CN114266435A - Rural smart energy management platform - Google Patents
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Abstract
The invention discloses a rural intelligent energy management platform which comprises a data acquisition layer, an information management layer, a service application layer and a comprehensive display layer. The data acquisition layer acquires rural on-site clean energy data such as distributed water, light, terrestrial heat, natural gas and the like by utilizing a cloud-edge cooperation technology, intelligent data processing and analysis are carried out by utilizing a weight coefficient cloud network algorithm, the information management layer stores the data into a local file and sends the data to the service application layer, the service application layer carries out comprehensive utilization optimization coordination control on a multi-energy system accessed into a rural energy supply area, the multi-energy system is connected with the comprehensive display layer and transmits data information, and panoramic visual display of a coordination control strategy, a regulation and control object and a control effect is realized. The intelligent energy management platform for the village provided by the invention aims at specialization, systematization and informatization, improves the on-site utilization level of renewable energy, promotes clean conversion of energy supply and energy supply of the village, and improves the electricity quality and electricity management level of village users.
Description
Technical Field
The invention belongs to the field of rural intelligent energy Internet construction and comprehensive energy service, and particularly relates to a rural intelligent energy management platform.
Background
The energy internet is a platform for interconnection, comprehensive utilization and optimized sharing of various energy sources taking electricity as a center. The energy Internet is a green, low-carbon, safe and efficient modern energy ecosystem which is based on an intelligent power grid, takes the Internet plus as a means and takes electric energy as a main carrier.
At present, top-level design of comprehensive energy is lacked in energy Internet construction, and the economical efficiency of projects, the actual running rate of loads and the like are less considered, so that some technical and management problems are encountered in the actual running process.
In order to help improve the bearing capacity and the operating efficiency of rural energy infrastructure, accelerate the transformation of rural energy supply and utilization habits, realize the replacement of clean energy and the comprehensive optimal configuration of energy, accelerate the construction of rural energy internet, a rural intelligent energy management platform is urgently needed to be developed.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a rural intelligent energy management platform which can fully play the complementary advantages of rural on-site clean energy such as distributed water, light, terrestrial heat, natural gas and the like, reasonably adjust various energy such as cold, heat, electricity and the like by combining a cloud edge cooperation technology, improve the on-site utilization level of renewable energy, promote the clean transformation of rural energy supply and energy supply, and improve the electricity quality and the electricity management level of rural users.
The invention adopts the following technical scheme.
A rural intelligent energy management platform comprises a data acquisition layer, an information management layer, a service application layer and a comprehensive display layer;
the data acquisition layer acquires the distributed rural in-place clean energy data by using a cloud edge cooperation technology;
the information management layer stores the acquired data into a file and sends the data to the service application layer;
the service application layer is used for carrying out comprehensive utilization optimization coordination control on the multi-energy system accessed into the energy supply area of the village, is connected with the comprehensive display layer and transmits data information;
and the comprehensive display layer realizes panoramic visual display of a coordination control strategy, a regulation and control object and a control effect.
Further, the cloud edge collaboration framework comprises a data end, an edge end and a cloud end; the data end consists of distributed rural on-site clean energy equipment, generates original data and sends the original data to the edge end;
the edge terminal stores the original data generated by each device collected from the data terminal and carries out pretreatment; then uploading relevant data required by the cloud application, and receiving data shared by the cloud;
the cloud end is responsible for collecting data of each edge end, further processing, analyzing and storing the data, and cloud end data processing results can be shared to the edge ends.
Furthermore, the cloud end is provided with a weight coefficient cloud network model which comprises an input layer, a middle layer and an output layer.
Further, the edge and the cloud end use a weight coefficient cloud network algorithm to perform data intelligent processing analysis, specifically:
selecting distributed rural on-site clean energy data to be analyzed at the edge end, and preprocessing disordered data through learning and training to obtain pure data; then clustering and classifying according to the characteristic attribute of the data;
the processed rural on-site clean energy data is input through an input layer of the weight coefficient cloud network model, and the weight and the threshold value in the weight coefficient cloud network are repeatedly adjusted to gradually approach the required result, so that the rural on-site clean energy data is finally enabled to minimize the output error.
