A remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things

Abstract

This invention relates to the field of industrial internet and chemical equipment operation and maintenance technology, specifically to a remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things (IoT). The system includes a field layer for the chemical utility equipment, a network transmission layer, a cloud data center layer, and a user terminal layer. The network transmission layer constructs a hybrid wireless communication network architecture incorporating 5G communication technology and low-power wide-area network (NB-IoT) technology. The cloud data center layer includes a big data storage server and a big data analysis server. The big data analysis server is equipped with a data analysis model based on deep learning, including convolutional neural network (CNN) and recurrent neural network (RNN) models. This invention enables early fault warning and accurate diagnosis, triggering tiered warnings when the fault probability exceeds 70%, assisting maintenance personnel in proactive handling, reducing manual intervention while improving equipment operational stability and energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of industrial internet and chemical equipment operation and maintenance technology, specifically to a remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things. Background Technology

[0002] In modern chemical production, utilities such as circulating water systems, refrigeration units, air compressors, and wastewater treatment devices are core infrastructures that ensure continuous and stable production. Their operational status directly determines production safety, continuity, and energy efficiency. With the advancement of Industry 4.0 and intelligent manufacturing, traditional operation and maintenance models relying on human experience are no longer suitable for the needs of high-quality industrial development, making digital and intelligent transformation imperative.

[0003] Currently, the operation and maintenance of chemical equipment still relies mainly on manual periodic inspections, which has obvious drawbacks: the inspection cycle is fixed and long, making it impossible to monitor the equipment status in real time; the equipment in the park is widely distributed, resulting in blind spots and incomplete coverage; manual inspections rely on experience and are affected by subjective factors, making it difficult to capture subtle anomalies, which can easily lead to missed or misdiagnosed early faults, and thus cause major losses such as downtime and safety accidents.

[0004] To address the aforementioned pain points, remote monitoring technology that integrates the Internet of Things (IoT) and Artificial Intelligence (AI) has become a research hotspot. Chinese patent CN112073461A discloses a cloud-edge collaborative industrial internet system that collects terminal data through edge gateways and processes it in the cloud, achieving universal management of industrial equipment data, providing basic infrastructure support for remote monitoring, and improving data management efficiency.

[0005] However, when these existing technologies are applied to complex chemical utility engineering scenarios, they have limitations due to insufficient specificity and are difficult to meet the needs of specialized operation and maintenance.

[0006] First, the communication network has poor adaptability. Equipment in chemical industrial parks is widely distributed, and the transmission requirements of different devices vary significantly: core equipment such as refrigeration units and air compressors have large data volumes and high real-time requirements; peripheral equipment such as remote circulating water pumps and sewage treatment devices are scattered, have limited power supply, and have stringent power consumption and battery life requirements. The existing system uses a single networking approach, which cannot meet both types of needs—adapting to core equipment results in high power consumption and soaring costs for peripheral devices, while focusing on peripheral devices leads to delays in core data transmission, affecting maintenance efficiency.

[0007] Second, the fault diagnosis dimensions are too narrow. Existing technologies mostly focus on numerical data such as vibration and current, lacking the ability to fuse and analyze multimodal data that includes both physical appearance and operating parameters. Early-stage faults in chemical equipment exhibit diverse characteristics, such as visible defects like pipe leaks and casing corrosion, which are difficult to detect using only numerical sensors. Incomplete early warning systems can easily lead to the malfunction worsening.

[0008] Third, there is a lack of system-wide collaborative management and control. The various subsystems of chemical utilities are closely interconnected; for example, the quality of circulating water directly affects the heat exchange efficiency of chiller units. Existing technologies focus on monitoring individual equipment or subsystems, lacking the ability to coordinate and analyze the overall situation, and are unable to formulate global optimization strategies. This leads to subsystems operating suboptimally for extended periods, resulting in high energy consumption, a tendency to trigger cascading failures, and increased costs due to ineffective maintenance. Summary of the Invention

[0009] To address the problems existing in the prior art, the present invention provides a remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things.

