Industrial Internet of Things data access and processing method, system and device and storage medium

Abstract

The invention relates to the technical field of computers, and provides an industrial Internet of Things data access and processing method, system and device and a storage medium. The industrial Internet of Things data access and processing method comprises the following steps: receiving heterogeneous original data from different industrial Internet of Things protocols in parallel through a plurality of protocol adapter micro-services; performing batch data verification, preprocessing and storage on the received heterogeneous original data, and sending data information to an equipment state prediction message queue; obtaining a task from the device state prediction message queue, creating an algorithm execution task according to a task type and a data validity condition, and pushing the algorithm execution task to an algorithm execution message queue; and obtaining the task from the algorithm execution message queue, dynamically loading and analyzing the corresponding algorithm file and the configuration parameter, and processing the algorithm task. According to the industrial Internet of Things data access and processing scheme disclosed by the invention, the access and processing efficiency of industrial Internet data can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to an industrial Internet of Things (IoT) data access and processing method, system, electronic device, and computer-readable storage medium. Background Technology

[0002] Data access frameworks based on multiple industrial IoT protocols need to access data from various industrial IoT protocols. This industrial IoT data comes from devices of different eras and manufacturers, and uses different protocols. Traditional solutions require the development of a separate acquisition program for each protocol, resulting in a large, complex, and difficult-to-manage and maintain system.

[0003] Industrial IoT data is constantly being generated, with the number of data points potentially reaching tens or even hundreds of thousands per second. Traditional software architectures and databases cannot handle such a massive number of connections and data write requests simultaneously, leading to data congestion, loss, extremely high latency, and system crashes.

[0004] Furthermore, industrial IoT data, such as process data, vibration waveform data, and vibration characteristic data, are large in volume and complex in format. Different data types require different processing methods and storage media. All of this can lead to inefficient data processing during industrial IoT data access, and may even result in congestion. Summary of the Invention

[0005] In view of this, the present disclosure provides an industrial Internet of Things (IoT) data access and processing method, system, electronic device, and computer-readable storage medium to solve the problem of low efficiency in industrial IoT data access and processing in the prior art.

[0006] A first aspect of this disclosure provides a central processing unit (CPU) method for industrial IoT data access and processing. The method includes: receiving heterogeneous raw data from different industrial IoT protocols in parallel via multiple protocol adapter microservices; performing batch data verification, preprocessing, and storage on the received heterogeneous raw data, and sending the data information to a device status prediction message queue. Data verification includes data existence verification, data compliance verification, and data correctness verification. The data information includes the heterogeneous raw data after data verification and preprocessing. Tasks are retrieved from the device status prediction message queue, and algorithm execution tasks are created based on task type and data validity conditions. The algorithm execution tasks are then pushed to an algorithm execution message queue. Finally, tasks are retrieved from the algorithm execution message queue, and the corresponding algorithm files and configuration parameters are dynamically loaded and parsed. Algorithm task processing is then performed.

[0007] A second aspect of this disclosure provides an industrial Internet of Things (IIoT) data access and processing system, comprising: a data receiving module, configured to receive heterogeneous raw data from different IIoT protocols in parallel through multiple protocol adapter microservices; a data processing module, configured to perform batch data verification, preprocessing, and storage on the received heterogeneous raw data, and send the data information to a device status prediction message queue, wherein data verification includes data existence verification, data compliance verification, and data correctness verification, and the data information includes heterogeneous raw data after data verification and preprocessing; a status prediction module, configured to obtain tasks from the device status prediction message queue, create algorithm execution tasks according to task type and data validity conditions, and push the algorithm execution tasks to an algorithm execution message queue; and a task processing module, configured to obtain tasks from the algorithm execution message queue, dynamically load and parse the corresponding algorithm files and configuration parameters, and perform algorithm task processing.

[0008] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0009] A fourth aspect of this disclosure provides a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0010] The beneficial effects of this disclosed embodiment compared with the prior art are: by innovatively integrating batch processing technology, microservice architecture and industrial IoT data processing requirements across fields and at a deep level, a new, efficient and flexible solution is formed, resulting in a series of significant technical effects such as increased throughput, reduced latency and enhanced flexibility. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure; Figure 2 This is a flowchart illustrating an industrial Internet of Things (IoT) data access and processing method provided in an embodiment of this disclosure. Figure 3 This is a schematic diagram of the structure of an industrial Internet of Things (IoT) data access and processing system provided in an embodiment of this disclosure; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will understand that this disclosure may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.

