Medical isotope production base evaluation method and system based on big data, and medium

Abstract

The invention discloses a medical isotope production base evaluation method based on big data, which constructs a'rigid screening-elastic grading 'double-layer space evaluation model, and improves the efficiency and scientificity of site selection. According to the method, multi-source spatial big data is acquired, firstly, rigid factors such as fault distance and ecological red lines are utilized to perform spatial mask processing on an evaluation area, and unsuitable units are quickly eliminated through a one-ticket override mechanism, so that a safety bottom line of site selection is ensured; on the basis, an elastic factor is quantitatively calculated by using a fuzzy five-element connection degree model for the primary selection area. According to the hierarchical processing technology, invalid refined calculation on an obviously unsuitable area is avoided, the data processing efficiency is greatly improved, and digitization and spatial visualization of an evaluation result are realized by converting multi-source data into a comprehensive five-element connection number; the technical problems that a traditional site selection method is high in qualitative subjectivity and lacks space dimension support are effectively solved.

Description

Technical Field

[0001] This invention belongs to the field of radiation protection and environmental protection. Specifically, it relates to a method for evaluating the suitability of the radiation environment of a medical isotope production base based on spatial big data support, a system for evaluating the suitability of the radiation environment of a medical isotope production base based on spatial big data support, and a computer-readable storage medium. Background Technology

[0002] With the rapid development of nuclear medicine technology, the "Medium- and Long-Term Development Plan for Medical Isotopes (2021-2035)" clearly states that medical isotopes play an irreplaceable role in the diagnosis and treatment of major diseases such as cardiovascular diseases and malignant tumors. To meet the ever-increasing demand for isotope production and ensure the orderly development of the industry, the scientific and rational selection and layout of medical isotope production bases is particularly important. However, the inventors have found that existing technologies for production base site selection and environmental suitability assessment still have many unresolved technical problems in practical applications, which are analyzed in detail below: First, existing environmental suitability assessment methods have significant shortcomings in data processing and screening mechanisms, failing to meet the needs of efficient and accurate site selection across large-scale regions. Traditional assessment methods, such as overall assessments of specific areas, often treat the assessment area as a homogeneous whole, ignoring the spatial heterogeneity within the geographic space. When facing complex geographical environments, existing technologies lack deep integration and utilization of multi-source spatial big data, such as GIS vector data and real-time radiation monitoring data, and have failed to establish an efficient, systematic processing flow from macro-level screening to micro-level grading. Specifically, existing technologies typically lack a tiered screening mechanism that organically combines "rigid constraints," such as geological faults and ecological red lines (veto indicators), with "flexible assessments," such as economic benefits and environmental capacity (gradually changing indicators). This leads to either inefficient detailed calculations of many obviously unsuitable areas due to the lack of rigid initial screening, reducing assessment efficiency, or the inability to accurately identify optimal micro-level site selection points among areas that pass the initial screening due to the lack of flexible grading based on big data. Consequently, the final site selection results lack scientific spatial layout guidance, making it difficult to achieve the optimal match between radiation safety and land use.

[0003] Secondly, the existing evaluation index system lacks specificity and cannot adapt to the special needs of medical isotope production technology, easily leading to a "supply-demand mismatch" in site selection results. Currently, the site selection evaluation of nuclear technology utilization projects mostly directly adopts the evaluation standards of nuclear power plants or general nuclear facilities. These standards mainly focus on extreme safety, such as extreme geological disasters and low-density population areas, and are applicable to long-half-life, high-risk nuclear power projects. However, they appear too simplistic or stringent for medical isotope production bases. Medical isotope products, especially short-half-life nuclides, have extremely high timeliness requirements. The site selection of their production bases must not only consider radiation safety but also economic and social factors such as convenient logistics and transportation, distance from downstream medical institutions, and synergistic benefits across the industrial chain.

[0004] Furthermore, existing indicator systems often adopt a "one-size-fits-all" approach, failing to distinguish the fundamental differences in environmental impact and economic needs between reactor production technology and accelerator production technology. For example, accelerator production technology is more flexible and less affected by natural conditions; applying the stringent site selection criteria for reactors would severely limit its rational industrial layout. Therefore, constructing a differentiated evaluation indicator system that ensures radiation safety while also considering the economic characteristics of different production processes is another pressing technical challenge.

