CN104682962A - Compression method for massive fuel gas data - Google Patents
Compression method for massive fuel gas data Download PDFInfo
- Publication number
- CN104682962A CN104682962A CN201510067670.XA CN201510067670A CN104682962A CN 104682962 A CN104682962 A CN 104682962A CN 201510067670 A CN201510067670 A CN 201510067670A CN 104682962 A CN104682962 A CN 104682962A
- Authority
- CN
- China
- Prior art keywords
- data
- compression
- max
- data point
- fan door
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The invention provides a compression method for massive fuel gas data. The compression method comprises the following steps: initializing parameters, comparing record time intervals, compressing 'rotation gate' data, decompressing data, calculating deviation and adjusting compression allowance according to the deviation. By adopting the compression method, negative feedback is adopted to adjust compression parameters in real time, so that relatively high compression precision can be kept constantly, the fuel gas data can be precisely compressed according to the use time regular of the fuel gas data, and the compression method is relatively good in application prospect.
Description
Technical field
The present invention relates to a kind of data compression method, especially a kind of method for compressing the combustion gas data of magnanimity.
Background technology
Along with the development of automatic technology, relevant technology and product are that the supervision foundation of gas ductwork and equipment running status information provides solid technical foundation.Combustion gas information monitoring system comprises the collection of combustion gas data, feedback, storage and analysis.The collection of data comprises the collection of operating pressure that pipe network and gas-fired equipment run, flow and the signal such as security alarm for special user, by means of communication, data feedback is gone back to data center, helps administrative staff to understand ruuning situation in time, exactly.All produce the process data of magnanimity in the supervisory systemss such as pipeline network of fuel gas in city, voltage regulating station, user's gas table tool every day, in monitor procedure, the data of long-term accumulation are the resources of gas enterprise preciousness, for diagnosis and management fault provides the firsthand information; Meanwhile, by analyzing the historical data of different time sections, can simulate the Changing Pattern of whole pipe network operation, the optimization for pipe network provides directly perceived and data foundation accurately.Therefore, ubiquity and is stored and utilize the application demand of these magnanimity creation datas, but saves historical data and there is certain difficulty: due to monitor data point enormous amount, needs the historical data of preserving very considerable, if not compressed direct storage, a lot of storage mediums will be needed.Therefore, the compression method of the historical data of research seems very important.Revolving door trend analysis (the Swinging Door Trend of usual employing, SDT) algorithm is the algorithm that in current process industrial, data compression is the most popular, the databases such as the Hyper Historian of PI, GE company Proficy Historian and Ai Kangnuo (ICONICS) company of the real-time data base OSI company of foreign well-known are all adopt SDT (" revolving door " compresses) technology, and the real-time data base manufacturer of China expands based on SDT technology.
Summary of the invention
The technical problem to be solved in the present invention is due to monitor data point enormous amount, and existing compression method is turned pale, poor property is poor, sincere higher, can not effectively reflect real data situation after data decompression.
