Alarm fusion processing method of multivariate time series data and storage medium

Abstract

The invention provides an alarm fusion processing method for multivariate time series data, a system deployed by the method is based on a B / S architecture, and the method comprises the following steps: collecting and processing industrial data from different sources, storing the industrial data into a local time series database, and carrying out real-time data supervision according to a preset alarm rule; when the alarm data spontaneously comes from the time sequence database, querying associated information of the alarm data in the time sequence database to fill fields to form corresponding alarm information, and storing the alarm information in an alarm fusion database; and when the alarm data is pushed by the third-party system through the API and the MQ, the alarm data is verified when being received by the interface and then is matched with the local data, the corresponding local data is updated if the data exists, and the corresponding local data is newly added and stored in the alarm fusion database if the related data does not exist. According to the technical scheme, complete link processing data from collection to cleaning to fusion is used, data consistency and unique and clear association among source data are guaranteed, and monitoring jump of a user is facilitated.

Description

Technical Field

[0001] The present application belongs to the field of time series data management, and specifically relates to an alarm fusion processing method and storage medium for multivariate time series data. Technical Background

[0002] In the production and operation process of industrial stations, most systems are deployed in a single-machine C / S architecture, and the isolation and interoperability between systems are poor, so there is a lack of advanced data fusion capabilities, which often leads to information dispersion, making it impossible to fully evaluate the operation status of the station, and it is difficult to form a complete alarm and early warning system. However, the industry is currently undergoing technological upgrades and business needs are also being updated. How to improve the construction of intelligent stations and the real-time monitoring and preventive treatment of on-site production processes has become a focus of industry development.

[0003] Technical content

[0004] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention proposes a method for network policy configuration and data transmission link establishment under the condition that there is only an intranet environment at the processing site and the production network and information network are not interoperable, thereby realizing data fusion and comprehensive early warning.

[0005] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0006] In a first aspect, the present invention provides an alarm fusion processing method for multivariate time series data. The system deployed by the method is based on a B / S architecture and includes:

[0007] After collecting and processing industrial data from different sources and storing them in the local time series database, real-time data supervision is performed according to the preset alarm rules;

[0008] When alarm data is spontaneously generated in the time series database, the associated information of the alarm data is queried in the time series database to fill in the fields to form the corresponding alarm information, and the alarm information is stored in the alarm fusion database; when the alarm data is pushed by a third-party system through API and MQ, it is verified when received by the interface and matched with the local data. If there is existing data, the corresponding local data is updated. If there is no relevant data, it is newly added and stored in the alarm fusion database.

[0009] Optionally, when the industrial data is transmitted through a universal industrial protocol, different protocols are directly collected and stored in real time, including: collection point number, IO address, collection time, type, data value, status, and frequency; the collection frequency is customized according to actual support.

[0010] Optionally, when the industrial data is collected via MQ, an adaptation layer interface needs to be established to connect to different MQ products, a push time needs to be added for identification and distinction, and the received data needs to be verified: duplicate push data is filtered out in the time series database, and if abnormal data is collected, the most recent time data is used to fill it.

[0011] Optionally, when the industrial data is directly collected through an API and a third-party database, it is periodically acquired through a scheduled call; the interface call return value is cleaned and then matched and verified with the time series database to ensure the uniqueness of data collection, and the data source and collection time fields are retained for identification.

[0012] Optionally, ensuring the uniqueness of data collection includes: eliminating redundant data or updating when the collected data is inconsistent with the data status of the local time series database, modifying the data status in the local time series data, and recording the modification time as required.

[0013] Optionally, the time series database can display data while filtering queries based on dimension fields; it can also perform attribute queries through an interface provided by the page.

[0014] Optionally, the alarm rules include analog high and low threshold settings, priority prompt alarm notification importance, and switch value configuration TRUE and FALSE to identify shutdown and start-stop alarms.

[0015] Optionally, the real-time data supervision according to preset alarm rules also includes monitoring the alarm occurrence time and processing it in real time through a page pop-up window or text message.

[0016] Optionally, when the alarm data is pushed by a third-party system through API and MQ, it also includes: time correction and replacement into a unified UTC format; redundant storage of the alarm data acquisition time and storage time.

[0017] In a second aspect, the present invention also provides a computer-readable medium, which stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded by a processor and executed by a method for alarm fusion processing of multivariate time series data.

[0018] This technical solution simplifies the unified integration of multivariate time series data and alarm data between different devices and different control software, and uses a complete link from collection to cleaning to fusion to process data, ensuring data consistency. The association between data from various sources is unique and clear, which is convenient for users to monitor and jump. Staff do not need to monitor multiple systems at the same time. After the alarm data is generated, it can be processed uniformly on the comprehensive page, ensuring the unity and interactivity of the system.

[0019] Instruction Manual

[0020] Figure 1 A schematic diagram of a flow chart of time series data collection in an alarm fusion processing method for multivariate time series data provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the process of processing alarm data in an alarm fusion processing method for multivariate time series data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0023] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0024] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". Relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0025] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0026] refer to Figure 1 and Figure 2 The embodiment of the present invention provides an alarm fusion processing method for multivariate time series data, comprising:

[0027] After collecting and processing industrial data from different sources and storing them in the local time series database, real-time data supervision is performed according to the preset alarm rules;

[0028] When alarm data is spontaneously generated in the time series database, the related information of the alarm data is queried in the time series database to fill in the fields to form the corresponding alarm information, and the alarm information is stored in the alarm fusion database; when the alarm data is pushed by a third-party system through API and MQ, it is verified when received by the interface and matched with the local data. If there is existing data, the corresponding local data is updated. If there is no relevant data, it is newly added and stored in the alarm fusion database.

[0029] Specifically, industrial data is divided into three categories according to the source:

[0030] (1) Industrial data using common industrial protocols such as OPC\MODBUS\IEC protocols. This part of the data can be directly collected and processed, and different protocols can be connected for data collection according to actual conditions. The collected real-time data is stored in the local time series database, and the collection frequency in this system supports custom modification according to actual site needs. This part is real-time collection and real-time storage, including collection point number, IO address, collection time, type, data value, status, frequency, etc. The collection frequency can be customized according to actual support; the collection time, data value, value type and other field formats are applicable and no additional processing is required.

[0031] (2) Industrial data pushed using MQ. This part of data is adapted to interface with different MQ products, which is scalable and requires the data link to be opened. Since this part of the data is pushed in real time, due to factors such as the on-site network and the underlying equipment time, it is necessary to increase the push time for identification and distinction. After the push connection is established, the data packet is pushed in real time. After receiving the data packet, the data is cleaned first. According to the timestamp, check whether there is abnormal data collected in the current time series database and whether there is repeated push data to remove redundant data. Perform data verification according to the custom collection frequency. If no normal data is received within the time period, the data collected in the most recent time is used as a filler to ensure that the data cycle is sufficient.

[0032] (3) Industrial data collected directly using APIs and databases. The collection and processing of this part of data is relatively complex. Since it is actively collected, it is necessary to set a collection frequency and obtain it periodically through scheduled calls. In order to ensure the real-time nature of the data, there are high requirements on the frequency. This part of the data is collected regularly, and the collection time is retained. The interface returns the query data for data cleaning, which is verified with the data in the local database. Duplicate collected data is removed or the data status field is updated before being put into the database to ensure the uniqueness of data collection. The data source and collection time field are retained for identification. Among them, updating the data status field specifically means that when the collected data is inconsistent with the data status of the local time series database, it is updated, the data status in the local time series database is modified, and the modification time is recorded as required.

[0033] The above data collection operations are performed for data cleaning and verification before being stored in the database. Different collection frequencies can be customized for data from different sources. Key monitoring data can be collected by heartbeat monitoring in seconds, and non-key data can be collected periodically on time or daily.

[0034] In particular, the data in the time series database is all the data after multi-dimensional fusion. When displaying data, you can filter and query according to the dimension field. Of course, the time series database also supports attribute query through the interface provided by the page, allowing users to customize the interface fields, which is more flexible. At the same time, the time series database supports the configuration of alarm rules by attributes and also supports batch configuration by data source and template. The alarm rules need to be engineered in the system in advance. The time series data will be included in the supervision after it is stored in the database. The data value is monitored in real time to match the alarm rules, and the alarm data output is generated according to the priority, alarm level, etc.

[0035] The alarm rules in this embodiment include setting high and low thresholds of analog quantity and adjusting the importance of alarm notification by priority; the switch quantity configuration TRUE and FALSE are used to mark alarms such as shutdown and start-stop.

[0036] Specifically, alarm data is divided into two categories according to the source, and different processing methods are adopted accordingly:

[0037] (1) Self-controlled alarm data, that is, monitoring the time series database. When the real-time value exceeds the threshold configured by the alarm rule, an alarm is triggered. The relevant information of the time series database, such as data source, priority, alarm level, etc., is queried. The alarm information is automatically filled in according to the time data source field in the time series database. The alarm information synchronously queries the related information and saves it in the alarm fusion database.

[0038] (2) When receiving alarm data through MQ or interfaces, the data is cleaned and verified, the time is corrected and replaced with a unified UTC format, and the incoming data is matched with the local data. If there is data, the required status of the corresponding data in the time series database is updated; if there is no data, an incremental update is performed.

[0039] For example, the liquid level of the No. 1 oil tank requires the engineering configuration of the collection point on this platform, the configuration of relevant information and the IO address of the underlying PLC. The platform will monitor the point value of this point in real time. If the configured alarm value is 30M and the monitored point value is 36M, then an alarm will be triggered and considered as a level 1 alarm. An alarm message will be generated. The identification information such as "No. 1 tank", "liquid level", priority, and alarm level in the alarm message needs to be queried, and then the field of the alarm data is expanded and then stored in the library. If the alarm is considered to be a level 2 alarm through an external system interface (usually built by the site and requires matching access), if it is manually confirmed or updated by service, we will make local modifications to the correctness of the data after receiving it, match the level 1 alarm data in the local database, and change the alarm level of the data in the local database to level 2. The operation time of the external system will also be saved by us as a basis for judgment.

[0040] In particular, externally transmitted alarms and real-time data are verified by alarms generated by rules, and the alarm levels are synchronized according to the rules if the bit names are consistent. Real-time data is stored in the same alarm fusion database based on alarms generated by alarm rules and alarms obtained by external means, and is identified according to the data source. The alarm time is guaranteed to be timely. The alarm occurrence time is monitored and processed in real time through page pop-ups or SMS. In particular, when the alarm data is fused and processed, the acquisition time and the storage time are redundantly stored to facilitate later tracing, and the data format is standardized and unified, and each unique attribute is redundant to facilitate later business display and processing.

[0041] In particular, alarm data and alarm information can be displayed in a unified manner, and the cockpit page can be customized and developed according to on-site needs. In the central control room, one picture can be used to manage everything, and data can be displayed in different dimensions according to different business scenarios on a comprehensive picture.

[0042] In this technical solution, the key point of the implementation process is the accuracy and real-time performance of the time series processing of multivariate data and the alarm fusion. The system deployed by the method of the present invention is based on the B / S architecture, and the development can be accessed through a web browser after the deployment is completed. At the same time, this method can complete the transmission and collection of data as long as the network communication between the systems is guaranteed. The method of the present invention can ensure that the real-time data from different systems maintains orderly consistency during the time series processing, and can keep the system self-generated alarm data and the third-party alarm data consistent after fusion to avoid information conflicts or duplications. In terms of real-time performance, it can process and respond to alarm information in real time to ensure that countermeasures are taken in time. By integrating the data sources to improve the accuracy of the alarm, reduce false alarms and missed alarms, and ensure the authenticity and reliability of the alarm information. At the same time, it can well guarantee possible future expansion, including new alarm sources, alarm types and processing capabilities. In addition, the method uses common standards in the industry to ensure the compatibility and interoperability of the system with other systems, which can largely ensure the flexibility and maintainability of the software product, and allow engineers to understand the software principles and obtain first-hand feedback, which is conducive to improving the fit and maturity of the software.

[0043] To sum up, the present invention mainly simplifies the unified integration of multivariate time series data and the integration of alarm data between different devices and different control software, uses a complete link from collection to cleaning to fusion to process data, ensures data consistency, and the association between data from each source is unique and clear, which is convenient for users to monitor and jump.

[0044] In addition, an embodiment of the present invention also provides a computer-readable medium, which stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded by a processor and executed by a method for alarm fusion processing of multivariate time series data.

[0045] The technical effect that can be achieved by this embodiment is basically the same as the technical effect that can be achieved by the aforementioned alarm fusion processing method for multivariate time series data, and will not be repeated here.

[0046] Since the system / device described in the above embodiments of the present invention is a system / device used to implement the method of the above embodiments of the present invention, a person skilled in the art can understand the specific structure and deformation of the system / device based on the method described in the above embodiments of the present invention, and thus will not be described in detail here. All systems / devices used in the method of the above embodiments of the present invention belong to the scope of protection of the present invention.

[0047] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0049] The electronic device includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0050] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0051] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0052] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for processing alarm fusion of multivariate time series data, characterized in that The system deployed by this method is based on B / S architecture, including: After collecting and processing industrial data from different sources and storing them in the local time series database, real-time data supervision is performed according to the preset alarm rules; When alarm data is spontaneously generated in the time series database, the associated information of the alarm data is queried in the time series database to fill in the fields to form the corresponding alarm information, and the alarm information is stored in the alarm fusion database; when the alarm data is pushed by a third-party system through API and MQ, it is verified when received by the interface and matched with the local data. If there is existing data, the corresponding local data is updated. If there is no relevant data, it is newly added and stored in the alarm fusion database.

2. The method for processing alarm fusion of multivariate time series data as claimed in claim 1, characterized in that When the industrial data is transmitted through the universal industrial protocol, different protocols are directly collected and stored in real time, including: collection point number, IO address, collection time, type, data value, status, and frequency; the collection frequency is customized according to actual support.

3. The method for fusion processing of multivariate time series data according to claim 1, characterized in that When the industrial data is collected through MQ, it is necessary to establish an adaptation layer interface to connect to different MQ products, add a push time for identification and distinction, and perform data verification on the received data: filter and remove duplicate push data in the time series database, and if abnormal data is collected, use the most recent time data to fill it.

4. The method for fusion processing of multivariate time series data as claimed in claim 1, characterized in that When the industrial data is directly collected through API and third-party database, it is periodically acquired through scheduled calls; the interface call return value is cleaned and then matched and verified with the time series database to ensure the uniqueness of data collection, and the data source and collection time fields are retained for identification.

5. The method for fusion processing of multivariate time series data as claimed in claim 4, characterized in that Ensuring the uniqueness of data collection includes: eliminating redundant data or updating when the collected data is inconsistent with the data status of the local time series database, modifying the data status in the local time series data, and recording the modification time as required.

6. The method for fusion processing of multivariate time series data according to claim 1, characterized in that The time series database can display data while filtering queries based on dimension fields; it can also perform attribute queries through the interface provided by the page.

7. The method for fusion processing of multivariate time series data as claimed in claim 1, characterized in that The alarm rules include analog high and low threshold settings, priority prompt alarm notification importance, and switch value configuration TRUE and FALSE to identify shutdown and start-stop alarms.

8. The method for fusion processing of multivariate time series data as claimed in claim 1, characterized in that The real-time data supervision according to the preset alarm rules also includes monitoring the alarm occurrence time and processing it in real time through a page pop-up window or text message.

9. The method for fusion processing of multivariate time series data as claimed in claim 1, characterized in that When the alarm data is pushed by a third-party system through API and MQ, it also includes: time correction and replacement into a unified UTC format; the alarm data acquisition time and storage time are redundantly stored.

10. A computer storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor according to any one of claims 1 to 9 as an alarm fusion processing method for multivariate time series data.

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