CN116523276B - High-efficiency energy utilization management platform based on intelligent control system - Google Patents
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Abstract
The invention relates to the field of control systems, and discloses a high-efficiency energy utilization management platform based on an intelligent control system, which comprises the following components: the energy monitoring module is used for carrying out multidimensional real-time energy monitoring on the plurality of distributed energy devices to obtain the real-time running state of the distributed energy devices; the energy data acquisition module is used for responding to the real-time running state of the distributed energy equipment, acquiring target energy data of the distributed energy equipment and determining the energy type of the target energy data based on the distributed energy equipment; the energy data analysis module is used for carrying out energy consumption analysis on the target energy data according to the energy type to obtain energy consumption data; the intelligent control system is used for determining energy consumption dynamics according to the energy consumption data, responding to the energy consumption dynamics to generate equipment adjustment information, and feeding the equipment adjustment information back to the corresponding distributed energy equipment so as to adjust the distributed energy equipment; the invention improves the rationality of energy utilization, the energy utilization rate and the energy management efficiency.
Description
Technical Field
The invention relates to the field of control systems, in particular to a high-efficiency energy utilization management platform based on an intelligent control system.
Background
Natural energy is an energy source existing or possessed in nature, and is a part of natural resources. Mainly solar energy (including light energy and heat energy), water energy, wave energy, tidal energy, wind energy, biomass energy and the like. The natural energy is almost renewable energy, natural energy and natural energy such as coal, petroleum and natural gas are commonly called natural energy, modern times are partially replaced by electric energy due to development of science and technology, serious pollution is generated in recent years due to burning of fossil energy such as coal and petroleum, and meanwhile, remote areas need to be developed, so that the utilization of the natural energy is paid attention, and the existing comprehensive utilization of the natural energy is unreasonable in energy utilization, so that the situation of energy surplus or energy shortage is caused.
Disclosure of Invention
The invention aims to solve the problems, and designs a high-efficiency energy utilization management platform based on an intelligent control system.
The first aspect of the invention provides a high-efficiency energy utilization management platform based on an intelligent control system, which comprises distributed energy equipment, an energy monitoring module, an energy data acquisition module, an energy data analysis module and an intelligent control system, wherein,
the energy monitoring module is used for carrying out multidimensional real-time energy monitoring on the distributed energy devices to obtain the real-time running state of the distributed energy devices, and sending the real-time running state to the energy data acquisition module;
the energy data acquisition module is used for responding to the real-time running state of the distributed energy equipment, acquiring target energy data of the distributed energy equipment, determining the energy type of the target energy data based on the distributed energy equipment and sending the target energy data and the energy type to the energy data analysis module;
the energy data analysis module is used for carrying out energy consumption analysis on the target energy data according to the energy type to obtain energy consumption data, and sending the energy consumption data to the intelligent control system;
the intelligent control system is used for determining energy consumption dynamics according to the energy consumption data, responding to the energy consumption dynamics to generate equipment adjusting information, and feeding the equipment adjusting information back to corresponding distributed energy equipment so as to adjust the distributed energy equipment.
Optionally, in a first implementation manner of the first aspect of the present invention, the energy monitoring module includes a signal issuing sub-module, a real-time monitoring sub-module, a status feedback sub-module, and a data conversion sub-module, where,
the release signal submodule is used for releasing a monitoring request signal and sending the monitoring request signal to each distributed energy device in the wireless network;
the real-time monitoring submodule is used for responding to the monitoring request signal and carrying out multidimensional real-time energy monitoring on each distributed energy device;
the state feedback sub-module is used for receiving real-time operation states fed back by the distributed energy devices, wherein the real-time operation states at least comprise device positions, corresponding network sites, corresponding device functions and corresponding energy types;
the data conversion sub-module is used for converting the real-time running state of each distributed energy device into a reliable data stream by utilizing a transmission control protocol and sending the reliable data stream to the energy data acquisition module.
Optionally, in a second implementation manner of the first aspect of the present invention, each of the distributed energy devices is wirelessly connected to the energy monitoring module through a wireless network.
