Water volatile organic compound sample collection and pretreatment system based on headspace principle

Abstract

The invention relates to the field of environmental monitoring, and discloses a system for collecting and preprocessing a volatile organic compound sample in water based on a headspace principle, which constructs a full-automatic physical execution chain through multi-thread interrupt control, PID closed-loop regulation and a mechanical arm state machine. Comprising the following steps: constructing a pre-packaged intelligent sample bottle, encrypting and storing a freeze-dried internal standard substance, a sealing parameter and a calibration curve in a UHF RFID tag, and eliminating the risk of manual intervention; on the basis of a multi-thread parallel control technology, constant-speed sampling, three-stage gradient filtering and environmental parameter binding are synchronously executed, and full-process automation of data acquisition is realized; a physical model is adopted to drive a state machine, balance time is dynamically calculated through a Henry constant, and nitrogen purging pressure closed-loop control is combined, so that the recovery rate of low-volatile substances is increased; a quantum random number generator and a topological data association technology are utilized to construct a structured data packet containing a timestamp, an environment parameter and a quantum check code, and it is ensured that data cannot be tampered.

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 volatile organic compounds that can quickly diffuse in water bodies and include 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 operations, 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 facilitates subsequent analysis by balancing the distribution of VOCs between the gas and liquid phases in the water sample to achieve the enrichment and separation of VOCs.

[0004] Deficiencies in the prior art: The traditional process of collecting and preprocessing water samples of VOCs often relies on manual operations, 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.

[0005] Key parameters in the process of sample collection and preprocessing (such as calibration curves of internal standards, sealing structure parameters, etc.) often exist in the form of paper records, which are easy to lose and error-prone. There is a lack of an effective data encryption and storage mechanism, and the integrity and traceability of data cannot be ensured.

[0006] 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. There is a lack of an effective filtering mechanism, and particulate matter and impurities in the water sample cannot be completely removed, affecting the accuracy of subsequent analysis results.

[0007] 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

[0008] 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 interrupt control, PID closed-loop regulation, and robotic arm state machine.

[0009] 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 water samples 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 environmental parameters and binding them to the RFID tag; driving 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 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 environmental parameters to generate a structured data packet containing a timestamp and a quantum random number verification code.

[0010] 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 freeze-dried internal standards from the 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.

[0011] 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. If it is overdue, an audible and visual alarm signal is triggered and the process is terminated; performing CRC-32 cyclic redundancy check on the calibration curve data block in the configuration file to generate a check result mark. If the check fails, the RFID tag data is read again; 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 preset filter screen switching rules, including: primary filter screen: enabled by default, and switched 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.

[0012] 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 a micro peristaltic pump to inject a water sample at a flow rate of 0.5 L / min ± 0.5%; collecting pressure sensor data in real time to generate a pressure data set aligned with time stamps; generating a filtration log including a filter screen switching time stamp, a turbidity value, and the mass of particulate matter based on a filtration instruction set and real-time feedback values of a turbidity sensor; collecting environmental data, binding the environmental data with the bottle identification code and sampling time stamp in an RFID tag to generate an encrypted environmental parameter packet.

[0013] Optionally, in the fourth implementation manner of the first aspect of the present invention, the following is included: calculating the concentration of each VOCs component according to a calibration curve data block and the headspace gas volume: ; 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.

[0014] Optionally, in the fifth implementation manner of the first aspect of the present invention, the following is included: 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 packet, and representing the node relationship 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 this random number as the quantum check code, and binding the quantum check code and 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 encryption key library.

[0015] 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 sealed 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 VOC concentration matrix; a packaging module, which is used to perform topological association based on the gaseous VOC concentration matrix, the RFID tag, and environmental parameters, and generate a structured data packet containing a timestamp and a quantum random number verification code.

[0016] 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, wherein 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.

[0017] 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.

[0018] The mechanism of the present invention is as follows: Digitalize physical properties such as fractal geometry parameters and sealing material characteristics, and achieve precise parameter-device matching through RFID tags. Realize microsecond-level synchronization of multi-threads through hardware interrupts to solve the problem of time accumulation error in traditional serial operations. Introduce the gas-liquid interface area as a key variable to significantly improve the mass transfer efficiency of low-volatile 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 caused by ignoring environmental fluctuations in traditional methods.

[0019] 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. All key parameters and information are encrypted and stored in RFID tags to ensure data integrity and traceability. By generating structured data packets containing timestamps and quantum random number verification codes, the security and reliability of the data are further enhanced. 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. Dynamically adjust the filtering process according to the real-time feedback values of the pressure sensor and the turbidity sensor to ensure the stability of the pretreatment effect. Calculate the concentration of each VOC component through the calibration curve data block and the headspace gas volume, generate the gaseous VOC concentration matrix, providing an accurate data basis for subsequent analysis. Use the quantum random number generator and the SHA3-256 hash value calculation as the quantum verification code to ensure the uniqueness and immutability of the data, further improving the accuracy of the analysis. 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 the encrypted key library to encrypt and store and transmit sensitive data to prevent data leakage and tampering, enhancing the security of the system. Description of the Drawings

