CN114331761A - Equipment parameter analysis and adjustment method and system for special transformer acquisition terminal - Google Patents
Equipment parameter analysis and adjustment method and system for special transformer acquisition terminal Download PDFInfo
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Abstract
The invention discloses a method and a system for analyzing and adjusting equipment parameters of a special transformer acquisition terminal, wherein the method comprises the following steps: performing abnormal data identification analysis on the first electric power data information to obtain first abnormal electric power data characteristics; inputting the first abnormal power data characteristic into a cyclic neural network for training, and constructing a first power abnormality analysis model; inputting second abnormal power data characteristics into the recurrent neural network for distributed training to obtain a second power abnormality analysis model; performing integrated training on model parameters of the first power anomaly analysis model and the second power anomaly analysis model to obtain an integrated power anomaly analysis model; and obtaining an electric power abnormity analysis result according to the integrated electric power abnormity analysis model, and controlling and managing the power consumption of the user based on the electric power abnormity analysis result. The technical problem that in the prior art, the collection and analysis of abnormal power data are not timely and accurate enough, so that the power management quality is influenced is solved.
Description
Technical Field
The invention relates to the field of control management, in particular to a method and a system for analyzing and adjusting equipment parameters of a special transformer acquisition terminal.
Background
The special transformer acquisition terminal is equipment for acquiring the electricity utilization information of a special transformer user, can realize the acquisition of electric energy meter data, the monitoring of the working condition of electric energy metering equipment and the quality of power supply and electric energy, the monitoring of customer electricity load and electric energy, and manages and bidirectionally transmits the acquired data. Therefore, the power utilization is controlled by the special transformer acquisition terminal, and the power management is of great significance.
However, the prior art has the technical problem that the quality of power management is affected due to the fact that the abnormal power data acquisition and analysis are not timely and accurate enough.
Disclosure of Invention
The method and the system for analyzing and adjusting the equipment parameters of the special transformer acquisition terminal solve the technical problem that in the prior art, the acquisition and analysis of abnormal power data are not timely and accurate enough to influence the power management quality, achieve the purpose of performing integrated analysis on the power data in a plurality of areas, establish an integrated power abnormal analysis model to timely process abnormal power results, improve the accuracy and the analysis efficiency of analysis results, and further guarantee the technical effect of the power management quality.
In view of the above problems, the present invention provides a method and a system for analyzing and adjusting device parameters of a specific transformer acquisition terminal.
In a first aspect, the present application provides a method for analyzing and adjusting device parameters of a dedicated transformer acquisition terminal, where the method includes: acquiring first electric power data information of a first area through a special transformer acquisition terminal; encrypting and uploading the first power data information, and transmitting the first power data information to a power load management platform for analysis; the power load management platform performs abnormal data identification analysis on the first power data information to obtain a first abnormal power data characteristic; inputting the first abnormal power data characteristic into a cyclic neural network for training, and constructing a first power abnormality analysis model; acquiring a second abnormal power data characteristic of a second area, inputting the second abnormal power data characteristic into the recurrent neural network for distributed training, and acquiring a second power abnormality analysis model; performing integrated training on model parameters of the first power anomaly analysis model and the second power anomaly analysis model to obtain an integrated power anomaly analysis model; and obtaining an electric power abnormity analysis result according to the integrated electric power abnormity analysis model, and controlling and managing the electricity consumption of the user based on the electric power abnormity analysis result.
On the other hand, this application still provides a special change acquisition terminal's equipment parameter analysis adjustment system, the system includes: the first obtaining unit is used for obtaining first electric power data information of a first area through a special transformer collecting terminal; the first processing unit is used for encrypting and uploading the first power data information, and transmitting the first power data information to a power load management platform for analysis; a second obtaining unit, configured to perform, by the power load management platform, abnormal data identification analysis on the first power data information to obtain a first abnormal power data feature; the first construction unit is used for inputting the first abnormal power data characteristics into a cyclic neural network for training and constructing a first power abnormality analysis model; a third obtaining unit, configured to obtain a second abnormal power data feature of a second area, input the second abnormal power data feature into the recurrent neural network for distributed training, and obtain a second power abnormality analysis model; a fourth obtaining unit, configured to perform integrated training on model parameters of the first power anomaly analysis model and the second power anomaly analysis model to obtain an integrated power anomaly analysis model; and the second processing unit is used for obtaining an electric power abnormity analysis result according to the integrated electric power abnormity analysis model and carrying out control management on the electricity consumption of the user based on the electric power abnormity analysis result.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program implements the steps of any of the methods when executed by the processor.
