CN117314684A - Distributed computing network intelligent scheduling system based on green energy - Google Patents
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Abstract
The invention discloses a distributed computing network intelligent scheduling system based on green energy, and relates to the technical field of green energy scheduling. The system comprises: a data acquisition module for acquiring user data including user location data, energy requirements, and data related to the requirements; and the data acquisition module is also used for acquiring provider data, wherein the provider data comprises production, storage and transmission data of the green energy source. The invention can collect the user demands on line, and formulate the energy scheduling scheme meeting the demands on line, has high feedback speed, can effectively reduce the risk of insufficient energy storage in the actual scheduling process, can collect the specific demands of the user, and can pertinently output the energy scheduling scheme according to the specific demands, thereby realizing more complex energy scheduling and more humanizedly meeting the user demands.
Description
Technical Field
The invention relates to the technical field of green energy scheduling, in particular to a distributed computing network intelligent scheduling system based on green energy.
Background
The green energy (green energy) generally refers to clean energy, and refers to energy which does not discharge pollutants and can be directly used for production and living. The economic system for green low-carbon cyclic development and the clean low-carbon safe and efficient energy system are comprehensively established in 2060 years, the consumption proportion of non-fossil energy reaches more than 80%, wind energy, solar energy, biomass energy, ocean energy, geothermal energy and the like are greatly developed, the consumption proportion of non-fossil energy is continuously improved, centralized and distributed combination is maintained, wind energy and solar energy are preferentially promoted to be developed and utilized in situ, water energy is developed according to local conditions, nuclear power is actively, safely and orderly developed, biomass energy is reasonably utilized, a novel power system taking new energy as a main body is constructed, and the consumption and regulation capacity of a power grid on renewable energy with high proportion are improved.
Because of the development and utilization of distributed and centralized wind energy and solar energy, current green energy suppliers are gradually increased, green energy functions are gradually increased in proportion, and in order to better respond to policies to protect the environment, users in many industries also gradually convert electricity demands into green energy. Taking 2022 as an example, the electricity consumption of the whole society of the first quarter reaches 13214 hundred million kilowatt-hours, the electricity consumption of urban and rural residents is 3417 hundred million kilowatt-hours, the electricity generation of the same year non-fossil energy source reaches 36.2%. In order to better meet the requirements of social users on green energy, green energy suppliers need to schedule photoelectricity or wind power, and trade through modes of an electric power market, a distributed power generation market and the like. The electric power market transaction is centralized and is mastered with more resources, but the current green power transaction is not popular because of larger distribution area of the required users, and the users are difficult to directly and quickly conduct the green power transaction. The distributed power generation marketization transaction is also called as 'partition wall electricity selling', can be carried out nearby with power users in the same power distribution network through a power transaction platform, has lower transmission loss, but has unstable energy supply of green electricity and inconvenient supply and demand information intercommunication of the two parties, so that the users cannot fully trust the distributed power generation marketization transaction, and high-efficiency green electricity transaction is realized. And after green electricity is traded by many users, the possibility of short-term large-scale electricity utilization or explosive electricity utilization exists, and when the situation is met, green electricity suppliers and green electricity suppliers are difficult to timely respond under the condition that information is poor, so that the situation of insufficient productivity occurs, and the users are further out of trust in the distributed power generation marketized trading.
Disclosure of Invention
The invention aims to provide a distributed computing network intelligent scheduling system based on green energy sources, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the utility model provides a distributed computing network intelligent scheduling system based on green energy which characterized in that includes:
a data acquisition module for acquiring user data including user location data, energy requirements, and data related to the requirements;
the data acquisition module is also used for acquiring provider data, wherein the provider data comprises production, storage and transmission data of green energy;
the data processing module is used for analyzing the energy demand of the user through the distributed computing network according to the position data, the energy demand and related data of the user and the production, storage and transmission data of the energy, and outputting an energy scheduling prefabrication scheme;
the data processing module is also used for acquiring the specific requirements of the user, adding the specific requirements of the user into the output energy scheduling prefabrication scheme, carrying out simulation analysis on the specific requirements of the user through the distributed computing network, and outputting the energy scheduling prefabrication scheme with the specific requirements;
the scheme processing module is used for carrying out energy scheduling according to the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements after a user confirms the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements and obtains confirmation feedback.
