CN117787659A - Smart city energy management system and method based on 5G - Google Patents
Smart city energy management system and method based on 5G Download PDFInfo
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Abstract
The invention provides a smart city energy management system and method based on 5G, wherein the method comprises the following steps: acquiring three-dimensional image data and city basic data of a smart city, and establishing a city BIM model; acquiring historical energy data of the smart city; determining a first energy management area in the smart city according to a city BIM model and historical energy data, and configuring a plurality of edge servers and a plurality of energy management terminals in the first energy management area; determining a first energy management scheme, a second energy management scheme and a third energy management scheme according to the city BIM model and the historical energy data; and determining a first current energy management scheme to perform energy management on the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme. Through the block division and hierarchical calculation and control structure, the energy management of the whole smart city can be more efficient and intelligent.
Description
Technical Field
The invention relates to the technical field of smart cities, in particular to a smart city energy management system and method based on 5G.
Background
With the continuous development and progress of technology, the buildings and various facilities of cities are more and more densely and intelligently arranged, so that the life of people is more convenient, and the energy consumption of the cities is higher and higher. How to effectively monitor and manage the energy application of the buildings and facilities so as to control the energy consumption of the buildings and facilities to a reasonable level is currently being solved.
Disclosure of Invention
The invention provides a smart city energy management system and a smart city energy management method based on 5G based on the problems, and the scheme of the invention can enable the energy management of the whole smart city to be more efficient and intelligent.
In view of this, an aspect of the present invention proposes a 5G-based smart city energy management system, comprising: the system comprises a cloud server, an edge server which is in communication connection with the cloud server through a first 5G communication network, and an energy management terminal which is in communication connection with the edge server through a second 5G communication network;
the cloud server is configured to:
acquiring three-dimensional image data and city basic data of a smart city, and establishing a city BIM model of the smart city;
acquiring historical energy data of the smart city;
Determining a first energy management area in the smart city according to the city BIM model and the historical energy data, and configuring a plurality of edge servers and a plurality of energy management terminals in the first energy management area;
determining a first energy management scheme, a second energy management scheme and a third energy management scheme according to the city BIM model and the historical energy data;
and determining a first current energy management scheme to perform energy management on the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme.
Optionally, after the step of determining the first energy management scheme, the second energy management scheme, and the third energy management scheme according to the city BIM model and the historical energy data, the cloud server is configured to:
controlling the energy management terminal to acquire first real-time energy data in the first energy management area, and sending the first real-time energy data to the corresponding edge server;
controlling the edge server to preprocess the first real-time energy data to obtain second real-time energy data;
Receiving the second real-time energy data sent by the edge server, and obtaining energy management scheme correction data according to the second real-time energy data;
the step of determining, according to the first energy management scheme, the second energy management scheme, and the third energy management scheme, that a first current energy management scheme performs energy management on the first energy management area, the cloud server being configured to:
and determining a first current energy management scheme to perform energy management on the first energy management area according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme.
Optionally, the step of determining a first energy management area in the smart city according to the city BIM model and the historical energy data, and configuring a plurality of edge servers and a plurality of energy management terminals in the first energy management area, the cloud server is configured to:
integrating and correlating the city BIM model data with the historical energy data, and determining an energy consumption mode and an energy distribution mode of each building/facility;
Determining a plurality of similar building/facility groups according to the energy consumption mode and/or the energy distribution mode, and determining a plurality of building/facility groups as a plurality of first energy management areas;
the edge server special for the area is arranged in each determined first energy management area, and the energy management terminal is configured at important buildings and important equipment in the first energy management area;
controlling the edge server to collect the energy management related data of the first energy management area in real time;
and controlling the edge server to analyze the energy management related data so as to optimize the control of each energy main body and coordinate the energy control and scheduling in the first energy management area.
Optionally, the step of determining a first energy management scheme, a second energy management scheme, and a third energy management scheme according to the city BIM model and the historical energy data, the cloud server is configured to:
after cleaning, labeling and clustering analysis are carried out on the historical energy data, marking is carried out on the historical energy data according to time sequences, building/facility types and several dimensions of an energy main body;
performing sequence prediction modeling and clustering by using a machine learning algorithm to obtain an energy consumption prediction model containing average energy consumption and change rules of each type of building/facility in different time periods and different external environments;
Binding the energy consumption prediction model with the urban BIM model, and carrying out digital twin modeling simulation of multiple scenes by utilizing a digital twin platform preset on the cloud server;
performing energy management simulation on the digital twin platform, obtaining simulation energy consumption results under different scenes, different management strategies and different control rules, and analyzing according to the simulation energy consumption results to obtain an optimal management strategy;
and decomposing the optimal management strategy into an end-side decision rule, an edge control strategy and a cloud platform-level scheduling rule according to the influence range of the optimal management strategy and the difference of action objects to respectively obtain the first energy management scheme, the second energy management scheme and the third energy management scheme.
