CN114866487A - Mass power grid dispatching data acquisition and storage system - Google Patents
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- 239000011159 matrix material Substances 0.000 claims description 6
- 238000013527 convolutional neural network Methods 0.000 claims description 5
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- 238000013135 deep learning Methods 0.000 claims description 4
- 238000009825 accumulation Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
- H04L47/2475—Traffic characterised by specific attributes, e.g. priority or QoS for supporting traffic characterised by the type of applications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
- H04L47/2441—Traffic characterised by specific attributes, e.g. priority or QoS relying on flow classification, e.g. using integrated services [IntServ]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
- H04L47/2483—Traffic characterised by specific attributes, e.g. priority or QoS involving identification of individual flows
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/32—Flow control; Congestion control by discarding or delaying data units, e.g. packets or frames
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- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention relates to a mass power grid dispatching data acquisition and storage system, which comprises a data acquisition module, a data processing module, a data analysis module, a data storage module and a data reading module, wherein the data acquisition module is used for acquiring and storing mass power grid dispatching data; the data acquisition module is used for acquiring scheduling data; the data processing module realizes the preprocessing of data; the data identification module realizes the identification of data functions; the data storage module realizes classified storage of data; the data reading module realizes the quick reading of data. The invention realizes the high-efficiency acquisition and storage of massive power grid dispatching data and solves the problem of low efficiency of data reading.
Description
Technical Field
The invention relates to the field of power grid dispatching data acquisition and storage, in particular to a mass power grid dispatching data acquisition and storage system.
Background
With the great increase of national electricity consumption, more and more power grid equipment is also invested, so that the data volume of power dispatching shows exponential increase, which declares the coming of big data era of power dispatching data network. At present, data in power dispatching service is rapidly increased every day, and the existing technology is difficult to meet the requirement of collecting and storing massive dispatching service data. The method mainly shows that massive data cannot be completely collected, and meanwhile, data analysis is difficult to carry out during data retrieval due to the single storage function, data reading is difficult, and the speed of rapid development and new service popularization of a power grid is severely limited. Due to the further development of the power dispatching data network, the amount of information data is continuously increased, so that a higher-capacity data acquisition and storage mode is required, and the traditional power grid acquisition and storage mode is difficult to meet the requirement.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a massive power grid dispatching data acquisition and storage system, which realizes the efficient acquisition and storage of massive power grid dispatching data and solves the problem of low efficiency of data reading.
In order to solve the technical problems, the technical scheme of the invention is as follows: a mass power grid dispatching data acquisition and storage system comprises a data acquisition module, a data processing module, a data analysis module, a data storage module and a data reading module; wherein the content of the first and second substances,
the data acquisition module realizes the acquisition of scheduling data;
the data processing module realizes the preprocessing of data;
the data identification module realizes the identification of data functions;
the data storage module realizes classified storage of data;
the data reading module realizes the quick reading of data.
Preferably, the data acquisition module is configured to acquire message data in a power grid, and the data acquisition module includes: the system comprises a main network data acquisition module and a distribution network data acquisition module; wherein the content of the first and second substances,
the main network data acquisition module is used for acquiring mass message data transmitted to the main station front-end processor from the plant station telecontrol device; the distribution network data acquisition module is used for acquiring mass message data sent by the substation distribution automation terminal.
Preferably, the data processing module analyzes the collected message data quintuple and judges whether the message conforms to a 104 protocol format; the method comprises the following specific steps:
step A1, searching whether the message after analysis has bytes of '0 x 68', if not, determining that the message does not conform to 104 protocol format, and then discarding the message, otherwise, entering step A2;
a2, intercepting subsequent message data starting from the byte of 0x 68;
step A3, judging whether the length of the application layer protocol data unit of the intercepted message is within 4 to 253, if not, judging that the message does not conform to 104 protocol format, and then discarding the message, otherwise, entering step A4;
step A4, judging whether the total length of the intercepted message is 2 bytes longer than the application layer protocol data unit length, if not, judging that the message does not conform to 104 protocol format, then discarding the message, otherwise, judging that the message conforms to 104 protocol format.
