CN116207864B - Method and system for controlling power equipment in low-voltage area based on Internet of things - Google Patents
Method and system for controlling power equipment in low-voltage area based on Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of the Internet of things, and discloses a method and a system for controlling power equipment in a low-voltage area based on the Internet of things, wherein the method comprises the following steps: performing wavelet analysis and wavelet transformation on historical operation data of the power equipment in the low-voltage transformer area to obtain a decomposition scale and a transformation coefficient; constructing the historical operation data according to the decomposition scale and the transformation coefficient; performing index decomposition on the target data to obtain index components, and extracting characteristic data in the target data according to the index components; calculating the operation state of each power device according to the characteristic data, and determining the distribution level of the power device according to the operation state; when receiving a power demand task, performing initial task allocation on the power equipment according to the power demand task and the allocation grade; optimizing the initial task allocation to obtain a target task allocation result of the power equipment, and controlling the power equipment according to the target task allocation result. The invention can improve the control effect of the power equipment.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to a method and a system for controlling power equipment in a low-voltage area based on the Internet of things.
Background
With the development of social economy, the demand of people for electric power is higher, the power grid structure is more complex, and the operation control difficulty of the electric power equipment is greatly increased. The low-voltage transformer area is an area for low-voltage power supply of the voltage transformer, the low-voltage transformer area directly provides service for users, and through dividing the low-voltage transformer area, power utilization management can be better carried out, and the aspects of personnel division, equipment maintenance and the like are more standard and scientific, so that the problem that needs of user power utilization are solved urgently is solved.
However, the existing control method of the power equipment mainly comprises the steps of analyzing the state data of the power equipment, establishing a comprehensive evaluation model of the running state of the power equipment, and realizing the state evaluation of the power equipment and the rapid monitoring of abnormal equipment, wherein the abnormal equipment can only be controlled, the possible abnormal conditions of the power equipment can not be timely predicted, the power equipment can not be reasonably allocated according to the power demand, and the abnormal conditions of the power equipment are avoided, so that the control effect of the power equipment is poor.
Disclosure of Invention
The invention provides a method and a system for controlling power equipment in a low-voltage area based on the Internet of things, and mainly aims to solve the problem that the control effect of the power equipment in the low-voltage area is poor.
In order to achieve the above object, the present invention provides a method for controlling power equipment in a low-voltage area based on the internet of things, including:
acquiring historical operation data of power equipment in a target low-voltage transformer area, performing wavelet analysis on the historical operation data to obtain initial noise reduction data, and performing wavelet transformation on the initial noise reduction data by utilizing a preset decomposition layer number to obtain a decomposition scale and a transformation coefficient of the historical operation data;
wavelet transforming the initial noise reduction data using the formula:
wherein,,indicating the number of decomposition layers as +.>The scale of decomposition during time,/->Indicating the number of decomposition layers as +.>Transform coefficient at time, < >>Representing +.f in the initial noise reduction data>Data of->Representing integer shift size, < > in wavelet transform>Indicate->The number of layers of the data is +.>Quantization scale of time->Representing the number of preset wavelet transform filters;
constructing target data of the historical operation data according to the decomposition scale and the transformation coefficient, performing index decomposition on the target data to obtain index components of the target data, and extracting characteristic data in the target data according to the index components;
Calculating the operation state of each piece of electric equipment according to the characteristic data, and determining the distribution grade of each piece of electric equipment according to the operation state;
when the target low-voltage area receives a power demand task, carrying out initial task allocation on the power equipment according to the power demand task and the allocation grade;
optimizing the initial task allocation to obtain a target task allocation result of the power equipment, and controlling the power equipment according to the target task allocation result.
Optionally, the said
Constructing target data of the historical operating data according to the decomposition scale and the transformation coefficient, wherein the target data comprises:
quantizing the decomposition scale to obtain a quantization scale, and selecting the transformation coefficient by using a preset dilution threshold to obtain a target coefficient;
and carrying out data reconstruction on the initial noise reduction data according to the quantization scale and the target coefficient to obtain target data of the historical operation data.
