Industrial production equipment monitoring and early warning system based on Internet of Things and edge intelligence

Abstract

The invention belongs to the technical field of industrial equipment monitoring, and discloses an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence, and the system collects multi-modal data in real time through a distributed sensor network, and carries out the real-time processing and feature extraction through the edge intelligence technology. Data fusion and optimization are carried out through a cloud deep learning model, a self-adaptive early warning and decision module dynamically adjusts a threshold value and optimizes an operation strategy, an environmental adaptability optimization module ensures the stability of equipment under extreme conditions, and an equipment health management and collaborative maintenance module realizes cross-equipment collaborative maintenance. The remote monitoring and interaction module provides man-machine interaction type control, and the cross-device collaborative learning module improves the model performance through federal learning. According to the system, the accuracy of equipment monitoring, the timeliness of early warning and the intelligent level of maintenance are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of industrial equipment monitoring technology, and in particular to an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence, which is suitable for equipment status monitoring, fault early warning and intelligent maintenance in complex industrial environments. Background Art

[0002] In modern industrial production, efficient and stable operation of equipment is the key to ensuring production continuity and safety. However, industrial equipment usually operates in a changeable and harsh environment, such as high temperature, high humidity, strong vibration or dust pollution, which significantly affects the performance and life of the equipment. Traditional equipment monitoring systems have the following shortcomings when dealing with such challenges: (1) Data collection limitations: Traditional systems rely on a single or a small number of sensors, which makes it difficult to fully capture the multi-dimensional changes in equipment status and environment, resulting in one-sided monitoring results.

[0003] (2) Static warning mechanism: The existing system has a fixed warning threshold and lacks the ability to adapt to dynamic changes in the environment, which can easily lead to false alarms or missed alarms.

[0004] (3) Inefficient maintenance: Maintenance plans are often based on fixed cycles rather than the actual status of the equipment, which increases costs and may lead to missed opportunities for fault prevention.

[0005] (4) Insufficient remote management: In distributed equipment scenarios, traditional systems rely on manual on-site intervention, and their remote monitoring and real-time response capabilities are limited.

[0006] In response to the above problems, the present invention proposes an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence to significantly improve the intelligence level of equipment management. Summary of the invention

[0007] In view of the shortcomings of the existing technology, the present invention provides an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence. By introducing technologies such as multimodal data fusion, edge intelligent computing, adaptive early warning thresholds, dynamic environment optimization, and cross-device collaborative learning, the accuracy of equipment monitoring, the timeliness of early warning, and the intelligent level of maintenance are significantly improved.

[0008] To achieve the above object, the present invention is implemented through the following technical solutions: Industrial production equipment monitoring and early warning system based on IoT and edge intelligence, including: A data acquisition module, used to collect multi-modal operation data and environmental data of the equipment through a distributed sensor network; The edge intelligent processing module is deployed on the device side and is used to perform real-time preprocessing and feature extraction on the collected data at the edge layer; The cloud data analysis module performs deep fusion analysis and model optimization on multimodal data uploaded from the edge; The adaptive warning and decision-making module dynamically generates warning signals and optimizes equipment operation strategies based on the collaborative analysis results of the edge and cloud; Environmental adaptability optimization module, used to dynamically adjust the operation mode according to real-time environmental data and equipment status; The equipment health management and collaborative maintenance module evaluates the equipment health status and generates a predictive maintenance plan that is collaborative across equipment; Remote monitoring and interaction module, used to achieve real-time monitoring of equipment status, fault diagnosis and human-machine interactive remote control through the Internet of Things platform; The cross-device collaborative learning module is used to achieve knowledge sharing and model optimization among multiple devices through federated learning technology.

[0009] How the system works: 1. The data acquisition module obtains the temperature, vibration, sound, humidity, current and environmental parameters (such as air pressure and dust concentration) of the equipment in real time through a distributed sensor network, and transmits the data to the edge intelligent processing module.

[0010] 2. The edge intelligent processing module uses lightweight neural networks (such as MobileNet) to perform real-time preprocessing and feature extraction of data, reducing the cloud computing burden and improving response speed.