[0020] Figure 1 It is a schematic diagram of an embodiment of the method for collecting and pretreating waterborne volatile organic compound samples based on the headspace principle in an embodiment of the present invention; Figure 2 It is a schematic diagram of another embodiment of the method for collecting and pretreating waterborne volatile organic compound samples based on the headspace principle in an embodiment of the present invention; Figure 3Schematic diagram of an embodiment of a system for collecting and preprocessing water volatile organic compound samples based on the headspace principle in an embodiment of the present invention; Figure 4 Schematic diagram of an embodiment of a device for collecting and preprocessing water volatile organic compound samples based on the headspace principle in an embodiment of the present invention. Detailed implementation manners

[0021] The embodiment of the present invention provides a system for collecting and preprocessing water volatile organic compound samples based on the headspace principle, and constructs a full-automatic physical execution chain through multi-threaded interrupt control, PID closed-loop regulation, and robotic arm state machine. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order different from that shown or described here. 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 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.

[0022] For ease of understanding, the specific process of the embodiment of the present invention will be described below. Please refer to Figure 1 , an embodiment of the method for collecting and preprocessing water volatile organic compound samples based on the headspace principle in the embodiment of the present invention includes: 101. Generate a programmable configuration file including a bottle identification code, an internal standard calibration curve, and seal structure parameters based on a predefined sampling protocol database and an internal standard calibration parameter library. The configuration file is encrypted by SHA-256 and stored in a UHF RFID tag; It can be understood that the execution subject of the present invention can be a system for collecting and preprocessing water volatile organic compound samples based on the headspace principle, or a terminal or a server. Specifically, it is not limited here. The embodiment of the present invention takes the server as the execution subject as an example for illustration.

[0023] It should be noted that in the server of the environmental monitoring laboratory, the preset sampling protocol database includes the following core fields: Bottle identification code: Adopt 16-bit hexadecimal encoding (A3F9B2E7-5C8D-4A01), which is uniquely bound to the UHF RFID tag of the physical sampling bottle.

[0024] Internal Standard Calibration Parameter Library: Stores calibration curve data for multiple common internal standards (tetrachloroethylene, toluene-d8), including concentration gradients (0.1 μg / L to 50 μg / L), response factors (the response value of toluene-d8 at 25°C is 1.32 ± 0.05), and validity periods (valid until December 31, 2025).

[0025] Sealing Structure Parameters: Define the material of the bottle mouth O-ring (fluororubber), pre-tightening torque (1.5 N·m ± 0.2), and sealing pressure threshold (≥101.3 kPa).

[0026] 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 body (Bottle_2025_00456) from the database.

[0027] 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 where x is the concentration (μg / L) and y is the gas chromatogram peak area ratio.

[0028] 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 to 50°C (based on the bottle body material being borosilicate glass 3.3).

[0029] Data Encryption and RFID Writing. The generated configuration file is encrypted using the SHA-256 algorithm to generate a 64-bit hash value: 8f3c7a...b2e45d and 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.

[0030] 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.

[0031] 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-level gradient filtering instruction set (20μm → 5μm → 1μm filter switching logic); 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 body identification code Bottle_2025_00456, the calibration curve equation of tetrachloroethylene y = 0.982x 3 −0.156x 2 +1.024x + 0.003, and the parameters of the fluororubber sealing ring) are read through near-field communication by the NFC reader-writer.

[0032] 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).

[0033] Validity check: The system automatically compares the expiration date of the internal standard substance (the expiration date of tetrachloroethylene is 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.

[0034] The peristaltic pump parameters are activated. Based on the parsed calibration parameters, the server sends control instructions to the micro peristaltic pump through the RS-485 bus: 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 real-time feedback through the built-in Hall sensor.

[0035] 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.

[0036] Three-level gradient filtration instruction generation. The server generates a three-level filter screen switching logic according to the water sample type (surface water containing suspended particles): Step-by-step filtration strategy: Primary filtration: Start the 20μm stainless steel filter screen and continue to operate until the feedback value of the turbidity sensor ≥ 3 NTU (60% of the preset threshold of 5 NTU); 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; Tertiary fine filtration: When the turbidity drops to 1.2 NTU, enable the 1μm polyethersulfone filter membrane until the effluent turbidity is stable < 0.8 NTU.

[0037] Parallel data recording: The filter screen switching timestamp and the pressure sensor reading (the pressure difference of the primary filter screen is 0.15 bar → the secondary is 0.28 bar) are synchronously written into the filtration log field of the RFID tag.

[0038] 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 the verification of data consistency between the local and the cloud. Physical tamper-proof: The RFID tag is encapsulated with epoxy resin. If illegal disassembly is detected (abnormal change in tag impedance > 5%), the stored data will be immediately erased and a security alarm will be sent.

