Adaptive counting rate processing method and device

Abstract

The invention provides an adaptive counting rate processing method and device, and relates to the technical field of industrial detection, and the method comprises the steps: obtaining the current cumulative time and cumulative particle number of continuous monitoring; setting a plurality of time thresholds with different sizes and a plurality of counting thresholds with different sizes; determining a counting strategy based on a successive relationship between the cumulative time arrival time threshold value and the cumulative particle number arrival counting threshold value; and calculating the current average counting rate based on the counting strategy. According to the invention, the metering mode can be dynamically adjusted to improve the adaptability of the measurement system in different intensity fields.

Description

Technical Field

[0001] The present application relates to the technical field of industrial detection, and in particular to a method and device for processing an adaptive count rate. Background Art

[0002] In the fields of radiation measurement, particle detection, and industrial testing technology, particularly in applications such as nuclear physics experiments, environmental radiation monitoring, and industrial nondestructive testing, detectors sense particle or radiation events and output corresponding electrical signal pulses. These pulses must be received and processed in real time by the measuring device. To quantitatively assess radiation field intensity or particle flux, the measuring device must accurately count the number of pulses per unit time, thereby calculating the average count rate to reflect the intensity of particle activity.

[0003] Existing hybrid count rate measurement methods typically employ only a single time threshold or a single count threshold as a criterion. While this fixed threshold mechanism can achieve basic measurement functionality within a specific count rate range, it can suffer from long response times and insufficient statistical accuracy in practical applications, particularly when facing wide dynamic ranges of count rates, from very low to very high. In particular, under conditions of drastic fluctuations in particle flux or instability of the measured object, a single threshold strategy cannot flexibly adapt to diverse measurement requirements. This makes it difficult for the measurement system to achieve an effective balance between fast response and high confidence, limiting its applicability and scalability in complex scenarios such as high-performance particle detection, precise radiation assessment, and real-time industrial diagnostics. Summary of the Invention

[0004] The present application provides a method and apparatus for processing an adaptive count rate, which can dynamically adjust the metering mode to improve the adaptability of the measurement system under different intensity fields.

[0005] In a first aspect of the present application, a method for processing an adaptive count rate is provided, the method comprising: Get the current cumulative time and cumulative number of particles of continuous monitoring; Set multiple time thresholds of different sizes and multiple count thresholds of different sizes; determining a counting strategy based on a sequential relationship between the cumulative time reaching the time threshold and the cumulative number of particles reaching the counting threshold; Based on the counting strategy, the current average counting rate is calculated.

[0006] On the basis of the above technical solution, preferably, the setting of multiple time thresholds of different sizes and multiple counting thresholds of different sizes specifically includes: Setting a first time threshold and a second time threshold, wherein the value of the first time threshold is different from the value of the second time threshold, and the first time threshold is greater than the second time threshold or the second time threshold is greater than the first time threshold; A first counting threshold and a second counting threshold are set, wherein the value of the first counting threshold is different from the value of the second counting threshold, and the first counting threshold is greater than the second counting threshold or the second counting threshold is greater than the first counting threshold.

[0007] On the basis of the above technical solution, preferably, determining the counting strategy based on the order of the cumulative time reaching the time threshold and the cumulative number of particles reaching the counting threshold specifically includes: In an initial stage, a first time threshold and a second time threshold are set among the plurality of time thresholds, and a first counting threshold and a second counting threshold are set among the plurality of counting thresholds; Setting the first time threshold to be smaller than the second time threshold, and setting the first counting threshold to be smaller than the second counting threshold; Determining a magnitude relationship between the accumulated time and the first time threshold, and simultaneously determining a magnitude relationship between the accumulated particle count and the first counting threshold; If it is determined that the cumulative number of particles reaches the first counting threshold and the cumulative time is less than the first time threshold, determining to adopt a fixed number timing strategy; Continue to determine the relationship between the accumulated time and the first time threshold, and simultaneously determine the relationship between the accumulated particle count and the second counting threshold; If it is determined that the accumulated time reaches the first time threshold and the accumulated number of particles is less than the second counting threshold, it is determined to adopt the first counting strategy; or if it is determined that the accumulated number of particles reaches the second counting threshold and the accumulated time is less than the first time threshold, it is determined to adopt the second counting strategy.

[0008] Based on the above technical solution, preferably, after determining the relationship between the accumulated time and the first time threshold and determining the relationship between the accumulated number of particles and the first counting threshold, the method further includes: If it is determined that the accumulated time reaches the first time threshold and the accumulated particle number is less than the first counting threshold, a timing strategy is adopted; Continue to determine the relationship between the accumulated time and the second time threshold, and simultaneously determine the relationship between the accumulated particle count and the first counting threshold; If it is determined that the accumulated time reaches the second time threshold and the accumulated number of particles is less than the first counting threshold, it is determined to adopt the third counting strategy; or if it is determined that the accumulated number of particles reaches the first counting threshold and the accumulated time is less than the second time threshold, it is determined to adopt the fourth counting strategy.