[0011] 3. The cloud data analysis module performs fusion analysis on multimodal data based on deep learning models (such as Transformer), extracts deep features and optimizes the global model.

[0012] 4. The adaptive warning and decision-making module combines the edge and cloud analysis results, generates warning signals through a dynamic threshold generation algorithm, and uses reinforcement learning to optimize equipment operating parameters.

[0013] 5. The environmental adaptability optimization module dynamically adjusts the operating mode through the environment-equipment coupling optimization algorithm to ensure the stability of the equipment under extreme conditions.

[0014] 6. The equipment health management and collaborative maintenance module evaluates equipment status and generates a maintenance plan based on a multi-equipment collaborative health prediction model.

[0015] 7. The remote monitoring and interaction module provides real-time data visualization, fault diagnosis and voice interactive control functions through the Internet of Things platform.

[0016] 8. The cross-device collaborative learning module uses federated learning technology to achieve model sharing and optimization among multiple devices while protecting data privacy.

[0017] As a further preferred technical solution of the present invention The data acquisition module comprises: Distributed sensor network, using low-power wide area network (LPWAN) technology to ensure high coverage and low energy consumption of data collection; The data preprocessing unit uses Kalman filtering and wavelet transform algorithms for denoising and signal enhancement. The Kalman filtering update equation is: in, For the calibration state, For gain, is the observed value.

[0018] The edge intelligent processing module includes: The lightweight feature extraction unit uses the MobileNet V3 model to extract data features. Its convolution operation is: in, is the convolution kernel weight, For input data, is the activation function; Real-time anomaly detection unit detects data anomalies based on the isolation forest algorithm.

[0019] The cloud data analysis module includes: The multimodal fusion unit uses the Transformer model for data fusion, and its attention mechanism calculation formula is: in, are query, key, and value matrices respectively, for dimension; The global model optimization unit updates the model parameters by gradient descent.

[0020] The adaptive early warning and decision-making module includes: Dynamic threshold generation unit, which adjusts the threshold based on the exponentially weighted moving average (EWMA) algorithm: in, is the smoothing factor, is the current data deviation.

[0021] The decision optimization unit uses the deep deterministic policy gradient (DDPG) algorithm to optimize the operation strategy; Environmental adaptability optimization modules include: Environmental sensing unit, real-time monitoring of temperature, humidity, air pressure and dust concentration; The coupled optimization unit balances equipment performance and energy consumption based on a multi-objective optimization algorithm (such as NSGA-II).

[0022] The equipment health management and collaborative maintenance module includes: Health assessment unit, combining Hidden Markov Model (HMM) and Graph Neural Network (GNN) to predict equipment status; Collaborative maintenance unit optimizes maintenance plans through collaborative analysis of multiple devices.

[0023] The cross-device collaborative learning module adopts a federated learning framework, and its aggregate update formula is: in, is the local model parameter of the kth device, is the device data volume, For the total amount.

[0024] The present invention provides an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence, which has the following significant advantages over the prior art: (1) Edge intelligence improves efficiency: Edge computing reduces data transmission delays and enables real-time anomaly detection and early warning.

[0025] (2) Multimodal fusion to enhance accuracy: Combining Transformer and GNN technologies, making full use of multi-source data to improve the accuracy of state assessment.

[0026] (3) Adaptability and dynamic optimization: Dynamic threshold and coupled optimization algorithm ensure the robustness and energy efficiency of the system in complex environments.

[0027] (4) Innovation of collaborative learning: Federated learning enables knowledge sharing among multiple devices, improves model generalization capabilities and protects data privacy.

[0028] (5) Intelligent maintenance: Collaborative prediction across devices reduces downtime and optimizes resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 :Schematic diagram of the overall system architecture; Figure 2 : Flowchart of the data acquisition module and the edge intelligent processing module; Figure 3 : A schematic diagram of the structure of the cloud data analysis module; Figure 4 : The workflow diagram of the adaptive warning and decision-making module; Figure 5 : Schematic diagram of environmental adaptability optimization module; Figure 6: The equipment health management and collaborative maintenance module architecture diagram; Figure 7 : Schematic diagram of the cross-device collaborative learning module. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example 1: Overall system implementation This system is deployed in an industrial equipment monitoring scenario in a chemical plant, involving multiple devices distributed in high temperature and high humidity environments. Figure 1 As shown, it includes the following modules: 1. Data acquisition module like Figure 2 As shown in the figure, a distributed sensor network is used to collect device temperature (0-200°C), vibration (0-50Hz), sound (20-20kHz), humidity (0-100%RH) and current (0-100A) data. The sensor transmits data to the edge node via the LoRa protocol, and the pre-processing unit uses Kalman filtering and wavelet transform denoising to ensure data quality.

