CN117411875A - Power data transmission system, method, device, equipment and storage medium - Google Patents
Power data transmission system, method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN117411875A CN117411875A CN202311714270.4A CN202311714270A CN117411875A CN 117411875 A CN117411875 A CN 117411875A CN 202311714270 A CN202311714270 A CN 202311714270A CN 117411875 A CN117411875 A CN 117411875A
- Authority
- CN
- China
- Prior art keywords
- message data
- data
- power consumption
- message
- compression
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 119
- 238000007906 compression Methods 0.000 claims abstract description 101
- 230000006835 compression Effects 0.000 claims abstract description 101
- 230000006837 decompression Effects 0.000 claims abstract description 69
- 238000012706 support-vector machine Methods 0.000 claims abstract description 54
- 238000012549 training Methods 0.000 claims description 60
- 230000005611 electricity Effects 0.000 claims description 20
- 230000006870 function Effects 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 11
- 238000002372 labelling Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 7
- 230000006872 improvement Effects 0.000 abstract description 2
- 230000006854 communication Effects 0.000 description 29
- 238000004891 communication Methods 0.000 description 28
- 238000013144 data compression Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 238000013145 classification model Methods 0.000 description 2
- 238000010295 mobile communication Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000005538 encapsulation Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000000869 ion-assisted deposition Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000005437 stratosphere Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 239000005436 troposphere Substances 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/04—Protocols for data compression, e.g. ROHC
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/22—Parsing or analysis of headers
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2212/00—Encapsulation of packets
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- Compression, Expansion, Code Conversion, And Decoders (AREA)
Abstract
The invention provides an electric power data transmission system, a method, a device, an electronic device and a storage medium, wherein compression end equipment and decompression end equipment for data exchange are arranged in electric power data transmission, the compression end equipment is used for acquiring electric message data, inputting the electric message data into a pre-trained support vector machine algorithm model, acquiring the message type of the electric message data, confirming the compression algorithm of the electric message data according to the message type, acquiring the compression message data, and then sending the compression message data to the decompression end equipment; the decompression terminal equipment decompresses the compressed message data to obtain the power utilization message data, and then sends the power utilization message data to the power utilization message receiving terminal. According to the method and the device, the linear inseparable power data content is classified through the support vector machine algorithm and matched with the optimal compression algorithm, so that the improvement of the circuit data transmission efficiency is realized.
Description
Technical Field
The embodiment of the application relates to the technical field of power communication, in particular to a power data transmission system, a method, a device, equipment and a storage medium.
Background
In a part of weak signal or no signal area, along with the popularization and application of the Beidou satellite navigation system, the electric power system starts to perform electric power data communication through the Beidou short message communication service. Because civil Beidou short message communication service has low service frequency and limited single transmission bandwidth, in practical application, if the communication is carried out through the Beidou short message, data lossless compression is needed to be carried out on transmission information of an electric power system.
The common lossless data compression method comprises a plurality of algorithms such as LZSS, LZW, LZMA and the like; the messages used for transmission in the power system also have message types such as IEC101 protocol messages, CDT protocol messages and the like; different message types correspond to different optimal compression methods. However, when power data communication is performed, a unified data compression method is used for all messages, so that the message compression efficiency is low.
Disclosure of Invention
The application provides a power data transmission system, a method, a device, electronic equipment and a storage medium, wherein the message type and the message characteristics are classified, so that the most suitable data compression mode is selected for data lossless compression, and the compression efficiency is improved.
In a first aspect, the present application provides a power data transmission system comprising:
the compression end device is in signal connection with the decompression end device;
the compression end equipment is used for obtaining the power consumption message data, inputting the power consumption message data into a pre-trained support vector machine algorithm model, obtaining the message type of the power consumption message data, confirming the compression algorithm of the power consumption message data according to the message type, obtaining the compression message data, and then sending the compression message data to the decompression end equipment;
the decompression terminal equipment decompresses the compressed message data to obtain the electricity consumption message data, and then sends the electricity consumption message data to an electricity consumption message receiving terminal.