[0039] 103. Based on the three-level gradient filtering instruction set, the following parallel threads are executed synchronously: Thread T1: Control the peristaltic pump to inject water samples and monitor the data of the pressure sensor in real time (sampling frequency 100Hz); 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 (threshold < 5NTU); Thread T3: Record the environmental parameters (temperature, humidity, air pressure) and bind them to the RFID tag; It should be noted that the environmental monitoring laboratory server receives the three-level gradient filtering instruction set generated in step 102 (filter screen switching logic: 20μm → 5μm → 1μm) and synchronously activates three independent threads: Thread T1: Control the micro peristaltic pump to inject surface water samples at 0.5L / min ± 0.5% (i.e., the flow range is 0.4975 - 0.5025L / min). At the same time, collect the pipeline pressure data through the pressure sensor (range 0 - 5bar) at a frequency of 100Hz. When the first-level filter screen is blocked, the pressure rises from the initial 0.15bar to 0.28bar, triggering a dynamic compensation algorithm to adjust the pump speed.

[0040] Thread T2: Based on the real-time feedback value of the turbidity sensor (range 0 - 50NTU, resolution 0.1NTU), execute the filter screen switching strategy. When the turbidity ≥ 3NTU (60% of the preset threshold 5NTU), switch to the next-level filter screen, and record the timestamp (2025-04-03T08:15:23Z), filter screen type, and turbidity value in the filtering log.

[0041] Thread T3: Call the environmental sensor group (temperature ± 0.5℃, humidity ± 2%RH, air pressure ± 0.5hPa accuracy) to collect data every 10 seconds, set the temperature to 23.5℃, humidity to 65%RH, 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 and writer.

[0042] 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: First-level filter screen stage: When it is detected during the operation of the 20μm stainless steel filter screen that the turbidity suddenly increases from 2.8NTU to 4.2NTU (surface water contains sediment particles), the system triggers the switching of the second-level filter screen in advance to avoid the safety threshold of the pressure difference exceeding 0.3bar.

[0043] Backwash linkage: When the 5μm ceramic filter is enabled, the backwash program is started synchronously (nitrogen pressure 0.5MPa, lasting 5 seconds) to remove the residue on the first-stage filter and record the number of backwashes (3 times for a single sampling) in the log.

[0044] Level 3 fine filtration verification: After the 1μm polyethersulfone filter membrane is activated, if the turbidity does not drop to <0.8NTU within 30 seconds, an alarm is triggered and an error code (E102: filter membrane is damaged) is generated, terminating the sampling process.

[0045] Multi-threaded data synchronization and security mechanism, time synchronization: three threads achieve μs-level time alignment through the NTP server, set the time deviation between the pressure peak (0.27bar) and the turbidity jump (4.1NTU) to <10ms, to ensure the accuracy of data association. Anti-interference design: the pressure sensor signal adopts a differential amplifier circuit (gain 100 times) to eliminate electromagnetic interference, and the turbidity probe is equipped with a self-cleaning brush (rotates once every 5 minutes) to prevent biofilm adhesion. Data integrity check: The filter log generates a CRC-32 check code (0x8A7D2F) every 10 records, which is written into the tamper-proof storage partition of the RFID tag together with the environmental parameters.

[0046] Performance verification and fault tolerance testing, simulating extreme scenarios in the laboratory: High turbidity impact test: After injecting a simulated water sample with a turbidity of 8 NTU, the system completed the three-stage filter switching within 12 seconds, and the log record response delay was less than 50ms.

[0047] Low temperature environment test: Under 5℃ working conditions, the peristaltic pump flow fluctuation rate is still less than 0.45% (the standard requires ±0.5%), and the pressure sensor temperature drift compensation algorithm suppresses the error within ±0.02bar.

[0048] 104. According to the filtration log and environmental parameters, the six-axis robot arm is driven to perform the following state transfers: State Q1: 4°C constant temperature oscillation (amplitude 5mm, frequency 2Hz), the duration is dynamically calculated by the preset Henry constant; State Q2: nitrogen purge (flow rate 10mL / min), the trigger condition is headspace pressure <101.3kPa±1%; State Q3: Generate equilibrium completion flag and gaseous VOCs concentration matrix; It should be noted that under the control of the environmental monitoring laboratory server, the six-axis robotic arm (Huiling Z-ArmS622 model, with a repeatability accuracy of ±0.02mm) performs the following operations: Robotic arm movement: Precisely 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 to 5 mm, the frequency to 2 Hz, and the duration is dynamically calculated according to the preset Henry's constant (Henry's constant of 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.

[0049] Environmental compensation: Combining 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.

[0050] Status Q2: Nitrogen purge trigger logic: 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 (threshold 101.3 kPa ± 1% lower limit) after oscillation, the server triggers the nitrogen purge instruction.

[0051] Purge control: The six - axis robotic arm transfers the sample bottle to the purge 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 purge pressure is 0.2 MPa. The turbidity log shows that the turbidity of the filtered water sample is 0.7 NTU, meeting the purge start condition.

[0052] Abnormal handling: If the pressure is still below 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.

[0053] Status Q3: Generation of gaseous VOCs concentration matrix: Data acquisition: After headspace equilibrium, the mass spectrometer detector (ThermoFisher TR - WaxMS chromatographic column) acquires signals of 55 VOCs and generates a 12×55 concentration matrix (rows correspond to sampling time points, columns correspond to compounds).

[0054] Dynamic calibration: Combining the calibration curve y = 0.982x 3 −0.156x 2 +1.024x + 0.003 of the internal standard (tetrachloroethylene) in the RFID tag, correct the concentrations of substances such as benzene and toluene. For example, the response value of benzene is adjusted from 3250 to 3287 (relative deviation ≤ 1.2%).

[0055] Flag bit generation: When the RSD of all compounds in the matrix is < 5%, the server writes the balance completion flag bit STATUS_OK and attaches the quantum random number check code QRN_8D4F to the data packet header.

[0056] Robotic arm collaboration and safety guarantee, multi-axis collaboration: 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: The 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 robotic arm movement trajectory (joint angles, acceleration) and the headspace pressure change curve are synchronously stored in the cloud.

[0057] 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 check 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); 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: RFID calibration data: Read the perchloroethylene calibration curve y = 0.982x 3 −0.156x 2 +1.024x + 0.003 from the UHF RFID tag, and correct the original response value of benzene, 3250, to obtain the calibrated concentration of 3287 μg / m 3 (relative deviation ≤ 1.2%).

[0058] 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%).

[0059] Structured data packet generation, timestamp synchronization: Use the 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 movement trajectory (joint angles, acceleration) to ensure the time consistency of multi-source data. Quantum random number check code: Call the quantum entropy source device (based on an optical quantum random number generator) to generate a 128-bit check code (QRN_8D4F-9B2E-7C1A) to prevent data tampering. This check code and the data packet hash value (SHA-256) are jointly encrypted and stored.

[0060] JSON-LD framework construction, data packets are organized according to the Linked Data framework, and the core fields include: Concentration matrix: rows represent time series (0 - 11), columns correspond to compounds (benzene, toluene), and the numerical unit is μg / m 3 (Example: The concentration of benzene at T5 is 3287 μg / m 3 ).

[0061] Metadata association: Calibration curve parameters (polynomial coefficients, expiration date until December 31, 2025).

[0062] Environmental compensation coefficients (temperature correction factor 0.8% / °C, humidity influence weight 0.2% / RH).

[0063] Equipment traceability chain: robotic arm model (Z - ArmS622), mass spectrometer serial number (TR - WaxMS#00456).

[0064] Encrypted storage and dual - channel writing, encrypted channel: The data packets are encrypted using the AES - 256 - GCM algorithm, the key is dynamically generated through a Hardware Security Module (HSM), and a digital signature (ECDSA - P384) is attached.

[0065] 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), with the storage path being / VOC / 2025 - 04 - 03 / Bottle_00456.jsonld.

[0066] Disaster recovery mechanism: If the network is interrupted, the data is temporarily stored in an edge computing node (NVIDIA Jetson AGX), and after recovery, it will be automatically resumed and the CRC - 32 consistency between the cloud and local files will be verified.

[0067] 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 redundant storage nodes (local NAS).

[0068] In the embodiments of the present invention, 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 structured data packet and metadata association 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 subsequent analysis equipment. The design of the backwashing program and filter screen switching logic extends the service life of the filter screen; the multi-thread parallel processing design enables the system to process 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 aspects such as integration, automation, encryption security, gradient filtering, and multi-thread parallel processing, this solution significantly improves the efficiency, accuracy, and security of the collection and pretreatment of volatile organic compound samples in water, providing strong technical support for the environmental monitoring field.

[0069] Please refer to Figure 2 , another embodiment of the method for collecting and pretreating volatile organic compound samples in water based on the headspace principle in the embodiments of the present invention includes: 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 a UHF RFID tag; Specifically, based on the predefined sampling protocol database, generate a unique bottle identification code (product 1a) 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: Generate the initial seed value through a hardware true random number generator (TRNG); Extract the preset concentration gradient points of the freeze-dried internal standard (perfluorotriphenylphosphine) from the internal standard calibration parameter library 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%); According to the preset sealing process rules, a set of sealing structure parameters (Product 1c) is generated, including: the thickness of the silicone gasket: 0.5 ± 0.02 mm (Shore hardness 60 ± 2); the torque of the aluminum screw cap: 3.5 ± 0.2 N·m; the shrinkage rate of the PFA heat-shrinkable sealing film: 30 ± 2% (under the condition of 130 °C hot air). Integrate Products 1a, 1b, and 1c 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). 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): the check code generated based on the TRNG module (model: Infineon SLI9670). Hardware implementation: Generate an initial seed value through the hardware true random number generator (TRNG) in the embedded system, generate 1000 entropy values per second, and extract the valid bits after verification by the NIST SP800 - 90B standard. Compilation of the calibration curve of the internal standard substance, concentration gradient configuration:

[0070] Data source: Determined using an Agilent 7890B GC - MS system with reference to the EPA8260B standard method; Validity period management: Set the validity period to 30 days (error ±3.2%) based on the stability experiment data, and trigger an automatic alarm when exceeding the threshold. Definition of sealing structure parameters, physical parameter configuration: Silicone gasket: Select Dow Corning SE - 4400 material, Shore hardness 61 ± 1 (measured value: 60.8); Aluminum cap torque: Use an Atlas Copco STW10 torque controller with a calibration accuracy of ±0.05 N·m; PFA heat - shrinkable film: Select Chemours PFA340, with a measured shrinkage rate of 31.2% at 130 °C; Process verification: Verify the sealing performance through a helium mass spectrometer leak detector (Leybold Phoenix L300i), leakage rate ≤ 1×10^-9 mbar·L / s. Encrypted storage: The SHA-256 algorithm is used to generate a digital digest (example: 8f3c7b...e9a2); it is written into an AlienHiggs-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 for writing.

[0071] 202. Read the programmable configuration file through the Near Field Communication (NFC) module, and 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 (0.5 L / min ± 0.5%); Generate a three-stage gradient filtration instruction set (20μm → 5μm → 1μm filter screen switching logic); 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 period 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 = 0.2 s; Generate a three-stage gradient filtration instruction set (product 2d) according to the preset filter screen switching rules, including: First-stage filter screen (20μm): Default enabled, switch to the next stage when the pressure sensor value > 50 kPa; Second-stage filter screen (5μm): The enabling condition is that the turbidity sensor value < 10 NTU and lasts for 5 seconds; Third-stage filter screen (1μm): The enabling condition is that the turbidity sensor value < 5 NTU and the pressure value < 30 kPa.

[0072] 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: NFC module reading and data decryption, Hardware configuration: An STM32 series NFC controller (supporting ISO14443A protocol) is used to read the encrypted configuration file stored in a 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); 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 (determined by Agilent 7890B GC-MS); Expiry 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%; Sub-step S2a: Validation of the expiry date of the internal standard. Validation 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 expiry date). If it is overdue (time difference ≥ 31 days), trigger the buzzer alarm (frequency 2 kHz, lasting for 10 seconds) and terminate the process. Output: Generate a status flag VALID, allowing subsequent processes to execute.

[0073] Sub-step S2b: Verification of the integrity of the calibration curve. CRC-32 verification process: Extract the calibration curve data block (length 128 bytes, including concentration gradient and response factors). Calculate the current data CRC value as 0x8F3C7B2D and compare it with the verification value stored in the RFID tag. If the verification fails (data corruption due to transmission interference), trigger the retry mechanism (up to 3 times). Fault tolerance design: After three retries all fail, activate the backup calibration curve (pre-stored in the local database).

[0074] Sub-step S2c: Activation of the 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 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.

[0075] Sub-step S2d: Generation of the 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 lasting for 3 seconds (pressure fluctuation < ±1 kPa). Record the switching timestamp: 2025-04-03T08:02:15.123.

[0076] Second-stage filter (5 μm) → Third-stage filter (1 μm): Turbidity sensor value < 10 NTU (measured 8.5 NTU ± 0.3) and lasting for 5 seconds (sampling interval 0.5 seconds).

[0077] Activate the tertiary filter when the pressure sensor value < 30 kPa (measured 28.7 kPa).

[0078] Filter log generation: includes filter screen 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).

[0079] 203. Based on the tertiary gradient filtering instruction set, simultaneously execute the following parallel threads: Thread T1: Control the peristaltic pump to inject the water sample and real-time monitor the pressure sensor data (sampling frequency 100 Hz); Thread T2: Dynamically adjust the switching timing of the tertiary filter screens, generate a filter 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; Specifically, according to the PID parameter set (product 2c), perform the following operations: 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 pressure sensor data (range 0 - 100 kPa, sampling frequency 100 Hz), generate a pressure data set aligned with the time stamp (product 3a); Based on the filtering instruction set (product 2d) and the real-time feedback value of the turbidity sensor (range 0 - 100 NTU, accuracy ± 0.5 NTU), perform: Switch from the primary filter screen (20 μm) to the secondary filter screen (5 μm): Trigger condition: Pressure value > 50 kPa and lasts for 3 seconds; Generate a record of the usage duration of the primary filter screen (accuracy ± 0.1 s); Switch from the secondary filter screen (5 μm) to the tertiary filter screen (1 μm): Trigger condition: Turbidity value < 5 NTU and pressure value < 30 kPa; Generate an estimated value of the particulate matter mass intercepted by the secondary filter screen (based on the pressure-turbidity linear model); Generate a filter log (product 3b) including the filter screen switching time stamp, turbidity value, and particulate matter mass; 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%), air pressure (80 - 110 kPa, ± 0.1 kPa); Bind the environmental data to 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 TLV structure (type - length - value).

[0080] 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 the three parallel threads under the tertiary gradient filtering instruction set: 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 (data is collected every 10 ms).

[0081] 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). The motor speed is adjusted in real time to ensure that the flow rate fluctuation range is ±0.002 L / min (measured pressure fluctuation: 48.5 kPa → 52.3 kPa → stabilized at 49.8 kPa).

[0082] Data recording: Generate a pressure data set aligned with the timestamp (2025-04-03T08:00:15.123 → 52.3 kPa, error ±0.05 kPa).

[0083] Thread T2: Dynamic filter screen switching (trigger condition and log generation), primary filter screen (20 μm) → secondary filter screen (5 μm) switching: Trigger condition: Pressure value > 50 kPa and lasts for 3 seconds (measured 52.3 kPa for 3.2 seconds). Record content: The usage time of the primary filter screen is 120.5 seconds (accuracy ±0.1 s), and the estimated mass of the intercepted particulate matter is 15.3 mg (based on the formula: particulate matter mass = 0.12 × pressure + 0.05 × turbidity).

[0084] Secondary filter screen (5 μm) → tertiary filter screen (1 μm) switching: Trigger condition: Turbidity value < 5 NTU (measured 4.8 NTU ±0.3) and pressure < 30 kPa (measured 28.7 kPa).

[0085] Generate log entry: Timestamp: 2025-04-03T08:02:30.456; Event: Secondary → tertiary filter screen switching; Turbidity value: 4.8 NTU; Particulate matter mass: 8.2 mg; 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).

[0086] Data Binding and Encryption: Associate environmental parameters with the bottle body identification code A3B5_202504030800_9D4E to generate an encrypted TLV structure packet (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 encryption with AES-256 (the key is dynamically generated by the Hardware Security Module HSM).

[0087] Parallel Thread Coordination Mechanism, Timing Synchronization: Schedule threads through the Real-Time Operating System (RTOS) of the embedded system to ensure that the timestamps of pressure data (T1), filter screen switching (T2), and environmental records (T3) are aligned (error < ±1 ms). Data Sharing: Use a thread-safe queue 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).

[0088] 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 bit and a gaseous VOCs concentration matrix; 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 thermoelectric cooling module); Amplitude: 5 ± 0.1 mm; Frequency: 2 ± 0.05 Hz; Calculate the equilibrium duration: ; 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); Drive the six-axis robotic arm to perform the oscillation action to generate a constant temperature oscillation completion flag (product 4a); Sub-step S4b: State Q2 (nitrogen purge trigger), real-time monitor the data of the headspace pressure sensor (range: 80 - 120 kPa, accuracy ±0.1 kPa); when the pressure value < 101.3 kPa ± 1%, start nitrogen purge: nitrogen purity ≥ 99.999%; flow control: 10 ± 0.2 mL / min (closed-loop regulation based on mass flowmeter); continuously purge until the pressure returns to 101.3 ± 0.5 kPa, generate a pressure balance flag (product 4b); Sub-step S4c: State Q3 (concentration matrix generation), calculate the concentration of each VOC component according to the calibration curve data block (product 1b) and the headspace gas volume: ; where, C i is the concentration of component i (μg / L), 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); Generate a gaseous VOCs concentration matrix (product 4c), including: the name of each component, concentration value, unit; equilibrium timestamp (synchronized with the environmental parameter package).

[0089] 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 filter logs and environmental parameters: 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).

[0090] 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 filter log of step 203); gas-liquid interface area A = 15 cm 2 (bottle geometric parameter); Calculation result: t = 0.12 × 0.5 / 15 × 3600 = 14.4 seconds; Execution action: The robotic arm oscillates along a sinusoidal curve trajectory for 14.4 seconds, generating the flag bit Q1_COMPLETE.

[0091] 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).

[0092] Trigger logic: When the pressure < 101.3 kPa ± 1% (i.e., < 100.3 kPa), the purge is started (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), taking 3 minutes and 15 seconds, generating the flag bit Q2_COMPLETE.

[0093] Status Q3: Generation of gaseous VOCs concentration matrix, calibration data call: Call the calibration curve (response factor of decafluorotriphenylphosphine) generated in step 201:

[0094] Example of concentration calculation (taking benzene as an example): Volume of headspace gas V g = 20 mL, Henry's constant K H = 0.08 mol / (L·kPa) Response factor R i = 18.67 (corresponding to the 10 μg / L calibration point) Calculated concentration: Ci = 18.67×20 / 0.08×0.5 = 9.34 μg / L Output matrix:

[0095] Data synchronization and binding of environmental parameters: Temperature 4.2 °C, humidity 65%, air pressure 101.1 kPa.

[0096] 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 local SD card through an encrypted channel, and the output format is JSON-LD (Linked Data Framework); Specifically, sub-step S5a: Data topology association. Bind the following data sources in a hierarchical relationship: Core data layer: Gaseous VOCs concentration matrix (Product 4c); Calibration and traceability layer: Internal standard calibration curve stored in the RFID tag (Product 1b); Environment association layer: Environment 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: Environment parameter → ending point: Concentration value). 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. 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. Encrypted storage: Encrypt the data packet using the AES-256 algorithm (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).

[0097] 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: Data topology 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); Calibration and traceability layer (Product 1b): Internal standard: Bis(perfluorophenyl) phenylphosphine, expiration date: 2025-05-03; Response factor: 184.95 (100 μg / L calibration point); Environment association layer (Product 3c): Temperature: 4.2 °C (sensor ID: TEMP-001); Humidity: 65%RH; Air pressure: 101.1 kPa; Topology construction rules: Node relationship: Environmental parameters (temperature, humidity) → Calibration curve validity → Concentration value; Directed edge definition: Temperature → Benzene concentration (affects the gas-liquid equilibrium rate); Calibration curve ID → Benzene concentration (data traceability); Quantum check code generation (S5b), hardware and process: Quantum random number generator: IDQuantique QRNG-1G4, generates 128-bit random numbers (example: 0x8f3c7b...e9a2); Hash calculation: Uses the SHA3-256 algorithm to generate quantum check codes (example hash value: a1b2c3...d4e5); Timestamp binding: Obtains UTC time (2025-04-03T08:15:23.456±1ms) through the GNSS module; Structured data encapsulation (S5c), JSON-LD framework construction: @context definition: Associates the Schema.org vocabulary (http: / / schema.org / chemicalConcentration). Maps the calibration curve ID to the internal database URI (http: / / lab.example / calibration / CAL-20250403-A3B5).

[0098] @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); The quantum check code and timestamp are bound to the root node to form an immutable identifier.

[0099] Encryption and storage: AES-256 encryption: Uses a hardware security module (HSM) to generate a dynamic key and encrypts the data packet in CBC mode.

[0100] Dual-channel writing: Cloud: Uploads to the AWS S3 bucket (path: s3: / / vocs-data / 20250403 / ) through the TLS1.3 protocol; Local: Writes to a SanDisk Extreme Pro SD card (FAT32 format, encrypted partition); Example of measured data, JSON-LD structure logic: Context layer: Declares that the data source is the internal laboratory standard (calibration curve) and the Schema.org semantic model.

[0101] 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 traceability of the data life cycle.

[0102] In the embodiments 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, traceability from sample collection to data processing is achieved, ensuring the authenticity and integrity of data; the flow rate of the micro peristaltic pump is controlled by PID closed-loop control to achieve precise control of water sample injection. Based on the standard method of gas chromatography-mass spectrometry (GC-MS), an internal standard calibration curve is generated to ensure the accuracy and reliability of measurement results; a three-stage gradient filtration system is adopted to effectively remove impurities and particulate matter in the water sample, improving the accuracy of subsequent analysis. Based on the real-time feedback of pressure sensors and turbidity sensors, the switching timing of the filter screen is dynamically adjusted to ensure the optimization of the filtration effect; 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. Topological correlation of environmental parameters with VOCs concentration data helps analyze the influence of environmental factors on VOCs concentration; encryption algorithms such as SHA-256 and AES-256 are used to encrypt and store configuration files and data packets to ensure data security, and a hardware security module (HSM) is used to store and manage keys to further improve system security; this method can adjust sampling protocols, calibration parameters, filtration instructions, etc. 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 pretreating water samples of volatile organic compounds in this solution is outstanding in terms of creativity, not only improving the efficiency and accuracy of sampling and pretreatment, but also ensuring the authenticity and security of data, providing strong technical support for water quality monitoring and analysis.

[0103] The method for collecting and pretreating water samples of volatile organic compounds based on the headspace principle in the embodiments of the present invention has been described above. Next, the system for collecting and pretreating water samples of volatile organic compounds based on the headspace principle in the embodiments of the present invention will be described. Please refer to Figure 3, an embodiment of the system for collecting and preprocessing waterborne volatile organic compound samples 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 sealing structure parameters 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 the micro peristaltic pump, and generate a three-level gradient filtering instruction set; an execution module 303, configured to synchronously execute 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 data of the pressure sensor 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 304, configured to drive 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 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 305, configured to perform topological association based on 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.

[0104] In the embodiments of the present invention, a programmable configuration file including a bottle identification code, an internal standard calibration curve, and sealing structure parameters 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 filtering 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 preprocessing 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 filtering 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 based on 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 the data.

[0105] Above Figure 3The water volatile organic compound sample collection and pretreatment system based on the headspace principle in the embodiments of the present invention will be described in detail from the perspective of modular functional entities. Next, the water volatile organic compound sample collection and pretreatment device based on the headspace principle in the embodiments of the present invention will be described in detail from the perspective of hardware processing.

[0106] Figure 4 FIG. 4 is a schematic structural diagram of a water volatile organic compound sample collection and pretreatment device provided in 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.

[0107] 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 the 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 synthetic 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.

[0108] The processor controls and manages the actions 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 transmitting 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.

[0109] Furthermore, the device 400 may further include a memory 404 for storing program codes and data for the device 400.

[0110] 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.

[0111] 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 respective embodiments.

[0112] 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.

