Monitoring system for electronic technology experiment

Abstract

The invention discloses a monitoring system for an electronic technology experiment, and relates to the technical field of electronic experiments, the monitoring system comprises an electronic technology experiment recording end, a monitoring center, a distributed early warning end and a data trend prediction end, and the electronic technology experiment recording end, the monitoring center, the distributed early warning end and the data trend prediction end are arranged. And recording equipment installed in each marked area and main responsibilities of the equipment in a table mode, accelerating module processing data by embedding other subsystems, constructing a distributed storage model, uploading experimental data to a cloud platform, and solving many problems existing in a traditional experimental monitoring mode. Experiment parameters can be accurately monitored in real time, electronic technology experiment management can be achieved through local area network cloud management and control, the intelligent level of monitoring management is improved, and on-site and remote synchronous supervision processing can be conveniently conducted on each experiment link.

Description

Technical Field

[0001] The invention relates to the technical field of electronic experiments, in particular to a monitoring system for electronic technology experiments. Background Art

[0002] Electronic technology experiments refer to practical activities that verify, test and study electronic components, circuits, systems and their working principles through experimental methods based on the principles of electronics and circuits. Electronic technology experiments are an important part of electronic engineering, electronic communications, automation and other disciplines, and are designed to help students or engineers understand the basic principles of electronic technology and improve their experimental operation and fault analysis capabilities.

[0003] Chinese patent announcement number CN 217181711 U discloses a pocket experiment system suitable for analog electronic technology experiments. The system of the utility model includes an experiment box, a double-headed USB data cable and a computer with a dual-channel oscilloscope platform based on LabVIEW. The experiment box is equipped with two USB external sound cards, a power module, a waveform signal generation module and a teaching experiment circuit module, wherein the waveform signal generation module generates a waveform signal and inputs it to the teaching experiment circuit module, and the signal output by the teaching experiment circuit module is collected and sent to the dual-channel oscilloscope platform through the USB external sound card, and finally the platform is used to select the data acquisition channel, process the data and display the waveform. The utility model can effectively reduce the configuration cost of the analog electronics laboratory, and the entire system structure is compact and easy to carry, which can meet the daily experimental needs of students.

[0004] However, the above scheme still has the following problems: it is impossible to achieve on-site and remote synchronous monitoring operations during electronic technology experiments, which makes it difficult to discover abnormalities in on-site experiments in the first time, causing subsequent experimental goals to deviate. At the same time, it is not convenient to conduct comprehensive query and management of each completed experimental process data. The overall intelligent operation level of the equipment and system is low, which reduces the efficiency and safety of the experiment to a certain extent. Therefore, the present invention needs to design a monitoring system for electronic technology experiments to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a monitoring system for electronic technology experiments in order to solve the above problems, thereby solving the problems mentioned in the background technology.

[0006] In order to solve the above problems, the present invention provides a technical solution:

[0007] A monitoring system for electronic technology experiments, the monitoring system comprising an electronic technology experiment recording terminal, a monitoring center, a distributed early warning terminal and a data trend prediction terminal. A staff member logs into the electronic technology experiment recording terminal to enter the monitoring system, thereby viewing the real-time operating data of the monitoring center, the distributed early warning terminal and the data trend prediction terminal one by one. The electronic technology experiment recording terminal maintains data intercommunication and sharing with the monitoring center, the distributed early warning terminal and the data trend prediction terminal respectively through a laboratory local area network.

[0008] As a preferred technical solution, the electronic technology experiment recording terminal is used to record all operations, data changes, system status and user operation information during the experiment, and is also used to provide a stable power supply and monitor the output quality of the power supply;

[0009] The monitoring center is used to achieve remote data access and experimental equipment control through the network, allowing experimenters to monitor and operate experiments at a remote location, and is also used to detect potential hazards such as harmful gases, radiation, and noise in the experimental environment;

[0010] The distributed early warning terminal is used to mark the locations where each monitoring device is installed, and to record the devices installed in each marked area and the main responsibilities of the devices in a table. It is also used to conduct comprehensive analysis and prediction model training on the data of multiple sensors by introducing artificial intelligence and multiple groups of algorithms.

[0011] The data trend prediction terminal is used to collect various signals in the experiment in real time, accelerate the module to process data by embedding other subsystems, and is also used to build a distributed storage model to upload the experimental data to the cloud platform.

[0012] As a preferred technical solution, the output end of the monitoring center is communicatively connected to the input end of the distributed early warning end, the output end of the distributed early warning end is communicatively connected to the input end of the data trend prediction end, and the electronic technology experiment recording end is bidirectionally communicatively connected to the data trend prediction end.