In order to solve the problems of the technologies described above, the invention provides a kind of compression method of magnanimity combustion gas data, comprising the steps:
Step 1, sets maximum time interval parameter T in real time according to combustion gas usage time interval
max, adjusting range parameter η, the compression tolerance Δ E of compression deviation amount, deviation Tolerance Parameters μ, anticipation error δ s and dead band threshold epsilon, wherein, dead band threshold epsilon=μ δ s, compression tolerance Δ E=Δ E
max-Δ E
min, Δ E
maxwith Δ E
minbe respectively the upper and lower bound of compression tolerance Δ E;
Step 2, in chronological sequence order reads a data point y in combustion gas data acquisition sequence
k(0<k≤n), and calculated data point y
kwith last data point y
k-1interval of delta t writing time, if Δ t < T
max, then step 4 is entered, if Δ t>=T
max, then step 3 is entered;
Step 3, directly stores last data point, and k is added 1, then return step 2;
Step 4, calculates " revolving door " and turns to data point y
kupper fan door slope during state and lower fan door slope, and by data point y
kupper fan door slope during state and lower fan door slope and last data point y
k-1upper fan door slope during state and lower fan door slope are made comparisons, and get the higher value of fan door slope as data point y
kupper fan door slope during state, takes off the smaller value of fan door slope as data point y
klower fan door slope during state, if now data point y
kupper fan door slope≤data point y during state
klower fan door slope during state, then store last data point y
k-1, terminate the data compression of this " revolving door " simultaneously, and by last data point y
k-1as the starting point of next " revolving door " data compression, then enter step 5, if now this data point state time upper this data point state of fan door slope > time lower fan door slope, then directly enter step 5;
Step 5, judges whether the data in current gas data acquisition sequence are compressed complete, if compress complete, then enters step 6, if uncompressed complete, then return step 2 after k being added 1;
Step 6, carries out data decompression to the data after compression, and adopts linear interpolation method to recover the data point not having to store, according to the data after decompress(ion)
and initial data y
1..., y
ncalculate worst error
and mean error
if δ
max>=δ s, then store δ
maxcorresponding initial data, then adjust according to deviation e=δ s-δ:
1) if | e| < ε, shows that deviation is in allowed band, does not then adjust Δ E, and enter step 7;
2) if e>=ε, show that mean error δ is less than normal, Δ E arranges on the low side, and compression ratio CR can be caused on the low side, then enter step 1 couple compression deviation amount Δ E at Δ E
maxdo in limited range to increase adjustment Δ E=Δ E+ η e/ ε, if Δ E > Δ E
max, then Δ E=Δ E
max;
3) if e < is-ε, show that mean error δ is bigger than normal, Δ E arranges higher, can reduce the decompress(ion) precision of the rear data of compression, then enter step 1 couple compression deviation amount Δ E at Δ E
mindo in limited range to reduce adjustment Δ E=Δ E-η e/ ε, if Δ E < Δ E
min, then Δ E=Δ E
min;
Step 7, judges whether that new combustion gas data acquisition sequence is to be compressed, if having, then returns step 2, if do not have, then terminate and exit.
Adopt the combustion gas data of " revolving door " data compression method to free rule to compress, adjust compression tolerance Δ E in time according to real-time decompress(ion) effect, make the Compress softwares precision that data compression process remains higher; Adopt data point y
kupper fan door slope during state and lower fan door slope and last data point y
k-1upper fan door slope during state and lower fan door slope are made comparisons, and get the higher value of fan door slope as data point y
kupper fan door slope during state, takes off the smaller value of fan door slope as data point y
klower fan door slope during state, as long as thus once in " revolving door " compression process upper fan door gradient maxima meet rotating condition with lower fan door slope minimum value and just perform compression, further increase compression accuracy, effectively prevent the data of sudden change to be missed; Adopt at δ
maxδ is stored during>=δ s
maxcorresponding initial data, can will occur that the data of sudden change store, effectively prevent the data of suddenling change to be missed, further increase compression accuracy.
As further restriction scheme of the present invention, maximum time interval parameter T in step 1
maxvalue within 0:00 ~ 6:00 time period is 1800 seconds, value within 6:00 ~ 8:00 time period is 300 seconds, value within 8:00 ~ 10:00 time period is 600 seconds, value within 10:00 ~ 12:00 time period is 300 seconds, value within 12:00 ~ 17:00 time period is 900 seconds, value within 17:00 ~ 19:00 time period is 300 seconds, and the value within 19:00 ~ 24:00 time period is 1200 seconds.Maximum time interval parameter T is adjusted in real time according to the temporal regularity that combustion gas uses
max, not only reduce compression power consumption, also improve data compression rate, further reduce the data volume after compression.
As further restriction scheme of the present invention, adjusting range parameter η=(the Δ E of compression deviation amount in step 1
max-Δ E
min)/12.Adopt this adjusting range parameter to meet combustion gas is actual when using adjusting range, carries out adjusting and single can be avoided to regulate the excessive and problem that causes compression accuracy to decline under this adjusting range parameter.
As further restriction scheme of the present invention, step 1 large deviations Tolerance Parameters μ size is 0.1 ~ 0.2.Adopt 0.1 ~ 0.2 deviation Tolerance Parameters can guarantee combustion gas data compression all the time in higher compression accuracy.
