A type based on 18 Early Warning Method and System for F-FDG Efficiency Calculation Model

Abstract

This invention relates to the field of computer processing technology, and more particularly to a method based on... 18 A method and system for early warning of F-FDG efficiency calculation models. The method includes the following steps: obtaining the corresponding fluoride ion yield influencing factors and 18 Factors affecting F-FDG synthesis; factors affecting fluoride ion yield and 18 The F-FDG composite impact factors are classified into primary and secondary impacts and their weights are determined to obtain the relative importance weights of each impact factor. Based on the relative importance weights of each impact factor, an efficiency calculation model is iteratively trained and efficiency is predicted to generate the current corresponding impact factor. 18 F-FDG production efficiency; based on preset primary and secondary early warning thresholds. 18 F-FDG production early warning, to generate corresponding 18 F-FDG production early warning and adjustment measures. This invention can comprehensively consider... 18 Accurately predict the corresponding influencing factors in the F-FDG production process. 18 F-FDG efficiency.

Description

Technical Field

[0001] This invention relates to the field of computer processing technology, and more particularly to a method based on... 18 Early warning method and system for F-FDG efficiency calculation model. Background Technology

[0002] Fluoride-deoxyglucose injection ( 18 Fibro-FDG (F-FDG), a radioactive tracer commonly used in positron emission tomography (PET) imaging, is widely applied in the medical field. 18 The production process of F-FDG typically requires efficient synthesis and quality control to ensure its effectiveness in clinical applications. Therefore, real-time monitoring and prediction are crucial. 18 F-FDG production efficiency and early warning of potential problems have become key factors in improving [product quality]. 18 The key to F-FDG production quality and efficiency is currently being studied. 18 Real-time monitoring of key parameters in the F-FDG production process (such as reaction temperature, reaction time, feed concentration, equipment performance, etc.), combined with 18 The calculation model for F-FDG production efficiency can assess efficiency fluctuations during the production process. However, traditional... 18 The F-FDG efficiency calculation method relies on process review and data traceability indicators, making it difficult to capture factors affecting production efficiency in real time during the production process. Furthermore, it lacks dynamic response analysis between various influencing factors, thus failing to achieve early warning effects. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a method based on... 18 A warning method and system for the F-FDG efficiency calculation model are proposed to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, a method based on 18 The early warning method for the F-FDG efficiency calculation model includes the following steps:

[0005] Step S1: By 18 F-FDG historical production process, obtaining factors affecting the yield of fluoride ions from cyclotron nuclear reactions, and 18 Factors affecting F-FDG synthesis include fluoride ion yield factors such as oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transport consumption in a closed target, proton beam target site, proton beam energy, proton beam current, and irradiation time. 18 Factors affecting F-FDG synthesis include precursor quantity during production preparation, reagent content, fluoride ion transport consumption, QMA loading, precursor purity, catalyst activity, reaction temperature, and reaction time. 18F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production;

[0006] Step S2: Factors affecting fluoride ion yield and 18 The primary and secondary influences of the F-FDG composite impact factor were classified to obtain... 18 The main and secondary influencing factors of F-FDG efficiency; 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights;

[0007] Step S3: Based on the corresponding impact factors 18 F-FDG relative importance weights 18 The efficiency calculation model is iteratively trained by analyzing the primary and secondary influencing factors corresponding to F-FDG efficiency to generate... 18 F-FDG efficiency calculation model;

[0008] Step S4: Get the current 18 The main and secondary influencing factors in the F-FDG production process are input into the trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction to generate the current corresponding 18 F-FDG efficiency; based on preset first-level and second-level warning thresholds, the current corresponding... 18 F-FDG efficiency 18 F-FDG production early warning, to generate corresponding 18 F-FDG production early warning adjustment measures.

[0009] Furthermore, step S1 includes the following steps:

[0010] Step S11: Use a mass spectrometer to obtain the abundance of oxygen-18 water produced by the nuclear reaction in the cyclotron, and use a flow sensor and a level sensor to monitor the flow rate and storage volume of oxygen-18 water in the target firing process in real time. Calculate the corresponding amount of oxygen-18 water used based on the integral of the flow rate and storage volume. Also, monitor each target loading point or position on the target loading track during the target firing process to obtain the amount of oxygen-18 water consumed in the sealed target.

[0011] Step S12: Use a cyclotron to monitor and obtain the number of single or double target sites corresponding to the proton beam during the target firing stage, so as to obtain the proton beam target sites; use a radiation monitoring sensor to accurately measure the energy, flux, and irradiation time of the proton beam during the target firing stage, so as to obtain the proton beam energy, proton beam flux, and irradiation time; use the corresponding oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transmission consumption in the closed target, proton beam target sites, proton beam energy, proton beam flux, and irradiation time as fluoride ion yield influencing factors;

[0012] Step S13: By 18 In the historical production process of F-FDG, the corresponding synthesis step uses a combination of capacitive level sensors and weighing sensors to accurately measure the volume and mass of the corresponding materials in the reaction tube to obtain the pre-production preparation volume; and uses a spectral analysis sensor to quickly and accurately detect the concentration and composition of the production reagents to obtain the reagent content, including trifluoromannose, anhydrous acetonitrile, K2.2.2 / K2CO3 rinsing solution and NaOH.

[0013] Step S14: Obtain the corresponding fluoride ion dose after the nuclear reaction and the fluoride ion dose after passing through the QMA column by obtaining the corresponding fluoride ion pipeline transport process in the synthesis step, so as to obtain the fluoride ion pipeline transport consumption and QMA full loading; obtain the precursor purity, catalyst activity, reaction temperature, reaction time, etc. in the synthesis step. 18 The number of F-FDG synthesis channels, including FDG1 and FDG2, was determined, and the corresponding automated synthesis efficiency was obtained through two dehydration processes and one nucleophilic reaction in the synthesis step. Simultaneously, the corresponding data were collected. 18 F-FDG yield; corresponding production preparation precursor quantities, reagent content, fluoride ion pipeline transport consumption, QMA full load, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production as 18 F-FDG synthesis influence factor.

[0014] Furthermore, the reaction tube mentioned in step S13 includes a QMA column, a long tC18 column, and a combined column, wherein the combined column includes an IC-H column, an Al2O3 column, and a tC18 column.

[0015] Furthermore, the automated synthesis efficiency obtained in step S14 through two dehydration processes and one nucleophilic reaction in the synthesis step includes:

[0016] The corresponding dehydration rate was obtained by performing two dehydration processes on the reaction reagents during the synthesis process, and the water content was obtained by using a desiccant to absorb water and distillation to remove water.

[0017] Based on the water removal rate, a statistical analysis of residual moisture was performed to obtain... 18 Residual moisture content before the F-FDG synthesis reaction;

[0018] Based on the residual moisture content, the corresponding nucleophilic reaction process in the synthesis step was analyzed. 18 F-FDG synthesis loss assessment to determine the effect of reagent moisture content on the synthesis loss. 18 The F-FDG synthesis reaction affects the loss;

[0019] By obtaining the corresponding nucleophilic reaction temperature and time during the nucleophilic reaction process in the synthesis step, and based on the nucleophilic reaction temperature and time, the nucleophilic reaction rate of the corresponding nucleophilic reaction process in the synthesis step is analyzed to obtain the corresponding nucleophilic reaction rate at the corresponding reaction temperature and time. 18 F-FDG synthesis reaction rate;

[0020] The corresponding raw material input amounts are obtained through a nucleophilic reaction process in the synthesis stage. 18 F-FDG synthesis yield, and based on 18 The effects of F-FDG synthesis reaction on losses and at corresponding reaction temperatures and durations 18 The reaction rate of F-FDG synthesis is affected by the amount of raw materials input and 18 F-FDG synthesis yield 18 Quantification of F-FDG synthesis rate to obtain 18 The automated synthesis efficiency corresponding to F-FDG.

[0021] Furthermore, obtaining the corresponding water content of the reaction reagents through two dehydration processes in the synthesis step includes the following steps:

[0022] The corresponding drying and water absorption reaction times and the distillation reaction times were obtained by performing two water removal processes in the synthesis step.

[0023] The amount of water absorbed during the drying process of the desiccant in the synthesis step was analyzed to obtain the amount of water absorbed by the reaction reagents during drying.

[0024] The amount of water migration in the distillation process of the corresponding reaction reagents was obtained by distilling and separating water in the synthesis process. Based on the drying water absorption reaction time and the distillation water reaction time, the probability statistics of water absorption during the drying process and the amount of water migration during the distillation process of the reaction reagents were statistically analyzed to obtain the water content of the corresponding reaction reagents.

[0025] Furthermore, step S2 includes the following steps:

[0026] Step S21: Factors affecting fluoride ion yield and18 The F-FDG composite impact factor performs pairwise correlation measurement calculations on each impact factor to quantify the correlation coefficients between each pair of impact factor combinations, and constructs the corresponding impact factor correlation matrix to generate... 18 F-FDG impact factor correlation matrix;

[0027] Step S22: Based on 18 The correlation matrix of F-FDG influence factors on the influence factor of fluoride ion yield and 18 An influence factor correlation grid was constructed for each influence factor within the F-FDG synthesis influence factor to integrate the fluoride ion yield influence factor and... 18 Each impact factor within the F-FDG composite impact factor is used as a node, and based on... 18 The correlation coefficient between any two pairs of influence factor combinations within the F-FDG influence factor correlation matrix determines the edges connecting them. If the correlation coefficient is greater than or equal to 0.85, the edges between their nodes are connected; if the correlation coefficient is less than 0.85, no action is taken, and the corresponding edges are generated. 18 F-FDG impact factor correlation network;

[0028] Step S23: For 18 The F-FDG influence factor correlation network was analyzed for node degree and betweenness centrality. The number of edges connected to a node was used as the degree of that node, and the betweenness centrality of the corresponding node was also analyzed to obtain the degree of each node. 18 The nodal degree and betweenness centrality of the F-FDG impact factor;

[0029] Step S24: Based on each 18 The influence factors of nodal degree and betweenness centrality on fluoride ion yield in the F-FDG influence factor and 18 The primary and secondary impacts of each impact factor within the F-FDG composite impact factor are classified, if corresponding 18 If the nodal degree of an F-FDG impact factor exceeds 5 or its betweenness centrality exceeds 8, it is determined as a major impact factor; otherwise, it is determined as a minor impact factor. 18 The main and secondary influencing factors of F-FDG efficiency;

[0030] Step S25: For 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights.

[0031] Furthermore, step S25 includes the following steps:

[0032] Step S251: By in18 F-FDG historical production process obtained corresponding 18 Historical production efficiency of F-FDG was used as the target layer.

[0033] Step S252: ... 18 The main influencing factors corresponding to F-FDG efficiency are used as the criterion layer, and... 18 The secondary influencing factors corresponding to F-FDG efficiency are used as the indicator layer; the target layer, criterion layer and indicator layer are constructed according to the hierarchical structure to build the corresponding expert evaluation system architecture;

[0034] Step S253: Experts score each impact factor by using the correlation coefficient between the criteria layer and the indicator layer within the expert evaluation system architecture, and construct a corresponding scoring judgment matrix based on the scoring results of each impact factor. The elements in the scoring judgment matrix represent the score ratio relationship between each impact factor.

[0035] Step S254: Obtain the corresponding maximum eigenvalue and its corresponding eigenvector through the scoring judgment matrix, and perform consistency check calculation based on the maximum eigenvalue and its corresponding eigenvector to calculate the corresponding consistency index and random consistency index, and calculate the ratio of the consistency index and the random consistency index to obtain the corresponding consistency ratio.

[0036] Step S255: If the consistency ratio is greater than or equal to 0.1, then the corresponding score is determined as the corresponding relative importance weight to obtain the corresponding weight for each influencing factor. 18 F-FDG relative importance weight; if the consistency ratio is less than 0.1, the process is returned to expert scoring until the scoring judgment matrix passes the consistency test.

[0037] Furthermore, step S3 includes the following steps:

[0038] Step S31: Based on the corresponding impact factors 18 F-FDG relative importance weights and combined with multiple linear regression to... 18 The efficiency calculation model is trained by considering the primary and secondary influencing factors corresponding to F-FDG efficiency, in order to... 18 The relative importance weights of F-FDG are calculated by weighting the primary and secondary influencing factors as inputs. 18 F-FDG efficiency is used as the output to generate 18 The F-FDG efficiency calculation initializes the model and outputs the corresponding predictions. 18 F-FDG efficiency;

[0039] Step S32: Predict 18 F-FDG efficiency compared to historical average 18A comparative analysis of F-FDG efficiency was conducted to obtain the deviation between the predicted efficiency and the historical average efficiency.