[0032] Distributed sensor network, using low-power wide area network (LPWAN) technology, to ensure high coverage and low energy consumption of data collection; The data preprocessing unit uses Kalman filtering and wavelet transform algorithms for denoising and signal enhancement. The Kalman filtering update equation is: in, For the calibration state, For gain, is the observed value.

[0033] 2. The edge intelligent processing module The edge node deploys a Raspberry Pi 4, runs the MobileNet V3 model to extract features (such as vibration frequency peak value, temperature change rate), and detects anomalies through the isolation forest algorithm. Abnormal data is uploaded to the cloud in real time, and normal data is stored locally.

[0034] The lightweight feature extraction unit uses the MobileNet V3 model to extract data features. Its convolution operation is in, is the convolution kernel weight, For input data, is the activation function; Real-time anomaly detection unit detects data anomalies based on the isolation forest algorithm.

[0035] 3. The cloud data analysis module like Figure 3 As shown in the figure, the cloud server uses the Transformer model to fuse multimodal data and generate a probability distribution of equipment status. For example, after fusing vibration and sound data, the bearing wear trend is detected with an accuracy rate of 95%.

[0036] The multimodal fusion unit uses the Transformer model for data fusion, and its attention mechanism calculation formula is: in, are query, key, and value matrices respectively, for dimension; The global model optimization unit updates the model parameters by gradient descent.

[0037] 4. The adaptive warning and decision-making module like Figure 4 As shown, the dynamic threshold generation unit adjusts the warning threshold based on the EWMA algorithm. For example, the temperature threshold is dynamically increased from 50°C to 60°C to adapt to environmental changes. The DDPG algorithm optimizes the equipment speed and reduces energy consumption by 10%.

[0038] Dynamic threshold generation unit, which adjusts the threshold based on the exponentially weighted moving average (EWMA) algorithm: in, is the smoothing factor, is the current data deviation.

[0039] The decision optimization unit uses the deep deterministic policy gradient (DDPG) algorithm to optimize the operation strategy.

[0040] 5. Environmental adaptability optimization module like Figure 5 As shown in the figure, when the environmental sensing unit detects that the humidity rises to 90%RH, the coupling optimization unit reduces the device power by 20% through the NSGA-II algorithm to ensure stable operation.

[0041] Environmental sensing unit, real-time monitoring of temperature, humidity, air pressure and dust concentration; The coupled optimization unit balances equipment performance and energy consumption based on a multi-objective optimization algorithm (such as NSGA-II).

[0042] 6. The equipment health management and collaborative maintenance module like Figure 6 As shown in the figure, the HMM and GNN models predict the remaining life of the equipment (e.g., the estimated failure time of the pump is 30 days), and the collaborative maintenance unit optimizes the maintenance sequence of multiple devices, reducing downtime by 15%.

[0043] Health assessment unit, combining Hidden Markov Model (HMM) and Graph Neural Network (GNN) to predict equipment status; Collaborative maintenance unit optimizes maintenance plans through collaborative analysis of multiple devices.

[0044] 7. Remote monitoring and interaction module Through the IoT platform, users can view equipment status (such as temperature curve) in real time, remotely adjust parameters, or perform shutdown operations through voice commands.

[0045] 8. The cross-device collaborative learning module like Figure 7 As shown in the figure, federated learning aggregates the local models of 10 devices and updates the global model, improving the anomaly detection accuracy to 98% without sharing the original data.

[0046] The cross-device collaborative learning module adopts a federated learning framework, and its aggregate update formula is: in, is the local model parameter of the kth device, is the device data volume, For the total amount.

[0047] The present invention provides an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence, which has the following significant advantages over the prior art: (1) Edge intelligence improves efficiency: Edge computing reduces data transmission delays and enables real-time anomaly detection and early warning.

