CN114677069A - Energy internet scheduling method based on big data - Google Patents
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Abstract
The invention relates to the technical field of energy scheduling, in particular to an energy internet scheduling method based on big data, which comprises a central control module, an acquisition module, a preprocessing module, a storage module, an analysis and modeling module, a man-machine interaction module and an energy transportation module, wherein the central control module also comprises rated data storage, energy transportation scheduling quantity analysis and rated range data, the central control module directly controls the energy transportation module, the man-machine interaction module can change and store the rated data, the man-machine interaction module can change the rated data range of the central control module, the preprocessing module and the analysis and modeling module feed back data to each other, and the energy scheduling can be integrally controlled by arranging the central control module, the user can allocate the energy needed by different departments conveniently.
Description
Technical Field
The invention relates to the technical field of energy scheduling, in particular to an energy internet scheduling method based on big data.
Background
The energy scheduling is to ensure that the energy of a company can be reasonably distributed, so that the energy of the company can be reasonably utilized on each post, the energy of the company can be stably output, a high energy consumption department and a low energy consumption department can stably perform production work, the production efficiency in the company can be improved by reasonably distributing the energy, and the production time is saved.
The problems caused are as follows:
traditional energy allocation work is often controlled by workers to each valve of the traditional energy allocation work, the flow rate and the output quantity of energy to each department are controlled, the traditional energy allocation work cannot effectively and reasonably conduct the energy, and the generation of energy surplus or energy shortage can be caused.
Traditional energy source allotment often controls through a plurality of valves or a plurality of controllers, can not realize integrated control to it.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an energy internet scheduling method based on big data, so as to solve the problems in the background art.
The invention provides the following technical scheme: the central control module also comprises rated data storage, energy transportation scheduling quantity analysis and rated range data, the central control module directly controls the energy transportation module, the human-computer interaction module can change and store the rated data of the human-computer interaction module, the human-computer interaction module can change the rated data range of the central control module, and the energy scheduling can be controlled in a centralized manner by arranging the central control module, so that a user can conveniently allocate energy required by different departments.
Furthermore, the rated data storage comprises rated acquisition time and rated acquisition quantity, the preprocessing module comprises a unique value removing module, a missing number supplementing module and output information, the data analyzing and modeling module comprises data comparison, model building, model output and feedback preprocessing, the preprocessing module and the analyzing and modeling module feed back data mutually, each group of information can be preprocessed through the preprocessing module, internal useless information is removed, and some incomplete information is supplemented.
Furthermore, the human-computer interaction module comprises data display comparison, automatic allocation, command output to the controller, manual allocation and command output to the controller, the energy transportation module executes the command by receiving the command, automatically allocates energy, manually controls the command and allocates the command, the human-computer interaction module can compare the original data range with the range obtained by selecting data, the human-computer interaction module can manually control the energy transportation module, and can carry out intelligent scheduling and manual scheduling on the scheduling of the energy transportation module through the human-computer interaction module, so that more choices are provided for users, and troubles caused by emergency scheduling reasons are prevented for the users.
Further, the storage module backs up the information of the storage module, the user can extract different groups of data stored by the storage module in different time periods through the human-computer interaction module, original data of the user can be compared and judged through the analysis and modeling module, the collected data can be effectively stored, and data are prevented from being lost.
Furthermore, the energy transportation module can analyze and execute the instruction sent by the controller to automatically allocate energy, the energy transportation module can execute the manual instruction to allocate energy according to the requirement of a user, and the energy can be effectively allocated through the energy transportation module to prevent the energy from being difficult to allocate.
Furthermore, the acquisition module can acquire data according to a rated numerical value set by a user of the acquisition module, and acquisition of the data can be controlled through the data acquisition module.
