Nuclear radiation sensor data processing method based on Kalman filtering and alarm instrument

Abstract

The invention relates to the technical field of data processing, and discloses a nuclear radiation sensor data processing method based on Kalman filtering and an alarm apparatus, and the method comprises the steps: continuously obtaining nuclear radiation observation data according to a preset sampling period; acquiring Kalman filtering observation data at a previous sampling moment, and predicting Kalman filtering observation data at a current sampling moment by using a preset Kalman filtering model according to the Kalman filtering observation data at the previous sampling moment to obtain a current prediction result; and correcting the current prediction result by using the observation data at the current sampling moment to obtain Kalman filtering observation data at the current sampling moment, and displaying the Kalman filtering observation data. According to the method, the dynamic system model is established based on the Kalman filter, the state at the current sampling moment is predicted based on the Kalman filtering observation data at the previous sampling moment, the evolution of the system state is accurately estimated in combination with the measurement data of the sensor, the dynamic change of the system is better coped with, and the accuracy and reliability of nuclear radiation detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method for processing nuclear radiation sensor data based on Kalman filtering and an alarm device. Background Art

[0002] Nuclear radiation monitoring is an important means to ensure public health and environmental safety. Traditional nuclear radiation monitoring systems usually use sensors to monitor the radiation level in the environment. However, sensor data is often affected by noise, interference, and uncertainty, resulting in inaccurate and unreliable monitoring results.

[0003] Existing traditional filtering techniques include moving average filtering, median filtering, etc. Moving average filtering is a simple and effective filtering method that smooths the signal by taking the average of data points within a certain window size to reduce the influence of noise. The larger the window, the slower the response of the filter, and the better the smoothing effect on low-frequency signals. Median filtering is a non-linear filtering method that sorts the data points within the data window and takes the median as the output value. Median filters are very effective in dealing with special types of noise such as salt-and-pepper noise and can effectively remove outliers and anomalies. These traditional filtering techniques are simple, easy to implement, and effective when processing sensor data and are applicable to various real-time data processing scenarios. However, traditional filtering techniques cannot handle more complex information scenarios and dynamic systems well, with low data processing accuracy and poor reliability. Summary of the Invention

[0004] In view of this, the present invention provides a method for processing nuclear radiation sensor data based on Kalman filtering and an alarm device to solve the problems of low data processing accuracy and poor reliability of traditional filtering techniques.

[0005] In a first aspect, the present invention provides a method for processing nuclear radiation sensor data based on Kalman filtering, the method comprising:

[0006] Continuously acquiring nuclear radiation observation data according to a preset sampling period;

[0007] Obtaining the Kalman filtering observation data at the previous sampling moment, and predicting the Kalman filtering observation data at the current sampling moment according to the Kalman filtering observation data at the previous sampling moment by using a preset Kalman filtering model to obtain the current prediction result;

[0008] Using the observation data at the current sampling moment to correct the current prediction result to obtain the Kalman filtering observation data at the current sampling moment and display it.

[0009] The data processing method of the nuclear radiation sensor based on Kalman filtering provided by the present invention establishes a dynamic system model based on the Kalman filter, predicts the state at the current sampling moment based on the Kalman filtering observation data at the previous sampling moment, combines the measurement data of the sensor to accurately estimate the evolution of the system state, better cope with the dynamic changes of the system, and improve the accuracy and reliability of nuclear radiation detection.

[0010] In an alternative embodiment, obtaining nuclear radiation observation data includes:

[0011] Collecting the raw data of nuclear radiation by using a nuclear radiation sensor;

[0012] Performing preprocessing on the raw data to obtain nuclear radiation observation data.

[0013] The data processing method of the nuclear radiation sensor based on Kalman filtering provided by the present invention preprocesses the raw data collected by the sensor, screens out effective observation data, reduces the influence and error of incorrect data on the subsequent prediction results, makes the observation data all real and effective data, and improves the accuracy of subsequent predictions.

[0014] In an alternative embodiment, the parameters preset for the Kalman filtering model include: the initial state vector and the initial state covariance matrix, the system state transition matrix and the process noise covariance matrix, the observation matrix and the measurement noise covariance matrix, where

[0015] The initial state vector is used to set the initial observation state estimation of the Kalman filter and serves as the Kalman filtering observation data at the initial moment;

[0016] The initial state covariance matrix is used to set the initial uncertainty of the Kalman filter;

[0017] The system state transition matrix is used to dynamically predict the Kalman filtering observation data at the current sampling moment based on the Kalman filtering observation data at the previous sampling moment;

[0018] The process noise covariance matrix is used to describe the uncertainty in the dynamic prediction process;

[0019] The observation matrix is used to describe the measurement method of the sensor and map the state variables to the actual measurement data;

[0020] The measurement noise covariance matrix is used to describe the measurement error of the sensor.