Further, when the weight coefficient cloud network model is used, the weight coefficient cloud network model is adjusted according to the following formula:
the formula for adjusting the weight coefficient of the output layer is as follows:
in the formula,. DELTA.wkiIs the weight coefficient deviation of each value of the output layer, eta is the adjustment constant,
are the weight coefficients of the values of the output layer,
the weight coefficients of the values of the intermediate layer,
is the output layer threshold;
the formula for adjusting the weight coefficient of the middle layer is as follows:
in the formula,. DELTA.wijThe weight coefficient deviation amount of each value of the intermediate layer,
and L is the weight coefficient of each value of the input layer, and the quantity of the middle layer influence factors.
Furthermore, on an input layer, firstly, the rural local clean energy data transmitted by the edge end is subjected to standardization processing, and then the calculation of the repeated adjustment weight coefficient is carried out;
assuming that the types of the inputted rural local clean energy data are m, the number of the samples is N, and the input data xijThe normalization is performed according to the following formula:
wherein i is 1, 2, …, N; j is 1, 2, …, m, ZijThe data after the standardization processing is carried out.
Further, the service application layer adopts a mode of layering, partitioning and coordination control, and realizes comprehensive energy network modeling and topology analysis on the multi-energy system accessed into the rural energy supply area.
Further, a dynamic game balancing strategy is used for realizing the topology modeling analysis of the comprehensive energy network, and the method specifically comprises the following steps:
in one round of the game, the power system determines a power supply and line new establishment scheme according to the load in the area, the natural gas network information of the ground source heat pump, the cold accumulation device and the gas turbine set (E)T,EL) Maximizing the electric power system profit; then, the power flow information on the gas engine set is transmitted to a natural gas network, and a heat source point, a gas source point and a pipeline new construction scheme (H) are determined by combining heat and gas loadsT,GT,HL,GL) Thereby maximizing the benefits of the heat system and the natural gas system; the decision of each subsystem enables the topology of the comprehensive energy system to be updated, and the next round is entered;
and after n rounds of the game, when any main body change strategy can not obtain larger benefits any more, a dynamic game equilibrium state is formed.
Furthermore, the hierarchical partitioning and coordination control mode of the service application layer is divided into a presentation module, a service interaction module, a service logic module and a data persistence module;
the display module provides unified UI packaging and two types of UI controls, namely a traditional label control and a React control;
the service interaction module comprises service interaction control and externally exposes RESTful service and Web service;
the business logic module comprises interface logic HTML/JSP, interface definition and 3 business development processes for service realization;
and the data persistence module is a persistence framework and is used as a component of the service application layer.
Further, the comprehensive display layer comprises a display screen, a computer desktop, a mobile APP or a sand table, and is used as a carrier for displaying the theme application and the analysis decision application.
Compared with the prior art, the data acquisition layer acquires the rural on-site clean energy data such as distributed water, light, terrestrial heat, natural gas and the like by utilizing the cloud edge cooperation technology, and the information management layer stores the acquired data in a local file and sends the data to the service application layer. The service application layer carries out comprehensive utilization, optimization and coordination control on the multi-energy system accessed into the energy supply area of the village, is connected with the comprehensive display layer and transmits data information, and realizes panoramic visual display of a coordination control strategy, a regulation and control object and a control effect.
The intelligent energy management platform for the village provided by the invention aims at specialization, systematization and informatization, improves the on-site utilization level of renewable energy, promotes clean conversion of energy supply and energy supply of the village, and improves the electricity quality and electricity management level of village users.
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FIG. 1 is a schematic diagram of a rural intelligent energy management platform according to the present invention;
FIG. 2 is a schematic diagram of a weight coefficient cloud network algorithm according to the present invention;
FIG. 3 is a data resource collaboration architecture diagram of the present invention;
FIG. 4 is a service application layer layered architecture diagram of the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the rural intelligent energy management platform of the present invention provides a general data mining and analyzing method by integrating multi-source data, provides an analysis decision support tool based on big data and an application development service capability for upper-layer applications, and mainly uses big data and cloud-edge cooperation technology. The technical architecture of the rural intelligent energy management platform relates to a data acquisition layer, an information management layer, a service application layer and a comprehensive display layer.