Preferably, the data acquisition module specifically includes:
acquiring position data of a user, wherein the position data is position data of an energy scheduling destination point;
acquiring energy requirements of users, wherein the energy requirements are scheduling requirements for green energy;
and acquiring data related to the demands of the user, wherein the data related to the demands are historical data of energy consumption of the user, and the historical data of the energy consumption of the user comprise total energy consumption in unit time, average energy consumption in unit time and specific energy consumption in unit time.
Preferably, the data acquisition module is further configured to predict, through a distributed computing network, an energy consumption situation of the user within a certain time in the future according to historical data of energy consumption of the user after acquiring the user data;
and obtaining a prediction result and feeding back the prediction result to the user.
Preferably, the specific requirement includes a fixed requirement for a fixed time, a fixed requirement for a non-fixed time, and a non-fixed requirement for a fixed time.
Preferably, the data acquisition module specifically includes, when acquiring the provider data:
acquiring green energy production data of an energy provider, wherein the green energy production data comprises current yield data and future predicted yield data of green energy;
acquiring green energy storage data of an energy supplier, wherein the green energy storage data comprises the total energy storage amount of the green energy;
and acquiring green energy transmission data of an energy provider, wherein the green energy transmission data comprises energy transmission route information and transmission equipment information.
Preferably, the data processing module specifically includes, when outputting the energy scheduling prefabrication scheme:
loading historical data of the energy consumption of the user, and analyzing the energy consumption habit of the user through a distributed computing network;
loading position data of a user;
loading energy production data and energy storage data of an energy supplier;
according to the energy transmission data and the user position data, analyzing the energy transmission loss through a distributed computing network;
loading current energy demand data of a user;
analyzing through a distributed computing network, judging whether an energy supplier can meet the current requirement of a user on energy, and if so, outputting an energy scheduling prefabrication scheme; if not, feeding back the user, and collecting the current new requirement of the user for energy, or jumping to the next user.
Preferably, the data acquisition module specifically includes, when outputting an energy scheduling prefabrication scheme with specific requirements:
whether the user has specific requirements or not is confirmed, if yes, an energy scheduling prefabrication scheme is loaded, and the specific requirements of the user are loaded;
analyzing whether the energy supplier can meet the specific requirements of the user through a distributed computing network, and if so, outputting an energy scheduling prefabrication scheme with the specific requirements; if not, feeding back the energy supplier, and waiting for a feedback result;
wherein, feedback is carried out to the energy supplier, and after waiting for the feedback result, the method further comprises the following steps:
checking whether the feedback result of the energy supplier can meet the specific requirement of the user again, and if the feedback result is not met, directly feeding back the user;
if the energy scheduling prefabrication scheme can be met, the energy suppliers wait for uploading the production, storage and transmission data of the energy again, and re-output the energy scheduling prefabrication scheme according to the re-uploaded energy production, storage and transmission data, and then analyze and output the energy scheduling prefabrication scheme with specific requirements through a distributed computing network according to the new energy scheduling prefabrication scheme.
Compared with the prior art, the invention has the beneficial effects that:
firstly, energy demand of a user and energy consumption historical data thereof are obtained, then future energy consumption conditions of the user are predicted firstly according to the energy consumption historical data of the user for reference of the user, the user can adjust the energy demand according to the prediction result, the energy demand of the user is confirmed more accurately, then the energy demand of the user is analyzed according to the energy demand of the user and energy production, storage and transmission data of an energy supplier are combined, whether the energy demand of the user can be met or not is analyzed through a distributed computing network, if the energy demand meets the energy demand, an energy dispatching prefabrication scheme meeting the user demand is output, if the user also has additional specific demand, the capacity or reserve of the energy supplier can meet the specific demand of the user continuously on the basis of the energy dispatching prefabrication scheme, if the energy demand meets the energy demand, otherwise, the energy supplier is fed back, the energy supplier is tried again after data is updated, the energy demand is collected on line, the energy demand of the user can be collected on line, the feedback speed is high, the risk of energy shortage in the actual dispatching process can be effectively reduced, meanwhile, the energy demand of the user can be met, the energy demand can be more easily controlled by the user is also can be met, the energy demand of the user can be more easily, the energy demand of the user can be more environment-friendly energy supply can be met, and the user can meet the energy demand, and the actual demand can be more can meet the requirements, and the requirements of the energy demand can be more green, and the energy supply requirements can meet the requirements, and the requirements can meet the requirements, and green requirements of the requirements can meet the requirements, and green requirements.
