Industrial time series data stream processing method, device, equipment and medium

Abstract

The invention discloses an industrial time series data stream processing method, device and equipment and a medium, and the industrial time series data stream processing method provided by the invention comprises the steps: obtaining an original data stream uploaded by industrial equipment, analyzing an object model identifier matched with the industrial equipment from the original data stream, and storing the object model identifier in a database; the object model identification is the unique identity identification of the object model, the original data flow is guided to a processing node corresponding to the object model, and the processing node recompiles the original data flow into a data format file matched with the object model. And the processing node processes the original data stream and recompiles the original data stream into the data format file matched with the physical model, so that the processing rule does not need to be configured manually, the maintenance cost is reduced, the processing efficiency is improved, and large scale is facilitated.

Description

Technical Field

[0001] This invention relates to data stream processing technology, and more particularly to an industrial time-series data stream processing method, apparatus, equipment, and medium. Background Technology

[0002] Sensor data in industrial scenarios (such as temperature, voltage, current, etc.) have the following characteristics: high-frequency sampling (millisecond to second level), multi-source heterogeneity (inconsistent device types, protocols, and formats), high noise and outliers (device jitter, transmission packet loss, electromagnetic interference), and strong timeliness requirements.

[0003] Traditional data processing methods are mostly offline batch processing methods. This method relies on manual configuration of processing rules, which has high maintenance costs, is difficult to scale, and has high latency, making it unable to meet the needs of real-time monitoring. Summary of the Invention

[0004] This invention provides an industrial time-series data streaming processing method, apparatus, equipment, and medium to reduce maintenance costs, improve processing efficiency, and facilitate large-scale application.

[0005] In a first aspect, the present invention provides a method for industrial time-series data streaming processing, comprising:

[0006] Obtain the raw data stream uploaded by industrial equipment;

[0007] Parse the object model identifier that matches the industrial equipment from the original data stream; the object model identifier is a unique identifier for the object model.

[0008] The original data stream is directed to the processing node corresponding to the object model;

[0009] The processing node rewrites the original data stream into a data format file that matches the object model.

[0010] Optionally, parsing the object model identifier matching the industrial equipment from the raw data stream includes:

[0011] A streaming editor is used to parse the object model identifier that matches the industrial equipment from the raw data stream.

[0012] Optionally, the processing node is a Node-Red node.

[0013] Optionally, the processing node rewrites the original data stream into a data format file that matches the object model, including:

[0014] The processing node parses the original data stream and searches for matching fields in the original data stream that match the object model;

[0015] Write the data value corresponding to the matching field into the matching field to form structured data;

[0016] The structured data is compiled into a data format file.

[0017] Optionally, industrial time-series data streaming methods also include:

[0018] If there are mismatched fields in the original data stream that do not match the object model, a modification prompt is sent to the maintenance personnel of the industrial equipment, and the maintenance personnel modify the mismatched fields in the data stream to be collected by the industrial equipment to the corresponding fields in the object model.

[0019] Optionally, industrial time-series data streaming methods also include:

[0020] If there is a mismatch field in the original data stream that does not match the object model, record the mismatch field and its corresponding data value;

[0021] The mismatched fields are modified to the corresponding fields in the object model to obtain the corrected structured data;

[0022] Insert the corrected structured data into the data format file.

[0023] Optionally, industrial time-series data streaming methods also include:

[0024] When a new industrial device is connected, its metadata is obtained.

[0025] Based on the metadata, a matching object model and its corresponding object model identifier are determined from the object model library.

[0026] In a second aspect, the present invention also provides an industrial time-series data streaming processing apparatus, comprising:

[0027] The data stream acquisition module is used to acquire the raw data stream uploaded by industrial equipment;

[0028] The object model identifier parsing module is used to parse the object model identifier that matches the industrial equipment from the original data stream. The object model identifier is a unique identifier for the object model.

[0029] A data flow guidance module is used to guide the original data flow to the processing node corresponding to the object model;

[0030] The data writing module is used by the processing node to rewrite the original data stream into a data format file that matches the object model.

[0031] Optionally, the object model identifier resolution module includes:

[0032] The identifier parsing submodule is used to parse the object model identifier that matches the industrial equipment from the raw data stream using a streaming editor.

[0033] Optionally, the processing node is a NodeRed node.

[0034] Optionally, the data writing module includes:

[0035] The field matching submodule is used by the processing node to parse the original data stream and find matching fields that match the object model from the original data stream;

[0036] The structured data generation submodule is used to write the data value corresponding to the matching field into the matching field to form structured data;

[0037] The file writing submodule is used to write the structured data into a data format file.