Beneficial effect of the present invention is: (1) adopts the combustion gas data of " revolving door " data compression method to free rule to compress, adjust compression tolerance Δ E in time according to real-time decompress(ion) effect, make the Compress softwares precision that data compression process remains higher; (2) adopt data point y
kupper fan door slope during state and lower fan door slope and last data point y
k-1upper fan door slope during state and lower fan door slope are made comparisons, and get the higher value of fan door slope as data point y
kupper fan door slope during state, takes off the smaller value of fan door slope as data point y
klower fan door slope during state, as long as thus once in " revolving door " compression process upper fan door gradient maxima meet rotating condition with lower fan door slope minimum value and just perform compression, further increase compression accuracy, effectively prevent the data of sudden change to be missed; (3) adopt at δ
maxδ is stored during>=δ s
maxcorresponding initial data, can will occur that the data of sudden change store, effectively prevent the data of suddenling change to be missed, further increase compression accuracy.
Accompanying drawing explanation
Fig. 1 is control feedback model of the present invention;
Fig. 2 is method flow diagram of the present invention.
Embodiment
At present " revolving door " data compression method in compression process by abandon some data thus reduce data capacity.The data requiring these to be dropped must not affect the restoration and reconstruction of data within the specific limits, therefore, and the decompression error of the important indicator evaluating compression process quality normally data.For this problem, the present invention is in data compression process, calculate the absolute deviation expected between compression performance and actual performance, automatically compression tolerance Δ E is adjusted according to absolute deviation size, and using the compression deviation amount of the Δ E after adjustment as next group data, make actual compression performance constantly close to expected performance like this.
Actual compression performance is generally weighed by compression ratio and error.Assuming that one group of combustion gas data amount check is n, be designated as y
1..., y
n, after compression, data amount check is m (m≤n); After decompress(ion), data amount check is still n, is designated as:
ze Ya Shrink is respectively than CR, relative error RE, absolute error AE, error of sum square SE and mean square deviation MSE definition:
And
Wherein, CR is used for the compressed capability of measure algorithm, and AE, RE, SE and MSE all can be used to the distortion factor weighing data.
Combustion gas data have the characteristic of monotone increasing, and most of combustion gas data are larger with changing in some hours before the meal in every day, and all the other times do not change substantially.The present invention is directed to the characteristic of combustion gas data, traditional " revolving door " data compression method is improved, weigh the compressed capability of data with compression ratio, weigh the distortion factor of data by the mean error of data.Compression ratio CR=0 indicates without compressed capability, and CR=1 represents that the data after compression are the half of former data, and the larger then compressed capability of CR value is stronger.Mean error illustrates the mean value of total data compressed error, is the error comprehensively examining rate total data, thus can assess the overall compression performance of one group of data exactly, and mean error δ is defined as
the present invention evaluates the absolute error of individual data simultaneously, and the data point corresponding to worst error error in a compressional zone being exceeded to anticipation error directly stores.
As shown in Figure 1, main thought of the present invention is: first improve combustion gas data " revolving door " compression method, finds out in compressional zone the worst error point exceeding anticipation error and stores; Then according to the thought of FEEDBACK CONTROL, corrected Calculation is carried out to the compression tolerance Δ E in " revolving door " compression method, the compression process of combustion gas data " revolving door " compression method is as controlled device, the control strategy of improving one's methods, as controller, is responsible for the compression tolerance Δ E in adjustment " revolving door " compression method; System is input as anticipation error δ
s, export the mean error δ into reality, export negative feedback to input, calculation deviation e=δ
s?δ, and give controller by a nonlinear dead-zone link ε.