Preferably, the data identification module identifies the message application service data by using a deep learning network, and the specific steps are as follows:
step B1, extracting the application service data in the message, and converting the hexadecimal application data into decimal;
step B2, intercepting the front 784 numbers in the decimal data after the binary conversion, discarding the redundant parts, complementing the insufficient parts with 0, and then performing normalization operation;
step B3, converting the 784 normalized numbers into a matrix with 28 row numbers and column numbers;
and step B4, transmitting the matrix into the trained deep convolutional neural network, classifying the application functions, and judging the types of the application functions.
Preferably, the trained deep convolutional neural network is obtained by changing the final classification number of the LeNet-5 network into 6 classes by using transfer learning.
Preferably, the application function types include: the system comprises a general calling function, a time setting function, a cycle data transmission function, an event collection function, a command transmission function and an accumulation amount transmission function.
Preferably, the data storage module realizes storage of different application function data, classifies, compresses and stores the data according to the category of the application service data, and stamps a timestamp on the data while storing the data, and the specific steps are as follows:
step C1, converting the 104 message data S of 16 system into 2 system data to obtain 2 system message data S 1 ；
Step C2, the 2 system message data S 1 Message transformation is carried out through BWT algorithm to obtain message data S 2 ；
Step C3, using counting method to count the message data S 2 Conversion into message data S 3 (ii) a The specific counting method of the counting method comprises the following steps:
capital letters A-Z are used to represent S 2 The number of seed numbers 1 in succession, wherein A represents 1, Z represents 26, and counting is resumed from A if there are more than 26; s is represented by lower case letters a-z 2 The number of seed numbers 0 in succession, wherein a represents 1 and z represents26, if the number of the parts exceeds 26, counting from a again; the counting method uses a letter to represent 1 or 0 which is continuously repeated, so that the size of original text data can be reduced preliminarily;
step C4, using LZW algorithm to convert message S 3 Compressing; the LZW algorithm is a lossless compression algorithm, and the compression rate can reach 3: 1, the compression and decompression speed is high due to the stable compression process;
and step C5, stamping a time stamp on the compressed message data and storing the message data.
Preferably, the data storage module has a priority storage control function, and the priority of data storage of different categories is limited according to a priority storage category set by a user.
Preferably, the data reading module reads data quickly according to the application service data type and the data storage timestamp interval set by the user.
The invention has the following beneficial effects:
firstly, the invention divides the collection mode into two collection modes to be carried out simultaneously, and the collection efficiency is higher than that of a single collection mode;
secondly, the invention identifies the 104 messages, only stores the 104 messages, and realizes high-efficiency storage;
thirdly, the invention identifies the category of the 104 messages, and can selectively store the related messages when the network is congested, so that the efficiency is higher than that of the non-classified storage;
the storage is stored according to the category and the timestamp is marked, so that the desired message data can be accurately read only by determining the reading time range and the reading type during reading, and the reading efficiency is higher compared with the reading efficiency of the message data without distinguishing the category.
Drawings
FIG. 1 is a schematic diagram of a mass power grid dispatch data acquisition and storage system of the present invention;
FIG. 2 is a schematic diagram of a process for message acquisition and identification 104 of a data processing module in the system of the present invention;
FIG. 3 is a schematic diagram of a message application function classification flow of a data identification module in the system of the present invention;
FIG. 4 is a schematic diagram illustrating a compressed storage flow of a data storage module in the system of the present invention;
FIG. 5 is a schematic diagram of a message conversion process of a data storage module in the system of the present invention;
FIG. 6 is a diagram illustrating a counting method of the data storage module in the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, the present invention is a system for acquiring and storing massive power grid dispatching data, which includes: the device comprises a data acquisition module, a data processing module, a data analysis module, a data storage module and a data reading module; wherein the content of the first and second substances,
the data acquisition module realizes the acquisition of scheduling data;
the data processing module realizes the preprocessing of data;
the data identification module realizes the identification of data functions;
the data storage module realizes classified storage of data;
the data reading module realizes the quick reading of data;
specifically, the data acquisition module is used for collecting message data in the power grid, and the data acquisition module includes: the system comprises a main network data acquisition module and a distribution network data acquisition module; the main network data acquisition module is used for acquiring mass message data transmitted to the master station front-end processor from the plant station telecontrol device; the distribution network data acquisition module is used for acquiring mass message data sent by the substation distribution automation terminal.
