Radon gas detection method, device, electronic equipment and storage medium

Abstract

The present application provides a radon gas detection method, apparatus, electronic device, and storage medium. The method includes: obtaining the number of first, second, and third particles generated by atomic decay of radon gas collected within a preset time period, where the first particle is emitted after the decay of radon-222, the second particle is emitted after the decay of polonium-218, and the third particle is emitted after the decay of polonium-214; determining a first humidity value based on a first ratio between the number of second or third particles and the number of first particles, where the smaller the first ratio, the greater the first humidity value; determining a first calibration coefficient based on the first humidity value, where the smaller the first humidity value, the smaller the first calibration coefficient; and determining a first radon gas concentration value based on the first calibration coefficient and the number of fifth particles, where the fifth particle is at least one of the second and third particles. This avoids the need for an additional humidity sensor and reduces product production costs.

Description

Technical Field

[0001] The present application relates to the field of radiation protection technology, and in particular to a radon gas detection method, device, electronic equipment and storage medium. Background Art

[0002] Radon is a radioactive element that exists as a gas at normal temperature and pressure. It typically forms near the surface of uranium-containing materials, such as soil or rock, and diffuses into the surrounding air. Radon gas is colorless and odorless, making its presence and concentration difficult for humans to detect. Because radon is radioactive, long-term exposure to high concentrations significantly increases the risk of cancer and is the second leading cause of lung cancer deaths after smoking.

[0003] Since the ambient humidity will have a certain impact on the concentration detection of radon gas, current detection instruments use additional humidity sensors to compensate for the concentration, but this will increase the production cost of the product. Summary of the Invention

[0004] The present application provides a radon gas detection method, device, electronic device and storage medium. The method reflects the current humidity state by measuring the ratio between the number of radon-222 generated during the atomic decay of radon and the number of particles emitted by polonium-218 or polonium-214, thereby compensating for the concentration. This avoids the need to introduce a humidity sensor to determine the humidity value when measuring the radon gas concentration, thereby reducing the production cost of the product.

[0005] In a first aspect, the present application provides a radon gas detection method, the method comprising:

[0006] Obtain the number of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period, where the first particles are particles emitted after the decay of radon-222, the second particles are particles emitted after the decay of polonium-218, and the third particles are particles emitted after the decay of polonium-214;

[0007] determining a first humidity value according to a first ratio between the number of fourth particles and the number of first particles, the fourth particles being the second particles or the third particles, and the smaller the first ratio, the greater the first humidity value;

[0008] determining a first calibration coefficient according to the first humidity value, wherein the smaller the first humidity value is, the smaller the first calibration coefficient is;

[0009] A first concentration value of radon gas is determined according to the first calibration coefficient and the number of fifth particles, where the fifth particles are at least one of the second particles and the third particles.

[0010] As can be seen in this application, since polonium-218 and polonium-214 are positively charged particles, there is a certain probability that they will be neutralized by water molecules (OH-) in the environment, and therefore they are significantly affected by humidity. However, radon-222 is a gas and is not neutralized by water molecules (OH-). Therefore, under different humidity conditions and a constant radon concentration, the number of collected particles emitted by radon-222 does not change much, but the number of collected particles emitted by polonium-218 and polonium-214 is significantly affected. Therefore, this application reflects changes in humidity by measuring the ratio of the number of collected particles emitted by polonium-218 or polonium-214 to the number of collected particles emitted by radon-222 at different humidity levels. If the ratio decreases, it indicates that the humidity is increasing. This avoids the need to introduce a humidity sensor to determine the humidity value when measuring radon concentration, thereby reducing the production cost of the product.

[0011] In a feasible example, determining the first calibration coefficient according to the first humidity value includes:

[0012] Obtaining a calibration coefficient-concentration curve corresponding to the first humidity value, where the calibration coefficient-concentration curve is used to represent calibration coefficients corresponding to different concentrations;

[0013] Determining a second ratio between the concentration corresponding to each of a plurality of coordinate points on the calibration coefficient-concentration curve and the calibration coefficient;

[0014] determining a target coordinate point corresponding to a second ratio corresponding to the number of fifth particles among the plurality of second ratios;

[0015] The calibration coefficient corresponding to the target coordinate point on the calibration coefficient-concentration curve is determined as the first calibration coefficient.

[0016] In this application, the ratio of the concentration to the calibration coefficient at each coordinate point in the calibration coefficient concentration curve is calculated, and the target coordinate point is located based on the matching relationship between the current number of fifth particles and the ratio. Finally, the corresponding calibration coefficient is extracted as a compensation parameter. In this way, through the coordinated optimization of humidity, concentration, and calibration coefficient, the effects of humidity and concentration on the calibration coefficient can be taken into account. At the same time, the use of pre-stored curve data can improve the efficiency of locating the calibration coefficient, enhancing the detection accuracy and anti-interference ability of radon gas detection instruments in environmental conditions with changing humidity and fluctuating concentration.

[0017] In a feasible example, determining the first calibration coefficient according to the first humidity value includes:

[0018] Obtain the calibration coefficient-humidity curve and the calibration coefficient-concentration curve. The calibration coefficient-humidity curve is used to represent the calibration coefficient corresponding to different humidity conditions, and the calibration coefficient-concentration curve is used to represent the calibration coefficient corresponding to different concentration conditions.

[0019] determining a second calibration coefficient corresponding to the first humidity value according to a calibration coefficient-humidity curve;

[0020] determining a second concentration value based on the second calibration coefficient and the number of the fifth particles;

[0021] A first calibration coefficient is determined according to the second concentration value and the calibration coefficient-concentration curve, where the first calibration coefficient corresponds to the second concentration value.

[0022] In this application, the initial concentration is first calculated using the initial second calibration coefficient corresponding to the current humidity. Based on the initial concentration, the first calibration coefficient is then determined from the calibration coefficient-concentration curve, enabling a feedback loop to adjust the calibration coefficients. This addresses the potential bias inherent in single-variable mapping and enhances the detection accuracy and anti-interference capabilities of radon gas detection instruments in environments with varying humidity and fluctuating concentrations.

[0023] In a feasible example, obtaining the number of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period includes:

[0024] Step 4-1, collecting pulse signals corresponding to single particles generated by atomic decay of radon gas multiple times to obtain multiple signal values;

[0025] Step 4-2, determining target particles corresponding to the multiple signal values, where the target particles are the first particles, the second particles, or the third particles;

[0026] Step 4-3, setting the target number corresponding to the target particle to N+1, where N is the number of target particles determined before multiple acquisitions of the pulse signal corresponding to a single particle;

[0027] Repeat steps 4-1 to 4-3 M times to obtain the number of first particles, second particles, and third particles, respectively. M is determined according to a preset time.

[0028] In the present application, within a preset time period, multiple signal values are obtained by collecting pulse signals corresponding to single particles generated by atomic decay of radon gas multiple times, and the target particles are determined based on the multiple signal values, which can improve the accuracy of particle type identification.

[0029] In a feasible example, determining target particles corresponding to multiple signal values includes:

[0030] Perform pulse waveform fitting on multiple signal values based on the acquisition order to obtain a target pulse waveform;

[0031] determining a target peak in a target pulse waveform;

[0032] When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the first peak value range corresponding to the first particle, determining the target particles corresponding to the multiple signal values as the first particles;

[0033] When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the second peak value range corresponding to the second particle, determining the target particles corresponding to the multiple signal values as the second particles;

[0034] When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the third peak value range corresponding to the third particle, the target particles corresponding to the multiple signal values are determined to be the third particles.

[0035] In this application, pulse waveform generation based on mathematical fitting to eliminate staircase effects can compensate for detail loss caused by sampling intervals. Furthermore, combining morphological parameters with multi-dimensional criteria such as peak range can improve the accuracy of particle type differentiation, providing more accurate basic data for subsequent humidity compensation and concentration calculations.