Optionally, in a third implementation manner of the first aspect of the present invention, the energy data acquisition module includes a data loading sub-module, a parameter checking sub-module, a result judging sub-module and a data converting sub-module, where,
the data loading sub-module is used for receiving the data stream which is transmitted by the energy monitoring module and contains the real-time running state of each distributed energy device, and carrying out data loading on the data stream to obtain a first energy data parameter;
the parameter checking sub-module is used for checking the parameter of the first energy data parameter, generating a parameter checking result and sending the parameter checking result value to the result judging sub-module;
the result judging sub-module is used for judging whether the parameter checking result is abnormal or not, if yes, repairing the first energy data parameter to obtain a second energy data parameter, and sending the second energy data parameter to the data conversion sub-module; if not, the first energy data parameters are sent to the data conversion sub-module;
the data conversion sub-module is used for converting the first energy data parameter or the second energy data parameter into target energy data through multi-round data conversion.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the repairing the first energy data parameter to obtain a second energy data parameter includes:
responding to the parameter checking result, extracting data to be repaired in the first energy data parameter, wherein the data to be repaired at least comprises repeated data, error data and missing data;
dividing the first energy data parameters according to the sequence length of the data to be repaired, carrying out normalization processing on each section of sequence of the first energy data parameters, extracting a front section and a rear section of sequence of the data to be repaired, and dividing the front section and the rear section of sequence into a training set and a testing set;
inputting the training set as an input layer into a first neural network model for network training, and optimizing model super parameters of the first neural network model through the test set;
and repairing the data sequence to be repaired by using the trained first neural network model to obtain the second energy data parameters.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the energy data analysis module includes a trend analysis sub-module, a data sorting sub-module and a data output sub-module, where,
the trend analysis submodule is used for responding to the energy type, acquiring corresponding target energy data and determining the degree of freedom of an analysis model according to the target energy data;
the data arrangement sub-module is used for arranging the target energy data based on the degree of freedom to obtain a training set vector, and sending the training set vector to the data output sub-module;
the data output sub-module is used for inputting the training set vector into an analysis model so as to output energy consumption data through the analysis model.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the determining a degree of freedom of the analysis model according to the target energy data includes:
dividing the target energy data according to different degrees of freedom to obtain a plurality of sample training sets;
clustering the data in each sample training set by adopting an AP clustering algorithm so as to perform rolling training on the analysis model;
and calculating the state division goodness of the analysis model, and determining the degree of freedom corresponding to the maximum value, thus obtaining the degree of freedom of the analysis model.
Optionally, in a seventh implementation manner of the first aspect of the present invention, the intelligent control system includes a change trend module, a state judgment module, a difference calculation module, an information feedback module, and a balance constraint module, where,
the change trend module is used for determining an energy change trend according to the energy consumption data to obtain energy consumption dynamics and sending the energy consumption dynamics to the state judgment module;
the state judging module is used for dynamically judging the current state of the distributed energy equipment according to energy consumption and sending the current state to the difference value calculating module, wherein the current state is one of energy surplus, energy shortage or energy normal;
the difference value calculating module is used for calculating the difference value between the energy consumption data and a preset energy consumption threshold value to obtain an energy adjustment value when the current state is energy surplus or energy shortage, and sending the energy adjustment value to the information feedback module;
the information feedback module is used for generating equipment adjusting information according to the energy adjusting data, determining the alarm level of the distributed energy equipment according to the equipment adjusting information, and feeding back the equipment adjusting information and the alarm level to the corresponding distributed energy equipment;
the balance constraint module is used for conducting balance constraint on the distributed energy equipment based on the equipment adjustment information.