Optionally, the step of determining, according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme, and the third energy management scheme, that the first current energy management scheme performs energy management on the first energy management area, the cloud server is configured to:
determining a plurality of energy management correction schemes according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme;
Respectively running each energy management correction scheme on the digital twin platform, and comparing the final effects of a plurality of energy management correction schemes to obtain a first comparison result;
comprehensively considering the first comparison result, the optimization space and the improvement effect of each energy management correction scheme, and determining the first current energy management scheme;
and sending the first current energy management scheme to the edge server and the energy management terminal corresponding to the first energy management area so as to manage energy.
Another aspect of the present invention provides a 5G-based smart city energy management method, comprising:
the method comprises the steps that a cloud server acquires three-dimensional image data and city basic data of a smart city, and a city BIM model of the smart city is built;
the cloud server acquires historical energy data of the smart city;
the cloud server determines a first energy management area in the smart city according to the city BIM model and the historical energy data, and configures a plurality of edge servers and a plurality of energy management terminals in the first energy management area;
the cloud server establishes communication connection with the edge server through a first 5G communication network;
The edge server establishes communication connection with the energy management terminal through a second 5G communication network;
the cloud server determines a first energy management scheme, a second energy management scheme and a third energy management scheme according to the urban BIM model and the historical energy data;
and the cloud server determines a first current energy management scheme to manage the energy of the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme.
Optionally, after the step of determining the first energy management scheme, the second energy management scheme, and the third energy management scheme by the cloud server according to the urban BIM model and the historical energy data, the method includes:
the energy management terminal acquires first real-time energy data in the first energy management area and sends the first real-time energy data to the corresponding edge server;
the edge server preprocesses the first real-time energy data to obtain second real-time energy data, and sends the second real-time energy data to the cloud server;
the cloud server obtains energy management scheme correction data according to the second real-time energy data;
The cloud server determines, according to the first energy management scheme, the second energy management scheme, and the third energy management scheme, that a first current energy management scheme performs energy management on the first energy management area, including:
and the cloud server determines a first current energy management scheme to perform energy management on the first energy management area according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme.
Optionally, the step that the cloud server determines a first energy management area in the smart city according to the city BIM model and the historical energy data, and configures a plurality of edge servers and a plurality of energy management terminals in the first energy management area includes:
integrating and correlating the city BIM model data with the historical energy data, and determining an energy consumption mode and an energy distribution mode of each building/facility;
determining a plurality of similar building/facility groups according to the energy consumption mode and/or the energy distribution mode, and determining a plurality of building/facility groups as a plurality of first energy management areas;
The edge server special for the area is arranged in each determined first energy management area, and the energy management terminal is configured at important buildings and important equipment in the first energy management area;
the edge server immediately collects the energy management related data of the first energy management area;
the edge server analyzes the energy management related data to optimize control of each energy main body, and is connected with the cloud server to coordinate energy control and scheduling in the first energy management area.
Optionally, the step of determining, by the cloud server, a first energy management scheme, a second energy management scheme, and a third energy management scheme according to the urban BIM model and the historical energy data includes:
after cleaning, labeling and clustering analysis are carried out on the historical energy data, marking is carried out on the historical energy data according to time sequences, building/facility types and several dimensions of an energy main body;
performing sequence prediction modeling and clustering by using a machine learning algorithm to obtain an energy consumption prediction model containing average energy consumption and change rules of each type of building/facility in different time periods and different external environments;
Binding the energy consumption prediction model with the urban BIM model, and carrying out digital twin modeling simulation of multiple scenes by utilizing a digital twin platform preset on the cloud server;
performing energy management simulation on the digital twin platform, obtaining simulation energy consumption results under different scenes, different management strategies and different control rules, and analyzing according to the simulation energy consumption results to obtain an optimal management strategy;
and decomposing the optimal management strategy into an end-side decision rule, an edge control strategy and a cloud platform-level scheduling rule according to the influence range of the optimal management strategy and the difference of action objects to respectively obtain the first energy management scheme, the second energy management scheme and the third energy management scheme.
Optionally, the step of determining, by the cloud server, that the first current energy management scheme performs energy management on the first energy management area according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme, and the third energy management scheme includes:
the cloud server determines a plurality of energy management correction schemes according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme;
Respectively running each energy management correction scheme on the digital twin platform, and comparing the final effects of a plurality of energy management correction schemes to obtain a first comparison result;
the cloud server comprehensively considers the first comparison result, the optimization space and the improvement effect of each energy management correction scheme, and determines the first current energy management scheme;
and sending the first current energy management scheme to the edge server and the energy management terminal corresponding to the first energy management area so as to manage energy.
By adopting the technical scheme, the smart city energy management method based on 5G comprises the steps that a cloud server acquires three-dimensional image data and city basic data of a smart city, and a city BIM model of the smart city is established; the cloud server acquires historical energy data of the smart city; the cloud server determines a first energy management area in the smart city according to the city BIM model and the historical energy data, and configures a plurality of edge servers and a plurality of energy management terminals in the first energy management area; the cloud server establishes communication connection with the edge server through a first 5G communication network; the edge server establishes communication connection with the energy management terminal through a second 5G communication network; the cloud server determines a first energy management scheme, a second energy management scheme and a third energy management scheme according to the urban BIM model and the historical energy data; and the cloud server determines a first current energy management scheme to manage the energy of the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme. Through the block division and hierarchical calculation and control structure, the energy management of the whole smart city can be more efficient and intelligent.
