An industrial equipment cross-region internet of things detection and early warning management system

Abstract

The application belongs to the technical field of industrial internet of things, and provides an industrial equipment cross-region internet of things detection and early warning management system, which comprises a collection communication module, a personalized cloud platform, and a client port; the collection communication module is used for collecting operation data of industrial equipment, and converts the data into digital signals for encoding processing and transmission; the personalized cloud platform is used for receiving, storing, and processing the digital signals, and intelligently analyzing and matching the data through an expert system; the client port is used for user accessing the personalized cloud platform to query operation states and early warning information of the equipment; the collection communication module and the personalized cloud platform perform remote communication and data transmission through an internet of things gateway; the application realizes quick and accurate conversion of analog signals to digital signals by using an efficient data processing module and an advanced encoding technology, which not only significantly improves the precision and speed of data collection, but also reduces delay and distortion in the data transmission process.

Description

Technical Field

[0001] This invention belongs to the field of industrial Internet of Things (IoT) technology, specifically a cross-regional IoT detection and early warning management system for industrial equipment. Background Technology

[0002] The Industrial Internet of Things (IIoT) refers to the application of IoT technology in industrial environments. By connecting various industrial equipment, sensors, machines, and systems, it enables the collection, exchange, analysis, and optimization of data. The core of the IIoT lies in improving production efficiency, reducing operating costs, enhancing equipment reliability and security, and optimizing the entire production process. The IIoT has a wide range of applications, including manufacturing, energy, transportation, and healthcare.

[0003] In the current field of Industrial Internet of Things (IIoT), although there are some cross-regional equipment monitoring and early warning systems, the data acquisition and transmission efficiency of these systems is low. In particular, during the process of converting analog signals into digital signals, inaccurate conversion or untimely processing often leads to data distortion or delay, affecting the accuracy of subsequent intelligent analysis and early warning.

[0004] To address these issues, those skilled in the art have proposed a cross-regional Internet of Things (IoT) detection and early warning management system for industrial equipment. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a cross-regional IoT detection and early warning management system for industrial equipment. This system solves the problem in existing technologies where inaccurate conversion or untimely processing during the analog-to-digital signal conversion process often leads to data distortion or delays, affecting the accuracy of subsequent intelligent analysis and early warning.

[0006] A cross-regional IoT detection and early warning management system for industrial equipment includes,

[0007] The data acquisition and communication module is used to acquire the operating data of industrial equipment, convert the data into digital signals for encoding and transmission.

[0008] A personalized cloud platform is used to receive, store, and process the digital signals, and to perform intelligent analysis and matching operations on the data through an expert system;

[0009] The client interface is used by users to access the personalized cloud platform and query the device's operating status and warning information.

[0010] The data acquisition and communication module communicates and transmits data remotely with the personalized cloud platform through an IoT gateway.

[0011] Preferably, the acquisition and communication module includes,

[0012] The data acquisition sensor is used to collect real-time data on voltage, current, temperature, vibration, and sound from industrial equipment, as well as hyperspectral data on the quality of metal powder in lubricating grease.

[0013] The data processing module is used to convert the analog signals collected by the acquisition sensor into digital signals and perform encoding processing;

[0014] The communication module is used to transmit the encoded digital signal to the personalized cloud platform.

[0015] Preferably, the data processing module uses the following formula for signal conversion: D = A × ADC resolution +offset, where D is the digital signal, A is the analog signal amplitude, and ADC resolution The resolution of the analog-to-digital converter is denoted by 'resolution', and 'offset' is the offset value.

[0016] Preferably, the personalized cloud platform includes,

[0017] A receiving module is used to receive digital signals transmitted by the communication module;

[0018] A cloud server is used to store and process the data received by the receiving module;

[0019] An expert system is used to perform intelligent analysis and matching operations on the data, and triggers an early warning mechanism when abnormal values ​​appear in the data.

[0020] Preferably, the expert system uses the following algorithm for outlier detection: Where Z is the standard score, X is the current data value, μ is the average of historical data, and σ is the standard deviation of historical data; when |Z| exceeds the preset threshold, it is judged as an outlier.

[0021] Preferably, the personalized cloud platforms are interconnected through a directed acyclic graph to achieve data sharing and collaborative computing.

[0022] Preferably, the client port includes multi-level ports, with different levels of ports having different access permissions.

[0023] Preferably, it also includes a data encryption module for encrypting the transmitted data to ensure the security of data transmission.

[0024] Preferably, the data encryption module uses the following encryption algorithm to encrypt the data: C = E(K,P), where C is the ciphertext, E is the encryption algorithm, K is the key, and P is the plaintext.

