A nuclear radiation monitoring method and system for improving gamma dose rate prediction accuracy

Abstract

The present invention relates to the field of nuclear radiation monitoring technology, and specifically to a nuclear radiation monitoring method and system for improving the accuracy of gamma dose rate prediction, comprising the following steps: collecting data based on an automatic monitoring station, recording gamma radiation dose rate data and time information in real time, collecting environmental information and meteorological information around the monitoring station, and generating radiation data collection records. In the present invention, by real-time recording and analysis of environmental and meteorological information, real-time adjustment of data processing parameters is performed to reduce the interference of environmental and meteorological parameters on prediction results, improve the quality of monitoring data and the reliability of prediction models, adjust the layout of monitoring stations, improve the efficiency and response capability of monitoring networks, combine data from multiple monitoring stations, adjust the predicted model parameters, optimize the stability and accuracy of prediction models in various environments, improve the ability of environmental safety and public health protection, and enhance the safety management capabilities of nuclear facilities and environmental radiation monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear radiation monitoring, and in particular to a nuclear radiation monitoring method and system for improving the prediction accuracy of gamma dose rate. Background Art

[0002] The field of nuclear radiation monitoring technology ensures environmental safety and hygiene by detecting and analyzing nuclear radiation. It uses detectors to monitor gamma rays and neutron radiation, evaluates radiation levels and radioactive contamination, and combines data processing and analysis technology, including real-time data transmission, long-term radiation trend analysis, and emergency response strategy formulation in the event of an accident. Nuclear radiation monitoring is of great significance to nuclear power plants, medical facilities, industrial applications, and environmental protection.

[0003] Among them, the nuclear radiation monitoring method that improves the accuracy of gamma dose rate prediction optimizes radiation safety management and emergency response by improving the prediction accuracy of gamma radiation dose rate in the environment. It helps monitoring personnel accurately predict changes in radiation levels around nuclear facilities with risks of radiation leakage and in environmental areas that require long-term monitoring, and take measures in advance to protect public and environmental safety. It helps monitoring personnel plan protective measures and reduce radiation exposure risks by improving the prediction accuracy and efficiency of the prediction model.

[0004] Traditional nuclear radiation monitoring technology has poor capabilities in dealing with rapidly changing environmental conditions. In areas where environmental and meteorological factors have a greater impact on radiation data, data processing parameters cannot be adjusted in real time, resulting in poor prediction results. The fixation and layout of monitoring sites are not adequately considered, and the impact of geographical and environmental factors on monitoring results cannot be fully considered, limiting the coverage efficiency and response capabilities of the monitoring system. It lacks the ability to dynamically respond to real-time data, reducing the efficiency and accuracy of emergency responses, and is inefficient when dealing with rapidly changing environments and complex application scenarios, making it difficult to effectively predict and manage radiation risks. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a nuclear radiation monitoring method and system for improving the prediction accuracy of gamma dose rate.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a nuclear radiation monitoring method for improving the prediction accuracy of gamma dose rate, comprising the following steps:

[0007] S1: Based on the data collected by the automatic monitoring station, the gamma radiation dose rate data and time information are recorded in real time, the environmental information and meteorological information around the monitoring station are collected, and the radiation data collection record is generated;

[0008] S2: Based on the radiation data collection records, analyze the impact of various environmental factors on the radiation dose data, identify key environmental factors that affect the data accuracy, and obtain key factor identification results;

[0009] S3: Based on the key factor identification results, evaluate the impact of various environmental factors, including atmospheric pressure and humidity, on the gamma radiation measurement data, adjust data processing parameters to reduce the impact of environmental and meteorological parameters, and generate processing parameter adjustment results;

[0010] S4: Based on the processing parameter adjustment result and in combination with the monitoring station location information, the influence of multiple monitoring locations on the monitoring effect is evaluated, and the deployment location of the monitoring station is adjusted to obtain the monitoring station location adjustment result;

[0011] S5: Analyze the gamma radiation data characteristics in combination with the monitoring station position adjustment result, evaluate the change trend of the radiation data, build a radiation dose prediction model, and obtain a dose data prediction model;

[0012] S6: Based on the dose data prediction model, combined with actual measurement data from multiple sites, the consistency with the predicted data is evaluated, the prediction model parameters are adjusted to optimize the prediction accuracy, and radiation dose prediction data is generated.

[0013] As a further solution of the present invention, the radiation data collection record includes the radiation dose rate value, data collection time information, and associated environmental meteorological information; the key factor identification result includes the influencing factor identification information, the influence degree analysis result, the change characteristics and pattern information of the influencing factor; the processing parameter adjustment result includes the data filtering threshold, the abnormal data processing rule, and the environmental meteorological impact suppression parameter; the monitoring station position adjustment result includes the monitoring station geographic coordinate data, the monitoring range information, and the coverage area information; the dose data prediction model includes the algorithm type information, the model input variable data, and the model evaluation index; the radiation dose prediction data includes the time series prediction data, the prediction value accuracy analysis result, and the model parameter configuration adjustment result.

