Multi-motor intelligent operation system based on wireless network
By combining Bluetooth Mesh and LoRa's wireless network architecture, the problems of insufficient communication delay, wiring complexity and energy consumption management of motor intelligent operation systems in industrial environments are solved, efficient and low-cost motor control and fault warning are achieved, and industrial process efficiency is improved.
Patent Information
- Application Number
- CN202510540325.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-27
- Publication Date
- 2025-08-12
AI Technical Summary
The existing motor intelligent operating systems face problems such as communication delay, high wiring complexity and insufficient energy consumption management in industrial environments, resulting in low efficiency and increased costs in industrial processes.
The wireless network architecture combined with Bluetooth Mesh subnet and LoRa wide area network is adopted, combined with multi-modal sensor arrays and edge computing nodes, and uses dynamic path optimization algorithms and adaptive energy consumption management strategies to achieve multi-protocol collaborative operation, including a positioning module based on RSSI fingerprint and N+1 redundancy strategy to ensure efficient communication and low power consumption.
It has achieved a 71.5% reduction in end-to-end communication latency, a 41.6% reduction in power consumption, a 38% reduction in equipment operating costs, and improved operation and maintenance efficiency by 34.9% through high-precision equipment tracking and fault warning.
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of motor intelligent operation, and in particular to a multi-motor intelligent operation system based on a wireless network. Background Art
[0002] The intelligent motor operation system utilizes advanced control algorithms and sensor technology to monitor and control the motor's operating status in real time. Its primary purpose is to improve motor control efficiency, stability, and energy efficiency, while reducing maintenance costs. The intelligent motor operation system monitors motor parameters such as speed, current, and temperature in real time and uses advanced control algorithms to intelligently adjust these parameters based on the actual load. This system automatically adjusts the motor's operating parameters based on actual load conditions, achieving precise speed control and ensuring stable equipment operation.
[0003] Existing intelligent operation systems have the following problems: High communication latency in industrial environments: Traditional systems often face the problem of insufficient data transmission speed, resulting in inefficient industrial processes.
[0004] High wiring complexity: A factory may require widely distributed equipment, and traditional bus calibration networks may cause connection anomalies in heterogeneous environments.
[0005] Insufficient energy consumption management: Wireless networks consume high amounts of energy and are difficult to regulate efficiently, impacting the energy consumption of production systems.
[0006] To address these challenges, a three-layer approach combining Bluetooth, mesh, and LoRa was designed to address the communication complexity issues inherent in industrial environments. Bluetooth is used for small-scale networking, mesh provides efficient data management and multi-hop transmission, and LoRa's instant service mode ensures low power consumption and high responsiveness for data transmission. This approach addresses the challenges of inefficient industrial processes, high wiring complexity, and insufficient energy management, necessitating the use of this wireless network-based multi-motor intelligent operation system. Summary of the Invention
[0007] In view of the deficiencies in the prior art, the present invention provides a multi-motor intelligent operation system based on a wireless network, which solves the technical problems raised in the above background technology.
[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-motor intelligent operation system based on a wireless network, comprising:
[0009] Perception layer: Consists of a multimodal sensor array and edge computing nodes. The multimodal sensor array includes temperature sensors, vibration sensors, and current / voltage sensors. The edge computing nodes use STM32 series microcontrollers to perform data preprocessing.
[0010] Network layer: Consists of a Bluetooth Mesh subnet and a LoRa wide area network. The Bluetooth Mesh subnet supports multi-hop self-organizing networks, and the LoRa wide area network achieves long-distance communication through a LoRa gateway. The network layer integrates the nRF52840 and SX1276 dual-mode communication chips.
[0011] Control layer: This includes a cloud-based machine learning platform that deploys an LSTM neural network for fault prediction and a real-time drive controller that generates PWM signals to dynamically adjust the motor speed.
[0012] The system achieves multi-protocol collaborative operation through a dynamic path optimization algorithm and an adaptive energy management strategy, where:
[0013] The dynamic path optimization algorithm is a hybrid routing strategy of the Dijkstra algorithm and the AODV protocol;
[0014] The adaptive energy management strategy dynamically switches the communication mode according to the motor load.
[0015] Preferably, the Bluetooth Mesh subnet includes:
[0016] Motor positioning module based on RSSI fingerprint, positioning error is less than 1 meter;
[0017] The self-organizing network topology reconstruction module implements route switching when a node fails, with a switching time of less than 200 milliseconds.
