CN115933507B - Intelligent regional power consumption energy saving method and system - Google Patents
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Abstract
The invention provides an intelligent regional power consumption energy-saving method and system, wherein the system is used for managing the power consumption conditions of users, buildings and public regions in the region and saving power consumption, and comprises a data real-time monitoring acquisition module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information acquisition module, a blockchain information management module and an alarm module. According to the method and the system provided by the invention, the cloud computing early warning analysis module with the built abnormality detection model is arranged, the external air temperature is monitored in real time through the temperature sensor for detecting the external temperature, different abnormality detection conditions for influencing and building different external temperatures are built according to the influence conditions of power supply and heat supply required by different external temperatures, and then the electric energy saving early warning judgment is carried out under the condition of different electric energy saving standard requirements, and an alarm is sent out when the energy saving requirement is exceeded, so that the electric energy saving is effectively carried out.
Description
Technical Field
The invention belongs to the technical field of electric energy monitoring and management, and particularly relates to an intelligent regional power consumption energy saving method and system.
Background
The electric power industry has the characteristics of high investment, high consumption, high emission and the like, and has become the largest carbon emission source in the global energy field. In recent decades, the power generation capacity of China is continuously increased, electricity becomes an indispensable part of life of people, and a series of related problems are generated while the high-efficiency and convenient social production life is ensured by the higher power consumption.
The types of electric equipment are more and more, the structures of the circuits are more and more complex, and the electric energy quality problems caused by the circuits have different degrees of influence on multiple parties.
Therefore, an electric energy quality monitoring system is built on a plurality of places, electric energy quality data are collected, most of current research is focused on disturbance detection, and relatively few electric energy quality index analysis and prediction are truly performed, so that the method has important engineering significance for reasonable early warning of electric energy quality steady-state indexes.
In order to respond to energy conservation and emission reduction, the power utilization management quality of the power industry is effectively improved, and besides the power parameters of each area are required to be rapidly and accurately obtained, a system and a method for carrying out power energy conservation management and alarm processing according to the power utilization condition of the monitored management area and the outside environment temperature are also required.
Disclosure of Invention
Aiming at the defects, the invention provides an electric energy saving early warning method and an electric energy saving early warning system for carrying out electric energy saving management and alarm processing according to the electricity consumption condition of a monitored management area and in combination with the external environment temperature.
The invention provides the following technical scheme: an intelligent regional power consumption energy-saving method comprises the following steps:
1) The micro-control module controls the data real-time monitoring and collecting module and the temperature sensor to be started, the data real-time monitoring and collecting module collects electric energy parameters of users, buildings and public areas in the area, and the temperature sensor collects outside air temperature data;
2) The micro control module transmits data to the display module for displaying the electricity consumption condition of the electric energy in real time, and sends the data to the relay and the wireless communication module, the relay is connected into the power strip to control the on-off of the live wire, and the control signal of the micro control module is received to realize the on-off of the power supply in the jack;
3) The wireless communication module transmits the electric energy parameter data to the cloud computing early warning analysis module, the cloud computing early warning analysis module performs data analysis, an anomaly detection model is built through the received external temperature, and the actual consumption Y s,n and the predicted consumption of the electric energy within the range of one monitoring cycle t are calculated Whether the difference is smaller than a threshold valueIf less than the threshold valueThe power use state is the normal energy-saving mode, if the threshold value is exceededThe electric energy use state in the normal energy-saving mode is not met, and the cloud computing early warning analysis module sends an alarm instruction to the alarm module;
4) The alarm module sends out an alarm notice;
5) The block chain information management module receives abnormal data obtained by analysis of the cloud computing early warning analysis module, electric energy use data of users, buildings and public areas in the areas where the data are acquired by the data real-time monitoring acquisition module and the temperature sensor, and external environment temperature, and is used for carrying out historical query, analysis and summarization when needed later.