[0036] In one possible example, after determining the target peak value in the target pulse waveform, the method further includes:

[0037] Determine a first slope corresponding to each signal value between the start signal value and the target peak value in the target pulse waveform;

[0038] Determine a second slope corresponding to each signal value between the target peak value and the endpoint signal value in the target pulse waveform;

[0039] determining a first mean value between the plurality of first slopes and a second mean value between the plurality of second slopes;

[0040] When the first mean value is greater than a first preset threshold value and the second mean value is less than a second preset threshold value, it is determined that the change trend of the target pulse waveform meets the preset change trend.

[0041] In this application, through the quantitative analysis of slope statistical characteristics, the morphological judgment is upgraded from qualitative description to quantitative constraint, which improves the recognition accuracy of target particles. The mean statistical method is used to effectively filter out single-point noise interference, which also improves the recognition accuracy of target particles.

[0042] In a feasible example, determining a first concentration value of radon gas according to the first calibration coefficient and the number of fourth particles includes:

[0043] Get airflow velocity;

[0044] The first calibration coefficient is adjusted according to the airflow velocity to obtain a third calibration coefficient, wherein the third calibration coefficient is smaller than the first calibration coefficient. The faster the airflow velocity, the greater the difference between the first calibration coefficient and the third calibration coefficient.

[0045] A first concentration value of radon gas is determined according to the third calibration coefficient and the number of fourth particles.

[0046] In this application, by acquiring the air flow velocity in real time to establish a dynamic correction mechanism, the technical effects of improving detection accuracy, enhancing environmental adaptability and expanding application scenarios can be achieved.

[0047] In a feasible example, the first calibration coefficient is adjusted according to the airflow velocity to obtain a third calibration coefficient, including:

[0048] When the air flow velocity is less than a preset velocity, determining a third calibration coefficient according to a difference between the first calibration coefficient and a first adjustment coefficient, wherein the first adjustment coefficient is determined according to a product of the first air flow velocity and the first weight;

[0049] When the first air flow velocity is greater than or equal to the preset speed, the third calibration coefficient is determined based on the product between the first calibration coefficient and the second adjustment coefficient, the second adjustment coefficient is determined based on the negative exponential function of the third adjustment coefficient with a natural number as the base, and the third adjustment coefficient is determined based on the product between the first air flow velocity and the second weight.

[0050] In this application, the airflow velocity is compared with a preset threshold to classify low- and high-wind speed operating conditions. Linear compensation and exponential compensation are then combined to adjust the calibration coefficients for low and high wind speed conditions differently. Linear compensation uses a simple subtraction operation to achieve gradual compensation for low wind speed conditions, while exponential compensation uses an exponential function to address the dramatic effects of high wind speed conditions. This significantly improves the adaptability and detection accuracy of the detection instrument in complex airflow environments.

[0051] In a second aspect, the present application provides a radon gas detection device, comprising:

[0052] an acquisition unit, configured to acquire the number of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period, wherein the first particles are particles emitted after the decay of radon-222, the second particles are particles emitted after the decay of polonium-218, and the third particles are particles emitted after the decay of polonium-214;

[0053] a processing unit, configured to determine a first humidity value according to a first ratio between the number of fourth particles and the number of first particles, wherein the fourth particles are the second particles or the third particles, and the smaller the first ratio, the larger the first humidity value;

[0054] The processing unit is further configured to determine a first calibration coefficient according to the first humidity value, wherein the smaller the first humidity value is, the smaller the first calibration coefficient is;

[0055] The processing unit is further configured to determine a first concentration value of radon gas according to the first calibration coefficient and the number of fifth particles, where the fifth particles are at least one of the second particles and the third particles.

[0056] In a third aspect, the present application provides an electronic device comprising 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 method of the first aspect.

[0057] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements some or all of the steps described in the first aspect of the present application.

[0058] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when processed and executed, implements some or all of the steps described in the first aspect of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0060] Figure 1 A schematic structural diagram of a radon gas detection system provided in an embodiment of the present application;

[0061] Figure 2 A schematic diagram of a radon gas detection method provided in an embodiment of the present application;

[0062] Figure 3 A flow chart of another radon gas detection method provided in an embodiment of the present application;

[0063] Figure 4 A flow chart of another radon gas detection method provided in an embodiment of the present application;

[0064] Figure 5 A schematic flow chart of another radon gas detection method provided in an embodiment of the present application;

[0065] Figure 6 A schematic diagram of a pulse waveform provided in an embodiment of the present application;

[0066] Figure 7 A block diagram of the functional units of a radon gas detection device provided in an embodiment of the present application;

[0067] Figure 8 A block diagram of the functional units of another radon gas detection device provided in an embodiment of the present application;

[0068] Figure 9 This is a structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0070] The terms "first," "second," and so on, in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps is not limited to the listed steps but may optionally include steps not listed, or may optionally include other steps inherent to the process, method, product, or apparatus.

[0071] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] Radon is a radioactive element that exists as a gas at room temperature and pressure. It typically forms near the surface of uranium-containing materials, such as soil or rock, and diffuses into the surrounding air. Radon is colorless and odorless, making its presence and concentration difficult for humans to detect. Because radon is radioactive, long-term exposure to high concentrations significantly increases the risk of cancer and is the second leading cause of lung cancer deaths after smoking. Radon concentration testing primarily relies on counting particles produced during radon decay. Radon decay produces positively charged daughters called polonium, which have a certain probability of being neutralized by water molecules (OH-). Therefore, ambient humidity can affect radon concentration calculations. Therefore, humidity is necessary to supplement radon concentrations. Furthermore, high humidity inhibits radon diffusion, resulting in a lower number of particles emitted by radon decay.

[0073] Today's radon gas detection instruments mainly use additional humidity sensors to detect the current humidity and thus compensate for the radon gas concentration. However, this will increase the production cost of the detection instrument.

[0074] Based on this, the present application obtains the corresponding numbers of first particles, second particles and third particles produced by atomic decay of radon gas collected within a preset time period, where the first particle is the particle emitted after the decay of radon-222, the second particle is the particle emitted after the decay of polonium-218, and the third particle is the particle emitted after the decay of polonium-214; then determines the first humidity value based on a first ratio between the number of fourth particles and the number of first particles, where the fourth particle is any one of the second particle and the third particle, and the smaller the first ratio, the larger the first humidity value; determines a first calibration coefficient based on the first humidity value, where the smaller the first humidity value, the smaller the first calibration coefficient; determines a first concentration value of radon gas based on the first calibration coefficient and the number of fifth particles, where the fifth particle is at least one of the second particle and the third particle.

[0075] Because polonium-218 and polonium-214 are positively charged particles with a certain probability of being neutralized by water molecules (OH-) in the environment, they are significantly affected by humidity. However, radon-222 is a gas and is not neutralized by water molecules (OH-). Therefore, under varying humidity conditions and with a constant radon concentration, the number of particles collected from radon-222 will not change much, but the number of particles collected from polonium-218 and polonium-214 will be significantly affected. Therefore, humidity changes can be reflected by measuring the ratio of the number of particles collected from polonium-218 or polonium-214 to the number of particles collected from radon-222 at different humidity levels. A smaller ratio indicates higher humidity. This eliminates the need for a humidity sensor to determine the humidity value when measuring radon concentration, thereby reducing product production costs.

[0076] The following is an introduction to the prior art involved in this application.

[0077] Analog-to-Digital Converter (ADC) is an electronic component that converts analog signals into digital signals.

[0078] Radon-222 refers to the part of radon that releases particles through alpha decay.

[0079] Polonium-218 refers to the daughter produced by the decay of radon-222.

