CN113269468A - Power dispatching system based on block chain and data processing method thereof - Google Patents
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Abstract
The invention discloses a power dispatching system based on a block chain and a data processing method thereof, belonging to the technical field of power dispatching and comprising a plurality of power monitoring areas, a data acquisition module, an analysis and prediction module, a data comparison module, a block chain sharing platform, a plurality of area power management companies and a power master dispatching platform; the system is provided with an analysis prediction module which is internally provided with a periodic load prediction model and can carry out staged prediction on the electric load of a plurality of areas in a future period of time, and meanwhile, the system can compare the collected power generation data with the predicted electric load of the future period of time, so that the power utilization and power generation conditions in the plurality of areas can be known, and the reasonable power scheduling in the areas is facilitated; in addition, the block chain sharing platform carries out data sharing on the power utilization and power generation conditions of each power monitoring area, so that any power management company can access through the account number, and reasonable power scheduling among the areas is facilitated.
Description
Technical Field
The invention relates to the technical field of power dispatching, in particular to a power dispatching system based on a block chain and a data processing method thereof.
Background
Through retrieval, chinese patent No. CN112865312A discloses a power scheduling system and a power data processing method, which can effectively collect power data and effectively find power failures according to high-performance processing on the power data, but cannot perform reasonable scheduling of power resources in and between areas; the power dispatching is an effective management means which is adopted for ensuring safe and stable operation of a power grid, reliable external power supply and orderly execution of various power production works; the specific work content of the power dispatching is that the safe and economic operation state of the power grid is judged according to data information fed back by various information acquisition equipment or information provided by monitoring personnel by combining actual operation parameters of the power grid, such as voltage, current, frequency, load and the like, comprehensively considering the development conditions of various production works, issuing an operation instruction through a telephone or an automatic system, and commanding field operators or an automatic control system to adjust, such as adjusting the output of a generator, adjusting load distribution, switching capacitors, reactors and the like, so that the continuous safe and stable operation of the power grid is ensured; at the present stage, the development of the power industry faces the increasing overall demand; the whole electric energy utilization rate is continuously reduced; the power supply capacity is insufficient, and seasonal and regional power failure often occurs; the problems that the development is unbalanced, the electric quantity is not supplied enough in the peak time period of power utilization, the electric quantity remains seriously in the peak time period of power utilization, the daily load peak-valley difference value is continuously increased, the contradiction between supply and demand is increasingly sharp and the like are solved; in order to ensure the safety and stability of a power grid, the invention of a power dispatching system based on a block chain and a data processing method thereof becomes more important;
most of the existing power dispatching systems configure the power grid of the area according to the number of power consumption population of the power consumption area, but due to various power load influence factors, a power supply company cannot accurately acquire the power consumption condition of the area, so that reasonable dispatching of power resources in the area and among the areas cannot be performed; therefore, the power dispatching system based on the block chain and the data processing method thereof are provided.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a power dispatching system based on a block chain and a data processing method thereof.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power dispatching system based on a block chain comprises a plurality of power monitoring areas, a data acquisition module, an analysis and prediction module, a data comparison module, a block chain sharing platform, a plurality of area power management companies and a power general dispatching platform;
the data acquisition module comprises a power load influence factor acquisition unit and a power plant equipment information acquisition unit, wherein the power load influence factor acquisition unit comprises a holiday acquisition subunit, a weather prediction subunit and an electricity price acquisition subunit; the regional power management companies are internally provided with regional power dispatching modules and correspond to the power monitoring regions one to one.
Further, the power load influence factor acquisition unit is used for acquiring power load influence information; the holiday acquiring subunit is used for acquiring holiday dates of a plurality of power monitoring areas in a future period of time to obtain holiday information of the plurality of areas; the weather forecasting subunit is used for acquiring weather conditions of a plurality of power monitoring areas within a period of time in the future to obtain weather information of the plurality of areas; the electricity price obtaining subunit is used for obtaining the current electricity price conditions and the electricity price conditions in a future period of time of a plurality of electric power monitoring areas, and obtaining electricity price information of the plurality of areas; the power plant equipment information acquisition unit is used for acquiring the power generation power information of the power generation equipment in the plurality of power monitoring areas, and the power generation information of the plurality of area power plants is obtained.
