CN108023597B - Numerical control system reliability data compression method - Google Patents
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Abstract
The invention relates to a method for compressing the reliability data of a numerical control system, which comprises the following steps: according to the reliability data type of the numerical control system, respectively adopting a static dictionary and an improved LZW algorithm of a dynamic dictionary to compress static data; compressing trigger data or time values by adopting an RLE algorithm of a PCX file; compressing process data by adopting an improved revolving door algorithm for storing deviation points; and compressing network transmission data by adopting a two-stage compression mode combining a static dictionary and a Huffman algorithm. The method can efficiently compress the reliability data of the numerical control system, and the decompression of the data does not influence the reliability test of the numerical control system.
Description
Technical Field
The invention relates to a data compression technology in the technical field of numerical control, in particular to a method for compressing reliability data of a numerical control system.
Background
The reliability data of the numerical control system is the basis for improving the reliability of products, and plays a key guiding role in positioning and eliminating early faults and researching and developing new products. For reliability testing work of a numerical control system, reliability data needs to be collected and stored for a long time, which requires a considerable storage space. In order to ensure that the reliable data can be stored in real time, save storage space and cost as much as possible, and reduce the bandwidth occupied in network transmission, the reliable data needs to be compressed.
Data compression algorithms can be classified into lossy compression and lossless compression according to their compression accuracy. Common lossless data compression algorithms include Huffman coding algorithm, run length coding algorithm, LZ series coding algorithm, and the like. Lossy compression methods can be classified into 3 types, i.e., a piecewise linear method, a vector quantization method, and a signal transformation method. The piecewise linear method also includes a boxcar method, a backward slope method, a revolving gate algorithm and a PLOT method.
The reliability data collected by the numerical control system mainly comprises the following steps: the device information data, configuration data, operation state data, diagnosis data and the like are classified into static data, trigger data, time values, process data and network transmission data according to the characteristics and the functions of the data. Different types of reliability data have different characteristics, static data is mainly descriptive text data, and the context has certain relation; the trigger type data is data which keeps the same state in a certain time; and the process data is floating point type data with a certain change rule.
The compression effect of one compression algorithm on different types of data is different, and the reliability data of the numerical control system is not of a single type, so that the reliability data of the numerical control system cannot be effectively compressed by using a single compression algorithm, and the compression algorithm for the different types of reliability data needs to be designed.
Disclosure of Invention
In order to solve the problems, the invention provides a method for compressing the reliability data of a numerical control system, which respectively compresses the reliability data according to the reliability data type of the numerical control system.
The technical scheme adopted by the invention for realizing the purpose is as follows: a method for compressing the reliability data of a numerical control system comprises the following steps:
step 1: judging the reliability data type of the numerical control system;
step 2: compressing static data by adopting an improved LZW algorithm of combining a static dictionary with a dynamic dictionary;
compressing trigger data or time values by adopting an RLE algorithm of a PCX file;
compressing process data by adopting an improved revolving door algorithm for storing deviation points;
and compressing network transmission data by adopting a two-stage compression mode combining a static dictionary and a Huffman algorithm.
The reliability data types include: static data, trigger-like data, time values, process data, network transmission data.
The method for compressing the static data by adopting the improved LZW algorithm of combining the static dictionary with the dynamic dictionary specifically comprises the following steps:
s1: initializing a dictionary, wherein the dictionary comprises character strings of which the occurrence frequency exceeds a set value;
s2: sequentially reading one character of the static data as a prefix;
s3: reading the next character as a suffix;
s4: checking whether a dictionary has a prefix and a suffix;
s5: if so, assigning a prefix to the prefix; transitioning to S7;
s6: if not, outputting prefix codes, adding the prefix plus suffix into the dictionary, assigning the suffix to the prefix, and turning to S7;
s7: checking whether the static data has unread bytes; if yes, returning to S3; if not, the prefix code is output.