Further, the abnormality detection model construction method is as follows:
S1: taking 24 hours of a day as a detection cycle, the cycle is represented by s, namely t=0, 1,2, …,23, s epsilon t, each cycle has n periods, and the consumption in the t time range of the previous i days is used for automatic regression, i=1, 2, …, p, and the regression of each day forms a step; constructing an anomaly detection model Y s,n of the s-th cycle and the n-th cycle:
Wherein Y is a data point in the elapsed time sequence; p is the order in autoregressive; XT1, XT2, and XT3 are the required power supplies due to external temperature; alpha is an anomaly detection calculation coefficient, and beta is an external temperature influence calculation coefficient; epsilon s is the value of white noise;
s2: constructing different abnormal detection conditions according to different external temperature influences;
S3: constructing training data sets X= { X 1,x2,…,xn } formed under different abnormal detection conditions, constructing a detection function m (X j; mu, delta) of each element X j conforming to unique variable gaussian distribution, j=1, 2, …,3, and performing detection iterative computation on subsequently detected data;
calculated m (x j; mu, delta) and threshold A comparison is made.
Further, in the step S2, different conditions for detecting abnormality are constructed by different external temperature influences as follows:
the T is the external temperature detected by the temperature sensor;
The XT1 abnormal detection condition represents the external temperature influence condition that power supply, heat preservation and heat supply are not needed under the condition that the external temperature is 20 ℃; the XT2 abnormal detection condition represents the external temperature influence condition of power supply, heat preservation and heat supply under the condition that the external temperature is 16 ℃; the abnormal XT3 detection condition represents the external temperature influence condition of power supply, heat preservation and heat supply under the condition that the external temperature is 5 ℃.
Further, in the step S3, the detection function m (x j; μ, δ) is as follows:
Wherein,
The invention also provides an intelligent regional power consumption energy saving system adopting the method, which is used for managing the power consumption conditions of users, buildings and public areas in the region and saving power consumption, and comprises a data real-time monitoring and acquisition module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information acquisition module, a blockchain information management module and an alarm module; the data real-time monitoring and collecting module is used for monitoring the electric energy use data of users, buildings and public areas in the area in real time; the temperature sensor is used for monitoring external environment temperature data in real time; the micro control module is used for controlling whether the data real-time monitoring acquisition module and the temperature sensor are started; the wireless communication module is used for transmitting the data integrated and collected by the micro control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used for analyzing the electric energy data, detecting abnormality according to the external environment temperature, and sending an instruction to the alarm module if the abnormality threshold value is exceeded, wherein the alarm module sends an alarm notification; otherwise, continuing to analyze the data and detect the abnormality; the information collector module is used for controlling the cloud computing early warning analysis module to receive the data of the wireless communication module in a staged manner; and the block chain information management module is used for managing the acquired data.
Further, the cloud computing early warning analysis module adopts a Hadoop cloud computing method, and a data storage platform is introduced and data processing is carried out.
Further, the cloud computing early warning analysis module comprises an early warning grading module, an electric energy trend prediction module, a data query module and a statistical report module.
Further, the alarm module informs the user of early warning in a short message notification mode.
Further, the block chain information management module comprises a user management module, a building management module and a region management module;
the user management module is used for collecting and managing personal information such as identity information, electricity utilization information and the like of a user;
The building management module is used for classifying and metering the electric energy data of a plurality of buildings in the monitored area;
the public area management module is used for classifying and metering public area electric energy data in the monitored area.
The beneficial effects of the invention are as follows:
1. The system and the method provided by the invention have the capability of monitoring various indexes of the electric energy quality in real time, can conveniently and rapidly inquire related data of an event and automatically modify information types, can set proper threshold values according to different regional differences, and can automatically capture disturbance of different electric energy quality indexes; the system can make a prejudgment on the development trend of the power distribution network through sensitivity analysis and statistics, and issues maintenance and treatment control commands to all levels of monitoring stations.
2. The system and the method provided by the invention mainly comprise the functions of real-time data monitoring, statistics report, grading early warning, trend prediction, historical data query and the like. Based on the method, the system can monitor and early warn the power quality trend of the intelligent power distribution network, perform data analysis and evaluation conclusion display on all the connected voltage quality monitoring devices, and add and delete corresponding circuits.
