Microwave digestion-liquid flash spectrometer strontium isotope rapid detection system for multiple types of samples

Abstract

The invention discloses a microwave digestion-liquid flash spectrometer strontium isotope rapid detection system for multiple types of samples. The automatic sample injection module automatically identifies, grabs and conveys a sample by means of an image and radio frequency identification technology. The microwave digestion module is provided with a self-adaptive program, temperature and pressure are dynamically regulated and controlled through a PID algorithm according to sample characteristics, and efficient and safe digestion is ensured. The automatic separation and purification module intelligently selects a solid-phase extraction column or an ion exchange resin column to purify a sample by matching spectral analysis with a database. The liquid flash spectrometer detection module accurately collects signals by means of a high-sensitivity element and an algorithm. The data processing and control system adopts a multi-layer perceptron neural network, so that the strontium isotope content can be intelligently judged, and the source of abnormal data can be traced. In addition, the system has the functions of automatic cleaning and maintenance, emergency detection flow and remote monitoring, all the modules are connected through standardized interfaces, and the expansibility is high. The system is high in automation degree and accurate in detection, and an efficient and reliable scheme is provided for strontium isotope detection.

Description

Technical Field

[0001] The present invention relates to a rapid detection system for strontium isotopes of a microwave digestion - liquid scintillation spectrometer for multi - category samples. Background Art

[0002] In the current field of strontium isotope detection, traditional detection technology has many problems that need to be solved. Low degree of automation: In the existing sample detection process, the identification, grabbing and transportation of multi-category samples often rely on manual operation. This is not only inefficient and slow in detection speed, but also difficult to meet the needs of rapid detection of a large number of samples. In addition, during the manual operation, it is easy to cause sample confusion and number errors due to human negligence, resulting in increased detection errors and affecting the reliability of the test results. Poor digestion efficiency and safety: Most traditional digestion equipment is only equipped with a single or a few digestion programs, and cannot perform personalized digestion according to the characteristics of different types of samples. This makes it difficult for some complex samples to be fully digested, affecting the accuracy of subsequent detection. At the same time, there is a lack of real-time temperature and pressure control systems, and parameters cannot be adjusted in time during the digestion process. Safety accidents such as digestion tank rupture caused by excessive temperature or excessive pressure are prone to occur, which poses a major safety hazard. Single separation and purification method: Traditional automatic separation and purification modules usually use a fixed separation and purification method, such as using only one of the solid phase extraction columns or ion exchange resin columns, and cannot automatically and flexibly switch according to the complex components after sample digestion. This results in the inability to accurately separate and purify multi-category samples with large composition differences, reducing the sensitivity and accuracy of the detection. Insufficient detection accuracy: Due to the accuracy limitations of equipment components, such as low luminous efficiency of scintillators, weak signal amplification capabilities of photomultiplier tubes, and poor processing accuracy of signal acquisition processors, some traditional liquid scintillation spectrometer detection modules are difficult to accurately convert the weak signals generated by the decay of strontium isotopes in samples into electrical signals and accurately collect and process them, resulting in large errors in the detection results and unable to meet the requirements of high-precision detection. Low data analysis efficiency: In terms of data processing and analysis, traditional technologies mostly rely on manual analysis of detection data to determine whether the content of strontium isotopes in samples is normal. This process not only consumes a lot of manpower and time, but also is prone to misjudgment due to the subjectivity of human judgment. At the same time, manual analysis is difficult to quickly process a large amount of complex data, and cannot generate detection reports in a timely manner, affecting the overall progress of the detection work. Complex system maintenance: After the detection of traditional detection systems is completed, the cleaning and maintenance of each module often needs to be performed manually, which is cumbersome and easy to miss. The long-term lack of an effective automatic cleaning and maintenance mechanism will lead to an increase in residual impurities in the system, affecting equipment performance, shortening equipment life, and increasing equipment failure rates, which in turn affects the long-term stable operation of the system. Limited system scalability: The connection interfaces between modules of traditional detection systems lack standardized design, which makes the system face many difficulties in assembly, disassembly and upgrading. When the system configuration needs to be adjusted according to changes in actual detection tasks, it is difficult to flexibly add or replace modules due to incompatible interfaces, which limits the application scope and adaptability of the system.

