Industrial data processing system and method

Abstract

The invention provides an industrial data processing system and method, and the system comprises an acquisition module which is used for periodically acquiring industrial data from an OPC server under the condition of establishing communication connection with the OPC-DA server; the conversion module is used for converting the industrial data into XML (Extensible Markup Language) data and converting the XML data into target protocol data according to a target protocol; and the industrial Internet of Things platform is used for carrying out analysis and Flink streaming calculation on the target protocol data by adopting an Flink data engine to obtain target data, and forwarding and subscribing the target data. According to the system, the automation management degree of the LNG receiving station is achieved, and the processing efficiency of industrial data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an industrial data processing system and method. Background Art

[0002] Currently, liquefied natural gas (LNG) receiving stations are an important link in the LNG supply chain, responsible for receiving, storing and regasifying LNG to meet local energy needs.

[0003] In the related technologies, distributed control systems DCS, power management systems PMS, vibration monitoring systems VMS and programmable control systems PLC are put into operation and newly built as production monitoring and management systems for LNG receiving stations. These systems operate independently, and different systems generate industrial data in different formats, requiring the design of multiple data transmission, parsing or calculation methods. Data management is difficult, resulting in isolated data that is difficult to use, causing the problem of low digitalization and intelligence levels in LNG receiving stations. Summary of the invention

[0004] The present invention provides an industrial data processing system and method, which are used to solve the problem that in the prior art, when conducting production monitoring and management of LNG receiving stations, a variety of data transmission, analysis or calculation methods need to be designed to meet different system function requirements, data management is difficult, and the utilization efficiency of industrial data is low. The system realizes the degree of automated management of LNG receiving stations and improves the processing efficiency of industrial data.

[0005] The present invention provides an industrial data processing system, comprising: A collection module, wherein the collection module is used to periodically collect industrial data from the OPC server when a communication connection is established with the OPC-DA server; A conversion module, the conversion module is used to convert the industrial data into XML data, and convert the XML data according to a target protocol to obtain target protocol data; wherein the target protocol includes at least one of the MQTT protocol, the Kafka protocol and the HTTP protocol; An industrial Internet of Things platform, wherein the industrial Internet of Things platform adopts a front-end and back-end separation architecture; the industrial Internet of Things platform is used to use a Flink data engine to parse the target protocol data and perform Flink streaming calculations to obtain target data, and forward and subscribe to the target data; the Flink streaming calculations include at least one of model calculations and alarm calculations.

[0006] According to an industrial data processing system provided by the present invention, the conversion module includes: OPC2WEB unit, the OPC2WEB unit is used to convert the industrial data into OPC-DA-XML data; A Node-Red unit, wherein the Node-Red unit includes configuration information determined based on a first user instruction; the Node-Red unit is used to perform normalization processing such as word segmentation, removal of abnormal characters and renaming on the XML data according to a second user instruction, and send the target protocol data to the industrial Internet of Things platform according to the configuration information; wherein the configuration information includes at least one of a target OPC server address, a port and a Client ID.

[0007] According to an industrial data processing system provided by the present invention, the industrial Internet of Things platform includes: An access module, the access module being used to receive the target protocol data according to the target protocol through multiple OpenAPIs; Flink data engine, the Flink data engine supports point mode data upload and device mode data upload, and the Flink data supports data conversion formats of JSON and binary data; the Flink data engine is used to parse and convert the time series data in the target protocol data, perform model calculation and alarm calculation, and obtain the target data; A forwarding module is used to forward and subscribe to the target data according to the standard storage service and the forwarding storage service.

[0008] According to an industrial data processing system provided by the present invention, the industrial Internet of Things platform also includes: The encapsulation module is used to encapsulate the target data according to an algorithm based on Docker encapsulation to obtain encapsulated data.

[0009] According to an industrial data processing system provided by the present invention, the industrial Internet of Things platform also includes: A storage module, wherein the storage module is used to store the time series data in the target data using a time series database based on IoTDB; A visualization module is used to visualize the target protocol data and the intermediate process corresponding to the target data.