Specifically, the data processing module analyzes the acquired message data quintuple and judges whether the message conforms to a 104 protocol format;
specifically, the data identification module identifies the message application service data by using a deep learning network;
specifically, the data storage module realizes storage of different application function data, classifies, compresses and stores the data according to the category of application service data, and stamps a time stamp on the data during storage;
specifically, the data storage module has a priority storage control function, and limits the data storage priorities of different categories according to the priority storage categories set by a user;
specifically, the data reading module reads data quickly according to the application service data type and the data storage timestamp interval set by the user.
Further, referring to fig. 2, the data processing module analyzes the collected five-tuple of the message data, and determines whether the message conforms to the 104-protocol format, including the following steps:
step A1, searching whether the message after analysis has bytes of '0 x 68', if not, determining that the message does not conform to 104 protocol format, and then discarding the message, otherwise, entering step A2;
a2, intercepting subsequent message data starting from the byte of 0x 68;
step A3, judging whether the length of the application layer protocol data unit of the intercepted message is within 4 to 253, if not, judging that the message does not conform to 104 protocol format, and then discarding the message, otherwise, entering step A4;
step A4, judging whether the total length of the intercepted message is 2 bytes longer than the length of an application layer protocol data unit, if not, judging that the message does not conform to 104 protocol format, and then discarding the message, otherwise, judging that the message conforms to 104 protocol format; if the format is 104 specification format, the bit after the "0 x 68" byte is the length of the application layer protocol data unit.
Referring to fig. 3, the data identification module identifies the message application service data by using a deep learning network, and the specific steps are as follows:
step B1, extracting the application service data in the message, and converting the hexadecimal application data into decimal;
step B2, intercepting the front 784 numbers in the decimal data after the binary conversion, discarding the redundant parts, complementing the insufficient parts with 0, and then performing normalization operation;
step B3, converting the normalized 784 number into a matrix with 28 rows and columns;
step B4, transmitting the matrix into the trained deep convolution neural network, classifying application functions and judging the types of the application functions; in the embodiment, the trained deep convolutional neural network is obtained by changing the final classification number of the LeNet-5 network into 6 classes by using transfer learning; in this embodiment, the application function types include: the system comprises a general calling function, a time setting function, a cycle data transmission function, an event collection function, a command transmission function and an accumulation amount transmission function.
Referring to fig. 4, the data storage module realizes storage of different application function data, classifies, compresses and stores the data according to the category of the application service data, and stamps a timestamp on the data while storing the data, and the specific steps are as follows:
step C1, converting the 104 message data S of 16 system into 2 system data to obtain 2 system message data S 1 ；
Step C2, the 2 system message data S 1 Message transformation is carried out through BWT algorithm to obtain message data S 2 ；
Step C3, using counting method to count the message data S 2 Conversion into message data S 3 (ii) a In this embodiment, the counting method is a special counting method, and the specific counting method is as follows: capital letters A-Z are used to represent S 2 The number of seed numbers 1 in succession, wherein A represents 1, Z represents 26, and counting is resumed from A if there are more than 26; s is represented by lower case letters a-z 2 The number of seed numbers 0 in succession, wherein a represents 1, z represents 26, and if there are more than 26 parts, counting is started again from a;
step C4, using LZW algorithm to convert message S 3 Compressing;
and step C5, stamping a time stamp on the compressed message data and storing the message data.
Referring to fig. 5, the message conversion method in the steps C1 and C2 is illustrated: the original 16-system message is: 6807 b6 dc d 82009, after binary conversion, becomes 1101000000001111011011011011100110110000010000000001001 and after BWT conversion, becomes 1111000000000111111111100000000000111111111000000000000, thus aligning some similar characters together for subsequent compression.
The counting method in the step C3 is implemented by using upper and lower case letters to respectively count the numbers 1 and 0 which are continuously arranged in the message, wherein the upper case letter a represents one number 1, the upper case letter Z represents twenty-six consecutive numbers 1, and similarly, the lower case letter represents the continuous number of the numbers 0. Referring to fig. 6, the BWT conversion result is 1111000000000111111111100000000000111111111000000000000, which is converted into dijkli by the counting method of step C3.
