LabVIEW-based multi-device remote management and control system

Abstract

The application relates to the technical field of intelligent monitoring and data management of industrial equipment, in particular to a multi-device remote management and control system based on LabVIEW; the technical scheme is as follows: a multi-device remote management and control system based on LabVIEW, which comprises a data acquisition and communication module, an intelligent diagnosis and early warning module, a remote control and parameter optimization module, a data management and analysis module and a man-machine interaction and visualization module; the intelligent diagnosis and early warning module is integrated in the system; after receiving an equipment alarm code, the module can automatically convert the alarm code into an intuitive text description, and simultaneously combines preset diagnosis rules and a machine learning model trained based on historical data to perform multidimensional analysis on real-time collected process parameters; the system can not only identify simple threshold overrun faults, but also perform early warning and reason speculation on potential faults or performance degradation by analyzing the associated changes and trend characteristics among parameters.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring and data management technology for industrial equipment, and in particular to a multi-device remote control system based on LabVIEW. Background Technology

[0002] The factory involves a wide variety of equipment, and issues such as lost parameters, lack of alarm text, lack of information records, and inaccurate on-site descriptions of equipment malfunctions lead to numerous pain points in equipment maintenance and management. Current technologies heavily rely on human experience and subjective judgment in equipment fault diagnosis, limiting their effectiveness. Alarm information generated during equipment operation is often presented in code form, lacking intuitive text descriptions. This makes it difficult for technicians to quickly understand the alarm meaning, forcing them to rely on their own experience or consult scattered documents for interpretation. Furthermore, the system only provides historical parameter curves for manual review and lacks the ability to automatically analyze abnormal data patterns and make causal inferences. This results in a time-consuming and inaccurate fault location process, prone to misjudgments due to differences in personnel experience, thus prolonging equipment fault recovery cycles and impacting overall production efficiency.

[0003] To address the aforementioned issues, this solution integrates an intelligent diagnosis and early warning module into the system. Upon receiving equipment alarm codes, this module automatically converts them into intuitive text descriptions. Simultaneously, it combines preset diagnostic rules with a machine learning model trained on historical data to perform multi-dimensional analysis of real-time collected process parameters. The system can not only identify simple threshold exceedance faults but also provide early warnings and cause predictions for potential faults or performance degradation by analyzing the correlation changes and trend characteristics between parameters. This eliminates the need for technical personnel's personal experience in the fault diagnosis process, allowing the system to automatically complete preliminary analysis and judgment and push handling suggestions to relevant personnel. Consequently, it significantly shortens the time interval from fault occurrence to cause identification, improving the accuracy and efficiency of maintenance response. Summary of the Invention

[0004] To overcome the limitations of existing technologies that heavily rely on human experience and subjective judgment of on-site personnel in equipment fault diagnosis, alarm information generated during equipment operation is mostly presented in code form, lacking intuitive text descriptions. This makes it difficult for technicians to quickly understand the meaning of the alarms, forcing them to rely on their own experience or consult scattered documents for interpretation. At the same time, the system can only provide historical parameter curves for manual viewing and does not have the ability to automatically analyze abnormal data patterns and make causal inferences. This makes the fault location process time-consuming, inaccurate, and prone to misjudgment due to differences in personnel experience, thereby prolonging the equipment fault recovery cycle and affecting overall production efficiency.

[0005] The technical solution of this invention is: a multi-device remote control system based on LabVIEW, comprising the following modules: Data acquisition and communication module: This module is responsible for real-time data communication with various devices, collecting device status, process parameters, and alarm information, and supports multiple industrial protocols. Intelligent Diagnosis and Early Warning Module: Based on the collected equipment data and alarm information, combined with preset rules and machine learning algorithms, it performs automatic fault diagnosis, cause analysis and early warning push, thereby improving fault handling efficiency; Remote control and parameter optimization module: Used to remotely set, adjust and optimize key parameters of the equipment under authorized and security mechanisms, and provide adaptive parameter suggestions based on historical data and real-time operating conditions; Data management and analysis module: used for structured storage of production data, equipment parameters, and alarm records; Human-Computer Interaction and Visualization Module: Provides multiple display methods including web pages, LabVIEW interfaces, and large-screen dashboards.

[0006] Preferably, the data acquisition and communication module includes: A11: Protocol parsing unit, including protocol parsing engine, message assembler and check processor, used to parse the communication protocols of different devices and unify the data format; A12: Communication driver unit, including network adapter, heartbeat maintainer and disconnection reconnection controller, used to establish network connection with the device and maintain communication link; A13: Data caching unit, including a memory buffer, data queue manager and timestamp marker, is used to temporarily store collected data and mitigate the impact of network fluctuations.

[0007] Preferably, the data acquisition and communication module includes the following steps when it is in operation: S11: Configure device communication parameters, including IP address, port number and protocol type, and create a device file in the system, associating its model with a data address mapping table; S12: Start the protocol parsing engine, load the corresponding communication protocol driver according to the device type, decode the received raw message, and extract the valid data fields; S13: Establish a TCP / IP or dedicated industrial network connection through the network adapter, periodically send heartbeat messages to the device to maintain link activity, and record the response status by the heartbeat keeper; S14: Continuously poll or subscribe to the equipment's data points, read real-time status values, process parameters and alarm codes, and send the raw data stream to the verification processor for format and integrity verification; S15: Attach timestamps to the verified data, categorize them by device number and parameter type, store them sequentially in the memory buffer, and then forward them to the next module by the data queue manager according to priority.

[0008] As a preferred option, the intelligent diagnosis and early warning module includes: A21: Rule diagnosis unit, including rule base, matching engine and fault code mapping table, used to judge and classify faults based on preset rules; A22: Machine learning unit, including feature extractor, model trainer and anomaly detector, used to train models through historical data to achieve intelligent early warning; A23: Early warning push unit, including message assembler, push channel selector and confirmation feedback processor, is used to push diagnostic results to relevant personnel in multiple ways.

[0009] As a preferred embodiment, the intelligent diagnosis and early warning module includes the following steps when it is in operation: S21: Receive real-time data stream and alarm codes from the acquisition module, parse the alarm codes, and convert them into corresponding text descriptions through the fault code mapping table; S22: The rule diagnosis unit calls the preset conditions in the rule base, matches the current parameter value with the rule, and determines whether a fault event is triggered; S23: The machine learning unit extracts relevant parameter sequences from the historical database, calculates statistical features through the feature extractor, and inputs them into the trained anomaly detection model for real-time inference; S24: If the rule matching or model output is abnormal, generate structured diagnostic data containing fault type, possible cause, occurrence time and related equipment information; S25: The early warning push unit selects SMS, email or internal message channel according to the preset list of recipients and push level, assembles alarm message and sends it, and records the sending status at the same time.

[0010] As a preferred embodiment, the remote control and parameter optimization module includes: A31: Command issuing unit, including command verifier, permission verifier and issuing queue manager, used to securely issue control commands to target devices; A32: Parameter optimization unit, including an optimization algorithm library, parameter recommender, and performance evaluator, is used to provide parameter adjustment suggestions based on the data model; A33: Safety control unit, including operation log recorder, two-factor authenticator and operation rollback controller, is used to ensure the security of remote operation and prevent misoperation.

[0011] As a preferred embodiment, the remote control and parameter optimization module includes the following steps when operating: S31: Receive control commands or parameter modification values ​​input by the user through the human-computer interaction interface. The command verifier checks the legality of the command format, value range and semantics. S32: The permission verifier calls the user's identity information and the device operation permission list to verify whether the current user has remote control permission for the device. S33: Add valid commands to the dispatch queue manager and schedule the command order according to the device's response capability and network conditions; S34: The parameter optimization unit obtains the historical operating data and process standards of the equipment from the database, calculates the recommended values ​​for parameter adjustment through the optimization algorithm library, and provides a confidence assessment. S35: The safety control unit records operation logs before and after instruction execution, including the operator, time, and instruction content, and prepares an operation rollback script for recovery in case of an anomaly.

[0012] As a preferred option, the data management and analysis module includes: A41: Data storage unit, including database connector, transaction manager and storage policy executor, for persistently storing data to a database or file system; A42: Query Analysis Unit, which includes a query parser, index optimizer, and aggregation calculation engine to support complex queries and multi-dimensional data analysis; A43: Report generation unit, including template manager, data filler and format exporter, used to automatically generate production reports, equipment operation reports, etc.

[0013] As a preferred option, the data management and analysis module includes the following steps when performing its functions: S41: Receive structured data from the acquisition module or diagnostic module, and select the corresponding storage strategy according to the data type; S42: The database connector establishes a connection with MySQL or SQL Server, starts the transaction manager, and checks the atomicity and consistency of data writes to avoid partial write failures. S43: Format the data according to the predetermined table structure, perform insert or update operations, and decide whether to back up the data to the specified Excel file according to the storage policy; S44: The query analysis unit receives the query conditions submitted by the user through the interface, and the query parser converts them into SQL statements and uses the index optimizer to improve query efficiency. S45: Perform aggregation calculations or multi-dimensional grouping statistics on the query result dataset and return the results to the visualization module for display.

[0014] As a preferred option, the human-computer interaction and visualization module includes: A51: Interface rendering unit, including graphics rendering engine, style manager and layout adapter, is responsible for the graphics rendering of web, client and large screen interfaces; A52: Interactive Response Unit, which includes an event listener, an operation mapping table, and a response scheduler, is used to process user operations and trigger corresponding system functions; A53: Visualization unit, including chart generator, curve plotter, and dynamic update controller, used to display data in the form of charts, curves, and animations.

[0015] The beneficial effects of this invention are: Existing technologies heavily rely on human experience and subjective judgment in equipment fault diagnosis. Alarm information generated during equipment operation is often presented in code form, lacking intuitive text descriptions. This makes it difficult for technicians to quickly understand the alarm meaning, forcing them to rely on their own experience or consult scattered documents for interpretation. Furthermore, the system only provides historical parameter curves for manual review and lacks the ability to automatically analyze abnormal data patterns and make causal inferences. This results in a time-consuming and inaccurate fault location process, easily leading to misjudgments due to differences in personnel experience, thus prolonging equipment fault recovery cycles and impacting overall production efficiency. This solution integrates an intelligent diagnosis and early warning module into the system. After receiving equipment alarm codes, this module can automatically convert them into intuitive text descriptions. Simultaneously, it combines preset diagnostic rules with a machine learning model trained on historical data to perform multi-dimensional analysis of real-time collected process parameters. The system can not only identify simple threshold exceedance faults, but also provide early warnings and cause inferences for potential faults or performance degradation by analyzing the correlation changes and trend characteristics between parameters. This eliminates the need for technical personnel's personal experience in the fault diagnosis process. The system can automatically complete preliminary analysis and judgment, and push handling suggestions to relevant personnel, thereby significantly shortening the time interval from fault occurrence to cause identification and improving the accuracy and efficiency of maintenance response. Attached Figure Description

[0016] Figure 1 The diagram shown is a schematic flowchart of a multi-device remote control system based on LabVIEW according to the present invention. Figure 2 The diagram illustrates the workflow of the data acquisition and communication module of a multi-device remote control system based on LabVIEW according to the present invention. Figure 3 The diagram shows the workflow of the intelligent diagnosis and early warning module of a multi-device remote control system based on LabVIEW according to the present invention. Figure 4 The diagram illustrates the workflow of the remote control and parameter optimization module of a multi-device remote management system based on LabVIEW according to the present invention. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Please see Figure 1-4 This invention provides an embodiment: a multi-device remote control system based on LabVIEW, comprising the following modules: Data acquisition and communication module: This module is responsible for real-time data communication with various devices, collecting device status, process parameters, and alarm information, and supports multiple industrial protocols. Intelligent Diagnosis and Early Warning Module: Based on the collected equipment data and alarm information, combined with preset rules and machine learning algorithms, it performs automatic fault diagnosis, cause analysis and early warning push, thereby improving fault handling efficiency; Remote control and parameter optimization module: Used to remotely set, adjust and optimize key parameters of the equipment under authorized and security mechanisms, and provide adaptive parameter suggestions based on historical data and real-time operating conditions; Data management and analysis module: used for structured storage of production data, equipment parameters, and alarm records; Human-Computer Interaction and Visualization Module: Provides multiple display methods including web pages, LabVIEW interfaces, and large-screen dashboards.

[0019] Preferably, the data acquisition and communication module includes: A11: Protocol parsing unit, including protocol parsing engine, message assembler and check processor, used to parse the communication protocols of different devices and unify the data format; A12: Communication driver unit, including network adapter, heartbeat maintainer and disconnection reconnection controller, used to establish network connection with the device and maintain communication link; A13: Data caching unit, including a memory buffer, data queue manager and timestamp marker, is used to temporarily store collected data and mitigate the impact of network fluctuations.

[0020] Preferably, the data acquisition and communication module includes the following steps when it is in operation: S11: Configure device communication parameters, including IP address, port number and protocol type, and create a device file in the system, associating its model with a data address mapping table; S12: Start the protocol parsing engine, load the corresponding communication protocol driver according to the device type, decode the received raw message, and extract the valid data fields; S13: Establish a TCP / IP or dedicated industrial network connection through the network adapter, periodically send heartbeat messages to the device to maintain link activity, and record the response status by the heartbeat keeper; S14: Continuously poll or subscribe to the equipment's data points, read real-time status values, process parameters and alarm codes, and send the raw data stream to the verification processor for format and integrity verification; S15: Attach timestamps to the verified data, categorize them by device number and parameter type, store them sequentially in the memory buffer, and then forward them to the next module by the data queue manager according to priority.

[0021] As a preferred option, the intelligent diagnosis and early warning module includes: A21: Rule diagnosis unit, including rule base, matching engine and fault code mapping table, used to judge and classify faults based on preset rules; A22: Machine learning unit, including feature extractor, model trainer and anomaly detector, used to train models through historical data to achieve intelligent early warning; A23: Early warning push unit, including message assembler, push channel selector and confirmation feedback processor, is used to push diagnostic results to relevant personnel in multiple ways.

[0022] As a preferred embodiment, the intelligent diagnosis and early warning module includes the following steps when it is in operation: S21: Receive real-time data stream and alarm codes from the acquisition module, parse the alarm codes, and convert them into corresponding text descriptions through the fault code mapping table; S22: The rule diagnosis unit calls the preset conditions in the rule base, matches the current parameter value with the rule, and determines whether a fault event is triggered; S23: The machine learning unit extracts relevant parameter sequences from the historical database, calculates statistical features through the feature extractor, and inputs them into the trained anomaly detection model for real-time inference; S24: If the rule matching or model output is abnormal, generate structured diagnostic data containing fault type, possible cause, occurrence time and related equipment information; S25: The early warning push unit selects SMS, email or internal message channel according to the preset list of recipients and push level, assembles alarm message and sends it, and records the sending status at the same time.

[0023] As a preferred embodiment, the remote control and parameter optimization module includes: A31: Command issuing unit, including command verifier, permission verifier and issuing queue manager, used to securely issue control commands to target devices; A32: Parameter optimization unit, including an optimization algorithm library, parameter recommender, and performance evaluator, is used to provide parameter adjustment suggestions based on the data model; A33: Safety control unit, including operation log recorder, two-factor authenticator and operation rollback controller, is used to ensure the security of remote operation and prevent misoperation.

[0024] As a preferred embodiment, the remote control and parameter optimization module includes the following steps when operating: S31: Receive control commands or parameter modification values ​​input by the user through the human-computer interaction interface. The command verifier checks the legality of the command format, value range and semantics. S32: The permission verifier calls the user's identity information and the device operation permission list to verify whether the current user has remote control permission for the device. S33: Add valid commands to the dispatch queue manager and schedule the command order according to the device's response capability and network conditions; S34: The parameter optimization unit obtains the historical operating data and process standards of the equipment from the database, calculates the recommended values ​​for parameter adjustment through the optimization algorithm library, and provides a confidence assessment. S35: The safety control unit records operation logs before and after instruction execution, including the operator, time, and instruction content, and prepares an operation rollback script for recovery in case of an anomaly.

[0025] As a preferred option, the data management and analysis module includes: A41: Data storage unit, including database connector, transaction manager and storage policy executor, for persistently storing data to a database or file system; A42: Query Analysis Unit, which includes a query parser, index optimizer, and aggregation calculation engine to support complex queries and multi-dimensional data analysis; A43: Report generation unit, including template manager, data filler and format exporter, used to automatically generate production reports, equipment operation reports, etc.

[0026] As a preferred option, the data management and analysis module includes the following steps when performing its functions: S41: Receive structured data from the acquisition module or diagnostic module, and select the corresponding storage strategy according to the data type; S42: The database connector establishes a connection with MySQL or SQL Server, starts the transaction manager, and checks the atomicity and consistency of data writes to avoid partial write failures. S43: Format the data according to the predetermined table structure, perform insert or update operations, and decide whether to back up the data to the specified Excel file according to the storage policy; S44: The query analysis unit receives the query conditions submitted by the user through the interface, and the query parser converts them into SQL statements and uses the index optimizer to improve query efficiency. S45: Perform aggregation calculations or multi-dimensional grouping statistics on the query result dataset and return the results to the visualization module for display.

[0027] As a preferred option, the human-computer interaction and visualization module includes: A51: Interface rendering unit, including graphics rendering engine, style manager and layout adapter, is responsible for the graphics rendering of web, client and large screen interfaces; A52: Interactive Response Unit, which includes an event listener, an operation mapping table, and a response scheduler, is used to process user operations and trigger corresponding system functions; A53: Visualization unit, including chart generator, curve plotter, and dynamic update controller, used to display data in the form of charts, curves, and animations.

[0028] Optionally, a LabVIEW-based multi-device remote control system further includes: Paperless electronic inspection module: It provides an electronic interface for equipment inspection, supports parameter range comparison, over-limit warnings, and uploading and storage of inspection records, replacing the traditional paper inspection process.

[0029] The paperless electronic inspection module includes an electronic inspection unit that can be set in the human-computer interaction and visualization module. The electronic inspection unit includes an inspection template loader, a parameter comparator, a status identifier, and a confirmation and submission processor. It is used to provide an electronic interface for equipment inspection and supports parameter range comparison, over-limit warnings, and uploading and storage of inspection records. Example 1

[0030] Background: In the current manufacturing environment for precision components such as automotive wood interior trim, factories widely deploy injection molding machines, welding machines, and various automated equipment from different manufacturers. These machines are typically equipped with PLC control systems from brands such as Siemens and Mitsubishi. However, existing equipment management methods generally face two major challenges: First, when equipment failures occur, alarm messages are often presented in code form that is difficult to understand intuitively. Maintenance personnel need to rely on personal experience or consult numerous paper manuals to interpret the information, making fault location time-consuming and prone to errors. Second, traditional monitoring systems mainly focus on passive data collection and historical recording, lacking the ability to proactively intervene in equipment and optimize process parameters. When production conditions change or equipment performance slowly deteriorates, timely adjustments and warnings cannot be made, leading to fluctuations in product quality or unplanned downtime.

[0031] Implementation steps: To address the aforementioned issues, a multi-device remote control system was developed based on LabVIEW and deployed in an automotive interior parts production workshop. The implementation process first involved networking all Engel injection molding machines, KM injection molding machines, and Siemens PLC devices at welding stations within the workshop, ensuring that each device had a unique IP address and supported OPCUA or Euromap standard protocols. Subsequently, a server running the system was deployed in the central control room, and communication connections were established with all devices through the factory intranet.

[0032] The data acquisition and communication module first configures the communication parameters of all devices and establishes a unified device file. Then, it continuously polls the data points of each device through the protocol parsing engine to obtain process parameters such as barrel temperature, clamping force, and cycle time, as well as device status codes in real time. It then sends the timestamped verification data to the subsequent modules. After receiving the data, the intelligent diagnosis and early warning module converts the alarm codes reported by the devices into intuitive text descriptions such as "mold temperature exceeds limit" and "hydraulic pressure is insufficient" in real time. On the other hand, it calls the preset rule base and the anomaly detection model trained based on historical data to comprehensively analyze the parameters, automatically identify complex potential faults such as "slow temperature drift accompanied by pressure fluctuations", and immediately pushes the diagnostic report to the process engineer and maintenance supervisor through WeChat.

[0033] When the system suggests optimizing the holding pressure parameters of a certain injection molding machine, engineers can log in to the system via the web from their office, submit adjustment instructions, and after authorization and validity verification, the instructions are securely added to the distribution queue and ultimately sent to the target equipment for execution by the communication module. At the same time, the safety control unit of this module will fully record "who, when, and what operation was performed" and generate a corresponding rollback instruction script for restoration when needed. The data management and analysis module will synchronously and structurally store all real-time data, alarm records, operation logs, and diagnostic results in the background MySQL database and automatically back them up to an Excel file in a designated directory on a regular basis, providing a complete data foundation for production traceability and report generation. Finally, the human-machine interaction and visualization module will centrally present the above information. The workshop director can view a real-time overview of the entire plant's equipment status through the large TV screen in the conference room, while process engineers can flexibly query the historical curves of any equipment on a computer webpage for in-depth analysis. Example 2

[0034] The system integrates a paperless electronic inspection function to replace the traditional paper-based inspection process, improving inspection efficiency and data traceability. Before starting their shift each day, operators can initiate the inspection function through the LabVIEW interface or a web page. The system automatically loads the equipment inspection item template from the background, displays the parameter description, actual value, and set range. If the actual value exceeds the set range, the item will be automatically highlighted in red as a warning, prompting the operator to confirm or record the reason for the abnormality. After the inspection is completed, the operator confirms and submits the inspection record with an electronic signature. The inspection record is automatically uploaded to the data management and analysis module for structured storage, supporting subsequent queries, statistics, and traceability, thus realizing paperless and information-based management of the entire inspection process.

[0035] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A multi-device remote control system based on LabVIEW; characterized in that: It consists of the following modules: Data acquisition and communication module: This module is responsible for real-time data communication with various devices, collecting device status, process parameters, and alarm information, and supports multiple industrial protocols. Intelligent Diagnosis and Early Warning Module: Based on the collected equipment data and alarm information, combined with preset rules and machine learning algorithms, it performs automatic fault diagnosis, cause analysis and early warning push, thereby improving fault handling efficiency; Remote control and parameter optimization module: Used to remotely set, adjust and optimize key parameters of the equipment under authorized and security mechanisms, and provide adaptive parameter suggestions based on historical data and real-time operating conditions; Data management and analysis module: used for structured storage of production data, equipment parameters, and alarm records; Human-Computer Interaction and Visualization Module: Provides multiple display methods including web pages, LabVIEW interfaces, and large-screen dashboards.

2. The multi-device remote control system based on LabVIEW according to claim 1, characterized in that: The data acquisition and communication module includes: A11: Protocol parsing unit, including protocol parsing engine, message assembler and check processor, used to parse the communication protocols of different devices and unify the data format; A12: Communication driver unit, including network adapter, heartbeat maintainer and disconnection reconnection controller, used to establish network connection with the device and maintain communication link; A13: Data caching unit, including a memory buffer, data queue manager and timestamp marker, is used to temporarily store collected data and mitigate the impact of network fluctuations.

3. The multi-device remote control system based on LabVIEW according to claim 2, characterized in that: The data acquisition and communication module, when operating, includes the following steps: S11: Configure device communication parameters, including IP address, port number and protocol type, and create a device file in the system, associating its model with a data address mapping table; S12: Start the protocol parsing engine, load the corresponding communication protocol driver according to the device type, decode the received raw message, and extract the valid data fields; S13: Establish a TCP / IP or dedicated industrial network connection through the network adapter, periodically send heartbeat messages to the device to maintain link activity, and record the response status by the heartbeat keeper; S14: Continuously poll or subscribe to the equipment's data points, read real-time status values, process parameters and alarm codes, and send the raw data stream to the verification processor for format and integrity verification; S15: Attach timestamps to the verified data, categorize them by device number and parameter type, store them sequentially in the memory buffer, and then forward them to the next module by the data queue manager according to priority.

4. The multi-device remote control system based on LabVIEW according to claim 1, characterized in that: The intelligent diagnosis and early warning module includes: A21: Rule diagnosis unit, including rule base, matching engine and fault code mapping table, used to judge and classify faults based on preset rules; A22: Machine learning unit, including feature extractor, model trainer and anomaly detector, used to train models through historical data to achieve intelligent early warning; A23: Early warning push unit, including message assembler, push channel selector and confirmation feedback processor, is used to push diagnostic results to relevant personnel in multiple ways.

5. A multi-device remote control system based on LabVIEW according to claim 4, characterized in that: The intelligent diagnosis and early warning module, when operating, includes the following steps: S21: Receive real-time data stream and alarm codes from the acquisition module, parse the alarm codes, and convert them into corresponding text descriptions through the fault code mapping table; S22: The rule diagnosis unit calls the preset conditions in the rule base, matches the current parameter value with the rule, and determines whether a fault event is triggered; S23: The machine learning unit extracts relevant parameter sequences from the historical database, calculates statistical features through the feature extractor, and inputs them into the trained anomaly detection model for real-time inference; S24: If the rule matching or model output is abnormal, generate structured diagnostic data containing fault type, possible cause, occurrence time and related equipment information; S25: The early warning push unit selects SMS, email or internal message channel according to the preset list of recipients and push level, assembles alarm message and sends it, and records the sending status at the same time.

6. A multi-device remote control system based on LabVIEW according to claim 1, characterized in that: The remote control and parameter optimization module includes: A31: Command issuing unit, including command verifier, permission verifier and issuing queue manager, used to securely issue control commands to target devices; A32: Parameter optimization unit, including an optimization algorithm library, parameter recommender, and performance evaluator, is used to provide parameter adjustment suggestions based on the data model; A33: Safety control unit, including operation log recorder, two-factor authenticator and operation rollback controller, is used to ensure the security of remote operation and prevent misoperation.

7. A multi-device remote control system based on LabVIEW according to claim 6, characterized in that: The remote control and parameter optimization module, when operating, includes the following steps: S31: Receive control commands or parameter modification values ​​input by the user through the human-computer interaction interface. The command verifier checks the legality of the command format, value range and semantics. S32: The permission verifier calls the user's identity information and the device operation permission list to verify whether the current user has remote control permission for the device. S33: Add valid commands to the dispatch queue manager and schedule the command order according to the device's response capability and network conditions; S34: The parameter optimization unit obtains the historical operating data and process standards of the equipment from the database, calculates the recommended values ​​for parameter adjustment through the optimization algorithm library, and provides a confidence assessment. S35: The safety control unit records operation logs before and after instruction execution, including the operator, time, and instruction content, and prepares an operation rollback script for recovery in case of an anomaly.

8. A multi-device remote control system based on LabVIEW according to claim 1, characterized in that: The data management and analysis module includes: A41: Data storage unit, including database connector, transaction manager and storage policy executor, for persistently storing data to a database or file system; A42: Query Analysis Unit, which includes a query parser, index optimizer, and aggregation calculation engine to support complex queries and multi-dimensional data analysis; A43: Report generation unit, including template manager, data filler and format exporter, used to automatically generate production reports and equipment operation reports.

9. A multi-device remote control system based on LabVIEW according to claim 8, characterized in that: The data management and analysis module, when performing its functions, includes the following steps: S41: Receive structured data from the acquisition module or diagnostic module, and select the corresponding storage strategy according to the data type; S42: The database connector establishes a connection with MySQL or SQL Server, starts the transaction manager, and checks the atomicity and consistency of data writes to avoid partial write failures. S43: Format the data according to the predetermined table structure, perform insert or update operations, and decide whether to back up the data to the specified Excel file according to the storage policy; S44: The query analysis unit receives the query conditions submitted by the user through the interface, and the query parser converts them into SQL statements and uses the index optimizer to improve query efficiency. S45: Perform aggregation calculations or multi-dimensional grouping statistics on the query result dataset and return the results to the visualization module for display.

10. A multi-device remote control system based on LabVIEW according to claim 1, characterized in that: The human-computer interaction and visualization module includes: A51: Interface rendering unit, including graphics rendering engine, style manager and layout adapter, is responsible for the graphics rendering of web, client and large screen interfaces; A52: Interactive Response Unit, which includes an event listener, an operation mapping table, and a response scheduler, is used to process user operations and trigger corresponding system functions; A53: Visualization unit, including chart generator, curve plotter, and dynamic update controller, used to display data in the form of charts, curves, and animations.

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