[0009] On the basis of the above technical solution, preferably, the calculating of the current average counting rate based on the counting strategy further includes: Calculating a current average counting rate using the first counting strategy, specifically by dividing the cumulative number of particles by the first time threshold to calculate the average counting rate; Calculating a current average count rate using the second counting strategy, specifically dividing the second counting threshold by the accumulated time to calculate the average count rate; Calculating a current average counting rate using the third counting strategy, specifically dividing the cumulative number of particles by the second time threshold to calculate the average counting rate; The fourth counting strategy is used to calculate the current average counting rate, specifically by dividing the first counting threshold by the accumulated time to calculate the average counting rate.

[0010] On the basis of the above technical solution, preferably, the setting of multiple time thresholds of different sizes and multiple counting thresholds of different sizes further includes: Obtaining the preset relative limit of counting rate error and the expected statistical confidence level; Under Poisson distribution, a counting error is determined according to the historical cumulative number of particles, where the counting error is the square root of the historical cumulative number of particles; determining a counting rate error based on the counting error and the historical cumulative particle count; When the counting rate error is less than or equal to the counting rate error relative limit, determining the historical cumulative particle count as the first counting threshold; Calculating a particle growth rate based on a plurality of the historical accumulated particle numbers; According to the particle growth rate, the expected number of particles that meets the expected statistical confidence level within a preset time period is calculated to obtain the second counting threshold.

[0011] On the basis of the above technical solution, preferably, the setting of multiple time thresholds of different sizes and multiple counting thresholds of different sizes further includes: Get multiple historical cumulative particle counts; Calculating an average flux intensity based on a change rate calculated from the plurality of historical cumulative particle counts and a collection time of the plurality of historical cumulative particle counts; Calculating a minimum observation time according to a quotient of a preset expected minimum count number and the average flux intensity to obtain the first time threshold; Based on a preset redundant time compensation mechanism, the second time threshold is calculated according to the first time threshold.

[0012] In a second aspect of the present application, an adaptive count rate processing device is provided, the device being configured to execute any one of the above-described adaptive count rate processing methods, the device comprising an acquisition module, a processing module, and an output module, wherein: The acquisition module is used to obtain the current cumulative time and cumulative particle number of continuous monitoring; The processing module is used to set multiple time thresholds of different sizes and multiple counting thresholds of different sizes; The processing module is configured to determine a counting strategy based on a sequential relationship between the cumulative time reaching the time threshold and the cumulative number of particles reaching the counting threshold; The output module is used to calculate the current average counting rate based on the counting strategy.

[0013] On the basis of the above technical solution, preferably, the acquisition module is used to set a first time threshold and a second time threshold, wherein the value of the first time threshold is different from the value of the second time threshold, the first time threshold is greater than the second time threshold or the second time threshold is greater than the first time threshold; The acquisition module is used to set a first counting threshold and a second counting threshold, wherein the value of the first counting threshold is different from the value of the second counting threshold, the first counting threshold is greater than the second counting threshold, or the second counting threshold is greater than the first counting threshold.

[0014] On the basis of the above technical solution, preferably, the acquisition module is used to set a first time threshold and a second time threshold among the multiple time thresholds, and set a first counting threshold and a second counting threshold among the multiple counting thresholds in the initial stage; The acquisition module is configured to set the first time threshold to be smaller than the second time threshold, and simultaneously set the first counting threshold to be smaller than the second counting threshold; The processing module is configured to determine a relationship between the accumulated time and the first time threshold, and simultaneously determine a relationship between the accumulated number of particles and the first counting threshold; The processing module is configured to determine to adopt a fixed number timing strategy if it is determined that the cumulative number of particles reaches the first counting threshold and the cumulative time is less than the first time threshold; The processing module is configured to continue determining a relationship between the accumulated time and the first time threshold, and simultaneously determine a relationship between the accumulated particle count and the second counting threshold; The processing module is configured to determine to adopt a first counting strategy if it is determined that the accumulated time reaches the first time threshold and the accumulated particle count is less than the second counting threshold; or to determine to adopt a second counting strategy if it is determined that the accumulated particle count reaches the second counting threshold and the accumulated time is less than the first time threshold.

[0015] On the basis of the above technical solution, preferably, the output module is used to adopt a timing strategy if it is determined that the cumulative time reaches the first time threshold and the cumulative number of particles is less than the first counting threshold; The processing module is configured to continue determining a relationship between the accumulated time and the second time threshold, and simultaneously determine a relationship between the accumulated number of particles and the first counting threshold; The processing module is configured to determine to adopt a third counting strategy if it is determined that the accumulated time reaches the second time threshold and the accumulated particle count is less than the first counting threshold; or to determine to adopt a fourth counting strategy if it is determined that the accumulated particle count reaches the first counting threshold and the accumulated time is less than the second time threshold.

