CN114866487B - Massive power grid dispatching data acquisition and storage system - Google Patents
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Abstract
The invention relates to a massive 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 data of a mass power grid; the data acquisition module is used for realizing the acquisition of scheduling data; the data processing module is used for preprocessing data; the data identification module realizes the identification of the data function; the data storage module realizes classified storage of data; the data reading module realizes quick reading of data. The invention realizes the efficient collection and storage of the dispatching data of the massive power grid, 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 massive power grid dispatching data acquisition and storage system.
Background
With the great increase of the national electricity consumption, the input of power grid equipment is more and more, so that the power dispatching data volume is exponentially increased, and the coming of the big data age of the power dispatching data network is announced. At present, data in power dispatching business is rapidly growing every day, and the acquisition and storage of mass dispatching business data are difficult to meet in the prior art. The method is mainly characterized in that massive data cannot be completely collected, meanwhile, the data analysis is difficult to develop when the data are read by a single storage function, and the data are difficult to read, so that the speed of rapid development and popularization of new business of a power grid is severely restricted. As the power dispatching data network further develops, the information data volume is also increasing, and a data acquisition and storage mode with higher capability is needed, and the traditional power grid acquisition and storage mode has hardly met the requirement.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a system for collecting and storing massive power grid dispatching data, which realizes the efficient collection and storage of the 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: the 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 data acquisition module is used for realizing the acquisition of scheduling data;
the data processing module is used for preprocessing data;
the data identification module realizes the identification of the data function;
the data storage module realizes classified storage of data;
the data reading module realizes 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: a main network data acquisition module and a distribution network data acquisition module; wherein,
the main network data acquisition module is used for acquiring massive message data sent to the master station front-end processor by the station telecontrol device; the distribution network data acquisition module is used for acquiring massive message data sent by the station distribution automation terminal.
Preferably, the data processing module analyzes the five-tuple of the acquired message data and judges whether the message accords with the 104 protocol format; the method comprises the following specific steps:
step A1, searching whether the analyzed message contains 0x68 bytes, if not, judging that the message does not conform to the 104 protocol format, then discarding the message, otherwise, entering step A2;
step A2, intercepting the subsequent message data starting from the byte 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 intercepted message does not accord with the 104 protocol format, discarding the intercepted message, otherwise, entering the step A4;
and A4, judging whether the total length of the intercepted message is more than 2 bytes than the length of the application layer protocol data unit, if not, judging that the message does not accord with the 104 protocol format, discarding the message, and otherwise, judging that the message accords with the 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 application service data in the message, and converting hexadecimal application data into decimal;
step B2, the front 784 numbers in the decimal data after the decimal conversion are intercepted, redundant parts are discarded, the insufficient parts are complemented by 0, and then normalization operation is carried out;
step B3, converting the normalized 784 numbers into a matrix with 28 rows and 28 columns;
and step B4, introducing the matrix into a trained deep convolutional neural network, classifying the application function, and judging the type of the application function.
Preferably, the trained deep convolutional neural network is obtained by changing the final classification number of the LeNet-5 network into 6 types by using transfer learning.
Preferably, the application function types include: a general calling function, a time setting function, a cyclic data transmission function, an event collection function, a command transmission function and an accumulated amount transmission function.
Preferably, the data storage module stores data with different application functions, classifies, compresses and stores the data according to the category of application service data, and time stamps the data while storing the data, and the specific steps are as follows:
step C1, converting the 16-system 104 message data S into 2-system data to obtain 2-system message data S 1 ；
Step C2, 2 the system message data S 1 Message transformation is carried out through BWT algorithm to obtain message data S 2 ；
Step C3, using a counting method to process the message data S 2 Conversion into message data S 3 The method comprises the steps of carrying out a first treatment on the surface of the The specific counting method of the counting method is as follows:
s is represented by capital letters A-Z 2 A number of consecutive 1 s, wherein a represents 1, z represents 26, and if there is a portion exceeding 26, counting again from a; s is represented by lower case letters a-z 2 A number 0 is a number which is continuous, wherein a represents 1 number, z represents 26 numbers, and if a part exceeding 26 exists, counting from a again; the counting method is characterized in that continuously repeated 1 or 0 is represented by a letter, so that the size of original text data can be primarily reduced;
step C4, using LZW algorithm to process the message S 3 Compressing; the LZW algorithm is a lossless compression algorithm, and the compression rate can reach 3 at the highest: 1, a stable compression process is provided, and the compression and decompression speeds are high;
and step C5, the compressed message data is subjected to time stamping and is stored.
Preferably, 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.
Preferably, the data reading module rapidly reads the data according to the application service data type and the data storage time stamp interval set by the user.