3. According to the system and the method, the cloud computing early warning analysis module with the built anomaly detection model is arranged, the temperature sensor for detecting the external temperature is used for monitoring the external air temperature in real time, different anomaly detection conditions for influencing and building different external temperatures are built according to the influence conditions of power supply and heat supply required by different external temperatures, and then the electric energy saving early warning judgment is carried out under the condition of different electric energy saving standard requirements, and an alarm is given when the energy saving requirement is exceeded, so that the electric energy saving is effectively carried out.
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The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
FIG. 1 is a flow chart of a method for saving energy in regional power according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram of the overall structure of the power saving system for regional power use according to embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of a cloud computing early warning analysis module in the regional power-saving system according to embodiment 3 of the present invention;
fig. 4 is a schematic structural diagram of a blockchain information management module in the regional power-saving system according to embodiment 4 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, a flowchart of an intelligent regional power-saving method provided in this embodiment includes the following steps:
1) The micro-control module controls the data real-time monitoring and collecting module and the temperature sensor to be started, the data real-time monitoring and collecting module collects electric energy parameters of users, buildings and public areas in the area, and the temperature sensor collects outside air temperature data;
2) The micro control module transmits data to the display module for displaying the electricity consumption condition of the electric energy in real time, and sends the data to the relay and the wireless communication module, the relay is connected into the power strip to control the on-off of the live wire, and the control signal of the micro control module is received to realize the on-off of the power supply in the jack;
3) The wireless communication module transmits the electric energy parameter data to the cloud computing early warning analysis module, the cloud computing early warning analysis module performs data analysis, an anomaly detection model is built through the received external temperature, and the actual consumption Y s,n and the predicted consumption of the electric energy within the range of one monitoring cycle t are calculated Whether the difference is smaller than a threshold valueIf less than the threshold valueThe power use state is the normal energy-saving mode, if the threshold value is exceededThe electric energy use state in the normal energy-saving mode is not met, and the cloud computing early warning analysis module sends an alarm instruction to the alarm module;
4) The alarm module sends out an alarm notice;
5) The block chain information management module receives abnormal data obtained by analysis of the cloud computing early warning analysis module, electric energy use data of users, buildings and public areas in the areas where the data are acquired by the data real-time monitoring acquisition module and the temperature sensor, and external environment temperature, and is used for carrying out historical query, analysis and summarization when needed later.
The method for constructing the abnormality detection model comprises the following steps:
S1: taking 24 hours of a day as a detection cycle, the cycle is represented by s, namely t=0, 1,2, …,23, s epsilon t, each cycle has n periods, and the consumption in the t time range of the previous i days is used for automatic regression, i=1, 2, …, p, and the regression of each day forms a step; constructing an anomaly detection model Y s,n of the s-th cycle and the n-th cycle:
Wherein Y is a data point in the elapsed time sequence; p is the order in autoregressive; XT1, XT2, and XT3 are the required power supplies due to external temperature; alpha is an anomaly detection calculation coefficient, and beta is an external temperature influence calculation coefficient; epsilon s is the value of white noise;
s2: constructing different abnormal detection conditions according to different external temperature influences;
S3: constructing training data sets X= { X 1,x2,…,xn } formed under different abnormal detection conditions, constructing a detection function m (X j; mu, delta) of each element X j conforming to unique variable gaussian distribution, j=1, 2, …,3, and performing detection iterative computation on subsequently detected data;
calculated m (x j; mu, delta) and threshold A comparison is made.
In step S2, different external temperature influences construct different anomaly detection conditions as follows:
T is the external temperature detected by the temperature sensor;
The XT1 abnormal detection condition represents the external temperature influence condition that power supply, heat preservation and heat supply are not needed under the condition that the external temperature is 20 ℃; the XT2 abnormal detection condition represents the external temperature influence condition of power supply, heat preservation and heat supply under the condition that the external temperature is 16 ℃; the abnormal XT3 detection condition represents the external temperature influence condition of power supply, heat preservation and heat supply under the condition that the external temperature is 5 ℃.
In step S3, the detection function m (x j; μ, δ) is as follows:
Wherein,
According to the system provided by the invention, the abnormal detection model is built, the external air temperature is monitored in real time through the temperature sensor for detecting the external temperature, different abnormal detection conditions for influencing and building different external temperatures are built according to the influence conditions of power supply and heat supply required by different external temperatures, and then the electric energy saving early warning judgment is carried out under the condition of different electric energy saving standard requirements, and an alarm is sent out when the energy saving requirement is exceeded, so that the electric energy saving is effectively carried out.
