Water supply and drainage water quality radiation pollution automatic detection method and system

Abstract

The invention discloses an automatic detection method and system for water quality radiation pollution of water supply and drainage, and the method comprises the steps: obtaining ray dose data, ray dose data, simulated particle path data and simulated photon energy distribution data obtained after instrument detection and simulation operation are carried out on a to-be-detected water supply and drainage sample; packaging the ray dose data, the ray dose data, the simulated particle path data and the simulated photon energy distribution data to obtain radiation analysis data; according to the radiation analysis data, inputting the radiation analysis data into a pre-fitted standard dose harmful relation model to obtain dose response coefficient characteristics; according to the dose response coefficient characteristics, inputting the dose response coefficient characteristics into a pre-fitted standard dose effect model to obtain dose effect coefficient characteristics; according to the radiation analysis data and the dose effect coefficient characteristics, pollution evaluation calculation is carried out, and radiation grading data are obtained. The method can improve the water supply and drainage radiation detection speed.

Description

Technical Field

[0001] The present invention relates to the field of radiation detection technology, and in particular to an automatic detection method and system for radiation pollution of water quality for water supply and drainage. Background Art

[0002] As an important infrastructure, the demand for radiation detection of water supply and drainage systems is currently increasing, especially for the monitoring and management of water supply and drainage systems, which requires more scientific and systematic response measures. In water supply and drainage projects, the types of radiation are relatively complex, mainly α, β and γ. Among them, β and γ are both external irradiation, and α particles have poor penetration ability, generally only able to penetrate 1-2mm of tissue, but their energy deposition is higher. γ rays have higher energy and extremely strong penetration, and can be transmitted through air and water flow, causing large areas of radioactive contamination. Therefore, during the design and operation of the water supply and drainage system, it is necessary to focus on the detection and protection of β and γ rays to ensure water supply safety.

[0003] Existing responses to radiation contamination incidents mostly remain at the level of preliminary estimates of the severity of the accident and what measures to take based on experience and subjective judgment. The response speed of this manual method is often very limited and cannot promptly reflect radiation contamination problems in water supply and drainage. Summary of the invention

[0004] The present invention provides a method and system for automatically detecting radiation pollution of water quality in water supply and drainage, so as to solve the problem of slow response speed of radiation detection in water supply and drainage.

[0005] In the first aspect, in order to solve the above technical problems, the present invention provides a method for automatically detecting radiation pollution in water quality of water supply and drainage, comprising: After the water supply and drainage samples to be tested are tested and simulated, the Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; The Radiation dose data, Packing the radiation dose data, the simulated particle path data and the simulated photon energy distribution data to obtain radiation analysis data; According to the radiation analysis data, inputting into a pre-fitted standard dose-harm relationship model, to obtain a dose response coefficient characteristic; According to the dose response coefficient characteristics, inputting into a pre-fitted standard dose effect model, to obtain the dose effect coefficient characteristics; According to the radiation analysis data and the dose effect coefficient characteristics, pollution assessment calculations are performed to obtain radiation classification data.

[0006] In an optional embodiment, the fitting process of the standard dose-harm relationship model includes: Obtain historical radiation dose data and historical hazard assessment data; Constructing a dose-harm linear model based on the historical radiation dose data and the historical hazard assessment data to obtain an initial dose-harm relationship model; An iterative fitting operation is performed according to the historical radiation dose data, the historical hazard assessment data and the initial dose-harm relationship model, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose-harm relationship model is obtained; The fitting process of the standard dose-effect model includes: Obtain historical radiation dose data, historical dose response coefficient characteristics, and historical dose effect data; According to the historical radiation dose data, the historical dose response coefficient characteristics and the historical dose effect data, a dose effect generalized linear mixed model is constructed to obtain an initial dose effect model; An iterative fitting operation is performed according to the historical radiation dose data, the historical dose response coefficient characteristics, the historical dose effect data and the initial dose effect model. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose effect model is obtained.

[0007] In an optional embodiment, after the water supply and drainage samples to be tested are subjected to instrument detection and simulation operations, the obtained Radiation dose data, including: Obtain the water supply and drainage samples to be tested and perform After the particle energy detection operation, we get Particle energy collection data; According to the The particle energy collection data is averaged and the result is Average particle energy; Obtaining the water supply and drainage sample to be detected, performing a photon detection operation, and obtaining photon quantity data; According to the photon quantity data and the The average particle energy Radiation dose calculation operation, get Radiation dose data; Said The formula for calculating radiation dose is as follows:

[0008] in, express Radiation dose data, Indicates the preset The inverse of the ray attenuation constant, Indicates the preset sample density, Indicates the preset dry weight density, express The average particle energy, Represents photon quantity data.