Further, the method further comprises the following steps:
the compression end device is further configured to add an identifier to the compressed message data according to the message type, where the identifier indicates a compression algorithm of the compressed message data, encapsulate the identified compressed message data, obtain encapsulated compressed message data, and send the encapsulated compressed message data to the decompression end device;
the decompression terminal equipment is also used for identifying the identifier, confirming a corresponding decompression algorithm according to the compression algorithm indicated by the identifier, decompressing the encapsulated compressed message data through the corresponding decompression algorithm, and obtaining the power consumption message data.
Further, the compression end device is further configured to perform a pre-training process of the support vector machine algorithm model, including:
acquiring an original protocol data stream, and extracting samples of the original protocol data stream according to a preset fixed code length to acquire training samples;
labeling the training samples according to the classification of the original protocol data stream, wherein the training samples with the same label indicate that the training samples have the same protocol data stream classification;
constructing a support vector machine algorithm model, and inputting the training sample into the support vector machine algorithm model for supervised training, wherein a kernel function of the support vector machine algorithm model is a Gaussian kernel function;
and acquiring the trained support vector machine algorithm model.
Further, the method further comprises the following steps:
the compression end device is further configured to add an identifier to the header of the compressed packet data, where the identifier includes a first identifier and a second identifier, the first identifier indicates that the compression algorithm of the compressed packet data is an LZW compression algorithm, and the second identifier indicates that the compression algorithm of the compressed packet data is an LZMA compression algorithm.
In a second aspect, the present application provides a power data transmission method applied to a compression end device, including:
acquiring power consumption message data;
inputting the power consumption message data into a pre-trained support vector machine algorithm model to obtain the message type of the power consumption message data;
and confirming a compression algorithm of the power consumption message data according to the message type, acquiring the compressed message data, and then sending the compressed message data to decompression terminal equipment, wherein the decompression terminal is used for decompressing the compressed message data to acquire the power consumption message data, and then sending the power consumption message data to a power consumption message receiving terminal.
Further, after the compressed message data is obtained, the method further includes:
adding an identifier to the compressed message data according to the message type, wherein the identifier indicates a compression algorithm of the compressed message data;
and encapsulating the identified compressed message data to obtain encapsulated compressed message data and transmitting the encapsulated compressed message data to the decompression terminal equipment, wherein the decompression terminal equipment is used for identifying the identifier, confirming a corresponding decompression algorithm according to a compression algorithm indicated by the identifier, decompressing the encapsulated compressed message data through the corresponding decompression algorithm, and obtaining the power consumption message data.
Further, the pre-training method of the support vector machine algorithm model further comprises the following steps:
acquiring an original protocol data stream, and extracting samples of the original protocol data stream according to a preset fixed code length to acquire training samples;
labeling the training samples according to the classification of the original protocol data stream, wherein the training samples with the same label indicate that the training samples have the same protocol data stream classification;
constructing a support vector machine algorithm model, and inputting the training sample into the support vector machine algorithm model for supervised training, wherein a kernel function of the support vector machine algorithm model is a Gaussian kernel function;
and acquiring the trained support vector machine algorithm model.
In a third aspect, the present application provides a power data transmission apparatus comprising:
the power consumption message acquisition module is used for acquiring power consumption message data;
the message type acquisition module is used for inputting the power consumption message data into a pre-trained support vector machine algorithm model to acquire the message type of the power consumption message data;
and the compressed message data transmitting module is used for confirming a compression algorithm of the power consumption message data according to the message type, acquiring the compressed message data, and transmitting the compressed message data to decompression terminal equipment, wherein the decompression terminal is used for decompressing the compressed message data, acquiring the power consumption message data and transmitting the power consumption message data to a power consumption information receiving terminal.
In a fourth aspect, the present application provides an electronic device, comprising:
at least one memory and at least one processor;
the memory is used for storing one or more programs;
the one or more programs, when executed by the at least one processor, cause the at least one processor to implement the steps of a power data transmission method as described in the second aspect.
In a fifth aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of a power data transmission method according to the second aspect.