The invention has the following beneficial effects:
1. the invention divides the acquisition modes into two acquisition modes to be carried out simultaneously, and the acquisition efficiency is higher than that of a single acquisition mode;
2. the invention recognizes 104 messages, only stores 104 messages, and realizes high-efficiency storage;
3. the invention identifies the category of the 104 message, and when the network is congested, the related message can be selectively stored, so that the efficiency is higher than that of the unclassified storage;
4. the invention stores according to the category and marks the time stamp, so that the invention can accurately read the wanted message data by only determining the reading time range and the reading type when reading, and has higher efficiency compared with the prior reading of the message data without distinguishing the category.
Drawings
FIG. 1 is a schematic diagram of a mass grid dispatching data acquisition and storage system of the present invention;
FIG. 2 is a flow chart of message acquisition and identification of a data processing module 104 in the system of the present invention;
FIG. 3 is a schematic diagram of a message application function classification flow of a data recognition module in the system of the present invention;
FIG. 4 is a schematic diagram of 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 flow of a data storage module in the system of the present invention;
FIG. 6 is a schematic diagram of a counting method of a 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 collecting and storing massive power grid dispatching data, comprising: 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 data acquisition module is used for realizing the acquisition of scheduling data;
the data processing module is used for preprocessing data;
the data identification module realizes the identification of the data function;
the data storage module realizes classified storage of data;
the data reading module is used for realizing quick reading of data;
specifically, the data acquisition module is used for gathering the message data in the electric wire netting, and the data acquisition module includes: a main network data acquisition module and a distribution network data acquisition module; the main network data acquisition module is used for acquiring massive message data sent to the master station front-end processor by the station telecontrol device; the distribution network data acquisition module is used for acquiring massive message data sent by the station distribution automation terminal.
Specifically, the data processing module analyzes the five-tuple of the acquired message data and judges whether the message accords with the 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 the storage of data with different application functions, classifies, compresses and stores the data through the category of application service data, and marks a time stamp on the data while storing the data;
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 category set by a user;
specifically, the data reading module rapidly reads the data according to the application service data type and the data storage time stamp interval set by the user.
Further, referring to fig. 2, the data processing module analyzes the five-tuple of the collected message data to determine whether the message accords with the 104 protocol format, and specifically includes the following steps:
step A1, searching whether the analyzed message contains 0x68 bytes, if not, judging that the message does not conform to the 104 protocol format, then discarding the message, otherwise, entering step A2;
step A2, intercepting the subsequent message data starting from the byte 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 intercepted message does not accord with the 104 protocol format, discarding the intercepted message, otherwise, entering the step A4;
step A4, judging whether the total length of the intercepted message is more than 2 bytes than the length of the application layer protocol data unit, if not, judging that the intercepted message does not accord with the 104 protocol format, discarding the intercepted message, otherwise, judging that the intercepted message accords with the 104 protocol format; in the case of the 104 reduction format, the "0x68" byte is followed by the length of the application layer protocol data unit.
Referring to fig. 3, the data recognition module uses the deep learning network to recognize the message application service data, and specifically includes the following steps:
step B1, extracting application service data in the message, and converting hexadecimal application data into decimal;
step B2, the front 784 numbers in the decimal data after the decimal conversion are intercepted, redundant parts are discarded, the insufficient parts are complemented by 0, and then normalization operation is carried out;
step B3, converting the normalized 784 numbers into a matrix with 28 rows and 28 columns;
step B4, the matrix is transmitted into a trained deep convolutional neural network, application function classification is carried out, and the type of the application function is judged; in the embodiment, the trained deep convolutional neural network is obtained by changing the final classification number of the LeNet-5 network into 6 types by using transfer learning; in this embodiment, the application function types include: a general calling function, a time setting function, a cyclic data transmission function, an event collection function, a command transmission function and an accumulated amount transmission function.
Referring to fig. 4, the data storage module stores data with different application functions, classifies, compresses and stores the data according to the category of application service data, and time stamps the data while storing the data, and the specific steps are as follows:
step C1, converting the 16-system 104 message data S into 2-system data to obtain 2-system message data S 1 ；
Step C2, 2 the system message data S 1 Message transformation is carried out through BWT algorithm to obtain message data S 2 ；
Step C3, using a counting method to process the message data S 2 Conversion into message data S 3 The method comprises the steps of carrying out a first treatment on the surface of the In this embodiment, the counting method is a special counting method, and the specific counting method is as follows: s is represented by capital letters A-Z 2 A number of consecutive 1 s, wherein a represents 1, z represents 26, and if there is a portion exceeding 26, counting again from a; s is represented by lower case letters a-z 2 A number 0 is a number which is continuous, wherein a represents 1 number, z represents 26 numbers, and if a part exceeding 26 exists, counting from a again;
step C4, using LZW algorithm to process the message S 3 Compressing;
and step C5, the compressed message data is subjected to time stamping and is stored.