[0014] The industrial Internet of Things (IoT) data access and processing method and system according to embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0015] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure. The application scenario may include terminal devices 101, 102, and 103, server 104, and network 105.

[0016] Terminal devices 101, 102, and 103 can be hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays that support communication with server 104, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. Terminal devices 101, 102, and 103 can be implemented as multiple software programs or software modules, or as a single software program or software module; this disclosure does not impose any limitations on this. Furthermore, various applications can be installed on terminal devices 101, 102, and 103, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0017] Server 104 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices with which it has established communication connections. This backend server can receive and analyze the requests sent by the terminal devices and generate processing results. Server 104 can be a single server, a server cluster consisting of several servers, or a cloud computing service center. This embodiment of the disclosure does not impose any limitations on these aspects.

[0018] It should be noted that server 104 can be either hardware or software. When server 104 is hardware, it can be various electronic devices that provide various services to terminal devices 101, 102, and 103. When server 104 is software, it can be multiple software programs or software modules that provide various services to terminal devices 101, 102, and 103, or it can be a single software program or software module that provides various services to terminal devices 101, 102, and 103. This disclosure does not limit the scope of the embodiments.

[0019] Network 105 can be a wired network using coaxial cable, twisted pair, and fiber optic connection, or it can be a wireless network that enables interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), Infrared, etc. This disclosure does not limit the scope of the network.

[0020] Users can establish a communication connection with server 104 via network 105 through terminal devices 101, 102 and 103 to receive or send information, etc.

[0021] It should be noted that the specific types, quantities, and combinations of terminal devices 101, 102, and 103, server 104, and network 105 can be adjusted according to the actual needs of the application scenario, and this disclosure embodiment does not impose any restrictions on this.

[0022] Figure 2 This is a schematic flowchart illustrating an industrial Internet of Things (IoT) data access and processing method provided in this disclosure. The method provided in this disclosure can be executed by any electronic device with computer processing capabilities, such as a terminal or a server.

[0023] like Figure 2 As shown, the industrial Internet of Things (IoT) data access and processing method of this disclosure includes: Step S201 involves receiving heterogeneous raw data from different Industrial IoT protocols in parallel through multiple protocol adapter microservices. This heterogeneous raw data can be structured process data, feature data, and unstructured vibration waveform data, but is not limited to these. The protocol adapter microservices support dynamic loading, enabling the addition of support for new Industrial IoT protocols without restarting the system.

[0024] Step S202 involves batch data verification, preprocessing, and storage of the received heterogeneous raw data, and sending the data information to the device status prediction message queue. Data verification includes data existence verification, data compliance verification, and data correctness verification. The data information includes the heterogeneous raw data after data verification and preprocessing. Among them, data correctness verification is used to check for outliers or data jumps that do not conform to physical logic in the data.

[0025] Step S203: Obtain a task from the device status prediction message queue, create an algorithm execution task based on the task type and data validity conditions, and push the algorithm execution task to the algorithm execution message queue.

[0026] Step S204: Obtain tasks from the algorithm execution message queue, dynamically load and parse the corresponding algorithm files and configuration parameters, and process the algorithm tasks.

[0027] Specifically, when dynamically loading and parsing the corresponding algorithm file and configuration parameters, the system dynamically loads matching data parsing templates and data processing flow rules from the configuration center based on the data's source protocol or the device identifier contained within the data. Specifically, the data parsing template may include data format parsing rules, processing parameters, and business algorithm configurations.

[0028] In step S204, if the task in the algorithm execution message queue contains a feature extraction instruction, the dataset to be extracted is obtained; the algorithm service is called to extract features from the dataset.

[0029] In some embodiments, after calling the algorithm service to extract features from the dataset, if inference needs to be performed, the inference task is written into the inference waiting queue after all online measurement point data of the same unit at the same collection time point have arrived; the task is retrieved from the inference waiting queue, the AI ​​algorithm model file bound to the unit is found and batch inference is performed, wherein the AI ​​algorithm model file is a model that has been pre-trained and stored.

[0030] In some embodiments, after processing the algorithm task, it is also necessary to store the algorithm execution results and logs, automatically generate a diagnostic report, and send the device status update information to the status update service so that the status update service can update the device's operating status and historical trends based on the received information.

[0031] The technical solution of this disclosure embodiment can be executed collaboratively by multiple loosely coupled microservices. The microservices include: a data receiving and storage microservice, used for batch receiving, verifying, preprocessing and storing data; a device status prediction microservice, used for creating algorithm execution tasks according to task type and data validity conditions; an algorithm task execution microservice, used for dynamically loading and parsing the corresponding algorithm file and configuration parameters to perform algorithm task processing; and a device status update microservice, used for updating the device's operating status and historical trends according to the received information.