Drawings
FIG. 1 is a main flow chart of a distributed computing network intelligent scheduling system based on green energy provided by the embodiment of the invention;
fig. 2 is a flowchart of user data acquisition when a distributed computing network intelligent scheduling system based on green energy provided in an embodiment of the present invention is implemented;
fig. 3 is a flowchart of energy scheduling prefabrication scheme formulation when the distributed computing network intelligent scheduling system based on green energy provided by the embodiment of the invention is implemented;
fig. 4 is a flowchart of a specific demand energy scheduling prefabrication scheme formulation when the distributed computing network intelligent scheduling system based on green energy is implemented, provided by the embodiment of the invention;
fig. 5 is a block diagram of a distributed computing network intelligent scheduling system based on green energy according to an embodiment of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
The main execution body of the method in this embodiment is a terminal, and the terminal may be a device such as a mobile phone, a tablet computer, a PDA, a notebook or a desktop, but of course, may be another device with a similar function, and this embodiment is not limited thereto.
Referring to fig. 1, the invention provides a distributed computing network intelligent scheduling method based on green energy, which is applied to intelligent scheduling scheme self-adaptive prefabrication of the green energy, and comprises the following steps:
step S100, obtaining position data, energy requirements and data related to the requirements of the user.
It can be understood that the destination where green energy needs to be conveyed can be known by acquiring the position data of the user, namely the position data of the energy scheduling destination point, and then the green line and the distance of green energy conveyance are known, so that preparation is made for subsequent energy conveyance consumption analysis, then the demand of the green energy can be known by acquiring the energy demand of the user, and the demand of the user can be compared with the supply of a supplier for analysis, so as to check whether the demand can be met or not, and prepare for a subsequent scheduling scheme.
Referring to fig. 2, the step S100 further includes the following steps:
step S110, obtaining position data of a user, wherein the position data is position data of an energy scheduling destination point;
step S120, obtaining the energy requirement of a user, wherein the energy requirement is the scheduling requirement for green energy;
step S130, obtaining data related to requirements of a user, wherein the data related to requirements are historical data of energy consumption of the user, and the historical data of the energy consumption of the user comprise total energy consumption in unit time, average energy consumption in unit time and specific energy consumption in unit time;
step S140, according to historical data of the energy consumption of the user, predicting the energy consumption condition of the user in a certain time in the future through a distributed computing network;
step S150, obtaining a prediction result and feeding back the prediction result to a user.
In this embodiment, in step S140, the energy consumption situation of the user in a certain time in the future is predicted through the distributed computing network according to the historical data of the energy consumption of the user, that is, the future energy consumption situation of the user can be predicted approximately according to the past energy consumption of the user, the prediction result is fed back to the user, a certain reference quantity is provided, a reference suggestion is provided for the energy demand data of the user, and the subsequent adjustment of the energy demand or the numerical value of the specific demand is facilitated.
Specifically, the prediction of the energy consumption condition of the user in a certain time in the future through the distributed computing network is performed by performing energy consumption prediction analysis through a support vector machine regression (SVR) model by python.
Step S200, obtaining production, storage and transmission data of green energy.
Specifically, the step S200 includes:
acquiring green energy production data of an energy provider, wherein the green energy production data comprises current yield data and future predicted yield data of green energy;
acquiring green energy storage data of an energy supplier, wherein the green energy storage data comprises the total energy storage amount of the green energy;
and acquiring green energy transmission data of an energy provider, wherein the green energy transmission data comprises energy transmission route information and transmission equipment information.
Optionally, the transmission route includes a wire medium and a distance, and the transmission device is a transformer.