The data acquisition layer acquires rural on-site clean energy data such as distributed water, light, terrestrial heat, natural gas and the like by utilizing a cloud-edge cooperation technology, the information management layer stores the acquired data into a file and sends the data to the service application layer, the service application layer performs comprehensive utilization optimization coordination control on a multi-energy system accessed into a rural energy supply area, the multi-energy system is connected with the comprehensive display layer and transmits data information, and panoramic visual display of a coordination control strategy, a regulation and control object and a control effect is realized.
The data acquisition layer acquires rural on-site clean energy data such as distributed water, light, terrestrial heat, natural gas and the like by utilizing a cloud-edge collaborative technology, wherein the cloud-edge collaborative frame model consists of a data end, an edge end and a cloud end.
(1) The data end is composed of distributed rural on-site clean energy equipment such as water, light, geothermal and natural gas, generates a large amount of raw data, is connected to the edge server, and transmits the generated data to the edge end.
(2) The edge end consists of an edge server or an edge gateway, has the capacity of storing and preprocessing data, and is responsible for collecting mass data generated by each device from the data end for storage and carrying out primary processing. And then uploading relevant data required by the cloud application, and receiving the data shared by the cloud.
(3) The cloud end is responsible for collecting data of each edge end, carrying out further processing analysis such as AI intelligent processing and data fusion on the mass data collected from the edge and storing the mass data, and sharing the cloud end data processing result to the edge end.
As shown in fig. 2, the edge and the cloud end perform intelligent data processing and analysis by using a weight coefficient cloud network algorithm. The method comprises the steps of firstly selecting to-be-analyzed distributed rural on-site clean energy data such as water, light, terrestrial heat and natural gas from an edge server, preprocessing disordered data through learning and training, filtering to obtain relatively pure data types, and then performing clustering, classification and other operations according to characteristic attributes of the data. Because the weight coefficient cloud network model comprises an input layer, an intermediate layer and an output layer, the processed rural on-site clean energy data is input through the input layer, and the weight and the threshold value in the weight coefficient cloud network are repeatedly adjusted to gradually approach the required result, so that the rural on-site clean energy data is minimized in output error, and the data processing precision is improved. When the weight coefficient cloud network model is used, the weight coefficient cloud network model is adjusted according to the following formula:
the formula for adjusting the weight coefficient of the output layer is as follows:
in the formula,. DELTA.wkiIs the weight coefficient deviation of each value of the output layer, eta is the adjustment constant,
are the weight coefficients of the values of the output layer,
the weight coefficients of the values of the intermediate layer,
is the output layer threshold.
The formula for adjusting the weight coefficient of the middle layer is as follows:
in the formula,. DELTA.wijThe weight coefficient deviation amount of each value of the intermediate layer,
and L is the weight coefficient of each value of the input layer, and the quantity of the middle layer influence factors.
The quadratic accurate function model corresponding to the input mode in the local clean energy information sample data of each country is as follows:
in the formula, JpIs a quadratic accurate function of each clean energy information sample.
The total accurate function expression of the N rural local clean energy data samples is as follows:
at the input layer, the edge server has complex information data, before the calculation by the formula, the rural local clean energy data is standardized to improve the learning precision.
Assuming that the types of the inputted rural local clean energy data are m, the number of the samples is N, and the input data xijThe normalization is performed according to the following formula:
wherein i is 1, 2, …, N; j is 1, 2, …, m, ZijThe data after the standardization processing is carried out.
The normalized formula is:
in the formula, yiOutputting a clean energy information sample; y isi' standardized clean energy information sample data; y ismax、yminOutputting a maximum value and a minimum value in the sample data of the clean energy information; 0<q<2，0<b<2. And then determining the number of nodes of the middle layer to be 6-8, wherein the value from the input layer to the middle layer is 0.3-0.5, and the value from the middle layer to the output layer is 0.1-0.2. And establishing a weight coefficient cloud network model according to the formula, so that weight coefficient cloud network computing can be realized.
