the specific structure of the investigation policy customization system based on remote meter reading data acquisition of the present invention will be further described.
In an investigation policy customization system based on remote meter reading data acquisition according to various embodiments of the present invention:
establishing a convolutional neural network model, and sending the established convolutional neural network model to the content prediction device for use, wherein the method comprises the following steps: the number of times of learning the convolutional neural network is proportional to the occupied area of the set block.
In an investigation policy customization system based on remote meter reading data acquisition according to various embodiments of the present invention:
the method for predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block comprises the following steps: the convolutional neural network model is a convolutional neural network after multiple times of learning are completed;
the step of predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block, by adopting a convolutional neural network model, comprises the following steps: taking the total power consumption amount of each time segment of the set number of time segments before the future time segment and the block information of each set block as the input information of the convolutional neural network model;
the step of predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block, by adopting a convolutional neural network model, comprises the following steps: and running the convolutional neural network model to obtain the total electricity consumption of the set neighborhood segmented at the future time, which is output by the convolutional neural network model.
In an investigation policy customization system based on remote meter reading data acquisition according to various embodiments of the present invention:
the method for acquiring the total electricity consumption in each time section of the set block by adopting the remote meter reading mode comprises the following steps of: setting each electric object in the neighborhood to comprise a household electric object and a department electric object;
the automobile adopts a remote meter reading mode to collect the total electricity consumption in each time section of a set block, the total electricity consumption in each time section of the set block is a cumulative value of the electricity consumption of each electricity consumption object in the set block in the time section, and the cumulative value comprises: the duration of the time segment is smaller than or equal to a set duration limit;
the method for obtaining the total power consumption of each time segment of the set neighborhood before the future time segment, wherein the total power consumption of each time segment of the set neighborhood respectively corresponds to the set number of each time segment before the future time segment, the future time segment and the set number of each time segment before the future time segment jointly occupy the whole time interval on the time axis, and the method comprises the following steps: the future time segment is the same as the duration of each of a set number of individual time segments preceding the future time segment;
each piece of block information of the set block comprises a occupied area of the set block, a traffic flow of people of the set block, a total number of electricity consumption objects of the set block and a historical electricity consumption peak value of the set block, wherein the historical electricity consumption peak value comprises: and the historical electricity consumption peak value of the set block is the electricity consumption total amount corresponding to the time segment with the largest electricity consumption total amount value before the future time segment of the set block.
In addition, in the investigation policy customization system based on remote meter reading data acquisition, the investigation processing device is further used for sending a numerical reliability request when the absolute value of the difference value between the received predicted total power consumption and the total power consumption in the future time section of the set neighborhood acquired by the power meter reading mechanism in a remote meter reading mode does not exceed the preset power consumption difference value.
In the foregoing specification, the invention has been described with reference to specific embodiments. However, it will be understood by those skilled in the art that various modifications and changes may be made without departing from the scope of the present invention as defined in the appended claims. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of present invention.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, any benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or element of any or all the claims. The terms "comprises," "comprising," or any other variation thereof, as used herein, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Claims (10)
1. An investigation strategy customization system based on remote meter reading data acquisition, which is characterized by comprising:
the electricity meter reading mechanism is used for collecting the total electricity consumption in each time section of the set block by adopting a remote meter reading mode, wherein the total electricity consumption in each time section of the set block is a cumulative value of the electricity consumption of each electricity consumption object in the set block in the time section;
the timing operation mechanism is connected with the electric quantity meter reading mechanism and is used for providing timing signals for the acquisition action of a remote meter reading mode adopted by the electric quantity meter reading mechanism;
the data input mechanism is connected with the electric quantity meter reading mechanism and is used for acquiring the total power consumption of each time segment of a preset number of preset blocks before a future time segment, wherein the future time segment and the preset number of each time segment before the future time segment jointly occupy a whole time interval on a time axis;
the system comprises a block analysis mechanism, a power generation mechanism and a power generation mechanism, wherein the block analysis mechanism is used for acquiring various block information of a set block, and the various block information of the set block comprises the occupied area of the set block, the people flow of the set block, the total number of power utilization objects of the set block and the historical power utilization peak value of the set block;
the content prediction device is respectively connected with the data input mechanism and the neighborhood analysis mechanism and is used for predicting the total electricity consumption of the set neighborhood in the future time section as the predicted total electricity consumption according to the total electricity consumption of the set neighborhood in each time section corresponding to the set number of time sections before the future time section and each piece of neighborhood information of the set neighborhood by adopting a convolutional neural network model;
the checking processing device is respectively connected with the electric quantity meter reading mechanism and the content predicting device and is used for sending out a field checking request when the absolute value of the difference value between the received predicted total power consumption and the total power consumption in the future time section of the set neighborhood acquired by the electric quantity meter reading mechanism in a remote meter reading mode exceeds the set electric quantity difference value;
the step of predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block, by adopting a convolutional neural network model, comprises the following steps: the value of the set number is positively correlated with the total number of the electric objects of the set neighborhood.