In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of any of the methods described above.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the technical scheme is that power data information is obtained through a special transformer acquisition terminal, the power data information is encrypted and uploaded, the power data information is transmitted to a power load management platform for analysis, abnormal power data features obtained through analysis are input into a recurrent neural network for training, a first power abnormality analysis model is built, a second power abnormality analysis model is built in a similar way, model parameters of the first power abnormality analysis model and the second power abnormality analysis model are subjected to integrated training, an integrated power abnormality analysis model is obtained, finally, a power abnormality analysis result which is a model output result is obtained according to the integrated power abnormality analysis model, and power consumption of a user is controlled and managed based on the power abnormality analysis result. And then, the technical effects that the power data of a plurality of areas are integrated and analyzed, so that an integrated power abnormity analysis model is constructed to timely process power abnormity results, the accuracy and the analysis efficiency of the analysis results are improved, and the power management quality is further ensured are achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flow chart of a method for analyzing and adjusting device parameters of a specific transformer acquisition terminal according to the present application;
fig. 2 is a schematic view illustrating a process of adjusting parameters of a special transformer acquisition terminal in the method for analyzing and adjusting device parameters of the special transformer acquisition terminal according to the present application;
fig. 3 is a schematic flow chart illustrating a process of obtaining a first abnormal power data characteristic in the method for analyzing and adjusting the device parameter of the special transformer acquisition terminal according to the present application;
fig. 4 is a schematic diagram illustrating a process of obtaining a power clustering detection data set in the method for analyzing and adjusting the device parameters of the special transformer acquisition terminal according to the present application;
fig. 5 is a schematic structural diagram of an equipment parameter analysis and adjustment system of a special transformer acquisition terminal according to the present application;
fig. 6 is a schematic structural diagram of an exemplary electronic device of the present application.
Description of reference numerals: a first obtaining unit 11, a first processing unit 12, a second obtaining unit 13, a first constructing unit 14, a third obtaining unit 15, a fourth obtaining unit 16, a second processing unit 17, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, an operating system 1151, an application 1152 and a user interface 1160.
Detailed Description
In the description of the present application, it will be appreciated by those skilled in the art that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, the present application may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied therein.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, an optical fiber, a compact disc read-only memory, an optical storage device, a magnetic storage device, or any combination thereof. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or system.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws.
The method, the device and the electronic equipment are described by the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings attached hereto.
Example one
As shown in fig. 1, the present application provides a method for analyzing and adjusting device parameters of a dedicated transformer acquisition terminal, where the method includes:
step S100: acquiring first electric power data information of a first area through a special transformer acquisition terminal;
step S200: encrypting and uploading the first power data information, and transmitting the first power data information to a power load management platform for analysis;
specifically, the special transformer acquisition terminal is a device for acquiring the electricity utilization information of a special transformer user, generally comprises a CPU module, a main control module, a liquid crystal display module, a standby module, a GPRS module, a lithium battery, a power supply and an interface module, can realize the acquisition of electric energy meter data, the monitoring of the working condition and the power supply quality of electric energy metering equipment and the monitoring of customer electricity utilization load and electric energy, and manages and bidirectionally transmits the acquired data. Therefore, the power utilization is controlled by the special transformer acquisition terminal, and the power management is of great significance. The method comprises the steps that first electric power data information of a first area is obtained through a special transformer acquisition terminal, and the first area is an electric power management area, such as a community, a factory, a school and the like.
The collected first electric power data information comprises voltage data, current data, power consumption data, electric power data, real-time power consumption information and the like, and an electric power data basis is provided for follow-up electric power abnormity real-time analysis. The first power data information is encrypted and uploaded, data transmission safety is guaranteed, the first power data information is transmitted to a power load management platform to be analyzed, the power load management platform is a basic platform for collecting and analyzing real-time power utilization information of a client side and is a power master station, and the collected power data information is monitored and analyzed to achieve management control over power abnormal information.
