CN110609864B - Chemical supply chain-oriented data visualization management method and device - Google Patents
Chemical supply chain-oriented data visualization management method and device Download PDFInfo
- Publication number
- CN110609864B CN110609864B CN201910816327.9A CN201910816327A CN110609864B CN 110609864 B CN110609864 B CN 110609864B CN 201910816327 A CN201910816327 A CN 201910816327A CN 110609864 B CN110609864 B CN 110609864B
- Authority
- CN
- China
- Prior art keywords
- data
- time
- source heterogeneous
- visualization
- heterogeneous data
- 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.)
- Active
Links
- 238000007726 management method Methods 0.000 title claims abstract description 39
- 238000013079 data visualisation Methods 0.000 title claims abstract description 23
- 239000000126 substance Substances 0.000 title claims abstract description 20
- 238000004458 analytical method Methods 0.000 claims abstract description 126
- 238000012800 visualization Methods 0.000 claims abstract description 65
- 230000002159 abnormal effect Effects 0.000 claims abstract description 62
- 238000000034 method Methods 0.000 claims abstract description 35
- 230000000007 visual effect Effects 0.000 claims description 30
- 238000013473 artificial intelligence Methods 0.000 claims description 28
- 230000004927 fusion Effects 0.000 claims description 16
- 230000007812 deficiency Effects 0.000 claims description 11
- 238000004220 aggregation Methods 0.000 claims description 4
- 230000002776 aggregation Effects 0.000 claims description 4
- 230000002123 temporal effect Effects 0.000 claims 2
- 238000003860 storage Methods 0.000 description 8
- 230000003287 optical effect Effects 0.000 description 5
- 238000004140 cleaning Methods 0.000 description 3
- 238000013523 data management Methods 0.000 description 3
- 238000012217 deletion Methods 0.000 description 3
- 230000037430 deletion Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000003672 processing method Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000013068 supply chain management Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
- G06F16/287—Visualization; Browsing
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a chemical supply chain-oriented data visualization management method and a chemical supply chain-oriented data visualization management device, wherein the method comprises the following steps: the method comprises the steps that multi-source heterogeneous data of each node on a supply chain are collected based on various industrial sensors and uploaded to a cloud server; performing anomaly analysis processing on the multi-source heterogeneous data based on time and space sequences to obtain anomaly analysis data, and storing the anomaly analysis data in a cloud database in a cloud server; and performing visualization processing on the abnormal analysis data according to different visualization scenes based on visualization scene analysis and according to industrial field environments, and pushing visualization results according to different customized scenes. According to the embodiment of the invention, the abnormity analysis can be rapidly carried out, and the abnormity analysis data can be globally visualized and pushed.
Description
Technical Field
The invention relates to the technical field of industrial internet, in particular to a chemical supply chain-oriented data visualization management method and device.
Background
Supply chain management in the chemical industry has extremely obvious wide-area sparse characteristics, and different channels such as raw materials, production, distribution and sale, so that a supply chain process needing to be managed has the management characteristics of thousands of chains, and the supply chain process often has application pain points such as weak informatization level, untimely information uploading and the like because a chemical industry area is located in a coastal industry gathering area.
The existing supply chain pipe in the chemical industry can not form credible supply chain data according to time and space information and provide credible unique identification; and the abnormity judgment can not be carried out according to the conditions that the data provided by the supply chain has data abnormity, deficiency, illegal tampering and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a data visualization management method and device for a chemical supply chain, which can quickly perform anomaly analysis according to time and space sequences and perform global visualization and pushing on anomaly analysis data.
In order to solve the technical problem, an embodiment of the present invention provides a data visualization management method for a chemical supply chain, where the method includes:
the method comprises the steps that multi-source heterogeneous data of each node on a supply chain are collected based on various industrial sensors and uploaded to a cloud server;
performing anomaly analysis processing on the multi-source heterogeneous data based on time and space sequences to obtain anomaly analysis data, and storing the anomaly analysis data in a cloud database in a cloud server;
and performing visualization processing on the abnormal analysis data according to different visualization scenes based on visualization scene analysis and according to industrial field environments, and pushing visualization results according to different customized scenes.
Optionally, the multisource heterogeneous data of each node on the supply chain is gathered based on all kinds of industrial sensors to upload to the high in the clouds server, include:
remotely acquiring multi-source heterogeneous data information of each node on a supply chain based on various industrial sensors to obtain multi-source heterogeneous data;
and uploading the multi-source heterogeneous data to the cloud server for aggregation.
Optionally, the performing, based on the time and space sequence, an anomaly analysis process on the multi-source heterogeneous data includes:
taking the time and space sequence as fixed attributes of the multi-source heterogeneous data, and performing information fusion with the multi-source heterogeneous data to obtain fused multi-source heterogeneous data; and the number of the first and second groups,
and carrying out anomaly analysis processing on the fused multi-source heterogeneous data.