Further, the analysis and prediction module is configured to sequentially input the holiday information of the multiple areas, the meteorological information of the multiple areas, and the electricity price information of the multiple areas into the periodic load prediction model for prediction, so as to obtain the power load information of the multiple areas within a period of time in the future, where the periodic load prediction model is specifically constructed as follows:
s1: acquiring data, namely acquiring historical load information of residents and commercial users in multiple cycles in each power monitoring area, and acquiring power load influence information, holiday information, weather information and electricity price information of time periods corresponding to the historical load information of the residents and the commercial users in the multiple cycles;
s2: data processing, namely performing data cleaning, integration, stipulation and transformation on historical load information of residents and commercial users in a plurality of periods in each power monitoring area in the step 1;
s3: performing correlation calculation, namely performing correlation calculation on historical load information of residents and commercial users in multiple periods in each power monitoring area after data preprocessing and holiday information, weather information and electricity price information one by using a Pearson correlation algorithm to obtain historical load information and power load influence information of multiple correlations;
s3: feature extraction, namely, dividing the historical load information and the power load influence information of each correlation in the step S2 into a training set of 70% and a verification set of 30% together;
s4: establishing and verifying a model, constructing an SVM (support vector machine) classifier, and inputting 70% of the training set in the step S3 into the SVM classifier for training to obtain a periodic load prediction model; then, the 30% verification set described in step S3 is used for verification, and if the target requirement is met, the model is output.
Further, the correlation calculation formula is as follows:
in the formula: r represents a coefficient of correlation of holidays, weather or electricity prices; x is the number ofiRepresenting holiday information, weather information or electricity price information; y isiAnd historical load information representing residential and commercial users for a plurality of cycles in each power monitoring area.
Furthermore, the data comparison module is used for comparing the power load information of the plurality of areas in a future period of time with the power generation information of the power plants of the plurality of power monitoring areas one by one to obtain the power utilization and power generation conditions of the plurality of power monitoring areas; and the block chain sharing platform is used for sharing and storing the power utilization and power generation conditions of the plurality of power monitoring areas by adopting a block chain technology.
Furthermore, the plurality of regional power management companies are used for logging in a block chain sharing platform to check the power utilization and power generation conditions of the power monitoring regions, and performing intra-regional power scheduling according to the built-in intra-regional power scheduling module; the electric power general dispatching platform is used for carrying out inter-area electric power dispatching according to the electricity utilization and electricity generation conditions of a plurality of electric power monitoring areas stored in the block chain sharing platform.
A data processing method based on a block chain is specifically as follows:
(1) data acquisition: acquiring historical load information of residents and commercial users in each power monitoring area in multiple periods;
(2) outlier identification and correction: performing abnormal value identification on the historical load information of residents and commercial users in each power monitoring area for a plurality of cycles in the step S1 by adopting a horizontal processing method, and correcting the identified abnormal value;
(3) missing value padding: carrying out data cleaning on historical load information of residents and commercial users in each power monitoring area in multiple cycles after abnormal value identification and correction so as to fill in missing values;
(4) data integration: carrying out data integration on historical load information of residents and commercial users which are subjected to data cleaning and pass through a plurality of periods in each power monitoring area so as to solve the problems of data inconsistency and redundancy;
(5) data normalization: and finally, normalizing the historical load information of residents and commercial users in each power monitoring area in multiple periods through data integration to narrow the data range.