[0005] Finally, regarding evaluation algorithms and quantitative models, existing technologies mostly employ deterministic evaluation methods such as the Delphi method, the Analytic Hierarchy Process (AHP), or the entropy method. These methods, when processing evaluation indicators, often absolutize the relationship between indicator values ​​and evaluation levels, ignoring the fuzziness and uncertainty that exist around critical values. For example, when an indicator value is only slightly above a certain threshold, its suitability level should not change abruptly. This deterministic approach struggles to accurately reflect the complex characteristics of radiation environment suitability, easily introduces subjective bias, and cannot effectively predict the future suitability development trend of the selected area.

[0006] Therefore, how to introduce mathematical models that can effectively handle fuzzy boundaries and quantify uncertainties in order to improve the objectivity and foresight of evaluation results is also a key problem that those skilled in the art need to tackle. Summary of the Invention

[0007] The technical problem solved by this invention is that existing nuclear facility site selection evaluation methods lack a dedicated indicator system for medical isotope production technology and cannot accurately quantify the spatial heterogeneity and fuzzy uncertainty of evaluation level boundaries under complex radiation environments. This invention provides a method for evaluating the radiation environment suitability of medical isotope production bases based on spatial big data support, a system for evaluating the radiation environment suitability of medical isotope production bases based on spatial big data support, and a computer-readable storage medium.

[0008] Technical solution To address the aforementioned technical problems, this invention discloses a method for evaluating medical isotope production bases based on big data, comprising the following steps: Step S1: Obtain multi-source spatial big data of the area to be evaluated, including geographic information data, meteorological and hydrological data and real-time radiation monitoring data; Step S2: Based on the production technology type of medical isotopes, construct a corresponding radiation environment suitability evaluation index system; the production technology type includes reactor production technology and accelerator production technology; the evaluation index system includes rigidity factors and elasticity factors; Step S3: Based on the rigidity factor, perform a preliminary spatial suitability screening of the region to be evaluated, and remove unsuitable evaluation units through spatial masking to obtain a preliminary suitable region; Step S4: Establish a fuzzy five-element connectivity model and perform quantitative calculations on each evaluation unit within the suitability preliminary selection area; specifically, this includes: obtaining the elasticity factor index value of each evaluation unit, selecting the corresponding positive or negative index connectivity formula according to the index nature to calculate the single index connectivity number, and using the index weights determined by the entropy weight method to perform weighted summation on the single index connectivity number to obtain the comprehensive five-element connectivity number. Step S5: Determine the radiation environment suitability level of each evaluation unit based on the comprehensive five-element correlation coefficient, and generate a recommended site selection map for medical isotope production bases.

[0009] Furthermore, in step S2, the rigid factor refers to a binary judgment index that constitutes an absolute constraint on site selection; the elastic factor refers to a graded judgment index that has a relative impact on site selection and exhibits a gradual change in suitability. The rigid factors include at least: distance from the active fault, population density threshold, and distribution of ecological red line areas; when any rigid factor of an evaluation unit does not meet the preset threshold, the evaluation unit is directly determined to be unsuitable and will not proceed to the calculation in step S4.

[0010] Furthermore, in step S2, the construction of a corresponding radiation environment suitability evaluation index system based on the production technology type of medical isotopes specifically includes: If the production technology type is reactor production technology, then an evaluation index system including four dimensions should be constructed, including natural foundation, economic and social factors, ecological impact, and radiation environment. If the production technology type is accelerator production technology, then an evaluation index system is constructed that includes three dimensions: economic and social impact, ecological impact, and radiation environment; the economic and social dimension index of the accelerator production technology includes the industrial chain synergy benefit index.

[0011] Furthermore, in step S4, the grading criteria of the fuzzy five-element connectivity model are divided into high suitability, medium-high suitability, medium suitability, medium-low suitability, and low suitability; S1, S2, S3, and S4 are set as four critical values ​​for the evaluation grading criteria; the calculation formula for the single-index connectivity number is divided into a positive index connectivity formula and a negative index connectivity formula.

[0012] Furthermore, for the positive factors where larger values ​​are considered better, S1, S2, S3, and S4 are arranged in descending order, and the formula for the positive index correlation degree is as follows: ; In the formula, X i i1, i2, i3 are the index values ​​of the positive factor; i1, i2, i3 are the difference coefficients with values ​​between [-1, 1]; J is the opposition coefficient.