Example 2
As shown in fig. 2, the intelligent regional power consumption energy saving system provided in this embodiment is used for managing the power consumption conditions of users, buildings and public areas in the region where the power energy saving early warning system is located and saving power, and is characterized in that the system comprises a data real-time monitoring and collecting module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information collector module, a blockchain information management module and an alarm module; the data real-time monitoring acquisition module is used for monitoring the electric energy use data of users, buildings and public areas in the area in real time; the temperature sensor is used for monitoring external environment temperature data in real time; the micro control module is used for controlling whether the data real-time monitoring acquisition module and the temperature sensor are started; the wireless communication module is used for transmitting the data integrated and collected by the micro control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used for analyzing the electric energy data, carrying out abnormality detection according to the external environment temperature, and sending an instruction to the alarm module when the external environment temperature exceeds an abnormality threshold value, and sending an alarm notification by the alarm module; otherwise, continuing to analyze the data and detect the abnormality; the information collector module is used for controlling the cloud computing early warning analysis module to receive the data of the wireless communication module in a staged manner; and the block chain information management module is used for managing the acquired data.
The data real-time monitoring module comprises a plurality of current transformers: current transformer 1, current transformer 2, up to current transformer n. The micro control module adopts STM32F103C8T6 as a core controller, the display module adopts an LCD data display circuit, and the wireless communication module can be a Wi-Fi wireless data transmission circuit or a 4G mobile data transmission circuit, a 5G mobile data transmission circuit and the like
The STM32F103C8T6 singlechip has strong calculation performance, excellent response of an interrupt system, low cost and low power consumption as a 32-bit embedded ARM processor, and the pin number is also suitable for the electric energy saving early warning system provided by the invention.
The current transformer adopts PZEM-004T alternating current multifunctional communication module, utilizes TTL serial port communication to communicate with the main controller, and the inside of the current transformer comprises a current transformer circuit and A/D conversion and data metering and measuring functions, so that four electric parameters of voltage, current, power and electric quantity can be obtained, and data can be kept when power is lost.
The display module selects the LCD12864 as an LCD liquid crystal screen, is used for displaying the electric energy use information and the electric energy use information, and constructs a good man-machine interaction interface together with the keys, so that dynamic information of the circuit system can be intuitively presented.
Example 2
Based on embodiment 1, the cloud computing early warning analysis module adopts a Hadoop cloud computing method, introduces a data storage platform and processes data.
Example 3
Based on embodiment 1, the alarm module informs the user to perform early warning in the form of a short message notification.
As shown in fig. 2, the intelligent regional power consumption energy saving system provided in this embodiment is used for managing power consumption conditions of users, buildings and public areas in the region where the regional power consumption energy saving system is located, and saving power consumption, and is characterized in that the system comprises a data real-time monitoring and collecting module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information collector module, a blockchain information management module and an alarm module; the data real-time monitoring acquisition module is used for monitoring the electric energy use data of users, buildings and public areas in the area in real time; the temperature sensor is used for monitoring external environment temperature data in real time; the micro control module is used for controlling whether the data real-time monitoring acquisition module and the temperature sensor are started; the wireless communication module is used for transmitting the data integrated and collected by the micro control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used for analyzing the electric energy data, carrying out abnormality detection according to the external environment temperature, and sending an instruction to the alarm module when the external environment temperature exceeds an abnormality threshold value, and sending an alarm notification by the alarm module; otherwise, continuing to analyze the data and detect the abnormality; the information collector module is used for controlling the cloud computing early warning analysis module to receive the data of the wireless communication module in a staged manner; and the block chain information management module is used for managing the acquired data.
As shown in fig. 3, the cloud computing early warning analysis module includes an early warning classification module, an electric energy trend prediction module, a data query module and a statistics report module.