The RLE algorithm compression triggering type data or time value adopting the PCX file specifically comprises the following steps:
s1: sequentially reading a character ch1, and setting the string length s to 1;
s2: read the next character ch 2;
s3: comparing whether ch1 and ch2 are the same;
s4: if the two are the same, adding 1 to S, and turning to S10;
s5: if not, determining whether at least one of s >1 or ch1>192 is true;
s6: if s >1 or ch1>192 is true, then judging whether s >63 is true;
s7: if S >63 is true, then output (63, ch1) and assign S-63 to S, go to S5;
s8: if S >63 is false, outputting (S, ch1), assigning ch2 to ch1, and turning to S10;
s9: if S >1 is false and ch1>192 is false, outputting a character ch1, assigning ch2 to ch1, and turning to S10;
s10: checking whether there are unread characters; if yes, go to S2; if not, the output ch1 is encoded.
The compression process data by adopting the improved revolving door algorithm for storing the deviation point specifically comprises the following steps:
setting compression precision e, a threshold value, an upper initial value up and a lower initial value down;
s1: performing difference processing on the data;
s2: sequentially reading the data cur _ d after the difference processing and the time cur _ t thereof;
s3: the upper gate up' is calculated as (cur _ d-stored _ d-e)/(cur _ t-stored _ t),
lower gate down' ═ cur _ d-stored _ d + e)/(cur _ t-stored _ t);
taking the large value of up and up 'as up and the small value of down and down' as down;
s4: if up is more than or equal to down, read _ d is saved,
calculating a predicted value, forecast _ d ═ ((read _ d-stored _ d)/(cur _ t-stored _ t-1)) × cur _ t;
if | forecast _ d-cur _ d | is > a threshold value, judging cur _ d as an offset value, and storing the cur _ d; reading the next data assignment to stored _ d, storing stored _ d, and reading the next data assignment to cur _ d; otherwise, stored _ d is read _ d;
then, up is equal to cur _ d-stored _ d-e, down is equal to cur _ d-stored _ d + e; transitioning to S6;
s5: if up < down, go to S6;
s6: if the read _ d is cur _ d, checking whether unread data still exists; if yes, returning to S2; if not, storing the stored _ d;
wherein cur _ d is current read data, previous data of the current data of read _ d, stored _ d is last stored data, and cur _ t is time corresponding to the current data; stored _ t is the time corresponding to the last saved data.
The two-stage compression mode of combining the static dictionary with the Huffman algorithm is adopted to compress network transmission data, and specifically comprises the following steps:
s1: initializing a dictionary, wherein the dictionary comprises character strings with the occurrence frequency exceeding a set value in an XML label;
s2: reading the character c1 and setting the character string s to null;
s3: judging whether c1 is "<";
s4: if c1 is "<", reading the next character c2, and judging whether c2 is ">;
s5: if c2 is ">", checking whether the dictionary contains the character string s; if yes, outputting the coding sum corresponding to the "<" sum s ">; if not, output < s >; transitioning to S8;
s6: if c2 is not ">", assign S + c2 to S, go to S4;
s7: if c1 is not "<", outputting c 1; transitioning to S8;
s8: counting the occurrence times of each character respectively, checking whether unread characters exist or not, and if yes, turning to S2; if not, a Huffman tree is constructed, Huffman coding is carried out, and Huffman coding of each character is output.
The invention has the following advantages and beneficial effects:
1. the improved LZW algorithm combining the static dictionary and the dynamic dictionary solves the problem that the adaptation process of the dictionary of the LZW algorithm is slow.
The improved RLE algorithm in PCX file stores non-repeated character directly into compressed file to compress data to maximum extent.
3. The improved revolving door algorithm can save deviation points in the process data without influencing the decompression and recovery of normal data.
4. The static dictionary is combined with the two-stage compression mode of the Huffman algorithm to compress, so that the problem that the compression ratio of network transmission data is low due to weak context relevance can be solved.