It can be understood that the output and the storage amount of the green energy can be known by acquiring the green energy production and storage data of the energy supplier, so as to analyze whether the requirements of users can be met.
And step S300, analyzing the energy demand of the user through a distributed computing network according to the position data, the energy demand and related data of the user and the production, storage and transmission data of the energy, and outputting an energy scheduling prefabrication scheme.
Specifically, referring to fig. 3, the step S300 includes the following steps:
step S310, loading historical data of the energy consumption of the user, and analyzing the energy consumption habit of the user through a distributed computing network;
step S320, loading the position data of the user;
step S330, loading energy production data and energy storage data of the energy supplier;
step S340, analyzing the energy transmission loss through a distributed computing network according to the energy transmission data and the user position data;
step S350, loading the current energy demand data of the user;
step S360, analyzing through a distributed computing network, whether an energy provider can meet the current requirement of a user on energy, if so, jumping to step S380, otherwise, continuing to execute step S370;
step S370, feeding back the user and re-acquiring a new specific demand application of the user, if the application is acquired, returning to step S360, re-executing the analysis step, otherwise, ending;
and step S380, outputting an energy scheduling prefabrication scheme.
The green energy transmission wire information and the transmission equipment passing through the transmission route can be analyzed by acquiring green energy transmission data of an energy supplier and combining with position data of a user, and the overall loss condition of the green energy supply transmission route is deduced by analyzing wire resistance and transformer loss.
Specifically, the wire resistance is an energy loss due to the resistance of the wire, the wire resistance loss is related to the wire resistance, the current and the wire length, and the wire resistance loss is calculated as follows:
；
wherein Pc is wire resistance loss, and the unit is watt (W); i is current, the unit is ampere (A), R is wire resistance, the unit is ohm (omega);
the transformer loss comprises transformer copper loss and transformer iron loss, the transformer copper loss is energy loss generated by the resistance of a transformer winding, the transformer copper loss is related to the resistance of the transformer winding, the current and the number of windings, and the transformer copper loss is calculated according to the following formula:
；
wherein,the transformer copper loss is expressed in watt (W), the I is current, and the unit is ampere (A),>the resistor is the resistance of the transformer winding, the unit is ohm (omega), and N is the number of windings;
the transformer iron loss is energy loss caused by magnetization and magnetic rotation of a transformer iron core, the transformer iron loss is related to the load factor of a transformer and the rated power of the transformer, and the transformer iron loss calculation formula is as follows:
；
wherein,the unit is watt (W) of iron loss of the transformer, K is the iron loss constant of the transformer, and is related to the design of the transformer, S is the load factor of the transformer, namely the ratio of actual output power to rated power; />The middle is the magnetic flux density of the transformer;
the total loss is as follows:
；
wherein,the total power consumption is expressed in watts (W);
therefore, by combining the green energy transmission data and the calculation of the formula, the total loss generated when the green energy is transmitted to the green energy scheduling destination point position of the user can be known, and the total amount of the green energy required to be output by the energy supplier can be analyzed by combining the green energy demand of the user; if the energy provider can meet and output the total amount of green energy, an energy scheduling prefabrication scheme can be manufactured according to the current requirements.
It should be noted that there may be more than one energy supply route, so when the distributed computing network analyzes the energy transmission loss, multiple selection calculation can be performed on the route, and the route with the lowest loss is comprehensively adopted for transmission, and meanwhile, the energy scheduling prefabrication scheme is also formulated according to the route.
Step S400, obtaining the specific requirements of the user, adding the specific requirements of the user into the output energy scheduling prefabrication scheme, carrying out simulation analysis on the specific requirements of the user through a distributed computing network, and outputting the energy scheduling prefabrication scheme with the specific requirements.
Further, the specific requirements include a fixed requirement in a fixed time, a fixed requirement in a non-fixed time, and a non-fixed requirement in a fixed time.
That is, the user may have a specific energy demand at a specific time, and it is also possible that the user may only be able to determine one of the specific time and the specific energy demand, for example: the building A of the user can perform large-scale light performance in the second week of each month in the last half year 2024, the power consumption is estimated to be three times of the power consumption in normal times when performing large-scale light performance, and the power consumption per week is about 2000kW in normal times, so that the specific requirement can be preset according to the actual condition: 2024.1-2024.6, and the green energy supply is 6000kW or more in the second week of each month.
