A data acquisition system for an industrial plant

Abstract

The application discloses a kind of data acquisition systems of industrial equipment, including edge computing platform, data lake module, static module, multiple dynamic modules;Edge computing platform configuration generates each dynamic module and static module;Dynamic module is collected from data source corresponding transmission protocol format data, and after parsing, it is sent into static module, and static module carries out edge computing to the data after parsing again Perception calculation, obtain edge computing result data and perception calculation result data, and static module is based on the type of edge computing result data From data lake, the database of the type data of storage is selected to store, and static module is also based on perception calculation result data Whether to judge whether to satisfy perception condition, if satisfying perception condition, then upload to edge computing platform.The application has universality, ease of use, simplicity, lower use threshold, less time-consuming, help enterprise reduce the cost of data acquisition.

Description

Technical Field

[0001] The present invention relates to the field of data acquisition systems, in particular to a data acquisition system for industrial equipment. Background Art

[0002] Industrial equipment data collection refers to the process of using various sensors, instruments, and monitoring systems to collect real-time or periodic information about the equipment's operating status, operating environment, and production data. This data includes, but is not limited to, parameters such as temperature, pressure, humidity, vibration, current, and voltage, as well as equipment operating hours, downtime, production output, and fault information.

[0003] With the continuous improvement of industrial automation, enterprises are increasingly demanding the monitoring and optimization of equipment operating status. Through equipment data collection, it is possible to monitor equipment operating status in real time, identify problems promptly, and implement measures to improve production efficiency and product quality. Traditional equipment maintenance is often based on fixed maintenance cycles or equipment operating hours, an approach that is wasteful and inadequate. Through equipment data collection, it is possible to monitor and analyze equipment operating status in real time, predict potential equipment failures, implement predictive maintenance, reduce maintenance costs, and avoid production interruptions. The core of intelligent manufacturing in the Industry 4.0 era is data-driven, and equipment data collection is the foundation of intelligent manufacturing. Through equipment data collection, information sharing and collaboration between devices can be achieved, improving production efficiency and flexibility, and enabling customized production.

[0004] Traditionally, data collection for industrial equipment requires automation engineers to develop data collection programs based on device information and manually deploy them to the data collection gateway. The widely varying protocols between devices require extensive experience from automation engineers. Furthermore, developing data collection programs is tedious and has a high barrier to entry, making it difficult for companies to collect large quantities of industrial equipment data. Summary of the Invention

[0005] The present invention provides a data acquisition system for industrial equipment to solve the problems of poor versatility and difficulty in large-scale data acquisition in the prior art industrial equipment data acquisition.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A data acquisition system for industrial equipment, characterized by comprising an edge computing platform, a data lake module, a static module, and multiple dynamic modules; the edge computing platform acquires data in multiple transmission protocol formats from a data source, and the edge computing platform configures and generates each dynamic module according to the acquired transmission protocol format, which executes each transmission protocol in a one-to-one correspondence, and the edge computing platform also configures and generates a static module common to each dynamic module; the dynamic module configured by the edge computing platform acquires data in a corresponding transmission protocol format from the data source, parses the data in the corresponding transmission protocol format, and then feeds the data into the static module; the static module performs edge computing on the data parsed by the dynamic module and then performs perception computing to obtain edge computing result data and perception computing result data, and the static module and each dynamic module configured by the edge computing platform respectively constitute a data pump for the corresponding transmission protocol format data;

[0008] The data lake module includes multiple databases that store different types of data. The static module selects a database that stores data of the corresponding type from the data lake based on the type of edge computing result data, and stores the edge computing result data in the corresponding database in the data lake module. The static module also determines whether the perception conditions are met based on the perception computing result data. If the perception conditions are met, the static module uploads the perception computing result data to the edge computing platform.

[0009] Furthermore, the dynamic module includes a data acquisition node and a data parsing node, wherein the data acquisition node is configured by the edge computing platform to collect data in a certain transmission protocol format, and after the data acquisition node collects data in the corresponding transmission protocol format from the data source, the data is transmitted to the data parsing node; the data parsing node is configured by the edge computing platform to parse data in the corresponding transmission protocol format, and the data parsing node parses the data in the corresponding transmission protocol format into a JSON format of key-value pairs, and then transmits the parsed data to the static module.