Drawings
FIG. 1 is a flow diagram of a modified LZW algorithm with a static dictionary combined with a dynamic dictionary;
FIG. 2 is a flowchart of the RLE algorithm using PCX files;
FIG. 3 is a flow chart of an improved turnstile algorithm;
FIG. 4 is a two-stage compression flow diagram of a combination of static dictionary and Huffman algorithm;
FIG. 5 is a graph of data prior to compression of process data;
FIG. 6 is a graph of data after compression of process data.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The method is a compression algorithm research aiming at different types of reliability data in the numerical control system. The reliability data collected by the numerical control system mainly comprises the following steps: the device information data, configuration data, operating state data, diagnostic data, etc. can be classified into static data, trigger-type data, time values, process data and network transmission data according to the characteristics and functions of the data.
Static data includes device information, configuration data, diagnostic data, and the like; the equipment information comprises the model number, the serial number, the software version, the versions of all hardware components and the like of the numerical control system; servo and motor model, serial number, software version, hardware version, etc.; machine tool model, serial number, etc. The configuration data comprises numerical control system and servo parameters, user processing programs and the like. The diagnostic data includes hardware diagnostic information, software diagnostic information, servo operational diagnostic information, and the like. The trigger data includes switching value, working mode, working state, etc. The process data mainly comprises data such as axis coordinates, feeding speed, feeding multiplying power, current, temperature, humidity and the like. Network-transmitted data is real-time data that needs to be transmitted to a remote third party.
The different types of reliability data have different characteristics, static data is mainly descriptive text data, the context has certain relation, the trigger data is data which keeps the same state within a certain time, and the time value represents the time corresponding to the trigger data; and the process data is floating point type data with a certain change rule.
As shown in fig. 1, is a flow chart of the improved LZW algorithm for static data compression. According to analysis, static data contains some terms with high frequency of occurrence, the LZW algorithm is an efficient lossless compression algorithm, and matched character strings do not need to be searched in the compression process, the algorithm is much simpler to realize than that of LZSS, but the LZW algorithm is a dictionary built step by step in the compression process, the self-adaptation process of the original data characteristics is slow, so that the data characteristics are simply improved by combining the LZW algorithm, before the original data is coded, the dictionary is initialized by utilizing the noun character strings, the noun character strings consist of the terms with high frequency of occurrence, then the compression is carried out by utilizing the LZW algorithm, namely the static dictionary and the dynamic dictionary are combined, and the problem that the self-adaptation process of the dictionary of the LZW algorithm is slow is solved. The specific implementation process comprises the following steps:
(1) initializing a dictionary by using the extracted common entries;
(2) reading a byte as a prefix;
(3) reading a byte as a suffix;
(4) checking whether a dictionary has a prefix and a suffix;
(5) if so, taking the prefix as the prefix and returning to the step (3);
(6) if not, outputting prefix codes, adding a prefix plus suffix into the dictionary, and returning to the step (3);
as shown in fig. 2, is a flowchart of RLE algorithm in PCX file for trigger class data and time values. The numerical control system data includes trigger data such as a switching value type and a periodic time value. After the time value difference is processed, the data has a large number of continuous characters, so that the RLE algorithm is adopted for the data to obtain a high compression rate.
When processing discontinuous repeated characters, the RLE algorithm can store two bytes (string length and character), and the original character only has one byte, namely the discontinuous repeated data is processed by using run length coding, but the storage space is enlarged, so that the RLE algorithm improved in the PCX file is adopted in the text, the non-repeated characters are directly stored in a compressed file, the original data or the compressed code is distinguished by using the high two bits of the byte, wherein the high two bits are all 1 to represent the string length, and the high two bits are not all 1 to represent the original character. One byte has a minimum of 192 when all of the upper two bits are 1, and has a maximum of 63 when all of the upper two bits are 0. The specific implementation process comprises the following steps:
(1) reading a character ch1, with a string length s equal to 1;
(2) reading a character ch 2;
(3) comparing whether ch1 and ch2 are the same;
(4) if the same, s is added with 1 and returns to (2);
(5) if not, judging whether s >1 or ch1>192 is true;
(6) if false, the character ch1 is output, ch1 is ch2, and the process returns to (2);
(7) if true, then judging s is greater than 63;
(8) if true, output (63, ch1), s-63, back to (5);
(9) if false, output (s, ch1), ch1 ═ ch2, return to (2).