As shown in fig. 3, the database is divided into a cloud database and an edge database according to the data acquisition requirement. The edge database contains three phases of data acquisition: (1) y-data: metadata collected from data sources including file, video, structured data, and the like; (2) h-data: performing data preprocessing operations such as data cleaning, data extraction and the like on massive metadata in the Y-data according to data acquisition requirements to obtain core data; (3) z-data: and carrying out primary data fusion on the core data in the H-data according to a corresponding data fusion model to obtain knowledge data.
Cloud K-data: the data from different edge terminals are received, data processing work such as one-step data fusion and data mining is carried out, and the data can be issued to the edge terminals and the edge sharing cloud data content according to specific requirements. Thus, only necessary data is transmitted to the cloud, greatly reducing the amount of data transmitted over the Internet and the use of network bandwidth.
The information management layer is used for storing, inquiring, standardizing and managing the received data, and is configured to support data processing advanced application by adopting a distributed file system, a distributed relational database, a NoSQL database, a real-time database and a memory database on the basis of hardware equipment and a magnetic disk, and store, inquire, standardize and manage mass data.
The service application layer is accessed to a multi-energy system in a rural energy supply area by adopting a mode of layered partitioning and coordination control according to the principle of 'area cooperative complementation and terminal bidirectional interaction', is coordinated with a cold-heat-electricity multi-energy complementary comprehensive utilization system such as a ground source heat pump and a cold accumulation device by combining typical devices such as a distributed power supply and load side interaction, realizes comprehensive energy network modeling and topology analysis, performs coordinated optimization control on sources, networks and loads in the whole rural energy supply area, improves the overall safety level and the operation efficiency of an energy network in the rural energy supply area, and maximally consumes clean energy.
And (3) realizing the topological modeling analysis of the comprehensive energy network by using a dynamic game balancing strategy. In one round of the game, the power system determines a power supply and line new establishment scheme according to the load in the area, the natural gas network information of the ground source heat pump, the cold accumulation device and the gas turbine set (E)T,EL) Maximizing the electric power system profit; then, the power flow information on the gas engine set is transmitted to a natural gas network, and a heat source point, a gas source point and a pipeline new construction scheme (H) are determined by combining heat and gas loadsT,GT,HL,GL) Thereby maximizing the benefits of the heat system and the natural gas system; and the decision of each subsystem enables the topology of the comprehensive energy system to be updated, and the next round is entered. After n rounds of game, when any main body change strategy can not obtain more income any more, a dynamic game equilibrium state is formed [ (E)T,EL)*,(HT,GT,HL,GL)*]。
In the formula IEFor the total profit of the power system, IHFor the total gain of the thermal system, IGIs the total revenue of the natural gas system.
As shown in fig. 4, the layered partitioning and coordination control mode of the service application layer is mainly divided into a presentation module, a service interaction module, a service logic module and a data persistence module in the technical layer.
The display module is responsible for providing unified UI packaging and providing two types of UI controls for realization, and the traditional label control and the React control can meet the development requirements of a traditional development mode and a rich client development mode.
The service interaction module comprises service interaction control (safety control, authorization control and access control), and the service interaction module externally exposes RESTful service and Web service; the MVC controller is implemented for business control of the business system and does not provide access to the outside (non-standard services).
The business logic module is a business development process, one module is divided into 3 parts (interface logic HTML/JSP, interface definition and service implementation), the business logic component is not directly exposed to the interface logic, and the interface accesses the business logic component exposed by the business logic component through a business interface (a platform injects service into the business interface).
And the data persistence module, namely the persistence framework, is only used as a component of the service application layer and is not related to the third-party platform.
The comprehensive display layer comprises a display screen, a computer desktop, a mobile APP or a sand table and is used as a carrier for displaying theme application and analysis and decision application, and the panoramic visual display of a coordination control strategy, a regulation and control object and a control effect can be realized.
The rural intelligent energy management platform is an integrated vertical solution formed by combining software and hardware. The user can directly realize recharging management, power consumption collection and electric charge calculation, charge settlement and automatic deduction processing through mobile application (App/public number/life number). The user can use third party payment modes such as payment treasured, little letter payment to purchase the electricity to appointed purchase point or through the network, and when the surplus sum of money was not enough in the user's table, the ammeter made internal relay or external load control switch action outage through exporting trip signal, avoids owing fee. Meanwhile, the functions of short message reminding, self-service power failure and restoration and the like can be realized.