[0038] Optionally, the industrial time-series data streaming device also includes:

[0039] The prompting module is used to send a modification prompt to the maintenance personnel of the industrial equipment when there are mismatched fields in the original data stream that do not match the object model. The maintenance personnel then modify the mismatched fields in the data stream to be collected from the industrial equipment to the corresponding fields in the object model.

[0040] Optionally, the industrial time-series data streaming device also includes:

[0041] The recording module is used to record the mismatch field and its corresponding data value when there is a mismatch field in the original data stream that does not match the object model;

[0042] The field correction module is used to modify the mismatched fields to the corresponding fields in the object model, so as to obtain the corrected structured data;

[0043] The data insertion module is used to insert the corrected structured data into the data format file.

[0044] Optionally, the industrial time-series data streaming device also includes:

[0045] The metadata acquisition module is used to acquire the metadata of newly connected industrial equipment when it is connected.

[0046] The identifier matching module is used to determine the matching object model and the object model identifier corresponding to the object model from the object model library based on the metadata.

[0047] Thirdly, the present invention also provides an electronic device, comprising:

[0048] One or more processors;

[0049] Storage device for storing one or more programs;

[0050] When the one or more programs are executed by the one or more processors, the one or more processors implement the industrial time-series data streaming processing method as described in the first aspect of the present invention.

[0051] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the industrial time-series data streaming processing method as described in the first aspect of the present invention.

[0052] The industrial time-series data streaming processing method provided by this invention acquires the raw data stream uploaded by industrial equipment, parses out the object model identifier matching the industrial equipment from the raw data stream (the object model identifier is the unique identifier of the object model), and directs the raw data stream to the processing node corresponding to the object model. The processing node rewrites the raw data stream into a data format file matching the object model. This invention is based on streaming data processing, directing the raw data stream to the processing node of the object model corresponding to the industrial equipment. The processing node processes the raw data stream and rewrites it into a data format file matching the object model. This eliminates the need for manual configuration of processing rules, reduces maintenance costs, improves processing efficiency, and facilitates scalability.

[0053] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart of an industrial time-series data streaming processing method provided by the present invention;

[0056] Figure 2 A schematic diagram of the structure of an industrial time-series data streaming processing device provided by the present invention;

[0057] Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention.

[0058] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0060] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0061] Figure 1 This is a flowchart of an industrial time-series data streaming processing method provided by the present invention. This embodiment is applicable to the real-time processing of time-series streaming data from industrial equipment in industrial scenarios. The method can be executed by the industrial time-series data streaming processing device provided by the present invention. This device can be implemented in software and / or hardware, and is typically configured in electronic devices, such as… Figure 1 As shown, this industrial time-series data streaming processing method includes the following steps:

[0062] S101. Obtain the raw data stream uploaded by the industrial equipment.

[0063] In this embodiment of the invention, the raw data stream uploaded in real time by the industrial equipment is acquired. For example, the industrial equipment may include sensors used in industrial settings, such as temperature sensors, current collectors, voltage collectors, etc., and this embodiment of the invention does not limit the scope of the invention.

[0064] For example, the maintenance personnel of industrial equipment pre-write the fields of the data to be collected. When collecting data, they collect the data corresponding to the pre-written fields in real time to form the raw data stream.

[0065] S102. Parse the object model identifier that matches the industrial equipment from the raw data stream. The object model identifier is the unique identifier of the object model.

[0066] In this embodiment of the invention, for industrial equipment already connected to the system, a corresponding object model is pre-matched to the industrial equipment, and an object model identifier is determined. The object model identifier serves as the unique identity of the object model. All raw data streams uploaded by the industrial equipment carry the object model identifier. The object model is a digital description of the equipment, defining the product's functions. The object model abstracts and summarizes the functions of products from different brands and categories, forming a "standard object model," facilitating the description, control, and understanding of product functions by all parties using a unified language. The object model consists of several "parameters," which typically exist in the form of fields. Parameters are further categorized into attributes, methods, and events based on the type of function they describe. Attributes generally describe the equipment's state and support reading and setting. Methods refer to the capabilities or methods that the equipment can be invoked externally, allowing the setting of input and output parameters; each parameter must be an attribute. Events describe events reported by the device to the cloud and can contain multiple input parameters; each parameter must be an attribute.

[0067] For example, when a newly connected industrial device is detected, the system automatically captures the metadata of the newly connected industrial device. Metadata refers to various information related to the device, including the device's basic attributes, status data, and tags, etc., and is widely used in the Internet of Things and device management. For example, metadata may include the industrial device's device ID, device type (e.g., sensor, actuator), real-time status data, attribute data (e.g., geographical location, manufacturer, etc.), etc., which are not limited here. Then, based on the metadata and pre-built mapping rules, the matching object model is determined from the object model library, and the object model identifier corresponding to the object model is determined.