The invention sets up the compression end equipment and decompression end equipment for data exchange in the electric data transmission, wherein the compression end equipment is used for obtaining the power consumption message data, inputting the power consumption message data into a pre-trained support vector machine algorithm model, obtaining the message type of the power consumption message data, confirming the compression algorithm of the power consumption message data according to the message type, obtaining the compression message data, and then sending the compression message data to the decompression end equipment; the decompression terminal equipment decompresses the compressed message data to obtain the power utilization message data, and then sends the power utilization message data to the power utilization message receiving terminal. According to the method and the device, the linear inseparable power data content is classified through the support vector machine algorithm and matched with the optimal compression algorithm, so that the improvement of the circuit data transmission efficiency is realized.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Drawings
FIG. 1 is a block diagram of a power data transmission system provided in one exemplary embodiment;
FIG. 2 is a schematic diagram of training sample extraction and labeling for a power data transmission system provided in one exemplary embodiment;
FIG. 3 is a flow chart of steps of a method of power data transmission provided in one exemplary embodiment;
FIG. 4 is a block diagram of a power data transmission device provided in one exemplary embodiment;
FIG. 5 is a schematic diagram of an electronic device provided in an exemplary embodiment;
fig. 6 is a schematic diagram of an electronic device provided in an exemplary embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following detailed description of the embodiments of the present application will be given with reference to the accompanying drawings.
It should be understood that the described embodiments are merely some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the embodiments of the present application, are within the scope of the embodiments of the present application.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims. In the description of this application, it should be understood that the terms "first," "second," "third," and the like are used merely to distinguish between similar objects and are not necessarily used to describe a particular order or sequence, nor should they be construed to indicate or imply relative importance. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The Beidou short message communication has the capability of quick response, the short message communication delay is about 0.5s, and the point-to-point communication delay is 1-5 s; meanwhile, the communication has strong interference resistance, and the communication under extreme weather conditions can be ensured by adopting S/L wave band satellite transmission to penetrate through a stratosphere and a troposphere; the device has low requirement, low price, high cost performance and convenient networking, and can take the command type user machine as a core to quickly form a one-to-many communication network. In a severe environment without mobile communication signals, the Beidou short message communication function is particularly important. The Beidou short message communication service covers China and surrounding countries and regions, has the characteristics of low cost, wide coverage, high reliability, access at any time and the like, can effectively supplement a ground mobile communication network so as to meet the service requirements of national economy and society in emergency communication, search and rescue and the like in areas without the ground network coverage, is an important component of a national emergency system, and has positive effects in aspects of civil security, disaster relief, field search and rescue and the like in recent years. And the electric power data information is used as an important communication content type, so that it is very important to explore how to ensure the transmission efficiency of the electric power data in the Beidou short message communication process.
Based on the foregoing considerations and the content in the background art, as shown in fig. 1, an embodiment of the present application provides a power data transmission system, including:
compression end equipment and decompression end equipment, compression end equipment and decompression end equipment signal connection.
The compression end device and the decompression end device are both edge devices. Edge devices primarily refer to switches, routers, routing switches, IADs and various MAN/WAN devices installed on the edge network, responsible for packet transfer between the access devices and the core/backbone network devices. An edge device is a physical device that applies data link layer (second layer) and network layer (third layer) technologies.
In the embodiment of the application, the compression end device is in signal connection with an external electricity consumption information acquisition device, and the external electricity consumption information acquisition device can be one or a plurality of external electricity consumption information acquisition devices and is used for acquiring electricity consumption information and transmitting the electricity consumption information to the compression end device in a wired or wireless communication mode; the compression end equipment is in signal connection with the decompression end equipment through Beidou satellites, the compression end equipment sends compressed electricity consumption data to the decompression end equipment through Beidou short messages, the decompression end equipment decompresses the data after receiving the compressed electricity consumption data, and then sends the decompressed electricity consumption data to an external electricity consumption receiving end in a wired or wireless mode.
Specifically, the edge device may also be an intelligent terminal device, including but not limited to various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like.
The compression end equipment is used for obtaining the power consumption message data, inputting the power consumption message data into a pre-trained support vector machine algorithm model, obtaining the message type of the power consumption message data, confirming the compression algorithm of the power consumption message data according to the message type, obtaining the compression message data, and then sending the compression message data to the decompression end equipment.
At present, the maximum length of a single short message of the Beidou No. three regional short message communication service is 14000 bits, which is approximately equivalent to 1000 Chinese characters, and the global short message communication service realizes the global message communication service by using 14 MEO satellites, wherein the maximum length of the single short message is 560 bits, which is approximately equivalent to 40 Chinese characters. Therefore, when the electric power data transmission is performed, the information acquired by the electric power consumption information acquisition terminal is compressed through the compression end edge equipment, and the electric power data transmission can be performed through the Beidou short message protocol.
