Raman spectrum-based quantitative analysis system for pesticide residues

Abstract

The application discloses a pesticide residue quantitative analysis system based on Raman spectrum, relates to the field of analytical chemistry and detection technology, and comprises a collection module, a processing module, an analysis module and a regulation and control module.The collection module is used for collecting samples, obtaining Raman scattering signals, converting the Raman scattering signals into original spectrum data, transmitting the original spectrum data to the processing module, calibrating the original spectrum data, obtaining calibrated spectrum data, transmitting the calibrated spectrum data to the analysis module, comparing the calibrated spectrum data with a pesticide standard spectrum library, outputting quantitative analysis results of pesticide residues, transmitting the quantitative analysis results to the regulation and control module, storing the pesticide standard spectrum library and the calibrated spectrum data, and using the calibrated spectrum data to correct sample detection deviation.The application realizes full-process automatic operation from sample processing to result output through a regulation and control unit, improves detection reliability through spectrum compensation and characteristic peak comparison, realizes efficient automation through task scheduling and time sequence synchronization, and has the abilities of hardware fault tolerance, permission management and multi-scene adaptation.

Description

Technical Field

[0001] This invention relates to the fields of analytical chemistry and detection technology, and in particular to a quantitative analysis system for pesticide residues based on Raman spectroscopy. Background Technology

[0002] In the field of food safety testing, Raman spectroscopy has become a core method for quantitative analysis of pesticide residues due to its advantages of being rapid and non-destructive, highly specific, sensitive, and capable of simultaneous analysis of multiple components. Based on the Raman scattering effect, this technology uses laser excitation to generate characteristic vibrational scattering signals from pesticide molecules in a sample. After processing, the molecular structure spectrum is obtained. Detection requires no complex pretreatment and can be completed in seconds to minutes. Samples can be analyzed in situ. Its specificity stems from the uniqueness of the Raman characteristic peaks of pesticide molecules. By comparing with a standard spectral library, the type and concentration of residues can be accurately identified. In practical applications, this technology can penetrate fruit and vegetable matrices and grain matrices, making it suitable for batch sample screening and quantification, improving detection accuracy in complex matrices. With the development of portable devices and intelligent technologies, Raman spectroscopy is being widely applied in agricultural product testing, providing strong support for food safety supervision.

[0003] However, existing detection systems suffer from two major problems. First, resource scheduling efficiency is low during multi-task collaborative processing. Spectral acquisition and data transmission tasks are prone to real-time degradation due to resource contention. Traditional thread allocation and priority strategies are difficult to dynamically adapt to load changes, resulting in data processing delays or resource waste. Second, raw spectral data is susceptible to interference from optical devices and environmental factors, leading to wavelength drift and noise issues. Inaccurate calibration can cause accumulated quantitative analysis errors. Furthermore, traditional quantitative methods based on a single characteristic peak are difficult to correct for matrix effects and cannot meet the high-precision requirements for low-concentration residue detection. Therefore, there is an urgent need for a system that can achieve hierarchical resource isolation, dynamic spectral calibration, and accurate quantitative analysis to improve the reliability and efficiency of pesticide residue detection. Summary of the Invention

[0004] The technical problem solved by this invention is that existing detection systems suffer from two major issues. First, resource scheduling efficiency is low during multi-task collaborative processing. Spectral acquisition and data transmission tasks are prone to real-time degradation due to resource contention. Traditional thread allocation and priority strategies are difficult to dynamically adapt to load changes, resulting in data processing delays or resource waste. Second, raw spectral data is susceptible to interference from optical devices and environmental factors, easily leading to wavelength drift and noise problems. Inaccurate calibration can cause quantitative analysis errors to accumulate. Furthermore, traditional quantitative methods based on a single characteristic peak are difficult to correct for matrix effects and cannot meet the high-precision requirements for low-concentration residue detection. Therefore, there is an urgent need for a system that can achieve hierarchical resource isolation, dynamic spectral calibration, and accurate quantitative analysis to improve the reliability and efficiency of pesticide residue detection.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a pesticide residue quantitative analysis system based on Raman spectroscopy, comprising an acquisition module, a processing module, an analysis module, and a control module;

[0006] The acquisition module is used to acquire samples and obtain Raman scattering signals, convert the Raman scattering signals into raw spectral data, and transmit them to the processing module;

[0007] The processing module is used to calibrate the original spectral data, obtain the calibrated spectral data, and transmit it to the analysis module;

[0008] The analysis module is used to compare the calibrated spectral data with the pesticide standard spectral library, output the quantitative analysis results of pesticide residues, and transmit them to the control module.