Optionally, before the information fusion with the multi-source heterogeneous data, the method further includes:
judging whether the acquisition time resolution of the multi-source heterogeneous data is higher than the time resolution of the time sequence or not, and if so, adding a subclass sequence number after the time stamp of the time sequence;
and judging whether the acquisition spatial resolution for acquiring the multi-source heterogeneous data is higher than the spatial resolution of the spatial sequence, and if so, adding a subclass sequence number behind the spatial stamp of the spatial sequence.
Optionally, the performing anomaly analysis processing on the multi-source heterogeneous data based on the time and space sequences to obtain anomaly analysis data further includes:
and carrying out anomaly analysis on the conditions of anomaly, deficiency and illegal tampering of the multi-source heterogeneous data by using an artificial intelligence algorithm based on the time and space sequences to obtain anomaly analysis data.
Optionally, the performing, by using an artificial intelligence algorithm based on the time and space sequences, anomaly analysis on the abnormal, missing and illegal tampering conditions of the multi-source heterogeneous data to obtain anomaly analysis data includes:
carrying out artificial preset exception rules on the artificial intelligence algorithm, and clustering, classifying and exception handling learning on the multi-source heterogeneous data by the artificial intelligence algorithm based on the preset exception rules; and the number of the first and second groups,
updating the preset abnormal rule in the clustering, classifying and abnormal processing learning process to obtain updated clustering, classifying and abnormal processing learning results;
and performing time and space domain processing on the updated clustering, classifying and exception handling learning result by using a dynamic time rule algorithm based on the time and space sequence to obtain exception analysis data.
Optionally, the step of obtaining the time and space sequence includes:
generating a time series based on a time series generator;
generating a spatial sequence based on a spatial sequence generator;
wherein the time series generator generates a unique time series based on national standard time or international standard time; the spatial sequence generator generates a unique spatial sequence based on a national standard geodetic coordinate system or an international standard geodetic coordinate system.
Optionally, the storing the anomaly analysis data in a cloud database in a cloud server includes:
the anomaly analysis data is stored into a cloud database in the cloud server based on a database management system;
the database management system is provided with appointed conditions, and the appointed conditions are used for automatically covering historical data.
Optionally, the performing, based on the visualization scene analysis, visualization processing on different visualization scenes on the anomaly analysis data according to an industrial field environment, and pushing a visualization result according to different customized scenes includes:
reading the abnormal analysis data, providing a software running mode by using visual scene analysis software according to an industrial field environment, selecting different visual scenes for visual processing, and obtaining a visual processing result;
transmitting the visualization processing result to a front end for information display, and pushing the information of the characteristics according to different visualization scenes;
wherein, the different visual scenes comprise a computer display, a projector or a mobile phone terminal.
In addition, an embodiment of the present invention further provides a data visualization management device for a chemical supply chain, where the device includes:
the data acquisition and uploading module is used for: the system comprises a cloud server, a plurality of industrial sensors and a plurality of cloud servers, wherein the cloud server is used for acquiring multi-source heterogeneous data of each node on a supply chain based on each industrial sensor and uploading the multi-source heterogeneous data to the cloud server;
an anomaly analysis module: the cloud database is used for carrying out anomaly analysis processing on the multi-source heterogeneous data based on time and space sequences, obtaining anomaly analysis data and storing the anomaly analysis data in the cloud server;
a visualization management module: and the system is used for performing visualization processing on different visualization scenes on the abnormal analysis data according to the industrial field environment based on visualization scene analysis, and pushing visualization results according to different customized scenes.
In the embodiment of the invention, by injecting time and space sequences into multi-source heterogeneous data of various supply chains and combining an artificial intelligence processing method, special conditions such as data deletion, cleaning, illegal tampering and the like can be automatically judged, so that a stable and reliable supply chain data management effect is formed, and global visualization capability is provided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a data visualization management method for a chemical supply chain in an embodiment of the present invention;
fig. 2 is a schematic structural composition diagram of a data visualization management apparatus for a chemical supply chain in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Referring to fig. 1, fig. 1 is a schematic flow chart of a data visualization management method for a chemical supply chain according to an embodiment of the present invention.
As shown in fig. 1, a data visualization management method for a chemical supply chain includes:
s11: the method comprises the steps that multi-source heterogeneous data of each node on a supply chain are collected based on various industrial sensors and uploaded to a cloud server;
in a specific implementation process of the present invention, the acquiring, based on various industrial sensors, multi-source heterogeneous data of each node in a supply chain and uploading the data to a cloud server includes: remotely acquiring multi-source heterogeneous data information of each node on a supply chain based on various industrial sensors to obtain multi-source heterogeneous data; and uploading the multi-source heterogeneous data to the cloud server for aggregation.