[0021] In the nuclear radiation monitoring, the data processing method of the nuclear radiation sensor based on Kalman filtering provided by the present invention estimates parameters such as the intensity, position, and movement trajectory of the radiation source. The Kalman filter can optimally estimate these parameters based on the system model and sensor data, and improve the accuracy of locating and tracking the radiation source.

[0022] In an alternative embodiment, a preset Kalman filter model is used to predict the Kalman filter observation data at the current sampling moment based on the Kalman filter observation data at the previous sampling moment, and the current prediction result is obtained, including:

[0023] Initialize the initial state vector and the initial state covariance matrix of the preset Kalman filter model;

[0024] Perform iterative prediction based on the initial state vector and the initial state covariance matrix to obtain the Kalman filter observation data at each sampling moment, and use the Kalman filter observation data at the current sampling moment as the current prediction result.

[0025] For the nuclear radiation sensor data processing method based on the Kalman filter provided by the present invention, when the Kalman filter receives new sensor data, the estimated value of the observation data is updated in real time without storing historical data, enabling the Kalman filter to achieve real-time processing and response to the nuclear radiation monitoring system, and ensuring the timeliness and accuracy of the monitoring results.

[0026] In an alternative embodiment, the current prediction result is corrected using the observation data at the current sampling moment to obtain the Kalman filter data at the current sampling moment, including:

[0027] Compare the observation data at the current sampling moment with the current prediction result to obtain a prediction error;

[0028] Correct the current prediction result according to the prediction error to obtain the Kalman filter data at the current sampling moment, and update the corresponding process noise covariance matrix in the current prediction result.

[0029] For the nuclear radiation sensor data processing method based on the Kalman filter provided by the present invention, the prediction result is corrected by comparing the error between the prediction result and the actual sampling observation data, making the corrected prediction result more accurate and reducing the uncertainty of the detection system about the current state.

[0030] In an alternative embodiment, correcting the current prediction result according to the prediction error includes:

[0031] Compare the uncertainty corresponding to the current prediction result with a preset threshold to obtain a prediction uncertainty result, and compare the measurement error corresponding to the observation data at the current sampling moment with a preset error threshold to obtain a measurement uncertainty result;

[0032] Determine the correction strength of the prediction error according to the prediction uncertainty result and the measurement uncertainty result;

[0033] Correct the current prediction result using the prediction error according to the correction strength.

[0034] In an alternative embodiment, the current prediction result is corrected according to the correction strength using the prediction error, including:

[0035] Determine the correction value of the prediction error using the correction strength, and add the correction value to the current prediction result to obtain the Kalman filter data at the current sampling moment.

[0036] The method for processing data of a nuclear radiation sensor based on Kalman filter provided by the present invention determines the correction strength of the prediction error on the current prediction result according to the prediction uncertainty result and the measurement uncertainty result, balances the proportion of the influence of the prediction result and the measurement result on the final state evaluation, and improves the accuracy of the final evaluation result.

[0037] In a second aspect, the present invention provides a data processing system for a nuclear radiation sensor based on Kalman filter. The system includes:

[0038] A data acquisition module for continuously acquiring nuclear radiation observation data according to a preset sampling period;

[0039] A state prediction module for acquiring the Kalman filter observation data at the previous sampling moment, and predicting the Kalman filter observation data at the current sampling moment according to the Kalman filter observation data at the previous sampling moment using a preset Kalman filter model to obtain the current prediction result;

[0040] A state correction module for correcting the current prediction result using the observation data at the current sampling moment to obtain and display the Kalman filter observation data at the current sampling moment.

[0041] In a third aspect, the present invention provides an alarm instrument, including: a controller, a power supply module, a CsI-SiPM detector, a PD detector, an ambient light sensor, and an alarm module. Among them, the power supply module is connected to the CsI-SiPM detector, the PD detector, the ambient light sensor, the alarm module, and the controller; the CsI-SiPM detector, the PD detector, the ambient light sensor, and the alarm module are all connected to the controller; the controller includes: a memory and a processor, which communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method according to the first aspect or any corresponding embodiment thereof.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the method according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0043] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 is a schematic flowchart of a method for processing nuclear radiation sensor data based on Kalman filtering according to an embodiment of the present invention;

[0045] Figure 2 is a schematic flowchart of another method for processing nuclear radiation sensor data based on Kalman filtering according to an embodiment of the present invention;

[0046] Figure 3 is a block diagram of the structure of a system for processing nuclear radiation sensor data based on Kalman filtering according to an embodiment of the present invention;

[0047] Figure 4 is a schematic diagram of the hardware structure of an alarm instrument according to an embodiment of the present invention;

[0048] Figure 5 is a schematic diagram of the hardware structure of a controller according to an embodiment of the present invention. Specific Embodiments

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0050] The embodiments of the present invention provide a method for processing nuclear radiation sensor data based on Kalman filtering, which predicts the state at the current sampling moment through a Kalman filter and corrects the prediction result in combination with the current measurement data to achieve the effect of improving the accuracy and reliability of nuclear radiation detection and evaluation data.

