Lossless compression processing method for width data of continuous casting billet

Abstract

The invention relates to a continuous casting blank width data lossless compression processing method. The method comprises the following steps that 1, an original data acquisition module carries out data acquisition; 2, a data compression processing module processes the data; step 3, data transmission; 4, a data decompression processing module decompresses the data; and 5, storing the data by a data storage module. According to the method, aiming at the characteristics of continuous casting slab production, an existing slab width data storage structure and an existing slab width data storage transmission method are improved, and position starting information and data sampling length intervals are increased, so that the problem that a large amount of redundant data is stored in a database system is solved, the data storage amount is reduced, and data redundancy is eliminated; the data storage space is reduced, the database efficiency is improved, and the data can be conveniently transmitted to an L3ERP system or a downstream hot rolling process for production control.

Description

Technical Field

[0001] The invention relates to a compression processing method, in particular to a lossless compression processing method for continuous casting billet width data, belonging to the technical field of steelmaking. Background Art

[0002] At present, in the continuous casting production process, the slab width data is generally stored in the process control system database in real time with the slab number, position information and width information as the main keywords. The position interval is generally 0.02 meters. For a 10-meter-long slab, more than 500 sets of data need to be saved. Due to the large amount of data, it is not convenient to transmit it to the ERP (Enterprise Resource Planning) system for data analysis, nor to the downstream hot rolling process for production control. In addition, there are many duplicates in the large amount of width data. In order to effectively reduce the amount of transmitted data, the continuous casting slab width data needs to be compressed to reduce storage space and facilitate data transmission.

[0003] Searching the existing "continuous casting width measurement", an invention patent was retrieved, "A method for online accurate measurement of continuous casting slab width", which mainly realizes real-time online slab width detection, information transmission, width correction, and width abnormality alarm through width detection components, communication systems, and correction models. There is no obvious correlation with the invention content of the present invention patent "lossless compression processing of width data" in the technical invention.

[0004] Searching the existing "lossless data compression", an invention patent was retrieved, "A high-speed program controller based on lossless compression data transmission technology", which mainly compresses the data into a binary data stream and controls the processing through the master-slave module in parallel to achieve the high-speed requirement without changing the data storage space. The invention patent is based on the characteristics of the continuous casting width data itself, compresses a large amount of repeated data, mainly saves data storage space, does not change the data encoding form, and does not pursue high-speed module transmission. After preliminary search, there is no suitable solution in the prior art to solve the above problems. Summary of the invention

[0005] The present invention aims at the problems existing in the prior art and provides a method for lossless compression processing of continuous casting slab width data. The method aims at the characteristics of continuous casting slab production, improves the existing slab width data storage structure and storage transmission method, adds position start information and data sampling length interval, thereby solving the problem of storing a large amount of redundant data in the database system, reducing the data storage volume, eliminating data redundancy, reducing data storage space, improving database efficiency, and facilitating transmission to the L3 ERP system or downstream hot rolling process for production control.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows: a method for lossless compression processing of continuous casting billet width data, characterized in that the method comprises the following steps:

[0007] Step 1: The original data acquisition module performs data acquisition;

[0008] Step 2: The data compression processing module processes the data;

[0009] Step 3: Data transmission;

[0010] Step 4: The data decompression processing module decompresses the data;

[0011] Step 5: The data storage module stores the data.

[0012] Among them, step 1: the original data acquisition module performs data acquisition; it is used to collect the slab position and width information uploaded by the on-site PLC to the steelmaking secondary process control system. The module calculates the position of the slab movement through the PLC according to the preset sampling length, and performs data acquisition at a regular period. The collected data mainly includes the slab number, slab position, and the slab position width value.

[0013] Among them, step 2: the data compression processing module processes the data: including the data filtering module and the data processing module: 1) data filtering module: this module filters the width data uploaded by the PLC, and the specific algorithm is: the data of the latter acquisition point is compared with the previous acquisition point, and the absolute value of the deviation is greater than or equal to the allowable deviation range Limit, and the data is filtered out to avoid dirty data and data fluctuations; the deviation range is obtained by looking up the table in the continuous casting database according to the sampling length uploaded by the PLC.

[0014] 2) Data processing module: Due to the continuity of continuous casting slab width data, the RLE (Run Length Encoding) algorithm is used to compress the "continuously repeated" data and improve the algorithm. The improved algorithm is as follows:

[0015] For the continuous and repeated width data in the original PLC upload data, the width shape of this section of slab can be uniquely determined by the start position, end position and width value. The table structure is represented by slab number, start position, end position and width.

[0016] For the single-point non-repetitive data in width in the original PLC upload data, position and width are used to represent it, and the table structure is determined by slab number, position and width.

[0017] Among them, step 3: data transmission, is specifically as follows: the data transmission: the continuous casting secondary process control system transmits the width continuously repeated data and the width single point non-repetitive data through two telegrams, telegram 1 includes: telegram number, slab number, start position [], end position [], width [] and telegram end mark; telegram 2 includes: telegram number, slab number, position [], width [] and telegram end mark.

[0018] Among them, step 4: the data decompression processing module decompresses the data, which is specifically as follows: the data decompression processing module and algorithm: after the ERP system or downstream process receives the data of telegram 1 and telegram 2, it first processes telegram 2, expands the data of telegram 2 according to the structure of telegram 1, and processes the position [] column to expand it into the start position and the end position; where the start position = position, and the end position = position. After the expansion is completed, the contents of telegram 1 and telegram 2 are integrated together and reordered according to the start position, as shown in the attached figure Figure 1 shown.

[0019] Among them, step 5: the data storage module stores the data, which is specifically as follows: the data storage module: stores the decompressed data, establishes a data table with the slab number and the starting position as keywords in the database, and provides data for subsequent processing.

[0020] Compared with the prior art, the present invention has the following advantages: 1) This technology aims at the characteristics of continuous casting slab production, and cleverly utilizes the RLE (Run Length Encoding) algorithm to compress those "continuously repeated" data. By compressing the existing slab width data, the compression ratio of the width data of the width-adjustable slab is greater than 5, and the compression ratio of the width data of the non-width-adjustable slab is greater than 189. 2) By improving the storage structure and storage transmission method, the position start information is added according to the data sampling length interval, thereby solving the problem of storing a large amount of redundant data in the database system, reducing the data storage volume, eliminating data redundancy, reducing data storage space, and saving data storage costs; 3) Through telegram integration, using the telegram number, slab number, start position [], end position [], width [] and telegram end mark, the continuous casting slab data can be losslessly transmitted to the ERP system or the downstream hot rolling process. The ERP system can easily restore the data to the width curve that needs to be controlled according to the decompression rules, thereby meeting the data requirements of this link. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The flowchart of data decompression process is shown in Figure 2. DETAILED DESCRIPTION

[0022] In order to deepen the understanding of the present invention, the present embodiment is described in detail below with reference to the accompanying drawings.

[0023] Example 1: See Figure 1 A method for lossless compression processing of continuous casting billet width data, the method comprising the following steps:

[0024] Step 1: The original data acquisition module performs data acquisition;

[0025] Step 2: The data compression processing module processes the data;

[0026] Step 3: Data transmission;

[0027] Step 4: The data decompression processing module decompresses the data;

[0028] Step 5: The data storage module stores the data.

[0029] Among them, step 1: the original data acquisition module performs data acquisition; it is used to collect the slab position and width information uploaded by the on-site PLC to the steelmaking secondary process control system. The module calculates the position of the slab movement through the PLC according to the preset sampling length, and performs data acquisition at a regular period. The collected data mainly includes the slab number, slab position, and the slab position width value.