Further, the algorithm of the analysis and modeling module for comparing data is as follows
using System;
using System.Collections.Generic;
namespace CsdnTest
{
class Program
{
static void Main(string[] args)
{
string sourceStr = "nominal data";
string targetStr = "selected data";
string[] source = sourceStr.Split(' ');
string[] target = targetStr.Split(' ');
Array.Sort(source);
Array.Sort(target)
List<string> diff = new List<string>();
List<string> same = new List<string>();
int i = 0, j = 0;
while (i < source.Length & j < target.Length)
{
int comp = source[i].CompareTo(target[j]);
if (comp == 0)
{
same.Add(source[i]);
i++;
j++;
}
else if (comp > 0)
{
diff.Add(target[j]);
j++;
}
else
{
diff.Add(source[i]);
i++;
}
}
for(int k = i; k < source.Length;k++)
diff.Add(source[k]);
for (int k = j; k < target.Length; k++)
diff.Add(target[k]);
}
}
through the algorithm, the analysis modeling module can accurately compare the data, and the trouble brought to a user by data disorder is prevented.
Further, an energy internet scheduling method based on big data comprises the following steps:
s1, firstly, storing the acquired duration and the acquired number into a rated data storage in the central controller through the man-machine interaction module;
s2, the central controller transmits the rated acquisition time and the rated acquisition number to the acquisition module, and the acquisition module captures and detects data according to the rated data of the central controller;
s3, the acquisition module transmits each group of acquired data to the preprocessing module, the preprocessing module judges and removes unique values in the data, missing data is supplemented by an average value method of each group of data, the supplemented whole group of data is sent to the storage module through the transmission information, and the storage module carries out segmented storage on each group of information;
s4, a user selects a certain group of data in a specified time to compare with original rated range data through a human-computer interaction module, a storage module transmits the group of data in the specified time to a data analysis and modeling module, the data analysis and modeling module compares the data with the original data to judge whether data is missing or not and whether the data contains unique data or not, if the data meets modeling requirements, a selected data model and an original data model are established and output back to the human-computer interaction module through the model, if the data does not meet the modeling requirements, information is fed back to a preprocessing module, the preprocessing module judges and supplements the data, and then the data is fed back to the data analysis and modeling to analyze and model;
s5, comparing the model of the original data and the selected data displayed by the man-machine interaction module, determining the allocation of the energy output module to the energy by the user through whether the automatic allocation is selected, if the automatic allocation is selected, outputting the compared data and the control command to the central controller, analyzing the energy transportation allocation quantity by the central controller and outputting the analyzed quantity to the energy allocation module, and if the manual allocation is selected, outputting the manual allocation command to the controller;
and S6, the energy transportation module receives the energy transportation dispatching amount analysis of the central controller and automatically distributes and dispatches the energy, and when the controller outputs the manual energy dispatching instruction to the energy control module, the controller executes the manual dispatching instruction.
Through the operation method, the user can know the data of each time period, and the user can conveniently schedule the energy.
The invention has the technical effects and advantages that:
1. the central control module is arranged, so that rated data of the central control module can be stored, energy scheduling of the central control module can be analyzed and processed by energy transportation scheduling quantity analysis, the most effective energy scheduling instruction can be sent to the energy scheduling management module, energy scheduling can be efficiently performed, and low energy scheduling efficiency caused by over-dispersion of energy scheduling valves is prevented.
2. The invention judges and removes the unique value in the data through a processing module by arranging a preprocessing module and a sub-modeling module, complements the missing data through the method of average value of each group of data, sends the complemented whole group of data to a storage module through transmission information, the storage module carries out segmented storage on each group of information, selects a certain group of data in a certain time through a human-computer interaction module to compare with the original rated range data, the storage module transmits a group of data in a certain time to a data analysis and modeling module, the data analysis and modeling module compares the data with the original data to judge whether the data is missing and whether the data contains the unique data, if the modeling requirements are met, the selected data model and the original data model are established and output to the human-computer interaction module through the model, if the modeling requirements are not met, the information is fed back to the preprocessing module, the preprocessing module judges and supplements the data, and then the data is fed back to the data analysis and modeling for analysis and modeling, the preprocessing module is favorable for judging and eliminating the unique value of the data, missing data is supplemented through the average number of each group of data, the fluctuation of the data can be objectively known by a user, one-sided observation is prevented, and the data analysis and modeling module is arranged, so that the user can accurately know the change of the data, and the scheduling decision is made.