[0003] The Chinese invention patent application of CN202211144754.5 discloses a microwave-assisted digestion method for detecting strontium isotopes in silkworm cocoons, including: 1) removing impurities and degumming; 2) pulverizing and acid digestion; 3) microwave-assisted digestion; 4) column packing; 5) separating strontium from other ions through an exchange column and enriching the content of strontium isotopes in the sample. The present invention uses a combination of perchloric acid and hydrogen peroxide to digest the organic matter in silkworm cocoons, which can completely digest the silkworm cocoon shell powder in a short time without contaminating the sample, facilitating the detection of strontium isotopes. From the perspective of automation, the automation level of this patented method is relatively low throughout the detection process. Key links such as impurity removal, degumming, pulverization, and subsequent column packing basically rely on manual operation. When faced with multi-category samples, it is not only difficult to achieve rapid detection, but also prone to problems such as sample confusion and numbering errors due to human negligence, seriously affecting the reliability of the detection results. For example, when batch detecting different-category samples, the efficiency of manual operation is much lower than that of an automated system, and the error rate increases significantly. In terms of the applicable sample range, this method is only designed for a specific sample, namely silkworm cocoons. When encountering other-category samples such as soil, ore, and water samples, the combination digestion method of perchloric acid and hydrogen peroxide may not be applicable, unable to meet the personalized digestion needs of different samples. The materials and components of different samples vary greatly. For example, ore samples may contain a large amount of insoluble minerals, and simple acid digestion combinations are difficult to fully digest, affecting the accuracy of subsequent detections. In terms of separation and purification, although this patent mentions separating strontium from other ions through an exchange column, the method is relatively single, lacking the ability to automatically and flexibly switch the separation and purification method according to the complex components after sample digestion. For multi-category samples with large compositional differences, this fixed separation method cannot achieve precise separation and purification, greatly reducing the detection sensitivity and accuracy. For example, when there are multiple interfering ions in the sample, a single exchange column may not be able to effectively remove them, resulting in deviation of the detection results. The universality of the detection equipment is insufficient. The equipment and processes involved in this method are closely designed around silkworm cocoon detection, and there is a lack of standardized interfaces between the equipment modules. This makes it face many difficulties in system assembly, disassembly, and upgrade, and it is difficult to flexibly adjust the system configuration according to the changes in the actual detection tasks, restricting the application scope and adaptability of the system. In terms of data processing and analysis, this patent does not mention an intelligent data processing method and most likely still relies on manual analysis of detection data to judge whether the content of strontium isotopes in the sample is normal. This process not only consumes a large amount of manpower and time, but also is prone to misjudgment due to the subjectivity of human judgment. At the same time, manual analysis is difficult to quickly process a large amount of complex data and cannot generate detection reports in a timely manner, seriously affecting the overall progress of the detection work.

[0004] In summary, it is of great practical significance and application value to develop a microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi-category samples that can overcome the above problems. Summary of the Invention

[0005] The object of the present invention is to provide a rapid detection system for strontium isotopes in a microwave digestion - liquid scintillation spectrometer for multi - category samples, so as to solve the problems raised in the above - mentioned background technology.

[0006] To solve the above - mentioned technical problems, the technical solution provided by the present invention is: a rapid detection system for strontium isotopes in a microwave digestion - liquid scintillation spectrometer for multi - category samples, including:

[0007] An automatic sampling module, which is used to automatically identify, grasp and transport multi - category samples to the microwave digestion module. This module includes a sample identification device that can identify the type and number of samples and a robotic arm that grasps the sample from the sample placement rack and transports it to the microwave digestion module according to the identification result;