Optionally, the performing index decomposition on the target data to obtain an index component of the target data includes:
obtaining an extremum of the target data, performing curve fitting according to the extremum to obtain an upper extremum curve and a lower extremum curve, and calculating a mean value curve of the upper extremum curve and the lower extremum curve;
Subtracting the mean curve from the target data to obtain a first index component of the target data;
and iterating the first index component serving as update data to obtain an update iteration component, stopping iterating when the update iteration component does not meet a preset constraint condition, and taking the first index component and the update iteration component as index components of the target data.
Optionally, the extracting feature data in the target data according to the index component includes:
performing interpolation filtering on the index component to obtain a demodulation signal of the index component;
and extracting index data of the demodulation signal, and constructing feature data corresponding to the index component according to the index data.
Optionally, the calculating the operation state of each of the electric devices according to the characteristic data includes:
extracting data features of the feature data, and carrying out weighted prediction on the data features by utilizing a hidden layer in a preset neural network to obtain a prediction score;
and performing an activation operation on the prediction scores to obtain the running state of each power device.
Optionally, the performing initial task allocation on the power device according to the power demand task and the allocation level includes:
Determining target power equipment required to be subjected to task allocation according to the power demand task, and sequencing the target power equipment according to the allocation grade to obtain a target power equipment sequence;
and randomly distributing the power demand tasks according to the target power equipment sequence to obtain initial task distribution of the power equipment.
Optionally, the optimizing the initial task allocation to obtain a target task allocation result of the power device includes:
calculating task overload amount distributed by the initial task, generating an objective function according to the task overload amount, and constructing an adaptability function based on the objective function;
the fitness function is constructed using the following formula:
wherein,,representing fitness function, ++>Representing an objective function +.>Representing the objective function at +.>Maximum value of the objective function at the time of iteration, +.>Indicate->Adjustment value at multiple iterations,/->Along with->Is increased by an increase in (a);
randomly generating a task allocation population, and calculating the fitness value of each task allocation in the task allocation population according to the fitness function;
selecting a target population from the task allocation population according to the fitness value, calculating a crossing value of the target total population, and generating an updated total population according to the crossing value;
And carrying out variation iteration on the updated total group until the iteration times of the variation iteration reach a preset iteration threshold value, and obtaining a target task allocation result of the power equipment.
Optionally, the reconstructing the initial noise reduction data according to the quantization scale and the target coefficient to obtain target data of the historical operation data includes:
and carrying out data reconstruction on the initial noise reduction data by using the following formula:
wherein,,representing target data->Representing +.f in the initial noise reduction data>Data of->Indicate->The number of layers of the data is +.>Quantization scale of time->Representing integer shift size, < > in wavelet transform>Representation->The number of layers of the data is +.>Target coefficient at time,/->Representing a preset number of wavelet transform filters.
Optionally, the calculating the intersection value of the target total group includes:
calculating the intersection value of the target total group by using the following formula:
wherein,,a crossing value representing said target total group, < >>Representing the maximum value of the fitness function in said target population,/->Mean value of fitness function representing the total population of said targets,/->Representing the minimum value of the fitness function in the target population, < > >Indicate->Multiple iterations(s)>Representing a preset iteration threshold.
In order to solve the above problems, the present invention further provides a power equipment control system for implementing a low-voltage station based on the internet of things, the system comprising:
the system comprises a decomposition scale and transformation coefficient calculation module, a wavelet analysis module and a wavelet analysis module, wherein the decomposition scale and transformation coefficient calculation module is used for acquiring historical operation data of power equipment in a target low-voltage transformer area, performing wavelet analysis on the historical operation data to obtain initial noise reduction data, and performing wavelet transformation on the initial noise reduction data by utilizing a preset decomposition layer number to obtain the decomposition scale and transformation coefficient of the historical operation data;
the characteristic data extraction module is used for constructing target data of the historical operation data according to the decomposition scale and the transformation coefficient, carrying out index decomposition on the target data to obtain index components of the target data, and extracting characteristic data in the target data according to the index components;
the distribution grade determining module is used for calculating the operation state of each piece of electric equipment according to the characteristic data and determining the distribution grade of each piece of electric equipment according to the operation state;
the initial task allocation module is used for carrying out initial task allocation on the power equipment according to the power demand task and the allocation grade when the target low-voltage area receives the power demand task;
And the power equipment control module is used for optimizing the initial task allocation to obtain a target task allocation result of the power equipment, and controlling the power equipment according to the target task allocation result.