[0018] Preferably, the dual-mode communication architecture of the network layer includes:
[0019] Frequency band adaptive switching module, when the Bluetooth Mesh signal strength is lower than -90dBm, it automatically switches to LoRa communication mode, and the switching time does not exceed 10 milliseconds;
[0020] In the data classification transmission module, key control instructions are transmitted via Bluetooth Mesh, and status monitoring data is transmitted via LoRa. The status monitoring data is compressed using CBOR coding with a compression rate of no less than 50%.
[0021] Preferably, the adaptive energy consumption management strategy includes:
[0022] The load prediction model based on the LSTM neural network uses motor current, speed, and historical load curve as input parameters, with a prediction window of 300 milliseconds and a prediction error of no more than 5%.
[0023] Dynamic sleep mechanism: idle nodes enter deep sleep mode with power consumption of no more than 5 microamps. The wake-up trigger condition is receiving a specific LoRa wake-up frame or a sensor threshold alarm.
[0024] Preferably, the cloud-based machine learning platform includes:
[0025] Multi-sensor data fusion module uses Kalman filtering algorithm, with data sampling frequency not less than 100Hz and noise reduced by more than 30%;
[0026] The fault prediction module provides bearing fault early warning based on vibration spectrum analysis and temperature trend prediction, with the warning time being more than 8 hours in advance.
[0027] Preferably, the real-time drive controller includes:
[0028] Cluster collaborative control module, based on Lyapunov stability theory to design multi-motor synchronous controller, with synchronization error less than 0.5%;
[0029] The fault-tolerant module adopts N+1 redundancy strategy, and the failover time is less than 50 milliseconds.
[0030] Preferably, in the multimodal sensor array of the perception layer:
[0031] The temperature sensor is SHT20 model with a measurement accuracy of ±0.3°C;
[0032] The vibration sensor is SG-2H model with a frequency response range of 0.5-10kHz.
[0033] Preferably, the communication parameters of the LoRa wide area network are:
[0034] Operating frequency 868MHz, bandwidth 125kHz, spreading factor SF=7;
[0035] The theoretical transmission distance exceeds 10 kilometers, and the packet loss rate is less than 1%.
[0036] Beneficial effects
[0037] The present invention provides a wireless network-based multi-motor intelligent operation system. In terms of communication performance, the present invention reduces end-to-end communication latency to 28.5ms (a 71.5% improvement over traditional systems) and maintains an ultra-low packet loss rate of 0.78% in complex electromagnetic environments through a dynamic switching mechanism between Bluetooth Mesh and LoRa dual-mode. In terms of energy consumption management, the system's overall power consumption is reduced by 41.6% by relying on an LSTM load prediction model and a dynamic sleep strategy. Combined with supercapacitor energy recovery technology (87.3% efficiency), the device's battery life is further extended. In terms of control accuracy, a multi-motor synchronization error of less than 0.47% is achieved based on an improved AODV protocol and a Lyapunov control algorithm. At the same time, high-precision device tracking of 0.75 meters is achieved through RSSI fingerprint positioning. In terms of reliability, the system compresses the fault switching time to 43ms through an N+1 redundancy strategy and self-organizing network reconstruction technology. Based on vibration spectrum analysis (FFT resolution 0.8Hz), it achieves an 8.2-hour advance warning of bearing failures. Ultimately, the operation and maintenance efficiency in industrial scenarios is improved by 34.9%, and the overall equipment operating cost is reduced by more than 38%. DETAILED DESCRIPTION
[0038] The following is a clear and complete description of the technical solutions in the embodiments 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 making any creative efforts are within the scope of protection of the present invention.
[0039] The present invention provides a technical solution: a multi-motor intelligent operation system based on a wireless network, comprising:
[0040] Perception layer: Consists of a multimodal sensor array and edge computing nodes. The multimodal sensor array includes temperature sensors, vibration sensors, and current / voltage sensors. The edge computing nodes use STM32 series microcontrollers to perform data preprocessing.
[0041] Network layer: Consists of a Bluetooth Mesh subnet and a LoRa wide area network. The Bluetooth Mesh subnet supports multi-hop self-organizing networks, and the LoRa wide area network achieves long-distance communication through a LoRa gateway. The network layer integrates the nRF52840 and SX1276 dual-mode communication chips.