Specifically, various industrial sensors are used for collecting data of each node on a supply chain, so that multi-source heterogeneous data is formed, each industrial sensor comprises an optical sensor, an infrared sensor and the like, the optical sensors firstly converge to the nearest route connected with the sensors after collecting data of the corresponding node, and the route is uploaded to a cloud server based on a wireless network transmission technology.
S12: performing anomaly analysis processing on the multi-source heterogeneous data based on time and space sequences to obtain anomaly analysis data, and storing the anomaly analysis data in a cloud database in a cloud server;
in a specific implementation process of the present invention, the performing anomaly analysis processing on the multi-source heterogeneous data based on the time and space sequences includes: taking the time and space sequence as fixed attributes of the multi-source heterogeneous data, and performing information fusion with the multi-source heterogeneous data to obtain fused multi-source heterogeneous data; and carrying out anomaly analysis processing on the fused multi-source heterogeneous data.
Further, before the information fusion with the multi-source heterogeneous data, the method further includes: judging whether the acquisition time resolution of the multi-source heterogeneous data is higher than the time resolution of the time sequence or not, and if so, adding a subclass sequence number after the time stamp of the time sequence; and judging whether the acquisition spatial resolution for acquiring the multi-source heterogeneous data is higher than the spatial resolution of the spatial sequence, and if so, adding a subclass sequence number behind the spatial stamp of the spatial sequence.
Further, the performing anomaly analysis processing on the multi-source heterogeneous data based on the time and space sequences to obtain anomaly analysis data further includes: and carrying out anomaly analysis on the conditions of anomaly, deficiency and illegal tampering of the multi-source heterogeneous data by using an artificial intelligence algorithm based on the time and space sequences to obtain anomaly analysis data.
Further, the abnormal analysis of the abnormal, missing and illegal tampering conditions of the multi-source heterogeneous data by using an artificial intelligence algorithm based on the time and space sequences to obtain abnormal analysis data includes: carrying out artificial preset exception rules on the artificial intelligence algorithm, and clustering, classifying and exception handling learning on the multi-source heterogeneous data by the artificial intelligence algorithm based on the preset exception rules; updating the preset abnormal rule in the clustering, classifying and abnormal processing learning process to obtain updated clustering, classifying and abnormal processing learning results; and performing time and space domain processing on the updated clustering, classifying and exception handling learning result by using a dynamic time rule algorithm based on the time and space sequence to obtain exception analysis data.
Further, the step of obtaining the time and space sequence includes: generating a time series based on a time series generator; generating a spatial sequence based on a spatial sequence generator; wherein the time series generator generates a unique time series based on national standard time or international standard time; the spatial sequence generator generates a unique spatial sequence based on a national standard geodetic coordinate system or an international standard geodetic coordinate system.
Further, the storing the anomaly analysis data in a cloud database in a cloud server includes: the anomaly analysis data is stored into a cloud database in the cloud server based on a database management system; the database management system is provided with appointed conditions, and the appointed conditions are used for automatically covering historical data.
Specifically, the corresponding time and space sequence is used as the fixed attribute of the collected multi-source heterogeneous data, and the corresponding time and space sequence and the corresponding multi-source heterogeneous data are used for fusion, so that the fused multi-source heterogeneous data is obtained; and then carrying out anomaly analysis processing on the fused multi-source heterogeneous data.
The fusion refers to fusion of corresponding time and space sequences on the multi-source heterogeneous data; before fusion is carried out by utilizing the corresponding time and space sequences and the corresponding multi-source heterogeneous data, firstly, whether the acquisition time resolution of the acquired multi-source heterogeneous data is higher than the time resolution of the time sequence is judged, and if so, a subclass serial number is added behind a time stamp of the time sequence; otherwise, no subclass sequence number needs to be added; whether the acquisition spatial resolution of the acquired multi-source heterogeneous data is higher than the spatial resolution of the spatial sequence or not needs to be judged, and if yes, a subclass serial number is added behind a spatial stamp of the spatial sequence; otherwise, the subclass sequence number does not need to be added.
The collected multi-source heterogeneous data may have the conditions of abnormity, deficiency, illegal tampering and the like, and corresponding abnormity analysis is needed, so that whether the conditions of corresponding abnormity, deficiency, illegal tampering and the like exist is determined; and the abnormal analysis is carried out by utilizing a corresponding artificial intelligence analysis algorithm according to the time and space sequence, so that corresponding abnormal analysis data is obtained.
For the artificial intelligence algorithm, an artificial preset exception rule is needed, so that the artificial intelligence algorithm performs clustering, classification, exception handling learning and the like on the multi-source heterogeneous data according to the preset exception rule, and accordingly the preset exception rule is updated correspondingly in the clustering, classification, exception handling learning and other processes, and a corresponding learning result is obtained; then, the learning result is processed automatically in Time and space domain by using Time and space sequence and Dynamic Time rule (DTW) algorithm to obtain abnormal analysis data.