Drawings
FIG. 1 is a schematic block diagram of a 5G-based smart city energy management system provided in one embodiment of the present invention;
fig. 2 is a flowchart of a smart city energy management method based on 5G according to an embodiment of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
The terms first, second and the like in the description and in the claims of the present application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
A system and method for 5G-based smart city energy management according to some embodiments of the present invention are described below with reference to fig. 1-2.
As shown in fig. 1, one embodiment of the present invention provides a 5G-based smart city energy management system, comprising: the system comprises a cloud server, an edge server which is in communication connection with the cloud server through a first 5G communication network, and an energy management terminal which is in communication connection with the edge server through a second 5G communication network;
the cloud server is configured to:
acquiring three-dimensional image data and city basic data (such as detailed information of positions, structures, functions and the like of various buildings and facilities) of a smart city, and establishing a city BIM model of the smart city;
Acquiring historical energy data (including historical energy supply main data, historical energy distribution data, historical energy consumption main data and the like; can be collected according to granularity of buildings, blocks and the like) of the smart city;
determining a first energy management area in the smart city according to the city BIM model and the historical energy data, and configuring a plurality of edge servers and a plurality of energy management terminals (the energy management terminals are connected with a plurality of energy main bodies) in the first energy management area;
determining a first energy management scheme, a second energy management scheme and a third energy management scheme according to the urban BIM model and the historical energy data (for example, the first energy management scheme is to directly manage each energy main body after interaction and decision among the energy management terminals, the second energy management scheme is to manage each energy main body through interaction and decision among edge servers, the third energy management scheme is to process and analyze the energy data of multiple dimensions of a cloud server integrated with a first energy management area and other energy management areas, manage each energy main body and the like, wherein the energy main bodies are energy generation equipment, energy distribution equipment, energy consumption equipment and the like);
And determining a first current energy management scheme to perform energy management on the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme.
By adopting the technical scheme of the embodiment, a cloud server acquires three-dimensional image data and city basic data of a smart city, and establishes a city BIM model of the smart city; the cloud server acquires historical energy data of the smart city; the cloud server determines a first energy management area in the smart city according to the city BIM model and the historical energy data, and configures a plurality of edge servers and a plurality of energy management terminals in the first energy management area; the cloud server establishes communication connection with the edge server through a first 5G communication network; the edge server establishes communication connection with the energy management terminal through a second 5G communication network; the cloud server determines a first energy management scheme, a second energy management scheme and a third energy management scheme according to the urban BIM model and the historical energy data; and the cloud server determines a first current energy management scheme to manage the energy of the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme. Through the block division and hierarchical calculation and control structure, the energy management of the whole smart city can be more efficient and intelligent.
It should be understood that the block diagram of the 5G-based smart city energy management system shown in fig. 1 is only illustrative, and the number of the illustrated modules is not limiting to the scope of the present invention.
In some possible embodiments of the present invention, after the step of determining the first energy management scheme, the second energy management scheme, and the third energy management scheme according to the city BIM model and the historical energy data, the cloud server is configured to:
controlling the energy management terminal to acquire first real-time energy data in the first energy management area, and sending the first real-time energy data to the corresponding edge server;
controlling the edge server to preprocess the first real-time energy data to obtain second real-time energy data;
receiving the second real-time energy data sent by the edge server, and obtaining energy management scheme correction data according to the second real-time energy data;
in the step, real-time energy data can be continuously and uninterruptedly collected from a pre-deployed intelligent ammeter, a sensor and metering equipment, and a connection relation is established with historical data; inputting the newly collected real-time data into an AI prediction model which is trained, and carrying out reasoning prediction; comparing and analyzing the prediction result with the actual energy consumption condition, and if the deviation exceeds a preset threshold value, judging that the current energy management strategy needs to be adjusted and optimized; the reinforcement learning optimization algorithm module of the cloud is started, simulation and calculation are rerun according to the latest conditions such as environmental change, actual load demand and the like, and a new optimal solution is found out, namely, the energy management scheme corrects data/scheme; the newly generated energy management scheme correction data/scheme is issued to an edge server and an energy management terminal at the end side, and strategy adjustment and parameter update are carried out, so that closed-loop autonomous optimization is realized; and the cloud platform continuously receives the feedback data, so that the management effect can be kept in an optimal state. In the whole process, the cloud server plays a role of brain, and ensures that urban energy sources are optimized and reasonably scheduled.
The step of determining, according to the first energy management scheme, the second energy management scheme, and the third energy management scheme, that a first current energy management scheme performs energy management on the first energy management area, the cloud server being configured to:
and determining a first current energy management scheme to perform energy management on the first energy management area according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme.