[0013] As a preferred technical solution, the electronic technology experiment recording end includes a laboratory log management module, an experimental data storage module, a power supply and safety control center and an automation control module, the output end of the experimental data storage module is communicatively connected to the input end of the laboratory log management module, the automation control module is integrated inside the power supply and safety control center, and the output end of the laboratory log management module is communicatively connected to the input end of the power supply and safety control center;

[0014] The laboratory log management module is used to record all operations, data changes, system status and user operation information during the experiment;

[0015] It is also used to facilitate the review and analysis of the experiment later to ensure the integrity and credibility of the data;

[0016] The experimental data storage module is used to manage experimental resources, personnel arrangements, and experimental progress, and improve experimental efficiency and avoid resource conflicts through reasonable scheduling and management;

[0017] It is also used to classify and store experiments of different groups and different dates, and to perform subsequent data processing and analysis to ensure that experimental data is not lost. The data analysis system can provide in-depth analysis of experimental results and help identify trends or potential problems in the experiment;

[0018] The power supply and safety control center is used to provide a stable power supply and monitor the output quality of the power supply, such as voltage fluctuations and short circuits;

[0019] It is also used to prevent power outages or fluctuations from affecting experimental results while ensuring the normal operation of equipment;

[0020] The automation control module is used to automatically adjust the current, voltage, and temperature variables according to preset parameters, and automatically adjust the working status of each instrument;

[0021] It is also used to improve the accuracy and consistency of the experimental process, reduce human intervention, and ensure the stability of experimental conditions, especially for experiments that require precise control.

[0022] As a preferred technical solution, the monitoring center includes a remote monitoring module, an environmental monitoring module, a visual detection module and a monitoring database. The output ends of the environmental monitoring module and the visual detection module are communicatively connected to the input end of the remote monitoring module, the output end of the remote monitoring module is communicatively connected to the input end of the monitoring database, and the output end of the monitoring database is communicatively connected to the input end of the distributed early warning end.

[0023] As a preferred technical solution, the remote monitoring module is used to achieve remote data access and experimental equipment control through the network, allowing experimenters to monitor and operate experiments at a remote location;

[0024] It is also used for long-running experiments or unified management of multiple experimental sites;

[0025] The environmental monitoring module is used to detect potential dangers of harmful gases, radiation, and noise in the experimental environment;

[0026] It is also used to protect the health and safety of experimenters and ensure that there are no environmental factors harmful to the human body during the experiment;

[0027] The visual inspection module is used to perform real-time video monitoring of the experimental process through various groups of cameras installed inside the laboratory, especially when it is necessary to observe physical phenomena or changes in experimental conditions during the experiment;

[0028] It is also used to ensure the transparency of the experiment, facilitate the review of specific details during the experiment, and assist in determining whether each device is operating normally;

[0029] The monitoring database is used to display real-time experimental data and equipment status through a graphical interface. The interface can be viewed and controlled through a touch screen, computer or mobile device. The monitoring information of different areas and monitoring data of different time periods are uniformly classified and labeled, which is convenient for manual search of the required monitoring information by searching a single entry;

[0030] It is also used for data trend charts, alarm information display, and historical data backtracking functions, allowing users to view the progress and status of experiments in real time;

[0031] It is also used to allow users to remotely access experimental data through the network. Users can remotely control and monitor through the Internet, local area network or Wi-Fi, and upload experimental data for backup and analysis.

[0032] As a preferred technical solution, the distributed early warning end includes a distributed management module, an alarm module, a real-time response module and an intelligent prediction model module. The output end of the alarm module is communicatively connected to the output end of the real-time response module, the output end of the real-time response module is communicatively connected to the output end of the distributed management module, and the distributed management module is bidirectionally communicatively connected to the intelligent prediction model module.

[0033] As a preferred technical solution, the distributed management module is used to mark the locations where each monitoring device is installed, recorded as a1, a2, ..., and for each marked area, the devices installed in this area and the main responsibilities of the devices are recorded in a table;

[0034] A1 is the laboratory control center, including the power controller, control panel, laboratory display screen and computer of the whole equipment. It monitors the operation data of each equipment in real time, including operation time, ambient temperature change during operation and other data for remote supervision.