[0010] Furthermore, the data acquisition node includes a timer, an acquisition selector and an acquisition connector. The timer sets the data acquisition cycle under the configuration of the edge computing platform. The acquisition selector selects the point data to be collected under the configuration of the edge computing platform. The acquisition connector connects to the data source under the configuration of the edge computing platform and performs data acquisition actions in the corresponding transmission protocol format.

[0011] Furthermore, the static module includes an edge computing node generated by the edge computing platform based on edge computing rules, a perception computing node generated by the edge computing platform based on perception computing rules, a message reporting node, and a data storage node; the data parsed by the dynamic module is first edge-computing processed by the edge computing node to obtain edge computing result data, and then the edge computing result data is distributed to the data storage node and the perception computing node at the same time;

[0012] The data storage node selects a database storing data of the corresponding type from the data lake based on the type of the edge computing result data, and stores the edge computing result data to the corresponding database in the data lake module;

[0013] The perception computing node performs perception computing processing on the edge computing result data to obtain the perception computing result data, and judges whether it meets the set perception conditions based on the perception computing result data. If it meets the set perception conditions, the perception computing node transmits the perception computing result data to the edge computing platform through the message reporting node. If it does not meet the set perception conditions, the perception computing node stops the data transmission.

[0014] Furthermore, the static module also includes an exception capture node, which monitors the perception computing result data transmitted by the message reporting node in real time, and determines whether the operation status of the data pump corresponding to the perception computing result data is abnormal. When the data pump is abnormal, the exception capture node captures the abnormal information, converts it into abnormal data in a standard format, and then transmits it to the edge computing platform through the message reporting node.

[0015] Furthermore, the data storage node includes a storage selector and a storage connector. The storage selector selects a database that stores data of the corresponding type in the data lake under the configuration of the edge computing platform. The storage connector is used to connect to the database of the corresponding type of data in the data lake and perform storage actions.

[0016] Furthermore, the data sources include equipment, instruments, sensors, PLC, SCADA, and DCS.

[0017] Furthermore, the databases in the data lake include relational databases, time series databases, in-memory databases, object-oriented databases, graph databases, and key-value databases.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] In this invention, once a data pump corresponding to a transmission protocol is created, it can be used without the involvement of automation engineers or code development. Ordinary personnel can simply fill in the corresponding parameter configurations to achieve data collection. The data pump type in this invention is adapted to the transmission protocol. Different devices using the same protocol can share the same data pump type, making the data pump type more focused and universal.

[0020] Compared with traditional methods, the data acquisition system for industrial equipment described in the present invention is more universal, easier to use, and simpler, has a lower usage threshold, and consumes less time, helping enterprises reduce data acquisition costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is an overall architecture diagram of an embodiment of the present invention.

[0022] Figure 2 1 is a schematic diagram of the data pump structure in an embodiment of the present invention.

[0023] Figure 3 Schematic diagram of a data pump type in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below with reference to the accompanying drawings and examples.

[0025] like Figure 1 As shown, this embodiment discloses a data acquisition system for industrial equipment, the overall architecture of which is as follows Figure 1 As shown, it is used to collect data from data sources, including edge computing platform, data collection platform, and data lake module.

[0026] The data source described in this embodiment is the information source for collecting data, including equipment, instruments, sensors, PLCs, SCADA, and DCS. Equipment is a general term for machines, devices, tools, and facilities used to complete specific production tasks in industrial production processes; instruments are detection devices that display measured information as digital data; sensors are detection devices that convert measured information into electrical signals or other required forms for output according to a certain rule; PLCs are programmable logic controllers that control various types of mechanical equipment or production processes through digital or analog input and output; SCADA is a computer-based production process control and scheduling automation system; and DCS is a new generation of instrument control systems based on microprocessors that adopts the design principles of decentralized control functions, centralized display operations, and a balance of decentralized autonomy and comprehensive coordination.

