An automatic detection method and system for water quality radiation pollution in water supply and drainage

Abstract

The present invention discloses an automatic detection method and system for water quality radiation pollution in water supply and drainage. The method includes: obtaining ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data after instrument detection and simulation operations on the water supply and drainage samples to be detected; packing the ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data to obtain radiation analysis data; inputting the radiation analysis data into a pre-fitted standard dose harmful relationship model to obtain dose response coefficient characteristics; inputting the dose response coefficient characteristics into a pre-fitted standard dose effect model to obtain dose effect coefficient characteristics; and performing pollution assessment calculations based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation grading data. This method can improve the speed of radiation detection in water supply and drainage.

Description

Technical Field

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

[0002] As an important infrastructure, the demand for radiation detection in water supply and drainage is currently on the rise. Especially for the monitoring and management of water supply and drainage systems, more scientific and systematic countermeasures are needed. In water supply and drainage engineering, the types of radiation are relatively complex, mainly including α, β, and γ. Among them, β and γ both belong to external irradiation, while α particles have poor penetration ability and can generally only penetrate 1-2 mm of tissue, but their energy deposition is relatively high. γ rays have high energy and extremely strong penetration ability, and can spread through air and water flow, causing large-area radioactive pollution. Therefore, in the design and operation of water supply and drainage systems, it is necessary to focus on the detection and protection of β and γ rays to ensure water supply safety.

[0003] Most of the existing responses to radiation pollution incidents stay at the preliminary estimation of the accident severity and the measures to be taken based on experience and subjective judgment. The response speed of this manual method is often very limited and cannot timely reflect the radiation pollution problems in water supply and drainage. Summary of the Invention

[0004] The present invention provides an automatic detection method and system for water quality radiation pollution in water supply and drainage to solve the problem of slow response speed in water supply and drainage radiation detection.

[0005] In a first aspect, to solve the above technical problems, the present invention provides an automatic detection method for water quality radiation pollution in water supply and drainage, including:

[0006] Obtaining the ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data obtained after instrument detection and simulation operation on the water supply and drainage sample to be detected;

[0007] 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;

[0008] Inputting the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain dose response coefficient characteristics;

[0009] Inputting the dose response coefficient characteristics into a pre-fitted standard dose-effect model to obtain dose effect coefficient characteristics;

[0010] Based on the radiation analysis data and the dose-effect coefficient characteristics, perform pollution assessment calculations to obtain radiation classification data.

[0011] In an alternative embodiment, the fitting process of the standard dose-harm relationship model includes:

[0012] Obtain historical radiation dose data and historical harm assessment data;

[0013] Based on the historical radiation dose data and the historical harm assessment data, construct a dose-harm linear model to obtain an initial dose-harm relationship model;

[0014] Based on the historical radiation dose data, the historical harm assessment data, and the initial dose-harm relationship model, perform iterative fitting operations. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, end the iteration and obtain the standard dose-harm relationship model;

[0015] The fitting process of the standard dose-effect model includes:

[0016] Obtain historical radiation dose data, historical dose-response coefficient characteristics, and historical dose-effect data;

[0017] Based on the historical radiation dose data, the historical dose-response coefficient characteristics, and the historical dose-effect data, construct a dose-effect generalized linear mixed model to obtain an initial dose-effect model;

[0018] Based on the historical radiation dose data, the historical dose-response coefficient characteristics, the historical dose-effect data, and the initial dose-effect model, perform iterative fitting operations. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, end the iteration and obtain the standard dose-effect model.

[0019] In an alternative embodiment, after performing instrument detection and simulation operations on the water supply and drainage sample to be detected, obtain ray dose data, including:

[0020] Obtain the particle energy detection operation on the water supply and drainage sample to be detected, and obtain particle energy set data;

[0021] Based on the particle energy set data, perform a mean calculation operation to obtain average particle energy;

[0022] Obtain the photon number data after performing photon detection operations on the water supply and drainage sample to be detected;

[0023] Based on the photon number data and the The average particle energy is used to perform a ray dose calculation operation to obtain ray dose data;

[0024] The ray dose calculation formula is as follows:

[0025]

[0026] Wherein, represents ray radiation dose data, represents a preset reciprocal of the ray attenuation constant, represents a preset sample density, represents a preset dry weight density, represents the average particle energy, represents photon number data.