According to the embodiment of the invention, through carrying out wavelet analysis and wavelet transformation on the historical operation data of the power equipment in the target low-voltage transformer area, the data noise in the historical data can be removed, and more accurate target data can be obtained, so that the control effect of the power equipment is improved; performing index decomposition on target data to obtain index components of each piece of power equipment, and further performing deep mining on characteristic data in historical data of the characteristic data in the target data; the running state of each power equipment is calculated so as to determine the distribution level, so that reasonable task distribution can be carried out on the power equipment, and the control effect of the power equipment is further improved; when the power demand task is received, the power equipment is subjected to initial task allocation according to the allocation grade, the initial task allocation is optimized, the power equipment is controlled according to the target task allocation result, the power equipment can be prevented from being abnormal, the reasonable allocation of the power demand task is realized, and the control effect of the power equipment in the low-voltage transformer area can be improved. Therefore, the method and the system for controlling the power equipment in the low-voltage area based on the Internet of things can solve the problem of poor control effect of the power equipment in the low-voltage area.
Drawings
Fig. 1 is a schematic flow chart of a method for implementing power equipment control in a low-voltage area based on the internet of things according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a target data index decomposition according to an embodiment of the present invention;
fig. 3 is a functional block diagram of a power equipment control system based on the internet of things for implementing a low-voltage transformer area according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
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 embodiment of the application provides a power equipment control method based on the Internet of things and used for realizing a low-voltage transformer area. The execution main body of the method for realizing the control of the power equipment in the low-voltage area based on the internet of things comprises at least one of the electronic equipment, such as a server, a terminal and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the method for controlling the power equipment in the low-voltage area based on the internet of things can be executed by software or hardware installed in the terminal equipment or the server equipment, and the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for implementing power equipment control in a low-voltage transformer area based on the internet of things according to an embodiment of the present invention is shown. In this embodiment, the method for implementing power equipment control in a low-voltage transformer area based on the internet of things includes:
s1, acquiring historical operation data of power equipment in a target low-voltage transformer area, performing wavelet analysis on the historical operation data to obtain initial noise reduction data, and performing wavelet transformation on the initial noise reduction data by utilizing a preset decomposition layer number to obtain a decomposition scale and a transformation coefficient of the historical operation data.
In the embodiment of the invention, the target low-voltage transformer area is a low-voltage power supply area needing intelligent control of the power equipment, the power equipment under the target low-voltage transformer area can be equipment such as a generator, a transformer, a switching device and the like, and the power consumption requirement under the target low-voltage transformer area can be met through the power equipment.
In the embodiment of the invention, the historical operation data is the equipment operation signal data of each power equipment in the past 1 year or the past 6 months, and because the signal has interference of other signals in the process of transmission or receiving, the data noise reduction can be carried out on the historical operation data to obtain more accurate target data.
In the embodiment of the invention, the Wavelet analysis is to use a Wavelet function to represent the historical operation data as a linear combination of a group of Wavelet bases, so as to compress or reduce the dimension of noise of the historical operation data and further to initially reduce the noise of the historical operation data, wherein the Wavelet analysis can be performed by using a Haar Wavelet function.
In the embodiment of the invention, the data in the specific frequency range of the initial noise reduction data can be decomposed through the decomposition layer number to obtain the conversion coefficients under different frequencies, but the energy of the signals in the initial noise reduction data is concentrated in some large conversion coefficients, and the energy of the noise is distributed in the whole wavelet domain. Therefore, after wavelet transformation, the amplitude of the transformation coefficient of the signal is larger than that of the noise, i.e. the transformation coefficient with larger amplitude is generally based on the original data, while the transformation coefficient with smaller amplitude is noise to a large extent. Therefore, by presetting a threshold value, the transform coefficient smaller than the threshold value is changed to 0, and the transform coefficient is removed as interference noise, thereby obtaining a target coefficient.
In the embodiment of the invention, wavelet transformation is performed on the initial noise reduction data by using the following formula:
Wherein,,indicating the number of decomposition layers as +.>The scale of decomposition during time,/->Indicating the number of decomposition layers as +.>Transform coefficient at time, < >>Representing +.f in the initial noise reduction data>Personal data，/>Representing integer shift size, < > in wavelet transform>Indicate->The number of layers of the data is +.>Quantization scale of time->Representing a preset number of wavelet transform filters.
