Electronic product remote real-time detection and fault diagnosis system

Abstract

The invention relates to the technical field of electronic products, in particular to a remote real-time detection and fault diagnosis system for electronic products, and solves the problems that in the prior art, data acquisition is insufficient in comprehensiveness and accuracy, and a traditional method can only acquire limited equipment parameters and is easily influenced by noise and interference, so that the data acquisition accuracy is low. The quality of acquired data is not high, and accurate fault diagnosis is difficult to support. A remote real-time detection and fault diagnosis system for an electronic product comprises a data acquisition module which is provided with a high-precision sensor array and is used for collecting operation state data of the electronic product in a real-time and high-precision manner, including but not limited to voltage, current, temperature, humidity, vibration frequency, power consumption, operation duration and possible abnormal signals. According to the invention, the comprehensiveness and accuracy of data acquisition are improved, the acquired equipment parameters are ensured to be comprehensive and accurate through a high-precision sensor array and a remote configuration upgrading function, and powerful data support is provided for fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic products, and in particular to a remote real-time detection and fault diagnosis system for electronic products. Background Art

[0002] With the widespread application of electronic products in various fields, their complexity and integration are constantly increasing, which puts higher requirements on the stability and reliability of the equipment. However, in actual use, electronic products are often affected by various environmental factors, such as temperature fluctuations, humidity changes, voltage instability, etc. These factors may cause equipment failures and affect the normal operation of the equipment. Traditional fault diagnosis methods often rely on the experience and judgment of on-site personnel, and there are problems such as low diagnostic efficiency and insufficient accuracy. In addition, with the wider distribution of electronic products, traditional on-site fault diagnosis methods also face huge challenges in terms of time and cost.

[0003] The data collection is not comprehensive and accurate enough. Traditional methods can only collect limited equipment parameters and are easily affected by noise and interference, resulting in low quality of collected data and difficulty in supporting accurate fault diagnosis.

[0004] The security and real-time performance of data transmission are difficult to guarantee. During the data transmission process, data may be stolen or tampered with, resulting in information leakage or misjudgment. At the same time, transmission delays may also cause delays in diagnostic results, affecting the timeliness of fault handling.

[0005] The intelligence level of fault diagnosis is low. Traditional methods mainly rely on manual experience and rule-based judgment. They lack intelligent diagnostic algorithms and models and are unable to cope with complex and changeable fault conditions.

[0006] User interactivity is poor, and it is difficult for users to obtain device status and diagnostic results in real time, nor can they interact with the system in a convenient way, which affects the efficiency of troubleshooting and user experience.

[0007] Limitations of data storage and management. The single management method for historical data and diagnostic records makes it difficult to support the storage and analysis of large-scale data, limiting the mining and utilization of data value. Summary of the invention

[0008] The purpose of the present invention is to provide a remote real-time detection and fault diagnosis system for electronic products, which solves the problem that the data collection in the prior art is insufficient in comprehensiveness and accuracy. Traditional methods can often only collect limited equipment parameters and are easily affected by noise and interference, resulting in low quality of collected data and difficulty in supporting accurate fault diagnosis.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] A remote real-time detection and fault diagnosis system for electronic products, including a data acquisition module, which is designed with a high-precision sensor array for real-time and high-precision collection of operating status data of electronic products, including but not limited to voltage, current, temperature, humidity, vibration frequency, power consumption, operating time and possible abnormal signals; at the same time, the module supports remote configuration and upgrade, and can dynamically adjust data acquisition strategies and parameters according to user needs or system optimization strategies to ensure the comprehensiveness and accuracy of data;

[0011] Data transmission module: This module uses the Advanced Encryption Standard (AES) to encrypt the collected data and establishes a secure connection with the remote server through a secure communication protocol (such as HTTPS or TLS) to transmit the data to the remote server, ensuring the security and integrity of the data during the transmission process; in addition, the data transmission module also includes a data compression function to reduce the amount of transmitted data and improve transmission efficiency;