Step S300: the power load management platform performs abnormal data identification analysis on the first power data information to obtain a first abnormal power data characteristic;
as shown in fig. 3, further to obtain the first abnormal electrical data characteristic, step S300 of the present application further includes:
step S310: obtaining a power clustering detection data set;
step S320: performing category marking on the power clustering detection data set to obtain a marked training power data set;
step S330: taking the marked training power data set as input data to carry out support vector machine model training to obtain a power abnormal feature recognition model;
step S340: inputting the first power data information into the power abnormal feature recognition model, and obtaining a first output result, wherein the first output result comprises the first abnormal power data feature.
Specifically, the power load management platform performs abnormal data identification and analysis on the first power data information, and first obtains a power clustering detection data set based on historical power data information. The power clustering detection data set is a result obtained by clustering and dividing historical power data information, such as current data and voltage data, and improves the accuracy of power data classification and division. And then, carrying out abnormal category marking on the power clustering detection data set, such as current abnormal data and voltage abnormal data, marking corresponding category labels on different categories of power data, and obtaining a corresponding marked training power data set.
And carrying out support vector machine model training by taking the label training power data set as input data, wherein the support vector machine algorithm can be applied to not only the linearly separable data set, but also the case that the data set is not linearly separable. In the field of machine learning, the method is a supervised learning model, and is generally used for pattern recognition, classification and regression analysis, namely, the method carries out supervised learning on a marked training detection data set, and then constructs the electric power abnormal feature recognition model according to a trained support vector machine model. And inputting the first power data information into the power abnormal feature recognition model to obtain a training output result of the model, namely the first abnormal power data feature, wherein the first abnormal power data feature is an abnormal data result in power data. Abnormal power data are identified in time through a support vector machine model, the identification accuracy and the identification efficiency of the abnormal power data are improved, and the power quality management efficiency is further improved.
Step S400: inputting the first abnormal power data characteristic into a cyclic neural network for training, and constructing a first power abnormality analysis model;
further, in the building of the first power anomaly analysis model, step S400 of the present application further includes:
step S410: obtaining an initial hidden layer value of the recurrent neural network, and obtaining a first input weight matrix based on the initial hidden layer value;
step S420: taking the first diagnosis and treatment characteristic information as input layer information, and training the recurrent neural network according to the input layer information and the first input weight matrix;
step S430: and taking the input layer information and the initial hidden layer value as a next hidden layer value, and sequentially carrying out iterative training to construct the first fracture and wound analysis model.
Specifically, the first abnormal power data feature is input into a recurrent neural network for training, the recurrent neural network is a recurrent neural network which takes sequence data as input, recurs in the evolution direction of the sequence and all nodes (recurrent units) are connected in a chain manner, and the recurrent neural network comprises an input layer, a hidden layer and an output layer. In the process of processing input information by the processing layer in the recurrent neural network, the processing layer not only processes the input information according to the current input information, but also stores output information of the previous time sequence, processes the output information as the input information of the current time sequence, and further obtains output, and the processing layer is continuously updated along with the advance of the time sequence. The recurrent neural network not only relates to the current input but also relates to the output at the last moment by using the neurons with self feedback, so that the recurrent neural network has short-term memory capability when processing time series data of any length.
The initial hidden layer value can be obtained in a self-defined mode, a first input weight matrix is obtained based on the initial hidden layer value, in the processing process, the current input information and the output information of the previous time sequence are predicted according to a certain weight ratio, namely the weight matrix is obtained, and in the updating process of the processing layer, the weight value in the weight matrix is stable and unchangeable. And when the output result of the recurrent neural network reaches a certain accuracy rate or convergence through sequential iterative training, finishing supervised training and constructing the first power anomaly analysis model. The first power anomaly analysis model is used for analyzing and processing power anomaly information, so that an output power anomaly result is more accurate and reasonable, and the accuracy and the management efficiency of abnormal power data management are guaranteed to be improved.