[0008] A microwave digestion module, which is provided with an adaptive digestion program for different samples and can efficiently digest the samples. The microwave digestion module includes a microwave generator, a digestion tank and a temperature - pressure control system. The temperature - pressure control system can monitor the temperature and pressure in the digestion tank in real time and adjust the power of the microwave generator according to a preset program to ensure the safety and efficiency of the digestion process;

[0009] An automatic separation and purification module, which is connected to the microwave digestion module and can automatically select a suitable separation and purification method according to the components of the digested sample, and uses a solid - phase extraction column or an ion - exchange resin column to separate and purify the digested sample;

[0010] A liquid scintillation spectrometer detection module, which is connected to the automatic separation and purification module and is used to detect strontium isotopes in the purified sample. The liquid scintillation spectrometer detection module includes a scintillator, a photomultiplier tube and a signal acquisition and processor, which can convert the signal generated by the decay of strontium isotopes in the sample into an electrical signal and perform precise acquisition and processing;

[0011] A data processing and control system, which is connected to the above - mentioned modules and is used to control the operation of each module, and uses an artificial intelligence algorithm to analyze and process the data of the liquid scintillation spectrometer detection module. It can automatically judge whether the content of strontium isotopes in the sample is within the normal range according to the detection data and generate a detection report;

[0012] A cleaning and maintenance module, which can automatically clean and maintain each module after the detection is completed to ensure the long - term stable operation of the system;

[0013] The modules of the system are connected by standardized interfaces, which is convenient for the assembly, disassembly and upgrade of the system.

[0014] The architecture and training method of the AI algorithm:

[0015] The data processing and control system of the present invention adopts the multi-layer perceptron (MLP) algorithm in the neural network architecture. This algorithm consists of an input layer, multiple hidden layers, and an output layer. The input layer receives the original detection data from the liquid scintillation spectrometer detection module, including information such as signal intensity and pulse frequency. The hidden layer performs non-linear transformation and feature extraction on the input data through a series of weight matrices and activation functions. The output layer outputs the judgment result of whether the strontium isotope content in the sample is normal and the relevant analysis data according to the processing results of the hidden layer.

[0016] The training method adopts the supervised learning method, and a large number of sample data with known strontium isotope content are used as the training set. These data include the liquid scintillation spectrometer detection data of different categories of samples (such as soil, ore, water samples, etc.) at different content levels and the corresponding actual strontium isotope content values. During the training process, the weights and biases of each layer in the neural network are continuously adjusted through the backpropagation algorithm to minimize the error between the prediction result and the actual value. For example, the mean square error (MSE) is used as the loss function, and the weights and biases are iteratively updated through the gradient descent method. After a large number of training rounds (such as 10,000 rounds), the neural network can accurately analyze and judge new detection data.

[0017] To verify its judgment accuracy, independent test set data is used for verification. The test set contains 200 sample data of different categories, and these samples do not participate in the training process. After testing, the judgment accuracy rate of this AI algorithm for whether the strontium isotope content in the sample is normal reaches more than 95%.

[0018] Temperature and pressure control parameters and dynamic adjustment logic:

[0019] In the microwave digestion module, the upper temperature limit varies according to different sample types. For general soil samples, the upper temperature limit is set at 200 °C; for ore samples, due to their more complex composition and greater digestion difficulty, the upper temperature limit can be set at 250 °C. In terms of the pressure threshold, for common samples, the upper pressure limit is set at 5 MPa.

[0020] The dynamic adjustment logic adopts the PID control algorithm. The PID controller calculates the power of the microwave generator that needs to be adjusted according to the deviation between the real-time monitored temperature and pressure values in the digestion tank and the preset values. The proportional (P) link outputs a control quantity according to the magnitude of the current deviation. The larger the deviation, the larger the control quantity. The integral (I) link integrates the deviation over a period of time in the past to eliminate the steady-state error of the system. The derivative (D) link outputs a control quantity according to the rate of change of the deviation, which is used to predict the change trend of the deviation and adjust the control quantity in advance. For example, when the temperature in the digestion tank is lower than the preset value, the PID controller calculates the power that needs to be increased for the microwave generator according to the deviation, and the increased amplitude is jointly determined by the P, I, and D links. As the temperature gradually approaches the preset value, the PID controller will gradually reduce the power adjustment amount to achieve precise control of the temperature and ensure that the digestion process is both efficient and safe.