2. The remote meter reading data acquisition based troubleshooting policy customization system of claim 1, further comprising:
and the cloud computing server is used for carrying out network data interaction with the checking processing device through a wireless network and carrying out on-site checking marking on a set block with an on-site checking request.
3. The remote meter reading data acquisition based troubleshooting policy customization system of claim 1, further comprising:
the model building device is connected with the content prediction device and used for building a convolutional neural network model and sending the built convolutional neural network model to the content prediction device for use.
4. The remote meter reading data acquisition-based troubleshooting policy customization system according to claim 3, wherein:
establishing a convolutional neural network model, and sending the established convolutional neural network model to the content prediction device for use, wherein the method comprises the following steps: the number of times of learning the convolutional neural network is proportional to the occupied area of the set block.
5. The remote meter reading data acquisition-based troubleshooting policy customization system according to any one of claims 2-4, wherein:
the method for predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block comprises the following steps: the convolutional neural network model is a convolutional neural network after multiple times of learning are completed.
6. The remote meter reading data acquisition-based troubleshooting policy customization system according to claim 5, wherein:
the method for predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block comprises the following steps: and taking the total power consumption amount of each time segment of the set number of time segments before the future time segment and the block information of each set block as the input information of the convolutional neural network model.
7. The remote meter reading data acquisition-based troubleshooting policy customization system according to claim 6, wherein:
the method for predicting the total electricity consumption of the set block in the future time section according to the total electricity consumption of the set block in the future time section, which corresponds to the set number of time sections before the future time section, and the block information of the set block comprises the following steps: and running the convolutional neural network model to obtain the total electricity consumption of the set neighborhood segmented at the future time, which is output by the convolutional neural network model.
8. The remote meter reading data acquisition-based troubleshooting policy customization system according to any one of claims 2-4, wherein:
the method for acquiring the total electricity consumption in each time section of the set block by adopting the remote meter reading mode comprises the following steps of: each electric object in the set neighborhood comprises a household electric object and a department electric object.
9. The remote meter reading data acquisition-based troubleshooting policy customization system according to claim 8, wherein:
the method for acquiring the total electricity consumption in each time section of the set block by adopting the remote meter reading mode comprises the following steps of: and the duration of the time segment is smaller than or equal to the set duration limit.
10. The remote meter reading data acquisition-based troubleshooting policy customization system according to claim 9, wherein:
acquiring the total amount of each electricity consumption of a set block corresponding to each time segment of a set number before a future time segment, wherein the future time segment and each time segment of the set number before the future time segment jointly occupy a whole time interval on a time axis, and the method comprises the following steps: the future time segment is the same as the duration of each of a set number of individual time segments preceding the future time segment;
each piece of block information of the set block comprises a occupied area of the set block, a traffic flow of people of the set block, a total number of electricity consumption objects of the set block and a historical electricity consumption peak value of the set block, wherein the historical electricity consumption peak value comprises: and the historical electricity consumption peak value of the set block is the electricity consumption total amount corresponding to the time segment with the largest electricity consumption total amount value before the future time segment of the set block.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117134480A (en) * | 2023-10-26 | 2023-11-28 | 常州满旺半导体科技有限公司 | Big data analysis-based power supply regulation monitoring system and method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117134480A (en) * | 2023-10-26 | 2023-11-28 | 常州满旺半导体科技有限公司 | Big data analysis-based power supply regulation monitoring system and method |
| CN117134480B (en) * | 2023-10-26 | 2024-01-12 | 常州满旺半导体科技有限公司 | Big data analysis-based power supply regulation monitoring system and method |
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