[0010] According to an industrial data processing system provided by the present invention, the industrial Internet of Things platform supports deployment in any configuration environment of a physical machine, a virtual machine, K8s and a Docker container.

[0011] The present invention also provides an industrial data processing method, comprising: In case of establishing a communication connection with an OPC-DA server, periodically collecting industrial data from the OPC server; Convert the industrial data into XML data, and convert the XML data according to a target protocol to obtain target protocol data; wherein the target protocol includes at least one of the MQTT protocol, the Kafka protocol, and the HTTP protocol; The target protocol data is parsed and Flink stream computing is performed using a Flink data engine to obtain target data, and the target data is forwarded and subscribed to; the Flink stream computing includes at least one of model computing and alarm computing.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the above-mentioned industrial data processing methods is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the industrial data processing method described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the industrial data processing method described above is implemented.

[0015] The industrial data processing system and method provided by the present invention periodically collects industrial data from the OPC server through a collection module, converts the industrial data into XML data through a conversion module, and converts the XML data into data supporting the target protocol. Finally, the target protocol data is parsed and Flink streaming is performed on the industrial Internet of Things platform using the Flink data engine to obtain the target data, and the target data is forwarded and subscribed, thereby realizing the degree of automated management of the LNG receiving station and improving the processing efficiency of industrial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is one of the structural schematic diagrams of the industrial data processing system provided by the present invention.

[0018] Figure 2 It is a flow chart of the working mechanism of the industrial Internet of Things platform provided by the present invention.

[0019] Figure 3 This is the second structural schematic diagram of the industrial data processing system provided by the present invention.

[0020] Figure 4 It is a flow chart of the industrial data processing method provided by the present invention.

[0021] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention.

[0022] Reference numerals: 110: acquisition module; 120: conversion module; 121: OPC2WEB unit; 122: Node-Red unit; 130: Industrial IoT platform; 131: Access module; 132: Flink data engine; 133: forwarding module; 134: encapsulation module; 135: storage module; 136: visualization module. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Combine the following Figure 1-Figure 4 The industrial data processing system and method of the present invention are described.

[0025] Figure 1 It is one of the structural diagrams of the industrial data processing system provided by the present invention. The industrial data processing system includes: an acquisition module 110, a conversion module 120 and an industrial Internet of Things platform 130.

[0026] The acquisition module 110 is used to periodically acquire industrial data from the OPC server when a communication connection is established with the OPC-DA server.

[0027] In this embodiment, the industrial data includes the integration of important equipment and process data of the main process technology of the liquefied natural gas (LNG) receiving station and power data; for example, the industrial data includes various data such as pressure, flow, temperature, voltage, current, power, electricity, and switch opening and closing status of the LNG receiving station.

[0028] In this embodiment, when the DCS system and PMS system of the LNG receiving station adopt the OPC DA protocol system to transmit data, the acquisition module 110 is first set to establish a communication connection with the on-site OPC server, and a communication test between the acquisition module 110 and OPC DA is carried out.

[0029] For example, the acquisition module 110 includes OPC data acquisition and display functions. First, the IP address of the target OPC server is selected through the acquisition module 110 to establish a communication connection between the two; then, an OPC group is established through the acquisition module 110, the acquisition cycle of the group is defined, and the acquisition method is set to use synchronous reading; then, OPC acquisition points are created, and communication tests are performed based on the selected points to communicate. Finally, after the communication test is successful, the industrial data collection is completed.

[0030] The conversion module 120 is used to convert the industrial data into XML data, and convert the XML data according to the target protocol to obtain target protocol data; wherein the target protocol includes at least one of the MQTT protocol, the Kafka protocol and the HTTP protocol.

[0031] In this embodiment, the access layer of the industrial Internet of Things platform 130 mainly uses the MQTT protocol for access, while also providing Kafka and HTTP access services, which can obtain target data through different data transmission protocols, thereby improving data reception efficiency.

[0032] In this embodiment, the conversion module 120 is used to convert industrial data in different formats into XML format. For example, the collected DA data is converted into OPC-DA-XML data through the conversion module 120 .