[0021] Decision-making mechanism of the separation and purification module:

[0022] The automatic separation and purification module adopts a decision-making mechanism that combines a spectroscopic analysis sensor with database matching. After the sample digestion, first, the spectroscopic analysis sensor is used to detect in real time parameters such as the ion species, concentration, and pH value in the digested solution. The spectroscopic analysis sensor can emit light of a specific wavelength, and the ions in the solution will absorb or scatter this light. By detecting the change of the light, the components in the solution are analyzed. For example, atomic absorption spectroscopy technology is used to detect the concentration of metal ions in the solution.

[0023] A large number of the best separation and purification methods and related elution procedures corresponding to different component samples are pre-stored in the database. When the spectroscopic analysis sensor detects the parameters of the sample solution, the system will match these parameters with the data in the database. The matching algorithm adopts a similarity-based matching method, calculates the similarity between the detected parameters and each record in the database, and selects the solid-phase extraction column or ion-exchange resin column and the elution procedure corresponding to the record with the highest similarity. For example, if it is detected that the digested solution contains high-concentration interfering ions such as calcium and magnesium and the pH value is acidic, through database matching, the system will select an ion-exchange resin column with high adsorption capacity for calcium and magnesium ions in acidic solution and use the corresponding acidic eluent for elution to achieve the best separation and purification effect.

[0024] Preferably, the adaptive digestion program automatically adjusts the temperature, pressure, microwave power, and digestion time parameters during the digestion process based on the analysis of factors such as the material, composition, and expected digestion difficulty of different samples, so as to achieve efficient and safe digestion of various samples.

[0025] Preferably, the intelligent separation selection mechanism of the automatic separation and purification module is to detect the ion species, concentration, and pH parameters in the digested sample solution in real time, and use a preset algorithm model to match the most suitable solid-phase extraction column or ion-exchange resin column, as well as the corresponding elution procedure, to achieve the best separation and purification effect.

[0026] Preferably, the AI report generation function of the data processing and control system can not only judge whether the strontium isotope content in the sample is normal, but also conduct a traceability analysis of abnormal data, associate it with possible sample sources, digestion processes, and separation and purification links, and generate a test report containing detailed detection information, result analysis, and traceability suggestions.

[0027] Preferably, the standardized interface design follows unified size specifications, electrical connection standards, and communication protocols, ensuring that each module can be quickly and accurately docked during assembly, does not affect the performance of the module itself during disassembly, and is convenient for the subsequent upgrade and replacement of new modules.

[0028] Preferably, the system is also equipped with an emergency detection process. When detecting a sudden emergency (such as a serious over-standard of strontium isotope content in the sample or a failure of a key component of the system), it can automatically trigger an emergency response mechanism, quickly switch to a standby detection channel (if any), or adjust the detection parameters to preferentially obtain key data, and promptly issue an alarm to the operator, while recording all data during the emergency process.

[0029] Preferably, the cleaning and maintenance module adopts different cleaning methods and maintenance means according to the material and function characteristics of different modules. For example, the digestion tank of the microwave digestion module is cleaned by high-temperature and high-pressure steam combined with chemical reagents, and the robotic arm of the automatic sampling module is lubricated and its accuracy is calibrated to ensure the long-term stable operation of each module.

[0030] Preferably, the sample identification device of the automatic sampling module combines image recognition technology and radio frequency identification technology to improve the accuracy and speed of sample type and number identification, ensuring that the robotic arm can accurately grab and transport samples.

[0031] Preferably, the signal acquisition processor of the liquid scintillation spectrometer detection module uses low-noise and high-sensitivity electronic components, combined with digital filtering algorithms and signal amplification technologies, to improve the acquisition accuracy and anti-interference ability of strontium isotope decay signals.