[0035] a2 is the laboratory personnel registration desk, which includes facial recognition equipment, infrared sensors, surveillance cameras, and personnel authentication display screens. It verifies the identity information of every person entering the laboratory to reduce data leakage;

[0036] a3 is the experimental log recording table, including the operation table, log book, and online recording memo. It can check whether there is any failure to record experimental data in time after the experiment is completed every day through remote monitoring. At the same time, it can remotely correct abnormal data in the record and correct it by reminding laboratory personnel;

[0037] a4...;

[0038] The alarm module is used to automatically analyze the collected data according to the preset experimental parameters and standards. When the experimental parameters exceed the set safety range, the system will immediately trigger an alarm and issue a warning to the experimenter;

[0039] It is also used to automatically record abnormal experimental events and generate logs for later tracing and analysis;

[0040] The real-time response module is used to conduct comprehensive analysis and prediction model training on the data of multiple sensors by introducing artificial intelligence and multiple sets of algorithms, thereby improving the accuracy of alarms and the timeliness of responses;

[0041] The multiple groups of algorithms include:

[0042] Fast Fourier transform algorithm, for frequency domain analysis, extracts the frequency components of the signal, and is particularly suitable for analyzing time domain signals in electronic experiments;

[0043] Fuzzy control algorithm, using fuzzy control algorithm, dynamic adjustment is performed through fuzzy description of experimental status;

[0044] Adaptive control algorithm, when the experimental environment and the parameters of each laboratory group change over time, the adaptive control algorithm can continuously adjust the control strategy according to the feedback of the system;

[0045] Machine learning algorithms are used to analyze experimental data in real time and detect potential anomalies. By training the model, it is possible to predict anomalies that may occur during the experiment and take appropriate intervention measures;

[0046] Genetic algorithms, which can be used for parameter optimization and system tuning, such as determining the optimal working conditions in experiments;

[0047] Regression analysis algorithms use regression models to predict the relationship between certain variables and help experimental design and result derivation;

[0048] Authentication algorithms to ensure identity verification and access control of experimental control systems and equipment;

[0049] Image segmentation and analysis algorithms are used to extract features and dynamic changes in experiments;

[0050] According to the specific needs of the experiment, the above algorithms can be combined to achieve accurate, efficient and safe experimental management and monitoring;

[0051] It is also used to dynamically adjust the alarm threshold according to the experimental environment or experimental progress to avoid too frequent false alarms caused by fixed threshold settings;

[0052] The intelligent prediction model module is used to construct a probability model to infer different events in the experimental process and determine potential failure modes. The probability model is a Bayesian network model, which is used to represent and infer uncertainty and causal relationships. It consists of nodes and directed edges. Each node represents a random variable, and the edge represents the conditional dependency between these variables.

[0053] As a preferred technical solution, the data trend prediction end includes an electronic experiment data collection module, a data trend analysis module, a prediction analysis model and an intelligent prediction port. The output end of the electronic experiment data collection module is communicatively connected to the input end of the data trend analysis module, the output end of the data trend analysis module is communicatively connected to the input end of the prediction analysis model, and the prediction analysis model and the intelligent prediction port are bidirectionally communicatively connected.

[0054] As a preferred technical solution, the electronic experiment data collection module is used to collect various signals in the experiment in real time, including physical quantities such as voltage, current, frequency, temperature, pressure, etc.;

[0055] It is also used to perform edge computing at the data collection end and accelerate the module to process data by embedding other subsystems, which can reduce the delay of data transmission and processing;

[0056] The subsystem includes:

[0057] The temperature and humidity control subsystem is used to control the temperature and humidity environment in the laboratory. Some electronic technology experiments need to be carried out under constant temperature and humidity conditions. The temperature and humidity control system can help provide a suitable experimental environment and reduce the impact of external environmental changes on experimental results;

[0058] The fault diagnosis and repair subsystem is used to automatically or semi-automatically diagnose problems and provide repair suggestions when equipment fails during the experiment. It is also used to reduce the response time when a failure occurs and improve the efficiency of equipment recovery.

[0059] Communication and network systems are used to connect various devices in the experiment with external monitoring systems, data acquisition systems, etc. to ensure real-time data transmission. They are also used to provide real-time and reliable data transmission pipelines to support the coordination between various devices in the experiment;

[0060] These auxiliary systems can be integrated with the monitoring system to ensure the efficient, stable and safe operation of electronic technology experiments through intelligent and automated management. Their combination not only improves the accuracy of the experiment, but also reduces human intervention, improves data reliability, and effectively reduces potential risks in the experiment.

[0061] The data trend analysis module is used to build a distributed storage model and upload experimental data to the cloud platform, which can not only solve the storage bottleneck but also provide higher scalability and backup mechanism;

[0062] The predictive analysis model is used to compress and store data using an efficient data compression algorithm or database management system to reduce storage space usage, while constructing an analysis model and performing autonomous predictive analysis processing, and achieving real-time synchronization of the model by continuously updating data;

[0063] The intelligent prediction port is used to optimize the user interface of the system, making it more concise and intuitive, and reducing the difficulty of operation;

[0064] It is also used to introduce functions such as voice recognition and intelligent assistants, allowing experimenters to interact with the system through voice or other natural language methods to improve user experience.