[0033] In this embodiment, the conversion module 120 includes a preset format conversion module 120. When converting the data format, the industrial data is filled into the module by pasting and copying operations to obtain the corresponding target protocol data.

[0034] In this embodiment, the conversion module 120 can be set as OPC2WEB software, and the OPC2WEB unit 121 is used to convert OPC UA data into a Web interface, which can help users view and control the data of industrial automation equipment more conveniently in a Web browser.

[0035] Specifically, the conversion module 120 includes: an OPC2WEB unit 121, which is used to convert industrial data into OPC-DA-XML data; a Node-Red unit 122, which includes configuration information determined based on a first user instruction; the Node-Red unit 122 is used to perform normalization processing such as word segmentation, removal of abnormal characters and renaming on the XML data according to a second user instruction, and send the target protocol data to the industrial Internet of Things platform according to the configuration information; wherein the configuration information includes at least one of the target OPC server address, port and Client ID.

[0036] In this embodiment, the OPC2WEB unit 121 receives the second user instruction and responds to the instruction to execute operations of word segmentation, removal of abnormal characters, and renaming of the XML data.

[0037] In this embodiment, a program is used to rename the collected points through two user instructions, such as removing illegal characters in the points and replacing " / " with "_", in preparation for collection on the industrial Internet of Things platform.

[0038] In this embodiment, the industrial Internet of Things platform is accessed and configured through a first user instruction, and information such as an access code and name is input.

[0039] For example, configure MQTT forwarding in Node-Red unit 122, edit the MQTT server address, port, and Client ID, etc. After the configuration is completed, confirm that the connection with the Industrial Internet of Things platform is successful.

[0040] The industrial Internet of Things platform adopts a front-end and back-end separation architecture; the industrial Internet of Things platform adopts the Flink data engine 132 to parse the target protocol data and perform Flink streaming calculations to obtain the target data, and forward and subscribe to the target data; the Flink streaming calculations include at least one of model calculations and alarm calculations.

[0041] In this embodiment, the Industrial Internet of Things platform supports deployment in any configuration environment of physical machines, virtual machines, K8s and Docker containers, which enhances the deployment flexibility of the Industrial Internet of Things platform and improves the generalization of the industrial data processing system.

[0042] In this embodiment, the industrial Internet of Things platform may include several functional components, including edge management, collection access, Internet of Things computing, basic applications, interface opening, and operation management, which perform different functions respectively.

[0043] In this embodiment, the industrial IoT platform provides a standard framework for the development of data collection, which can customize the collection of industrial data of multiple protocols.

[0044] In this embodiment, the target protocol data is MQTT data; the steps for configuring MQTT data access on the industrial IoT platform include: the first step: click "Add" on the main interface, and the access new configuration window pops up; the second step, execute the configuration of new access content, including: input access code, Chinese name of access point, data upload mode (such as point mode), access method (such as MQTT), access authentication (user authentication), client ID and set whether the data is uploaded anonymously, etc.; the second step, after the new access content is configured, it can be queried and modified. In this embodiment, the industrial IoT platform can parse, calculate, and forward time series data through a real-time computing engine based on Flink; the basic application of the industrial IoT platform uses Springboot+Vue technology, and adopts a front-end and back-end separation architecture, and uses message queues to forward and subscribe data.

[0045] The industrial data processing system provided by the embodiment of the present invention periodically collects industrial data from the OPC server through the collection module 110, converts the industrial data into XML data through the conversion module 120, and converts the XML data into data that supports the target protocol. Finally, the target protocol data is parsed and Flink streaming is performed by the Flink data engine 132 through the Industrial Internet of Things to obtain the target data, and the target data is forwarded and subscribed, thereby realizing the degree of automated management of the LNG receiving station and improving the processing efficiency of industrial data.

[0046] In some embodiments, the industrial Internet of Things platform includes: an access module 131, the access module 131 is used to receive target protocol data according to the target protocol through multiple OpenAPIs; a Flink data engine 132, the Flink data engine 132 supports point mode data upload and device mode upload, and Flink data supports JSON and binary data data conversion formats; the Flink data engine 132 is used to parse and convert, model calculate and alarm calculate the time series data in the target protocol data to obtain the target data; the forwarding module 133, the forwarding module 133 is used to forward and subscribe to the target data according to the standard storage service and the forwarding storage service.