S2, constructing target data of the historical operation data according to the decomposition scale and the transformation coefficient, performing index decomposition on the target data to obtain index components of the target data, and extracting characteristic data in the target data according to the index components.
In the embodiment of the invention, the historical operation data is subjected to data reconstruction through the quantization scale and the target coefficient, so that the data can be further subjected to noise reduction, and more accurate target data can be obtained.
The constructing the target data of the historical operating data according to the decomposition scale and the transformation coefficient comprises the following steps:
quantizing the decomposition scale to obtain a quantization scale, and selecting the transformation coefficient by using a preset dilution threshold to obtain a target coefficient;
and carrying out data reconstruction on the initial noise reduction data according to the quantization scale and the target coefficient to obtain target data of the historical operation data.
In the embodiment of the invention, the data reconstruction is performed on the initial noise reduction data by using the following formula:
wherein,,representing target data->Representing +.f in the initial noise reduction data>Data of->Indicate->The number of layers of the data is +.>Quantization scale of time->Representing integer shift size, < > in wavelet transform>Representation->The number of layers of the data is +.>Target coefficient at time,/->Representing a preset number of wavelet transform filters.
In the embodiment of the invention, the index decomposition is to obtain different index components corresponding to the target data by decomposing nonlinear and nonstationary signals, and further obtain the characteristic data in the target data according to the index components.
In an embodiment of the present invention, referring to fig. 2, the performing index decomposition on the target data to obtain an index component of the target data includes:
s21, obtaining an extremum of the target data, performing curve fitting according to the extremum to obtain an upper extremum curve and a lower extremum curve, and calculating a mean value curve of the upper extremum curve and the lower extremum curve;
s22, subtracting the mean curve from the target data to obtain a first index component of the target data;
s23, iterating the first index component serving as update data to obtain an update iteration component, stopping iterating when the update iteration component does not meet a preset constraint condition, and taking the first index component and the update iteration component as index components of the target data.
In the embodiment of the invention, an upper extremum curve and a lower extremum curve are generated according to the maximum extremum point and the minimum extremum point of the target data, and the average value corresponding to each point on the extremum curve is calculated, so that an average value curve is obtained, and a curve obtained by subtracting the target data from the average value curve is an index component corresponding to the target data.
In the embodiment of the present invention, the constraint condition is a condition of an index component, including: the number of extreme points and the number of zero crossing points must be equal or differ by at most not more than one and the average value of the upper and lower extreme curves is zero.
In the embodiment of the invention, after each index component in the target data is decomposed, the index component needs to be demodulated, namely, the information contained in each index component is recovered, so that the characteristic data, such as the characteristic data of the working time, the historical fault time, the fault frequency and the like of each power equipment, can be extracted according to the index component.
In an embodiment of the present invention, the extracting feature data in the target data according to the index component includes:
performing interpolation filtering on the index component to obtain a demodulation signal of the index component;
and extracting index data of the demodulation signal, and constructing feature data corresponding to the index component according to the index data.
In the embodiment of the invention, the interpolation filtering is interpolation among discrete digital signal points, and a preset low-pass filter is utilized to filter the signals at the same time, so that noise during feature extraction is removed, more accurate index data is obtained, and further more accurate feature data is obtained.
And S3, calculating the operation state of each piece of electric equipment according to the characteristic data, and determining the distribution grade of each piece of electric equipment according to the operation state.
In the embodiment of the invention, the running state of the power equipment can represent the states of good, overload, crisis and the like of the power equipment, and the power demand which can be continuously born by each power equipment is determined according to the running state, so that the distribution grade of each power equipment is determined.
In an embodiment of the present invention, the calculating the operation state of each electrical device according to the feature data includes:
extracting data features of the feature data, and carrying out weighted prediction on the data features by utilizing a hidden layer in a preset neural network to obtain a prediction score;
and performing an activation operation on the prediction scores to obtain the running state of each power device.
According to the embodiment of the invention, the neural network is a pre-trained running state prediction model, the neural network comprises an input layer, a hidden layer and an output layer, the data characteristics are predicted in a weighting mode through each neuron in the hidden layer to obtain the state prediction score of each piece of power equipment, then the prediction score is activated to calculate the running state of each piece of power equipment, and the running state of each piece of power equipment is obtained.