Referring to fig. 5, an example is given for the message conversion method in the above steps C1 and C2: the original 16-system message is: 68 07 b6 dc d8 20 09, after binary conversion, becomes 1101 0000 0000 1111 0110 1101 1011 1001 1011 0000 0100 0000 0001 001, and after BWT conversion, 1111 0000 0000 0111 1111 1110 0000 0000 0011 1111 1110 0000 0000 000 is obtained, so that some similar characters are arranged together, and the subsequent compression operation is facilitated.
The counting method in the step C3 is a technique of respectively aligning digits 1 and digits 0 in the message in sequence by case letters, wherein the case letter a represents a digit 1, the case letter Z represents twenty-six consecutive digits 1, and the case letter Z represents the consecutive number of digits 0. Referring to fig. 6, 1111 0000 0000 0111 1111 1110 0000 0000 0011 1111 1110 0000 0000 000 is obtained after BWT conversion, and is converted to DiJkIl by the counting method of step C3.
The invention is not related in part to the same or implemented in part by the prior art.
The foregoing is a further detailed description of the invention in connection with specific embodiments, and it is not intended that the invention be limited to such description. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (8)
1. A system for collecting and storing massive power grid dispatching data is characterized in that: the 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 data acquisition module is used for realizing the acquisition of scheduling data;
the data processing module is used for preprocessing data;
the data identification module realizes the identification of the data function;
the data storage module realizes classified storage of data;
the data reading module is used for realizing quick reading of data;
the data storage module realizes the storage of data with different application functions, classifies, compresses and stores the data by the category of application service data, and marks a time stamp on the data while storing the data, and the specific steps are as follows:
step C1, converting the 16-system 104 message data S into 2-system data to obtain 2-system message data S 1 ；
Step C2, 2 the system message data S 1 Message transformation is carried out through BWT algorithm to obtain message data S 2 ；
Step C3, using a counting method to process the message data S 2 Conversion into message data S 3 The method comprises the steps of carrying out a first treatment on the surface of the The specific counting method of the counting method is as follows:
s is represented by capital letters A-Z 2 A number of consecutive 1 s, wherein a represents 1, z represents 26, and if there is a portion exceeding 26, counting again from a;
s is represented by lower case letters a-z 2 A number 0 is a number which is continuous, wherein a represents 1 number, z represents 26 numbers, and if a part exceeding 26 exists, counting from a again;
step C4, using LZW algorithm to process the message S 3 Compressing;
and step C5, the compressed message data is subjected to time stamping and is stored.
2. The mass grid dispatching data collection and storage system of claim 1, wherein: the data acquisition module is used for acquiring message data in a power grid, and comprises: a main network data acquisition module and a distribution network data acquisition module; wherein,
the main network data acquisition module is used for acquiring massive message data sent to the master station front-end processor by the station telecontrol device; the distribution network data acquisition module is used for acquiring massive message data sent by the station distribution automation terminal.
3. The mass grid dispatching data collection and storage system of claim 1, wherein: the data processing module analyzes the five-tuple of the acquired message data and judges whether the message accords with the 104 protocol format; the method comprises the following specific steps:
step A1, searching whether the analyzed message contains 0x68 bytes, if not, judging that the message does not conform to the 104 protocol format, then discarding the message, otherwise, entering step A2;
step A2, intercepting the subsequent message data starting from the byte 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 intercepted message does not accord with the 104 protocol format, discarding the intercepted message, otherwise, entering the step A4;
and A4, judging whether the total length of the intercepted message is more than 2 bytes than the length of the application layer protocol data unit, if not, judging that the message does not accord with the 104 protocol format, discarding the message, and otherwise, judging that the message accords with the 104 protocol format.
4. The mass grid dispatching data collection and storage system of claim 1, wherein: the data identification module utilizes the deep learning network to identify the message application service data, and the specific steps are as follows:
step B1, extracting application service data in the message, and converting hexadecimal application data into decimal;
step B2, the front 784 numbers in the decimal data after the decimal conversion are intercepted, redundant parts are discarded, the insufficient parts are complemented by 0, and then normalization operation is carried out;
step B3, converting the normalized 784 numbers into a matrix with 28 rows and 28 columns;
and step B4, introducing the matrix into a trained deep convolutional neural network, classifying the application function, and judging the type of the application function.
5. The mass grid dispatching data collection 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 types by using transfer learning.
6. The mass grid dispatching data collection and storage system of claim 4, wherein: the application function types include: a general calling function, a time setting function, a cyclic data transmission function, an event collection function, a command transmission function and an accumulated amount transmission function.
7. The mass grid dispatching data collection 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 a user.
8. The mass grid dispatching data collection 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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