Step S500: acquiring a second abnormal power data characteristic of a second area, inputting the second abnormal power data characteristic into the recurrent neural network for distributed training, and acquiring a second power abnormality analysis model;
step S600: performing integrated training on model parameters of the first power anomaly analysis model and the second power anomaly analysis model to obtain an integrated power anomaly analysis model;
specifically, in order to analyze the abnormal power data more accurately and comprehensively, and to collect the power data in other areas in the same way, the number of the second areas may be one or more, and the collected second abnormal power data characteristics are input to the recurrent neural network for distributed training to obtain a corresponding second power abnormality analysis model. And performing integrated training on model parameters of the first power abnormality analysis model and the second power abnormality analysis model based on the training result of the recurrent neural network, such as an abnormality type parameter, an abnormality duration parameter, model corresponding weights and the like. And updating parameters of the cyclic neural network according to the model parameters, and constructing an integrated power anomaly analysis model after integrated training, so that the output result of the integrated power anomaly analysis model after parameter federation learning is more reasonable and accurate, and the application range is more comprehensive.
Step S700: and obtaining an electric power abnormity analysis result according to the integrated electric power abnormity analysis model, and controlling and managing the electricity consumption of the user based on the electric power abnormity analysis result.
Specifically, according to the integrated power abnormality analysis model constructed as described above, abnormality analysis is performed on an area that needs to be subjected to power management control, and corresponding power abnormality analysis results, such as power abnormality types, abnormality causes, severity, and the like, are obtained. And controlling and managing the power consumption of the user based on the power abnormality analysis result so as to further make a power abnormality solution, for example, the user needs to perform corresponding power consumption penalty and power limit management due to abnormal power consumption caused by power stealing. By integrating and analyzing the electric power data of the plurality of areas, an integrated electric power abnormity analysis model is constructed to timely process the electric power abnormity result, the accuracy and the analysis efficiency of the analysis result are improved, and the electric power management quality is further ensured.
As shown in fig. 2, further, the steps of the present application further include:
step S810: acquiring parameter configuration information of the special transformer acquisition terminal according to the electric power master station file;
step S820: carrying out feasibility analysis on the parameter configuration information to obtain a terminal feasibility analysis result;
step S830: acquiring sensitivity information of the special transformer acquisition terminal based on the electric power data acquisition amount and the actual electric power data difference value of the data acquisition period;
step S840: obtaining an operation acquisition quality coefficient according to the terminal feasibility analysis result and the sensitivity information;
step S850: and adjusting parameters of the special transformer acquisition terminal based on the operation acquisition quality coefficient.
Specifically, for better management of power consumers, a power master station establishes a user profile, wherein the power master station profile comprises a profile number, an electric energy meter profile, a transformer profile, a collector terminal profile, configuration parameters and the like of each user in a power utilization area. And acquiring parameter configuration information of the special transformer acquisition terminal of the power user according to the power master station file, wherein the parameter configuration information comprises a terminal IP address, a master station acquisition period, a metering point sequence, a transformation ratio multiplying power, a decimal place and the like. And performing feasibility analysis on the parameter configuration information, namely performing usability evaluation on the parameters of the user acquisition terminal to obtain a terminal feasibility analysis result, namely the accuracy of acquiring the user power information by the terminal setting parameters.
And acquiring sensitivity information of the special transformer acquisition terminal based on the electric power data acquisition amount and the actual power utilization data difference value of the data acquisition period, wherein the smaller the difference value is, the better the sensitivity information of the special transformer acquisition terminal is, and the more accurate the acquired data is. And determining an operation acquisition quality coefficient in combination according to the terminal feasibility analysis result and the sensitivity information, wherein the operation acquisition quality coefficient is the accuracy of the special transformer acquisition terminal in acquiring the power utilization information of the power users, and the acquisition accuracy is higher when the quality coefficient is higher. And adjusting parameters of the special transformer acquisition terminal based on the operation acquisition quality coefficient, for example, adjusting a terminal acquisition period or a variable ratio parameter, so as to ensure the timeliness and accuracy of data acquisition of the special transformer acquisition terminal, and improve the management efficiency of the power management quality.
As shown in fig. 4, further to obtain the power clustering detection data set, step S310 of the present application further includes:
step S311: constructing a historical power data information base;
step S312: traversing and cleaning the power data in the historical power data information base, and carrying out normalization processing according to a preset data format to generate a standard power data information set;
step S313: clustering and dividing the standard electric power data information set to obtain an electric power data clustering result;
step S314: and constructing the power clustering detection data set based on main feature analysis of the power data clustering result.
