Data acquisition system of industrial equipment

Abstract

The invention discloses a data acquisition system for industrial equipment. The data acquisition system comprises an edge computing platform, a data lake module, a static module and a plurality of dynamic modules, the edge computing platform configures and generates each dynamic module and each static module; the dynamic module collects data in a corresponding transmission protocol format from a data source, analyzes the data and then sends the data to the static module, the static module conducts edge calculation on the analyzed data and then conducts perceptual calculation, and edge calculation result data and perceptual calculation result data are obtained. And the static module selects a database for storing data of a corresponding type from the data lake for storage based on the type of the edge calculation result data, and the static module also judges whether a sensing condition is met or not based on the sensing calculation result data, and uploads the sensing condition to the edge calculation platform if the sensing condition is met. The method has the advantages of universality, usability, conciseness, lower use threshold and less time consumption, and helps enterprises to reduce the cost of data acquisition.

Description

Technical Field

[0001] The 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, monitoring systems and other equipment to collect information such as the operating status, working environment, and production data of industrial equipment in real time or regularly. These data include but are not limited to parameters such as equipment temperature, pressure, humidity, vibration, current, voltage, and equipment working time, downtime, output, and fault information.

[0003] With the continuous improvement of the level of industrial automation, enterprises have an increasing demand for monitoring and optimizing the operating status of equipment. Through equipment data collection, the operating status of equipment can be monitored in real time, problems can be discovered in time and measures can be taken to improve production efficiency and product quality. Traditional equipment maintenance is often based on fixed maintenance cycles or equipment operation time, which is a wasteful and inadequate method. Through equipment data collection, real-time monitoring and analysis of the operating status of equipment can be achieved, possible equipment failures can be predicted, predictive maintenance of equipment can be achieved, maintenance costs can be reduced, and production interruptions can be avoided. The core of intelligent manufacturing in the Industrial 4.0 era is data-driven, and equipment data collection is the basis for realizing intelligent manufacturing. Through equipment data collection, information sharing and collaboration between equipment can be achieved, production efficiency and flexibility can be improved, and customized production can be achieved.

[0004] Traditional industrial equipment data collection requires automation engineers to operate, develop corresponding data collection programs according to equipment information, and manually deploy the programs into the data collection gateway. The protocols of different devices vary greatly, requiring automation engineers to have rich experience, and the development of data collection programs is cumbersome and has a high threshold, which is not conducive to enterprises 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: 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 a data source, and the edge computing platform generates each dynamic module according to the acquired transmission protocol format configuration to execute various transmission protocols one by one, and the edge computing platform configures and generates a static module common to each dynamic module; the dynamic module configured by the edge computing platform collects data in a corresponding transmission protocol format from the data source, 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, and each dynamic module configured by the static module and 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 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.

[0007] Furthermore, the dynamic module includes a data collection node and a data parsing node, wherein the data collection node is configured by the edge computing platform to collect data in a certain transmission protocol format, and after the data collection node collects data in the corresponding transmission protocol format from the data source, the data transfers the data 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 transfers the parsed data to the static module.

[0008] Furthermore, the data collection 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, and the collection connector connects to the data source under the edge computing platform configuration and performs data collection actions in the corresponding transmission protocol format.

[0009] 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-computed 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 a 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 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.

[0010] 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.

[0011] Furthermore, the data storage node includes a storage selector and a storage connector. The storage selector selects a database storing 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.

[0012] Furthermore, the data sources include equipment, instruments, sensors, PLC, SCADA, and DCS.

[0013] 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.

[0014] Compared with the prior art, the present invention has the following advantages: In the present invention, after the creation of the data pump corresponding to the transmission protocol is completed, there is no need for automation engineers to participate in the use process, and no code development is required. Ordinary personnel can realize data collection after filling in the corresponding parameter configuration. In the present invention, the data pump type is adapted for the transmission protocol, and different devices of the same protocol share the same type of data pump, so the data pump type is also more focused and universal.

[0015] 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 threshold for use, and consumes less time, helping enterprises reduce the cost of data acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1It is an overall architecture diagram of an embodiment of the present invention.

[0017] Figure 2 1 is a schematic diagram of the structure of a data pump in an embodiment of the present invention. Figure 3 Schematic diagram of data pump types in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0019] like Figure 1 As shown, this embodiment discloses a data acquisition system for industrial equipment, and the overall architecture 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.

[0020] The data source described in this embodiment is the information source of the collected data, including equipment, instruments, sensors, PLC, SCADA, and DCS. Among them, equipment is a general term for machines, devices, tools, and facilities used to complete specific production tasks in the industrial production process; instruments are detection devices that display the measured information as digital; sensors are detection devices that convert the measured information into electrical signals or other required forms for output according to a certain rule; PLC is a programmable logic controller that controls 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; DCS is a new generation of instrument control system based on microprocessors, adopting the design principles of decentralized control functions, centralized display operations, and taking into account both division and autonomy and comprehensive coordination.

[0021] The data lake described in this embodiment is a repository for collected data, including multiple databases, namely relational databases, time series databases, memory databases, object-oriented databases, graph databases, and key-value databases. Among them, relational databases are databases that use relational models to organize data; time series databases are databases specifically for time series data; memory databases are databases that directly operate on data in memory; object-oriented databases are databases based on the idea of ​​object-oriented programming; graph databases are databases that use points and edges as basic storage units and are designed with the principle of efficient storage and query of graph data; and key-value databases are databases that store data in the form of key-value pairs.