[0009] In an optional embodiment, after the water supply and drainage samples to be tested are subjected to instrument detection and simulation operations, the obtained Radiation dose data, including: Obtain the water supply and drainage samples to be tested and perform After particle energy detection, we get The actual distribution of particle energy, Particle energy collection data and Particle number data; According to the The particle energy collection data is averaged and the result is Average particle energy; According to the The actual distribution of particle energy is estimated by performing distribution estimation operations to obtain an estimate Particle energy distribution; According to the estimated Particle energy distribution, The average particle energy and Particle number data, Radiation dose calculation operation, get Radiation dose data; Said The formula for calculating radiation dose is as follows:

[0010] in, express Radiation dose data, Indicates the preset The inverse of the ray attenuation constant, express The average particle energy, express Particle number data, Indicates the preset The linear attenuation coefficient of the particle, Indicates the preset sample density, Indicates an estimate Particle energy distribution.

[0011] In an optional implementation, a dose-harm linear model is constructed based on the historical radiation dose data and the historical hazard assessment data to obtain an initial dose-harm relationship model, including: The dose-harm linear model is formulated as follows:

[0012] in, Represents biological Historical harmful assessment data, Represents biological The dose response coefficient, Indicates biological Historical radiation doses.

[0013] In an optional implementation, an iterative fitting operation is performed according to the historical radiation dose data, the historical hazard assessment data and the initial dose-harm relationship model, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose-harm relationship model is obtained, including: Initializing the initial dose-harm relationship model parameters to obtain a first dose-harm relationship model; Performing dose hazard theoretical calculation according to the first dose-harm relationship model and the historical radiation dose data to obtain first theoretical hazard assessment data; According to the first theoretical hazard assessment data and the historical hazard assessment data, error calculation is performed and the first dose-harm relationship model parameters are updated to obtain a second dose-harm relationship model; Continue iterating, and when it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, end the iteration and obtain the standard dose-harm relationship model.

[0014] In an optional embodiment, a dose-effect generalized linear mixed model is constructed based on the historical radiation dose data, the historical dose response coefficient characteristics and the historical dose effect data to obtain an initial dose effect model, including: The formula for the dose-effect generalized linear mixed model is as follows:

[0015] in, Represents biological Historical dose-effect data, Represents biological The historical dose response coefficient, Indicates biological Historical radiation dose data, is a nonlinear coefficient, represents the square term of the dose, Represents a predetermined random error that follows a specific distribution.

[0016] In an optional implementation, an iterative fitting operation is performed according to the historical radiation dose data, the historical dose response coefficient characteristics, the historical dose effect data and the initial dose effect model, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose effect model is obtained, including: Initializing the initial dose-effect model to obtain a first dose-effect relationship model; Performing a dose-effect theoretical calculation according to the first dose-effect relationship model, the historical radiation dose data, and the historical dose response coefficient characteristics to obtain first theoretical dose-effect evaluation data; According to the first theoretical dose-effect evaluation data and the historical dose-effect data, error calculation is performed and the first dose-effect relationship model parameters are updated to obtain a second dose-effect relationship model; Continue to iterate, and when it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, end the iteration and obtain the standard dose-effect model.

[0017] In an optional implementation, a contamination assessment calculation is performed based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data, including: According to the radiation analysis data, a data extraction operation is performed to obtain Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; According to the Radiation dose data, The radiation dose data and the dose effect coefficient characteristics are used to perform radiation calculations to obtain particle radiation characteristics; Performing radiation statistical operations according to the particle radiation characteristics, the simulated particle path data and the simulated photon energy distribution data to obtain radiation assessment data; According to the radiation assessment data, the pollution degree is graded to obtain radiation graded data.

[0018] In a second aspect, the present invention provides an automatic detection system for radiation pollution of water quality in water supply and drainage, comprising: The input module is used to obtain the data obtained after instrument detection and simulation operation of the water supply and drainage samples to be tested. Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; Radiation analysis module, used to convert the Radiation dose data, Packing the radiation dose data, the simulated particle path data and the simulated photon energy distribution data to obtain radiation analysis data; A dose response coefficient estimation module is used to input the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain a dose response coefficient characteristic; A dose effect coefficient estimation module is used to input the dose response coefficient characteristics into a pre-fitted standard dose effect model to obtain the dose effect coefficient characteristics; The radiation contamination range estimation module is used to perform contamination assessment calculations based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data.

[0019] Compared with the prior art, the present invention has the following beneficial effects: (1) Traditional radiation contamination detection methods mainly rely on manual experience, subjective judgment of the severity of the accident and the adoption of measures. However, the present invention automatically obtains radiation dose data, simulated particle path data and simulated photon energy distribution data, and packages them into radiation analysis data, which greatly reduces the time and subjective errors of manual operation. This automated processing method can quickly obtain and analyze data, thereby significantly improving detection efficiency.