In the embodiment of the invention, the better the running state of the power equipment is, the better the running of the power tasks can be, and further, the higher task allocation grade is obtained, and the reasonable task allocation can be carried out on the power equipment by determining the allocation grade of each power equipment, so that the control effect of the power equipment is improved.
And S4, when the target low-voltage area receives the power demand task, carrying out initial task allocation on the power equipment according to the power demand task and the allocation grade.
In the embodiment of the invention, the power demand task is a new power task of the power equipment in the target low-voltage area, and the power task needs to be reasonably distributed so as to meet the equipment requirement of the power demand task.
In the embodiment of the present invention, the performing initial task allocation on the power device according to the power demand task and the allocation level includes:
determining target power equipment required to be subjected to task allocation according to the power demand task, and sequencing the target power equipment according to the allocation grade to obtain a target power equipment sequence;
and randomly distributing the power demand tasks according to the target power equipment sequence to obtain initial task distribution of the power equipment.
In the embodiment of the invention, when a new power demand task is received, a part of power equipment does not participate in the execution of the power demand task, the corresponding initial task is allocated to 0, the power demand task is allocated randomly according to a target power equipment sequence only by the task allocation of target power equipment which participates in the execution of the power demand task, for example, 10 power demand tasks are allocated randomly to 5 power equipment in the target power equipment sequence, and the required 5 power equipment is determined according to the target power equipment sequence, so that the target power equipment with higher allocation level is allocated to more tasks, the execution effect of the power demand task is ensured, and the initial task allocation of the power equipment is realized.
And S5, optimizing the initial task allocation to obtain a target task allocation result of the power equipment, and controlling the power equipment according to the target task allocation result.
In the embodiment of the invention, the initial task allocation may be random allocation, so that part of the power equipment is overloaded and part of the power equipment is idle, and the control effect of the power equipment is poor.
In the embodiment of the present invention, the optimizing the initial task allocation to obtain the target task allocation result of the power device includes:
calculating task overload amount distributed by the initial task, generating an objective function according to the task overload amount, and constructing an adaptability function based on the objective function;
randomly generating a task allocation population, and calculating the fitness value of each task allocation in the task allocation population according to the fitness function;
selecting a target population from the task allocation population according to the fitness value, calculating a crossing value of the target total population, and generating an updated total population according to the crossing value;
and carrying out variation iteration on the updated total group until the iteration times of the variation iteration reach a preset iteration threshold value, and obtaining a target task allocation result of the power equipment.
In the embodiment of the invention, the task overload can be obtained according to the difference value between the power demand task which can be carried under the running state of each power device and the distribution demand task under the initial task distribution; the power demand tasks which can be carried under the running state can be determined according to the preset working standard table of each power device, the task overload amount represents the total resource overload amount of the power device under the initial task allocation, the power device is possibly abnormal, the power demand tasks cannot be normally executed, and the control effect of the power device is poor.
In the embodiment of the invention, the minimum value of the task overload is taken as an objective function, and an fitness function is constructed according to the objective function, so that fitness is obtained, an updated total group with the greatest fitness is selected from task distribution groups, iteration is carried out on the task distribution groups, and an optimal objective distribution result is obtained.
In the embodiment of the invention, the fitness function is constructed by using the following formula:
wherein,,representing fitness function, ++>Representing an objective function +.>Representing the objective function at +.>Maximum value of the objective function at the time of iteration, +.>Indicate->Adjustment value at multiple iterations,/->Along with->Is increased by an increase in (a).
In the embodiment of the invention, a preset number of target populations can be selected to participate in the next iteration to ensure the task allocation effect of the updated total population, the intersection value of the target total population is calculated, and the scheme of the updated population is determined, so that the updated total population is obtained.
In the embodiment of the invention, the intersection value of the target total group is calculated by using the following formula:
calculating the intersection value of the target total group by using the following formula:
wherein,,representing the intersection of the target populationValue of->Representing the maximum value of the fitness function in said target population,/->Mean value of fitness function representing the total population of said targets,/- >Representing the minimum value of the fitness function in the target population, < >>Indicate->Multiple iterations(s)>Representing a preset iteration threshold.
In the embodiment of the invention, the alternative value is randomly generated, so that a variation scheme of task allocation is ensured to be generated, iteration is performed on the updated total group, the optimization effect is ensured, the optimization of initial task allocation is realized, and the control effect of the power equipment is improved.