[0025] Preferably, it also includes a data backup and recovery module, which is used to back up the stored data regularly and restore the data when needed.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. This invention achieves rapid and accurate conversion from analog to digital signals by employing a high-efficiency data processing module and advanced coding technology. This improvement not only significantly enhances the accuracy and speed of data acquisition but also reduces latency and distortion during data transmission, providing a more reliable data foundation for subsequent intelligent analysis and early warning. At the same time, the system also enables remote communication and data transmission between the acquisition and communication module and the personalized cloud platform through an IoT gateway, further improving the efficiency and stability of data transmission.

[0028] 2. This invention employs an intelligent outlier detection algorithm. This algorithm can perform in-depth analysis and matching operations on current data based on historical data, promptly detect outliers in the data, and trigger an early warning mechanism. This not only improves the accuracy and timeliness of the early warning, but also enables the system to provide more refined early warnings for different types of equipment failures, thus providing a strong guarantee for the stable operation of industrial production.

[0029] 3. This invention achieves comprehensive protection for transmitted data through a data encryption module and advanced encryption algorithms. This improvement not only effectively prevents data leakage and tampering during transmission but also enhances the overall security of the system, providing a solid barrier for data security in industrial production. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of an industrial equipment cross-regional Internet of Things (IoT) detection and early warning management system. Detailed Implementation

[0031] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0032] Example 1

[0033] As attached Figure 1 As shown, this invention provides a cross-regional IoT detection and early warning management system for industrial equipment, including,

[0034] The data acquisition and communication module is used to collect operating data from industrial equipment, convert the data into digital signals for encoding and transmission, and adopts low-power, high-reliability sensor and communication technologies to ensure the accuracy and stability of data acquisition. Through digital signal encoding and processing, it effectively improves data transmission efficiency and security, laying a solid foundation for subsequent intelligent analysis. With the help of Internet of Things technology, the system is not limited by geographical location and can realize unified management of cross-regional equipment.

[0035] The personalized cloud platform is used to receive, store, and process digital signals, and to perform intelligent analysis and matching operations on the data through an expert system. The expert system built into the personalized cloud platform can perform in-depth mining and intelligent analysis of the collected data based on big data analysis and machine learning algorithms, accurately identify abnormal device patterns, provide more accurate early warning information, and reduce the possibility of false alarms and missed alarms. At the same time, it can be personalized according to the characteristics of different devices and user needs, such as setting specific monitoring parameters and early warning thresholds, making management more refined and meeting the management needs in different scenarios.

[0036] The client interface is used by users to access the personalized cloud platform, query the device's operating status and warning information. The client interface provides an intuitive data visualization interface, allowing users to easily view the device's operating status, historical data trends and warning information without a professional background, greatly improving the convenience of information acquisition and user experience.

[0037] Among them, the data acquisition and communication module and the personalized cloud platform communicate and transmit data remotely through the Internet of Things gateway. Through the close cooperation between the data acquisition and communication module and the Internet of Things gateway, the real-time acquisition and remote transmission of device data are realized, which greatly shortens the time from fault detection to response and improves maintenance efficiency.

[0038] The data acquisition and communication module, the personalized cloud platform, and the client interface are seamlessly connected through an IoT gateway, forming a closed-loop monitoring and early warning system. This highly integrated design ensures smooth information transmission and efficient processing, improving the overall performance and stability of the system.

[0039] As can be seen from the above, by integrating advanced Internet of Things (IoT) technology, big data analytics, and artificial intelligence (AI) technology, the system enables real-time monitoring, intelligent early warning, and efficient management of the operating status of industrial equipment. This not only improves the reliability and stability of the equipment but also reduces the operating costs and risks for enterprises.

[0040] Example 2

[0041] As attached Figure 1 As shown, this embodiment is basically the same as the previous embodiment, except that the acquisition and communication module includes,

[0042] The data acquisition sensors are used to collect real-time data on voltage, current, temperature, vibration, and sound from industrial equipment, as well as hyperspectral data on the metal powder content of lubricating grease. The data acquisition sensors are the "eyes" and "ears" of the system, responsible for collecting various relevant data from industrial equipment in real time. These data can comprehensively reflect the operating status of the equipment and provide a foundation for subsequent intelligent analysis and early warning.

[0043] The data acquisition sensors include voltage and current sensors, used to monitor the electrical parameters of the equipment, ensuring that the equipment operates under rated voltage and current, and avoiding overload or underload; temperature sensors, used to monitor the temperature status of the equipment, preventing equipment damage or fire accidents caused by overheating; vibration sensors, used to monitor the vibration of the equipment, and promptly detect wear or loosening of components such as bearings and gears; sound sensors, used to monitor the sound generated by the equipment during operation, and to determine the operating status of the equipment through sound characteristic analysis, such as whether there are abnormal noises or excessive noise; and hyperspectral sensors, used to collect mass data of metal powder in lubricating grease, and through hyperspectral analysis technology, the content of metal powder in the grease can be accurately measured, thereby predicting the wear degree and lifespan of the equipment.