[0065] The beneficial effects of the present invention are as follows: the present invention constructs a complete monitoring system by setting up an electronic technology experiment recording terminal, a monitoring center, a distributed early warning terminal and a data trend prediction terminal. In actual use, the electronic technology experiment recording terminal is used to record all operations, data changes, system status and user operation information during the experiment, and is also used to provide a stable power supply and monitor the output quality of the power supply. The monitoring center is used to achieve remote data access and experimental equipment control through the network, allowing experimenters to monitor and operate experiments at a remote location, and is also used to detect potential dangers of harmful gases, radiation, and noise in the experimental environment. The distributed early warning terminal is used to mark the installation location of each monitoring device, and to record the equipment installed in each marked area and the main responsibilities of the equipment in this area in a table format. It is also used to introduce people Artificial intelligence and multiple groups of algorithms are used to conduct comprehensive analysis and prediction model training on the data of multiple sensors. The data trend prediction end is used to collect various signals in the experiment in real time. By embedding other subsystems, the module processes data to accelerate the construction of a distributed storage model and uploads the experimental data to the cloud platform, which solves many inadequacies of traditional experimental monitoring methods. It can not only monitor the experimental parameters in real time and accurately, but also automatically alarm and adjust the experimental equipment to ensure the smooth progress of the experiment. At the same time, it greatly improves the efficiency and safety of the experiment. It manages, visualizes and stores the electronic technology experimental data and the corresponding analysis results, which helps to realize the electronic technology experimental management through LAN cloud control, improve the intelligent level of monitoring management, and facilitate on-site and remote synchronous supervision and processing of each experimental link. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] For ease of explanation, the present invention is described in detail with reference to the following specific implementations and the accompanying drawings.

[0067] Figure 1 It is a flow chart of a monitoring system for an electronic technology experiment of the present invention;

[0068] Figure 2 It is an overall topological diagram of a monitoring system for an electronic technology experiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0070] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0071] It should be understood that although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0072] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0073] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity 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 commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0074] It should be particularly noted that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0075] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0076] The embodiment provided in this application is an embodiment of a method for analyzing the correlation between driving behavior factors and driving risks based on big data.

[0077] like Figure 1-Figure 2 As shown, this specific implementation adopts the following technical solutions:

[0078] A monitoring system for electronic technology experiments, the monitoring system comprising an electronic technology experiment recording terminal, a monitoring center, a distributed early warning terminal and a data trend prediction terminal. A staff member logs into the electronic technology experiment recording terminal to enter the monitoring system, thereby viewing the real-time operation data of the monitoring center, the distributed early warning terminal and the data trend prediction terminal one by one. The electronic technology experiment recording terminal maintains data intercommunication and sharing with the monitoring center, the distributed early warning terminal and the data trend prediction terminal respectively through a laboratory local area network. The output terminal of the monitoring center is communicatively connected with the input terminal of the distributed early warning terminal, the output terminal of the distributed early warning terminal is communicatively connected with the input terminal of the data trend prediction terminal, and the electronic technology experiment recording terminal is bidirectionally communicatively connected with the data trend prediction terminal.

[0079] S101. Laboratory staff tested the on-site and remote hardware equipment one by one, and all of them could be started and shut down normally before starting the monitoring system;

[0080] S102, sequentially constructing a remote monitoring module, an environmental monitoring module, a visual detection module and a monitoring database, maintaining the output ends of the environmental monitoring module and the visual detection module in communication connection with the input end of the remote monitoring module, maintaining the output end of the remote monitoring module in communication connection with the input end of the monitoring database, and maintaining the output end of the monitoring database in communication connection with the input end of the distributed early warning terminal;

[0081] S102, the remote monitoring module realizes remote data access and experimental equipment control through the network, allowing the experimenter to monitor and operate the experiment at a remote location;

[0082] S102, remote monitoring module for long-term running experiments or unified management of multiple experimental points;

[0083] S103, the environmental monitoring module detects potential hazards of harmful gases, radiation, and noise in the experimental environment;

[0084] S104, environmental monitoring module protects the health and safety of experimenters and ensures that there are no environmental factors harmful to the human body during the experiment;

[0085] S105, the visual inspection module performs real-time video monitoring of the experimental process through various groups of cameras installed inside the laboratory, especially when it is necessary to observe physical phenomena or changes in experimental conditions during the experiment;

[0086] S106, the visual inspection module ensures the transparency of the experiment, facilitates the review of the specific details of the experiment process, and assists in determining whether each device is operating normally;

[0087] S107, the monitoring database displays real-time experimental data and equipment status through a graphical interface, which can be viewed and controlled through a touch screen, computer or mobile device, and uniformly classifies and labels the monitoring information of different areas and monitoring data of different time periods, so that it is convenient for manual search of the required monitoring information by searching a single entry;

[0088] S108, the monitoring database is also used for data trend graphs, alarm information display, and historical data backtracking functions, which facilitates users to view the progress and status of the experiment in real time;

[0089] S109. The monitoring database is also used to allow users to remotely access experimental data through the network. Users can remotely control and monitor through the Internet, local area network or Wi-Fi, and upload experimental data for backup and analysis.