[0032] Preferably, the data processing and control system also has a remote monitoring function. Operators can remotely connect to the system through the network, view the system operation status, detection data, and equipment parameter information in real time, and can remotely issue control commands to remotely operate and maintain the system.

[0033] The advantages of the present invention are as follows: High degree of automation: The sample recognition device, robotic arm, and sample placement rack of the automatic sample injection module work together to automatically identify, grasp, and transport various types of samples without manual intervention, greatly improving the detection efficiency and reducing human errors.

[0034] Efficient and safe digestion: The microwave digestion module sets multiple digestion programs for different samples. At the same time, the temperature and pressure control system monitors the temperature and pressure in the digestion tank in real time and adjusts the power of the microwave generator, which can not only ensure the efficient digestion of various samples but also ensure the safety and stability of the digestion process.

[0035] Intelligent separation and purification: The automatic separation and purification module uses solid-phase extraction columns or ion-exchange resin columns, which can automatically select the appropriate separation and purification method according to the components of the digested samples, improving the accuracy and effect of separation and purification.

[0036] Accurate and reliable detection: The liquid scintillation spectrometer detection module converts the signals generated by the decay of strontium isotopes in the sample into electrical signals through scintillators, photomultiplier tubes, and signal acquisition processors, and accurately collects and processes them, ensuring the accuracy of the detection results.

[0037] Intelligent and convenient data analysis: The data processing and control system uses artificial intelligence algorithms to automatically judge whether the content of strontium isotopes in the sample is within the normal range based on the detection data and generate a detection report, saving the time of manual analysis and improving the efficiency and accuracy of data processing.

[0038] Simple system maintenance: The cleaning and maintenance module automatically cleans and maintains each module after the detection is completed, extending the service life of the system and ensuring the long-term stable operation of the system.

[0039] Strong system scalability: The modules are connected by standardized interfaces, which is convenient for the assembly, disassembly, and upgrade of the system. The system configuration can be flexibly adjusted according to actual needs to adapt to different detection tasks and application scenarios. Detailed implementation manners

[0040] For the purposes of the following detailed description, it should be understood that the present invention may employ various alternative variations and step sequences, unless expressly specified to the contrary. Additionally, except where otherwise indicated in any operating instance or otherwise, all numbers expressing, for example, quantities of ingredients used in the specification and claims are to be understood as being modified in all instances by the term "about". Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that vary depending upon the desired properties sought to be obtained by the present invention. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in accordance with the number of reported significant digits and by applying ordinary rounding techniques.

[0041] While the numerical ranges and parameters setting forth the broad scope of the present invention are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. However, any numerical value inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements.

[0042] In addition, it should be understood that any numerical range recited herein is intended to include all sub-ranges subsumed therein. For example, a range of "1 to 10" is intended to include all sub-ranges between (and including) the recited minimum value of 1 and the recited maximum value of 10, that is, having a minimum value equal to or greater than 1 and a maximum value equal to or less than 10.

[0043] Example 1: Automatic Sampling and Digestion Process for Multi-Category Samples

[0044] Sample Preparation: Samples of different categories are placed on the sample placement rack, and the sample identification device identifies the sample type and number, such as identifying soil samples, ore samples, etc.

[0045] Automatic Sampling: The robotic arm grabs the corresponding sample from the sample placement rack according to the identification result of the sample identification device and transports it to the digestion tank of the microwave digestion module.

[0046] Microwave Digestion: The microwave digestion module calls a preset digestion program according to the sample type. The temperature and pressure control system monitors the temperature and pressure in the digestion tank in real time. For example, when digesting soil samples, when the temperature approaches the preset upper limit, the power of the microwave generator is automatically adjusted to ensure the safety and efficiency of the digestion process and fully digest the organic matter and minerals in the soil samples.

[0047] Example 2: Automatic Separation, Purification and Detection Process

[0048] Post-Digestion Treatment: The samples after microwave digestion are transported to the automatic separation and purification module, which automatically selects a solid-phase extraction column or an ion-exchange resin column for separation and purification according to the components of the digested samples. For example, for the digested samples containing multiple metal ions, an ion-exchange resin column is selected to remove interfering ions and purify strontium elements.