[0020] (2) Existing detection methods often have difficulty accurately determining the type and extent of radiation contamination when faced with complex water supply and drainage systems. The present invention obtains multiple data types (such as radiation dose, particle path, and photon energy distribution) through instrument detection and simulation operations, and analyzes them in combination with the standard dose-harm relationship model and dose-effect model. This multi-dimensional data analysis method can more comprehensively reflect the characteristics of radiation contamination, thereby improving detection accuracy.

[0021] (3) In radiation contamination incidents, rapid response is key. Traditional methods rely on manual judgment and have a slow response speed. The present invention can complete contamination assessment in a short time and quickly generate radiation classification data through automated detection and model analysis. This rapid response capability can provide timely and accurate information for emergency response, thereby effectively reducing the impact of radiation contamination on the water supply and drainage system.

[0022] (4) There may be complex water flow and radiation propagation environments in water supply and drainage systems. The present invention can better adapt to such complex environments by simulating particle paths and photon energy distribution. For example, the propagation characteristics of gamma rays in water flow can be analyzed by simulating photon energy distribution data, thereby improving the applicability of the detection method in complex environments.

[0023] In summary, the present invention significantly improves the efficiency, accuracy and emergency response capability of radiation contamination detection in water supply and drainage systems through automated detection, multi-dimensional data analysis and scientific model evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for automatically detecting radiation pollution in water quality of water supply and drainage provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of an automatic detection system for radiation pollution of water quality in water supply and drainage provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Reference Figure 1 The embodiment of the present invention provides a method for automatically detecting radiation pollution in water quality of water supply and drainage, comprising the following steps: S11, obtain the water supply and drainage samples to be tested after instrument detection and simulation operation. Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; S12, the Radiation dose data, Packing the radiation dose data, the simulated particle path data and the simulated photon energy distribution data to obtain radiation analysis data; S13, inputting the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain a dose response coefficient characteristic; S14, inputting the dose response coefficient characteristics into a pre-fitted standard dose effect model to obtain dose effect coefficient characteristics; S15, performing pollution assessment calculation according to the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data.

[0027] In step S11, it should be noted that obtaining the water supply and drainage samples to be tested is the first step in the detection process. This step needs to ensure the representativeness and integrity of the samples so as to accurately reflect the radiation contamination status of the overall water quality. The samples are obtained by professionals at the water source, water supply pipeline or user end, and are collected using sterile containers according to the prescribed methods and time intervals. After the samples are collected, they need to be properly preserved to avoid contamination or changes to ensure the accuracy of the analysis. During the sample acquisition process, it is necessary to record information such as the source of the sample, the collection time, and the environmental conditions. The storage conditions of the samples also need to be strictly controlled. Exemplarily, the present invention adopts storage in a low temperature environment to prevent changes in the properties of the samples caused by microbial activity. Of course, other storage methods can also be selected according to different application scenarios, and the present invention is not limited to this.

[0028] It should be noted that The radiation dose estimation operation uses a gamma spectrometer to probe water and wastewater samples to identify and measure the Ray energy. Through the detection of the instrument, we can get Radiation dose data, which reflects the The radiation intensity of the rays. The radiation dose data is obtained by measuring the The energy distribution of the rays is then used to calculate the dose. Radiation dose estimation operation Energy spectrometer to detect water supply and drainage samples ray. Radiation dose data reflects the The radiation intensity of the rays. Radiation dose data are based on The spectrometer detected The number of particles and energy distribution are calculated. The energy deposition simulation operation simulates the energy deposition process of particles in water supply and drainage samples through computer simulation. This operation can obtain simulated particle path data, which show the propagation path and energy deposition of particles in the sample. The photon energy distribution simulation operation simulates the energy distribution of photons in water supply and drainage samples. This operation can obtain simulated photon energy distribution data, which reflects the energy deposition pattern of photons in the sample. Radiation dose data, The radiation dose data, simulated particle path data, and simulated photon energy distribution data are packaged to integrate these data into a radiation analysis data set. This data set contains a comprehensive analysis of the radiation characteristics of the water supply and drainage samples and can be used for subsequent radiation risk assessment and decision support. The data packaging operation ensures the organization and storage of all relevant data for further analysis and application.

[0029] It should be noted that after obtaining the water supply and drainage samples to be tested and conducting instrument detection and simulation operations, the Radiation dose data, including: Obtain the water supply and drainage samples to be tested and perform After the particle energy detection operation, we get Particle energy collection data; According to the The particle energy collection data is averaged and the result is Average particle energy; Obtaining the water supply and drainage sample to be detected, performing a photon detection operation, and obtaining photon quantity data; According to the photon quantity data and the The average particle energy Radiation dose calculation operation, get Radiation dose data; Said The formula for calculating radiation dose is as follows:

[0030] in, express Radiation dose data, Indicates the preset The inverse of the ray attenuation constant, Indicates the preset sample density, Indicates the preset dry weight density, express The average particle energy, Represents photon quantity data.