Specifically, referring to fig. 4, the step S400 further includes the following steps:
step S410, confirm whether users have specific demands, if not, finish, otherwise, continue to carry out step S420;
step S420, loading an energy scheduling prefabrication scheme and loading specific requirements of users;
step S430, analyzing through the distributed computing network, if the energy provider can meet the specific requirement of the user, if so, jumping to step S470, otherwise, continuing to execute step S440;
step S440, feeding back the energy supplier, if the energy supplier can meet the specific requirement of the user again, if not, ending, otherwise, continuing to execute step S450;
step S450, waiting for the energy supplier to upload the production, storage and transmission data of the energy again;
step S460, re-outputting the energy scheduling prefabrication scheme according to the production, storage and transmission data of the re-uploaded energy, and returning to step S420;
step S470, outputting the energy scheduling prefabrication scheme with the specific requirements.
That is, it is first required to confirm whether the user has a specific requirement, and then analyze the specific requirement according to the user to confirm whether the energy provider can meet the requirement, for example, the specific requirement of the user is preset as follows: 2024.1-2024.6, the second week of each month is more than 6000kW of green energy supply, at this time, it is required to analyze according to the data uploaded by the energy supplier, confirm that the currently loaded energy production and storage data meets the energy demand of the user base, and sufficient surplus can meet the specific demand of the user, or can meet the energy demand from the energy production data, or can meet the energy storage data, then generate and output an energy scheduling prefabrication scheme according to the specific demand, if the energy demand cannot be met, feed back the specific demand to the energy supplier, wait for the energy supplier to upload new energy production and storage data again, and re-execute step S136 and step S138 again, modify the original energy scheduling prefabrication scheme, analyze again through a distributed computing network on this basis, see whether the specific demand can be met or not, and if the energy demand can be met, on the basis of the original energy scheduling prefabrication scheme, pertinently adjust the energy scheduling prefabrication scheme according to the specific demand of the user, and output the energy scheduling prefabrication scheme with the specific demand.
Step S500, the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements is confirmed for the user, and after confirmation feedback is obtained, energy scheduling is carried out according to the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements.
Specifically, no matter the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements is formulated, the energy scheduling prefabrication scheme is output to a user preferentially, the user is waited for to feed back, and if the user feeds back and confirms the scheme, green energy scheduling can be performed according to the scheme subsequently.
In this embodiment, firstly, the energy demand of the user and the energy consumption history data thereof are obtained, then, the future energy consumption condition is predicted according to the energy consumption history data of the user for reference by the user, the user can adjust the energy demand according to the prediction result, more accurately confirms the own energy demand, then, the energy demand of the user is collected on line according to the energy demand of the user and the energy production, storage and transmission data of the energy supplier are combined, whether the energy demand of the user can be met or not is analyzed through a distributed computing network, if the energy demand is met, an energy dispatching prefabrication scheme meeting the user demand is output, if the user has additional specific demand, the energy capacity or reserve energy of the energy supplier can meet the specific demand of the user on the basis of the energy dispatching prefabrication scheme, otherwise, the energy supplier is fed back, the energy supplier is tried again after the data is updated, the energy demand of the user can be collected on line by the method, the energy dispatching scheme meeting the demand of the user on line is met, the feedback speed is high, the energy demand is effectively reduced, meanwhile, the energy demand of the user can be met, the energy demand of the user is not met, the actual energy demand is more easily can be met, the energy demand of the user is more environment-friendly demand is met, the user can be met, the energy demand of the user can be more easily is better, and the energy demand of the user can be met, and the energy demand can be more has better energy demand can meet the energy demand, and can meet the requirements, and better requirements, and can meet the requirements of the energy demand of the user, and can meet the requirements.