[0044] The data processing module is used to convert the analog signals collected by the acquisition sensors into digital signals and perform encoding processing, providing a reliable foundation for subsequent data processing and transmission;

[0045] The communication module is used to transmit the encoded digital signals to the personalized cloud platform. In order to achieve cross-regional communication and data transmission, the communication module can adopt wireless communication technologies such as Wi-Fi, 4G / 5G, LoRa, NB-IoT, etc. These technologies have advantages such as long transmission distance, low power consumption, and strong anti-interference ability, which can ensure the real-time performance and reliability of data.

[0046] Specifically, the data processing module uses the following formula for signal conversion: D = A × ADC resolution +offset, where D is the digital signal, A is the analog signal amplitude, and ADC resolution The resolution of the analog-to-digital converter is given by `offset`, which is the offset to ensure that the analog signal is accurately converted into a digital signal.

[0047] Furthermore, personalized cloud platforms include,

[0048] The receiving module is used to receive digital signals transmitted by the communication module. To ensure the integrity and accuracy of the data, the receiving module adopts reliable communication protocols and verification mechanisms, such as TCP / IP protocol and CRC check. At the same time, the receiving module also has a data caching function, which can temporarily store the received data and wait for the cloud server to process it.

[0049] The cloud server is used to store and process the data received by the receiving module. The cloud server is the core of storing and processing the data received by the receiving module. The cloud server adopts a distributed architecture and has high availability, scalability and security. Through the cloud server, massive amounts of data can be stored, queried and analyzed, providing strong support for subsequent intelligent analysis and early warning. The cloud server also supports data backup and recovery functions to ensure data security and reliability.

[0050] Expert systems are used for intelligent analysis and matching operations on data. When outliers appear in the data, an early warning mechanism is triggered. Expert systems use advanced algorithms and models, such as machine learning and deep learning, to deeply mine and analyze the data.

[0051] Furthermore, the expert system employs the following algorithm for outlier detection: Where Z is the standard score, X is the current data value, μ is the average of historical data, and σ is the standard deviation of historical data; when |Z| exceeds the preset threshold, it is judged as an outlier. This algorithm can promptly detect abnormal changes in the operating status of the equipment and trigger an early warning mechanism to remind users to perform timely maintenance and repair. Through the analysis and mining of historical data, the expert system can predict the future operating status and performance trend of the equipment, providing a scientific basis for the maintenance and management of the equipment.

[0052] Furthermore, personalized cloud platforms achieve data sharing and collaborative computing through distributed connections via directed acyclic graphs. This fully utilizes the computing and data resources across multiple cloud platforms, improving the overall system performance and reliability. Simultaneously, data sharing and collaborative computing enable cross-regional device monitoring and early warning, providing strong support for enterprises' cross-regional operations.

[0053] Furthermore, the client port includes multi-level ports, with different levels of access permissions. Higher-level ports can access all device operating data and alert information, while lower-level ports can only access partial data and information. This design ensures system security and flexibility. The client port also supports various device access methods, such as PCs, mobile phones, and tablets. Users can access the system anytime, anywhere through the client port to view device operating status and alert information. The client port provides rich functions and a user interface, such as data visualization, report generation, and alert settings, facilitating remote monitoring and management of devices.

[0054] As shown above, by integrating a data acquisition and communication module, a personalized cloud platform, and a client interface, comprehensive monitoring and intelligent early warning of industrial equipment are achieved. Specifically, various sensors collect real-time data from industrial equipment, such as voltage, current, temperature, vibration, and sound. This data comprehensively reflects the operating status of the equipment, providing a foundation for subsequent intelligent analysis and early warning. The data collected by the sensors is converted into digital signals by the communication module and transmitted to the personalized cloud platform. The expert system performs intelligent analysis and matching calculations on the data. When abnormal values ​​are found, an early warning mechanism is triggered, reminding users to perform timely maintenance and repair. Each component of the system has been carefully designed and optimized to ensure the real-time performance, accuracy, and reliability of the data. At the same time, the system also supports data sharing and collaborative computing, and provides rich functions and operation interfaces for the client interface, such as data visualization, report generation, and early warning settings, facilitating remote monitoring and management of equipment and providing strong support for enterprises' cross-regional operations.

[0055] Example 3

[0056] As attached Figure 1 As shown, this embodiment is basically the same as the previous embodiment, except that it also includes a data encryption module, which is used to encrypt the transmitted data to ensure the security of data transmission. The data encryption module protects the confidentiality and integrity of the data. The data encryption module uses advanced encryption algorithms to encrypt the data to ensure that the data is not stolen or tampered with during transmission.