In some possible embodiments of the present invention, the step of determining a first energy management area in the smart city according to the city BIM model and the historical energy data, and configuring a plurality of edge servers and a plurality of energy management terminals in the first energy management area, the cloud server is configured to:
integrating and correlating the city BIM model data with the historical energy consumption data, and determining an energy consumption mode and an energy distribution mode of each building/facility;
in this step, three-dimensional BIM model data (which contains fine spatial structure information of the building) of all important public and commercial buildings and facilities in the smart city range is collected by a pre-stage; for example, for a building, acquiring detailed energy consumption original data (including various energy data such as electricity, water, gas and the like, and the granularity of the data can reach the room level) in the past year; analyzing consumption distribution conditions and characteristics of various energy sources by utilizing a data mining technology, and mining typical energy consumption modes and energy consumption load characteristics; carrying out association binding on the obtained mode and the characteristic label and rooms/facilities in the three-dimensional BIM data to form an energy information model; aiming at some important energy utilization equipment, the BIM data are also required to be subjected to link association to obtain the accurate spatial position and operation and maintenance metadata of the BIM data; the energy consumption mode and the energy distribution mode of each building/facility can be obtained by combining the previous processing and analysis results; the space distribution condition of energy consumption and distribution can be displayed on a three-dimensional visual platform, and the thermodynamic diagram can clearly show the energy gathering area; through the fusion of modeling information and energy consumption data, energy management decisions can be intuitively and scientifically analyzed and made.
Determining a similar plurality of building/facility groups according to the energy consumption mode and/or the energy distribution mode, and determining a plurality of the building/facility groups as a plurality of the first energy management areas (e.g., a office building area, a business area, a residential area, etc.);
the edge server special for the area is arranged in each determined first energy management area, and the energy management terminal is configured at important buildings and important equipment in the first energy management area;
controlling the edge server to collect the energy management related data of the first energy management area in real time;
and controlling the edge server to analyze the energy management related data so as to optimize the control of each energy main body and coordinate the energy control and scheduling in the first energy management area.
In this embodiment, the energy management of the whole smart city can be more efficient and intelligent through the block division and hierarchical calculation and control structure.
It can be understood that in the embodiment of the invention, terminals such as intelligent electric meters, water meters, sensors and metering devices can be distributed in a large area in an intelligent city, and are connected to an edge server and a cloud server in real time through a 5G network to monitor the energy use conditions such as electricity, water, gas and the like with high precision; the terminals upload a large amount of energy source using raw data to an edge server or a large data warehouse of a cloud platform for storage in real time or at regular time through a safety interface, and the data carry explicit geographic space identifiers of buildings, floors, rooms and the like; carrying out multidimensional analysis by utilizing big data analysis and AI algorithms such as machine learning, deep learning and the like, establishing energy consumption prediction models of various buildings and facilities, evaluating key parameters affecting energy consumption, and forming a knowledge graph and a consumption label; matching and binding the analysis result and the BIM three-dimensional model to realize space position, accurately drawing energy consumption distribution diagrams and thermodynamic diagrams of buildings and blocks, and finding out key monitoring areas, key monitoring equipment, key monitoring buildings and the like; and combining with the integration of external data such as real-time weather, people flow and the like, establishing a dynamically updated energy demand prediction analysis model, and providing basis for the edge server and the energy scheduling in the whole city.
In some possible embodiments of the present invention, the step of determining a first energy management scheme, a second energy management scheme, and a third energy management scheme according to the city BIM model and the historical energy data, the cloud server is configured to:
after cleaning, labeling and clustering analysis are carried out on the historical energy data, marking is carried out on the historical energy data according to time sequences, building/facility types and several dimensions of an energy main body;
performing sequence prediction modeling and clustering by using a machine learning algorithm (such as LSTM) to obtain an energy consumption prediction model containing average energy consumption and change rules of each type of building/facility in different time periods and different external environments;
binding the energy consumption prediction model with the urban BIM model, and carrying out digital twin modeling simulation of multiple scenes (such as multiple scenes of daytime, nighttime, weekends and the like) by utilizing a digital twin platform preset on the cloud server;
performing energy management simulation on the digital twin platform, obtaining simulation energy consumption results under different scenes, different management strategies and different control rules, and analyzing according to the simulation energy consumption results to obtain an optimal management strategy;
And decomposing the optimal management strategy into an end-side decision rule, an edge control strategy and a cloud platform-level scheduling rule according to the influence range of the optimal management strategy and the difference of action objects to respectively obtain the first energy management scheme, the second energy management scheme and the third energy management scheme.
In this embodiment, the AI decision system of the cloud server obtains a data distribution rule and an optimal decision model under different situations according to the city BIM model and the historical energy data of different time-space dimensions, different building types, different energy main body types and the like; for these models, the AI decision system can autonomously formulate different management strategies/schemes by reinforcement learning or the like. Due to the different characteristics of the area and the device, these decisions can be divided into three main categories:
(1) The decision rule (i.e. the first energy management scheme) of the terminal side (equipment side) is that each energy main body is directly managed after interaction and decision among the energy management terminals;
(2) The edge control strategy (namely a second energy management scheme) is to manage each energy main body through interaction and decision among edge servers;
(3) The cloud platform-level scheduling rule (namely, a third energy management scheme) is that the cloud server synthesizes the energy data of multiple dimensions of the first energy management area and other energy management areas, and then manages each energy main body after processing and analyzing;
In this embodiment, these policy considerations range and influence from end-side to global, layer-by-layer. For example, the end-side strategy can simply and directly control a single device, and the global decision coordinates the supply and demand of the whole city, and meanwhile, the schemes can be dynamically updated and combined to be used cooperatively; in order to ensure the effect, digital twin and simulation technology can be utilized to verify the effect of different strategy combinations, find the optimal solution, issue for use and dynamically adjust and optimize. In the embodiment, the urban energy can be managed more flexibly and efficiently through a multi-stage linkage type decision system.