[0013] Furthermore, for the negative factors where smaller values ​​are preferred, S1, S2, S3, and S4 are arranged in ascending order, and the formula for the correlation degree of the negative indicators is as follows: ; X t is the index value of the negative factor; i1, i2, i3 are the difference coefficients with values ​​between [-1, 1]; J is the opposition coefficient.

[0014] Furthermore, step S5 also includes a step of analyzing the suitability development trend based on set pair potential: A comprehensive five-element connection number is constructed using the formula u=a+bi+cj+dk+el, where a, b, c, d, and e are connection components, corresponding to connection components from high suitability to low suitability, and satisfying a+b+c+d+e=1; i, j, k, and l are difference coefficients, taking values ​​of [-1,1], used to calculate the algebraic value of the comprehensive five-element connection number u as the set pair potential; Based on the numerical range of the set potential, the development trend is divided into five levels: anti-potential, partial anti-potential, equilibrium potential, partial homopotential, and homopotential, in order to predict the dynamic change trend of suitability of medical isotope production bases.

[0015] Furthermore, the elasticity factor specifically includes: Spatial elasticity factors: including atmospheric diffusion capacity and water exchange rate, are obtained by collecting spatial distribution data and are used to characterize the ability of environmental media to dilute radionuclides; Comprehensive elasticity factors include economic benefit assessment and social acceptance, obtained by collecting data at an appropriate time scale; the economic benefit assessment indicators include the internal rate of return.

[0016] This invention also discloses a radiation environment suitability evaluation system for medical isotope production bases based on spatial big data support, which can execute the above-mentioned big data-based evaluation method for medical isotope production bases, including: The data acquisition module is used to acquire multi-source spatial big data, including geographic information data and radiation monitoring data; The index construction module is used to construct an evaluation index system that includes rigidity and elasticity factors based on reactor production technology or accelerator production technology. The region filtering module is used to perform spatial masking on the evaluation region based on the rigidity factor, and to remove unsuitable regions. The model calculation module is used to call the positive and negative indicator correlation formulas to calculate the comprehensive five-element correlation coefficient of the initially selected region. The evaluation generation module is used to output the radiation environment suitability level and site distribution map based on the comprehensive five-element connection number.

[0017] The present invention also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described evaluation method for medical isotope production bases based on big data.

[0018] Beneficial effects The present invention has the following beneficial effects: 1. This invention constructs a two-layer spatial evaluation model of "rigid screening - flexible grading," improving the efficiency and scientific rigor of site selection. By acquiring multi-source spatial big data, this invention first uses rigid factors, such as fault distance and ecological red lines, to spatially mask the evaluation area, quickly eliminating unsuitable units through a "one-vote veto" mechanism to ensure a safe baseline for site selection. Based on this, a fuzzy five-element connectivity model is used to quantitatively calculate the flexible factors for the initially selected area. This layered processing technique avoids ineffective fine-grained calculations for obviously unsuitable areas, greatly improving data processing efficiency. Furthermore, by transforming multi-source data into a comprehensive five-element connectivity coefficient, it achieves the digitization and spatial visualization of evaluation results, effectively solving the technical problems of traditional site selection methods being highly subjective in terms of qualitative assessment and lacking spatial dimensional support.

[0019] 2. By constructing differentiated indicator systems for different production processes, the problem of supply and demand mismatch in isotope site selection is solved. This invention breaks through the traditional single-mode evaluation of nuclear facilities that "emphasizes safety but neglects economics." It constructs dedicated evaluation indicator systems for reactor production technology and accelerator production technology, addressing their fundamental differences. For fission-dependent reactor technology, the focus is on natural environmental and radiation dimensions; while for the more flexible accelerator technology, indicators such as supply chain synergy benefits are introduced, particularly in the socio-economic dimension. This technical approach fully adapts to the industry characteristics of medical isotopes—short half-lives and the need for rapid distribution near medical institutions—achieving a balance between radiation safety and economic benefits, such as logistical efficiency and market synergy, through technological means. This fills the gap in existing technologies for dedicated evaluation systems in the field of medical isotopes.

