Radon detection method and device, electronic equipment and storage medium

Abstract

The application provides a radon detection method and device, electronic equipment and storage medium. The method comprises: collecting a plurality of first electric signals from an electric signal flowing through a comparator in a target time range according to a preset time interval; determining a second electric signal corresponding to a time of occurrence of an interrupt signal from the comparator from the plurality of first electric signals in response to receiving the interrupt signal; determining a first noise value according to a plurality of third electric signals; compensating a first range value according to the first noise value to determine a second range value, the first range value being used to represent a signal peak value range of radon corresponding particles in a noise-eliminated state, the plurality of third electric signals being signals different from the second electric signal in the plurality of first electric signals; determining a fourth electric signal in which a signal peak value meets the second range value from the second electric signal; and determining a concentration value of radon gas according to a number of the fourth electric signals. The accuracy of radon gas concentration detection can be improved.

Description

Technical Field

[0001] This application relates to the field of radiation protection technology, and in particular to a radon detection method, device, electronic device and storage medium. Background Technology

[0002] Radon, a radioactive element, exists as a gas at room temperature and pressure. It typically forms near the surface of uranium-containing materials such as soil or rocks and diffuses into the surrounding air. Current detection instruments determine radon concentration by analyzing the peak range of detected pulse signals to identify radon progeny, and then using the effective count to determine the ambient radon concentration. However, over time, the background noise of these instruments can increase, potentially interfering with the determination of the pulse signal's source and thus affecting radon concentration calculations. Therefore, improving the accuracy of radon concentration detection is a critical technical challenge that needs to be addressed in this field. Summary of the Invention

[0003] This application provides a radon gas detection method, apparatus, electronic device, and storage medium, which can improve the accuracy of radon gas concentration detection.

[0004] In a first aspect, this application provides a radon gas detection method. This method is applied to a microcontroller in a radon gas detection system. The radon gas detection system further includes a comparator and a pre-detection device. The pre-detection device collects radon gas and generates an electrical signal based on the particles produced after radon decay. The comparator generates an interrupt signal when the electrical signal exceeds a preset threshold. The method includes:

[0005] Multiple first electrical signals are acquired from the electrical signals flowing through the comparator at preset time intervals within the target time range;

[0006] In response to receiving an interrupt signal from the comparator, a second electrical signal corresponding to the time at which the interrupt signal occurs is determined from a plurality of first electrical signals;

[0007] The first noise value is determined based on multiple third electrical signals;

[0008] The first range value is compensated based on the first noise value to determine the second range value. The first range value is used to represent the signal peak range of the radon-corresponding particle under noise-eliminating conditions. The multiple third electrical signals are signals that are different from the second electrical signals among the multiple first electrical signals.

[0009] Determine a fourth electrical signal whose peak value satisfies the second range value from the second electrical signal;

[0010] The concentration of radon gas is determined based on the amount of the fourth electrical signal.

[0011] As can be seen, in this application, the microcontroller periodically samples the electrical signal flowing through the comparator at preset time intervals to form a time series of multiple first electrical signals. Whenever the comparator detects a signal exceeding a preset threshold and issues an interrupt signal, the second electrical signal corresponding to the time of the interrupt signal is regarded as a potential radon progeny event. The remaining third electrical signals reflect the background state of the system when there are no significant particle impacts. By statistically analyzing the third electrical signals, a first noise value is obtained to characterize the background noise level in the current environment. This noise value is used to dynamically compensate the preset first range value to generate a second range value that adapts to the current operating conditions, thereby correcting the baseline drift caused by water molecule or aerosol deposition. The portion of the second electrical signal whose signal peak falls within the second range value is selected to obtain the fourth electrical signal, which confirms the valid radon progeny decay event. Finally, the radon concentration value is calculated based on the number of fourth electrical signals. The entire process realizes the transformation from static threshold identification to adaptive signal discrimination based on actual noise state, improving the accuracy and stability of radon detection in complex environments.

[0012] In a feasible example, determining the second electrical signal corresponding to the occurrence time of the interrupt signal from a plurality of first electrical signals includes:

[0013] Determine the first count value and the second count value. The first count value refers to the count value when the acquisition of multiple first electrical signals begins, and the second count value refers to the count value when the interrupt signal is received.

[0014] The first difference is determined based on the difference between the second count value and the first count value;

[0015] The first sequence value is determined based on the ratio between the first difference and the preset time interval;

[0016] The signal located at the first sequence value is determined from multiple first electrical signals as the second electrical signal.

[0017] In this application, by acquiring count values ​​at the start and interruption trigger times to form a relative time offset, and converting it into a sampling sequence index to locate the target signal, a high-precision alignment between physical events and digital signals can be achieved. Furthermore, a second electrical signal can be determined from multiple first electrical signals through simple calculation.

[0018] In a feasible example, a second range value is determined by compensating for a first range value based on a first noise value, including:

[0019] The first signal value is determined based on the median value of the first range.

[0020] The second signal value is determined based on the sum of the first noise value and the first signal value;

[0021] The second range value is determined based on the magnitude of the first noise value and the second signal value. The first difference and the second difference are of the same magnitude. The first difference is the difference between the maximum value in the second range and the second signal value. The second difference is the absolute value of the difference between the minimum value in the second range and the second signal value. The larger the first noise value, the larger the first difference.

[0022] In this application, a dynamic compensation mechanism is used to make the identification window shift synchronously with the background noise and adaptively expand. This can not only accommodate signal fluctuations caused by environmental interference, but also prevent misjudgment or missed detection caused by static thresholds under pollution accumulation or variable operating conditions. Finally, the dynamic window is used to screen the second electrical signal to confirm the fourth electrical signal that meets the energy characteristics. This can improve the accuracy and robustness of radon concentration calculation under long-term operation or variable environment.

[0023] In a feasible example, a first noise value is determined based on multiple third electrical signals, including:

[0024] Determine the noise value corresponding to each of the multiple third electrical signals;

[0025] The first noise value is determined by averaging the noise values ​​corresponding to each of the multiple third electrical signals.

[0026] In this application, by determining the noise value corresponding to each of the multiple third electrical signals and determining the first noise value based on the average of the noise values ​​corresponding to each of the multiple third electrical signals, the interference of random fluctuations on noise judgment in complex or changing environments can be reduced, thereby enhancing the ability to identify real radon progeny events and ultimately improving the accuracy and stability of radon concentration calculation.

[0027] In a feasible example, the noise value corresponding to each of the multiple third electrical signals is determined, including:

[0028] Determine the first and second values ​​in each third electrical signal, where the first value is the first quartile value in the sequence of each third electrical signal, and the second value is the third quartile value in the sequence of each third electrical signal.

[0029] The interquartile range of each third electrical signal is determined based on the absolute value of the difference between the first and second values.

[0030] The noise value corresponding to each third electrical signal is determined based on the ratio of the interquartile range of each third electrical signal to the first coefficient, where the first coefficient is the ratio between the interquartile range and the standard deviation in a general normal distribution.