Acquisition with respect to time and spatial sequences; wherein, the time and space sequences are the only physical characteristic attribute data; the time sequence is generated by a time sequence generator, and the time sequence generator generates a unique time sequence according to national standard time or international standard time; the space sequence is generated by a space sequence generator, and the space sequence generator generates a unique space sequence according to a national standard geodetic coordinate system or an international standard geodetic coordinate system; after the time and space sequences are obtained, the two sequences are used as inherent attributes of data, are fused with the collected multi-source heterogeneous data, and are stored in a unified mode; before storage, judging whether the acquisition time resolution of the acquired multi-source heterogeneous data is higher than the time resolution of the time sequence, if so, adding a subclass serial number behind a time stamp of the time sequence; otherwise, no subclass sequence number needs to be added; whether the acquisition spatial resolution of the acquired multi-source heterogeneous data is higher than the spatial resolution of the spatial sequence or not needs to be judged, and if yes, a subclass serial number is added behind a spatial stamp of the spatial sequence; otherwise, the subclass sequence number does not need to be added.
After the abnormal analysis data is obtained through analysis, the obtained abnormal analysis data needs to be stored in a database on the cloud server, and the data are stored in the database in the cloud server through a database management system (DBMS); the database management system can realize automatic coverage of historical data according to appointed conditions; in the analysis and storage processes, if analysis abnormal data and various storage abnormal data are obtained, data abnormal prompts are asynchronously triggered and transmitted to a front-end interface for data abnormal prompting.
S13: and performing visualization processing on the abnormal analysis data according to different visualization scenes based on visualization scene analysis and according to industrial field environments, and pushing visualization results according to different customized scenes.
In a specific implementation process of the present invention, the performing visualization processing on the abnormal analysis data according to different visualization scenes based on the visualization scene analysis and according to an industrial field environment, and pushing a visualization result according to different customized scenes includes: reading the abnormal analysis data, providing a software running mode by using visual scene analysis software according to an industrial field environment, selecting different visual scenes for visual processing, and obtaining a visual processing result; transmitting the visualization processing result to a front end for information display, and pushing the information of the characteristics according to different visualization scenes; wherein, the different visual scenes comprise a computer display, a projector or a mobile phone terminal.
Specifically, the pushing visualization result is a process of supply chain abnormity warning for the outside; the method comprises the steps of reading abnormal analysis data stored in a cloud server and collected multi-source heterogeneous data (including contents such as global features, local features and abnormal features of the data, and the like), and selecting different visual scenes such as a computer display, a projector and a mobile phone end according to industrial field environments and corresponding software operation modes by utilizing visual scene analysis software to perform corresponding visualization on the data, so that a visual result is obtained; and pushing the corresponding visualization result to the front end of the corresponding visualization scene for information display, and pushing the information of the characteristics according to the same visualization scene.
In the embodiment of the invention, by injecting time and space sequences into multi-source heterogeneous data of various supply chains and combining an artificial intelligence processing method, special conditions such as data deletion, cleaning, illegal tampering and the like can be automatically judged, so that a stable and reliable supply chain data management effect is formed, and global visualization capability is provided.
Examples
Referring to fig. 2, fig. 2 is a schematic structural composition diagram of a data visualization management apparatus for a chemical supply chain according to an embodiment of the present invention.
As shown in fig. 2, a data visualization management apparatus for chemical supply chain, the apparatus includes:
the data acquisition and uploading module 21: the system comprises a cloud server, a plurality of industrial sensors and a plurality of cloud servers, wherein the cloud server is used for acquiring multi-source heterogeneous data of each node on a supply chain based on each industrial sensor and uploading the multi-source heterogeneous data to the cloud server;
in a specific implementation process of the present invention, the acquiring, based on various industrial sensors, multi-source heterogeneous data of each node in a supply chain and uploading the data to a cloud server includes: remotely acquiring multi-source heterogeneous data information of each node on a supply chain based on various industrial sensors to obtain multi-source heterogeneous data; and uploading the multi-source heterogeneous data to the cloud server for aggregation.
Specifically, various industrial sensors are used for collecting data of each node on a supply chain, so that multi-source heterogeneous data is formed, each industrial sensor comprises an optical sensor, an infrared sensor and the like, the optical sensors firstly converge to the nearest route connected with the sensors after collecting data of the corresponding node, and the route is uploaded to a cloud server based on a wireless network transmission technology.