[0020] 3. Utilizing an improved fuzzy set pair analysis algorithm, this invention achieves precise quantification and trend prediction of evaluation index uncertainty. It employs an improved fuzzy set pair analysis method based on positive and negative index connectivity formulas. Through refined piecewise function calculations, it overcomes the shortcomings of traditional evaluation methods, such as the analytic hierarchy process (AHP), which tend to be overly absolute when index values ​​are near the critical values ​​of adjacent levels. This technical solution not only accurately reflects the degree of membership of evaluation units across the five levels from "high suitability" to "low suitability" by calculating the five-element connectivity, but also analyzes the dynamic development trend of suitability through the calculation of set pair potentials, such as anti-potential and homo-potential. This mathematical quantification of fuzzy boundaries and dynamic trends significantly improves the objectivity and foresight of evaluation results in complex and ever-changing radiation environments. Attached Figure Description

[0021] Figure 1 This is a radiation thermal map of Mianyang City in Embodiment 6 of the present invention. Detailed Implementation

[0022] Example 1 This embodiment corresponds to a method for evaluating the suitability of radiation environment for medical isotope production bases based on spatial big data support according to the present invention. The method includes the following steps: Step S1: Acquisition of Multi-Source Spatial Big Data Acquire multi-source spatial big data of the area to be evaluated. This multi-source spatial big data encompasses geographic information data, meteorological and hydrological data, and real-time radiation monitoring data. This data forms the basis for subsequent evaluations, ensuring that the evaluation results have actual physical spatial support.

[0023] Step S2: Construction of Evaluation Index System Based on the specific production technology type of the proposed medical isotope production base, a corresponding radiation environment suitability evaluation index system is constructed. The production technology types are specifically divided into two categories: reactor production technology and accelerator production technology. Within the constructed evaluation index system, the evaluation indicators are clearly divided into two categories: rigid factors and elastic factors.

[0024] Step S3: Initial screening of spatial suitability A preliminary spatial suitability screening of the evaluation area is performed based on rigidity factors. This step employs spatial masking technology to directly mark areas that do not meet the rigidity factor requirements as unsuitable evaluation units and eliminate them, thereby obtaining a preliminary selection of suitable areas. This step enables the rapid elimination of obviously unsuitable areas.

[0025] Step S4: Quantitative Calculation A fuzzy five-element connectivity model was established to quantitatively calculate the connectivity coefficients of each evaluation unit within the initial suitability selection area. The specific steps included: first, obtaining the specific index values ​​of each evaluation unit for each elasticity factor; second, based on the nature of the indicators (i.e., larger values ​​are better or smaller values ​​are better), selecting the corresponding positive or negative connectivity coefficient formulas to calculate the single-indicator connectivity coefficients; and finally, combining the weights of each indicator to calculate the comprehensive five-element connectivity coefficient for each evaluation unit.

[0026] Step S5: Level Determination and Map Generation. Based on the calculated comprehensive five-element correlation coefficient, the radiation environment suitability level of each evaluation unit is determined. Based on the level determination results of each evaluation unit, a recommended site selection map for medical isotope production bases is generated using a geographic information system, visually displaying the spatial distribution of suitability.

[0027] Example 2 This embodiment further optimizes the indicator system and screening process involved in steps S2 and S3 of embodiment 1.

[0028] In this embodiment, the rigid factor refers to a binary judgment index that constitutes an absolute constraint on site selection, and its judgment result is only suitable or unsuitable. The flexible factor refers to a hierarchical judgment index that has a relative impact on site selection and exhibits a gradual change in suitability.

[0029] Preferably, the rigid factor refers to a binary judgment index that constitutes an absolute constraint on site selection, and its judgment result is only suitable or unsuitable. The rigid factor includes at least: distance from the active fault, population density threshold, and distribution of ecological red line areas. For example, it is set that the distance from the active fault must be greater than 5 kilometers, the population density must be less than 1,000 people per square kilometer, and it must not be located within the ecological red line area.

[0030] In step S3, if any rigidity factor of an evaluation unit fails to meet a preset threshold, the system will directly determine that the evaluation unit is unsuitable, and the evaluation unit will not proceed to the calculation process in the subsequent step S4. This approach effectively reduces the computational load and ensures the safety and compliance of site selection.