[0027] In an alternative embodiment, after obtaining instrument detection and simulation operations on the water supply and drainage sample to be detected, the ray dose data obtained includes:

[0028] Obtain the particle energy detection on the water supply and drainage sample to be detected, and obtain the true distribution of particle energy, the particle energy set data, and the particle number data;

[0029] According to the particle energy set data, perform a mean calculation operation to obtain the average particle energy;

[0030] According to the true distribution of particle energy, perform a distribution estimation operation to obtain an estimated particle energy distribution;

[0031] According to the estimated particle energy distribution, the average particle energy, and the particle number data, perform a ray dose calculation operation to obtain ray dose data;

[0032] The ray dose calculation formula is as follows:

[0033]

[0034] Among them, represents the ray radiation dose data, represents the preset reciprocal of the ray attenuation constant, represents the average particle energy, represents the particle number data, represents the preset linear attenuation coefficient of the particle, represents the preset sample density, represents the estimated particle energy distribution.

[0035] In an alternative embodiment, according to the historical radiation dose data and the historical harmful assessment data, a dose-harm linear model is constructed to obtain an initial dose-harm relationship model, including:

[0036] The formula of the dose-harm linear model is as follows:

[0037]

[0038] Among them, represents the historical harmful assessment data of the organism , represents the dose response coefficient of the organism , represents the historical radiation dose to the organism .

[0039] In an alternative embodiment, according to the historical radiation dose data, the historical harmful assessment data, and the initial dose-harm relationship model, an iterative fitting operation is performed. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, the iteration ends and a standard dose-harm relationship model is obtained, including:

[0040] Initialize the parameters of the initial dose-harm relationship model to obtain a first dose-harm relationship model;

[0041] According to the first dose-harm relationship model and the historical radiation dose data, perform dose-harm theoretical calculations to obtain first theoretical harmful assessment data;

[0042] According to the first theoretical harmful assessment data and the historical harmful assessment data, perform error calculations and update the parameters of the first dose-harm relationship model to obtain a second dose-harm relationship model;

[0043] Continue the iteration. 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.

[0044] In an alternative 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:

[0045] The formula of the dose-effect generalized linear mixed model is as follows:

[0046]

[0047] Wherein, represents the historical dose-effect data of the organism , represents the historical dose-response coefficient of the organism , represents the historical radiation dose data of the organism , is a non-linear coefficient, represents the square term of the dose, represents a random error that is preset and follows a specific distribution.

[0048] In an alternative embodiment, an iterative fitting operation is performed based on 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 ended and a standard dose-effect model is obtained, including:

[0049] Initialize the initial dose-effect model to obtain a first dose-effect relationship model;

[0050] Based on the first dose-effect relationship model, the historical radiation dose data, and the historical dose-response coefficient characteristics, perform dose-effect theoretical calculations to obtain first theoretical dose-effect evaluation data;

[0051] Based on the first theoretical dose-effect evaluation data and the historical dose-effect data, perform error calculations and update the parameters of the first dose-effect relationship model to obtain a second dose-effect relationship model;

[0052] Continue the iteration. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, end the iteration and obtain a standard dose-effect model.

[0053] In an alternative embodiment, pollution assessment calculations are performed based on the radiation analysis data and the dose-effect coefficient characteristics to obtain radiation classification data, including:

[0054] Based on the radiation analysis data, perform data extraction operations to obtain ray dose data, Ray dose data, simulated particle path data, and simulated photon energy distribution data;

[0055] According to the Ray dose data, the Ray dose data and the dose-effect coefficient characteristics, perform radiation calculations to obtain particle radiation characteristics;

[0056] According to the particle radiation characteristics, the simulated particle path data, and the simulated photon energy distribution data, perform radiation statistical operations to obtain radiation assessment data;

[0057] According to the radiation assessment data, perform pollution level classification to obtain radiation classification data.

[0058] In a second aspect, the present invention provides an automatic detection system for water quality radiation pollution in water supply and drainage, including:

[0059] An input module for obtaining, after instrument detection and simulation operations on a water supply and drainage sample to be detected, the Ray dose data, Ray dose data, simulated particle path data, and simulated photon energy distribution data;

[0060] A radiation analysis module for packing 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;

[0061] A dose response coefficient estimation module for inputting the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain dose response coefficient characteristics;

[0062] A dose effect coefficient estimation module for inputting the dose response coefficient characteristics into a pre-fitted standard dose effect model to obtain dose effect coefficient characteristics;

[0063] A radiation pollution range estimation module for performing pollution assessment calculations based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] (1) Traditional radiation pollution detection methods mainly rely on manual experience, subjectively judging the severity of accidents and taking measures. In contrast, the present invention automatically obtains ray dose data, simulated particle path data, and simulated photon energy distribution data, and packages them as radiation analysis data, greatly reducing the time of manual operation and subjective errors. This automated processing method can quickly obtain and analyze data, thus significantly improving the detection efficiency.