Compared with the prior art, the invention has the beneficial effects that:
1. the power dispatching system based on the block chain is provided with an analysis prediction module, a periodic load prediction model generated by a machine learning algorithm is built in the analysis prediction module, periodic prediction can be carried out on power loads of a plurality of areas in a future period of time, and meanwhile, the system can know power utilization and power generation conditions in the plurality of areas by comparing collected power generation data with the predicted power loads in the future period of time, so that reasonable power dispatching in the areas is facilitated;
2. the power dispatching system based on the block chain is provided with a block chain sharing platform, the platform is used for storing power utilization and power generation conditions of each power monitoring area and sharing the power utilization and power generation conditions, and any power management company can access the power dispatching system through an account number, so that reasonable power dispatching among the areas is facilitated;
3. according to the data processing method based on the block chain, through identification and correction of abnormal data values, missing value filling, data integration and data normalization, accuracy, integrity and effectiveness of historical load information of residents and commercial users in multiple periods in each power monitoring area are improved, prediction accuracy of a periodic load prediction model is further improved, and improvement of reasonability of power scheduling in the areas and among the areas is facilitated.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic overall structure diagram of a power dispatching system based on a block chain according to the present invention;
fig. 2 is an overall flowchart of a data processing method based on a block chain according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1, the embodiment discloses a power scheduling system based on a block chain, which includes a plurality of power monitoring areas, a data acquisition module, an analysis and prediction module, a data comparison module, a block chain sharing platform, a plurality of area power management companies, and a power general scheduling platform;
the system comprises a data acquisition module, a power plant equipment information acquisition module and a power load influence factor acquisition module, wherein the data acquisition module comprises a power load influence factor acquisition unit and a power plant equipment information acquisition unit, and the power load influence factor acquisition unit comprises a holiday acquisition subunit, a weather prediction subunit and an electricity price acquisition subunit; a plurality of regional power management companies are internally provided with regional power dispatching modules and correspond to a plurality of power monitoring regions one to one.
The power load influence factor acquisition unit is used for acquiring power load influence information;
the holiday acquiring subunit is used for acquiring holiday dates of a plurality of power monitoring areas in a future period of time to obtain holiday information of the plurality of areas;
the weather forecasting subunit is used for acquiring weather conditions of a plurality of power monitoring areas within a period of time in the future to obtain weather information of the plurality of areas;
the electricity price obtaining subunit is used for obtaining the current electricity price conditions and the electricity price conditions in a future period of time of a plurality of electric power monitoring areas, and obtaining electricity price information of the plurality of areas;
the power plant equipment information acquisition unit is used for acquiring the power generation power information of the power generation equipment in the plurality of power monitoring areas, and the power generation information of the plurality of area power plants is obtained.
The analysis and prediction module is used for sequentially inputting the holiday information of a plurality of areas, the meteorological information of the plurality of areas and the electricity price information of the plurality of areas into the periodic load prediction model for prediction to obtain the power load information of the plurality of areas in a period of time in the future;
specifically, the specific construction process of the periodic load prediction model is as follows:
s1: acquiring data, namely acquiring historical load information of residents and commercial users in multiple cycles in each power monitoring area, and acquiring power load influence information, holiday information, weather information and electricity price information of time periods corresponding to the historical load information of the residents and the commercial users in the multiple cycles;
s2: data processing, namely performing data cleaning, integration, stipulation and transformation on historical load information of residents and commercial users in a plurality of periods in each power monitoring area in the step 1;
s3: performing correlation calculation, namely performing correlation calculation on historical load information of residents and commercial users in multiple periods in each power monitoring area after data preprocessing and holiday information, weather information and electricity price information one by utilizing a Pearson correlation algorithm to obtain historical load information and power load influence information of multiple correlations,
specifically, the correlation calculation formula is as follows:
in the formula: r represents a coefficient of correlation of holidays, weather or electricity prices; x is the number ofiRepresenting holiday information, weather information or electricity price information; y isiAnd historical load information representing residential and commercial users for a plurality of cycles in each power monitoring area.
S3: feature extraction, namely, dividing the historical load information and the power load influence information of each correlation in the step S2 into a training set of 70% and a verification set of 30% together;
s4: establishing and verifying a model, constructing an SVM classifier, and inputting the training set of the step S370% into the SVM classifier for training to obtain a periodic load prediction model; and then, verifying by using the verification set of the step S330%, and outputting the model if the target requirement is met.
The data comparison module is used for comparing the power load information of the plurality of areas in a future period of time with the power generation information of the power plants of the plurality of power monitoring areas one by one to obtain the power utilization and power generation conditions of the plurality of power monitoring areas;
the block chain sharing platform is used for sharing and storing the power utilization and power generation conditions of a plurality of power monitoring areas by adopting a block chain technology.