[0080] Polonium-214 refers to the decay product of polonium-218, and can be collectively referred to as radon daughters together with polonium-218.

[0081] Alpha particles are particles emitted when radioactive substances undergo alpha decay.

[0082] Radon-222 decays by emitting an alpha particle with an energy of approximately 5.49 MeV. The resulting polonium (Po)-218 has a half-life of approximately 3 minutes, after which it emits an alpha particle with an energy of approximately 6.0 MeV. The resulting lead (Lead)-214 has a half-life of approximately 27 minutes, after which it undergoes beta decay to form bismuth-214. Bismuth-214 has a half-life of 20 minutes, after which it undergoes beta decay to form polonium-214. Polonium-214 has a half-life of approximately 164 microseconds, after which it emits an alpha particle with an energy of approximately 7.7 MeV, forming lead-210, which has a half-life of 22 years.

[0083] Bq / m³ (Becquerel per cubic meter) is used to express the radioactive activity of radon in one cubic meter of air. 100 Bq / m³ means that in one cubic meter of air, radon atoms decay 100 times per second.

[0084] The following introduces the system architecture involved in this application.

[0085] See also Figure 1 , Figure 1 A schematic diagram of the structure of a radon gas detection system provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the radon gas detection system 100 includes an electrostatic collection chamber 101 , a photodiode 102 , a preamplifier circuit 103 , a linear pulse amplifying circuit 104 , a comparator 105 and a controller 106 .

[0086] The electrostatic collection cavity 101 captures α particles generated during the decay of radon gas and concentrates them into the detection area of the photodiode 102 mainly through the action of the electrostatic field.

[0087] The photodiode 102 is mainly used to convert the kinetic energy of the α particles into a light signal, and then convert it into an electrical signal through the photoelectric effect.

[0088] The preamplifier circuit 103 is mainly used to preliminarily amplify and shape the weak electrical signal output by the photodiode 102 to improve the signal-to-noise ratio and match the input requirements of subsequent circuits.

[0089] The linear pulse amplifier circuit 104 is mainly used to further amplify the amplified pulse signal and adjust its amplitude and width to meet the input requirements of the comparator.

[0090] The comparator 105 is used to compare the analog signal output by the linear pulse amplifying circuit 104 with a preset threshold value, and also output an interrupt signal (high / low level) for triggering ADC sampling.

[0091] The controller 106 includes an ADC detection port for converting analog electrical signals into digital signals and performing analysis and processing.

[0092] Specifically, after the measured gas enters the electrostatic collection chamber 101, it will undergo atomic decay, and the generated alpha particles will be converted into electrical signals by the photodiode 102. After obtaining the electrical signal, the preamplifier circuit 103 will shape and amplify it. The linear pulse amplifier circuit 104 will further amplify the electrical signal and adjust the pulse width. After denoising by the comparator 105, it is input to the ADC detection port of the controller 106, and the analog electrical signal is converted into a digital signal for analysis and processing.

[0093] Based on this, the controller 106 obtains the corresponding numbers of first particles, second particles and third particles generated after the atomic decay of the radon gas collected within a preset time period, where the first particles are particles emitted after the decay of radon-222, the second particles are particles emitted after the decay of polonium-218, and the third particles are particles emitted after the decay of polonium-214; then, a first humidity value is determined based on a first ratio between the number of fourth particles and the number of first particles, where the fourth particle is any one of the second particle and the third particle, and the smaller the first ratio, the larger the first humidity value; a first calibration coefficient is determined based on the first humidity value, where the smaller the first humidity value, the smaller the first calibration coefficient; and a first concentration value of the radon gas is determined based on the first calibration coefficient and the number of fifth particles, where the fifth particle is at least one of the second particle and the third particle.

[0094] Since polonium-218 and polonium-214 are positively charged particles, there's a certain probability they'll be neutralized by water molecules (OH-) in the environment, making them significantly affected by humidity. However, radon-222 is a gas and isn't neutralized by water molecules (OH-). Therefore, under varying humidity conditions and with a constant radon concentration, the number of particles collected from radon-222 doesn't change much, but the number of particles collected from polonium-218 and polonium-214 is significantly affected. Therefore, this application measures the ratio of the number of particles collected from polonium-218 or polonium-214 to the number of particles collected from radon-222 at varying humidity levels to reflect humidity changes. A smaller ratio indicates higher humidity. This avoids the need for an additional humidity sensor to determine humidity values, thereby reducing product production costs.

[0095] Based on this, an embodiment of the present application provides a radon gas detection method, which is described in detail below with reference to the accompanying drawings.

[0096] In the first embodiment, the main process of the radon gas detection method is described below.

[0097] See also Figure 2 , Figure 2 A schematic flow chart of a radon gas detection method provided in an embodiment of the present application is provided. The method is applied to the above-mentioned controller, such as Figure 2 As shown, the method includes the following steps.

[0098] Step S201 , obtaining the numbers of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period.

[0099] The first particle is emitted from the decay of radon-222 (half-life approximately 3.8 days), the second particle is emitted from the decay of polonium-218 (half-life approximately 3 minutes), and the third particle is emitted from the decay of polonium-214 (half-life only 164 microseconds). Their energies are approximately 5.49 MeV, 6.0 MeV, and 7.69 MeV, respectively. The number of first, second, and third particles can be determined by capturing radon gas in the environment using an electrostatic collection chamber. The electric field then moves the charged particles generated by the decay to the surface of a photodiode. The photoelectric effect generates current pulses, which are then continuously sampled by a preamplifier, a linear pulse amplifier, and a comparator triggering the ADC module. Finally, a filtering algorithm analyzes the ADC data and calculates the count value of each particle.

[0100] Step S202 : determining a first humidity value according to a first ratio between the quantity of the fourth particles and the quantity of the first particles.

[0101] The fourth particle is either the second or third particle. The smaller the first ratio, the larger the first humidity value. The fourth particle can be a general term for particles emitted by polonium-218 or polonium-214. Both are positively charged daughters that readily neutralize water molecules (OH⁻) in the air. The first humidity value can be obtained by mapping the first ratio to a preset calibration curve. For example, as humidity increases, the probability of water molecules neutralizing polonium particles increases, resulting in a decrease in the fourth particle count. Radon-222 is less likely to be neutralized by water molecules (OH⁻), further decreasing the first ratio and, in this case, increasing the corresponding first humidity value. This process calculates the ratio from the count values extracted in the previous steps and combines it with the corresponding calibration curve to quantitatively assess humidity conditions. This avoids the direct use of a humidity sensor while leveraging the sensitivity of polonium particles to humidity to indirectly reflect ambient humidity.

[0102] Step S203: determining a first calibration coefficient according to the first humidity value.

[0103] Among them, the smaller the first humidity value, the smaller the first calibration coefficient. The first calibration coefficient can be a correction parameter used to convert counting data into actual concentration, and its value is positively correlated with humidity. The calibration coefficient can be obtained by querying the calibration curve stored in the microcontroller FLASH. The curve is established based on the relationship between counts and concentrations at different humidity levels measured in advance in a radon gas generator with a constant concentration. For example, at low humidity, the polonium particle count is high, and the calibration coefficient K value is small at this time; conversely, at high humidity, the polonium particle count decreases, and the K value needs to be increased to compensate for the concentration calculation deviation. This process dynamically adjusts the calibration coefficient so that the concentration calculation result can adapt to the count changes under different humidity conditions, thereby improving detection accuracy.

[0104] Step S204 : determining a first concentration value of radon gas according to the first calibration coefficient and the number of the fifth particles.

[0105] The fifth particle is at least one of the second and third particles. The first concentration value may be the final radon concentration output in Bq / m³, calculated by multiplying the calibration coefficient by the count data. The number of fifth particles may be the count value of polonium-218 and / or polonium-214.