Because the power data formats may be different when the power data is transmitted, and the corresponding optimal compression methods are different, further processing needs to be performed on the transmitted data, so that the data compression and decompression efficiency is improved, and the efficiency of the whole data transmission process is further improved.
In a preferred embodiment, the compression end device adds an identifier to the compressed message data according to the message type, where the identifier indicates a compression algorithm of the compressed message data, encapsulates the identified compressed message data, obtains encapsulated compressed message data, and sends the encapsulated compressed message data to the decompression end device. And the decompression terminal equipment identifies the identifier, confirms a corresponding decompression algorithm according to the compression algorithm indicated by the identifier, decompresses the encapsulated compressed message data through the corresponding decompression algorithm, and acquires the power consumption message data.
In a specific example, considering that the CDT protocol message and the IEC101 protocol message are commonly used at present to transmit electric data, the optimal lossless compression algorithm corresponding to the CDT protocol message is an LZW compression algorithm, and the optimal lossless compression algorithm corresponding to the IEC101 protocol message is an LZMA compression algorithm. Therefore, for original electricity consumption data, firstly, identifying whether the message type of the original electricity consumption data is CDT protocol message or IEC101 protocol message through a classification algorithm; if the message type is identified as CDT protocol message, compressing the segment of message by LZW compression algorithm, and adding an identifier at the head of the compression result, wherein the identifier can be one or more bits, and is used for indicating the compression algorithm adopted by the current compressed message, for example, 0 is added as the identifier at the head of the compression result by adopting the LZW compression algorithm; a header of a compression result by adopting the LZMA compression algorithm is added with 1 as an identifier. And then, after the compressed data result added with the identifier is packaged, the compressed data result is sent to decompression terminal equipment through a Beidou short message protocol. The decompression terminal equipment receives the encapsulation data and identifies the identifier, if the identifier of the message is identified to be 0, the segment of the message is decompressed through a decompression algorithm corresponding to the LZW compression algorithm, if the identifier of the message is identified to be 1, the segment of the message is decompressed through a decompression algorithm corresponding to the LZMA compression algorithm, and finally the original electricity consumption data is obtained.
In a preferred example, the compression end device further includes a message type classification module, configured to perform a pre-training process of the message classification model and identify a message type through the trained message classification model. In the embodiment of the application, the identification of the message type is realized by constructing a support vector machine algorithm model, and the training process of the algorithm model comprises the following steps:
acquiring an original protocol data stream, and extracting samples of the original protocol data stream according to a preset fixed code length to acquire training samples;
labeling the training samples according to the classification of the original protocol data stream, wherein the training samples with the same label indicate that the training samples have the same protocol data stream classification;
constructing a support vector machine algorithm model, and inputting the training sample into the support vector machine algorithm model for supervised training, wherein a kernel function of the support vector machine algorithm model is a Gaussian kernel function;
and acquiring the trained support vector machine algorithm model.
In a specific example, as shown in fig. 2, training samples are extracted from the original CDT protocol data stream and the original IEC101 protocol data stream, and the sample extraction is repeated for the original protocol data stream with the message content set to be 12 bytes in fixed code length as the sample extraction feature. Labeling the extracted training samples, adding a label for each sample, for example, setting the label to be 1 for the samples in the CDT protocol data stream; for samples to which IEC101 is assigned in the data stream, label is set to 2. And then is divided into a training set, a testing set and a verification set. The training set is used for model training and determining model parameters; the test set is used for determining the network structure and adjusting the super parameters of the model; the validation set is used to verify the generalization ability of the model. For a message content training sample with fixed code length, the training sample belongs to a linear inseparable sample, so that a kernel function is defined as a Gaussian kernel function, and sample points are mapped to a high-dimensional feature space to be linearly separable, so that an optimal hyperplane is searched; and inputting the training samples into the constructed support vector machine algorithm model, performing supervised training of a support vector machine on the training set, and finally obtaining a trained support vector machine algorithm model, wherein the trained support vector machine algorithm model can realize identification and classification of the data types of the messages. The identification process comprises the following steps: the compression end equipment reads an original message data stream to be compressed, and extracts message content with a fixed code length of 12 bytes from the original message data stream to be used as a single Item; inputting the Item into a trained support vector machine algorithm model to extract the characteristics of the message and classify the message; and then compressing the message according to the optimal compression algorithm corresponding to the classification to obtain a compression result.