[0012] The fault diagnosis module is located on the remote server. This module uses machine learning algorithms, deep learning models, and preset logical rules to conduct in-depth analysis of the transmitted data. It can automatically detect abnormal conditions of electronic products and generate detailed diagnostic reports, including fault types, possible causes, recommended solutions, and predicted fault impact ranges. At the same time, the fault diagnosis module also includes an adaptive learning mechanism that can continuously optimize the fault diagnosis algorithm based on new fault data and user feedback to improve the accuracy and efficiency of fault diagnosis.

[0013] The user interface module is designed with an intuitive graphical user interface (GUI) that supports multi-platform access (such as web pages, mobile applications, and desktop applications), provides users with diagnostic results and operational suggestions, and supports user feedback and online help functions; the user interface module also includes an interactive troubleshooting tool that helps users quickly locate and solve problems by guiding users through a series of troubleshooting steps, thereby improving the self-help and efficiency of troubleshooting;

[0014] Data storage module, which uses distributed database technology to efficiently and long-term store historical data and diagnostic records, and supports big data analysis and fault prediction algorithms; at the same time, the data storage module also includes data backup and recovery functions to ensure data reliability and availability and prevent data loss or damage;

[0015] Alarm module: When a fault is diagnosed, the module automatically triggers an alarm notification through multiple communication methods (such as SMS, email, and instant messaging) and provides urgent suggestions for troubleshooting. The alarm module also supports custom alarm thresholds and alarm recipients, which can be personalized according to user preferences and needs. At the same time, the alarm module also includes a fault prediction function, which predicts potential faults based on historical data and machine learning algorithms, and notifies users in advance so that they can take preventive measures.

[0016] System configuration module, which provides flexible configuration options, allowing users to configure system parameters according to actual needs, such as data collection frequency, alarm mode, data storage strategy, etc.; at the same time, the system configuration module also supports permission management functions to ensure the security and compliance of the system and prevent unauthorized access and operation.

[0017] Preferably, the data acquisition module further includes signal filtering and preprocessing functions to ensure the quality of the acquired data and reduce the impact of noise and interference on fault diagnosis.

[0018] Preferably, the data transmission module also supports a breakpoint resume function to ensure that the transmission can be automatically resumed when an interruption occurs during the data transmission process, thereby ensuring the integrity and continuity of the data.

[0019] Preferably, the fault diagnosis module also includes a fault pattern recognition function, which can identify common fault patterns based on historical fault data and machine learning algorithms, and provide users with targeted solutions and preventive measures.

[0020] Preferably, the user interface module also supports a multi-language support function, and can provide user interfaces and operation guides in different languages ​​according to user preferences and needs.

[0021] Preferably, the data storage module also includes a data encryption function to ensure that the stored data cannot be accessed or leaked without authorization.

[0022] Preferably, the alarm module also includes an alarm history recording function, which can save all alarm information and user response conditions, and provide data support for subsequent fault analysis and system optimization.

[0023] The present invention has at least the following beneficial effects:

[0024] Improve the comprehensiveness and accuracy of data collection: Through high-precision sensor arrays and remote configuration upgrade functions, ensure that the collected equipment parameters are comprehensive and accurate, providing strong data support for fault diagnosis.

[0025] Ensure the security and real-time nature of data transmission: Use advanced encryption standards and secure communication protocols to ensure the security and integrity of data during transmission; at the same time, data compression and breakpoint resume functions improve transmission efficiency and ensure the real-time nature of data.

[0026] Improve the intelligence level of fault diagnosis: Use machine learning algorithms, deep learning models, and adaptive learning mechanisms to achieve intelligent and automated fault diagnosis and improve the accuracy and efficiency of diagnosis.

[0027] Enhanced user interactivity: Provides an intuitive graphical user interface and multi-platform access support, allowing users to obtain device status and diagnostic results in real time; interactive troubleshooting tools guide users through the troubleshooting steps, improving the self-help and efficiency of troubleshooting.