[0068] After obtaining the raw data stream uploaded by the industrial equipment, the object model identifier matching the industrial equipment is parsed from the raw data stream.

[0069] In this embodiment of the invention, the industrial time-series data streaming processing method is based on Node-RED, an open-source visual programming tool designed to simplify the development and management of Internet of Things (IoT) systems and event-driven applications. Through an intuitive drag-and-drop interface, it allows developers to quickly build complex data flows and logic controls. The core of Node-RED is a node-based visual programming model. Each node represents a functional module, divided into input nodes, processing nodes, and output nodes. By connecting these nodes, users can design complete workflows. Input nodes receive data; for example, they receive raw data streams uploaded by industrial equipment. Processing nodes transform, filter, or perform logical processing on the data, such as using custom logic written in JavaScript. Output nodes send the processed data to a target, such as a database, file, or user interface.

[0070] For example, Node-RED's streaming editor is used to parse object model identifiers matching industrial equipment from raw data streams. A streaming editor is a non-interactive text processing tool specifically designed for filtering, replacing, inserting, and deleting text. It processes input data line by line in a stream, making it suitable for handling large-scale text files or automated script tasks.

[0071] S103. Direct the original data stream to the processing node corresponding to the object model.

[0072] In this embodiment of the invention, each object model corresponds to a processing node, which is used to process the data of the corresponding object model. For example, after parsing the object model identifier matching the industrial equipment from the original data stream, the processing node corresponding to the object model is determined according to the object model identifier, and then the original data stream is directed to the processing node corresponding to the object model.

[0073] S104. The processing node rewrites the original data stream into a data format file that matches the object model.

[0074] In this embodiment of the invention, the Node-RED processing node processes the raw data, filters out data that is irrelevant to the object model, and rewrites the raw data stream into a data format file that matches the object model.

[0075] In some embodiments of the present invention, the processing node parses the original data stream and searches for matching fields that match the object model. Exemplarily, this matching process can employ keyword matching or semantic matching methods; the present invention does not limit this. The data value corresponding to the matching field is written into the matching field to form structured data, and then the structured data is compiled into a data format file, such as a JSON file.

[0076] In some embodiments of the present invention, when the maintenance personnel pre-write the fields to be collected, there may be errors in the writing, resulting in a mismatch between the fields in the original data stream and the fields in the object model. When there are mismatched fields in the original data stream that do not match the object model, for the existing mismatched data, the mismatched fields and their corresponding data values ​​can be recorded, the mismatched fields can be modified to the corresponding fields in the object model, and the corrected structured data can be obtained. Then, the corrected structured data can be inserted into the data format file to ensure that the data is not lost.

[0077] In some embodiments of the present invention, when there are mismatched fields in the original data stream that do not match the object model, a modification prompt is sent to the maintenance personnel of the industrial equipment. The maintenance personnel modify the mismatched fields in the data stream to be collected by the industrial equipment to the corresponding fields in the object model. This can dynamically respond to the baseline drift problem caused by changes in operating conditions and improve adaptability.

[0078] The industrial time-series data streaming processing method provided by this invention acquires the raw data stream uploaded by industrial equipment, parses out the object model identifier matching the industrial equipment from the raw data stream (the object model identifier is the unique identifier of the object model), and directs the raw data stream to the processing node corresponding to the object model. The processing node rewrites the raw data stream into a data format file matching the object model. This invention is based on streaming data processing, directing the raw data stream to the processing node of the object model corresponding to the industrial equipment. The processing node processes the raw data stream and rewrites it into a data format file matching the object model. This eliminates the need for manual configuration of processing rules, reduces maintenance costs, improves processing efficiency, and facilitates scalability.

[0079] Figure 2 This is a schematic diagram of the structure of an industrial time-series data streaming processing device provided by the present invention, as shown below. Figure 2 As shown, the industrial time-series data streaming processing device includes:

[0080] The data stream acquisition module 201 is used to acquire the raw data stream uploaded by the industrial equipment;

[0081] The object model identifier parsing module 202 is used to parse the object model identifier that matches the industrial equipment from the original data stream, wherein the object model identifier is a unique identifier of the object model;

[0082] Data flow guidance module 203 is used to guide the original data flow to the processing node corresponding to the object model;

[0083] The data writing module 204 is used by the processing node to rewrite the original data stream into a data format file that matches the object model.