[0010] To achieve the above objectives, the technical solution of the present invention is as follows:

[0011] A remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things includes a field layer for chemical utility equipment, a network transmission layer, a cloud data center layer, and a user terminal layer.

[0012] The field layer of the chemical utility equipment is used to collect operating status data of the chemical utility equipment; the field layer of the chemical utility equipment includes sensor groups and intelligent gateways installed in the circulating water system, refrigeration unit, air compressor and sewage treatment device; the sensor groups include vibration sensors for monitoring mechanical faults, current sensors for monitoring electrical loads and water quality sensors for monitoring water quality parameters;

[0013] The network transmission layer is used to transmit the data collected by the sensor group to the cloud. The network transmission layer constructs a hybrid wireless communication network architecture that includes 5G communication technology and low-power wide area network (NB-IoT) technology. For the refrigeration unit and the air compressor, which have large data volumes and high real-time requirements, 5G base stations are set up and network slicing technology is used for data transmission. For the distributed and power-sensitive circulating water system and the sewage treatment device, the NB-IoT protocol is used for data transmission.

[0014] The cloud data center layer includes a big data storage server and a big data analysis server; the big data analysis server is equipped with a data analysis model based on deep learning, which includes a convolutional neural network (CNN) model and a recurrent neural network (RNN) model; the CNN model is used to process image data of equipment appearance inspection to identify surface anomalies, and the RNN model is used to process time series data of vibration, current and water quality to predict performance degradation trends.

[0015] The user terminal layer is used to receive early warning information and display device operating parameters;

[0016] The system also includes a closed-loop control mechanism, in which the cloud data center layer generates control commands based on data analysis results and feeds them back to the field layer of the chemical utility equipment through the network transmission layer to automatically adjust the equipment operating parameters.

[0017] Furthermore, the network access parameters of the NB-IoT protocol are equipped with an adaptive heartbeat cycle mechanism. Under normal device operation, the heartbeat cycle is set to 30-60 minutes. When the smart gateway detects that the sensor data exceeds the preset safety threshold, it automatically shortens the heartbeat cycle to 1-5 minutes.

[0018] Furthermore, the water quality sensor includes a dissolved oxygen sensor based on the fluorescence quenching principle and a chemical oxygen demand sensor based on the ultraviolet spectroscopy principle; the vibration sensor is an acceleration-type piezoelectric sensor.

[0019] Furthermore, the big data analysis server is also equipped with a multi-device linkage analysis model, which is used to analyze the correlation between changes in water quality parameters of the circulating water system and the heat exchange efficiency of the chiller unit. When the increased thermal resistance of the circulating water system due to fouling leads to an increase in the condensing temperature of the chiller unit, the cloud data center layer automatically sends instructions to adjust the dosage of chemicals or the opening of the drain valve of the circulating water system through a closed-loop control mechanism.

[0020] Furthermore, the image data processed by the Convolutional Neural Network (CNN) model originates from explosion-proof cameras or inspection robots installed at the equipment site; the Recurrent Neural Network (RNN) model employs a variant of the Long Short-Term Memory (LSTM) network to capture long-term equipment performance degradation characteristics, and triggers a graded early warning mechanism when the predicted failure probability exceeds 70%.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This invention provides a remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things (IoT). The data acquisition layer, targeting key equipment such as circulating water systems and chiller units, is equipped with various dedicated sensor groups and intelligent gateways to comprehensively and accurately collect operating parameters, laying a solid data foundation for operation and maintenance decisions. Network transmission adopts a hybrid architecture of 5G and NB-IoT, differentiated to meet equipment needs, and balancing transmission quality and energy consumption control. The cloud data center is equipped with CNN and RNN deep learning models. CNN efficiently identifies abnormal equipment appearance, while RNN accurately captures performance degradation trends in time-series data such as vibration and water quality. Combined with a multi-device linkage analysis model, it can uncover the correlation between circulating water quality and chiller unit heat exchange efficiency, enabling early warning and accurate diagnosis of faults. A tiered warning is triggered when the fault probability exceeds 70%, assisting maintenance personnel in proactive handling. A closed-loop control mechanism directly translates cloud analysis results into control commands, such as automatically adjusting the dosage of chemicals or the opening of the drain valve when the thermal resistance of circulating water scale increases, reducing manual intervention while improving equipment operational stability and energy efficiency. The NB-IoT protocol's adaptive heartbeat cycle mechanism sets a heartbeat cycle of 30-60 minutes under normal conditions, and shortens it to 1-5 minutes when data is abnormal, balancing timely transmission and low power consumption. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention is further described in detail below through specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] A remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things includes a field layer for chemical utility equipment, a network transmission layer, a cloud data center layer, and a user terminal layer.