[0040] Step S33: Compare the deviation between the prediction efficiency and the historical average efficiency based on a preset deviation threshold of 1%. If the deviation between the prediction efficiency and the historical average efficiency is less than or equal to the deviation threshold of 1%, no optimization is performed on the model; if the deviation between the prediction efficiency and the historical average efficiency is greater than the deviation threshold of 1%, the model is re-optimized. 18 The corresponding F-FDG efficiency calculation initial model 18 The relative importance weights of F-FDG are determined using criteria weights, and the redefined weights are then... 18 F-FDG relative importance weight iterative optimization corresponding to 18 The initial model for F-FDG efficiency calculation is used to generate... 18 F-FDG efficiency calculation model.

[0041] Furthermore, step S4 includes the following steps:

[0042] Step S41: Obtain the current 18 The main and secondary influencing factors in the F-FDG production process;

[0043] Step S42: Set the current 18 The primary and secondary influencing factors in the F-FDG production process are input into a trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction, which compares the corresponding primary and secondary influencing factors with... 18 The weights of each influencing factor within the F-FDG efficiency calculation model are weighted to generate the current corresponding... 18 F-FDG efficiency;

[0044] Step S43: Preset the corresponding first-level warning threshold of 85% and the second-level warning threshold of 70%;

[0045] Step S44: Based on the preset first-level warning threshold of 85% and the second-level warning threshold of 70%, adjust the current corresponding... 18 F-FDG efficiency 18 F-FDG production warning, if the current corresponding 18 When the efficiency of F-FDG is below 85% of the first-level warning threshold, a corresponding minor warning signal is issued to indicate the presence of an impact. 18 Potential factors affecting F-FDG efficiency should be identified, and production personnel should be alerted to conduct further inspections and analyses promptly; if the current corresponding 18 When the efficiency of F-FDG falls below 70% of the secondary warning threshold, a corresponding severe warning signal will be issued to indicate the occurrence of [a problem / issue].18 A warning was issued regarding a significant drop in F-FDG production, urging production personnel to immediately halt production to prevent the production of defective products, investigate whether the pipeline transmission system was leaking or blocked, and, if necessary, activate the pre-set emergency plan for radioactive material transmission pipeline leaks, and simultaneously activate... 18 During the F-FDG production process, backup targets and backup synthesis modules are prioritized for use. When the radiation dose in the target chamber drops to an acceptable level for personnel, accelerator inspection, maintenance, and troubleshooting are conducted to generate the corresponding... 18 F-FDG production early warning adjustment measures.

[0046] Furthermore, the present invention also provides a method based on 18 The early warning system based on the F-FDG efficiency calculation model is used to perform the above-mentioned functions. 18 The early warning method of the F-FDG efficiency calculation model, which is based on 18 The early warning system for the F-FDG efficiency calculation model includes:

[0047] 18 The F-FDG impact factor analysis module is used to analyze the impact factor of the F-FDG impact factor. 18 F-FDG historical production process, obtaining factors affecting the yield of fluoride ions from cyclotron nuclear reactions, and 18 Factors affecting F-FDG synthesis include fluoride ion yield factors such as oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transport consumption in a closed target, proton beam target site, proton beam energy, proton beam current, and irradiation time. 18 Factors affecting F-FDG synthesis include precursor quantity during production preparation, reagent content, fluoride ion transport consumption, QMA loading, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production;

[0048] The impact factor weight determination module is used to determine the weight of the impact factor based on... 18 The influence of historical F-FDG production efficiency on fluoride ion yield and 18 The primary and secondary influences of the F-FDG composite impact factor were classified to obtain... 18 The main and secondary influencing factors of F-FDG efficiency; 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights;

[0049] The efficiency calculation model training module is used to train models based on the corresponding efficiency factors. 18 F-FDG relative importance weights18 The efficiency calculation model is iteratively trained by analyzing the primary and secondary influencing factors corresponding to F-FDG efficiency to generate... 18 F-FDG efficiency calculation model;

[0050] 18 The F-FDG production early warning module is used to obtain current production information. 18 The main and secondary influencing factors in the F-FDG production process are input into the trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction to generate the current corresponding 18 F-FDG efficiency; based on preset first-level and second-level warning thresholds, the current corresponding... 18 F-FDG efficiency 18 F-FDG production early warning, to generate corresponding 18 F-FDG production early warning adjustment measures.

[0051] The beneficial effects of this invention are:

[0052] 1. The present invention is based on 18 The early warning method for the F-FDG efficiency calculation model, compared with the prior art, has the following advantages: 18 During F-FDG production, by analyzing factors affecting fluoride ion yield, such as target material (oxygen-18 water) abundance, target material (oxygen-18 water) dosage, target material (oxygen-18 water) transport consumption in the sealed target, proton beam target site, proton beam energy, proton beam current, and irradiation time, we can understand the role of fluoride ion yield in the oxygen-18 water nuclear reaction. 18 The interaction between O and the proton beam, and the key factors affecting fluoride ion yield, are studied to facilitate precise control. 18 The reaction conditions in the F-FDG production process provide data support, which helps to optimize the efficiency of the fluorination reaction. Meanwhile, 18 Factors affecting F-FDG synthesis include precursor quantity during production preparation, reagent content, fluoride ion transport consumption, QMA loading, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production is an influencing factor 18 Key factors affecting the final yield and purity of F-FDG: Understanding these influencing factors can help optimize [the process]. 18 The amount of raw materials and reagents used in the F-FDG synthesis process, the selection of the synthesis reaction pathway, and the level of automation in the production process enable real-time monitoring of factors affecting production efficiency. Secondly, by analyzing the factors influencing fluoride ion yield and...18 Classifying the primary and secondary influences of F-FDG composite influencing factors helps to systematically identify the impacts on... 18 This study identifies the factors that have the greatest impact on F-FDG efficiency and provides key areas of focus for further optimization of the production process. It effectively filters out the most influential factors from a large pool of influencing factors. 18 The most influential primary and secondary factors in F-FDG efficiency are identified. Furthermore, determining the criterion weights is a crucial step in quantifying the relative importance of each influencing factor. By establishing a reasonable weight allocation model, the impact of each influencing factor on efficiency can be accurately calculated. 18 The contribution of F-FDG efficiency is determined, providing a theoretical basis for subsequent model optimization. During weight determination, the correlation between influencing factors can be assessed using methods such as analytic hierarchy process (AHP) and expert scoring, thereby accurately determining the weight of each factor. This allows for better dynamic response analysis of the influencing factors. Then, based on each influencing factor... 18 Using the relative importance weights of F-FDG for iterative training of the efficiency calculation model, an accurate model can be constructed. 18 The F-FDG efficiency prediction model, through repeated optimization and model training of various influencing factors, combines theoretical research with practical production, thereby improving the accuracy and practicality of predictions. This step facilitates the comprehensive analysis of influencing factors using historical production data through machine learning, data mining, and other techniques, uncovering the impact of these factors. 18 The underlying patterns of F-FDG efficiency are explored through continuous optimization and model adjustment, gradually improving the stability and reliability of the efficiency calculation model. The trained efficiency calculation model not only provides... 18 The optimization of the F-FDG production process provides theoretical support and can effectively guide production decisions in actual production, thereby improving... 18 F-FDG production efficiency. Finally, by training... 18 The F-FDG efficiency calculation model, when applied to actual production processes, can monitor and predict current efficiency in real time. 18 The efficiency of F-FDG production, in order to improve the current 18 When the primary and secondary influencing factors in the F-FDG production process are input into the trained model, the model can provide corresponding... 18 The F-FDG efficiency prediction value provides a quantitative basis for adjusting the production process. Furthermore, based on the set primary and secondary early warning thresholds, the model can... 18When F-FDG production efficiency deviates from the normal range, it automatically issues tiered warning signals. A level one warning alerts potential risks in the production process, while a level two warning helps production personnel take more specific countermeasures, such as adjusting reaction conditions or changing raw materials. This mechanism effectively reduces production risks and avoids resource waste and time delays caused by low production efficiency. Simultaneously, through real-time... 18 F-FDG production early warning and adjustment measures can react immediately and adjust production strategies, thereby improving efficiency. 18 The corresponding early warning effect during the F-FDG production process.

[0053] 2. The present invention is based on 18 The early warning system of the F-FDG efficiency calculation model is composed of... 18 The F-FDG impact factor analysis module, impact factor weight determination module, and efficiency calculation model training module are included. 18 The F-FDG production early warning module is composed of components capable of realizing any production early warning system described in this invention. 18 The early warning method of the F-FDG efficiency calculation model is used to jointly implement the operation between computer programs running on various modules based on... 18 The early warning method of the F-FDG efficiency calculation model relies on the collaborative internal structure of the system, which significantly reduces repetitive work and manpower input, enabling it to quickly and effectively provide more accurate and efficient early warnings based on... 18 The early warning process of the F-FDG efficiency calculation model simplifies the process based on... 18 Operational procedures of the early warning system based on the F-FDG efficiency calculation model. Attached Figure Description

[0054] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0055] Figure 1 This invention is based on 18 A flowchart illustrating the steps of the early warning method for the F-FDG efficiency calculation model;

[0056] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0057] Figure 3 for Figure 1 A detailed flowchart of step S2. Detailed Implementation

[0058] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0059] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0060] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0061] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method based on 18 The early warning method for the F-FDG efficiency calculation model includes the following steps:

[0062] Step S1: By 18 F-FDG historical production process, obtaining factors affecting the yield of fluoride ions from cyclotron nuclear reactions, and 18 Factors affecting F-FDG synthesis include fluoride ion yield factors such as oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transport consumption in a closed target, proton beam target site, proton beam energy, proton beam current, and irradiation time. 18 Factors affecting F-FDG synthesis include precursor quantity during production preparation, reagent content, fluoride ion transport consumption, QMA loading, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production;

[0063] Step S2: Factors affecting fluoride ion yield and 18The F-FDG synthesis influencing factors were divided into primary and secondary influencing factors to obtain the primary and secondary influencing factors corresponding to the 18F-FDG efficiency; 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights;

[0064] Step S3: Based on the corresponding impact factors 18 F-FDG relative importance weights 18 The efficiency calculation model is iteratively trained by analyzing the primary and secondary influencing factors corresponding to F-FDG efficiency to generate... 18 F-FDG efficiency calculation model;

[0065] Step S4: Get the current 18 The main and secondary influencing factors in the F-FDG production process are input into the trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction to generate the current corresponding 18 F-FDG efficiency; based on preset first-level and second-level warning thresholds, the current corresponding... 18 F-FDG efficiency 18 F-FDG production early warning, to generate corresponding 18 F-FDG production early warning adjustment measures.