[0066] (2) Existing detection methods often have difficulty accurately judging the type and degree of radiation pollution when facing complex water supply and drainage systems. The present invention obtains multiple data types (such as ray 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 pollution, thereby improving the detection accuracy.

[0067] (3) In radiation pollution incidents, rapid response is crucial. Traditional methods rely on manual judgment and thus have a slow response speed. The present invention can complete pollution assessment within a short time and quickly generate radiation grading data through automated detection and model analysis. This rapid response ability can provide timely and accurate information for emergency treatment, thereby effectively reducing the impact of radiation pollution on the water supply and drainage system.

[0068] (4) The water supply and drainage system may have a complex water flow and radiation propagation environment. The present invention can better adapt to this complex environment by simulating particle paths and photon energy distributions. For example, the propagation characteristics of γ rays in water can be analyzed through simulated photon energy distribution data, thereby improving the applicability of the detection method in complex environments.

[0069] In summary, the present invention significantly improves the detection efficiency, accuracy, and emergency response ability of radiation pollution in the water supply and drainage system through automated detection, multi-dimensional data analysis, and scientific model evaluation. Brief Description of the Drawings

[0070] Figure 1 is a schematic flow chart of the automatic detection method for water quality radiation pollution in the water supply and drainage provided by the embodiment of the present invention;

[0071] Figure 2 is a schematic structural diagram of the automatic detection system for water quality radiation pollution in the water supply and drainage provided by the embodiment of the present invention. Detailed Embodiments

[0072] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0073] Referring to Figure 1 , an automatic detection method for water quality radiation pollution in water supply and drainage provided by an embodiment of the present invention includes the following steps:

[0074] S11, after obtaining instrument detection and simulation operations on the water supply and drainage sample to be detected, obtain ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data;

[0075] S12, package 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;

[0076] S13, input the radiation analysis data into a pre-fitted standard dose harmful relationship model to obtain dose response coefficient characteristics;

[0077] S14, input the dose response coefficient characteristics into a pre-fitted standard dose effect model to obtain dose effect coefficient characteristics;

[0078] S15, perform pollution assessment calculations based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data.

[0079] In step S11, it should be noted that obtaining the water supply and drainage sample to be detected is the first step in the detection process. This step needs to ensure the representativeness and integrity of the sample so that it can accurately reflect the radiation pollution status of the overall water quality. The sample is obtained by professional personnel at the water source, water supply pipeline, or user end, and collected using a sterile container according to the specified method and time interval. After the sample is collected, it needs to be properly stored to avoid contamination or change to ensure the accuracy of the analysis. During the sample acquisition process, information such as the sample source, collection time, and environmental conditions needs to be recorded. The storage conditions of the sample also need to be strictly controlled. Exemplarily, the present invention adopts storage in a low-temperature environment to prevent changes in the sample properties caused by microbial activities. Of course, according to different application scenarios, other storage methods can also be selected, and the present invention does not limit this.

[0080] It should be noted that The ray dose estimation operation uses a gamma spectrometer to detect water supply and drainage samples to identify and measure the ray energy in the samples. Through the detection of the instrument, ray dose data can be obtained, and these data reflect the radiation intensity of the rays in the samples. The acquisition of ray dose data is through the energy distribution measurement of rays by the spectrometer, and then the dose is calculated according to the energy distribution. The ray dose estimation operation uses a spectrometer to detect rays in water supply and drainage samples. Ray dose data reflect the radiation intensity of the rays in the samples. Ray dose data are calculated according to the number and energy distribution of particles detected by the spectrometer. The energy deposition simulation operation is to simulate 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 samples. The photon energy distribution simulation operation is to simulate the energy distribution of photons in water supply and drainage samples. Through this operation, simulated photon energy distribution data can be obtained, which reflect the energy deposition pattern of photons in the samples. Combine ray dose data, ray dose data, simulated particle path data and simulated photon energy distribution data for data packaging operation, that is, integrate these data into a radiation analysis data set. This data set contains a comprehensive analysis of the radiation characteristics of 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.