Optionally, in an eighth implementation manner of the first aspect of the present invention, the calculating a difference between the energy consumption data and a preset energy consumption threshold to obtain an energy adjustment value includes:
constructing a second neural network model, initializing a weight and a threshold of the model, and encoding the initial values of the weight and the threshold of the second neural network model by using a particle swarm algorithm, wherein one particle represents one weight or threshold parameter;
inputting historical energy data, preprocessing the data, and taking an error value obtained by training a second neural network model as an adaptability value of the current position;
updating the optimal position of each particle and the optimal position of the particle population, calculating the fitness value of the current position, judging whether the termination condition of the particle swarm algorithm is met, and if so, obtaining the optimal initial weight and threshold of the second neural network model;
and calculating the difference value between the energy consumption data and a preset energy consumption threshold value, judging whether the BPNN training termination condition is met, and if so, outputting an energy regulation value.
Optionally, in a ninth implementation manner of the first aspect of the present invention, the operation method of the high-efficiency energy utilization management platform based on the intelligent control system includes the following steps:
step one, multidimensional real-time energy monitoring is carried out on a plurality of distributed energy devices to obtain the real-time running state of the distributed energy devices;
step two, responding to the real-time running state of the distributed energy equipment, collecting target energy data of the distributed energy equipment, and determining the energy type of the target energy data based on the distributed energy equipment;
thirdly, carrying out energy consumption analysis on the target energy data according to the energy type to obtain energy consumption data;
and step four, determining energy consumption dynamics according to the energy consumption data, responding to the energy consumption dynamics to generate equipment adjusting information, and feeding the equipment adjusting information back to corresponding distributed energy equipment so as to adjust the distributed energy equipment.
According to the technical scheme, the high-efficiency energy utilization management platform comprises distributed energy equipment, an energy monitoring module, an energy data acquisition module, an energy data analysis module and an intelligent control system, and the intelligent control system is used for energy scheduling, so that the situation of energy surplus or energy shortage is avoided, and the energy utilization rationality, the energy utilization rate and the energy management efficiency are improved.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
FIG. 1 is a schematic diagram of a first embodiment of an intelligent control system-based high-efficiency energy utilization management platform according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a second embodiment of an intelligent control system-based high-efficiency energy utilization management platform according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a third embodiment of an intelligent control system-based high-efficiency energy utilization management platform according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, which is a schematic diagram of a first embodiment of a high-efficiency energy utilization management platform based on an intelligent control system according to an embodiment of the present invention, where the high-efficiency energy utilization management platform includes a distributed energy device, an energy monitoring module, an energy data acquisition module, an energy data analysis module, and an intelligent control system,
the energy monitoring module 101 is configured to monitor multiple distributed energy devices in real time in multiple dimensions, obtain a real-time running state of the distributed energy devices, and send the real-time running state to the energy data acquisition module;
the energy data acquisition module 102 is configured to respond to a real-time operation state of the distributed energy device, acquire target energy data of the distributed energy device, determine an energy type of the target energy data based on the distributed energy device, and send the target energy data and the energy type to the energy data analysis module;
the energy data analysis module 103 is configured to perform energy consumption analysis on the target energy data according to the energy type, obtain energy consumption data, and send the energy consumption data to the intelligent control system;
the intelligent control system 104 is configured to determine an energy consumption dynamic according to the energy consumption data, generate device adjustment information in response to the energy consumption dynamic, and feed back the device adjustment information to the corresponding distributed energy device to adjust the distributed energy device.
The high-efficiency energy utilization management platform comprises distributed energy equipment, an energy monitoring module, an energy data acquisition module, an energy data analysis module and an intelligent control system, wherein energy scheduling is performed through the intelligent control system, the situation of energy surplus or energy shortage is avoided, and the energy utilization rationality, the energy utilization rate and the energy management efficiency are improved.
Referring to fig. 2, a second embodiment of an intelligent control system-based high-efficiency energy utilization management platform is shown, wherein the high-efficiency energy utilization management platform comprises a distributed energy device, an energy monitoring module, an energy data acquisition module, an energy data analysis module, and an intelligent control system,
the energy monitoring module 101 is configured to monitor multiple distributed energy devices in real time in multiple dimensions, obtain a real-time running state of the distributed energy devices, and send the real-time running state to the energy data acquisition module;
the energy data acquisition module 102 is configured to respond to a real-time operation state of the distributed energy device, acquire target energy data of the distributed energy device, determine an energy type of the target energy data based on the distributed energy device, and send the target energy data and the energy type to the energy data analysis module;
the energy data analysis module 103 is configured to perform energy consumption analysis on the target energy data according to the energy type, obtain energy consumption data, and send the energy consumption data to the intelligent control system;
the intelligent control system 104 is configured to determine an energy consumption dynamic according to the energy consumption data, generate device adjustment information in response to the energy consumption dynamic, and feed back the device adjustment information to the corresponding distributed energy device to adjust the distributed energy device.