As shown in fig. 3, is a flow chart of an improved turnstile algorithm for process data. The compression of process data for reliability test of a numerical control system needs the characteristic of keeping original data to the greatest extent, namely for reliability evaluation and test, some deviation data are also stored, and the decompression and recovery of normal data cannot be influenced, so that the data cannot be compressed by directly applying a revolving door algorithm.
The revolving door algorithm decides whether to retain the value of the previous time point according to whether the compression offset coverage area formed by the current value and the last saved value can cover all the points between the two, which cannot process the offset data, so that the offset data needs to be improved to meet the requirement of the reliability test. The main improvement points are as follows: and setting a threshold value for judging deviation data, and if the difference between the data and the predicted data exceeds the threshold value, determining the data to be the deviation data and needing to be stored. In order to ensure the accuracy of the data during decompression, the two data before and after the deviation data also need to be stored, so that the decompressed data is not influenced by the deviation data. If the predicted value is calculated for each data in order to determine whether the data is a deviation data, it takes a lot of time, which requires a certain method to reduce the amount of calculation. The analysis finds that: the deviation data can only appear in the latter data of the saved data, that is, the data which is formed by the coverage area formed by the saved data and the last saved value can not cover all the points between the two, and only the predicted value of the data needs to be calculated to analyze whether the data is the deviation data. The predicted value is calculated using a linear extrapolation of the previous data from the previously stored data and the deviation data. In order to further improve the storage efficiency, the data can be compressed after difference preprocessing. The specific implementation process comprises the following steps:
(1) performing difference processing on the data, and setting compression precision e, a threshold value, up and down, wherein cur _ d is currently read data, the previous data of the current data of read _ d, and stored _ d is the last stored data;
(2) reading data cur _ d, time cur _ t;
(3) calculating an upper gate up ═ cur _ d-stored _ d-e)/(cur _ t-stored _ t), a lower gate down ═ cur _ d-stored _ d + e)/(cur _ t-stored _ t), up ═ max (up, up '), down ═ min (down, down');
(4) judging up < down;
(5) if the current value is false, saving the read _ d, and calculating a predicted value, namely ((read _ d-stored _ d)/(cur _ t-stored _ t-1)). curr _ t; judging a | forecast _ d-cur _ d | > threshold value;
(6) if true, judging cur _ d as an offset value, storing cur _ d, reading data and assigning value to stored _ d, storing stored _ d, reading data and assigning value to cur _ d;
(7) if false, stored _ d is read _ d;
(8) updating up-cur _ d-stored _ d-e and down-cur _ d-stored _ d + e;
(9) return to (2) read _ d ═ cur _ d.
Fig. 4 is a flow chart of two-stage compression for network transmission data. The transmission format of the network transmission data adopts an XML format, different data need different XML tags in order to describe the meaning of the data, the tags contain different types of data, and the context relevance of the data is not strong.
The LZ-series algorithm can achieve a better compression effect on data with strong context relevance, so that the LZ-series algorithm cannot achieve a better compression rate on the type of data. Huffman coding statistically codes bytes, which requires scanning twice the original data, consumes more time, and is not suitable for directly compressing data. By analyzing the network data transmitted by using the XML format, the occurrence frequency of character strings in the XML label is higher and is relatively fixed, so that the character strings can be compressed and subjected to character statistics in a static dictionary mode, and then compressed by Huffman coding. The specific implementation process comprises the following steps:
(1) acquiring a constructed static dictionary;
(2) reading the character c1, s ═ c ";
(3) judging c1 ═ < ";
(4) if false, output c 1;
(5) if true, read character c2, judge c2 ═ ">;
(6) if false, s + c2 returns to (5);
(7) if true, checking whether the dictionary contains s;
(8) if yes, outputting "<" + coding + ">;
(9) if not, outputting "<" + s + ">;
(10) counting the occurrence times of the characters, and returning to the step (2) if the characters exist;
and constructing a Huffman tree, performing Huffman coding and outputting a result.
The execution effect of the method of the invention is as follows:
1. the results of the tests for compressing static data using the LZW algorithm, the LZSS algorithm, and the modified LZW algorithm, respectively, are shown in table 1.