[0031] It should be noted that The radiation dose estimation operation begins with the water supply and drainage samples to be inspected. Particle energy detection operation, this step is done by using This is done with a spectrometer, which measures The energy distribution of the ray. Through this detection, we can get Particle energy ensemble data, which is a set of data containing multiple measurements reflecting the Next, The particle energy collection data is averaged to obtain The average energy of the particles. This operation calculates the average energy of all detected particles by statistical methods. The average value of the particle energy, which represents the The average energy level of the ray. At the same time, the photon detection operation is performed to obtain the photon quantity data. This step also uses The spectrometer is completed, and the instrument records the number of photons detected in a specific time. This number reflects The intensity of the ray. With the photon number data and The average energy of the particles can be Radiation dose calculation operation. This operation uses preset parameters and detected data to estimate the radiation dose through a specific calculation formula. In this process, the number of photons and The average particle energy is calculated using the preset The inverse of the ray attenuation constant, for example, is set The reciprocal value of the radiation attenuation constant is 0.01, the sample density is set to 1, and the dry weight density is set to 0.5. Of course, other numerical settings can be selected according to different application scenarios, and the present invention does not limit this. Finally, through the comprehensive calculation of these data and parameters, we get Radiation dose data , which represents the water supply and drainage samples to be tested Radiation dose.

[0032] It should be noted that after obtaining the water supply and drainage samples to be tested and conducting instrument detection and simulation operations, the Radiation dose data, including: Obtain the water supply and drainage samples to be tested and perform After particle energy detection, we get The actual distribution of particle energy, Particle energy collection data and Particle number data; According to the The particle energy collection data is averaged and the result is Average particle energy; According to the The actual distribution of particle energy is estimated by performing distribution estimation operations to obtain an estimate Particle energy distribution; According to the estimated Particle energy distribution, The average particle energy and Particle number data, Radiation dose calculation operation, get Radiation dose data; Said The formula for calculating radiation dose is as follows:

[0033] in, express Radiation dose data, Indicates the preset The inverse of the ray attenuation constant, express The average particle energy, express Particle number data, Indicates the preset The linear attenuation coefficient of the particle, Indicates the preset sample density, Indicates an estimate Particle energy distribution.

[0034] It should be noted that The radiation dose estimation operation first requires the water supply and drainage samples to be tested to be Particle energy detection, this step uses Energy spectrometer to measure the The energy distribution of particles. Through this detection, we can get The actual distribution of particle energy, Particle energy collection data and Particle number data. The true particle energy distribution provides detailed information about the particle energy, while Particle energy ensemble data is a set of data containing multiple measurements, reflecting the Energy characteristics of particles. The particle count data records the total number of beta particles detected in a specific period of time. The particle energy collection data is averaged to obtain The average energy of the particles. This operation calculates the average energy of all detected particles by statistical methods. The average value of the particle energy, which represents the The average energy level of the rays. Then, according to The actual distribution of particle energy is estimated by performing distribution estimation operation to obtain the estimated Particle energy distribution. With an estimated Particle energy distribution, The average particle energy and Particle number data can be used Radiation dose calculation operation. This operation uses preset parameters and detected data to estimate the radiation dose through a specific calculation formula. In this process, The radiation dose estimation operation also starts with Detection of particle energy, collection True distribution of particle energy, energy collection data and Particle number data. Using this data, calculate The average energy of the particles and estimate Particle energy distribution. Using the preset The inverse of the ray attenuation constant, for example, is set The reciprocal value of the ray attenuation constant is 0.005. The linear attenuation coefficient of the particle is 0.0001. Of course, other numerical settings may be selected according to different application scenarios, and the present invention is not limited thereto.

[0035] In step S13, the fitting process of the standard dose-harm relationship model includes: acquiring historical radiation dose data and historical hazard assessment data; constructing a dose-harm linear model based on the historical radiation dose data and the historical hazard assessment data to obtain an initial dose-harm relationship model; performing an iterative fitting operation based on the historical radiation dose data, the historical hazard assessment data and the initial dose-harm relationship model, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, terminating the iteration and obtaining the standard dose-harm relationship model.

[0036] It should be noted that, in step S13, the standard dose-harm relationship model fitting process first obtains historical radiation dose data and historical hazard assessment data. These data are derived from previous experiments, measurements or long-term environmental monitoring records, providing the necessary input information for the model. Subsequently, a dose-harm linear model is constructed using these historical data to obtain an initial dose-harm relationship model, which is a simplified linear model used to preliminarily describe the relationship between radiation dose and harmful effects. Next, an iterative fitting operation is performed based on the historical radiation dose data, the historical hazard assessment data and the initial dose-harm relationship model. This process involves continuous adjustment and optimization of model parameters to better fit the actual data. Each iteration updates the model parameters based on the difference between the model prediction results and the actual observed data. When the number of iterations reaches the preset maximum number of iterations, the iterative process ends. Exemplarily, the present invention presets a maximum number of iterations of 1000 times. Of course, this value can be adjusted according to the complexity of the model and the computing resources, and the present invention does not limit this. The model obtained at this time is the standard dose-harm relationship model, which can more accurately describe the relationship between radiation dose and harmful effects.