[0081] It should be noted that after obtaining the instrument detection and simulation operations on the water supply and drainage samples to be detected, the ray dose data obtained include:

[0082] After obtaining the particle energy detection operation on the water supply and drainage samples to be detected, particle energy set data are obtained; According to the

[0083] particle energy set data, a mean calculation operation is performed to obtain the average particle energy; After obtaining the photon detection operation on the water supply and drainage samples to be detected, photon number data are obtained;

[0084] ​

[0085] Based on the photon number data and the average particle energy, perform ray dose calculation operations to obtain ray dose data;

[0086] The ray dose calculation formula is as follows:

[0087]

[0088] where represents ray radiation dose data, represents the preset reciprocal of the ray attenuation constant, represents the preset sample density, represents the preset dry weight density, represents average particle energy, represents photon number data.

[0089] It should be noted that the ray dose estimation operation starts with performing a particle energy detection operation on the water supply and drainage sample to be detected. This step is completed by using a spectrometer, which can measure the energy distribution of rays in the sample. Through this detection, particle energy set data can be obtained. This is a dataset containing multiple measurement values, reflecting the energy characteristics of rays in the sample. Next, perform a mean calculation operation on the particle energy set data to obtain the average particle energy. This operation calculates the average value of all detected particle energies through statistical methods. This value represents the average energy level of rays in the sample. At the same time, perform a photon detection operation to obtain photon number data. This step is also completed by using a spectrometer. The instrument records the number of photons detected within a specific time, and this number reflects the intensity of rays. With the photon number data and the average particle energy, the ray dose calculation operation can be performed. This operation uses the preset parameters and the detected data to estimate the radiation dose of rays through a specific calculation formula. In this process, combining the photon number and the average particle energy, using the preset The reciprocal of the ray attenuation constant. Exemplarily, in the present invention, the value of the reciprocal of the ray attenuation constant is set to 0.01, and the value of the sample density is set to 1, and the value of the dry weight density is set to 0.5. Of course, according to different application scenarios, other numerical settings can also be selected, and the present invention does not limit this. Finally, through the comprehensive calculation of these data and parameters, ray dose data are obtained, which represents the ray radiation dose of the water supply and drainage sample to be detected.

[0090] It should be noted that after obtaining the instrument detection and simulation operation on the water supply and drainage sample to be detected, the ray dose data obtained includes:

[0091] After obtaining the particle energy detection on the water supply and drainage sample to be detected, the true distribution of particle energy, the particle energy set data, and the particle number data are obtained;

[0092] According to the particle energy set data, a mean calculation operation is performed to obtain the average particle energy;

[0093] According to the true distribution of particle energy, a distribution estimation operation is performed to obtain the estimated particle energy distribution;

[0094] According to the estimated particle energy distribution, the average particle energy, and the particle number data, a ray dose calculation operation is performed to obtain ray dose data;

[0095] The ray dose calculation formula is as follows:

[0096]

[0097] Among them, represents the ray radiation dose data, represents the preset reciprocal of the ray attenuation constant, represents the average particle energy, represents the particle number data, Represents a preset Linear attenuation coefficient of particles, Represents the preset sample density, Represents an estimate Particle energy distribution.

[0098] It should be noted that, The ray dose estimation operation first requires Performing particle energy detection on the water supply and drainage samples to be detected. This step uses A spectrometer to measure the Energy distribution of particles in the sample. Through this detection, the True distribution of particle energy, Particle energy set data and Particle number data can be obtained. The true distribution of particle energy provides detailed information about the particle energy, while The particle energy set data is a data set containing multiple measurement values, reflecting the Energy characteristics of particles in the sample. The particle number data records the total number of β particles detected within a specific time. Next, A mean calculation operation is performed on the particle energy set data to obtain The average energy of particles. This operation calculates the average value of all detected Particle energies through statistical methods. This value represents the Average energy level of the rays in the sample. Then, based on The true distribution of particle energy, a distribution estimation operation is performed to obtain the estimated Particle energy distribution. With the estimated <C Particle energy distribution, Particle average energy and Particle number data, the Ray dose calculation operation can be performed. This operation uses the preset parameters and the detected data, and estimates the Radiation dose of the rays through specific calculation formulas. During this process, The ray dose estimation operation also starts with The detection of particle energy, collecting The true distribution of particle energy, energy set data and Particle number data. Through these data, the Average energy of particles is calculated, and the Particle energy distribution is estimated. Using the preset Reciprocal of the ray attenuation constant. Exemplarily, the present invention sets The reciprocal value of the ray attenuation constant is 0.005, The numerical value of the linear attenuation coefficient of the particles is 0.0001. Of course, according to different application scenarios, other numerical settings can also be selected, and the present invention does not limit this.

[0099] In step S13, the fitting process of the standard dose-harm relationship model includes: obtaining historical radiation dose data and historical harm assessment data; constructing a dose-harm linear model based on the historical radiation dose data and the historical harm assessment data to obtain an initial dose-harm relationship model; and performing an iterative fitting operation based on the historical radiation dose data, the historical harm assessment data, and the initial dose-harm relationship model. When it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, the iteration ends and the standard dose-harm relationship model is obtained.

[0100] It should be noted that in step S13, the fitting process of the standard dose-harm relationship model first obtains historical radiation dose data and historical harm 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. Then, based on the historical radiation dose data, the historical harm assessment data, and the initial dose-harm relationship model, an iterative fitting operation is performed. This process involves continuously adjusting and optimizing the 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 iteration process ends. Exemplarily, the preset maximum number of iterations in the present invention is 1000 times. Of course, this value can be adjusted according to the complexity of the model and 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.

[0101] It should be noted that constructing a dose-harm linear model based on the historical radiation dose data and the historical harm assessment data to obtain an initial dose-harm relationship model includes:

[0102] The formula of the dose-harm linear model is as follows:

[0103]

[0104] Wherein, represents the historical harm assessment data of the organism , represents the dose response coefficient of the organism , Represents the historical radiation dose of an organism of the organism.

[0105] It should be noted that the process of constructing the dose-harm linear model is based on historical radiation dose data and historical harm assessment data. Historical radiation dose data refers to the radiation doses recorded during past radiation measurements on organisms or the environment, while historical harm assessment data refers to the assessment records of the harmful effects on organisms caused by radiation. These data provide the basic input information for the model to analyze the relationship between radiation dose and harmful effects. In this process, among them represents the historical harm assessment data of an organism of the organism, which can be a quantitative indicator of the harmful effects observed in the organism under a specific radiation dose, such as the mortality rate, growth inhibition rate, or other health indicators of the organism. represents the dose-response coefficient of an organism of the organism, which is used to describe the sensitivity of the organism to radiation, that is, the degree of harmful effects caused by a unit radiation dose. Represents the historical radiation dose of an organism of the organism, which is the radiation dose received by the organism in the past.

[0106] It should be noted that according to the historical radiation dose data, the historical harm assessment data, and the initial dose-harm relationship model, an iterative fitting operation is performed. When it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, the iteration ends and the standard dose-harm relationship model is obtained, including:

[0107] Initialize the parameters of the initial dose-harm relationship model to obtain the first dose-harm relationship model;

[0108] According to the first dose-harm relationship model and the historical radiation dose data, perform dose-harm theoretical calculations to obtain the first theoretical harm assessment data;

[0109] According to the first theoretical harm assessment data and the historical harm assessment data, perform error calculations and update the parameters of the first dose-harm relationship model to obtain the second dose-harm relationship model;

[0110] Continue the iteration. When it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, the iteration ends and the standard dose-harm relationship model is obtained. [[ID=3!4]]

[0111] It should be noted that the iterative fitting operation is carried out on the existing initial dose-harm relationship model, aiming to more accurately reflect the relationship between historical radiation doses and harm 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 parameters such as the dose-response coefficient in the model, thereby obtaining the first dose-harm relationship model. Subsequently, based on the first dose-harm relationship model and historical radiation dose data, dose-harm theoretical calculations are performed. This step involves substituting the radiation dose data into the model to predict harmful effects, thereby obtaining the first theoretical harm assessment data. These theoretical data are predictions of actual harmful effects, and they will be compared with the actual historical harm assessment data. Then, by comparing the first theoretical harm assessment data and the historical harm assessment data, error calculations are performed, which involve 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, each time using new model parameters for theoretical calculations and updating the model 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.

[0112] 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. When it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, the iteration ends and the standard dose-effect model is obtained.

[0113] It should be noted that 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 includes:

[0114] The formula of the dose-effect generalized linear mixed model is as follows:

[0115]

[0116] where represents the historical dose-effect data of organism , represents the historical dose-response coefficient of organism , represents for organism historical radiation dose data is a non - linear coefficient representing the square term of the dose represents a random error that is preset and follows a specific distribution

[0117] It should be noted that, based on 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. When it is determined that the number of iterations is greater than or equal to the preset maximum number of iterations, the iteration ends and a standard dose - effect model is obtained, including:

[0118] Initialize the initial dose - effect model to obtain a first dose - effect relationship model;

[0119] According to the first dose - effect relationship model, the historical radiation dose data, and the historical dose - response coefficient characteristics, perform dose - effect theoretical calculations to obtain first - theoretical dose - effect evaluation data;

[0120] According to the first - theoretical dose - effect evaluation data and the historical dose - effect data, perform error calculations and update the parameters of the first dose - effect relationship model to obtain a second dose - effect relationship model;

[0121] Continue the iteration. 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.

[0122] It should be noted that in step S14, the primary task in the fitting process of the standard dose - effect model is to obtain historical radiation dose data, historical dose - response coefficient characteristics, and historical dose - effect data. These data provide the basic input information for the model. Among them, the historical radiation dose data and the historical dose - response coefficient characteristics can be sourced from previous measurement or experimental records, while the historical dose - effect data comes from long - term observations and studies of organisms or the environment.

[0123] Using these historical data, construct a dose - effect generalized linear mixed model to obtain the initial dose - effect model. Where 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 non - linear coefficient used to describe the non - linear relationship between dose and effect represents the square term of the dose, and Denote random errors that are preset and follow a specific distribution. Exemplarily, in the present invention, it is set as a normal distribution with a mean of 0 and a variance of 1. Of course, other distributions can also be selected according to different application scenarios, and the present invention does not limit this.

[0124] After obtaining the initial model, iterative fitting operations are performed. This process begins with initializing the initial dose-effect model to obtain the first dose-effect relationship model. Then, based on the first dose-effect relationship model, historical radiation dose data, and historical dose-response coefficient characteristics, dose-effect theoretical calculations are carried out 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 historical dose-effect data, error calculations are performed, which involve calculating the difference between the model prediction value and the actual observed value. Based on 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 aims 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 this time, the iteration ends and the 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 computing resources. The present invention does not limit this.

[0125] In step S15, based on the radiation analysis data and the dose-effect coefficient characteristics, pollution assessment calculations are performed to obtain radiation grading data, including:

[0126] Based on the radiation analysis data, data extraction operations are performed to obtain ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data;

[0127] Based on the ray dose data, the ray dose data, and the dose-effect coefficient characteristics, radiation calculations are performed to obtain particle radiation characteristics;

[0128] Based on the particle radiation characteristics, the simulated particle path data, and the simulated photon energy distribution data, radiation statistical operations are performed to obtain radiation evaluation data;

[0129] Based on the radiation evaluation data, pollution level grading is performed to obtain radiation grading data.

[0130] It should be noted that in step S15, the pollution assessment calculation is based on the radiation analysis data and the dose-effect coefficient characteristics to obtain the radiation grading data. This process first involves extraction operations on the radiation analysis data to obtain Ray dose data, Ray 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 pollution assessment. Next, using Ray dose data, Ray dose data and dose-effect coefficient characteristics to perform radiation calculations to obtain particle radiation characteristics. This step involves quantitative analysis of the relationship between particle energy, dose, and effect, as well as comparison and comprehensive evaluation of the effects of different types of radiation. Based on 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, based on the radiation assessment data, pollution level grading is performed to obtain radiation grading data. This step involves setting thresholds based on the radiation assessment data and dividing the radiation levels into different grades. Exemplarily, the present invention sets the grades as low, medium, and high radiation pollution grades. And the low radiation pollution grade is set as 0 - 10 mSv / year, the medium radiation pollution grade is set as 10 - 50 mSv / year, and the high radiation pollution grade is set as greater than 50 mSv / year. Of course, according to different application scenarios, other grade setting methods can also be selected, and the present invention does not limit this

[0131] In summary, the present invention realizes the automatic detection effect by designing a method for automatic detection of water quality radiation pollution in water supply and drainage, and can improve the detection speed and reduce the time cost brought by manual detection.

[0132] Referring to Figure 2 , an embodiment of the present invention provides an automatic detection system for water quality radiation pollution in water supply and drainage, including:

[0133] An input module, configured to obtain, after instrument detection and simulation operations on the water supply and drainage sample to be detected, Ray dose data, Ray dose data, simulated particle path data, and simulated photon energy distribution data;

[0134] A radiation analysis module, configured to package 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;

[0135] A dose-response coefficient estimation module, configured to input the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain dose-response coefficient characteristics;

[0136] The dose-effect coefficient estimation module is configured to input the dose-response coefficient characteristics into a pre-fitted standard dose-effect model to obtain dose-effect coefficient characteristics;

[0137] The radiation pollution range estimation module is configured to perform pollution assessment calculations based on the radiation analysis data and the dose-effect coefficient characteristics to obtain radiation grading data

[0138] The fitting process of the standard dose-harm relationship model includes:

[0139] Obtain historical radiation dose data and historical harm assessment data;

[0140] According to the historical radiation dose data and the historical harm assessment data, construct a dose-harm linear model to obtain an initial dose-harm relationship model;

[0141] According to the historical radiation dose data, the historical harm assessment data, and the initial dose-harm relationship model, perform iterative fitting operations. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, end the iteration and obtain a standard dose-harm relationship model;

[0142] The fitting process of the standard dose-effect model includes:

[0143] Obtain historical radiation dose data, historical dose-response coefficient characteristics, and historical dose-effect data;

[0144] According to the historical radiation dose data, the historical dose-response coefficient characteristics, and the historical dose-effect data, construct a dose-effect generalized linear mixed model to obtain an initial dose-effect model;

[0145] According to the historical radiation dose data, the historical dose-response coefficient characteristics, the historical dose-effect data, and the initial dose-effect model, perform iterative fitting operations. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, end the iteration and obtain a standard dose-effect model.

[0146] It should be noted that the automatic detection device for water quality radiation pollution in water supply and drainage provided in the embodiments of the present invention is used to execute all the process steps of the method for automatically detecting water quality radiation pollution in water supply and drainage in the above embodiments. The working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0147] The embodiments of the present invention also provide 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 1The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments.

[0148] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0149] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may further include input / output devices, network access devices, a bus, etc.

[0150] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and lines.

[0151] The memory can be used to store the computer program and / or module. By running or executing the computer program and / or module stored in the memory, and invoking the data stored in the memory, the processor can implement various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the sound playback function, the image playback function, etc.); the data storage area can store the data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0152] Among them, 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, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included 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, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0153] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement this without creative effort.

[0154] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An automatic detection method for water quality radiation pollution in water supply and drainage, characterized in that, Executed by a computer, including: After obtaining instrument detection and simulation operations on the water supply and drainage samples to be tested, the ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data; Pack 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; Inputting the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain dose response coefficient characteristics, including: Obtaining historical radiation dose data and historical harm assessment data; Constructing a dose-harm linear model based on the historical radiation dose data and the historical harm assessment data to obtain an initial dose-harm relationship model; Performing an iterative fitting operation based on the historical radiation dose data, the historical harm assessment data, and the initial dose-harm relationship model. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, end the iteration and obtain the standard dose-harm relationship model; Inputting the dose response coefficient characteristics into a pre-fitted standard dose-effect model to obtain dose effect coefficient characteristics; 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. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, end the iteration and obtain the standard dose effect model, including: Initializing the initial dose effect model to obtain a first dose effect relationship model; Performing dose effect theoretical calculations based on the first dose effect relationship model, the historical radiation dose data, and the historical dose response coefficient characteristics to obtain first theoretical dose effect assessment data; Performing error calculations based on the first theoretical dose effect assessment data and the historical dose effect data and updating the parameters of the first dose effect relationship model to obtain a second dose effect relationship model; Continue the iteration. When it is determined that the number of iterations is greater than or equal to a preset maximum number of iterations, end the iteration and obtain the standard dose effect model; Performing pollution assessment calculations based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data; Among them, after obtaining instrument detection and simulation operations on the water supply and drainage sample to be detected, the ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data, including: Obtaining simulated particle path data by simulating the energy deposition process of particles in the water supply and drainage sample; Obtaining simulated photon energy distribution data by simulating the energy distribution of photons in the water supply and drainage sample; Among them, performing pollution assessment calculations based on the radiation analysis data and the dose effect coefficient characteristics to obtain radiation classification data, including: Based on the radiation analysis data, perform a data extraction operation to obtain ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data; According to the said ray dose data, the said ray dose data and the dose effect coefficient characteristics, perform radiation calculations to obtain particle radiation characteristics; Performing radiation statistical operations based on the particle radiation characteristics, the simulated particle path data, and the simulated photon energy distribution data to obtain radiation assessment data; The radiation statistical operation includes statistical analysis of the propagation path, energy deposition pattern, and energy distribution of particles in the medium to evaluate the potential impact of radiation on the environment or organisms; Performing pollution level classification based on the radiation assessment data to obtain radiation classification data.

2. The automatic detection method for water quality radiation pollution in water supply and drainage according to claim 1, characterized in that After obtaining the instrument detection and simulation operation on the water supply and drainage sample to be detected, the ray dose data includes: Obtain the water supply and drainage sample to be detected, and perform After the particle energy detection operation, obtain Particle energy set data; According to the particle energy set data, perform a mean calculation operation to obtain the average particle energy; Obtaining photon number data after performing photon detection operations on the water supply and drainage sample to be detected; Based on the photon number data and the average particle energy, perform a ray dose calculation operation to obtain ray dose data; The ray dose calculation formula is as follows: ； Among them, represents ray radiation dose data, represents a preset reciprocal of the ray attenuation constant, represents a preset sample density, represents a preset dry weight density, represents average particle energy, represents photon number data.

3. The automatic detection method for water quality radiation pollution in water supply and drainage according to claim 1, characterized in that After obtaining the instrument detection and simulation operation on the water supply and drainage sample to be detected, the ray dose data includes: Obtain the water supply and drainage sample to be detected, and perform After particle energy detection, obtain The true distribution of particle energy, The particle energy set data and The particle number data; According to the particle energy set data, perform a mean calculation operation to obtain the average particle energy; According to the true distribution of the particle energy, perform a distribution estimation operation to obtain an estimated particle energy distribution; According to the estimation the particle energy distribution, the average particle energy, and the particle number data, perform ray dose calculation operations to obtain ray dose data; The ray dose calculation formula is as follows: ； Among them, represents ray radiation dose data, represents a preset reciprocal of the ray attenuation constant, represents average particle energy, represents particle number data, represents a preset linear attenuation coefficient of the particle, represents a preset sample density, represents an estimate of particle energy distribution.

4. The automatic detection method for water quality radiation pollution in water supply and drainage according to claim 1, characterized in that Construct a dose-harm linear model based on the historical radiation dose data and the historical harm assessment data to obtain an initial dose-harm relationship model, including: The formula of the dose-harm linear model is as follows: ； Among them, represents the historical harmful assessment data of the organism , represents the dose-response coefficient of the organism , represents the historical radiation dose to the organism .

5. The automatic detection method for water quality radiation pollution in water supply and drainage according to claim 2, characterized in that, Based on the historical radiation dose data, the historical harm assessment data, and the initial dose-harm relationship model, perform an iterative fitting operation. 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 a standard dose-harm relationship model, including: Initialize the parameters of the initial dose-harm relationship model to obtain a first dose-harm relationship model; Based on the first dose-harm relationship model and the historical radiation dose data, perform dose-harm theoretical calculations to obtain first theoretical harm assessment data; Based on the first theoretical harm assessment data and the historical harm assessment data, perform error calculations and update the parameters of the first dose-harm relationship model to obtain a second dose-harm relationship model; Continue the iteration. 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 a standard dose-harm relationship model.

6. The automatic detection method for water quality radiation pollution in water supply and drainage according to claim 2, characterized in that, Construct 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, including: The formula of the dose-effect generalized linear mixed model is as follows: ； Among them, represents the historical dose-effect data of the organism , represents the historical dose-response coefficient of the organism , represents the historical radiation dose data of the organism , is a non-linear coefficient representing the square term of the dose represents a preset random error that follows a specific distribution.

7. An automatic detection system for water quality radiation pollution in water supply and drainage, characterized in that, A method for automatically detecting water quality radiation pollution in water supply and drainage as described in any one of claims 1 to 6, including: An input module for obtaining, after instrument detection and simulation operations are performed on a water supply and drainage sample to be detected, ray dose data, ray dose data, simulated particle path data, and simulated photon energy distribution data; A radiation analysis module for packing 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; A dose-response coefficient estimation module for inputting the radiation analysis data into a pre-fitted standard dose-harm relationship model to obtain dose-response coefficient characteristics; A dose-effect coefficient estimation module for inputting the dose-response coefficient characteristics into a pre-fitted standard dose-effect model to obtain dose-effect coefficient characteristics; A radiation pollution range estimation module for performing pollution assessment calculations based on the radiation analysis data and the dose-effect coefficient characteristics to obtain radiation classification data.

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