[0027] The data lake described in this embodiment is a repository for collected data and includes multiple database types, including relational databases, time-series databases, in-memory databases, object-oriented databases, graph databases, and key-value databases. Relational databases use a relational model to organize data; time-series databases are databases specifically designed for time-series data; in-memory databases store data in memory and operate directly on it; object-oriented databases are based on object-oriented programming concepts; graph databases use nodes and edges as basic storage units and are designed for efficient storage and querying of graph data; and key-value databases store data using key-value pairs.

[0028] The data acquisition platform described in this embodiment is an operating platform that hosts and runs data pumps. A single data acquisition platform can simultaneously run multiple different types of data pumps. The data pumps consist of static and dynamic modules. The dynamic modules of each data pump share a static module, and the transmission protocols executed by the dynamic modules of each data pump correspond to the transmission protocol type of the data in the data source. A data pump is a general term for programs that perform data acquisition. It is used to obtain collected data from the data source, filter, parse, and calculate it, and then store the processed data in the data lake. Data pumps are categorized by transmission protocol. Data collected using different transmission protocols requires the use of data pumps of the corresponding transmission protocol type.

[0029] The edge computing platform described in this embodiment is a management platform that configures, generates, and deploys data pumps within the data collection platform, and provides a presentation function for collected data and related logs. The edge computing platform generates a corresponding data pump based on the user's configuration information and deploys the data pump to the data collection platform for data collection.

[0030] Specifically, the edge computing platform obtains data in multiple transmission protocol formats from the data source, and the edge computing platform configures and deploys various dynamic modules in the data collection platform according to the obtained transmission protocol format to execute various transmission protocols one by one, and the edge computing platform configures and deploys static modules in the data collection platform. The dynamic module configured by the edge computing platform collects data in the corresponding transmission protocol format from the data source that executes the corresponding transmission protocol, and parses the data in the corresponding transmission protocol format and sends it to the static module. The static module performs edge computing on the data parsed by the dynamic module and then performs perception computing to obtain edge computing result data and perception computing result data. In addition, the static module selects a database that stores data of the corresponding type from the data lake based on the type of the edge computing result data, and stores the edge computing result data in the corresponding database in the data lake module. The static module also determines whether the perception conditions are met based on the perception computing result data. If the perception conditions are met, the static module uploads the perception computing result data to the edge computing platform. At the same time, the static module also captures data pump abnormality information and reports it to the edge computing platform. Figure 2 As shown in the figure, the static modules and each dynamic module configured by the edge computing platform constitute a data pump for data in the corresponding transmission protocol format. The static modules configured and deployed by the edge computing platform in the data acquisition platform are universal modules. Data pumps for data in different transmission protocol formats correspond to dynamic modules one by one, and all types of data pumps use the same static module.

[0031] Among them, such as Figure 2 As shown, the dynamic modules in each data pump include a data acquisition node and a data parsing node. The data acquisition node is configured by the edge computing platform to collect data in a certain transmission protocol format. After collecting data in the corresponding transmission protocol format from the data source, the data acquisition node transmits the data to the data parsing node. The data acquisition node includes a timer, a collection selector, and a collection connector. The timer sets the data collection cycle under the edge computing platform configuration. The collection selector selects the point data to be collected under the edge computing platform configuration. The collection connector connects to the data source and performs data collection actions in the corresponding transmission protocol format under the edge computing platform configuration.

[0032] In the dynamic module, the data parsing node is configured by the edge computing platform to parse the data in the corresponding transmission protocol format, and the data parsing node parses the data in the corresponding transmission protocol format into the JSON format of key-value pairs, and then transmits the parsed data to the static module.

[0033] The static module includes edge computing nodes generated by the edge computing platform based on edge computing rules, perception computing nodes generated by the edge computing platform based on perception computing rules, as well as message reporting nodes, data storage nodes, and exception capture nodes. Data parsed by the dynamic module is first processed by the edge computing nodes to obtain edge computing result data, which is then distributed to both the data storage nodes and perception computing nodes.