[0031] Flexibility factors are graded indicators that have a relative impact on site selection and exhibit a gradual change in suitability. Flexibility factors are divided into spatial flexibility factors and comprehensive flexibility factors.

[0032] Spatial elasticity factors include atmospheric diffusion capacity and water exchange rate. These factors are obtained by collecting spatial distribution data and reflect the environment's ability to dilute and diffuse radioactive materials.

[0033] Comprehensive resilience factors include economic benefit assessment and social acceptance, which are obtained by collecting data at appropriate time scales.

[0034] For different types of production technology, the construction method of the radiation environment suitability evaluation index system is as follows: If the production technology type is reactor production technology, considering its nuclear fission risk and high dependence on the natural environment, an evaluation index system including four dimensions is constructed, namely natural basis, economic and social, ecological impact and radiation environment.

[0035] If the production technology is accelerator production technology, considering its flexibility and the requirements for market response speed, an evaluation index system including three dimensions: economic and social impact, ecological impact, and radiation environment should be constructed.

[0036] More preferably, the economic and social dimensions of accelerator production technology include a specific indicator of supply chain synergy benefits to assess the synergistic efficiency between the base and downstream medical institutions and logistics distribution systems.

[0037] In step S4, the weights of each indicator are determined by the entropy weight method to ensure the objectivity of the weight allocation.

[0038] More preferably, the radiation environment suitability evaluation index system constructed according to the type of medical isotope production technology is as follows: Regarding reactor production technology, due to the high risks involved in nuclear fission reactions, reactors require extremely stringent natural geological conditions. Therefore, an evaluation index system was constructed that includes four dimensions: natural foundation, economic and social impact, ecological impact, and radiation environment, as shown in Table 1.

[0039] Table 1. Evaluation Index System for Radiation Environment Suitability of Reactor Production Base

[0040] Regarding accelerator production technology, given its relative flexibility and less susceptibility to natural factors, greater emphasis is placed on its proximity to medical institutions and supply chain collaboration. Therefore, an evaluation index system encompassing three dimensions—economic and social impact, ecological impact, and radiation environment—was constructed, as shown in Table 2.

[0041] Table 2. Evaluation Index System for the Suitability of Radiation Environment for Accelerator Production Isotopes

[0042] Example 3 This embodiment further optimizes the fuzzy five-element connectivity model and specific calculation formula in step S4 of embodiment 1.

[0043] In this embodiment, the fuzzy five-element connectivity model is clearly divided into five levels: high suitability (Level I), medium-high suitability (Level II), medium suitability (Level III), medium-low suitability (Level IV), and low suitability (Level V).

[0044] To achieve the above-mentioned grading, S1, S2, S3, and S4 are set as four critical values ​​for the evaluation level standards. Depending on the nature of the indicators, the single-indicator correlation coefficient is calculated using the following formulas.

[0045] For positive factors where larger values ​​are generally better, such as the internal rate of return or the atmospheric diffusion coefficient, S1, S2, S3, and S4 are arranged in descending order. The formula for the correlation degree of positive indicators is as follows: ; In the formula, Xi is the index value of the positive factor, i1, i2, and i3 are the difference coefficients, and J is the opposition coefficient.

[0046] For negative factors where smaller values ​​are preferred, such as radiation dose rate or population density, S1, S2, S3, and S4 are arranged in ascending order. The formula for the correlation degree of negative indicators is as follows: ; In the formula, Xt is the index value of the negative factor, i1, i2, and i3 are the difference coefficients, and J is the opposition coefficient.

[0047] Preferably, in step S5, in addition to determining the current suitability level, the method of fuzzy set pair analysis is also used to establish a set of index values ​​X. l (l=1,2,…,m) and the set of evaluation levels Y k The formula for the degree of connection (k=1,2,…,5), the fuzzy five-element degree of connection formula is as follows: u=a+bi+cj+dk+el.

[0048] In the formula, a, b, c, d, and e are the correlation components, representing the correlation between the evaluation value and the suitability level I-V standards, and satisfy the normalization condition, i.e., a+b+c+d+e=1. i, j, k, and l are the difference coefficients, with values ​​ranging from [-1, 1].

[0049] More preferably, i∈[0,1], partial same difference; j=0, neutral difference, or take a fixed value such as j∈[-0.5,0.5]; k∈[-1,0], partial opposite difference; l=-1, completely opposite.