[0009] The control module is used to store the pesticide standard spectral library and calibrated spectral data. The calibrated spectral data is used to correct sample detection deviations. Through the control unit, the entire process from sample processing to result output is automated.

[0010] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, the acquisition module includes an excitation light source and an optical probe;

[0011] The Raman scattering signal is acquired through the optical probe;

[0012] The Raman scattering signal is converted into raw spectral data by a photodetector;

[0013] The raw spectral data includes wavenumber information, intensity core information, and pixel information.

[0014] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, the calibration of the original spectral data includes a wavelength drift dynamic compensation mechanism.

[0015] The wavelength drift dynamic compensation mechanism specifically includes:

[0016] The pixel information and the wavenumber information have an original mapping relationship;

[0017] The calibrated spectral data is generated by the wavenumber calibration laser built into the acquisition module;

[0018] The mapping relationship between pixel information and wavenumber information of the original spectral data is corrected in real time using cubic spline interpolation.

[0019] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, the control unit includes a task priority division unit, a resource occupancy monitoring unit, a dynamic thread allocation unit, a time constraint feedback unit, and a task conflict resolution unit.

[0020] The task priority division unit is used to preset priority levels according to the real-time requirements of the detection task, specifically including:

[0021] The raw spectral data acquisition task executed by the acquisition module is configured with the highest priority;

[0022] The data transmission task is configured as medium priority, and the data transmission task includes the process of transmitting raw spectral data from the acquisition module to the processing module and calibrated spectral data from the processing module to the analysis module.

[0023] The result calculation task is configured with low priority;

[0024] The resource usage monitoring unit is used to collect CPU core load, memory usage, data bus bandwidth parameters, and total number of CPU cores in real time.

[0025] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, the dynamic thread allocation unit specifically includes:

[0026] The original spectral data acquisition task reserves 0 independent CPU cores as dedicated processing threads.

[0027] The data transmission task dynamically allocates threads based on the size of the data in a single transmission.

[0028] When the amount of data transmitted in a single instance is greater than or equal to the first threshold, p transmission threads are automatically activated for parallel processing.

[0029] When the amount of data transmitted in a single instance is less than the second threshold, q transmission threads are activated.

[0030] The result calculation task is configured with an elastic thread pool, with the maximum number of threads not exceeding the third threshold of the total number of CPU cores, and each thread is bound to an independent L2 cache partition;

[0031] The time constraint feedback unit is used to accumulate the processing time of a single sample in real time.

[0032] When the processing time of any task reaches the preset time for the entire process, the cross-task thread borrowing mechanism is triggered;

[0033] The cross-task thread borrowing mechanism includes:

[0034] Prioritize reclaiming n threads from the low-load result calculation task thread pool and allocating them to high-priority tasks that have timed out.

[0035] After borrowing a thread, the remaining processing steps of the result calculation task are automatically merged into a single thread for execution;

[0036] The built-in data transmission congestion monitoring module automatically suspends the recording of non-urgent system logs when the continuous data bus occupancy rate reaches i.

[0037] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, the task conflict resolution unit adopts a hierarchical resource isolation strategy, specifically including:

[0038] Implement a memory space exclusive mechanism for the spectral acquisition task, allocate a dedicated area in physical memory, and prohibit other tasks from accessing it;

[0039] The data transmission task uses a double-buffered queue, specifically including:

[0040] The raw spectral data of the high-priority queue adopts a first-in-first-out (FIFO) strategy;

[0041] Historical spectral data in the low-priority queue employs a flow control strategy, automatically delaying transmission when CPU core load, memory utilization, and real-time bandwidth utilization of the spectral data transmission bus exceed the fourth threshold.