The multiple regional power management companies are used for logging in the block chain sharing platform to check the power utilization and power generation conditions of the power monitoring regions and scheduling power in the regions according to the power scheduling modules in the built-in regions;
the power general dispatching platform is used for carrying out inter-area power dispatching according to power utilization and power generation conditions of a plurality of power monitoring areas stored in the block chain sharing platform.
Referring to fig. 2, the present embodiment discloses a data processing method based on a block chain, where the data processing method specifically includes:
(1) data acquisition: acquiring historical load information of residents and commercial users in each power monitoring area in multiple periods;
(2) outlier identification and correction: performing abnormal value identification on the historical load information of residents and commercial users in each power monitoring area for a plurality of cycles in the step S1 by adopting a horizontal processing method, and correcting the identified abnormal value;
specifically, the abnormal value identification and correction steps are as follows:
the method comprises the following steps: outlier identification, which is formulated as follows:
max[|Pt+1-Pt|，|Pt-Pt-1|]＞zt (2)
in the formula: pt+1Load value at time t +1, PtIs a load value at time t, Pt-1Is a load value at time t-1, ztChanging a threshold value for the set load data;
step two: correcting an abnormal value;
first, P is obtainedtAverage AVG of five load data in close proximitya：
Then, P is obtainedt+1Average AVG of three load data in close proximityb：
Then, P is obtainedt+1Average AVG of two load data in close proximityc：
Finally, the corrected power load value P 'is obtained by weighting't：
P′t＝ωAAVGa+ωBAVGb+ωCAVGc (6)
In the formula: ω is the weighted weight.
(3) Missing value padding: carrying out data cleaning on historical load information of residents and commercial users in each power monitoring area in multiple cycles after abnormal value identification and correction so as to fill in missing values;
specifically, the missing value refers to that, during the process of collecting and recording the power load data, the data may not be recorded normally due to a short-time failure of the system, so as to cause a missing of a part of the power load.
(4) Data integration: carrying out data integration on historical load information of residents and commercial users which are subjected to data cleaning and pass through a plurality of periods in each power monitoring area so as to solve the problems of data inconsistency and redundancy;
(5) data normalization: and finally, normalizing the historical load information of residents and commercial users in each power monitoring area in multiple periods through data integration to narrow the data range.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. A power dispatching system based on a block chain is characterized by comprising a plurality of power monitoring areas, a data acquisition module, an analysis prediction module, a data comparison module, a block chain sharing platform, a plurality of area power management companies and a power general dispatching platform;
the data acquisition module comprises a power load influence factor acquisition unit and a power plant equipment information acquisition unit, wherein the power load influence factor acquisition unit comprises a holiday acquisition subunit, a weather prediction subunit and an electricity price acquisition subunit; the regional power management companies are internally provided with regional power dispatching modules and correspond to the power monitoring regions one to one.
2. The power dispatching system based on the blockchain as claimed in claim 1, wherein the power load influence factor collecting unit is configured to obtain power load influence information; the holiday acquiring subunit is used for acquiring holiday dates of a plurality of power monitoring areas in a future period of time to obtain holiday information of the plurality of areas; the weather forecasting subunit is used for acquiring weather conditions of a plurality of power monitoring areas within a period of time in the future to obtain weather information of the plurality of areas; the electricity price obtaining subunit is used for obtaining the current electricity price conditions and the electricity price conditions in a future period of time of a plurality of electric power monitoring areas, and obtaining electricity price information of the plurality of areas; the power plant equipment information acquisition unit is used for acquiring the power generation power information of the power generation equipment in the plurality of power monitoring areas, and the power generation information of the plurality of area power plants is obtained.