[0032] The technical solution of this disclosure can efficiently receive various IoT data in batches, including but not limited to process data, vibration waveform data, vibration characteristic data and other common industrial IoT data. It can reduce the latency of IoT data, process industrial data in real time, and facilitate cleaning and processing between various services.

[0033] This disclosure provides an open platform architecture based on data orientation, enabling multi-protocol data reception, custom data format matching, custom data cleaning and filtering, and dynamic loading. It utilizes a microservice architecture for rapid deployment, achieving efficient processing and concurrent fast storage of structured and unstructured sensor data.

[0034] This program receives raw data from third parties and hardware sensors in batches, performs batch feature extraction, batch inference, batch storage, and batch business processing, and ultimately efficiently provides AI-automated diagnostic suggestions in batches. Specifically, it can generate automated diagnostic suggestions based on the results of AI model inference and provide them to upper-layer business applications.

[0035] During the data access and analysis process, data existence, compliance, and correctness verification are performed. For verified data, dynamic data type matching and parameter loading are conducted, and then the algorithm selection is completed through business configuration.

[0036] The technical solution of this disclosure provides a batch processing framework based on the Spring framework to handle various types of batch processing tasks, such as importing / exporting process data and IoT vibration data, and generating reports. By defining components such as Job, Step, ItemReader, ItemProcessor, and ItemWriter in the Spring framework, reusable batch processing applications can be built, easily achieving batch processing concurrency, fault tolerance, and retry functionality. It can also be easily integrated with other Spring components such as Spring Boot and Spring Data to form a flexible and easy-to-use batch processing framework for fast, efficient, and reliable batch processing of industrial IoT data.

[0037] The ItemProcessor component performs AI model inference in batches, meaning it performs model inference on multiple data entries within a batch simultaneously to improve processing efficiency. The ItemWriter component supports concurrent writes to heterogeneous data sources, including time-series databases and relational databases, to achieve rapid persistence of processing results.

[0038] Based on the batch processing framework, dynamically configured data can be organized into batch processing tasks. For example, data can be read in batches using the defined ItemReader component, feature extraction and AI model inference processing can be performed on the batch data using the defined ItemProcessor component, and the processing results can be stored concurrently in batches to the target database using the defined ItemWriter component.

[0039] The technical solution of this disclosure constructs a unified and open data entry point, which can solve the access challenges of multi-protocol, high-concurrency, and heterogeneous data in industrial scenarios. By integrating batch processing and real-time processing capabilities, it can achieve full-process automation from raw data access to AI intelligent diagnosis, aiming to reduce data latency, improve data processing efficiency, and provide clean, compliant, and timely data services for upper-layer applications.

[0040] The technical solution of this disclosure provides a data-oriented open architecture, designed around data flow, offering standardized access and processing interfaces, allowing flexible integration of third-party systems and algorithms, and avoiding vendor lock-in. This open architecture decouples the system into a series of loosely coupled services with single responsibilities, enabling rapid deployment, independent scaling, and high availability. The technical solution of this disclosure employs batch processing optimization, deeply integrating and extending the Spring Batch batch processing framework to meet the needs of efficient batch processing of massive amounts of data in industrial scenarios, natively supporting concurrency, fault tolerance, and retry mechanisms.

[0041] The open architecture in this disclosure can seamlessly integrate AI models such as machine learning and deep learning models, perform batch inference on the processed data, and finally automatically generate diagnostic suggestions or analysis reports, completing the closed loop from "data" to "insight" and realizing the provision of AI-automated diagnostic suggestions.

[0042] In one embodiment, sensor data is accessed through protocols such as MQTT for existence, compliance, and correctness verification. Failed data is placed in a dead-letter queue. Verified data is dynamically loaded with corresponding processing parameters and algorithm configurations based on its type.

[0043] In the AI ​​data batch processing workflow, the data sender can be a third-party data sender or various platforms. The AI ​​data batch processing workflow involves the following modules: Data reception and storage module includes vibration waveform reception, vibration data filtering, vibration waveform feature extraction, vibration waveform graph plotting, vibration feature reception, vibration feature data storage, process data reception, and process data storage. Equipment status prediction module includes start / stop status prediction, operating condition prediction, and diagnostic report plotting. Equipment status update module includes operating status updates and historical trend updates. Equipment model learning module includes equipment model learning and updating. Unit management module includes unit management, equipment management, configuration management, and unit algorithm configuration. Algorithm management module includes version management, call verification configuration, and call parameter configuration. IoT hardware management module includes sensor management, data acquisition gateway management, IoT gateway management, and AI machine management. Model management module includes equipment model management and unit model management. Product management module includes configuration management, model management, and dictionary management. Scheduled task module includes offline monitoring, automatic learning detection, offline monitoring, and email notification.