[0049] Liquid Scintillation Spectrometer Detection: The purified samples enter the liquid scintillation spectrometer detection module. The signals generated by the decay of strontium isotopes in the samples are converted into optical signals by the scintillator, and then the optical signals are converted into electrical signals by the photomultiplier tube. Finally, the signal acquisition and processing unit accurately acquires and processes these electrical signals to obtain relevant data of strontium isotopes in the samples.

[0050] Example 3: Data Processing and System Maintenance Process

[0051] Data processing: The liquid scintillation spectrometer detection module transmits the detection data to the data processing and control system. This system uses artificial intelligence algorithms to analyze and process the data. For example, based on a large amount of historical data and a preset normal range, the system automatically determines whether the content of strontium isotopes in the sample is normal and generates a detailed detection report, which includes sample information, detection data, analysis results, etc.

[0052] System maintenance: After the detection is completed, the cleaning and maintenance module starts, automatically cleaning the robotic arm of the automatic sampling module, the digestion tank of the microwave digestion module, the column of the automatic separation and purification module, and the relevant components of the liquid scintillation spectrometer detection module to remove residual samples and impurities. At the same time, it checks and maintains each module to ensure the long-term stable operation of the system.

[0053] Example 4: System scalability application process

[0054] Requirement analysis: When new sample types need to be detected or the detection accuracy needs to be improved, the system is evaluated to determine the modules that need to be added or upgraded.

[0055] Module replacement and upgrade: Since the modules in the system are connected by standardized interfaces, the existing modules can be easily disassembled and replaced with modules more suitable for the new requirements. For example, to improve the detection speed, replace it with a more efficient liquid scintillation spectrometer detection module; to adapt to new sample types, add specific digestion procedures or separation and purification methods.

[0056] System debugging and operation: After installing the new module, the system is debugged to ensure normal communication and collaborative work among the modules, enabling the system to adapt to new detection tasks and application scenarios.

[0057] Example 5: Multi-round digestion and purification process for complex samples

[0058] Initial digestion: For samples with complex compositions, such as industrial waste residue samples containing a large amount of insoluble substances, the automatic sampling module transports them to the microwave digestion module. The microwave digestion module first uses a mild digestion procedure for preliminary treatment, and the temperature and pressure control system strictly monitors the reaction process to prevent dangerous situations caused by excessive pressure.

[0059] Preliminary separation and secondary digestion: The sample after the initial digestion enters the automatic separation and purification module for preliminary solid-liquid separation and impurity removal. Then, the preliminarily treated sample is sent back to the microwave digestion module again. According to the residual components after the initial digestion, the digestion procedure is adjusted, and more targeted microwave power and time are used for secondary digestion to ensure the full release of strontium elements in the sample.

[0060] Deep purification and detection: The sample after secondary digestion enters the automatic separation and purification module again for deep purification. A more refined solid-phase extraction column or ion-exchange resin column is selected to further remove interfering substances. Finally, the purified sample enters the liquid scintillation spectrometer detection module for detection to obtain accurate strontium isotope data.

[0061] Example 6: Optimization of the detection process for batch samples

[0062] Preparation of batch samples: A large number of samples of the same type to be detected, such as soil samples collected from multiple soil monitoring points, are placed on the sample placement rack in an orderly manner. The sample identification device quickly scans and records the number and position information of each sample.

[0063] Continuous automatic sampling and digestion: The robotic arm grabs the samples in sequence according to the preset order and transports them to the microwave digestion module. The microwave digestion module adopts a parallel processing method to digest multiple samples simultaneously. The temperature and pressure control system monitors the temperature and pressure of each digestion tank in real time, and dynamically adjusts the power of the microwave generator according to the overall situation of the samples to ensure that all samples can be digested efficiently and safely.