Specifically, a historical power data information base is constructed, the historical power data information base is historical power data information acquired by the power load management platform, power data in the historical power data information base are subjected to traversal cleaning, data consistency is checked, invalid values, missing values and the like are processed, normalization processing is performed according to a preset data format, data format standardization is guaranteed, and a processed standard power data information set is generated. The standard power data information set is subjected to clustering division, the data set is grouped into a plurality of classes consisting of similar objects, the analysis process is an unsupervised learning process, and corresponding power data clustering results are obtained, for example, the clustering division is carried out according to the power data types.
Based on the main feature analysis of the power data clustering result, the main feature analysis is a linear dimensionality reduction method, and aims to reduce the dimensionality of the original features under the condition of ensuring that the information quantity is not lost as much as possible, so that the loss of the information quantity after dimensionality reduction is minimum. According to the power data after dimensionality reduction, the power clustering detection data set is constructed, the power clustering detection data set is a result obtained by clustering and dividing historical power data information, for example, the historical power data information is divided into current data, voltage data and the like, the accuracy of classification and division of the power data is improved, and further the construction accuracy of a power anomaly analysis model is improved.
Further, the method further comprises the following steps:
step S910: constructing a first power abnormal waveform according to the first abnormal power data characteristic;
step S920: obtaining a preset power signal threshold, and analyzing the first power abnormal waveform based on the preset power signal threshold to obtain an abnormal power signal set;
step S930: marking the abnormal power signal set, and carrying out statistics based on the period and the amplitude of a marking point to obtain a first abnormal damage coefficient;
step S940: and formulating an electricity utilization maintenance control scheme based on the first abnormal damage coefficient.
Specifically, in order to process the power failure more quickly, a first power abnormal waveform is constructed according to the first abnormal power data characteristic, the first power abnormal waveform is a fault waveform and comprises the whole process of the power failure, the collected waveform is not generally subjected to filtering processing, and the authenticity and the real-time property of fault information are kept as much as possible. The preset electric power signal threshold is an electric power signal variation range in normal operation, the first electric power abnormal waveform is analyzed based on the preset electric power signal threshold, and an abnormal electric power signal set exceeding the threshold range is obtained. And carrying out data marking on the abnormal power signal set, carrying out statistics on the period and the amplitude of the marked points, solving the current, the voltage amplitude and the phase in the fault process, the fault property and the fault duration, and evaluating to obtain a first abnormal damage coefficient, wherein the larger the abnormal damage coefficient is, the larger the power damage degree is, and emergency treatment is needed. Based on first abnormal damage coefficient, formulate the power consumption and overhaul control scheme, the power consumption is overhauld the control scheme and is combined fault feature and damage degree to carry out the maintenance scheme of formulating for in time resume power consumption safety, guarantee that power failure handles in time, and then guarantee power quality control efficiency.
Further, step S200 of the present application further includes:
step S210: selecting a power encryption algorithm according to the data acquisition grade of the first area;
step S220: encrypting the first power data information based on the power encryption algorithm to obtain first encrypted power data information;
step S230: and uploading the first encrypted power data information to the power load management platform.
Specifically, the data acquisition grade of the first area is the power data security grade of the power utilization acquisition area, for example, the security grade of the power utilization information of the military region is high, and a power encryption algorithm is selected according to the data acquisition grade of the first area. The basic process of data encryption is to process the original file or data in plain text according to a certain algorithm to make it become an unreadable segment of code as "ciphertext", so that the original content can be displayed only after inputting the corresponding key, and the purpose of protecting the data from being stolen and read by an illegal person is achieved through the way. The higher the data security level is, the higher the corresponding encryption algorithm level is, and the first power data information is encrypted based on the power encryption algorithm to obtain encrypted first encrypted power data information. And uploading the first encrypted electric power data information to the electric power load management platform for analysis, so that the data transmission safety is ensured, the data acquisition accuracy is improved, and the electric power data management safety is further ensured.