[0050] More preferably, i=2 / 3, j=0, k=-2 / 3, l=-1.

[0051] Calculate the set-pair potential and classify the development trend into five levels based on the value of the set-pair potential: counter-trend, partial counter-trend, equilibrium, partial homogeneity, and homogeneity. [-1.0, -0.6) belongs to counter-trend, [-0.6, -0.2) belongs to partial counter-trend, [-0.2, 0.2] belongs to equilibrium, (0.2, 0.6] belongs to partial homogeneity, and (0.6, 1.0] belongs to homogeneity.

[0052] By determining the range of the set potential, the dynamic trend of suitability changes of medical isotope production sites can be predicted. For example, if the set potential is in the same potential range, it indicates that the suitability of the region is stable and has a trend of improvement; if it is in the opposite potential range, it indicates that the region faces greater risks and challenges, and its suitability may decline.

[0053] Example 5 This embodiment discloses a system and a computer-readable storage medium that implements the above embodiments 1-4.

[0054] A radiation environment suitability assessment system for medical isotope production bases based on spatial big data support. The system mainly includes the following modules: The data acquisition module is used to connect to relevant databases and monitoring networks to acquire multi-source spatial big data, including geographic information data and radiation monitoring data.

[0055] The indicator construction module is used to store the indicator library and automatically construct an evaluation indicator system that includes rigidity factors and elasticity factors based on the reactor production technology or accelerator production technology selected by the user.

[0056] The region filtering module is used to perform spatial analysis calculations, and performs spatial masking on the evaluation region based on the rigidity factor to remove unsuitable regions.

[0057] The model calculation module is used to calculate the comprehensive five-element connection number of the initially selected region using the positive and negative index connection degree formulas in the aforementioned embodiments.

[0058] The evaluation generation module is used to output the radiation environment suitability level and site distribution map based on the comprehensive five-element connection number.

[0059] Furthermore, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can implement the steps of any one of the methods in Embodiments 1 to 4 described above.

[0060] Example 6 This embodiment uses Mianyang City, Sichuan Province as the area to be evaluated to illustrate the specific application of the present invention.

[0061] (1) Region description Mianyang City is located in northwestern Sichuan Province, on the northwestern edge of the Chengdu Plain, within the Sichuan Basin. Its geographical coordinates are approximately 31°26′N-33°03′N, 104°19′E-105°43′E. It covers a total area of ​​approximately 20,000 square kilometers, with terrain mainly consisting of hills and low mountains, averaging 500-800 meters above sea level. Influenced by the Longmenshan Fault, it experiences moderate seismic activity (historically, there have been earthquakes of magnitude 6 or higher). The climate is mild and humid, with an average annual temperature of 16-17℃, annual rainfall of 900-1200 mm, and an average wind speed of 3-5 m / s, which is conducive to atmospheric diffusion but prone to temperature inversions in winter. The population is approximately 5.3 million (2023 data), with a density of approximately 260 people / km². 2 The urbanization rate is approximately 60%. The economy is primarily driven by the technology industry, with a GDP of approximately 300 billion RMB (2023). The Nuclear Technology Application Industrial Park (Mianyang Science and Technology City) focuses on nuclear medicine and radiation protection, with the industrial chain encompassing upstream raw material supply and downstream medical institution collaboration. Ecologically, it belongs to the ecological barrier zone of the upper reaches of the Yangtze River, with a forest coverage rate of approximately 45% and national nature reserves (such as the Pingwu Wanglang Nature Reserve). Biodiversity is high, but soil permeability is moderate, making it susceptible to pollution accumulation. The background radiation environment is low (gamma dose rate approximately 0.05-0.1 μSv / h), with no history of significant nuclear pollution, making it suitable for a medical isotope production base; however, the risk of radiation spread needs to be assessed.

[0062] (2) Comparison of data acquisition and processing Spatial big data (such as satellite remote sensing, GIS data, and real-time monitoring) was used to collect data on rigidity and elasticity factors. Data sources included the National Radiation Environment Monitoring Network, reports from the Sichuan Provincial Department of Ecology and Environment, and simulation models. The evaluation unit was assumed to be a 10km × 10km grid in Mianyang City, comprising 202 units.