[0066] In the embodiments of this invention, please refer to Figure 1 As shown, this invention is based on 18 A flowchart illustrating the steps of the early warning method based on the F-FDG efficiency calculation model is shown in this example. 18 The early warning method of the F-FDG efficiency calculation model includes the following steps:

[0067] Step S1: By 18 F-FDG historical production process, obtaining factors affecting the yield of fluoride ions from cyclotron nuclear reactions, and 18 Factors affecting F-FDG synthesis include fluoride ion yield factors such as oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transport consumption in a closed target, proton beam target site, proton beam energy, proton beam current, and irradiation time. 18 Factors affecting F-FDG synthesis include precursor quantity during production preparation, reagent content, fluoride ion transport consumption, QMA loading, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production;

[0068] In an embodiment of the present invention, by 18 Relevant influencing factors were obtained during the historical production process of F-FDG. Regarding the fluoride ion yield, the abundance of oxygen-18 water was determined using mass spectrometry. Mass spectrometer parameters were set to specific ranges to accurately identify the isotopic peaks of oxygen-18 water, such as setting the mass number scan range to 18-20 amu and the resolution to 1000. This allowed for the acquisition of oxygen-18 water abundance values. The amount of oxygen-18 water used was calculated using a combination of flow and level sensors. The flow sensor, installed on the delivery pipeline, measures the flow rate in real time using the principle of electromagnetic induction. The level sensor, installed in the storage tank, measures the liquid level based on the principle of hydrostatic pressure. The total flow rate is obtained by integrating the velocity data, and the usage is calculated by combining it with the liquid level change. The transmission consumption in the closed target is measured by installing high-precision mass flow meters at key positions on the target transfer track, with an accuracy of 0.1 grams. The proton beam target position is determined by the target position switching signal obtained from the cyclotron control system, making it clear whether it is a single target position or a dual target position. The proton beam energy is measured using an energy detector, such as a magnetic spectrometer; the proton beam current is measured using an ionization chamber detector, based on the characteristic that radiation ionizes the gas to generate current; the irradiation time is timed by a high-precision timer from the start of proton beam irradiation to the end of irradiation. 18 Factors affecting F-FDG synthesis: Pre-production volume was determined by measuring the material volume in the reaction tube using a capacitive level sensor and measuring the mass using a weighing sensor. Reagent content was measured using a spectroscopic sensor, such as a UV-Vis spectrophotometer, according to Lambert-Beer's law, to determine the concentration and composition of production reagents (e.g., trifluoromannose, anhydrous acetonitrile). Fluoride ion transfer consumption and QMA full-load capacity were measured at the beginning of the pipeline and after passing through the QMA column using radioactivity meters; the difference between these measurements was the transfer consumption. The activity of the QMA column at saturation was the full-load capacity. Precursor purity was determined by comparing the chromatogram with that of a standard using high-performance liquid chromatography (HPLC). Catalyst activity was determined through specific catalytic reaction experiments, measuring the catalytic rate under specified conditions. Reaction temperature was monitored in real-time using a thermocouple temperature sensor inserted into the reaction vessel. Reaction time was timed from start to finish using a high-precision timer. 18 The number of F-FDG synthesis channels can be determined by checking the operating status of the synthesis equipment, while the automated synthesis efficiency is calculated using the appropriate method. 18 F-FDG yield was directly measured using a radioactivity meter. These specific tools and methods were used to comprehensively obtain the factors influencing fluoride ion yield. 18 F-FDG synthesis influence factor.

[0069] Step S2: Factors affecting fluoride ion yield and 18 The primary and secondary influences of the F-FDG composite impact factor were classified to obtain... 18The main and secondary influencing factors of F-FDG efficiency; 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights;

[0070] In this embodiment of the invention, the factors influencing fluoride ion yield are analyzed using Python's pandas and numpy libraries. 18 The F-FDG synthesis influencing factors are classified into primary and secondary influences. First, the Pearson correlation coefficients between each pairwise combination of influencing factors are calculated using the DataFrame.corr() method. For example, the correlation coefficient between the number of targets and the corresponding production preparation column volume is 0.7, and the correlation coefficient between the automated synthesis rate and the target water usage is 0.85. A correlation coefficient threshold is set, such as 0.85. Influencing factors with correlation coefficients greater than or equal to 0.85 are identified as primary influencing factors, and those less than 0.85 are identified as secondary influencing factors. After determining the primary and secondary influencing factors, the Analytic Hierarchy Process (AHP) is used to determine the criterion weights. A hierarchical structure model is constructed using yaahp software, with the primary influencing factors as the criterion layer and the secondary influencing factors as the indicator layer, with the objective being... 18 F-FDG efficiency involves organizing experts in relevant fields (such as chemical process experts and production engineers) to score the relative importance of each influencing factor, constructing a judgment matrix, and the software automatically calculating the largest eigenvalue and its corresponding eigenvector. A consistency test is then performed; if the consistency ratio is less than 0.1, the test is passed. The eigenvectors are then normalized to obtain the values ​​corresponding to each influencing factor. 18 F-FDG relative importance weights.

[0071] Step S3: Based on the corresponding impact factors 18 F-FDG relative importance weights 18 The efficiency calculation model is iteratively trained by analyzing the primary and secondary influencing factors corresponding to F-FDG efficiency to generate... 18 F-FDG efficiency calculation model;

[0072] In this embodiment of the invention, the scikit-learn library of Python is used to base the analysis on the corresponding impact factors. 18 F-FDG relative importance weights 18 The efficiency calculation model for F-FDG efficiency is iteratively trained by considering the primary and secondary influencing factors. The primary and secondary influencing factors from historical production data are organized into a feature matrix, with each row representing the influencing factor data for a single production process. 18Using F-FDG efficiency as a label, a multiple linear regression model object is created using the `LinearRegression()` function. The weight vector and feature matrix are weighted and calculated, and model training begins. For example, if the prediction error is large after the first training round, the weights are adjusted (e.g., by re-determining weights using AHP based on the training results), and training is repeated. This iterative training continues until the model's prediction error meets the requirements (e.g., the mean squared error (i.e., the bias) is less than a preset value), and the model is generated. 18 The F-FDG efficiency calculation model is then used to save the model as a Python pickle file for later use.

[0073] Step S4: Get the current 18 The main and secondary influencing factors in the F-FDG production process are input into the trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction to generate the current corresponding 18 F-FDG efficiency; based on preset first-level and second-level warning thresholds, the current corresponding... 18 F-FDG efficiency 18 F-FDG production early warning, to generate corresponding 18 F-FDG production early warning adjustment measures.

[0074] In this embodiment of the invention, by using the current 18 During F-FDG production, the corresponding primary and secondary influencing factors are acquired through a real-time data acquisition system. For example, the number of shots is obtained by real-time monitoring of the firing stage using sensors, and the automation synthesis rate is obtained from the control system of the automated synthesis equipment. This real-time data is then organized into feature vectors in the same format as those used during model training, and the trained model is loaded using the pickle.load() function. 18 The F-FDG efficiency calculation model inputs feature vectors into the model for prediction, generating the current corresponding... 18 F-FDG efficiency, assuming a preset first-level warning threshold of 85% and a second-level warning threshold of 70% (determined based on historical production data and process requirements), will be... 18 F-FDG Efficiency and Historical Standards 18 The percentage of F-FDG efficiency (obtained from historical production data) is calculated by comparison. If the current... 18 When the F-FDG efficiency percentage is below 85% but above 70%, a text message is sent to production personnel via the production early warning system's SMS platform to alert them of the potential impact. 18Potential factors affecting F-FDG efficiency: If it falls below 70%, not only will a text message be sent, but a warning message will also be displayed on the production workshop screen, and the automated control system will be activated to suspend production equipment. Simultaneously, maintenance, process parameter adjustments, and replacement of substandard reagents will be initiated. 18 F-FDG production early warning and adjustment measures are implemented to ensure stable production and product quality.

[0075] Furthermore, step S1 includes the following steps:

[0076] Step S11: Use a mass spectrometer to obtain the abundance of oxygen-18 water produced by the nuclear reaction in the cyclotron, and use a flow sensor and a level sensor to monitor the flow rate and storage volume of oxygen-18 water in the target firing process in real time. Calculate the corresponding amount of oxygen-18 water used based on the integral of the flow rate and storage volume. Also, monitor each target loading point or position on the target loading track during the target firing process to obtain the amount of oxygen-18 water consumed in the sealed target.

[0077] Step S12: Use a cyclotron to monitor and obtain the number of single or double target sites corresponding to the proton beam during the target firing stage, so as to obtain the proton beam target sites; use a radiation monitoring sensor to accurately measure the energy, flux, and irradiation time of the proton beam during the target firing stage, so as to obtain the proton beam energy, proton beam flux, and irradiation time; use the corresponding oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transmission consumption in the closed target, proton beam target sites, proton beam energy, proton beam flux, and irradiation time as fluoride ion yield influencing factors;

[0078] Step S13: By 18 In the historical production process of F-FDG, the corresponding synthesis step uses a combination of capacitive level sensors and weighing sensors to accurately measure the volume and mass of the corresponding materials in the reaction tube to obtain the pre-production preparation volume; and uses a spectral analysis sensor to quickly and accurately detect the concentration and composition of the production reagents to obtain the reagent content, including trifluoromannose, anhydrous acetonitrile, K2.2.2 / K2CO3 rinsing solution and NaOH.

[0079] Step S14: Obtain the corresponding fluoride ion dose after the nuclear reaction and the fluoride ion dose after passing through the QMA column by obtaining the corresponding fluoride ion pipeline transport process in the synthesis step, so as to obtain the fluoride ion pipeline transport consumption and QMA full loading; obtain the precursor purity, catalyst activity, reaction temperature, reaction time, etc. in the synthesis step. 18 The number of F-FDG synthesis channels, including FDG1 and FDG2, was determined, and the corresponding automated synthesis efficiency was obtained through two dehydration processes and one nucleophilic reaction in the synthesis step. Simultaneously, the corresponding data were collected. 18F-FDG yield; corresponding production preparation precursor quantities, reagent content, fluoride ion pipeline transport consumption, QMA full load, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production as 18 F-FDG synthesis influence factor.

[0080] As an embodiment of the present invention, reference Figure 2 As shown, Figure 1 A detailed flowchart of step S1 is shown below. In this embodiment, step S1 includes the following steps:

[0081] Step S11: Use a mass spectrometer to obtain the abundance of oxygen-18 water produced by the nuclear reaction in the cyclotron, and use a flow sensor and a level sensor to monitor the flow rate and storage volume of oxygen-18 water in the target firing process in real time. Calculate the corresponding amount of oxygen-18 water used based on the integral of the flow rate and storage volume. Also, monitor each target loading point or position on the target loading track during the target firing process to obtain the amount of oxygen-18 water consumed in the sealed target.

[0082] In this embodiment of the invention, when acquiring data related to oxygen-18 water generated by the cyclotron nuclear reaction, a mass spectrometer is connected to the nuclear reaction product collection device. The scanning parameters of the mass spectrometer are set to accurately detect the isotopic abundance of oxygen-18 water. For example, the scanning mass range is set to 18-20 amu, and the resolution is adjusted to 1000 to clearly distinguish the mass spectral peaks of oxygen-18 water from other impurities, thereby accurately obtaining the abundance of oxygen-18 water. A flow sensor is installed on the oxygen-18 water delivery pipeline in the target-shooting stage. Its working principle is based on electromagnetic induction or ultrasonic measurement to measure the water flow rate in real time. A level sensor is installed in the oxygen-18 water storage tank. Inside, a hydrostatic level gauge is used to calculate the liquid level by measuring the liquid pressure, thereby obtaining the storage capacity. The flow rate data from the flow sensor is integrated with time. For example, within 10 minutes, the flow rate data is integrated to obtain a total flow rate of 5 ml. On the target transfer track, a high-precision mass flow meter is installed at each target transfer stop or position, with an accuracy of 0.1 g. When oxygen-18 water enters the sealed target through the target transfer track, the mass flow meter monitors its mass change in real time, thereby obtaining the transfer consumption in the sealed target. For example, in a certain target transfer process, the mass before entering the sealed target is 10 g, and after the transfer it becomes 9.8 g, and the transfer consumption is 0.2 g.

[0083] Step S12: Use a cyclotron to monitor and obtain the number of single or double target sites corresponding to the proton beam during the target firing stage, so as to obtain the proton beam target sites; use a radiation monitoring sensor to accurately measure the energy, flux, and irradiation time of the proton beam during the target firing stage, so as to obtain the proton beam energy, proton beam flux, and irradiation time; use the corresponding oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transmission consumption in the closed target, proton beam target sites, proton beam energy, proton beam flux, and irradiation time as fluoride ion yield influencing factors;

[0084] In this embodiment of the invention, the number of single or dual target sites corresponding to the proton beam during the target firing phase is obtained by utilizing the monitoring system built into the cyclotron accelerator. This monitoring system determines the number of target sites by identifying target switching signals inside the accelerator. If the signal indicates that only one target site is currently active, the proton beam is a single target site; if activation signals for two targets are detected, it is a dual target site. The radiation monitoring sensor uses an ionization chamber detector, whose working principle is based on the characteristic that radiation ionizes gas to generate current, accurately measuring the energy, flow rate, and irradiation duration of the proton beam during the target firing phase. The detector is placed on the proton beam transmission path. The proton beam current is calculated by measuring the magnitude of the ionization current. For example, if the measured current is 10 microamps, the proton beam current is calculated to be 100 nanoamps. The irradiation time is recorded using a high-precision timer. For example, if the irradiation time is 30 minutes, the proton beam energy is measured by an energy detector. For example, if the measured value is 16 MeV, the energy is measured to be 16 MeV. At the same time, the previously measured oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transmission consumption in the sealed target, proton beam target position, proton beam energy, proton beam current, and irradiation time are compiled and summarized as a fluoride ion yield influencing factor.