[0014] As a further solution of the present invention, based on the automatic monitoring station data collection, real-time recording of gamma radiation dose rate data and time information, collecting environmental information and meteorological information around the monitoring station, the steps of generating radiation data collection records are specifically as follows:

[0015] S101: Based on the data collected by the automatic monitoring station, gamma radiation dose rate data and associated time information are analyzed and recorded to generate time data records;

[0016] S102: Based on the time data record, collect and record the meteorological information and environmental information around the monitoring station in real time, including temperature, humidity, wind speed, and atmospheric pressure, to generate environmental meteorological information;

[0017] S103: Based on the environmental meteorological information and in combination with the time information of the radiation measurement data, the timestamp format is adjusted and the data is synchronized to generate a radiation data collection record.

[0018] As a further solution of the present invention, based on the radiation data collection records, the influence of various environmental factors on the radiation dose data is analyzed, and the key environmental factors affecting the data accuracy are identified. The steps of obtaining the key factor identification results are specifically as follows:

[0019] S201: According to the radiation data collection record, identifying and recording various environmental factor data in the data, including temperature, humidity and air pressure, and generating environmental variable record data;

[0020] S202: Based on the environmental variable record data, analyzing the correlation of multiple environmental factors to the gamma radiation dose measurement data, evaluating the influence of the target factors on the measurement accuracy, and generating an environmental factor analysis record;

[0021] S203: Analyze and record the change patterns and characteristics of various environmental factors according to the environmental factor analysis records and in combination with environmental data information to generate key factor identification results.

[0022] As a further solution of the present invention, according to the key factor identification result, multiple environmental factors are evaluated, including the influence of atmospheric pressure and humidity on the gamma radiation measurement data, and data processing parameters are adjusted to reduce the influence of environmental and meteorological parameters. The steps of generating the processing parameter adjustment result are specifically as follows:

[0023] S301: Based on the key factor identification result, analyze the influence of various environmental factors on radiation measurement, including temperature, humidity, and atmospheric pressure, adjust measurement parameters by real-time monitoring of environmental data, and generate environmental impact assessment information;

[0024] S302: Based on the environmental impact assessment information, adjust the sensitivity, filtering threshold, and noise suppression level of data acquisition, optimize data quality and reduce noise interference caused by environmental changes, and generate parameter optimization records;

[0025] S303: Integrate the parameter optimization records, evaluate changes in radiation monitoring data, perform trend analysis and anomaly detection on the monitoring data, adjust the data processing flow to optimize data accuracy, and generate processing parameter adjustment results.

[0026] As a further solution of the present invention, based on the processing parameter adjustment result, combined with the monitoring station location information, the impact of multiple monitoring locations on the monitoring effect is evaluated, and the monitoring station deployment location is adjusted. The steps of obtaining the monitoring station location adjustment result are specifically as follows:

[0027] S401: Based on the processing parameter adjustment result, by analyzing the consistency and deviation of the monitoring station data, evaluating the accuracy and reliability of the data of multiple monitoring stations, recording the location information of the monitoring stations, and obtaining monitoring effect evaluation information;

[0028] S402: Based on the monitoring effect evaluation information, considering the terrain, densely populated areas and industrial activity distribution, evaluating the impact of multiple locations on the monitoring data, adjusting the site layout through simulation, and generating a site layout analysis result;

[0029] S403: According to the site layout analysis result and in combination with the impact of environmental influences on collection efficiency and accuracy, the actual deployment position of the monitoring station is adjusted to generate a monitoring station position adjustment result.

[0030] As a further solution of the present invention, in combination with the monitoring station position adjustment result, the gamma radiation data characteristics are analyzed, the change trend of the radiation data is evaluated, and a radiation dose prediction model is established. The steps of obtaining the dose data prediction model are specifically as follows:

[0031] S501: Based on the monitoring station position adjustment result, using the automatic monitoring station at the current position to collect radiation data, analyzing data characteristics, including peak and valley values, average values, and medians, and generating a data characteristic summary;

[0032] S502: using the data feature summary, calculating the rate of change and pattern of radiation data features, evaluating the change trend and random fluctuation characteristics of gamma radiation dose data, and generating change trend evaluation information;

[0033] S503: Based on the change trend assessment information, a radiation dose prediction model is constructed to predict radiation dose data at multiple times and locations, and a dose data prediction model is generated.

[0034] As a further solution of the present invention, based on the dose data prediction model, combined with actual measurement data of multiple sites, the consistency with the predicted data is evaluated, and the prediction model parameters are adjusted to optimize the prediction accuracy. The steps of generating radiation dose prediction data are specifically as follows:

[0035] S601: using the dose data prediction model, combined with radiation data collected by multiple monitoring stations, evaluating the consistency between the model output result and the actual data, and generating a prediction value evaluation result;

[0036] S602: According to the prediction value evaluation result, adjust the model parameters through iterative training, including environmental parameter weights and noise processing thresholds, optimize the adaptive prediction ability of the model, and generate a model parameter adjustment result;

[0037] S603: Apply the model parameter adjustment result, input environmental variable parameters, predict the radiation doses at multiple locations, and generate radiation dose prediction data.