[0037] It should be noted that, based on the historical radiation dose data and the historical hazard assessment data, a dose hazard linear model is constructed to obtain an initial dose hazard relationship model, including: The dose-harm linear model is formulated as follows:

[0038] in, Represents biological Historical harmful assessment data, Represents biological The dose response coefficient, Indicates biological Historical radiation doses.

[0039] It should be noted that the process of constructing a dose-harm linear model is based on historical radiation dose data and historical hazard assessment data. Historical radiation dose data refers to the radiation dose recorded when radiation measurements were taken on organisms or the environment in the past, while historical hazard assessment data refers to the assessment records of harmful effects of radiation on organisms. These data provide basic input information for the model to analyze the relationship between radiation dose and harmful effects. In this process, Represents biological Historical hazard assessment data, which can be a quantification of the adverse effects observed in organisms at specific radiation doses, such as mortality, growth inhibition, or other health indicators of the organisms. Represents biological The dose response coefficient is used to describe the sensitivity of an organism to radiation, that is, the degree of harmful effects caused by a unit radiation dose. Indicates biological The historical radiation dose is the radiation dose that the organism has received in the past.

[0040] It should be noted that, according to the historical radiation dose data, the historical hazard assessment data and the initial dose-harm relationship model, an iterative fitting operation is performed, and when it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, the iteration is terminated and the standard dose-harm relationship model is obtained, including: Initializing the initial dose-harm relationship model parameters to obtain a first dose-harm relationship model; Performing dose hazard theoretical calculation according to the first dose-harm relationship model and the historical radiation dose data to obtain first theoretical hazard assessment data; According to the first theoretical hazard assessment data and the historical hazard assessment data, error calculation is performed and the first dose-harm relationship model parameters are updated to obtain a second dose-harm relationship model; Continue iterating, and when it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, end the iteration and obtain the standard dose-harm relationship model.

[0041] It should be noted that the iterative fitting operation is performed on the basis of the existing initial dose-harm relationship model, and the purpose is to more accurately reflect the relationship between the historical radiation dose and the harmful assessment data by continuously adjusting the model parameters. This process begins with initializing the parameters of the initial dose-harm relationship model, assigning initial values ​​to the parameters such as the dose response coefficient in the model, and thus obtaining the first dose-harm relationship model. Subsequently, based on the first dose-harm relationship model and the historical radiation dose data, a dose-harm theory calculation is performed. This step is to predict the harmful effects by substituting the radiation dose data into the model, thereby obtaining the first theoretical harmful assessment data. These theoretical data are predictions of the actual harmful effects, and they will be compared with the actual historical harmful assessment data. Next, by comparing the first theoretical harmful assessment data with the historical harmful assessment data, an error calculation is performed, which involves calculating the difference between the model prediction value and the actual observed value. This error will be used to guide the update of the model parameters, thereby obtaining the second dose-harm relationship model. This process will be repeated, and each time the new model parameters are used for theoretical calculations, and the model is updated to reduce the prediction error. When the number of iterations reaches the preset maximum number of iterations, the iterative process ends. The model obtained at this time is the standard dose-harm relationship model, which can more accurately describe the relationship between radiation dose and harmful effects.

[0042] In step S14, the fitting process of the standard dose-effect model includes: obtaining historical radiation dose data, historical dose response coefficient characteristics and historical dose effect data; constructing a dose-effect generalized linear mixed model based on the historical radiation dose data, the historical dose response coefficient characteristics and the historical dose effect data to obtain an initial dose-effect model; performing an iterative fitting operation based on the historical radiation dose data, the historical dose response coefficient characteristics, the historical dose effect data and the initial dose-effect model, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, terminating the iteration and obtaining the standard dose-effect model.

[0043] It should be noted that, based on the historical radiation dose data, the historical dose response coefficient characteristics and the historical dose effect data, a dose effect generalized linear mixed model is constructed to obtain an initial dose effect model, including: The formula for the dose-effect generalized linear mixed model is as follows:

[0044] in, Represents biological Historical dose-effect data, Represents biological The historical dose response coefficient, Indicates biological Historical radiation dose data, is a nonlinear coefficient, represents the square term of the dose, Represents a predetermined random error that follows a specific distribution.