[0113] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0114] 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 such an 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 memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0115] 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 pretreating volatile organic compound samples in water based on the headspace principle, characterized in that: The headspace-based method for collecting and pretreating volatile organic compounds in water includes: Based on the predefined sampling protocol database and internal standard calibration parameter library, a programmable configuration file including a bottle identification code, an internal standard calibration curve and sealing structure parameters is generated, and the programmable configuration file is encrypted and stored in an RFID tag; The programmable configuration file is read through the near field communication module, and after parsing the encrypted data, the validity period of the internal standard and the integrity of the calibration curve are verified, the flow control parameters of the micro peristaltic pump are activated, and a three-level gradient filtering instruction set is generated; According to the three-level gradient filtering instruction set, the following parallel threads are executed synchronously: 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-stage filter and generate the filtration log based on the feedback value of the turbidity sensor; Thread T3: Record environmental parameters and bind to RFID tags; According to the filtered log and environmental parameters, the six-axis robot is driven to perform the following state transitions: State Q1: Constant temperature oscillation at 4°C, the duration is dynamically calculated by the preset Henry constant; State Q2: Nitrogen purge, the trigger condition is headspace pressure <101.3kPa±1%; State Q3: Generate the balance completion flag and gaseous VOCs concentration matrix; Based on the topological association of the gaseous VOCs concentration matrix with the RFID tags and environmental parameters, a structured data packet containing a timestamp and a quantum random number check code is generated.

2. The method for collecting and pretreating volatile organic compounds in water based on the headspace principle according to claim 1, characterized in that: include: Based on the predefined sampling protocol database, a unique bottle identification code is generated according to the following rules; Extracting preset concentration gradient points of the freeze-dried internal standard from an internal standard calibration parameter library to generate an internal standard calibration curve; Generate a sealing structure parameter set according to preset sealing process rules; TLV structure encoding is used to integrate the bottle identification code, internal standard calibration curve, and sealing structure parameter set into a programmable configuration file.

3. The method for collecting and pretreating volatile organic compounds in water based on the headspace principle according to claim 1, characterized in that: include: By comparing the production date of the internal standard stored in the RFID tag with the current date of the embedded system clock, an expiration status indicator is generated. If it is overdue, an audible and visual alarm signal is triggered and the process is terminated; Perform CRC-32 cyclic redundancy check on the calibration curve data block in the configuration file and generate a check result mark. If the check fails, re-read the RFID tag data; Parse the peristaltic pump parameters in the configuration file and generate a PID closed-loop control parameter set; Generate a three-level gradient filtering instruction set based on the preset filter switching rules, including: First-level filter: enabled by default, switches to the next level when the pressure sensor value is > 50kPa; Secondary filter: Enabled when the turbidity sensor value is less than 10NTU and lasts for 5 seconds; Level 3 filter: Enabled when the turbidity sensor value is less than 5NTU and the pressure value is less than 30kPa.

4. The method for collecting and pretreating volatile organic compounds in water based on the headspace principle according to claim 3, characterized in that: include: According to the PID closed-loop control parameter set, perform the following operations: Control the micro peristaltic pump to inject water sample at a flow rate of 0.5L / min±0.5%; Collect pressure sensor data in real time to generate a pressure data set with time-stamp alignment; Generate a filter log containing filter switching timestamps, turbidity values, and particulate matter mass based on the filter instruction set and real-time feedback from the turbidity sensor; 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 pretreating volatile organic compounds in water based on the headspace principle according to claim 4, characterized in that: include: Calculate the concentration of each VOCs component based on the calibration curve data block and the headspace gas volume: , in, C i is the concentration of component i, R i is the GC-MS response factor, V g is the headspace volume, K H,i represents the Henry's law constant of the i-th volatile organic compound; Generate a gaseous VOCs concentration matrix.

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

7. A volatile organic compound sample collection and pretreatment system in water based on the headspace principle, characterized in that: The volatile organic compound sample collection and pretreatment system in water based on the headspace principle includes: A headspace bottle module, for generating a programmable configuration file including 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, wherein the programmable configuration file is encrypted and stored in an RFID tag; A control module is used to read a programmable configuration file through a near field communication module, parse the encrypted data, 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, and generate a three-level gradient filtering instruction set; An execution module is used to synchronously execute 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-stage filter and generate the filtration log based on the feedback value of the turbidity sensor; Thread T3: Record environmental parameters and bind to RFID tags; The control module is used to drive the six-axis robot to perform the following state transitions based on the filtered logs and environmental parameters: State Q1: Constant temperature oscillation at 4°C, the duration is dynamically calculated by the preset Henry constant; State Q2: Nitrogen purge, the trigger condition is headspace pressure <101.3kPa±1%; State Q3: Generate the balance completion flag and gaseous VOCs concentration matrix; The encapsulation module is used to perform topological association with the RFID tags and environmental parameters based on the gaseous VOCs concentration matrix, and generate a structured data packet containing a timestamp and a quantum random number check code.

8. A volatile organic compound sample collection and pretreatment device in water based on the headspace principle, characterized in that: The headspace-based volatile organic sample collection and pretreatment device for water comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the device for collecting and pretreating samples of volatile organic compounds in water based on the head space principle to perform the method for collecting and pretreating samples of volatile organic compounds in water based on the head space principle as described in any one of claims 1-6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the method for collecting and pretreating volatile organic compound samples in water based on the head space principle as described in any one of claims 1 to 6 is implemented.

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