On the basis of the above embodiment, as shown in fig. 5, the present invention further provides a distributed computing network intelligent scheduling system based on green energy, which is configured to support the distributed computing network intelligent scheduling method based on green energy of the above embodiment, where the distributed computing network intelligent scheduling system based on green energy includes:
the data acquisition module 10 is used for acquiring the position data, the energy requirement and the data related to the requirement of the user, acquiring the production, storage and transmission data of the green energy and acquiring the specific requirement of the user.
The data processing module 20 is specifically configured to obtain location data of a user, where the location data is location data of an energy scheduling destination point; acquiring energy requirements of users, wherein the energy requirements are scheduling requirements for green energy; acquiring data related to demands of a user, wherein the data related to the demands is historical data of energy consumption of the user, and the historical data of the energy consumption of the user comprises total energy consumption in unit time, average energy consumption in unit time and specific energy consumption in unit time; acquiring green energy production data of an energy provider, wherein the green energy production data comprises current yield data and future predicted yield data of green energy; acquiring green energy storage data of an energy supplier, wherein the green energy storage data comprises the total energy storage amount of the green energy; and acquiring green energy transmission data of an energy provider, wherein the green energy transmission data comprises energy transmission route information and transmission equipment information.
The data processing module 20 is configured to analyze the energy demand of the user through the distributed computing network according to the location data, the energy demand and related data of the user, and the production, storage and transmission data of the energy, and output an energy scheduling prefabrication scheme.
Specifically, the data processing module 20 is configured to predict, according to historical data of energy consumption of the user, energy consumption conditions of the user within a certain time in the future through a distributed computing network; obtaining a prediction result and feeding back the prediction result to a user; the data processing module 20 is further configured to load historical data of energy consumption of the user, and analyze energy consumption habits of the user through the distributed computing network; loading position data of a user; loading energy production data and energy storage data of an energy supplier; according to the energy transmission data and the user position data, analyzing the energy transmission loss through a distributed computing network; loading current energy demand data of a user; analyzing through a distributed computing network, judging whether an energy supplier can meet the current requirement of a user on energy, and if so, outputting an energy scheduling prefabrication scheme; if not, feeding back the user, and collecting the current new requirement of the user for energy, or jumping to the next user.
The data processing module 20 is further configured to add the specific requirement of the user to the output energy scheduling prefabrication scheme, perform simulation analysis on the specific requirement of the user through the distributed computing network, and output the energy scheduling prefabrication scheme with the specific requirement.
Specifically, the data processing module 20 is further configured to confirm whether the user has a specific requirement, and if so, load an energy scheduling prefabrication scheme and load the specific requirement of the user; analyzing whether the energy supplier can meet the specific requirements of the user through a distributed computing network, and if so, outputting an energy scheduling prefabrication scheme with the specific requirements; if not, feeding back the energy supplier, and waiting for a feedback result;
then, checking whether the feedback result of the energy supplier can meet the specific requirement of the user again, and if the feedback result is unsatisfied, directly feeding back the user; if the energy scheduling prefabrication scheme can be met, the energy suppliers wait for uploading the production, storage and transmission data of the energy again, and re-output the energy scheduling prefabrication scheme according to the re-uploaded energy production, storage and transmission data, and then analyze and output the energy scheduling prefabrication scheme with specific requirements through a distributed computing network according to the new energy scheduling prefabrication scheme.
The data processing module 20 is further configured to predict, according to historical data of the energy consumption of the user, the energy consumption situation of the user within a certain time in the future through the distributed computing network, obtain a prediction result, and feed back the prediction result to the user.
The scheme processing module 30 is configured to perform energy scheduling according to the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements after the user confirms the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements and obtains confirmation feedback.