[0090] S201, sequentially constructing a distributed management module, an alarm module, a real-time response module, and an intelligent prediction model module, maintaining a communication connection between the output end of the alarm module and the output end of the real-time response module, maintaining a communication connection between the output end of the real-time response module and the output end of the distributed management module, and maintaining a two-way communication connection between the distributed management module and the intelligent prediction model module;

[0091] S202, the distributed management module marks the locations where each monitoring device is installed, recorded as a1, a2, ..., and records the devices installed in each marked area and the main responsibilities of the devices in a table;

[0092] S203, a1 is the laboratory control center, including the power controller, control panel, laboratory display screen and computer of the whole equipment, which can monitor the operation data of each equipment in real time, including operation time, environmental temperature change during operation and other data for remote supervision;

[0093] a2 is the laboratory personnel registration desk, which includes facial recognition equipment, infrared sensors, surveillance cameras, and personnel authentication display screens. It verifies the identity information of every person entering the laboratory to reduce data leakage;

[0094] a3 is the experimental log recording table, including the operation table, log book, and online recording memo. It can check whether there is any failure to record experimental data in time after the experiment is completed every day through remote monitoring. At the same time, it can remotely correct abnormal data in the record and correct it by reminding laboratory personnel;

[0095] a4...;

[0096] S204, the alarm module automatically analyzes the collected data according to the preset experimental parameters and standards. When the experimental parameters exceed the set safety range, the system will immediately trigger an alarm and issue a warning to the experimenter;

[0097] S205, the alarm module automatically records abnormal events in the experiment and generates a log for later tracing and analysis;

[0098] S206, the real-time response module is used to conduct comprehensive analysis and prediction model training on the data of multiple sensors by introducing artificial intelligence and multiple sets of algorithms, so as to improve the accuracy of alarms and the timeliness of responses;

[0099] S207, multiple groups of algorithms include:

[0100] Fast Fourier transform algorithm, for frequency domain analysis, extracts the frequency components of the signal, and is particularly suitable for analyzing time domain signals in electronic experiments;

[0101] Fuzzy control algorithm, using fuzzy control algorithm, dynamic adjustment is performed through fuzzy description of experimental status;

[0102] Adaptive control algorithm, when the experimental environment and the parameters of each laboratory group change over time, the adaptive control algorithm can continuously adjust the control strategy according to the feedback of the system;

[0103] Machine learning algorithms are used to analyze experimental data in real time and detect potential anomalies. By training the model, it is possible to predict anomalies that may occur during the experiment and take appropriate intervention measures;

[0104] Genetic algorithms, which can be used for parameter optimization and system tuning, such as determining the optimal working conditions in experiments;

[0105] Regression analysis algorithms use regression models to predict the relationship between certain variables and help experimental design and result derivation;

[0106] Authentication algorithms to ensure identity verification and access control of experimental control systems and equipment;

[0107] Image segmentation and analysis algorithms are used to extract features and dynamic changes in experiments;

[0108] According to the specific needs of the experiment, the above algorithms can be combined to achieve accurate, efficient and safe experimental management and monitoring;

[0109] S208, the real-time response module is also used to dynamically adjust the alarm threshold according to the experimental environment or experimental progress to avoid too frequent false alarms caused by fixed threshold settings;

[0110] S209, the intelligent prediction model module constructs a probability model to infer different events in the experimental process and determine potential failure modes. The probability model is a Bayesian network model, which is used to represent and infer uncertainty and causal relationships. It consists of nodes and directed edges. Each node represents a random variable, and the edge represents the conditional dependency between these variables.

[0111] S301, sequentially constructing an electronic experiment data collection module, a data trend analysis module, a prediction analysis model, and an intelligent prediction port, maintaining a communication connection between the output end of the electronic experiment data collection module and the input end of the data trend analysis module, maintaining a communication connection between the output end of the data trend analysis module and the input end of the prediction analysis model, and maintaining a two-way communication connection between the prediction analysis model and the intelligent prediction port;

[0112] S302, the electronic experiment data collection module collects various signals in the experiment in real time, including voltage, current, frequency, temperature, pressure and other physical quantities;

[0113] S303, the electronic experiment data collection module can reduce the delay of data transmission and processing by performing edge computing at the data collection end and accelerating the module to process data by embedding other subsystems;

[0114] S304, the subsystem includes:

[0115] The temperature and humidity control subsystem is used to control the temperature and humidity environment in the laboratory. Some electronic technology experiments need to be carried out under constant temperature and humidity conditions. The temperature and humidity control system can help provide a suitable experimental environment and reduce the impact of external environmental changes on experimental results;

[0116] The fault diagnosis and repair subsystem is used to automatically or semi-automatically diagnose problems and provide repair suggestions when equipment fails during the experiment. It is also used to reduce the response time when a failure occurs and improve the efficiency of equipment recovery.