The embodiment of the application sets compression end equipment and decompression end equipment for data exchange in power data transmission, wherein the compression end equipment is used for acquiring power consumption message data, inputting the power consumption message data into a pre-trained support vector machine algorithm model, acquiring the message type of the power consumption message data, confirming a compression algorithm of the power consumption message data according to the message type, acquiring the compression message data, and then sending the compression message data to the decompression end equipment; the decompression terminal equipment decompresses the compressed message data to obtain the power utilization message data, and then sends the power utilization message data to the power utilization message receiving terminal. According to the embodiment of the application, the power data types are classified through the support vector machine algorithm model and matched with the optimal compression algorithm, so that the data compression efficiency is improved; meanwhile, an identifier is added to the compressed message data, so that the decompression speed of the decompression terminal equipment is improved; thereby achieving the purpose of improving the power data transmission efficiency.
The embodiment of the application also provides a power data transmission method applied to the compression end equipment, as shown in fig. 3, comprising the following steps:
s201: and obtaining the power consumption message data.
S202: and inputting the power consumption message data into a pre-trained support vector machine algorithm model to obtain the message type of the power consumption message data.
S203: and confirming a compression algorithm of the power consumption message data according to the message type, acquiring the compressed message data, and then sending the compressed message data to decompression terminal equipment, wherein the decompression terminal is used for decompressing the compressed message data to acquire the power consumption message data, and then sending the power consumption message data to a power consumption message receiving terminal.
In a preferred embodiment, after the obtaining the compressed message data, the method further includes:
adding an identifier to the compressed message data according to the message type, wherein the identifier indicates a compression algorithm of the compressed message data;
and encapsulating the identified compressed message data to obtain encapsulated compressed message data and transmitting the encapsulated compressed message data to the decompression terminal equipment, wherein the decompression terminal equipment is used for identifying the identifier, confirming a corresponding decompression algorithm according to a compression algorithm indicated by the identifier, decompressing the encapsulated compressed message data through the corresponding decompression algorithm, and obtaining the power consumption message data.
In a preferred embodiment, the method for pre-training the support vector machine algorithm model further includes:
acquiring an original protocol data stream, and extracting samples of the original protocol data stream according to a preset fixed code length to acquire training samples;
labeling the training samples according to the classification of the original protocol data stream, wherein the training samples with the same label indicate that the training samples have the same protocol data stream classification;
constructing a support vector machine algorithm model, and inputting the training sample into the support vector machine algorithm model for supervised training, wherein a kernel function of the support vector machine algorithm model is a Gaussian kernel function;
and acquiring the trained support vector machine algorithm model.
It should be noted that, the power data transmission method and the power data transmission system provided in the foregoing embodiments belong to the same concept, and detailed implementation processes of the power data transmission method and the power data transmission system are shown in system embodiments, which are not repeated herein.
The present application also provides a power data transmission apparatus 300, as shown in fig. 4, including:
the electricity consumption message obtaining module 301 is configured to obtain electricity consumption message data;
the message type obtaining module 302 is configured to input the power consumption message data into a pre-trained support vector machine algorithm model, and obtain a message type of the power consumption message data;
and the compressed message data sending module 303 is configured to confirm a compression algorithm of the power consumption message data according to the message type, obtain compressed message data, and send the compressed message data to a decompression terminal device, where the decompression terminal is configured to decompress the compressed message data, obtain the power consumption message data, and send the power consumption message data to a power consumption information receiving terminal.
In an exemplary embodiment, the compressed message data sending module 303 is further configured to:
adding an identifier to the compressed message data according to the message type, wherein the identifier indicates a compression algorithm of the compressed message data;
and encapsulating the identified compressed message data to obtain encapsulated compressed message data and transmitting the encapsulated compressed message data to the decompression terminal equipment, wherein the decompression terminal equipment is used for identifying the identifier, confirming a corresponding decompression algorithm according to a compression algorithm indicated by the identifier, decompressing the encapsulated compressed message data through the corresponding decompression algorithm, and obtaining the power consumption message data.