The anomaly analysis module 22: the cloud database is used for carrying out anomaly analysis processing on the multi-source heterogeneous data based on time and space sequences, obtaining anomaly analysis data and storing the anomaly analysis data in the cloud server;
in a specific implementation process of the present invention, the performing anomaly analysis processing on the multi-source heterogeneous data based on the time and space sequences includes: taking the time and space sequence as fixed attributes of the multi-source heterogeneous data, and performing information fusion with the multi-source heterogeneous data to obtain fused multi-source heterogeneous data; and carrying out anomaly analysis processing on the fused multi-source heterogeneous data.
Further, before the information fusion with the multi-source heterogeneous data, the method further includes: judging whether the acquisition time resolution of the multi-source heterogeneous data is higher than the time resolution of the time sequence or not, and if so, adding a subclass sequence number after the time stamp of the time sequence; and judging whether the acquisition spatial resolution for acquiring the multi-source heterogeneous data is higher than the spatial resolution of the spatial sequence, and if so, adding a subclass sequence number behind the spatial stamp of the spatial sequence.
Further, the performing anomaly analysis processing on the multi-source heterogeneous data based on the time and space sequences to obtain anomaly analysis data further includes: and carrying out anomaly analysis on the conditions of anomaly, deficiency and illegal tampering of the multi-source heterogeneous data by using an artificial intelligence algorithm based on the time and space sequences to obtain anomaly analysis data.
Further, the abnormal analysis of the abnormal, missing and illegal tampering conditions of the multi-source heterogeneous data by using an artificial intelligence algorithm based on the time and space sequences to obtain abnormal analysis data includes: carrying out artificial preset exception rules on the artificial intelligence algorithm, and clustering, classifying and exception handling learning on the multi-source heterogeneous data by the artificial intelligence algorithm based on the preset exception rules; updating the preset abnormal rule in the clustering, classifying and abnormal processing learning process to obtain updated clustering, classifying and abnormal processing learning results; and performing time and space domain processing on the updated clustering, classifying and exception handling learning result by using a dynamic time rule algorithm based on the time and space sequence to obtain exception analysis data.
Further, the step of obtaining the time and space sequence includes: generating a time series based on a time series generator; generating a spatial sequence based on a spatial sequence generator; wherein the time series generator generates a unique time series based on national standard time or international standard time; the spatial sequence generator generates a unique spatial sequence based on a national standard geodetic coordinate system or an international standard geodetic coordinate system.
Further, the storing the anomaly analysis data in a cloud database in a cloud server includes: the anomaly analysis data is stored into a cloud database in the cloud server based on a database management system; the database management system is provided with appointed conditions, and the appointed conditions are used for automatically covering historical data.
Specifically, the corresponding time and space sequence is used as the fixed attribute of the collected multi-source heterogeneous data, and the corresponding time and space sequence and the corresponding multi-source heterogeneous data are used for fusion, so that the fused multi-source heterogeneous data is obtained; and then carrying out anomaly analysis processing on the fused multi-source heterogeneous data.
The fusion refers to fusion of corresponding time and space sequences on the multi-source heterogeneous data; before fusion is carried out by utilizing the corresponding time and space sequences and the corresponding multi-source heterogeneous data, firstly, whether the acquisition time resolution of the acquired multi-source heterogeneous data is higher than the time resolution of the time sequence is judged, and if so, a subclass serial number is added behind a time stamp of the time sequence; otherwise, no subclass sequence number needs to be added; whether the acquisition spatial resolution of the acquired multi-source heterogeneous data is higher than the spatial resolution of the spatial sequence or not needs to be judged, and if yes, a subclass serial number is added behind a spatial stamp of the spatial sequence; otherwise, the subclass sequence number does not need to be added.
The collected multi-source heterogeneous data may have the conditions of abnormity, deficiency, illegal tampering and the like, and corresponding abnormity analysis is needed, so that whether the conditions of corresponding abnormity, deficiency, illegal tampering and the like exist is determined; and the abnormal analysis is carried out by utilizing a corresponding artificial intelligence analysis algorithm according to the time and space sequence, so that corresponding abnormal analysis data is obtained.
For the artificial intelligence algorithm, an artificial preset exception rule is needed, so that the artificial intelligence algorithm performs clustering, classification, exception handling learning and the like on the multi-source heterogeneous data according to the preset exception rule, and accordingly the preset exception rule is updated correspondingly in the clustering, classification, exception handling learning and other processes, and a corresponding learning result is obtained; then, the learning result is processed automatically in Time and space domain by using Time and space sequence and Dynamic Time rule (DTW) algorithm to obtain abnormal analysis data.