[0047] Figure 2 It is a flow chart of the working mechanism of the industrial IoT platform provided by the present invention. Figure 2In the embodiment shown, an industrial Internet of things (IoT) platform is provided. The bottom layer of the platform relies on big data technology to realize real-time computing and processing of massive data. At the same time, computing resources can be dynamically adjusted according to the number of device points, that is, the access layer of the IoT platform is mainly accessed through the MQTT protocol, and Kafka and HTTP access services can also be provided. Among them, the key nodes of the main data processing flow can be personalized and deleted in a visual configuration manner to meet the needs of different business scenarios. The IoT platform supports two modes of data upload, point mode data upload and device mode upload, to realize data parsing and conversion, and supports its own JSON and binary data formats by default, and can also customize the data format according to needs. In terms of data calculation, data processing and calculation services are provided, which can support calculations in the form of time alignment, precision elimination, range and legality verification, logical and mathematical operations, interface calls, etc. In terms of data forwarding and storage, the platform supports standard storage services, corresponding to storing time series data in a time series database (including point mode data storage, device mode data storage, device mode alignment data storage, alarm data storage, and statistical indicator data storage). The platform also supports forwarding storage services, corresponding to storing data in HBase, IoTDB, and HDFS for storage.

[0048] In some embodiments, the industrial IoT platform further includes: a packaging module 134, which is used to package the target data according to an algorithm based on Docker packaging to obtain packaged data.

[0049] In this embodiment, the algorithm based on Docker encapsulation can be a model function, which can provide the ability to encapsulate some complex logic and meet the user's needs for reuse of local models; users can encapsulate part of the logic unit into a "function" for use in different models.

[0050] Specifically, the model function can be determined in the following two ways: (1) Drag-and-drop function creation: Users can directly create functions by dragging computing components. Users can also use graphical methods to build models, circle some of the logic, and save them as functions.

[0051] (2) Script-based model creation method: Users create functions by editing scripts.

[0052] In this embodiment, the industrial Internet of Things platform supports two data structure definitions: device mode and point mode. The device mode encapsulates and sends multiple attributes of each device as a whole, while the point mode encapsulates and sends data for each measuring point as a whole. The measuring point can be associated with the dynamic attributes of the device, and the data structure can be automatically converted and stored, so as to achieve the purpose of accessing data with one structure, but being able to store and use it in two structures.

[0053] The industrial data processing system provided by the embodiment of the present invention receives target protocol data according to the target protocol through multiple OpenAPIs of the access module 131, parses and converts the time series data in the target protocol data, performs model calculation and alarm calculation through the Flink data engine 132 to obtain the target data, and finally forwards and subscribes to the target data according to the standard storage service and the forwarding storage service through the forwarding module 133, thereby realizing the automated design of data reception, analysis, calculation and forwarding of the industrial Internet of Things platform, and improving the processing efficiency of massive industrial data.

[0054] In some embodiments, the industrial Internet of Things platform also includes: a storage module 135, which is used to store the time series data in the target data using a time series database based on IoTDB; a visualization module 136, which is used to visualize the target protocol data and the intermediate process corresponding to the target data.

[0055] In this embodiment, the IoTDB-based time series database includes a high-performance time series database based on Apache IoTDB.

[0056] In this embodiment, the industrial Internet of Things platform adopts a high-performance time series database based on APache IoTDB as a storage tool for sensor data. The time series database can support high-speed write access for millions of devices, and has a high data compression ratio. The disk occupancy rate for long-term data storage is extremely low, and the response time for large-span data queries can be as low as milliseconds, thereby solving the problems of high storage cost and low writing and query analysis efficiency caused by the huge number of device collection points and high data collection frequency.