[0064] Batch separation, purification and detection: The digested samples enter the automatic separation and purification module in batches. A suitable separation and purification method is uniformly selected according to the sample type, such as using an ion-exchange resin column for batch processing. The purified samples enter the liquid scintillation spectrometer detection module in sequence. The signal acquisition and processor quickly acquires and processes the data. The data processing and control system analyzes a large amount of detection data synchronously to generate a detection report for batch samples, greatly improving the detection efficiency.

[0065] Example 7: Application process for emergency detection scenarios

[0066] Receiving of emergency samples: In sudden environmental incidents or emergency detection tasks, such as the detection of water samples suspected of being radioactively contaminated, the samples are quickly received and placed on the sample placement rack. The sample identification device quickly identifies the sample type and the emergency level label.

[0067] Starting the rapid detection process: The robotic arm immediately grabs the sample to the microwave digestion module. The microwave digestion module calls the rapid digestion program for such samples, shortening the digestion time while ensuring the digestion effect. The automatic separation and purification module and the liquid scintillation spectrometer detection module also switch to the rapid detection mode, reducing the processing time of intermediate links.

[0068] Real-time data feedback and report generation: The liquid scintillation spectrometer detection module transmits the detection data to the data processing and control system in real time. The system quickly analyzes the data, judges whether the content of strontium isotopes in the sample is abnormal, and generates an emergency detection report in the first time to provide timely and accurate data support for emergency decision-making.

[0069] Comparison experiment data:

[0070] Detection speed: Select 100 samples of different categories and detect them using the rapid detection system of the present invention and traditional detection techniques respectively. Due to manual injection, a single digestion procedure, and manual data analysis in traditional detection techniques, it takes 48 hours to complete the detection of 100 samples. While the rapid detection system of the present invention, through the automatic injection module, adaptive digestion program, and data processing of artificial intelligence algorithms, only needs 8 hours to complete the detection of the same number of samples, and the detection speed is increased by 6 times.

[0071] Accuracy: Use the system of the present invention and traditional detection techniques to detect 20 standard samples with known strontium isotope contents. Due to problems such as the precision limitation of equipment components and the single separation and purification method in traditional detection techniques, the average relative error of the detection results is 10%. While the system of the present invention, through the optimized liquid scintillation spectrometer detection module and intelligent separation and purification module, the average relative error of the detection results is only 2%, and the accuracy has been significantly improved.

[0072] Safety: During the microwave digestion process, perform safety tests on the two techniques. Due to the lack of real-time temperature and pressure control in traditional digestion equipment, 5 safety accidents of digestion tank rupture occurred in 100 digestion experiments. While the microwave digestion module of the present invention, through real-time monitoring and adjustment by the temperature and pressure control system, no safety accidents occurred in 1000 digestion experiments, and the safety has been greatly guaranteed.

[0073] Compatibility case of the standardized interface:

[0074] In order to test the compatibility of the standardized interface, replace the original liquid scintillation spectrometer detection module in the system of the present invention with a new liquid scintillation spectrometer detection module produced by another manufacturer with higher detection accuracy. During the replacement process, since the modules are connected by standardized interfaces and follow unified size specifications, electrical connection standards, and communication protocols, the entire replacement process only takes 2 hours and no large-scale adjustment of other parts of the system is required. After the replacement is completed, perform performance tests on the system, including tests on indicators such as the detection accuracy and detection speed of known samples. The test results show that the system after replacing the module has a 10% improvement in detection accuracy compared to before, and the detection speed remains unchanged, proving that the standardized interface has good compatibility and can facilitate the replacement and upgrade of modules and improve the system performance.