[0030] Among them, step 2: the data compression processing module processes the data: including the data filtering module and the data processing module: 1) Data filtering module: This module filters the width data uploaded by the PLC. The specific algorithm is: the data of the latter acquisition point is compared with the previous acquisition point. If the absolute value of the deviation is greater than or equal to the allowed deviation range Limit, the data is filtered out to avoid dirty data and data fluctuations; the deviation range is obtained by looking up the table in the continuous casting database according to the sampling length uploaded by the PLC;

[0031] 2) Data processing module: Due to the continuity of continuous casting slab width data, the RLE (Run Length Encoding) algorithm is used to compress the "continuously repeated" data and improve the algorithm. The improved algorithm is as follows:

[0032] For the continuous and repeated width data in the original PLC upload data, the width shape of this section of slab can be uniquely determined by the start position, end position and width value. The table structure is represented by slab number, start position, end position and width.

[0033] For the single-point non-repetitive data in width in the original PLC upload data, position and width are used to represent it, and the table structure is determined by slab number, position and width.

[0034] Among them, step 3: data transmission, is specifically as follows: the data transmission: the continuous casting secondary process control system transmits the width continuously repeated data and the width single point non-repetitive data through two telegrams, telegram 1 includes: telegram number, slab number, start position [], end position [], width [] and telegram end mark; telegram 2 includes: telegram number, slab number, position [], width [] and telegram end mark.

[0035] Among them, step 4: the data decompression processing module decompresses the data, which is specifically as follows: the data decompression processing module and algorithm: after the ERP system or downstream process receives the data of telegram 1 and telegram 2, it first processes telegram 2, expands the data of telegram 2 according to the structure of telegram 1, and processes the position [] column to expand it into the start position and the end position; where the start position = position, and the end position = position. After the expansion is completed, the contents of telegram 1 and telegram 2 are integrated together and reordered according to the start position, as shown in the attached figure Figure 1 shown.

[0036] Among them, step 5: the data storage module stores the data, which is specifically as follows: the data storage module: stores the decompressed data, establishes a data table with the slab number and the starting position as keywords in the database, and provides data for subsequent processing.

[0037] Example 2: See Figure 1 A method for lossless compression processing of continuous casting billet width data, the method comprising the following steps:

[0038] This embodiment is described by taking the continuous casting of width-adjustable slabs in a steel plant as an example.

[0039] Step 1: Raw data acquisition module: collect width data at a fixed length through the on-site PLC and upload it to the secondary steelmaking control system. The information mainly includes slab number, slab position, and width information. The corresponding relationship between the length information and width data of a slab is shown in Table 1 below:

[0040]

[0041]

[0042]

[0043]

[0044] The data sampling length intervals in Table 1 are shown in Table 2 below:

[0045] Slab number Fixed length size 40993658010 0.02

[0046] Table 2 Fixed length dimensions

[0047] Step 2.1: Data filtering and processing module, firstly searches the fixed length size and deviation range experience table 3 in the continuous casting secondary database system according to the fixed length size uploaded by the primary PLC, determines the deviation range as 0.1, and filters the width data uploaded by the PLC. The data with fluctuations greater than 0.1 meters are considered dirty data and removed, thereby avoiding dirty data and data fluctuations.

[0048] Fixed length size Deviation range 0.02 0.1 0.05 0.2

[0049] Table 3 Experience table of fixed length dimensions and deviation range

[0050] Step 2.2: Data compression processing. Due to the continuity of the width of the continuous casting slab, the RLE algorithm is used to process two types of data:

[0051] 1) For the continuous repetitive width data in the original PLC upload data, the width shape of this section of the slab can be uniquely determined by the start position, end position and width. The table structure is represented by the slab number, start position, end position and width. The following Table 4 records the complete continuous repetitive width data information of the slab.

[0052]

[0053]

[0054] Table 4 Width continuous repetition data table

[0055] 2) For the single-point non-repetitive data of width in the original PLC upload data, position and width are used to represent it, and the table structure is determined by slab number, position and width. The following Table 5 records the complete single-point non-repetitive data information of the slab in width.

