An online temperature measurement system for tobacco leaf loosening and rehydration drum based on RFID passive technology

Abstract

The present invention relates to the technical field of online temperature measurement inside a loosening and conditioning drum, and in particular to an online temperature measurement system for a tobacco loosening and conditioning drum based on RFID passive technology. Its technical solution includes an RFID passive temperature measurement tag module, an RFID reader / writer module, an edge computing and intelligent control module, and a visualization and early warning module. The present invention uses RFID passive temperature measurement technology to achieve cable-free temperature measurement inside the drum, and combines high-temperature resistant magnetic adsorption tags with intelligent readers / writers to overcome the problem of accurate temperature perception under complex working conditions; the edge computing module integrates multi-dimensional compensation algorithms and predictive control to significantly improve the accuracy of temperature and humidity control; the self-cleaning and energy recovery system ensures the long-term and stable operation of the equipment, and the blockchain evidence storage technology realizes reliable traceability of data throughout the entire process. The overall solution improves process stability, reduces operation and maintenance costs, and enhances production safety.

Description

Technical Field

[0001] The present invention relates to the technical field of online temperature measurement inside a loosening and conditioning drum, and in particular to an online temperature measurement system for a tobacco loosening and conditioning drum based on RFID passive technology. Background Art

[0002] In the cigarette production process, the loosening and rehydration process has a crucial impact on the quality of tobacco leaves. This process typically involves placing the cut tobacco leaves in a rotating drum, where a lifting plate acts to loosen and fully moisten the leaves. Temperature control is a key parameter for ensuring tobacco leaf quality. However, due to the high temperature and humidity in the semi-enclosed environment of the loosening and rehydration drum, traditional temperature measurement methods (such as thermocouples or infrared thermometers) lack reliable operation. Therefore, a wireless temperature measurement system that can reliably operate within the loosening and rehydration drum is urgently needed.

[0003] Therefore, this application proposes an online temperature measurement system for a tobacco leaf loosening and rehydration drum based on RFID passive technology. Summary of the Invention

[0004] The purpose of the present invention is to address the problem in the background technology that the traditional temperature measurement methods such as thermocouples or infrared thermometers are difficult to work stably because the loosening and conditioning drum is in a semi-closed environment with high temperature and high humidity. An online temperature measurement system for the loosening and conditioning drum of tobacco leaves based on RFID passive technology is proposed.

[0005] The technical solution of the present invention is an online temperature measurement system for a tobacco loosening and rehydration drum based on RFID passive technology, comprising:

[0006] The RFID passive temperature measurement tag module is evenly fixed on the inner wall of the drum or the surface of the copy board. It includes a high-temperature resistant packaging shell, a temperature sensor for real-time temperature sensing, and a passive RFID chip that stores temperature data and transmits it through radio frequency signal reflection modulation.

[0007] The RFID reader module is deployed outside the drum outlet and includes:

[0008] Helical antenna array for multi-tag group reading in rotating state;

[0009] Adaptive frequency modulation unit, dynamically adjusts the communication frequency band according to the drum speed;

[0010] Edge computing and intelligent control modules are used to process temperature data and adjust steam valves and hot air temperatures in real time, triggering warnings based on temperature thresholds;

[0011] The visualization and early warning module provides real-time temperature display and over-limit alarm functions.

[0012] Optionally, the RFID passive temperature measurement tag module is fixed to the inner wall of the drum through a high-temperature resistant magnetic adsorption structure, specifically including:

[0013] Flexible ceramic substrate, made of silicon nitride ceramic, with a thickness of 2mm and a bending radius adapted to the curvature of the inner wall of the drum (R = 500-800mm);

[0014] Rare earth permanent magnets, embedded in the substrate, are made of neodymium iron boron material (grade N52), with a magnetic attraction force ≥ 20N, a mechanical vibration frequency tolerance ≤ 200HZ, and a magnetic attenuation rate of <1% / year;

[0015] The high-temperature resistant adhesive layer is applied to the contact surface between the substrate and the roller, and adopts aluminum silicate fiber glue (temperature resistance ≥300℃), and the shear strength after curing is ≥5MPa.

[0016] Optionally, the RFID reader module further includes:

[0017] The multi-channel diversity receiving unit uses 2×2 Multiple-Input Multiple-Output (MIMO) space-time coding technology and a maximum ratio combining algorithm to improve the signal-to-noise ratio (gain ≥ 6dB) of the received signal.

[0018] The self-calibration unit has a built-in reference tag (accuracy ±0.1°C) and automatically triggers a calibration process every 30 minutes: the reader reads the temperature value of the reference tag and compares it with the preset baseline value. If the deviation is greater than 0.5°C, the readings of other temperature measurement tags are corrected using a linear regression algorithm.

[0019] Optionally, the edge computing and intelligent control module specifically includes:

[0020] The data preprocessing unit uses a sliding window filtering algorithm to denoise the raw temperature data and uses timestamp alignment technology to eliminate data timing deviation caused by drum rotation;

[0021] The dynamic PID controller calculates the steam valve opening adjustment in real time (with an accuracy of ±1%) based on the temperature distribution data of the drum divided into 8 sectors (three temperature measurement tags are installed in each sector), and optimizes the hot air temperature set point through fuzzy logic algorithm;

[0022] The machine learning model uses a long short-term memory (LSTM) network to train historical temperature data (time span ≥ 6 months) to predict temperature trends within the next 10 minutes. When the prediction deviation is greater than 5%, process parameter adjustment instructions are triggered in advance.

[0023] The distributed temperature compensation algorithm establishes a compensation model based on the drum rotation angle sensor and material thickness radar measurement data:

[0024] T 修正 =T 原始 +k1·θ+k2·d

[0025] Wherein, k1 is the angle compensation coefficient, k2 is the material thickness compensation coefficient, θ is the drum rotation angle, and d is the material stacking thickness;

[0026] The process parameter optimization database stores the optimal temperature and humidity curves for different tobacco varieties (such as flue-cured tobacco and air-cured tobacco), and recommends control strategies in real time through fuzzy logic matching. The matching rules include:

[0027] The correlation mapping between tobacco leaf moisture content (17%-21%) and temperature setting value;

[0028] Drum speed (5-15rpm) and steam flow (0.5-2.0m 3 / h) dynamic relationship table.

[0029] Optionally, the visualization and warning module includes the following submodules:

[0030] The 3D thermal map display interface reconstructs the internal temperature field distribution of the drum based on the finite element analysis algorithm and supports dynamic rotation of the viewing angle;

[0031] Sound and light alarm unit: when the temperature exceeds the predicted deviation by more than 5%, it triggers the buzzer (frequency 2-4kHz) and the LED flashes (red warning), and links the PLC to control the drum to stop protection;

[0032] The blockchain evidence storage unit uses the Hyperledger Fabric framework to upload temperature data and operation records to the chain, generating an unalterable hash value to achieve full life cycle traceability of the process.

[0033] The blockchain evidence storage unit is used to perform the following operations:

[0034] Receive temperature data from the RFID reader module, pre-process the data, and use the SHA-256 hash algorithm to generate a 256-bit hash value;

[0035] Bind the hash value to the PLC operation record and timestamp (accuracy 1ms), and write it into the private chain block. The block header includes the hash value of the previous block and the Merkle tree root.

[0036] Provides a Representational State Transfer Application Programming Interface (RESTful API) to support third-party audit platforms accessing blockchain data through digital certificate authentication;

[0037] The cross-platform data exchange unit is built based on the OPC Unified Architecture (OPC UA) protocol and specifically implements:

[0038] Define the following standardized data nodes:

[0039] Real-time temperature node: including tag ID (16-bit encoding), temperature value (floating point type, resolution 0.1°C), and timestamp (ISO8601 format);

[0040] Alarm status node: includes alarm code (4-digit hexadecimal), alarm level (enumeration type: warning / serious / urgent), and trigger timestamp;

[0041] Process parameter nodes: including steam valve opening (percentage, 0-100%), hot air temperature setting value (integer, unit ℃).

[0042] Optionally, the system further includes:

[0043] The self-cleaning module is configured on the surface of the reader antenna and includes:

[0044] High-pressure air flow nozzle, injection pressure ≥ 0.5MPa, injection cycle is once every 10 minutes (duration 2s), air flow temperature ≥ 80℃ to prevent condensation;

[0045] A micro camera detects the degree of contamination on the antenna surface through image recognition and triggers a cleaning instruction when the stain coverage rate is greater than 10%;

[0046] Energy recovery unit, comprising:

[0047] The permanent magnet generator installed at the end of the drum shaft has an output power of ≥50W;

[0048] The supercapacitor energy storage module (capacity ≥ 100F) converts kinetic energy into electrical energy and provides auxiliary power supply for the reader module (endurance time ≥ 8h).

[0049] Optionally, the error correction formula of the linear regression algorithm in the self-calibration unit is:

[0050]

[0051] Where: ΔT is the correction value, α is the reference temperature weight coefficient, β is the label temperature distribution weight coefficient, T 基准 is the preset reference label base temperature value, T 参考 is the actual temperature of the current reference tag, T 标签，i is the original temperature value of the i-th temperature measurement tag, and n is the number of valid temperature measurement tags.

[0052] Optionally, the dynamic calculation formula of the material thickness compensation coefficient k2 in the distributed temperature compensation algorithm is:

[0053]

[0054] Where: ΔT max is the maximum allowable temperature deviation, δ max is the maximum threshold of material thickness, γ is the attenuation coefficient, and d is the real-time measured material accumulation thickness; when d>δ max When , k2 is calculated according to the saturation value.

[0055] Optionally, the formula for generating the hash value in the blockchain evidence storage unit is:

[0056] H 区块 =SHA-256(T 均值 ||σ T ||t 时间戳 ||PLC operation code)

[0057] Among them: H 区块 is the block hash value, T 均值 is the average temperature of all temperature measuring tags in the drum; σ T is the temperature standard deviation, t 时间戳 is the data collection time (accuracy 1ms); || represents the data concatenation operation; SHA-256 is a hash algorithm that outputs a 256-bit fixed-length string.

[0058] Optionally, the cleaning efficiency η of the self-cleaning module is evaluated by the following formula:

[0059]

[0060] Among them: A 残留 A is the area of ​​stains remaining on the antenna surface after cleaning. 初始 is the stain coverage area before cleaning, P is the high-pressure airflow jet pressure, t is the duration of a single jet, μ is the airflow and pollutant adhesion coefficient, ρ is the air density, v is the airflow velocity, P0 is the reference pressure, and α is the ambient temperature correction coefficient; T 环境 is the ambient temperature outside the drum. When η<85%, the cleaning process is triggered to repeat until η≥90%.

[0061] Compared with the prior art, this application has at least one of the following beneficial technical effects:

[0062] RFID backscatter modulation technology is used to achieve passive power supply for the temperature measurement tag inside the drum, breaking through the wiring limitations of traditional wired temperature measurement and adapting to complex industrial environments.

[0063] The integration of angle compensation and material thickness compensation algorithms, combined with the LSTM prediction model, enables precise temperature control and early warning, and optimizes steam valve adjustment strategies.

[0064] Stable communication in complex electromagnetic environments is ensured through spiral antenna arrays, frequency hopping spread spectrum technology, and dynamic calibration of reference tags; the self-cleaning system and shaft-end power generation and energy storage technology enhance the equipment's continuous operation capability.

[0065] Production safety is ensured through dual-redundant monitoring of temperature thresholds and a rapid alarm response mechanism; blockchain evidence storage technology ensures that data cannot be tampered with and supports full life cycle traceability.

[0066] A standardized data interaction interface is built based on the OPC UA protocol to support seamless integration of third-party systems and optimized matching of process parameters.

[0067] The magnetic adsorption flexible installation design simplifies the label replacement process, and the self-cleaning system reduces the need for manual maintenance, significantly reducing operation and maintenance costs.

[0068] The present invention uses RFID passive temperature measurement technology to achieve cable-free temperature measurement inside the drum, and combines high-temperature resistant magnetic adsorption tags with intelligent readers and writers to overcome the problem of accurate temperature perception under complex working conditions; the edge computing module integrates multi-dimensional compensation algorithms and predictive control to significantly improve the accuracy of temperature and humidity control; the self-cleaning and energy recovery systems ensure the long-term and stable operation of the equipment, and the blockchain evidence storage technology realizes reliable traceability of data throughout the entire process. The overall solution improves process stability, reduces operation and maintenance costs, and enhances production safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a principle block diagram of an online temperature measurement system for tobacco leaf loosening and rehydration drum based on RFID passive technology. DETAILED DESCRIPTION

[0070] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments.

[0071] Example 1

[0072] like Figure 1 As shown, the present invention proposes an online temperature measurement system for a tobacco leaf loosening and rehydration drum based on RFID passive technology, including an RFID passive temperature measurement tag module, an RFID reader / writer module, an edge computing and intelligent control module, and a visualization and early warning module. Each module is described in detail below.

[0073] 1. RFID passive temperature measurement tag module is evenly fixed on the inner wall of the loose rehumidification drum or the surface of the copy board. Each temperature measurement tag includes:

[0074] High-temperature resistant ceramic package shell, made of alumina ceramic material, coated with 50-100μm thick polytetrafluoroethylene anti-metal interference coating, temperature resistance grade ≥150℃, mechanical impact strength ≥50G;

[0075] Ultra-low power temperature sensor, using MEMS thermopile structure, with measurement accuracy of ±0.3°C, dynamically adjustable sampling frequency (1-10Hz), and support for sleep mode to reduce power consumption;

[0076] Passive RFID chip with integrated EEPROM memory (capacity ≥ 2KB) to store temperature data and tag ID information, and uses backscatter modulation technology to encode temperature data into RF signals;

[0077] The RFID passive temperature measurement tag module is fixed to the inner wall of the drum through a high-temperature resistant magnetic adsorption structure, specifically including:

[0078] Flexible ceramic substrate, made of silicon nitride ceramic, with a thickness of 2mm and a bending radius adapted to the curvature of the inner wall of the drum (R = 500-800mm);

[0079] Rare earth permanent magnets, embedded in the substrate, are made of neodymium iron boron material (grade N52), with a magnetic attraction force ≥ 20N, a mechanical vibration frequency tolerance ≤ 200Hz, and a magnetic attenuation rate of <1% / year;

[0080] The high-temperature resistant adhesive layer is applied to the contact surface between the substrate and the roller, and is made of aluminum silicate fiber glue (temperature resistance >300℃). The shear strength after curing is ≥5MPa.

[0081] The RFID passive temperature measurement tag module utilizes alumina ceramic and polytetrafluoroethylene coating to achieve a wide operating temperature range of -40°C to 300°C, meeting the 150°C high-temperature requirements of tobacco processing. The MEMS thermopile sensor supports 1-10Hz dynamic sampling, and combined with a sleep mode, it extends the lifespan of a single tag to over five years. The flexible silicon nitride substrate adapts to curved surfaces of R500-R800mm. The N52-grade neodymium iron boron magnet provides 20N of adhesion, and the aluminum silicate adhesive achieves a shear strength of 5MPa at 300°C.

[0082] 2. RFID reader / writer module, deployed outside the drum outlet, including:

[0083] Helical antenna array, composed of copper spiral coils, with signal gain ≥8dBi, penetration strength ≥10dB, supports multi-tag group reading (maximum number of concurrent tags ≥200), and coverage radius ≥8m;

[0084] The adaptive frequency modulation unit dynamically adjusts the communication frequency band (860-960MHz) based on the real-time speed signal fed back by the drum speed sensor, and adopts frequency hopping spread spectrum technology (FHSS) to suppress electromagnetic interference, with a frequency hopping interval of ≤1ms;

[0085] Multi-channel diversity receiving unit, using 2×2 MIMO space-time coding technology, and maximum ratio combining algorithm to improve the signal-to-noise ratio of signal reception (gain ≥ 6dB);

[0086] The self-calibration unit has a built-in reference tag (accuracy ±0.1°C) and automatically triggers a calibration process every 30 minutes: the reader reads the temperature value of the reference tag and compares it with the preset baseline value. If the deviation is greater than 0.5°C, the readings of other temperature measurement tags are corrected using a linear regression algorithm. The error correction formula of the linear regression algorithm in the self-calibration unit is:

[0087]

[0088] Where: ΔT is the correction value, α is the reference temperature weight coefficient, β is the label temperature distribution weight coefficient,

[0089] T 基准 is the preset reference label base temperature value, T 参考 is the actual temperature of the current reference tag, T 标签，i is the original temperature value of the i-th temperature measurement tag, and n is the number of valid temperature measurement tags.

[0090] The RFID reader module uses a spiral antenna array with 8dBi gain and frequency hopping spread spectrum technology to achieve an 8-meter coverage radius at 10dB penetration strength. It has a built-in ±0.1°C reference tag and uses a linear regression algorithm to achieve 0.5°C dynamic correction to eliminate the effects of ambient temperature drift. It supports concurrent reading of 200 tags through 2×2 MIMO technology, adapting to the needs of rapid data acquisition at a drum speed of 15rpm.

[0091] 3. Edge computing and intelligent control module, including:

[0092] The data preprocessing unit uses a sliding window filtering algorithm to denoise the raw temperature data and uses timestamp alignment technology to eliminate data timing deviation caused by drum rotation;

[0093] The dynamic PID controller calculates the steam valve opening adjustment in real time (with an accuracy of ±1%) based on the temperature distribution data of the drum divided into 8 sectors (three temperature measurement tags are installed in each sector), and optimizes the hot air temperature set point through fuzzy logic algorithm;

[0094] The machine learning model uses an LSTM neural network to train historical temperature data (time span ≥ 6 months) to predict temperature trends within the next 10 minutes. When the prediction deviation is greater than 5%, process parameter adjustment instructions are triggered in advance.

[0095] The edge computing and intelligent control module further includes:

[0096] The distributed temperature compensation algorithm establishes a compensation model based on the drum rotation angle sensor and material thickness radar measurement data:

[0097] T 修正 =T 原始 +k1·θ+k2·d

[0098] Where k1 is the angle compensation coefficient (0.05°C / °), k2 is the material thickness compensation coefficient (0.1°C / mm), θ is the drum rotation angle, and d is the material stacking thickness. The dynamic calculation formula of the material thickness compensation coefficient k2 in the distributed temperature compensation algorithm is:

[0099]

[0100] Where: ΔT max is the maximum allowable temperature deviation, δ max is the maximum threshold of material thickness, γ is the attenuation coefficient, and d is the real-time measured material accumulation thickness; when d>δ max When , k2 is calculated according to the saturation value.

[0101] In the edge computing and intelligent control module, the multimodal compensation algorithm integrates the angle sensor (0.05°C / ° compensation) and the radar thickness measurement (0.1°C / mm compensation) to establish a three-dimensional temperature compensation model. The LSTM neural network is used to predict the temperature trend within 10 minutes, and the fuzzy PID controller is used to adjust the steam valve with an accuracy of ±1%. Through the parallel processing of temperature data from 8 sectors, the response time is less than 50ms, and a data throughput of 200 tags / second is supported.

[0102] 4. Visualization and early warning module, including:

[0103] The 3D thermal map display interface reconstructs the internal temperature field distribution of the drum based on the finite element analysis algorithm and supports dynamic rotation of the viewing angle;

[0104] The sound and light alarm unit triggers a buzzer (frequency 2-4kHz) and a flashing LED (red warning) when the temperature exceeds the preset process range (65-95°C) or the predicted deviation is greater than 5%, and the PLC is linked to control the drum to stop for protection:

[0105] The blockchain evidence storage unit uploads temperature data and operation records to the chain through the Hyperledger Fabric framework, generates an unalterable hash value, and realizes the traceability of the entire life cycle of the process.

[0106] The visualization and warning module includes the following submodules:

[0107] (a) Blockchain evidence storage unit, used to perform the following operations:

[0108] Receive temperature data from the RFID reader module, pre-process the data, and use the SHA-256 hash algorithm to generate a 256-bit hash value; the generation formula of the hash value in the blockchain evidence unit is:

[0109] H 区块 =SHA-256(T 均值 ||σ T ||t 时间戳 ||PLC operation code)

[0110] Among them: H 区块 is the block hash value, T 均值 is the average temperature of all temperature measurement tags in the drum;

[0111] σ T is the temperature standard deviation, t 时间戳 is the data collection time (accuracy 1ms); || represents the data concatenation operation; SHA-256 is a hash algorithm that outputs a 256-bit fixed-length string.

[0112] The process parameter optimization database stores the optimal temperature and humidity curves for different tobacco varieties (such as flue-cured tobacco and air-cured tobacco), and recommends control strategies in real time through fuzzy logic matching. The matching rules include:

[0113] The correlation mapping between tobacco leaf moisture content (17%-21%) and temperature setting value;

[0114] Drum speed (5-15rpm) and steam flow (0.5-2.0m 3 / h) dynamic relationship table;

[0115] Bind the hash value to the PLC operation record and timestamp (accuracy 1ms), and write it into the private chain block. The block header includes the hash value of the previous block and the Merkle tree root.

[0116] Provides a RESTful API interface to support third-party audit platforms to access blockchain data through digital certificate authentication;

[0117] (b) Cross-platform data interaction unit, built based on the OPC UA protocol, specifically implements:

[0118] Define the following standardized data nodes:

[0119] Real-time temperature node: includes tag ID (16-bit encoding), temperature value (floating point type, resolution 0.1°C), and timestamp (ISO 8601 format);

[0120] Alarm status node: includes alarm code (4-digit hexadecimal), alarm level (enumeration type: warning / serious / urgent), and trigger timestamp;

[0121] Process parameter nodes: including steam valve opening (percentage, 0-100%), hot air temperature setting value (integer, unit ℃)

[0122] The visualization and warning module has the following benefits:

[0123] Industrial-grade safety design: dual-redundant monitoring of 65-95°C temperature threshold, 2-4kHz sound and light alarm response time <100ms;

[0124] Blockchain traceability system: SHA-256 hash algorithm + Hyperledger Fabric framework, enabling temperature data to be uploaded to the chain and stored in seconds;

[0125] Intelligent decision support: Based on the fuzzy matching model of 12-18% moisture content, it provides the optimal temperature and humidity curve recommendation strategy.

[0126] Example 2

[0127] This embodiment is based on the first embodiment, and the system further includes:

[0128] The self-cleaning module is configured on the surface of the reader antenna and includes:

[0129] High-pressure air flow nozzle, injection pressure ≥ 0.5MPa, injection cycle is once every 10 minutes (duration 2s), air flow temperature ≥ 80℃ to prevent condensation;

[0130] A micro camera detects the degree of contamination on the antenna surface through image recognition and triggers a cleaning instruction when the stain coverage rate is greater than 10%;

[0131] Energy recovery unit, comprising:

[0132] The permanent magnet generator installed at the end of the drum shaft has an output power of ≥50W;

[0133] The supercapacitor energy storage module (capacity ≥ 100F) converts kinetic energy into electrical energy and provides auxiliary power supply for the reader module (endurance time ≥ 8h).

[0134] The cleaning efficiency η of the self-cleaning module is evaluated by the following formula:

[0135]

[0136] Among them: A 残留 A is the area of ​​residual stains on the antenna surface after cleaning. 初始 is the stain coverage area before cleaning, P is the high-pressure airflow jet pressure, t is the duration of a single jet, μ is the airflow and pollutant adhesion coefficient, ρ is the air density, v is the airflow velocity, P0 is the reference pressure, and α is the ambient temperature correction coefficient; T 环境 is the ambient temperature outside the drum. When η<85%, the cleaning process is triggered to repeat until η≥90%.

[0137] In this embodiment, high-pressure airflow cleaning: 0.5MPa @ 80°C airflow achieves a 10-minute cleaning cycle. Combined with image recognition (>10% contamination trigger), the antenna cleaning efficiency is ≥90%. The Bernoulli equation is used to optimize the nozzle design, and the cleaning efficiency formula η = 1-A residual / A initial is used to achieve a dynamic balance between energy consumption and cleaning effect.

[0138] In the energy recovery system, the 50W output of the permanent magnet generator is combined with a 100F supercapacitor to achieve a kinetic energy-electrical energy conversion efficiency of ≥85%; the 8-hour energy storage support system completes data preservation and safe shutdown in the event of a sudden power outage, meeting the continuous production requirements of the tobacco industry.

[0139] The system in this embodiment uses passive temperature measurement technology to overcome the wiring challenges of traditional wired temperature measurement. Self-cleaning and energy recovery technologies improve operational reliability, and blockchain-based evidence storage meets the traceability requirements of the tobacco industry. Actual measurement data shows improved system temperature control accuracy and reduced energy consumption.

[0140] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art may make various alternative improvements and combinations to the above specific embodiments.

Claims

1. An online temperature measurement system for tobacco loosening and rehydration drum based on RFID passive technology, characterized in that: include: The RFID passive temperature measurement tag module is evenly fixed on the inner wall of the drum or the surface of the copy board. It includes a high-temperature resistant packaging shell, a temperature sensor for real-time temperature sensing, and a passive RFID chip that stores temperature data and transmits it through radio frequency signal reflection modulation. The RFID passive temperature measurement tag module is fixed to the inner wall of the drum through a high-temperature resistant magnetic adsorption structure, specifically including: Flexible ceramic substrate, made of silicon nitride ceramic, with a bending radius adapted to the curvature of the inner wall of the drum; Rare earth permanent magnets are embedded in the substrate; High temperature resistant adhesive layer, applied on the contact surface between substrate and roller; The RFID reader module is deployed outside the drum outlet and includes: Helical antenna array for multi-tag group reading in rotating state; Adaptive frequency modulation unit, dynamically adjusts the communication frequency band according to the drum speed; Edge computing and intelligent control modules are used to process temperature data and adjust steam valves and hot air temperatures in real time, triggering warnings based on temperature thresholds; The edge computing and intelligent control module specifically includes: The data preprocessing unit uses a sliding window filtering algorithm to denoise the raw temperature data and uses timestamp alignment technology to eliminate data timing deviation caused by drum rotation; The dynamic PID controller calculates the steam valve opening adjustment in real time based on the temperature distribution data of the drum divided into 8 sectors, and optimizes the hot air temperature set point through fuzzy logic algorithm; The machine learning model uses a long short-term memory network to train historical temperature data to predict temperature trends within the next 10 minutes. When the prediction deviation is greater than 5%, process parameter adjustment instructions are triggered in advance. The distributed temperature compensation algorithm establishes a compensation model based on the drum rotation angle sensor and material thickness radar measurement data: T 修正 =T 原始 +k1·θ+k2·d Wherein, k1 is the angle compensation coefficient, k2 is the material thickness compensation coefficient, θ is the drum rotation angle, and d is the material stacking thickness; The process parameter optimization database stores the optimal temperature and humidity curves for different tobacco varieties and recommends control strategies in real time through fuzzy logic matching. The matching rules include: Correlation mapping between tobacco leaf moisture content and temperature setting values; Dynamic relationship table between drum speed and steam flow rate; The RFID reader module also includes: Multi-channel diversity receiving unit, using 2×2 multiple-input multiple-output space-time coding technology; Self-calibration unit, with built-in reference tag, automatically triggers calibration process every 30 minutes: the reader reads the temperature value of the reference tag and compares it with the preset benchmark value. If the deviation is greater than 0.5°C, the readings of other temperature measurement tags are corrected through linear regression algorithm; The visualization and early warning module provides real-time temperature display and over-limit alarm functions.

2. The online temperature measurement system for tobacco loosening and conditioning drum based on RFID passive technology according to claim 1 is characterized in that: The visualization and warning module includes the following submodules: The 3D thermal map display interface reconstructs the internal temperature field distribution of the drum based on the finite element analysis algorithm and supports dynamic rotation of the viewing angle; Sound and light alarm unit: when the temperature exceeds the predicted deviation by more than 5%, the buzzer and LED will flash, and the PLC will be linked to control the drum to stop for protection; The blockchain evidence storage unit uses the Hyperledger Fabric framework to upload temperature data and operation records to the chain, generating an unalterable hash value and tracing the entire life cycle of the process. The blockchain evidence storage unit is used to perform the following operations: Receive temperature data from the RFID reader module, pre-process the data, and use the SHA-256 hash algorithm to generate a 256-bit hash value; Bind the hash value with the PLC operation record and timestamp, and write it into the private chain block. The block header includes the hash value of the previous block and the Merkle tree root. Provides a presentation layer state transfer application programming interface to support third-party audit platforms to access blockchain data through digital certificate authentication; The cross-platform data exchange unit is built based on the OPC unified architecture protocol and is specifically used to define the following standardized data nodes: Real-time temperature node: including tag ID, temperature value, and timestamp; Alarm status node: including alarm code, alarm level, and trigger timestamp; Process parameter nodes: including steam valve opening and hot air temperature setting value.

3. The online temperature measurement system for tobacco loosening and conditioning drum based on RFID passive technology according to claim 1 is characterized in that: The system further comprises: The self-cleaning module is configured on the surface of the reader antenna and includes: High-pressure air flow nozzle, injection pressure ≥ 0.5MPa, injection cycle is once every 10 minutes, air flow temperature ≥ 80℃ to prevent condensation; A micro camera detects the degree of contamination on the antenna surface through image recognition and triggers a cleaning instruction when the stain coverage rate is greater than 10%; Energy recovery unit, comprising: A permanent magnet generator mounted on the drum shaft end; The supercapacitor energy storage module converts kinetic energy into electrical energy and provides auxiliary power for the reader module.

4. The online temperature measurement system for tobacco loosening and conditioning drum based on RFID passive technology according to claim 3 is characterized in that: The error correction formula of the linear regression algorithm in the self-calibration unit is: Where: ΔT is the correction value, α is the reference temperature weight coefficient, β is the label temperature distribution weight coefficient, T 基准 is the preset reference label base temperature value, T 参考 is the actual temperature of the current reference tag, T 标签，i is the original temperature value of the i-th temperature measurement tag, and n is the number of valid temperature measurement tags.

5. The online temperature measurement system for tobacco loosening and conditioning drum based on RFID passive technology according to claim 1 is characterized in that: The dynamic calculation formula of the material thickness compensation coefficient k2 in the distributed temperature compensation algorithm is: Where: ΔT max is the maximum allowable temperature deviation, δ max is the maximum threshold of material thickness, γ is the attenuation coefficient, and d is the real-time measured material accumulation thickness; when d>δ max When , k2 is calculated according to the saturation value.

6. The online temperature measurement system for tobacco loosening and conditioning drum based on RFID passive technology according to claim 2 is characterized in that: The formula for generating the hash value in the blockchain evidence storage unit is: H 区块 =SHA-256(T 均值 ||σ T ||t 时间戳 ||PLC operation code) Among them: H 区块 is the block hash value, T 均值 is the average temperature of all temperature measurement tags in the drum; σ T is the temperature standard deviation, t 时间戳 is the data collection time; || indicates the data concatenation operation; SHA-256 is a hash algorithm that outputs a 256-bit fixed-length string.

7. The online temperature measurement system for tobacco loosening and conditioning drum based on RFID passive technology according to claim 3 is characterized in that: The cleaning efficiency η of the self-cleaning module is evaluated by the following formula: Among them: A 残留 A is the area of ​​residual stains on the antenna surface after cleaning. 初始 is the stain coverage area before cleaning, P is the high-pressure airflow jet pressure, t is the duration of a single jet, μ is the airflow and pollutant adhesion coefficient, ρ is the air density, v is the airflow velocity, P0 is the reference pressure, and α is the ambient temperature correction coefficient; T 环境 is the ambient temperature outside the drum. When η<85%, the cleaning process is triggered to repeat until η≥90%.

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