In some possible embodiments of the present invention, the step of determining, according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme, and the third energy management scheme, that the first current energy management scheme performs energy management on the first energy management area, the cloud server is configured to:
determining a plurality of energy management correction schemes according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme;
respectively running each energy management correction scheme on the digital twin platform, and comparing the final effects of a plurality of energy management correction schemes to obtain a first comparison result;
Comprehensively considering the first comparison result, the optimization space and the improvement effect of each energy management correction scheme, and determining the first current energy management scheme;
and sending the first current energy management scheme to the edge server and the energy management terminal corresponding to the first energy management area so as to manage energy.
In the embodiment, dynamic optimization control of the distributed energy management unit can be efficiently and accurately realized through calculation, scheduling and command of the cloud.
Referring to fig. 2, another embodiment of the present invention provides a smart city energy management method based on 5G, including:
the cloud server acquires three-dimensional image data and city basic data (such as detailed information of positions, structures, functions and the like of various buildings and facilities) of a smart city, and establishes a city BIM model of the smart city;
the cloud server acquires historical energy data (including historical energy supply main data, historical energy distribution data, historical energy consumption main data and the like; can be collected according to granularity of buildings, blocks and the like) of the smart city;
the cloud server determines a first energy management area in the smart city according to the city BIM model and the historical energy data, and configures a plurality of edge servers and a plurality of energy management terminals (the energy management terminals are connected with a plurality of energy main bodies) in the first energy management area;
The cloud server establishes communication connection with the edge server through a first 5G communication network;
the edge server establishes communication connection with the energy management terminal through a second 5G communication network;
the cloud server determines a first energy management scheme, a second energy management scheme and a third energy management scheme according to the urban BIM model and the historical energy data (for example, the first energy management scheme is to directly manage each energy main body after interaction and decision among the energy management terminals, the second energy management scheme is to manage each energy main body through interaction and decision among the edge servers, the third energy management scheme is to process and analyze the energy data of multiple dimensions of a first energy management area and other energy management areas of the cloud server, and manage each energy main body after analysis, and the like, wherein the energy main bodies are energy generating equipment, energy distribution equipment, energy consumption equipment and the like);
and the cloud server determines a first current energy management scheme to manage the energy of the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme.
In some possible embodiments of the present invention, after the step of determining the first energy management scheme, the second energy management scheme, and the third energy management scheme according to the city BIM model and the historical energy data, the cloud server includes:
the energy management terminal acquires first real-time energy data in the first energy management area and sends the first real-time energy data to the corresponding edge server;
the edge server preprocesses the first real-time energy data to obtain second real-time energy data, and sends the second real-time energy data to the cloud server;
the cloud server obtains energy management scheme correction data according to the second real-time energy data;
in the step, real-time energy data can be continuously and uninterruptedly collected from a pre-deployed intelligent ammeter, a sensor and metering equipment, and a connection relation is established with historical data; inputting the newly collected real-time data into an AI prediction model which is trained, and carrying out reasoning prediction; comparing and analyzing the prediction result with the actual energy consumption condition, and if the deviation exceeds a preset threshold value, judging that the current energy management strategy needs to be adjusted and optimized; the reinforcement learning optimization algorithm module of the cloud is started, simulation and calculation are rerun according to the latest conditions such as environmental change, actual load demand and the like, and a new optimal solution is found out, namely, the energy management scheme corrects data/scheme; the newly generated energy management scheme correction data/scheme is issued to an edge server and an energy management terminal at the end side, and strategy adjustment and parameter update are carried out, so that closed-loop autonomous optimization is realized; and the cloud platform continuously receives the feedback data, so that the management effect can be kept in an optimal state. In the whole process, the cloud server plays a role of brain, and ensures that urban energy sources are optimized and reasonably scheduled.
The cloud server determines, according to the first energy management scheme, the second energy management scheme, and the third energy management scheme, that a first current energy management scheme performs energy management on the first energy management area, including:
and the cloud server determines a first current energy management scheme to perform energy management on the first energy management area according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme.