[0031] In this application, the interquartile range is obtained by determining the first and third quartile values ​​in the sequence of the third electrical signal and calculating the absolute value of their difference. This index focuses on the dispersion of the middle 50% of the data, significantly reducing the impact of outliers such as residual particle events or transient interference on noise assessment. Furthermore, by utilizing the fixed proportional relationship between the interquartile range and the standard deviation under a normal distribution, the interquartile range is converted into an equivalent standard deviation value, which serves as the noise value corresponding to the third electrical signal. The resulting noise value set is used to subsequently calculate the average value to determine the first noise value, improving the realism and stability of the overall noise estimation. Finally, the optimized noise estimate is used to dynamically adjust the first range value to generate the second range value, enhancing the accuracy of identifying real radon progeny signals under non-ideal environments and thus improving the reliability of radon concentration calculation.

[0032] In a feasible example, the method also includes:

[0033] Obtain a second noise value, which is a noise value determined before the target time range;

[0034] Based on the second noise value, a third noise value corresponding to the target time range is obtained through prediction.

[0035] The preset threshold is adjusted based on the third noise value and the first range value so that the preset threshold is greater than the third noise value and less than the minimum value in the first range value.

[0036] In this application, historical noise data is introduced to predict the current periodic noise level. A dynamic threshold boundary is set by combining the predicted third noise value and the first range value, so that the comparator threshold can respond to changes in environmental noise in advance. This can reduce false noise triggering and missed detection of real signals under pollution accumulation conditions, and improve the accuracy of distinguishing between noise and the electrical signals corresponding to radon progeny. This can reduce the number of signals to be compared with the signal peaks in the subsequent process, thereby improving processing efficiency and enhancing the stability and reliability of radon detection in complex real-world scenarios.

[0037] In a feasible example, determining the predicted third noise value based on the second noise value includes:

[0038] The system acquires a first temperature value, a first humidity value, a first usage duration, and a first time period. The first temperature value and the first humidity value are the temperature and humidity values ​​at the time when the second noise value is determined. The first usage duration is the usage duration of the radon detection system at the time when the second noise value is determined. The first time period is the corresponding time period when the second noise value is determined.

[0039] The initial model is trained based on the first temperature value, the first humidity value, the first usage duration, the first time period, and the second noise value to obtain the target model;

[0040] The second temperature value, second humidity value, second usage duration, and second time period are obtained when the collection of multiple first electrical signals begins. The second temperature value, second humidity value, second usage duration, and second time period are then input into the target model to obtain the third noise value.

[0041] In this application, by constructing multi-dimensional historical samples containing environmental and operational states to support model training, the model learns the complex relationship between noise evolution and external conditions, and performs forward inference based on the current environment and equipment state to achieve forward-looking noise estimation. This achieves accurate prediction of background noise in changing environments, and dynamically adjusts the comparator's preset threshold to avoid expected background interference and retain an effective signal response window, thus maintaining the technical effect of radon detection stability and accuracy.

[0042] Secondly, this application provides a radon gas detection device, which is applied to a microcontroller in a radon gas detection system. The radon gas detection system further includes a comparator and a pre-detection device. The pre-detection device is used to collect radon gas and generate an electrical signal based on the particles produced after radon decay. The comparator is used to generate an interrupt signal when the electrical signal exceeds a preset threshold. The device includes:

[0043] The acquisition unit is used to acquire multiple first electrical signals from the electrical signals flowing through the comparator at preset time intervals within a target time range;

[0044] The processing unit is configured to, in response to receiving an interrupt signal from a comparator, determine a second electrical signal from a plurality of first electrical signals corresponding to the time at which the interrupt signal occurs;

[0045] Processing unit, configured to determine a first noise value based on multiple third electrical signals;

[0046] The processing unit is used to compensate the first range value according to the first noise value and determine the second range value. The first range value is used to represent the signal peak range of the radon-corresponding particle under noise-eliminating conditions. The multiple third electrical signals are signals that are different from the second electrical signals among the multiple first electrical signals.

[0047] The processing unit is configured to determine, from the second electrical signal, a fourth electrical signal whose peak value satisfies a second range value;

[0048] The processing unit is used to determine the radon concentration value based on the quantity of the fourth electrical signal.

[0049] Thirdly, this application provides an electronic device including a processor, a memory, and a communication interface. The processor, memory, and communication interface are interconnected and perform communication with each other. The memory stores executable program code, the communication interface is used for wireless communication, and the processor is used to retrieve the executable program code stored in the memory and execute some or all of the steps described in any of the methods in the first aspect.

[0050] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements some or all of the steps described in the first aspect of this application.

[0051] Fifthly, this application provides a computer program product, including a computer program that, when processed and executed, implements some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A schematic diagram of the structure of a radon gas detection system provided in an embodiment of this application;

[0054] Figure 2 A schematic flowchart of a radon gas detection method provided in an embodiment of this application;

[0055] Figure 3 This is a schematic diagram of the structure of a pulse waveform signal and an interrupt signal provided in an embodiment of this application;

[0056] Figure 4 A schematic flowchart of another radon detection method provided in an embodiment of this application;

[0057] Figure 5 A schematic flowchart illustrating another radon detection method provided in this application embodiment;

[0058] Figure 6 A functional unit block diagram of a radon gas detection device provided in an embodiment of this application;

[0059] Figure 7 A functional unit block diagram of another radon gas detection device provided in an embodiment of this application;

[0060] Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0061] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0062] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps is not limited to the steps listed, but may optionally include steps not listed, or may optionally include other steps inherent to these processes, methods, products, or apparatuses.

[0063] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0064] Currently, radon detection instruments primarily use electrostatic collection methods. The principle of this method is as follows: a radon concentration difference exists inside and outside the high-pressure collection chamber. Radon in the test environment diffuses into the diffusion chamber, establishing a new radioactive equilibrium. Radon diffuses into the high-pressure chamber and decays, producing radon progeny particles, which are collected by a photodiode under electrical influence. The alpha particles generated by these decays cause the photodiode to produce a corresponding pulse signal. This signal is transmitted through a signal line to an amplification circuit, where it is amplified and then acquired by a microcontroller's analog-to-digital converter (ADC). The energy of the particles is inferred from the ADC data to determine whether the pulse was generated by radon progeny particles (e.g., polonium-218, polonium-214). Finally, the effective counts over a period of time are tallied, and a specific algorithm is used to determine the current radon concentration in the environment.

[0065] Since the diffusion chamber is not sealed, in addition to radon in the air, water molecules and aerosols can also enter. When these impurities enter the diffusion chamber, there is a certain probability that they will come into contact with the photodiode, causing background noise on the photodiode. In this case, the signal amplified by the amplifier circuit is actually an electrical signal superimposed on the pulse electrical signal generated by alpha particles hitting the photodiode and the background noise. When the background noise is small, the impact is not significant, but as time accumulates, the background noise may become larger and larger, even interfering with the judgment of the pulse signal source, and thus affecting the calculation of radon concentration.

[0066] This application acquires multiple first signals within a detection cycle and classifies them into second signals that may belong to radon progeny, and other third signals. The background noise is then calculated using the third signals. A new signal peak range is determined by combining the local noise with the signal peak range corresponding to radon progeny in a clean state. This new signal peak range is then compared with the peak value of the second signals to identify signals belonging to radon progeny, thereby achieving accurate calculation of radon concentration. This improves the accuracy of radon concentration detection.