In the embodiment of the invention, the power equipment is controlled through the target task allocation result, for example, 10 power demand tasks are allocated to 8 power equipment with high grade and small task amount, the power equipment is sequentially executed according to the allocated power demand tasks, for example, tasks 2 and 5 are allocated to the power equipment A, the task execution of the power equipment A is sequentially obtained as tasks 2 and 5, the task execution of each power equipment is determined through the integrated control of the power demand task allocation power equipment, and the work task allocation of each power equipment is reasonable, so that the reasonable allocation of the power demand tasks is realized, and the control effect of the power equipment is effectively improved.
According to the embodiment of the invention, through carrying out wavelet analysis and wavelet transformation on the historical operation data of the power equipment in the target low-voltage transformer area, the data noise in the historical data can be removed, and more accurate target data can be obtained, so that the control effect of the power equipment is improved; performing index decomposition on target data to obtain index components of each piece of power equipment, and further performing deep mining on characteristic data in historical data of the characteristic data in the target data; the running state of each power equipment is calculated so as to determine the distribution level, so that reasonable task distribution can be carried out on the power equipment, and the control effect of the power equipment is further improved; when the power demand task is received, the power equipment is subjected to initial task allocation according to the allocation grade, the initial task allocation is optimized, the power equipment is controlled according to the target task allocation result, the power equipment can be prevented from being abnormal, the reasonable allocation of the power demand task is realized, and the control effect of the power equipment in the low-voltage transformer area can be improved. Therefore, the method for controlling the power equipment in the low-voltage transformer area based on the Internet of things can solve the problem of poor control effect of the power equipment in the low-voltage transformer area.
Fig. 3 is a functional block diagram of a power equipment control system based on the internet of things for realizing a low-voltage transformer area according to an embodiment of the present invention.
The power equipment control system 300 for realizing the low-voltage transformer area based on the Internet of things can be installed in electronic equipment. According to the implemented functions, the power equipment control system 300 implemented under the low-voltage transformer area based on the internet of things may include a decomposition scale and transformation coefficient calculation module 301, a feature data extraction module 302, an allocation level determination module 303, an initial task allocation module 304, and a power equipment control module 305. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the decomposition scale and transformation coefficient calculation module 301 is configured to obtain historical operation data of the power device in the target low-voltage transformer area, perform wavelet analysis on the historical operation data to obtain initial noise reduction data, and perform wavelet transformation on the initial noise reduction data by using a preset decomposition layer number to obtain a decomposition scale and transformation coefficient of the historical operation data;
The feature data extraction module 302 is configured to construct target data of the historical operation data according to the decomposition scale and the transformation coefficient, perform index decomposition on the target data to obtain an index component of the target data, and extract feature data in the target data according to the index component;
the allocation level determining module 303 is configured to calculate an operation state of each of the electrical devices according to the feature data, and determine an allocation level of each of the electrical devices according to the operation state;
the initial task allocation module 304 is configured to perform initial task allocation on the power device according to the power demand task and the allocation level when the target low-voltage area receives the power demand task;
the power equipment control module 305 is configured to optimize the initial task allocation to obtain a target task allocation result of the power equipment, and control the power equipment according to the target task allocation result.
In detail, each module in the system 300 for controlling electric equipment in a low-voltage area based on the internet of things in the embodiment of the present invention adopts the same technical means as the method for controlling electric equipment in a low-voltage area based on the internet of things in fig. 1 to 2, and can produce the same technical effects, which are not described herein.
The invention also provides electronic equipment, which can comprise a processor, a memory, a communication bus and a communication interface, and can also comprise a computer program which is stored in the memory and can run on the processor, such as a power equipment control method program under a low-voltage area based on the Internet of things.
The processor may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and the like. The processor is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory (for example, executes a power device Control method program or the like in a low-voltage station area based on the internet of things), and invokes data stored in the memory to perform various functions of the electronic device and process the data.
The memory includes at least one type of readable storage medium including flash memory, removable hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory may also include both internal storage units and external storage devices of the electronic device. The memory can be used for storing application software installed in electronic equipment and various data, such as codes of a power equipment control method program for realizing a low-voltage station area based on the Internet of things, and can be used for temporarily storing data which is output or is to be output.
The communication bus may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory and at least one processor or the like.