[0063] 1) Comparison of rigid factors (binary judgment, threshold such as population density <1000 people / km²) 2 ) Taking Mianyang as an example, the distance to the active fault is >5km (suitable threshold; in reality, some areas of Mianyang are >10km, with a suitability rate of 80%). Population density is <1000 people / km².2 (Suitable; Mianyang average 260 people / km) 2 (All suitable). Distribution of ecological red line areas: non-red line areas (suitable; 80% suitable outside Mianyang urban area). Screening results: 20% of units were removed (ecological protection red line, areas with poor geological stability), and the suitability was set to the lowest value of 0. The remaining 80% of units were entered into the flexible assessment.

[0064] The suitable areas in Mianyang account for approximately 80%, encompassing Fucheng District, Youxian District (excluding the northwest), the entirety of Santai County, the entirety of Yanting County, the entirety of Zitong County, the southwest of Jiangyou City, and the southern part of Anzhou District. The unsuitable areas account for approximately 20%, encompassing the entirety of Beichuan County, the entirety of Pingwu County, the northern part of Jiangyou City, and the northwest of Anzhou District. The main limiting factors are: most of these areas are within ecological protection red lines and are habitats for rare animals such as giant pandas; the terrain is steep, with high altitudes (up to 5440 meters), and complex geological conditions.

[0065] 2) Comparison of elasticity factor data (five-level classification, average value of example grid): Table 3 Elasticity Factor Analysis

[0066] Data processing: Rigid factors were analyzed using GIS overlay, and elastic factors were normalized (in the [0,1] interval) after screening. The weights were determined by the entropy weight method (0.3 for radiation environment and 0.23 for others).

[0067] (3) Evaluation process Step 1: Rigid screening to remove unsuitable units.

[0068] Step 2: Elastic Grading and Fuzzy Set Pair Analysis. For positive indicators (such as economic return on investment), the quinary correlation coefficient is calculated using an optimization formula. The comprehensive correlation coefficient μ = 0.65 + 0.15i + 0.1j + 0.05k + 0.05l. The set pair potential μ = 0.45 (partially homopair potential), indicating a medium-high overall suitability.

[0069] Step 3: Suitability scores across different dimensions: Natural basis: 0.75; Economic and social: 0.85; Ecological impact: 0.80; Radiation environment: 0.88. The overall average score is 0.82, indicating a strong advantage in the economic and radiation dimensions.

[0070] Results: The suitability score of Mianyang Science and Technology City is 0.82 (highly suitable), and it is recommended as a site for the base.

[0071] (4) Chart visualization like Figure 1 The spatial distribution map of radiation environment suitability is shown. This map simulates a 10*10km grid in Mianyang. Green represents high suitability areas and red represents low suitability areas. Overall, the radiation environment suitability is high in the center of the grid and slightly lower at the edges, indicating that the urban area is suitable.

[0072] The preferred embodiments of the invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for evaluating medical isotope production bases based on big data, characterized in that, Includes the following steps: Step S1: Obtain multi-source spatial big data of the area to be evaluated, including geographic information data, meteorological and hydrological data and real-time radiation monitoring data; Step S2: Based on the production technology type of medical isotopes, construct a corresponding radiation environment suitability evaluation index system; the production technology type includes reactor production technology and accelerator production technology; the evaluation index system includes rigidity factors and elasticity factors; Step S3: Based on the rigidity factor, perform a preliminary spatial suitability screening of the region to be evaluated, and remove unsuitable evaluation units through spatial masking to obtain a preliminary suitable region; Step S4: Establish a fuzzy five-element connectivity model and perform quantitative calculations on each evaluation unit within the suitability preliminary selection area; specifically, this includes: obtaining the elasticity factor index value of each evaluation unit, selecting the corresponding positive or negative index connectivity formula according to the index nature to calculate the single index connectivity number, and using the index weights determined by the entropy weight method to perform weighted summation on the single index connectivity number to obtain the comprehensive five-element connectivity number. Step S5: Determine the radiation environment suitability level of each evaluation unit based on the comprehensive five-element correlation coefficient, and generate a recommended site selection map for medical isotope production bases.