In this embodiment, firstly, the energy demand of the user and the energy consumption history data thereof are obtained, then, the future energy consumption condition is predicted according to the energy consumption history data of the user for reference by the user, the user can adjust the energy demand according to the prediction result, more accurately confirms the own energy demand, then, the energy demand of the user is collected on line according to the energy demand of the user and the energy production, storage and transmission data of the energy supplier are combined, whether the energy demand of the user can be met or not is analyzed through a distributed computing network, if the energy demand is met, an energy dispatching prefabrication scheme meeting the user demand is output, if the user has additional specific demand, the energy capacity or reserve energy of the energy supplier can meet the specific demand of the user on the basis of the energy dispatching prefabrication scheme, otherwise, the energy supplier is fed back, the energy supplier is tried again after the data is updated, the energy demand of the user can be collected on line by the method, the energy dispatching scheme meeting the demand of the user on line is met, the feedback speed is high, the energy demand is effectively reduced, meanwhile, the energy demand of the user can be met, the energy demand of the user is not met, the actual energy demand is more easily can be met, the energy demand of the user is more environment-friendly demand is met, the user can be met, the energy demand of the user can be more easily is better, and the energy demand of the user can be met, and the energy demand can be more has better energy demand can meet the energy demand, and can meet the requirements, and better requirements, and can meet the requirements of the energy demand of the user, and can meet the requirements.
Further, the distributed computing network intelligent scheduling system based on the green energy source can operate the distributed computing network intelligent scheduling method based on the green energy source, and specific implementation can be seen in method embodiments, which are not described herein.
On the basis of the embodiment, the invention further provides electronic equipment, which comprises:
the device comprises a processor and a memory, wherein the processor is in communication connection with the memory;
in this embodiment, the memory may be implemented in any suitable manner, for example: the memory can be read-only memory, mechanical hard disk, solid state disk or U disk, etc.; the memory is used for storing executable instructions executed by at least one of the processors;
in this embodiment, the processor may be implemented in any suitable manner, e.g., the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an application specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), a programmable logic controller, and an embedded microcontroller, etc.; the processor is used for executing the executable instructions to realize the intelligent dispatching method of the green energy-based distributed computing network.
On the basis of the embodiment, the invention further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the intelligent dispatching method of the distributed computing network based on the green energy when being executed by a processor.
Those of ordinary skill in the art will appreciate that the various illustrative modules and method steps described in connection with the embodiments disclosed herein are capable of being implemented as electronic hardware, or as a combination of computer software and electronic hardware, whether or not the functions are performed in hardware or software, depending on the particular application and design constraints of the solution, that a person skilled in the art can use different methods to implement the described functions for each particular application, but such implementation should not be considered to be beyond the scope of the present invention.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and module described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus, system and method may be implemented in other manners, for example, the apparatus embodiments described above are merely illustrative, for example, the modules are divided into only one logic function, and there may be other manners of dividing actually being implemented, for example, a plurality of modules or units may be combined or may be integrated into another apparatus, or some features may be omitted or not performed, and another point, the coupling or direct coupling or communication connection between each other that is shown or discussed may be through some interfaces, indirect coupling or communication connection of apparatuses or devices may be in electrical, mechanical or other forms.
The modules described as separate components may or may not be physically separate, and components displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units, and some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
Said functions, if implemented in the form of software functional modules and sold or used as separate products, may be stored in a computer readable storage medium, based on which the technical solution of the present invention is essentially or partly contributing to the prior art or part of the technical solution may be embodied in the form of a software product stored in a storage medium comprising instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention, whereas the aforementioned storage medium comprises: a usb disk, a removable hard disk, a read-only memory server, a random access memory server, a magnetic disk or an optical disk, or other various media capable of storing program instructions.
In addition, it should be noted that the combination of the technical features described in the present invention is not limited to the combination described in the claims or the combination described in the specific embodiments, and all the technical features described in the present invention may be freely combined or combined in any manner unless contradiction occurs between them.
It should be noted that the above-mentioned embodiments are merely specific embodiments of the present invention, and it is obvious that the present invention is not limited to the above-mentioned embodiments, and that many similar variations are possible therewith, and all modifications directly derived or suggested from the disclosure of the present invention should be included in the scope of the present invention by those skilled in the art.
The foregoing description of the preferred embodiments of the invention is not intended to limit the scope of the invention, but rather to cover any modifications, equivalents, improvements or the like within the spirit and scope of the present invention.