[0042] Control layer: This includes a cloud-based machine learning platform that deploys an LSTM neural network for fault prediction and a real-time drive controller that generates PWM signals to dynamically adjust the motor speed.
[0043] The system achieves multi-protocol collaborative operation through a dynamic path optimization algorithm and an adaptive energy management strategy, where:
[0044] The dynamic path optimization algorithm is a hybrid routing strategy of the Dijkstra algorithm and the AODV protocol;
[0045] The adaptive energy management strategy dynamically switches the communication mode according to the motor load.
[0046] This embodiment is further configured such that the Bluetooth Mesh subnet includes:
[0047] Motor positioning module based on RSSI fingerprint, positioning error is less than 1 meter;
[0048] The self-organizing network topology reconstruction module implements route switching when a node fails, with a switching time of less than 200 milliseconds.
[0049] This embodiment is further configured such that the dual-mode communication architecture of the network layer includes:
[0050] Frequency band adaptive switching module, when the Bluetooth Mesh signal strength is lower than -90dBm, it automatically switches to LoRa communication mode, and the switching time does not exceed 10 milliseconds;
[0051] In the data classification transmission module, key control instructions are transmitted via Bluetooth Mesh, and status monitoring data is transmitted via LoRa. The status monitoring data is compressed using CBOR coding with a compression rate of no less than 50%.
[0052] This embodiment is further configured such that the adaptive energy consumption management strategy includes:
[0053] The load prediction model based on the LSTM neural network uses motor current, speed, and historical load curve as input parameters, with a prediction window of 300 milliseconds and a prediction error of no more than 5%.
[0054] Dynamic sleep mechanism: idle nodes enter deep sleep mode with power consumption of no more than 5 microamps. The wake-up trigger condition is receiving a specific LoRa wake-up frame or a sensor threshold alarm.
[0055] This embodiment is further configured such that the cloud-based machine learning platform includes:
[0056] Multi-sensor data fusion module uses Kalman filtering algorithm, with data sampling frequency not less than 100Hz and noise reduced by more than 30%;
[0057] The fault prediction module provides bearing fault early warning based on vibration spectrum analysis and temperature trend prediction, with the warning time being more than 8 hours in advance.
[0058] This embodiment is further configured such that the real-time drive controller includes:
[0059] Cluster collaborative control module, based on Lyapunov stability theory to design multi-motor synchronous controller, with synchronization error less than 0.5%;
[0060] The fault-tolerant module adopts N+1 redundancy strategy, and the failover time is less than 50 milliseconds.
[0061] This embodiment is further configured such that, in the multimodal sensor array of the perception layer:
[0062] The temperature sensor is SHT20 model with a measurement accuracy of ±0.3°C;
[0063] The vibration sensor is SG-2H model with a frequency response range of 0.5-10kHz.
[0064] This embodiment is further configured such that the communication parameters of the LoRa wide area network are:
[0065] Operating frequency 868MHz, bandwidth 125kHz, spreading factor SF=7;
[0066] The theoretical transmission distance exceeds 10 kilometers, and the packet loss rate is less than 1%.
[0067] The detailed connection means are well-known technologies in this field. The following mainly introduces the working principle and process. The specific operations are as follows.
[0068] Example: The feasibility and superiority of the technical solution are verified through the following two typical application scenarios: In the AGV fleet collaborative control scenario, the Bluetooth Mesh network built based on the nRF52840 chip covers 50 AGV devices. Each AGV is equipped with an STM32H743 controller to collect vibration data in real time (SG-2H sensor, sampling rate 1kHz). A 2ms dedicated communication window is allocated to each AGV through the TDMA time slot allocation mechanism to achieve a 100ms periodic broadcast of path planning instructions. The end-to-end delay of the control instruction is measured to be 28.5ms. At the same time, the SX1276 is deployed. The LoRa gateway uploads the operating status data (CBOR encoding) with a compression rate of 62% to the cloud every 5 minutes. After analyzing the vibration spectrum with an LSTM model (FFT resolution 0.8Hz), it successfully achieves an 8.2-hour advance warning of bearing wear. In the oil field pumping unit group monitoring scenario, a monitoring network with a radius of 12 kilometers is built using LoRa modules (frequency 868MHz, SF=7). The electrical parameters of 200 pumping units are collected in real time (current accuracy ±0.5%). When the motor winding temperature is detected to exceed 82°C, it automatically switches to Bluetooth M The esh emergency control channel completes fault node routing reconstruction within 43ms by improving the AODV protocol and triggers the N+1 redundancy strategy to distribute the load to the backup motor. Combined with the 48V supercapacitor energy storage system (energy recovery efficiency 87.3%), the overall system energy consumption is reduced by 41.6%. Actual measured data shows that in a strong electromagnetic interference environment, the communication packet loss rate is only 0.78%, the motor positioning accuracy reaches 0.75 meters, and the multi-motor synchronous control error is stable within 0.47%. Compared with the traditional RS485 bus system, the operation and maintenance response speed is improved by 34.9%.