[0034] In the static module, the data storage node selects a database from the data lake that stores data of the corresponding type based on the edge computing result data type and stores the edge computing result data in the corresponding database in the data lake module. The data storage node contains a storage selector and a storage connector. The storage selector selects the database in the data lake that stores the corresponding data type under the edge computing platform configuration. The storage connector is used to connect to the database of the corresponding data type in the data lake and perform the storage action.

[0035] In the static module, the perception computing node performs perception computing processing on the edge computing result data to obtain the perception computing result data, and judges whether it meets the set perception conditions based on the perception computing result data. If it meets the set perception conditions, the perception computing node transmits the perception computing result data to the edge computing platform through the message reporting node. If it does not meet the set perception conditions, the perception computing node stops the data transmission.

[0036] In the static module, the exception capture node monitors the perception computing result data transmitted by the message reporting node in real time, and determines whether the operation status of the data pump corresponding to the perception computing result data is abnormal. When the data pump has an abnormality, the exception capture node captures the abnormal information and converts it into abnormal data in a standard format, and then transmits it to the edge computing platform through the message reporting node.

[0037] The data pumps formed in this embodiment are divided into different types according to the transmission protocol of the data source, and the dynamic module type in the data pump corresponds to the data pump type. Figure 3As shown, taking OPC and S7 data as examples, the OPC data pump collects OPC protocol data, an industry standard proposed by the OPC Foundation; the Modbus data pump collects Modbus protocol data, an industry standard for industrial communication protocols; and the S7 data pump collects S7 protocol data, a Siemens PLC-specific protocol. The dynamic module in the OPC data pump is an OPC dynamic module, specifically used to collect OPC protocol data; the dynamic module in the S7 data pump is an S7 dynamic module, specifically used to collect S7 protocol data. The static module is a universal module, meaning that both the OPC and S7 data pumps share the same static module. Therefore, in this embodiment, the data pump type is independent of the manufacturer and model of the data source, but only of the transmission protocol. Data sources from different manufacturers or models with the same transmission protocol use the same data pump for data collection. For acquisition protocols that are not compatible in industrial production environments, a dynamic module corresponding to the protocol should be created and then combined with the universal static module to form a data pump for the corresponding protocol, used to collect data for that protocol.

[0038] In this embodiment, a data pump instance is a data pump that collects data for a specific scenario and is a specific implementation of the data pump. The edge computing platform generates corresponding data pump instances based on the specific data source, data lake, edge computing algorithm, and perceptual computing algorithm, and performs matching data collection. Different data pump instances of the same type of data pump share the same dynamic and static modules, but different configuration parameters.

[0039] The edge computing platform configures the generation process of the data pump instance as follows:

[0040] a. Select the corresponding data pump type according to the data source transmission protocol type and go to step b;

[0041] b. Generate the corresponding data collection node and data parsing node according to the data source collection cycle, collection points and connection information, and go to step c;

[0042] c. Generate the corresponding edge computing node according to the edge computing rules and go to step d;

[0043] d. Generate the corresponding perception computing node according to the perception computing rules and go to step e;

[0044] e. Generate the corresponding data storage node based on the data lake type and connection information, and go to step f;

[0045] f. Generate a general exception capture node and message reporting node according to a fixed specification, and go to step g;

[0046] g. Combine the data collection nodes, data parsing nodes, edge computing nodes, perception computing nodes, data storage nodes, exception capture nodes, and message reporting nodes generated in the previous steps to form a corresponding data pump instance.

[0047] The preferred embodiments of the present invention are described in detail above with reference to the accompanying drawings. The embodiments described in the present invention are merely descriptions of the preferred embodiments of the present invention and do not limit the concept and scope of the present invention. The various specific technical features described in the above specific embodiments can be combined in any suitable manner unless there is any contradiction. Such combinations should also be regarded as the contents disclosed in this disclosure as long as they do not violate the concept of the present invention. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0048] The present invention is not limited to the specific details of the above-mentioned embodiments. Within the scope of the technical concept of the present invention and without departing from the design concept of the present invention, various modifications and improvements made to the technical solution of the present invention by those skilled in the art should fall within the scope of protection of the present invention. The technical content for which protection is sought in the present invention has been fully recorded in the claims.