In an exemplary example, the message type obtaining module 302 is further configured to:
acquiring an original protocol data stream, and extracting samples of the original protocol data stream according to a preset fixed code length to acquire training samples;
labeling the training samples according to the classification of the original protocol data stream, wherein the training samples with the same label indicate that the training samples have the same protocol data stream classification;
constructing a support vector machine algorithm model, and inputting the training sample into the support vector machine algorithm model for supervised training, wherein a kernel function of the support vector machine algorithm model is a Gaussian kernel function;
and acquiring the trained support vector machine algorithm model.
It should be noted that, the power data transmission device and the power data transmission system provided in the foregoing embodiments belong to the same concept, and detailed implementation processes of the power data transmission device and the power data transmission system are shown in system embodiments, which are not repeated herein.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing relevant data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of power data transmission.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of power data transmission. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The embodiment of the application further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements a power data transmission method according to any one of the above embodiments.
Embodiments of the present application may take the form of a computer program product embodied on one or more storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having program code embodied therein. Computer-readable storage media include both non-transitory and non-transitory, 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 for a computer 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, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, may be used to store information that may be accessed by the computing device.
It is to be understood that the embodiments of the present application are not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be made without departing from the scope thereof. The scope of embodiments of the present application is limited only by the appended claims.
The above examples merely represent a few implementations of the examples of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the embodiments of the present application, which are all within the scope of the embodiments of the present application.
Claims (10)
1. A power data transmission system, comprising:
the compression end device is in signal connection with the decompression end device;
the compression end equipment is used for obtaining the power consumption message data, inputting the power consumption message data into a pre-trained support vector machine algorithm model, obtaining the message type of the power consumption message data, confirming the compression algorithm of the power consumption message data according to the message type, obtaining the compression message data, and then sending the compression message data to the decompression end equipment;
the decompression terminal equipment decompresses the compressed message data to obtain the electricity consumption message data, and then sends the electricity consumption message data to an electricity consumption message receiving terminal.
2. A power data transmission system according to claim 1, further comprising:
the compression end device is further configured to add an identifier to the compressed message data according to the message type, where the identifier indicates a compression algorithm of the compressed message data, encapsulate the identified compressed message data, obtain encapsulated compressed message data, and send the encapsulated compressed message data to the decompression end device;
the decompression terminal equipment is also used for identifying the identifier, confirming a corresponding decompression algorithm according to the compression algorithm indicated by the identifier, decompressing the encapsulated compressed message data through the corresponding decompression algorithm, and obtaining the power consumption message data.
3. The power data transmission system according to claim 1, wherein the compression end device is further configured to perform a pre-training process of the support vector machine algorithm model, including:
acquiring an original protocol data stream, and extracting samples of the original protocol data stream according to a preset fixed code length to acquire training samples;
labeling the training samples according to the classification of the original protocol data stream, wherein the training samples with the same label indicate that the training samples have the same protocol data stream classification;
constructing a support vector machine algorithm model, and inputting the training sample into the support vector machine algorithm model for supervised training, wherein a kernel function of the support vector machine algorithm model is a Gaussian kernel function;
and acquiring the trained support vector machine algorithm model.
4. A power data transmission system according to claim 2, further comprising:
the compression end device is further configured to add an identifier to the header of the compressed packet data, where the identifier includes a first identifier and a second identifier, the first identifier indicates that the compression algorithm of the compressed packet data is an LZW compression algorithm, and the second identifier indicates that the compression algorithm of the compressed packet data is an LZMA compression algorithm.
5. A method for transmitting power data, applied to a compression end device, comprising:
acquiring power consumption message data;
inputting the power consumption message data into a pre-trained support vector machine algorithm model to obtain the message type of the power consumption message data;
and confirming a compression algorithm of the power consumption message data according to the message type, acquiring the compressed message data, and then sending the compressed message data to decompression terminal equipment, wherein the decompression terminal is used for decompressing the compressed message data to acquire the power consumption message data, and then sending the power consumption message data to a power consumption message receiving terminal.