[0106] Before the current step, calibrate the device by placing it in a radon generator at a constant concentration and humidity. After a period of time, a certain number of counts will be collected for the first, second, and third particles. Because radon is a gas, it is not affected by the electric field within the diffusion chamber and can exist anywhere within the chamber. Therefore, the alpha particles it emits have a relatively low probability of hitting the photodiode within the diffusion chamber. Polonium, on the other hand, is a positively charged particle. The electric field within the diffusion chamber moves it to the photodiode, where it subsequently decays, and the alpha particles it emits have a high probability of hitting the photodiode. Therefore, the first particle counts will be lower than the second and third particle counts. In this case, directly inferring radon concentration from the first particle counts will result in inaccurate results due to the low count rate. While the second and third particles are not generated by the radon parent, they are part of the decay chain and their source is also radon. Therefore, the radon concentration can be indirectly inferred from the second and third particle counts. Since polonium-214 decays from polonium-218 and is also positively charged, the second particle count is approximately equal to the third particle count when the concentration reaches equilibrium. By calibrating the radon generator at different concentration points, the relationship between concentration and count rate can be demonstrated. Specifically, the concentration is equal to the number of fourth particles multiplied by the corresponding calibration factor. Calibration can also be performed using the sum of the second and third particle counts. This increases the count rate while also improving the accuracy of concentration detection.

[0107] It can be seen that the present application obtains the particles emitted by radon-222, polonium-218 and polonium-214 after decay, uses the ratio of radon-222 to polonium daughter counts to indirectly infer the ambient humidity, and dynamically adjusts the calibration coefficient based on the humidity state. Finally, the concentration value is calculated in combination with the polonium daughter counts, which can achieve the following technical effects: humidity compensation is achieved through the energy spectrum analysis and ratio relationship of atomic decay products, avoiding the hardware cost of adding additional humidity sensors, and using polonium daughter counts instead of parent counts to significantly improve the detection sensitivity in low concentration environments.

[0108] Embodiment 2: The radon gas detection method is described in detail below based on the method for determining the first calibration coefficient.

[0109] See also Figure 3 , Figure 3 This is a flow chart of another radon gas detection method provided in an embodiment of the present application, which is applied to the above controller, such as Figure 3 As shown, the method includes the following steps.

[0110] Step S301 , obtaining the numbers of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period.

[0111] Step S302 : determining a first humidity value according to a first ratio between the quantity of the fourth particles and the quantity of the first particles.

[0112] Step S303: Obtain a calibration coefficient-concentration curve corresponding to the first humidity value.

[0113] Among them, the calibration coefficient-concentration curve is used to represent the calibration coefficient corresponding to different concentrations. The calibration coefficient-concentration curve can be a pre-established parameter mapping relationship used to characterize the correlation between ambient humidity, radon gas concentration and the calibration coefficient, and can be generated by experimental calibration or data fitting. Exemplarily, the calibration coefficient-concentration curve can include multiple calibration coefficient-concentration curves, each corresponding to different humidity conditions, and each calibration coefficient-concentration curve contains calibration coefficients corresponding to different concentrations. For example, when the humidity is 50%, the calibration coefficient corresponding to the concentration of 200Bq / m³ may be 0.8, while the calibration coefficient corresponding to the concentration of 400Bq / m³ may be 0.6.

[0114] Step S304 : determining a second ratio between the concentration corresponding to each of the plurality of coordinate points on the calibration coefficient-concentration curve and the calibration coefficient.

[0115] The second ratio is the ratio of concentration to calibration coefficient, i.e., the value obtained by dividing the concentration value of each coordinate point on the curve by the corresponding calibration coefficient value. For example, if the concentration at a coordinate point is C1 and the calibration coefficient is K1, the second ratio is C1 / K1. Calculating this ratio can convert the relationship between concentration and calibration coefficient into an intermediate variable, facilitating the subsequent matching process. It is understood that the multiple coordinate points on the calibration coefficient-concentration curve can be multiple coordinate points selected from the calibration coefficient-concentration curve based on preset concentration intervals.

[0116] Step S305 : determining a target coordinate point corresponding to a second ratio corresponding to the number of fifth particles among the plurality of second ratios.

[0117] The target coordinate point may be a coordinate position on the calibration coefficient-concentration curve where the second ratio and the number of the fifth particles are identical. It will be appreciated that, because the multiple second ratios are determined from multiple coordinate points selected on the calibration coefficient-concentration curve, there may not be a second ratio among the multiple second ratios that is exactly the same as the number of the fifth particles. In this case, the second ratio with the smallest difference from the number of the fifth particles may be selected as the second ratio corresponding to the number of the fifth particles among the multiple second ratios.

[0118] Step S306 : determining the calibration coefficient corresponding to the target coordinate point on the calibration coefficient-concentration curve as the first calibration coefficient.

[0119] As can be seen, the above process calls the calibration coefficient concentration curve in the storage unit, calculates the ratio of the concentration to the calibration coefficient at each coordinate point, locates the target coordinate point based on the matching relationship between the current number of fifth particles and the ratio, and finally extracts the corresponding calibration coefficient as the compensation parameter. In this way, through the coordinated optimization of humidity, concentration, and calibration coefficient, the effects of humidity and concentration on the calibration coefficient can be taken into account. At the same time, the use of pre-stored curve data can improve the efficiency of locating the calibration coefficient, enhancing the detection accuracy and anti-interference ability of the radon gas detection instrument in environmental conditions with changing humidity and fluctuating concentration.

[0120] Step S307 : determining a first concentration value of radon gas according to the first calibration coefficient and the number of the fifth particles.

[0121] Embodiment 3: The radon gas detection method is described in detail below based on another method for determining the first calibration coefficient.

[0122] See also Figure 4 , Figure 4 A flow chart of another radon gas detection method provided in an embodiment of the present application is provided, wherein the method is applied to the above controller, such as Figure 4 As shown, the method includes the following steps.

[0123] Step S401 : obtaining the number of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period.

[0124] Step S402 : determining a first humidity value according to a first ratio between the quantity of the fourth particles and the quantity of the first particles.

[0125] Step S403: Obtain a calibration coefficient-humidity curve and a calibration coefficient-concentration curve.

[0126] The calibration coefficient-humidity curve is used to represent the calibration coefficients corresponding to different humidity conditions, and the calibration coefficient-concentration curve is used to represent the calibration coefficients corresponding to different concentration conditions.

[0127] The calibration coefficient-humidity curve can be a pre-stored data set that records the corresponding relationship between different humidity values and calibration coefficients under fixed concentration conditions, and can be retrieved through the storage unit of the controller. The calibration coefficient-concentration curve can be a pre-stored data set that records the corresponding relationship between different concentration values and calibration coefficients under fixed humidity conditions, and can be retrieved through the same storage unit.

[0128] By calling these two curves simultaneously, multi-dimensional parameter constraints can be provided for the dynamic calibration of subsequent calibration coefficients. For example, when humidity and concentration change simultaneously, the limitations of single variable mapping can be avoided.

[0129] Step S404: determining a second calibration coefficient corresponding to the first humidity value according to the calibration coefficient-humidity curve.

[0130] The second calibration coefficient can be an initial compensation parameter obtained by matching the calibration coefficient humidity curve based on the current measured humidity value. For example, if the current humidity is 60%, K1 = 0.75 can be obtained by searching the curve data point. This process, through the direct mapping relationship between humidity and calibration coefficient, preliminarily establishes compensation for the impact of ambient humidity on test results.

[0131] Step S405 : determining a second concentration value according to the second calibration coefficient and the number of the fifth particles.

[0132] The second concentration value may be determined according to the product of the second calibration coefficient and the number of the fifth particles.