[0057] Specifically, the data encryption module uses the following encryption algorithm to encrypt data: C = E(K,P), where C is the ciphertext, i.e. the encrypted data; E is the encryption algorithm, which is the core of data encryption and determines the strength and security of the encryption; K is the key, which is an indispensable part of the encryption and decryption process, and its complexity and confidentiality are directly related to the security of the data; and P is the plaintext, i.e. the data to be encrypted.

[0058] The preferred encryption algorithm is AES, a symmetric encryption algorithm that uses the same key for both encryption and decryption. It is efficient, secure, and easy to implement. To ensure the security and availability of the key, a key management mechanism combining a Key Distribution Center (KDC) and a Key Management Service (KMS) is adopted. The KDC is responsible for key generation and distribution, while the KMS is responsible for key storage, backup, and recovery. This mechanism ensures the security of the key during generation, distribution, storage, and use.

[0059] Furthermore, it also includes a data backup and recovery module, which is used to regularly back up the stored data and restore the data when needed. The data backup and recovery module is responsible for regularly backing up the stored data and restoring the data when needed. The data backup and recovery module can effectively prevent system paralysis caused by data loss or damage.

[0060] As can be seen from the above, by integrating the data acquisition and communication module, the personalized cloud platform, and the client interface, as well as adding the data encryption module and the data backup and recovery module, the security and reliability of the system have been further improved, ensuring the confidentiality, integrity, and availability of the data. Through this system, comprehensive monitoring and intelligent early warning of industrial equipment can be achieved.

[0061] All standard parts used in this invention can be purchased commercially, and irregularly shaped parts can be customized according to the description and drawings. The specific connection methods for each part all employ conventional methods such as bolts, rivets, and welding, which are mature technologies in the prior art. The machinery, parts, and equipment all use conventional models in the prior art, and the circuit connections also use conventional connection methods in the prior art, which will not be detailed here. Any content not described in detail in this specification belongs to the prior art known to those skilled in the art.

[0062] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0063] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0064] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0065] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0066] The accompanying drawings of the embodiments disclosed in this invention only involve structures relevant to the embodiments disclosed in this invention. Other structures can be referred to with common designs. Unless otherwise specified, the same embodiment and different embodiments of this invention can be combined with each other.

[0067] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cross-regional Internet of Things (IoT) detection and early warning management system for industrial equipment, characterized in that: include, The data acquisition and communication module is used to acquire the operating data of industrial equipment, convert the data into digital signals for encoding and transmission. A personalized cloud platform is used to receive, store, and process the digital signals, and to perform intelligent analysis and matching operations on the data through an expert system; The client interface is used by users to access the personalized cloud platform and query the device's operating status and warning information. The data acquisition and communication module communicates and transmits data remotely with the personalized cloud platform through an IoT gateway.

2. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 1, characterized in that: The data acquisition and communication module includes, The data acquisition sensor is used to collect real-time data on voltage, current, temperature, vibration, and sound from industrial equipment, as well as hyperspectral data on the quality of metal powder in lubricating grease. The data processing module is used to convert the analog signals collected by the acquisition sensor into digital signals and perform encoding processing; The communication module is used to transmit the encoded digital signal to the personalized cloud platform.

3. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 2, characterized in that: The data processing module performs signal conversion using the following formula: D = A × ADC resolution +offset, where D is the digital signal, A is the analog signal amplitude, and ADC resolution The resolution of the analog-to-digital converter is denoted by 'resolution', and 'offset' is the offset value.

4. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 3, characterized in that: The personalized cloud platform includes, A receiving module is used to receive digital signals transmitted by the communication module; A cloud server is used to store and process the data received by the receiving module; An expert system is used to perform intelligent analysis and matching operations on the data, and triggers an early warning mechanism when abnormal values ​​appear in the data.

5. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 4, characterized in that: The expert system uses the following algorithm for outlier detection. Where Z is the standard score, X is the current data value, μ is the average of historical data, and σ is the standard deviation of historical data; when |Z| exceeds the preset threshold, it is judged as an outlier.

6. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 5, characterized in that: The personalized cloud platforms are interconnected through a directed acyclic graph to achieve data sharing and collaborative computing.

7. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 6, characterized in that: The client port includes multiple levels of ports, with different levels of ports having different access permissions.

8. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 7, characterized in that: It also includes a data encryption module, which is used to encrypt the transmitted data to ensure the security of data transmission.

9. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 8, characterized in that: The data encryption module uses the following encryption algorithm to encrypt data: C = E(K,P), where C is the ciphertext, E is the encryption algorithm, K is the key, and P is the plaintext.

10. The cross-regional IoT detection and early warning management system for industrial equipment as described in claim 9, characterized in that: It also includes a data backup and recovery module, which is used to back up the stored data regularly and restore the data when needed.

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