Water Volatile Organic Compounds Sampling and Pretreatment System Based on Headspace Principle

Abstract

The present invention relates to the field of environmental monitoring, and discloses a system for collecting and preprocessing water volatile organic compound samples based on the headspace principle. An automatic physical execution chain is constructed through multi-threaded interruption control, PID closed-loop regulation, and robotic arm state machine. It includes: constructing a pre-packaged intelligent sample bottle, encrypting and storing freeze-dried internal standards, sealing parameters, and calibration curves in a UHF RFID tag to eliminate the risk of manual intervention; based on multi-threaded parallel control technology, synchronously performing isokinetic sampling, three-stage gradient filtration, and environmental parameter binding to achieve full-process automation of data collection; adopting a physical model-driven state machine, dynamically calculating the equilibrium time through Henry's constant, and combining closed-loop control of nitrogen purge pressure to improve the recovery rate of low-volatility substances; using a quantum random number generator and topological data association technology to construct a structured data packet containing timestamps, environmental parameters, and quantum verification codes to ensure the integrity of the data.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring, and particularly to a system for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle. Background Art

[0002] The monitoring of volatile organic compounds (VOCs) in water is of great significance for environmental protection, water quality safety and public health. VOCs are a class of organic compounds that are volatile and can rapidly diffuse in water bodies, including various substances harmful to human health, such as benzene, toluene, chloroform, etc. Therefore, accurately and efficiently collecting and preprocessing water samples of VOCs is a key step in subsequent analysis and detection work.

[0003] Traditional methods for collecting and preprocessing water samples of VOCs often have problems such as complex operation, long time consumption, and low automation. With the progress of technology, especially the application of headspace technology, automatic control technology, Internet of Things technology and quantum computing technology, the process of collecting and preprocessing water samples of VOCs is gradually realizing automation and intelligence. The headspace technology realizes the enrichment and separation of VOCs by balancing the distribution of VOCs between the gas and liquid phases in the water sample, providing convenience for subsequent analysis.

[0004] Deficiencies in the prior art:

[0005] The traditional process of collecting and preprocessing water samples of VOCs often relies on manual operation, with cumbersome steps and long time consumption. It lacks an intelligent control system and cannot automatically adjust the collection and preprocessing parameters according to the characteristics of the water sample.

[0006] The key parameters (such as the calibration curve of the internal standard substance, the sealing structure parameters, etc.) in the process of sample collection and preprocessing often exist in the form of paper records, which are easy to lose and make mistakes. It lacks an effective data encryption and storage mechanism and cannot ensure the integrity and traceability of data.

[0007] Traditional preprocessing methods often cannot dynamically adjust the processing process according to real-time parameters such as the turbidity and pressure of the water sample, resulting in unstable preprocessing effects. It lacks an effective filtering mechanism and cannot completely remove particulate matter and impurities in the water sample, affecting the accuracy of subsequent analysis results.

[0008] Therefore, we propose a system for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle to solve the above problems. Summary of the Invention

[0009] The present invention provides a system for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle, which constructs a full-automatic physical execution chain through multi-threaded interruption control, PID closed-loop regulation, and robotic arm state machine.

[0010] The first aspect of the present invention provides a method for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle. The method for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle includes: generating a programmable configuration file containing a bottle identification code, an internal standard calibration curve, and sealing structure parameters based on a predefined sampling protocol database and an internal standard calibration parameter library, encrypting the programmable configuration file and storing it in an RFID tag; reading the programmable configuration file through a near-field communication module, verifying the validity period of the internal standard and the integrity of the calibration curve after parsing the encrypted data, and activating the flow control parameters of a micro peristaltic pump to generate a three-level gradient filtration instruction set; according to the three-level gradient filtration instruction set, synchronously execute the following parallel threads: Thread T1: controlling the peristaltic pump to inject the water sample and real-time monitoring the data of the pressure sensor; Thread T2: dynamically adjusting the switching timing of the three-level filter screens and generating a filtration log based on the feedback value of the turbidity sensor; Thread T3: recording the environmental parameters and binding them to the RFID tag; according to the filtration log and the environmental parameters, driving a six-axis robotic arm to perform the following state transitions: State Q1: constant temperature oscillation at 4°C, and the duration is dynamically calculated by a preset Henry's constant; State Q2: nitrogen purging, and the triggering condition is that the headspace pressure < 101.3 kPa ± 1%; State Q3: generating a balance completion flag bit and a gaseous VOCs concentration matrix; performing topological association on the gaseous VOCs concentration matrix, the RFID tag, and the environmental parameters to generate a structured data packet containing a timestamp and a quantum random number check code.

[0011] Optionally, in the first implementation manner of the first aspect of the present invention, it includes: generating a unique bottle identification code based on a predefined sampling protocol database according to the following rules; extracting preset concentration gradient points of a freeze-dried internal standard from an internal standard calibration parameter library to generate an internal standard calibration curve; generating a set of sealing structure parameters according to preset sealing process rules; integrating the bottle identification code, the internal standard calibration curve, and the set of sealing structure parameters into a programmable configuration file by using TLV structure encoding.

[0012] Optionally, in the second implementation manner of the first aspect of the present invention, it includes: generating a validity period status identifier by comparing the production date of the internal standard stored in the RFID tag with the current date of the embedded system clock, and triggering an audible and visual alarm signal and terminating the process if it is overdue; performing CRC-32 cyclic redundancy check on the calibration curve data block in the configuration file to generate a check result flag, and re-reading the RFID tag data if the check fails; parsing the peristaltic pump parameters in the configuration file to generate a PID closed-loop control parameter set; generating a three-level gradient filtration instruction set according to a preset filter screen switching rule, including: primary filter screen: default enabled, switching to the next level when the pressure sensor value > 50 kPa; secondary filter screen: the enabling condition is that the turbidity sensor value < 10 NTU and lasts for 5 seconds; tertiary filter screen: the enabling condition is that the turbidity sensor value < 5 NTU and the pressure value < 30 kPa.

[0013] Optionally, in the third implementation manner of the first aspect of the present invention, the following operations are performed according to the PID closed-loop control parameter set: controlling the micro peristaltic pump to inject the water sample at a flow rate of 0.5 L / min ± 0.5%; collecting the pressure sensor data in real time to generate a pressure data set aligned with the time stamp; generating a filtration log including the filter screen switching time stamp, the turbidity value, and the particulate matter mass based on the filtration instruction set and the real-time feedback value of the turbidity sensor; collecting the environmental data, binding the environmental data with the bottle identification code and the sampling time stamp in the RFID tag to generate an encrypted environmental parameter package.

[0014] Optionally, in the fourth implementation manner of the first aspect of the present invention, it includes: calculating the concentration of each VOCs component according to the calibration curve data block and the headspace gas volume:

[0015] ;

[0016] wherein, C i is the concentration of component i, R i is the GC-MS response factor, V g is the headspace gas volume, K H,i represents the Henry's law constant of the i-th volatile organic compound; generating a gaseous VOCs concentration matrix.

[0017] Optionally, in the fifth implementation manner of the first aspect of the present invention, it includes: generating a topological data tree according to the gaseous VOCs concentration matrix, the internal standard calibration curve stored in the RFID tag, and the environmental parameter package, and the node relationship is represented by a directed edge; generating a 128-bit random number through a hardware quantum random number generator, calculating the SHA3-256 hash value based on the random number as the quantum check code, and binding the quantum check code with the UTC time stamp to the root node of the topological data tree; generating a final structured data packet according to the topological data tree, the quantum check code, and the encrypted key library.

[0018] The second aspect of the present invention provides a system for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle. The system for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle includes: a headspace vial module, which is used to generate a programmable configuration file containing vial identification codes, internal standard calibration curves, and sealing structure parameters based on a predefined sampling protocol database and an internal standard calibration parameter library. The programmable configuration file is encrypted and stored in an RFID tag; a control module, which is used to read the programmable configuration file through a near-field communication module, verify the validity period of the internal standard and the integrity of the calibration curve after parsing the encrypted data, activate the flow control parameters of a micro peristaltic pump, and generate a three-level gradient filtration instruction set; an execution module, which is used to synchronously execute the following parallel threads according to the three-level gradient filtration instruction set: Thread T1: control the peristaltic pump to inject water samples and monitor the pressure sensor data in real time; Thread T2: dynamically adjust the switching timing of the three-level filter screens and generate a filtration log based on the feedback value of a turbidity sensor; Thread T3: record environmental parameters and bind them to the RFID tag; a regulation module, which is used to drive a six-axis robotic arm to perform the following state transitions according to the filtration log and environmental parameters: State Q1: constant temperature oscillation at 4°C, and the duration is dynamically calculated based on a preset Henry's constant; State Q2: nitrogen purging, and the triggering condition is that the headspace pressure < 101.3 kPa ± 1%; State Q3: generate a balance completion flag bit and a gaseous VOCs concentration matrix; a packaging module, which is used to topologically associate the gaseous VOCs concentration matrix with the RFID tag and environmental parameters to generate a structured data packet containing a timestamp and a quantum random number verification code.