[0117] Communication and network systems are used to connect various devices in the experiment with external monitoring systems, data acquisition systems, etc. to ensure real-time data transmission. They are also used to provide real-time and reliable data transmission pipelines to support the coordination between various devices in the experiment;

[0118] S305. These auxiliary systems can be integrated with the monitoring system to ensure the efficient, stable and safe operation of electronic technology experiments through intelligent and automated management. Their combination not only improves the accuracy of the experiment, but also reduces human intervention, improves the reliability of data, and effectively reduces potential risks in the experiment;

[0119] S306, the data trend analysis module builds a distributed storage model and uploads the experimental data to the cloud platform, which can not only solve the storage bottleneck but also provide higher scalability and backup mechanism;

[0120] S307. The prediction analysis model uses an efficient data compression algorithm or database management system to compress and store data, thereby reducing storage space usage. At the same time, the analysis model is constructed, and autonomous prediction analysis processing is performed. Real-time synchronization of the model is achieved by continuously updating data.

[0121] S308, the user interface of the intelligent prediction port optimization system is made more concise and intuitive, and the operation difficulty is reduced;

[0122] S309, the intelligent prediction port introduces functions such as voice recognition and intelligent assistant, enabling experimenters to interact with the system through voice or other natural language methods to improve user experience.

[0123] S401, sequentially constructing a laboratory log management module, an experimental data storage module, a power supply and safety control center, and an automation control module, maintaining a communication connection between the output end of the experimental data storage module and the input end of the laboratory log management module, maintaining the automation control module integrated inside the power supply and safety control center, and maintaining a communication connection between the output end of the laboratory log management module and the input end of the power supply and safety control center;

[0124] S402, the laboratory log management module records all operations, data changes, system status and user operation information during the experiment;

[0125] S403, laboratory log management module facilitates the review and analysis of experiments at a later stage to ensure the integrity and credibility of the data;

[0126] S404, the experimental data storage module manages experimental resources, personnel arrangements, and experimental progress, and improves experimental efficiency and avoids resource conflicts through reasonable scheduling and management;

[0127] S405, the experimental data storage module classifies and stores each group of experiments and experiments on different dates, and performs subsequent data processing and analysis to ensure that the experimental data is not lost. The data analysis system can provide in-depth analysis of the experimental results and help identify trends or potential problems in the experiment;

[0128] S406, the power supply and safety control center provides a stable power supply and monitors the output quality of the power supply, such as voltage fluctuations and short circuits;

[0129] S407, the power supply and safety control center can also prevent power outages or fluctuations from affecting the experimental results, while ensuring the normal operation of the equipment;

[0130] S408, the automation control module automatically adjusts the current, voltage, and temperature variables according to the preset parameters, and automatically adjusts the working state of each instrument;

[0131] S409, the automated control module improves the accuracy and consistency of the experimental process, reduces human intervention, and ensures the stability of experimental conditions, especially for experiments that require precise control.

[0132] Example

[0133] S1. Laboratory staff tested the on-site and remote hardware equipment one by one, and all of them could be started and shut down normally before starting the monitoring system;

[0134] S2. Test and install laboratory and remote monitoring equipment;

[0135] S3. Connect to other subsystems for auxiliary use:

[0136]

[0137] S4. Access related algorithms for auxiliary use:

[0138]

[0139] S5. Mark the locations where each monitoring device is installed as a1, a2, a3 and a4, and record the devices installed in each marked area and the main responsibilities of the devices in a table;

[0140] S6 and a1 are the laboratory control centers, including the power controller, control panel, laboratory display screen and computer of the whole equipment. They monitor the operation data of each equipment in real time, including operation time, ambient temperature change during operation, etc., for remote supervision.

[0141] a2 is the laboratory personnel registration desk, which includes facial recognition equipment, infrared sensors, surveillance cameras, and personnel authentication display screens. It verifies the identity information of every person entering the laboratory to reduce data leakage;

[0142] a3 is the experimental log recording table, including the operation table, log book, and online recording memo. It can check whether there is any failure to record experimental data in time after the experiment is completed every day through remote monitoring. At the same time, it can remotely correct abnormal data in the record and correct it by reminding laboratory personnel;

[0143] a4 is the experimental process monitoring station, which includes multiple sets of monitoring equipment, waste collection equipment, lighting equipment and warning lights. It facilitates remote supervision and intervention by monitoring different experimental processes separately and synchronously.