In some possible embodiments of the present invention, the cloud server determines a first energy management area in the smart city according to the city BIM model and the historical energy data, and configures a plurality of edge servers and a plurality of energy management terminals in the first energy management area, including:
integrating and correlating the city BIM model data with the historical energy data, and determining an energy consumption mode and an energy distribution mode of each building/facility;
in this step, three-dimensional BIM model data (which contains fine spatial structure information of the building) of all important public and commercial buildings and facilities in the smart city range is collected by a pre-stage; for example, for a building, acquiring detailed energy consumption original data (including various energy data such as electricity, water, gas and the like, and the granularity of the data can reach the room level) in the past year; analyzing consumption distribution conditions and characteristics of various energy sources by utilizing a data mining technology, and mining typical energy consumption modes and energy consumption load characteristics; carrying out association binding on the obtained mode and the characteristic label and rooms/facilities in the three-dimensional BIM data to form an energy information model; aiming at some important energy utilization equipment, the BIM data are also required to be subjected to link association to obtain the accurate spatial position and operation and maintenance metadata of the BIM data; the energy consumption mode and the energy distribution mode of each building/facility can be obtained by combining the previous processing and analysis results; the space distribution condition of energy consumption and distribution can be displayed on a three-dimensional visual platform, and the thermodynamic diagram can clearly show the energy gathering area; through the fusion of modeling information and energy consumption data, energy management decisions can be intuitively and scientifically analyzed and made.
Determining a similar plurality of building/facility groups according to the energy consumption mode and/or the energy distribution mode, and determining a plurality of the building/facility groups as a plurality of the first energy management areas (e.g., a office building area, a business area, a residential area, etc.);
the edge server special for the area is arranged in each determined first energy management area, and the energy management terminal is configured at important buildings and important equipment in the first energy management area;
the edge server immediately collects the energy management related data of the first energy management area;
the edge server analyzes the energy management related data to optimize control of each energy main body, and is connected with the cloud server to coordinate energy control and scheduling in the first energy management area.
In this embodiment, the energy management of the whole smart city can be more efficient and intelligent through the block division and hierarchical calculation and control structure.
It can be understood that in the embodiment of the invention, terminals such as intelligent electric meters, water meters, sensors and metering devices can be distributed in a large area in an intelligent city, and are connected to an edge server and a cloud server in real time through a 5G network to monitor the energy use conditions such as electricity, water, gas and the like with high precision; the terminals upload a large amount of energy source using raw data to an edge server or a large data warehouse of a cloud platform for storage in real time or at regular time through a safety interface, and the data carry explicit geographic space identifiers of buildings, floors, rooms and the like; carrying out multidimensional analysis by utilizing big data analysis and AI algorithms such as machine learning, deep learning and the like, establishing energy consumption prediction models of various buildings and facilities, evaluating key parameters affecting energy consumption, and forming a knowledge graph and a consumption label; matching and binding the analysis result and the BIM three-dimensional model to realize space position, accurately drawing energy consumption distribution diagrams and thermodynamic diagrams of buildings and blocks, and finding out key monitoring areas, key monitoring equipment, key monitoring buildings and the like; and combining with the integration of external data such as real-time weather, people flow and the like, establishing a dynamically updated energy demand prediction analysis model, and providing basis for the edge server and the energy scheduling in the whole city.
In some possible embodiments of the present invention, the cloud server determines a first energy management scheme, a second energy management scheme, and a third energy management scheme according to the city BIM model and the historical energy data, including:
after cleaning, labeling and clustering analysis are carried out on the historical energy data, marking is carried out on the historical energy data according to time sequences, building/facility types and several dimensions of an energy main body;
performing sequence prediction modeling and clustering by using a machine learning algorithm (such as LSTM) to obtain an energy consumption prediction model containing average energy consumption and change rules of each type of building/facility in different time periods and different external environments;
binding the energy consumption prediction model with the urban BIM model, and carrying out digital twin modeling simulation of multiple scenes (such as multiple scenes of daytime, nighttime, weekends and the like) by utilizing a digital twin platform preset on the cloud server;
performing energy management simulation on the digital twin platform, obtaining simulation energy consumption results under different scenes, different management strategies and different control rules, and analyzing according to the simulation energy consumption results to obtain an optimal management strategy;
And decomposing the optimal management strategy into an end-side decision rule, an edge control strategy and a cloud platform-level scheduling rule according to the influence range of the optimal management strategy and the difference of action objects to respectively obtain the first energy management scheme, the second energy management scheme and the third energy management scheme.
In this embodiment, the AI decision system of the cloud server obtains a data distribution rule and an optimal decision model under different situations according to the city BIM model and the historical energy data of different time-space dimensions, different building types, different energy main body types and the like; for these models, the AI decision system can autonomously formulate different management strategies/schemes by reinforcement learning or the like. Due to the different characteristics of the area and the device, these decisions can be divided into three main categories:
(1) The decision rule (i.e. the first energy management scheme) of the terminal side (equipment side) is that each energy main body is directly managed after interaction and decision among the energy management terminals;
(2) The edge control strategy (namely a second energy management scheme) is to manage each energy main body through interaction and decision among edge servers;
(3) The cloud platform-level scheduling rule (namely, a third energy management scheme) is that the cloud server synthesizes the energy data of multiple dimensions of the first energy management area and other energy management areas, and then manages each energy main body after processing and analyzing;
In this embodiment, these policy considerations range and influence from end-side to global, layer-by-layer. For example, the end-side strategy can simply and directly control a single device, and the global decision coordinates the supply and demand of the whole city, and meanwhile, the schemes can be dynamically updated and combined to be used cooperatively; in order to ensure the effect, digital twin and simulation technology can be utilized to verify the effect of different strategy combinations, find the optimal solution, issue for use and dynamically adjust and optimize. In the embodiment, the urban energy can be managed more flexibly and efficiently through a multi-stage linkage type decision system.