Acquisition with respect to time and spatial sequences; wherein, the time and space sequences are the only physical characteristic attribute data; the time sequence is generated by a time sequence generator, and the time sequence generator generates a unique time sequence according to national standard time or international standard time; the space sequence is generated by a space sequence generator, and the space sequence generator generates a unique space sequence according to a national standard geodetic coordinate system or an international standard geodetic coordinate system; after the time and space sequences are obtained, the two sequences are used as inherent attributes of data, are fused with the collected multi-source heterogeneous data, and are stored in a unified mode; before storage, judging whether the acquisition time resolution of the acquired multi-source heterogeneous data is higher than the time resolution of the time sequence, if so, adding a subclass serial number behind a time stamp of the time sequence; otherwise, no subclass sequence number needs to be added; whether the acquisition spatial resolution of the acquired multi-source heterogeneous data is higher than the spatial resolution of the spatial sequence or not needs to be judged, and if yes, a subclass serial number is added behind a spatial stamp of the spatial sequence; otherwise, the subclass sequence number does not need to be added.
After the abnormal analysis data is obtained through analysis, the obtained abnormal analysis data needs to be stored in a database on the cloud server, and the data are stored in the database in the cloud server through a database management system (DBMS); the database management system can realize automatic coverage of historical data according to appointed conditions; in the analysis and storage processes, if analysis abnormal data and various storage abnormal data are obtained, data abnormal prompts are asynchronously triggered and transmitted to a front-end interface for data abnormal prompting.
The visualization management module 23: and the system is used for performing visualization processing on different visualization scenes on the abnormal analysis data according to the industrial field environment based on visualization scene analysis, and pushing visualization results according to different customized scenes.
In a specific implementation process of the present invention, the performing visualization processing on the abnormal analysis data according to different visualization scenes based on the visualization scene analysis and according to an industrial field environment, and pushing a visualization result according to different customized scenes includes: reading the abnormal analysis data, providing a software running mode by using visual scene analysis software according to an industrial field environment, selecting different visual scenes for visual processing, and obtaining a visual processing result; transmitting the visualization processing result to a front end for information display, and pushing the information of the characteristics according to different visualization scenes; wherein, the different visual scenes comprise a computer display, a projector or a mobile phone terminal.
Specifically, the pushing visualization result is a process of supply chain abnormity warning for the outside; the method comprises the steps of reading abnormal analysis data stored in a cloud server and collected multi-source heterogeneous data (including contents such as global features, local features and abnormal features of the data, and the like), and selecting different visual scenes such as a computer display, a projector and a mobile phone end according to industrial field environments and corresponding software operation modes by utilizing visual scene analysis software to perform corresponding visualization on the data, so that a visual result is obtained; and pushing the corresponding visualization result to the front end of the corresponding visualization scene for information display, and pushing the information of the characteristics according to the same visualization scene.
In the embodiment of the invention, by injecting time and space sequences into multi-source heterogeneous data of various supply chains and combining an artificial intelligence processing method, special conditions such as data deletion, cleaning, illegal tampering and the like can be automatically judged, so that a stable and reliable supply chain data management effect is formed, and global visualization capability is provided.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic or optical disk, or the like.
In addition, the chemical supply chain-oriented data visualization management method and device provided by the embodiment of the invention are described in detail, a specific example is adopted to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (8)
1. A chemical supply chain-oriented data visualization management method is characterized by comprising the following steps:
the method comprises the steps that multi-source heterogeneous data of each node on a supply chain are collected based on various industrial sensors and uploaded to a cloud server;
performing anomaly analysis processing on the multi-source heterogeneous data based on time and space sequences to obtain anomaly analysis data, and storing the anomaly analysis data in a cloud database in a cloud server;
based on visual scene analysis, performing visual processing on the abnormal analysis data according to different visual scenes in an industrial field environment, and pushing visual results according to different customized scenes;
the time and space sequence-based anomaly analysis processing is performed on the multi-source heterogeneous data to obtain anomaly analysis data, and the method further comprises the following steps:
carrying out anomaly analysis on the conditions of anomaly, deficiency and illegal tampering of the multi-source heterogeneous data by using an artificial intelligence algorithm based on time and space sequences to obtain anomaly analysis data;
the method for analyzing the abnormity, the deficiency and the illegal tampering of the multi-source heterogeneous data by using an artificial intelligence algorithm based on the time and space sequences to obtain abnormal analysis data comprises the following steps:
carrying out artificial preset exception rules on the artificial intelligence algorithm, and clustering, classifying and exception handling learning on the multi-source heterogeneous data by the artificial intelligence algorithm based on the preset exception rules; and the number of the first and second groups,
updating the preset abnormal rule in the clustering, classifying and abnormal processing learning process to obtain updated clustering, classifying and abnormal processing learning results;
and performing time and space domain processing on the updated clustering, classifying and exception handling learning result by using a dynamic time rule algorithm based on the time and space sequence to obtain exception analysis data.
2. The data visualization management method according to claim 1, wherein the collecting and uploading multi-source heterogeneous data of each node on a supply chain to a cloud server based on various industrial sensors comprises:
remotely acquiring multi-source heterogeneous data information of each node on a supply chain based on various industrial sensors to obtain multi-source heterogeneous data;
and uploading the multi-source heterogeneous data to the cloud server for aggregation.