In this embodiment, the energy monitoring module 101 includes a signal issuing sub-module, a real-time monitoring sub-module, a status feedback sub-module, and a data conversion sub-module, wherein,
the release signal submodule 1011 is used for releasing a monitoring request signal and sending the monitoring request signal to each distributed energy device in the wireless network, wherein each distributed energy device is in wireless connection with the energy monitoring module through the wireless network;
a real-time monitoring submodule 1012, configured to perform multidimensional real-time energy monitoring on each distributed energy device in response to the monitoring request signal;
the state feedback sub-module 1013 is configured to receive a real-time operation state fed back by each distributed energy device, where the real-time operation state at least includes a device location, a corresponding network site, a corresponding device function, and a corresponding energy type;
the data conversion sub-module 1014 is configured to convert the real-time operation status of each distributed energy device into a reliable data stream by using the transmission control protocol, and send the data stream to the energy data acquisition module.
In this embodiment, the energy data acquisition module 102 includes a data loading sub-module, a parameter checking sub-module, a result judging sub-module, and a data converting sub-module, wherein,
the data loading submodule 1021 is used for receiving the data stream which is transmitted by the energy monitoring module and contains the real-time running state of each distributed energy device, and carrying out data loading on the data stream to obtain a first energy data parameter;
the parameter checking sub-module 1022 is configured to perform parameter checking on the strikingness of the first energy data parameter, generate a parameter checking result, and send a parameter checking result value result judging sub-module;
the result judging submodule 1023 is used for judging whether the parameter checking result is abnormal or not, if so, repairing the first energy data parameter to obtain a second energy data parameter, and sending the second energy data parameter to the data converting submodule; if not, the first energy data parameters are sent to the data conversion sub-module;
the data conversion submodule 1024 is configured to convert the first energy data parameter or the second energy data parameter into target energy data through multiple rounds of data conversion.
In this embodiment, repairing the first energy data parameter to obtain a second energy data parameter includes:
responding to a parameter checking result, extracting data to be repaired in a first energy data parameter, wherein the data to be repaired at least comprises repeated data, error data and missing data;
dividing a first energy data parameter according to the sequence length of the data to be repaired, carrying out normalization processing on each section of sequence of the first energy data parameter, extracting a front section and a rear section of sequence of the data to be repaired, and dividing the front section and the rear section of sequence into a training set and a testing set;
inputting the training set into the first neural network model as an input layer for network training, and optimizing the model super parameters of the first neural network model through the testing set;
and repairing the data sequence to be repaired by using the trained first neural network model to obtain the second energy data parameters.
In this embodiment, the energy data analysis module 103 includes a trend analysis sub-module, a data sorting sub-module, and a data output sub-module, wherein,
the trend analysis sub-module 1031 is configured to obtain corresponding target energy data in response to the energy type, and determine a degree of freedom of an analysis model according to the target energy data;
the data arrangement submodule 1032 is used for arranging the target energy data based on the degrees of freedom to obtain a training set vector, and sending the training set vector to the data output submodule;
a data output submodule 1033 for inputting the training set vector into the analytical model to output energy consumption data through the analytical model.
In this embodiment, determining the degree of freedom of the analysis model according to the target energy data includes:
dividing target energy data according to different degrees of freedom to obtain a plurality of sample training sets;
clustering the data in each sample training set by adopting an AP clustering algorithm so as to perform rolling training on the analysis model;
in this embodiment, clustering is to divide a data set into different classes or clusters according to a specific standard (such as distance), so that the similarity of data objects in the same cluster is as large as possible, and the variability of data objects not in the same cluster is also as large as possible. That is, the data of the cluster backlashes are gathered together as much as possible, and the data of different classes are separated as much as possible. The AP clustering algorithm is a neighbor propagation algorithm or an affinity propagation algorithm, and the representative elements are finally selected to complete clustering by continuously transmitting information between points. The AP clustering algorithm achieves the aim of efficient and accurate data clustering by iteratively transmitting attraction degree information and attribution degree information among data points in a given data set. The similarity between a pair of data points is taken as input, and the true valuable information is exchanged between the data points until an optimal set of class representative points and clusters are gradually formed. At this point the sum of the similarity of all data points to their nearest class representation point is the largest.
And calculating the state division goodness of the analysis model, and determining the degree of freedom corresponding to the maximum value, thus obtaining the degree of freedom of the analysis model.
In this embodiment, the intelligent control system 104 includes a change trend module, a state judgment module, a difference calculation module, an information feedback module, and a balance constraint module, wherein,
the change trend module 1041 is configured to determine an energy change trend according to the energy consumption data, obtain an energy consumption dynamic, and send the energy consumption dynamic to the state judgment module;
the state judging module 1042 is configured to dynamically judge a current state of the distributed energy device according to energy consumption, and send the current state to the difference calculating module, where the current state is one of energy surplus, energy shortage and energy normal;
the difference calculating module 1043 is configured to calculate a difference between the energy consumption data and a preset energy consumption threshold when the current state is energy surplus or energy shortage, obtain an energy adjustment value, and send the energy adjustment value to the information feedback module;
the information feedback module 1044 is configured to generate device adjustment information according to the energy adjustment data, determine an alarm level of the distributed energy device according to the device adjustment information, and feed back the device adjustment information and the alarm level to the corresponding distributed energy device;
the balancing constraint module 1045 is configured to perform balancing constraint on the distributed energy device based on the device adjustment information.
In this embodiment, calculating a difference between the energy consumption data and a preset energy consumption threshold to obtain an energy adjustment value includes:
constructing a second neural network model, initializing a weight and a threshold of the model, and encoding the initial values of the weight and the threshold of the second neural network model by using a particle swarm algorithm, wherein one particle represents one weight or threshold parameter;
inputting historical energy data, preprocessing the data, and taking an error value obtained by training a second neural network model as an adaptability value of the current position;
updating the optimal position of each particle and the optimal position of the particle population, calculating the fitness value of the current position, judging whether the termination condition of the particle swarm algorithm is met, and if so, obtaining the optimal initial weight and threshold of the second neural network model;
and calculating the difference value between the energy consumption data and a preset energy consumption threshold value, judging whether the BPNN training termination condition is met, and if so, outputting an energy regulation value.
In summary, the high-efficiency energy utilization management platform of the embodiment comprises distributed energy equipment, an energy monitoring module, an energy data acquisition module, an energy data analysis module and an intelligent control system, and energy scheduling is performed through the intelligent control system, so that the situation of energy surplus or energy shortage is avoided, and the energy utilization rationality, the energy utilization rate and the energy management efficiency are improved.
Referring to fig. 3, a third embodiment of an intelligent control system-based high-efficiency energy utilization management platform according to the present invention is shown, and the operation method of the intelligent control system-based high-efficiency energy utilization management platform includes the following steps:
step one, multidimensional real-time energy monitoring is carried out on a plurality of distributed energy devices to obtain the real-time running state of the distributed energy devices;
step two, in response to the real-time running state of the distributed energy equipment, acquiring target energy data of the distributed energy equipment, and determining the energy type of the target energy data based on the distributed energy equipment;
thirdly, performing energy consumption analysis on the target energy data according to the energy type to obtain the energy consumption data;
and step four, determining energy consumption dynamics according to the energy consumption data, responding to the energy consumption dynamics to generate equipment adjusting information, and feeding the equipment adjusting information back to the corresponding distributed energy equipment so as to adjust the distributed energy equipment.