3. The system according to claim 1, wherein the analysis prediction module is configured to sequentially input the holiday information of a plurality of areas, the meteorological information of a plurality of areas, and the electricity price information of a plurality of areas into the periodic load prediction model for prediction, so as to obtain the power load information of a plurality of areas in a future period of time, and the periodic load prediction model is specifically configured as follows:
s1: acquiring data, namely acquiring historical load information of residents and commercial users in multiple cycles in each power monitoring area, and acquiring power load influence information, holiday information, weather information and electricity price information of time periods corresponding to the historical load information of the residents and the commercial users in the multiple cycles;
s2: data processing, namely performing data cleaning, integration, stipulation and transformation on historical load information of residents and commercial users in a plurality of periods in each power monitoring area in the step 1;
s3: performing correlation calculation, namely performing correlation calculation on historical load information of residents and commercial users in multiple periods in each power monitoring area after data preprocessing and holiday information, weather information and electricity price information one by using a Pearson correlation algorithm to obtain historical load information and power load influence information of multiple correlations;
s3: feature extraction, namely, dividing the historical load information and the power load influence information of each correlation in the step S2 into a training set of 70% and a verification set of 30% together;
s4: establishing and verifying a model, constructing an SVM (support vector machine) classifier, and inputting 70% of the training set in the step S3 into the SVM classifier for training to obtain a periodic load prediction model; then, the 30% verification set described in step S3 is used for verification, and if the target requirement is met, the model is output.
4. The system according to claim 3, wherein the correlation calculation formula is as follows:
in the formula: r represents a coefficient of correlation of holidays, weather or electricity prices; x is the number ofiRepresenting holiday information, weather information or electricity price information; y isiAnd historical load information representing residential and commercial users for a plurality of cycles in each power monitoring area.
5. The power scheduling system based on the block chain as claimed in claim 1, wherein the data comparing module is configured to compare the power load information of the plurality of areas in a future period with the power generation information of the power plants of the plurality of power monitoring areas one by one, so as to obtain power utilization and power generation conditions of the plurality of power monitoring areas; and the block chain sharing platform is used for sharing and storing the power utilization and power generation conditions of the plurality of power monitoring areas by adopting a block chain technology.
6. The power dispatching system based on the blockchain as claimed in claim 1, wherein the plurality of regional power management companies are configured to log in a blockchain sharing platform to check power utilization and power generation conditions of the power monitoring region, and perform intra-regional power dispatching according to a built-in intra-regional power dispatching module; the electric power general dispatching platform is used for carrying out inter-area electric power dispatching according to the electricity utilization and electricity generation conditions of a plurality of electric power monitoring areas stored in the block chain sharing platform.
7. A data processing method based on a block chain is characterized in that the data processing method specifically comprises the following steps:
(1) data acquisition: acquiring historical load information of residents and commercial users in each power monitoring area in multiple periods;
(2) outlier identification and correction: performing abnormal value identification on the historical load information of residents and commercial users in each power monitoring area for a plurality of cycles in the step S1 by adopting a horizontal processing method, and correcting the identified abnormal value;
(3) missing value padding: carrying out data cleaning on historical load information of residents and commercial users in each power monitoring area in multiple cycles after abnormal value identification and correction so as to fill in missing values;
(4) data integration: carrying out data integration on historical load information of residents and commercial users which are subjected to data cleaning and pass through a plurality of periods in each power monitoring area so as to solve the problems of data inconsistency and redundancy;