3. The data visualization management method according to claim 1, wherein the performing anomaly analysis processing on the multi-source heterogeneous data based on the temporal and spatial sequences comprises:
taking the time and space sequence as fixed attributes of the multi-source heterogeneous data, and performing information fusion with the multi-source heterogeneous data to obtain fused multi-source heterogeneous data; and the number of the first and second groups,
and carrying out anomaly analysis processing on the fused multi-source heterogeneous data.
4. The data visualization management method according to claim 3, further comprising, before the information fusion with the multi-source heterogeneous data:
judging whether the acquisition time resolution of the multi-source heterogeneous data is higher than the time resolution of the time sequence or not, and if so, adding a subclass sequence number after the time stamp of the time sequence;
and judging whether the acquisition spatial resolution for acquiring the multi-source heterogeneous data is higher than the spatial resolution of the spatial sequence, and if so, adding a subclass sequence number behind the spatial stamp of the spatial sequence.
5. The data visualization management method according to claim 1, wherein the step of obtaining the temporal and spatial sequence comprises:
generating a time series based on a time series generator;
generating a spatial sequence based on a spatial sequence generator;
wherein the time series generator generates a unique time series based on national standard time or international standard time; the spatial sequence generator generates a unique spatial sequence based on a national standard geodetic coordinate system or an international standard geodetic coordinate system.
6. The data visualization management method according to claim 1, wherein the storing the anomaly analysis data in a cloud database in a cloud server comprises:
the anomaly analysis data is stored into a cloud database in the cloud server based on a database management system;
the database management system is provided with appointed conditions, and the appointed conditions are used for automatically covering historical data.
7. The data visualization management method according to claim 1, wherein the visualizing the abnormal analysis data based on the visualization scenario analysis is performed with visualization processing of different visualization scenarios according to an industrial field environment, and the pushing of the visualization results according to different customized scenarios comprises:
reading the abnormal analysis data, providing a software running mode by using visual scene analysis software according to an industrial field environment, selecting different visual scenes for visual processing, and obtaining a visual processing result;
transmitting the visualization processing result to a front end for information display, and pushing the information of the characteristics according to different visualization scenes;
wherein, the different visual scenes comprise a computer display, a projector or a mobile phone terminal.
8. A chemical supply chain-oriented data visualization management device is characterized by comprising:
the data acquisition and uploading module is used for: the system comprises a cloud server, a plurality of industrial sensors and a plurality of cloud servers, wherein the cloud server is used for acquiring multi-source heterogeneous data of each node on a supply chain based on each industrial sensor and uploading the multi-source heterogeneous data to the cloud server;
an anomaly analysis module: the cloud database is used for carrying out anomaly analysis processing on the multi-source heterogeneous data based on time and space sequences, obtaining anomaly analysis data and storing the anomaly analysis data in the cloud server;
a visualization management module: the system is used for carrying out visualization processing on different visualization scenes on the abnormal analysis data according to the industrial field environment based on visualization scene analysis and pushing visualization results according to different customized scenes;
the anomaly analysis module: the system is also used for carrying out anomaly analysis on the conditions of anomaly, deficiency and illegal tampering of the multi-source heterogeneous data by utilizing an artificial intelligence algorithm based on the time and space sequence to obtain anomaly analysis data;
the anomaly analysis module: the artificial intelligence algorithm is also used for artificially presetting abnormal rules, and clustering, classifying and abnormal processing learning are carried out on the multi-source heterogeneous data by the artificial intelligence algorithm based on the preset abnormal rules; updating the preset abnormal rule in the clustering, classifying and abnormal processing learning process to obtain updated clustering, classifying and abnormal processing learning results; and performing time and space domain processing on the updated clustering, classifying and exception handling learning result by using a dynamic time rule algorithm based on the time and space sequence to obtain exception analysis data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910816327.9A CN110609864B (en) | 2019-08-30 | 2019-08-30 | Chemical supply chain-oriented data visualization management method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910816327.9A CN110609864B (en) | 2019-08-30 | 2019-08-30 | Chemical supply chain-oriented data visualization management method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110609864A CN110609864A (en) | 2019-12-24 |