The communication interface is used for communication between the electronic equipment and other equipment, and comprises a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Only an electronic device having components is shown, and it will be understood by those skilled in the art that the structures shown in the figures do not limit the electronic device, and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and the power source may be logically connected to the at least one processor 501 through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Specifically, the specific implementation method of the above instruction by the processor may refer to descriptions of related steps in the corresponding embodiment of the drawings, which are not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, implements the steps of implementing a method and system for controlling an electric device under a low-voltage transformer area based on the internet of things as described above:
storage media includes both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media may include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. The method for controlling the power equipment in the low-voltage area based on the Internet of things is characterized by comprising the following steps:
acquiring historical operation data of power equipment in a target low-voltage transformer area, performing wavelet analysis on the historical operation data to obtain initial noise reduction data, and performing wavelet transformation on the initial noise reduction data by utilizing a preset decomposition layer number to obtain a decomposition scale and a transformation coefficient of the historical operation data;
wavelet transforming the initial noise reduction data using the formula:
wherein,,indicating the number of decomposition layers as +.>The scale of decomposition during time,/->Indicating the number of decomposition layers as +. >Transform coefficient at time, < >>Representing +.f in the initial noise reduction data>Data of->Representing integer shift size, < > in wavelet transform>Indicate->The number of layers of the data is +.>Quantization scale of time->Representing the number of preset wavelet transform filters;
constructing target data of the historical operation data according to the decomposition scale and the transformation coefficient, performing index decomposition on the target data to obtain index components of the target data, and extracting characteristic data in the target data according to the index components;
calculating the operation state of each piece of electric equipment according to the characteristic data, and determining the distribution grade of each piece of electric equipment according to the operation state;
the performing index decomposition on the target data to obtain an index component of the target data includes:
obtaining an extremum of the target data, performing curve fitting according to the extremum to obtain an upper extremum curve and a lower extremum curve, and calculating a mean value curve of the upper extremum curve and the lower extremum curve;
subtracting the mean curve from the target data to obtain a first index component of the target data;
iterating the first index component as update data to obtain an update iteration component, stopping iterating when the update iteration component does not meet a preset constraint condition, and taking the first index component and the update iteration component as index components of the target data;
The extracting feature data in the target data according to the index component includes:
performing interpolation filtering on the index component to obtain a demodulation signal of the index component;
extracting index data of the demodulation signal, and constructing feature data corresponding to the index component according to the index data;
the calculating the operation state of each power device according to the characteristic data comprises the following steps:
extracting data features of the feature data, and carrying out weighted prediction on the data features by utilizing a hidden layer in a preset neural network to obtain a prediction score;
performing an activation operation on the prediction scores to obtain the running state of each piece of power equipment;
when the target low-voltage area receives a power demand task, carrying out initial task allocation on the power equipment according to the power demand task and the allocation grade;
optimizing the initial task allocation to obtain a target task allocation result of the power equipment, and controlling the power equipment according to the target task allocation result.
2. The method for controlling the power equipment in the low-voltage area based on the internet of things according to claim 1, wherein the constructing the target data of the historical operating data according to the decomposition scale and the transformation coefficient comprises:
Quantizing the decomposition scale to obtain a quantization scale, and selecting the transformation coefficient by using a preset dilution threshold to obtain a target coefficient;
and carrying out data reconstruction on the initial noise reduction data according to the quantization scale and the target coefficient to obtain target data of the historical operation data.
3. The method for controlling the power equipment in the low-voltage area based on the internet of things according to claim 1, wherein the performing initial task allocation on the power equipment according to the power demand task and the allocation level comprises:
determining target power equipment required to be subjected to task allocation according to the power demand task, and sequencing the target power equipment according to the allocation grade to obtain a target power equipment sequence;
and randomly distributing the power demand tasks according to the target power equipment sequence to obtain initial task distribution of the power equipment.