[0084] Optionally, the object model identifier resolution module 202 includes:

[0085] The identifier parsing submodule is used to parse the object model identifier that matches the industrial equipment from the raw data stream using a streaming editor.

[0086] Optionally, the processing node is a NodeRed node.

[0087] Optionally, the data writing module 204 includes:

[0088] The field matching submodule is used by the processing node to parse the original data stream and find matching fields that match the object model from the original data stream;

[0089] The structured data generation submodule is used to write the data value corresponding to the matching field into the matching field to form structured data;

[0090] The file writing submodule is used to write the structured data into a data format file.

[0091] Optionally, the industrial time-series data streaming device also includes:

[0092] The prompting module is used to send a modification prompt to the maintenance personnel of the industrial equipment when there are mismatched fields in the original data stream that do not match the object model. The maintenance personnel then modify the mismatched fields in the data stream to be collected from the industrial equipment to the corresponding fields in the object model.

[0093] Optionally, the industrial time-series data streaming device also includes:

[0094] The recording module is used to record the mismatch field and its corresponding data value when there is a mismatch field in the original data stream that does not match the object model;

[0095] The field correction module is used to modify the mismatched fields to the corresponding fields in the object model, so as to obtain the corrected structured data;

[0096] The data insertion module is used to insert the corrected structured data into the data format file.

[0097] Optionally, the industrial time-series data streaming device also includes:

[0098] The metadata acquisition module is used to acquire the metadata of newly connected industrial equipment when it is connected.

[0099] The identifier matching module is used to determine the matching object model and the object model identifier corresponding to the object model from the object model library based on the metadata.

[0100] The aforementioned industrial time-series data streaming processing device can execute the industrial time-series data streaming processing method provided in the foregoing embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the industrial time-series data streaming processing method.

[0101] Figure 3 This is a schematic diagram of an electronic device provided by the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0102] like Figure 3 As shown, the electronic device includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0103] Multiple components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0104] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as industrial time-series data streaming methods.

[0105] In some embodiments, the industrial timing data streaming method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the industrial timing data streaming method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the industrial timing data streaming method by any other suitable means (e.g., by means of firmware).

[0106] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0108] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0110] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0111] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0112] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the industrial time-series data streaming processing method provided in any embodiment of this application.

[0113] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0115] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for industrial time-series data streaming processing, characterized in that, include: Obtain the raw data stream uploaded by industrial equipment; Parse the object model identifier that matches the industrial equipment from the original data stream; the object model identifier is a unique identifier for the object model. The original data stream is directed to the processing node corresponding to the object model; The processing node rewrites the original data stream into a data format file that matches the object model.

2. The industrial time-series data streaming processing method according to claim 1, characterized in that, Parsing the object model identifier matching the industrial equipment from the raw data stream includes: A streaming editor is used to parse the object model identifier that matches the industrial equipment from the raw data stream.

3. The industrial time-series data streaming processing method according to claim 1, characterized in that, The processing node is a Node-Red node.

4. The industrial time-series data streaming processing method according to claim 3, characterized in that, The processing node rewrites the original data stream into a data format file that matches the object model, including: The processing node parses the original data stream and searches for matching fields in the original data stream that match the object model; Write the data value corresponding to the matching field into the matching field to form structured data; The structured data is compiled into a data format file.

5. The industrial time-series data streaming processing method according to claim 4, characterized in that, Also includes: If there are mismatched fields in the original data stream that do not match the object model, a modification prompt is sent to the maintenance personnel of the industrial equipment, and the maintenance personnel modify the mismatched fields in the data stream to be collected by the industrial equipment to the corresponding fields in the object model.

6. The industrial time-series data streaming processing method according to claim 4, characterized in that, Also includes: If there is a mismatch field in the original data stream that does not match the object model, record the mismatch field and its corresponding data value; The mismatched fields are modified to the corresponding fields in the object model to obtain the corrected structured data; Insert the corrected structured data into the data format file.

7. The industrial time-series data streaming processing method according to claim 1, characterized in that, Also includes: When a new industrial device is connected, its metadata is obtained. Based on the metadata, a matching object model and its corresponding object model identifier are determined from the object model library.

8. An industrial time-series data streaming processing device, characterized in that, include: The data stream acquisition module is used to acquire the raw data stream uploaded by industrial equipment; The object model identifier parsing module is used to parse the object model identifier that matches the industrial equipment from the original data stream. The object model identifier is a unique identifier for the object model. A data flow guidance module is used to guide the original data flow to the processing node corresponding to the object model; The data writing module is used by the processing node to rewrite the original data stream into a data format file that matches the object model.

9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the industrial time-series data streaming processing method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the industrial time-series data streaming processing method as described in any one of claims 1-7.

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