[0056]

[0057]

[0058]

[0059] Table 5 Width single point non-repetitive data table

[0060] Step 3: The data transmission, message 1 is as follows:

[0061]

[0062] Message 2 is as follows:

[0063] Telegram number Slab number Location[] width[] End of message mark 10002 40993658010 Table 5 Position array Table 5 Width array 1

[0064] Through this compression algorithm, the data is compressed from the original 520 groups, 2080 data, to 53 groups of continuous repeated data, 159 (start position + end position + width) + 3 (telegram number + slab number + end mark) data, and 97 groups of single-point non-repetitive data, 194 (position + width) + 3 (telegram number + slab number + end mark) data, with a compression ratio of up to 5.79 times.

[0065] The original data volume = 2080, the compressed data volume = 162 + 197 = 359;

[0066] Compression ratio = 2080 / 359 = 5.79;

[0067] Step 4: After the ERP system or downstream process receives the data of telegram 1 and telegram 2, it first processes telegram 2 and expands the position [] column to the start position and end position; where the start position = position and the end position = position. After the expansion is completed, the contents of telegram 1 and telegram 2 are integrated and re-sorted according to the start position, as shown in Table 6 below.

[0068]

[0069]

[0070]

[0071]

[0072] Table 6 Data table after integration and sorting

[0073] Step 5: The data storage module stores the data in Table 6, and establishes a data table in the database with the slab number and the starting position as keywords to provide data for subsequent processing.

[0074] Example 3: See Figure 1 A method for lossless compression processing of continuous casting billet width data, the method comprising the following steps:

[0075] This embodiment is described by taking a non-width-adjustable slab in a steel plant as an example.

[0076] Step 1: Raw data acquisition module: collect width data at a fixed length through the on-site PLC and upload it to the secondary steelmaking control system. The information mainly includes slab number, slab position, and width information. The corresponding relationship between the length information and width data of a slab is shown in Table 7 below:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] The data sampling length intervals in Table 7 are as follows in Table 8:

[0083] Slab number Fixed length size 40993658050 0.02

[0084] Table 8 Fixed length dimensions

[0085] Step 2.1: Data filtering and processing module, firstly searches the fixed length size and deviation range experience table 9 in the continuous casting secondary database system according to the fixed length size uploaded by the primary PLC, determines the deviation range as 0.1, filters the width data uploaded by the PLC, and removes the dirty data if the data fluctuation is greater than 0.1 meters, thus avoiding dirty data and data fluctuation.

[0086] Fixed length size Deviation range 0.02 0.1 0.05 0.2

[0087] Table 9 Experience table of fixed length dimensions and deviation range

[0088] Step 2.2: Data compression processing. Due to the continuity of the width of the continuous casting slab, the RLE algorithm is used to process two types of data:

[0089] 1) For the continuous repetitive width data in the original PLC upload data, the width shape of this section of the slab can be uniquely determined by the start position, end position and width. The table structure is represented by the slab number, start position, end position and width. The following Table 10 records the complete continuous repetitive width data information of the slab.

[0090] Slab number Starting position End position width 40993658050 0.006 10.33 1.320

[0091] Table 10 Width continuous repetition data table

[0092] 2) For the non-repeated data of the single width point in the original PLC uploaded data, since there is no width adjustment for this slab, the width data are all repeated, so this step is ignored.

[0093] Step 3: Data transfer:

[0094] Telegram 1 is as follows:

[0095]

[0096] Message 2 is as follows:

[0097]

[0098] Through this compression algorithm, the data is compressed from the original 520 groups, 2080 data, to 1 group of continuous repeated data, 3 (start position + end position + width) + 3 (telegram number + slab number + end mark) data, and 1 group of single-point non-repetitive data, 2 (position + width) + 3 (telegram number + slab number + end mark) data, with a compression ratio of up to 189.09 times.