[0042] When multiple tasks request resources conflict, the task triggering order is marked by hardware timestamps. Tasks with the same priority are allocated resources in chronological order, and the task with the earlier time sequence is allocated resources first.

[0043] The intermediate results generated during the quantitative calculation process of the analysis module are synchronously stored in the CPU register and memory buffer. If the intermediate results are found to be inconsistent, the task is automatically restarted.

[0044] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, the photodetector array is equipped with a temperature compensation circuit, and the detector operating temperature is monitored in real time by an integrated platinum resistance temperature sensor.

[0045] When the temperature fluctuation exceeds the set threshold, the transimpedance amplifier gain compensation unit is automatically triggered.

[0046] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, wherein: the pesticide standard spectral library pre-stores standard spectral shift thresholds for more than s pesticides;

[0047] When the peak position shift of the measured calibrated spectral data exceeds the corresponding threshold, the system automatically marks the anomaly and triggers the signal resampling procedure of the dual-wavelength reference channel.

[0048] The calibrated spectral data were compared with the pesticide standard spectral library using the characteristic peak intensity ratio method.

[0049] The characteristic peak intensity ratio method performs quantitative calibration by calculating the intensity ratio of the Raman spectral characteristic peak corresponding to the pesticide residue to be detected to the internal standard peak, and outputs the quantitative analysis results of the pesticide residue.

[0050] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, the data interaction interface of the control module supports the IEEE 1588 precision clock synchronization protocol.

[0051] The acquisition module, processing module, analysis module, and control module achieve μs-level precision detection timing synchronization through hardware timestamps, enabling coordinated spectral acquisition and sample processing actions.

[0052] The excitation source integrates a wavelength locker, which monitors the laser wavelength in real time through a fiber Bragg grating;

[0053] When the laser wavelength drift exceeds a set threshold, the laser temperature control current is automatically adjusted.

[0054] As a preferred embodiment of the pesticide residue quantitative analysis system based on Raman spectroscopy described in this invention, the human-machine interface of the control module is configured with multi-level permission management, specifically including:

[0055] Inspectors can log in using fingerprint recognition and view real-time test curves;

[0056] Administrator accounts support standard spectral library updates and calibration parameter threshold settings.

[0057] The beneficial effects of this invention are as follows: By employing excitation source wavelength locking, detector temperature compensation, and wavelength drift dynamic compensation mechanisms, the accuracy and stability of the original spectral data are ensured. Combined with the characteristic peak intensity ratio method, comparison with the standard spectral library, and dual-wavelength reference channel resampling mechanism, the reliability of quantitative analysis is improved. The processing and control module achieves efficient task scheduling and conflict resolution through task priority classification, dynamic thread allocation, cross-task resource collaboration, and hierarchical resource isolation strategies. Full-process automated operation reduces human error, and precise clock synchronization ensures accurate timing. It also has hardware fault tolerance and data transmission blockage monitoring capabilities. The human-machine interface has multi-level permission management to ensure security and is compatible with different hardware configurations, allowing for flexible adaptation to multiple scenarios while balancing scalability and stability. Attached Figure Description

[0058] Figure 1This is a schematic diagram of the basic process of a pesticide residue quantitative analysis system based on Raman spectroscopy provided in one embodiment of the present invention. Detailed Implementation

[0059] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0060] Example 1, referring to Figure 1 As an embodiment of the present invention, a pesticide residue quantitative analysis system based on Raman spectroscopy is provided, including an acquisition module, a processing module, an analysis module and a control module;

[0061] The acquisition module is used to acquire samples and obtain Raman scattering signals, convert the Raman scattering signals into raw spectral data, and transmit them to the processing module.

[0062] The processing module is used to calibrate the raw spectral data, obtain the calibrated spectral data, and transmit it to the analysis module;

[0063] The analysis module is used to compare the calibrated spectral data with the pesticide standard spectral library, output the quantitative analysis results of pesticide residues, and transmit them to the control module;

[0064] The control module is used to store the pesticide standard spectral library and calibrated spectral data. The calibrated spectral data is used to correct sample detection deviations. Through the control unit, the entire process from sample processing to result output is automated.

[0065] In one embodiment, the system comprises four core components: an acquisition module, a processing module, an analysis module, and a control module. The acquisition module acquires the Raman scattering signal of the sample, converts it into raw spectral data including wavenumber, intensity, and pixel information via photoelectric conversion, and transmits this data to the processing module. The processing module uses a wavelength drift dynamic compensation calibration mechanism (such as correcting the pixel-wavenumber mapping relationship using a calibrated laser combined with cubic spline interpolation) to calibrate the raw spectral data, generating calibrated spectral data, which is then transmitted to the analysis module. The analysis module compares the calibrated data with a database of pre-stored standard spectra of at least 500 pesticides, analyzing the characteristic peak intensity ratios, and outputs the pesticide residue quantification results to the control module. The control module integrates an automated control unit for task priority division and dynamic thread allocation, achieving fully automated control from sample acquisition to result output. It also stores a standard spectral library and calibration data for detection deviation correction, ensuring efficient and accurate system operation.

[0066] The acquisition module includes an excitation source and an optical probe;

[0067] Raman scattering signals are acquired using an optical probe;

[0068] The Raman scattering signal is converted into raw spectral data by a photodetector;

[0069] The raw spectral data includes wavenumber information, intensity core information, and pixel information.

[0070] Calibrate the raw spectral data, including a wavelength drift dynamic compensation mechanism;

[0071] Wavelength drift dynamic compensation mechanisms specifically include:

[0072] There is an original mapping relationship between pixel information and wavenumber information;

[0073] The calibrated spectral data is generated by the wavenumber calibration laser built into the acquisition module;

[0074] The mapping relationship between pixel information and wavenumber information of the original spectral data is corrected in real time using cubic spline interpolation.

[0075] In one embodiment, the acquisition module is equipped with an excitation light source and an optical probe. The optical probe acquires the Raman scattering signal of the sample, which is then converted by a photodetector to generate raw spectral data including wavenumber information, intensity core information, and pixel information. The processing module uses a wavelength drift dynamic compensation mechanism to calibrate the raw spectral data. Based on the original mapping relationship between pixel information and wavenumber information, calibration data is generated using the wavenumber calibration laser built into the acquisition module. The mapping relationship between pixel information and wavenumber information is corrected in real time using cubic spline interpolation to eliminate wavelength drift error and ensure the accuracy of the calibrated spectral data.

[0076] The control unit includes a task priority allocation unit, a resource usage monitoring unit, a dynamic thread allocation unit, a time constraint feedback unit, and a task conflict resolution unit;

[0077] The task priority division unit is used to preset priority levels according to the real-time requirements of the detection task, specifically including:

[0078] The raw spectral data acquisition task executed by the acquisition module is configured with the highest priority;

[0079] The data transmission task is configured as medium priority. The data transmission task includes the process of transmitting raw spectral data from the acquisition module to the processing module and calibrated spectral data from the processing module to the analysis module.

[0080] The result calculation task is configured with low priority;

[0081] The resource usage monitoring unit is used to collect CPU core load, memory usage, data bus bandwidth parameters, and total number of CPU cores in real time.

[0082] In one embodiment, the control unit includes a task priority allocation unit, a resource usage monitoring unit, a dynamic thread allocation unit, a time constraint feedback unit, and a task conflict resolution unit. The task priority allocation unit presets three priority levels according to the real-time requirements of the detection task: the raw spectral data acquisition task executed by the acquisition module is set as the highest priority to ensure the real-time performance of data acquisition; the data transmission task (including the transmission process of raw spectral data from the acquisition module to the processing module and calibrated spectral data from the processing module to the analysis module) is configured as a medium priority; and the result calculation task is set as a low priority. The resource usage monitoring unit collects parameters such as CPU core load, memory usage, data bus bandwidth, and total number of CPU cores in real time to provide real-time data support for dynamic resource scheduling.

[0083] Dynamic thread allocation units, specifically including:

[0084] The raw spectral data acquisition task reserves 0 independent CPU cores as dedicated processing threads;

[0085] The data transmission task dynamically allocates threads based on the size of the data in a single transmission.

[0086] When the amount of data transmitted in a single instance is greater than or equal to the first threshold, p transmission threads are automatically activated for parallel processing.

[0087] When the amount of data transmitted in a single instance is less than the second threshold, q transmission threads are activated.

[0088] The result calculation task is configured with an elastic thread pool, with the maximum number of threads not exceeding the third threshold of the total number of CPU cores, and each thread is bound to an independent L2 cache partition;

[0089] A time constraint feedback unit is used to accumulate the total processing time of a single sample in real time.

[0090] When the processing time of any task reaches the preset time for the entire process, the cross-task thread borrowing mechanism is triggered;

[0091] Cross-task thread borrowing mechanisms include:

[0092] Prioritize reclaiming n threads from the low-load result calculation task thread pool and allocating them to high-priority tasks that have timed out.

[0093] After borrowing a thread, the remaining processing steps of the result calculation task are automatically merged into a single thread for execution;

[0094] The built-in data transmission congestion monitoring module automatically suspends the recording of non-urgent system logs when the continuous data bus occupancy rate reaches i.

[0095] In one embodiment, the dynamic thread allocation unit implements a differentiated thread scheduling strategy based on task characteristics: o (usually 2-4, set according to the total number of CPU cores and the real-time requirements of the acquisition task) independent CPU cores are reserved as dedicated processing threads for the raw spectral data acquisition task; the number of threads for the data transmission task is dynamically adjusted according to the amount of data transmitted at one time. When the data volume is greater than or equal to 1MB (first threshold), p (usually 4-8, ensuring efficient parallel transmission) threads are activated for parallel processing; when the data volume is less than 1MB (second threshold), q (usually 1-2) threads are activated; the result calculation task is configured with an elastic thread pool, with the maximum number of threads not exceeding 50% of the total number of CPU cores (third threshold), and each thread is bound to an independent L2 cache partition to improve computational efficiency, with time constraint feedback. The unit monitors the entire processing time of a single sample in real time. When the processing time of any task reaches 80% of the average processing time of the entire single sample process (typically 400ms; this threshold is set based on system hardware configuration such as 4-8 CPU cores, 10GB / s data bus bandwidth, and the real-time requirements of pesticide residue detection; actual measured average processing time of a single sample process is 500ms), a cross-task thread borrowing mechanism is triggered. This mechanism prioritizes reclaiming n (usually 1-2) threads from the low-load result calculation task thread pool to support high-priority tasks, while merging the remaining steps of the calculation task into a single thread for execution. The built-in data transmission blocking monitoring module automatically pauses non-urgent system log recording to ensure core data transmission when the data bus occupancy rate reaches 100% (i value) for 50ms consecutively (m value).

[0096] The task conflict resolution unit adopts a hierarchical resource isolation strategy, specifically including:

[0097] Implement a memory space exclusive mechanism for the spectral acquisition task, allocate a dedicated area in physical memory, and prohibit other tasks from accessing it;

[0098] Data transfer tasks use double-buffered queues, specifically including:

[0099] The raw spectral data of the high-priority queue adopts a first-in-first-out (FIFO) strategy;

[0100] Historical spectral data in the low-priority queue employs a flow control strategy, automatically delaying transmission when CPU core load, memory utilization, and real-time bandwidth utilization of the spectral data transmission bus exceed the fourth threshold.

[0101] When multiple tasks request resources conflict, the task triggering order is marked by hardware timestamps. Tasks with the same priority are allocated resources in chronological order, and tasks with earlier time order are allocated resources first.

[0102] The intermediate results generated during the quantitative calculation process of the analysis module are stored synchronously in the CPU register and memory buffer. If the intermediate results are found to be inconsistent, the task is automatically restarted.

[0103] In one embodiment, the task conflict resolution unit employs a hierarchical resource isolation strategy to ensure the stable operation of the acquisition module, processing module, analysis module, and control module: A memory space exclusive mechanism is implemented for the spectral acquisition task, allocating a dedicated area in physical memory and prohibiting other tasks from accessing it, ensuring that core data acquisition is not interfered with; the data transmission task uses a dual-buffered queue, with the high-priority queue using a first-in-first-out (FIFO) strategy to ensure real-time performance of raw spectral data, and the low-priority queue using a flow control strategy for historical spectral data. Transmission is automatically delayed when any parameter—CPU core load, memory utilization, or spectral data transmission bus bandwidth utilization—exceeds 80% (the fourth threshold, set based on resource critical safety values ​​to avoid overload affecting core tasks); when multiple tasks conflict over resource requests, the task triggering order is marked by a hardware timestamp, and tasks of the same priority are allocated resources according to their time sequence, ensuring that the task that triggers first gets priority access to resources; intermediate results of quantitative calculations by the analysis module are synchronously stored in the CPU register and memory buffer, and if an inconsistency is detected, the task is automatically restarted to ensure the accuracy and reliability of the calculation process.

[0104] The photodetector array is equipped with a temperature compensation circuit, which monitors the detector's operating temperature in real time through an integrated platinum resistance temperature sensor.

[0105] When the temperature fluctuation exceeds the set threshold, the transimpedance amplifier gain compensation unit is automatically triggered.

[0106] The photodetector array is equipped with a temperature compensation circuit, which monitors the detector's operating temperature in real time through an integrated platinum resistance temperature sensor.

[0107] When the temperature fluctuation exceeds the set threshold, the transimpedance amplifier gain compensation unit is automatically triggered.

[0108] The pesticide standard spectral library pre-stores standard spectral offset thresholds for more than s pesticides;

[0109] When the peak position shift of the measured calibrated spectral data exceeds the corresponding threshold, the system automatically marks the anomaly and triggers the signal resampling procedure of the dual-wavelength reference channel.

[0110] The calibrated spectral data were compared with the pesticide standard spectral library using the characteristic peak intensity ratio method.

[0111] The characteristic peak intensity ratio method performs quantitative calibration by calculating the intensity ratio of the Raman spectral characteristic peak corresponding to the pesticide residue to be detected to the internal standard peak, and outputs the quantitative analysis results of pesticide residue.

[0112] In one embodiment, the photodetector array integrates a temperature compensation mechanism. A built-in platinum resistance temperature sensor monitors the operating temperature in real time. When temperature fluctuations exceed ±0.5℃ (a set threshold determined based on the detector's sensitivity temperature characteristic curve to ensure the temperature drift's impact on signal acquisition is less than 0.1%), the transimpedance amplifier gain compensation unit is automatically triggered to maintain signal acquisition stability. A pesticide standard spectral library pre-stores standard spectral shift thresholds for at least 500 pesticides (a typical single-peak position shift threshold is 0.5 cm). -1 Based on the spectrometer resolution and pesticide characteristic peak broadening characteristics, when the peak position shift of the measured calibration spectrum exceeds the corresponding threshold, an anomaly is automatically marked and a dual-wavelength reference channel resampling procedure is initiated. Quantitative analysis is performed using the characteristic peak intensity ratio method. By calculating the intensity ratio between the Raman characteristic peak of the pesticide to be detected and the internal standard peak (the internal standard peak is selected from the chemical bond vibration peaks that are stably present in the sample matrix), the intensity ratio range of the characteristic peaks of the corresponding pesticide in the standard spectral library is directly compared, and the quantitative analysis results of pesticide residues are output, ensuring detection accuracy and repeatability.

[0113] The data interaction interface of the control module supports the IEEE 1588 precision clock synchronization protocol;

[0114] The acquisition module, processing module, analysis module and control module achieve μs-level precision detection timing synchronization through hardware timestamps, enabling coordinated spectral acquisition and sample processing actions;

[0115] The excitation source integrates a wavelength locker, which monitors the laser wavelength in real time via a fiber Bragg grating;

[0116] When the laser wavelength drift exceeds the set threshold, the laser temperature control current is automatically adjusted.

[0117] The human-machine interface of the control module is configured with multi-level permission management, specifically including:

[0118] Inspectors can log in using fingerprint recognition and view real-time test curves;

[0119] Administrator accounts support standard spectral library updates and calibration parameter threshold settings.

[0120] In one embodiment, the control module is configured with a data interaction interface based on the IEEE 1588 precision clock synchronization protocol. It achieves μs-level precision synchronization with the acquisition, processing, and analysis modules via hardware timestamps, ensuring precise coordination between spectral acquisition and sample processing. The excitation light source integrates a wavelength locker, and the laser wavelength is monitored in real-time via a fiber Bragg grating. When the wavelength drift exceeds a preset threshold of ±0.05nm (the threshold is set based on the laser temperature coefficient of 0.08~0.1nm / ℃ and the spectrometer resolution of 0.06nm, ensuring that the impact of wavelength fluctuation on spectral analysis is less than 0.1%), the laser temperature control current is automatically adjusted to maintain wavelength stability. The control module's human-machine interface employs a multi-level access control mechanism: testing personnel can log in via fingerprint recognition to view real-time detection curves; administrator accounts support advanced functions such as standard spectral library updates and calibration parameter threshold settings, ensuring operational security and standardized data management.

[0121] This invention ensures the accuracy and stability of raw spectral data through excitation source wavelength locking, detector temperature compensation, and wavelength drift dynamic compensation mechanisms. It enhances the reliability of quantitative analysis by combining characteristic peak intensity ratio method with standard spectral library comparison and dual-wavelength reference channel resampling mechanism. The processing and control module achieves efficient task scheduling and conflict resolution through task priority classification, dynamic thread allocation, cross-task resource collaboration, and hierarchical resource isolation strategies. Full-process automated operation reduces human error, precise clock synchronization ensures accurate timing, and it also features hardware fault tolerance and data transmission blockage monitoring capabilities. The human-machine interface has multi-level permission management to ensure security, and it is compatible with different hardware configurations, flexibly adaptable to multiple scenarios, and balances scalability and stability.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media in which computer-usable program code is contained. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A pesticide residue quantitative analysis system based on Raman spectroscopy, characterized in that, It includes a data acquisition module, a processing module, an analysis module, and a control module; The acquisition module is used to acquire samples and obtain Raman scattering signals, convert the Raman scattering signals into raw spectral data, and the Raman scattering signals are converted into raw spectral data by a photodetector. Transmitted to the processing module; The processing module is used to calibrate the original spectral data, obtain the calibrated spectral data, and transmit it to the analysis module; The analysis module is used to compare the calibrated spectral data with the pesticide standard spectral library, output the quantitative analysis results of pesticide residues, and transmit them to the control module. The control module is used to store the pesticide standard spectral library and calibrated spectral data. The calibrated spectral data is used to correct sample detection deviations. Through the control unit, the entire process from sample processing to result output is automated. The control unit includes a task priority division unit, a resource usage monitoring unit, a dynamic thread allocation unit, a time constraint feedback unit, and a task conflict resolution unit. The task conflict resolution unit adopts a hierarchical resource isolation strategy, specifically including: Implement a memory space exclusive mechanism for the spectral acquisition task, allocate a dedicated area in physical memory, and prohibit other tasks from accessing it; Data transfer tasks use double-buffered queues, specifically including: The raw spectral data of the high-priority queue adopts a first-in-first-out (FIFO) strategy; Historical spectral data in the low-priority queue employs a flow control strategy, automatically delaying transmission when CPU core load, memory utilization, and real-time bandwidth utilization of the spectral data transmission bus exceed the fourth threshold. When multiple tasks request resources conflict, the task triggering order is marked by hardware timestamps. Tasks with the same priority are allocated resources in chronological order, and tasks with earlier time order are allocated resources first. The intermediate results generated during the quantitative calculation process of the analysis module are stored synchronously in the CPU register and memory buffer. The task is automatically restarted when inconsistencies are found in the intermediate results. The photodetector array is equipped with a temperature compensation circuit, which monitors the detector's operating temperature in real time through an integrated platinum resistance temperature sensor. When the temperature fluctuation exceeds the set threshold, the transimpedance amplifier gain compensation unit is automatically triggered.

2. The pesticide residue quantitative analysis system based on Raman spectroscopy as described in claim 1, characterized in that: The acquisition module includes an excitation light source and an optical probe; The Raman scattering signal is acquired through the optical probe; The raw spectral data includes wavenumber information, intensity core information, and pixel information.