[0057] In this embodiment, the IoT platform provides data source connections for at least seven types of data storage, including Kafka, InfluxDB, CirroData-TimeS, MQTT, MySQL, HBase, and HDFS. The industrial IoT platform can achieve unified management of the above data sources, which can be directly obtained and used in the transfer configuration.

[0058] In this embodiment, the default time series database of the industrial Internet of Things platform is IoTDB, which can store time series data in a tree structure and correspond to the actual industrial scene; the storage module 135 defines the measurement point encoding according to the hierarchical relationship, for example, by symbol separation, length separation and custom script three methods to implement custom storage rules for measurement points, thereby enhancing the adaptability of the storage structure.

[0059] In this embodiment, if data storage fails, the failed data will be transferred to the Kafka data source. The user can discover the data storage failure through link monitoring or monitoring applications. After the transfer problem is troubleshooted and solved, the compensation task is started in the storage task. The compensation task will consume the storage failure data in Kafka, and then store the data in the database corresponding to Kafka.

[0060] In this embodiment, the industrial Internet of Things platform also includes a display module. For example, the display module is a high-definition LCD display or other types of display screens, which is used to visualize the input data, intermediate processing data, and output data of the industrial Internet of Things platform, so that users can intuitively understand the data change process based on the displayed data, and provide new ideas for subsequent research.

[0061] The industrial data processing system provided by the embodiment of the present invention uses a time series database based on IoTDB to store time series data in the target data through a storage module, and visualizes the target protocol data and the intermediate process corresponding to the target data through a visualization module, thereby improving data storage efficiency and display effect, and further improving data processing efficiency.

[0062] Figure 3 This is the second structural diagram of the industrial data processing system provided by the present invention. Figure 3 In the illustrated embodiment, the industrial data processing system includes an acquisition module 110, a conversion module 120 and an industrial Internet of Things platform 130; wherein the conversion module 120 also includes an OPC2WEB unit 121 and a Node-Red unit 122; the industrial Internet of Things platform 130 includes an access module 131, a Flink data engine 132, a forwarding module 133, an encapsulation module 134, a storage module 135 and a visualization module 136.

[0063] The industrial data processing system provided by the present invention is described below. The industrial data processing system described below and the industrial data processing method described above can be referenced to each other.

[0064] Figure 4 It is a flow chart of the industrial data processing method provided by the present invention, such as Figure 4 As shown, the industrial data processing method includes the following steps: Step 410: When a communication connection is established with the OPC-DA server, industrial data is periodically collected from the OPC server.

[0065] In this step, industrial data includes the integration of important equipment and process data of the main process technology of the liquefied natural gas (LNG) receiving station and power data; for example, industrial data includes various data such as pressure, flow, temperature, voltage, current, power, electricity, and switch opening and closing status of the LNG receiving station.

[0066] In this embodiment, when the DCS system and PMS system of the LNG receiving station adopt the OPC DA protocol system to transmit data, the acquisition module is first set to establish a communication connection with the on-site OPC server, and a communication test between the acquisition module and OPC DA is carried out.

[0067] For example, the acquisition module includes OPC data acquisition and display functions. First, the IP address of the target OPC server is selected through the acquisition module to establish a communication connection between the two. Then, an OPC group is established through the acquisition module, the acquisition cycle of the group is defined, and the acquisition method is set to synchronous reading. Then, OPC acquisition points are created, and communication tests are performed based on the selected points to communicate. Finally, after the communication test is successful, the industrial data collection is completed.

[0068] Step 420: convert the industrial data into XML data, and convert the XML data according to the target protocol to obtain target protocol data; wherein the target protocol includes at least one of the MQTT protocol, the Kafka protocol and the HTTP protocol.

[0069] In this step, a conversion module may be used to convert industrial data in different formats into XML format. For example, the collected DA data may be converted into OPC-DA-XML data through the conversion module.

[0070] In this embodiment, the conversion module includes a preset format conversion module. When converting the data format, the industrial data is filled into the module by pasting and copying operations to obtain the corresponding target protocol data.

[0071] In this embodiment, the conversion module can be set to OPC2WEB software, and the OPC2WEB unit is used to convert OPC UA data into a Web interface, which can help users view and control the data of industrial automation equipment more conveniently in a Web browser.