As shown in Figure 2, the compression method of magnanimity combustion gas data of the present invention, specifically comprises the steps:
Step 1, sets maximum time interval parameter T in real time according to combustion gas usage time interval
max, adjusting range parameter η, the compression tolerance Δ E of compression deviation amount, deviation Tolerance Parameters μ, anticipation error δ s and dead band threshold epsilon, wherein, dead band threshold epsilon=μ δ s, compression tolerance Δ E=Δ E
max-Δ E
min, Δ E
maxwith Δ E
minbe respectively the upper and lower bound of compression tolerance Δ E;
Step 2, in chronological sequence order reads a data point y in combustion gas data acquisition sequence
k(0<k≤n), and calculated data point y
kwith last data point y
k-1interval of delta t writing time, if Δ t < T
max, then step 4 is entered, if Δ t>=T
max, then step 3 is entered;
Step 3, directly stores last data point, and k is added 1, then return step 2;
Step 4, calculates " revolving door " and turns to data point y
kupper fan door slope during state and lower fan door slope, and by data point y
kupper fan door slope during state and lower fan door slope and last data point y
k-1upper fan door slope during state and lower fan door slope are made comparisons, and get the higher value of fan door slope as data point y
kupper fan door slope during state, takes off the smaller value of fan door slope as data point y
klower fan door slope during state, if now data point y
kupper fan door slope≤data point y during state
klower fan door slope during state, then store last data point y
k-1, terminate the data compression of this " revolving door " simultaneously, and by last data point y
k-1as the starting point of next " revolving door " data compression, then enter step 5, if now this data point state time upper this data point state of fan door slope > time lower fan door slope, then directly enter step 5;
Step 5, judges whether the data in current gas data acquisition sequence are compressed complete, if compress complete, then enters step 6, if uncompressed complete, then return step 2 after k being added 1;
Step 6, carries out data decompression to the data after compression, and adopts linear interpolation method to recover the data point not having to store, according to the data after decompress(ion)
and initial data y
1..., y
ncalculate worst error
and mean error
if δ
max>=δ s, then store δ
maxcorresponding initial data, then adjust according to deviation e=δ s-δ:
1) if | e| < ε, shows that deviation is in allowed band, does not then adjust Δ E, and enter step 7;
2) if e>=ε, show that mean error δ is less than normal, Δ E arranges on the low side, and compression ratio CR can be caused on the low side, then enter step 1 couple compression deviation amount Δ E at Δ E
maxdo in limited range to increase adjustment Δ E=Δ E+ η e/ ε, if Δ E > Δ E
max, then Δ E=Δ E
max;
3) if e < is-ε, show that mean error δ is bigger than normal, Δ E arranges higher, can reduce the decompress(ion) precision of the rear data of compression, then enter step 1 couple compression deviation amount Δ E at Δ E
mindo in limited range to reduce adjustment Δ E=Δ E-η e/ ε, if Δ E < Δ E
min, then Δ E=Δ E
min;
Step 7, judges whether that new combustion gas data acquisition sequence is to be compressed, if having, then returns step 2, if do not have, then terminate and exit.
According to the temporal regularity that combustion gas uses, maximum time interval parameter T in step 1 of the present invention
maxvalue within 0:00 ~ 6:00 time period is 1800 seconds, value within 6:00 ~ 8:00 time period is 300 seconds, value within 8:00 ~ 10:00 time period is 600 seconds, value within 10:00 ~ 12:00 time period is 300 seconds, value within 12:00 ~ 17:00 time period is 900 seconds, value within 17:00 ~ 19:00 time period is 300 seconds, and the value within 19:00 ~ 24:00 time period is 1200 seconds.Maximum time interval parameter T is adjusted in real time according to the temporal regularity that combustion gas uses
max, not only reduce compression power consumption, also improve data compression rate, further reduce the data volume after compression.
According to the feature of the combustion gas data gathered, in step 1 of the present invention, the adjusting range setting parameter of compression deviation amount is: η=(Δ E
max-Δ E
min)/12.Adopt this adjusting range parameter to meet combustion gas is actual when using adjusting range, carries out adjusting and single can be avoided to regulate the excessive and problem that causes compression accuracy to decline under this adjusting range parameter.
According to the feature of the combustion gas data gathered, the size of step 1 large deviations Tolerance Parameters μ of the present invention is set as: 0.1 ~ 0.2.Preferably can be set as 0.15, can adapt with the feature of combustion gas data better.Adopt 0.1 ~ 0.2 deviation Tolerance Parameters can guarantee combustion gas data compression all the time in higher compression accuracy.