[0133] Step S406 : determining a first calibration coefficient according to the second concentration value and the calibration coefficient-concentration curve, where the first calibration coefficient corresponds to the second concentration value.

[0134] The aforementioned process first calculates the initial concentration using the initial second calibration coefficient corresponding to the current humidity. Based on the initial concentration, the first calibration coefficient is then determined from the calibration coefficient-concentration curve, enabling a feedback loop to adjust the calibration coefficients. This resolves potential bias in single-variable mapping and enhances the accuracy and anti-interference capabilities of radon gas detectors in environments with varying humidity and fluctuating concentrations.

[0135] Step S407 : determining a first concentration value of radon gas according to the first calibration coefficient and the number of the fifth particles.

[0136] Example 4: The radon gas detection method is described in detail below based on the details of obtaining the number of target particles.

[0137] See also Figure 5 , Figure 5 A flow chart of another radon gas detection method provided in an embodiment of the present application is provided, wherein the method is applied to the above controller, such as Figure 5 As shown, the method includes the following steps.

[0138] In step S501 , pulse signals corresponding to single particles generated by atomic decay of radon gas are collected multiple times to obtain multiple signal values.

[0139] Among them, the pulse signal corresponding to a single particle generated after the atomic decay of radon gas is transmitted to the controller by the comparator, and when the comparator transmits the pulse signal, it also transmits an interrupt signal for controlling the ADC to perform multiple acquisitions.

[0140] Multiple acquisitions can refer to multiple analog-to-digital conversions performed continuously by the ADC after a single interrupt signal triggers. This involves continuous sampling at fixed time intervals to form a waveform sequence containing both noise and valid signals. Multiple acquisitions can be implemented by the controller initiating the ADC's continuous acquisition mode upon receiving an interrupt signal, for example, continuously acquiring 100 signal values at 5μs intervals.

[0141] Step S502: determining target particles corresponding to a plurality of signal values.

[0142] The target particle is the first particle, the second particle, or the third particle. The target particle can be determined by detecting multiple collected signal values through a filtering algorithm.

[0143] In a specific example, determining target particles corresponding to multiple signal values includes: performing pulse waveform fitting on the multiple signal values based on the acquisition order to obtain a target pulse waveform; determining a target peak in the target pulse waveform; when it is determined that the changing trend of the target pulse waveform meets the preset changing trend and the target peak is within the first peak range corresponding to the first particle, determining that the target particles corresponding to the multiple signal values are first particles; when it is determined that the changing trend of the target pulse waveform meets the preset changing trend and the target peak is within the second peak range corresponding to the second particle, determining that the target particles corresponding to the multiple signal values are second particles; when it is determined that the changing trend of the target pulse waveform meets the preset changing trend and the target peak is within the third peak range corresponding to the third particle, determining that the target particles corresponding to the multiple signal values are third particles.

[0144] Pulse waveform fitting can be a process of mathematically fitting continuously acquired signal values to eliminate stair-step effects through curve fitting. This can be achieved through cubic spline interpolation or an exponential decay model. Exemplarily, the fitting method includes using different models for the rising and falling edges to match the photodiode response characteristics. The target peak value can be the highest point in the fitted waveform curve, which can be extracted from the fitting results using a peak detection algorithm. Exemplarily, the peak value can be used to reflect the total ionization energy released by the interaction between alpha particles and the photodiode. The preset change trend can be the fluctuation state of the waveform, for example, initially in an upward fluctuation state, reaching a peak, and then in a downward fluctuation state.

[0145] The first peak range can be the signal value interval corresponding to a specific particle energy. For example, the range can be set to [450, 460] for the first particle. The preset change trend can be a combined constraint of parameters such as the waveform rise time and half-height width. For example, the morphology can include the requirement that the rising edge steepness is greater than a threshold and the waveform symmetry parameter is within a specified range. The second and third peak ranges can be signal value intervals corresponding to different particle energies. For example, the second and third peak ranges can be set to [560, 570] and [670, 680], respectively, to distinguish the second and third particles.

[0146] As can be seen, by generating a pulse waveform based on mathematical fitting to eliminate the staircase effect, the loss of detail caused by the sampling interval can be compensated. Furthermore, combining morphological parameters with the multi-dimensional judgment criteria of peak range can improve the accuracy of particle type differentiation, providing more accurate basic data for subsequent humidity compensation and concentration calculations.

[0147] Furthermore, after determining the target peak value in the target pulse waveform, the method also includes: determining the first slope corresponding to each signal value between the starting signal value and the target peak value in the target pulse waveform; determining the second slope corresponding to each signal value between the target peak value and the end signal value in the target pulse waveform; determining a first mean between multiple first slopes, and a second mean between multiple second slopes; when the first mean value is greater than a first preset threshold value and the second mean value is less than a second preset threshold value, determining that the change trend of the target pulse waveform meets the preset change trend.

[0148] The first slope can be a quantitative parameter that characterizes the local steepness of the rising segment of the waveform, and can be calculated by the ratio of the signal value difference between adjacent sampling points to the acquisition time interval. For example, the calculation formula for the first slope corresponding to the later signal value among the signal values of adjacent sampling points is:

[0149]

[0150] Wherein, ΔV / Δt represents the first slope, V1 and V2 represent the signal values of two adjacent points respectively, and Δt represents the time interval.

[0151] The second slope can be a quantitative parameter that characterizes the local decay rate of the waveform's descending segment. Its acquisition method is similar to the first slope, but the calculation range is limited to the signal value interval from the target peak to the endpoint. This is because the pulse waveforms corresponding to the first, second, and third particles should first rise and then fall. Therefore, when performing the calculation, the multiple signal values can be directly divided into two parts based on the peak value. The slope corresponding to the signal values in each part can then be calculated.

[0152] The first mean value can be a statistical characteristic value reflecting the overall upward trend of the waveform, which is obtained by adding all the first slope values and dividing by the number of sampling points. The second mean value can be a statistical characteristic value reflecting the overall attenuation trend of the waveform, and its calculation method is consistent with the first mean value. This is because in the process of acquiring the signal value, there may be a certain error in the signal value, then the left side of the peak of the generated target pulse waveform may not ensure that the slope corresponding to each signal value on the waveform is a positive value, and at the same time, the right side of the peak of the generated target pulse waveform may not ensure that the slope corresponding to each signal value on the waveform is a negative value. Therefore, the first mean value is used to reflect the slope of the overall upward trend of the waveform, and the second mean value is used to reflect the slope of the overall downward trend of the waveform.

[0153] Based on this, the first preset threshold can be a reference value for the slope of the rising segment set according to the energy characteristics of the target particles. The second preset threshold can be a reference value for the slope of the falling segment set according to the attenuation characteristics of the target particles. The first preset threshold should be greater than zero, and the second preset threshold should be less than zero.

[0154] It can be seen that through the quantitative analysis of the slope statistical characteristics, the morphological judgment is upgraded from qualitative description to quantitative constraint, which improves the recognition accuracy of the target particles. In addition, the use of mean statistics can effectively filter out single-point noise interference, which also improves the recognition accuracy of the target particles.

[0155] For example, see Figure 6 , Figure 6 A schematic diagram of a pulse waveform provided in an embodiment of the present application is shown in FIG. Figure 6 The figure shows the pulse waveform corresponding to the first particle (peak 452), the second particle (peak 561), and the third particle (peak 674). The horizontal axis of the pulse waveform corresponds to the acquisition number, and the vertical axis corresponds to the ADC channel address (signal value). It can be understood that the peak values reflect the energy of the alpha particles. For example, peak 452 represents a 5.49 MeV alpha particle emitted by radon-222, peak 561 represents a 6 MeV alpha particle emitted by the radon daughter polonium-218, and peak 674 represents a 7.69 MeV alpha particle emitted by the radon daughter polonium-214.