| CN110609864B true CN110609864B (en) | 2020-06-23 |
Family
ID=68890792
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910816327.9A Active CN110609864B (en) | 2019-08-30 | 2019-08-30 | Chemical supply chain-oriented data visualization management method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110609864B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111859302A (en) * | 2020-07-28 | 2020-10-30 | 上海积成能源科技有限公司 | Method for discovering abnormal data by applying generalized extreme student inspection algorithm in resident electricity safety analysis |
| CN112699113B (en) * | 2021-01-12 | 2022-08-05 | 上海交通大学 | Industrial manufacturing process operation monitoring system driven by time sequence data stream |
| CN113702862B (en) * | 2021-08-27 | 2022-10-14 | 广东省科学院电子电器研究所 | DC power supply state panoramic monitoring method and device based on cloud deployment |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109937344A (en) * | 2016-09-29 | 2019-06-25 | 通腾运输公司 | For generating the method and system of the profile data of the section of electronic map |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201298073Y (en) * | 2008-07-23 | 2009-08-26 | 中国国际海运集装箱(集团)股份有限公司 | Container and positioning tracker therefor |
| CN101493911A (en) * | 2009-01-09 | 2009-07-29 | 上海海事大学 | Virtual inventory management system for harbor equipment manufacturing enterprise |
| JP5595738B2 (en) * | 2010-01-05 | 2014-09-24 | 株式会社東芝 | Nuclear medicine diagnostic equipment |
| US10678225B2 (en) * | 2013-03-04 | 2020-06-09 | Fisher-Rosemount Systems, Inc. | Data analytic services for distributed industrial performance monitoring |
-
2019
- 2019-08-30 CN CN201910816327.9A patent/CN110609864B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109937344A (en) * | 2016-09-29 | 2019-06-25 | 通腾运输公司 | For generating the method and system of the profile data of the section of electronic map |
Non-Patent Citations (2)
| Title |
|---|
| "基于互相关分析和SOM神经网络的异常值检测平台";路辉 刘雅娴;《计算机测量与控制》;20180531;第46-49页 * |
| "基于物联网的农机生产线数据采集与分析系统开发";王培屹;《万方数据库》;20190816;第1章-第5章 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110609864A (en) | 2019-12-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US12039712B2 (en) | Underground infrastructure feature identification using multi-sensor inspection data analysis | |
| CN116188821B (en) | Copyright detection method, system, electronic device and storage medium | |
| CN110609864B (en) | Chemical supply chain-oriented data visualization management method and device | |
| CN105425698A (en) | Integrated management and control method and system for three-dimensional digital transformer station | |
| CN111080775A (en) | Server routing inspection method and system based on artificial intelligence | |
| CN112186901B (en) | Panoramic sensing monitoring method and system for transformer substation | |
| CN115547003A (en) | External damage prevention alarm method and system for power transmission line | |
| CN104991918A (en) | WEBGIS based online big data display method | |
| CN111400114A (en) | Deep recursion network-based big data computer system fault detection method and system | |
| CN116597390B (en) | Method and device for detecting construction hidden danger around power transmission line and computer equipment | |
| JP2016522517A (en) | Device that displays trends related to process variables | |
| CN115496460B (en) | Intelligent farmer system based on digital twinning | |
| CN113808134B (en) | Oil tank layout information generation method, oil tank layout information generation device, electronic apparatus, and medium | |
| CN117523146A (en) | System and method for quickly constructing digital twin scene based on target detection | |
| CN115061386B (en) | Intelligent driving automatic simulation test system and related equipment | |
| CN111522570B (en) | Target library updating method and device, electronic equipment and machine-readable storage medium | |
| CN111368651B (en) | Vehicle identification method and device and electronic equipment | |
| CN112669470A (en) | Camera marking method and device based on BIM | |
| CN112199418B (en) | State identification method, device and equipment for industrial object | |
| US12117974B2 (en) | Methods and systems for construct identification and analysis | |
| CN116662645B (en) | Video event tracing analysis method and system, storage medium and electronic equipment | |
| CN117520436B (en) | Meteorological service pilot-scale product display method, device, system and medium | |
| CN112783986B (en) | Object grouping compiling method and device based on label, storage medium and terminal | |
| CN107809613A (en) | Video index creation method and device, computer-readable recording medium, terminal | |
| JP7279776B2 (en) | Event management device and method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231016 Address after: 300453 Junlan Mingdi, Binhai Zhongguancun Science and Technology Park, Binhai New Area Economic and Technological Development Zone, Tianjin City 1/2/3/4-107 (Trusteeship No. 129 of Hexintai (Tianjin) Business Secretary Co., Ltd.) Patentee after: Sinotrans Easy Logistics Technology (Tianjin) Co.,Ltd. Address before: No. 110, Gate 1, Building 11, Xihu Lane, Xihu Road, Nankai District, Tianjin, 300000 Patentee before: Zhao Yi Effective date of registration: 20231016 Address after: No. 110, Gate 1, Building 11, Xihu Lane, Xihu Road, Nankai District, Tianjin, 300000 Patentee after: Zhao Yi Address before: 510000 Room 401, No. 243, science Avenue, high tech Industrial Development Zone, Guangzhou, Guangdong Patentee before: GUANGZHOU QIHUA CO.,LTD. |
|
| TR01 | Transfer of patent right |