TABLE 1 compression efficiency of static data under different compression algorithms
The test result shows that the LZW algorithm combined with the static dictionary reduces the process of gradually establishing the dictionary for the noun character strings with higher occurrence frequency, so that the character strings can be directly encoded and compressed without the self-adaptive process of gradually adding characters. Compared with LZSS and LZW algorithms, the method has the advantages of reducing the compression time, improving the compression rate and having better compression effect.
2. The compression effect of the trigger class data is shown in table 2.
Table 2 compression results of trigger class data
From the test results, it is seen that a higher compression rate is obtained using the RLE algorithm since the trigger class data has a large number of consecutive characters. And the compression rate is further improved through the improvement of the compression of the discontinuous characters. The data obtained after the time value is subjected to difference value preprocessing has the characteristic of continuity, and a good compression effect is also obtained by applying the RLE algorithm for compression.
3. The simulation of process data by sin (t) 100 is shown in table 3, where the compression effect of the deviation points are set at t 140 and t 680.
TABLE 3 Process data compression results
From the test results, the improved revolving door algorithm achieves good compression results. FIG. 5 is a graph of data prior to compression of process data; as shown in fig. 6, a graph of data after decompression of process data. Comparing fig. 5 and fig. 6, it can be seen that the algorithm performs special processing on the deviation point, stores the deviation point exceeding the threshold, and also stores the data adjacent to the deviation point in front of and behind the deviation point, so that the recovery of other data is not influenced during decompression, and the absolute error of the data is kept in a certain range.
4. Table 4 shows the test results of compressing the network transmission data respectively by using the Huffman algorithm, the LZW algorithm, the LZSS algorithm, and the static dictionary in combination with the Huffman algorithm.
Table 4 compression results of network transmission data under different compression algorithms
The test result shows that the two-stage compression mode combining the static dictionary coding and the Huffman coding shortens the compression time of the Huffman coding, and has greater advantages in compression rate and is very helpful for saving network bandwidth although the compression time and the decompression time are less than those of the LZW algorithm and the LZSS algorithm.
Claims (4)
1. A numerical control system reliability data compression method is characterized by comprising the following steps:
step 1: judging the reliability data type of the numerical control system;
step 2: compressing static data by adopting an improved LZW algorithm of combining a static dictionary with a dynamic dictionary; the method specifically comprises the following steps: s1: initializing a dictionary, wherein the dictionary comprises character strings of which the occurrence frequency exceeds a set value; s2: sequentially reading one character of the static data as a prefix; s3: reading the next character as a suffix; s4: checking whether a dictionary has a prefix and a suffix; s5: if so, assigning a prefix to the prefix; transitioning to S7; s6: if not, outputting prefix codes, adding the prefix plus suffix into the dictionary, assigning the suffix to the prefix, and turning to S7; s7: checking whether the static data has unread bytes; if yes, returning to S3; if not, outputting prefix codes;
compressing trigger data or time values by adopting an RLE algorithm of a PCX file;
compressing process data by adopting an improved revolving door algorithm for storing deviation points; the method specifically comprises the following steps:
setting compression precision e, a threshold value, an upper initial value up and a lower initial value down;
s1: performing difference processing on the data;
s2: sequentially reading the data cur _ d after the difference processing and the time cur _ t thereof;
s3: the upper gate up' is calculated as (cur _ d-stored _ d-e)/(cur _ t-stored _ t),
lower gate down' ═ cur _ d-stored _ d + e)/(cur _ t-stored _ t);
taking the large value of up and up 'as up and the small value of down and down' as down;
s4: if up is more than or equal to down, read _ d is saved,
calculating a predicted value, forecast _ d ═ ((read _ d-stored _ d)/(cur _ t-stored _ t-1)) × cur _ t;
if | forecast _ d-cur _ d | is > a threshold value, judging cur _ d as an offset value, and storing the cur _ d; reading the next data assignment to stored _ d, storing stored _ d, and reading the next data assignment to cur _ d; otherwise, stored _ d is read _ d;
then, up is equal to cur _ d-stored _ d-e, down is equal to cur _ d-stored _ d + e; transitioning to S6;
s5: if up < down, go to S6;
s6: if the read _ d is cur _ d, checking whether unread data still exists; if yes, returning to S2; if not, storing the stored _ d;
wherein cur _ d is current read data, previous data of the current data of read _ d, stored _ d is last stored data, and cur _ t is time corresponding to the current data; stored _ t is the time corresponding to the last saved data;
and compressing network transmission data by adopting a two-stage compression mode combining a static dictionary and a Huffman algorithm.
