Anti-interference radon detection method and device, electronic equipment and storage medium

Abstract

The application discloses an anti-interference radon detection method and device, electronic equipment and a storage medium. The method comprises the following steps: if the signal amplitude of an analog electric signal output by a radon sensor is greater than a preset trigger threshold, an interrupt signal is generated; the analog electric signal is sampled to obtain pulse sampling data; a first time domain feature and a first energy domain feature corresponding to the pulse sampling data are determined; when the first time domain feature falls within a preset dynamic period interval, the first energy domain feature is sent to a buffer area to be judged; the number of first data in the buffer area to be judged at the current time is detected; when the number of first data is equal to a preset number, a preset energy spectrum consistency threshold is obtained; a first standard deviation is determined; a target judgment result is determined according to the first standard deviation and the preset energy spectrum consistency threshold; and a target radon concentration is determined according to a target count value of a preset effective event counter. The application improves the accuracy of radon detection.

Description

Technical Field

[0001] This application relates to the field of radon detection technology, and in particular to an interference-resistant radon detection method, apparatus, electronic device and storage medium. Background Technology

[0002] In environmental radiation monitoring, radon is the main source of natural radiation received by the public, and the accuracy of its concentration detection is directly related to the safety of people's living environment.

[0003] Currently, traditional radon detection methods typically employ single-dimensional pulse feature discrimination. For example, amplitude-based dynamic thresholding methods only determine whether the pulse amplitude falls within a preset window, while period-based filtering methods only determine whether the pulse period is within an effective range. However, real-world environments contain various complex interferences such as electromagnetic coupling noise, mechanical vibration, and circuit resonance. The pulses generated by these interferences may overlap with the actual alpha pulses generated by radon in terms of amplitude or period, leading to false positives from single-dimensional discrimination and consequently, low accuracy in radon detection.

[0004] Therefore, improving the accuracy of radon detection has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides an anti-interference radon detection method, apparatus, electronic device, and storage medium, which improves the accuracy of radon detection.

[0006] In a first aspect, embodiments of this application provide an interference-resistant radon detection method, applied to a control module in a radon detection system. The system further includes a radon sensor and a comparison module. The method includes: The comparison module detects the analog electrical signal output by the radon gas sensor in real time. If the amplitude of the analog electrical signal is greater than a preset trigger threshold, an interrupt signal is generated. Upon detecting the interrupt signal, the pulse signal in the analog electrical signal is sampled to obtain pulse sampling data; Determine the first time-domain feature and the first energy-domain feature corresponding to the pulse sampling data; Obtain the preset dynamic period interval; When the first time-domain feature falls within the preset dynamic period interval, the first energy-domain feature is sent to the decision buffer. The number of data items in the buffer to be decided at the current moment is detected to obtain the first data count; When the number of the first data is equal to the preset number, a preset energy spectrum consistency threshold is obtained; N time-domain features stored in the decision buffer are determined; the value of N is equal to the preset number; the first standard deviation corresponding to the N time-domain features is determined; the target decision result is determined according to the first standard deviation and the preset energy spectrum consistency threshold; the target decision result includes any one of the following: a set of valid pulse signals and a set of invalid pulse signals; The target decision result is counted by a preset valid event counter. After the count is completed, the pending decision buffer is cleared. The target radon concentration is determined based on the target count value of the preset valid event counter.

[0007] Secondly, embodiments of this application provide an anti-interference radon gas detection device, applied to a control module in a radon gas detection system. The system further includes a radon gas sensor and a comparison module. The device comprises a comparison unit, a sampling unit, a determination unit, and a radon gas detection unit, wherein: The comparison unit is used to detect the analog electrical signal output by the radon gas sensor in real time through the comparison module. If the amplitude of the detected analog electrical signal is greater than a preset trigger threshold, an interrupt signal is generated. The sampling unit is used to sample the pulse signal in the analog electrical signal after detecting the interrupt signal to obtain pulse sampling data; The determining unit is used to determine the first time-domain feature and the first energy-domain feature corresponding to the pulse sampling data; The radon detection unit is used to acquire a preset dynamic period interval; when the first time-domain feature falls within the preset dynamic period interval, the first energy-domain feature is sent to a decision buffer; the number of data in the decision buffer at the current time is detected to obtain a first data count; when the first data count is equal to a preset count, a preset energy spectrum consistency threshold is acquired; N time-domain features stored in the decision buffer are determined; the value of N is equal to the preset count; a first standard deviation corresponding to the N time-domain features is determined; a target decision result is determined based on the first standard deviation and the preset energy spectrum consistency threshold; the target decision result includes any one of the following: a set of valid pulse signals and a set of invalid pulse signals; a preset valid event counter is used to count according to the target decision result, and after counting is completed, the decision buffer is cleared; the target radon concentration is determined based on the target count value of the preset valid event counter.

[0008] Thirdly, embodiments of this application provide an electronic device, including: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the first aspect of embodiments of this application.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of embodiments of this application.

[0010] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of embodiments of this application. The computer program product may be a software installation package.

[0011] Implementing this application will have the following beneficial effects: As can be seen, the anti-interference radon detection method, apparatus, electronic equipment, and storage medium described in this application firstly eliminates interference with periods significantly deviating from the true alpha pulses through periodic filtering; secondly, the energy domain characteristics (i.e., signal amplitude) after initial screening are sent to the decision buffer, and when a preset number of pulses are stored, the standard deviation of the entire amplitude group is calculated and compared with the energy spectrum consistency threshold; the energy of true alpha particles is concentrated, and the standard deviation of the corresponding pulse group is small; the pulse amplitudes generated by composite interference (e.g., electromagnetic noise, vibration) are dispersed, and the standard deviation is large. The standard deviation test can effectively eliminate interference that "deceives" the initial screening in terms of period but has dispersed energy, reducing false positives; at the same time, the continuously cleared and reused decision buffer design avoids the randomness of single-pulse discrimination, achieving robust discrimination from both the time domain and energy domain dimensions, thereby improving the accuracy of radon detection. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0013] Figure 1 This is an application scenario diagram of a radon gas detection system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a radon gas detection system provided in an embodiment of this application; Figure 3 This is a flowchart of an anti-interference radon gas detection method provided in an embodiment of this application; Figure 4This is a flowchart of a method for obtaining a preset dynamic periodic interval provided in an embodiment of this application; Figure 5 This is a flowchart of a method for determining a target judgment result provided in an embodiment of this application; Figure 6 This is a schematic diagram of another radon gas detection system provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an anti-interference radon gas detection device provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] 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.

