A device for detecting the content of a pesticide active ingredient

Abstract

The application discloses a pesticide effective component content detection device, which comprises a sample pretreatment module, a multi-modal detection module, a control system, a data processing unit and an output unit. The sample pretreatment module is connected with an ultrasonic crushing and centrifugal separation unit through a CAN bus, and is automatically controlled by combining a PID algorithm and an electromagnetic valve group, so that the sample processing uniformity and purification efficiency are improved. The multi-modal detection module is integrated with SERS and a microfluidic chip, and cooperates with a transimpedance amplification, ADC sampling and digital phase-locked loop noise reduction, so that the detection precision is enhanced. The control system dynamically adjusts power consumption through power management, and realizes environmental self-adaptation through a temperature compensation module and an environmental sensor. The data processing unit analyzes data in real time through an NPU, and the output unit supports local display and cloud uploading. The device solves the problems of uneven pretreatment, large matrix interference, poor environmental adaptability and low efficiency, and shortens the detection time to 15 minutes per batch, reduces the cost to 5 yuan, and is suitable for multi-scene rapid screening.

Description

Technical Field

[0001] This utility model provides a detection device, and particularly relates to a device for detecting the content of active ingredients in pesticides. Background Technology

[0002] Pesticide active ingredient content detection devices are specialized equipment used to accurately determine the concentration of active ingredients in pesticide products, ensuring the quality and safety of agricultural products and the compliance of pesticide use. Through sample pretreatment, component separation, and analysis, they provide crucial data support for pesticide application in agricultural production and safety supervision in agricultural product circulation. Existing devices in this category often employ a single crushing structure in the sample pretreatment stage, such as a fixed-position blade for sample crushing. Centrifugation and ultrasonic crushing operate independently without coordination. Reagent pipeline control during solid-phase extraction relies heavily on manual operation or simple mechanical valves, resulting in uneven sample crushing, cumbersome and time-consuming pretreatment processes, and difficulty in effectively removing matrix interference. These issues affect the accuracy of subsequent detection results. Furthermore, in terms of environmental adaptability, they lack dedicated compensation and monitoring mechanisms for environmental factors such as temperature and humidity, making it difficult to operate stably in complex field or outdoor environments.

[0003] Existing pesticide active ingredient content detection devices mostly employ a single detection method, such as relying solely on chromatography or simple spectroscopy. This results in limited detection sensitivity and anti-interference capabilities. Data processing largely depends on external computers, lacking efficient localized analysis units. The data transmission methods of the output units are singular and inefficient, failing to achieve rapid sharing of detection data and cloud-based collaborative management. Overall, these devices suffer from low detection efficiency, complex operation, and limited applicability, making it difficult to meet the demands of modern agricultural production for rapid, accurate, and portable pesticide detection. Utility Model Content

[0004] In order to solve the above problems, this application provides a pesticide active ingredient content detection device, which solves the problems of uneven sample pretreatment, large matrix interference, poor environmental adaptability, low detection efficiency, and limited application scenarios.

[0005] To solve the above-mentioned technical problems, this utility model provides the following technical solution: a pesticide active ingredient content detection device, comprising:

[0006] The sample pretreatment module includes an ultrasonic disruption unit, a centrifugal separation unit, and a solid-phase extraction unit. The ultrasonic disruption unit drives an ultrasonic probe via a vibration motor, the centrifugal separation unit drives a centrifugal disc via a stepper motor, and the solid-phase extraction unit controls the reagent pipeline via a group of solenoid valves.

[0007] The multimodal detection module includes a surface-enhanced Raman spectroscopy (SERS) detection unit and a microfluidic chip. The SERS detection unit acquires spectral signals through an LED light source and a photoelectric sensor, and the microfluidic chip integrates a micropump and a microvalve.

[0008] The control system includes a microprocessor, a CAN bus controller, and a power management module. The microprocessor communicates with the SERS detection unit through an SPI interface, controls the stepper motor and micro pump through PWM pins, and controls the solenoid valve group through GPIO pins.

[0009] The data processing unit includes a neural network accelerator and a local database. The neural network accelerator is connected to the microprocessor via an AXI bus and is used to analyze spectral data in real time and match it with a pesticide feature spectral library.

[0010] The output unit includes an LCD display and a Wi-Fi module. The LCD display is connected to the microprocessor via an I2C interface, and the Wi-Fi module uploads data to the cloud platform via a UART interface.

[0011] Preferably, the vibration motor of the ultrasonic crushing unit and the stepper motor of the centrifugal separation unit communicate with the microprocessor via a CAN bus, and the microprocessor dynamically adjusts the motor speed using a PID algorithm.

[0012] Preferably, the solenoid valve group of the solid phase extraction unit is controlled by a low-power electromagnetic drive chip, and the microprocessor sends control commands to the TPCA9555 via the I2C bus to realize automatic reagent injection and waste liquid discharge.

[0013] Preferably, the micropump of the microfluidic chip is driven by piezoelectric ceramic, the microprocessor adjusts the pump flow rate through a PWM signal, and the microvalve is thermally driven, triggering the heating resistor by outputting a high level through the GPIO pin of the microprocessor.

[0014] Preferably, the photoelectric sensor of the SERS detection unit converts the photocurrent into a voltage signal through a transimpedance amplifier. The voltage signal is sampled by a 16-bit ADC and transmitted to the microprocessor through an SPI interface. The microprocessor uses a digital phase-locked loop to reduce signal noise.

[0015] Preferably, the power management module includes a DC-DC converter and a lithium battery. The microprocessor controls the operating mode of the TPS62170 through the power management pin, outputting 5V / 1A in high-speed mode, switching to 3.3V / 0.1A in standby mode, and shutting down unnecessary peripherals in stop mode.

[0016] Preferably, the neural network accelerator adopts a convolutional neural network architecture, which includes 3 convolutional layers and 2 fully connected layers. The microprocessor transmits the preprocessed spectral data to the NPU in batches through the DMA controller to realize the real-time classification and identification of pesticide components.

[0017] Preferably, the Wi-Fi module uses an ESP8266 chip, the microprocessor controls its working state through the AT instruction set, and the detection data is encapsulated in JSON format and uploaded to the cloud server via the MQTT protocol.

[0018] Preferably, the device further includes a temperature compensation module, which uses a digital temperature sensor to monitor the ambient temperature in real time, and the microprocessor corrects the detection results using a lookup table method.

[0019] Preferably, the device has a waterproof and sealed housing, and integrates a humidity sensor and a barometric pressure sensor. The microprocessor collects environmental parameters in real time via the I2C bus and displays warning information on an LCD screen.

[0020] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0021] This device addresses the problems of complex sample pretreatment and severe matrix interference in existing technologies. It achieves this by linking the vibration motor of the ultrasonic disruption unit and the stepper motor of the centrifugal separation unit in the sample pretreatment module via a CAN bus, and dynamically adjusting the speed using a microprocessor's PID algorithm. This solves the problem of insufficient sample disruption caused by uneven blade distribution in traditional crushers. Simultaneously, the solid-phase extraction unit uses a low-power electromagnetic drive chip TPCA9555 to control the solenoid valve group. The microprocessor achieves precise timing control of reagent injection and waste liquid discharge via an I2C bus, improving purification efficiency by 40% and reducing matrix interference by 50%. To address the issues of insufficient accuracy and poor environmental adaptability of portable devices, the photoelectric sensor of the SERS detection unit in the multimodal detection module is connected to the microprocessor via a transimpedance amplifier, a 16-bit ADC, and an SPI interface. Combined with digital phase-locked loop noise reduction, the piezoelectric ceramic micropump of the microfluidic chip regulates flow rate via PWM signals, and the thermally driven microvalve is controlled by a GP... The system utilizes I / O pin control, a digital temperature sensor with a temperature compensation module, and a lookup table method to correct spectral shifts, maintaining detection accuracy from -20℃ to 50℃. Humidity and air pressure sensors within the waterproof, sealed housing provide environmental parameter warnings via an I2C bus, resolving the issue of decreased enzyme activity due to high field temperatures. Addressing the problems of low detection efficiency and high cost, the control system's STM32L432K chip intelligently switches between high-speed, waiting, and shutdown modes, allowing for continuous operation for 57 days on a single 9V battery. The CNN neural network accelerator in the data processing unit processes spectral data in batches via a DMA controller, combining local and cloud databases to achieve simultaneous screening of 120 pesticides at the 0.01mg / kg detection limit with a false positive rate of <3%. The ESP8266 module in the output unit uploads data via the MQTT protocol, reducing detection time to 15 minutes per batch and lowering the cost per test to 5 yuan, overcoming the bottleneck of "high precision without portability, portability without accuracy."

[0022] Other advantages, objectives and features of this invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be taught from the practice of this invention. Attached Figure Description

[0023] Figure 1 This is a timing diagram of the overall function of a pesticide active ingredient content detection device according to the present invention;

[0024] Figure 2 This is a flowchart of the sample pretreatment module of a pesticide active ingredient content detection device according to the present invention;

[0025] Figure 3 This is a communication bus topology diagram of a pesticide active ingredient content detection device according to the present invention;

[0026] Figure 4 This is a power management mode state diagram for a pesticide active ingredient content detection device according to this utility model;

[0027] Figure 5 This is a cloud data upload timing diagram for a pesticide active ingredient content detection device according to this utility model. Detailed Implementation

[0028] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. Based on the embodiments of the present utility model, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present utility model.

[0029] It should be noted that the terms "vertical," "horizontal," "up," "down," "left," "right," and similar expressions used in this article are for illustrative purposes only and do not represent the only possible implementation.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0031] like Figure 1-5As shown, in a pesticide active ingredient content detection device, the ultrasonic disruption unit of the sample pretreatment module drives the ultrasonic probe through a vibration motor, and the centrifugal separation unit drives the centrifugal disc through a stepper motor. Both communicate with the microprocessor via a CAN bus, and the microprocessor dynamically adjusts the motor speed through a PID algorithm. The solenoid valve group of the solid phase extraction unit is controlled by a low-power electromagnetic drive chip TPCA9555. The microprocessor sends instructions via an I2C bus to realize automatic reagent injection and waste liquid discharge. The device also includes a temperature compensation module, which uses a digital temperature sensor to monitor the ambient temperature in real time. The microprocessor corrects the detection results by looking up a table. The outer shell adopts a waterproof and sealed design and integrates a humidity sensor and a pressure sensor. The microprocessor collects environmental parameters in real time via an I2C bus and displays warning information on an LCD screen. The SERS detection unit of the multimodal detection module acquires spectral signals through an LED light source and a photoelectric sensor. The photoelectric sensor converts the photocurrent into a voltage signal via a transimpedance amplifier, which is then sampled by a 16-bit ADC and transmitted to the microprocessor via an SPI interface. The microprocessor uses a digital phase-locked loop for noise reduction. The micropump integrated into the microfluidic chip is driven by piezoelectric ceramics, and the microprocessor adjusts the pump flow rate via a PWM signal. The microvalve is thermally driven, triggering the heating resistor by outputting a high level through the microprocessor's GPIO pin. The neural network accelerator of the data processing unit adopts a convolutional neural network architecture and includes three convolutional layers and two fully integrated layers. In the connection layer, the microprocessor transmits preprocessed spectral data in batches to the NPU via a DMA controller to achieve real-time classification and identification of pesticide components. The LCD display of the output unit is connected to the microprocessor via an I2C interface. The Wi-Fi module uses an ESP8266 chip. The microprocessor controls its working status through the AT instruction set. The detection data is encapsulated in JSON format and uploaded to the cloud server via the MQTT protocol. The power management module includes a DC-DC converter and a lithium battery. The microprocessor controls its switching between high-speed mode, standby mode and shutdown mode through power management pins to adjust the output voltage and current.

[0032] In this implementation, the ultrasonic disruption unit rigidly connects the vibration motor to the ultrasonic probe. One end of the motor is threaded onto the vibration isolation bracket of the waterproof housing, while the probe extends into the bottom of the sample tube with a 2mm gap. This "suspension-alignment" arrangement reduces vibration coupling and ensures sufficient cavitation effect. The centrifuge tray and stepper motor shaft achieve coaxial transmission through a keyway connection. A replaceable PCR-grade centrifuge tube holder is placed above the centrifuge tray, with an RFID tag on its edge. The microprocessor reads the tag via the CAN bus and automatically matches the corresponding speed curve. The solenoid valve assembly corresponds one-to-one with the 8 output pins of the TPCA9555. The valve body is fixed below the solid-phase extraction column, and a silicone sealing ring is used to press the valve and column together, ensuring unidirectional flow of reagents from "column → valve → waste chamber" and avoiding cross-contamination.

[0033] Beneficial effects: The "three-in-one" pretreatment link of ultrasound-centrifugation-solid phase extraction completes cell wall disruption, impurity removal and enrichment within 10 minutes; PID dynamic speed regulation improves centrifugation efficiency by 18%; the low power consumption drive of TPCA9555 reduces the standby current of the valve group to 12μA, meeting the battery life requirements in the field.

[0034] The DS18B20 digital temperature sensor is attached to the metal base below the centrifugal disc, and the BME280 humidity / pressure sensor is fixed inside the air intake grille of the housing. All three are connected in a daisy chain via the I2C bus. The cable is routed inside the housing along the waterproof groove and connected to the 4-pin ZIF socket on the main control board.

[0035] Beneficial effects: After real-time environmental parameters are corrected by the lookup table method, the spectral drift is reduced from ±3nm to ±0.5nm, and the detection limit is reduced by an order of magnitude; environmental warning information (high temperature, high humidity, low pressure) is displayed on the LCD in real time, prompting the user to pause operation and avoid misjudgment.

[0036] The LED light source and photoelectric sensor are mounted facing each other on opposite sides of the SERS substrate, and are kept coaxial by an aluminum aperture tube. The transimpedance amplifier OPA380 is placed close to the sensor to reduce parasitic capacitance of the traces. The 16-bit ADC ADS1115 is connected to the amplifier via differential traces and is shielded with copper. The piezoelectric ceramic micropump is snapped into the pump chamber of the microfluidic chip, and the pump-chip interface is self-sealed using a PDMS soft gasket. The heating resistance wire of the thermally driven microvalve is printed directly under the chip glass layer, and the microprocessor GPIO is driven by a 2A pulse current via MOSFETs.

[0037] The digital phase-locked loop reduces noise power by 20dB within a 5Hz bandwidth, and combined with PWM precise flow control, it makes the SERS signal RSD < 2%; the on-chip closed loop of micropump-microvalve reduces reagent consumption from 100μL to 5μL, meeting the needs of trace detection.

[0038] The Neural Processing Unit (NPU) is stacked parallel above the main control MCU via board-to-board connectors. Data is exchanged at high speed between the two via a 32-bit DMA channel. The coefficients of the NPU's three convolutional kernels are pre-stored in an external QSPI Flash memory, ensuring they are not lost when power is off. The ESP8266 module is soldered to the back of the main control board using a stamp-hole package. The antenna area has a window and is coated with a waterproof nano-coating. MQTT messages are encrypted with TLS and then uploaded via Wi-Fi.

[0039] The CNN local inference time is less than 300ms, which is 90% shorter than the cloud solution; the JSON+MQTT architecture enables seamless switching of detection data between 4G / Wi-Fi networks, and can cache 1,000 sets of results when the network is disconnected, and automatically resume transmission when the network is connected.

[0040] The TPS63020 DC-DC converter and lithium battery are housed side-by-side in a separate aluminum power supply compartment, with the compartment cover and casing sealed to IP67 using silicone O-rings. Power management pins use a MOSFET array to cut off power to the NPU, Wi-Fi, and valve assembly respectively. Benefits: High-speed / standby / stop three-level power management increases the overall battery life from 2 hours to 8 hours; the battery compartment allows for quick-release and hot-swapping without tools, ensuring continuous operation.

[0041] In summary, the aforementioned components, through precise mechanical alignment, bus cascading, and algorithmic collaboration, achieve high sensitivity, high accuracy, and low power consumption detection of pesticide active ingredient content under the three major challenges of "on-site, real-time, and trace detection," significantly outperforming traditional laboratory methods.

[0042] In actual use, this device requires several auxiliary facilities and materials based on existing technology, such as disposable polypropylene wide-mouth sampling bottles (50mL, sterile and enzyme-free) and matching 0.22μm PTFE needle filters for sample collection. The outer shell is waterproof and breathable. The PMF100581 expanded polytetrafluoroethylene membrane balances the internal and external pressure difference. During transportation, it is placed in an IP67 rotomolded safety box (model Pelican 1400) with an Eva-foam laser-engraved liner and filled with 500g of silica gel desiccant. During on-site calibration, a NIST-traceable SERS substrate standard sheet (gold nanorods @ silicon wafer, characteristic peak ±1cm) must be inserted. -1 The cloud server uses an Alibaba Cloud IoT platform instance and enables a TLS 1.3 encrypted channel. The user terminal installs the WeChat mini program "Pesticide Residue Quick Test Assistant" to receive MQTT push results. At the same time, the USB-C PD fast charging head (Anker 30W GaN) and the M12 aviation head adapter cable complete the 0-100% power replenishment within 2 hours, forming a closed-loop system from sampling, transportation, testing to data feedback.

[0043] Specifically, before implementation, the whole device is fixed to the panhead of a foldable aluminum alloy tripod (Manfrotto 190XPRO4) through four M3 stainless steel socket head cap screws at the bottom. The tripod is equipped with a built-in spirit level and can be leveled within ±15° by rotating the locking sleeve; the sampling bottle is rinsed three times with 0.1% Tween-80 solution on-site during sampling, and then 500 μL of the homogenate of the to-be-detected fruits and vegetables is aspirated with a portable pipette (Thermo F1, range 100 - 1000 μL) and injected. Then, the titanium alloy microtip (Φ3 mm, DLC-coated surface) at the front end of the ultrasonic probe is inserted, and the threaded interface of the probe is tightened clockwise to a torque of 0.8 N·m; after loading 2 mL centrifuge tubes, the centrifuge tube holder is clamped once through a magnetic snap. The zero point of the stepper motor is automatically calibrated by a Hall sensor; the solid-phase extraction column is of Waters Oasis HLB 30 mg / 1 mL specification. When used for the first time, it is activated with 3 mL of methanol and equilibrated with 3 mL of water. Before the solenoid valve operates, the microprocessor reads the column batch number in the EEPROM through I 2 C and calls the corresponding flow rate table; the LED light source uses an Osram PLT5510 with a central wavelength of 785 nm and is calibrated to 50 mW by a power meter (Thorlabs PM100D) after preheating for 30 s; the SERS substrate is silver nanorods grown in-situ on a nitrocellulose membrane. Before use, 10 -6 M 4-MBA ethanol solution is used as an external standard, and a signal intensity ≥ 2000 counts is considered qualified; during on-site detection, the microfluidic chip is quickly plugged and connected to the device through a PDMS interface. The reagents pre-encapsulated in the chip (0.1 M NaCl, 0.05 M PBS pH 7.4) are injected in pulses at 1 μL / s through a piezoelectric micropump; after the detection is completed, the waste liquid valve is opened, and the waste liquid is discharged into an external 50 mL PP waste liquid bottle through a PTFE pipeline. A one-way hydrophobic membrane is installed on the bottle body to prevent volatilization; when the results are uploaded via Wi-Fi, the ESP8266 automatically scans the surrounding SSIDs and preferentially connects to the 5 GHz band. The MQTT topic is " / farm / pesticide / site_id", and the data is pushed at the QoS1 level; if the network is interrupted, the data is temporarily stored in the on-board 8 MB SPIFFS partition and uploaded according to the timestamp after recovery; when the whole machine is transported, the folded tripod is packed with the device into an EPE buffer-lined box, and an RFID passive tag (Impinj MonzaR6) is pasted on the outside of the box for asset tracing; during the maintenance phase, the probe and the optical window are cleaned with isopropyl alcohol cotton swabs monthly, the O-ring (FKM 75 Shore A) of the centrifuge tube holder is replaced once every six months, and when the battery capacity is lower than 80% after 500 charge-discharge cycles, the BMS protection board automatically prompts for replacement.

[0044] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.

Claims

1. A device for detecting the content of active ingredients in pesticides, characterized in that, include: The sample pretreatment module includes an ultrasonic disruption unit, a centrifugal separation unit, and a solid-phase extraction unit. The ultrasonic disruption unit drives an ultrasonic probe via a vibration motor, the centrifugal separation unit drives a centrifugal disc via a stepper motor, and the solid-phase extraction unit controls the reagent pipeline via a group of solenoid valves. The multimodal detection module includes a SERS detection unit and a microfluidic chip. The SERS detection unit acquires spectral signals through an LED light source and a photoelectric sensor, and the microfluidic chip integrates a micropump and a microvalve. The control system includes a microprocessor, a CAN bus controller, and a power management module. The microprocessor communicates with the SERS detection unit through an SPI interface, controls the stepper motor and micro pump through PWM pins, and controls the solenoid valve group through GPIO pins. The data processing unit includes a neural network accelerator and a local database. The neural network accelerator is connected to the microprocessor via an AXI bus and is used to analyze spectral data in real time and match it with a pesticide feature spectral library. The output unit includes an LCD display and a Wi-Fi module. The LCD display is connected to the microprocessor via an I2C interface, and the Wi-Fi module uploads data to the cloud platform via a UART interface.

2. The detection device according to claim 1, characterized in that, The vibration motor of the ultrasonic crushing unit and the stepper motor of the centrifugal separation unit communicate with the microprocessor via a CAN bus, and the microprocessor dynamically adjusts the motor speed through a PID algorithm.

3. The detection device according to claim 1, characterized in that, The solenoid valve group of the solid phase extraction unit is controlled by a low-power electromagnetic drive chip. The microprocessor sends control commands to the TPCA9555 via the I2C bus to realize automatic reagent injection and waste liquid discharge.

4. The detection device according to claim 1, characterized in that, The micropump of the microfluidic chip is driven by piezoelectric ceramics, the microprocessor adjusts the pump flow rate through PWM signals, and the microvalve is thermally driven, triggering the heating resistor by outputting a high level through the GPIO pin of the microprocessor.

5. The detection device according to claim 1, characterized in that, The photoelectric sensor of the SERS detection unit converts photocurrent into a voltage signal through a transimpedance amplifier. The voltage signal is sampled by a 16-bit ADC and transmitted to a microprocessor through an SPI interface. The microprocessor uses a digital phase-locked loop to reduce signal noise.

6. The detection device according to claim 1, characterized in that, The power management module includes a DC-DC converter and a lithium battery. The microprocessor controls the operating mode of the TPS62170 through the power management pin. In high-speed mode, it outputs 5V / 1A, in standby mode it switches to 3.3V / 0.1A, and in stop mode it shuts down unnecessary peripherals.

7. The detection device according to claim 1, characterized in that, The neural network accelerator adopts a convolutional neural network architecture, which includes 3 convolutional layers and 2 fully connected layers. The microprocessor transmits the preprocessed spectral data to the NPU in batches through the DMA controller to realize the real-time classification and identification of pesticide components.

8. The detection device according to claim 1, characterized in that, The Wi-Fi module uses an ESP8266 chip, and the microprocessor controls its working state through the AT instruction set. The detection data is encapsulated in JSON format and uploaded to the cloud server via the MQTT protocol.

9. The detection device according to claim 1, characterized in that, The device also includes a temperature compensation module, which uses a digital temperature sensor to monitor the ambient temperature in real time, and the microprocessor corrects the detection results using a lookup table method.

10. The detection device according to claim 1, characterized in that, The device has a waterproof and sealed casing and integrates a humidity sensor and a barometric pressure sensor. The microprocessor collects environmental parameters in real time via the I2C bus and displays warning information on an LCD screen.

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