Claims (5)
1. a compression method for magnanimity combustion gas data, is characterized in that, comprises the steps:
Step 1, sets maximum time interval parameter T in real time according to combustion gas usage time interval
max, adjusting range parameter η, the compression tolerance Δ E of compression deviation amount, deviation Tolerance Parameters μ, anticipation error δ s and dead band threshold epsilon, wherein, dead band threshold epsilon=μ δ s, compression tolerance Δ E=Δ E
max-Δ E
min, Δ E
maxwith Δ E
minbe respectively the upper and lower bound of compression tolerance Δ E;
Step 2, in chronological sequence order reads a data point y in combustion gas data acquisition sequence
k(0<k≤n), and calculated data point y
kwith last data point y
k-1interval of delta t writing time, if Δ t < T
max, then step 4 is entered, if Δ t>=T
max, then step 3 is entered;
Step 3, directly stores last data point, and k is added 1, then return step 2;
Step 4, calculates " revolving door " and turns to data point y
kupper fan door slope during state and lower fan door slope, and by data point y
kupper fan door slope during state and lower fan door slope and last data point y
k-1upper fan door slope during state and lower fan door slope are made comparisons, and get the higher value of fan door slope as data point y
kupper fan door slope during state, takes off the smaller value of fan door slope as data point y
klower fan door slope during state, if now data point y
kupper fan door slope≤data point y during state
klower fan door slope during state, then store last data point y
k-1, terminate the data compression of this " revolving door " simultaneously, and by last data point y
k-1as the starting point of next " revolving door " data compression, then enter step 5, if now this data point state time upper this data point state of fan door slope > time lower fan door slope, then directly enter step 5;
Step 5, judges whether the data in current gas data acquisition sequence are compressed complete, if compress complete, then enters step 6, if uncompressed complete, then return step 2 after k being added 1;
Step 6, carries out data decompression to the data after compression, and adopts linear interpolation method to recover the data point not having to store, according to the data after decompress(ion)
and initial data y
1..., y
ncalculate worst error
and mean error
if δ
max>=δ s, then store δ
maxcorresponding initial data, then adjust according to deviation e=δ s-δ:
1) if | e| < ε, shows that deviation is in allowed band, does not then adjust Δ E, and enter step 7;
2) if e>=ε, show that mean error δ is less than normal, Δ E arranges on the low side, and compression ratio CR can be caused on the low side, then enter step 1 couple compression deviation amount Δ E at Δ E
maxdo in limited range to increase adjustment Δ E=Δ E+ η e/ ε, if Δ E > Δ E
max, then Δ E=Δ E
max;
3) if e < is-ε, show that mean error δ is bigger than normal, Δ E arranges higher, can reduce the decompress(ion) precision of the rear data of compression, then enter step 1 couple compression deviation amount Δ E at Δ E
mindo in limited range to reduce adjustment Δ E=Δ E-η e/ ε, if Δ E < Δ E
min, then Δ E=Δ E
min;
Step 7, judges whether that new combustion gas data acquisition sequence is to be compressed, if having, then returns step 2, if do not have, then terminate and exit.
2. the compression method of magnanimity combustion gas data according to claim 1, is characterized in that: maximum time interval parameter T in described step 1
maxvalue within 0:00 ~ 6:00 time period is 1800 seconds, value within 6:00 ~ 8:00 time period is 300 seconds, value within 8:00 ~ 10:00 time period is 600 seconds, value within 10:00 ~ 12:00 time period is 300 seconds, value within 12:00 ~ 17:00 time period is 900 seconds, value within 17:00 ~ 19:00 time period is 300 seconds, and the value within 19:00 ~ 24:00 time period is 1200 seconds.
3. the compression method of magnanimity combustion gas data according to claim 1 and 2, is characterized in that: adjusting range parameter η=(the Δ E of compression deviation amount in described step 1
max-Δ E
min)/12.
4. the compression method of magnanimity combustion gas data according to claim 1 and 2, is characterized in that: described step 1 large deviations Tolerance Parameters μ size is 0.1 ~ 0.2.