[0015] 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 or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0016] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0017] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0018] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0019] 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.

[0020] The electronic devices described in this application embodiment may include smartphones (such as Android phones, iOS phones, Windows Phones, etc.), tablet computers, PDAs, laptops, video matrices, monitoring platforms, mobile internet devices (MIDs), or wearable devices, etc. The above are merely examples and not exhaustive, and include but are not limited to the above devices.

[0021] Of course, the aforementioned electronic devices can also be servers, such as cloud servers.

[0022] The following describes the relevant content, concepts, meanings, technical issues, technical solutions, and beneficial effects involved in the embodiments of this application.

[0023] First, let me explain some of the technical terms or phrases used in this application: Radon detection refers to the process of measuring the radon concentration in soil, water, or atmosphere using radon detection equipment. It can also refer to the technical activities of measuring, analyzing, and evaluating the content of radon and its decay products in indoor and outdoor air. Radon is a colorless, odorless, and tasteless natural radioactive inert gas. Radon detection is an important part of environmental radiation monitoring. Its detection objects include radon itself and radon decay products (e.g., Po-218, Po-214). The measurement results are usually expressed as radon concentration (unit: becquerel per cubic meter, Bq / m³).

[0024] Radon sensor: A detection element or device that converts the physical effects of radon gas and the alpha particles released by its decay products into a measurable electrical signal (e.g., a voltage pulse or current).

[0025] Alpha particle: A particle consisting of two protons and two neutrons, carrying a positive charge of 2 units, and is essentially a helium-4 atomic nucleus.

[0026] Alpha pulse: The radon gas sensor converts the physical effects (e.g., ionization charge or scintillation photon) produced by the incidence of an alpha particle into a transient electrical signal waveform that can be analyzed and processed by subsequent circuitry.

[0027] ADC module: also known as Analog-to-Digital Converter, is used to convert analog electrical signals into digital signals.

[0028] Please see Figure 1 , Figure 1 This is an application scenario diagram of a radon detection system provided in an embodiment of this application. As can be seen, during the detection process, the radon source continuously diffuses radon gas outward and omnidirectionally radiates alpha particles. Some of the diffused radon gas enters the radon detection system. The radon sensor built into the system captures the alpha particles generated by radon decay and converts the radioactive signal carried by the alpha particles into a recognizable electrical signal. The radon detection system calculates the concentration parameter of radon gas in the environment based on the electrical signal, thus completing the quantitative detection of environmental radon content.

[0029] Please see Figure 2 , Figure 2 This is a schematic diagram of a radon detection system provided in an embodiment of this application; the radon detection system (hereinafter referred to as the system) may include: a radon sensor, a comparison module, and a control module; wherein: Radon sensor: Used to convert radon gas and the alpha particles released from its decay products into electrical pulse signals.

[0030] Comparison module: Used to monitor the analog electrical signal output by the radon gas sensor in real time. When the signal amplitude exceeds the preset trigger threshold, an interrupt signal is generated to notify the control module to start sampling.

[0031] Control module: After receiving the interrupt signal, it controls the ADC module to sample, extract pulse features, execute algorithms such as periodic filtering and energy spectrum consistency verification, complete pulse decision and counting, and finally output radon concentration.

[0032] Please see Figure 3 , Figure 3 This is a flowchart of an anti-interference radon gas detection method provided in an embodiment of this application; a control module applied to a radon gas detection system, the system further including: a radon gas sensor and a comparison module, the method including but not limited to the following steps: S301. The analog electrical signal output by the radon gas sensor is detected in real time by the comparison module. If the amplitude of the analog electrical signal is greater than a preset trigger threshold, an interrupt signal is generated.

[0033] In this embodiment, the preset trigger threshold can be preset in advance or defaulted; the comparison module may include any of the following: a dedicated voltage comparison chip, a comparison circuit, an analog comparator, etc., which are not limited here.

[0034] In a specific embodiment, the positive input terminal of the comparison module is connected to the signal output terminal of the radon sensor, and the negative input terminal is connected to a preset fixed reference voltage (i.e., a preset trigger threshold). This preset trigger threshold is set to be higher than the baseline noise level in the sensor's output signal. The comparison module continuously monitors the analog electrical signal output by the radon sensor and compares it with the preset trigger threshold. When the instantaneous amplitude of the analog electrical signal is lower than the preset trigger threshold, the output of the comparison module remains at a logic low level, indicating that no pulse event has occurred. When the radon sensor detects the α pulse signal, the instantaneous amplitude of its output analog electrical signal rises and exceeds the preset trigger threshold. The output of the comparison module immediately flips from logic low to logic high, generating a rising edge triggered interrupt signal, which is then transmitted to the control module.

[0035] In some embodiments, the analog electrical signal output by the radon sensor can be directly input into the comparison module for comparison without additional amplification; for example, when the pulse amplitude output by the radon sensor is about 100mV~200mV, the preset trigger threshold can be set to 150mV.

[0036] In some embodiments, the analog electrical signal output by the radon gas sensor can be amplified first, and then the amplified analog electrical signal can be input into the comparison module for comparison. For example, when the amplified analog electrical signal is increased to 1V~2V, the preset trigger threshold can be set to 1.5V.

[0037] In this way, by comparing the analog electrical signal output by the radon sensor in real time and generating an interrupt signal when the signal amplitude is greater than the preset trigger threshold, an event-driven low-latency response can be achieved. This allows the control module to be woken up only when a valid pulse arrives to perform sampling and processing, and to remain in a dormant state at other times, thereby significantly reducing system power consumption and alleviating the computational burden on the control module.

[0038] S302. After detecting the interrupt signal, the pulse signal in the analog electrical signal is sampled to obtain pulse sampling data.

[0039] In this embodiment of the application, the radon detection system may further include an ADC module.

[0040] In a specific embodiment, the comparison module can send an interrupt signal to the control module. After receiving the interrupt signal, the control module starts the ADC module to sample the pulse signal in the analog electrical signal at a preset sampling rate to obtain pulse sampling data.