In some possible embodiments of the present invention, the step of determining, by the cloud server, that the first current energy management scheme performs energy management on the first energy management area according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme, and the third energy management scheme includes:
the cloud server determines a plurality of energy management correction schemes according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme;
respectively running each energy management correction scheme on the digital twin platform, and comparing the final effects of a plurality of energy management correction schemes to obtain a first comparison result;
The cloud server comprehensively considers the first comparison result, the optimization space and the improvement effect of each energy management correction scheme, and determines the first current energy management scheme;
and sending the first current energy management scheme to the edge server and the energy management terminal corresponding to the first energy management area so as to manage energy.
In the embodiment, the dynamic optimization control of the distributed energy management unit can be efficiently and accurately realized through the calculation, scheduling and command of the cloud end
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the present application, wherein specific examples are provided herein to illustrate the principles and embodiments of the present application, the above examples being provided solely to assist in the understanding of the methods of the present application and the core ideas thereof; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Although the present invention is disclosed above, the present invention is not limited thereto. Variations and modifications, including combinations of the different functions and implementation steps, as well as embodiments of the software and hardware, may be readily apparent to those skilled in the art without departing from the spirit and scope of the invention.
Claims (10)
1. A 5G-based smart city energy management system, comprising: the system comprises a cloud server, an edge server which is in communication connection with the cloud server through a first 5G communication network, and an energy management terminal which is in communication connection with the edge server through a second 5G communication network;
the cloud server is configured to:
Acquiring three-dimensional image data and city basic data of a smart city, and establishing a city BIM model of the smart city;
acquiring historical energy data of the smart city;
determining a first energy management area in the smart city according to the city BIM model and the historical energy data, and configuring a plurality of edge servers and a plurality of energy management terminals in the first energy management area;
determining a first energy management scheme, a second energy management scheme and a third energy management scheme according to the city BIM model and the historical energy data;
and determining a first current energy management scheme to perform energy management on the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme.
2. The 5G-based smart city energy management system of claim 1, wherein after the step of determining a first energy management scheme, a second energy management scheme, and a third energy management scheme from the city BIM model and the historical energy data, the cloud server is configured to:
controlling the energy management terminal to acquire first real-time energy data in the first energy management area, and sending the first real-time energy data to the corresponding edge server;
Controlling the edge server to preprocess the first real-time energy data to obtain second real-time energy data;
receiving the second real-time energy data sent by the edge server, and obtaining energy management scheme correction data according to the second real-time energy data;
the step of determining, according to the first energy management scheme, the second energy management scheme, and the third energy management scheme, that a first current energy management scheme performs energy management on the first energy management area, the cloud server being configured to:
and determining a first current energy management scheme to perform energy management on the first energy management area according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme.
3. The 5G-based smart city energy management system of claim 2, wherein the steps of determining a first energy management area in the smart city from the city BIM model and the historical energy data, and configuring a plurality of edge servers and a plurality of energy management terminals within the first energy management area, the cloud server configured to:
Integrating and correlating the city BIM model data with the historical energy data, and determining an energy consumption mode and an energy distribution mode of each building/facility;
determining a plurality of similar building/facility groups according to the energy consumption mode and/or the energy distribution mode, and determining a plurality of building/facility groups as a plurality of first energy management areas;
the edge server special for the area is arranged in each determined first energy management area, and the energy management terminal is configured at important buildings and important equipment in the first energy management area;
controlling the edge server to collect the energy management related data of the first energy management area in real time;
and controlling the edge server to analyze the energy management related data so as to optimize the control of each energy main body and coordinate the energy control and scheduling in the first energy management area.
4. The 5G-based smart city energy management system of claim 3, wherein the step of determining a first energy management scheme, a second energy management scheme, and a third energy management scheme from the city BIM model and the historical energy data, the cloud server is configured to:
After cleaning, labeling and clustering analysis are carried out on the historical energy data, marking is carried out on the historical energy data according to time sequences, building/facility types and several dimensions of an energy main body;
performing sequence prediction modeling and clustering by using a machine learning algorithm to obtain an energy consumption prediction model containing average energy consumption and change rules of each type of building/facility in different time periods and different external environments;
binding the energy consumption prediction model with the urban BIM model, and carrying out digital twin modeling simulation of multiple scenes by utilizing a digital twin platform preset on the cloud server;
performing energy management simulation on the digital twin platform, obtaining simulation energy consumption results under different scenes, different management strategies and different control rules, and analyzing according to the simulation energy consumption results to obtain an optimal management strategy;
and decomposing the optimal management strategy into an end-side decision rule, an edge control strategy and a cloud platform-level scheduling rule according to the influence range of the optimal management strategy and the difference of action objects to respectively obtain the first energy management scheme, the second energy management scheme and the third energy management scheme.