[0038] A nuclear radiation monitoring system for improving the prediction accuracy of gamma dose rate, the nuclear radiation monitoring system for improving the prediction accuracy of gamma dose rate is used to execute the above-mentioned nuclear radiation monitoring method for improving the prediction accuracy of gamma dose rate, the system comprises:

[0039] The site data acquisition module collects data based on the automatic monitoring station, collects gamma radiation dose rate data and time information, and simultaneously records the surrounding environment and meteorological information to generate radiation data acquisition records;

[0040] The environmental factor evaluation module identifies and analyzes key environmental factors that affect the accuracy of the acquisition equipment based on the radiation data acquisition records, evaluates the impact of various environmental factors on the radiation dose data, and generates processing parameter adjustment results;

[0041] The site layout adjustment module evaluates and adjusts the deployment position of the monitoring station based on the processing parameter adjustment result, optimizes the monitoring point layout to improve the monitoring efficiency and coverage, and generates the monitoring station position adjustment result;

[0042] The radiation dose assessment module analyzes the gamma radiation data characteristics, including the change trend and periodic fluctuation, based on the monitoring station position adjustment result, constructs a radiation dose prediction model, and generates a dose data prediction model;

[0043] The model parameter optimization module is based on the dose data prediction model and combines the actual measurement data of multiple monitoring stations to analyze the consistency between the model prediction results and the actual data, adjust the model parameters and perform predictions to generate radiation dose prediction data.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by real-time recording and analysis of environmental and meteorological information, real-time adjustment of data processing parameters is performed, the interference of environmental and meteorological factors on prediction results is reduced, the quality of monitoring data and the reliability of prediction models are improved, the layout of monitoring stations is adjusted, the efficiency and responsiveness of the monitoring network are enhanced, the predicted model parameters are adjusted in combination with the data of multiple monitoring stations, the stability and accuracy of the prediction model in a changing environment are optimized, the risks caused by prediction errors are reduced, the ability to protect environmental safety and public health is improved, the safety management of nuclear facility and environmental radiation monitoring is enhanced, a scientific basis is provided for protective measures, and the safety of the public and the environment is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0047] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0048] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0049] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0050] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0051] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0052] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0053] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] Embodiment 1

[0057] See also Figure 1 The present invention provides a technical solution, a nuclear radiation monitoring method for improving the prediction accuracy of gamma dose rate, comprising the following steps:

[0058] S1: Based on the data collected by the automatic monitoring station, the gamma radiation dose rate data and time information are recorded in real time, the environmental information and meteorological information around the monitoring station are collected, and the radiation data collection record is generated;

[0059] S2: Based on the radiation data collection records, analyze the impact of various environmental factors on radiation dose data, identify the key environmental factors that affect data accuracy, and obtain key factor identification results;

[0060] S3: Based on the key factor identification results, evaluate the impact of various environmental factors, including atmospheric pressure and humidity, on gamma radiation measurement data, adjust data processing parameters to reduce the impact of environmental and meteorological parameters, and generate processing parameter adjustment results;

[0061] S4: Based on the processing parameter adjustment results and combined with the monitoring station location information, the impact of multiple monitoring locations on the monitoring effect is evaluated, the deployment location of the monitoring station is adjusted, and the monitoring station location adjustment result is obtained;

[0062] S5: Combined with the results of the monitoring station position adjustment, analyze the gamma radiation data characteristics, evaluate the change trend of the radiation data, build a radiation dose prediction model, and obtain a dose data prediction model;

[0063] S6: Based on the dose data prediction model, combined with the actual measurement data of multiple sites, the consistency with the predicted data is evaluated, the prediction model parameters are adjusted to optimize the prediction accuracy, and the radiation dose prediction data is generated.

[0064] Radiation data collection records include radiation dose rate values, data collection time information, and associated environmental meteorological information. Key factor identification results include influencing factor identification information, impact degree analysis results, and changing characteristics and pattern information of influencing factors. Processing parameter adjustment results include data filtering thresholds, abnormal data processing rules, and environmental meteorological impact suppression parameters. Monitoring station location adjustment results include monitoring station geographic coordinate data, monitoring range information, and coverage area information. The dose data prediction model includes algorithm type information, model input variable data, and model evaluation indicators. Radiation dose prediction data includes time series prediction data, prediction value accuracy analysis results, and model parameter configuration adjustment results.

[0065] See also Figure 2 , based on the data collected by the automatic monitoring station, real-time recording of gamma radiation dose rate data and time information, collection of environmental information and meteorological information around the monitoring station, and the steps for generating radiation data collection records are as follows:

[0066] S101: Based on the data collected by the automatic monitoring station, the gamma radiation dose rate data and the associated time information are analyzed and recorded, and the specific process of generating the time data record is as follows;

[0067] In sub-step S101, based on the data collected by the automatic monitoring station, the gamma radiation dose rate data and the associated time information are analyzed and recorded, and data verification and cleaning are performed to exclude any abnormal data points, including records where the gamma radiation dose rate is below the detection limit or above the upper limit of the equipment. The missing time points are filled in using the linear interpolation method to ensure the integrity of the time series. The formula is: ,in, is the radiation dose rate at the interpolation time, is the interpolation time point, and is the time of the known data point, and for and The radiation dose rate at each moment is recorded and a time data record is generated.