[0028] Optimize data storage and management: Use distributed database technology to achieve efficient and long-term data storage; support big data analysis and fault prediction algorithms to mine data value; data backup and recovery functions ensure data reliability and availability. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0030] Figure 1 It is a schematic diagram of the main flow of the electronic product remote real-time detection and fault diagnosis system of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0032] Embodiment 1, refer to Figure 1 , a remote real-time detection and fault diagnosis system for electronic products, including a data acquisition module, which is designed with a high-precision sensor array for real-time and high-precision collection of operating status data of electronic products, including but not limited to voltage, current, temperature, humidity, vibration frequency, power consumption, operating time and possible abnormal signals; at the same time, the module supports remote configuration and upgrade, and can dynamically adjust data collection strategies and parameters according to user needs or system optimization strategies to ensure the comprehensiveness and accuracy of data;

[0033] Data transmission module: This module uses the Advanced Encryption Standard (AES) to encrypt the collected data and establishes a secure connection with the remote server through a secure communication protocol (such as HTTPS or TLS) to transmit the data to the remote server, ensuring the security and integrity of the data during the transmission process; in addition, the data transmission module also includes a data compression function to reduce the amount of transmitted data and improve transmission efficiency;

[0034] The fault diagnosis module is located on the remote server. This module uses machine learning algorithms, deep learning models, and preset logical rules to conduct in-depth analysis of the transmitted data. It can automatically detect abnormal conditions of electronic products and generate detailed diagnostic reports, including fault types, possible causes, recommended solutions, and predicted fault impact ranges. At the same time, the fault diagnosis module also includes an adaptive learning mechanism that can continuously optimize the fault diagnosis algorithm based on new fault data and user feedback to improve the accuracy and efficiency of fault diagnosis.

[0035] The user interface module is designed with an intuitive graphical user interface (GUI) that supports multi-platform access (such as web pages, mobile applications, and desktop applications), provides users with diagnostic results and operational suggestions, and supports user feedback and online help functions; the user interface module also includes an interactive troubleshooting tool that helps users quickly locate and solve problems by guiding users through a series of troubleshooting steps, thereby improving the self-help and efficiency of troubleshooting;

[0036] Data storage module, which uses distributed database technology to efficiently and long-term store historical data and diagnostic records, and supports big data analysis and fault prediction algorithms; at the same time, the data storage module also includes data backup and recovery functions to ensure data reliability and availability and prevent data loss or damage;

[0037] Alarm module: When a fault is diagnosed, the module automatically triggers an alarm notification through multiple communication methods (such as SMS, email, and instant messaging) and provides urgent suggestions for troubleshooting. The alarm module also supports custom alarm thresholds and alarm recipients, which can be personalized according to user preferences and needs. At the same time, the alarm module also includes a fault prediction function, which predicts potential faults based on historical data and machine learning algorithms, and notifies users in advance so that they can take preventive measures.

[0038] System configuration module, which provides flexible configuration options, allowing users to configure system parameters according to actual needs, such as data collection frequency, alarm mode, data storage strategy, etc.; at the same time, the system configuration module also supports permission management functions to ensure the security and compliance of the system and prevent unauthorized access and operation.

[0039] The present invention relates to an enhanced remote real-time detection and fault diagnosis system for electronic products, aiming to provide a comprehensive, efficient and user-friendly solution for real-time monitoring of the operating status of electronic products, timely detection and diagnosis of faults, and rich user interaction and data analysis functions.

[0040] The core components of the system include the data acquisition module, which can collect multiple key parameters of electronic products in real time and with high precision, providing a comprehensive and accurate data basis for fault diagnosis. The data transmission module uses advanced encryption and communication technology to ensure the security and integrity of data during transmission.