In the embodiment of the invention, the real-time running state of the distributed energy equipment is obtained by carrying out multidimensional real-time energy monitoring on a plurality of distributed energy equipment, the target energy data of the distributed energy equipment is collected in response to the real-time running state of the distributed energy equipment, the energy type of the target energy data is determined based on the distributed energy equipment, the energy consumption analysis is carried out on the target energy data according to the energy type to obtain the energy consumption data, the energy consumption dynamics is determined according to the energy consumption data, the equipment regulation information is generated in response to the energy consumption dynamics, and the equipment regulation information is fed back to the corresponding distributed energy equipment so as to regulate the distributed energy equipment; the invention avoids the situation of energy surplus or energy shortage and improves the energy utilization rationality, the energy utilization rate and the energy management efficiency.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The high-efficiency energy utilization management platform based on the intelligent control system is characterized by comprising distributed energy equipment, an energy monitoring module, an energy data acquisition module, an energy data analysis module and an intelligent control system, wherein,
the energy monitoring module is used for carrying out multidimensional real-time energy monitoring on the distributed energy devices to obtain the real-time running state of the distributed energy devices, and sending the real-time running state to the energy data acquisition module;
the energy data acquisition module is used for responding to the real-time running state of the distributed energy equipment, acquiring target energy data of the distributed energy equipment, determining the energy type of the target energy data based on the distributed energy equipment and sending the target energy data and the energy type to the energy data analysis module;
the energy data analysis module is used for carrying out energy consumption analysis on the target energy data according to the energy type to obtain energy consumption data, and sending the energy consumption data to the intelligent control system;
the intelligent control system is used for determining energy consumption dynamics according to the energy consumption data, responding to the energy consumption dynamics to generate equipment adjusting information, and feeding the equipment adjusting information back to corresponding distributed energy equipment so as to adjust the distributed energy equipment;
the energy data analysis module comprises a trend analysis sub-module, a data arrangement sub-module and a data output sub-module, wherein,
the trend analysis submodule is used for responding to the energy type, acquiring corresponding target energy data and determining the degree of freedom of an analysis model according to the target energy data;
the data arrangement sub-module is used for arranging the target energy data based on the degree of freedom to obtain a training set vector, and sending the training set vector to the data output sub-module;
the data output sub-module is used for inputting the training set vector into an analysis model so as to output energy consumption data through the analysis model;
the determining the degree of freedom of the analysis model according to the target energy data comprises the following steps:
dividing the target energy data according to different degrees of freedom to obtain a plurality of sample training sets;
clustering the data in each sample training set by adopting an AP clustering algorithm so as to perform rolling training on the analysis model;
calculating the state division goodness of the analysis model, and determining the degree of freedom corresponding to the maximum value to obtain the degree of freedom of the analysis model;
the intelligent control system comprises a change trend module, a state judging module, a difference value calculating module, an information feedback module and a balance constraint module, wherein,
the change trend module is used for determining an energy change trend according to the energy consumption data to obtain energy consumption dynamics and sending the energy consumption dynamics to the state judgment module;
the state judging module is used for dynamically judging the current state of the distributed energy equipment according to energy consumption and sending the current state to the difference value calculating module, wherein the current state is one of energy surplus, energy shortage or energy normal;
the difference value calculating module is used for calculating the difference value between the energy consumption data and a preset energy consumption threshold value to obtain an energy adjustment value when the current state is energy surplus or energy shortage, and sending the energy adjustment value to the information feedback module;
the information feedback module is used for generating equipment adjusting information according to the energy adjusting value, determining the alarm level of the distributed energy equipment according to the equipment adjusting information, and feeding back the equipment adjusting information and the alarm level to the corresponding distributed energy equipment;
the balance constraint module is used for carrying out balance constraint on the distributed energy equipment based on the equipment adjustment information;
the calculating the difference value between the energy consumption data and a preset energy consumption threshold value to obtain an energy regulation value comprises the following steps:
constructing a second neural network model, initializing a weight and a threshold of the model, and encoding the initial values of the weight and the threshold of the second neural network model by using a particle swarm algorithm, wherein one particle represents one weight or threshold parameter;
inputting historical energy data, preprocessing the data, and taking an error value obtained by training a second neural network model as an adaptability value of the current position;
updating the optimal position of each particle and the optimal position of the particle population, calculating the fitness value of the current position, judging whether the termination condition of the particle swarm algorithm is met, and if so, obtaining the optimal initial weight and threshold of the second neural network model;
and calculating the difference value between the energy consumption data and a preset energy consumption threshold value, judging whether the BPNN training termination condition is met, and if so, outputting an energy regulation value.