[0025] The field layer for chemical engineering equipment is used to collect operational status data of chemical engineering equipment. The field layer for chemical engineering equipment includes sensor groups and intelligent gateways installed in the circulating water system, refrigeration unit, air compressor and sewage treatment device. The sensor groups include vibration sensors for monitoring mechanical faults, current sensors for monitoring electrical loads and water quality sensors for monitoring water quality parameters.

[0026] The network transmission layer is used to transmit the data collected by the sensor group to the cloud. The network transmission layer constructs a hybrid wireless communication network architecture that includes 5G communication technology and low-power wide area network (NB-IoT) technology. For refrigeration units and air compressors with large data volume and high real-time requirements, 5G base stations are set up and network slicing technology is used for data transmission. For distributed and power-sensitive circulating water systems and sewage treatment devices, the NB-IoT protocol is used for data transmission.

[0027] The cloud data center layer includes big data storage servers and big data analysis servers. The big data analysis servers are equipped with data analysis models based on deep learning, including convolutional neural network (CNN) models and recurrent neural network (RNN) models. The CNN model is used to process image data of equipment appearance inspection to identify surface anomalies, and the RNN model is used to process time series data of vibration, current and water quality to predict performance degradation trends.

[0028] The user terminal layer is used to receive early warning information and display device operating parameters;

[0029] The system also includes a closed-loop control mechanism. The cloud data center layer generates control commands based on data analysis results and feeds them back to the field layer of chemical utility equipment through the network transmission layer to automatically adjust the equipment operating parameters.

[0030] In one embodiment of the present invention, the network access parameters of the NB-IoT protocol are equipped with an adaptive heartbeat cycle mechanism. Under normal operating conditions, the heartbeat cycle is set to 30-60 minutes. When the smart gateway detects that the sensor data exceeds the preset safety threshold, the heartbeat cycle is automatically shortened to 1-5 minutes.

[0031] In one embodiment of the present invention, the water quality sensor includes a dissolved oxygen sensor based on the principle of fluorescence quenching and a chemical oxygen demand sensor based on the principle of ultraviolet spectroscopy; the vibration sensor is an acceleration-type piezoelectric sensor.

[0032] In one embodiment of the present invention, the big data analysis server is further equipped with a multi-device linkage analysis model, which is used to analyze the correlation between changes in water quality parameters of the circulating water system and heat exchange efficiency of the chiller unit; when the increased fouling thermal resistance of the circulating water system is detected, which leads to an increase in the condensing temperature of the chiller unit, the cloud data center layer automatically sends instructions to adjust the dosage of chemicals or the opening of the drain valve of the circulating water system through a closed-loop control mechanism.

[0033] In one embodiment of the present invention, the image data processed by the convolutional neural network (CNN) model comes from explosion-proof cameras or inspection robots set up at the equipment site; the recurrent neural network (RNN) model adopts a variant of the long short-term memory network (LSTM) to capture long-term equipment performance degradation characteristics, and triggers a graded early warning mechanism when the predicted failure probability exceeds 70%.

[0034] The specific steps for operating the system of this invention are as follows:

[0035] Step 1: On-site operational status data acquisition

[0036] The system utilizes a field layer for collecting equipment operating status data. This field layer includes sensor groups and intelligent gateways installed in the circulating water system, refrigeration unit, air compressor, and sewage treatment device. The sensor groups consist of vibration sensors, current sensors, and water quality sensors.