[0085] Step S13: By 18 In the historical production process of F-FDG, the corresponding synthesis step uses a combination of capacitive level sensors and weighing sensors to accurately measure the volume and mass of the corresponding materials in the reaction tube to obtain the pre-production preparation volume; and uses a spectral analysis sensor to quickly and accurately detect the concentration and composition of the production reagents to obtain the reagent content, including trifluoromannose, anhydrous acetonitrile, K2.2.2 / K2CO3 rinsing solution and NaOH.

[0086] In an embodiment of the present invention, by 18In the synthesis stage of the historical F-FDG production process, capacitive level sensors were used to measure the material inside the reaction tubes. These sensors were installed on the QMA column, the long tC18 column, and the combined columns (including IC-H column, AL2O3 column, and tC18 column). By detecting changes in capacitance, the volume of material inside the column was accurately measured, and data was recorded every 15 seconds. At the same time, Sartorius weighing sensors were installed at the bottom of each reaction tube to measure the mass of the material using gravity sensing, and data was recorded every 20 seconds. The volume and mass data were combined to obtain the pre-production volume. For example, in the reaction tube, a capacitive level sensor measures a material volume of 200 ml, and a weighing sensor measures a material mass of 220 g. This determines the column preparation volume for production. A spectral analysis sensor is used to detect the production reagents. By analyzing the absorption of light at specific wavelengths, the concentration and composition of the reagents can be quickly and accurately determined. For instance, for 1 ml of trifluoromannose or anhydrous acetonitrile, the spectral analysis sensor determines the concentration of trifluoromannose to be 0.5 mol / L and the concentration of anhydrous acetonitrile to be 0.5 mol / L by analyzing the position and intensity of the absorption peak at a specific wavelength. The same method is used to detect the content of reagents such as 1.5 ml of K2.2.2 / K2CO3 eluent and NaOH. These data are then summarized as the reagent content.

[0087] Step S14: Obtain the corresponding fluoride ion dose after the nuclear reaction and the fluoride ion dose after passing through the QMA column by obtaining the corresponding fluoride ion pipeline transport process in the synthesis step, so as to obtain the fluoride ion pipeline transport consumption and QMA full loading; obtain the precursor purity, catalyst activity, reaction temperature, reaction time, etc. in the synthesis step. 18 The number of F-FDG synthesis channels, including FDG1 and FDG2, was determined, and the corresponding automated synthesis efficiency was obtained through two dehydration processes and one nucleophilic reaction in the synthesis step. Simultaneously, the corresponding data were collected. 18 F-FDG yield; corresponding production preparation precursor quantities, reagent content, fluoride ion pipeline transport consumption, QMA full load, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production as 18 F-FDG synthesis influence factor.

[0088] In this embodiment of the invention, during the fluoride ion transport process in the synthesis stage, radioactivity meters are installed at the beginning of the pipeline and after passing through the QMA column. Their working principle is based on a scintillator detecting the flashes of light generated by radioactive particles, and a photomultiplier tube converting the flashes into electrical signals for measurement. This allows the acquisition of the fluoride ion dose after the nuclear reaction and the fluoride ion dose after passing through the QMA column. The difference between the two yields the fluoride ion transport consumption. For example, if the initial dose is 100 MBq, the dose after passing through the QMA column is 90 MBq, and the transport consumption is 10 MBq. The QMA full load is obtained by measuring the radioactivity after the QMA column is saturated with fluoride ions. Furthermore, by obtaining corresponding reaction parameters during the synthesis process, the purity of the precursor is detected using high-performance liquid chromatography (HPLC). The precursor sample is injected into the HPLC system, and the purity is determined by comparing it with the chromatogram of the standard. Catalyst activity is determined through specific catalytic reaction experiments, such as measuring the catalytic rate of a certain reaction under certain conditions. The reaction temperature is measured using a thermocouple temperature sensor installed on the wall of the reaction vessel to display the temperature in real time, such as a reaction temperature of 60°C. The reaction time is recorded by a timer, and the duration from the start to the end of the reaction is the reaction time, such as a reaction time of 20 minutes. In addition, it is also necessary to determine... 18 The number of synthesis channels for F-FDG synthesis, such as FDG1 and FDG2 channels, is obtained by reading relevant configuration parameters from the synthesis equipment control system. During the two dehydration processes and one nucleophilic reaction in the synthesis stage, the execution time and completion status of each step are recorded by the automated control system. The corresponding reaction impact losses, reaction rates, and feedstock input amounts are calculated. 18 F-FDG synthesis yield, achieving automated synthesis rate, for example, this synthesis rate can also be achieved by transferring 1000 mCi of F-18 ions, through chemical reactions, purification, separation in the synthesizer, and finally producing... 18 F-FDG, with an activity meter reading of 500 mCi, indicates a synthesis efficiency of 50%. Meanwhile, in... 18 A high-precision weighing sensor is installed on the F-FDG product collection container to measure the collected data in real time. 18 F-FDG quality, according to 18 The density of F-FDG converted to volume yields... 18 F-FDG production, along with previously measured parameters such as precursor quantity, reagent content, fluoride ion pipeline transport consumption, QMA full load, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production summary as follows 18 F-FDG composite impact factor, used to analyze the impact 18 Various factors in the synthesis of F-FDG.

[0089] Furthermore, the reaction tube mentioned in step S13 includes a QMA column, a long tC18 column, and a combined column, wherein the combined column includes an IC-H column, an Al2O3 column, and a tC18 column.

[0090] Furthermore, the automated synthesis rate obtained in step S14 through two dehydration processes and one nucleophilic reaction in the synthesis step includes:

[0091] The corresponding dehydration rate was obtained by performing two dehydration processes on the reaction reagents during the synthesis process, and the water content was obtained by using a desiccant to absorb water and distillation to remove water.

[0092] In this embodiment of the invention, the dehydration rate is obtained by performing two dehydration processes on the reaction reagents during the synthesis stage. First, a desiccant water absorption method is used, with molecular sieves selected as the desiccant. The desiccant is added to the reaction reagents at a certain ratio (e.g., 1:10, molecular sieve mass: reaction reagent volume), and thoroughly stirred and mixed for 30 minutes to allow the desiccant to fully contact and adsorb water. The water content of the reaction reagents before and after adsorption is measured using a Karl Fischer moisture analyzer. For example, if the water content before adsorption is 5%, the water content after adsorption is 3%. Then, distillation is performed to remove water. Water: Place the reaction reagent in a distillation apparatus, set a suitable distillation temperature (e.g., boiling point of the reaction reagent + 10℃) and vacuum degree (e.g., 0.08MPa), and perform distillation for 30 minutes. Measure the water content after distillation again using a Karl Fischer moisture analyzer. Assuming the water content drops to 1%, the water removal rate is calculated as: (initial water content - final water content) ÷ initial water content × 100%, i.e., (5% - 1%) ÷ 5% × 100% = 80%. Through this operation, the corresponding water removal rate is finally obtained, and the water content at each stage is determined.

[0093] Preferably, a statistical analysis of residual moisture is performed on the moisture content based on the water removal rate to obtain... 18 Residual moisture content before the F-FDG synthesis reaction;

[0094] In this embodiment of the invention, residual moisture statistical analysis is performed on the moisture content based on the water removal rate to obtain... 18 Given the residual moisture before the F-FDG synthesis reaction, with a dehydration rate of 80%, an initial total reagent volume of 100 mL, and an initial moisture content of 5%, the initial moisture content is 100 × 5% = 5 mL. After two dehydration cycles, the remaining moisture content is the initial moisture content × (1 - dehydration rate), i.e., 5 × (1 - 80%) = 1 mL. Converting this 1 mL moisture content to its percentage in the total current reagent volume, we get 1 ÷ (100 - 5 + 1) × 100% ≈ 1.02% (because the total reagent volume changes slightly during dehydration). This percentage is... 18The residual moisture content before the F-FDG synthesis reaction is accurately calculated using this method based on the dehydration rate and initial moisture content.

[0095] Preferably, the nucleophilic reaction process corresponding to the synthesis step is analyzed based on the residual moisture content. 18 F-FDG synthesis loss assessment to determine the effect of reagent moisture content on the synthesis loss. 18 The F-FDG synthesis reaction affects the loss;

[0096] In this embodiment of the invention, the nucleophilic reaction process corresponding to the synthesis step is analyzed based on the residual moisture content. 18 F-FDG synthesis loss assessment was conducted to determine the effect of reagent moisture content on the synthesis loss. 18 The F-FDG synthesis reaction affects losses. A reaction loss model was established based on historical experimental data. This model shows a positive correlation between residual moisture and synthesis reaction losses. It is assumed that for every 0.1% increase in residual moisture... 18 The F-FDG synthesis yield loss is 5%. Given that the current residual moisture content is 1.02%, compared to the ideal anhydrous state (assuming an ideal loss of 0%), the residual moisture content has increased by 1.02%. According to the model calculation, the impact of the synthesis reaction on the loss is (1.02% ÷ 0.1%) × 5% = 51%. This method of comparing the model with historical data assesses the impact of the reagent moisture content on the synthesis yield. 18 The impact of the F-FDG synthesis reaction on the loss.

[0097] Preferably, the nucleophilic reaction temperature and time are obtained during the nucleophilic reaction process in the synthesis step, and the nucleophilic reaction rate is analyzed for each nucleophilic reaction process in the synthesis step based on the nucleophilic reaction temperature and time, so as to obtain the corresponding nucleophilic reaction rate at the corresponding reaction temperature and time. 18 F-FDG synthesis reaction rate;

[0098] In this embodiment of the invention, during a nucleophilic reaction in the synthesis process, a high-precision thermocouple temperature sensor is used to measure the nucleophilic reaction temperature in real time. The probe of the thermocouple temperature sensor is inserted into the reaction system to ensure accurate temperature measurement, and temperature data is recorded every 10 seconds. Simultaneously, a timer in the automated control system records the nucleophilic reaction time, starting from the beginning of the reaction and stopping at its end. For example, in a certain nucleophilic reaction, the thermocouple temperature sensor measures the reaction temperature to be 80°C at the beginning of the reaction, gradually increasing to 90°C and remaining stable. The total reaction time is 120 minutes. Using chemical kinetic principles, based on the reaction temperature and time data, and in conjunction with the Arrhenius equation... (where k is the reaction rate constant, A is the pre-exponential factor, and E is the exponential factor)a (where R is the activation energy of the reaction, R is the gas constant, and T is the absolute temperature). Calculate the activation energy at the corresponding reaction temperature and duration. 18 The reaction rate of F-FDG synthesis was determined experimentally or by consulting relevant literature to obtain the pre-exponential factor A and the activation energy E of the nucleophilic reaction. a The measured temperature data is converted into absolute temperature and substituted into the equation to calculate the reaction rate constant k at different times, thus obtaining... 18 The reaction rate for F-FDG synthesis is expressed by the equation v = k × [reactant concentration], where k is the rate constant at the corresponding reaction temperature and duration. For example, if the initial reactant concentration is 0.2 mol / L, and calculations show that the rate constant k = 0.0029 min at 90 °C and 120 min is calculated. -1 This indicates that at this temperature, the reaction rate is a certain proportion of reactants reacting per minute, thus yielding the corresponding reaction rate at a reaction temperature of 90℃ and a duration of 120 minutes. 18 The F-FDG synthesis reaction rate was 0.0029 min. -1 ×0.2mol / L=0.00058mol / (L·min), finally obtaining the corresponding values ​​at the corresponding reaction temperature and time. 18 F-FDG synthesis reaction rate.

[0099] Preferably, the corresponding raw material input amount and... are obtained through a corresponding nucleophilic reaction process in the synthesis step. 18 F-FDG synthesis yield, and based on 18 The effects of F-FDG synthesis reaction on losses and at corresponding reaction temperatures and durations 18 The reaction rate of F-FDG synthesis is affected by the amount of raw materials input and 18 F-FDG synthesis yield 18 Quantification of F-FDG synthesis rate to obtain 18 The automated synthesis efficiency corresponding to F-FDG.

[0100] In this embodiment of the invention, the amount of raw materials input and... are obtained during a single nucleophilic reaction in the synthesis process. 18 F-FDG synthesis yield, and based on 18 The effects of F-FDG synthesis reaction on losses and at corresponding reaction temperatures and durations 18 The reaction rate of F-FDG synthesis is affected by the amount of raw materials input and 18 F-FDG synthesis yield 18 Quantification of F-FDG synthesis rate: It is known that 1000 mCi of F-18 ions were transported as the raw material input, resulting in the final product... 18The activity of F-FDG measured by an activity meter was 500 mci. For example, under ideal conditions with no loss, the corresponding synthesis reaction loss is 0%, and the corresponding synthesis reaction rate at the given temperature and time is 0.00058 mol / (L·min). Assuming the theoretical yield calculated based on the synthesis reaction rate and reaction time under ideal conditions (no loss) is 1000 mci, then the actual synthesis rate is: actual yield ÷ (theoretical yield × (1 - synthesis reaction loss)) × 100%, i.e., 500 ÷ (1000 × (1 - 0%)) × 100% = 50%. The final result is... 18 The automated synthesis efficiency for F-FDG is 50% (in mci).

[0101] Furthermore, the corresponding water removal process is obtained through two water removal processes in the synthesis stage. 18 The process of F-FDG synthesis to achieve water removal includes the following steps:

[0102] The corresponding drying and water absorption reaction times and the distillation reaction times were obtained by performing two water removal processes in the synthesis step.

[0103] In this embodiment of the invention, during the two dehydration processes in the synthesis stage, for the desiccant water absorption process, when the desiccant is put into use, the timer in the automated control system is started to start timing. When it is confirmed that the desiccant is saturated with water and no longer has a significant water absorption effect, the timing is stopped, and this time is recorded as the drying water absorption reaction time. For example, starting from the timer when the desiccant is put into the reaction system, after 30 minutes, by observing the color change of the desiccant (assuming that the color change of the desiccant indicates water saturation) and using a Karl Fischer moisture analyzer to detect that the moisture content of the reaction system no longer decreases, it is determined that the desiccant water absorption is complete. At this time, the drying water absorption reaction time is recorded as 30 minutes. For the distillation and separation of water process, when the distillation device is started, the timer in the automated control system is also started to start timing. When the amount of water distilled reaches the expected value, or when the moisture content at the outlet is monitored by the online moisture sensor and stabilized at the target low value, the timing is stopped, and the distillation water reaction time is obtained. For example, after the distillation device is started, after 45 minutes, the online moisture sensor shows that the moisture content at the outlet is stable at 2%, reaching the expected low moisture content, and the distillation water reaction time is recorded as 45 minutes.

[0104] Preferably, the desiccant water absorption process at the synthesis stage is analyzed to obtain the water absorption amount. 18 F-FDG drying water absorption;

[0105] In this embodiment of the invention, a high-precision Karl Fischer moisture analyzer is used to detect the moisture content of the desiccant before and after water absorption during the desiccant's water absorption process, thereby analyzing the moisture content. 18The water absorption capacity of F-FDG desiccant is calculated as follows: Before use, a desiccant sample of mass m1 grams is taken and its initial moisture content is measured using a Karl Fischer moisture analyzer. The initial moisture content is w1%, which is then calculated as m1 × w1% grams. After the desiccant has absorbed water, another sample of the same mass m1 grams is taken and its moisture content is measured. The current moisture content is w2%, which is then calculated as m1 × w2% grams. The final moisture content is obtained by calculating m1 × (w2% - w1%). 18 For F-FDG desiccant, assuming a 100g sample has an initial moisture content w1% = 0.1% and a post-absorption moisture content w2% = 5%, then... 18 The dry water absorption of F-FDG is 100 × (5% - 0.1%) = 4.9 grams, and the final result is... 18 F-FDG drying water absorption.

[0106] Preferably, the corresponding water is obtained through a distillation process at the synthesis stage. 18 The migration of water during F-FDG distillation was analyzed, and the corresponding values ​​for the two water removal processes were compared based on the drying and distillation reaction times. 18 F-FDG drying water absorption and 18 The migration rate of water during F-FDG distillation was statistically analyzed to determine the probability of water removal during synthesis. 18 F-FDG synthesis dehydration probability.

[0107] In this embodiment of the invention, the mass of distilled water flowing out is measured by a mass flow meter installed at the outlet of the distillation apparatus during the water separation process, thereby obtaining the water content. 18 The amount of water migration during F-FDG distillation, for example, during the distillation process, the mass flow meter records that the mass of water flowing out from the start of distillation to the end of distillation is 50 grams, i.e. 18 The migration of water by F-FDG distillation is 50 grams. Meanwhile, based on the drying and water absorption reaction time t1 (e.g., 30 minutes) and the distillation water reaction time t2 (e.g., 45 minutes), combined with... 18 F-FDG drying water absorption (m) 吸 (e.g., 4.9 grams) and 18 F-FDG distilled water migration m 蒸 (e.g., 50 grams) To calculate the probability of water removal through synthesis, first calculate the total theoretical water removal amount m. 总理论 Assuming the calculated amount based on the reaction formula and chemical principles is 100 grams, the actual amount of water removed is m. 实际 =m 吸 +m 蒸 = 4.9 + 50 = 54.9 grams, total reaction time 30 + 45 = 75 minutes, probability of dehydration during synthesis Assuming the theoretical total reaction time is 90 minutes, then P = 54.9 / 100 × 75 / 90 × 100% ≈ 45.75%, which gives the corresponding probability of water removal during 18F-FDG synthesis.

[0108] Furthermore, step S2 includes the following steps:

[0109] Step S21: Factors affecting fluoride ion yield and 18 The F-FDG composite impact factor performs pairwise correlation measurement calculations on each impact factor to quantify the correlation coefficients between each pair of impact factor combinations, and constructs the corresponding impact factor correlation matrix to generate... 18 F-FDG impact factor correlation matrix;

[0110] Step S22: Based on 18 The correlation matrix of F-FDG influence factors on the influence factor of fluoride ion yield and 18 An influence factor correlation grid was constructed for each influence factor within the F-FDG synthesis influence factor to integrate the fluoride ion yield influence factor and... 18 Each impact factor within the F-FDG composite impact factor is used as a node, and based on... 18 The correlation coefficient between any two pairs of influence factor combinations within the F-FDG influence factor correlation matrix determines the edges connecting them. If the correlation coefficient is greater than or equal to 0.85, the edges between their nodes are connected; if the correlation coefficient is less than 0.85, no action is taken, and the corresponding edges are generated. 18 F-FDG impact factor correlation network;

[0111] Step S23: For 18 The F-FDG influence factor correlation network was analyzed for node degree and betweenness centrality. The number of edges connected to a node was used as the degree of that node, and the betweenness centrality of the corresponding node was also analyzed to obtain the degree of each node. 18 The nodal degree and betweenness centrality of the F-FDG impact factor;

[0112] Step S24: Based on each 18 The influence factors of nodal degree and betweenness centrality on fluoride ion yield in the F-FDG influence factor and 18 The primary and secondary impacts of each impact factor within the F-FDG composite impact factor are classified, if corresponding 18 If the nodal degree of an F-FDG impact factor exceeds 5 or its betweenness centrality exceeds 8, it is determined as a major impact factor; otherwise, it is determined as a minor impact factor. 18 The main and secondary influencing factors of F-FDG efficiency;

[0113] Step S25: For 18The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights.

[0114] As an embodiment of the present invention, reference Figure 3 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0115] Step S21: Factors affecting fluoride ion yield and 18 The F-FDG composite impact factor performs pairwise correlation measurement calculations on each impact factor to quantify the correlation coefficients between each pair of impact factor combinations, and constructs the corresponding impact factor correlation matrix to generate... 18 F-FDG impact factor correlation matrix;

[0116] In this embodiment of the invention, the factors influencing fluoride ion yield are analyzed using Python's pandas and numpy libraries. 18 The influencing factors in F-FDG synthesis are combined pairwise for correlation measurement. Assuming the fluoride ion yield influencing factor is stored in a pandas DataFrame object `fluoride_factors_df` containing columns such as target number and target water abundance, and the 18F-FDG synthesis influencing factor is stored in `18F-FDG_synthesis_factors_df` containing columns such as production preparation column quantity and reagent content, the two DataFrames are merged into a new DataFrame `all_factors_df`. The Pearson correlation coefficients between all factors are calculated using the `all_factors_df.corr()` method, which automatically performs correlation measurement for every pair of factors. For example, the correlation coefficient between target number and production preparation column quantity is calculated to be 0.6, and the correlation coefficient between target water abundance and reagent content is 0.3. These correlation coefficients are then organized into a two-dimensional matrix to construct the corresponding influencing factor correlation matrix, ultimately generating... 18 The F-FDG impact factor correlation matrix consists of rows and columns corresponding to each impact factor, and matrix elements representing the correlation coefficients between pairs of impact factor combinations.

[0117] Step S22: Based on 18 The correlation matrix of F-FDG influence factors on the influence factor of fluoride ion yield and 18 An influence factor correlation grid was constructed for each influence factor within the F-FDG synthesis influence factor to integrate the fluoride ion yield influence factor and... 18Each impact factor within the F-FDG composite impact factor is used as a node, and based on... 18 The correlation coefficient between any two pairs of influence factor combinations within the F-FDG influence factor correlation matrix determines the edges connecting them. If the correlation coefficient is greater than or equal to 0.85, the edges between their nodes are connected; if the correlation coefficient is less than 0.85, no action is taken, and the corresponding edges are generated. 18 F-FDG impact factor correlation network;

[0118] In this embodiment of the invention, the NetworkX library of Python is used as a basis. 18 To construct an impact factor correlation grid using the F-FDG impact factor correlation matrix, first, create an empty undirected graph object G = nx.Graph() to correlate the fluoride ion yield impact factor and... 18 Each impact factor within the F-FDG composite impact factor is added as a node to the graph. The specific operation is, for example, `G.add_nodes_from(all_factors_df.columns)`, which iterates through the graph. 18 The F-FDG influence factor correlation matrix checks if each correlation coefficient is greater than or equal to 0.85. If the correlation coefficient is greater than or equal to 0.85, such as a correlation coefficient of 0.9 between the number of targets and a certain 18F-FDG synthesis influence factor (assumed to be the automated synthesis rate), then the edge corresponding to these two nodes is connected in the graph using the `G.add_edge('number of targets', 'automated synthesis rate')` method. If the correlation coefficient is less than 0.85, such as a correlation coefficient of 0.5 between target water abundance and production preparation column quantity, then no processing is performed. After judging and processing all factor combinations, the corresponding edges are connected to generate the graph. 18 The F-FDG impact factor correlation network visually demonstrates the strong correlations between various impact factors.

[0119] Step S23: For 18 The F-FDG influence factor correlation network was analyzed for node degree and betweenness centrality. The number of edges connected to a node was used as the degree of that node, and the betweenness centrality of the corresponding node was also analyzed to obtain the degree of each node. 18 The nodal degree and betweenness centrality of the F-FDG impact factor;

[0120] In this embodiment of the invention, by continuing to use the NetworkX library... 18The F-FDG impact factor association network was analyzed for node degree and betweenness centrality. For node degree analysis, the `nx.degree(G)` method was used, which returns a dictionary containing each node and its corresponding degree (i.e., the number of connected edges). For example, `degree_dict = dict(nx.degree(G))` yields a degree of 3 for the node representing the number of hits, meaning it is connected to the other three impact factor nodes via edges. For betweenness centrality analysis, the `nx.betweenness_centrality(G)` method was used, which calculates the betweenness centrality of each node. For example, `betweenness_dict = nx.betweenness_centrality(G)` yields a betweenness centrality of 9 for a given impact factor node (assumed to be the automated synthesis rate). (In actual calculations, this may vary depending on the network configuration.) Through these calculations, the betweenness centrality of each impact factor node was finally obtained. 18 The nodal degree and betweenness centrality corresponding to the F-FDG impact factor.

[0121] Step S24: Based on each 18 The influence factors of nodal degree and betweenness centrality on fluoride ion yield in the F-FDG influence factor and 18 The primary and secondary impacts of each impact factor within the F-FDG composite impact factor are classified, if corresponding 18 If the nodal degree of an F-FDG impact factor exceeds 5 or its betweenness centrality exceeds 8, it is determined as a major impact factor; otherwise, it is determined as a minor impact factor. 18 The main and secondary influencing factors of F-FDG efficiency;

[0122] In embodiments of the present invention, by according to previously obtained various 18 The influence factors of nodal degree and betweenness centrality on fluoride ion yield in the F-FDG influence factor and 18 The F-FDG composite impact factors are categorized into primary and secondary impact factors by traversing a dictionary of nodal degrees and betweenness centrality. For each impact factor nodal, the nodal degree is determined to be greater than 5 or the betweenness centrality greater than 8. For example, if the nodal degree of the target number node is 6, which exceeds 5, then the target number node is identified as a primary impact factor. Similarly, if the betweenness centrality of an impact factor node (assuming a certain reaction tube quantity) is 5, which is less than 8, and the nodal degree is 3, which is less than 5, then that reaction tube quantity is identified as a secondary impact factor. This process is repeated for all impact factor nodes to obtain... 18 Identify the primary and secondary influencing factors of F-FDG efficiency to clarify which factors affect it. 18 The impact of F-FDG efficiency is even more critical.

[0123] Step S25: For 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights.

[0124] In this embodiment of the invention, the Analytic Hierarchy Process (AHP) is used to determine... 18 The criterion weights for the primary and secondary influencing factors of F-FDG efficiency are determined as follows: First, a judgment matrix is ​​constructed. For primary influencing factors, assuming they include the number of shots fired and the automated synthesis rate, the relative importance of these factors is compared pairwise based on expert experience or actual production data. For example, the number of shots fired is slightly more important than the automated synthesis rate, and the corresponding element in the judgment matrix is ​​set to 3 (using a 1-9 scale, where 1 indicates equal importance and 9 indicates absolute importance). After constructing the judgment matrix, the weight vector is calculated using the eigenvector method. By calculating the largest eigenvalue of the judgment matrix and its corresponding eigenvector, and normalizing the eigenvector, the weights corresponding to each primary influencing factor are obtained. For secondary influencing factors, the judgment matrix is ​​constructed and the weights are calculated in the same way, ultimately yielding the weights corresponding to each influencing factor. 18 F-FDG relative importance weights, these weights can be used for subsequent analysis. 18 Optimization of the F-FDG efficiency calculation model and formulation of early warning methods.

[0125] Furthermore, step S25 includes the following steps:

[0126] Step S251: By in 18 F-FDG historical production process obtained corresponding 18 Historical production efficiency of F-FDG was used as the target layer.

[0127] In an embodiment of the present invention, by 18 In the F-FDG historical production data storage system, queries can be made to retrieve the data corresponding to each production process within a past period (such as the past year). 18 Data such as F-FDG production output and production time are used to calculate the output using the formula "F-FDG production output and production time". 18 F-FDG historical production efficiency = 18 The calculation is obtained by dividing the total F-FDG production by the total number of production cycles. 18 Historical average production efficiency of F-FDG, for example, a total of 174,000 mCi produced in the past year. 18 For F-FDG, the total production time is 500 hours. 18 The historical production efficiency of F-FDG is 174000÷500=348mci / hour. This efficiency value is used as the target layer for subsequent correlation analysis with influencing factors to determine the degree of influence of each factor on production efficiency.

[0128] Step S252: ... 18 The main influencing factors corresponding to F-FDG efficiency are used as the criterion layer, and... 18 The secondary influencing factors corresponding to F-FDG efficiency are used as the indicator layer; the target layer, criterion layer and indicator layer are constructed according to the hierarchical structure to build the corresponding expert evaluation system architecture;

[0129] In this embodiment of the invention, by relying on previously obtained... 18 The primary influencing factors of F-FDG efficiency (such as the number of targets and the rate of automated synthesis) are used as the criterion layer, and the secondary influencing factors (such as the volume of a certain reaction tube and the content of a certain component in the reagent) are used as the indicator layer. An expert evaluation system architecture is constructed according to a hierarchical structure using professional project management tools (such as Microsoft Project) or online analytic hierarchy process (AHP) tools (such as YaAHP). In the tool, the target layer is created first, and input... 18 The F-FDG historical production efficiency descriptions and values ​​are then used to add a criterion layer node under the target layer, adding each major influencing factor as a node. Next, indicator layer nodes are added under the criterion layer nodes, linking the corresponding minor influencing factors with each major influencing factor to form a complete hierarchical structure system, providing a clear framework for expert scoring.

[0130] Step S253: Experts score each impact factor by using the correlation coefficient between the criteria layer and the indicator layer within the expert evaluation system architecture, and construct a corresponding scoring judgment matrix based on the scoring results of each impact factor. The elements in the scoring judgment matrix represent the score ratio relationship between each impact factor.

[0131] In this embodiment of the invention, a team of experts in relevant fields (such as nuclear engineers and process researchers) scores the correlation coefficients between each influencing factor in the criterion layer and indicator layer within the expert evaluation system architecture. A detailed scoring table is prepared, listing all combinations of influencing factors in the criterion and indicator layers. For each group of influencing factors, experts base their assessments on their own experience and understanding of the relevant factors. 18 A thorough understanding of the F-FDG production process, scored using a 1-9 scale. For example, regarding the influencing factors of the number of targets and the automated synthesis rate, if experts believe that the number of targets significantly impacts... 18The impact of F-FDG production efficiency is slightly more important than the automation synthesis rate. Fill in 3 in the corresponding position in the scoring table. After collecting the scoring results of all experts, organize them into a scoring judgment matrix. For example, assuming that there are 3 major influencing factors in the criterion layer and 5 minor influencing factors in the indicator layer, the scoring judgment matrix is ​​a (3+5)×(3+5) matrix. The matrix elements represent the score ratio relationship between each influencing factor. For example, the element in the i-th row and j-th column represents the importance score of the i-th influencing factor relative to the j-th influencing factor.

[0132] Step S254: Obtain the corresponding maximum eigenvalue and its corresponding eigenvector through the scoring judgment matrix, and perform consistency check calculation based on the maximum eigenvalue and its corresponding eigenvector to calculate the corresponding consistency index and random consistency index, and calculate the ratio of the consistency index and the random consistency index to obtain the corresponding consistency ratio.

[0133] In this embodiment of the invention, the scoring judgment matrix is ​​calculated using Python's NumPy library. The eigenvalues ​​and eigenvectors of the scoring judgment matrix are calculated using the `numpy.linalg.eig()` function. The largest eigenvalue and its corresponding eigenvector are extracted. For example, assuming the calculated largest eigenvalue is 8.5, the corresponding eigenvector is [0.3, 0.2, 0.1, 0.2, 0.1, 0.05, 0.05, 0.0]. Then, the consistency index (CI) is calculated using the formula "CI = (largest eigenvalue - matrix order) ÷ (matrix order - 1)". Assuming the matrix order is 8, then CI = (8.5 - 8) ÷ (8 - 1) ≈ 0.071. The random consistency index (RI) can be obtained by consulting a pre-defined random consistency index table. For an 8th order matrix, the RI value is assumed to be 1.41. The consistency index CI is then compared with the random consistency index RI to obtain the consistency ratio (CR), i.e., CR = CI ÷ RI ≈ 0.071 ÷ 1.41 ≈ 0.05. Finally, the corresponding consistency ratio is obtained.

[0134] Step S255: If the consistency ratio is greater than or equal to 0.1, then the corresponding score is determined as the corresponding relative importance weight to obtain the corresponding weight for each influencing factor. 18 F-FDG relative importance weight; if the consistency ratio is less than 0.1, the process is returned to expert scoring until the scoring judgment matrix passes the consistency test.

[0135] In this embodiment of the invention, the calculated consistency ratio is compared with 0.1. If the consistency ratio is greater than or equal to 0.1 (e.g., the previously calculated consistency ratio is 0.05, which is less than 0.1), the scoring result of the scoring judgment matrix is ​​discarded, experts are reorganized to score, and the new scoring results are reorganized into a scoring judgment matrix. The calculation process of step S254 is repeated until the consistency ratio is greater than or equal to 0.1. When the consistency ratio meets the requirements, the feature vector corresponding to the scoring judgment matrix at this time is normalized to obtain the corresponding feature vectors of each influence factor. Weights, for example, normalizing the feature vector [0.3,0.2,0.1,0.2,0.1,0.05,0.05,0.0] yields [0.3÷1.0,0.2÷1.0,0.1÷1.0,0.2÷1.0,0.1÷1.0,0.05÷1.0,0.05÷1.0,0.0÷1.0] = [0.3,0.2,0.1,0.2,0.1,0.05,0.05,0.0]. These weight values ​​are the 18F-FDG relative importance weights for each influencing factor, which can be used for subsequent analysis. 18 Optimization of the F-FDG efficiency calculation model and formulation of early warning methods.

[0136] Furthermore, step S3 includes the following steps:

[0137] Step S31: Based on the corresponding impact factors 18 F-FDG relative importance weights and combined with multiple linear regression to... 18 The efficiency calculation model is trained by considering the primary and secondary influencing factors corresponding to F-FDG efficiency, in order to... 18 The relative importance weights of F-FDG are calculated by weighting the primary and secondary influencing factors as inputs. 18 F-FDG efficiency is used as the output to generate 18 The F-FDG efficiency calculation initializes the model and outputs the corresponding predictions. 18 F-FDG efficiency;

[0138] In this embodiment of the invention, multiple linear regression is performed using Python's scikit-learn library to... 18 The efficiency calculation model is trained using the primary and secondary influencing factors corresponding to F-FDG efficiency, and the previously obtained influencing factors are used to calculate the efficiency. 18The relative importance weights of F-FDG are organized into a weight vector, for example, a weight vector of [0.3, 0.2, 0.1, 0.2, 0.1, 0.05, 0.05, 0.0]. The data of primary influencing factors (such as the number of targets, automated synthesis rate, etc.) and secondary influencing factors (such as the volume of a reaction tube, the content of a component in the reagent, etc.) are organized into a feature matrix. Each row represents data from one production process, and each column corresponds to an influencing factor. For example, a row of data in the feature matrix might be [2 targets, 80% automated synthesis rate, 1.5 ml anhydrous acetonitrile, 5 g / L trifluoromannose content, etc.]. A multiple linear regression model object is created using the `LinearRegression()` function, and the weights of each influencing factor in the weight vector are weighted and calculated in conjunction with the corresponding influencing factors in the feature matrix. 18 F-FDG efficiency is used as an output label, for example, in the prediction obtained through model calculation. 18 The F-FDG efficiency is 1200 mci, generating... 18 The F-FDG efficiency calculation initializes the model and outputs the corresponding predictions. 18 F-FDG efficiency.

[0139] Step S32: Predict 18 F-FDG Efficiency and Historical Standards 18 A comparative analysis of F-FDG efficiency was conducted to obtain the deviation between the predicted efficiency and the standard efficiency.

[0140] In this embodiment of the invention, by means of... 18 Extracting historical standards from the F-FDG historical production data storage system 18 F-FDG efficiency data, which are data that have been deemed to meet standards in past production processes. 18 F-FDG production efficiency value, assuming historical standards 18 The F-FDG efficiency is 1000 mci, which will convert the previously output predictions. 18 F-FDG efficiency (e.g., 1200 mci) and historical standards 18 A comparative analysis of F-FDG efficiency was conducted, and the deviation between the predicted efficiency and the standard efficiency was calculated. The formula is: "Deviation = |Predicted Efficiency||Standard ... 18 F-FDG Efficiency - Historical Standards 18 F-FDG efficiency | ÷ historical standard 18 "F-FDG efficiency × 100%" In this example, the deviation = |1200-1000|÷2×100% = 10%, which gives the deviation value between the predicted efficiency and the standard efficiency, and is used to determine whether the model needs to be optimized.

[0141] Step S33: Compare the deviation between the predicted efficiency and the standard efficiency based on a preset deviation threshold of 1%. If the deviation between the predicted efficiency and the standard efficiency is less than or equal to the deviation threshold of 1%, no optimization is performed on the model; if the deviation between the predicted efficiency and the standard efficiency is greater than the deviation threshold of 1%, the model is re-optimized. 18 The corresponding F-FDG efficiency calculation initial model 18 The relative importance weights of F-FDG are determined using criteria weights, and the redefined weights are then... 18 F-FDG relative importance weight iterative optimization corresponding to 18 The initial model for F-FDG efficiency calculation is used to generate... 18 F-FDG efficiency calculation model.

[0142] In this embodiment of the invention, the deviation between the predicted efficiency and the standard efficiency (e.g., 10%) is compared with a preset deviation threshold of 1%. Since 10% is greater than 1%, it is necessary to re-evaluate. 18 The corresponding F-FDG efficiency calculation initial model 18 The relative importance weights of F-FDG are determined based on the criteria weights. Experts in relevant fields (such as nuclear engineers and process researchers) are reorganized and, following step S25, re-score the influence factors of the criterion and indicator layers. A scoring judgment matrix is ​​constructed, the largest eigenvalue and its corresponding eigenvector are calculated, and consistency checks are performed to obtain the newly determined weights. 18 The F-FDG relative importance weights are applied to the multiple linear regression model, and the redefined weights are used to determine the relative importance weights. 18 The F-FDG efficiency calculation initially optimizes the model iteratively. For example, the new weight vector is [0.25, 0.22, 0.12, 0.2, 0.1, 0.06, 0.04, 0.01]. Weighted calculations and model training are then performed again to generate a new... 18 F-FDG efficiency calculation model to improve the model's accuracy. 18 The accuracy of F-FDG efficiency prediction ultimately generates 18 F-FDG efficiency calculation model.

[0143] Furthermore, step S4 includes the following steps:

[0144] Step S41: Obtain the current 18 The main and secondary influencing factors in the F-FDG production process;

[0145] In this embodiment of the invention, by using the current 18During F-FDG production, various sensors installed on the production equipment and the automated control system collect data in real time to obtain the corresponding primary and secondary influencing factors. For primary influencing factors, such as the number of targets, mechanical sensors with position coding functions monitor the use of single and double targets in the target firing process in real time, and the number is counted as the number of targets. The automated synthesis rate is directly read from the control system of the automated synthesis equipment. For secondary influencing factors, such as the volume of a certain reaction tube, capacitive level sensors and weighing sensors are installed on reaction tubes such as QMA columns, long tC18 columns, and combined columns to measure the volume and mass of the material in the column in real time, thereby obtaining the reaction tube volume. The content of a certain component in the reagent is obtained by detecting the production reagent through spectral analysis sensors. For example, for 1 ml of trifluoromannose / anhydrous acetonitrile reagent, the concentration of trifluoromannose and anhydrous acetonitrile is determined by analyzing its absorption of light at a specific wavelength. These real-time collected primary and secondary influencing factor data are organized and stored for subsequent use. 18 F-FDG efficiency prediction.

[0146] Step S42: Set the current 18 The primary and secondary influencing factors in the F-FDG production process are input into a trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction, which compares the corresponding primary and secondary influencing factors with... 18 The weights of each influencing factor within the F-FDG efficiency calculation model are weighted to generate the current corresponding... 18 F-FDG efficiency;

[0147] In this embodiment of the invention, the current value obtained in step S41 is used... 18 The primary and secondary influencing factors in the F-FDG production process are input into a trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction, assuming well-trained... 18 The F-FDG efficiency calculation model is stored in a Python pickle file. The model is loaded using the pickle.load() function. Major influencing factors (e.g., 2 targets, 75% automated synthesis rate, etc.) and minor influencing factors (e.g., 1 ml reaction tube volume, 6 g / L concentration of a reagent component, etc.) are organized into feature vectors. These feature vectors are then weighted with the weights corresponding to each influencing factor within the model (e.g., weight vector [0.25, 0.22, 0.12, 0.2, 0.1, 0.06, 0.04, 0.01]). For example, by calculating (2 × 0.25 + 75% × 0.22 + 1 × 0.12 + 6 × 0.2 + ...), the current corresponding feature vector is finally generated.18 F-FDG efficiency, assuming the current calculation is obtained 18 The F-FDG dose is 1000 mcg.

[0148] Step S43: Preset the corresponding first-level warning threshold of 85% and the second-level warning threshold of 70%;

[0149] In an embodiment of the present invention, by 18 The configuration file for the F-FDG production early warning system explicitly sets the corresponding preset primary warning threshold at 85% and the secondary warning threshold at 70%. These thresholds were determined through multiple analyses and experiments based on historical production data and production process requirements. For example, through statistical analysis of a large amount of past production data, it was found that when... 18 When F-FDG efficiency is below 85%, although production can still be maintained, some potential factors affecting production efficiency already exist; while when 18 When the efficiency of F-FDG is less than 70%, 18 F-FDG production will drop significantly, severely impacting production efficiency. These thresholds will be stored in the system's database for later use in early warning assessments.

[0150] Step S44: Based on the preset first-level warning threshold of 85% and the second-level warning threshold of 70%, adjust the current corresponding... 18 F-FDG efficiency 18 F-FDG production warning, if the current corresponding 18 When the efficiency of F-FDG is below 85% of the first-level warning threshold, a corresponding minor warning signal is issued to indicate the presence of an impact. 18 Potential factors affecting F-FDG efficiency should be identified, and production personnel should be alerted to conduct further inspections and analyses promptly; if the current corresponding 18 When the efficiency of F-FDG falls below 70% of the secondary warning threshold, a corresponding severe warning signal will be issued to indicate the occurrence of [a problem / issue]. 18 A warning was issued regarding a significant drop in F-FDG production, urging production personnel to immediately halt production to prevent the production of defective products, investigate whether the pipeline transmission system was leaking or blocked, and, if necessary, activate the pre-set emergency plan for radioactive material transmission pipeline leaks, and simultaneously activate... 18 During the F-FDG production process, backup targets and backup synthesis modules are prioritized for use. When the radiation dose in the target chamber drops to an acceptable level for personnel, accelerator inspection, maintenance, and troubleshooting are conducted to generate the corresponding... 18 F-FDG production early warning adjustment measures.

[0151] In this embodiment of the invention, the current corresponding warning threshold is determined based on a preset first-level warning threshold of 85% and a second-level warning threshold of 70%. 18 F-FDG efficiency18 F-FDG production early warning system includes a real-time data comparison module that continuously compares the calculated current data. 18 The efficiency of F-FDG is compared with two warning thresholds. If the current corresponding 18 If the F-FDG efficiency is 80%, which is below the first-level warning threshold of 85%, the system will immediately issue a minor warning signal. For example, it will activate a low-frequency flashing light and a soft alert sound through the audible and visual alarm in the production workshop, and at the same time, a prompt box will pop up on the production monitoring screen to inform the system of the impact. 18 Potential factors affecting F-FDG efficiency suggest that production personnel should promptly conduct further checks and analyses of production parameters and equipment operating status. 18 When the F-FDG efficiency drops to 60%, below the secondary warning threshold of 70%, the system issues a severe warning signal. The audible and visual alarm emits a high-frequency bright light and a sharp alarm sound, and a conspicuous red warning message is displayed on the production monitoring screen. 18 Upon receiving an early warning of a significant drop in F-FDG production, production personnel immediately halted production according to established procedures. Using pipeline inspection equipment, such as leak detectors, they checked the pipeline transmission system for leaks or blockages. If a leak was detected, the pre-set emergency plan for radioactive material transmission pipeline leaks was immediately activated, including activating leak control devices and evacuating nearby personnel. Simultaneously, a rapid switch to... 18 During the F-FDG production process, backup targets and backup synthesis modules are prioritized for use to ensure sufficient supply to patients requiring medication. Once the radiation dose in the target chamber has been reduced to an acceptable level through ventilation and other measures, professional maintenance personnel conduct a comprehensive inspection and maintenance of the accelerator, investigate the cause of any malfunctions, record the entire process, and ultimately generate the corresponding... 18 F-FDG Production Early Warning and Adjustment Measures Report.

[0152] Furthermore, the present invention also provides a method based on 18 The early warning system based on the F-FDG efficiency calculation model is used to perform the above-mentioned functions. 18 The early warning method of the F-FDG efficiency calculation model, which is based on 18 The early warning system for the F-FDG efficiency calculation model includes:

[0153] 18 The F-FDG impact factor analysis module is used to analyze the impact factor of the F-FDG impact factor. 18 F-FDG historical production process, obtaining factors affecting the yield of fluoride ions from cyclotron nuclear reactions, and 18 Factors affecting F-FDG synthesis include fluoride ion yield factors such as oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transport consumption in a closed target, proton beam target site, proton beam energy, proton beam current, and irradiation time. 18Factors affecting F-FDG synthesis include precursor quantity during production preparation, reagent content, fluoride ion transport consumption, QMA loading, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production;

[0154] The impact factor weight determination module is used to determine the weight of the impact factor based on... 18 The influence of historical F-FDG production efficiency on fluoride ion yield and 18 The primary and secondary influences of the F-FDG composite impact factor were classified to obtain... 18 The main and secondary influencing factors of F-FDG efficiency; 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights;

[0155] The efficiency calculation model training module is used to train models based on the corresponding efficiency factors. 18 F-FDG relative importance weights 18 The efficiency calculation model is iteratively trained by analyzing the primary and secondary influencing factors corresponding to F-FDG efficiency to generate... 18 F-FDG efficiency calculation model;

[0156] 18 The F-FDG production early warning module is used to obtain current production information. 18 The main and secondary influencing factors in the F-FDG production process are input into the trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction to generate the current corresponding 18 F-FDG efficiency; based on preset first-level and second-level warning thresholds, the current corresponding... 18 F-FDG efficiency 18 F-FDG production early warning, to generate corresponding 18 F-FDG production early warning adjustment measures.

[0157] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0158] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method based on 18 The early warning method of the F-FDG efficiency calculation model is characterized by, Includes the following steps: Step S1: By 18 F-FDG historical production process, obtaining factors affecting the yield of fluoride ions from cyclotron nuclear reactions, and 18 Factors affecting F-FDG synthesis include fluoride ion yield factors such as oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transport consumption in a closed target, proton beam target site, proton beam energy, proton beam current, and irradiation time. 18 Factors affecting F-FDG synthesis include precursor quantity during production preparation, reagent content, fluoride ion transport consumption, QMA loading, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production; Step S2: Factors affecting fluoride ion yield and 18 The primary and secondary influences of the F-FDG composite impact factor were classified to obtain... 18 The main and secondary influencing factors of F-FDG efficiency; right 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights; Step S3: Based on the corresponding impact factors 18 F-FDG relative importance weights 18 The efficiency calculation model is iteratively trained by analyzing the primary and secondary influencing factors corresponding to F-FDG efficiency to generate... 18 F-FDG efficiency calculation model; wherein, step S3 includes the following steps: Step S31: Based on the corresponding impact factors 18 F-FDG relative importance weights and combined with multiple linear regression to... 18 The efficiency calculation model is trained by considering the primary and secondary influencing factors corresponding to F-FDG efficiency, in order to... 18 The relative importance weights of F-FDG are calculated by weighting the primary and secondary influencing factors as inputs. 18 F-FDG efficiency is used as the output to generate 18 The F-FDG efficiency calculation initializes the model and outputs the corresponding predictions. 18 F-FDG production efficiency; Step S32: Predict 18 F-FDG Efficiency and Historical Standards 18 A comparative analysis of F-FDG efficiency was conducted to obtain the deviation between the predicted efficiency and the standard efficiency. Step S33: Compare the deviation between the predicted efficiency and the standard efficiency based on a preset deviation threshold of 1%. If the deviation between the predicted efficiency and the standard efficiency is less than or equal to the deviation threshold of 1%, no optimization is performed on the model; if the deviation between the predicted efficiency and the standard efficiency is greater than the deviation threshold of 1%, the model is re-optimized. 18 The corresponding F-FDG efficiency calculation initial model 18 The relative importance weights of F-FDG are determined using criteria weights, and the redefined weights are then... 18 F-FDG relative importance weight iterative optimization corresponding to 18 The initial model for F-FDG efficiency calculation is used to generate... 18 F-FDG efficiency calculation model; Step S4: Get the current 18 The main and secondary influencing factors in the F-FDG production process are input into the trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction to generate the current corresponding 18 F-FDG efficiency; based on preset first-level and second-level warning thresholds, the current corresponding... 18 F-FDG efficiency 18 F-FDG production early warning, to generate corresponding 18 F-FDG production early warning adjustment measures.

2. The method based on claim 1 18 The early warning method of the F-FDG efficiency calculation model is characterized by, Step S1 includes the following steps: Step S11: Use a mass spectrometer to obtain the abundance of oxygen-18 water produced by the nuclear reaction in the cyclotron, and use a flow sensor and a level sensor to monitor the flow rate and storage volume of oxygen-18 water in the target firing process in real time. Calculate the corresponding amount of oxygen-18 water used based on the integral of the flow rate and storage volume. Also, monitor each target loading point or position on the target loading track during the target firing process to obtain the amount of oxygen-18 water consumed in the sealed target. Step S12: Use a cyclotron to monitor and obtain the number of single or double target sites corresponding to the proton beam during the target firing stage, so as to obtain the proton beam target sites; use a radiation monitoring sensor to accurately measure the energy, flux, and irradiation time of the proton beam during the target firing stage, so as to obtain the proton beam energy, proton beam flux, and irradiation time; use the corresponding oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transmission consumption in the closed target, proton beam target sites, proton beam energy, proton beam flux, and irradiation time as fluoride ion yield influencing factors; Step S13: By 18 In the historical production process of F-FDG, the corresponding synthesis step uses a combination of capacitive level sensors and weighing sensors to accurately measure the volume and mass of the corresponding materials in the reaction tube to obtain the pre-production preparation volume; and uses a spectral analysis sensor to quickly and accurately detect the concentration and composition of the production reagents to obtain the reagent content, including trifluoromannose, anhydrous acetonitrile, K2.2.2 / K2CO3 rinsing solution and NaOH. Step S14: Obtain the corresponding fluoride ion dose after the nuclear reaction and the fluoride ion dose after passing through the QMA column by obtaining the corresponding fluoride ion pipeline transport process in the synthesis step, so as to obtain the fluoride ion pipeline transport consumption and QMA full loading; obtain the precursor purity, catalyst activity, reaction temperature, reaction time, etc. in the synthesis step. 18 The number of F-FDG synthesis channels, including FDG1 and FDG2, was determined, and the corresponding automated synthesis efficiency was obtained through two dehydration processes and one nucleophilic reaction in the synthesis step. Simultaneously, the corresponding data were collected. 18 F-FDG yield; corresponding production preparation precursor quantities, reagent content, fluoride ion pipeline transport consumption, QMA full load, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production as 18 F-FDG synthesis influence factor.

3. The method based on claim 2 18 The early warning method of the F-FDG efficiency calculation model is characterized by, The reaction tube described in step S13 includes an activated QMA column, a long tC18 column, and a combined column, wherein the combined column includes an IC-H column, an Al2O3 column, and a tC18 column.

4. The method based on claim 2 18 The early warning method of the F-FDG efficiency calculation model is characterized by, The automated synthesis efficiency obtained in step S14 through two dehydration processes and one nucleophilic reaction in the synthesis process includes: The corresponding dehydration rate was obtained by performing two dehydration processes on the reaction reagents during the synthesis process, and the water content was obtained by using a desiccant to absorb water and distillation to remove water. Based on the water removal rate, a statistical analysis of residual moisture was performed to obtain... 18 Residual moisture content before the F-FDG synthesis reaction; Based on the residual moisture content, the corresponding nucleophilic reaction process in the synthesis step was analyzed. 18 F-FDG synthesis loss assessment to determine the effect of reagent moisture content on the synthesis loss. 18 The F-FDG synthesis reaction affects the loss; By obtaining the corresponding nucleophilic reaction temperature and time during the nucleophilic reaction process in the synthesis step, and based on the nucleophilic reaction temperature and time, the nucleophilic reaction rate of the corresponding nucleophilic reaction process in the synthesis step is analyzed to obtain the corresponding nucleophilic reaction rate at the corresponding reaction temperature and time. 18 F-FDG synthesis reaction rate; The corresponding raw material input amounts are obtained through a nucleophilic reaction process in the synthesis stage. 18 F-FDG synthesis yield, and based on 18 The effects of F-FDG synthesis reaction on losses and at corresponding reaction temperatures and durations 18 The reaction rate of F-FDG synthesis is affected by the amount of raw materials input and 18 F-FDG synthesis yield 18 Quantification of F-FDG synthesis rate to obtain 18 The automated synthesis efficiency corresponding to F-FDG.

5. The method based on claim 4 18 The early warning method of the F-FDG efficiency calculation model is characterized by, The method of obtaining the corresponding moisture content of the reaction reagents through two dehydration processes in the synthesis step includes the following steps: The corresponding drying and water absorption reaction times and the distillation reaction times were obtained by performing two water removal processes in the synthesis step. The amount of water absorbed during the drying process of the desiccant in the synthesis step was analyzed to obtain the amount of water absorbed by the reaction reagents during drying. The amount of water migration in the distillation process of the corresponding reaction reagents was obtained by distilling and separating water in the synthesis process. Based on the drying water absorption reaction time and the distillation water reaction time, the probability statistics of water absorption during the drying process and the amount of water migration during the distillation process of the reaction reagents were statistically analyzed to obtain the water content of the corresponding reaction reagents.

6. The method based on claim 1 18 The early warning method of the F-FDG efficiency calculation model is characterized by, Step S2 includes the following steps: Step S21: Factors affecting fluoride ion yield and 18 The F-FDG composite impact factor performs pairwise correlation measurement calculations on each impact factor to quantify the correlation coefficients between each pair of impact factor combinations, and constructs the corresponding impact factor correlation matrix to generate... 18 F-FDG impact factor correlation matrix; Step S22: Based on 18 The correlation matrix of F-FDG influence factors on the influence factor of fluoride ion yield and 18 An influence factor correlation grid was constructed for each influence factor within the F-FDG synthesis influence factor to integrate the fluoride ion yield influence factor and... 18 Each impact factor within the F-FDG composite impact factor is used as a node, and based on... 18 The correlation coefficient between any two pairs of influence factor combinations within the F-FDG influence factor correlation matrix determines the edges connecting them. If the correlation coefficient is greater than or equal to 0.85, the edges between their nodes are connected; if the correlation coefficient is less than 0.85, no action is taken, and the corresponding edges are generated. 18 F-FDG impact factor correlation network; Step S23: For 18 The F-FDG influence factor correlation network was analyzed for node degree and betweenness centrality. The number of edges connected to a node was used as the degree of that node, and the betweenness centrality of the corresponding node was also analyzed to obtain the degree of each node. 18 The nodal degree and betweenness centrality of the F-FDG impact factor; Step S24: Based on each 18 The influence factors of nodal degree and betweenness centrality on fluoride ion yield in the F-FDG influence factor and 18 The primary and secondary impacts of each impact factor within the F-FDG composite impact factor are classified, if corresponding 18 If the nodal degree of an F-FDG impact factor exceeds 5 or its betweenness centrality exceeds 8, it is determined as a major impact factor; otherwise, it is determined as a minor impact factor. 18 The main and secondary influencing factors of F-FDG efficiency; Step S25: For 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights.

7. The method based on claim 6 18 The early warning method of the F-FDG efficiency calculation model is characterized by, Step S25 includes the following steps: Step S251: By in 18 F-FDG historical production process obtained corresponding 18 F-FDG production efficiency, and use it as the target layer; Step S252: ... 18 The main influencing factors corresponding to F-FDG production efficiency are used as the criterion layer, and the following are... 18 The secondary influencing factors corresponding to F-FDG production efficiency are used as the indicator layer; the target layer, criterion layer and indicator layer are constructed according to the hierarchical structure to build the corresponding expert evaluation system architecture. Step S253: Experts score each impact factor by using the correlation coefficient between the criteria layer and the indicator layer within the expert evaluation system architecture, and construct a corresponding scoring judgment matrix based on the scoring results of each impact factor. The elements in the scoring judgment matrix represent the score ratio relationship between each impact factor. Step S254: Obtain the corresponding maximum eigenvalue and its corresponding eigenvector through the scoring judgment matrix, and perform consistency check calculation based on the maximum eigenvalue and its corresponding eigenvector to calculate the corresponding consistency index and random consistency index, and calculate the ratio of the consistency index and the random consistency index to obtain the corresponding consistency ratio. Step S255: If the consistency ratio is greater than or equal to 0.1, then the corresponding score is determined as the corresponding relative importance weight to obtain the corresponding weight for each influencing factor. 18 F-FDG relative importance weight; if the consistency ratio is less than 0.1, the process is returned to expert scoring until the scoring judgment matrix passes the consistency test.

8. The method based on claim 1 18 The early warning method of the F-FDG efficiency calculation model is characterized by, Step S4 includes the following steps: Step S41: Obtain the current 18 The main and secondary influencing factors in the F-FDG production process; Step S42: Set the current 18 The primary and secondary influencing factors in the F-FDG production process are input into a trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction, which compares the corresponding primary and secondary influencing factors with... 18 The weights of each influencing factor within the F-FDG efficiency calculation model are weighted to generate the current corresponding... 18 F-FDG efficiency; Step S43: Set the corresponding first-level warning threshold of 85% and the second-level warning threshold of 70%; Step S44: Based on the preset first-level warning threshold of 85% and the second-level warning threshold of 70%, adjust the current corresponding... 18 F-FDG efficiency 18 F-FDG production warning, if the current corresponding 18 When the efficiency of F-FDG is below 85% of the first-level warning threshold, a corresponding minor warning signal will be issued to indicate the presence of an impact. 18 Potential factors affecting F-FDG efficiency should be identified, and production personnel should be alerted to conduct further inspections and analyses promptly; if the current corresponding 18 When the efficiency of F-FDG falls below 70% of the secondary warning threshold, a corresponding severe warning signal will be issued to indicate the occurrence of [a problem / issue]. 18 A warning was issued regarding a significant drop in F-FDG production, urging production personnel to immediately halt production to prevent the production of defective products, investigate whether the pipeline transmission system was leaking or blocked, and, if necessary, activate the pre-set emergency plan for radioactive material transmission pipeline leaks, and simultaneously activate... 18 During the F-FDG production process, backup targets and backup synthesis modules are prioritized for supply and use. Once the radiation dose in the target firing chamber drops to an acceptable level for personnel, accelerator inspection, maintenance, and troubleshooting are conducted to generate the corresponding... 18 F-FDG production early warning adjustment measures.

9. A method based on 18 The early warning system of the F-FDG efficiency calculation model is characterized by, For performing the based as described in claim 1 18 The early warning method of the F-FDG efficiency calculation model, which is based on 18 The early warning system for the F-FDG efficiency calculation model includes: 18 The F-FDG impact factor analysis module is used to analyze the impact factor of the F-FDG impact factor. 18 F-FDG historical production process, obtaining factors affecting the yield of fluoride ions from cyclotron nuclear reactions, and 18 Factors affecting F-FDG synthesis include fluoride ion yield factors such as oxygen-18 water abundance, oxygen-18 water usage, oxygen-18 water transport consumption in a closed target, proton beam target site, proton beam energy, proton beam current, and irradiation time. 18 Factors affecting F-FDG synthesis include precursor quantity during production preparation, reagent content, fluoride ion transport consumption, QMA loading, precursor purity, catalyst activity, reaction temperature, and reaction time. 18 F-FDG synthesis channel number, automated synthesis efficiency and 18 F-FDG production; The impact factor weight determination module is used to determine the weight of the impact factor based on... 18 The influence of historical F-FDG production efficiency on fluoride ion yield and 18 The primary and secondary influences of the F-FDG composite impact factor were classified to obtain... 18 The main and secondary influencing factors of F-FDG efficiency; 18 The primary and secondary influencing factors corresponding to F-FDG efficiency are weighted according to the criteria to obtain the corresponding values ​​for each influencing factor. 18 F-FDG relative importance weights; The efficiency calculation model training module is used to train models based on the corresponding efficiency factors. 18 F-FDG relative importance weights 18 The efficiency calculation model is iteratively trained by analyzing the primary and secondary influencing factors corresponding to F-FDG efficiency to generate... 18 F-FDG efficiency calculation model; 18 The F-FDG production early warning module is used to obtain current production information. 18 The main and secondary influencing factors in the F-FDG production process are input into the trained... 18 In the F-FDG efficiency calculation model 18 F-FDG efficiency prediction to generate the current corresponding 18 F-FDG efficiency; based on preset first-level and second-level warning thresholds, the current corresponding... 18 F-FDG efficiency 18 F-FDG production early warning, to generate corresponding 18 F-FDG production early warning adjustment measures.

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