Compared with the prior art, the data acquisition layer acquires the rural on-site clean energy data such as distributed water, light, terrestrial heat, natural gas and the like by utilizing the cloud edge cooperation technology, and the information management layer stores the acquired data in a local file and sends the data to the service application layer. The service application layer carries out comprehensive utilization, optimization and coordination control on the multi-energy system accessed into the energy supply area of the village, is connected with the comprehensive display layer and transmits data information, and realizes panoramic visual display of a coordination control strategy, a regulation and control object and a control effect.
The intelligent energy management platform for the village provided by the invention aims at specialization, systematization and informatization, improves the on-site utilization level of renewable energy, promotes clean conversion of energy supply and energy supply of the village, and improves the electricity quality and electricity management level of village users.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. A rural intelligent energy management platform is characterized by comprising a data acquisition layer, an information management layer, a service application layer and a comprehensive display layer;
the data acquisition layer acquires the distributed rural in-place clean energy data by using a cloud edge cooperation technology;
the information management layer stores the acquired data into a file and sends the data to the service application layer;
the service application layer is used for carrying out comprehensive utilization optimization coordination control on the multi-energy system accessed into the energy supply area of the village, is connected with the comprehensive display layer and transmits data information;
and the comprehensive display layer realizes panoramic visual display of a coordination control strategy, a regulation and control object and a control effect.
2. The rural intelligent energy management platform of claim 1,
the cloud edge collaboration frame comprises a data end, an edge end and a cloud end; the data end consists of distributed rural on-site clean energy equipment, generates original data and sends the original data to the edge end;
the edge terminal stores the original data generated by each device collected from the data terminal and carries out pretreatment; then uploading relevant data required by the cloud application, and receiving data shared by the cloud;
the cloud end is responsible for collecting data of each edge end, further processing, analyzing and storing the data, and cloud end data processing results can be shared to the edge ends.
3. The rural intelligent energy management platform of claim 2,
the cloud end is provided with a weight coefficient cloud network model which comprises an input layer, a middle layer and an output layer.
4. The rural intelligent energy management platform of claim 3,
the edge end and the cloud end use a weight coefficient cloud network algorithm to perform data intelligent processing analysis, and specifically:
selecting distributed rural on-site clean energy data to be analyzed at the edge end, and preprocessing disordered data through learning and training to obtain pure data; then clustering and classifying according to the characteristic attribute of the data;
the processed rural on-site clean energy data is input through an input layer of the weight coefficient cloud network model, and the weight and the threshold value in the weight coefficient cloud network are repeatedly adjusted to gradually approach the required result, so that the rural on-site clean energy data is finally enabled to minimize the output error.
5. The rural intelligent energy management platform of claim 4,
when the weight coefficient cloud network model is used, the weight coefficient cloud network model is adjusted according to the following formula:
the formula for adjusting the weight coefficient of the output layer is as follows:
in the formula,. DELTA.wkiIs the weight coefficient deviation of each value of the output layer, eta is the adjustment constant, Ok pIs a weight coefficient of each value of the output layer, Oi pIs the weight coefficient of each value in the middle layer, tk pIs the output layer threshold;
the formula for adjusting the weight coefficient of the middle layer is as follows:
in the formula,. DELTA.wijThe amount of weight coefficient deviation of each value of the intermediate layer, Oj pAnd L is the weight coefficient of each value of the input layer, and the quantity of the middle layer influence factors.
6. The rural intelligent energy management platform of claim 5,
on an input layer, firstly, carrying out standardization processing on rural on-site clean energy data conveyed by an edge end, and then carrying out calculation of repeatedly adjusting weight coefficients;
assuming that the types of the inputted rural local clean energy data are m, the number of the samples is N, and the input data xijThe normalization is performed according to the following formula:
wherein i is 1, 2, …, N; j is 1, 2, …, m, ZijThe data after the standardization processing is carried out.
7. The rural intelligent energy management platform of claim 1,
and the service application layer adopts a mode of layering, partitioning and coordination control to realize comprehensive energy network modeling and topology analysis on the multi-energy system accessed into the rural energy supply area.