[0099] Original data volume = 2080, compressed data volume = 6 + 5 = 11

[0100] Compression ratio = 2080 / 11 = 189.09

[0101] Step 4: After the ERP system or downstream process receives the data of telegram 1 and telegram 2, it first processes telegram 2 and expands the position [] column to the start position and end position; where the start position = position and the end position = position. After the expansion is completed, the contents of telegram 1 and telegram 2 are integrated and re-sorted according to the start position, as shown in Table 11 below.

[0102] Slab number Starting position End position width 40993658050 0.006 10.33 1.320

[0103] Table 11 Data table after integration and sorting

[0104] Step 5: The data storage module: stores the data in Table 11, and establishes a data table with the slab number and the starting position as keywords in the database to provide data for subsequent processing.

[0105] It should be noted that the above embodiments are not intended to limit the protection scope of the present invention, and equivalent changes or substitutions made on the basis of the above technical solutions all fall within the protection scope of the claims of the present invention.

Claims

1. A method for lossless compression of continuous casting billet width data, characterized in that: The method comprises the following steps: Step 1: The original data acquisition module performs data acquisition; Step 2: The data compression processing module processes the data; Step 3: Data transmission; Step 4: The data decompression processing module decompresses the data; Step 5: The data storage module stores the data.

2. The method for lossless compression of continuous casting billet width data according to claim 1, characterized in that: Step 1: The original data acquisition module performs data acquisition; It is used to collect the slab position and width information uploaded by the on-site PLC to the secondary process control system of steelmaking. The module calculates the moving position of the slab through PLC according to the preset sampling length, and collects data at a regular period. The collected data mainly includes the slab number, slab position, and the slab position width value.

3. The method for lossless compression of continuous casting billet width data according to claim 2, characterized in that: Step 2: The data compression processing module processes the data: including the data filtering module and the data processing module: 1) Data filtering module: This module filters the width data uploaded by the PLC. The specific algorithm is: the data of the latter acquisition point is compared with the previous acquisition point. If the absolute value of the deviation is greater than or equal to the allowed deviation range Limit, the data is filtered out to avoid dirty data and data fluctuations; the deviation range is obtained by looking up the table in the continuous casting database according to the sampling length uploaded by the PLC; 2) Data processing module: Due to the continuity of continuous casting slab width data, the RLE (Run Length Encoding) algorithm is used to compress "continuously repeated" data and improve the algorithm. The improved algorithm is as follows: For the continuous and repeated width data in the original PLC upload data, the width shape of this section of slab can be uniquely determined by the start position, end position and width value. The table structure is represented by slab number, start position, end position and width. For the single-point non-repetitive data in width in the original PLC upload data, position and width are used to represent it, and the table structure is determined by slab number, position and width.

4. The method for lossless compression of continuous casting billet width data according to claim 3, characterized in that: Step 3: Data transmission, as follows: The data transmission: The continuous casting secondary process control system transmits the width continuously repeated data and the width single point non-repetitive data through two telegrams, and telegram 1 includes: telegram number, slab number, start position [], end position [], width [] and telegram end mark; telegram 2 includes: telegram number, slab number, position [], width [] and telegram end mark.

5. The method for lossless compression of continuous casting billet width data according to claim 3 or 4, characterized in that: Step 4: The data decompression processing module decompresses the data, as follows: The data decompression processing module and algorithm: After the ERP system or downstream process receives the data of telegram 1 and telegram 2, it first processes telegram 2, expands the data of telegram 2 according to the structure of telegram 1, and processes the position [] column to expand it into the starting position and the ending position; where the starting position = position, the ending position = position. After the expansion is completed, the contents of telegram 1 and telegram 2 are integrated together and reordered according to the starting position.

6. The method for lossless compression of continuous casting billet width data according to claim 5, characterized in that: Step 5: The data storage module stores the data, as follows: The data storage module stores the decompressed data, and establishes a data table in the database with the slab number and the starting position as keywords to provide data for subsequent processing.

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