3. The pesticide residue quantitative analysis system based on Raman spectroscopy as described in claim 2, characterized in that: The calibration of the raw spectral data includes a wavelength drift dynamic compensation mechanism; The wavelength drift dynamic compensation mechanism specifically includes: The pixel information and the wavenumber information have an original mapping relationship; The calibrated spectral data is generated by the wavenumber calibration laser built into the acquisition module; The mapping relationship between pixel information and wavenumber information of the original spectral data is corrected in real time using cubic spline interpolation.

4. The pesticide residue quantitative analysis system based on Raman spectroscopy as described in claim 3, characterized in that: The task priority division unit is used to preset priority levels according to the real-time requirements of the detection task, specifically including: The raw spectral data acquisition task executed by the acquisition module is configured with the highest priority; The data transmission task is configured as medium priority, and the data transmission task includes the process of transmitting raw spectral data from the acquisition module to the processing module and calibrated spectral data from the processing module to the analysis module. The result calculation task is configured with low priority; The resource usage monitoring unit is used to collect CPU core load, memory usage, data bus bandwidth parameters, and total number of CPU cores in real time.

5. The pesticide residue quantitative analysis system based on Raman spectroscopy as described in claim 4, characterized in that: The dynamic thread allocation unit specifically includes: The original spectral data acquisition task reserves 0 independent CPU cores as dedicated processing threads. The data transmission task dynamically allocates threads based on the size of the data in a single transmission. When the amount of data transmitted in a single instance is greater than or equal to the first threshold, p transmission threads are automatically activated for parallel processing. When the amount of data transmitted in a single instance is less than the second threshold, q transmission threads are activated. The result calculation task is configured with an elastic thread pool, with the maximum number of threads not exceeding the third threshold of the total number of CPU cores, and each thread is bound to an independent L2 cache partition; The time constraint feedback unit is used to accumulate the processing time of a single sample in real time. When the processing time of any task reaches the preset time for the entire process, the cross-task thread borrowing mechanism is triggered; The cross-task thread borrowing mechanism includes: Prioritize reclaiming n threads from the low-load result calculation task thread pool and allocating them to high-priority tasks that have timed out. After borrowing a thread, the remaining processing steps of the result calculation task are automatically merged into a single thread for execution; The built-in data transmission congestion monitoring module automatically suspends the recording of non-urgent system logs when the continuous data bus occupancy rate reaches i.

6. The pesticide residue quantitative analysis system based on Raman spectroscopy as described in claim 5, characterized in that: The pesticide standard spectral library pre-stores standard spectral offset thresholds for more than s pesticides; When the peak position shift of the measured calibrated spectral data exceeds the corresponding threshold, the system automatically marks the anomaly and triggers the signal resampling procedure of the dual-wavelength reference channel. The calibrated spectral data were compared with the pesticide standard spectral library using the characteristic peak intensity ratio method. The characteristic peak intensity ratio method performs quantitative calibration by calculating the intensity ratio of the Raman spectral characteristic peak corresponding to the pesticide residue to be detected to the internal standard peak, and outputs the quantitative analysis results of the pesticide residue.

7. The pesticide residue quantitative analysis system based on Raman spectroscopy as described in claim 6, characterized in that: The data interaction interface of the control module supports the IEEE 1588 precision clock synchronization protocol; The acquisition module, processing module, analysis module, and control module achieve μs-level precision detection timing synchronization through hardware timestamps, enabling coordinated spectral acquisition and sample processing actions. The excitation source integrates a wavelength locker, which monitors the laser wavelength in real time through a fiber Bragg grating; When the laser wavelength drift exceeds a set threshold, the laser temperature control current is automatically adjusted.

8. The pesticide residue quantitative analysis system based on Raman spectroscopy as described in claim 7, characterized in that: The human-machine interface of the control module is configured with multi-level permission management, specifically including: Inspectors can log in using fingerprint recognition and view real-time test curves; Administrator accounts support standard spectral library updates and calibration parameter threshold settings.

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