3. According to the invention, the man-machine interaction module is arranged, the model comparison of original data and selected data is displayed through the man-machine interaction module, a user determines the allocation of the energy output module to the energy through whether automatic allocation is selected, if automatic allocation is selected, the compared data and a control command are output to the central controller, the central controller analyzes the energy transportation allocation amount and outputs the analyzed energy transportation allocation amount to the energy allocation module, if manual allocation is selected, a manual allocation instruction is output to the controller, the controller can scientifically perform scheduling work through analyzing the acquired amount through automatic allocation, and the user can perform emergency scheduling through manual scheduling, so that the problem of insufficient energy is prevented.
Drawings
Fig. 1 is a block diagram of a big data energy internet scheduling system according to the present invention.
FIG. 2 is a block diagram of the operation of the central control module of the present invention.
FIG. 3 is a block diagram of the operation of the preprocessing module of the present invention.
FIG. 4 is a block diagram of the operation of the data analysis and detection module of the present invention.
FIG. 5 is a block diagram of the operation of the human-computer interaction module of the present invention.
Fig. 6 is a block diagram of the operation of the energy transport module of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Embodiments of the application are applicable to computer systems/servers that are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the computer system/server include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.
The computer system/server may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Example 1
The invention provides an energy internet scheduling method based on big data, which comprises the following modules and steps: the energy resource management system comprises a central control module, an acquisition module, a preprocessing module, a storage module, an analysis and modeling module, a man-machine interaction module and an energy transportation module, wherein the central control module further comprises rated data storage, energy transportation scheduling quantity analysis and rated range data, the central control module directly controls the energy transportation module, the man-machine interaction module can change and store the rated data of the man-machine interaction module, and the man-machine interaction module can change the rated data range of the central control module.
The energy internet scheduling method based on big data comprises the following modules and steps, wherein the rated data storage comprises rated acquisition time and rated acquisition quantity, the preprocessing module comprises the steps of removing a unique value, supplementing a missing number value and outputting information, the data analyzing and modeling module comprises the steps of data comparison, judging whether modeling is met or not, model building, model outputting and feedback preprocessing, and the preprocessing module and the analyzing and modeling module mutually feed back data.
A big data-based energy internet scheduling method comprises the following modules and steps, wherein a human-computer interaction module comprises data display comparison, automatic allocation or not, command output to a controller, manual allocation and command output to the controller, the energy transportation module comprises a receiving command, automatic energy allocation, manual control command and allocation command execution, the human-computer interaction module can compare the original data range with the range obtained by selecting data, and the human-computer interaction module can manually control the energy transportation module.
The energy internet scheduling method based on big data is characterized in that the storage module backs up the information of the storage module, a user can extract different groups of data stored in the storage module at different time periods through a human-computer interaction module, and original data of the user can be compared and judged through an analysis and modeling module;
in this embodiment, it is specifically explained that all the data can be prevented from being lost by effectively storing the acquired data.
An energy Internet scheduling method based on big data, the energy transportation module can analyze and execute the instruction sent by the controller to automatically allocate energy, and the energy transportation module can execute the manual instruction to allocate energy according to the requirement of a user;
in this embodiment, it is specifically explained that energy can be effectively distributed to prevent energy scheduling difficulty.
According to the energy internet scheduling method based on big data, an acquisition module can acquire data of a rated numerical value set by a user of the acquisition module;
in this embodiment, the data acquisition module can control the acquisition.
The energy internet scheduling method based on big data is characterized in that an algorithm for comparing data by the analysis and modeling module is as follows
using System;
using System.Collections.Generic;
namespace CsdnTest
{
class Program
{
static void Main(string[] args)
{
string sourceStr = "nominal data";
string targetStr = "selected data";
string[] source = sourceStr.Split(' ');
string[] target = targetStr.Split(' ');
Array.Sort(source);
Array.Sort(target)
List<string> diff = new List<string>();
List<string> same = new List<string>();
int i = 0, j = 0;
while (i < source.Length & j < target.Length)
{
int comp = source[i].CompareTo(target[j]);
if (comp == 0)
{
same.Add(source[i]);
i++;
j++;
}
else if (comp > 0)
{
diff.Add(target[j]);
j++;
}
else
{
diff.Add(source[i]);
i++;
}
}
for(int k = i; k < source.Length;k++)
diff.Add(source[k]);
for (int k = j; k < target.Length; k++)
diff.Add(target[k]);
}
}
in this embodiment, the data is determined by an algorithm, and the data can be compared to prevent the problem caused by the data deviation.
An energy internet scheduling method based on big data comprises the following steps:
s1, firstly, storing the acquired duration and the acquired number into a rated data storage in the central controller through the man-machine interaction module;
in this embodiment, it is specifically stated that, first, a user stores rated information into a central processing unit through a human-computer interaction module, so that the user can perform normal processing on a problem;
s2, the central controller transmits the rated acquisition time and the rated acquisition number to the acquisition module, and the acquisition module captures and detects data according to the rated data of the central controller;
in this embodiment, it is specifically stated that the acquisition module is constrained by the rated acquisition duration and the rated acquisition quality, and the acquisition module acquires data according to the rated acquisition duration or the rated acquisition quality, so as to prevent a large difference in data values in a group of data from being caused, so that the data cannot achieve a display effect;
s3, the acquisition module transmits each group of acquired data to the preprocessing module, the preprocessing module judges and removes the unique value in the data, missing data is supplemented by the average value method of each group of data, the supplemented whole group of data is sent to the storage module through the transmission information, and the storage module carries out segmented storage on each group of information;
in this embodiment, it is specifically described that the preprocessing module removes the unique value in each group of data, and completes missing data, so as to ensure stable data quality of each group of data and ensure data accuracy;
s4, a user selects a certain group of data in a specified time to compare with original rated range data through a human-computer interaction module, a storage module transmits the group of data in the specified time to a data analysis and modeling module, the data analysis and modeling module compares the data with the original data to judge whether data is missing or not and whether the data contains unique data or not, if the data meets modeling requirements, a selected data model and an original data model are established and output back to the human-computer interaction module through the model, if the data does not meet the modeling requirements, information is fed back to a preprocessing module, the preprocessing module judges and supplements the data, and then the data is fed back to the data analysis and modeling to analyze and model;
in this embodiment, it is specifically described that the accuracy of information is ensured by comparing the original information of the information to be processed;
s5, comparing the model of the original data and the selected data displayed by the man-machine interaction module, determining the allocation of the energy output module to the energy by the user through whether the automatic allocation is selected, if the automatic allocation is selected, outputting the compared data and the control command to the central controller, analyzing the energy transportation allocation quantity by the central controller and outputting the analyzed quantity to the energy allocation module, and if the manual allocation is selected, outputting the manual allocation command to the controller;
in this embodiment, it is specifically described that manual scheduling of the mobile terminal can be automatically controlled through the human-computer interaction module, and resources of the mobile terminal can also be manually scheduled, and through the human-computer interaction module, intelligent scheduling and manual scheduling of the mobile terminal can be performed, so that more choices are provided for users, and troubles caused by emergency scheduling reasons are prevented for the users;
and S6, the energy transportation module receives the energy transportation dispatching amount analysis of the central controller and automatically distributes and dispatches the energy, and when the controller outputs the manual energy dispatching instruction to the energy control module, the controller executes the manual dispatching instruction.
The points to be finally explained are: first, in the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" should be understood broadly, and may be a mechanical connection or an electrical connection, or a communication between two elements, and may be a direct connection, and "upper," "lower," "left," and "right" are only used to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed;
secondly, the method comprises the following steps: in the drawings of the disclosed embodiments of the invention, only the structures related to the disclosed embodiments are referred to, other structures can refer to common designs, and the same embodiment and different embodiments of the invention can be combined with each other without conflict;
and finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (7)
1. The energy internet scheduling method based on big data is characterized in that a central control module, an acquisition module, a preprocessing module, a storage module, an analysis and modeling module, a human-computer interaction module and an energy transportation module are adopted, the central control module further comprises rated data storage, energy transportation scheduling quantity analysis and rated range data, the central control module directly controls the energy transportation module, the human-computer interaction module changes and stores the rated data of the human-computer interaction module, and the human-computer interaction module changes the rated data range of the central control module; the method comprises the following steps:
s1, firstly, storing the acquired duration and the acquired number into a rated data storage in the central controller through the man-machine interaction module;
s2, the central controller transmits the rated acquisition time and the acquisition number to the acquisition module, and the acquisition module captures and detects data according to the rated data of the central controller;
s3, the acquisition module transmits each group of acquired data to the preprocessing module, the preprocessing module judges and removes the unique value in the data, missing data is supplemented by the average value method of each group of data, the supplemented whole group of data is sent to the storage module through the transmission information, and the storage module carries out segmented storage on each group of information;
s4, a user selects a certain group of data in a specified time to compare with original rated range data through a human-computer interaction module, a storage module transmits the group of data in the specified time to a data analysis and modeling module, the data analysis and modeling module compares the data with the original data to judge whether data is missing or not and whether the data contains unique data or not, if the data meets modeling requirements, a selected data model and an original data model are established and output back to the human-computer interaction module through the model, if the data does not meet the modeling requirements, information is fed back to a preprocessing module, the preprocessing module judges and supplements the data, and then the data is fed back to the data analysis and modeling to analyze and model;
s5, comparing the model of the original data and the selected data displayed by the man-machine interaction module, determining the allocation of the energy output module to the energy by the user through whether the automatic allocation is selected, if the automatic allocation is selected, outputting the compared data and the control command to the central controller, analyzing the energy transportation allocation quantity by the central controller and outputting the analyzed quantity to the energy allocation module, and if the manual allocation is selected, outputting the manual allocation command to the controller;
and S6, the energy transportation module receives the energy transportation dispatching amount analysis of the central controller and automatically distributes and dispatches the energy, and when the controller outputs the manual energy dispatching instruction to the energy control module, the controller executes the manual dispatching instruction.
2. The big-data-based energy internet scheduling method according to claim 1, wherein: the system comprises a preprocessing module, an analysis and modeling module, a model establishing module, a model outputting module and a feedback preprocessing module, wherein the preprocessing module and the analysis and modeling module feed back data mutually.
3. The big data based energy internet scheduling method according to claim 1, wherein: the man-machine interaction module comprises data display comparison, automatic allocation, command output to the controller, manual allocation and command output to the controller, the energy transportation module comprises a receiving command, automatic energy allocation, manual control command and allocation command execution, the man-machine interaction module can compare the original data range with the range obtained by selecting data, and the man-machine interaction module manually controls the energy transportation module.
4. The big data based energy internet scheduling method according to claim 1, wherein: the storage module backs up the information of the user, the user extracts different groups of data stored in the storage module at different time periods through the human-computer interaction module, and the original data of the user is compared and judged through the analysis and modeling module.
5. The big-data-based energy internet scheduling method according to claim 1, wherein: the energy transportation module analyzes and executes the instruction sent by the controller to automatically allocate energy, and the energy transportation module executes the manual instruction to allocate energy according to the requirements of users.
6. The big data based energy internet scheduling method according to claim 1, wherein: the acquisition module can acquire data for a rated numerical value set by a user of the acquisition module.
7. The big data based energy internet scheduling method according to claim 1, wherein: the algorithm used by the analysis and modeling module to compare the data is as follows,
using System;
using System.Collections.Generic;
namespace CsdnTest
{
class Program
{
static void Main(string[] args)
{
string sourceStr = "nominal data";
string targetStr = "selected data";
string[] source = sourceStr.Split(' ');
string[] target = targetStr.Split(' ');
Array.Sort(source);
Array.Sort(target)
List<string> diff = new List<string>();
List<string> same = new List<string>();
int i = 0, j = 0;
while (i < source.Length & j < target.Length)
{
int comp = source[i].CompareTo(target[j]);
if (comp == 0)
{
same.Add(source[i]);
i++;
j++;
}
else if (comp > 0)
{
diff.Add(target[j]);
j++;
}
else
{
diff.Add(source[i]);
}
}。
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