Industrial equipment high-precision positioning and fault early warning system based on ultra-wideband RFID

Abstract

The invention discloses an industrial equipment high-precision positioning and fault early warning system based on ultra-wideband RFID, and particularly relates to the technical field of industrial Internet of Things, which comprises an ultra-wideband RFID tag module, a multi-node reader-writer network, an edge computing gateway and a cloud management platform, the UWB RFID tag module is integrated with a UWB positioning unit, a vibration sensor and a temperature sensor, and acquires equipment position, vibration and temperature data in real time; the multi-node reader-writer network realizes meter-scale positioning of equipment less than or equal to 0.5 meter based on a TOF positioning principle; a positioning algorithm and a self-learning fault early warning model are built in the edge computing gateway, a threshold value is dynamically adjusted, and abnormal early warning is triggered; and the cloud management platform completes data storage, visual display, track tracing and multi-channel early warning pushing. The system realizes integrated management of positioning, monitoring and early warning, adapts to the severe environment of the intelligent manufacturing workshop, is accurate in positioning, reliable in early warning and convenient to deploy, and can remarkably improve the equipment management efficiency and the production safety.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) technology, and more specifically, to a high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID. Background Technology

[0002] In the production management of smart manufacturing workshops, precise position tracking and real-time fault monitoring of industrial equipment (such as CNC machine tools, robotic arms, and conveying equipment) are core requirements for ensuring production continuity and improving scheduling efficiency. However, existing technical solutions have many pain points and are difficult to meet the refined management requirements of smart manufacturing: Traditional industrial equipment positioning relies heavily on conventional RFID technology, which uses signal strength for positioning. The positioning error is usually between 1 and 5 meters, which cannot achieve high-precision scheduling, area control, and abnormal movement identification of equipment, making it difficult to adapt to the dynamic management scenarios of modern workshops. Equipment fault monitoring often relies on independently deployed single monitoring devices such as vibration sensors and temperature sensors, which require separate data acquisition and transmission lines. This not only results in high deployment costs and cumbersome maintenance, but also makes it impossible to achieve collaborative analysis of "location-status" data when the monitoring system and the positioning system operate independently, leading to delayed fault response and low equipment management efficiency. Although Ultra-Wideband (UWB) technology has advantages such as high positioning accuracy, strong anti-interference ability and fast transmission rate, it has been initially applied in the field of indoor positioning. However, at present, there is no integrated system solution that deeply integrates UWB technology with RFID technology and integrates equipment positioning, multi-dimensional status monitoring and intelligent fault early warning functions. It cannot take into account both positioning accuracy and fault monitoring comprehensiveness. Traditional positioning and monitoring equipment has a low protection level and is difficult to adapt to the harsh environment commonly found in smart manufacturing workshops, such as oil stains, dust, vibration, and temperature and humidity fluctuations. This results in a high failure rate and short service life, further increasing operation and maintenance costs.

[0003] Therefore, an integrated system is proposed that combines high-precision positioning, multi-dimensional status monitoring, and intelligent fault early warning functions, and is adaptable to harsh workshop environments, thus solving the problems of "inaccurate positioning, single function, fragmented data, and poor adaptability" in existing technologies. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID, comprising an ultra-wideband RFID tag module, a multi-node reader network, an edge computing gateway, and a cloud management platform, wherein the modules work together to achieve high-precision positioning, status monitoring, and fault early warning for industrial equipment; The ultra-wideband RFID tag module is installed on the surface of the industrial equipment to be monitored. It integrates a UWB positioning unit, a vibration sensor, a temperature sensor, a data processing unit, and a wireless transmission unit. It is used to collect equipment position, vibration, and temperature data in real time, and then transmit the data after preprocessing. The multi-node reader network consists of at least three ultra-wideband RFID readers, which are deployed in key locations in the smart manufacturing workshop. Each reader has a unique ID and preset coordinates, and is used to receive signals from the tag module and obtain signal flight time and signal strength data, which are then transmitted to the edge computing gateway. The edge computing gateway is wirelessly connected to the multi-node reader network, and has a built-in positioning algorithm and fault early warning model. It calculates the real-time position of the device through TOF data from at least 3 readers, with a positioning error of ≤0.5 meters. It also has preset adjustable vibration and temperature thresholds, and generates a fault early warning signal when the detected data exceeds the threshold. The cloud management platform communicates with the edge computing gateway and includes a data storage module, a visualization module, and an early warning push module, enabling data storage, real-time display, and multi-channel early warning push.