[0051] According to an embodiment of the present invention, an embodiment of a method for processing nuclear radiation sensor data based on Kalman filtering is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0052] In this embodiment, a method for processing nuclear radiation sensor data based on Kalman filtering is provided, which can be used in the above computer system. Figure 1 It is a flowchart of a method for processing nuclear radiation sensor data based on Kalman filtering according to an embodiment of the present invention, as Figure 1 shown. The process includes the following steps:

[0053] Step S101, continuously obtain nuclear radiation observation data according to a preset sampling period.

[0054] Specifically, nuclear radiation sensors are used to collect nuclear radiation data in the environment. An α-particle sensor detects α rays, a β-particle sensor detects β rays, and a γ-particle sensor detects γ rays. Raw data is obtained from the nuclear radiation sensors. The raw data includes information such as radiation counts. Due to changes in the sensitivity of the nuclear radiation sensors or external environmental influences, the raw data fluctuates greatly. After preprocessing the raw data, nuclear radiation observation data is obtained. The sampling period can be set according to actual situations. For example, if the period is one second, nuclear radiation observation data is obtained every second.

[0055] Step S102, obtain the Kalman filtering observation data at the previous sampling moment, and use a preset Kalman filtering model to predict the Kalman filtering observation data at the current sampling moment according to the Kalman filtering observation data at the previous sampling moment, and obtain the current prediction result.

[0056] Specifically, the raw data directly collected by nuclear radiation sensors is often affected by noise, interference, and uncertainties, resulting in inaccurate and unreliable monitoring results. Kalman filtering is an efficient recursive filter that can estimate the state of a dynamic system from a series of incomplete and noisy measurements. For each sampling moment, a Kalman filtering observation data will be predicted. The state prediction at the current sampling moment needs to be realized using a preset Kalman filtering model according to the Kalman filtering observation data at the previous sampling moment.

[0057] Step S103, use the observation data at the current sampling moment to correct the current prediction result, and obtain and display the Kalman filtering observation data at the current sampling moment.

[0058] Specifically, the observation data at the current sampling moment collected by nuclear radiation sensors is not accurate directly due to changes in the sensitivity of the nuclear radiation sensors or external environmental influences, and the current prediction result obtained by using a preset Kalman filtering model is also inaccurate. Therefore, the two are combined, and the observation data at the current sampling moment is used to correct the current prediction result to obtain more accurate Kalman filtering observation data at the current sampling moment, and it is displayed to the user in a visual form through a monitoring display module so that the user can monitor the nuclear radiation level in real time.

[0059] Input the preprocessed data into a preset Kalman filter. The preset Kalman filter estimates the system state by combining the dynamic model of the system and the observation data, suppresses noise, and provides an accurate state estimate to obtain a smooth filtering result.

[0060] At each new sampling moment, the system repeats the process of first predicting and then correcting with new measurement data, continuously updating the state estimate and uncertainty.

[0061] The method for processing nuclear radiation sensor data based on Kalman filtering provided in this embodiment establishes a dynamic system model based on the Kalman filter, predicts the state at the current sampling moment based on the Kalman filtering observation data at the previous sampling moment, combines the measurement data of the sensor to accurately estimate the evolution of the system state, better responds to the dynamic changes of the system, and improves the accuracy and reliability of nuclear radiation detection.

[0062] In this embodiment, a method for processing nuclear radiation sensor data based on Kalman filtering is provided, which can be used in the above computer system. Figure 2 It is a flowchart of the method for processing nuclear radiation sensor data based on Kalman filtering according to an embodiment of the present invention, as Figure 2 shown, and the process includes the following steps:

[0063] Step S201, continuously obtain nuclear radiation observation data according to a preset sampling period.

[0064] Specifically, the above step S201 includes:

[0065] Step S2011, collect the original data of nuclear radiation using a nuclear radiation sensor.

[0066] Specifically, use an α-particle sensor to detect the radiation count of α-rays, use a β-particle sensor to detect the radiation count of β-rays, and use a γ-particle sensor to detect the radiation count of γ-rays. The radiation counts of various rays are used as the original data of nuclear radiation, which is only an example and not limited thereto.

[0067] Step S2012, preprocess the original data to obtain nuclear radiation observation data.

[0068] Specifically, the preprocessing of the original data includes but is not limited to data correction, outlier removal, etc. There may be inaccurate data in the original data due to measurement errors, recording errors, or other reasons. Through preprocessing processes such as correction and outlier removal, these errors can be reduced, making the nuclear radiation observation data closer to the real situation.