[0156] Step S503: setting the target number corresponding to the target particles to N+1.

[0157] Where N is the number of target particles determined before multiple acquisitions of pulse signals corresponding to a single particle are performed. For example, when multiple signal values are determined to correspond to the first particle, the current value of the counter corresponding to the first particle is incremented by 1. It is understood that in actual applications, not every acquisition cycle will capture a single particle from the first, second, or third particles. If the target pulse waveform does not meet the pulse waveform corresponding to the first, second, or third particle, acquisition will be repeated in the next cycle.

[0158] Step S504, repeating steps S501 to S503 M times to obtain the number of first particles, second particles, and third particles respectively.

[0159] Among them, M is determined according to the preset duration. It can be understood that the preset duration is also the standard duration of the experiment corresponding to the aforementioned calibration coefficient-concentration curve and the calibration coefficient-humidity curve. The operation of repeating the step M times can be achieved by calculating the ratio of the preset duration to the duration of a single acquisition cycle. For example, if the preset duration is 1 hour and each acquisition cycle takes 1 second, then M=3600 times. The determination of the M value can be based on the duration requirement of the detection task and the system resource allocation strategy, for example, by setting the total detection duration and calculating the corresponding number of cycles to balance accuracy and energy consumption.

[0160] It can be seen that within the preset time period, multiple signal values are obtained by repeatedly collecting the pulse signals corresponding to single particles generated by the atomic decay of radon gas, and the target particles are determined based on the multiple signal values, which can improve the accuracy of particle type identification.

[0161] Step S505 , obtaining the number of target particles generated by atomic decay of radon gas collected within a preset time period.

[0162] The target particle is a first particle, a second particle or a third particle. The first particle is a particle emitted after the decay of radon-222, the second particle is a particle emitted after the decay of polonium-218, and the third particle is a particle emitted after the decay of polonium-214.

[0163] Step S506 : determining a first humidity value according to a first ratio between the number of the fourth particles and the number of the first particles.

[0164] The fourth particle is any one of the second particle and the third particle, and the smaller the first ratio is, the larger the first humidity value is.

[0165] Step S507: determining a first calibration coefficient according to the first humidity value.

[0166] The smaller the first humidity value is, the smaller the first calibration coefficient is.

[0167] Step S508 : determining a first concentration value of radon gas according to the first calibration coefficient and the number of the fifth particles.

[0168] The fifth particle is at least one of the second particle and the third particle.

[0169] Furthermore, the first concentration value of radon gas is determined based on the first calibration coefficient and the number of fourth particles, including: obtaining the airflow velocity; adjusting the first calibration coefficient based on the airflow velocity to obtain a third calibration coefficient, the third calibration coefficient being smaller than the first calibration coefficient, and the faster the airflow velocity, the greater the difference between the first calibration coefficient and the third calibration coefficient; and determining the first concentration value of radon gas based on the third calibration coefficient and the number of fourth particles.

[0170] Airflow velocity can be a physical quantity representing the rate of ambient air flow, and can be obtained using a built-in or external airflow sensor. Exemplary sensors include one or more of thermal, differential pressure, or ultrasonic sensors. For example, a thermal sensor infers airflow velocity by detecting changes in the rate of heat dissipation from a component. Its operating principle is based on the relationship between heat transfer and air flow rate in thermodynamics.

[0171] The third calibration coefficient can be a dynamically adjusted parameter after the first calibration coefficient is corrected by the air flow velocity. The difference between the first calibration coefficient and the third calibration coefficient is positively correlated with the air flow velocity. For example, when the air flow velocity increases from 0.1m / s to 0.5m / s, the decrease in the third calibration coefficient relative to the initial value will increase with the increase in speed.

[0172] Adjusting the first calibration coefficient according to the airflow velocity can be achieved by establishing a mapping relationship between the airflow velocity and the calibration coefficient correction factor. In a specific embodiment, the mapping relationship is obtained through experimental calibration.

[0173] It can be seen that by obtaining the air flow velocity in real time to establish a dynamic correction mechanism, the technical effects of improving detection accuracy, enhancing environmental adaptability and expanding application scenarios can be achieved.

[0174] Furthermore, the first calibration coefficient is adjusted according to the air flow velocity to obtain a third calibration coefficient, including: when the air flow velocity is less than the preset speed, the third calibration coefficient is determined according to the difference between the first calibration coefficient and the first adjustment coefficient, and the first adjustment coefficient is determined according to the product between the first air flow velocity and the first weight; when the first air flow velocity is greater than or equal to the preset speed, the third calibration coefficient is determined according to the product between the first calibration coefficient and the second adjustment coefficient, the second adjustment coefficient is determined according to the negative exponential function of the third adjustment coefficient with a natural number as the base, and the third adjustment coefficient is determined according to the product between the first air flow velocity and the second weight.

[0175] The preset speed is a pre-set threshold parameter, such as 0.5 m / s, which is used as a criterion for distinguishing low-speed and high-speed operating conditions. The first adjustment coefficient is an intermediate variable in the linear compensation model, and its value is determined by the product of the airflow velocity and the first weight. The first weight is an experimentally calibrated linear compensation coefficient, such as 0.02 / (m / s), which is stored in the memory unit of the microcontroller. When the airflow velocity is less than the preset speed, the adjustment formula of the first calibration coefficient is as follows:

[0176]

[0177] Wherein, T3 is used to represent the third calibration coefficient, T1 is used to represent the first calibration coefficient, v is used to represent the airflow velocity, and a is used to represent the first weight.

[0178] The second adjustment coefficient is an intermediate variable in the exponential compensation model. Its value is generated by the negative exponential function of a natural number. The exponential part of this exponential function is composed of the product of the airflow velocity and the second weight. The second weight is an experimentally calibrated exponential compensation coefficient, such as 0.1 / (m / s), which is also determined through experimental fitting. When the airflow velocity is greater than or equal to the preset velocity, the adjustment formula of the first calibration coefficient is as follows:

[0179]

[0180] Among them, e is used to represent a natural number, and b is used to represent the second weight.

[0181] As can be seen, by comparing airflow velocity with a preset threshold to classify low and high wind speed conditions, a combination of linear and exponential compensation allows for differential adjustment of the calibration coefficients for low and high wind speed conditions. Linear compensation achieves gradual compensation for low wind speed conditions through simple subtraction, while exponential compensation addresses the dramatic effects of high wind speed conditions through exponential function calculations. This significantly improves the instrument's adaptability and detection accuracy in complex airflow environments.

[0182] In accordance with the above-mentioned embodiment, please refer to Figure 7 , Figure 7 This is a block diagram of the functional units of a radon gas detection device provided in an embodiment of the present application. The radon gas detection device is the above-mentioned controller or a part of the controller, such as Figure 7 As shown, the radon gas detection device 70 includes:

[0183] An acquisition unit 701 is configured to acquire the number of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period, wherein the first particles are particles emitted by the decay of radon-222, the second particles are particles emitted by the decay of polonium-218, and the third particles are particles emitted by the decay of polonium-214.

[0184] a processing unit 702 for determining a first humidity value according to a first ratio between the number of fourth particles and the number of first particles, where the fourth particles are the second particles or the third particles, and the smaller the first ratio, the larger the first humidity value;

[0185] The processing unit 702 is further configured to determine a first calibration coefficient according to the first humidity value, wherein the smaller the first humidity value is, the smaller the first calibration coefficient is;

[0186] The processing unit 702 is further configured to determine a first concentration value of radon gas according to the first calibration coefficient and the number of fifth particles, where the fifth particles are at least one of the second particles and the third particles.