[0045] It should be noted that, according to the historical radiation dose data, the historical dose response coefficient characteristics, the historical dose effect data and the initial dose effect model, an iterative fitting operation is performed, and when it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, the iteration is terminated and a standard dose effect model is obtained, including: Initializing the initial dose-effect model to obtain a first dose-effect relationship model; Performing a dose-effect theoretical calculation according to the first dose-effect relationship model, the historical radiation dose data, and the historical dose response coefficient characteristics to obtain first theoretical dose-effect evaluation data; According to the first theoretical dose-effect evaluation data and the historical dose-effect data, error calculation is performed and the first dose-effect relationship model parameters are updated to obtain a second dose-effect relationship model; Continue to iterate, and when it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, end the iteration and obtain the standard dose-effect model.

[0046] It should be noted that in step S14, the primary task of the standard dose-effect model fitting process is to obtain historical radiation dose data, historical dose response coefficient characteristics and historical dose effect data. These data provide basic input information for the model, wherein the historical radiation dose data and historical dose response coefficient characteristics can be derived from previous measurements or experimental records, while the historical dose effect data comes from long-term observation and research on organisms or environments.

[0047] Using these historical data, a dose-effect generalized linear mixed model was constructed to obtain the initial dose-effect model. represents the historical dose-effect data of organism j, represents the historical dose-response coefficient of organism j, represents the historical radiation dose data for organism j, is a nonlinear coefficient used to describe the nonlinear relationship between dose and effect. represents the square term of the dose, and It represents a random error that is preset and obeys a specific distribution. For example, the present invention is set to a normal distribution with a mean of 0 and a variance of 1. Of course, other distributions can be selected according to different application scenarios, and the present invention is not limited to this.

[0048] After obtaining the initial model, an iterative fitting operation is performed. This process starts with initializing the initial dose-effect model to obtain the first dose-effect relationship model. Then, according to the first dose-effect relationship model, historical radiation dose data and historical dose response coefficient characteristics, a dose-effect theoretical calculation is performed to obtain the first theoretical dose-effect evaluation data. These theoretical data are predictions of the actual dose effect based on the current parameter settings of the model. According to the first theoretical dose-effect evaluation data and the historical dose-effect data, an error calculation is performed, which involves calculating the difference between the predicted value of the model and the actual observed value. According to these errors, the parameters of the first dose-effect relationship model are updated to obtain the second dose-effect relationship model. This process is repeated, and each iteration is intended to reduce the prediction error and improve the accuracy of the model. The iterative process continues until it is determined that the number of iterations is greater than the preset maximum number of iterations, at which time the iteration ends and a standard dose-effect model is obtained. Exemplarily, the present invention sets the maximum number of iterations to 1000 times, and this value can be adjusted according to the complexity of the model and the computing resources, and the present invention is not limited to this.

[0049] In step S15, a pollution assessment calculation is performed based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data, including: According to the radiation analysis data, a data extraction operation is performed to obtain Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; According to the Radiation dose data, The radiation dose data and the dose effect coefficient characteristics are used to perform radiation calculations to obtain particle radiation characteristics; Performing radiation statistical operations according to the particle radiation characteristics, the simulated particle path data and the simulated photon energy distribution data to obtain radiation assessment data; According to the radiation assessment data, the pollution degree is graded to obtain radiation graded data.

[0050] It should be noted that in step S15, the pollution assessment calculation is performed based on the radiation analysis data and the dose effect coefficient characteristics to obtain the radiation classification data. This process first involves the extraction operation of the radiation analysis data, from which the Radiation dose data, Radiation dose data, simulated particle path data, and simulated photon energy distribution data. These data are obtained through previous radiation dose measurements and simulation operations, and they provide basic information for contamination assessment. Next, use Radiation dose data, The radiation dose data and the dose effect coefficient characteristics are used for radiation calculation to obtain the particle radiation characteristics. This step involves the quantitative analysis of the relationship between particle energy, dose and effect, as well as the comparison and comprehensive evaluation of the effects of different types of radiation. According to the particle radiation characteristics, simulated particle path data and simulated photon energy distribution data, radiation statistical operations are performed. This includes statistical analysis of the propagation path of particles in the medium, energy deposition pattern and energy distribution to evaluate the potential impact of radiation on the environment or organisms. Finally, according to the radiation assessment data, the degree of pollution is graded to obtain radiation grading data. This step involves setting thresholds according to the radiation assessment data and dividing the radiation level into different levels. Exemplarily, the present invention is set as low, medium and high radiation pollution levels. In addition, the low radiation pollution level is set to 0-10mSv / year, the medium radiation pollution level is set to 10-50 mSv / year, and the high radiation pollution level is set to greater than 50mSv / year. Of course, depending on the application scenario, other level setting methods can also be selected, and the present invention is not limited to this. In summary, the present invention achieves automatic detection effect by designing a method for automatic detection of radiation pollution in water quality of water supply and drainage, and can improve the detection speed and reduce the time cost caused by manual detection.