2. The method for compressing the reliability data of the numerical control system according to claim 1, wherein the reliability data types comprise: static data, trigger-like data, time values, process data, network transmission data.
3. The method for compressing the reliability data of the numerical control system according to claim 1, wherein the RLE algorithm for compressing the trigger data or the time value by using the PCX file specifically comprises:
s1: sequentially reading a character ch1, and setting the string length s to 1;
s2: read the next character ch 2;
s3: comparing whether ch1 and ch2 are the same;
s4: if the two are the same, adding 1 to S, and turning to S10;
s5: if not, determining whether at least one of s >1 or ch1>192 is true;
s6: if s >1 or ch1>192 is true, then judging whether s >63 is true;
s7: if S >63 is true, then output (63, ch1) and assign S-63 to S, go to S5;
s8: if S >63 is false, outputting (S, ch1), assigning ch2 to ch1, and turning to S10;
s9: if S >1 is false and ch1>192 is false, outputting a character ch1, assigning ch2 to ch1, and turning to S10;
s10: checking whether there are unread characters; if yes, go to S2; if not, the output ch1 is encoded.
4. The method for compressing the reliability data of the numerical control system according to claim 1, wherein the compressing of the network transmission data by the two-stage compression method combining the static dictionary and the Huffman algorithm specifically comprises:
s1: initializing a dictionary, wherein the dictionary comprises character strings with the occurrence frequency exceeding a set value in an XML label;
s2: reading the character c1 and setting the character string s to null;
s3: judging whether c1 is "<";
s4: if c1 is "<", reading the next character c2, and judging whether c2 is ">;
s5: if c2 is ">", checking whether the dictionary contains the character string s; if yes, outputting the coding sum corresponding to the "<" sum s ">; if not, output < s >; transitioning to S8;
s6: if c2 is not ">", assign S + c2 to S, go to S4;
s7: if c1 is not "<", outputting c 1; transitioning to S8;
s8: counting the occurrence times of each character respectively, checking whether unread characters exist or not, and if yes, turning to S2; if not, a Huffman tree is constructed, Huffman coding is carried out, and Huffman coding of each character is output.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102638579A (en) * | 2012-03-29 | 2012-08-15 | 深圳市高正软件有限公司 | Data processing method and system based on data transmission of mobile equipment |
| CN105357260A (en) * | 2015-09-28 | 2016-02-24 | 深圳市深信服电子科技有限公司 | System for realizing virtual desktop, data caching method for VDI (Virtual Desktop Infrastructure) and VDI caching device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7609179B2 (en) * | 2008-01-08 | 2009-10-27 | International Business Machines Corporation | Method for compressed data with reduced dictionary sizes by coding value prefixes |
-
2016
- 2016-10-28 CN CN201610960271.0A patent/CN108023597B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102638579A (en) * | 2012-03-29 | 2012-08-15 | 深圳市高正软件有限公司 | Data processing method and system based on data transmission of mobile equipment |
| CN105357260A (en) * | 2015-09-28 | 2016-02-24 | 深圳市深信服电子科技有限公司 | System for realizing virtual desktop, data caching method for VDI (Virtual Desktop Infrastructure) and VDI caching device |
Non-Patent Citations (2)
| Title |
|---|
| LZW*:一个改进的LZW数据压缩算法;张凤林 等;《小型微型计算机系统》;20061031;第27卷(第10期);全文 * |
| 实时数据库数据压缩算法探讨与改进;刘红霞 等;《化工自动化及仪表》;20100610;第37卷(第6期);全文 * |
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