[0044] In the data receiving batch processing flow, after acquiring waveform data, it can be determined whether the current point exists and whether the timestamp of the data type corresponding to the current measurement point already exists in the database. When using algorithms for data filtering, algorithm feature extraction and algorithm graph drawing can be performed. Then, it is determined whether the current value exceeds the customer-defined threshold; if not, the filtered graph and the original data are compressed and stored. Afterwards, data information is sent to the device status prediction queue. The same process is followed when receiving edge feature data and hardware status information.

[0045] In the batch execution process of state prediction, tasks to be executed are retrieved from the equipment state prediction queue. The type of task to be executed is determined, and the bound vibration type point information, unit equipment information, and algorithm information are obtained through data IDs. Next, it is determined whether the current task needs to be manually re-executed. If not, it is determined whether the sensor axis corresponding to the current data point is a high-frequency acceleration waveform along the z-axis. If so, all high-frequency acceleration data IDs of the main shafts within the offset time range corresponding to the current axial acquisition time are obtained, and it is determined whether the number of obtained data IDs exceeds a specified ratio to the total number of main shafts. If satisfied, an algorithm execution task is created, the algorithm call prerequisites are obtained through the configuration file provided by the algorithm, and data is pushed to the algorithm execution service queue.

[0046] In the batch execution process of algorithm tasks, execution tasks are retrieved from the algorithm task queue. Algorithm information and equipment information are obtained through the task ID, and the existence of the equipment and algorithm is checked. If they exist, the algorithm compressed file is retrieved and parsed to obtain the parameters required for the corresponding algorithm call. Then, it is checked whether all necessary parameters for algorithm execution are available. If so, the algorithm task is processed and the execution call is recorded in the log. Afterwards, the algorithm results are stored, the algorithm log is stored, and the algorithm task status is updated. Data is pushed to the status update service, and the algorithm generates and saves a file to a designated path.

[0047] In the batch execution process of feature extraction, it is determined whether the current task has been deleted. If not, the feature extraction instruction is received from the queue, the dataset to be extracted is obtained, the algorithm service is called to perform feature extraction, the feature extraction results are recorded, and the feature extraction task status is updated. Then, it is determined whether this type needs to be inferred, and all online measurement point information of the unit is obtained. After that, it is determined whether the data for this collection time point of the unit exists in the waiting queue. If it exists, it is determined whether the data for this collection time point of the unit in the waiting queue already includes all online measurement points. If it does, the data is written to the start / stop inference waiting queue, and then the data is written to the operating condition inference waiting queue.

[0048] In the batch inference execution process, inference task instructions are received from the queue of tasks, and it is determined whether the current task has been canceled. If not, it is determined whether the current unit is bound to a specific algorithm. If so, the corresponding file for the bound algorithm is located and invoked. Next, it is determined whether the current unit's training status is successful, and the model file is located based on the unit ID and its trained model ID. Then, the algorithm model file is decompressed and inference is performed. If the inference is successful, the inference result is saved.

[0049] The industrial IoT data access and processing method according to the embodiments of this disclosure innovatively integrates batch processing technology, microservice architecture and industrial IoT data processing requirements across fields and at a deep level, forming a new, efficient and flexible solution, and producing a series of significant technical effects such as improving throughput, reducing latency and enhancing flexibility.

[0050] like Figure 3 As shown, the industrial IoT data access and processing system provided in this disclosure includes: The data receiving module 301 is used to receive heterogeneous raw data from different industrial IoT protocols in parallel through multiple protocol adapter microservices.

[0051] The data processing module 302 is used to perform batch data verification, preprocessing and storage on the received heterogeneous raw data, and send the data information to the device status prediction message queue. The data verification includes data existence verification, data compliance verification and data correctness verification. The data information includes heterogeneous raw data after data verification and preprocessing.

[0052] The status prediction module 303 is used to obtain tasks from the device status prediction message queue, create algorithm execution tasks according to task type and data validity conditions, and push the algorithm execution tasks to the algorithm execution message queue.

[0053] The task processing module 304 is used to retrieve tasks from the algorithm execution message queue, dynamically load and parse the corresponding algorithm files and configuration parameters, and perform algorithm task processing.