[0019] The third aspect of the present invention provides a device for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle, including: a memory and at least one processor, where instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the device for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle executes the above-mentioned method for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle.

[0020] The fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored. When the instructions run on a computer, the computer is made to execute the above-mentioned method for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle.

[0021] The mechanism of the present invention is as follows:

[0022] Digitize physical properties such as fractal geometry parameters and sealing material characteristics, and achieve precise matching of parameters - equipment through RFID tags. Achieve microsecond - level synchronization of multi - threads through hardware interrupts to solve the problem of time - cumulative error in traditional serial operations. Introduce the gas - liquid interface area as a key variable to significantly improve the mass transfer efficiency of low - volatility substances (such as chlorobenzene). Bind the quantum random source to the UTC timestamp to achieve spatio - temporal uniqueness authentication of data packets, support full - life - cycle traceability in laboratory analysis, and use environmental parameters as necessary inputs for concentration calculation to solve the concentration deviation problem caused by ignoring environmental fluctuations in traditional methods.

[0023] Beneficial effects: Automatically generate configuration files through predefined databases and parameter libraries, and read and execute them through the near - field communication module, achieving a high degree of automation in the sample collection and pretreatment process. Through the design of parallel threads and state transitions, the entire process is made more efficient and orderly, reducing manual intervention and improving work efficiency.

[0024] All key parameters and information are encrypted and stored in RFID tags, ensuring data integrity and traceability. By generating structured data packets containing timestamps and quantum random number verification codes, the security and reliability of data are further enhanced.

[0025] Adopt the PID closed - loop control parameter set and the three - level gradient filtering instruction set to achieve precise control of the peristaltic pump flow rate and the timing of filter screen switching. According to the real - time feedback values of the pressure sensor and the turbidity sensor, dynamically adjust the filtering process to ensure the stability of the pretreatment effect.

[0026] Calculate the concentration of each VOCs component through the calibration curve data block and the headspace gas volume, generate the gaseous VOCs concentration matrix, providing an accurate data basis for subsequent analysis. Use the quantum random number generator and SHA3 - 256 hash value calculation as the quantum verification code to ensure the uniqueness and immutability of data, further improving the accuracy of analysis.

[0027] Through mechanisms such as CRC - 32 cyclic redundancy check, validity status identification, and audible and visual alarm signals, ensure the correctness of the configuration file and the effectiveness of the internal standard substance. Use an encrypted key library to encrypt and store and transmit sensitive data, prevent data leakage and tampering, and enhance the security of the system. Description of the Drawings

[0028] Figure 1 It is a schematic diagram of an embodiment of the method for collecting and pretreating water - borne volatile organic compound samples based on the headspace principle in the embodiments of the present invention.

[0029] Figure 2Another schematic diagram of the method for collecting and pretreating water volatile organic compound samples based on the headspace principle in the embodiments of the present invention;

[0030] Figure 3 A schematic diagram of an embodiment of the system for collecting and pretreating water volatile organic compound samples based on the headspace principle in the embodiments of the present invention;

[0031] Figure 4 A schematic diagram of an embodiment of the equipment for collecting and pretreating water volatile organic compound samples based on the headspace principle in the embodiments of the present invention. Detailed implementation manners

[0032] The embodiments of the present invention provide a system for collecting and pretreating water volatile organic compound samples based on the headspace principle, and construct a full-automatic physical execution chain through multi-thread interruption control, PID closed-loop regulation, and robotic arm state machine. Terms such as "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0033] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to Figure 1 An embodiment of the method for collecting and pretreating water volatile organic compound samples based on the headspace principle in the embodiments of the present invention includes:

[0034] 101. Generate a programmable configuration file including bottle identification codes, internal standard calibration curves, and sealing structure parameters based on a predefined sampling protocol database and internal standard calibration parameter library. The configuration file is encrypted by SHA-256 and stored in a UHF RFID tag;

[0035] It can be understood that the execution subject of the present invention can be a system for collecting and pretreating water volatile organic compound samples based on the headspace principle, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present invention are described by taking the server as the execution subject as an example.

[0036] It should be noted that in the server of the environmental monitoring laboratory, the preset sampling protocol database includes the following core fields:

[0037] Bottle Identification Code: It adopts a 16 - bit hexadecimal code (A3F9B2E7 - 5C8D - 4A01), which is uniquely bound to the UHF RFID tag of the physical sampling bottle.

[0038] Internal Standard Calibration Parameter Library: It stores calibration curve data of multiple common internal standards (tetrachloroethylene, toluene - d8), including concentration gradient (0.1 μg / L - 50 μg / L), response factor (the response value of toluene - d8 at 25 °C is 1.32 ± 0.05), and validity period (valid before December 31, 2025).

[0039] Sealing Structure Parameters: Define the material of the O - ring at the bottle mouth (fluororubber), pre - tightening torque (1.5 N·m ± 0.2), and sealing pressure threshold (≥101.3 kPa).

[0040] Configuration File Generation Logic: When the user selects the "Surface Water VOCs Monitoring" protocol, the system executes: Extract the identification code of the corresponding bottle (Bottle_2025_00456) from the database.

[0041] Call the calibration curve data of tetrachloroethylene in the internal standard parameter library to generate a cubic polynomial fitting formula: y = 0.982x 3 −0.156x 2 +1.024x + 0.003

[0042] Where x is the concentration (μg / L) and y is the gas chromatography peak area ratio.

[0043] Combine the sealing parameters to generate structured fields: O - ring compression ratio: 30% ± 2% (corresponding to the elastic modulus of fluororubber); temperature adaptability range: - 10 °C - 50 °C (based on the bottle body material being borosilicate glass 3.3).

[0044] Data Encryption and RFID Writing: The generated configuration file is encrypted by the SHA - 256 algorithm to generate a 64 - bit hash value:

[0045] 8f3c7a...b2e45d, and is written into the EEPROM storage area of the UHF RFID tag (occupying 512 bytes). Dynamic key authentication is enabled during tag writing to ensure that only authorized readers can decrypt.

[0046] Data Verification Mechanism: The server - side synchronously generates a quantum random number verification code (QRN_8D4F), which is stored in the cloud database together with the hash value for subsequent data integrity verification.

[0047] 102. Read the programmable configuration file through the Near Field Communication (NFC) module. After parsing the encrypted data, perform the following operations: verify the validity period of the internal standard and the integrity of the calibration curve; activate the flow control parameters of the micro peristaltic pump; generate a three-stage gradient filtration instruction set (20μm → 5μm → 1μm filter screen switching logic);

[0048] It should be noted that under the control of the environmental monitoring laboratory server, the encrypted configuration file stored in the UHF RFID tag (including the bottle identification code Bottle_2025_00456, the calibration curve equation of tetrachloroethylene y = 0.982x 3 −0.156x 2 +1.024x + 0.003, the parameters of the fluororubber sealing ring) is read through near field communication by the NFC reader / writer.

[0049] Dynamic key decryption: The server calls the preset AES-256 key to decrypt the configuration file, generates a plaintext data packet, and verifies the data integrity through the SHA-256 hash value (8f3c7a...b2e45d).

[0050] Validity check: The system automatically compares the validity period of the internal standard (the validity period of tetrachloroethylene is up to December 31, 2025) with the current UTC timestamp (April 3, 2025). If it is overdue, an alarm will be triggered and the process will be terminated.

[0051] Activation of peristaltic pump parameters. Based on the parsed calibration parameters, the server sends control instructions to the micro peristaltic pump through the RS-485 bus:

[0052] Flow accuracy control: Set the pump body flow rate to 0.5 L / min, with an error range of ±0.5% (i.e., 0.4975 - 0.5025 L / min), and the flow data is fed back in real time through the built-in Hall sensor.

[0053] Dynamic compensation mechanism: When the environmental temperature fluctuates by more than ±2°C (the laboratory temperature rises from 25°C to 28°C), the system automatically adjusts the motor speed to compensate for the change in the elastic modulus of the silicone tube caused by thermal expansion.

[0054] Generation of three-stage gradient filtration instructions. The server generates a three-stage filter screen switching logic according to the water sample type (surface water containing suspended particles):

[0055] Step-by-step filtration strategy:

[0056] Primary filtration: Start the 20μm stainless steel filter screen and continue to run until the feedback value of the turbidity sensor ≥ 3 NTU (60% of the preset threshold of 5 NTU);

[0057] Secondary switching: Switch to the 5μm ceramic filter membrane, and at the same time start the backwashing program to remove the residual particles on the primary filter screen;

[0058] Tertiary fine filtration: When the turbidity drops to 1.2 NTU, a 1μm polyethersulfone filter membrane is enabled until the turbidity of the effluent stabilizes at <0.8 NTU.

[0059] Parallel data recording: The filter screen switching timestamp and the pressure sensor readings (primary filter screen differential pressure 0.15 bar → secondary 0.28 bar) are synchronously written into the filtration log field of the RFID tag.

[0060] Safety and anti-interference design, dual-channel verification: When the NFC module verifies the internal standard, it synchronously sends a quantum random number verification code (QRN_8D4F) to the cloud server to achieve local and cloud data consistency verification. Physical anti-tampering: The RFID tag is encapsulated with epoxy resin. If illegal disassembly is detected (abnormal change in tag impedance > 5%), the stored data is immediately erased and a security alarm is sent.

[0061] 103. Based on the tertiary gradient filtration instruction set, the following parallel threads are executed synchronously: Thread T1: Controls the peristaltic pump to inject the water sample and monitors the pressure sensor data in real time (sampling frequency 100 Hz); Thread T2: Dynamically adjusts the switching timing of the tertiary filter screens and generates a filtration log based on the feedback value of the turbidity sensor (threshold < 5 NTU); Thread T3: Records the environmental parameters (temperature, humidity, air pressure) and binds them to the RFID tag;

[0062] It should be noted that the environmental monitoring laboratory server receives the tertiary gradient filtration instruction set generated in step 102 (filter screen switching logic: 20μm → 5μm → 1μm) and synchronously activates three independent threads:

[0063] Thread T1: Controls the micro peristaltic pump to inject the surface water sample at 0.5 L / min ± 0.5% (i.e., the flow range is 0.4975 - 0.5025 L / min). At the same time, the pipeline pressure data is collected through the pressure sensor (range 0 - 5 bar) at a frequency of 100 Hz. When the primary filter screen is blocked, the pressure rises from the initial 0.15 bar to 0.28 bar, triggering the dynamic compensation algorithm to adjust the pump speed.

[0064] Thread T2: Based on the real-time feedback value of the turbidity sensor (range 0 - 50 NTU, resolution 0.1 NTU), the filter screen switching strategy is executed. When the turbidity ≥ 3 NTU (60% of the preset threshold 5 NTU), switch to the next-level filter screen, and record the timestamp (2025-04-03T08:15:23Z), filter screen type, and turbidity value in the filtration log.

[0065] Thread T3: Call the environmental sensor group (temperature accuracy of ±0.5°C, humidity accuracy of ±2%RH, air pressure accuracy of ±0.5hPa) to collect data every 10 seconds, set the temperature to 23.5°C, humidity to 65%RH, and air pressure to 101.2kPa, and encrypt and write the data into the extended storage area of the bottle label through the UHF RFID reader-writer.

[0066] Dynamic filter screen switching algorithm and exception handling. In thread T2, the server uses a fuzzy PID control model to optimize the filter screen switching:

[0067] Primary filter screen stage: When the turbidity suddenly increases from 2.8 NTU to 4.2 NTU (surface water contains sediment particles) during the operation of the 20μm stainless steel filter screen, the system triggers the secondary filter screen switching in advance to avoid the safety threshold of the pressure difference exceeding 0.3 bar.

[0068] Backwashing linkage: When the 5μm ceramic filter membrane is enabled, the backwashing program is started synchronously (nitrogen pressure 0.5MPa, lasting for 5 seconds) to remove the residues on the primary filter screen, and the number of backwashing times (accumulated 3 times for single sampling) is recorded in the log.

[0069] Tertiary fine filtration verification: After the 1μm polyethersulfone filter membrane is enabled, if the turbidity does not drop to <0.8 NTU within 30 seconds, an alarm is triggered and an error code (E102: filter membrane damaged) is generated to terminate the sampling process.

[0070] Multi-threaded data synchronization and security mechanism, time synchronization: The three threads achieve μs-level time alignment through the NTP server, set the time deviation of the pressure peak value (0.27 bar) and turbidity jump (4.1 NTU) to <10 ms to ensure the accuracy of data correlation. Anti-interference design: The differential amplifier circuit (gain 100 times) is used for the pressure sensor signal to eliminate electromagnetic interference, and the turbidity probe is equipped with a self-cleaning brush (rotating once every 5 minutes) to prevent biofilm attachment. Data integrity verification: The CRC-32 check code (0x8A7D2F) is generated for every 10 records of the filtration log and written into the tamper-proof storage area of the RFID tag together with the environmental parameters.

[0071] Performance verification and fault tolerance test, simulate extreme scenarios in the laboratory:

[0072] High turbidity shock test: Inject a simulated water sample with a turbidity of 8 NTU, and the system completes the tertiary filter screen switching within 12 seconds, and the response delay recorded in the log is <50 ms.

[0073] Low temperature environment test: Under the working condition of 5°C, the flow rate volatility of the peristaltic pump is still <0.45% (standard requirement ±0.5%), and the temperature drift compensation algorithm of the pressure sensor suppresses the error within ±0.02 bar.

[0074] 104. Drive a six-axis robotic arm to perform the following state transitions based on the filtration log and environmental parameters: State Q1: Constant temperature oscillation at 4°C (amplitude 5 mm, frequency 2 Hz), and the duration is dynamically calculated according to the preset Henry's constant; State Q2: Nitrogen purging (flow rate 10 mL / min), and the triggering condition is that the headspace pressure < 101.3 kPa ± 1%; State Q3: Generate a balance completion flag and a gaseous VOCs concentration matrix.

[0075] It should be noted that under the control of the environmental monitoring laboratory server, a six-axis robotic arm (model Huiling Z-ArmS622, repeat positioning accuracy ±0.02 mm) performs the following operations:

[0076] Robotic arm movement: Accurately transfer the water sample bottle (identification code Bottle_2025_00456) after three-stage filtration from the filtration station to the 4°C constant temperature oscillation chamber. The gripper uses a vacuum adsorption method to ensure no displacement error of the bottle body. Oscillation parameters: Set the amplitude at 5 mm and the frequency at 2 Hz, and the duration is dynamically calculated according to the preset Henry's constant (Henry's constant for benzene H = 5.5×10 -3 atm·m 3 / mol). When the benzene concentration in the water sample is 200 μg / L, the oscillation time t = H·V / k (k is the empirical coefficient, taking 0.85), and the calculated result is 28 minutes.

[0077] Environmental compensation: Combining the environmental parameters (temperature 23.5°C, humidity 65%RH), the server automatically corrects the power of the oscillation motor to ensure that the temperature fluctuation in the chamber ≤ ±0.2°C.

[0078] State Q2: Nitrogen purging trigger logic:

[0079] Pressure monitoring: The headspace pressure sensor (range 0 - 200 kPa, accuracy ±0.5%) feeds back data in real time. When the pressure drops to 100.8 kPa (lower limit value of the threshold 101.3 kPa ± 1%) after oscillation, the server triggers the nitrogen purging instruction.

[0080] Purging control: The six-axis robotic arm transfers the sample bottle to the purging station, connects to the high-purity nitrogen interface (purity 99.999%), purges at a constant flow rate of 10 mL / min for 30 seconds, and the purging pressure is 0.2 MPa. The turbidity log shows that the turbidity of the filtered water sample is 0.7 NTU, meeting the purging start condition.

[0081] Abnormal handling: If the pressure is still lower than the threshold after purging, the system automatically switches to the redundant gas path (spare nitrogen cylinder) and records the fault code E205 to the RFID tag.

[0082] State Q3: Generation of gaseous VOCs concentration matrix:

[0083] Data collection: After headspace equilibration, a mass spectrometry detector (ThermoFisher TR-WaxMS chromatographic column) collects signals of 55 VOCs, generating a 12×55 concentration matrix (rows correspond to sampling time points, and columns correspond to compounds).

[0084] Dynamic calibration: Combining the calibration curve of the internal standard (tetrachloroethylene) in the RFID tag, y = 0.982x 3 −0.156x 2 +1.024x + 0.003, the concentrations of substances such as benzene and toluene are corrected. For example, the response value of benzene is adjusted from 3250 to 3287 (relative deviation ≤ 1.2%).

[0085] Flag bit generation: When the RSD of all compounds in the matrix < 5%, the server writes the balance completion flag bit STATUS_OK, and at the same time attaches the quantum random number verification code QRN_8D4F to the data packet header.

[0086] Robotic arm coordination and safety guarantee, multi-axis coordination: The first to third axes of the robotic arm are responsible for translational positioning (repetitive accuracy ±0.1 mm), and the fourth to sixth axes control the rotation of the gripper (angle resolution 0.01°) to avoid wear of the bottle mouth sealing ring. Collision detection: A force sensor (range 0 - 10 N) monitors the clamping force in real time. When the pressure > 3 N, an emergency stop is triggered to prevent the glass bottle from breaking. Data traceability: The movement trajectory of the robotic arm (joint angles, accelerations) and the headspace pressure change curve are synchronously stored in the cloud.

[0087] 105. Topologically associate the gaseous VOCs concentration matrix with RFID calibration data and environmental parameters, and execute: Generate a structured data packet containing a timestamp (UTC synchronization) and a quantum random number verification code; Write it to the cloud database and the local SD card through an encrypted channel, and the output format is JSON-LD (Linked Data Framework);

[0088] It should be noted that the server receives the gaseous VOCs concentration matrix (12×55 matrix, containing the concentration data of 55 compounds such as benzene and toluene at 12 time points) generated in step 104, and combines the following multi-source data for fusion:

[0089] RFID calibration data: Read the calibration curve of tetrachloroethylene, y = 0.982x, from the UHF RFID tag 3 −0.156x 2 +1.024x + 0.003, correct the original response value of benzene, 3250, to obtain the calibrated concentration of 3287 μg / m 3 (relative deviation ≤ 1.2%).

[0090] Environmental parameters: Bind the turbidity (0.7 NTU), temperature (23.5 °C), humidity (65% RH), and air pressure (101.2 kPa) in the filtered log to compensate for the temperature drift effect of the chromatographic detector (for every 1 °C increase in temperature, the benzene response value decreases by 0.8%).

[0091] Structured data packet generation, timestamp synchronization: Use a UTC atomic clock module (accuracy ±1 ms) to mark the data acquisition time (2025-04-03T08:30:45.123Z) and align it with the robotic arm's motion trajectory (joint angles, accelerations) to ensure the time consistency of multi-source data. Quantum random number verification code: Call a quantum entropy source device (based on an optical quantum random number generator) to generate a 128-bit verification code (QRN_8D4F-9B2E-7C1A) to prevent data tampering. This verification code is encrypted and stored together with the data packet hash value (SHA-256).

[0092] JSON-LD framework construction, data packets are organized according to the linked data framework, and the core fields include:

[0093] Concentration matrix: Rows represent the time series (0~11), columns correspond to compounds (benzene, toluene), and the numerical unit is μg / m 3 (Example: The concentration of benzene at time T5 is 3287 μg / m 3 ).

[0094] Metadata association:

[0095] Calibration curve parameters (polynomial coefficients, valid until 2025-12-31).

[0096] Environmental compensation coefficients (temperature correction factor 0.8% / °C, humidity influence weight 0.2% / RH).

[0097] Equipment traceability chain: Robotic arm model (Z-ArmS622), mass spectrometer serial number (TR-WaxMS#00456).

[0098] Encrypted storage and dual-channel writing, encrypted channel: Use the AES-256-GCM algorithm to encrypt data packets. The key is dynamically generated by a hardware security module (HSM) and an additional digital signature (ECDSA-P384) is attached.

[0099] Cloud synchronization: The encrypted data packets are fragmented and uploaded to an AWS S3 bucket (region eu-central-1), and at the same time written to a local SD card (SanDisk Extreme Pro 1TB), and the storage path is / VOC / 2025-04-03 / Bottle_00456.jsonld.

[0100] Disaster recovery mechanism: If the network is interrupted, the data is temporarily stored in the edge computing node (NVIDIA Jetson AGX), and after recovery, it will automatically resume transmission and verify the CRC-32 consistency between the cloud and local files.

[0101] Offline verification: Laboratory personnel can compare the quantum verification code in the RFID tag with the cloud hash value to detect whether data loss or tampering has occurred during transmission (error tolerance threshold: single-byte difference < 0.001%). Exception handling: If the verification fails (hash values do not match), the system automatically triggers the data retransmission protocol (RTP) and retrieves backup data from the redundant storage node (local NAS).

[0102] In the embodiments of the present invention, the automated design reduces manual operation steps and improves sampling efficiency. Through precise control algorithms and sensors, the accuracy and reliability of the sampling process are ensured; the use of data encryption and quantum random number verification codes effectively prevents data tampering and loss. The design of the association between structured data packets and metadata makes the data traceable; the three-level gradient filtering instruction set and dynamic adjustment strategy effectively remove suspended particulate matter in the water sample, protecting the subsequent analysis equipment. The design of the backwashing program and filter screen switching logic extends the service life of the filter screen; the multi-threaded parallel processing design enables the system to handle multiple tasks simultaneously, improving flexibility. Through environmental parameter compensation and dynamic calibration, the system can adapt to sampling requirements under different environmental conditions; the structured data packet is organized using the JSON-LD framework, facilitating data exchange and sharing with other systems. The encrypted storage and dual-channel writing design ensure the reliability and availability of the data. In summary, through innovative designs in integration, automation, encryption security, gradient filtering, multi-threaded parallel processing, etc., this solution significantly improves the efficiency, accuracy, and security of the collection and pretreatment of water samples containing volatile organic compounds, providing strong technical support for the environmental monitoring field.

[0103] Please refer to Figure 2 , another embodiment of the method for collecting and pretreating water samples containing volatile organic compounds based on the headspace principle in the embodiments of the present invention includes:

[0104] 201. Generate a programmable configuration file containing the bottle identification code, internal standard calibration curve, and sealing structure parameters based on a predefined sampling protocol database and internal standard calibration parameter library. The configuration file is encrypted using SHA-256 and stored in the UHF RFID tag.

[0105] Specifically, based on a predefined sampling protocol database, a unique bottle body identification code (Product 1a) is generated according to the following rules: Encoding format: 16-bit HEX code (the first 4 bits represent the production batch, the middle 8 bits are the timestamp, and the last 4 bits are the check code); Generation method: An initial seed value is generated through a hardware true random number generator (TRNG); From the internal standard calibration parameter library, preset concentration gradient points of the freeze-dried internal standard (decafluorotriphenylphosphine) are extracted to generate a calibration curve data block (Product 1b) containing the following content: Concentration gradient: at least 5 points (0.1 μg / L, 1 μg / L, 10 μg / L, 50 μg / L, 100 μg / L); Response factor: Determined based on the gas chromatography-mass spectrometry (GC-MS) standard method; Validity period: Set according to the internal standard stability experiment data (error tolerance ±5%);

[0106] According to the preset sealing process rules, a set of sealing structure parameters (Product 1c) is generated, including: Silicone gasket thickness: 0.5 ± 0.02 mm (Shore hardness 60 ± 2); Aluminum screw cap torque: 3.5 ± 0.2 N·m; PFA heat-shrinkable sealing film shrinkage rate: 30 ± 2% (under the condition of 130 °C hot air); Product 1a, 1b, and 1c are integrated into a programmable configuration file (Product 1) according to the following rules: Data format: Encoded using the TLV (Type-Length-Value) structure; Encryption method: Generate a digital digest through the SHA-256 algorithm and write it into the UHF RFID tag (operating frequency 902 - 928 MHz);

[0107] It should be noted that the encoding rule adopts a 16-bit HEX code structure (example: A3B5_202503151200_9D4E); The first 4 bits (A3B5): represent the production batch, generated according to the "2024-Q3-UV batch rule" in the sampling protocol database; The middle 8 bits (20250315): UTC timestamp (March 15, 2025); The last 4 bits (9D4E): Check code generated based on the TRNG module (model: Infineon SLI9670);

[0108] Hardware implementation: An initial seed value is generated through the hardware true random number generator (TRNG) in the embedded system, generating 1000 entropy values per second. After verification by the NIST SP800-90B standard, the valid bits are extracted;

[0109] Compilation of the internal standard calibration curve, concentration gradient configuration:

[0110]

[0111] Data source: Determined using an Agilent 7890B GC-MS system in accordance with the EPA8260B standard method; Expiry date management: The expiry date is set at 30 days (error ±3.2%) based on stability test data, and an automatic alarm is triggered when the threshold is exceeded.

[0112] Definition of sealing structure parameters and physical parameter configuration: Silicone gasket: Dow Corning SE-4400 material is selected, with a Shore hardness of 61 ± 1 (measured value: 60.8); Aluminum cap torque: An Atlas Copco STW10 torque controller is used, with a calibration accuracy of ±0.05 N·m; PFA heat shrinkable film: Chemours PFA340 is selected, and the measured shrinkage rate at 130 °C is 31.2%; Process verification: The helium mass spectrometer leak detector (Leybold Phoenix L300i) is used to verify the sealing performance, and the leakage rate ≤ 1×10^-9 mbar·L / s.

[0113] Encrypted storage: The SHA-256 algorithm is used to generate a digital digest (example: 8f3c7b...e9a2); It is written into an Alien Higgs-4 RFID tag through an Impinj R700 UHF reader (902 - 928 MHz), with a storage capacity of 8 KB; Key management: A hardware security module (HSM) is used to store the master key, and a temporary session key is generated each time it is written.

[0114] 202. Read the programmable configuration file through the near field communication (NFC) module, and perform the following operations after parsing the encrypted data: Verify the expiry date of the internal standard and the integrity of the calibration curve; Activate the flow control parameters of the micro peristaltic pump (0.5 L / min ± 0.5%); Generate a three-level gradient filtering instruction set (20μm → 5μm → 1μm filter switching logic).

[0115] Specifically, by comparing the production date of the internal standard stored in the RFID tag with the current date of the embedded system clock, a validity status flag (product 2a) is generated. If it is overdue, an audible and visual alarm signal is triggered and the process is terminated; Perform a CRC-32 cyclic redundancy check on the calibration curve data block in the configuration file to generate a check result flag (product 2b). If the check fails, the RFID tag data is read again (up to 3 retries); Parse the peristaltic pump parameters (flow rate 0.5 L / min ± 0.5%) in the configuration file to generate a PID closed-loop control parameter set (product 2c), including the proportionality coefficient K p = 2.5, integral time T i = 0.8 s, derivative time T d= 0.2s; Generate a three - level gradient filtration instruction set (product 2d) according to the preset filter screen switching rule, including: Primary filter screen (20μm): Default enabled, switch to the next level when the pressure sensor value > 50 kPa; Secondary filter screen (5μm): Enabled when the turbidity sensor value < 10 NTU and lasts for 5 seconds; Tertiary filter screen (1μm): Enabled when the turbidity sensor value < 5 NTU and the pressure value < 30 kPa.

[0116] It should be noted that the following takes the specific execution of step 202 in a certain water quality sampling as an example to illustrate how to read the configuration file through the NFC module and execute four sub - steps:

[0117] NFC module reading and data decryption, hardware configuration: Adopt STM32 series NFC controller (supporting ISO14443A protocol) to read the encrypted configuration file stored in the UHF RFID tag (operating frequency 902 - 928 MHz). Data decryption: Verify the digital digest through the SHA - 256 algorithm, decrypt the TLV structure data, and extract the following key parameters: Bottle identification code: A3B5_202504030800_9D4E (production batch A3B5, timestamp April 3, 2025, 08:00, check code 9D4E);

[0118] Internal standard calibration curve: Concentration gradient points: 0.1, 1, 10, 50, 100 μg / L; Response factors: 0.15, 1.82, 18.67, 92.31, 184.95 (measured based on Agilent7890B GC - MS); Expiration date: May 3, 2025 (error ±5%); Sealing parameters: Silicone gasket thickness 0.52 mm (Shore hardness 61), aluminum cap torque 3.45 N·m, PFA film shrinkage rate 29.8%;

[0119] Sub - step S2a: Internal standard expiration date verification, verification logic: Extract the production date of the internal standard (March 15, 2025) from the configuration file and compare it with the current system time (April 3, 2025). Calculate the time difference: 19 days (not exceeding the 30 - day expiration date). If it is overdue (time difference ≥ 31 days), trigger the buzzer alarm (frequency 2 kHz, lasting 10 seconds) and terminate the process. Output: Generate a status identifier VALID to allow the subsequent process to execute.

[0120] Sub-step S2b: Calibration Curve Integrity Verification, CRC-32 Check Process: Extract the calibration curve data block (128 bytes in length, containing the concentration gradient and response factor). Calculate the current data CRC value as 0x8F3C7B2D and compare it with the check value stored in the RFID tag. If the check fails (data corruption due to transmission interference), trigger the retry mechanism (up to 3 times). Fault Tolerance Design: After three consecutive retry failures, activate the backup calibration curve (pre-stored in the local database).

[0121] Sub-step S2c: Activation of Peristaltic Pump Flow Control Parameters, PID Closed-loop Control Implementation: Parameter Settings: Proportional coefficient Kp = 2.5, integral time Ti = 0.8 s, derivative time Td = 0.2 s. Hardware Linkage: Adjust the motor speed of the micro peristaltic pump (model Cole-Parmer EW-07554-20) through the frequency converter, with a target flow rate of 0.5 L / min ± 0.5% (measured flow rate of 0.498 L / min, error -0.4%). Real-time Feedback: Integrate a high-precision flow sensor (range 0 - 2 L / min, accuracy ± 0.1%), sample once every 100 ms, and dynamically correct the PID parameters.

[0122] Sub-step S2d: Generation of Three-stage Gradient Filtration Instruction Set, Trigger Conditions and Execution Logic: First-stage filter (20 μm) → Second-stage filter (5 μm): Pressure sensor value > 50 kPa (measured 52.3 kPa) and lasts for 3 seconds (pressure fluctuation < ± 1 kPa). Record the switching timestamp: 2025-04-03T08:02:15.123.

[0123] Second-stage filter (5 μm) → Third-stage filter (1 μm): Turbidity sensor value < 10 NTU (measured 8.5 NTU ± 0.3) and lasts for 5 seconds (sampling interval 0.5 seconds).

[0124] Activate the third-stage filter when the pressure sensor value < 30 kPa (measured 28.7 kPa).

[0125] Filtration Log Generation: Includes filter switching time, turbidity value, pressure value, and particulate matter mass estimation (based on the pressure-turbidity linear model: Particulate matter = 0.12 × Pressure + 0.05 × Turbidity).

[0126] 203. Based on the three-stage gradient filtration instruction set, synchronously execute the following parallel threads: Thread T1: Control the peristaltic pump to inject the water sample and monitor the pressure sensor data in real time (sampling frequency 100 Hz); Thread T2: Dynamically adjust the switching timing of the three-stage filter and generate a filtration log based on the feedback value of the turbidity sensor (threshold < 5 NTU); Thread T3: Record the environmental parameters (temperature, humidity, air pressure) and bind them to the RFID tag;

[0127] Specifically, according to the PID parameter set (Product 2c), the following operations are performed: Control the micro peristaltic pump to inject the water sample at a flow rate of 0.5 L / min ± 0.5%; Real-time collect the data of the pressure sensor (range 0 - 100 kPa, sampling frequency 100 Hz), and generate a pressure data set aligned with the time stamp (Product 3a);

[0128] Based on the filtration instruction set (Product 2d) and the real-time feedback value of the turbidity sensor (range 0 - 100 NTU, accuracy ± 0.5 NTU), perform the following: Switch of the primary filter (20 μm) → secondary filter (5 μm): Trigger condition: The pressure value > 50 kPa and lasts for 3 seconds; Generate the usage duration record of the primary filter (accuracy ± 0.1 s); Switch of the secondary filter (5 μm) → tertiary filter (1 μm): Trigger condition: The turbidity value < 5 NTU and the pressure value < 30 kPa; Generate the estimated mass of the particulate matter intercepted by the secondary filter (based on the pressure-turbidity linear model); Generate a filtration log containing the filter switch time stamp, turbidity value, and particulate matter mass (Product 3b);

[0129] Record and associate the environmental data in the following way: Collect the temperature (range -20 °C to 50 °C, ± 0.1 °C), humidity (0 - 100% RH, ± 2%), and air pressure (80 - 110 kPa, ± 0.1 kPa); Bind the environmental data with the bottle identification code and sampling time stamp in the RFID tag; Generate an encrypted environmental parameter package (Product 3c), and the data format is the TLV structure (type - length - value).

[0130] It should be noted that the following takes the specific execution of Step 203 in a certain water quality sampling as an example to illustrate the collaborative operation of three parallel threads under the three-level gradient filtration instruction set:

[0131] Thread T1: Pressure synchronization control, Hardware configuration: Peristaltic pump model: Cole-Parmer EW-07554-20, Flow control accuracy ± 0.5% (target flow rate 0.5 L / min, measured 0.498 L / min, error -0.4%). Pressure sensor: Honeywell 26PC series, range 0 - 100 kPa, sampling frequency 100 Hz (collect data every 10 ms).

[0132] PID parameter execution: Proportional coefficient Kp = 2.5, Integral time Ti = 0.8 s, Derivative time Td = 0.2 s (from the parameter set generated in Step 202). Real-time adjust the motor speed to ensure that the flow rate fluctuation range is ± 0.002 L / min (measured pressure fluctuation: 48.5 kPa → 52.3 kPa → stabilized to 49.8 kPa).

[0133] Data Record: Generate a pressure data set aligned with the timestamp (2025-04-03T08:00:15.123 → 52.3 kPa, error ±0.05 kPa).

[0134] Thread T2: Dynamic filter switch (trigger condition and log generation), primary filter (20μm) → secondary filter (5μm) switch: Trigger condition: Pressure value > 50 kPa and lasts for 3 seconds (measured 52.3 kPa for 3.2 seconds). Record content: The usage duration of the primary filter is 120.5 seconds (accuracy ±0.1 s), and the estimated mass of intercepted particulate matter is 15.3 mg (based on the formula: particulate matter mass = 0.12 × pressure + 0.05 × turbidity).

[0135] Secondary filter (5μm) → tertiary filter (1μm) switch: Trigger condition: Turbidity value < 5 NTU (measured 4.8 NTU ±0.3) and pressure < 30 kPa (measured 28.7 kPa).

[0136] Generate log entry: Timestamp: 2025-04-03T08:02:30.456; Event: Secondary → tertiary filter switch; Turbidity value: 4.8 NTU; Particulate matter mass: 8.2 mg;

[0137] Thread T3: Environmental parameter binding (data collection and encryption), sensor configuration: Temperature: PT100 platinum resistance sensor (range -20°C to 50°C, measured water temperature 4.2°C ±0.05°C). Humidity: Sensirion SHT35 (range 0-100%RH, measured ambient humidity 65% ±1.5%). Air pressure: Bosch BMP388 (range 80-110 kPa, measured air pressure 101.2 kPa ±0.08 kPa).

[0138] Data binding and encryption: Associate environmental parameters with the bottle identification code A3B5_202504030800_9D4E, generate a TLV structure encrypted package (example): Type: 0x01 (temperature) | Length: 4 bytes | Value: 4.2°C; Type: 0x02 (humidity) | Length: 4 bytes | Value: 65%; Type: 0x03 (air pressure) | Length: 4 bytes | Value: 101.2 kPa; Write to the RFID tag after encrypting with AES-256 (the key is dynamically generated by the hardware security module HSM).

[0139] Parallel thread coordination mechanism, timing synchronization: The real-time operating system (RTOS) of the embedded system is used to schedule threads to ensure that the timestamps of pressure data (T1), filter screen switching (T2), and environmental records (T3) are aligned (error < ±1 ms). Data sharing: Thread-safe queues are used to transfer pressure and turbidity data to avoid data competition. Fault tolerance design: If any thread crashes (sensor failure), the system triggers an exception handler to save the current state and restart the affected thread (maximum retry count: 3 times).

[0140] 204. According to the filtration log and environmental parameters, drive the six-axis robotic arm to perform the following state transitions: State Q1: Constant temperature oscillation at 4°C (amplitude 5 mm, frequency 2 Hz), and the duration is dynamically calculated based on the preset Henry's constant; State Q2: Nitrogen purging (flow rate 10 mL / min), and the trigger condition is that the headspace pressure < 101.3 kPa ± 1%; State Q3: Generate a balance completion flag and a gaseous VOCs concentration matrix;

[0141] Specifically, sub-step S4a: State Q1 (constant temperature oscillation control), set the constant temperature oscillation parameters: Temperature: 4 ± 0.5°C (based on PID control of the semiconductor refrigeration module); Amplitude: 5 ± 0.1 mm; Frequency: 2 ± 0.05 Hz;

[0142] Calculate the equilibrium duration: ;

[0143] Wherein, K H is the preset Henry's constant (unit: mol / (L·kPa)), V l is the liquid volume (unit: L), A is the gas-liquid interface area (unit: cm²);

[0144] Drive the six-axis robotic arm to perform the oscillation action to generate a constant temperature oscillation completion flag (product 4a);

[0145] Sub-step S4b: State Q2 (nitrogen purging trigger), monitor the headspace pressure sensor data in real time (range: 80 - 120 kPa, accuracy ±0.1 kPa); When the pressure value < 101.3 kPa ± 1%, start nitrogen purging: Nitrogen purity ≥ 99.999%; Flow control: 10 ± 0.2 mL / min (closed-loop regulation based on the mass flowmeter); Continue purging until the pressure returns to 101.3 ± 0.5 kPa to generate a pressure balance flag (product 4b);

[0146] Sub-step S4c: State Q3 (concentration matrix generation), calculate the concentration of each VOCs component according to the calibration curve data block (product 1b) and the headspace gas volume:

[0147] ;

[0148] Wherein, C i is the concentration (μg / L) of component i . R i is the GC-MS response factor, V g is the headspace gas volume (mL), K H,i represents the Henry's law constant of the i-th volatile organic compound (VOC);

[0149] Generate a gaseous VOCs concentration matrix (product 4c), including: component names, concentration values, units; equilibrium timestamp (synchronized with the environmental parameter package).

[0150] It should be noted that the following takes the specific execution of step 204 in a certain water quality sampling as an example to illustrate the state transition control logic driven by filtered logs and environmental parameters:

[0151] State Q1: Constant temperature oscillation control (dynamic equilibrium duration calculation and execution), hardware configuration: six-axis robotic arm: using the ABB IRB1200 series, integrated with a semiconductor refrigeration module (temperature control accuracy ±0.3°C). Oscillation parameters: temperature 4.2°C (measured fluctuation ±0.4°C), amplitude 5.1 mm (error ±0.08 mm), frequency 2.03 Hz (PID regulation error ±0.02 Hz).

[0152] Equilibrium duration calculation: Input parameters: Henry's constant K H = 0.12 mol / (L·kPa) (pre-stored in the parameter library); liquid volume V l = 0.5 L (from the filtered log of step 203); gas-liquid interface area A = 15 cm 2 (bottle geometry parameter);

[0153] Calculation result: t = 0.12×0.5 / 15×3600 = 14.4 s;

[0154] Execution action: The robotic arm oscillates in a sinusoidal curve trajectory for 14.4 s, generating a flag Q1_COMPLETE.

[0155] Status Q2: Nitrogen purge triggered (pressure closed-loop control and purge), sensors and purge system: Pressure sensor: Honeywell 26PC series (range 80 - 120 kPa, measured headspace pressure 100.8 kPa, error ±0.08 kPa). Nitrogen supply: Using a liquid nitrogen vaporization system (purity 99.999%), the flow rate is controlled by a Brooks SLA5850 mass flow meter (set at 10.1 mL / min, measured 10.05 mL / min).

[0156] Trigger logic: Purge is started when the pressure < 101.3 kPa ± 1% (i.e., < 100.3 kPa) (purge is triggered when the measured pressure drops to 99.8 kPa). Purge process: Continuously purge until the pressure returns to 101.3 ± 0.5 kPa (measured 101.1 kPa to meet the standard), which takes 3 minutes and 15 seconds, generating the flag bit Q2_COMPLETE.

[0157] Status Q3: Generation of gaseous VOCs concentration matrix, calibration data call:

[0158] Call the calibration curve (response factor of decafluorotriphenylphosphine) generated in step 201:

[0159]

[0160] Example of concentration calculation (taking benzene as an example):

[0161] Volume of headspace gas V g = 20 mL, Henry's constant K H = 0.08 mol / (L·kPa)

[0162] Response factor R i = 18.67 (corresponding to the 10 μg / L calibration point)

[0163] Calculated concentration: Ci = 18.67×20 / 0.08×0.5 = 9.34 μg / L

[0164] Output matrix:

[0165]

[0166] Data synchronization binds environmental parameters: temperature 4.2°C, humidity 65%, air pressure 101.1 kPa.

[0167] 205. Topologically associate the gaseous VOCs concentration matrix with RFID calibration data and environmental parameters, and execute: Generate a structured data packet containing a timestamp (UTC synchronization) and a quantum random number verification code; Write it to the cloud database and the local SD card through an encrypted channel, and the output format is JSON-LD (Linked Data Framework);

[0168] Specifically, sub-step S5a: Data topological association. Bind the following data sources in a hierarchical relationship: Core data layer: Gaseous VOCs concentration matrix (Product 4c); Calibration traceability layer: Internal standard calibration curve stored in the RFID tag (Product 1b); Environmental association layer: Environmental parameter package (Product 3c); Generate a topological data tree with spatio-temporal correlation (Product 5a), and the node relationship is represented by a directed edge (starting point: environmental parameter → ending point: concentration value).

[0169] Sub-step S5b: Quantum check code generation. Generate a 128-bit random number through a hardware quantum random number generator (QRNG), and perform the following operations based on this random number: Calculate the SHA3-256 hash value as the quantum check code (Product 5b); Bind the check code and the UTC timestamp (synchronization accuracy ±1ms) to the root node of the topological data tree.

[0170] Sub-step S5c: Structured data encapsulation. Encapsulate the data according to the following rules: Data format: JSON-LD (Linked Data Framework), including the following fields: @context: Define the data lineage ("The concentration value is derived from calibration curve ID: xxxx"); @graph: Embed the topological data tree (Product 5a) and the quantum check code (Product 5b); @timestamp: UTC timestamp in ISO8601 format.

[0171] Encryption storage: Use the AES-256 algorithm to encrypt the data packet (key length 256 bits, CBC mode); Synchronously write to the cloud database (through the TLS1.3 protocol) and the local SD card (FAT32 format); Generate the final structured data packet (Product 5).

[0172] It should be noted that the following takes the execution of step 205 in a certain water quality sampling as an example to illustrate how to associate the gaseous VOCs concentration matrix with multi-source data and generate an encrypted data packet that complies with the JSON-LD standard:

[0173] Data topological association (S5a), input data: Core data layer (Product 4c): Benzene concentration: 9.34 μg / L (calibration curve ID: CAL-20250403-A3B5); Toluene concentration: 5.67 μg / L; Equilibrium timestamp: 2025-04-03T08:15:23.456 (UTC synchronization);

[0174] Calibration traceability layer (Product 1b): Internal standard: Bis(perfluorophenyl) phenylphosphine, expiration date: 2025-05-03; Response factor: 184.95 (100 μg / L calibration point);

[0175] Environment Association Layer (Product 3c): Temperature: 4.2°C (Sensor ID: TEMP-001); Humidity: 65%RH; Air Pressure: 101.1 kPa;

[0176] Topology Construction Rules: Node Relationship: Environmental Parameters (Temperature, Humidity) → Calibration Curve Validity → Concentration Value; Directed Edge Definition: Temperature → Benzene Concentration (Affects Gas-Liquid Equilibrium Rate); Calibration Curve ID → Benzene Concentration (Data Traceability);

[0177] Quantum Checksum Generation (S5b), Hardware and Process: Quantum Random Number Generator: IDQuantique QRNG-1G4, Generates 128-bit Random Number (Example: 0x8f3c7b...e9a2); Hash Calculation: Uses SHA3-256 Algorithm to Generate Quantum Checksum (Example Hash Value: a1b2c3...d4e5); Timestamp Binding: Obtains UTC Time (2025-04-03T08:15:23.456±1ms) through GNSS Module;

[0178] Structured Data Encapsulation (S5c), JSON-LD Framework Construction:

[0179] @context Definition: Associates Schema.org Vocabulary (http: / / schema.org / chemicalConcentration). Maps Calibration Curve ID to Internal Database URI (http: / / lab.example / calibration / CAL-20250403-A3B5).

[0180] @graph Embedding: The Topology Data Tree Contains: Environmental Parameter Nodes (Temperature, Humidity, Air Pressure); Calibration Curve Nodes (Validity Period, Response Factor); Concentration Matrix Nodes (Each VOC Component and Value);

[0181] The Quantum Checksum and Timestamp are Bound to the Root Node to Form an Immutable Identifier.

[0182] Encryption and Storage: AES-256 Encryption: Uses Hardware Security Module (HSM) to Generate Dynamic Key, Encrypts Data Packets in CBC Mode.

[0183] Dual-Channel Writing: Cloud: Uploads to AWS S3 Bucket (Path: s3: / / vocs-data / 20250403 / ) through TLS1.3 Protocol; Local: Writes to SanDisk Extreme Pro SD Card (FAT32 Format, Encrypted Partition);

[0184] Example of measured data, JSON-LD structure logic: Context layer: Declare that the data source is the internal standard of the laboratory (calibration curve) and the Schema.org semantic model.

[0185] Data layer: The benzene concentration value is associated with the calibration curve ID, and the environmental temperature influence coefficient (0.12 / °C) is marked. The quantum check code a1b2c3...d4e5 is bound to the timestamp 2025-04-03T08:15:23.456 to ensure the traceability of the data life cycle.

[0186] In the embodiment of the present invention, the entire sampling and pretreatment process is highly automated, reducing human intervention, improving work efficiency and accuracy. Through RFID tags and encryption technology, the whole process from sample collection to data processing is traceable, ensuring the authenticity and integrity of the data; the flow rate of the micro peristaltic pump is controlled by PID closed-loop control to achieve precise control of the water sample injection. Based on the gas chromatography-mass spectrometry (GC-MS) standard method, the internal standard calibration curve is generated to ensure the accuracy and reliability of the measurement results; a three-stage gradient filtration system is adopted to effectively remove impurities and particulate matters in the water sample, improving the accuracy of subsequent analysis. Based on the real-time feedback of the pressure sensor and the turbidity sensor, the switching timing of the filter screen is dynamically adjusted to ensure the optimization of the filtration effect; the environmental parameters such as temperature, humidity and air pressure during the sampling process are recorded in real time, providing an important basis for data analysis. Topologically associating the environmental parameters with the VOCs concentration data helps to analyze the influence of environmental factors on the VOCs concentration; encryption algorithms such as SHA-256 and AES-256 are used to encrypt and store the configuration file and data packet to ensure the security of the data. The hardware security module (HSM) is used to store and manage the keys to further improve the security of the system; this method can adjust the sampling protocol, calibration parameters and filtration instructions according to actual needs, with high flexibility. By adding sensors and actuators, the functions and application scope of the system can be further expanded. In summary, the method for collecting and preprocessing water samples of volatile organic compounds in the present solution is excellent in creativity, not only improving the efficiency and accuracy of sampling and pretreatment, but also ensuring the authenticity and security of the data, providing strong technical support for water quality monitoring and analysis.

[0187] The method for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle in the embodiment of the present invention has been described above. Next, the system for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle in the embodiment of the present invention will be described. Please refer to Figure 3, an embodiment of the waterborne volatile organic compounds sample collection and pretreatment system based on the headspace principle in the embodiments of the present invention includes: a headspace bottle module 301, configured to generate a programmable configuration file including a bottle identification code, an internal standard calibration curve, and a sealing structure parameter based on a predefined sampling protocol database and an internal standard calibration parameter library, and encrypt and store the programmable configuration file in an RFID tag; a control module 302, configured to read the programmable configuration file through a near-field communication module, verify the validity period of the internal standard and the integrity of the calibration curve after parsing the encrypted data, activate the flow control parameters of a micro peristaltic pump, and generate a three-level gradient filtration instruction set; an execution module 303, configured to synchronously execute the following parallel threads according to the three-level gradient filtration instruction set: thread T1: control the peristaltic pump to inject water samples and monitor the data of a pressure sensor in real time; thread T2: dynamically adjust the switching timing of the three-level filter screen and generate a filtration log based on the feedback value of a turbidity sensor; thread T3: record environmental parameters and bind them to the RFID tag; a regulation module 304, configured to drive a six-axis robotic arm to perform the following state transitions according to the filtration log and environmental parameters: state Q1: constant temperature oscillation at 4°C, and the duration is dynamically calculated by a preset Henry constant; state Q2: nitrogen purging, and the trigger condition is that the headspace pressure < 101.3 kPa ± 1%; state Q3: generate a balance completion flag bit and a gaseous VOCs concentration matrix; a packaging module 305, configured to perform topological association according to the gaseous VOCs concentration matrix, the RFID tag, and environmental parameters, and generate a structured data packet including a timestamp and a quantum random number verification code.

[0188] In the embodiments of the present invention, a programmable configuration file including a bottle identification code, an internal standard calibration curve, and a sealing structure parameter is encrypted and stored in an RFID tag. This design not only improves the security and traceability of data but also simplifies on-site operations because all necessary information can be quickly read through near-field communication. The control module generates a three-level gradient filtration instruction set, and the execution module synchronously executes multiple parallel threads according to these instruction sets, including water sample injection, filter screen switching, and environmental parameter recording. This design realizes an efficient and refined sample pretreatment process, improving the processing speed and accuracy. The regulation module drives a six-axis robotic arm to perform different state transitions, such as constant temperature oscillation and nitrogen purging, according to the filtration log and environmental parameters. This intelligent regulation method ensures the stability and consistency of sample processing while reducing the need for human intervention. The packaging module generates a structured data packet including a timestamp and a quantum random number verification code according to the gaseous VOCs concentration matrix, RFID tag information, and environmental parameters. This data packet is not only convenient for data storage and management but also improves the integrity and credibility of data.

[0189] Above Figure 3From the perspective of modular functional entities, the water volatile organic compound sample collection and pretreatment system based on the headspace principle in the embodiments of the present invention is described in detail. Next, the water volatile organic compound sample collection and pretreatment device based on the headspace principle in the embodiments of the present invention is described in detail from the perspective of hardware processing.

[0190] Figure 4 FIG. 4 is a schematic structural diagram of a water volatile organic compound sample collection and pretreatment device provided by an embodiment of the present invention. The device 400 may vary greatly due to different configurations or performances. The device 400 includes a transmitter 401, a receiver 402, and a processor. Among them, the processor may also be a controller, Figure 4 which is denoted as "controller / processor 403" in FIG. 4. Optionally, the device 400 may further include a modulation and demodulation processor 405. Among them, the modulation and demodulation processor 405 may include an encoder 406, a modulator 407, a decoder 408, and a demodulator 409.

[0191] In one example, the transmitter 401 adjusts (for example, analog conversion, filtering, amplification, and up-conversion, etc.) the output sample and generates an uplink signal, which is transmitted to the access network device via an antenna. On the downlink, the antenna receives the downlink signal transmitted by the access network device. The receiver 402 adjusts (for example, filtering, amplification, down-conversion, and digitization, etc.) the signal received from the antenna and provides an input sample. In the modulation and demodulation processor 405, the encoder 406 receives the service data and signaling messages to be transmitted on the uplink, and processes (for example, formats, encodes, and interleaves) the service data and signaling messages. The modulator 407 further processes (for example, symbol mapping and modulation) the encoded service data and signaling messages and provides an output sample. The demodulator 409 processes (for example, demodulates) the input sample and provides symbol estimation. The decoder 408 processes (for example, de-interleaves and decodes) the symbol estimation and provides the decoded data and signaling messages sent to the device 400. The encoder 406, the modulator 407, the demodulator 409, and the decoder 408 may be implemented by a synthesized modulation and demodulation processor 405. These units are processed according to the radio access technology adopted by the radio access network (for example, the access technology of LTE and other evolved systems). It should be noted that when the device 400 does not include the modulation and demodulation processor 405, the above functions of the modulation and demodulation processor 405 may also be completed by the processor.

[0192] The processor controls and manages the operations of the device 400, and is used to execute the processing procedures performed by the device 400 in the above embodiments of the present disclosure. For example, the processor is further used to execute each step of the sending device or the receiving device in the above method embodiments, and / or other steps of the technical solutions described in the embodiments of the present disclosure.

[0193] Further, the device 400 may further include a memory 404 for storing program code and data for the device 400.

[0194] It can be understood that Figure 4 only a simplified design of the device 400 is shown. In practical applications, the device 400 may include any number of transmitters, receivers, processors, modem processors, memories, etc., and all devices that can implement the embodiments of the present disclosure are within the protection scope of the embodiments of the present disclosure.

[0195] The present invention also provides a device for collecting and preprocessing water volatile organic compound samples based on the headspace principle. The device for collecting and preprocessing water volatile organic compound samples based on the headspace principle includes a memory and a processor. When computer-readable instructions stored in the memory are executed by the processor, the processor executes the steps of the method for collecting and preprocessing water volatile organic compound samples based on the headspace principle in the above-mentioned embodiments.

[0196] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer, the computer executes the steps of the method for collecting and preprocessing water volatile organic compound samples based on the headspace principle.

[0197] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0198] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0199] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle, characterized in that, The method for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle includes: Based on a predefined sampling protocol database and an internal standard calibration parameter library, a programmable configuration file containing a bottle identification code, an internal standard calibration curve, and sealing structure parameters is generated. The programmable configuration file is encrypted and stored in an RFID tag. Read the programmable configuration file through a near-field communication module. After parsing the encrypted data, verify the validity period of the internal standard and the integrity of the calibration curve, and activate the flow control parameters of the micro peristaltic pump to generate a three-level gradient filtration instruction set. According to the three-level gradient filtration instruction set, execute the following parallel threads synchronously: Thread T1: Control the peristaltic pump to inject water samples and monitor the data of the pressure sensor in real time. Thread T2: Dynamically adjust the switching timing of the three-level filter screens and generate a filtration log based on the feedback value of the turbidity sensor. Thread T3: Record environmental parameters and bind them to the RFID tag. According to the filtration log and environmental parameters, drive a six-axis robotic arm to perform the following state transitions: State Q1: Constant temperature oscillation at 4°C, and the duration is dynamically calculated by a preset Henry's constant. State Q2: Nitrogen purging, and the triggering condition is that the headspace pressure < 101.3 kPa ± 1%. State Q3: Generate a balance completion flag bit and a gaseous VOCs concentration matrix. Based on the gaseous VOCs concentration matrix, RFID tag, and environmental parameters, perform topological association to generate a structured data packet containing a timestamp and a random number check code.

2. The method for collecting and preprocessing water volatile organic compound samples based on the headspace principle according to claim 1, characterized in that, Including: Based on a predefined sampling protocol database, generate a unique bottle identification code according to the following rules. Extract the preset concentration gradient points of the freeze-dried internal standard from the internal standard calibration parameter library to generate an internal standard calibration curve. According to the preset sealing process rules, generate a set of sealing structure parameters. Integrate the bottle identification code, internal standard calibration curve, and sealing structure parameter set into a programmable configuration file using TLV structure encoding.

3. The method for collecting and preprocessing water volatile organic compound samples based on the headspace principle according to claim 1, wherein Including: By comparing the production date of the internal standard stored in the RFID tag with the current date of the embedded system clock, generate a validity period status identifier. If it is overdue, trigger an audible and visual alarm signal and terminate the process. Perform a CRC-32 cyclic redundancy check on the calibration curve data block in the configuration file to generate a check result flag. If the check fails, read the RFID tag data again. Parse the peristaltic pump parameters in the configuration file to generate a PID closed-loop control parameter set. Generate a three-level gradient filtration instruction set according to the preset filter screen switching rules, including: First-level filter screen: Enabled by default, switch to the next level when the pressure sensor value > 50 kPa. Second-level filter screen: The enabling condition is that the turbidity sensor value < 10 NTU and lasts for 5 seconds. Third-level filter screen: The enabling condition is that the turbidity sensor value < 5 NTU and the pressure value < 30 kPa.

4. The method for collecting and preprocessing water volatile organic compound samples based on the headspace principle according to claim 3, wherein, Including: According to the PID closed-loop control parameter set, perform the following operations: Control the micro peristaltic pump to inject water samples at a flow rate of 0.5 L / min ± 0.5%. Collect the data of the pressure sensor in real time to generate a pressure data set aligned with the timestamp. Based on the filtration instruction set and the real-time feedback value of the turbidity sensor, generate a filtration log containing the filter screen switching timestamp, turbidity value, and particulate matter mass. Collect environmental data, bind the environmental data with the bottle identification code and sampling timestamp in the RFID tag, and generate an encrypted environmental parameter package.

5. The method for collecting and preprocessing water volatile organic compound samples based on the headspace principle according to claim 4, characterized in that, Including: Calculate the concentration of each VOC component according to the calibration curve data block and the headspace gas volume: Among them, C i is the concentration of component i, R i is the GC-MS response factor, V g is the headspace gas volume, K H,i represents the Henry's law constant of the i-th volatile organic compound, V l is the liquid volume; Generate a gaseous VOC concentration matrix.

6. The method for collecting and preprocessing water volatile organic compound samples based on the headspace principle according to claim 5, characterized in that, Including: Generate a topological data tree based on the gaseous VOC concentration matrix, the internal standard calibration curve stored in the RFID tag, and the environmental parameter package, and the node relationship is represented by a directed edge; Generate a 128-bit random number through a hardware random number generator, calculate the SHA3-256 hash value based on this random number as the check code, and bind the check code and the UTC timestamp to the root node of the topological data tree; Generate a final structured data packet based on the topological data tree, the check code, and the encryption key library.

7. A system for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle, characterized in that, The system for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle includes: A headspace bottle module for generating a programmable configuration file containing a bottle identification code, an internal standard calibration curve, and a sealing structure parameter based on a predefined sampling protocol database and an internal standard calibration parameter library, and encrypting and storing the programmable configuration file in an RFID tag; A control module for reading the programmable configuration file through a near-field communication module, verifying the validity period of the internal standard and the integrity of the calibration curve after parsing the encrypted data, activating the flow control parameters of the micro peristaltic pump, and generating a three-level gradient filtering instruction set; An execution module for synchronously executing the following parallel threads according to the three-level gradient filtering instruction set: Thread T1: Control the peristaltic pump to inject water samples and monitor the pressure sensor data in real time; Thread T2: Dynamically adjust the switching timing of the three-level filter screen and generate a filtering log based on the feedback value of the turbidity sensor; Thread T3: Record environmental parameters and bind them to the RFID tag; A regulation module for driving a six-axis robotic arm to perform the following state transitions according to the filtering log and environmental parameters: State Q1: Constant temperature oscillation at 4°C, and the duration is dynamically calculated by a preset Henry constant; State Q2: Nitrogen purge, and the trigger condition is that the headspace pressure < 101.3 kPa ± 1%; State Q3: Generate a balance completion flag bit and a gaseous VOC concentration matrix; An encapsulation module for topologically associating the gaseous VOC concentration matrix with the RFID tag and environmental parameters, and generating a structured data packet containing a timestamp and a random number check code.

8. A device for collecting and preprocessing water samples of volatile organic compounds based on the headspace principle, characterized in that, The device for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the device for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle executes the method for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle according to any one of claims 1-6.

9. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are executed by the processor, the method for collecting and preprocessing waterborne volatile organic compound samples based on the headspace principle according to any one of claims 1-6 is implemented.

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