[0041] On the remote server, the fault diagnosis module uses machine learning algorithms and preset logical rules to conduct in-depth analysis of the collected data, automatically detect abnormal conditions of electronic products, and generate detailed diagnostic reports. These reports not only contain the fault type and possible causes, but also provide recommended solution steps to help users quickly locate and solve problems.

[0042] The user interface module is designed with an intuitive graphical user interface that supports multi-platform access, allowing users to view diagnostic results and operational suggestions anytime and anywhere. At the same time, the user interface module also provides interactive troubleshooting tools to guide users through a series of steps to locate and solve problems, improving the efficiency and accuracy of troubleshooting.

[0043] The data storage module uses distributed database technology to efficiently and long-term save historical data and diagnostic records. These data can be used for subsequent data analysis and fault prediction, helping users better understand the performance and usage of electronic products and prevent potential failures in advance.

[0044] When the fault diagnosis module detects a fault, the alarm module will immediately trigger an alarm notification, ensuring that users can learn about the fault information and take appropriate measures in the first place through various communication methods. At the same time, the alarm module also provides emergency suggestions for fault handling, and supports custom alarm thresholds and alarm recipients to meet the personalized needs of users.

[0045] Finally, the system configuration module provides flexible configuration options and permission management functions, allowing users to personalize the system according to their own usage habits and needs, and ensure the security and compliance of the system.

[0046] According to the above embodiments, it can be seen that:

[0047] Improve the comprehensiveness and accuracy of data collection: Through high-precision sensor arrays and remote configuration upgrade functions, ensure that the collected equipment parameters are comprehensive and accurate, providing strong data support for fault diagnosis.

[0048] Ensure the security and real-time nature of data transmission: Use advanced encryption standards and secure communication protocols to ensure the security and integrity of data during transmission; at the same time, data compression and breakpoint resume functions improve transmission efficiency and ensure the real-time nature of data.

[0049] Improve the intelligence level of fault diagnosis: Use machine learning algorithms, deep learning models, and adaptive learning mechanisms to achieve intelligent and automated fault diagnosis and improve the accuracy and efficiency of diagnosis.

[0050] Enhanced user interactivity: Provides an intuitive graphical user interface and multi-platform access support, allowing users to obtain device status and diagnostic results in real time; interactive troubleshooting tools guide users through the troubleshooting steps, improving the self-help and efficiency of troubleshooting.

[0051] Optimize data storage and management: Use distributed database technology to achieve efficient and long-term data storage; support big data analysis and fault prediction algorithms to mine data value; data backup and recovery functions ensure data reliability and availability.

[0052] Example 2, refer to Figure 1 ,The data acquisition module further includes signal filtering and ,preprocessing functions to ensure the quality of the acquired data and ,reduce the impact of noise and interference on fault diagnosis.

[0053] The data transmission module also supports the breakpoint resume function to ensure that the transmission can be automatically resumed when an interruption occurs during the data transmission process, ensuring the integrity and continuity of the data.

[0054] The fault diagnosis module also includes a fault pattern recognition function, which can identify common fault modes based on historical fault data and machine learning algorithms, and provide users with targeted solutions and preventive measures.

[0055] The user interface module also supports multi-language support, and can provide user interfaces and operation guides in different languages ​​according to user preferences and needs.

[0056] The data storage module also includes a data encryption function to ensure that the stored data cannot be accessed or disclosed without authorization.

[0057] The alarm module also includes an alarm history recording function, which can save all alarm information and user response status, providing data support for subsequent fault analysis and system optimization.

[0058] According to the above embodiments, the present invention significantly improves the performance and user experience of the electronic product remote real-time detection and fault diagnosis system by solving the problems existing in the background technology, and provides comprehensive guarantee for the stable operation of electronic products.