6. The method for transmitting power data according to claim 5, wherein after the compressed message data is obtained, further comprising:
adding an identifier to the compressed message data according to the message type, wherein the identifier indicates a compression algorithm of the compressed message data;
and encapsulating the identified compressed message data to obtain encapsulated compressed message data and transmitting the encapsulated compressed message data to the decompression terminal equipment, wherein the decompression terminal equipment is used for identifying the identifier, confirming a corresponding decompression algorithm according to a compression algorithm indicated by the identifier, decompressing the encapsulated compressed message data through the corresponding decompression algorithm, and obtaining the power consumption message data.
7. The power data transmission method according to claim 5, wherein the pre-training method of the support vector machine algorithm model further comprises:
acquiring an original protocol data stream, and extracting samples of the original protocol data stream according to a preset fixed code length to acquire training samples;
labeling the training samples according to the classification of the original protocol data stream, wherein the training samples with the same label indicate that the training samples have the same protocol data stream classification;
constructing a support vector machine algorithm model, and inputting the training sample into the support vector machine algorithm model for supervised training, wherein a kernel function of the support vector machine algorithm model is a Gaussian kernel function;
and acquiring the trained support vector machine algorithm model.
8. An electric power data transmission apparatus, characterized by comprising:
the power consumption message acquisition module is used for acquiring power consumption message data;
the message type acquisition module is used for inputting the power consumption message data into a pre-trained support vector machine algorithm model to acquire the message type of the power consumption message data;
and the compressed message data transmitting module is used for confirming a compression algorithm of the power consumption message data according to the message type, acquiring the compressed message data, and transmitting the compressed message data to decompression terminal equipment, wherein the decompression terminal is used for decompressing the compressed message data, acquiring the power consumption message data and transmitting the power consumption message data to a power consumption information receiving terminal.
9. An electronic device, comprising:
at least one memory and at least one processor;
the memory is used for storing one or more programs;
when executed by the at least one processor, causes the at least one processor to implement the steps of a power data transmission method as claimed in any one of claims 5 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of a power data transmission method according to any one of claims 5 to 7.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202311714270.4A CN117411875A (en) | 2023-12-14 | 2023-12-14 | Power data transmission system, method, device, equipment and storage medium |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202311714270.4A CN117411875A (en) | 2023-12-14 | 2023-12-14 | Power data transmission system, method, device, equipment and storage medium |
Publications (1)
Publication Number | Publication Date |
---|---|
CN117411875A true CN117411875A (en) | 2024-01-16 |
Family
ID=89500220
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202311714270.4A Pending CN117411875A (en) | 2023-12-14 | 2023-12-14 | Power data transmission system, method, device, equipment and storage medium |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN117411875A (en) |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8560843B1 (en) * | 2010-09-24 | 2013-10-15 | Symantec Corporation | Encrypted universal resource identifier (URI) based messaging |
US20170262221A1 (en) * | 2016-03-11 | 2017-09-14 | EMC IP Holding Company LLC | Methods and apparatuses for data migration of a storage device |
CN110532466A (en) * | 2019-08-21 | 2019-12-03 | 广州华多网络科技有限公司 | Processing method, device, storage medium and the equipment of platform training data is broadcast live |
CN113055017A (en) * | 2019-12-28 | 2021-06-29 | 华为技术有限公司 | Data compression method and computing device |
US20220337857A1 (en) * | 2021-04-12 | 2022-10-20 | Tencent America LLC | Techniques for signaling neural network topology, parameters, and processing information in video stream |
WO2022224069A1 (en) * | 2021-04-22 | 2022-10-27 | Nokia Technologies Oy | Syntax and semantics for weight update compression of neural networks |
CN116208772A (en) * | 2023-05-05 | 2023-06-02 | 浪潮电子信息产业股份有限公司 | Data processing method, device, electronic equipment and computer readable storage medium |
