CN116300717A - Big data monitoring system based on industrial production - Google Patents
Big data monitoring system based on industrial production Download PDFInfo
- Publication number
- CN116300717A CN116300717A CN202310056342.4A CN202310056342A CN116300717A CN 116300717 A CN116300717 A CN 116300717A CN 202310056342 A CN202310056342 A CN 202310056342A CN 116300717 A CN116300717 A CN 116300717A
- Authority
- CN
- China
- Prior art keywords
- data
- unit
- time sequence
- monitoring
- monitoring system
- 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
- 238000012544 monitoring process Methods 0.000 title claims abstract description 61
- 238000009776 industrial production Methods 0.000 title claims abstract description 27
- 238000004364 calculation method Methods 0.000 claims abstract description 24
- 238000000605 extraction Methods 0.000 claims abstract description 23
- 238000007418 data mining Methods 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 19
- 238000004519 manufacturing process Methods 0.000 claims abstract description 18
- 238000004140 cleaning Methods 0.000 claims abstract description 4
- 238000013500 data storage Methods 0.000 claims abstract description 4
- 239000007788 liquid Substances 0.000 claims abstract description 4
- 238000007726 management method Methods 0.000 claims abstract description 4
- 239000000463 material Substances 0.000 claims description 8
- 238000013075 data extraction Methods 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 5
- 238000007781 pre-processing Methods 0.000 abstract description 4
- 238000005065 mining Methods 0.000 abstract description 2
- 238000000034 method Methods 0.000 description 15
- 230000008569 process Effects 0.000 description 9
- 230000001364 causal effect Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 239000012535 impurity Substances 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31282—Data acquisition, BDE MDE
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Factory Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a big data monitoring system based on industrial production, which comprises a data acquisition unit, a central processing unit, a data mining unit, a time sequence extraction unit, a variable monitoring unit, a time sequence standardization calculation unit, a data warehouse unit and a data backup unit, wherein the data acquisition unit is used for acquiring data of a large data object; the variable monitoring unit is used for transmitting all monitored temperature, flow, liquid level, pressure and gas concentration variables in the production process to the terminal control interface through the sensor, and the data enter a data storage management stage through an OLTP processing program to carry out a series of data cleaning; according to the industrial production-based big data monitoring system, the operation rules are mined in the historical data, the historical data are scanned, various data preprocessing and data mining technologies are used for the historical data, so that massive data with high continuity and high relevance are processed, and the control level of the industrial monitoring system can be improved through mining.
Description
Technical Field
The invention relates to the technical field of data monitoring, in particular to a big data monitoring system based on industrial production.
Background
With the rapid development of industrial informatization and computer technology, the content of data information in the industrial production process is increasingly abundant, so that a large data age is met, how to extract needed data from a large amount of data in the industrial production has become a focus of attention, and in a process control system, process variable data changing with time is monitored and recorded in a distributed control system. With the support of industrial production data record storage technology, a large amount of data are accumulated in many factories, wherein each factory generates data calculated by tb every day, and the historical data comprise a plurality of information such as equipment running state, operator operation record and record of material quantity change of each part in the production process. However, in essence, these data are characterized by "many", "miscellaneous", etc., and are not random samples, but rather are all data; precision is not sought, but confounding; the method is not required to be causal, but is related, and the data generated by the process industry has the characteristics of mass property, continuity, strong relevance and the like, so that the working capacity of a detection system can be emphasized, and therefore, a big data monitoring system based on industrial production is proposed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a big data monitoring system based on industrial production, which solves the problems of large and miscellaneous data workload and low monitoring level of the existing big data monitoring system based on industrial production.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the big data monitoring system based on industrial production comprises a data acquisition unit, a central processing unit, a data mining unit, a time sequence extraction unit, a variable monitoring unit, a time sequence standardization calculation unit, a data warehouse unit and a data backup unit;
the data acquisition unit is connected with a monitoring end in a signal manner at one end of the data acquisition unit, and a plurality of production devices are monitored in the monitoring end and are used for monitoring information transmission of data;
the data mining unit is used for extracting record information comprising equipment running conditions, operator operation records and material quantity changes in the production process from historical data based on a time line, but basically, the data have the characteristics of multiple, impurity and the like, are good and bad, do not require random samples, are all data, do not require accuracy, and are mixed; the method is free from causal relation, and is characterized in that an operation rule is mined from historical data according to the data condition, and the control level of an industrial monitoring system can be improved by scanning the historical data and using various data preprocessing and data mining technologies;
the variable monitoring unit is used for transmitting all monitored temperature, flow, liquid level, pressure and gas concentration variables in the production process to the terminal control interface through the sensor, the data enter a data storage management stage through an OLTP processing program, a series of data cleaning is carried out, the data are transmitted to a data warehouse after being processed, then the data are divided into data marts and enter the OLTP processing stage, and then valuable information in industrial data is extracted out to form rules by using a data mining algorithm, so that the data are used for guiding a series of activities such as auxiliary decision making and operation.
Preferably, the central processing unit is used for processing data in the system, is responsible for reading instructions, decoding the instructions and executing the instructions;
the time sequence extraction unit is used for arranging the numerical values of the same statistical index in the time line according to the time sequence of occurrence, namely, arranging the time sequence, searching historical data according to the time sequence, and carrying out characteristic extraction on the historical data for prediction purposes, wherein the time sequence data is in a data form generated by the time sequence of the data, the data combined by the time and the numerical values is called as a time sequence, and the characteristics of the time sequence are completely represented through a data extraction formula;
the time sequence standardized calculation unit converts the time sequence standardized calculation unit into a symbol sequence under the condition of not limiting the time sequence length to form a character string with any length, and the time sequence standardized calculation formula is used for calculating, so that the process calculation is simpler, the data noise can be better removed, and the algorithm is more efficient.
Preferably, the data warehouse unit refers to a structured data environment of a decision support system and an online analysis application data source, and the data warehouse researches and solves the problem of acquiring information from a database, and is characterized by theme-oriented, integration, stability and time-varying property;
and the data backup unit establishes a copy file of the main database on a backup machine which is separated from the production machine where the main database is located, thereby being convenient for storing and checking the data and preventing the data from being lost.
Preferably, the monitoring data in the data acquisition unit comprises equipment running conditions, operator operation records and material quantity changes in the production process.
Preferably, the data extraction formula in the time sequence extraction unit is as follows:
T＝[t 1 (x 1 ,y 1 ),t 2 (x 2 ,y 2 ),t 3 (x 3 ,y 3 ),...,t n (x n ,y n )]wherein
t i (x i ,y i ) Representing a single data point in the time series data, i= (1, 2,3,) n, x i Representing the data value size, y i The data at the moment can be searched in the time sequence by the formula, and the similar sequence can be searched in the time sequence, and the statistics of a trend shape in the time sequence can be carried out according to the timeThe sequence history data serves the purpose of predicting the sequence history data and the like.
Preferably, the timing normalization calculation formula in the timing normalization calculation unit is as follows:
L l = (L- μ)/σ, where L is raw data, L l For the standardized data, mu and sigma are respectively the mean value and standard deviation of the original data, and after the time sequence data is standardized, each monitored data variable is conveniently monitored in real time in the industrial production process, and when the monitored process variable encounters certain interference or faults, fluctuation is generated, namely, the monitored quantity is warned.
Preferably, the output end of the data acquisition unit is connected with the input end of the central processing unit, the input end of the data acquisition unit is connected with the output end of the monitoring unit, the output end of the central processing unit is connected with the input ends of the data mining unit, the time sequence extraction unit, the variable monitoring unit, the time sequence standardization calculation unit and the data warehouse unit, the output end of the data mining unit is connected with the input end of the time sequence extraction unit, the output end of the time sequence extraction unit is connected with the input end of the variable monitoring unit, the output end of the variable monitoring unit is connected with the input end of the time sequence standardization calculation unit, and the output end of the data warehouse unit is connected with the input end of the data backup unit.
Compared with the prior art, the invention provides a big data monitoring system based on industrial production, which has the following beneficial effects:
1. according to the industrial production-based big data monitoring system, the data mining unit is used for extracting record information comprising equipment running conditions, operator operation records and material quantity changes in the production process from historical data according to a time line, but basically, the data have the characteristics of multiple, impurity and the like, are good and bad, do not require random samples, are all data, do not require accuracy, and are mixed; the control level of the industrial monitoring system can be improved by scanning the historical data and using various data preprocessing and data mining techniques on the historical data without causal, but related, mining the operation rules from the historical data for the data situation.
2. According to the industrial production-based big data monitoring system, the similar sequence can be searched in the time sequence through the data extraction formula in the time sequence extraction unit, statistics is carried out on a trend shape in the time sequence, and the purposes of predicting the trend shape according to time sequence historical data and the like are achieved.
3. According to the industrial production-based big data monitoring system, after the time sequence data is standardized through the time sequence standardized calculation formula in the time sequence standardized calculation unit, each monitored data variable is monitored in real time conveniently in the industrial production process, and when the monitored process variable encounters certain interference or faults, fluctuation is generated, namely, the monitoring quantity is warned.
Drawings
FIG. 1 is a block diagram of a big data monitoring system based on industrial production of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
Embodiment one:
referring to fig. 1, the big data monitoring system based on industrial production comprises a data acquisition unit, a central processing unit, a data mining unit, a time sequence extraction unit, a variable monitoring unit, a time sequence standardization calculation unit, a data warehouse unit and a data backup unit;
the data acquisition unit is connected with a monitoring end in a signal manner at one end of the data acquisition unit, and a plurality of production devices are monitored in the monitoring end and are used for monitoring information transmission of data;
the data mining unit is used for extracting record information comprising equipment running conditions, operator operation records and material quantity changes in the production process from historical data based on a time line, but basically, the data have the characteristics of multiple, impurity and the like, are good and bad, do not require random samples, are all data, do not require accuracy, and are mixed; the method is free from causal relation, and is characterized in that an operation rule is mined from historical data according to the data condition, and the control level of an industrial monitoring system can be improved by scanning the historical data and using various data preprocessing and data mining technologies;
the variable monitoring unit is used for transmitting all monitored temperature, flow, liquid level, pressure and gas concentration variables in the production process to the terminal control interface through the sensor, the data enter a data storage management stage through an OLTP processing program, a series of data cleaning is carried out, the data are transmitted to a data warehouse after being processed, then the data are divided into data marts and enter the OLTP processing stage, and then valuable information in industrial data is extracted out to form rules by using a data mining algorithm, so that the data are used for guiding a series of activities such as auxiliary decision making and operation.
In this embodiment, the central processing unit is used for processing data in the system, and is responsible for reading instructions, decoding the instructions and executing the instructions;
the time sequence extraction unit is used for arranging the numerical values of the same statistical index in the time line according to the time sequence of occurrence, namely, arranging the time sequence, searching historical data according to the time sequence, and carrying out characteristic extraction on the historical data for prediction purposes, wherein the time sequence data is in a data form generated by the time sequence of the data, the data combined by the time and the numerical values is also called as a time sequence, and the characteristics of the time sequence are completely represented through a data extraction formula;
the time sequence standardized calculation unit converts the time sequence standardized calculation unit into a symbol sequence under the condition of not limiting the time sequence length to form a character string with any length, and the time sequence standardized calculation formula is used for calculating, so that the process calculation is simpler, the data noise can be better removed, and the algorithm is more efficient.
In this embodiment, a data warehouse unit, which refers to a structured data environment of a decision support system and an online analysis application data source, researches and solves the problem of obtaining information from a database, the data warehouse being characterized by topic-oriented, integration, stability and time-varying properties;
and the data backup unit establishes a copy file of the main database on a backup machine which is separated from the production machine where the main database is located, thereby being convenient for storing and checking the data and preventing the data from being lost.
In this embodiment, the monitoring data in the data acquisition unit includes equipment operating conditions, operator operating records, and material quantity changes during production.
In this embodiment, the data extraction formula in the timing sequence extraction unit is as follows:
T＝[t 1 (x 1 ,y 1 ),t 2 (x 2 ,y 2 ),t 3 (x 3 ,y 3 ),...,t n (x n ,y n )]wherein
t i (x i ,y i ) Representing a single data point in the time series data, i= (1, 2,3,) n, x i Representing the data value size, y i The time of the data can be searched in the time sequence by the formula, and the similar sequence can be searched in the time sequence, and the statistics of a trend shape in the time sequence can be performed, so that the purposes of predicting the time sequence according to historical data of the time sequence and the like are achieved.
In this embodiment, the timing normalization calculation formula in the timing normalization calculation unit is as follows:
L l = (L- μ)/σ, where L is raw data, L l For the standardized data, mu and sigma are respectively the mean value and standard deviation of the original data, and after the time sequence data is standardized, each monitored data variable is conveniently monitored in real time in the industrial production process, and when the monitored process variable encounters certain interference or faults, fluctuation is generated, namely, the monitored quantity is warned.
In this embodiment, the output end of the data acquisition unit is connected to the input end of the central processing unit, the input end of the data acquisition unit is connected to the output end of the monitoring unit, the output end of the central processing unit is connected to the input ends of the data mining unit, the time sequence extraction unit, the variable monitoring unit, the time sequence standardization calculation unit and the data warehouse unit, the output end of the data mining unit is connected to the input end of the time sequence extraction unit, the output end of the time sequence extraction unit is connected to the input end of the variable monitoring unit, the output end of the variable monitoring unit is connected to the input end of the time sequence standardization calculation unit, and the output end of the data warehouse unit is connected to the input end of the data backup unit.
It is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and further, that the terms "comprise," "include," or any other variation thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The big data monitoring system based on industrial production is characterized by comprising a data acquisition unit, a central processing unit, a data mining unit, a time sequence extraction unit, a variable monitoring unit, a time sequence standardization calculation unit, a data warehouse unit and a data backup unit;
the data acquisition unit is connected with a monitoring end in a signal manner at one end of the data acquisition unit, and a plurality of production devices are monitored in the monitoring end and are used for monitoring information transmission of data;
the data mining unit is used for extracting record information comprising equipment running conditions, operator operation records and material quantity changes in the production process from the historical data based on the time line;
the variable monitoring unit is used for transmitting all monitored temperature, flow, liquid level, pressure and gas concentration variables in the production process to the terminal control interface through the sensor, the data enter a data storage management stage through an OLTP processing program, a series of data cleaning is carried out, the data are transmitted to a data warehouse after being processed, then the data are divided into data marts and enter the OLTP processing stage, and then valuable information in industrial data is extracted out to form rules by using a data mining algorithm, so that the data are used for guiding a series of activities such as auxiliary decision making and operation.
2. The industrial production-based big data monitoring system according to claim 1, wherein the central processing unit is used for data processing inside the system, is responsible for reading instructions, decoding the instructions and executing the instructions;
the time sequence extraction unit is used for arranging the numerical values of the same statistical index in the time line according to the time sequence of occurrence, namely, arranging the time sequence, searching historical data according to the time sequence, and carrying out characteristic extraction on the historical data for prediction purposes, wherein the time sequence data is in a data form generated by the time sequence of the data, the data combined by the time and the numerical values is called as a time sequence, and the characteristics of the time sequence are completely represented through a data extraction formula;
and the time sequence standardization calculating unit converts the time sequence length into a symbol sequence under the condition of not limiting the time sequence length to form a character string with any length, and calculates the character string through a time sequence standardization calculating formula.
3. The industrial-based big data monitoring system of claim 1, wherein: the data warehouse unit refers to a structured data environment of a decision support system and an online analysis application data source, and is used for researching and solving the problem of acquiring information from a database, and is characterized by theme-oriented, integration, stability and time-varying property;
and the data backup unit is used for establishing copy files of the main database on a backup machine which is separated from the production machine where the main database is located.
4. The industrial production-based big data monitoring system of claim 1, wherein the monitoring data in the data collection unit includes equipment operating conditions, operator operating records, and material quantity changes during production.
5. The industrial production-based big data monitoring system according to claim 2, wherein the data extraction formula in the time series extraction unit is as follows:
T＝[t 1 (x 1 ,y 1 ),t 2 (x 2 ,y 2 ),t 3 (x 3 ,y 3 ),...,t n (x n ,y n )]wherein
t i (x i ,y i ) Representing a single data point in the time series data, i= (1, 2,3,) n, x i Representing the data value size, y i The data is at that time.
6. The industrial production-based big data monitoring system according to claim 2, wherein the timing normalization calculation formula in the timing normalization calculation unit is as follows:
L l = (L- μ)/σ, where L is raw data, L l For normalized data, μ and σ are the mean and standard deviation, respectively, of the raw data.
7. The industrial production-based big data monitoring system according to claim 1, wherein the output end of the data acquisition unit is connected to the input end of the central processing unit, the input end of the data acquisition unit is connected to the output end of the monitoring unit, the output end of the central processing unit is connected to the input ends of the data mining unit, the time sequence extraction unit, the variable monitoring unit, the time sequence standardization calculating unit and the data warehouse unit, the output end of the data mining unit is connected to the input end of the time sequence extraction unit, the output end of the time sequence extraction unit is connected to the input end of the variable monitoring unit, the output end of the variable monitoring unit is connected to the input end of the time sequence standardization calculating unit, and the output end of the data warehouse unit is connected to the input end of the data backup unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310056342.4A CN116300717A (en) | 2023-01-18 | 2023-01-18 | Big data monitoring system based on industrial production |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310056342.4A CN116300717A (en) | 2023-01-18 | 2023-01-18 | Big data monitoring system based on industrial production |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116300717A true CN116300717A (en) | 2023-06-23 |
Family
ID=86834946
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310056342.4A Pending CN116300717A (en) | 2023-01-18 | 2023-01-18 | Big data monitoring system based on industrial production |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116300717A (en) |
-
2023
- 2023-01-18 CN CN202310056342.4A patent/CN116300717A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110347116B (en) | Machine tool state monitoring system and monitoring method based on operation data flow | |
| CN105653427A (en) | Log monitoring method based on abnormal behavior detection | |
| CN111506478A (en) | Method for realizing alarm management control based on artificial intelligence | |
| CN111027615B (en) | Middleware fault early warning method and system based on machine learning | |
| CN113934720A (en) | Data cleaning method and equipment and computer storage medium | |
| CN115883163A (en) | Network safety alarm monitoring method | |
| CN115935286A (en) | Abnormal point detection method, device and terminal for railway bearing state monitoring data | |
| JP4275359B2 (en) | Data analysis method | |
| CN113657700A (en) | Mine safety production real-time monitoring method and system based on big data | |
| CN112650739A (en) | Data storage processing method and device for coal mine data middling station | |
| CN116300717A (en) | Big data monitoring system based on industrial production | |
| CN112416732A (en) | Hidden Markov model-based data acquisition operation anomaly detection method | |
| CN114722081B (en) | Streaming data time sequence transmission method and system based on transfer library mode | |
| CN111209158B (en) | Mining monitoring method and cluster monitoring system for server cluster | |
| CN113535458B (en) | Abnormal false alarm processing method and device, storage medium and terminal | |
| CN111522710B (en) | Data compression method, device and medium based on big data | |
| CN112699005A (en) | Server hardware fault monitoring method, electronic equipment and storage medium | |
| CN116861204B (en) | Intelligent manufacturing equipment data management system based on digital twinning | |
| CN117520964B (en) | Motor pre-diagnosis method based on artificial intelligence | |
| CN117574180B (en) | Fuel production and emission system data correlation control management system | |
| CN114548177A (en) | Equipment health assessment method, system and equipment | |
| CN115576770A (en) | Data lake monitoring system, method, device, equipment and storage medium | |
| Pavlov et al. | Relevance and Problems for Application of Big Data in Engineering Industry | |
| CN117411780A (en) | Network log anomaly detection method based on multi-source data characteristics | |
| CN116647860A (en) | Index association alarm analysis method for 5G core network, electronic device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |