A filtering extraction method for detecting metal elements in water quality

Abstract

The application discloses a filtering and extracting method for water quality metal element detection and relates to the technical field of water sample extraction. By monitoring the key features of an SPE system in a solid phase extraction process in real time, whether the SPE system is abnormal is judged. If the SPE system is abnormal, the corresponding key features in the SPE system are immediately adjusted, and whether the SPE system can continue to be used for subsequent extraction is judged again. If the SPE system can continue to be used for subsequent extraction, the water sample after solid phase extraction is continuously used for metal element extraction in subsequent steps. If the SPE system cannot continue to be used for subsequent extraction, the solid phase extraction process is immediately stopped, an alarm is sent out, manual solid phase extraction of the water sample is performed again, and metal element extraction in subsequent steps is continued. In the filtering and extracting application of actual water quality metal element detection, if the SPE system is found to be faulty in time and is corrected, the purification effect can be ensured to be effective, so that the influence on the accuracy and reliability of overall water quality monitoring is reduced.

Description

Technical Field

[0001] This invention relates to the field of water sample extraction technology, and specifically to a filtration extraction method for detecting metal elements in water. Background Technology

[0002] In water quality monitoring, the detection of metal elements is crucial for assessing environmental pollution. However, due to the typically low concentration of metal elements in water and the frequent presence of impurities in water samples, direct detection faces challenges such as poor sensitivity and severe interference. Therefore, filtration extraction methods have become a standard pretreatment step to improve detection accuracy. This method first uses a microporous membrane for filtration pretreatment to remove large particulate impurities from the water. Subsequently, solid-phase extraction (SPE) is employed to enrich the target metal ions in the water using a selective adsorbent (such as a chelating resin). Finally, the adsorbed metal ions are eluted and concentrated using an eluent for subsequent quantitative detection. This method offers significant advantages in trace metal detection, effectively improving the enrichment efficiency of metal ions, reducing background interference, and thus significantly enhancing detection sensitivity.

[0003] Although solid-phase extraction (SPE) technology is widely used in the detection of metal elements in water, its stability and reliability remain key factors affecting the detection results. In practical applications, if the SPE system malfunctions and is not detected and corrected in a timely manner, the purification effect may decrease significantly. This lack of real-time monitoring and fault warning mechanisms may prevent the timely detection of such problems, thereby affecting the accuracy and reliability of overall water quality monitoring. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned above and provide a filtration and extraction method for detecting metal elements in water.

[0005] This invention proposes a filtration extraction method for detecting metal elements in water, the method comprising: Multiple sensor modules are set up to monitor key characteristics of the SPE system that affect the solid-phase extraction process in real time, and to determine whether the SPE system is abnormal based on the key characteristics. If the SPE system malfunctions, the corresponding key features in the SPE system should be adjusted immediately, and the SPE system should be used to determine whether it can continue to be used for subsequent extraction based on the adjusted key features. If possible, the water sample after solid-phase extraction will be further processed for metal element extraction in subsequent steps. If not, immediately stop the solid phase extraction process and issue an alarm. At the same time, switch to manual solid phase extraction of the water sample and continue the subsequent metal element extraction steps on the water sample after manual solid phase extraction.

[0006] Optionally, the steps for real-time monitoring of key characteristics of the SPE system affecting the solid-phase extraction process and determining whether the SPE system has malfunctioned based on these key characteristics are as follows: By setting up a micro-flow sensor to monitor the flow rate of liquid through the SPE column in real time, and calculating the flow rate anomaly coefficient based on the flow rate, the flow rate of the SPE column in the SPE system is analyzed to determine whether the flow rate is abnormal. The working pressure of the SPE system is monitored in real time by setting up a pressure sensor, and the pressure anomaly coefficient is calculated based on the working pressure; the pressure of the SPE system is analyzed to determine whether it is abnormal. Temperature and humidity anomaly coefficients are calculated by setting temperature and humidity sensors to monitor the temperature and humidity of the SPE system environment in real time during the solid phase extraction process; and the anomaly of temperature and humidity in the SPE system environment is analyzed. Determine whether the SPE system is malfunctioning based on the flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient.

[0007] Optionally, the calculation steps for the velocity anomaly coefficient are as follows: The flow rate of the SPE column in the SPE system during solid-phase extraction was monitored in real time using a microflow sensor, and a time-series flow rate sequence was obtained. The initial fluctuation coefficient is obtained by dividing the standard deviation and mean of the flow velocity sequence by the standard deviation. Linear regression is performed on the flow velocity sequence to extract the trend slope of the flow velocity change. The absolute value of the slope is compared with the preset standard slope, and the difference between the absolute value of the slope and the preset standard slope is calculated. The difference is then divided by the preset standard slope to obtain the flow velocity trend anomaly coefficient. The initial fluctuation coefficient and the velocity trend anomaly coefficient are weighted and summed to obtain the velocity anomaly coefficient.

[0008] Optionally, the calculation steps for the pressure anomaly coefficient are as follows: The working pressure of the SPE system is monitored in real time by a pressure sensor to obtain a time-series pressure sequence. The pressure in the pressure sequence is compared with the preset standard pressure range when the SPE system is working. If the pressure is not within the preset standard pressure range when the SPE system is working, the corresponding pressure in the pressure sequence is recorded as abnormal pressure. The pressure anomaly coefficient is obtained by dividing the total number of abnormal pressures in the pressure sequence by the total number of pressures in the pressure sequence.

[0009] Optionally, the calculation steps for the temperature and humidity anomaly coefficient are as follows: The temperature and humidity of the SPE system environment during solid-phase extraction are monitored in real time by temperature and humidity sensors, and the time when the temperature and humidity are within the preset standard temperature and humidity range is integrated to obtain the temperature anomaly and humidity anomaly of the SPE system environment during solid-phase extraction. The temperature and humidity anomalies are weighted and summed to obtain the temperature and humidity anomaly coefficient.

[0010] Optionally, the steps for determining whether the SPE system is experiencing anomalies based on the flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are as follows: The flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are compared with the preset flow velocity anomaly coefficient threshold, preset pressure anomaly coefficient threshold, and preset temperature and humidity anomaly coefficient threshold, respectively. If the flow rate abnormality coefficient is not less than the preset flow rate abnormality coefficient threshold, or the pressure abnormality coefficient is not less than the preset pressure abnormality coefficient threshold, or the temperature and humidity abnormality coefficient is not less than the preset temperature and humidity abnormality coefficient threshold, the SPE system is immediately judged to be abnormal and an alarm is issued. The corresponding flow rate, pressure and ambient temperature and humidity of the SPE system are immediately adjusted. If the flow velocity anomaly coefficient is less than the preset flow velocity anomaly coefficient threshold, the pressure anomaly coefficient is less than the preset pressure anomaly coefficient threshold, and the temperature and humidity anomaly coefficient is less than the preset temperature and humidity anomaly coefficient threshold, then the flow velocity anomaly coefficient, pressure anomaly coefficient and temperature and humidity anomaly coefficient are weighted and summed to obtain the anomaly value. Compare the outlier with the preset outlier threshold. If the outlier is not less than the preset outlier threshold, immediately adjust the flow rate, pressure and ambient temperature and humidity of the SPE system. If the outlier is less than the preset outlier threshold, there is no need to adjust the flow rate, pressure, and ambient temperature and humidity of the SPE system; continue solid-phase extraction and subsequent metal element extraction steps on the water sample.

[0011] Optionally, the step of determining whether the SPE system can continue to be used for subsequent extraction based on the adjusted key features is as follows: Calculate the flow rate anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient of the adjusted SPE system. If the adjusted flow rate anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are all less than the corresponding preset thresholds, and the anomaly values ​​are less than the preset anomaly value thresholds, then there is no need to adjust the flow rate, pressure, and ambient temperature and humidity of the SPE system; continue solid phase extraction and subsequent metal element extraction steps on the water sample; otherwise, immediately stop the solid phase extraction process, issue an alarm, and switch to manual solid phase extraction of the water sample, and continue the subsequent metal element extraction steps on the water sample after manual solid phase extraction.

[0012] The beneficial effects of this invention are: This invention proposes a filtration extraction method for detecting metal elements in water. By setting up multiple sensor modules, it monitors key characteristics of the solid-phase extraction (SPE) system in real time, and determines whether the SPE system is malfunctioning based on these characteristics. If an SPE system malfunctions, the corresponding key characteristics are immediately adjusted, and the system's continued usability for subsequent extractions is assessed based on the adjusted characteristics. If usable, the water sample after SPE extraction continues with subsequent metal element extraction steps. If not, the SPE process is immediately stopped, an alarm is issued, and manual SPE extraction is performed on the water sample again. The manually extracted water sample then continues with subsequent metal element extraction steps. In this way, in practical applications of filtration extraction for detecting metal elements in water, any SPE system malfunction can be detected and corrected promptly, ensuring effective purification. This real-time monitoring and fault warning mechanism allows for timely detection of such problems, thereby reducing the impact on the accuracy and reliability of overall water quality monitoring. Attached Figure Description

[0013] The invention will now be further described with reference to the accompanying drawings.

[0014] Figure 1 This is a flowchart of a filtration and extraction method for detecting metal elements in water. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0017] This invention provides a filtration extraction method for detecting metal elements in water. See also... Figure 1 , Figure 1 This is a flowchart illustrating a filtration and extraction method for detecting metal elements in water, provided as an embodiment of the present invention. The method includes the following steps: Multiple sensor modules are set up to monitor key characteristics of the SPE system that affect the solid-phase extraction process in real time, and to determine whether the SPE system is abnormal based on the key characteristics. If the SPE system malfunctions, the corresponding key features in the SPE system should be adjusted immediately, and the SPE system should be used to determine whether it can continue to be used for subsequent extraction based on the adjusted key features. If possible, the water sample after solid-phase extraction will be further processed for metal element extraction in subsequent steps. If not, immediately stop the solid phase extraction process and issue an alarm. At the same time, switch to manual solid phase extraction of the water sample and continue the subsequent metal element extraction steps on the water sample after manual solid phase extraction.

[0018] Based on the filtration and extraction method for detecting metal elements in water provided by the embodiments of the present invention, if the SPE system malfunctions in the actual application of filtration and extraction for detecting metal elements in water, it can be detected and corrected in a timely manner, ensuring effective purification. This real-time monitoring and fault warning mechanism can enable the above-mentioned problems to be detected in a timely manner, thereby reducing the impact on the accuracy and reliability of overall water quality monitoring.

[0019] It should be noted that the filtration extraction method for detecting metal elements in water generally includes four main steps: filtration pretreatment, metal ion enrichment, elution concentration, and subsequent detection. The specific process is as follows: First, the collected water sample is filtered through a microporous membrane to remove suspended solids, silt, and other large particulate impurities, reducing interference with subsequent operations. Next, the filtered water sample is introduced into a solid-phase extraction column filled with materials that selectively adsorb metal ions, such as chelating resins or functionalized silica gels. As the water sample passes through, the target metal ions are adsorbed onto the surface of the adsorbent. After adsorption, the metal ions are eluted from the adsorbent using an appropriate eluent (e.g., an acidic solution), concentrating them in the eluent. Finally, the eluent is transferred to a subsequent analytical instrument (such as atomic absorption spectrometry (AAS) or inductively coupled plasma mass spectrometry (ICP-MS) for quantitative detection. This process is characterized by clear steps, relatively simple operation, and strong adaptability, making it particularly suitable for the extraction and analysis of trace metal ions. It is widely used in environmental monitoring, drinking water safety assessment, and other fields.

[0020] It's important to note that an SPE system, or Solid Phase Extraction system, is an automated or semi-automated device for sample pretreatment. It primarily enriches and purifies target analytes through selective adsorption and elution. In water metal element detection, the SPE system plays a crucial role, its core objective being the selective extraction and concentration of target metal ions from complex water samples, thereby improving detection sensitivity and accuracy. Specifically, an SPE system typically consists of a sample introduction unit, a flow path control device, a solid phase extraction column, and an elution and collection unit. During system operation, the pretreated water sample is loaded into the extraction column. Metal ions selectively bind to the adsorbent (such as chelating resin) packed within the column, while other non-target components are washed away. Subsequently, a specific eluent is added to release the metal ions from the adsorbent and collect them. This process not only effectively removes background impurities and reduces interference but also enriches metal elements, initially at very low concentrations, above the detection limit, thus meeting the requirements of subsequent instrumental analysis. Compared with traditional liquid-liquid extraction methods, the SPE system has the advantages of simple operation, low solvent consumption, and high degree of automation, and has become an indispensable key unit in the detection of heavy metals in water.

[0021] In one embodiment, multiple sensor modules are set up to monitor key characteristics of the SPE system that affect the solid-phase extraction process in real time, and to determine whether the SPE system is abnormal based on the key characteristics. Specifically, the steps for real-time monitoring of key characteristics of the SPE system affecting the solid-phase extraction process and determining whether the SPE system has malfunctioned based on these key characteristics are as follows: By setting up a micro-flow sensor to monitor the flow rate of liquid through the SPE column in real time, and calculating the flow rate anomaly coefficient based on the flow rate, the flow rate of the SPE column in the SPE system is analyzed to determine whether the flow rate is abnormal. The working pressure of the SPE system is monitored in real time by setting up a pressure sensor, and the pressure anomaly coefficient is calculated based on the working pressure; the pressure of the SPE system is analyzed to determine whether it is abnormal. Temperature and humidity anomaly coefficients are calculated by setting temperature and humidity sensors to monitor the temperature and humidity of the SPE system environment in real time during the solid phase extraction process; and the anomaly of temperature and humidity in the SPE system environment is analyzed. Determine whether the SPE system is malfunctioning based on the flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient.

[0022] It should be noted that, to achieve real-time monitoring and anomaly identification of the solid phase extraction (SPE) system's operating status, multiple sensor modules are set up to collect key operating parameters and determine whether the system is in an abnormal state. Specifically, the method includes the following steps: First, by setting micro-flow sensors before and after the SPE column, the flow rate of liquid flowing through the SPE column is monitored in real time, and the collected real-time flow rate data is compared with the set standard flow rate range to calculate the flow rate anomaly coefficient, thereby determining whether there are problems such as blockage, leakage, or pump control abnormalities in the SPE column; Second, a pressure sensor is configured to monitor the working pressure of the SPE system, and by comparing the actual pressure with the normal operating range, a pressure anomaly coefficient is calculated to identify whether the system is experiencing excessive pump load, abnormal changes in column resistance, etc. In addition, temperature and humidity sensors are used to record the temperature and humidity of the environment in which the SPE system is located in real time, and by comparing them with standard environmental values, a temperature and humidity anomaly coefficient is obtained to assess whether there is a risk of system instability due to environmental changes (such as overheating or humidity). Ultimately, the above three abnormal coefficients are used as the basis for judgment. If one or more of the parameters exceed the preset threshold, the system will determine that it is in an abnormal state, thereby providing decision support for subsequent adjustment and processing.

[0023] Specifically, in one embodiment, the calculation steps for the flow velocity anomaly coefficient are as follows: The flow rate of the SPE column in the SPE system during solid-phase extraction was monitored in real time using a microflow sensor, and a time-series flow rate sequence was obtained. The initial fluctuation coefficient is obtained by dividing the standard deviation and mean of the flow velocity sequence by the standard deviation. Linear regression is performed on the flow velocity sequence to extract the trend slope of the flow velocity change. The absolute value of the slope is compared with the preset standard slope, and the difference between the absolute value of the slope and the preset standard slope is calculated. The difference is then divided by the preset standard slope to obtain the flow velocity trend anomaly coefficient. The initial fluctuation coefficient and the velocity trend anomaly coefficient are weighted and summed to obtain the velocity anomaly coefficient.

[0024] It should be noted that the data required for calculating the flow velocity anomaly coefficient is primarily acquired through microflow sensors installed at the inlet and outlet of the SPE system. These sensors collect instantaneous flow velocity data of the liquid flowing through the SPE column in real time with high temporal resolution (e.g., once per second or higher), and record this data chronologically to form a flow velocity time series. The sensors are typically connected to a data acquisition module or embedded control system, enabling real-time transmission of flow velocity data to a host computer or local processing unit for subsequent statistical calculations and trend analysis. To ensure data accuracy and timeliness, the system can also be configured with data caching and outlier removal mechanisms to perform preliminary processing on the collected raw flow velocity data, thereby providing high-quality, continuous input data for calculating the flow velocity anomaly coefficient.

[0025] It should be noted that the flow rate anomaly coefficient is a quantitative indicator that comprehensively evaluates the stability and trend of flow rate in an SPE column within an SPE system. It aims to reflect fluctuations and deviations in flow rate during operation. A larger flow rate anomaly coefficient indicates more severe flow rate fluctuations or a deviation from the expected stable state, which usually signifies an abnormality in the SPE system's operation. This is because the flow rate in an SPE system has a significant impact on the adsorption and elution processes of metal ions. Flow rates that are too high or too low can reduce the enrichment efficiency of metal ions and may even affect subsequent elution results. For example, if the flow rate is too high, metal ions may not fully contact the adsorbent, failing to achieve complete adsorption and thus reducing purification efficiency; while a flow rate that is too low may lead to excessively long processing times or adsorbent saturation, affecting the system's processing efficiency and stability. If flow rate anomalies are not detected and corrected in time, the adsorption process of metal ions within the SPE column may be insufficient, or metal ions may not be completely eluted during elution, ultimately leading to inaccurate detection results or decreased sensitivity. In severe cases, it can even significantly reduce the accuracy and reliability of the entire water quality monitoring process. Therefore, real-time monitoring and adjustment of the flow rate anomaly coefficient is crucial for ensuring the stable operation of the SPE system and improving the purification efficiency of metal elements.

[0026] In one embodiment, the calculation steps for the pressure anomaly coefficient are as follows: The working pressure of the SPE system is monitored in real time by a pressure sensor to obtain a time-series pressure sequence. The pressure in the pressure sequence is compared with the preset standard pressure range when the SPE system is working. If the pressure is not within the preset standard pressure range when the SPE system is working, the corresponding pressure in the pressure sequence is recorded as abnormal pressure. The pressure anomaly coefficient is obtained by dividing the total number of abnormal pressures in the pressure sequence by the total number of pressures in the pressure sequence.

[0027] It should be noted that the data required for calculating the pressure anomaly coefficient is acquired in real time by pressure sensors installed in the SPE system. These pressure sensors are typically installed at critical locations within the SPE system, such as the SPE column inlet or outlet, to monitor the system's operating pressure in real time. The pressure sensors can collect pressure data at a high frequency and transmit this data chronologically to the data acquisition system or control unit. The collected pressure data is then compared with a preset standard pressure range. If the monitored pressure value exceeds the standard range, the system will mark it as abnormal pressure. Furthermore, the pressure sensors typically possess high accuracy and stability, ensuring that the collected data reflects the true operating status of the system. The data acquisition system processes and stores the data fed back from the sensors in real time and uses it for subsequent calculations of the pressure anomaly coefficient, providing crucial information for anomaly diagnosis and fault early warning in the SPE system.

[0028] It is important to note that the pressure anomaly coefficient quantifies the degree to which the operating pressure in an SPE system deviates from the preset standard pressure range, specifically reflecting whether abnormal pressure conditions exist during system operation. A higher pressure anomaly coefficient indicates that the pressure value frequently or severely exceeds the preset standard range within the monitoring period, typically signifying a system malfunction or operational instability. Pressure is crucial for the normal operation of an SPE system, as it directly affects the velocity and uniformity of liquid flow through the SPE column, thus influencing the adsorption and elution processes of metal ions. Excessive system pressure can lead to over-compaction of the adsorbent, affecting liquid flowability and potentially damaging system pipelines or devices; conversely, insufficient pressure can result in inadequate flow rate, affecting the enrichment efficiency of metal ions and potentially causing incomplete adsorption, thereby reducing purification efficiency. Failure to detect and correct pressure anomalies in a timely manner can lead to insufficient enrichment and incomplete elution of metal ions within the SPE column, ultimately resulting in a significant decrease in metal element extraction efficiency and impacting the accuracy and reliability of water quality testing. Therefore, timely monitoring of the pressure anomaly coefficient and implementation of corrective measures are key to ensuring stable operation of the SPE system and improving the accuracy of test results.

[0029] In one embodiment, the calculation steps for the temperature and humidity anomaly coefficient are as follows: The temperature and humidity of the SPE system environment during solid-phase extraction are monitored in real time by temperature and humidity sensors, and the time when the temperature and humidity are within the preset standard temperature and humidity range is integrated to obtain the temperature anomaly and humidity anomaly of the SPE system environment during solid-phase extraction. The temperature and humidity anomalies are weighted and summed to obtain the temperature and humidity anomaly coefficient.

[0030] It should be noted that the data required for calculating the temperature and humidity anomaly coefficient is acquired through temperature and humidity sensors installed in the environment surrounding the SPE system. These sensors monitor the temperature and humidity of the environment in which the SPE system is located in real time and transmit the data to the data acquisition system at a high frequency. Temperature and humidity sensors are typically equipped with high-precision measurement capabilities, providing accurate temperature and humidity values ​​even under highly variable environmental conditions. The data acquisition system compares the real-time temperature and humidity data provided by the sensors with preset standard temperature and humidity ranges and records the time spent within these ranges. Using this data, the system can calculate the anomaly degree of temperature and humidity, reflecting the potential impact of environmental changes on the SPE system. Finally, the temperature and humidity anomalies are weighted and summed to obtain the temperature and humidity anomaly coefficient, which serves as the basis for determining whether the environment affects the stability of the SPE system.

[0031] It should be noted that the temperature and humidity anomaly coefficient measures the degree to which the temperature and humidity in the SPE system's operating environment deviate from the preset standard range. It integrates fluctuations in ambient temperature and humidity, reflecting the potential impact of environmental changes on the operational stability of the SPE system. A higher temperature and humidity anomaly coefficient means that the temperature and humidity in the SPE system's environment frequently or significantly exceed the standard range, which may lead to unstable operating conditions and consequently affect the purification effect. Temperature and humidity have a direct impact on the SPE process because they can affect the performance of the adsorbent, the fluidity of the liquid, and the rate of chemical reactions. For example, at excessively high temperatures, solubility and reaction rates may increase, leading to incomplete or excessive contact between metal ions and the adsorbent, thus affecting the enrichment effect; while excessive humidity may cause the adsorbent to absorb water and swell, affecting its adsorption performance and even causing equipment corrosion and malfunction. Failure to monitor and adjust temperature and humidity anomalies in a timely manner may result in low metal ion purification efficiency or even complete failure in the SPE process, thus affecting the accuracy and reliability of water quality testing.

[0032] In one embodiment, the steps for determining whether the SPE system is experiencing an anomaly based on the flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are as follows: The flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are compared with the preset flow velocity anomaly coefficient threshold, preset pressure anomaly coefficient threshold, and preset temperature and humidity anomaly coefficient threshold, respectively. If the flow rate abnormality coefficient is not less than the preset flow rate abnormality coefficient threshold, or the pressure abnormality coefficient is not less than the preset pressure abnormality coefficient threshold, or the temperature and humidity abnormality coefficient is not less than the preset temperature and humidity abnormality coefficient threshold, the SPE system is immediately judged to be abnormal and an alarm is issued. The corresponding flow rate, pressure and ambient temperature and humidity of the SPE system are immediately adjusted. If the flow velocity anomaly coefficient is less than the preset flow velocity anomaly coefficient threshold, the pressure anomaly coefficient is less than the preset pressure anomaly coefficient threshold, and the temperature and humidity anomaly coefficient is less than the preset temperature and humidity anomaly coefficient threshold, then the flow velocity anomaly coefficient, pressure anomaly coefficient and temperature and humidity anomaly coefficient are weighted and summed to obtain the anomaly value. Compare the outlier with the preset outlier threshold. If the outlier is not less than the preset outlier threshold, immediately adjust the flow rate, pressure and ambient temperature and humidity of the SPE system. If the outlier is less than the preset outlier threshold, there is no need to adjust the flow rate, pressure, and ambient temperature and humidity of the SPE system; continue solid-phase extraction and subsequent metal element extraction steps on the water sample.

[0033] It should be noted that the calculated results of the flow rate anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient can be used to comprehensively determine whether the SPE system is experiencing an anomaly and take corresponding adjustment measures. The specific steps are as follows: First, compare the flow rate anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient with preset thresholds. If any of these coefficients exceeds the preset threshold, it indicates that the system has experienced an anomaly. In this case, the system will immediately issue an alarm and adjust the flow rate, pressure, and ambient temperature and humidity according to the anomaly to restore the system to normal operating status. For example, if the flow rate anomaly coefficient is too high, it may be due to the flow rate being too fast or too slow; in this case, the pump speed or pipe resistance can be adjusted to correct the flow rate. If the pressure anomaly coefficient is too high, it may be due to pipe blockage or excessive pump load; the pressure needs to be adjusted or the system cleaned. If all anomaly coefficients are below their respective thresholds, they are weighted and summed to obtain a comprehensive anomaly value, which is then compared with the preset anomaly value threshold. If the overall abnormal value is not less than the preset threshold, it indicates that the system is still at potential risk and the flow rate, pressure, and environmental conditions need to be adjusted immediately. If the overall abnormal value is less than the threshold, the system is considered to be within the normal range and no adjustment is needed; solid-phase extraction and subsequent metal element extraction steps can continue. Timely detection and correction of abnormalities during this process helps avoid potential risks in system operation, thereby ensuring the accuracy of purification effects and water quality test results. For example, in high ambient humidity, the system may experience adsorbent expansion or equipment corrosion; timely humidity adjustment can avoid interference with the purification process and improve the extraction efficiency of metal ions. In one embodiment, the step of determining whether the SPE system can continue to be used for subsequent extraction based on the adjusted key features is as follows: Calculate the flow rate anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient of the adjusted SPE system. If the adjusted flow rate anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are all less than the corresponding preset thresholds, and the anomaly values ​​are less than the preset anomaly value thresholds, then there is no need to adjust the flow rate, pressure, and ambient temperature and humidity of the SPE system; continue solid phase extraction and subsequent metal element extraction steps on the water sample; otherwise, immediately stop the solid phase extraction process, issue an alarm, and switch to manual solid phase extraction of the water sample, and continue the subsequent metal element extraction steps on the water sample after manual solid phase extraction.

[0034] It is important to note that after adjusting the key characteristics of the SPE system, the crucial factor in determining whether the system can continue to be used for subsequent extraction steps lies in evaluating the adjusted flow rate anomaly coefficients, pressure anomaly coefficients, and temperature and humidity anomaly coefficients. The specific steps are as follows: First, recalculate all anomaly coefficients of the adjusted SPE system, especially the anomalies in flow rate, pressure, and temperature and humidity. If, after adjustment, all flow rate, pressure, and temperature and humidity anomaly coefficients are less than their respective preset thresholds, and the weighted overall anomaly value is also lower than the preset anomaly value threshold, it indicates that the system has returned to normal operating conditions, and solid-phase extraction and subsequent metal element extraction steps can continue. For example, if the flow rate returns to the set range after adjustment, the pressure returns to normal, and the ambient temperature and humidity return to the standard range, it means that the SPE system can continue to operate stably, and the enrichment and purification process of metal elements is unaffected. However, if the adjusted anomaly coefficients still exceed the thresholds, or the overall anomaly value is still too high, it means that the system has not returned to a stable state. In this case, the solid-phase extraction process should be stopped immediately, and an alarm should be issued. The system will switch to manual intervention, with operators re-performing solid-phase extraction on the water sample to ensure the entire extraction process is unaffected by equipment malfunction, thus guaranteeing the accuracy and effectiveness of subsequent metal element extraction. This early warning and automatic adjustment mechanism can promptly identify and resolve potential problems, preventing reduced purification efficiency or inaccurate test results due to equipment failure. For example, if the ambient temperature and humidity are too high and the system cannot automatically adjust back to normal, manual intervention can control environmental conditions and re-perform solid-phase extraction, thereby ensuring the accuracy of metal ion enrichment.

[0035] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. A filtration extraction method for detecting metal elements in water, characterized in that, Includes the following steps: Multiple sensor modules are configured to monitor key characteristics of the SPE system affecting the solid-phase extraction (SPE) process in real time, and to determine whether the SPE system has experienced any abnormalities based on these key characteristics. Specifically, the process involves: using a micro-flow sensor to monitor the liquid flow rate through the SPE column in real time, calculating a flow rate anomaly coefficient, and analyzing whether the SPE column flow rate is abnormal; using a pressure sensor to monitor the operating pressure of the SPE system in real time, calculating a pressure anomaly coefficient, and analyzing whether the SPE system pressure is abnormal; and using temperature and humidity sensors to monitor the temperature and humidity of the environment surrounding the SPE system during solid-phase extraction, calculating a temperature and humidity anomaly coefficient, and analyzing whether the temperature and humidity of the environment surrounding the SPE system are abnormal. Determine whether the SPE system is malfunctioning based on the flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient. If the SPE system malfunctions, the corresponding key features in the SPE system should be adjusted immediately, and the SPE system should be used to determine whether it can continue to be used for subsequent extraction based on the adjusted key features. If possible, the water sample after solid-phase extraction will be further processed for metal element extraction in subsequent steps. If not, immediately stop the solid phase extraction process and issue an alarm. At the same time, switch to manual solid phase extraction of the water sample and continue the subsequent metal element extraction steps on the water sample after manual solid phase extraction.