[0075] As described above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A rapid detection system for strontium isotopes in a microwave digestion - liquid scintillation spectrometer for multi - category samples, characterized in that, Comprising: An automatic sampling module for automatically identifying, grasping, and transporting multi-category samples to the microwave digestion module. This module includes a sample identification device capable of identifying the sample type and number, and a robotic arm that grasps and transports the sample from the sample placement rack to the microwave digestion module according to the identification result; A microwave digestion module equipped with an adaptive digestion program for different samples, capable of efficiently digesting the samples. The microwave digestion module includes a microwave generator, a digestion tank, and a temperature and pressure control system. The temperature and pressure control system can monitor the temperature and pressure in the digestion tank in real time and adjust the power of the microwave generator according to a preset program to ensure the safe and efficient digestion process; An automatic separation and purification module connected to the microwave digestion module, capable of automatically selecting a suitable separation and purification method according to the components of the digested sample, and separating and purifying the digested sample using a solid-phase extraction column or an ion exchange resin column; A liquid scintillation spectrometer detection module connected to the automatic separation and purification module for detecting strontium isotopes in the purified sample. The liquid scintillation spectrometer detection module includes a scintillator, a photomultiplier tube, and a signal acquisition and processor, which can convert the signal generated by the decay of strontium isotopes in the sample into an electrical signal and perform precise acquisition and processing; A data processing and control system connected to the above-mentioned modules for controlling the operation of each module, and using an artificial intelligence algorithm to analyze and process the data of the liquid scintillation spectrometer detection module. It can automatically judge whether the content of strontium isotopes in the sample is within the normal range according to the detection data and generate a detection report; A cleaning and maintenance module capable of automatically cleaning and maintaining each module after the detection is completed to ensure the long-term stable operation of the system; The modules of the system are connected by standardized interfaces, which is convenient for the assembly, disassembly, and upgrade of the system.

2. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, wherein, The adaptive digestion program automatically adjusts the temperature, pressure, microwave power, and digestion time parameters during the digestion process based on the analysis of factors such as the material, composition, and expected digestion difficulty of different samples, so as to achieve efficient and safe digestion of various samples.

3. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, characterized in that, The intelligent separation selection mechanism of the automatic separation and purification module is to match the most suitable solid-phase extraction column or ion exchange resin column and the corresponding elution program by real-time detecting the ion species, concentration, and pH parameters in the digested sample solution using a preset algorithm model to achieve the best separation and purification effect.

4. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, wherein, The AI report generation function of the data processing and control system can not only judge whether the content of strontium isotopes in the sample is normal, but also conduct traceability analysis on abnormal data, associate it with possible sample sources, digestion processes, and separation and purification links, and generate a detection report containing detailed detection information, result analysis, and traceability suggestions.

5. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, characterized in that, The standardized interface design follows unified size specifications, electrical connection standards, and communication protocols, ensuring that each module can be quickly and accurately docked during assembly, does not affect the performance of the module itself during disassembly, and is convenient for the upgrade and replacement of subsequent new modules.

6. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, wherein, The system is also equipped with an emergency detection process. When a sudden emergency is detected (such as a serious over - standard of strontium isotope content in the sample or a failure of key components of the system), it can automatically trigger the emergency response mechanism, quickly switch to the standby detection channel (if any), or adjust the detection parameters to preferentially obtain key data, and send an alarm to the operator in a timely manner, while recording all data during the emergency process.

7. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, characterized in that, The cleaning and maintenance module adopts different cleaning methods and maintenance means according to the material and functional characteristics of different modules. For example, the digestion tank of the microwave digestion module is cleaned by high - temperature and high - pressure steam combined with chemical reagent cleaning, and the robotic arm of the automatic sampling module is lubricated and its precision is calibrated to ensure the long - term stable operation of each module.

8. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, wherein, The sample identification device of the automatic sampling module combines image recognition technology and radio frequency identification technology to improve the accuracy and speed of sample type and number identification, ensuring that the robotic arm can accurately grab and transport samples.

9. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, characterized in that, The signal acquisition processor of the liquid scintillation spectrometer detection module uses low - noise and high - sensitivity electronic components, combined with digital filtering algorithms and signal amplification techniques, to improve the acquisition accuracy and anti - interference ability of strontium isotope decay signals.

10. The microwave digestion - liquid scintillation spectrometer strontium isotope rapid detection system for multi - category samples according to claim 1, wherein, The data processing and control system also has a remote monitoring function. Operators can remotely connect to the system through the network, view the system operation status, detection data, and equipment parameter information in real time, and can remotely issue control commands to remotely operate and maintain the system.