[0067] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a radon gas detection system provided in an embodiment of this application, as shown below. Figure 1 As shown, the radon detection system 100 includes a microcontroller 110, a comparator 120, and a pre-detection device 130. The pre-detection device 130 includes an electrostatic collection chamber 131, a photodiode 132, a preamplifier circuit 133, and a linear pulse amplifier circuit 134.

[0068] The microcontroller 110 is used for signal acquisition and analysis, and may specifically include an ADC detection port for converting analog electrical signals into digital signals and performing analysis and processing.

[0069] The pre-detection device 130 is used to collect radon gas and generate an electrical signal. Specifically, the electrostatic collection cavity 131 mainly uses an electrostatic field to capture and concentrate the alpha particles generated during the decay of radon gas into the detection area of ​​the photodiode 132. The photodiode 132 is mainly used to convert the kinetic energy of the alpha particles into a light signal, and then into an electrical signal through the photoelectric effect. The pre-amplifier circuit 133 is mainly used to initially amplify and shape the weak electrical signal output by the photodiode 132 to improve the signal-to-noise ratio and match the input requirements of subsequent circuits. The linear pulse amplifier circuit 134 is mainly used to further amplify the amplified pulse signal and adjust its amplitude and width to meet the input requirements of the comparator 120.

[0070] Comparator 120 is used to compare the electrical signal (analog signal) output by linear pulse amplifier circuit 134 with a preset threshold, and also outputs an interrupt signal (high / low level) to trigger ADC sampling.

[0071] Specifically, after radon gas enters the electrostatic collection chamber 131, it undergoes atomic decay. The resulting alpha particles are converted into electrical signals by the photodiode 132. The preamplifier circuit 133 obtains the electrical signals and shapes and amplifies them. The linear pulse amplifier circuit 134 further amplifies the electrical signals and adjusts the pulse width. After passing through the comparator 120, the signals are input to the ADC detection port of the microcontroller 110, converting the analog electrical signals into digital signals for analysis and processing.

[0072] In this application, the microcontroller 110 acquires multiple first electrical signals from the electrical signals flowing through the comparator 120 at preset time intervals within a target time range; in response to receiving an interrupt signal from the comparator 120, it determines a second electrical signal corresponding to the time of occurrence of the interrupt signal from the multiple first electrical signals; it determines a first noise value based on multiple third electrical signals; it compensates a first range value based on the first noise value to determine a second range value, where the first range value represents the signal peak range of radon-corresponding particles under noise-eliminating conditions; the multiple third electrical signals are signals among the multiple first electrical signals that are different from the second electrical signals; it determines a fourth electrical signal from the second electrical signals whose signal peak value satisfies the second range value; and it determines the radon concentration value based on the number of fourth electrical signals. This improves the accuracy of radon concentration detection.

[0073] Based on this, the present application provides a radon gas detection method, and the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0074] Example 1: The main process of the radon gas detection method is described below.

[0075] Please see Figure 2 , Figure 2 This is a flowchart illustrating a radon detection method provided in an embodiment of this application. The method is applied to the aforementioned microcontroller, such as... Figure 2 As shown, the method includes the following steps.

[0076] In one embodiment, a radon gas detection method is provided, the radon gas detection method comprising:

[0077] Step S201: Collect multiple first electrical signals from the electrical signals flowing through the comparator at preset time intervals within the target time range.

[0078] The target time range can be the time interval covered by a complete detection cycle, which can be used to limit the acquisition window of the first electrical signal and ensure that all signals are analyzed under the same time reference. The preset time interval can be the time difference between two adjacent signal samplings, which can be used to ensure that the time series of the first electrical signal is uniformly distributed. In this embodiment, the preset time interval can be driven by the internal timer of the microcontroller to control the ADC to read the input signal voltage value at a fixed frequency.

[0079] The electrical signal flowing through the comparator can be a continuous analog voltage signal output from the pre-detection device and sent to the comparator input. It can be used to carry information about radon progeny decay events and environmental noise components, and is the data source for the first electrical signal. Each of the multiple first electrical signals can be a set of discrete voltage value sequences collected at preset time intervals within the target time range, which can be used to construct a complete input signal time series.

[0080] Step S202: In response to receiving an interrupt signal from the comparator, determine the second electrical signal corresponding to the time of occurrence of the interrupt signal from a plurality of first electrical signals.

[0081] The interrupt signal can be a digital pulse signal output by a comparator, used to indicate that the current input electrical signal has exceeded a preset threshold. It can be used to mark the occurrence time of potential radon progeny decay events, allowing the microcontroller to locate the corresponding second electrical signal. The second electrical signal can be the first electrical signal acquired at the time the interrupt signal occurs, representing a candidate valid event possibly caused by radon progeny. It can be used as a set of valid events to be verified, participating in the subsequent filtering process based on the updated range value.

[0082] Optionally, determining the second electrical signal corresponding to the occurrence time of the interrupt signal from a plurality of first electrical signals includes: determining a first count value and a second count value, wherein the first count value refers to the count value when the collection of a plurality of first electrical signals begins, and the second count value refers to the count value when the interrupt signal is received; determining a first difference based on the difference between the second count value and the first count value; determining a first sequence value based on the ratio between the first difference and a preset time interval; and determining the signal located at the first sequence value from a plurality of first electrical signals as the second electrical signal.

[0083] The first count value can be the value recorded by the microcontroller's internal timer or counter at the initial moment of starting the acquisition of multiple first electrical signals, and can be used to provide a time start marker for the signal acquisition process. The second count value can be the real-time reading value of the counter captured by the microcontroller at the instant the interrupt signal issued by the comparator is received, and can be used to mark the precise count moment when the interrupt event occurred.

[0084] The first difference can be the numerical difference between the second count value and the first count value, representing the number of count units elapsed from the start of acquisition to the interruption trigger. It can be used to quantify the time offset of the interruption event relative to the acquisition start point. The preset time interval can be the time difference between two adjacent signal samples. The first sequence value is used to indicate the position of the sampling point corresponding to the interruption time in the sequence, and can be used to accurately locate the specific element among multiple first electrical signals that should be marked as the second electrical signal.

[0085] Determining the signal located at the first sequence value from multiple first electrical signals as the second electrical signal can be achieved by using the first sequence value as an array index, accessing the buffer storing multiple first electrical signals, and retrieving the data element at the corresponding position as the second electrical signal.

[0086] In this embodiment, by acquiring count values ​​at the start and interruption trigger times to form a relative time offset, and converting it into a sampling sequence index to locate the target signal, a high-precision alignment between physical events and digital signals can be achieved. Furthermore, the second electrical signal can be determined from multiple first electrical signals through simple calculation.

[0087] Optionally, in one embodiment, the method further includes: obtaining a second noise value, the second noise value being a noise value determined before a target time range; predicting based on the second noise value to obtain a third noise value corresponding to the target time range; and adjusting a preset threshold based on the third noise value and a first range value so that the preset threshold is greater than the third noise value and less than the minimum value in the first range value.

[0088] The second noise value can be a quantified value of the background noise determined in one or more detection periods prior to the current target time range. It can be used as historical noise reference data to support the prediction of subsequent noise trends. The second noise value can be obtained by reading the noise statistics saved at the end of the previous detection period from a storage unit or historical data cache.