[0072] Specifically, the conversion module includes: an OPC2WEB unit, which is used to convert industrial data into OPC-DA-XML data; a Node-Red unit, which includes configuration information determined based on a first user instruction; the Node-Red unit is used to perform normalization processing such as word segmentation, removal of abnormal characters and renaming on the XML data according to a second user instruction, and send the target protocol data to the industrial Internet of Things platform according to the configuration information; wherein the configuration information includes at least one of the target OPC server address, port and Client ID.

[0073] In this embodiment, the OPC2WEB unit receives the second user instruction and responds to the instruction to execute operations of word segmentation, removal of abnormal characters and renaming of the XML data.

[0074] In this embodiment, a program is used to rename the collected points through two user instructions, such as removing illegal characters in the points and replacing " / " with "_", in preparation for collection on the industrial Internet of Things platform.

[0075] In this embodiment, the industrial Internet of Things platform is accessed and configured through a first user instruction, and information such as an access code and name is input.

[0076] For example, configure MQTT forwarding in the Node-Red unit, edit the MQTT server address, port, and ClientID, etc. After the configuration is completed, confirm that the connection with the Industrial IoT platform is successful.

[0077] Step 430: Use the Flink data engine to parse the target protocol data and perform Flink stream computing to obtain the target data, and forward and subscribe to the target data; the Flink stream computing includes at least one of model computing and alarm computing.

[0078] In this step, the time series data can be parsed, calculated, and forwarded through the real-time computing engine based on Flink of the Industrial Internet of Things platform. The basic application of the Industrial Internet of Things platform applies Springboot+Vue technology, adopts a front-end and back-end separation architecture, and uses message queues to forward and subscribe to data.

[0079] In this embodiment, the Industrial Internet of Things platform supports deployment in any configuration environment of physical machines, virtual machines, K8s and Docker containers, which enhances the deployment flexibility of the Industrial Internet of Things platform and improves the generalization of the industrial data processing system.

[0080] In this embodiment, the industrial Internet of Things platform may include several functional components, including edge management, collection access, Internet of Things computing, basic applications, interface opening, and operation management, which perform different functions respectively.

[0081] In this embodiment, the access layer of the industrial Internet of Things platform mainly uses the MQTT protocol for access, while also providing Kafka and HTTP access services, which can obtain target data through different data transmission protocols, thereby improving data reception efficiency.

[0082] In this embodiment, the industrial IoT platform provides a standard framework for the development of data collection, which can customize the collection of industrial data of multiple protocols.

[0083] In this embodiment, the target protocol data is MQTT data; the steps for configuring MQTT data access on the industrial IoT platform include: the first step: click "Add" on the main interface to pop up the access new configuration window; the second step, execute the configuration of new access content, including: enter the access code, the Chinese name of the access point, the data upload mode (such as point mode), the access method (such as MQTT), the access authentication (user authentication), the client ID and set whether the data is uploaded anonymously, etc.; the second step, after the new access content is configured, it can be queried and modified.

[0084] The industrial data processing method provided in the embodiment of the present invention periodically collects industrial data from the OPC server, converts the industrial data into XML data, and converts the XML data into data that supports the target protocol. The Flink data engine is used to parse the target data and perform Flink streaming calculations to obtain the target data, and the target data is forwarded and subscribed to, thereby realizing the degree of automated management of the LNG receiving station and improving the processing efficiency of industrial data.

[0085] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the industrial data processing method, which includes: periodically collecting industrial data from the OPC server when establishing a communication connection with the OPC-DA server; converting the industrial data into XML data, and converting the XML data according to the target protocol to obtain target data; wherein the target protocol includes at least one of the MQTT protocol, the Kafka protocol and the HTTP protocol; using the Flink data engine to parse the target data and perform Flink stream computing to obtain the target data, and forwarding and subscribing the target data; the Flink stream computing includes at least one of the model computing and the alarm computing.