(5) data normalization: and finally, normalizing the historical load information of residents and commercial users in each power monitoring area in multiple periods through data integration to narrow the data range.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113687119A (en) * | 2021-09-03 | 2021-11-23 | 山东卓文信息科技有限公司 | Power consumption and energy consumption calculating device and method based on non-invasive load monitoring |
| CN114819750A (en) * | 2022-06-23 | 2022-07-29 | 湖南工商大学 | Intelligent power supply dynamic hierarchical management method |
| CN116070795A (en) * | 2023-03-29 | 2023-05-05 | 山东历控能源有限公司 | Intelligent energy management and control method and system based on Internet of things |
| CN116822888A (en) * | 2023-07-04 | 2023-09-29 | 上海宏灿信息科技股份有限公司 | Intelligent dispatching command platform based on big data technology |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108988495A (en) * | 2018-08-01 | 2018-12-11 | 国网汇通金财（北京）信息科技有限公司 | A kind of micro-capacitance sensor power dispatching method and system based on block chain |
| CN109492836A (en) * | 2018-12-29 | 2019-03-19 | 上海电力学院 | Load forecast and Research on electricity price prediction system based on shot and long term memory network |
| CN110610280A (en) * | 2018-10-31 | 2019-12-24 | 山东大学 | Short-term prediction method, model, device and system for power load |
| CN111008795A (en) * | 2020-03-09 | 2020-04-14 | 国网电子商务有限公司 | Block chain-based power dispatching system data processing method and device |
| CN111090890A (en) * | 2020-03-19 | 2020-05-01 | 国网电子商务有限公司 | New energy power utilization data processing method and device based on block chain and participating node |
| CN111668839A (en) * | 2020-07-16 | 2020-09-15 | 江苏电力交易中心有限公司 | Distributed power resource optimization scheduling method and system under block chain environment |
| CN111985701A (en) * | 2020-07-31 | 2020-11-24 | 国网上海市电力公司 | Power utilization prediction method based on power supply enterprise big data model base |
| CN112434974A (en) * | 2020-12-14 | 2021-03-02 | 上海玳鸽信息技术有限公司 | Block chain-based power scheduling method and device, electronic equipment and storage medium |
-
2021
- 2021-06-17 CN CN202110672377.1A patent/CN113269468A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108988495A (en) * | 2018-08-01 | 2018-12-11 | 国网汇通金财（北京）信息科技有限公司 | A kind of micro-capacitance sensor power dispatching method and system based on block chain |
| CN110610280A (en) * | 2018-10-31 | 2019-12-24 | 山东大学 | Short-term prediction method, model, device and system for power load |
| CN109492836A (en) * | 2018-12-29 | 2019-03-19 | 上海电力学院 | Load forecast and Research on electricity price prediction system based on shot and long term memory network |
| CN111008795A (en) * | 2020-03-09 | 2020-04-14 | 国网电子商务有限公司 | Block chain-based power dispatching system data processing method and device |
| CN111090890A (en) * | 2020-03-19 | 2020-05-01 | 国网电子商务有限公司 | New energy power utilization data processing method and device based on block chain and participating node |
| CN111668839A (en) * | 2020-07-16 | 2020-09-15 | 江苏电力交易中心有限公司 | Distributed power resource optimization scheduling method and system under block chain environment |
| CN111985701A (en) * | 2020-07-31 | 2020-11-24 | 国网上海市电力公司 | Power utilization prediction method based on power supply enterprise big data model base |
| CN112434974A (en) * | 2020-12-14 | 2021-03-02 | 上海玳鸽信息技术有限公司 | Block chain-based power scheduling method and device, electronic equipment and storage medium |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113687119A (en) * | 2021-09-03 | 2021-11-23 | 山东卓文信息科技有限公司 | Power consumption and energy consumption calculating device and method based on non-invasive load monitoring |
| CN113687119B (en) * | 2021-09-03 | 2024-02-27 | 山东卓文信息科技有限公司 | Non-invasive load monitoring-based electricity consumption calculation device and method |
| CN114819750A (en) * | 2022-06-23 | 2022-07-29 | 湖南工商大学 | Intelligent power supply dynamic hierarchical management method |
| CN114819750B (en) * | 2022-06-23 | 2022-09-16 | 湖南工商大学 | Intelligent power supply dynamic hierarchical management method |
| CN116070795A (en) * | 2023-03-29 | 2023-05-05 | 山东历控能源有限公司 | Intelligent energy management and control method and system based on Internet of things |
| CN116070795B (en) * | 2023-03-29 | 2023-07-11 | 山东历控能源有限公司 | Intelligent energy management and control method and system based on Internet of things |
| CN116822888A (en) * | 2023-07-04 | 2023-09-29 | 上海宏灿信息科技股份有限公司 | Intelligent dispatching command platform based on big data technology |
| CN116822888B (en) * | 2023-07-04 | 2024-03-15 | 上海宏灿信息科技股份有限公司 | Intelligent dispatching command platform based on big data technology |
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