8. The rural intelligent energy management platform of claim 7,
the method for realizing the topology modeling analysis of the comprehensive energy network by using the dynamic game balancing strategy specifically comprises the following steps:
in one round of the game, the power system determines a power supply and line new establishment scheme according to the load in the area, the natural gas network information of the ground source heat pump, the cold accumulation device and the gas turbine set (E)T,EL) Maximizing the electric power system profit; then, the power flow information on the gas engine set is transmitted to a natural gas network, and a heat source point, a gas source point and a pipeline new construction scheme (H) are determined by combining heat and gas loadsT,GT,HL,GL) Thereby maximizing the benefits of the heat system and the natural gas system; the decision of each subsystem enables the topology of the comprehensive energy system to be updated, and the next round is entered;
and after n rounds of the game, when any main body change strategy can not obtain larger benefits any more, a dynamic game equilibrium state is formed.
9. The rural intelligent energy management platform of claim 7,
the service application layer is divided into a presentation module, a service interaction module, a service logic module and a data persistence module;
the display module provides unified UI packaging and two types of UI controls, namely a traditional label control and a React control;
the service interaction module comprises service interaction control and externally exposes RESTful service and Web service;
the business logic module comprises interface logic HTML/JSP, interface definition and 3 business development processes for service realization;
and the data persistence module is a persistence framework and is used as a component of the service application layer.
10. The rural intelligent energy management platform of claim 1,
the comprehensive display layer comprises a display screen, a computer desktop, a mobile APP or a sand table and is used as a carrier for displaying the subject application and the analysis decision application.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109242446A (en) * | 2018-10-16 | 2019-01-18 | 南京工业大学 | A kind of city intelligent energy panorama control platform |
| CN110443398A (en) * | 2019-05-13 | 2019-11-12 | 华北电力大学（保定） | A kind of regional complex energy resource system optimizing operation method based on repeated game model |
| WO2020189235A1 (en) * | 2019-03-20 | 2020-09-24 | 国立大学法人大阪大学 | Trained model, control device, friction stir welding system, neural network system, and method for generating trained model |
| CN112165513A (en) * | 2020-09-11 | 2021-01-01 | 武汉大学 | Cloud edge collaborative platform architecture for watershed water environment ecological intelligent management |
| CN112862352A (en) * | 2021-03-05 | 2021-05-28 | 三峡大学 | Joint planning method for wide-area comprehensive energy system |
| CN112926945A (en) * | 2021-03-12 | 2021-06-08 | 国网吉林省电力有限公司电力科学研究院 | Flood prevention key part regulating and controlling system and method based on real object ID |
-
2021
- 2021-11-08 CN CN202111312579.1A patent/CN114266435A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109242446A (en) * | 2018-10-16 | 2019-01-18 | 南京工业大学 | A kind of city intelligent energy panorama control platform |
| WO2020189235A1 (en) * | 2019-03-20 | 2020-09-24 | 国立大学法人大阪大学 | Trained model, control device, friction stir welding system, neural network system, and method for generating trained model |
| CN110443398A (en) * | 2019-05-13 | 2019-11-12 | 华北电力大学（保定） | A kind of regional complex energy resource system optimizing operation method based on repeated game model |
| CN112165513A (en) * | 2020-09-11 | 2021-01-01 | 武汉大学 | Cloud edge collaborative platform architecture for watershed water environment ecological intelligent management |
| CN112862352A (en) * | 2021-03-05 | 2021-05-28 | 三峡大学 | Joint planning method for wide-area comprehensive energy system |
| CN112926945A (en) * | 2021-03-12 | 2021-06-08 | 国网吉林省电力有限公司电力科学研究院 | Flood prevention key part regulating and controlling system and method based on real object ID |
Non-Patent Citations (1)
| Title |
|---|
| 周福举，等: "基于BP神经网络的线损不良数据辨识及其预测", 电力行业节能, 31 December 2020 (2020-12-31), pages 27 - 31 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115296974A (en) * | 2022-07-20 | 2022-11-04 | 深圳供电局有限公司 | Scheduling information decision support system |
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