[0068] S102: Based on the time data record, the meteorological information and environmental information around the monitoring station are collected and recorded in real time, including temperature, humidity, wind speed, and atmospheric pressure. The specific process of generating environmental meteorological information is as follows;

[0069] In sub-step S102, based on the time data record, the meteorological information and environmental information around the monitoring station are collected and recorded in real time, including temperature, humidity, wind speed, and atmospheric pressure. The collected environmental data are standardized and the Z score method is used for data standardization. The formula is: ,in, is the original data point, is the mean value of the data, is the standard deviation, generating environmental meteorological information.

[0070] S103: Based on the environmental meteorological information and in combination with the time information of the radiation measurement data, the timestamp format is adjusted and the data is synchronized to generate the radiation data collection record.

[0071] In sub-step S103, based on the environmental meteorological information and the time information of the radiation measurement data, the timestamp format is adjusted and the data is synchronized. The data synchronization technology is used to ensure the integrity of the time series, and the interpolation method is used to process missing data. The formula is: ,in, is the interpolated data value, and are the values ​​of two adjacent data points, For the The timestamp of each data point, for The timestamp of each data point, It is the time point that needs to be interpolated to generate the radiation data collection record.

[0072] See also Figure 3 Based on the radiation data collection records, analyze the impact of various environmental factors on radiation dose data, identify the key environmental factors that affect data accuracy, and obtain the key factor identification results in the following steps:

[0073] S201: According to the radiation data collection record, a plurality of environmental factor data in the data is identified and recorded, including temperature, humidity and air pressure, and the specific process of generating environmental variable recording data is as follows;

[0074] In sub-step S201, based on the radiation data collection records, various environmental factors in the data are identified and recorded, including temperature, humidity and air pressure, and the quality of the environmental data is evaluated, outliers are removed, and the mean and standard deviation of each factor are calculated using statistical methods for subsequent analysis. The formula is: ,in, Represents environmental factors, including temperature, humidity, and air pressure. is the sample mean, is the sample standard deviation, Represents a normally distributed random variable, generating environmental variable record data.

[0075] S202: Based on the environmental variable record data, the correlation of various environmental factors on the gamma radiation dose measurement data is analyzed, the influence of the target factors on the measurement accuracy is evaluated, and the specific process of generating the environmental factor analysis record is as follows;

[0076] In sub-step S202, based on the environmental variable record data, the correlation of various environmental factors on the gamma radiation dose measurement data is analyzed, the influence of the target factors on the measurement accuracy is evaluated, and the Pearson correlation coefficient method is used to quantitatively evaluate the correlation between environmental factors and radiation dose. The formula is: ,in, is the Pearson correlation coefficient, and represent the observed values ​​of environmental factors and gamma radiation dose, respectively. and They are the corresponding average values, generating environmental factor analysis records.

[0077] S203: According to the environmental factor analysis record, combined with environmental data information, the change patterns and characteristics of various environmental factors are analyzed and recorded, and the specific process of generating key factor identification results is as follows;

[0078] In sub-step S203, based on the environmental factor analysis records, combined with environmental data information, the change patterns and characteristics of various environmental factors are analyzed and recorded, and the time series analysis technology is used to identify and describe the seasonal and trend changes of environmental factors. The formula is ,in, It's time The environmental factor value, is the intercept term in the formula, representing the Under the influence of environmental factors The baseline value of It's time The linear coefficient represents the environmental factors Linear trends over time, With the sine function Multiplication, indicating environmental factors The sinusoidal component of the seasonal variation of With the cosine function Multiplication, representing seasonal changes in environmental factors, is used to analyze the periodic variation corresponding to the cosine waveform. is the angular frequency corresponding to the seasonal period, is the error term, which identifies the changing patterns and characteristics of key environmental factors and generates key factor identification results.

[0079] See also Figure 4 ,According to the key factor identification results, evaluate the impact of various environmental factors, including atmospheric pressure and humidity, on gamma radiation measurement data, adjust data processing parameters to reduce the impact of environmental and meteorological parameters, and generate the processing parameter adjustment results in the following steps:

[0080] S301: Based on the key factor identification results, the impact of various environmental factors on radiation measurement is analyzed, including temperature, humidity, and atmospheric pressure, and the measurement parameters are adjusted by real-time monitoring of environmental data to generate environmental impact assessment information. The specific process is as follows;

[0081] In sub-step S301, based on the key factor identification results, the influence of temperature, humidity, atmospheric pressure and other environmental factors on radiation measurement is analyzed. The real-time monitoring system is used to dynamically adjust the measurement parameters to match the environmental changes. The partial least squares regression model is used to quantitatively analyze the relationship between environmental variables and radiation measurement. The formula is: ,in, Indicates the radiation measurement results, Represents environmental variables, including temperature, humidity, and atmospheric pressure, is the model coefficient, representing the environmental variable The intensity of the impact on radiation measurements, It is an error term that reflects the variation not resolved by the model, adjusts the measurement parameters in real time, ensures measurement accuracy, and generates environmental impact assessment information.

[0082] S302: Based on the environmental impact assessment information, the sensitivity, filtering threshold, and noise suppression level of data acquisition are adjusted to optimize data quality and reduce noise interference caused by environmental changes. The specific process of generating parameter optimization records is as follows;

[0083] In sub-step S302, based on the environmental impact assessment information, the data acquisition parameters are adjusted, including the optimization of sensitivity, filter threshold and noise suppression level. The filter parameters are adjusted using adaptive filtering technology to minimize the impact of environmental and meteorological factors on the measurement data. The formula is: ,in, is the filtered data, is the current original data, is the filtering result of the previous data point, To adjust the factors, dynamically adjust according to environmental changes, optimize data quality, reduce noise interference caused by environmental changes, and generate parameter optimization records.

[0084] S303: Integrate parameter optimization records, evaluate changes in radiation monitoring data, perform trend analysis and anomaly detection on monitoring data, adjust data processing flow to optimize data accuracy, and generate processing parameter adjustment results.

[0085] In sub-step S303, the parameter optimization records are integrated to evaluate the changes in radiation monitoring data, and the trend analysis and anomaly detection of the monitoring data are performed using the moving average and exponential smoothing methods. The formula is: ,in, is the data after smoothing. For the current monitoring data, is the smoothing coefficient, Smooth data for the previous time point, analyze trends and anomalies in the data, optimize the data processing flow, improve data accuracy, and generate processing parameter adjustment results.

[0086] See also Figure 5 Based on the processing parameter adjustment results, combined with the monitoring station location information, the impact of multiple monitoring locations on the monitoring effect is evaluated, and the monitoring station deployment location is adjusted. The specific steps to obtain the monitoring station location adjustment result are as follows:

[0087] S401: Based on the processing parameter adjustment result, the K-means clustering algorithm is used to analyze the consistency and deviation of the monitoring station data, evaluate the accuracy and reliability of the data of multiple monitoring stations, record the location information of the monitoring stations, and obtain the monitoring effect evaluation information. The specific process is as follows;

[0088] In sub-step S401, based on the processing parameter adjustment results, the accuracy and reliability of the data from multiple monitoring stations are evaluated by analyzing the consistency and deviation of the monitoring station data, statistical analysis methods are used to evaluate the trend and discreteness of the data, the consistency of the data and the deviation between multiple monitoring stations are analyzed, the location information of the monitoring stations is recorded, and the monitoring effect evaluation information is obtained.

[0089] The K-means clustering algorithm is based on the formula:

[0090] ;

[0091] Calculate the cluster quality assessment value, where, is the improved clustering quality evaluation value, which indicates the quality of the entire clustering and is used to measure the accuracy and effect of clustering. is the number of clusters, that is, how many groups the data is divided into, For the The weight coefficient of each cluster is determined based on the distance between the monitoring station and the radiation source, reflecting the importance of the monitoring station data. is the specific sample point data, representing the radiation measurement value of a monitoring station at the target time. For the The center point of a cluster represents the center and average value of all data in the target cluster. For the The set of sample points in a cluster includes the data of all monitoring stations in the target cluster. The specific time point for data collection. For the The average time point in the clusters represents the average of all data collection times in the target cluster. is the background radiation level parameter of the monitoring station, For the The average value of a specific parameter of a monitoring station in a cluster, is the cluster index number, that is, the cluster sequence number, is the weight coefficient of the environmental parameter factor, which is used to adjust the influence of environmental parameters on clustering quality assessment. is the weight coefficient of the time factor, which is used to adjust the impact of time information on clustering quality assessment. is the weight coefficient of the background radiation level factor of the monitoring station.

[0092] The specific execution process of the formula is as follows:

[0093] Increase the distance of the monitoring station's geographical location relative to the known radiation source and calculate the weight coefficient , where monitoring stations closer to the radiation source are assigned higher weights, and the monitoring station data are grouped into Each cluster represents a set of monitoring stations with similar data characteristics. The weighted sum of square distances between the sample points within each cluster and the cluster center is calculated. Environmental factors, time, and background radiation levels of the monitoring stations are considered to evaluate the quality of the clustering, the accuracy and effectiveness of the clustering process, and the consistency of the monitoring data and identify abnormal data.

[0094] S402: Based on the monitoring effect evaluation information, considering the terrain, densely populated areas and industrial activity distribution, evaluating the impact of multiple locations on the monitoring data, adjusting the site layout through simulation, and generating the site layout analysis results. The specific process is as follows;

[0095] In sub-step S402, based on the monitoring effect evaluation information, taking into account the terrain, densely populated areas and industrial activity distribution, the impact of multiple locations on the monitoring data is evaluated, and the station layout is simulated and adjusted using geographic information system technology and multivariate spatial analysis. The formula is: ,in, It's location The impact score, It is The weights of various influencing factors, including topography, population density, and industrial activity, The factors affecting the location The site layout analysis results are generated by taking into account various environmental factors.

[0096] S403: According to the site layout analysis results, combined with the impact of environmental influences on collection efficiency and accuracy, the actual deployment location of the monitoring station is adjusted, and the process of generating the monitoring station location adjustment result is specifically as follows;

[0097] In sub-step S403, according to the site layout analysis results, combined with the impact of environmental influences on collection efficiency and accuracy, the actual deployment location of the monitoring station is adjusted, and the optimization algorithm is used to determine the optimal monitoring station location to minimize environmental interference and improve data quality. The formula is: ,in, is the optimized monitoring station location, It is The weight of each evaluation criterion, including signal strength, data integrity, is in position No. The function of the evaluation criteria generates the monitoring station position adjustment result.

[0098] See also Figure 6 , combined with the results of the monitoring station position adjustment, analyze the characteristics of gamma radiation data, evaluate the changing trend of radiation data, establish a radiation dose prediction model, and obtain the dose data prediction model in the following steps:

[0099] S501: Based on the monitoring station position adjustment result, the radiation data is collected using the automatic monitoring station at the current position, and the data characteristics are analyzed, including peak and valley values, average values, and medians, and the process of generating a data characteristic summary is specifically as follows;

[0100] In sub-step S501, based on the results of the monitoring station position adjustment, the radiation data is collected using the automatic monitoring station at the current location, the data is reviewed, any readings that deviate from the normal range are identified and excluded, including data points with abnormally high or low radiation levels, key statistical indicators of the data are calculated, including peak, valley, mean and median, and basic descriptive statistical analysis of the data is provided. The formula is , , , ,in, Represents the radiation data of a single monitoring point within the target time. is the maximum value, is the calculated minimum value, is the average value, The median is used to generate a summary of the data characteristics.

[0101] S502: Using the data feature summary, calculating the rate of change and pattern of the radiation data feature, evaluating the change trend and random fluctuation characteristics of the gamma radiation dose data, and generating the change trend evaluation information, the specific process is as follows;

[0102] In sub-step S502, the data feature summary is used to calculate the rate of change of radiation data features using statistical methods. The trend of data changes is determined by comparing the measured values ​​at consecutive time points. The Pearson correlation coefficient is used to analyze the correlation between differentiated environmental factors and radiation levels, including the correlation between temperature, humidity and radiation values, to evaluate the trend of gamma radiation dose data and random fluctuation characteristics. The formula is: , ,in, represents the trend slope, represents the standard deviation of the data, For time point, Generate change trend assessment information for the corresponding radiation value.

[0103] S503: Based on the change trend assessment information, a radiation dose prediction model is constructed to predict radiation dose data at multiple times and locations. The specific process of generating the dose data prediction model is as follows;

[0104] In sub-step S503, based on the change trend evaluation information, a radiation dose prediction model is constructed, and the radiation dose data is predicted using the exponential smoothing method. The formula is: ,in, This is the predicted radiation dose value corresponding to a future time point , represents an estimate of the radiation dose predicted based on current and past data, This is the smoothing parameter, which ranges from 0 to 1 and determines the current observation value in the new forecast. Relative to the previous forecast value The importance of For the current time point The actual observed radiation dose value is the basic data point for building the prediction model, reflecting the recent radiation level. For time point The predicted radiation dose value is predicted based on the data at the previous time point. It is used to calculate the predicted value at the next time point and generate a dose data prediction model by iteratively updating the predicted value.

[0105] See also Figure 7 , based on the dose data prediction model, combined with the actual measurement data of multiple sites, the consistency with the predicted data is evaluated, and the prediction model parameters are adjusted to optimize the prediction accuracy. The specific steps for generating radiation dose prediction data are as follows:

[0106] S601: Using the dose data prediction model, combined with radiation data collected by multiple monitoring stations, the consistency between the model output result and the actual data is evaluated, and the specific process of generating the prediction value evaluation result is as follows;

[0107] In sub-step S601, the dose data prediction model is used to combine the radiation data collected by multiple monitoring stations to evaluate the consistency between the model output results and the actual data. The mean square error is used as the evaluation criterion to measure the difference between the model prediction value and the actual observation value. The calculation formula is: ,in, The model is The predicted radiation dose at each monitoring station, is the actual observed radiation dose, is the number of monitoring stations, which evaluates the accuracy and reliability of the model prediction and generates the prediction value evaluation results.

[0108] S602: According to the prediction value evaluation result, the model parameters are adjusted through iterative training, including the environmental parameter weights and the noise processing threshold, to optimize the adaptive prediction capability of the model, and the process of generating the model parameter adjustment result is specifically as follows;

[0109] In sub-step S602, according to the prediction value evaluation results, the model parameters are adjusted through iterative training, including the environmental parameter weights and the noise processing threshold, and the gradient descent method is used to optimize the parameters to minimize the prediction error. The optimization formula is: ,in, represents the model parameters, including the environmental parameter weights, is the learning rate, is the loss function MSE with respect to the parameters The gradient of the model is used to optimize the adaptive prediction ability of the model and generate model parameter adjustment results.

[0110] S603: Apply the model parameter adjustment result, input the environmental variable parameters, predict the radiation doses at multiple locations, and generate the radiation dose prediction data in the following steps;

[0111] In sub-step S603, the model parameter adjustment results are applied, the environmental variable parameters are input, and the radiation doses at multiple locations are predicted. The adjusted model is used in combination with real-time environmental data to perform radiation dose prediction. The formula is: ,in, is the predicted radiation dose, is the current environment data, Generate radiation dose prediction data for the adjusted model parameters.

[0112] See also Figure 8 , a nuclear radiation monitoring system for improving the prediction accuracy of gamma dose rate, the nuclear radiation monitoring system for improving the prediction accuracy of gamma dose rate is used to execute the above-mentioned nuclear radiation monitoring method for improving the prediction accuracy of gamma dose rate, the system comprises:

[0113] The site data acquisition module collects data based on the automatic monitoring station, collects gamma radiation dose rate data and time information, and simultaneously records the surrounding environment and meteorological information to generate radiation data acquisition records;

[0114] The environmental factor assessment module identifies and analyzes the key environmental factors that affect the accuracy of the acquisition equipment based on the radiation data acquisition records, evaluates the impact of various environmental factors on the radiation dose data, and generates processing parameter adjustment results;

[0115] The site layout adjustment module evaluates and adjusts the deployment location of the monitoring station based on the processing parameter adjustment results, optimizes the layout of monitoring points to improve monitoring efficiency and coverage, and generates the monitoring station location adjustment results;

[0116] The radiation dose assessment module analyzes the gamma radiation data characteristics, including the change trend and periodic fluctuation, based on the monitoring station position adjustment results, builds a radiation dose prediction model, and generates a dose data prediction model;

[0117] The model parameter optimization module is based on the dose data prediction model and combines the actual measurement data of multiple monitoring stations to analyze the consistency between the model prediction results and the actual data, adjust the model parameters and make predictions to generate radiation dose prediction data.

[0118] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A nuclear radiation monitoring method for improving the prediction accuracy of gamma dose rate, characterized in that: The following steps are involved: Based on the data collected by the automatic monitoring station, the gamma radiation dose rate data and time information are recorded in real time, the environmental information and meteorological information around the monitoring station are collected, and the radiation data collection record is generated; Based on the radiation data collection records, analyzing the impact of various environmental factors on the radiation dose data, identifying key environmental factors that affect the data accuracy, and obtaining key factor identification results; According to the key factor identification results, evaluate the influence of various environmental factors, including atmospheric pressure and humidity, on the gamma radiation measurement data, adjust data processing parameters to reduce the influence of environmental and meteorological parameters, and generate processing parameter adjustment results; According to the key factor identification results, multiple environmental factors, including the influence of atmospheric pressure and humidity on gamma radiation measurement data, are evaluated, and data processing parameters are adjusted to reduce the influence of environmental and meteorological parameters. The specific steps of generating processing parameter adjustment results are as follows: Based on the key factor identification results, analyze the impact of various environmental factors on radiation measurement, including temperature, humidity, and atmospheric pressure, adjust measurement parameters through real-time monitoring of environmental data, and generate environmental impact assessment information; Based on the environmental impact assessment information, adjust the sensitivity, filtering threshold, and noise suppression level of data acquisition to optimize data quality and reduce noise interference caused by environmental changes, and generate parameter optimization records; Integrate the parameter optimization records, evaluate changes in radiation monitoring data, perform trend analysis and anomaly detection on the monitoring data, adjust the data processing flow to optimize data accuracy, and generate processing parameter adjustment results; Based on the processing parameter adjustment result and in combination with the monitoring station location information, the influence of multiple monitoring locations on the monitoring effect is evaluated, and the monitoring station deployment location is adjusted to obtain the monitoring station location adjustment result; In combination with the monitoring station position adjustment result, the gamma radiation data characteristics are analyzed, the change trend of the radiation data is evaluated, a radiation dose prediction model is constructed, and a dose data prediction model is obtained; Combined with the monitoring station position adjustment results, the gamma radiation data characteristics are analyzed, the change trend of the radiation data is evaluated, and a radiation dose prediction model is established. The specific steps of obtaining the dose data prediction model are as follows: Based on the monitoring station position adjustment result, using the automatic monitoring station at the current position to collect radiation data, analyzing data characteristics, including peak and valley values, average values, and medians, and generating a data characteristic summary; Using the data feature summary, calculating the rate of change and pattern of radiation data features, evaluating the change trend and random fluctuation characteristics of gamma radiation dose data, and generating change trend evaluation information; Based on the change trend assessment information, a radiation dose prediction model is constructed to predict radiation dose data at multiple times and locations, and a dose data prediction model is generated; Based on the dose data prediction model, combined with actual measurement data from multiple sites, the consistency with the predicted data is evaluated, the prediction model parameters are adjusted to optimize the prediction accuracy, and radiation dose prediction data is generated.

2. The method for nuclear radiation monitoring for improving the prediction accuracy of gamma dose rate according to claim 1, characterized in that: The radiation data collection record includes the radiation dose rate value, data collection time information, and associated environmental meteorological information. The key factor identification result includes the influencing factor identification information, the impact degree analysis result, the change characteristics and pattern information of the influencing factors. The processing parameter adjustment result includes the data filtering threshold, abnormal data processing rules, and environmental meteorological impact suppression parameters. The monitoring station location adjustment result includes the monitoring station geographic coordinate data, monitoring range information, and coverage area information. The dose data prediction model includes algorithm type information, model input variable data, and model evaluation indicators. The radiation dose prediction data includes time series prediction data, prediction value accuracy analysis results, and model parameter configuration adjustment results.

3. The method for nuclear radiation monitoring for improving the prediction accuracy of gamma dose rate according to claim 1, characterized in that: Based on the data collected by the automatic monitoring station, the gamma radiation dose rate data and time information are recorded in real time, and the environmental information and meteorological information around the monitoring station are collected. The specific steps for generating radiation data collection records are as follows: Based on the data collected by the automatic monitoring station, the gamma radiation dose rate data and the associated time information are analyzed and recorded to generate time data records; Based on the time data record, the meteorological information and environmental information around the monitoring station are collected and recorded in real time, including temperature, humidity, wind speed, and atmospheric pressure, to generate environmental meteorological information; Based on the environmental meteorological information and in combination with the time information of the radiation measurement data, the timestamp format is adjusted and the data is synchronized to generate a radiation data collection record.

4. The method for nuclear radiation monitoring for improving the prediction accuracy of gamma dose rate according to claim 1, characterized in that: Based on the radiation data collection records, the impact of various environmental factors on the radiation dose data is analyzed, and the key environmental factors that affect the data accuracy are identified. The steps of obtaining the key factor identification results are specifically as follows: According to the radiation data collection record, identifying and recording various environmental factor data in the data, including temperature, humidity and air pressure, and generating environmental variable record data; Based on the environmental variable record data, analyzing the correlation of various environmental factors to the gamma radiation dose measurement data, evaluating the impact of target factors on measurement accuracy, and generating environmental factor analysis records; According to the environmental factor analysis records, combined with environmental data information, the change patterns and characteristics of various environmental factors are analyzed and recorded to generate key factor identification results.

5. The method for nuclear radiation monitoring for improving the prediction accuracy of gamma dose rate according to claim 1, characterized in that: Based on the processing parameter adjustment result, combined with the monitoring station location information, the impact of multiple monitoring locations on the monitoring effect is evaluated, and the monitoring station deployment location is adjusted. The steps of obtaining the monitoring station location adjustment result are specifically as follows: Based on the processing parameter adjustment result, the consistency and deviation of the monitoring station data are analyzed, the accuracy and reliability of the data of multiple monitoring stations are evaluated, the location information of the monitoring stations is recorded, and the monitoring effect evaluation information is obtained; Based on the monitoring effect evaluation information, considering the terrain, densely populated areas and distribution of industrial activities, the impact of multiple locations on the monitoring data is evaluated, and the site layout is adjusted through simulation to generate a site layout analysis result; According to the site layout analysis results, combined with the impact of environmental influences on collection efficiency and accuracy, the actual deployment location of the monitoring station is adjusted to generate a monitoring station location adjustment result.

6. The method for nuclear radiation monitoring for improving the prediction accuracy of gamma dose rate according to claim 1, characterized in that: Based on the dose data prediction model, combined with actual measurement data of multiple sites, the consistency with the predicted data is evaluated, and the prediction model parameters are adjusted to optimize the prediction accuracy. The steps of generating radiation dose prediction data are specifically as follows: Using the dose data prediction model, combined with radiation data collected by multiple monitoring stations, the consistency between the model output results and the actual data is evaluated to generate a prediction value evaluation result; According to the prediction value evaluation results, the model parameters are adjusted through iterative training, including environmental parameter weights and noise processing thresholds, so as to optimize the adaptive prediction ability of the model and generate model parameter adjustment results; The model parameter adjustment results are applied, environmental variable parameters are input, radiation doses at multiple locations are predicted, and radiation dose prediction data are generated.

7. A nuclear radiation monitoring system for improving the prediction accuracy of gamma dose rate, which is implemented according to the nuclear radiation monitoring method for improving the prediction accuracy of gamma dose rate according to any one of claims 1 to 6, characterized in that: The system comprises: The site data acquisition module collects data based on the automatic monitoring station, collects gamma radiation dose rate data and time information, and simultaneously records the surrounding environment and meteorological information to generate radiation data acquisition records; The environmental factor evaluation module identifies and analyzes key environmental factors that affect the accuracy of the acquisition equipment based on the radiation data acquisition records, evaluates the impact of various environmental factors on the radiation dose data, and generates processing parameter adjustment results; The site layout adjustment module evaluates and adjusts the deployment position of the monitoring station based on the processing parameter adjustment result, optimizes the monitoring point layout to improve the monitoring efficiency and coverage, and generates the monitoring station position adjustment result; The radiation dose assessment module analyzes the gamma radiation data characteristics, including the change trend and periodic fluctuation, based on the monitoring station position adjustment result, constructs a radiation dose prediction model, and generates a dose data prediction model; The model parameter optimization module is based on the dose data prediction model and combines the actual measurement data of multiple monitoring stations to analyze the consistency between the model prediction results and the actual data, adjust the model parameters and perform predictions to generate radiation dose prediction data.

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