In summary, the method and the system for analyzing and adjusting the device parameters of the dedicated transformer acquisition terminal provided by the present application have the following technical effects:
the technical scheme is that power data information is obtained through a special transformer acquisition terminal, the power data information is encrypted and uploaded, the power data information is transmitted to a power load management platform for analysis, abnormal power data features obtained through analysis are input into a recurrent neural network for training, a first power abnormality analysis model is built, a second power abnormality analysis model is built in a similar way, model parameters of the first power abnormality analysis model and the second power abnormality analysis model are subjected to integrated training, an integrated power abnormality analysis model is obtained, finally, a power abnormality analysis result which is a model output result is obtained according to the integrated power abnormality analysis model, and power consumption of a user is controlled and managed based on the power abnormality analysis result. And then, the technical effects that the power data of a plurality of areas are integrated and analyzed, so that an integrated power abnormity analysis model is constructed to timely process power abnormity results, the accuracy and the analysis efficiency of the analysis results are improved, and the power management quality is further ensured are achieved.
Example two
Based on the same inventive concept as the equipment parameter analysis and adjustment method of the special transformer acquisition terminal in the foregoing embodiment, the present invention further provides an equipment parameter analysis and adjustment system of the special transformer acquisition terminal, as shown in fig. 5, the system includes:
the first obtaining unit 11 is used for obtaining first electric power data information of a first area through a special transformer collecting terminal;
the first processing unit 12, the first processing unit 12 is configured to encrypt and upload the first power data information, and transmit the encrypted and uploaded first power data information to a power load management platform for analysis;
a second obtaining unit 13, where the second obtaining unit 13 is configured to perform abnormal data identification analysis on the first power data information by the power load management platform, so as to obtain a first abnormal power data feature;
a first constructing unit 14, where the first constructing unit 14 is configured to input the first abnormal power data feature into a recurrent neural network for training, and construct a first power abnormality analysis model;
a third obtaining unit 15, where the third obtaining unit 15 is configured to obtain a second abnormal power data feature of a second area, and input the second abnormal power data feature into the recurrent neural network for distributed training to obtain a second power abnormality analysis model;
a fourth obtaining unit 16, where the fourth obtaining unit 16 is configured to perform integrated training on model parameters of the first power abnormality analysis model and the second power abnormality analysis model to obtain an integrated power abnormality analysis model;
and the second processing unit 17 is configured to obtain an electric power abnormality analysis result according to the integrated electric power abnormality analysis model, and perform control management on the user power consumption based on the electric power abnormality analysis result.
Further, the system further comprises:
the fifth obtaining unit is used for obtaining the parameter configuration information of the special transformer acquisition terminal according to the electric power master station file;
a sixth obtaining unit, configured to perform feasibility analysis on the parameter configuration information to obtain a terminal feasibility analysis result;
a seventh obtaining unit, configured to obtain sensitivity information of the special transformer acquisition terminal based on a power data acquisition amount and an actual power data difference in a data acquisition period;
an eighth obtaining unit, configured to obtain an operation acquisition quality coefficient according to the terminal feasibility analysis result and the sensitivity information;
and the first adjusting unit is used for adjusting parameters of the special transformer acquisition terminal based on the running acquisition quality coefficient.
Further, the system further comprises:
a ninth obtaining unit configured to obtain a power cluster detection data set;
a tenth obtaining unit, configured to perform category labeling on the power clustering detection data set to obtain a labeled training power data set;
an eleventh obtaining unit, configured to perform support vector machine model training using the labeled training power dataset as input data, and obtain a power abnormality feature recognition model;
a twelfth obtaining unit, configured to input the first power data information into the power abnormality feature recognition model, and obtain a first output result, where the first output result includes the first abnormality power data feature.
Further, the system further comprises:
the second construction unit is used for constructing a historical power data information base;
the first generation unit is used for traversing and cleaning the power data in the historical power data information base, and carrying out normalization processing according to a preset data format to generate a standard power data information set;
a thirteenth obtaining unit, configured to perform cluster division on the standard electric power data information set to obtain an electric power data cluster result;
and the third construction unit is used for constructing the power clustering detection data set based on main feature analysis of the power data clustering result.