2. The intelligent control system-based high-efficiency energy utilization management platform as recited in claim 1, wherein the energy monitoring module comprises a release signal sub-module, a real-time monitoring sub-module, a status feedback sub-module and a data conversion sub-module,
the release signal submodule is used for releasing a monitoring request signal and sending the monitoring request signal to each distributed energy device in the wireless network;
the real-time monitoring submodule is used for responding to the monitoring request signal and carrying out multidimensional real-time energy monitoring on each distributed energy device;
the state feedback sub-module is used for receiving real-time operation states fed back by the distributed energy devices, wherein the real-time operation states at least comprise device positions, corresponding network sites, corresponding device functions and corresponding energy types;
the data conversion sub-module is used for converting the real-time running state of each distributed energy device into a reliable data stream by utilizing a transmission control protocol and sending the reliable data stream to the energy data acquisition module.
3. The intelligent control system-based high-efficiency energy utilization management platform according to claim 1, wherein each of the distributed energy devices is wirelessly connected to the energy monitoring module via a wireless network.
4. The intelligent control system-based high-efficiency energy utilization management platform as claimed in claim 1, wherein the energy data acquisition module comprises a data loading sub-module, a parameter checking sub-module, a result judging sub-module and a data conversion sub-module,
the data loading sub-module is used for receiving the data stream which is transmitted by the energy monitoring module and contains the real-time running state of each distributed energy device, and carrying out data loading on the data stream to obtain a first energy data parameter;
the parameter checking sub-module is used for checking the correctness of the first energy data parameter, generating a parameter checking result and sending the parameter checking result to the result judging sub-module;
the result judging sub-module is used for judging whether the parameter checking result is abnormal or not, if yes, repairing the first energy data parameter to obtain a second energy data parameter, and sending the second energy data parameter to the data conversion sub-module; if not, the first energy data parameters are sent to the data conversion sub-module;
the data conversion sub-module is used for converting the first energy data parameter or the second energy data parameter into target energy data through multi-round data conversion.
5. The intelligent control system-based high-efficiency energy utilization management platform as claimed in claim 4, wherein said repairing the first energy data parameter to obtain a second energy data parameter comprises:
responding to the parameter checking result, extracting data to be repaired in the first energy data parameter, wherein the data to be repaired at least comprises repeated data, error data and missing data;
dividing the first energy data parameters according to the sequence length of the data to be repaired, carrying out normalization processing on each section of sequence of the first energy data parameters, extracting a section of sequence before and after the data to be repaired, and dividing the sequence into a training set and a testing set;
inputting the training set as an input layer into a first neural network model for network training, and optimizing model super parameters of the first neural network model through the test set;
and repairing the data sequence to be repaired by using the trained first neural network model to obtain the second energy data parameters.
6. The intelligent control system-based high-efficiency energy utilization management platform as claimed in claim 1, wherein the intelligent control system-based high-efficiency energy utilization management platform operation method comprises the following steps:
step one, multidimensional real-time energy monitoring is carried out on a plurality of distributed energy devices to obtain the real-time running state of the distributed energy devices;
step two, responding to the real-time running state of the distributed energy equipment, collecting target energy data of the distributed energy equipment, and determining the energy type of the target energy data based on the distributed energy equipment;
thirdly, carrying out energy consumption analysis on the target energy data according to the energy type to obtain energy consumption data;
and step four, determining energy consumption dynamics according to the energy consumption data, responding to the energy consumption dynamics to generate equipment adjusting information, and feeding the equipment adjusting information back to corresponding distributed energy equipment so as to adjust the distributed energy equipment.
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