Claims

1. A data acquisition system for industrial equipment, characterized in that: It includes an edge computing platform, a data lake module, a static module, and multiple dynamic modules. The edge computing platform obtains data in multiple transmission protocol formats from the data source, and generates each dynamic module according to the acquired transmission protocol format. The dynamic modules execute various transmission protocols one by one, and the static modules are generated by the edge computing platform. The dynamic module configured by the edge computing platform collects data in the corresponding transmission protocol format from the data source, parses the data in the corresponding transmission protocol format, and sends it to the static module. The static module performs edge computing on the data parsed by the dynamic module and then performs perception computing to obtain edge computing result data and perception computing result data. The static module and each dynamic module configured by the edge computing platform respectively constitute a data pump for the corresponding transmission protocol format data; The data lake module includes multiple databases storing different types of data. The static module selects a database storing corresponding types of data from the data lake based on the type of edge computing result data, and stores the edge computing result data in the corresponding database in the data lake module. The static module also determines whether the perception condition is met based on the perception computing result data. If the perception condition is met, the static module uploads the perception computing result data to the edge computing platform. The static module includes an edge computing node generated by the edge computing platform based on edge computing rules, a perception computing node generated by the edge computing platform based on perception computing rules, a message reporting node, and a data storage node; the data parsed by the dynamic module is first edge-computing processed by the edge computing node to obtain edge computing result data, and then the edge computing result data is distributed to the data storage node and the perception computing node at the same time; The data storage node selects a database storing data of the corresponding type from the data lake based on the type of the edge computing result data, and stores the edge computing result data to the corresponding database in the data lake module; The perception computing node performs perception computing processing on the edge computing result data to obtain the perception computing result data, and determines whether the perception computing result data meets the set perception conditions based on the perception computing result data. If the set perception conditions are met, the perception computing node transmits the perception computing result data to the edge computing platform through the message reporting node. If the set perception conditions are not met, the perception computing node stops the data transmission. The static module also includes an exception capture node, which monitors the perception computing result data transmitted by the message reporting node in real time, and determines whether the operation status of the data pump corresponding to the perception computing result data is abnormal. When the data pump is abnormal, the exception capture node captures the abnormal information, converts it into abnormal data in a standard format, and then transmits it to the edge computing platform through the message reporting node.

2. The data acquisition system for industrial equipment according to claim 1, characterized in that: The dynamic module includes a data acquisition node and a data parsing node, wherein the data acquisition node is configured by the edge computing platform to collect data in a certain transmission protocol format, and after the data acquisition node collects data in the corresponding transmission protocol format from the data source, the data is transmitted to the data parsing node; the data parsing node is configured by the edge computing platform to parse data in the corresponding transmission protocol format, and the data parsing node parses the data in the corresponding transmission protocol format into a JSON format of key-value pairs, and then transmits the parsed data to the static module.

3. The data acquisition system for industrial equipment according to claim 2, characterized in that: The data acquisition node includes a timer, an acquisition selector and an acquisition connector. The timer sets the data acquisition cycle under the configuration of the edge computing platform. The acquisition selector selects the point data to be collected under the configuration of the edge computing platform. The acquisition connector connects to the data source under the configuration of the edge computing platform and performs data acquisition actions in the corresponding transmission protocol format.

4. The data acquisition system for industrial equipment according to claim 1, characterized in that: The data storage node includes a storage selector and a storage connector. The storage selector selects a database in the data lake that stores data of the corresponding type under the configuration of the edge computing platform. The storage connector is used to connect to the database of the corresponding type of data in the data lake and perform storage actions.

5. The data acquisition system for industrial equipment according to any one of claims 1 to 4, characterized in that: The data sources include equipment, instruments, sensors, PLC, SCADA, and DCS.

6. The data acquisition system for industrial equipment according to any one of claims 1 to 4, characterized in that: The databases in the data lake include relational databases, time series databases, in-memory databases, object-oriented databases, graph databases, and key-value databases.

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