[0041] The preset sampling rate can be preset in advance or set to the default value, for example, 1 MHz.

[0042] It should be explained that the analog electrical signal refers to the continuous analog signal output by the radon gas sensor and its front-end circuit; the pulse signal refers to the transient pulse segment in the analog electrical signal corresponding to a single alpha particle event.

[0043] S303. Determine the first time-domain feature and the first energy-domain feature corresponding to the pulse sampling data.

[0044] In this embodiment of the application, pulse sampling data can be analyzed to obtain first time-domain features and first energy-domain features.

[0045] In some embodiments, the pulse sampling data includes *a* sample data points and *a* sampling times, each sampling time corresponding to one sample data point, where *a* is an integer greater than 1; the first time-domain feature includes a first signal period; the first energy-domain feature includes a first signal amplitude; determining the first time-domain feature and the first energy-domain feature corresponding to the pulse sampling data includes: S11. Acquire the first pulse sampling data within a first preset time period; the end time of the first preset time period is earlier than the generation time of the interrupt signal; S12. Determine the target baseline based on the first pulse sampling data; S13. Determine the sampling amplitude based on the target baseline and the preset baseline adjustment coefficient; S14. Determine the first sampled data that is greater than the sampling amplitude among the a sampled data to obtain the first sampled data; the first sampled data corresponds to the first sampling time among the a sampling times. S15. Determine the sample data with the latest sampling time among the a sample data and which is greater than the sampling amplitude to obtain the second sample data; the second sample data corresponds to the second sampling time among the a sample data. S16. Determine the first signal period based on the second sampling time and the first sampling time; S17. Determine the amplitude of the first signal according to the preset amplitude calculation algorithm and the pulse sampling data.

[0046] In this embodiment, the first preset time period, the preset baseline adjustment coefficient, and the preset amplitude calculation algorithm can all be preset in advance or defaulted.

[0047] In a specific embodiment, pulse sampling data within a first preset time period can be queried from the preset database of the control module to obtain the first pulse sampling data; wherein, the preset database is a database in the control module used to store pulse sampling data.

[0048] In some embodiments, the first preset time period can be the most recent 60 seconds.

[0049] Then, the target baseline can be determined based on the first pulse sampling data. Specifically, the first pulse sampling data may include multiple pulse sampling values. The average value of these multiple pulse sampling values ​​is calculated and used as the target baseline. Alternatively, the maximum and minimum values ​​of these multiple pulse sampling values ​​can be removed first, and the average value of the remaining pulse sampling values ​​can be calculated and used as the target baseline. Next, the sampling amplitude can be determined based on the target baseline and a preset baseline adjustment coefficient, as follows: Sampling amplitude = target baseline × (1 + preset baseline adjustment coefficient); The sampling amplitude can be obtained by calculating using the formula above.

[0050] In some embodiments, the preset baseline adjustment factor can be 10%.

[0051] Then, the first sampled data that is greater than the sampling amplitude among the a sampled data can be determined to obtain the first sampled data. Specifically, the a sampled data can be arranged in chronological order to obtain a sampling sequence. The sampling sequence is traversed to find the first sampled data that is greater than the sampling amplitude, which is the first sampled data. If no sampled data that meets the conditions is found after traversing, it means that there is no sampled data that meets the requirements in the pulse signal. At this time, the pulse sampled data is determined to be invalid and no further processing is performed.

[0052] Next, we can determine the sampled data with the latest sampling time among the a sampled data and that is greater than the sampling amplitude, and obtain the second sampled data. Specifically, we can also traverse the above sampling sequence to find the last sampled data that is greater than the sampling amplitude, which is the second sampled data.

[0053] Then, the first signal period can be determined based on the second sampling time and the first sampling time. Specifically, the duration, i.e. the first signal period, can be obtained by subtracting the first sampling time from the second sampling time. Then, the first signal amplitude can be determined based on the preset amplitude calculation algorithm and the pulse sampling data. The preset amplitude calculation algorithm can include any of the following: maximum value difference method, integral area method, peak fitting method, etc., which are not limited here.

[0054] In some embodiments, the preset amplitude calculation algorithm can be the maximum difference method, which obtains the maximum sampled data in the pulse sampling data and uses the maximum sampled data to subtract the target baseline to obtain the first signal amplitude.

[0055] In some embodiments, the preset amplitude calculation algorithm can be the integral area method, which integrates the increment of the pulse sampling data relative to the target baseline and uses the integral value as the first signal amplitude.

[0056] In some embodiments, the pulse sampling data, the first time-domain feature, and the first energy-domain feature can be organized into a structured data object and stored in a preset database. The structured data object adopts the following format: {Pulse event i: (T_i, V_i)}; Where i represents the pulse number; T_i represents the time domain feature of the i-th pulse (i.e., the first time domain feature); V_i represents the energy domain feature of the i-th pulse (i.e., the first energy domain feature); for example, a certain extraction result can be: {Pulse event 1: (T_1=320μs, V_1=150mV)}.

[0057] Thus, by using the first pulse sampling data to determine the target baseline and calculating the sampling amplitude based on the baseline and the preset baseline adjustment coefficient, the threshold is adaptively adjusted to the current noise level, suppressing the influence of baseline drift on the period measurement. By locating the first and last sampling points in the pulse sequence that are greater than the sampling amplitude, and using them as the start time of the rising edge and the end time of the falling edge, respectively, the pulse period is calculated, providing stable and reliable time-domain characteristics for subsequent filtering based on the dynamic period range. Combined with the preset amplitude calculation algorithm, the signal amplitude is extracted synchronously, realizing the parallel acquisition of time-domain and energy-domain characteristics, laying the data foundation for subsequent screening.

[0058] S304. Obtain the preset dynamic period interval.

[0059] In some embodiments, please refer to Figure 4 , Figure 4 This is a flowchart illustrating a method for obtaining a preset dynamic period interval according to an embodiment of this application. The method for obtaining the preset dynamic period interval includes, for example: Figure 4 The steps shown are as follows: S21. Determine the target sensor model corresponding to the radon gas sensor; S22. Determine the first reference dynamic period interval based on the target sensor model; S23. Acquire background noise pulse data; the background noise pulse data is the pulse data collected by the radon gas sensor in the presence of only background noise; S24. Determine multiple noise periods corresponding to the background noise pulse data; S25. Determine the preset dynamic period interval based on the plurality of noise periods and the first reference dynamic period interval.