[0069] The method for processing nuclear radiation sensor data based on Kalman filtering provided in this embodiment preprocesses the original data collected by the sensor, screens out effective observation data, reduces the influence and error of incorrect data on subsequent prediction results, makes the observation data all real and effective data, and improves the accuracy of subsequent prediction.

[0070] Step S202: Obtain the Kalman filter observation data at the previous sampling moment, and use the preset Kalman filter model to predict the Kalman filter observation data at the current sampling moment based on the Kalman filter observation data at the previous sampling moment, so as to obtain the current prediction result.

[0071] In some optional implementation manners, the parameters of the preset Kalman filter model include: an initial state vector and an initial state covariance matrix, a system state transition matrix and a process noise covariance matrix, an observation matrix and a measurement noise covariance matrix.

[0072] The initial state vector is used to set the initial observation state estimate of the Kalman filter and serves as the Kalman filter observation data at the initial moment.

[0073] The initial state covariance matrix is used to set the initial uncertainty of the Kalman filter.

[0074] The system state transition matrix is used to dynamically predict the Kalman filter observation data at the current sampling moment based on the Kalman filter observation data at the previous sampling moment.

[0075] The process noise covariance matrix is used to describe the uncertainty in the dynamic prediction process.

[0076] The observation matrix is used to describe the measurement method of the sensor and map the state variables to the actual measurement data.

[0077] The measurement noise covariance matrix is used to describe the measurement error of the sensor.

[0078] Specifically, the core of Kalman filtering lies in using a mathematical model to predict the state of the system at the next sampling moment. The mathematical model is constructed based on the understanding of the physical characteristics of the system (such as the motion equation) and external influencing factors. The preset Kalman filter model includes two main parts: a state transition model and an observation model. The state transition model describes how to predict the next state from the current state, while the observation model defines the relationship between the actual observation and the true state of the system.

[0079] The parameters of the two models will be continuously updated during the state monitoring and prediction process. The parameters of the state transition model include: the system state transition matrix and the process noise covariance matrix, which are used to describe the dynamic characteristics of the system and model uncertainty. System state transition matrix: Describes the dynamic characteristics of the system and is used to predict the current state from the previous state. Adjusting this matrix can ensure that the filter can adapt to the dynamic changes of the radiation source or sensor. For example, if the state changes rapidly in the system model, the state transition matrix needs to be adjusted to better reflect the actual dynamic behavior. Process noise covariance matrix: Used to describe the uncertainty or noise in the system dynamic process. Appropriate adjustment of the process noise matrix helps to optimize the stability and accuracy of the Kalman filter. If the uncertainty of the system is large, the matrix should have a higher value; if the behavior of the system is more certain, a smaller value can be used.

[0080] The parameters of the observation model include: the observation matrix and the measurement noise covariance matrix, which are used to describe the measurement characteristics and measurement errors of the sensor. Observation matrix: Maps the state of the system to the measurement space and is used to calculate the observation value from the state variables. In nuclear radiation monitoring, the observation matrix may describe the measurement method of the detector, such as the relationship between position and radiation intensity. Adjusting the observation matrix can help the Kalman filter more accurately map the state variables to the actual measurement data. Measurement noise covariance matrix: Reflects the measurement errors and noise characteristics of the sensor. In practical applications, the measurement noise is usually unstable, especially under different environmental conditions. Adjusting the measurement noise covariance matrix can optimize the measurement weight of the filter according to the actual distribution of the measurement noise.

[0081] For the initial moment of state monitoring, there is no data from the previous sampling moment as a reference, so it is necessary to set the initial state estimation vector and uncertainty of the Kalman filter. Initial state vector: Sets the estimation of the system state by the filter at the initial moment. For example, the initial radiation intensity or position, etc. An accurate initial estimation helps to improve the performance of the filter. Initial state covariance matrix: Sets the uncertainty of the initial state estimation and determines the tolerance of the filter to the state error at the initial stage. A larger initial covariance matrix indicates that the filter is more sensitive to the uncertainty of the initial state, while a smaller initial covariance matrix indicates that the filter has more confidence in the initial state estimation.

[0082] The method for processing nuclear radiation sensor data based on Kalman filtering provided in this embodiment estimates parameters such as the intensity, position, and movement trajectory of the radiation source in nuclear radiation monitoring. The Kalman filter can optimally estimate these parameters based on the system model and sensor data, improving the accuracy of locating and tracking the radiation source.

[0083] Specifically, the above step S202 includes:

[0084] Step S2021, initialize the initial state vector and the initial state covariance matrix of the preset Kalman filter model.

[0085] Specifically, before the nuclear radiation monitoring system starts running, first make a preliminary estimate of the current state. For example, the radiation count of the detector, and the estimation result of the current state is used as the initial state vector. Since the initial estimation result may not be accurate, an initial state covariance matrix (uncertainty range) also needs to be set simultaneously to represent the possible errors in the initial estimation result.

[0086] Step S2022, perform iterative prediction based on the initial state vector and the initial state covariance matrix to obtain the Kalman filter observation data at each sampling moment, and use the Kalman filter observation data at the current sampling moment as the current prediction result.

[0087] Specifically, based on the radiation count at the previous sampling moment, predict the radiation count at the current sampling moment according to the system operation law (such as the change trend of the radiation count). At the same time, evaluate the uncertainty of the current prediction result. The influencing factors of the uncertainty include but are not limited to: possible random changes within the system, such as the performance fluctuations of the detector or environmental impacts.

[0088] For the nuclear radiation sensor data processing method based on the Kalman filter provided in this embodiment, when the Kalman filter receives new sensor data, it updates the estimated value of the observation data in real time without storing historical data, enabling the Kalman filter to achieve real-time processing and response to the nuclear radiation monitoring system, ensuring the timeliness and accuracy of the monitoring results.

[0089] Step S203, use the observation data at the current sampling moment to correct the current prediction result, obtain the Kalman filter observation data at the current sampling moment and display it.

[0090] Specifically, the above step S203 includes:

[0091] Step S2031, compare the observation data at the current sampling moment with the current prediction result to obtain the prediction error.

[0092] Specifically, use the nuclear radiation detector to collect the actual radiation count data at the current moment. Since the directly collected data will be affected by noise, such as the random error of electronic devices or the background radiation in the environment, the actual radiation count data is preprocessed and used as the observation data at the current sampling moment. Compare the radiation count in the current prediction result with the measured actual radiation count, and calculate the difference (i.e., "residual") between the two as the prediction error, which reflects the error between the prediction and the actual measurement.

[0093] Step S2032: Correct the current prediction result according to the prediction error to obtain the Kalman filter data at the current sampling moment, and update the corresponding process noise covariance matrix in the current prediction result.

[0094] Specifically, correct the current measurement result according to the prediction error. If the prediction uncertainty is large, it indicates that the observed data at the current sampling moment is relatively reliable, so the observed data at the current sampling moment is more relied on to correct the current prediction result. If it is achieved by assigning weights to the observed data and the current prediction result at the current sampling moment, the weight of the observed data at the current sampling moment is greater than the weight of the current prediction result. If the noise of the nuclear radiation data collected by the nuclear radiation sensor at the current moment is large, the observed data at the current sampling moment may have a large deviation, and the correction strength for the current prediction result should be small, and the monitoring system relies more on the current prediction result. If it is achieved by assigning weights to the observed data and the current prediction result at the current sampling moment, the weight of the observed data at the current sampling moment is less than the weight of the current prediction result. The Kalman filter data at the current sampling moment obtained by correcting the current prediction result is more accurate, and its uncertainty will be reduced, and it is necessary to update the corresponding process noise covariance matrix according to the current prediction result.

[0095] The method for processing nuclear radiation sensor data based on Kalman filter provided in this embodiment corrects the prediction result by comparing the error between the prediction result and the actual sampled observed data, making the corrected prediction result more accurate, and at the same time reducing the uncertainty of the detection system about the current state.

[0096] In some alternative embodiments, correcting the current prediction result according to the prediction error includes:

[0097] Step a1: Compare the uncertainty corresponding to the current prediction result with a preset threshold to obtain a prediction uncertainty result, and compare the measurement error corresponding to the observed data at the current sampling moment with a preset error threshold to obtain a measurement uncertainty result.

[0098] Specifically, if the uncertainty corresponding to the current prediction result is less than the preset threshold, it indicates that the prediction uncertainty is small and the accuracy of the prediction result is high; otherwise, it indicates that the prediction uncertainty is large and the accuracy of the prediction result is low. If the measurement error corresponding to the observed data at the current sampling moment is less than the preset error threshold, it indicates that the accuracy of the observed data at the current sampling moment is high; otherwise, it indicates that the accuracy of the observed data at the current sampling moment is low.

[0099] Step a2: Determine the correction strength of the prediction error according to the prediction uncertainty result and the measurement uncertainty result.

[0100] Specifically, if the uncertainty corresponding to the current prediction result is less than the preset threshold, and / or the measurement error corresponding to the observed data at the current sampling moment is not less than the preset error threshold, it indicates that the prediction result for the current moment is more accurate, and the proportion should be larger, and the correction strength of the prediction error should be set smaller, such as 30%, just for example, but not limited thereto; if the uncertainty corresponding to the current prediction result is not less than the preset threshold, and / or the measurement error corresponding to the observed data at the current sampling moment is less than the preset error threshold, it indicates that the observed data at the current sampling moment is more accurate, and the proportion should be larger, and the correction strength of the prediction error should be set larger, such as 80%, just for example, but not limited thereto.

[0101] Step a3, correct the current prediction result according to the correction strength using the prediction error.

[0102] In some alternative embodiments, correcting the current prediction result according to the correction strength using the prediction error includes:

[0103] Determine the correction value of the prediction error using the correction strength, and add the correction value to the current prediction result to obtain the Kalman filter data at the current sampling moment.

[0104] Specifically, multiply the correction strength by the prediction error to obtain the correction value of the prediction error, and add the correction value of the prediction error to the current prediction result to obtain the Kalman filter data at the current sampling moment. The corrected current prediction result will be more accurate, and the uncertainty of the monitoring system about the current state will be reduced, indicating that the monitoring system has a clearer understanding of the current state.

[0105] The method for processing nuclear radiation sensor data based on Kalman filter provided in this embodiment determines the correction strength of the prediction error on the current prediction result according to the prediction uncertainty result and the measurement uncertainty result, balances the influence proportion of the prediction result and the measurement result on the final state evaluation, and improves the accuracy of the final evaluation result.

[0106] In this embodiment, a nuclear radiation sensor data processing system based on Kalman filter is also provided. This system is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0107] This embodiment provides a nuclear radiation sensor data processing system based on Kalman filter, as Figure 3 shown, including:

[0108] The data acquisition module 301 is used to continuously acquire nuclear radiation observation data according to a preset sampling period.

[0109] The state prediction module 302 is used to acquire the Kalman filter observation data at the previous sampling moment, and use a preset Kalman filter model to predict the Kalman filter observation data at the current sampling moment based on the Kalman filter observation data at the previous sampling moment, so as to obtain the current prediction result.

[0110] The state correction module 303 is used to correct the current prediction result by using the observation data at the current sampling moment, so as to obtain the Kalman filter observation data at the current sampling moment and display it.

[0111] In some alternative embodiments, the data acquisition module 301 includes:

[0112] The raw data acquisition unit is used to acquire the raw data of nuclear radiation by using a nuclear radiation sensor.

[0113] The raw data preprocessing unit is used to preprocess the raw data to obtain nuclear radiation observation data.

[0114] In some alternative embodiments, the state prediction module 302 includes:

[0115] The state initialization unit is used to initialize the initial state vector and the initial state covariance matrix of the preset Kalman filter model.

[0116] The iterative prediction unit is used to perform iterative prediction according to the initial state vector and the initial state covariance matrix to obtain the Kalman filter observation data at each sampling moment, and use the Kalman filter observation data at the current sampling moment as the current prediction result.

[0117] In some alternative embodiments, the state correction module 303 includes:

[0118] The prediction error calculation unit is used to compare the observation data at the current sampling moment with the current prediction result to obtain a prediction error.

[0119] The correction prediction and uncertainty update unit is used to correct the current prediction result according to the prediction error to obtain the Kalman filter data at the current sampling moment, and update the corresponding process noise covariance matrix in the current prediction result.

[0120] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding above embodiments, and will not be elaborated here.

[0121] The data processing system of the nuclear radiation sensor based on Kalman filtering in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0122] An embodiment of the present invention also provides an alarm instrument having the above Figure 3 shown data processing system of the nuclear radiation sensor based on Kalman filtering.

[0123] Traditional instruments for detecting ionizing radiation generally use Geiger-Müller (GM) tubes, which are widely used in nuclear radiation detection, radioactive substance monitoring, and radiation measurement in laboratories and industries. Based on the principle of the GM tube, ion pairs are generated by the interaction of ionizing radiation with gas molecules, and these ion pairs are used to trigger current pulses to detect the presence and intensity of radiation. However, the GM tube has a low response sensitivity to optical signals, which can lead to inaccurate measurement results, and there is also a problem of high operating voltage, which poses a certain safety hazard during portable use.

[0124] This embodiment provides an alarm instrument, as Figure 4 shown, including: a controller, a power supply module, a CsI-SiPM detector, a PD detector, an ambient light sensor, and an alarm module. Among them, the power supply module is connected to the CsI-SiPM detector, the PD detector, the ambient light sensor, the alarm module, and the controller; the CsI-SiPM detector, the PD detector, the ambient light sensor, and the alarm module are all connected to the controller; the controller includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the data processing method of the nuclear radiation sensor based on Kalman filtering in any one of the above embodiments.

[0125] Specifically, Figure 4 in, the controller, the ambient light sensor, and the alarm module are all integrated on a circuit board, and the power supply module supplies power to the controller, the ambient light sensor, and the alarm module respectively through the lines on the circuit board. The controller, the power supply module, the CsI-SiPM detector, the photoelectric (Photoelectric Detector, PD) detector, the ambient light sensor, and the alarm module are all arranged inside the alarm instrument housing. The detection ends of the CsI-SiPM detector and the PD detector extend out of the housing to collect the real-time radiation dose rate outside the housing, and the real-time radiation dose rate is used as nuclear radiation observation data.

[0126] Specifically, Figure 4In it, the CsI-SiPM detector is a combined structure of an SiPM detector and a CsI scintillator. The SiPM detector consists of many tiny photodiodes, which are used to detect the arrival time and light intensity of photons. The circuit of the SiPM detector is composed of a high-voltage circuit and a signal processing circuit. The high-voltage circuit uses a low-power power chip to reduce the system power consumption and extend the usage time. The CsI scintillator is used for detection by utilizing the flashes generated by ionizing radiation in certain substances. When there is radiation in the environment, the CsI scintillator emits visible light during the de-excitation process of the ionized or excited atoms or molecules under the action of radiation energy. The visible light generated by the CsI scintillator is converted into an electrical signal by the SiPM detector and then output to the controller.

[0127] Specifically, Figure 4 In it, the PD detector is a photodetector that utilizes the photoelectric effect, that is, when photons are incident on a semiconductor material, the energy is absorbed and electrons are released, thereby generating a current or voltage. The circuit of the PD detector is composed of a charge-sensitive preamplifier, a multi-stage amplifier circuit, and a comparator circuit. After the PD detector captures the radiation signal and generates charges, the charge-sensitive preamplifier converts the weak charge signal into a voltage pulse signal, and then amplifies the weak voltage signal to the required amplitude through the multi-stage amplifier circuit. The comparator is used to convert the voltage pulse into a rectangular pulse and input it into the controller. Compared with the GM tube, the PD detector only requires a few volts of bias voltage, has low power consumption, is suitable for portable devices and battery power supply, and also has better performance at high counting rates.

[0128] Specifically, Figure 4 In it, the controller simultaneously collects the electrical signals output by the CsI-SiPM detector and the PD detector, and realizes automatic range switching according to the data of the two detectors. For example, when the ambient radiation dose is lower than mSv / h, the data output by the CsI-SiPM detector is used; when the ambient radiation dose is higher than mSv / h, the data output by the PD detector is used.

[0129] It should be noted that the controller can respectively compare the magnitudes of the electrical signals output by the CsI-SiPM detector and the PD detector with the built-in voltage range, and perform automatic switching of the range circuit according to the comparison results, such as level signals. Those skilled in the art can use the existing technology to set the range switching method of the controller. That is, this embodiment only protects the structure of the alarm instrument and does not protect the range switching method.

[0130] Specifically, Figure 4Among them, the alarm instrument in this embodiment uses a CsI-SiPM detector and a PD detector in combination to detect the radiation dose rate. Compared with the traditional GM tube, the SiPM detector of the CsI-SiPM detector has a high response sensitivity to optical signals. The ambient light sensor is used to detect the intensity of the ambient light inside the housing of the alarm instrument, preventing the ambient light from entering the inside of the housing due to damage to the housing or gaps at the joints and affecting the measurement results.

[0131] Optionally, Figure 4 Among them, the alarm module may include alarm devices such as a buzzer, a vibration motor, and an alarm light. When the controller determines that the radiation dose in the environment is higher than the preset threshold according to the electrical signals sent by the CsI-SiPM detector and the PD detector, the controller controls the alarm devices in the alarm module to turn on. When the ambient light sensor recognizes that the light intensity exceeds the preset brightness threshold, after the ambient light sensor outputs a sensing signal to the controller, the controller can display through the screen of the alarm instrument, or by turning on the buzzer, vibration motor, alarm light, etc. of the alarm instrument to remind the user to pay attention to the influence of external light and prompt the user to replace the housing of the alarm instrument or reassemble the housing.

[0132] Optionally, Figure 4 Among them, the power supply module includes a rechargeable battery and a voltage conversion module, which can output different levels of supply voltage to supply power to different devices.

[0133] For the alarm instrument provided in this embodiment, the CsI-SiPM detector can sensitively detect weak radiation and efficiently convert the radiation energy into visible light signals. Moreover, the CsI-SiPM detector has a high response sensitivity to optical signals, and the PD detector requires a low bias voltage, making the alarm instrument have good safety and sensitivity while being convenient to carry. The SiPM detector has the advantages of high gain, high sensitivity, and low operating voltage, and performs excellently in the detection of weak radiation. It can cooperate with the CsI scintillator to efficiently convert the weaker radiation energy into visible light signals, and the CsI-SiPM detector only requires a bias voltage of dozens of volts, which is safer than the traditional GM tube. This embodiment also uses an ambient light sensor to detect the ambient light leaking into the housing of the alarm instrument, eliminate the interference of the ambient light on the measurement results, and improve the reliability of the measurement results. The integrated design of the alarm instrument reduces complexity, lowers costs, and improves portability.

[0134] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a controller provided by an alternative embodiment of the present invention. As Figure 5As shown, the controller includes: one or more processors 10, a memory 20, and interfaces for connecting the components, including a high-speed interface and a low-speed interface. Each component communicates and connects with each other using different buses and can be installed on a common motherboard or in other ways as needed. The processor can process instructions executed within the controller, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple controllers can be connected, and each device provides part of the necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 5 Taking one processor 10 as an example in

[0135] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0136] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0137] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the controller, etc. In addition, the memory 20 can include a high-speed random access memory and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the controller through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0138] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0139] The controller further includes a communication interface 30 for the controller to communicate with other devices or communication networks.

[0140] Embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the methods described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0141] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for processing data of a nuclear radiation sensor based on Kalman filtering, characterized in that, The method includes: Continuously obtaining nuclear radiation observation data according to a preset sampling period; Obtaining the Kalman filter observation data at the previous sampling moment, and using a preset Kalman filter model to predict the Kalman filter observation data at the current sampling moment according to the Kalman filter observation data at the previous sampling moment, to obtain the current prediction result; Using the observation data at the current sampling moment to correct the current prediction result, to obtain the Kalman filter observation data at the current sampling moment and display it.

2. The method according to claim 1, wherein Obtaining nuclear radiation observation data includes: Collecting the raw data of nuclear radiation by using a nuclear radiation sensor; Performing preprocessing on the raw data to obtain nuclear radiation observation data.

3. The method according to claim 1, wherein The parameters of the preset Kalman filter model include: an initial state vector and an initial state covariance matrix, a system state transition matrix and a process noise covariance matrix, an observation matrix and a measurement noise covariance matrix, where The initial state vector is used to set the initial observation state estimate of the Kalman filter, as the Kalman filter observation data at the initial moment; The initial state covariance matrix is used to set the initial uncertainty of the Kalman filter; The system state transition matrix is used to dynamically predict the Kalman filter observation data at the current sampling moment according to the Kalman filter observation data at the previous sampling moment; The process noise covariance matrix is used to describe the uncertainty in the dynamic prediction process; The observation matrix is used to describe the measurement method of the sensor, and maps the state variable to the actual measurement data; The measurement noise covariance matrix is used to describe the measurement error of the sensor.

4. The method according to claim 3, wherein Using a preset Kalman filter model to predict the Kalman filter observation data at the current sampling moment according to the Kalman filter observation data at the previous sampling moment, to obtain the current prediction result, includes: Initializing the initial state vector and the initial state covariance matrix of the preset Kalman filter model; Performing iterative prediction according to the initial state vector and the initial state covariance matrix to obtain the Kalman filter observation data at each sampling moment, and using the Kalman filter observation data at the current sampling moment as the current prediction result.

5. The method according to claim 3, characterized in that Using the observation data at the current sampling moment to correct the current prediction result to obtain the Kalman filter data at the current sampling moment, includes: Comparing the observation data at the current sampling moment with the current prediction result to obtain a prediction error; Correcting the current prediction result according to the prediction error to obtain the Kalman filter data at the current sampling moment, and updating the corresponding process noise covariance matrix in the current prediction result.

6. The method according to claim 5, characterized in that, Correcting the current prediction result according to the prediction error includes: Comparing the uncertainty corresponding to the current prediction result with a preset threshold to obtain a prediction uncertainty result, and comparing the measurement error corresponding to the observation data at the current sampling moment with a preset error threshold to obtain a measurement uncertainty result; Determining the correction strength of the prediction error according to the prediction uncertainty result and the measurement uncertainty result; Correcting the current prediction result according to the correction strength by using the prediction error.

7. The method according to claim 6, characterized in that, Correcting the current prediction result according to the correction strength by using the prediction error, includes: Determine the correction value of the prediction error using the correction strength, and add the correction value to the current prediction result to obtain the Kalman filter data at the current sampling moment.

8. A data processing system for a nuclear radiation sensor based on Kalman filtering, characterized in that, The system includes: A data acquisition module, configured to continuously acquire nuclear radiation observation data according to a preset sampling period; A state prediction module, configured to acquire the Kalman filter observation data at the previous sampling moment, and predict the Kalman filter observation data at the current sampling moment using a preset Kalman filter model based on the Kalman filter observation data at the previous sampling moment to obtain the current prediction result; A state correction module, configured to correct the current prediction result using the observation data at the current sampling moment to obtain and display the Kalman filter observation data at the current sampling moment.

9. An alarm instrument, characterized in that, Including: A power supply module, a CsI-SiPM detector, a PD detector, an ambient light sensor, an alarm module, and a controller, where The power supply module is connected to the CsI-SiPM detector, the PD detector, the ambient light sensor, the alarm module, and the controller; The CsI-SiPM detector, the PD detector, the alarm module, and the ambient light sensor are all connected to the controller; The controller includes a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

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