Example 4
As shown in fig. 2-4, the intelligent regional power consumption energy saving system provided in this embodiment is used for managing power consumption conditions of users, buildings and public areas in the region where the regional power consumption energy saving system is located, and saving power consumption, and is characterized in that the system includes a data real-time monitoring and collecting module, a temperature sensor, a micro-control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information collector module, a blockchain information management module and an alarm module; the data real-time monitoring acquisition module is used for monitoring the electric energy use data of users, buildings and public areas in the area in real time; the temperature sensor is used for monitoring external environment temperature data in real time; the micro control module is used for controlling whether the data real-time monitoring acquisition module and the temperature sensor are started; the wireless communication module is used for transmitting the data integrated and collected by the micro control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used for analyzing the electric energy data, carrying out abnormality detection according to the external environment temperature, and sending an instruction to the alarm module when the external environment temperature exceeds an abnormality threshold value, and sending an alarm notification by the alarm module; otherwise, continuing to analyze the data and detect the abnormality; the information collector module is used for controlling the cloud computing early warning analysis module to receive the data of the wireless communication module in a staged manner; and the block chain information management module is used for managing the acquired data.
As shown in fig. 3, the cloud computing early warning analysis module includes an early warning classification module, an electric energy trend prediction module, a data query module and a statistics report module.
As shown in fig. 4, the blockchain information management module includes a user management module, a building management module, and a region management module;
The user management module is used for collecting and managing personal information such as identity information, electricity utilization information and the like of the user;
The building management module is used for classifying and metering the electric energy data of a plurality of buildings in the monitored area;
And the public area management module is used for classifying and metering public area electric energy data in the monitored area.
And comparing and analyzing electricity consumption conditions of the buildings and the areas based on the cloud computing early warning analysis module, and outputting the electricity consumption conditions in forms of reports, curves, graphs and the like. The system can classify and collect the total electricity consumption of each user, each building and each area, and calculate the data result required by the user through an algorithm, such as calculating the electricity consumption of a certain type of equipment, a certain user and a certain area in different time periods of day, week, month and the like. Through real-time on-line monitoring, carry out early warning suggestion to the unusual condition that appears in the power consumption in-process, the extravagant root and the hidden danger that exist in the analysis out electric energy consumption in-process are uploaded to monitoring management center through the network, and according to the condition of predetermineeing in the intelligent contract, early warning is carried out to the power consumption object that exceeds standard, realizes accurate rectification, optimizes the power consumption scheme, improves the rate of utilization of electric energy.
Example 5
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the respective embodiments may be combined in any manner as long as there is no structural conflict. The present invention is not limited to the specific embodiments disclosed herein, but encompasses all technical solutions falling within the scope of the claims.
Claims (8)
1. An intelligent regional power consumption energy-saving method is characterized by comprising the following steps:
the micro-control module controls the data real-time monitoring and collecting module and the temperature sensor to be started, the data real-time monitoring and collecting module collects electric energy parameters of users, buildings and public areas in the area, and the temperature sensor collects outside air temperature data;
The micro control module transmits data to the display module for displaying the electricity consumption condition of the electric energy in real time, and sends the data to the relay and the wireless communication module, the relay is connected into the power strip to control the on-off of the live wire, and the control signal of the micro control module is received to realize the on-off of the power supply in the jack;
The wireless communication module transmits the electric energy parameter data to the cloud computing early warning analysis module, the cloud computing early warning analysis module performs data analysis, an anomaly detection model is built through the received external temperature, and the actual consumption of the electric energy in the range of one monitoring cycle t is calculated And predicting consumptionWhether the difference is smaller than a threshold valueIf less than the threshold valueThe power use state is the normal energy-saving mode, if the threshold value is exceededIf the power consumption state is not the power consumption state in the normal energy-saving mode, the cloud computing early-warning analysis module sends an alarm instruction to the alarm module;
the method for constructing the abnormality detection model comprises the following steps:
S1: taking 24 hours of a day as a detection cycle, the cycle being represented by s, i.e. t=0, 1,2, …,23, s e t, each cycle having n periods therein, and performing an auto-regression using the consumption of the previous i days in the t time range, i=1, 2, …, p, the regression per day forming a first order; constructing an anomaly detection model for the s-th cycle and the n-th cycle ；
；
Wherein Y is a data point in the elapsed time sequence; p is the order in autoregressive; XT1, XT2, and XT3 are the required power supplies due to external temperature; alpha is an anomaly detection calculation coefficient, and beta is an external temperature influence calculation coefficient; is the value of white noise;
s2: constructing different abnormal detection conditions according to different external temperature influences;
s3: constructing training data sets formed under different abnormality detection conditions Constructing each element conforming to a unique variable gaussian distributionIs a function of the detection of (2)Detecting and iterating the data detected subsequently;
Calculated and obtained Comparing with a threshold value;
The alarm module sends out an alarm notice;
The block chain information management module receives abnormal data obtained by analysis of the cloud computing early warning analysis module, electric energy use data of users, buildings and public areas in the areas where the data are acquired by the data real-time monitoring acquisition module and the temperature sensor, and external environment temperature, and is used for carrying out historical query, analysis and summarization when needed later.