[0069] It should be noted that, in this document, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.
Claims
1. A multi-motor intelligent operation system based on a wireless network, characterized in that: include: Perception layer: Consists of a multimodal sensor array and edge computing nodes. The multimodal sensor array includes temperature sensors, vibration sensors, and current / voltage sensors. The edge computing nodes use STM32 series microcontrollers to perform data preprocessing. Network layer: Consists of a Bluetooth Mesh subnet and a LoRa wide area network. The Bluetooth Mesh subnet supports multi-hop self-organizing networks, and the LoRa wide area network achieves long-distance communication through a LoRa gateway. The network layer integrates the nRF52840 and SX1276 dual-mode communication chips. Control layer: This includes a cloud-based machine learning platform that deploys an LSTM neural network for fault prediction and a real-time drive controller that generates PWM signals to dynamically adjust the motor speed. The system achieves multi-protocol collaborative operation through a dynamic path optimization algorithm and an adaptive energy management strategy, where: The dynamic path optimization algorithm is a hybrid routing strategy of the Dijkstra algorithm and the AODV protocol; The adaptive energy management strategy dynamically switches the communication mode according to the motor load.
2. The multi-motor intelligent operation system based on wireless network according to claim 1 is characterized in that , the Bluetooth Mesh subnet includes: Motor positioning module based on RSSI fingerprint, positioning error is less than 1 meter; The self-organizing network topology reconstruction module implements route switching when a node fails, with a switching time of less than 200 milliseconds.
3. The multi-motor intelligent operation system based on wireless network according to claim 1 is characterized in that ,The dual-mode communication architecture of the network layer includes: Frequency band adaptive switching module, when the Bluetooth Mesh signal strength is lower than -90dBm, it automatically switches to LoRa communication mode, and the switching time does not exceed 10 milliseconds; In the data classification transmission module, key control instructions are transmitted via Bluetooth Mesh, and status monitoring data is transmitted via LoRa. The status monitoring data is compressed using CBOR coding with a compression rate of no less than 50%.
4. The wireless network-based multi-motor intelligent operation system according to claim 1 is characterized in that ,The adaptive energy consumption management strategy includes: The load prediction model based on the LSTM neural network uses motor current, speed, and historical load curve as input parameters, with a prediction window of 300 milliseconds and a prediction error of no more than 5%. Dynamic sleep mechanism: idle nodes enter deep sleep mode with power consumption of no more than 5 microamps. The wake-up trigger condition is receiving a specific LoRa wake-up frame or a sensor threshold alarm.
5. The wireless network-based multi-motor intelligent operation system according to claim 1 is characterized in that ,The cloud machine learning platform includes: Multi-sensor data fusion module uses Kalman filtering algorithm, with data sampling frequency not less than 100Hz and noise reduced by more than 30%; The fault prediction module provides bearing fault early warning based on vibration spectrum analysis and temperature trend prediction, with the warning time being more than 8 hours in advance.
6. The wireless network-based multi-motor intelligent operation system according to claim 1 is characterized in that ,The real-time drive controller includes: Cluster collaborative control module, based on Lyapunov stability theory to design multi-motor synchronous controller, with synchronization error less than 0.5%; The fault-tolerant module adopts N+1 redundancy strategy, and the failover time is less than 50 milliseconds.
7. The wireless network-based multi-motor intelligent operation system according to claim 1 is characterized in that , in the multimodal sensor array of the perception layer: The temperature sensor is SHT20 model with a measurement accuracy of ±0.3°C; The vibration sensor is SG-2H model with a frequency response range of 0.5-10kHz.
8. The wireless network-based multi-motor intelligent operation system according to claim 1 is characterized in that , the communication parameters of the LoRa wide area network are: Operating frequency 868MHz, bandwidth 125kHz, spreading factor SF=7; The theoretical transmission distance exceeds 10 kilometers, and the packet loss rate is less than 1%.