The parts not involved in the present invention are the same as or implemented using the prior art.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (9)
1. A mass power grid dispatching data acquisition and storage system is characterized in that: the device comprises a data acquisition module, a data processing module, a data analysis module, a data storage module and a data reading module; wherein the content of the first and second substances,
the data acquisition module realizes the acquisition of scheduling data;
the data processing module realizes the preprocessing of data;
the data identification module realizes the identification of data functions;
the data storage module realizes classified storage of data;
the data reading module realizes the quick reading of data.
2. The mass power grid dispatching data acquisition and storage system of claim 1, wherein: the data acquisition module is used for collecting message data in the power grid, and the data acquisition module comprises: the system comprises a main network data acquisition module and a distribution network data acquisition module; wherein the content of the first and second substances,
the main network data acquisition module is used for acquiring mass message data transmitted to the main station front-end processor from the plant station telecontrol device; the distribution network data acquisition module is used for acquiring mass message data sent by the substation distribution automation terminal.
3. The mass power grid dispatching data acquisition and storage system of claim 1, wherein: the data processing module analyzes the acquired message data quintuple and judges whether the message conforms to a 104 protocol format; the method comprises the following specific steps:
step A1, searching whether the message after analysis has bytes of '0 x 68', if not, determining that the message does not conform to 104 protocol format, and then discarding the message, otherwise, entering step A2;
a2, intercepting subsequent message data starting from the byte of 0x 68;
step A3, judging whether the length of the application layer protocol data unit of the intercepted message is within 4 to 253, if not, judging that the message does not conform to 104 protocol format, then discarding the message, otherwise, entering step A4;
step A4, judging whether the total length of the intercepted message is 2 bytes longer than the application layer protocol data unit length, if not, judging that the message does not conform to 104 protocol format, then discarding the message, otherwise, judging that the message conforms to 104 protocol format.
4. The mass power grid dispatching data acquisition and storage system of claim 1, wherein: the data identification module identifies the message application service data by using a deep learning network, and comprises the following specific steps:
step B1, extracting the application service data in the message, and converting the hexadecimal application data into decimal;
step B2, intercepting the front 784 numbers in the decimal data after the binary conversion, discarding the redundant parts, complementing the insufficient parts with 0, and then performing normalization operation;
step B3, converting the normalized 784 number into a matrix with 28 rows and columns;
and step B4, transmitting the matrix into the trained deep convolutional neural network, classifying the application functions, and judging the types of the application functions.
5. The mass power grid dispatching data acquisition and storage system of claim 4, wherein: the trained deep convolutional neural network is obtained by changing the final classification number of the LeNet-5 network into 6 classes by utilizing transfer learning.
6. The mass power grid dispatching data acquisition and storage system of claim 4, wherein: the application function types include: the system comprises a general calling function, a time setting function, a cycle data transmission function, an event collection function, a command transmission function and an accumulation amount transmission function.
7. The mass power grid dispatching data acquisition and storage system of claim 1, wherein: the data storage module realizes the storage of different application function data, classifies, compresses and stores the data according to the category of application service data, and stamps a time stamp on the data while storing, and the specific steps are as follows:
step C1, converting the 104 message data S of 16 system into 2 system data to obtain 2 system message data S 1 ；
Step C2, the 2 system message data S 1 Message transformation is carried out through BWT algorithm to obtain message data S 2 ；
Step C3, using counting method to count the message data S 2 Conversion into message data S 3 (ii) a The specific counting method of the counting method comprises the following steps:
capital letters A-Z are used to represent S 2 The number of successive seed numbers 1, wherein A represents 1, Z represents 26, and if there is a portion exceeding 26, starting from A againCounting;
s is represented by lower case letters a-z 2 The number of seed numbers 0 in succession, wherein a represents 1, z represents 26, and if there are more than 26 parts, counting is started again from a;
step C4, using LZW algorithm to convert message S 3 Compressing;
and step C5, stamping a time stamp on the compressed message data and storing the message data.
8. The mass power grid dispatching data acquisition and storage system of claim 1, wherein: the data storage module has a priority storage control function, and limits the data storage priority of different categories according to the priority storage category set by the user.
9. The mass power grid dispatching data acquisition and storage system of claim 1, wherein: and the data reading module is used for rapidly reading the data according to the application service data type and the data storage time stamp interval set by the user.
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