2. The method for evaluating medical isotope production bases based on big data according to claim 1, characterized in that, In step S2, the rigid factor refers to a binary judgment index that constitutes an absolute constraint on site selection; the elastic factor refers to a graded judgment index that has a relative impact on site selection and exhibits a gradual change in suitability. The rigid factors include at least: distance from the active fault, population density threshold, and distribution of ecological red line areas; when any rigid factor of an evaluation unit does not meet the preset threshold, the evaluation unit is directly determined to be unsuitable and will not proceed to the calculation in step S4.

3. The method for evaluating medical isotope production bases based on big data according to claim 1 or 2, characterized in that, In step S2, the construction of a radiation environment suitability evaluation index system based on the production technology type of medical isotopes specifically includes: If the production technology type is reactor production technology, then an evaluation index system including four dimensions should be constructed, including natural foundation, economic and social factors, ecological impact, and radiation environment. If the production technology type is accelerator production technology, then an evaluation index system is constructed that includes three dimensions: economic and social impact, ecological impact, and radiation environment; the economic and social dimension index of the accelerator production technology includes the industrial chain synergy benefit index.

4. The method for evaluating medical isotope production bases based on big data according to claim 1, characterized in that, In step S4, the grading criteria of the fuzzy five-element connectivity model are divided into high suitability, medium-high suitability, medium suitability, medium-low suitability, and low suitability; S1, S2, S3, and S4 are set as four critical values ​​for the evaluation grading criteria; the calculation formula for the single-index connectivity number is divided into a positive index connectivity formula and a negative index connectivity formula.

5. The method for evaluating medical isotope production bases based on big data according to claim 1, characterized in that, For the positive factors where larger values ​​are considered better, S1, S2, S3, and S4 are arranged in descending order, and the formula for the positive index correlation degree is as follows: ; In the formula, X i i1, i2, i3 are the index values ​​of the positive factor; i1, i2, i3 are the difference coefficients with values ​​between [-1, 1]; J is the opposition coefficient.

6. The method for evaluating medical isotope production bases based on big data according to claim 5, characterized in that, For the negative factors where smaller values ​​are preferred, S1, S2, S3, and S4 are arranged in ascending order, and the formula for the correlation degree of the negative indicators is as follows: ; X t is the index value of the negative factor; i1, i2, i3 are the difference coefficients with values ​​between [-1, 1]; J is the opposition coefficient.

7. The method for evaluating medical isotope production bases based on big data according to claim 1, characterized in that, Step S5 also includes a step of analyzing the suitability development trend based on set potential: A comprehensive five-element connection number is constructed using the formula u=a+bi+cj+dk+el, where a, b, c, d, and e are connection components, corresponding to connection components from high suitability to low suitability, and satisfying a+b+c+d+e=1. i, j, k, l are the difference coefficients, with values ​​of [-1, 1], used to calculate the algebraic value of the comprehensive five-element connection number u as the set pair potential; Based on the numerical range of the set potential, the development trend is divided into five levels: anti-potential, partial anti-potential, equilibrium potential, partial homopotential, and homopotential, in order to predict the dynamic change trend of suitability of medical isotope production bases.

8. The method for evaluating medical isotope production bases based on big data according to claim 1, characterized in that, The elasticity factor specifically includes: Spatial elasticity factors: including atmospheric diffusion capacity and water exchange rate, are obtained by collecting spatial distribution data and are used to characterize the ability of environmental media to dilute radionuclides; Comprehensive elasticity factors include economic benefit assessment and social acceptance, obtained by collecting data at an appropriate time scale; the economic benefit assessment indicators include the internal rate of return.

9. A radiation environment suitability evaluation system for medical isotope production bases based on spatial big data support, characterized in that, The method for evaluating medical isotope production bases based on big data, as described in any one of claims 1 to 8, includes: The data acquisition module is used to acquire multi-source spatial big data, including geographic information data and radiation monitoring data; The index construction module is used to construct an evaluation index system that includes rigidity and elasticity factors based on reactor production technology or accelerator production technology. The region filtering module is used to perform spatial masking on the evaluation region based on the rigidity factor, and to remove unsuitable regions. The model calculation module is used to call the positive and negative indicator correlation formulas to calculate the comprehensive five-element correlation coefficient of the initially selected region. The evaluation generation module is used to output the radiation environment suitability level and site distribution map based on the comprehensive five-element connection number.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the big data-based evaluation method for medical isotope production bases as described in any one of claims 1 to 8.

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