[0051] Reference Figure 2 The embodiment of the present invention provides an automatic detection system for radiation pollution of water quality in water supply and drainage, comprising: The input module is used to obtain the data obtained after instrument detection and simulation operation of the water supply and drainage samples to be tested. Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; Radiation analysis module, used to convert the Radiation dose data, Packing the radiation dose data, the simulated particle path data and the simulated photon energy distribution data to obtain radiation analysis data; A dose response coefficient estimation module is used to input the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain a dose response coefficient characteristic; A dose effect coefficient estimation module is used to input the dose response coefficient characteristics into a pre-fitted standard dose effect model to obtain the dose effect coefficient characteristics; The radiation contamination range estimation module is used to perform contamination assessment calculations based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data. The fitting process of the standard dose-harm relationship model includes: Obtain historical radiation dose data and historical hazard assessment data; Constructing a dose-harm linear model based on the historical radiation dose data and the historical hazard assessment data to obtain an initial dose-harm relationship model; An iterative fitting operation is performed according to the historical radiation dose data, the historical hazard assessment data and the initial dose-harm relationship model, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose-harm relationship model is obtained; The fitting process of the standard dose-effect model includes: Obtain historical radiation dose data, historical dose response coefficient characteristics, and historical dose effect data; According to the historical radiation dose data, the historical dose response coefficient characteristics and the historical dose effect data, a dose effect generalized linear mixed model is constructed to obtain an initial dose effect model; An iterative fitting operation is performed according to the historical radiation dose data, the historical dose response coefficient characteristics, the historical dose effect data and the initial dose effect model. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose effect model is obtained.

[0052] It should be noted that the automatic detection device for radiation contamination of water quality for water supply and drainage provided in an embodiment of the present invention is used to execute all the process steps of the automatic detection method for radiation contamination of water quality for water supply and drainage in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be elaborated on again.

[0053] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented, for example Figure 1 Alternatively, the processor implements the functions of each module / unit in the above-mentioned device embodiments when executing the computer program.

[0054] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0055] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0056] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and various interfaces and lines are used to connect various parts of the entire electronic device.

[0057] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0058] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0059] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0060] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for automatically detecting radiation pollution in water quality of water supply and drainage, characterized in that: Executed by a computer, including: After the water supply and drainage samples to be tested are tested and simulated, the Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; The Radiation dose data, Packing the radiation dose data, the simulated particle path data and the simulated photon energy distribution data to obtain radiation analysis data; According to the radiation analysis data, inputting into a pre-fitted standard dose-harm relationship model, to obtain a dose response coefficient characteristic; According to the dose response coefficient characteristics, inputting into a pre-fitted standard dose effect model, to obtain the dose effect coefficient characteristics; According to the radiation analysis data and the dose effect coefficient characteristics, pollution assessment calculations are performed to obtain radiation classification data.

2. The method for automatic detection of radiation pollution of water quality of water supply and drainage according to claim 1 is characterized in that: The fitting process of the standard dose-harm relationship model includes: Obtain historical radiation dose data and historical hazard assessment data; Constructing a dose-harm linear model based on the historical radiation dose data and the historical hazard assessment data to obtain an initial dose-harm relationship model; An iterative fitting operation is performed according to the historical radiation dose data, the historical hazard assessment data and the initial dose-harm relationship model, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose-harm relationship model is obtained; The fitting process of the standard dose-effect model includes: Obtain historical radiation dose data, historical dose response coefficient characteristics, and historical dose effect data; According to the historical radiation dose data, the historical dose response coefficient characteristics and the historical dose effect data, a dose effect generalized linear mixed model is constructed to obtain an initial dose effect model; An iterative fitting operation is performed according to the historical radiation dose data, the historical dose response coefficient characteristics, the historical dose effect data and the initial dose effect model. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose effect model is obtained.

3. The method for automatic detection of radiation pollution of water quality of water supply and drainage according to claim 1 is characterized in that: After the water supply and drainage samples to be tested are tested and simulated, the Radiation dose data, including: Obtain the water supply and drainage samples to be tested and perform After the particle energy detection operation, we get Particle energy collection data; According to the The particle energy collection data is averaged and the result is Average particle energy; Obtaining the water supply and drainage sample to be detected, performing a photon detection operation, and obtaining photon quantity data; According to the photon quantity data and the The average particle energy Radiation dose calculation operation, get Radiation dose data; Said The formula for calculating radiation dose is as follows: ； in, express Radiation dose data, Indicates the preset The inverse of the ray attenuation constant, Indicates the preset sample density, Indicates the preset dry weight density, express The average particle energy, Represents photon quantity data.

4. The method for automatic detection of radiation pollution in water quality of water supply and drainage according to claim 1 is characterized in that: After the water supply and drainage samples to be tested are tested and simulated, the Radiation dose data, including: Obtain the water supply and drainage samples to be tested and perform After particle energy detection, we get The actual distribution of particle energy, Particle energy collection data and Particle number data; According to the The particle energy collection data is averaged and the result is Average particle energy; According to the The actual distribution of particle energy is estimated by performing distribution estimation operations to obtain an estimate Particle energy distribution; According to the estimated Particle energy distribution, The average particle energy and Particle number data, Radiation dose calculation operation, get Radiation dose data; Said The formula for calculating radiation dose is as follows: ； in, express Radiation dose data, Indicates the preset The inverse of the ray attenuation constant, express The average particle energy, express Particle number data, Indicates the preset The linear attenuation coefficient of the particle, Indicates the preset sample density, Indicates an estimate Particle energy distribution.

5. The method for automatic detection of radiation pollution in water quality of water supply and drainage according to claim 2 is characterized in that: According to the historical radiation dose data and the historical hazard assessment data, a dose hazard linear model is constructed to obtain an initial dose hazard relationship model, including: The dose-harm linear model is formulated as follows: ； in, Represents biological Historical harmful assessment data, Represents biological The dose response coefficient, Indicates biological Historical radiation doses.

6. The method for automatic detection of radiation pollution in water quality of water supply and drainage according to claim 2 is characterized in that: An iterative fitting operation is performed according to the historical radiation dose data, the historical hazard assessment data and the initial dose-harm relationship model, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose-harm relationship model is obtained, including: Initializing the initial dose-harm relationship model parameters to obtain a first dose-harm relationship model; Performing dose hazard theoretical calculation according to the first dose-harm relationship model and the historical radiation dose data to obtain first theoretical hazard assessment data; According to the first theoretical hazard assessment data and the historical hazard assessment data, error calculation is performed and the first dose-harm relationship model parameters are updated to obtain a second dose-harm relationship model; Continue iterating, and when it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, end the iteration and obtain the standard dose-harm relationship model.

7. The method for automatic detection of radiation pollution in water quality of water supply and drainage according to claim 2 is characterized in that: According to the historical radiation dose data, the historical dose response coefficient characteristics and the historical dose effect data, a dose effect generalized linear mixed model is constructed to obtain an initial dose effect model, including: The formula for the dose-effect generalized linear mixed model is as follows: ； in, Represents biological Historical dose-effect data, Represents biological The historical dose response coefficient, Indicates biological Historical radiation dose data, is a nonlinear coefficient, represents the square term of the dose, Represents a predetermined random error that follows a specific distribution.

8. The method for automatic detection of radiation pollution in water quality of water supply and drainage according to claim 2 is characterized in that: According to the historical radiation dose data, the historical dose response coefficient characteristics, the historical dose effect data and the initial dose effect model, an iterative fitting operation is performed, and when it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration is terminated and a standard dose effect model is obtained, including: Initializing the initial dose-effect model to obtain a first dose-effect relationship model; Performing a dose-effect theoretical calculation according to the first dose-effect relationship model, the historical radiation dose data, and the historical dose response coefficient characteristics to obtain first theoretical dose-effect evaluation data; According to the first theoretical dose-effect evaluation data and the historical dose-effect data, error calculation is performed and the first dose-effect relationship model parameters are updated to obtain a second dose-effect relationship model; Continue to iterate, and when it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, end the iteration and obtain the standard dose-effect model.

9. The method for automatic detection of radiation pollution in water quality of water supply and drainage according to claim 1 is characterized in that: According to the radiation analysis data and the dose effect coefficient characteristics, a pollution assessment calculation is performed to obtain radiation classification data, including: According to the radiation analysis data, a data extraction operation is performed to obtain Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; According to the Radiation dose data, The radiation dose data and the dose effect coefficient characteristics are used to perform radiation calculations to obtain particle radiation characteristics; Performing radiation statistical operations according to the particle radiation characteristics, the simulated particle path data and the simulated photon energy distribution data to obtain radiation assessment data; According to the radiation assessment data, the pollution degree is graded to obtain radiation graded data.

10. An automatic detection system for radiation pollution of water quality in water supply and drainage, characterized in that: include: The input module is used to obtain the data obtained after instrument detection and simulation operation of the water supply and drainage samples to be tested. Radiation dose data, Radiation dose data, simulated particle path data and simulated photon energy distribution data; Radiation analysis module, used to convert the Radiation dose data, Packing the radiation dose data, the simulated particle path data and the simulated photon energy distribution data to obtain radiation analysis data; A dose response coefficient estimation module is used to input the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain a dose response coefficient characteristic; A dose effect coefficient estimation module is used to input the dose response coefficient characteristics into a pre-fitted standard dose effect model to obtain the dose effect coefficient characteristics; The radiation contamination range estimation module is used to perform contamination assessment calculations based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data.

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