Claims (7)
1. The utility model provides a distributed computing network intelligent scheduling system based on green energy which characterized in that includes:
a data acquisition module for acquiring user data including user location data, energy requirements, and data related to the requirements;
the data acquisition module is also used for acquiring provider data, wherein the provider data comprises production, storage and transmission data of green energy;
the data processing module is used for analyzing the energy demand of the user through the distributed computing network according to the position data, the energy demand and related data of the user and the production, storage and transmission data of the energy, and outputting an energy scheduling prefabrication scheme;
the data processing module is also used for acquiring the specific requirements of the user, adding the specific requirements of the user into the output energy scheduling prefabrication scheme, carrying out simulation analysis on the specific requirements of the user through the distributed computing network, and outputting the energy scheduling prefabrication scheme with the specific requirements;
the scheme processing module is used for carrying out energy scheduling according to the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements after a user confirms the energy scheduling prefabrication scheme or the energy scheduling prefabrication scheme with specific requirements and obtains confirmation feedback.
2. The intelligent scheduling system for green energy-based distributed computing network according to claim 1, wherein the data acquisition module, when acquiring user data, specifically comprises:
acquiring position data of a user, wherein the position data is position data of an energy scheduling destination point;
acquiring energy requirements of users, wherein the energy requirements are scheduling requirements for green energy;
and acquiring data related to the demands of the user, wherein the data related to the demands are historical data of energy consumption of the user, and the historical data of the energy consumption of the user comprise total energy consumption in unit time, average energy consumption in unit time and specific energy consumption in unit time.
3. The intelligent dispatching system of the distributed computing network based on the green energy according to claim 2, wherein the data acquisition module is further used for predicting the energy consumption condition of the user in a certain time in the future through the distributed computing network according to the historical data of the energy consumption of the user after acquiring the user data;
and obtaining a prediction result and feeding back the prediction result to the user.
4. The green energy based distributed computing network intelligent scheduling system of claim 1, wherein the specific demand comprises a fixed demand for a fixed time, a fixed demand for a non-fixed time, and a non-fixed demand for a fixed time.
5. The intelligent green energy-based dispatching system for a distributed computing network according to claim 1, wherein the data acquisition module, when acquiring the provider data, specifically comprises:
acquiring green energy production data of an energy provider, wherein the green energy production data comprises current yield data and future predicted yield data of green energy;
acquiring green energy storage data of an energy supplier, wherein the green energy storage data comprises the total energy storage amount of the green energy;
and acquiring green energy transmission data of an energy provider, wherein the green energy transmission data comprises energy transmission route information and transmission equipment information.
6. The intelligent dispatching system of the green energy-based distributed computing network according to claim 1, wherein the data processing module specifically comprises, when outputting an energy dispatching prefabrication scheme:
loading historical data of the energy consumption of the user, and analyzing the energy consumption habit of the user through a distributed computing network;
loading position data of a user;
loading energy production data and energy storage data of an energy supplier;
according to the energy transmission data and the user position data, analyzing the energy transmission loss through a distributed computing network;
loading current energy demand data of a user;
analyzing through a distributed computing network, judging whether an energy supplier can meet the current requirement of a user on energy, and if so, outputting an energy scheduling prefabrication scheme; if not, feeding back the user, and collecting the current new requirement of the user for energy, or jumping to the next user.
7. The intelligent scheduling system for green energy-based distributed computing network according to claim 1, wherein the data acquisition module, when outputting an energy scheduling prefabrication scheme with specific requirements, specifically comprises:
whether the user has specific requirements or not is confirmed, if yes, an energy scheduling prefabrication scheme is loaded, and the specific requirements of the user are loaded;
analyzing whether the energy supplier can meet the specific requirements of the user through a distributed computing network, and if so, outputting an energy scheduling prefabrication scheme with the specific requirements; if not, feeding back the energy supplier, and waiting for a feedback result;
wherein, feedback is carried out to the energy supplier, and after waiting for the feedback result, the method further comprises the following steps:
checking whether the feedback result of the energy supplier can meet the specific requirement of the user again, and if the feedback result is not met, directly feeding back the user;
if the energy scheduling prefabrication scheme can be met, the energy suppliers wait for uploading the production, storage and transmission data of the energy again, and re-output the energy scheduling prefabrication scheme according to the re-uploaded energy production, storage and transmission data, and then analyze and output the energy scheduling prefabrication scheme with specific requirements through a distributed computing network according to the new energy scheduling prefabrication scheme.
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