[0144] S7. Collect various signals in the experiment, including voltage, current, frequency, temperature, pressure and other physical quantities. By performing edge computing at the data acquisition end and accelerating the module to process data by embedding other subsystems, the delay of data transmission and processing can be reduced.

[0145] S8. Build a distributed storage model and upload experimental data to the cloud platform, which can not only solve the storage bottleneck, but also provide higher scalability and backup mechanism. Use efficient data compression algorithms or database management systems to compress and store data, reduce storage space occupancy, and build analysis models at the same time, conduct autonomous prediction analysis and processing, and achieve real-time synchronization of the model by continuously updating data.

[0146] S9. Record all operations, data changes, system status and user operation information during the experiment, review and analyze the experiment to ensure the integrity and credibility of the data, classify and store each group of experiments and experiments on different dates, and perform subsequent data processing and analysis to ensure that the experimental data is not lost. The data analysis system can provide in-depth analysis of the experimental results and help identify trends or potential problems in the experiment;

[0147] S10. Realize remote data access and experimental equipment control through the network, allowing experimenters to monitor and operate experiments at remote locations, unified management of long-running experiments or multiple experimental sites, detect potential hazards of harmful gases, radiation, and noise in the experimental environment, protect the health and safety of experimenters, and ensure that there are no environmental factors harmful to the human body during the experiment;

[0148] S11. Provide a stable power supply and monitor the output quality of the power supply, such as voltage fluctuations and short circuits, to complete an electronic technology experiment monitoring and query process.

[0149] Specifically: In actual applications, there are multiple distributed early warning terminals, which are used in conjunction with the monitoring system including the electronic technology experiment recording terminal, the monitoring center, and the data trend prediction terminal. The multiple distributed early warning terminals are located in different geographical locations. The present invention constructs a complete monitoring system by setting the electronic technology experiment recording terminal, the monitoring center, the distributed early warning terminal and the data trend prediction terminal. In actual use, the electronic technology experiment recording terminal is used to record all operations, data changes, system status and user operation information during the experiment, and is also used to provide a stable power supply and monitor the output quality of the power supply. The monitoring center is used to achieve remote data access and experimental equipment control through the network, allowing experimenters to monitor and operate experiments at remote locations. It is also used to detect harmful gases, radiation, and noise potential dangers in the experimental environment. The distributed early warning terminal is used to mark the installation locations of each monitoring device, and for each marked area The domain records the equipment installed in this area and the main responsibilities of the equipment in a table format. It is also used to conduct comprehensive analysis and prediction model training on the data of multiple sensors by introducing artificial intelligence and multiple sets of algorithms. The data trend prediction end is used to collect various signals in the experiment in real time, and accelerate the module to process data by embedding other subsystems. It is also used to build a distributed storage model and upload the experimental data to the cloud platform, which solves many inadequacies of traditional experimental monitoring methods. It can not only monitor the experimental parameters in real time and accurately, but also automatically alarm and adjust the experimental equipment to ensure the smooth progress of the experiment. At the same time, it greatly improves the efficiency and safety of the experiment, manages, visualizes and stores electronic technology experimental data and corresponding analysis results, which helps to realize electronic technology experimental management through LAN cloud control, improves the intelligent level of monitoring management, and facilitates on-site and remote synchronous supervision and processing of each experimental link.

[0150] Those of ordinary skill in the art will appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, equipment and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] The monitoring system includes an electronic technology experiment recording terminal, a monitoring center, a distributed early warning terminal, and a data trend prediction terminal, which may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0153] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program instructions, such as a USB flash drive, a mobile hard disk, a read-only storage server, a random access storage server, a magnetic disk or an optical disk.

[0154] In addition, it should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0155] It should be noted that the above examples are only specific embodiments of the present invention, and the present invention is obviously not limited to the above examples, and there are many similar variations. All variations directly derived or associated from the contents disclosed by the technicians in this field should fall within the protection scope of the present invention.

[0156] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A monitoring system for electronic technology experiments, characterized in that: The monitoring system includes an electronic technology experiment recording terminal, a monitoring center, a distributed early warning terminal and a data trend prediction terminal. The staff enters the monitoring system by logging into the electronic technology experiment recording terminal, and then checks the real-time operation data of the monitoring center, the distributed early warning terminal and the data trend prediction terminal one by one. The electronic technology experiment recording terminal maintains data intercommunication and sharing with the monitoring center, the distributed early warning terminal and the data trend prediction terminal respectively through the laboratory local area network.