Further, the system further comprises:
a fourth construction unit, configured to construct a first abnormal power waveform according to the first abnormal power data characteristic;
a fourteenth obtaining unit, configured to obtain a preset power signal threshold, analyze the first power abnormal waveform based on the preset power signal threshold, and obtain an abnormal power signal set;
a fifteenth obtaining unit, configured to mark the abnormal power signal set, and perform statistics based on a mark point period and an amplitude to obtain a first abnormal damage coefficient;
and the first formulating unit is used for formulating a power utilization overhaul control scheme based on the first abnormal damage coefficient.
Further, the system further comprises:
the first selection unit is used for selecting a power encryption algorithm according to the data acquisition grade of the first area;
a sixteenth obtaining unit, configured to encrypt the first power data information based on the power encryption algorithm, and obtain first encrypted power data information;
the first uploading unit is used for uploading the first encrypted power data information to the power load management platform.
Further, the system further comprises:
a seventeenth obtaining unit, configured to obtain an initial hidden layer value of the recurrent neural network, and obtain a first input weight matrix based on the initial hidden layer value;
a first training unit, configured to train the recurrent neural network according to the input layer information and the first input weight matrix, with the first abnormal power data feature as input layer information;
and the fifth construction unit is used for performing iterative training in sequence by taking the input layer information and the initial hidden layer value as a next hidden layer value to construct the first power anomaly analysis model.
Various changes and specific examples of the device parameter analysis and adjustment method for the special transformer acquisition terminal in the first embodiment of fig. 1 are also applicable to the device parameter analysis and adjustment system for the special transformer acquisition terminal in this embodiment, and through the foregoing detailed description of the device parameter analysis and adjustment method for the special transformer acquisition terminal, those skilled in the art can clearly know the implementation method of the device parameter analysis and adjustment system for the special transformer acquisition terminal in this embodiment, so that the detailed description is omitted here for brevity of the description.
In addition, the present application further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected via the bus, respectively, and when the computer program is executed by the processor, the processes of the above-mentioned method for controlling output data are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Exemplary electronic device
In particular, referring to fig. 6, the present application further provides an electronic device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In this application, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of the method embodiments of controlling output data described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In this application, a bus architecture (represented by bus 1110), bus 1110 may include any number of interconnected buses and bridges, bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Processor 1120 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits in hardware or instructions in software in a processor. The processor described above includes: general purpose processors, central processing units, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro-control units or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in this application may be implemented or performed. For example, the processor may be a single core processor or a multi-core processor, which may be integrated on a single chip or located on multiple different chips.
Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in connection with the present application may be performed directly by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software modules may reside in random access memory, flash memory, read only memory, programmable read only memory, erasable programmable read only memory, registers, and the like, as is known in the art. The readable storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, it will not be further described in this application.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in the subject application, the memory 1150 can further include memory remotely located from the processor 1120, which can be coupled to a server via a network. One or more portions of the above-described network may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and a combination of two or more of the above. For example, the cellular telephone network and the wireless network may be global mobile communications devices, code division multiple access devices, global microwave interconnect access devices, general packet radio service devices, wideband code division multiple access devices, long term evolution devices, LTE frequency division duplex devices, LTE time division duplex devices, long term evolution advanced devices, universal mobile communications devices, enhanced mobile broadband devices, mass machine type communications devices, ultra-reliable low-latency communications devices, and the like.
It will be appreciated that the memory 1150 in the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described herein includes, but is not limited to, the above-described and any other suitable types of memory.
In the present application, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various device programs, such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media player, browser, used to realize various application services. A program implementing the method of the present application may be included in the application 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer device-executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements each process of the above method for controlling output data, and can achieve the same technical effect, and is not described herein again to avoid repetition.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A device parameter analysis and adjustment method for a special transformer acquisition terminal is characterized by comprising the following steps:
acquiring first electric power data information of a first area through a special transformer acquisition terminal;
encrypting and uploading the first power data information, and transmitting the first power data information to a power load management platform for analysis;
the power load management platform performs abnormal data identification analysis on the first power data information to obtain a first abnormal power data characteristic;
inputting the first abnormal power data characteristic into a cyclic neural network for training, and constructing a first power abnormality analysis model;
acquiring a second abnormal power data characteristic of a second area, inputting the second abnormal power data characteristic into the recurrent neural network for distributed training, and acquiring a second power abnormality analysis model;
performing integrated training on model parameters of the first power anomaly analysis model and the second power anomaly analysis model to obtain an integrated power anomaly analysis model;
and obtaining an electric power abnormity analysis result according to the integrated electric power abnormity analysis model, and controlling and managing the electricity consumption of the user based on the electric power abnormity analysis result.