[0037] The specific data collection method is as follows: An acceleration-type piezoelectric sensor is used as a vibration sensor to monitor data related to mechanical faults in the equipment; a current sensor is used to monitor the electrical load data of the equipment; and water quality sensors are used to monitor water quality parameters, including a dissolved oxygen sensor based on the fluorescence quenching principle and a chemical oxygen demand sensor based on the ultraviolet spectroscopy principle. The intelligent gateway performs preliminary aggregation and processing of the data collected by each sensor.

[0038] Step Two: Remote Data Transmission

[0039] The data collected from the field layer and processed by the smart gateway is transmitted to the cloud data center layer through the network transport layer. This network transport layer constructs a hybrid wireless communication network architecture that incorporates 5G communication technology and low-power wide-area network (NB-IoT) technology, and adopts a differentiated transmission strategy.

[0040] For refrigeration units and air compressors with large data volumes and high real-time requirements, 5G base stations are set up and network slicing technology is used for data transmission to ensure high-speed and real-time data transmission.

[0041] For distributed and power-sensitive circulating water systems and wastewater treatment devices, the NB-IoT protocol is used for data transmission. Simultaneously, the NB-IoT protocol's network access parameters include an adaptive heartbeat cycle mechanism. Under normal device operation, the heartbeat cycle is set to 30-60 minutes; when the smart gateway detects sensor data exceeding a preset safety threshold, it automatically shortens the heartbeat cycle to 1-5 minutes, reducing power consumption while ensuring timely transmission of abnormal data.

[0042] Step 3: Cloud Data Processing and Analysis

[0043] The big data storage server in the cloud data center layer stores the data transmitted to the cloud, while the big data analysis server processes and analyzes the data using built-in data analysis models and multi-device collaborative analysis models.

[0044] The data analysis model includes a convolutional neural network (CNN) model and a recurrent neural network (RNN) model. The CNN model is used to process image data from equipment appearance inspection to identify surface anomalies. The image data comes from explosion-proof cameras or inspection robots set up at the equipment site. The RNN model uses a variant of the long short-term memory network (LSTM) to process time series data of vibration, current, and water quality to capture long-term equipment performance degradation characteristics. When the predicted failure probability exceeds 70%, a graded early warning mechanism is triggered.

[0045] The multi-equipment linkage analysis model is used to analyze the correlation between changes in water quality parameters of the circulating water system and the heat exchange efficiency of the chiller unit, and to identify the operational correlation effects between different equipment.

[0046] Step 4: Early Warning Display and Closed-Loop Control

[0047] The user terminal layer receives early warning information output from the cloud data center layer and displays equipment operating parameters, allowing maintenance personnel to monitor the equipment's operating status in real time.

[0048] Simultaneously, the system activates a closed-loop control mechanism: the cloud data center layer generates control commands based on data analysis results. If the analysis reveals that increased fouling thermal resistance in the circulating water system leads to a rise in the condensing temperature of the chiller unit, the system automatically sends commands through the network transmission layer to the field layer of the chemical utility equipment. The field layer then automatically adjusts the dosage of chemicals or the opening of the drain valve in the circulating water system based on the commands. For other equipment malfunctions, the closed-loop control mechanism also automatically adjusts the equipment operating parameters, enabling remote intelligent control of the equipment.

[0049] This invention provides a remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things (IoT). The data acquisition layer, targeting key equipment such as circulating water systems and chiller units, is equipped with various dedicated sensor groups and intelligent gateways to comprehensively and accurately collect operating parameters, laying a solid data foundation for operation and maintenance decisions. Network transmission adopts a hybrid architecture of 5G and NB-IoT, differentiated to meet equipment needs, and balancing transmission quality and energy consumption control. The cloud data center is equipped with CNN and RNN deep learning models. CNN efficiently identifies abnormal equipment appearance, while RNN accurately captures performance degradation trends in time-series data such as vibration and water quality. Combined with a multi-device linkage analysis model, it can uncover the correlation between circulating water quality and chiller unit heat exchange efficiency, enabling early warning and accurate diagnosis of faults. A tiered warning is triggered when the fault probability exceeds 70%, assisting maintenance personnel in proactive handling. A closed-loop control mechanism directly translates cloud analysis results into control commands, such as automatically adjusting the dosage of chemicals or the opening of the drain valve when the thermal resistance of circulating water scale increases, reducing manual intervention while improving equipment operational stability and energy efficiency. The NB-IoT protocol's adaptive heartbeat cycle mechanism sets a heartbeat cycle of 30-60 minutes under normal conditions, and shortens it to 1-5 minutes when data is abnormal, balancing timely transmission and low power consumption.