4. The method for controlling the power equipment in the low-voltage area based on the internet of things according to claim 1, wherein the optimizing the initial task allocation to obtain the target task allocation result of the power equipment comprises:
Calculating task overload amount distributed by the initial task, generating an objective function according to the task overload amount, and constructing an adaptability function based on the objective function;
the fitness function is constructed using the following formula:
wherein,,representing fitness function, ++>Representing an objective function +.>Representing the objective function at +.>Maximum value of the objective function at the time of iteration, +.>Indicate->Adjustment value at multiple iterations,/->Along with->Is increased by an increase in (a);
randomly generating a task allocation population, and calculating the fitness value of each task allocation in the task allocation population according to the fitness function;
selecting a target population from the task allocation populations according to the fitness value, calculating a crossing value of the target population, and generating an updated population according to the crossing value;
and carrying out variation iteration on the updated population until the iteration times of the variation iteration reach a preset iteration threshold value, and obtaining a target task allocation result of the power equipment.
5. The method for controlling the power equipment in the low-voltage area based on the internet of things according to claim 2, wherein the performing data reconstruction on the initial noise reduction data according to the quantization scale and the target coefficient to obtain the target data of the historical operation data comprises:
And carrying out data reconstruction on the initial noise reduction data by using the following formula:
wherein,,representing target data->Representing +.f in the initial noise reduction data>Data of->Indicate->The number of layers of the data is +.>Quantization scale of time->Representing integer shift size, < > in wavelet transform>Representation->The number of layers of the data is +.>Target coefficient at time,/->Representing a preset number of wavelet transform filters.
6. The method for controlling power equipment in a low-voltage area based on the internet of things according to claim 4, wherein the calculating the intersection value of the target population comprises:
calculating the intersection value of the target population by using the following formula:
wherein,,cross value representing said target population, +.>Representing the maximum value of the fitness function in the target population,representing the mean of the fitness function in the target population,/>representing the minimum value of the fitness function in said target population,/->Indicate->Multiple iterations(s)>Representing a preset iteration threshold.
7. An electric power equipment control system based on the internet of things for realizing under a low-voltage station area, which is characterized in that the system comprises:
the system comprises a decomposition scale and transformation coefficient calculation module, a wavelet analysis module and a wavelet analysis module, wherein the decomposition scale and transformation coefficient calculation module is used for acquiring historical operation data of power equipment in a target low-voltage transformer area, performing wavelet analysis on the historical operation data to obtain initial noise reduction data, and performing wavelet transformation on the initial noise reduction data by utilizing a preset decomposition layer number to obtain the decomposition scale and transformation coefficient of the historical operation data;
Wherein the initial noise reduction data is wavelet transformed using the following formula:
wherein,,indicating the number of decomposition layers as +.>The scale of decomposition during time,/->Indicating the number of decomposition layers as +.>Transform coefficient at time, < >>Representing +.f in the initial noise reduction data>Data of->Representing integer shift size, < > in wavelet transform>Indicate->The number of layers of the data is +.>Quantization scale of time->Representing the number of preset wavelet transform filters;
the characteristic data extraction module is used for constructing target data of the historical operation data according to the decomposition scale and the transformation coefficient, carrying out index decomposition on the target data to obtain index components of the target data, and extracting characteristic data in the target data according to the index components;
the distribution grade determining module is used for calculating the operation state of each piece of electric equipment according to the characteristic data and determining the distribution grade of each piece of electric equipment according to the operation state;
the performing index decomposition on the target data to obtain an index component of the target data includes:
obtaining an extremum of the target data, performing curve fitting according to the extremum to obtain an upper extremum curve and a lower extremum curve, and calculating a mean value curve of the upper extremum curve and the lower extremum curve;
Subtracting the mean curve from the target data to obtain a first index component of the target data;
iterating the first index component as update data to obtain an update iteration component, stopping iterating when the update iteration component does not meet a preset constraint condition, and taking the first index component and the update iteration component as index components of the target data;
the extracting feature data in the target data according to the index component includes:
performing interpolation filtering on the index component to obtain a demodulation signal of the index component;
extracting index data of the demodulation signal, and constructing feature data corresponding to the index component according to the index data;
the calculating the operation state of each power device according to the characteristic data comprises the following steps:
extracting data features of the feature data, and carrying out weighted prediction on the data features by utilizing a hidden layer in a preset neural network to obtain a prediction score;
performing an activation operation on the prediction scores to obtain the running state of each piece of power equipment;
the initial task allocation module is used for carrying out initial task allocation on the power equipment according to the power demand task and the allocation grade when the target low-voltage area receives the power demand task;
And the power equipment control module is used for optimizing the initial task allocation to obtain a target task allocation result of the power equipment, and controlling the power equipment according to the target task allocation result.
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