5. the compression method of magnanimity combustion gas data according to claim 3, is characterized in that: described step 1 large deviations Tolerance Parameters μ size is 0.1 ~ 0.2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510067670.XA CN104682962A (en) | 2015-02-09 | 2015-02-09 | Compression method for massive fuel gas data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510067670.XA CN104682962A (en) | 2015-02-09 | 2015-02-09 | Compression method for massive fuel gas data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104682962A true CN104682962A (en) | 2015-06-03 |
Family
ID=53317617
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510067670.XA Pending CN104682962A (en) | 2015-02-09 | 2015-02-09 | Compression method for massive fuel gas data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104682962A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106126728A (en) * | 2016-07-04 | 2016-11-16 | 上海电气集团股份有限公司 | A kind of method of real time data distributed parallel compression |
| CN108540136A (en) * | 2018-03-13 | 2018-09-14 | 华侨大学 | A kind of compression method being suitable for agriculture sensing data |
| CN108667463A (en) * | 2018-03-27 | 2018-10-16 | 江苏中科羿链通信技术有限公司 | Monitoring data compression method |
| CN111680012A (en) * | 2020-06-12 | 2020-09-18 | 吉林省电力科学研究院有限公司 | Data compression method for monitoring data of heating system |
| CN112182034A (en) * | 2019-07-03 | 2021-01-05 | 河南许继仪表有限公司 | Data compression method and device |
| CN112965976A (en) * | 2021-02-26 | 2021-06-15 | 中国人民解放军海军工程大学 | Electromagnetic energy system service time sequence data compression method, non-transient readable recording medium and data processing device |
| CN113258933A (en) * | 2021-05-28 | 2021-08-13 | 山西阳光三极科技股份有限公司 | Multi-interval self-adaptive revolving door algorithm |
| CN113300388A (en) * | 2021-06-11 | 2021-08-24 | 华北电力大学（保定） | Wind power fluctuation stabilizing method based on improved revolving door algorithm |
| CN114640355A (en) * | 2022-03-30 | 2022-06-17 | 北京诺司时空科技有限公司 | Lossy compression and decompression method, system, storage medium and equipment of time sequence database |
| CN114969060A (en) * | 2022-08-01 | 2022-08-30 | 浙江木链物联网科技有限公司 | Industrial equipment time sequence data compression storage method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201993755U (en) * | 2011-01-30 | 2011-09-28 | 上海振华重工(集团)股份有限公司 | Data filtration, compression and storage system of real-time database |
| CN102510287A (en) * | 2011-11-03 | 2012-06-20 | 电子科技大学 | Method for rapidly compressing industrial real-time data |
| CN102622367A (en) * | 2011-01-30 | 2012-08-01 | 上海振华重工(集团)股份有限公司 | Method for filtering and compressing process data |
-
2015
- 2015-02-09 CN CN201510067670.XA patent/CN104682962A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201993755U (en) * | 2011-01-30 | 2011-09-28 | 上海振华重工(集团)股份有限公司 | Data filtration, compression and storage system of real-time database |
| CN102622367A (en) * | 2011-01-30 | 2012-08-01 | 上海振华重工(集团)股份有限公司 | Method for filtering and compressing process data |
| CN102510287A (en) * | 2011-11-03 | 2012-06-20 | 电子科技大学 | Method for rapidly compressing industrial real-time data |
Non-Patent Citations (4)
| Title |
|---|
| 于松涛: "《流程工业生产管理信息系统关键技术研究》", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 于松涛等: "《基于容差动态调整的旋转门(SDT)改进算法》", 《北京化工大学学报（自然科学版）》 * |
| 曲奕霖: "《流程工业历史数据的压缩策略与压缩方法研究》", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 曲奕霖: "《用于过程数据压缩的自控精度SDT算法》", 《计算机工程》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106126728A (en) * | 2016-07-04 | 2016-11-16 | 上海电气集团股份有限公司 | A kind of method of real time data distributed parallel compression |
| CN108540136A (en) * | 2018-03-13 | 2018-09-14 | 华侨大学 | A kind of compression method being suitable for agriculture sensing data |
| CN108540136B (en) * | 2018-03-13 | 2021-06-29 | 华侨大学 | Compression method suitable for agricultural sensing data |
| CN108667463A (en) * | 2018-03-27 | 2018-10-16 | 江苏中科羿链通信技术有限公司 | Monitoring data compression method |
| CN108667463B (en) * | 2018-03-27 | 2021-11-02 | 江苏中科羿链通信技术有限公司 | Monitoring data compression method |
| CN112182034A (en) * | 2019-07-03 | 2021-01-05 | 河南许继仪表有限公司 | Data compression method and device |
| CN111680012A (en) * | 2020-06-12 | 2020-09-18 | 吉林省电力科学研究院有限公司 | Data compression method for monitoring data of heating system |
| CN112965976A (en) * | 2021-02-26 | 2021-06-15 | 中国人民解放军海军工程大学 | Electromagnetic energy system service time sequence data compression method, non-transient readable recording medium and data processing device |
| CN113258933B (en) * | 2021-05-28 | 2022-09-16 | 山西阳光三极科技股份有限公司 | Multi-interval self-adaptive revolving door algorithm |
| CN113258933A (en) * | 2021-05-28 | 2021-08-13 | 山西阳光三极科技股份有限公司 | Multi-interval self-adaptive revolving door algorithm |
| CN113300388A (en) * | 2021-06-11 | 2021-08-24 | 华北电力大学（保定） | Wind power fluctuation stabilizing method based on improved revolving door algorithm |
| CN113300388B (en) * | 2021-06-11 | 2022-09-16 | 华北电力大学（保定） | Wind power fluctuation stabilizing method based on improved revolving door algorithm |
| CN114640355A (en) * | 2022-03-30 | 2022-06-17 | 北京诺司时空科技有限公司 | Lossy compression and decompression method, system, storage medium and equipment of time sequence database |
| CN114640355B (en) * | 2022-03-30 | 2023-04-18 | 北京诺司时空科技有限公司 | Lossy compression and decompression method, system, storage medium and equipment of time sequence database |
| CN114969060A (en) * | 2022-08-01 | 2022-08-30 | 浙江木链物联网科技有限公司 | Industrial equipment time sequence data compression storage method and device |
| CN114969060B (en) * | 2022-08-01 | 2022-11-04 | 浙江木链物联网科技有限公司 | Industrial equipment time sequence data compression storage method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104682962A (en) | Compression method for massive fuel gas data | |
| CN102376026B (en) | Industrial enterprise power utilization load optimizing system | |
| CN103742901B (en) | A kind of defining method of Properties of CFB power consumption analysis optimization target values system | |
| CN101609986A (en) | Multilevel joint coordination automatic voltage control method based on decision tree | |
| CN113760625B (en) | Evaluation method and monitoring system for model predictive controller performance | |
| CN114004296A (en) | Method and system for reversely extracting monitoring points based on power load characteristics | |
| JP2022535442A (en) | Methods and apparatus for facilitating storage of data from industrial automation control systems or power systems | |
| CN104834975A (en) | Power network load factor prediction method based on intelligent algorithm optimization combination | |
| CN102073773A (en) | Main steam pressure tracking optimization method for steam turbine | |
| CN111930790B (en) | Valve inlet temperature prediction method of valve cooling equipment based on time sequence analysis | |
| CN203069670U (en) | Intelligent electric meter having function of monitoring power supply reliability and voltage qualified rate | |
| Goosen et al. | Efficient monitoring of mine compressed air savings | |
| CN116388150A (en) | Matching short-term load prediction method based on digital twin and neural network technology | |
| CN117034763A (en) | One-stop intelligent management system of digital energy air compression system | |
| CN116680661A (en) | Multi-dimensional data-based automatic gas regulator pressure monitoring method | |
| CN109869198B (en) | Method for monitoring efficiency of steam turbine full-life high-pressure cylinder based on valve position correction | |
| CN111612181A (en) | Fault tree-based boiler abnormal working condition diagnosis and operation optimization method | |
| CN116296354A (en) | System and method for monitoring and optimizing flow characteristics of high-speed valve of steam turbine in real time | |
| CN103606916B (en) | A kind of transformer station evaluation of power supply capability method | |
| CN106650154B (en) | Nuclear power conventional island equipment reliability monitoring method based on thermal performance index | |
| CN103631991A (en) | Parameter identification system and method of prime mover speed regulating system | |
| CN115146977A (en) | Enterprise energy efficiency data management method and system based on Internet of things | |
| Xiang et al. | Energy conservation co-operative control mechanism based on gray prediction for structural air compressor | |
| CN111552911B (en) | Quantitative analysis method for technical line loss influence factors based on multi-scene generation | |
| CN102231081A (en) | Energy utilization state diagnosis method for process industrial equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150603 |