[0022] The data acquisition platform described in this embodiment is an operation platform that carries and runs data pumps. One data acquisition platform can run multiple different types of data pumps at the same time. The data pump is composed of a static module and a dynamic module. The dynamic modules of each data pump share a static module. The transmission protocol executed by the dynamic module of each data pump corresponds to the transmission protocol type of the data in the data source. The 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, and store the processed data in the data lake. Data pumps are divided into multiple categories according to the transmission protocol. The collected data of different transmission protocols need to be collected using data pumps of the corresponding transmission protocol type.

[0023] The edge computing platform described in this embodiment is a management platform for configuring, generating and deploying data pumps in the data collection platform, and provides the function of presenting collected data and related logs. The edge computing platform generates a corresponding data pump according to the user's configuration information, and deploys the data pump to the data collection platform for data collection.

[0024] 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 formats 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, each dynamic module configured by the static module and the edge computing platform constitutes a data pump corresponding to the data in the transmission protocol format. The static module configured and deployed by the edge computing platform in the data acquisition platform is a universal module. The data pumps of different transmission protocol format data correspond to dynamic modules one by one, and all types of data pumps use the same static module.

[0025] Among them, Figure 2As 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, and the data acquisition node collects data in the corresponding transmission protocol format from the data source, and then transmits the data to the data parsing node. The data acquisition node includes a timer, a collection selector, and a collection connector, wherein 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, and the collection connector connects to the data source under the edge computing platform configuration and performs the data collection action in the corresponding transmission protocol format.

[0026] 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.

[0027] 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. The data parsed by the dynamic module is first 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 perception computing node at the same time.

[0028] In the static module, the data storage node 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 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 edge computing platform configuration. The storage connector is used to connect to the database of the corresponding type of data in the data lake and perform storage actions.

[0029] 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.

[0030] 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 is abnormal, 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.

[0031] 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 types in the data pumps correspond to the data pump types. Figure 3 As shown, taking the data in the data source as OPC data and S7 data as an example, the OPC data pump is a data pump for collecting OPC protocol data, and OPC is an industrial standard proposed by the OPC Foundation; the Modbus data pump is a data pump for collecting Modbus protocol data, and Modbus is an industry standard for industrial communication protocols; the S7 data pump is a data pump for collecting S7 protocol data, and S7 is a Siemens PLC dedicated protocol. The dynamic module in the OPC data pump is an OPC dynamic module, which is dedicated to collecting data of the OPC protocol; the dynamic module in the S7 data pump is an S7 dynamic module, which is dedicated to collecting data of the S7 protocol, and the static module is a common module, that is, the OPC data pump and the S7 data pump use the same static module. Therefore, the type of data pump in this embodiment has nothing to do with the manufacturer and model of the data source, but is only related to the transmission protocol. For data sources of different manufacturers or models, if the transmission protocol is the same, the same type of data pump is used for data collection. For the acquisition protocol that cannot be adapted in the industrial production environment, a dynamic module of the corresponding protocol should be created, and then combined with the general static module to form a data pump of the corresponding protocol for collecting data of the protocol.

[0032] In this embodiment, the data pump instance is a data pump that collects data for a specific scenario and is a specific program of the data pump. The edge computing platform generates a corresponding data pump instance based on the specific data source, data lake, edge computing algorithm, and perception computing algorithm to collect matching data. In the same type of data pump, different data pump instances have the same dynamic modules and static modules, but different configuration parameters.

[0033] The edge computing platform configures the generation process of the data pump instance as follows: a. According to the transmission protocol type of the data source, select the corresponding data pump type and go to step b; b. Generate corresponding data collection nodes and data analysis nodes according to the data source collection cycle, collection points and connection information, and go to step c; c. Generate the corresponding edge computing node according to the edge computing rules and go to step d; d. Generate the corresponding perception computing node according to the perception computing rule, and go to step e; e. Generate corresponding data storage nodes according to the type and connection information of the data lake, and go to step f; f. Generate a general exception capture node and message reporting node according to fixed specifications, and go to step g; 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.

[0034] The preferred embodiments of the present invention are described in detail above in conjunction with 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 without contradiction, and such combinations should also be regarded as the contents disclosed in the present 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.

[0035] The present invention is not limited to the specific details of the above-mentioned embodiments. Within 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 technical personnel in this field should fall within the protection scope of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A data acquisition system for industrial equipment, It is 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 the edge computing platform generates each dynamic module according to the acquired transmission protocol format configuration to execute various transmission protocols one by one, and the edge computing platform configures and generates a static module common to each dynamic module; The dynamic module configured by the edge computing platform collects data in the corresponding transmission protocol format from the data source, 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. The static module and each dynamic module configured by the edge computing platform respectively constitute a data pump for the data in the corresponding transmission protocol format. 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 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.

2. The data acquisition system for industrial equipment according to claim 1, It is characterized in that The dynamic module includes a data collection node and a data parsing node, wherein the data collection node is configured by the edge computing platform to collect data in a certain transmission protocol format, and after the data collection 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, It is characterized in that The data collection 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, and the collection connector connects to the data source under the edge computing platform configuration and performs data collection actions in the corresponding transmission protocol format.

4. The data acquisition system for industrial equipment according to claim 1, It is characterized in that 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-computed 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 a 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 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.

5. The data acquisition system for industrial equipment according to claim 4, It is characterized in that 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 and converts it into abnormal data in a standard format, and then transmits it to the edge computing platform through the message reporting node.

6. The data acquisition system for industrial equipment according to claim 4, It is characterized in that The data storage node includes a storage selector and a storage connector. The storage selector selects a database storing data of a 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 data of a corresponding type in the data lake and perform storage actions.

7. A data acquisition system for industrial equipment according to any one of claims 1 to 6, It is characterized in that The data sources include equipment, instruments, sensors, PLC, SCADA, and DCS.

8. A data acquisition system for industrial equipment according to any one of claims 1 to 6, It is 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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