2. The monitoring system for electronic technology experiments according to claim 1 is characterized in that: The electronic technology experiment recording terminal is used to record all operations, data changes, system status and user operation information during the experiment, and is also used to provide a stable power supply and monitor the output quality of the power supply; The monitoring center is used to achieve remote data access and experimental equipment control through the network, allowing experimenters to monitor and operate experiments at a remote location, and is also used to detect potential hazards such as harmful gases, radiation, and noise in the experimental environment; The distributed early warning terminal is used to mark the locations where each monitoring device is installed, and to record the devices installed in each marked area and the main responsibilities of the devices in a table. It is also used to conduct comprehensive analysis and prediction model training on the data of multiple sensors by introducing artificial intelligence and multiple groups of algorithms. The data trend prediction terminal is used to collect various signals in the experiment in real time, accelerate the module to process data by embedding other subsystems, and is also used to build a distributed storage model to upload the experimental data to the cloud platform.

3. The monitoring system for electronic technology experiments according to claim 1 is characterized in that: The output end of the monitoring center is communicatively connected to the input end of the distributed early warning end, the output end of the distributed early warning end is communicatively connected to the input end of the data trend prediction end, and the electronic technology experiment recording end is bidirectionally communicatively connected to the data trend prediction end.

4. The monitoring system for electronic technology experiments according to claim 3 is characterized in that: The electronic technology experiment recording end includes a laboratory log management module, an experiment data storage module, a power supply and safety control center and an automation control module. The output end of the experiment data storage module is communicatively connected to the input end of the laboratory log management module. The automation control module is integrated inside the power supply and safety control center. The output end of the laboratory log management module is communicatively connected to the input end of the power supply and safety control center. The laboratory log management module is used to record all operations, data changes, system status and user operation information during the experiment; The experimental data storage module is used to manage experimental resources, personnel arrangements, and experimental progress; The power supply and safety control center is used to provide a stable power supply and monitor the output quality of the power supply; The automation control module is used to automatically adjust current, voltage and temperature variables according to preset parameters.

5. The monitoring system for electronic technology experiments according to claim 1 is characterized in that: The monitoring center includes a remote monitoring module, an environmental monitoring module, a visual detection module and a monitoring database. The output ends of the environmental monitoring module and the visual detection module are both communicatively connected to the input end of the remote monitoring module, the output end of the remote monitoring module is communicatively connected to the input end of the monitoring database, and the output end of the monitoring database is communicatively connected to the input end of the distributed early warning end.

6. The monitoring system for electronic technology experiments according to claim 5 is characterized in that: The remote monitoring module is used to achieve remote data access and experimental equipment control through the network; The environmental monitoring module is used to detect potential dangers of harmful gases, radiation, and noise in the experimental environment; The visual inspection module is used to perform real-time video monitoring of the experimental process through various groups of cameras installed inside the laboratory; The monitoring database is used to display real-time experimental data and equipment status through a graphical interface, which can be viewed and controlled through a touch screen, computer or mobile device.

7. The monitoring system for electronic technology experiments according to claim 1 is characterized in that: The distributed early warning end includes a distributed management module, an alarm module, a real-time response module and an intelligent prediction model module. The output end of the alarm module is communicatively connected to the output end of the real-time response module, the output end of the real-time response module is communicatively connected to the output end of the distributed management module, and the distributed management module is bidirectionally communicatively connected to the intelligent prediction model module.

8. The monitoring system for electronic technology experiments according to claim 7 is characterized in that: The distributed management module is used to mark the locations where each monitoring device is installed, recorded as a1, a2, ..., and record the devices installed in each marked area and the main responsibilities of the devices in a table; The alarm module is used to automatically analyze the collected data according to preset experimental parameters and standards; The real-time response module is used to conduct comprehensive analysis and prediction model training on data from multiple sensors by introducing artificial intelligence and multiple sets of algorithms; The intelligent prediction model module is used to construct a probability model to infer different events in the experimental process and determine potential failure modes.

9. The monitoring system for electronic technology experiments according to claim 1 is characterized in that: The data trend prediction end includes an electronic experiment data collection module, a data trend analysis module, a prediction analysis model and an intelligent prediction port. The output end of the electronic experiment data collection module is communicatively connected to the input end of the data trend analysis module, the output end of the data trend analysis module is communicatively connected to the input end of the prediction analysis model, and the prediction analysis model and the intelligent prediction port are bidirectionally communicatively connected.

10. The monitoring system for electronic technology experiments according to claim 9, characterized in that: The electronic experiment data collection module is used to collect various signals in the experiment in real time; It is also used to accelerate the module to process data by performing edge computing at the data collection end and embedding other subsystems; The data trend analysis module is used to build a distributed storage model and upload experimental data to the cloud platform; The predictive analysis model is used to compress and store data using an efficient data compression algorithm or a database management system, while constructing an analysis model; The intelligent prediction port is used to optimize the user interface of the system.

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