[0089] The third noise value can be a predicted background noise level applicable to the current target time range, obtained based on the second noise value. It can be used to provide a prior estimate of the noise state in the current detection period to support the dynamic setting of a preset threshold. In this embodiment, the third noise value can be used to evolve and estimate the second noise value using mathematical methods such as trend extrapolation, linear regression, or exponential smoothing.

[0090] The preset threshold can be a voltage threshold used to determine whether an input signal is valid, i.e., whether it is a valid event that may be caused by radon progeny. The preset threshold can be used to determine when to generate an interrupt signal, thereby affecting the capture sensitivity of valid events and the probability of noise false triggering. In a specific embodiment, the preset threshold can be set through system initialization and can be updated by the microcontroller by writing it into the comparator configuration register according to the operating status.

[0091] Adjusting the preset threshold based on the third noise value and the first range value can be achieved by using the third noise value as the lower limit reference and the minimum value of the first range value as the upper limit reference, and then resetting the specific value of the preset threshold within this range. Furthermore, this operation can be implemented by setting the preset threshold to the geometric mean between the third noise value and the minimum value of the first range value, or by using a proportional offset to set it to a safety margin value close to the lower limit. This allows the comparator's trigger threshold to adaptively update with environmental changes, balancing noise immunity and signal capture integrity.

[0092] In this embodiment, historical noise data is introduced to predict the noise level of the current period. A dynamic threshold boundary is set by combining the predicted third noise value and the first range value, so that the comparator threshold can respond to changes in environmental noise in advance. This can reduce false noise triggering and missed detection of real signals under pollution accumulation conditions, and improve the accuracy of distinguishing between noise and the electrical signals corresponding to radon progeny. This can reduce the number of signals for subsequent peak comparison, thereby improving processing efficiency and ultimately enhancing the stability and reliability of radon detection in complex real-world scenarios.

[0093] Optionally, determining the predicted third noise value based on the second noise value includes: acquiring a first temperature value, a first humidity value, a first usage duration, and a first time period, wherein the first temperature value and the first humidity value are the temperature and humidity values ​​at the time of determining the second noise value, the first usage duration is the usage duration of the radon detection system at the time of determining the second noise value, and the first time period is the corresponding time period at the time of determining the second noise value; training an initial model based on the first temperature value, the first humidity value, the first usage duration, the first time period, and the second noise value to obtain a target model; acquiring the second temperature value, the second humidity value, the second usage duration, and the second time period at the time when multiple first electrical signals are started to be collected, and inputting the second temperature value, the second humidity value, the second usage duration, and the second time period into the target model to obtain the third noise value.

[0094] The first temperature value can be an air temperature measurement used to characterize the environment in which the radon detection system operates when determining the second noise value. It can be used as a key environmental factor affecting water molecule activity and aerosol deposition rate, participating in noise evolution modeling. Furthermore, the first temperature value can be acquired in real time through a built-in or external digital temperature sensor and simultaneously labeled and stored with the noise data.

[0095] The first humidity value can be a relative percentage measurement reflecting the water vapor content in the air when determining the second noise value. It can reflect the water molecule concentration level in the environment and directly affect the surface contamination rate and background noise growth trend of the photodiode. In this embodiment, the first humidity value can be detected by a humidity sensor, converted into an electrical signal, and output as a standardized value after calibration.

[0096] The first usage duration can be used to characterize the cumulative operating time of the radon detection system since its activation, up to the moment the second noise value is determined. It can also be used to characterize the degree of equipment aging and the historical load of pollutant accumulation within the detection chamber, and to predict future noise changes. In one specific embodiment, the first usage duration can be continuously recorded by a real-time clock or counter inside the microcontroller, and a snapshot can be saved at the end of each noise assessment cycle. Furthermore, the first usage duration can reflect the long-term degradation state of the system, and, together with environmental parameters, influence the target model training process.

[0097] The first time period can be used to identify the intraday time or periodic time characteristics corresponding to the second noise value, and can be used to capture the periodic patterns of noise changes (such as diurnal differences) to enhance prediction accuracy.

[0098] The initial model can be a mathematical structure with learning capabilities, used to establish a mapping relationship between input variables and output noise values. It can serve as a starting point for training, learning the influence of the environment and operating parameters on noise through historical data. In an exemplary embodiment, the initial model can be constructed using a machine learning framework, such as a linear regression model, a decision tree ensemble model, or a shallow neural network.

[0099] The initial model is trained based on the first temperature value, first humidity value, first usage duration, first time period, and second noise value to obtain the target model. This can be achieved by using a training sample set comprised of these five data types, inputting it into the initial model for supervised learning, and adjusting internal weights or parameters until the prediction error converges. Furthermore, this operation can be implemented by training a linear regression model using batch gradient descent and optimizing the hyperparameters of a random forest using cross-validation. This allows the model to grasp the inherent patterns of noise changes with environmental and operational states, enabling cross-period prediction capabilities.

[0100] The second temperature value, second humidity value, second usage duration, and second time period correspond to the first temperature value, first humidity value, first usage duration, and first time period, respectively. The second temperature value can be an ambient temperature measurement value used to characterize the collection of multiple first electrical signals at the start of the current target time range; the second humidity value can be a relative humidity measurement value used to characterize the ambient air at the start of the current detection cycle; the second usage duration can be a total cumulative operating time of the radon detection system at the start of the current detection cycle; and the second time period can be a time period within the day used to identify the start of the current detection cycle.

[0101] The second temperature value, second humidity value, second usage duration, and second time period are input into the target model to obtain the third noise value. This can be achieved by organizing the four collected parameters into the input format required by the target model and performing forward inference to obtain the output result.

[0102] In this embodiment, by constructing multi-dimensional historical samples containing environmental and operational states to support model training, the model learns the complex relationship between noise evolution and external conditions, and performs forward inference based on the current environment and equipment state to achieve forward-looking noise estimation. This can achieve accurate prediction of background noise in changing environments, and dynamically adjust the comparator preset threshold to avoid expected background interference and retain an effective signal response window, thus maintaining the technical effect of radon gas detection stability and accuracy.

[0103] Step S203: Determine the first noise value based on multiple third electrical signals.

[0104] The multiple third electrical signals can be the remaining signal samples after excluding the second electrical signals from the multiple first electrical signals. They can be used to reflect the background level of the system during periods without significant particle impacts and to estimate the current background noise level. In this embodiment, the multiple third electrical signals can be obtained by performing a set subtraction operation on the entire set of first electrical signals to remove all second electrical signals.

[0105] The first noise value can be a quantitative indicator derived from the statistics of multiple third electrical signals, characterizing the background noise intensity of the current system. It can be used to provide a basis for dynamically adjusting the signal recognition range to compensate for noise shifts caused by environmental factors. In a specific embodiment, the first noise value can be obtained by calculating statistical quantities such as the mean, variance, or peak distribution of multiple third electrical signals.

[0106] Step S204: Compensate the first range value based on the first noise value to determine the second range value.

[0107] The first range value represents the signal peak range of radon particles under noise-free conditions, and the multiple third electrical signals are signals among the multiple first electrical signals that are different from the second electrical signal. The first range value can be the theoretical range of the electrical signal peak generated by the decay of radon progeny (such as polonium-218, polonium-214) under ideal noise-free conditions or when the noise is lower than a preset noise value. It can be used as an initial reference range for comparison and correction with the compensated second range value.

[0108] The second range value can be a dynamic signal identification interval obtained by offsetting or expanding the first range value in combination with the current first noise value. It can be used to adapt to the actual noise level of the current operating environment and improve the accuracy and robustness of signal identification. In this embodiment, the second range value can be obtained by floating the lower limit of the first range value upward by a certain amount (such as adding the noise mean) or by expanding the interval width to adapt to noise fluctuations.

[0109] Step S205: Determine a fourth electrical signal from the second electrical signal whose peak value satisfies the second range value.

[0110] The fourth electrical signal can be a further filtered signal from the second electrical signal, whose peak value falls within the second range. This signal can be used to confirm valid events truly originating from radon progeny decay and to eliminate false positives caused by noise enhancement. In this embodiment, the fourth electrical signal can be obtained by checking whether the amplitude of each of the second electrical signals is within the updated second range.

[0111] Step S206: Determine the radon concentration value based on the number of fourth electrical signals.

[0112] The radon concentration value can be a physical quantity representing the radon activity per unit volume of air, typically expressed in Bq / m³ (becquerels per cubic meter). It can be used in the final output to reflect the actual radon content level in the measured environment. In one specific embodiment, the radon concentration value can be calculated from the number of fourth electrical signals, the sampling time length, and the system sensitivity factor. Specifically, determining the radon concentration value based on the number of fourth electrical signals can be achieved by dividing the cumulative number of fourth electrical signals by the sampling time length and multiplying by the system calibration coefficient to obtain the count rate per unit time, which is then converted into the radon concentration.

[0113] For example, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of a pulse waveform signal and an interrupt signal provided in an embodiment of this application, as shown below. Figure 3The graph shown includes pulse waveform signals and interrupt signals on the coordinate axes, where the horizontal axis represents time in milliseconds (ms) and the vertical axis represents voltage in millivolts (mV). In typical data acquisition scenarios, the rising edge of the interrupt signal can be used to wake up the microcontroller and enable the ADC function for data acquisition. The pulse waveform signal is generated when alpha particles emitted by radon and its progeny strike a photodiode. The peak value of the waveform reflects the energy of the alpha particles. Acquired particles include alpha particles emitted by radon-222 (5.49 MeV), alpha particles emitted by the radon progeny polonium-218 (6 MeV), and alpha particles emitted by the radon progeny polonium-214 (7.69 MeV). The higher the energy of the alpha particles, the higher the peak value of the pulse waveform signal. However, alpha particles may experience energy decay before striking the photodiode, so the peak value of the pulse signal for each particle type is not unique. Concentration calculations can be performed based on the quantity of a single type of radon progeny, for example, using the quantity of alpha particles emitted by polonium-218.

[0114] In this application, the microcontroller periodically samples the electrical signal flowing through the comparator at preset time intervals, forming a time series of multiple first electrical signals. Whenever the comparator detects a signal exceeding a preset threshold and issues an interrupt signal, the second electrical signal corresponding to the time of the interrupt signal is regarded as a potential radon progeny event. The remaining third electrical signals reflect the background state of the system when there are no significant particle impacts. By statistically analyzing the third electrical signals, a first noise value is obtained to characterize the background noise level in the current environment. This noise value is used to dynamically compensate a preset first range value to generate a second range value that adapts to the current operating conditions, thereby correcting the baseline drift caused by water molecule or aerosol deposition. The portion of the second electrical signals whose signal peak falls within the second range value is selected to obtain a fourth electrical signal, which confirms a valid radon progeny decay event. Finally, the radon concentration value is calculated based on the number of fourth electrical signals. The entire process realizes the transformation from static threshold identification to adaptive signal discrimination based on actual noise state, improving the accuracy and stability of radon detection in complex environments.

[0115] Example 2: The radon detection method will be described in detail below based on the details of determining the second range value.

[0116] Please see Figure 4 , Figure 4 This is a flowchart illustrating another radon detection method provided in an embodiment of this application. This method is applied to the aforementioned microcontroller, such as... Figure 4 As shown, the method includes the following steps.

[0117] Step S401: Collect multiple first electrical signals from the electrical signals flowing through the comparator at preset time intervals within the target time range.

[0118] Step S402: In response to receiving an interrupt signal from the comparator, determine the second electrical signal corresponding to the time of occurrence of the interrupt signal from a plurality of first electrical signals.

[0119] Step S403: Determine the first noise value based on multiple third electrical signals.

[0120] Step S404: Determine the first signal value based on the median value of the first range.

[0121] Here, the first range value represents the signal peak range of radon-corresponding particles under noise-eliminating conditions, and the multiple third electrical signals are signals among the multiple first electrical signals that are different from the second electrical signal. The median value can be the midpoint of the signal peak interval corresponding to the first range value, i.e., the arithmetic center value of the upper and lower limits, and can be used as a basic reference point for determining the first signal value, and for establishing the initial response benchmark for dynamic compensation.

[0122] Step S405: Determine the second signal value based on the sum of the first noise value and the first signal value.

[0123] The second signal value can be a new response benchmark obtained by superimposing the current background noise level on the first signal value. It reflects the effective signal start position under actual operating conditions and can be used as the center anchor point of the second range value to ensure that the recognition window shifts synchronously with changes in the environment.

[0124] Step S406: Determine the second range value based on the magnitude of the first noise value and the second signal value.

[0125] The first difference and the second difference are of the same magnitude. The first difference is the difference between the maximum value in the second range and the second signal value. The second difference is the absolute value of the difference between the minimum value in the second range and the second signal value. The larger the first noise value, the larger the first difference.

[0126] The first difference can be the numerical difference between the upper limit of the second range value and its internal center point (the second signal value). It can be used to define the acceptable upper deviation boundary of the second range value to prevent the loss of effective signals due to high-amplitude noise. The second difference can be the absolute value of the difference between the lower limit of the second range value and its center point (the second signal value). It can be used to define the acceptable lower boundary of the second range value to ensure that it can still accommodate real signal fluctuations when noise increases.

[0127] The second range value is determined based on the magnitude of the first noise value and the second signal value. This can be achieved by determining the extent of expansion based on the amplitude of the first noise value, and by setting new upper and lower limits symmetrically around the second signal value, forming an updated recognition interval. Furthermore, this operation can be implemented by calculating the difference and generating the range according to a preset functional relationship (such as linear or piecewise linear), or by using a machine learning model to predict the optimal range width. This allows for the construction of a dynamic energy window that adapts to the current noise environment, improving the accuracy of signal classification under complex conditions.

[0128] The larger the initial noise value, the larger the initial difference. This can be achieved by designing a monotonically increasing relational function or lookup table, so that the initial difference increases accordingly with the initial noise value. Furthermore, this operation can be implemented by employing a linear proportional amplification strategy or by setting multiple noise intervals with corresponding fixed difference levels, thereby enhancing the system's fault tolerance in high-noise environments and preventing the exclusion of valid signals due to pulse broadening or amplitude fluctuations. Conversely, the smaller the initial noise value, the smaller the initial difference, thus improving the system's accuracy in low-noise environments.

[0129] In this embodiment, a dynamic compensation mechanism is used to make the identification window shift synchronously with the background noise and adaptively expand. This can not only accommodate signal fluctuations caused by environmental interference, but also prevent misjudgment or missed detection caused by static thresholds under pollution accumulation or variable operating conditions. Finally, the dynamic window is used to screen the second electrical signal to confirm the fourth electrical signal that meets the energy characteristics. This can improve the technical effect of radon concentration calculation accuracy and robustness under long-term operation or variable environment.

[0130] Step S407: Determine a fourth electrical signal from the second electrical signal whose peak value satisfies the second range value.

[0131] Step S408: Determine the radon concentration value based on the number of fourth electrical signals.

[0132] Example 3: The radon detection method will be described in detail below based on the details of determining the first noise value.

[0133] Please see Figure 5 , Figure 5 This is a flowchart illustrating another radon detection method provided in an embodiment of this application. This method is applied to the aforementioned microcontroller, such as... Figure 5 As shown, the method includes the following steps.

[0134] Step S501: Collect multiple first electrical signals from the electrical signals flowing through the comparator at preset time intervals within the target time range.

[0135] Step S502: In response to receiving an interrupt signal from the comparator, determine the second electrical signal corresponding to the time of occurrence of the interrupt signal from a plurality of first electrical signals.

[0136] Step S503: Determine the noise value corresponding to each of the multiple third electrical signals.

[0137] The noise value corresponding to each third electrical signal can be a numerical value used to characterize its local level features, reflecting the state of the system background noise within the preset time interval. In an exemplary embodiment, the noise value corresponding to each third electrical signal can be obtained by sampling the voltage amplitude of each third electrical signal, baseline fitting, or short-time energy calculation.

[0138] Optionally, in one embodiment, determining the noise value corresponding to each of the plurality of third electrical signals includes: determining a first value and a second value in each third electrical signal, wherein the first value is the first quartile value in the sequence of each third electrical signal, and the second value is the third quartile value in the sequence of each third electrical signal; determining the interquartile range of each third electrical signal based on the absolute value of the difference between the first value and the second value; and determining the noise value corresponding to each third electrical signal based on the ratio of the interquartile range of each third electrical signal to a first coefficient, wherein the first coefficient is the ratio between the interquartile range and the standard deviation in a general normal distribution.

[0139] During signal acquisition, some electronic components in the pre-detection device may generate transient noise, potentially leading to abnormal transient pulse signals. These abnormal transient pulse signals can occur due to factors such as power supply voltage drops or mechanical vibrations. Therefore, this embodiment uses the interquartile range (IQR) method to determine the noise value, which can reduce the impact of transient interference on noise assessment.

[0140] Each third electrical signal sequence can be an ordered set of data points continuously sampled from the third electrical signal over time. This set can provide basic data for analyzing local signal distribution characteristics and supports the extraction of statistical measures such as quartiles. Furthermore, each third electrical signal sequence can be obtained by the microcontroller performing high-density continuous sampling of the electrical signal flowing through the comparator within an uninterrupted time period to form a short-segment signal sequence.

[0141] The first quartile value can be the data value located at the 25th percentile when an ordered numerical sequence is divided into four equal parts. It represents the upper limit of the lower portion of the sequence and can be used as a lower bound reference for the signal basis fluctuation range, participating in the interquartile range calculation to assess the degree of dispersion. For example, the first quartile value can be obtained by determining the value of the 25th percentile by linear interpolation or nearest neighbor selection after sorting the sequence of each third electrical signal in ascending order.

[0142] The third quartile value can be the data value located at the 75th percentile when an ordered numerical sequence is divided into four equal parts. It represents the lower limit of the higher part of the sequence and can be used as the starting point of the fluctuation in the upper half of the signal. Combined with the first quartile, it reflects the dispersion of the middle 50% of the data. In this embodiment, the third quartile value can be achieved by locating the value at the 75th percentile after sorting the sequence of each third electrical signal.

[0143] The first value can be the first quartile value (Q1) of each third electrical signal sequence in this scheme, which can be used as the low boundary input of the interquartile range to measure the starting position of the signal distribution center region. The second value can be the third quartile value (Q3) of each third electrical signal sequence in this scheme, which can be used as the high boundary input of the interquartile range to measure the ending position of the signal distribution center region.

[0144] The interquartile range (ICM) can be the absolute value of the difference between the third quartile and the first quartile, representing the discrete range of the middle 50% of the data. It can be used to quantify the fluctuation intensity of a third electrical signal sequence and has robust statistical properties that are insensitive to outliers. For example, the ICM can be obtained by calculating the absolute value of the difference between the second and first values, thereby achieving a signal dispersion index that is insensitive to outliers and effectively characterizing the true fluctuation level of the background noise.

[0145] The first coefficient can be the theoretical ratio between the interquartile range and the standard deviation in a normal distribution, approximately 1.349. It can be used as a conversion factor to convert the interquartile range into an equivalent standard deviation, facilitating subsequent noise modeling and compensation. In this embodiment, the first coefficient can be obtained by derivation from probability and statistics theory, and is a constant under an ideal Gaussian noise model.

[0146] In this embodiment, the interquartile range is obtained by determining the first and third quartile values ​​in the sequence of the third electrical signal and calculating the absolute value of their difference. This index focuses on the dispersion of the middle 50% of the data, significantly reducing the impact of outliers such as residual particle events or transient interference on noise assessment. Furthermore, by utilizing the fixed proportional relationship between the interquartile range and the standard deviation under a normal distribution, the interquartile range is converted into an equivalent standard deviation value, which is used as the noise value corresponding to the third electrical signal. The resulting noise value set is used to calculate the average value to determine the first noise value, improving the realism and stability of the overall noise estimation. Finally, the optimized noise estimation is used to dynamically adjust the first range value to generate the second range value, enhancing the accuracy of identifying real radon progeny signals under non-ideal environments and thus improving the reliability of radon concentration calculation.

[0147] Step S504: Determine the first noise value based on the average value of the noise values ​​corresponding to each of the multiple third electrical signals.

[0148] The average value can be an arithmetic central tendency measure obtained by summing multiple values ​​and dividing by the total number, which can be used to provide a stable and representative noise level estimate. In this embodiment, it can be obtained by adding the noise values ​​corresponding to all the third electrical signals and then dividing by the total number of noise values.

[0149] In this embodiment, by determining the noise value corresponding to each of the multiple third electrical signals and determining the first noise value based on the average of the noise values ​​corresponding to each of the multiple third electrical signals, the interference of random fluctuations on noise judgment in complex or changing environments can be reduced, thereby enhancing the ability to identify real radon progeny events and ultimately improving the accuracy and stability of radon concentration calculation.

[0150] Step S505: Compensate the first range value based on the first noise value to determine the second range value.

[0151] The first range value is used to represent the signal peak range of the radon-corresponding particle under noise-eliminating conditions, and the multiple third electrical signals are signals that are different from the second electrical signals among the multiple first electrical signals.

[0152] Step S506: Determine a fourth electrical signal from the second electrical signal whose peak value satisfies the second range value.

[0153] Step S507: Determine the radon concentration value based on the number of fourth electrical signals.

[0154] For embodiments consistent with those shown above, please refer to... Figure 6 , Figure 6This application provides a functional unit block diagram of a radon detection device, wherein the radon detection device is the aforementioned microcontroller or a part thereof, such as... Figure 6 As shown, the radon gas detection device 60 includes:

[0155] The acquisition unit 601 is used to acquire multiple first electrical signals from the electrical signals flowing through the comparator at preset time intervals within a target time range;

[0156] The processing unit 602 is configured to, in response to receiving an interrupt signal from a comparator, determine a second electrical signal from a plurality of first electrical signals corresponding to the time at which the interrupt signal occurs;

[0157] Processing unit 602 is used to determine a first noise value based on a plurality of third electrical signals;

[0158] Processing unit 602 is used to compensate for a first range value based on a first noise value and determine a second range value. The first range value is used to represent the signal peak range of the radon-corresponding particle under noise-eliminating conditions. The multiple third electrical signals are signals that are different from the second electrical signals among the multiple first electrical signals.

[0159] Processing unit 602 is used to determine a fourth electrical signal whose peak value satisfies a second range value from the second electrical signal;

[0160] The processing unit 602 is used to determine the concentration value of radon gas based on the quantity of the fourth electrical signal.

[0161] In one feasible embodiment, in determining the second electrical signal corresponding to the occurrence time of the interrupt signal from a plurality of first electrical signals, the processing unit 602 is specifically configured to:

[0162] Determine the first count value and the second count value. The first count value refers to the count value when the acquisition of multiple first electrical signals begins, and the second count value refers to the count value when the interrupt signal is received.

[0163] The first difference is determined based on the difference between the second count value and the first count value;

[0164] The first sequence value is determined based on the ratio between the first difference and the preset time interval;

[0165] The signal located at the first sequence value is determined from multiple first electrical signals as the second electrical signal.

[0166] In a feasible embodiment, in determining a second range value by compensating for a first range value based on a first noise value, the processing unit 602 is specifically configured to:

[0167] The first signal value is determined based on the median value of the first range.

[0168] The second signal value is determined based on the sum of the first noise value and the first signal value;

[0169] The second range value is determined based on the magnitude of the first noise value and the second signal value. The first difference and the second difference are of the same magnitude. The first difference is the difference between the maximum value in the second range and the second signal value. The second difference is the absolute value of the difference between the minimum value in the second range and the second signal value. The larger the first noise value, the larger the first difference.

[0170] In one feasible embodiment, in determining the first noise value based on a plurality of third electrical signals, the processing unit 602 is specifically configured to:

[0171] Determine the noise value corresponding to each of the multiple third electrical signals;

[0172] The first noise value is determined by averaging the noise values ​​corresponding to each of the multiple third electrical signals.

[0173] In one feasible embodiment, in determining the noise value corresponding to each of the plurality of third electrical signals, the processing unit 602 is specifically configured to:

[0174] Determine the first and second values ​​in each third electrical signal, where the first value is the first quartile value in the sequence of each third electrical signal, and the second value is the third quartile value in the sequence of each third electrical signal.

[0175] The interquartile range of each third electrical signal is determined based on the absolute value of the difference between the first and second values.

[0176] The noise value corresponding to each third electrical signal is determined based on the ratio of the interquartile range of each third electrical signal to the first coefficient, where the first coefficient is the ratio between the interquartile range and the standard deviation in a general normal distribution.

[0177] In one feasible embodiment, the processing unit 602 is further configured to:

[0178] Obtain a second noise value, which is a noise value determined before the target time range;

[0179] Based on the second noise value, a third noise value corresponding to the target time range is obtained through prediction.

[0180] The preset threshold is adjusted based on the third noise value and the first range value so that the preset threshold is greater than the third noise value and less than the minimum value in the first range value.

[0181] In one feasible embodiment, in determining the predicted third noise value based on the second noise value, the processing unit 602 is specifically configured to:

[0182] The system acquires a first temperature value, a first humidity value, a first usage duration, and a first time period. The first temperature value and the first humidity value are the temperature and humidity values ​​at the time when the second noise value is determined. The first usage duration is the usage duration of the radon detection system at the time when the second noise value is determined. The first time period is the corresponding time period when the second noise value is determined.

[0183] The initial model is trained based on the first temperature value, the first humidity value, the first usage duration, the first time period, and the second noise value to obtain the target model;

[0184] The second temperature value, second humidity value, second usage duration, and second time period are obtained when the collection of multiple first electrical signals begins. The second temperature value, second humidity value, second usage duration, and second time period are then input into the target model to obtain the third noise value.

[0185] It is understood that since the method embodiments and the device embodiments are different presentations of the same technical concept, the content of the method embodiment section in this application should be adapted to the device embodiment section in a synchronous manner, and will not be repeated here.

[0186] When using integrated units, such as Figure 7 As shown, Figure 7 This is a block diagram of the functional units of another radon gas detection device provided in an embodiment of this application. Figure 7 The radon detection device 60 includes a processing module 712 and a communication module 711. The processing module 712 controls and manages the operation of the radon detection device 60, such as the steps of the processing unit 602, and / or performs other processes according to the techniques described herein. The communication module 711 supports interaction between the radon detection device 60 and other devices, such as the steps of the acquisition unit 601. Figure 7 As shown, the radon detection device 60 may also include a storage module 713, which is used to store the program code and data of the radon detection device 60.

[0187] The processing module 712 can be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication module 711 can be a transceiver, RF circuitry, or a communication interface, etc. The storage module 713 can be a memory.

[0188] All relevant content in each scenario involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here. The radon detection device 60 described above can perform the above... Figure 2 , Figure 4 as well as Figure 5 The radon gas detection method shown.

[0189] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0190] Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of this application. Figure 8As shown, the electronic device 800 may include one or more of the following components: processor 801, memory 802 and communication interface 803. The processor 801, memory 802 and communication interface 803 are interconnected and perform communication between them. The memory 802 may store one or more computer programs, which may be configured to implement the methods described in the above embodiments when executed by one or more processors 801.

[0191] Processor 801 may include one or more processing cores. Processor 801 connects to various parts within the electronic device 800 using various interfaces and lines, and performs various functions and processes data of the electronic device 800 by running or executing instructions, programs, code sets, or instruction sets stored in memory 802, and by calling data stored in memory 802. Optionally, processor 801 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 801 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. It is understood that the aforementioned modem may also not be integrated into processor 801, but may be implemented separately through a communication chip.

[0192] The memory 802 may include random access memory (RAM) or read-only memory (ROM). The memory 802 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 802 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described above. The data storage area may also store data created by the electronic device 800 during use.

[0193] It is understood that the electronic device 800 may include more or fewer structural elements than those shown in the above block diagram, such as a power module, physical buttons, WiFi (Wireless Fidelity) module, speaker, Bluetooth module, sensor, etc., without limitation.

[0194] The aforementioned electronic device 800 may be a microcontroller or a part of a microcontroller.

[0195] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements some or all of the steps of any of the radon detection methods described in the above method embodiments.

[0196] This application also provides a computer program product, including a computer program that, when executed by a processor, implements some or all of the steps of any of the radon detection methods described in the above method embodiments. This computer program product can be a software installation package.

[0197] It should be noted that, for the sake of simplicity, all of the aforementioned embodiments of the radon detection method are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0198] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce a good effect.

[0199] Those skilled in the art will understand that all or part of the steps in the various methods of any of the above-described radon detection method embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk, or an optical disk, etc.

[0200] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principle and implementation of a radon detection method, device, electronic device, and storage medium of this application. The description of the above embodiments is only for the purpose of helping to understand the method and its core ideas of this application. At the same time, for those skilled in the art, based on the ideas of a radon detection method, device, electronic device, and storage medium of this application, there will be changes in the specific implementation and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

[0201] This application is described with reference to flowchart illustrations and / or block diagrams of methods, hardware products, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0202] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0203] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0204] It is understood that any product that is controlled or configured to perform the processing method of the flowchart described in the method embodiment of the radon gas detection method of this application, such as the terminal and computer program product of the above flowchart, falls within the scope of the related products described in this application.

[0205] Obviously, those skilled in the art can make various modifications and variations to the radon detection method, apparatus, electronic device, and storage medium provided in this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for detecting radon gas, characterized in that, The method is applied to a microcontroller in a radon detection system. The radon detection system further includes a comparator and a pre-detection device. The pre-detection device collects radon gas and generates an electrical signal based on the particles produced after radon decay. The comparator generates an interrupt signal when the electrical signal exceeds a preset threshold. The method includes: Multiple first electrical signals are acquired from the electrical signals flowing through the comparator at preset time intervals within a target time range; In response to receiving an interrupt signal from the comparator, a second electrical signal corresponding to the time at which the interrupt signal occurs is determined from the plurality of first electrical signals; The first noise value is determined based on multiple third electrical signals; The first range value is compensated based on the first noise value to determine the second range value. The first range value is used to represent the signal peak range of the radon-corresponding particle under noise-eliminating conditions. The plurality of third electrical signals are signals among the plurality of first electrical signals that are different from the second electrical signal. Determine a fourth electrical signal whose peak value satisfies the second range value from the second electrical signal; The concentration of radon gas is determined based on the number of the fourth electrical signals. Wherein, the step of compensating the first range value based on the first noise value to determine the second range value includes: The first signal value is determined based on the median value of the first range. The second signal value is determined based on the sum of the first noise value and the first signal value; A second range value is determined based on the magnitude of the first noise value and the second signal value. The first difference and the second difference are of the same magnitude. The first difference is the difference between the maximum value in the second range and the second signal value. The second difference is the absolute value of the difference between the minimum value in the second range and the second signal value. The larger the first noise value, the larger the first difference. The step of determining the first noise value based on multiple third electrical signals includes: Determine the noise value corresponding to each of the plurality of third electrical signals; The first noise value is determined based on the average of the noise values ​​corresponding to each of the plurality of third electrical signals; Determining the noise value corresponding to each of the plurality of third electrical signals includes: Determine a first value and a second value in each of the third electrical signals, wherein the first value is the first quartile value in the sequence of each of the third electrical signals, and the second value is the third quartile value in the sequence of each of the third electrical signals; The interquartile range of each third electrical signal is determined based on the absolute value of the difference between the first value and the second value. The noise value corresponding to each third electrical signal is determined based on the ratio of the interquartile range of each third electrical signal to the first coefficient, where the first coefficient is the ratio between the interquartile range and the standard deviation in a general normal distribution.

2. The method according to claim 1, characterized in that, Determining the second electrical signal corresponding to the time of occurrence of the interrupt signal from the plurality of first electrical signals includes: A first count value and a second count value are determined, wherein the first count value refers to the count value when the acquisition of the plurality of first electrical signals begins, and the second count value refers to the count value when an interrupt signal is received; The first difference is determined based on the difference between the second count value and the first count value; The first sequence value is determined based on the ratio between the first difference and the preset time interval; The signal located at the first sequence value is determined from the plurality of first electrical signals as the second electrical signal.

3. The method according to claim 1, characterized in that, The method further includes: Obtain a second noise value, the second noise value being a noise value determined prior to the target time range; Based on the second noise value, a third noise value corresponding to the target time range is obtained by prediction. The preset threshold is adjusted based on the third noise value and the first range value, so that the preset threshold is greater than the third noise value and less than the minimum value in the first range value.

4. The method according to claim 3, characterized in that, The step of determining the predicted third noise value based on the second noise value includes: The system acquires a first temperature value, a first humidity value, a first usage duration, and a first time period. The first temperature value and the first humidity value are the temperature and humidity values ​​at the time when the second noise value is determined. The first usage duration is the usage duration of the radon detection system at the time when the second noise value is determined. The first time period is the corresponding time period when the second noise value is determined. The initial model is trained based on the first temperature value, the first humidity value, the first usage duration, the first time period, and the second noise value to obtain the target model; The second temperature value, second humidity value, second usage duration, and second time period are obtained when the collection of the plurality of first electrical signals begins, and the second temperature value, second humidity value, second usage duration, and second time period are input into the target model to obtain the third noise value.

5. A radon gas detection device, characterized in that, The device is applied to a microcontroller in a radon detection system. The radon detection system further includes a comparator and a pre-detection device. The pre-detection device collects radon gas and generates an electrical signal based on the particles produced after radon decay. The comparator generates an interrupt signal when the electrical signal exceeds a preset threshold. The device includes: The acquisition unit is used to acquire multiple first electrical signals from the electrical signals flowing through the comparator at preset time intervals within a target time range; A processing unit is configured to, in response to receiving an interrupt signal from the comparator, determine a second electrical signal from the plurality of first electrical signals corresponding to the time at which the interrupt signal occurs; Processing unit, configured to determine a first noise value based on multiple third electrical signals; The processing unit is configured to compensate for the first range value based on the first noise value and determine the second range value. The first range value is used to represent the signal peak range of the radon-corresponding particle under noise-eliminating conditions. The plurality of third electrical signals are signals among the plurality of first electrical signals that are different from the second electrical signal. Processing unit, configured to determine from the second electrical signal a fourth electrical signal whose peak value satisfies the second range value; The processing unit is used to determine the radon concentration value based on the quantity of the fourth electrical signal; In the aspect of compensating for the first range value based on the first noise value and determining the second range value, the processing unit is specifically configured to: The first signal value is determined based on the median value of the first range. The second signal value is determined based on the sum of the first noise value and the first signal value; A second range value is determined based on the magnitude of the first noise value and the second signal value. The first difference and the second difference are of the same magnitude. The first difference is the difference between the maximum value in the second range and the second signal value. The second difference is the absolute value of the difference between the minimum value in the second range and the second signal value. The larger the first noise value, the larger the first difference. In the aspect of determining the first noise value based on a plurality of third electrical signals, the processing unit is specifically configured to: Determine the noise value corresponding to each of the plurality of third electrical signals; The first noise value is determined based on the average of the noise values ​​corresponding to each of the plurality of third electrical signals; In determining the noise value corresponding to each of the plurality of third electrical signals, the processing unit is specifically configured to: Determine a first value and a second value in each of the third electrical signals, wherein the first value is the first quartile value in the sequence of each of the third electrical signals, and the second value is the third quartile value in the sequence of each of the third electrical signals; The interquartile range of each third electrical signal is determined based on the absolute value of the difference between the first value and the second value. The noise value corresponding to each third electrical signal is determined based on the ratio of the interquartile range of each third electrical signal to the first coefficient, where the first coefficient is the ratio between the interquartile range and the standard deviation in a general normal distribution.

6. An electronic device, the device comprising a processor, a memory, and executable program code stored in the memory, characterized in that, The processor is configured to retrieve the executable program code stored in the memory to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-4.

Images