[0060] In this embodiment, the target sensor model corresponding to the radon gas sensor can be determined. Specifically, the control module can obtain the instruction manual of the radon gas sensor and extract the target sensor model from the manual. Alternatively, the control module can obtain the currently connected radon gas sensor model identifier, i.e., the target sensor model, by reading the internal configuration register (or identifying the pin level). Then, the first reference dynamic period interval can be determined based on the target sensor model. Specifically, a pre-stored mapping relationship between preset sensor models and reference dynamic period intervals can be used to determine the first reference dynamic period interval corresponding to the target sensor model.

[0061] Then, background noise pulse data can be acquired. Specifically, under conditions where there is no radioactive source and only ambient background noise exists, the control module collects pulse data from the radon gas sensor over a continuous period of time. These pulses originate from non-alpha particle events such as circuit noise and mechanical vibration, thus obtaining background noise pulse data. Then, multiple noise periods corresponding to the background noise pulse data can be determined. Specifically, the background noise pulse data can include multiple background noise pulses, and the noise period of each background noise pulse is calculated to obtain multiple noise periods. Finally, a preset dynamic period interval can be determined based on the multiple noise periods and the first reference dynamic period interval.

[0062] Thus, by determining the sensor model to obtain the corresponding reference period range, and dynamically adjusting the period range using the actual collected background noise period, the periodic filtering window (i.e., the preset dynamic period range) can adaptively match the current sensor characteristics and environmental noise level. This effectively avoids the false filtering of the true α pulse period due to sensor individual differences, aging drift, or changes in ambient temperature. At the same time, it improves the ability to suppress interference of different periods in the background noise, thereby enhancing the robustness of periodic filtering and the overall accuracy of radon detection.

[0063] In some embodiments, determining the preset dynamic period interval based on the plurality of noise periods and the first reference dynamic period interval includes: S31. Determine the maximum noise period and the minimum noise period among the plurality of noise periods; S32. Determine the second reference dynamic period interval based on the maximum noise period and the minimum noise period; S33. Determine the overlapping period interval between the first reference dynamic period interval and the second reference dynamic period interval to obtain the target overlapping period interval; S34. Determine the target overlap degree based on the target overlap period interval and the first reference dynamic period interval; S35. When the target overlap is greater than the preset overlap, the preset dynamic period interval is determined according to the second reference dynamic period interval. S36. When the target overlap is less than or equal to the preset overlap, determine the first cycle upper limit and the first cycle lower limit corresponding to the first reference dynamic cycle interval; determine the second cycle upper limit based on the first cycle upper limit and the maximum noise cycle; determine the second cycle lower limit based on the first cycle lower limit and the minimum noise cycle; determine the preset dynamic cycle interval based on the second cycle upper limit and the second cycle lower limit.

[0064] In this embodiment, the preset overlap degree can be preset in advance or defaulted.

[0065] In a specific embodiment, the periods of multiple noise periods can be compared pairwise to find the maximum and minimum noise periods. Then, based on the maximum and minimum noise periods, a second reference dynamic period interval is determined. Specifically, the minimum noise period can be used as the lower limit of the interval, and the maximum noise period can be used as the upper limit of the interval to obtain the second reference dynamic period interval.

[0066] Then, the overlapping period interval between the first reference dynamic period interval and the second reference dynamic period interval can be determined to obtain the target overlapping period interval. Specifically, assuming the first reference dynamic period interval is [Amin, Amax] and the second reference dynamic period interval is [Bmin, Bmax], the larger value between Amin and Bmin is taken as the starting boundary of the overlapping interval, and the smaller value between Amax and Bmax is taken as the ending boundary of the overlapping interval. If the starting boundary is less than the ending boundary, the target overlapping period interval is formed; otherwise, it is determined that the target overlapping period interval does not exist.

[0067] Next, the target overlap degree can be determined based on the target overlap period interval and the first reference dynamic period interval. Specifically, the upper and lower limits of the target period of the target overlap period interval can be determined first, and the length of the first interval can be obtained by subtracting the lower limit of the target period from the upper limit. Similarly, the length of the second interval of the first reference dynamic period interval can be determined, and the target overlap degree can be obtained by dividing the length of the first interval by the length of the second interval. When the target overlap degree is greater than the preset overlap degree, the second reference dynamic period interval can be directly determined as the preset dynamic period interval. When the target overlap is less than or equal to the preset overlap, the upper limit and lower limit of the first period corresponding to the first reference dynamic period interval can be determined first. Then, the upper limit of the second period can be determined based on the upper limit of the first period and the maximum noise period. Specifically, the first average value of the upper limit of the first period and the maximum noise period can be calculated and used as the upper limit of the second period. The lower limit of the second period can be determined based on the lower limit of the first period and the minimum noise period. Similarly, the second average value of the lower limit of the first period and the minimum noise period can be calculated and used as the lower limit of the second period. Finally, the upper limit of the second period can be used as the upper limit of the interval, and the lower limit of the second period can be used as the lower limit of the interval to form the preset dynamic period interval. For example, if the lower limit of the second period is 180μs and the upper limit of the second period is 400μs, then the preset dynamic period interval is [180μs, 400μs].

[0068] In some embodiments, the preset overlap can be 80%.

[0069] Thus, by introducing an overlap factor, when the background noise periodic distribution highly overlaps with the sensor's inherent periodic range, a second reference interval based on noise extrema is directly used as the dynamic periodic interval. This ensures the filtering window closely matches the actual noise characteristics, improving screening accuracy. When the overlap factor is low, instead of blindly discarding or forcibly using the noise interval, the endpoints of the first reference interval are combined with the noise extrema to obtain the expanded upper and lower limits of the second period. This approach moderately widens the interval to increase tolerance and avoid false negatives while preserving the constraints of the sensor's physical characteristics. This mechanism achieves adaptive adjustment of the periodic interval, balancing the rigor and robustness of the screening process, and effectively reducing the risk of missed and false detections.

[0070] In some embodiments, the preset dynamic period interval is updated once every certain period of time (e.g., one week), and the update process is as follows: Assuming that initially, the preset dynamic period interval is [200μs, 500μs], and the interval width is 300μs; during system operation, background noise pulses (i.e., pulse signals output by the sensor when there is no radiation source) are continuously collected. Whenever a preset number of background pulses are collected, an adaptive update of the preset dynamic period interval is performed. Specifically, the median value of the period Tmed of the preset number of background pulses is calculated, and this median value is used as the new interval center. The initial width of 300μs is kept unchanged, and the updated preset dynamic period interval is calculated as [(Tmed-150)μs, (Tmed+150)μs].

[0071] In some embodiments, the preset quantity can be 1000.

[0072] S305. When the first time domain feature falls within the preset dynamic period interval, the first energy domain feature is sent to the decision buffer.

[0073] In this embodiment of the application, when the first time-domain feature is within a preset dynamic period interval, it is determined that the pulse sampling data has passed the period filtering and its period conforms to the expected range of the real α pulse; at this time, the first time-domain feature of the pulse is stored in the decision buffer as a data sample for subsequent energy spectrum consistency test.

[0074] If the first time-domain feature does not fall within the preset dynamic period interval, the pulse sampling data is directly discarded and does not enter the decision buffer.

[0075] S306. Detect the number of data items in the buffer to be decided at the current time to obtain the first data item count.

[0076] In this embodiment, the control module can read the number of energy domain feature data stored in the decision buffer in real time and record this number as the first data count. The decision buffer adopts a first-in-first-out queue structure; the data count is incremented by one for each pulse energy domain feature stored, and resets to zero each time the decision buffer is emptied. By detecting the current data count, the control module determines whether the decision buffer has met the preset number required for subsequent energy spectrum consistency checks.

[0077] S307. When the number of the first data is equal to the preset number, obtain the preset energy spectrum consistency threshold; determine the N time-domain features stored in the decision buffer; the value of N is equal to the preset number; determine the first standard deviation corresponding to the N time-domain features; determine the target decision result based on the first standard deviation and the preset energy spectrum consistency threshold; the target decision result includes any one of the following: a set of valid pulse signals and a set of invalid pulse signals.

[0078] In this embodiment of the application, the preset number can be preset in advance or defaulted.

[0079] In some embodiments, the preset number can be 20, that is, N=20.

[0080] In a specific embodiment, when the number of first data points equals a preset number, a preset energy spectrum consistency threshold is obtained; then, all time-domain features stored in the decision buffer can be read to obtain N time-domain features; then, the first standard deviation of these N time-domain features can be calculated according to the standard deviation calculation formula; then, the target decision result can be determined according to the first standard deviation and the preset energy spectrum consistency threshold.

[0081] When the number of initial data points is less than the preset number, it indicates that the decision buffer has not yet been filled with a sufficient number of energy domain features, making it impossible to perform an energy spectrum consistency check. At this point, the control module does not make any decision, does not clear the buffer, but continues to wait for subsequent pulses to be periodically filtered and stored in the decision buffer until the number of data points in the decision buffer reaches the preset number.

[0082] Thus, when the buffer to be decided is filled with a preset number of data, the standard deviation of the entire amplitude is calculated and compared with the energy spectrum consistency threshold; the standard deviation of the true α pulse group is small, while the standard deviation of the interference pulse group is large, thereby eliminating interference from energy dispersion, reducing false positives, and improving the accuracy of radon detection.

[0083] In some embodiments, obtaining the preset energy spectrum consistency threshold includes: S41. Acquire b sets of pulse data; the b sets of pulse data are pulse data of a preset radon gas source collected by the radon gas sensor under a preset environment; each set of pulse data includes N pulse data; b is an integer greater than 1; S42. Determine the b standard deviations corresponding to the b sets of pulse data; each standard deviation corresponds to a set of pulse data. S43. Determine the median and the first interquartile range corresponding to the b standard deviations; S44. Determine the reference standard deviation interval based on the median and the first interquartile range; S45. Remove the standard deviations that are not within the reference standard deviation interval from the b standard deviations to obtain c standard deviations; c is a positive integer less than or equal to b. S46. Determine the maximum value and the second interquartile range corresponding to the c standard deviations; S47. Determine the preset energy spectrum consistency threshold based on the maximum value, the second interquartile range, the preset consistency coefficient, and the first preset calculation formula.

[0084] In this embodiment of the application, the preset environment and the preset radon gas source can be preset or defaulted in advance. For example, the preset environment can be: a shielded room without external interference and constant temperature and humidity conditions; the preset radon gas source can be an Am-241 source (monoenergetic α).

[0085] In a specific embodiment, the radon gas sensor can be placed in a preset environment to continuously collect pulse data from a preset radon gas source, resulting in b sets of pulse data. Then, b standard deviations corresponding to the b sets of pulse data can be determined. Specifically, for each set of pulse data, N energy domain features are extracted, and the standard deviations of these N energy domain features are calculated according to the standard deviation calculation formula. In this way, b standard deviations can be obtained.

[0086] Then, the median and first interquartile range corresponding to b standard deviations can be determined. Specifically, these b standard deviations can be arranged in ascending order to obtain the standard deviation sequence σ: σ1, σ2, ..., σ b If b is odd, the median M is the middle value of the standard deviation sequence σ; if b is even, the median M is the average of the two middle values ​​of the standard deviation sequence σ. Then, the value at the 25th percentile in the standard deviation sequence σ, i.e., the first quartile Q1, can be obtained. Similarly, the value at the 75th percentile in the standard deviation sequence σ, i.e., the third quartile Q3, can be obtained. The first quartile distance is obtained by subtracting the first quartile Q1 from the third quartile Q3. For example, if b = 20, the 5th value in the standard deviation sequence σ corresponds to the 25th percentile, and the 15th value corresponds to the 75th percentile, then: Q1=σ5;Q3=σ 15 IQR = Q3 - Q1.

[0087] Then, the reference standard deviation interval can be determined based on the median and the first quartile interval. Specifically, a preset adjustment factor can be obtained, where the value of the preset adjustment factor ranges from 1 to 3. The reference standard deviation interval is determined based on the preset adjustment factor, the median, and the first quartile interval as follows: Reference standard deviation interval = [M - α × IQR, M + α × IQR]; Where M represents the median; α represents the preset adjustment coefficient; and IQR represents the first quartile interval.

[0088] In some embodiments, the preset adjustment coefficient α can be 1.5.

[0089] Then, remove the standard deviations that are not within the reference standard deviation interval from the b standard deviations, resulting in c standard deviations. Specifically, each of the b standard deviations can be compared with the reference standard deviation interval. If a standard deviation is less than the lower limit or greater than the upper limit of the interval, it is removed; if it is within the interval, it is retained. Thus, c standard deviations are obtained. Further, the maximum value and the second interquartile range corresponding to the c standard deviations can be determined. Specifically, the maximum value among the c standard deviations can be found. In addition, the method for obtaining the second interquartile range can be the same as the method for obtaining the first interquartile range, and will not be repeated here. Finally, the preset energy spectrum consistency threshold can be determined based on the maximum value, the second interquartile range, the preset consistency coefficient, and the first preset calculation formula. The first preset calculation formula is as follows: σ_threshold=σ max +k×IQR2; Where σ_threshold represents the preset energy spectrum consistency threshold; σ max represents the maximum value; k represents the preset consistency coefficient; IQR2 represents the second interquartile range.

[0090] In this way, by collecting pulse data from multiple standard sources and calculating the amplitude standard deviation of each group, a reference interval is constructed using the median and interquartile range to eliminate outlier groups. Then, the energy spectrum consistency threshold is dynamically calculated based on the maximum value and interquartile range of the clean data. This effectively avoids the contamination of calibration results by single sporadic interference or acquisition anomalies, and makes the threshold truly reflect the energy concentration characteristics of the alpha source itself. This allows for accurate differentiation of composite interference with dispersed energy in subsequent real-time detection, thereby improving the overall reliability of radon detection.

[0091] In some embodiments, please refer to Figure 5 , Figure 5 This is a flowchart of a method for determining a target decision result provided in an embodiment of this application. The step of determining the target decision result based on the first standard deviation and the preset energy spectrum consistency threshold includes: S51. When the first standard deviation is less than or equal to the preset energy spectrum consistency threshold, it is determined that the target decision result includes the set of valid pulse signals; S52. When the first standard deviation is greater than the preset energy spectrum consistency threshold, it is determined that the target decision result includes the set of invalid pulse signals.

[0092] In this embodiment of the application, when the first standard deviation is less than or equal to the preset energy spectrum consistency threshold, it indicates that the amplitude dispersion of the N pulses in the decision buffer is within the normal range and conforms to the physical characteristics of the energy concentration of real alpha particles. At this time, the target decision result can be determined as a set of effective pulse signals. When the first standard deviation is greater than the preset energy spectrum consistency threshold, it indicates that the amplitude dispersion of the N pulses in the current buffer is too large and does not conform to the energy distribution characteristics of real alpha particles. At this time, the target decision result can be determined as a set of invalid pulse signals.

[0093] Thus, by directly utilizing the statistical difference between the small standard deviation of the real α pulse group and the large standard deviation of the interference pulse group, the effective pulse group and the invalid interference group can be accurately distinguished by comparing the standard deviation with the energy spectrum consistency threshold. This eliminates the need for complex calculations and results in high computational efficiency.

[0094] In some embodiments, a fusion confidence score mechanism can be introduced, and the fusion confidence score function is defined as follows: Score = w1 × f(T) + w2 × g(σ); Wherein, Score is the fusion confidence score, a dimensionless numerical value used to comprehensively evaluate the credibility of the pulse group originating from real alpha particles; f(T) is the preset period conformity function, whose value increases as the pulse period T approaches the center of the preset dynamic period interval; g(σ) is the preset energy spectrum consistency function, whose value increases as the standard deviation σ of the buffer pulse amplitude decreases; w1 is the preset time domain weighting coefficient; w2 is the preset energy domain weighting coefficient, and w1+w2=1.

[0095] In some embodiments, in low-noise environments such as laboratories, w2 > w1 is set to make the decision result more dependent on the energy spectrum consistency test; in industrial sites with strong vibrations, w2 < w1 is set to make the decision result more dependent on periodic filtering. The values ​​of w1 and w2 are obtained through pre-calibration experiments under different noise scenarios and stored in the control module. When the calculated score is greater than the preset confidence threshold (e.g., 0.9), the current pulse group is determined to be a valid event (i.e., the target decision result is a set of valid pulse signals); otherwise, it is determined to be an invalid event (i.e., the target decision result is a set of invalid pulse signals). This fusion decision method can be adaptively adjusted according to the actual application scenario to further improve the robustness and accuracy of radon detection.

[0096] S308. Count the target decision result using a preset valid event counter. After counting is completed, clear the pending decision buffer.

[0097] In this embodiment, the preset valid event counter can be preset in advance or defaulted; it is used to accumulate the number of pulses that are determined to be valid pulse signals.

[0098] In some embodiments, the step of counting according to the target decision result using a preset valid event counter includes: S61. When the target decision result includes the set of valid pulse signals, obtain the value in the preset valid event counter to get a first value; determine a second value based on the first value and the preset number; update the value in the preset valid event counter to the second value. S62. When the target decision result includes the set of invalid pulse signals, keep the value in the preset valid event counter unchanged.

[0099] In this embodiment of the application, when the target judgment result includes a set of valid pulse signals, the value in the preset valid event counter is read to obtain a first value; based on the first value and a preset number, a second value is determined. Specifically, the first value and the preset number can be added together to obtain the second value; then, the value in the preset valid event counter is updated to the second value. When the target decision result includes a set of invalid pulse signals, keep the value in the preset valid event counter unchanged.

[0100] In this way, by accumulating N counts at once when a pulse group is determined to be valid, the extra computational overhead of counting each individual pulse is avoided, thus improving counting efficiency. At the same time, only reliable pulse groups that pass the two-level screening are counted, and invalid pulse groups do not change the counter value, ensuring that the final count value only reflects the real α pulse event, thereby significantly improving the accuracy and reliability of radon concentration measurement.

[0101] S309. Determine the target radon concentration based on the target count value of the preset valid event counter.

[0102] In this embodiment, the value in the preset valid event counter can be read to obtain the target count value, and the target radon concentration can be determined based on the target count value. For example, a preset mapping relationship between count values ​​and radon concentration can be stored in advance, and the target radon concentration corresponding to the target count value can be determined based on the mapping relationship.

[0103] In some embodiments, please refer to Figure 6 , Figure 6 This is a schematic diagram of another radon detection system provided in this application embodiment. The radon detection system includes: a high-voltage power supply, a semiconductor radon sensor, a preamplifier circuit, a linear pulse amplifier, a comparison module, an ADC sampling unit, and an MCU (control module). The MCU includes a preset valid event counter and a pulse amplitude analysis unit. The high-voltage power supply and the semiconductor radon sensor can be placed in an electrostatic collection chamber. During system operation, the high-voltage power supply provides the operating voltage to the semiconductor radon sensor. The semiconductor radon sensor, placed in the electrostatic collection chamber, converts alpha particles generated by the decay of radon and its progeny into charge signals. The input of the preamplifier circuit is connected to the output of the semiconductor radon sensor to convert the charge signals into voltage pulses. The input of the linear pulse amplifier is connected to the output of the preamplifier circuit to further amplify and shape the voltage pulses. The input of the comparison module is connected to the output of the linear pulse amplifier to compare the amplified pulse signal with a preset trigger threshold. When the pulse amplitude exceeds the threshold, an interrupt signal is generated and sent to the MCU. The input of the ADC sampling unit is connected to the output of a linear pulse amplifier and, under the control of the MCU, performs analog-to-digital conversion on the pulse signal. Upon receiving an interrupt signal, the MCU starts the ADC sampling unit, acquires pulse sampling data, and transmits the data to the pulse amplitude analysis unit. The pulse amplitude analysis unit extracts the period and amplitude characteristics of the pulse, performs period filtering and energy spectrum consistency checks, and transmits the decision result to a preset valid event counter for counting. The MCU calculates the radon concentration based on the count value of the preset valid event counter.

[0104] In summary, the anti-interference radon detection method described in this application firstly eliminates interference with periods significantly deviating from the true alpha pulses through periodic filtering; secondly, the energy domain characteristics (i.e., signal amplitude) after initial screening are sent to the decision buffer. When a preset number of signals are stored, the standard deviation of the entire amplitude group is calculated and compared with the energy spectrum consistency threshold. True alpha particles have concentrated energy, resulting in a small standard deviation for the corresponding pulse group; pulse amplitudes generated by composite interference (e.g., electromagnetic noise, vibration) are dispersed, resulting in a large standard deviation. The standard deviation test effectively eliminates interference that "deceives" the initial screening in terms of period but has dispersed energy, reducing false positives. At the same time, the continuously cleared and reused decision buffer design avoids the randomness of single-pulse discrimination, achieving robust discrimination from both the time and energy domains, thereby improving the accuracy of radon detection.

[0105] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of an anti-interference radon gas detection device provided in an embodiment of this application. It is applied to the control module of a radon gas detection system. The system further includes a radon gas sensor and a comparison module. The anti-interference radon gas detection device 700 includes a comparison unit 701, a sampling unit 702, a determination unit 703, and a radon gas detection unit 704, wherein: The comparison unit 701 is used to detect the analog electrical signal output by the radon gas sensor in real time through the comparison module. If the amplitude of the detected analog electrical signal is greater than a preset trigger threshold, an interrupt signal is generated. The sampling unit 702 is used to sample the pulse signal in the analog electrical signal after detecting the interrupt signal to obtain pulse sampling data; The determining unit 703 is used to determine the first time-domain feature and the first energy-domain feature corresponding to the pulse sampling data; The radon detection unit 704 is used to acquire a preset dynamic period interval; when the first time-domain feature falls within the preset dynamic period interval, the first energy-domain feature is sent to a decision buffer; the number of data in the decision buffer at the current time is detected to obtain a first data count; when the first data count is equal to a preset count, a preset energy spectrum consistency threshold is acquired; N time-domain features stored in the decision buffer are determined; the value of N is equal to the preset count; a first standard deviation corresponding to the N time-domain features is determined; a target decision result is determined based on the first standard deviation and the preset energy spectrum consistency threshold; the target decision result includes any one of the following: a set of valid pulse signals and a set of invalid pulse signals; a preset valid event counter is used to count according to the target decision result, and after counting is completed, the decision buffer is cleared; the target radon concentration is determined based on the target count value of the preset valid event counter.

[0106] In specific implementations, the anti-interference radon gas detection device 700 described in the embodiments of the present invention can also perform other implementations described in the anti-interference radon gas detection method provided in the embodiments of the present invention, which will not be repeated here.

[0107] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include a processor, a memory, a communication interface, and one or more programs. The processor, memory, and communication interface can be interconnected via a bus. The one or more programs are stored in the memory and configured to be executed by the processor. In this embodiment, the programs include instructions for performing some or all of the steps described in the above method embodiments.

[0108] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (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, cells, 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 unit can be a communication interface, transceiver, transceiver circuit, etc., and the storage unit can be a memory.

[0109] The memory can be volatile or non-volatile, or a combination of both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0110] It is understood that electronic devices may include more or fewer structural elements than those shown in the above block diagram, such as power modules, physical buttons, Wi-Fi modules, speakers, Bluetooth modules, sensors, display modules, etc., without limitation.

[0111] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0112] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0113] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all 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 and modules involved are not necessarily essential to this application.

[0114] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0116] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0117] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Additionally, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0118] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0119] The aforementioned computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The 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 integrates one or more available media.

[0120] The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0121] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on the processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented through a software program that runs on the processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0122] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. An interference-resistant radon gas detection method, characterized in that, A control module applied in a radon detection system, the system further comprising: a radon sensor and a comparison module, the method comprising: The comparison module detects the analog electrical signal output by the radon gas sensor in real time. If the amplitude of the analog electrical signal is greater than a preset trigger threshold, an interrupt signal is generated. Upon detecting the interrupt signal, the pulse signal in the analog electrical signal is sampled to obtain pulse sampling data; Determine the first time-domain feature and the first energy-domain feature corresponding to the pulse sampling data; Obtain the preset dynamic period interval; When the first time-domain feature falls within the preset dynamic period interval, the first energy-domain feature is sent to the decision buffer. The number of data items in the buffer to be decided at the current moment is detected to obtain the first data count; When the number of the first data is equal to the preset number, a preset energy spectrum consistency threshold is obtained; N time-domain features stored in the decision buffer are determined; the value of N is equal to the preset number; the first standard deviation corresponding to the N time-domain features is determined; the target decision result is determined according to the first standard deviation and the preset energy spectrum consistency threshold; the target decision result includes any one of the following: a set of valid pulse signals and a set of invalid pulse signals; The target decision result is counted by a preset valid event counter. After the count is completed, the pending decision buffer is cleared. The target radon concentration is determined based on the target count value of the preset valid event counter; The step of obtaining the preset dynamic period interval includes: Determine the target sensor model corresponding to the radon gas sensor; The first reference dynamic period interval is determined based on the target sensor model; Acquire background noise pulse data; the background noise pulse data is the pulse data collected by the radon gas sensor in the presence of only background noise; Determine multiple noise periods corresponding to the background noise pulse data; The preset dynamic period interval is determined based on the plurality of noise periods and the first reference dynamic period interval.

2. The method as described in claim 1, characterized in that, The pulse sampling data includes a sampling data points and a sampling times, with each sampling time corresponding to one sampling data point, where a is an integer greater than 1; the first time-domain feature includes the first signal period; the first energy-domain feature includes the first signal amplitude. Determining the first time-domain feature and the first energy-domain feature corresponding to the pulse sampling data includes: Acquire the first pulse sampling data within a first preset time period; the end time of the first preset time period is earlier than the generation time of the interrupt signal; Determine the target baseline based on the first pulse sampling data; The sampling amplitude is determined based on the target baseline and the preset baseline adjustment coefficient; The first sampled data point among the a sampled data points that is greater than the sampling amplitude is determined to obtain the first sampled data point; the first sampled data point corresponds to the first sampling time point among the a sampled data points. The sampling data with the latest sampling time and greater than the sampling amplitude among the a sampling data is obtained as the second sampling data; the second sampling data corresponds to the second sampling time among the a sampling times. The first signal period is determined based on the second sampling time and the first sampling time; The amplitude of the first signal is determined based on a preset amplitude calculation algorithm and the pulse sampling data.

3. The method as described in claim 1, characterized in that, Determining the preset dynamic period interval based on the plurality of noise periods and the first reference dynamic period interval includes: Determine the maximum and minimum noise periods among the plurality of noise periods; The second reference dynamic period interval is determined based on the maximum noise period and the minimum noise period; Determine the overlapping period interval between the first reference dynamic period interval and the second reference dynamic period interval to obtain the target overlapping period interval; The target overlap degree is determined based on the target overlap period interval and the first reference dynamic period interval; When the target overlap is greater than the preset overlap, the preset dynamic period interval is determined according to the second reference dynamic period interval; When the target overlap is less than or equal to the preset overlap, the first cycle upper limit and the first cycle lower limit corresponding to the first reference dynamic cycle interval are determined; the second cycle upper limit is determined based on the first cycle upper limit and the maximum noise cycle; the second cycle lower limit is determined based on the first cycle lower limit and the minimum noise cycle; and the preset dynamic cycle interval is determined based on the second cycle upper limit and the second cycle lower limit.

4. The method according to any one of claims 1-3, characterized in that, The process of obtaining the preset energy spectrum consistency threshold includes: Acquire b sets of pulse data; the b sets of pulse data are pulse data of a preset radon gas source collected by the radon gas sensor under a preset environment; each set of pulse data includes N pulse data; b is an integer greater than 1; Determine b standard deviations corresponding to the b sets of pulse data; each standard deviation corresponds to a set of pulse data. Determine the median and first interquartile range corresponding to the b standard deviations; The reference standard deviation interval is determined based on the median and the first interquartile range; Remove the standard deviations that are not within the reference standard deviation interval from the b standard deviations to obtain c standard deviations; c is a positive integer less than or equal to b. Determine the maximum value and the second interquartile range corresponding to the c standard deviations; The preset energy spectrum consistency threshold is determined based on the maximum value, the second interquartile range, the preset consistency coefficient, and the first preset calculation formula.

5. The method according to any one of claims 1-3, characterized in that, The step of determining the target decision result based on the first standard deviation and the preset energy spectrum consistency threshold includes: When the first standard deviation is less than or equal to the preset energy spectrum consistency threshold, the target decision result is determined to include the set of valid pulse signals; When the first standard deviation is greater than the preset energy spectrum consistency threshold, the target decision result is determined to include the set of invalid pulse signals.

6. The method according to any one of claims 1-3, characterized in that, The step of counting based on the target decision result using a preset valid event counter includes: When the target decision result includes the set of valid pulse signals, the value in the preset valid event counter is obtained to obtain a first value; a second value is determined based on the first value and the preset number; and the value in the preset valid event counter is updated to the second value. When the target decision result includes the set of invalid pulse signals, the value in the preset valid event counter remains unchanged.

7. An interference-resistant radon gas detection device, used to perform the method as described in any one of claims 1-6, characterized in that, A control module for use in a radon detection system, the system further comprising: a radon sensor and a comparison module, the device comprising: a comparison unit, a sampling unit, a determination unit, and a radon detection unit, wherein: The comparison unit is used to detect the analog electrical signal output by the radon gas sensor in real time through the comparison module. If the amplitude of the detected analog electrical signal is greater than a preset trigger threshold, an interrupt signal is generated. The sampling unit is used to sample the pulse signal in the analog electrical signal after detecting the interrupt signal to obtain pulse sampling data; The determining unit is used to determine the first time-domain feature and the first energy-domain feature corresponding to the pulse sampling data; The radon detection unit is used to acquire a preset dynamic period interval; when the first time-domain feature falls within the preset dynamic period interval, the first energy-domain feature is sent to a decision buffer; the number of data in the decision buffer at the current time is detected to obtain a first data count; when the first data count is equal to a preset count, a preset energy spectrum consistency threshold is acquired; N time-domain features stored in the decision buffer are determined; the value of N is equal to the preset count; a first standard deviation corresponding to the N time-domain features is determined; a target decision result is determined based on the first standard deviation and the preset energy spectrum consistency threshold; the target decision result includes any one of the following: a set of valid pulse signals and a set of invalid pulse signals; a preset valid event counter is used to count according to the target decision result, and after counting is completed, the decision buffer is cleared; the target radon concentration is determined based on the target count value of the preset valid event counter.

8. An electronic device, characterized in that, include: Processor, memory, communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, A computer program for storing electronic data interchange is provided, wherein the computer program causes a computer to perform the method as described in any one of claims 1-6.

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