[0006] Preferably, in the ultra-wideband RFID tag module, the UWB positioning unit uses a DW1000 chip, supports the TOF positioning algorithm, and operates at a frequency of 3.1-10.6GHz; the vibration sensor is an ADXL345 with a sampling frequency ≥1kHz and a measurement range of 0-50g; the temperature sensor is a DS18B20 with a measurement accuracy of ±0.5℃ and an operating temperature range of -40℃ to 125℃; the data processing unit uses an STM32L431 microcontroller, and the wireless transmission unit is a Bluetooth 5.0 transmission unit.

[0007] Preferably, the ultra-wideband RFID tag module adopts an IP67 waterproof and dustproof design, the shell material is ABS engineering plastic, the weight is ≤50g, and it provides two fixing methods: magnetic and bolt. The magnetic type uses a strong magnetic suction cup with a suction force of ≥5kg, and the bolt type is fixed with M4 bolts. The installation time is ≤5 minutes.

[0008] Preferably, the ultra-wideband RFID tag module is battery powered with a battery capacity of ≥2000mAh, supports low power consumption mode, enters sleep mode when idle, wakes up at an interval of 1-60 seconds, has a single charge life of ≥6 months, and is equipped with a USB Type-C interface with a charging time of ≤2 hours.

[0009] Preferably, the multi-node reader network follows the "triangle coverage" deployment principle to ensure that each device location can be covered by at least 3 readers. The reader installation height is 2.5-3 meters, and data synchronization is achieved through LoRa wireless communication with a synchronization accuracy of ≤10ns and a communication distance of ≥100 meters.

[0010] Preferably, the readers in the multi-node reader network are ultra-wideband RFID readers of model DWM1001-DEV, with a working frequency of 3.1-10.6GHz, a receiving sensitivity of -96dBm, a communication distance of ≥50 meters in open environments, an aluminum alloy shell with heat dissipation function, an operating temperature of -20℃~60℃, an IP65 protection rating, and support for PoE power supply and simultaneous access of more than 500 tag modules.

[0011] Preferably, the positioning algorithm of the edge computing gateway, by receiving the signal flight times t1, t2, and t3 between the three readers and the tag module, and combining them with the preset coordinates (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3) of the readers, establishes the following system of equations to solve for the real-time coordinates (x, y, z) of the device: (x-x1)²+(y-y1)²+(z-z1)²=(c×t1)² (x-x2)²+(y-y2)²+(z-z2)²=(c×t2)² (x-x3)²+(y-y3)²+(z-z3)²=(c×t3)² Where c is the speed of light 3×10 8 m / s.

[0012] Preferably, the fault warning model of the edge computing gateway supports self-learning function. By analyzing the vibration and temperature historical data of the device during normal operation over the past 3 months, the vibration threshold and temperature threshold are automatically adjusted monthly. When the vibration data collected for 3 consecutive times exceeds the threshold or the temperature data exceeds the threshold for 5 consecutive minutes, it is judged as abnormal and a fault warning signal containing device ID, abnormality type, current location and abnormal data is generated, with a false alarm rate of ≤1%.

[0013] Preferably, the data storage module of the cloud management platform uses a MySQL database, with location data recorded every 10 seconds and vibration and temperature data recorded every minute, with a storage duration of ≥1 year, and supports data export in Excel and CSV formats; the visualization module is based on web development, marking the equipment location in real time through a workshop map, distinguishing the normal, warning, and fault states of the equipment with different colors, and clicking on the equipment icon allows viewing the vibration curve, temperature curve, and historical positioning trajectory of the past 24 hours.

[0014] Preferably, the early warning push module of the cloud management platform pushes early warning information within 10 seconds after the fault early warning signal is generated through three methods: push notification via the mobile APP of the equipment manager, push SMS to the preset mobile phone number, and trigger the audible and visual alarm in the corresponding area of ​​the workshop. The platform also supports the early warning processing flow, and can mark the status as "viewed", "processing" or "resolved" and record the processing log.

[0015] Preferably, the cloud management platform also includes an equipment trajectory tracking module, which can query the location movement trajectory of industrial equipment for any time period within a maximum of one year, with a trajectory recording interval of ≤10 seconds. The system as a whole is adapted to the working environment of intelligent manufacturing workshops from -20℃ to 60℃ and has environmental adaptability to oil, dust and vibration.

[0016] The technical effects and advantages of this invention are as follows: The solution combines ultra-wideband technology with TOF positioning algorithm. Through the triangular coverage deployment of a multi-node reader network, the device coordinates are solved by combining the signal data of multiple readers. The positioning error is less than 0.5 meters, which is far better than the positioning accuracy of 1-5 meters of traditional RFID technology. It fully meets the refined management needs of intelligent manufacturing workshops such as high-precision scheduling of equipment, area intrusion prevention and abnormal movement identification. Breaking through the limitations of traditional independent deployment of positioning and monitoring systems, this technology achieves full integration of positioning, vibration monitoring, temperature monitoring, fault early warning, and trajectory tracing for the first time. It eliminates the need for separate deployment of multiple systems and wiring, reducing initial deployment costs and enabling collaborative analysis of location and status data. This significantly improves equipment management efficiency and solves management blind spots caused by data fragmentation. The fault warning model of the edge computing gateway supports self-learning function. By analyzing the normal operation data of the equipment over the past 3 months, it dynamically adjusts the vibration and temperature thresholds, with a false alarm rate of ≤1%, which is far lower than the false alarm rate of ≥5% of traditional fixed threshold systems. At the same time, the warning signal is pushed through multiple channels such as mobile APP, SMS and workshop sound and light alarms within 10 seconds after it is generated. Combined with the closed-loop processing process of the cloud platform, it can quickly locate faulty equipment and problem type, greatly shorten the fault response time, and reduce production interruption losses caused by equipment failure. The multi-node reader network achieves data synchronization through LoRa wireless communication, supporting the simultaneous access of more than 500 ultra-wideband RFID tag modules, which can meet the multi-device monitoring needs of large-scale intelligent manufacturing workshops; the cloud management platform supports the storage of historical data and equipment trajectory tracking for up to one year, and the web-based visual interface supports multi-terminal access, facilitating subsequent function expansion and system upgrades. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0018] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] As attached Figure 1 The system shown is a high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID. It includes an ultra-wideband RFID tag module, a multi-node reader network, an edge computing gateway, and a cloud management platform. The modules work together to achieve high-precision positioning, status monitoring, and fault early warning for industrial equipment. The ultra-wideband RFID tag module is installed on the surface of the industrial equipment to be monitored. It integrates a UWB positioning unit, a vibration sensor, a temperature sensor, a data processing unit, and a wireless transmission unit. It is used to collect equipment position, vibration, and temperature data in real time, and then transmit the data after preprocessing. The multi-node reader network consists of at least three ultra-wideband RFID readers, which are deployed in key locations in the smart manufacturing workshop. Each reader has a unique ID and preset coordinates, and is used to receive signals from the tag module and obtain signal flight time and signal strength data, which are then transmitted to the edge computing gateway. The edge computing gateway is wirelessly connected to the multi-node reader network, and has a built-in positioning algorithm and fault early warning model. It calculates the real-time position of the device through TOF data from at least 3 readers, with a positioning error of ≤0.5 meters. It also has preset adjustable vibration and temperature thresholds, and generates a fault early warning signal when the detected data exceeds the threshold. The cloud management platform communicates with the edge computing gateway and includes a data storage module, a visualization module, and an early warning push module, enabling data storage, real-time display, and multi-channel early warning push.

[0020] In practice, the ultra-wideband RFID tag module integrates a multi-dimensional sensing unit, collects location data through the UWB positioning unit (3.1-10.6GHz band + TOF algorithm), collects equipment status data through vibration / temperature sensors, and transmits the data wirelessly after preprocessing by the data processing unit. The multi-node reader network is deployed according to the "three-point positioning" principle. It obtains TOF and signal strength data by receiving tag signals, providing basic data support for positioning. The edge computing gateway is based on the TOF triangulation algorithm, which integrates data from at least three readers to solve the device coordinates. At the same time, it monitors status data through preset adjustable thresholds and triggers abnormal warnings. The cloud-based management platform enables full lifecycle management of data, and completes storage, display, and early warning push through the collaboration of multiple modules.

[0021] This breaks through the limitations of traditional industrial RFID's single positioning and independent sensor's single monitoring, realizing the integration of positioning, monitoring, and early warning, and solving the problem of location-state data fragmentation; The positioning error is ≤0.5 meters, which is far superior to traditional RFID (1-5 meter error), meeting the high-precision scheduling and area control requirements of smart manufacturing workshops; The multi-module collaborative architecture adapts to dynamic equipment management scenarios, improving equipment management efficiency and production safety, and laying the foundation for future functional expansion. In the ultra-wideband RFID tag module, the UWB positioning unit uses a DW1000 chip, supports the TOF positioning algorithm, and operates at a frequency of 3.1-10.6GHz; the vibration sensor is an ADXL345 with a sampling frequency ≥1kHz and a measurement range of 0-50g; the temperature sensor is a DS18B20 with a measurement accuracy of ±0.5℃ and an operating temperature range of -40℃ to 125℃; the data processing unit uses an STM32L431 microcontroller, and the wireless transmission unit is a Bluetooth 5.0 transmission unit.

[0022] In practical implementation, the UWB positioning chip (DW1000) ensures the hardware support for the TOF algorithm, the vibration sensor (ADXL345) collects vibration data at a sampling frequency of ≥1kHz and a measurement range of 0-50g, the temperature sensor (DS18B20) collects data over a wide temperature range (-40℃~125℃) with an accuracy of ±0.5℃, the microcontroller (STM32L431) coordinates data preprocessing, and the Bluetooth 5.0 transmission unit ensures stable short-range data transmission. The component parameters are precisely matched to the needs of industrial scenarios, and the vibration / temperature data acquisition accuracy far exceeds that of independent sensor systems (vibration sampling ≤500Hz, temperature accuracy ±1℃), providing high-quality data for fault early warning. The integrated hardware design reduces the size and weight of the module, and the use of low-power components further enhances the environmental adaptability and battery life of the tag module.

[0023] The ultra-wideband RFID tag module adopts an IP67 waterproof and dustproof design, with the outer shell made of ABS engineering plastic and a weight of ≤50g. It provides two fixing methods: magnetic and bolt. The magnetic type uses a strong magnetic suction cup with a suction force of ≥5kg, while the bolt type is fixed with M4 bolts. The installation time is ≤5 minutes.

[0024] In practical implementation, in response to the harsh environment of oil, dust and vibration in industrial workshops, the label module adopts an IP67 waterproof and dustproof design (sealed shell structure), and selects wear-resistant and impact-resistant ABS engineering plastic as the shell material. At the same time, it is designed with two fixing structures: magnetic and bolt. The magnetic type relies on the adsorption force of a strong magnetic chuck, and the bolt type is fastened with a standard M4 bolt, which is suitable for equipment with different materials and vibration intensities.

[0025] This ensures that the protection level meets the standards, guaranteeing that the label module works stably in the harsh environment of the workshop and avoiding malfunctions caused by oil and dust. Weighing ≤50g and easy to install / disassemble, it reduces the difficulty of on-site deployment and is compatible with different types of industrial equipment, such as metal / non-magnetic and low-vibration / high-vibration equipment, thus reducing maintenance costs.

[0026] The ultra-wideband RFID tag module is battery powered with a battery capacity of ≥2000mAh. It supports low power consumption mode, enters sleep mode when idle, and has an adjustable wake-up interval of 1-60 seconds. The battery life on a single charge is ≥6 months. It is also equipped with a USB Type-C interface and the charging time is ≤2 hours.

[0027] In practical implementation, the ultra-wideband RFID tag module adopts a "large capacity battery + low power sleep wake-up" design: a 2000mAh lithium polymer battery provides basic power, the microcontroller supports sleep mode, stops unnecessary work when idle, and only wakes up to collect data at adjustable intervals of 1-60 seconds, reducing invalid power consumption. A single charge provides ≥6 months of battery life, far superior to traditional RFID tags (≤3 months) and ordinary UWB tags (≤4 months), reducing the maintenance costs associated with frequent charging; The wake-up interval is adjustable to adapt to the monitoring frequency requirements of different devices, balancing monitoring accuracy and battery life.

[0028] The multi-node reader network follows the "triangle coverage" deployment principle to ensure that each device location is covered by at least 3 readers. The reader installation height is 2.5-3 meters, and data synchronization is achieved through LoRa wireless communication with a synchronization accuracy of ≤10ns and a communication distance of ≥100 meters.

[0029] In practice, the reader network uses LoRa wireless communication technology (433MHz band) to achieve data synchronization between nodes. It leverages LoRa's low power consumption and long-distance transmission characteristics (communication distance ≥100 meters) to ensure the time synchronization accuracy and data interaction stability of multiple readers. At the same time, it optimizes the communication protocol to support the concurrent access of more than 500 tags and distinguishes the data source through the unique ID of the reader to avoid channel conflicts when monitoring multiple devices.

[0030] This solves the positioning deviation problem caused by data asynchrony among multiple readers and ensures the accuracy of the TOF positioning algorithm. To meet the monitoring needs of large-scale workshops (such as hundreds of pieces of equipment) while avoiding data congestion and improving system scalability; Wireless synchronization reduces cabling costs, adapts to complex workshop layouts, and improves deployment flexibility.

[0031] The readers in the multi-node reader network are ultra-wideband RFID readers of model DWM1001-DEV, with a working frequency of 3.1-10.6GHz, a receiving sensitivity of -96dBm, a communication distance of ≥50 meters in open environments, an aluminum alloy shell with heat dissipation function, an operating temperature of -20℃~60℃, an IP65 protection rating, and support for PoE power supply and simultaneous access of more than 500 tag modules.

[0032] In practical implementation, the DWM1001-DEV reader is selected. Its 3.1-10.6GHz operating frequency band and -96dBm receiving sensitivity ensure signal reception stability. The aluminum alloy shell and IP65 protection rating are suitable for workshop temperature and humidity (-20℃~60℃) and dusty environments. The POE power supply design simplifies wiring, and the LoRa synchronization mechanism ensures that the time synchronization accuracy between nodes is ≤10ns. It solves the positioning blind zone problem caused by unreasonable deployment of traditional readers and ensures stable positioning accuracy of ≤0.5 meters. The reader has strong anti-interference and environmental resistance capabilities, reducing the impact of high temperatures and dust in the workshop on the equipment. PoE power supply reduces wiring and power supply costs. It supports the access of more than 500 tags, meeting the concurrent needs of large-scale device monitoring and avoiding data conflicts.

[0033] The positioning algorithm of the edge computing gateway, by receiving the signal flight times t1, t2, and t3 between the three readers and the tag module, and combining them with the preset coordinates (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3) of the readers, establishes the following system of equations to solve for the real-time coordinates (x, y, z) of the device: (x-x1)²+(y-y1)²+(z-z1)²=(c×t1)² (x-x2)²+(y-y2)²+(z-z2)²=(c×t2)² (x-x3)²+(y-y3)²+(z-z3)²=(c×t3)² Where c is the speed of light 3×10 8 m / s.

[0034] In practical implementation, based on the "linear relationship between signal flight time and distance," the edge computing gateway incorporates a Time-of-Flight (TOF) positioning algorithm. It receives the signal flight times (t1, t2, t3) from three readers and tags, and combines this with the readers' preset three-dimensional coordinates ((x1, y1, z1), (x2, y2, z2), (x3, y3, z3) to establish a spatial distance equation set. This equation is then used to determine the distance using the speed of light (3 × 10⁻⁶). 8The distance is converted from m / s to obtain the real-time three-dimensional coordinates (x, y, z) of the device; it breaks through the accuracy limitations of traditional RFID "signal strength positioning" and achieves meter-level high-precision positioning of ≤0.5 meters, meeting the needs of refined management such as industrial equipment scheduling and area intrusion prevention; The algorithm runs directly on the edge gateway, reducing cloud computing pressure and data transmission latency, and ensuring the real-time nature of the positioning results.

[0035] The fault warning model of the edge computing gateway supports self-learning function. By analyzing the vibration and temperature historical data of the device during normal operation over the past 3 months, it automatically adjusts the vibration threshold and temperature threshold every month. When the vibration data collected for 3 consecutive times exceeds the threshold or the temperature data exceeds the threshold for 5 consecutive minutes, it is judged as abnormal and generates a fault warning signal containing device ID, abnormality type, current location and abnormal data. The false alarm rate is ≤1%.

[0036] In practice, the fault early warning model introduces a self-learning mechanism, builds a benchmark database based on vibration and temperature data from the equipment's historical normal operation, and dynamically adjusts the threshold range through statistical analysis (such as mean, variance, and trend fitting) to overcome the limitations of "fixed thresholds". At the same time, it sets a false alarm rate control target (≤1%) and filters out random fluctuation interference through algorithm optimization.

[0037] The threshold is adapted to the individual operating characteristics of the equipment, avoiding false alarms / missed alarms caused by equipment aging and changes in operating conditions. The accuracy of the early warning is significantly better than that of the traditional fixed threshold system (false alarm rate ≥5%). Reduce management costs caused by invalid warnings, ensure that managers focus on real faults, and shorten fault response cycles.

[0038] The cloud management platform's data storage module uses a MySQL database. Location data is recorded every 10 seconds, and vibration and temperature data are recorded every minute, with a storage duration of ≥1 year. It supports data export in Excel and CSV formats. The visualization module is developed based on the web and marks the equipment location in real time on the workshop map. Different colors are used to distinguish the normal, warning, and fault states of the equipment. Clicking on the equipment icon allows users to view the vibration curve, temperature curve, and historical location trajectory for the past 24 hours.

[0039] The cloud management platform's early warning push module pushes early warning information within 10 seconds of the fault early warning signal being generated through three methods: push notification via the equipment manager's mobile APP, push via SMS to a preset mobile number, and trigger via the corresponding audible and visual alarm in the workshop area. The platform also supports early warning processing procedures, allowing users to mark statuses as "viewed," "processing," or "resolved" and record processing logs.

[0040] In practice, the data storage module uses a MySQL database and records data at a frequency of "location data every 10 seconds and vibration / temperature data every minute" to ensure data integrity and traceability, and supports export in Excel / CSV format. The visualization module is developed based on the web. It marks the location of equipment on the electronic map of the workshop (color distinguishes status) and displays the trend in the form of curves to achieve "visualized control". The early warning push module adopts a multi-channel parallel design of "APP + SMS + sound and light alarm" to ensure that early warning information can quickly reach management personnel. It also supports early warning status marking and log recording to form a closed-loop management. The data storage time is ≥1 year, and it supports historical data traceability and analysis to provide data support for equipment maintenance plan optimization and root cause analysis of failures. The visual interface intuitively presents the device's "location-status" association information, reducing the operational difficulty for managers and improving management efficiency; Multi-channel early warning push within 10 seconds solves the problem of information omission in traditional single push methods, and reduces production losses caused by equipment failure when combined with closed-loop management process.

[0041] The cloud management platform also includes an equipment trajectory tracking module, which can query the location movement trajectory of industrial equipment for any time period within a maximum of one year. The trajectory recording interval is ≤10 seconds. The system is adapted to the working environment of intelligent manufacturing workshops from -20℃ to 60℃ and has environmental adaptability to oil, dust and vibration.

[0042] In practical implementation, the cloud management platform adds a device trajectory tracking module, which records device location data at intervals of ≤10 seconds / data. Combined with historical data from the data storage module (storage duration ≥1 year), the device movement trajectory is generated by connecting coordinates, supporting backtracking queries for any time period. Enables traceability of equipment scheduling history and abnormal movements, providing data support for production process optimization and responsibility identification; It helps detect issues such as redundant equipment movement and out-of-bounds operation, thereby improving workshop scheduling efficiency and regional control accuracy.

[0043] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. 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 high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID, characterized in that, It includes an ultra-wideband RFID tag module, a multi-node reader network, an edge computing gateway, and a cloud management platform. These modules work together to achieve high-precision positioning, status monitoring, and fault early warning for industrial equipment. The ultra-wideband RFID tag module is installed on the surface of the industrial equipment to be monitored. It integrates a UWB positioning unit, a vibration sensor, a temperature sensor, a data processing unit, and a wireless transmission unit. It is used to collect equipment position, vibration, and temperature data in real time, and then transmit the data after preprocessing. The multi-node reader network consists of at least three ultra-wideband RFID readers, which are deployed in key locations in the smart manufacturing workshop. Each reader has a unique ID and preset coordinates, and is used to receive signals from the tag module and obtain signal flight time and signal strength data, which are then transmitted to the edge computing gateway. The edge computing gateway is wirelessly connected to the multi-node reader network, and has a built-in positioning algorithm and fault early warning model. It calculates the real-time position of the device through TOF data from at least 3 readers, with a positioning error of ≤0.5 meters. It also has preset adjustable vibration and temperature thresholds, and generates a fault early warning signal when the detected data exceeds the threshold. The cloud management platform communicates with the edge computing gateway and includes a data storage module, a visualization module, and an early warning push module, enabling data storage, real-time display, and multi-channel early warning push.

2. The high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 1, characterized in that, In the ultra-wideband RFID tag module, the UWB positioning unit supports the TOF positioning algorithm and operates at a frequency of 3.1-10.6GHz; the vibration sensor has a sampling frequency of ≥1kHz and a measurement range of 0-50g; the temperature sensor has a measurement accuracy of ±0.5℃ and an operating temperature range of -40℃ to 125℃; the data processing unit is a microcontroller, and the wireless transmission unit is a Bluetooth 5.0 transmission unit.

3. The high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 1, characterized in that, The ultra-wideband RFID tag module adopts an IP67 waterproof and dustproof design, with the outer shell made of ABS engineering plastic and a weight of ≤50g. It provides two fixing methods: magnetic and bolt. The magnetic type uses a strong magnetic suction cup with a suction force of ≥5kg, while the bolt type is fixed with M4 bolts.

4. The high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 1, characterized in that, The ultra-wideband RFID tag module is battery powered with a battery capacity of ≥2000mAh. It supports low power consumption mode, enters sleep mode when idle, and has an adjustable wake-up interval of 1-60 seconds. The battery life on a single charge is ≥6 months, and it is equipped with a USB Type-C interface.

5. The high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 1, characterized in that, The multi-node reader network follows the triangular coverage deployment principle to ensure that each device location is covered by at least 3 readers. The reader installation height is 2.5-3 meters, and data synchronization is achieved through LoRa wireless communication with a synchronization accuracy of ≤10ns and a communication distance of ≥100 meters.

6. The high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 1, characterized in that, The readers in the multi-node reader network operate at a frequency of 3.1-10.6GHz, have a receiving sensitivity of -96dBm, a communication distance of ≥50 meters in open environments, an aluminum alloy shell with heat dissipation function, an operating temperature of -20℃~60℃, an IP65 protection rating, and support PoE power supply and simultaneous access of more than 500 tag modules.

7. The high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 1, characterized in that, The positioning algorithm of the edge computing gateway, by receiving the signal flight times t1, t2, and t3 between the three readers and the tag module, and combining them with the preset coordinates (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3) of the readers, establishes the following system of equations to solve for the real-time coordinates (x, y, z) of the device: (x-x1)²+(y-y1)²+(z-z1)²=(c×t1)² (x-x2)²+(y-y2)²+(z-z2)²=(c×t2)² (x-x3)²+(y-y3)²+(z-z3)²=(c×t3)² Where c is the speed of light 3×10 8 m / s.

8. A high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 7, characterized in that, The fault warning model of the edge computing gateway supports self-learning function. By analyzing the vibration and temperature historical data of the device during normal operation over the past 3 months, it automatically adjusts the vibration threshold and temperature threshold every month. When the vibration data collected for 3 consecutive times exceeds the threshold or the temperature data exceeds the threshold for 5 consecutive minutes, it is judged as abnormal and generates a fault warning signal containing device ID, abnormality type, current location and abnormal data. The false alarm rate is ≤1%.

9. A high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 1, characterized in that, The cloud management platform's data storage module uses a MySQL database. Location data is recorded every 10 seconds, and vibration and temperature data are recorded every minute. The storage duration is ≥1 year, and it supports data export in Excel and CSV formats. The visualization module is developed based on the web. It marks the location of equipment in real time through a workshop map and distinguishes the normal, warning and fault status of equipment with different colors. Clicking on the equipment icon can view the vibration curve, temperature curve and historical location trajectory of the past 24 hours.

10. A high-precision positioning and fault early warning system for industrial equipment based on ultra-wideband RFID according to claim 9, characterized in that, The cloud management platform's early warning push module pushes early warning information within 10 seconds of the fault early warning signal being generated through three methods: push notification via the equipment management personnel's mobile APP, push SMS to a preset mobile number, and triggering of the corresponding area's audible and visual alarm. The platform also supports early warning processing procedures, allowing users to mark statuses as "viewed," "processing," or "resolved" and record processing logs.

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