2. The filtration and extraction method for detecting metal elements in water according to claim 1, characterized in that, The calculation steps for the velocity anomaly coefficient are as follows: The flow rate of the SPE column in the SPE system during solid-phase extraction was monitored in real time using a microflow sensor, and a time-series flow rate sequence was obtained. Calculate the standard deviation and mean of the flow velocity sequence, and divide the standard deviation by the mean to obtain the initial fluctuation coefficient; Linear regression is performed on the flow velocity sequence to extract the trend slope of the flow velocity change. The absolute value of the slope is compared with the preset standard slope, and the difference between the absolute value of the slope and the preset standard slope is calculated. The difference is then divided by the preset standard slope to obtain the flow velocity trend anomaly coefficient. The initial fluctuation coefficient and the velocity trend anomaly coefficient are weighted and summed to obtain the velocity anomaly coefficient.

3. The filtration and extraction method for detecting metal elements in water according to claim 1, characterized in that, The calculation steps for the pressure anomaly coefficient are as follows: The working pressure of the SPE system is monitored in real time by a pressure sensor to obtain a pressure sequence based on time order. The pressure in the pressure sequence is compared with the preset standard pressure range when the SPE system is working. If the pressure is not within the preset standard pressure range when the SPE system is working, the pressure in the corresponding pressure sequence is recorded as abnormal pressure. The pressure anomaly coefficient is obtained by dividing the total number of abnormal pressures in the pressure sequence by the total number of pressures in the pressure sequence.

4. The filtration and extraction method for detecting metal elements in water according to claim 1, characterized in that, The calculation steps for the temperature and humidity anomaly coefficient are as follows: The temperature and humidity of the SPE system environment during solid-phase extraction are monitored in real time by temperature and humidity sensors. The temperature and humidity during the time when the system is not within the preset standard temperature and humidity range are integrated to obtain the temperature and humidity anomalies of the SPE system environment during solid-phase extraction. The temperature and humidity anomalies are weighted and summed to obtain the temperature and humidity anomaly coefficient.

5. The filtration and extraction method for detecting metal elements in water according to claim 1, characterized in that, The steps to determine whether an SPE system is malfunctioning based on flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are as follows: The flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are compared with the preset flow velocity anomaly coefficient threshold, preset pressure anomaly coefficient threshold, and preset temperature and humidity anomaly coefficient threshold, respectively. If the flow rate abnormality coefficient is not less than the preset flow rate abnormality coefficient threshold, or the pressure abnormality coefficient is not less than the preset pressure abnormality coefficient threshold, or the temperature and humidity abnormality coefficient is not less than the preset temperature and humidity abnormality coefficient threshold, then the SPE system is immediately judged to be abnormal, an alarm is issued, and the corresponding flow rate, pressure or ambient temperature and humidity of the SPE system are immediately adjusted. If the flow velocity anomaly coefficient is less than the preset flow velocity anomaly coefficient threshold, the pressure anomaly coefficient is less than the preset pressure anomaly coefficient threshold, and the temperature and humidity anomaly coefficient is less than the preset temperature and humidity anomaly coefficient threshold, then the flow velocity anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are weighted and summed to obtain the anomaly value; the anomaly value is compared with the preset anomaly value threshold, and if the anomaly value is not less than the preset anomaly value threshold, then the flow velocity, pressure, and ambient temperature and humidity of the SPE system are immediately adjusted; If the outlier is less than the preset outlier threshold, there is no need to adjust the flow rate, pressure, and ambient temperature and humidity of the SPE system; continue solid-phase extraction and subsequent metal element extraction steps on the water sample.

6. The filtration and extraction method for detecting metal elements in water according to claim 5, characterized in that, The steps for determining whether the SPE system can continue to be used for subsequent extraction based on the adjusted key characteristics are as follows: Calculate the flow rate anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient of the adjusted SPE system. If the adjusted flow rate anomaly coefficient, pressure anomaly coefficient, and temperature and humidity anomaly coefficient are all less than the corresponding preset thresholds, and the anomaly values ​​are less than the preset anomaly value thresholds, then it is not necessary to adjust the flow rate, pressure, and ambient temperature and humidity of the SPE system; continue solid-phase extraction and subsequent metal element extraction steps on the water sample; otherwise, immediately stop the solid-phase extraction process, issue an alarm, and switch to manual solid-phase extraction of the water sample, and continue the subsequent metal element extraction steps on the water sample after manual solid-phase extraction.

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