[0050] The foregoing descriptions have outlined some exemplary embodiments of the present invention. It is understood that these embodiments are merely illustrative and do not constitute a limitation on the scope of protection of the present invention. Features in these embodiments can be rearranged in a suitable manner, and the resulting solutions remain within the scope of protection claimed by the present invention. All other embodiments obtained by those skilled in the art based on the foregoing embodiments without inventive effort, i.e., all modifications, equivalent substitutions, and improvements made within the spirit and principles of this application, fall within the scope of protection claimed by the present invention.

Claims

1. A remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things, characterized in that, This includes the on-site layer of chemical utility equipment, the network transmission layer, the cloud data center layer, and the user terminal layer; The field layer of the chemical utility equipment is used to collect operating status data of the chemical utility equipment; the field layer of the chemical utility equipment includes sensor groups and intelligent gateways installed in the circulating water system, refrigeration unit, air compressor and sewage treatment device; the sensor groups include vibration sensors for monitoring mechanical faults, current sensors for monitoring electrical loads and water quality sensors for monitoring water quality parameters; The network transmission layer is used to transmit the data collected by the sensor group to the cloud; the network transmission layer constructs a hybrid wireless communication network architecture that includes 5G communication technology and low-power wide area network NB-IoT technology. For the refrigeration unit and the air compressor, which have large data volumes and high real-time requirements, 5G base stations are set up and network slicing technology is used for data transmission; for the distributed and power-sensitive circulating water system and the sewage treatment device, the NB-IoT protocol is used for data transmission. The cloud data center layer includes a big data storage server and a big data analysis server; the big data analysis server is equipped with a data analysis model based on deep learning, which includes a convolutional neural network (CNN) model and a recurrent neural network (RNN) model; the CNN model is used to process image data of equipment appearance inspection to identify surface anomalies, and the RNN model is used to process time series data of vibration, current and water quality to predict performance degradation trends. The user terminal layer is used to receive early warning information and display device operating parameters; The system also includes a closed-loop control mechanism, in which the cloud data center layer generates control commands based on data analysis results and feeds them back to the field layer of the chemical utility equipment through the network transmission layer to automatically adjust the equipment operating parameters.

2. The remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things as described in claim 1, characterized in that, The NB-IoT protocol's network access parameters include an adaptive heartbeat cycle mechanism. Under normal device operation, the heartbeat cycle is set to 30-60 minutes. When the smart gateway detects that the sensor data exceeds the preset safety threshold, it automatically shortens the heartbeat cycle to 1-5 minutes.

3. The remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things as described in claim 1, characterized in that, The water quality sensors include a dissolved oxygen sensor based on the fluorescence quenching principle and a chemical oxygen demand sensor based on the ultraviolet spectroscopy principle; the vibration sensor is an acceleration-type piezoelectric sensor.

4. The remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things as described in claim 1, characterized in that, The big data analysis server is also equipped with a multi-device linkage analysis model, which is used to analyze the correlation between changes in water quality parameters of the circulating water system and the heat exchange efficiency of the chiller unit. When the increased thermal resistance of the circulating water system due to fouling leads to an increase in the condensing temperature of the chiller unit, the cloud data center layer automatically sends instructions to adjust the dosage of chemicals or the opening of the drain valve of the circulating water system through a closed-loop control mechanism.

5. The remote intelligent operation and maintenance system for chemical utility equipment based on the Internet of Things as described in claim 1, characterized in that, The image data processed by the Convolutional Neural Network (CNN) model comes from explosion-proof cameras or inspection robots installed at the equipment site; the Recurrent Neural Network (RNN) model adopts a variant of the Long Short-Term Memory (LSTM) network to capture long-term equipment performance degradation characteristics, and triggers a graded early warning mechanism when the predicted failure probability exceeds 70%.