[0187] In a feasible embodiment, in determining the first calibration coefficient according to the first humidity value, the processing unit 702 is specifically configured to:

[0188] Obtaining a calibration coefficient-concentration curve corresponding to the first humidity value, where the calibration coefficient-concentration curve is used to represent calibration coefficients corresponding to different concentrations;

[0189] Determining a second ratio between the concentration corresponding to each of a plurality of coordinate points on the calibration coefficient-concentration curve and the calibration coefficient;

[0190] determining a target coordinate point corresponding to a second ratio corresponding to the number of fifth particles among the plurality of second ratios;

[0191] The calibration coefficient corresponding to the target coordinate point on the calibration coefficient-concentration curve is determined as the first calibration coefficient.

[0192] In a feasible embodiment, in determining the first calibration coefficient according to the first humidity value, the processing unit 702 is specifically configured to:

[0193] Obtain the calibration coefficient-humidity curve and the calibration coefficient-concentration curve. The calibration coefficient-humidity curve is used to represent the calibration coefficient corresponding to different humidity conditions, and the calibration coefficient-concentration curve is used to represent the calibration coefficient corresponding to different concentration conditions.

[0194] determining a second calibration coefficient corresponding to the first humidity value according to a calibration coefficient-humidity curve;

[0195] determining a second concentration value based on the second calibration coefficient and the number of the fifth particles;

[0196] A first calibration coefficient is determined according to the second concentration value and the calibration coefficient-concentration curve, where the first calibration coefficient corresponds to the second concentration value.

[0197] In a feasible embodiment, in terms of obtaining the number of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period, the obtaining unit 701 is specifically configured to:

[0198] Step 4-1, collecting pulse signals corresponding to single particles generated by atomic decay of radon gas multiple times to obtain multiple signal values;

[0199] Step 4-2, determining target particles corresponding to the multiple signal values, where the target particles are the first particles, the second particles, or the third particles;

[0200] Step 4-3, setting the target number corresponding to the target particle to N+1, where N is the number of target particles determined before multiple acquisitions of the pulse signal corresponding to a single particle;

[0201] Repeat steps 4-1 to 4-3 M times to obtain the number of first particles, second particles, and third particles, respectively. M is determined according to a preset time.

[0202] In a feasible embodiment, in terms of determining target particles corresponding to multiple signal values, the acquisition unit 701 is specifically configured to:

[0203] Perform pulse waveform fitting on multiple signal values based on the acquisition order to obtain a target pulse waveform;

[0204] determining a target peak in a target pulse waveform;

[0205] When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the first peak value range corresponding to the first particle, determining the target particles corresponding to the multiple signal values as the first particles;

[0206] When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the second peak value range corresponding to the second particle, determining the target particles corresponding to the multiple signal values as the second particles;

[0207] When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the third peak value range corresponding to the third particle, the target particles corresponding to the multiple signal values are determined to be the third particles.

[0208] In a feasible embodiment, after determining the target peak value in the target pulse waveform, the acquiring unit 701 is further configured to:

[0209] Determine a first slope corresponding to each signal value between the start signal value and the target peak value in the target pulse waveform;

[0210] Determine a second slope corresponding to each signal value between the target peak value and the endpoint signal value in the target pulse waveform;

[0211] determining a first mean value between the plurality of first slopes and a second mean value between the plurality of second slopes;

[0212] When the first mean value is greater than a first preset threshold value and the second mean value is less than a second preset threshold value, it is determined that the change trend of the target pulse waveform meets the preset change trend.

[0213] In a feasible embodiment, in determining the first concentration value of radon gas according to the first calibration coefficient and the number of the fourth particles, the processing unit 702 is specifically configured to:

[0214] Get airflow velocity;

[0215] The first calibration coefficient is adjusted according to the airflow velocity to obtain a third calibration coefficient, wherein the third calibration coefficient is smaller than the first calibration coefficient. The faster the airflow velocity, the greater the difference between the first calibration coefficient and the third calibration coefficient.

[0216] A first concentration value of radon gas is determined according to the third calibration coefficient and the number of fourth particles.

[0217] In a feasible embodiment, in terms of adjusting the first calibration coefficient according to the airflow velocity to obtain the third calibration coefficient, the processing unit 702 is specifically configured to:

[0218] When the air flow velocity is less than a preset velocity, determining a third calibration coefficient according to a difference between the first calibration coefficient and a first adjustment coefficient, wherein the first adjustment coefficient is determined according to a product of the first air flow velocity and the first weight;

[0219] When the first air flow velocity is greater than or equal to the preset speed, the third calibration coefficient is determined based on the product between the first calibration coefficient and the second adjustment coefficient, the second adjustment coefficient is determined based on the negative exponential function of the third adjustment coefficient with a natural number as the base, and the third adjustment coefficient is determined based on the product between the first air flow velocity and the second weight.

[0220] It can be understood that since the method embodiment and the device embodiment are different presentation forms of the same technical concept, the content of the method embodiment part in this application should be synchronously adapted to the device embodiment part and will not be repeated here.

[0221] In the case of integrated units, such as Figure 8 As shown, Figure 8 This is a block diagram of the functional units of another radon gas detection device provided in an embodiment of the present application. Figure 8In the embodiment, the radon gas detection device 70 includes: a processing module 812 and a communication module 811. The processing module 812 is used to control and manage the actions of the radon gas detection device 70, for example, the steps of the acquisition unit 701 and the processing unit 702, and / or other processes for executing the technology described herein. The communication module 811 is used to support the interaction between the radon gas detection device 70 and other devices. Figure 8 As shown, the radon gas detection device 70 may further include a storage module 813 , and the storage module 813 is used to store program codes and data of the radon gas detection device 70 .

[0222] The processing module 812 may 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 device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 811 may be a transceiver, an RF circuit, or a communication interface, and the like. The storage module 813 may be a memory.

[0223] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here. The above radon gas detection device 70 can perform the above Figure 2 Radon gas detection method shown.

[0224] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. 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 transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred 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 accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more 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.

[0225] Figure 9 This is a structural block diagram of an electronic device provided in an embodiment of the present application. Figure 9 As shown, the electronic device 900 may include one or more of the following components: a processor 901, a memory 902 and a communication interface 903. The processor 901, the memory 902 and the communication interface 903 are interconnected and perform communication with each other. The memory 902 may store one or more computer programs, and the one or more computer programs may be configured to implement the methods described in the above embodiments when executed by one or more processors 901.

[0226] The processor 901 may include one or more processing cores. The processor 901 uses various interfaces and lines to connect the various parts of the entire electronic device 900, and performs various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets or instruction sets stored in the memory 902, and calling data stored in the memory 902. Optionally, the processor 901 can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 901 can integrate one or more combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. It is understandable that the above-mentioned modem may not be integrated into the processor 901, but may be implemented separately through a communication chip.

[0227] The memory 902 may include a random access memory (RAM) or a read-only memory (ROM). The memory 902 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the electronic device 900 during use.

[0228] It is understandable that the electronic device 900 may include more or fewer structural elements than those in the above structural block diagram, for example, a power module, physical buttons, a WiFi (Wireless Fidelity) module, a speaker, a Bluetooth module, a sensor, etc., which are not limited here.

[0229] The electronic device 900 may be a controller or a part of a controller.

[0230] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements part or all of the steps of any radon gas detection method described in the above method embodiments.

[0231] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements some or all of the steps of any of the radon gas detection methods described in the above method embodiments. The computer program product may be a software installation package.

[0232] It should be noted that for the sake of simplicity, the method embodiments of any of the aforementioned radon gas detection methods are described as a series of action combinations. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions involved are not necessarily required by this application.

[0233] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality of components or steps. The fact that certain measures are recited in different dependent claims does not mean that these measures cannot be combined to produce good results.

[0234] Those skilled in the art will appreciate that all or part of the steps in the various methods of any of the above-mentioned radon gas detection method embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0235] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application's radon gas detection method, device, electronic device, and storage medium. The description of the above embodiments is only used to help understand the method and its core idea of the present application. At the same time, for those skilled in the art, based on the idea of the present application's radon gas detection method, device, electronic device, and storage medium, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0236] The present application is described with reference to the flowcharts and / or block diagrams of the methods, hardware products, and computer program products of the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0237] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0238] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0239] It can be understood that any product that is controlled or configured to execute the processing method of the flowchart described in the method embodiment of a radon gas detection method of the present application, such as the terminal and computer program product in the above flowchart, falls within the scope of the related products described in this application.

[0240] Obviously, those skilled in the art may make various modifications and variations to the radon gas detection method, device, electronic device, and storage medium provided herein without departing from the spirit and scope of the present application. Thus, if such modifications and variations fall within the scope of the present claims and their equivalents, the present application is intended to encompass such modifications and variations.

Claims

1. A radon gas detection method, characterized in that: The method comprises: Obtaining the number of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period, wherein the first particles are particles emitted after the decay of radon-222, the second particles are particles emitted after the decay of polonium-218, and the third particles are particles emitted after the decay of polonium-214; determining a first humidity value according to a first ratio between the number of fourth particles and the number of the first particles, the fourth particles being the second particles or the third particles, and the smaller the first ratio, the larger the first humidity value; determining a first calibration coefficient according to the first humidity value, wherein the smaller the first humidity value is, the smaller the first calibration coefficient is; determining a first concentration value of radon gas according to the first calibration coefficient and the number of fifth particles, wherein the fifth particles are at least one of the second particles and the third particles; Determining the first calibration coefficient according to the first humidity value includes: Obtaining a calibration coefficient-concentration curve corresponding to the first humidity value, wherein the calibration coefficient-concentration curve is used to represent calibration coefficients corresponding to different concentrations; Determining a second ratio between the concentration corresponding to each of a plurality of coordinate points on the calibration coefficient-concentration curve and the calibration coefficient; determining a target coordinate point corresponding to a second ratio corresponding to the number of the fifth particles among the plurality of second ratios; Determining a calibration coefficient corresponding to a target coordinate point on the calibration coefficient-concentration curve as the first calibration coefficient; Alternatively, determining a first calibration coefficient according to the first humidity value includes: Obtaining a calibration coefficient-humidity curve and a calibration coefficient-concentration curve, wherein the calibration coefficient-humidity curve is used to represent the calibration coefficient corresponding to different humidity conditions, and the calibration coefficient-concentration curve is used to represent the calibration coefficient corresponding to different concentration conditions; determining a second calibration coefficient corresponding to the first humidity value according to the calibration coefficient-humidity curve; determining a second concentration value according to the second calibration coefficient and the number of the fifth particles; The first calibration coefficient is determined according to the second concentration value and the calibration coefficient-concentration curve, and the first calibration coefficient corresponds to the second concentration value.

2. The method according to claim 1, characterized in that The obtaining of the number of first particles, second particles, and third particles generated by atomic decay of radon gas collected within a preset time period includes: Step 4-1, collecting pulse signals corresponding to single particles generated by atomic decay of radon gas multiple times to obtain multiple signal values; Step 4-2, determining target particles corresponding to the multiple signal values, wherein the target particles are first particles, second particles, or third particles; Step 4-3, setting the target number corresponding to the target particle to N+1, where N is the number of target particles determined before multiple acquisitions of the pulse signal corresponding to the single particle; Repeat steps 4-1 to 4-3 M times to obtain the number of the first particles, the second particles, and the third particles, respectively, where M is determined according to the preset time length.

3. The method according to claim 2, characterized in that Determining target particles corresponding to the multiple signal values includes: Performing pulse waveform fitting on the plurality of signal values based on an acquisition order to obtain a target pulse waveform; determining a target peak value in the target pulse waveform; When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the first peak value range corresponding to the first particle, determining that the target particle corresponding to the multiple signal values is the first particle; When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the second peak value range corresponding to the second particle, determining that the target particles corresponding to the multiple signal values are second particles; When it is determined that the change trend of the target pulse waveform meets the preset change trend and the target peak value is within the third peak value range corresponding to the third particle, the target particles corresponding to the multiple signal values are determined to be third particles.

4. The method according to claim 3, characterized in that After determining the target peak value in the target pulse waveform, the method further includes: Determining a first slope corresponding to each signal value between a start signal value and a target peak value in the target pulse waveform; Determining a second slope corresponding to each signal value between the target peak value and the endpoint signal value in the target pulse waveform; determining a first mean value between a plurality of said first slopes and a second mean value between a plurality of said second slopes; When the first mean value is greater than a first preset threshold value and the second mean value is less than a second preset threshold value, it is determined that the change trend of the target pulse waveform meets a preset change trend.

5. The method according to claim 1, wherein The determining a first concentration value of radon gas according to the first calibration coefficient and the number of the fourth particles includes: Get airflow velocity; Adjusting the first calibration coefficient according to the airflow velocity to obtain a third calibration coefficient, wherein the third calibration coefficient is smaller than the first calibration coefficient, and the faster the airflow velocity, the greater the difference between the first calibration coefficient and the third calibration coefficient; A first concentration value of radon gas is determined according to the third calibration coefficient and the number of the fourth particles.

6. A radon gas detection device, characterized in that: The device comprises: an acquisition unit, configured to acquire the number of first particles, second particles, and third particles respectively generated by atomic decay of radon gas collected within a preset time period, wherein the first particles are particles emitted after the decay of radon-222, the second particles are particles emitted after the decay of polonium-218, and the third particles are particles emitted after the decay of polonium-214; a processing unit, configured to determine a first humidity value according to a first ratio between the number of fourth particles and the number of the first particles, wherein the fourth particles are the second particles or the third particles, and the smaller the first ratio, the larger the first humidity value; The processing unit is further configured to determine a first calibration coefficient according to the first humidity value, wherein the smaller the first humidity value is, the smaller the first calibration coefficient is; The processing unit is further configured to determine a first concentration value of radon gas based on the first calibration coefficient and a number of fifth particles, where the fifth particles are at least one of the second particles and the third particles; In terms of determining the first calibration coefficient according to the first humidity value, the processing unit is specifically configured to: Obtaining a calibration coefficient-concentration curve corresponding to the first humidity value, wherein the calibration coefficient-concentration curve is used to represent calibration coefficients corresponding to different concentrations; Determining a second ratio between the concentration corresponding to each of a plurality of coordinate points on the calibration coefficient-concentration curve and the calibration coefficient; determining a target coordinate point corresponding to a second ratio corresponding to the number of the fifth particles among the plurality of second ratios; Determining a calibration coefficient corresponding to a target coordinate point on the calibration coefficient-concentration curve as the first calibration coefficient; Alternatively, in determining the first calibration coefficient according to the first humidity value, the processing unit is specifically configured to: Obtaining a calibration coefficient-humidity curve and a calibration coefficient-concentration curve, wherein the calibration coefficient-humidity curve is used to represent the calibration coefficient corresponding to different humidity conditions, and the calibration coefficient-concentration curve is used to represent the calibration coefficient corresponding to different concentration conditions; determining a second calibration coefficient corresponding to the first humidity value according to the calibration coefficient-humidity curve; determining a second concentration value according to the second calibration coefficient and the number of the fifth particles; The first calibration coefficient is determined according to the second concentration value and the calibration coefficient-concentration curve, and the first calibration coefficient corresponds to the second concentration value.

7. An electronic device comprising a processor, a memory, and an executable program code stored in the memory, wherein: The processor is used to call the executable program code stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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