[0059] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A remote real-time detection and fault diagnosis system for electronic products, characterized in that: include: Data acquisition module, which is designed with a high-precision sensor array to collect the operating status data of electronic products in real time and with high precision, including but not limited to voltage, current, temperature, humidity, vibration frequency, power consumption, operating time and possible abnormal signals; at the same time, the module supports remote configuration and upgrade, and can dynamically adjust data collection strategies and parameters according to user needs or system optimization strategies to ensure the comprehensiveness and accuracy of data; Data transmission module, which uses the Advanced Encryption Standard (AES) to encrypt the collected data and establishes a secure connection with the remote server through a secure communication protocol (such as HTTPS or TLS) to transmit the data to the remote server, ensuring the security and integrity of the data during transmission; In addition, the data transmission module also includes a data compression function to reduce the amount of transmitted data and improve transmission efficiency; The fault diagnosis module is located on the remote server. This module uses machine learning algorithms, deep learning models, and preset logical rules to conduct in-depth analysis of the transmitted data. It can automatically detect abnormal conditions of electronic products and generate detailed diagnostic reports, including fault types, possible causes, recommended solutions, and predicted fault impact ranges. At the same time, the fault diagnosis module also includes an adaptive learning mechanism that can continuously optimize the fault diagnosis algorithm based on new fault data and user feedback to improve the accuracy and efficiency of fault diagnosis. The user interface module is designed with an intuitive graphical user interface (GUI) that supports multi-platform access (such as web pages, mobile applications, and desktop applications), provides users with diagnostic results and operational suggestions, and supports user feedback and online help functions. The user interface module also includes an interactive troubleshooting tool that guides users through a series of troubleshooting steps to help users quickly locate and solve problems, thereby improving the self-help and efficiency of troubleshooting; Data storage module, which uses distributed database technology to efficiently and long-term store historical data and diagnostic records, and supports big data analysis and fault prediction algorithms; at the same time, the data storage module also includes data backup and recovery functions to ensure data reliability and availability and prevent data loss or damage; Alarm module: When a fault is diagnosed, the module automatically triggers an alarm notification through various communication methods (such as SMS, email, instant message push) and provides emergency suggestions for troubleshooting; The alarm module also supports custom alarm thresholds and alarm recipients, which can be personalized according to user preferences and needs. At the same time, the alarm module also includes a fault prediction function, which predicts potential faults based on historical data and machine learning algorithms and notifies users in advance so that users can take preventive measures. System configuration module, which provides flexible configuration options, allowing users to configure system parameters according to actual needs, such as data collection frequency, alarm mode, data storage strategy, etc.; at the same time, the system configuration module also supports permission management functions to ensure the security and compliance of the system and prevent unauthorized access and operation.

2. The electronic product remote real-time detection and fault diagnosis system according to claim 1, characterized in that: The data acquisition module further includes signal filtering and preprocessing functions to ensure the quality of the acquired data and reduce the impact of noise and interference on fault diagnosis.

3. The electronic product remote real-time detection and fault diagnosis system according to claim 1, characterized in that: The data transmission module also supports the breakpoint resume function to ensure that the transmission can be automatically resumed when an interruption occurs during the data transmission process, ensuring the integrity and continuity of the data.

4. The electronic product remote real-time detection and fault diagnosis system according to claim 1, characterized in that: The fault diagnosis module also includes a fault pattern recognition function, which can identify common fault modes based on historical fault data and machine learning algorithms, and provide users with targeted solutions and preventive measures.

5. The electronic product remote real-time detection and fault diagnosis system according to claim 1, characterized in that: The user interface module also supports multi-language support, and can provide user interfaces and operation guides in different languages ​​according to user preferences and needs.

6. The electronic product remote real-time detection and fault diagnosis system according to claim 5, characterized in that: The data storage module also includes a data encryption function to ensure that the stored data cannot be accessed or disclosed without authorization.

7. The electronic product remote real-time detection and fault diagnosis system according to claim 6, characterized in that: The alarm module also includes an alarm history recording function, which can save all alarm information and user response status, providing data support for subsequent fault analysis and system optimization.

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