[0048] (2) Multimodal fusion to enhance accuracy: Combining Transformer and GNN technologies, making full use of multi-source data to improve the accuracy of state assessment.

[0049] (3) Adaptability and dynamic optimization: Dynamic threshold and coupled optimization algorithm ensure the robustness and energy efficiency of the system in complex environments.

[0050] (4) Innovation of collaborative learning: Federated learning enables knowledge sharing among multiple devices, improves model generalization capabilities and protects data privacy.

[0051] (5) Intelligent maintenance: Collaborative prediction across devices reduces downtime and optimizes resource allocation.

[0052] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence, characterized by: include: A data acquisition module, used to collect multi-modal operation data and environmental data of the equipment through a distributed sensor network; The edge intelligent processing module performs real-time preprocessing and feature extraction on the collected data at the edge layer; The cloud data analysis module performs deep fusion analysis and model optimization on multimodal data; The adaptive warning and decision-making module dynamically generates warning signals and optimizes equipment operation strategies; Environmental adaptability optimization module, used to dynamically adjust the operation mode according to environmental data; The equipment health management and collaborative maintenance module evaluates the health status and generates cross-equipment collaborative maintenance plans; Remote monitoring and interaction module, used to achieve real-time monitoring and interactive control through the Internet of Things platform; The cross-device collaborative learning module is used to achieve multi-device model optimization through federated learning.

2. According to claim 1, the industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence is characterized in that: The data acquisition module includes a distributed sensor network and a data preprocessing unit, using the LoRa protocol and Kalman filter algorithm; Distributed sensor network, using low-power wide area network (LPWAN) technology, to ensure high coverage and low energy consumption of data collection; The data preprocessing unit uses Kalman filtering and wavelet transform algorithms for denoising and signal enhancement. The Kalman filtering update equation is: in, For the calibration state, For gain, is the observed value.

3. The industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence according to claim 1 is characterized in that: The edge intelligent processing module uses the MobileNet V3 model to extract features and detects anomalies through the isolation forest algorithm; The lightweight feature extraction unit uses the MobileNet V3 model to extract data features. Its convolution operation is: in, is the convolution kernel weight, For input data, is the activation function; Real-time anomaly detection unit detects data anomalies based on the isolation forest algorithm.

4. The industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence according to claim 1 is characterized in that: The cloud data analysis module uses the Transformer model to perform multimodal data fusion; The multimodal fusion unit uses the Transformer model for data fusion, and its attention mechanism calculation formula is: in, are query, key, and value matrices respectively, for dimension; The global model optimization unit updates the model parameters by gradient descent.

5. The industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence according to claim 1 is characterized in that: The adaptive warning and decision module includes a dynamic threshold generation unit based on the EWMA algorithm and a decision optimization unit based on the DDPG algorithm; Dynamic threshold generation unit, which adjusts the threshold based on the exponentially weighted moving average (EWMA) algorithm: in, is the smoothing factor, is the current data deviation; The decision optimization unit uses the deep deterministic policy gradient (DDPG) algorithm to optimize the operation strategy.

6. The industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence according to claim 1 is characterized in that: The environmental adaptability optimization module uses the NSGA-II algorithm to perform environment-equipment coupling optimization; Environmental sensing unit, real-time monitoring of temperature, humidity, air pressure and dust concentration; The coupled optimization unit balances equipment performance and energy consumption based on a multi-objective optimization algorithm (such as NSGA-II).

7. The industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence according to claim 1 is characterized in that: The equipment health management and collaborative maintenance module combines HMM and GNN models to predict equipment status; Health assessment unit, combining Hidden Markov Model (HMM) and Graph Neural Network (GNN) to predict equipment status; Collaborative maintenance unit optimizes maintenance plans through collaborative analysis of multiple devices.

8. The industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence according to claim 1 is characterized in that: The cross-device collaborative learning module adopts a federated learning framework to achieve multi-device model sharing; The cross-device collaborative learning module adopts a federated learning framework, and its aggregate update formula is: in, is the local model parameter of the kth device, is the device data volume, For the total amount.

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