2. The method of claim 1, wherein the method comprises:
acquiring parameter configuration information of the special transformer acquisition terminal according to the electric power master station file;
carrying out feasibility analysis on the parameter configuration information to obtain a terminal feasibility analysis result;
acquiring sensitivity information of the special transformer acquisition terminal based on the electric power data acquisition amount and the actual electric power data difference value of the data acquisition period;
obtaining an operation acquisition quality coefficient according to the terminal feasibility analysis result and the sensitivity information;
and adjusting parameters of the special transformer acquisition terminal based on the operation acquisition quality coefficient.
3. The method of claim 1, wherein said obtaining a first abnormal power data characteristic comprises:
obtaining a power clustering detection data set;
performing category marking on the power clustering detection data set to obtain a marked training power data set;
taking the marked training power data set as input data to carry out support vector machine model training to obtain a power abnormal feature recognition model;
inputting the first power data information into the power abnormal feature recognition model, and obtaining a first output result, wherein the first output result comprises the first abnormal power data feature.
4. The method of claim 3, wherein the obtaining a power cluster detection dataset comprises:
constructing a historical power data information base;
traversing and cleaning the power data in the historical power data information base, and carrying out normalization processing according to a preset data format to generate a standard power data information set;
clustering and dividing the standard electric power data information set to obtain an electric power data clustering result;
and constructing the power clustering detection data set based on main feature analysis of the power data clustering result.
5. The method of claim 1, wherein the method comprises:
constructing a first power abnormal waveform according to the first abnormal power data characteristic;
obtaining a preset power signal threshold, and analyzing the first power abnormal waveform based on the preset power signal threshold to obtain an abnormal power signal set;
marking the abnormal power signal set, and carrying out statistics based on the period and the amplitude of a marking point to obtain a first abnormal damage coefficient;
and formulating an electricity utilization maintenance control scheme based on the first abnormal damage coefficient.
6. The method of claim 1, wherein the cryptographically uploading the first power data information comprises:
selecting a power encryption algorithm according to the data acquisition grade of the first area;
encrypting the first power data information based on the power encryption algorithm to obtain first encrypted power data information;
and uploading the first encrypted power data information to the power load management platform.
7. The method of claim 1, wherein the constructing the first power anomaly analysis model comprises:
obtaining an initial hidden layer value of the recurrent neural network, and obtaining a first input weight matrix based on the initial hidden layer value;
taking the first abnormal power data characteristic as input layer information, and training the recurrent neural network according to the input layer information and the first input weight matrix;
and taking the input layer information and the initial hidden layer value as a next hidden layer value, and sequentially performing iterative training to construct the first power abnormity analysis model.
8. The utility model provides a device parameter analysis adjustment system of special change acquisition terminal which characterized in that, the system includes:
the first obtaining unit is used for obtaining first electric power data information of a first area through a special transformer collecting terminal;
the first processing unit is used for encrypting and uploading the first power data information, and transmitting the first power data information to a power load management platform for analysis;
a second obtaining unit, configured to perform, by the power load management platform, abnormal data identification analysis on the first power data information to obtain a first abnormal power data feature;
the first construction unit is used for inputting the first abnormal power data characteristics into a cyclic neural network for training and constructing a first power abnormality analysis model;
a third obtaining unit, configured to obtain a second abnormal power data feature of a second area, input the second abnormal power data feature into the recurrent neural network for distributed training, and obtain a second power abnormality analysis model;
a fourth obtaining unit, configured to perform integrated training on model parameters of the first power anomaly analysis model and the second power anomaly analysis model to obtain an integrated power anomaly analysis model;
and the second processing unit is used for obtaining an electric power abnormity analysis result according to the integrated electric power abnormity analysis model and carrying out control management on the electricity consumption of the user based on the electric power abnormity analysis result.
9. An electronic device for analyzing and adjusting device parameters of a specific transformer acquisition terminal, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the transceiver, the memory and the processor are connected via the bus, and wherein the computer program, when executed by the processor, implements the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
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