5. The 5G-based smart city energy management system of claim 4, wherein the step of determining a first current energy management scheme to energy manage the first energy management area in accordance with the energy management scheme correction data, the first energy management scheme, the second energy management scheme, and the third energy management scheme, the cloud server is configured to:
Determining a plurality of energy management correction schemes according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme;
respectively running each energy management correction scheme on the digital twin platform, and comparing the final effects of a plurality of energy management correction schemes to obtain a first comparison result;
comprehensively considering the first comparison result, the optimization space and the improvement effect of each energy management correction scheme, and determining the first current energy management scheme;
and sending the first current energy management scheme to the edge server and the energy management terminal corresponding to the first energy management area so as to manage energy.
6. A 5G-based smart city energy management method, comprising:
the method comprises the steps that a cloud server acquires three-dimensional image data and city basic data of a smart city, and a city BIM model of the smart city is built;
the cloud server acquires historical energy data of the smart city;
the cloud server determines a first energy management area in the smart city according to the city BIM model and the historical energy data, and configures a plurality of edge servers and a plurality of energy management terminals in the first energy management area;
The cloud server establishes communication connection with the edge server through a first 5G communication network;
the edge server establishes communication connection with the energy management terminal through a second 5G communication network;
the cloud server determines a first energy management scheme, a second energy management scheme and a third energy management scheme according to the urban BIM model and the historical energy data;
and the cloud server determines a first current energy management scheme to manage the energy of the first energy management area according to the first energy management scheme, the second energy management scheme and the third energy management scheme.
7. The 5G-based smart city energy management method of claim 6, wherein after the step of the cloud server determining a first energy management scheme, a second energy management scheme, and a third energy management scheme from the city BIM model and the historical energy data, comprising:
the energy management terminal acquires first real-time energy data in the first energy management area and sends the first real-time energy data to the corresponding edge server;
the edge server preprocesses the first real-time energy data to obtain second real-time energy data, and sends the second real-time energy data to the cloud server;
The cloud server obtains energy management scheme correction data according to the second real-time energy data;
the cloud server determines, according to the first energy management scheme, the second energy management scheme, and the third energy management scheme, that a first current energy management scheme performs energy management on the first energy management area, including:
and the cloud server determines a first current energy management scheme to perform energy management on the first energy management area according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme.
8. The 5G-based smart city energy management method of claim 7, wherein the cloud server determines a first energy management area in the smart city based on the city BIM model and the historical energy data, and configures a plurality of edge servers and a plurality of energy management terminals within the first energy management area, comprising:
integrating and correlating the city BIM model data with the historical energy data, and determining an energy consumption mode and an energy distribution mode of each building/facility;
Determining a plurality of similar building/facility groups according to the energy consumption mode and/or the energy distribution mode, and determining a plurality of building/facility groups as a plurality of first energy management areas;
the edge server special for the area is arranged in each determined first energy management area, and the energy management terminal is configured at important buildings and important equipment in the first energy management area;
the edge server immediately collects the energy management related data of the first energy management area;
the edge server analyzes the energy management related data to optimize control of each energy main body, and is connected with the cloud server to coordinate energy control and scheduling in the first energy management area.
9. The 5G-based smart city energy management method of claim 8, wherein the cloud server determines a first energy management scheme, a second energy management scheme, and a third energy management scheme from the city BIM model and the historical energy data, comprising:
after cleaning, labeling and clustering analysis are carried out on the historical energy data, marking is carried out on the historical energy data according to time sequences, building/facility types and several dimensions of an energy main body;
Performing sequence prediction modeling and clustering by using a machine learning algorithm to obtain an energy consumption prediction model containing average energy consumption and change rules of each type of building/facility in different time periods and different external environments;
binding the energy consumption prediction model with the urban BIM model, and carrying out digital twin modeling simulation of multiple scenes by utilizing a digital twin platform preset on the cloud server;
performing energy management simulation on the digital twin platform, obtaining simulation energy consumption results under different scenes, different management strategies and different control rules, and analyzing according to the simulation energy consumption results to obtain an optimal management strategy;
and decomposing the optimal management strategy into an end-side decision rule, an edge control strategy and a cloud platform-level scheduling rule according to the influence range of the optimal management strategy and the difference of action objects to respectively obtain the first energy management scheme, the second energy management scheme and the third energy management scheme.
10. The 5G-based smart city energy management method of claim 9, wherein the cloud server determines a first current energy management scheme to manage energy for the first energy management area based on the energy management scheme correction data, the first energy management scheme, the second energy management scheme, and the third energy management scheme, comprising:
The cloud server determines a plurality of energy management correction schemes according to the energy management scheme correction data, the first energy management scheme, the second energy management scheme and the third energy management scheme;
respectively running each energy management correction scheme on the digital twin platform, and comparing the final effects of a plurality of energy management correction schemes to obtain a first comparison result;
the cloud server comprehensively considers the first comparison result, the optimization space and the improvement effect of each energy management correction scheme, and determines the first current energy management scheme;
and sending the first current energy management scheme to the edge server and the energy management terminal corresponding to the first energy management area so as to manage energy.
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