[0054] Since the functional modules of the industrial IoT data access and processing system in the example embodiments of this disclosure correspond to the steps of the example embodiments of the industrial IoT data access and processing method described above, for details not disclosed in the device embodiments of this disclosure, please refer to the embodiments of the industrial IoT data access and processing method described above.

[0055] The industrial IoT data access and processing system according to the embodiments of this disclosure innovatively integrates batch processing technology, microservice architecture and industrial IoT data processing requirements across fields and at a deep level, forming a new, efficient and flexible solution, and producing a series of significant technical effects such as increased throughput, reduced latency and enhanced flexibility.

[0056] Figure 4 This is a schematic diagram of the electronic device 4 provided in an embodiment of this disclosure. Figure 4 As shown, the electronic device 4 of this embodiment includes a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, it implements the steps in the various method embodiments described above. Alternatively, when the processor 401 executes the computer program 403, it implements the functions of each module in the various device embodiments described above.

[0057] Electronic device 4 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 4 may include, but is not limited to, processor 401 and memory 402. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or different components.

[0058] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0059] The memory 402 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM of the electronic device 4. The memory 402 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 4. The memory 402 can also include both internal and external storage units of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device.

[0060] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0061] If the integrated module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0062] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for industrial Internet of Things (IoT) data access and processing, characterized in that, include: Through multiple protocol adapter microservices, heterogeneous raw data from different industrial IoT protocols can be received in parallel. The received heterogeneous raw data is subjected to batch data verification, preprocessing and storage, and the data information is sent to the device status prediction message queue. The data verification includes data existence verification, data compliance verification and data correctness verification. The data information includes the heterogeneous raw data after data verification and preprocessing. Tasks are retrieved from the device status prediction message queue, and algorithm execution tasks are created based on task type and data validity conditions. The algorithm execution tasks are then pushed to the algorithm execution message queue. Tasks are retrieved from the algorithm execution message queue, the corresponding algorithm files and configuration parameters are dynamically loaded and parsed, and the algorithm tasks are processed.

2. The method according to claim 1, characterized in that, The algorithm task processing includes: if the task in the algorithm execution message queue contains a feature extraction instruction, obtaining the dataset to be extracted; The algorithm service is invoked to extract features from the dataset.

3. The method according to claim 2, characterized in that, After invoking the algorithm service to extract features from the dataset, the method further includes: If inference needs to be performed, wait for all online measurement data from the same unit at the same acquisition time to arrive before writing the inference task into the inference waiting queue. Tasks are retrieved from the inference waiting queue, and the AI ​​algorithm model files bound to the crew are located and batch inference is performed. The AI ​​algorithm model files are pre-trained and stored models.

4. The method according to claim 1, characterized in that, After performing the algorithm task processing, the method further includes: The system stores the algorithm execution results and logs, automatically generates diagnostic reports, and sends device status update information to the status update service so that the status update service can update the device's operating status and historical trends based on the received information.

5. The method according to claim 1, characterized in that, The heterogeneous raw data includes at least one of the following: structured process data, feature data, and unstructured vibration waveform data.

6. The method according to claim 1, characterized in that, The method is executed collaboratively by multiple loosely coupled microservices, including a data receiving and storage microservice for batch receiving, verification, preprocessing, and storing data. The device status prediction microservice is used to create algorithms to execute tasks based on task type and data validity conditions. The algorithm task execution microservice is used to dynamically load and parse the corresponding algorithm files and configuration parameters to process the algorithm tasks. The device status update microservice is used to update the device's operating status and historical trends based on the received information.

7. The method according to claim 1, characterized in that, The dynamic loading and parsing of the corresponding algorithm file and configuration parameters includes: dynamically loading the matching data parsing template and data processing flow rules from the configuration center according to the data source protocol or the device identifier contained in the data.

8. An industrial Internet of Things (IoT) data access and processing system, characterized in that, include: The data receiving module is used to receive heterogeneous raw data from different industrial IoT protocols in parallel through multiple protocol adapter microservices; The data processing module is used to perform batch data verification, preprocessing and storage on the received heterogeneous raw data, and send the data information to the device status prediction message queue. The data verification includes data existence verification, data compliance verification and data correctness verification. The data information includes the heterogeneous raw data after data verification and preprocessing. The status prediction module is used to obtain tasks from the device status prediction message queue, create algorithm execution tasks according to task type and data validity conditions, and push the algorithm execution tasks to the algorithm execution message queue. The task processing module is used to retrieve tasks from the algorithm execution message queue, dynamically load and parse the corresponding algorithm files and configuration parameters, and perform algorithm task processing.

9. 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 computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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