CN116701411A (en) * | 2023-08-07 | 2023-09-05 | 北京谷器数据科技有限公司 | Multi-field data archiving method, device, medium and equipment |
CN116800796A (en) * | 2023-07-06 | 2023-09-22 | 东软睿驰汽车技术(大连)有限公司 | Method, device, equipment and medium for transmitting internet of vehicles data |
-
2023
- 2023-12-14 CN CN202311714270.4A patent/CN117411875A/en active Pending
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8560843B1 (en) * | 2010-09-24 | 2013-10-15 | Symantec Corporation | Encrypted universal resource identifier (URI) based messaging |
US20170262221A1 (en) * | 2016-03-11 | 2017-09-14 | EMC IP Holding Company LLC | Methods and apparatuses for data migration of a storage device |
CN110532466A (en) * | 2019-08-21 | 2019-12-03 | 广州华多网络科技有限公司 | Processing method, device, storage medium and the equipment of platform training data is broadcast live |
CN113055017A (en) * | 2019-12-28 | 2021-06-29 | 华为技术有限公司 | Data compression method and computing device |
US20220337857A1 (en) * | 2021-04-12 | 2022-10-20 | Tencent America LLC | Techniques for signaling neural network topology, parameters, and processing information in video stream |
WO2022224069A1 (en) * | 2021-04-22 | 2022-10-27 | Nokia Technologies Oy | Syntax and semantics for weight update compression of neural networks |
CN116208772A (en) * | 2023-05-05 | 2023-06-02 | 浪潮电子信息产业股份有限公司 | Data processing method, device, electronic equipment and computer readable storage medium |
CN116800796A (en) * | 2023-07-06 | 2023-09-22 | 东软睿驰汽车技术(大连)有限公司 | Method, device, equipment and medium for transmitting internet of vehicles data |
CN116701411A (en) * | 2023-08-07 | 2023-09-05 | 北京谷器数据科技有限公司 | Multi-field data archiving method, device, medium and equipment |
Non-Patent Citations (3)
Title |
---|
HONGRUI ZHANG;ZENGQIAN HOU;: "Metallogenesis within continental collision zones: Comparisons of modern collisional orogens", SCIENCE CHINA(EARTH SCIENCES), no. 12, 21 November 2018 (2018-11-21), XP036662272, DOI: 10.1007/s11430-017-9303-0 * |
刘虎;: "基于低速串行通信链路传输的格式化短报文压缩方法", 电子元器件与信息技术, no. 01, 20 January 2020 (2020-01-20) * |
李欣然;钟俊;: "基于BWT改进的LZSS算法在报文压缩中的应用", 现代电子技术, no. 15, 2 August 2018 (2018-08-02) * |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107292808B (en) | Image processing method and device and image coprocessor | |
CN114448563B (en) | Semantic code transmission method and electronic equipment | |
US20080191015A1 (en) | Data Transfer System, Data Acquisition Device, Data Acquisition Method, Data Accumulation Device, Data Transmission Method, and Program for the Same | |
CN107992822B (en) | Image processing method and apparatus, computer device, computer-readable storage medium | |
US10817460B2 (en) | RDMA data sending and receiving methods, electronic device, and readable storage medium | |
CN104410822B (en) | Video frequency monitoring method and vehicle-mounted video monitoring device | |
CN104205161A (en) | System, method, and computer program product for decompression of block compressed images | |
CN112801719A (en) | User behavior prediction method, user behavior prediction device, storage medium, and apparatus | |
CN113114707B (en) | Rule filtering method for power chip Ethernet controller | |
CN112823519B (en) | Video decoding method, device, electronic equipment and computer readable storage medium | |
US20240112372A1 (en) | Method and apparatus for compressing and decompressing 3d map | |
CN112256774B (en) | Power data processing method, device, computer equipment and storage medium | |
CN113220651B (en) | Method, device, terminal equipment and storage medium for compressing operation data | |
CN113343895A (en) | Target detection method, target detection device, storage medium, and electronic apparatus | |
CN117411875A (en) | Power data transmission system, method, device, equipment and storage medium | |
CN106878149A (en) | A kind of foreign-going ship communication means and information switch endpoint based on wechat public number | |
CN107889191B (en) | Connection method, device and equipment of wireless local area network and computer readable storage medium | |
CN108062956A (en) | A kind of audio recognition method and system of single host multiple terminals | |
CN105574453A (en) | Two-dimensional code processing method and mobile terminal | |
WO2013153265A1 (en) | Methods and apparatuses for facilitating face image analysis | |
CN107026698A (en) | The detection of Wireless Fidelity clear channel assessment (CCA) and transmission decisions in portable equipment are made | |
EP4344200A1 (en) | Methods and apparatus for encoding and decoding 3d map | |
CN112783512B (en) | Application package processing method, device, equipment and storage medium | |
CN117880562A (en) | Data processing method, device, equipment and storage medium | |
CN116709228A (en) | Data transmission method of air interface and electronic equipment |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |