Laboratory intelligent network connection display system

Abstract

The invention relates to a laboratory intelligent network connection display system, which relates to the technical field of Internet of Things, and comprises an equipment end, a system center and a display end, the equipment end comprises a monitoring platform and a wireless transmitting module; the system center comprises a data processing module; the display end comprises a terminal display and a mobile phone end applet; the wireless transmitting module is in wireless communication with the data processing module, and the data processing module is connected with the display and the mobile phone small program through wifi. The laboratory intelligent network connection display system can reflect the operation, standby and alarm states of equipment in real time, improves the response speed of the equipment states, reduces the dependence on manual inspection through automatic monitoring, can carry out centralized monitoring and management on a plurality of laboratories, improves the management efficiency, and reduces the labor intensity of workers. Different brands and types of equipment are considered in design, and equipment with various control modes and communication protocols is supported through the wireless transmitting module and the data processing module.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an intelligent networked display system for a laboratory. Background Art

[0002] With the development and application of sensor technology, modern network technology and artificial intelligence, the Internet of Things technology is known as the third innovation of the information industry. The Internet of Things technology has a very broad technical scope and application field. On the one hand, it utilizes many existing technologies, including semiconductor technology, computer technology, modern communication network technology, and even nanotechnology, biotechnology and other high-tech and cross-cutting technologies; on the other hand, it has its own unique characteristics, which can acquire, transmit and process information in the physical world, and publish the processing results to users in the form of services. In terms of the existing technology of the Internet of Things and its application in the laboratory, the use of the Internet of Things technology can greatly improve the overall teaching quality and better ensure laboratory safety.

[0003] Chinese patent CN116107254A discloses a WI-FI wireless-based intelligent laboratory control system, including an intelligent laboratory Internet of Things cloud platform, a mobile phone terminal, a laboratory wireless coverage POE, a sensor terminal, a lighting control module, a curtain control module, an intelligent door lock, an automatic window, a multimedia, a projection, a central control module, an air conditioning control module, an infrared module, a single-chip main control module, a WIFI wireless gateway, and an intelligent laboratory Internet of Things cloud platform: a Java-based WEB control platform for sensor data upload and display, a status inspection and monitoring platform for laboratory equipment, and a remote control platform; through the Internet of Things technology and intelligent control technology to build an intelligent laboratory, the laboratory can be controlled by a cloud platform, a mobile terminal, on-site voice, and an intelligent panel, and three classroom modes are provided: before class, during class, and after class, thereby improving the degree of intelligent control of the laboratory.

[0004] However, with the development of industrial intelligence and the increase in labor costs, the timeliness and onlineness of laboratory equipment operation are becoming more and more important. In the laboratory, the existing laboratory control system has low timeliness in monitoring the equipment status. The system cannot reflect the operation, standby and alarm status of the equipment in time, which reduces the response speed to the equipment status. Secondly, excessive reliance on manual inspections increases labor costs. Finally, laboratory equipment of different brands and types may use different control methods and communication protocols, making it difficult to achieve unified monitoring and management. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a laboratory intelligent network display system, which has the advantages of real-time reflection of the operation, standby and alarm status of the equipment, and solves the problem of low timeliness of equipment status monitoring and the system's inability to timely reflect the operation, standby and alarm status of the equipment.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solutions: a laboratory intelligent network display system, comprising a device end, a system hub and a display end;

[0007] The device end includes a monitoring platform and a wireless transmission module;

[0008] The system hub includes a data processing module;

[0009] The display end includes a terminal display and a mobile phone applet;

[0010] Wireless communication is used between the wireless transmission module and the data processing module, and Wi-Fi is used to connect the data processing module to the display and the mobile app;

[0011] in,

[0012] Monitoring platform: used to monitor the operating status, standby status and alarm status of the equipment;

[0013] Wireless transmission module: transmits the monitored device status to the data processing module;

[0014] in,

[0015] Data processing module: receives the model transmitted by the wireless transmission module, processes the response, and issues a fault alarm;

[0016] Display end: Display the running status of each device through the terminal display and the mobile app;

[0017] The device side also includes an intelligent sensor module, an AI fault prediction module and an edge computing module;

[0018] The system hub also includes an integrated management platform, a data encryption module, a cloud computing service module, a user behavior analysis module and a remote assistance module.

[0019] Further, the intelligent sensor module includes a data acquisition unit, a state monitoring unit, an anomaly detection unit, a predictive maintenance unit, and an edge computing unit;

[0020] Said

[0021] Data acquisition unit: collects various operating parameters of the equipment in real time, including temperature, humidity, pressure, and flow;

[0022] Status monitoring unit: monitors the real-time working status of the equipment, including operation, standby and fault;

[0023] Anomaly detection unit: analyzes the collected data and identifies abnormal conditions in equipment operation;

[0024] Predictive Maintenance Unit: Predict potential equipment failures and perform maintenance in advance.

[0025] Furthermore, the data collection and analysis includes a data analysis unit, a machine learning algorithm, a prediction model and a fault diagnosis unit.

[0026] Said

[0027] Data analysis unit: continuously collects data from smart sensor modules, including equipment performance indicators, operating conditions, and historical maintenance records;

[0028] Machine learning algorithms: Identify unusual patterns and trends in equipment operation;

[0029] Predictive Models: Build and train predictive models to predict the future performance of equipment based on historical data and current operating conditions;

[0030] Fault Diagnosis Unit: Determines the cause of a fault and possible solutions.

[0031] Furthermore, the monitoring platform detects the equipment status through a data acquisition unit, which specifically includes but is not limited to temperature sensors, pressure sensors, flow sensors, vibration sensors, current / voltage sensors, position sensors, humidity sensors, chemical sensors, optical sensors, and sound sensors.

[0032] Furthermore, the edge computing module includes a data processing unit, an analysis engine, a storage unit, a communication interface, and a security module.

[0033] Said

[0034] Data processing unit: real-time processing of collected data, including data cleaning, formatting and preliminary analysis;

[0035] Analytical Engine: Apply machine learning algorithms and statistical models to conduct in-depth analysis of data to identify abnormal patterns and trends in device operation;

[0036] Storage unit: temporary or long-term storage of processed data;

[0037] Communication interface: exchanges data with sensors, system hubs, and display terminals on the device side, supporting both wireless and wired communication protocols;

[0038] Security module: Ensure the security of data during collection, processing and transmission, including data encryption and access control.

[0039] Furthermore, the integrated management platform of the system hub includes device management, data integration, user management, alarm and event management, reporting and analysis, configuration and maintenance, data backup and recovery,

[0040] Said

[0041] Device management: monitor and manage all connected devices, including their status, configuration, and performance;

[0042] Data integration: Integrate data from different devices and sensors;

[0043] User management: manage user accounts, including permission allocation, user authentication and access control;

[0044] Alarm and event management: handling alarms and events generated by the system, including triggering, recording and notification of alarms;

[0045] Reporting and Analysis: Generate reports on equipment performance and system operation;

[0046] Configuration and maintenance: Allows users to configure system parameters, including monitoring thresholds, alarm settings, and device configuration;

[0047] Data backup and recovery: Back up critical data regularly and provide data recovery capabilities when needed.

[0048] Furthermore, the data encryption module includes a symmetric encryption algorithm and an asymmetric encryption algorithm, specifically:

[0049] Symmetric encryption algorithm AES: uses 128, 192 or 256-bit keys to encrypt data through multiple rounds of transformations (including byte substitution, row shift, column confusion and round key addition);

[0050] Symmetric encryption algorithm DES: uses a 56-bit key (effective key length) and encrypts through a 16-round Feistel structure;

[0051] Asymmetric encryption algorithm RSA: Based on the difficulty of large number decomposition, a pair of keys (public key and private key) is used. The public key is used for encryption and the private key is used for decryption. The encryption process usually involves performing modular exponentiation operations on plaintext data and the public key.

[0052] Asymmetric encryption algorithm ECC: Based on the discrete logarithm problem of points on the elliptic curve, it uses a smaller key length to achieve the same security level as RSA.

[0053] Furthermore, the encryption process of the asymmetric encryption algorithm RSA can be described by the following simplified mathematical expression:

[0054] C=M e mod n,

[0055] Among them: C is the ciphertext, M is the plaintext, e is the public key exponent, and n is the modulus, which is usually the product of two large prime numbers.

[0056] Furthermore, the cloud computing service module specifically includes the following units:

[0057] Data storage service unit: used to store device status data, log files and other related information;

[0058] Computing service unit: provides flexible computing resources;

[0059] Database service unit: provides various database services, including relational database and non-relational database;

[0060] Big Data Processing Unit: Provides big data processing and analysis tools for processing and analyzing large-scale data sets;

[0061] Machine Learning Service Unit: Provides machine learning platforms and tools to support data modeling, training, and deployment of machine learning models.

[0062] Furthermore, the user behavior analysis module is used to collect data on user interactions with the system, track the user's behavior path in the system, use machine learning and data mining techniques to identify user behavior patterns, analyze the changing trends of user behavior over time, and predict future behavior patterns; the remote assistance module is used to provide secure remote access functions, allowing technical support personnel to view and control user screens, support secure file transfer between technical support personnel and users, provide voice and video communication functions, and provide collaboration tools.

[0063] Compared with the prior art, the technical solution of this application has the following beneficial effects:

[0064] 1. The laboratory intelligent network display system can reflect the operation, standby and alarm status of the equipment in real time, improve the response speed to the equipment status, reduce the dependence on manual inspections through automated monitoring, thereby reducing labor costs, and can centrally monitor and manage multiple laboratories, improving management efficiency. The design takes into account equipment of different brands and types, and supports equipment with multiple control methods and communication protocols through wireless transmission modules and data processing modules. Compared with existing technologies, the system provides a lower-cost solution while simplifying the installation and maintenance process. Through real-time monitoring and timely alarms, it helps to improve the utilization rate of equipment and reduce downtime caused by failures.

[0065] 2. The laboratory's intelligent network display system has an automatic fault push function, which enables users to learn about equipment fault information in the first time and speed up fault response and processing. Through data encryption modules and security measures, the security of data transmission and storage is ensured. Through the user behavior analysis module, the system performance and user experience can be optimized. The remote assistance module provides timely technical support and improves the efficiency of problem solving. The edge computing module can process and analyze data near the data source, reducing data transmission delays and network loads. Using the cloud computing service module, the system can flexibly process large amounts of data and improve data processing and analysis capabilities. The integrated management platform of the system center provides equipment management, data integration, user management and other functions, which simplifies system management and maintenance. Through the intelligent sensor module and AI fault prediction module, the system can predict potential equipment failures and perform maintenance in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] See also Figure 1 , a laboratory intelligent network display system in this embodiment includes a device end, a system hub and a display end;

[0069] The equipment side includes a monitoring platform and a wireless transmission module;

[0070] The system hub includes a data processing module;

[0071] The display end includes the terminal display and the mobile app;

[0072] Wireless communication is used between the wireless transmission module and the data processing module, and Wi-Fi is used to connect the data processing module to the display and the mobile app.

[0073] in,

[0074] Monitoring platform: used to monitor the operating status, standby status and alarm status of the equipment;

[0075] Wireless transmission module: transmits the monitored device status to the data processing module;

[0076] in,

[0077] Data processing module: receives the model transmitted by the wireless transmission module, processes it and issues a fault alarm;

[0078] Display end: Display the running status of each device through the terminal display and the mobile app;

[0079] The device side also includes intelligent sensor modules, AI fault prediction modules, and edge computing modules;

[0080] The system hub also includes an integrated management platform, data encryption module, cloud computing service module, user behavior analysis module and remote assistance module.

[0081] Among them, the intelligent sensor module includes a data acquisition unit, a condition monitoring unit, an anomaly detection unit, a predictive maintenance unit, and an edge computing unit;

[0082] Data acquisition unit: collects various operating parameters of the equipment in real time, including temperature, humidity, pressure, and flow;

[0083] Status monitoring unit: monitors the real-time working status of the equipment, including operation, standby and fault;

[0084] Anomaly detection unit: analyzes the collected data and identifies abnormal conditions in equipment operation;

[0085] Predictive Maintenance Unit: Predict potential equipment failures and perform maintenance in advance.

[0086] It should be noted that the laboratory intelligent network display system RES30-SE transmits the equipment's operating status, standby status, and alarm status to the LiCe RCS30 wireless cloud data processing module terminal through the wireless mode through the LiCe RC508 wireless transmitter module installed on a single device. At the same time, it is uploaded to the WECAN intelligent cloud platform through WIFI and can be displayed in real time on the user's mobile phone interface or transmitted to the laboratory billboard display through WIFI. It can display the status of each device in real time, enhance the user's real-time understanding of the status of laboratory equipment, and improve the equipment utilization rate. If a fault occurs, RCS30 will also issue a voice or sound and light alarm.

[0087] Among them, data collection and analysis include data analysis unit, machine learning algorithm, prediction model and fault diagnosis unit.

[0088] Data analysis unit: continuously collects data from smart sensor modules, including equipment performance indicators, operating conditions, and historical maintenance records;

[0089] Machine learning algorithms: Identify unusual patterns and trends in equipment operation;

[0090] Predictive Models: Build and train predictive models to predict the future performance of equipment based on historical data and current operating conditions;

[0091] Fault Diagnosis Unit: Determines the cause of a fault and possible solutions.

[0092] Among them, the monitoring platform detects the equipment status through the data acquisition unit, which specifically includes but is not limited to temperature sensors, pressure sensors, flow sensors, vibration sensors, current / voltage sensors, position sensors, humidity sensors, chemical sensors, optical sensors, and sound sensors.

[0093] Among them, the edge computing module includes a data processing unit, an analysis engine, a storage unit, a communication interface, and a security module.

[0094] Data processing unit: real-time processing of collected data, including data cleaning, formatting and preliminary analysis;

[0095] Analytical Engine: Apply machine learning algorithms and statistical models to conduct in-depth analysis of data to identify abnormal patterns and trends in device operation;

[0096] Storage unit: temporary or long-term storage of processed data;

[0097] Communication interface: exchanges data with sensors, system hubs, and display terminals on the device side, supporting both wireless and wired communication protocols;

[0098] Security module: Ensure the security of data during collection, processing and transmission, including data encryption and access control.

[0099] The integrated management platform at the center of the system includes equipment management, data integration, user management, alarm and event management, reporting and analysis, configuration and maintenance, data backup and recovery.

[0100] Device management: monitor and manage all connected devices, including their status, configuration, and performance;

[0101] Data integration: Integrate data from different devices and sensors;

[0102] User management: manage user accounts, including permission allocation, user authentication and access control;

[0103] Alarm and event management: handling alarms and events generated by the system, including triggering, recording and notification of alarms;

[0104] Reporting and Analysis: Generate reports on equipment performance and system operation;

[0105] Configuration and maintenance: Allows users to configure system parameters, including monitoring thresholds, alarm settings, and device configuration;

[0106] Data backup and recovery: Back up critical data regularly and provide data recovery capabilities when needed.

[0107] The data encryption module includes symmetric encryption algorithm and asymmetric encryption algorithm, specifically:

[0108] Symmetric encryption algorithm AES: uses 128, 192 or 256-bit keys to encrypt data through multiple rounds of transformations (including byte substitution, row shift, column confusion and round key addition);

[0109] Symmetric encryption algorithm DES: uses a 56-bit key (effective key length) and encrypts through a 16-round Feistel structure;

[0110] Asymmetric encryption algorithm RSA: Based on the difficulty of large number decomposition, a pair of keys (public key and private key) is used. The public key is used for encryption and the private key is used for decryption. The encryption process usually involves performing modular exponentiation operations on plaintext data and the public key.

[0111] Asymmetric encryption algorithm ECC: Based on the discrete logarithm problem of points on the elliptic curve, it uses a smaller key length to achieve the same security level as RSA.

[0112] Among them, the asymmetric encryption algorithm RSA, the encryption process can be described by the following simplified mathematical expression:

[0113] C=M e mod n,

[0114] Among them: C is the ciphertext, M is the plaintext, e is the public key exponent, and n is the modulus, which is usually the product of two large prime numbers.

[0115] The cloud computing service module specifically includes the following units:

[0116] Data storage service unit: used to store device status data, log files and other related information;

[0117] Computing service unit: provides flexible computing resources;

[0118] Database service unit: provides various database services, including relational database and non-relational database;

[0119] Big Data Processing Unit: Provides big data processing and analysis tools for processing and analyzing large-scale data sets;

[0120] Machine Learning Service Unit: Provides machine learning platforms and tools to support data modeling, training, and deployment of machine learning models.

[0121] Among them, the user behavior analysis module is used to collect data on user interaction with the system, track the user's behavior path in the system, use machine learning and data mining techniques to identify user behavior patterns, analyze the changing trends of user behavior over time, and predict future behavior patterns; the remote assistance module is used to provide secure remote access functions, allowing technical support personnel to view and control user screens, support secure file transfer between technical support personnel and users, provide voice and video communication functions, and provide collaboration tools.

[0122] At present, most laboratory equipment has many brands and adopts various control methods, including host computer control, PLC control, and control substrate control. In addition, the communication port protocols are also various, and some equipment cannot even communicate. The current laboratory LIMS system needs to rely on the equipment communication terminal and the open communication protocols of various equipment manufacturers. In addition, according to different controllers and communication ports, the system needs to correspond, and most of them require wired source connection, so the workload is large, and the production and debugging cycles are long, the cost is relatively expensive, and the bugs need to be upgraded and modified later. Compared with this system, this system has lower cost and is more practical. It can respond to the most basic operation information of the laboratory in real time, and has an automatic fault push function, so that users can get the information at the first time.

[0123] This application,

[0124] It improves the utilization rate and response speed of equipment and reduces labor costs.

[0125] Laboratory safety is enhanced through real-time monitoring and automatic alarms.

[0126] User behavior analysis and remote assistance improve user experience and system maintenance efficiency.

[0127] The beneficial effects of the above embodiments are:

[0128] It can reflect the operation, standby and alarm status of the equipment in real time, improve the response speed to the equipment status, reduce the dependence on manual inspections through automated monitoring, and thus reduce labor costs. It can centrally monitor and manage multiple laboratories, improve management efficiency, and the design takes into account equipment of different brands and types. Through wireless transmission modules and data processing modules, it supports equipment with multiple control methods and communication protocols. Compared with existing technologies, this system provides a lower-cost solution and simplifies the installation and maintenance process. Through real-time monitoring and timely alarms, it helps to improve the utilization rate of equipment and reduce downtime caused by failures.

[0129] It has an automatic fault push function, so that users can learn about equipment fault information in the first time, speed up fault response and processing, ensure the security of data transmission and storage through data encryption module and security measures, optimize system performance and user experience through user behavior analysis module, provide timely technical support and improve problem solving efficiency, and perform data processing and analysis near the data source through edge computing module, reducing data transmission delay and network load. By using cloud computing service module, the system can flexibly process large amounts of data and improve data processing and analysis capabilities. The integrated management platform of the system center provides equipment management, data integration, user management and other functions, which simplifies system management and maintenance. Through intelligent sensor modules and AI fault prediction modules, the system can predict potential equipment failures and perform maintenance in advance.

[0130] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0131] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A laboratory intelligent network display system, characterized in that: Including device side, system hub and display side; The device end includes a monitoring platform and a wireless transmission module; The system hub includes a data processing module; The display end includes a terminal display and a mobile phone applet; Wireless communication is used between the wireless transmission module and the data processing module, and Wi-Fi is used to connect the data processing module to the display and the mobile app; in, Monitoring platform: used to monitor the operating status, standby status and alarm status of the equipment; Wireless transmission module: transmits the monitored device status to the data processing module; in, Data processing module: receives the model transmitted by the wireless transmission module, processes the response, and issues a fault alarm; Display end: Display the running status of each device through the terminal display and the mobile app; The device side also includes an intelligent sensor module, an AI fault prediction module and an edge computing module; The system hub also includes an integrated management platform, a data encryption module, a cloud computing service module, a user behavior analysis module and a remote assistance module.

2. According to claim 1, a laboratory intelligent network display system is characterized by: The intelligent sensor module includes a data acquisition unit, a status monitoring unit, an anomaly detection unit, a predictive maintenance unit, and an edge computing unit; Said Data acquisition unit: collects various operating parameters of the equipment in real time, including temperature, humidity, pressure, and flow; Status monitoring unit: monitors the real-time working status of the equipment, including operation, standby and fault; Anomaly detection unit: analyzes the collected data and identifies abnormal conditions in equipment operation; Predictive Maintenance Unit: Predict potential equipment failures and perform maintenance in advance.

3. According to claim 1, a laboratory intelligent network display system is characterized in that: The data collection and analysis includes a data analysis unit, a machine learning algorithm, a prediction model and a fault diagnosis unit. Said Data analysis unit: continuously collects data from smart sensor modules, including equipment performance indicators, operating conditions, and historical maintenance records; Machine learning algorithms: Identify unusual patterns and trends in equipment operation; Predictive Models: Build and train predictive models to predict the future performance of equipment based on historical data and current operating conditions; Fault Diagnosis Unit: Determines the cause of a fault and possible solutions.

4. The laboratory intelligent network display system according to claim 2, characterized in that: The monitoring platform detects the equipment status through a data acquisition unit, which specifically includes but is not limited to temperature sensors, pressure sensors, flow sensors, vibration sensors, current / voltage sensors, position sensors, humidity sensors, chemical sensors, optical sensors, and sound sensors.

5. The laboratory intelligent network display system according to claim 1, characterized in that: The edge computing module includes a data processing unit, an analysis engine, a storage unit, a communication interface, and a security module. Said Data processing unit: real-time processing of collected data, including data cleaning, formatting and preliminary analysis; Analytical Engine: Apply machine learning algorithms and statistical models to conduct in-depth analysis of data to identify abnormal patterns and trends in device operation; Storage unit: temporary or long-term storage of processed data; Communication interface: exchanges data with sensors, system hubs, and display terminals on the device side, supporting both wireless and wired communication protocols; Security module: Ensure the security of data during collection, processing and transmission, including data encryption and access control.

6. The laboratory intelligent network display system according to claim 1, characterized in that: The integrated management platform of the system hub includes device management, data integration, user management, alarm and event management, reporting and analysis, configuration and maintenance, data backup and recovery, Said Device management: monitor and manage all connected devices, including their status, configuration, and performance; Data integration: Integrate data from different devices and sensors; User management: manage user accounts, including permission allocation, user authentication and access control; Alarm and event management: handling alarms and events generated by the system, including triggering, recording and notification of alarms; Reporting and Analysis: Generate reports on equipment performance and system operation; Configuration and maintenance: Allows users to configure system parameters, including monitoring thresholds, alarm settings, and device configuration; Data backup and recovery: Back up critical data regularly and provide data recovery capabilities when needed.

7. The laboratory intelligent network display system according to claim 1, characterized in that: The data encryption module includes a symmetric encryption algorithm and an asymmetric encryption algorithm. Symmetric encryption algorithm AES: uses 128, 192 or 256-bit keys to encrypt data through multiple rounds of transformations (including byte substitution, row shift, column confusion and round key addition); Symmetric encryption algorithm DES: uses a 56-bit key (effective key length) and encrypts through a 16-round Feistel structure; Asymmetric encryption algorithm RSA: Based on the difficulty of large number decomposition, a pair of keys (public key and private key) is used. The public key is used for encryption and the private key is used for decryption. The encryption process usually involves performing modular exponentiation operations on plaintext data and the public key. Asymmetric encryption algorithm ECC: Based on the discrete logarithm problem of points on the elliptic curve, it uses a smaller key length to achieve the same security level as RSA.

8. The laboratory intelligent network display system according to claim 7, characterized in that: The asymmetric encryption algorithm RSA, the encryption process can be described by the following simplified mathematical expression: C=M e mod n, Among them: C is the ciphertext, M is the plaintext, e is the public key exponent, and n is the modulus, which is usually the product of two large prime numbers.

9. The laboratory intelligent network display system according to claim 1, characterized in that: The cloud computing service module specifically includes the following units: Data storage service unit: used to store device status data, log files and other related information; Computing service unit: provides flexible computing resources; Database service unit: provides various database services, including relational database and non-relational database; Big Data Processing Unit: Provides big data processing and analysis tools for processing and analyzing large-scale data sets; Machine Learning Service Unit: Provides machine learning platforms and tools to support data modeling, training, and deployment of machine learning models.

10. The laboratory intelligent network display system according to claim 1, characterized in that: The user behavior analysis module is used to collect data on user interaction with the system, track the user's behavior path in the system, identify user behavior patterns using machine learning and data mining techniques, analyze the changing trend of user behavior over time, and predict future behavior patterns; The remote assistance module is used to provide secure remote access capabilities, allowing technical support personnel to view and control user screens, support secure file transfer between technical support personnel and users, provide voice and video communication capabilities, and provide collaboration tools.

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