2. The intelligent regional power-saving method according to claim 1, wherein in the step S2, different external temperature influences construct different abnormality detection conditions as follows:
；
；
the T is the external temperature detected by the temperature sensor;
The XT1 abnormal detection condition represents the external temperature influence condition that power supply, heat preservation and heat supply are not needed under the condition that the external temperature is 20 ℃; the XT2 abnormal detection condition represents the external temperature influence condition of power supply, heat preservation and heat supply under the condition that the external temperature is 16 ℃; the abnormal XT3 detection condition represents the external temperature influence condition of power supply, heat preservation and heat supply under the condition that the external temperature is 5 ℃.
3. The intelligent regional power-saving method according to claim 1, wherein in the step S3, the detection function isThe following are provided:
；
Wherein: ，。
4. An intelligent regional electricity-saving system adopting the method according to any one of claims 1-3, wherein the intelligent regional electricity-saving system is used for managing electricity consumption conditions of users, buildings and public areas in the region and saving electricity, and is characterized by comprising a data real-time monitoring and acquisition module, a temperature sensor, a micro control module, a display module, a relay, a wireless communication module, a cloud computing early warning analysis module, an information acquisition module, a blockchain information management module and an alarm module; the data real-time monitoring and collecting module is used for monitoring the electric energy use data of users, buildings and public areas in the area in real time; the temperature sensor is used for monitoring external environment temperature data in real time; the micro control module is used for controlling whether the data real-time monitoring acquisition module and the temperature sensor are started; the wireless communication module is used for transmitting the data integrated and collected by the micro control module to the cloud computing early warning analysis module; the cloud computing early warning analysis module is used for analyzing the electric energy use data, detecting abnormality according to the external environment temperature, and sending an instruction to the alarm module if the abnormality threshold value is exceeded, wherein the alarm module sends an alarm notification; otherwise, continuing to analyze the data and detect the abnormality; the information collector module is used for controlling the cloud computing early warning analysis module to receive the data of the wireless communication module in a staged manner; and the block chain information management module is used for managing the acquired data.
5. The intelligent regional power consumption energy saving system according to claim 4, wherein the cloud computing early warning analysis module adopts a Hadoop cloud computing method, introduces a data storage platform and performs data processing.
6. The intelligent regional power-saving system of claim 4, wherein the cloud computing early-warning analysis module comprises an early-warning classification module, an electric energy trend prediction module, a data query module and a statistics report module.
7. The intelligent regional power-saving system according to claim 4, wherein the alarm module is configured to notify the user of the early warning in the form of a short message notification.
8. The intelligent district power saving system of claim 4 wherein the blockchain information management module comprises a user management module, a building management module, a public district management module;
the user management module is used for collecting and managing personal information such as identity information, electricity utilization information and the like of a user;
The building management module is used for classifying and metering the electric energy data of a plurality of buildings in the monitored area;
the public area management module is used for classifying and metering public area electric energy data in the monitored area.
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| CN108964269A (en) * | 2018-07-03 | 2018-12-07 | 沈阳电电科技有限公司 | Power distribution network O&M and total management system |
| CN114677025A (en) * | 2022-03-30 | 2022-06-28 | 北京数智勘实科技有限公司 | Intelligent management system and management method for catalyst operation |
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| CN114677025A (en) * | 2022-03-30 | 2022-06-28 | 北京数智勘实科技有限公司 | Intelligent management system and management method for catalyst operation |
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