[0086] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0087] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the industrial data processing method provided by the above methods, and the method includes: while establishing a communication connection with the OPC-DA server, periodically collecting industrial data from the OPC server; converting the industrial data into XML data, and converting the XML data according to the target protocol to obtain target data; using the Flink data engine to parse the target protocol data and perform Flink streaming calculations to obtain target data, and forwarding and subscribing to the target data; Flink streaming calculations include at least one of model calculations and alarm calculations.

[0088] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the industrial data processing method provided by the above-mentioned methods, the method comprising: periodically collecting industrial data from the OPC server while establishing a communication connection with the OPC-DA server; converting the industrial data into XML data, and converting the XML data according to the target protocol to obtain target protocol data; using the Flink data engine to parse the target protocol data and perform Flink streaming calculations to obtain target data, and forwarding and subscribing to the target data; the Flink streaming calculations include at least one of model calculations and alarm calculations.

[0089] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0090] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An industrial data processing system, characterized in that: include: A collection module, wherein the collection module is used to periodically collect industrial data from the OPC server when a communication connection is established with the OPC-DA server; A conversion module, the conversion module is used to convert the industrial data into XML data, and convert the XML data according to a target protocol to obtain target protocol data; wherein the target protocol includes at least one of the MQTT protocol, the Kafka protocol and the HTTP protocol; An industrial Internet of Things platform, wherein the industrial Internet of Things platform adopts a front-end and back-end separation architecture; the industrial Internet of Things platform is used to use a Flink data engine to parse the target protocol data and perform Flink streaming calculations to obtain target data, and forward and subscribe to the target data; the Flink streaming calculations include at least one of model calculations and alarm calculations.

2. The industrial data processing system according to claim 1, characterized in that: The conversion module comprises: OPC2WEB unit, the OPC2WEB unit is used to convert the industrial data into OPC-DA-XML data; A Node-Red unit, wherein the Node-Red unit includes configuration information determined based on a first user instruction; the Node-Red unit is used to perform normalization processing such as word segmentation, removal of abnormal characters and renaming on the XML data according to a second user instruction, and send the target protocol data to the industrial Internet of Things platform according to the configuration information; wherein the configuration information includes at least one of a target OPC server address, a port and a Client ID.

3. The industrial data processing system according to claim 1, characterized in that: The industrial IoT platform includes: An access module, the access module being used to receive the target protocol data according to the target protocol through multiple OpenAPIs; Flink data engine, the Flink data engine supports point mode data upload and device mode data upload, and the Flink data supports data conversion formats of JSON and binary data; the Flink data engine is used to parse and convert the time series data in the target protocol data, perform model calculation and alarm calculation, and obtain the target data; A forwarding module is used to forward and subscribe to the target data according to the standard storage service and the forwarding storage service.

4. The industrial data processing system according to claim 3, characterized in that: The industrial IoT platform also includes: The encapsulation module is used to encapsulate the target data according to an algorithm based on Docker encapsulation to obtain encapsulated data.

5. The industrial data processing system according to claim 3, characterized in that: The industrial IoT platform also includes: A storage module, wherein the storage module is used to store the time series data in the target data using a time series database based on IoTDB; A visualization module is used to visualize the target protocol data and the intermediate process corresponding to the target data.

6. The industrial data processing system according to any one of claims 1 to 5, characterized in that: The industrial IoT platform supports deployment in any configuration environment of physical machines, virtual machines, K8s and Docker containers.

7. An industrial data processing method corresponding to the industrial data processing system according to claim 1, characterized in that: include: In case of establishing a communication connection with an OPC-DA server, periodically collecting industrial data from the OPC server; Convert the industrial data into XML data, and convert the XML data according to a target protocol to obtain target protocol data; wherein the target protocol includes at least one of the MQTT protocol, the Kafka protocol, and the HTTP protocol; A Flink data engine is used to parse the target protocol data and perform Flink stream computing to obtain target data, and the target data is forwarded and subscribed to; the Flink stream computing includes at least one of model computing and alarm computing.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the industrial data processing method as claimed in claim 7 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the industrial data processing method as claimed in claim 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the industrial data processing method as claimed in claim 7 is implemented.

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