[0016] On the basis of the above technical solution, preferably, the processing module is used to calculate the current average counting rate using the first counting strategy, specifically by dividing the cumulative number of particles by the first time threshold to calculate the average counting rate; The processing module is configured to calculate a current average counting rate using the second counting strategy, specifically by dividing the second counting threshold by the accumulated time to calculate the average counting rate; The processing module is configured to calculate a current average counting rate using the third counting strategy, specifically by dividing the cumulative number of particles by the second time threshold to calculate the average counting rate; The processing module is configured to calculate a current average counting rate using the fourth counting strategy, specifically by dividing the first counting threshold by the accumulated time to calculate the average counting rate.

[0017] On the basis of the above technical solution, preferably, the acquisition module is used to obtain a preset counting rate error relative limit and an expected statistical confidence level; The processing module is configured to determine a counting error based on a historical cumulative number of particles under a Poisson distribution, wherein the counting error is a square root of the historical cumulative number of particles; The processing module is configured to determine a counting rate error based on the counting error and the historical cumulative particle count; The processing module is configured to determine that the historical cumulative particle count is the first counting threshold when the counting rate error is less than or equal to the counting rate error relative limit; The processing module is used to calculate the particle growth rate according to the plurality of historical accumulated particle numbers; The processing module is configured to calculate an expected number of particles meeting the expected statistical confidence level within a preset time period according to the particle growth rate, and obtain the second counting threshold.

[0018] On the basis of the above technical solution, preferably, the acquisition module is used to obtain multiple historical cumulative particle counts; The processing module is configured to calculate an average flux intensity based on a change rate calculated from the plurality of historical cumulative particle counts and a collection time duration of the plurality of historical cumulative particle counts; The processing module is configured to calculate a minimum observation time according to a quotient of a preset expected minimum count number and the average flux intensity to obtain the first time threshold; The processing module is configured to calculate the second time threshold according to the first time threshold based on a preset redundant time compensation mechanism.

[0019] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0020] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0021] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application introduces multiple time and count thresholds of varying sizes, and dynamically determines the currently applicable counting strategy based on the order in which the accumulated time and the accumulated particle count reach their respective thresholds. This allows the system to adaptively switch between timed and fixed-count counting, automatically adjusting the measurement method based on the actual particle flux intensity. In high-flux environments, short times or large counts are prioritized for rapid response, while in low-flux environments, the sampling period is extended or the count accumulation is increased to ensure statistical accuracy. This achieves precise response and real-time adaptation to a wide dynamic particle field, significantly improving the flexibility, reliability, and versatility of the measurement system.

[0022] 2. By setting two time thresholds and two count thresholds with different values, a multi-level judgment basis is provided for subsequent strategy switching, giving the measurement system flexible trigger boundaries, thereby supporting rapid adaptive responses in different flux fields and improving the refinement of system strategy selection.

[0023] 3. By setting the precedence judgment relationship between two time thresholds and two counting thresholds, an adaptive strategy framework based on the timing and counting triggering logic is constructed, which can automatically switch the counting mode according to the flux level in actual measurement, effectively balancing the response time and statistical accuracy.

[0024] 4. It further covers the situation where the cumulative time is reached first but the number of particles is insufficient, expands the processing path under low-flux fields, and generates the third and fourth counting strategies through delayed observation or early termination judgment, which significantly enhances the fault tolerance and robustness of the system under sparse counting conditions.

[0025] 5. The calculation method of the average counting rate under the four counting strategies is clarified, so that an accurate correspondence is established between the counting strategy and the calculation model, ensuring that consistent and statistically valid counting rate estimation results can be output based on the determined parameters under any judgment path.

[0026] 6. By introducing the relative limit of counting rate error and the statistical confidence level, a dynamic counting threshold generation mechanism based on the Poisson distribution error model is constructed, so that the first counting threshold and the second counting threshold have statistical constraint logic, realizing the transformation of threshold from empirical setting to data-driven adjustment, and improving the system's precision control capability.

[0027] 7. The particle flux intensity is estimated based on the historical cumulative particle count, and the first time threshold is inferred from the expected count. The second time threshold is then generated through redundancy compensation. This makes the time threshold data-adaptable and error-tolerant, effectively ensuring that observation time that meets accuracy requirements can be accumulated under different flux fields, thereby enhancing the stability and availability of the measurement system in low-flux conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 1 is a flow chart of an adaptive count rate processing method disclosed in an embodiment of the present application; Figure 2 This is a module diagram of an adaptive count rate processing device disclosed in an embodiment of the present application; Figure 3 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0029] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0031] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0032] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0033] In applications such as radiation measurement, particle detection, and industrial inspection, in order to achieve quantitative assessment of particle flux or radiation intensity, it is necessary to count the electrical signal pulses output by the detector in real time and calculate the average count rate. However, existing hybrid counting methods generally use a single threshold as the judgment basis, which is difficult to cope with the wide dynamic count rate changes from extremely low to extremely high in practice. In particular, when the flux fluctuates or the target is unstable, response lag or insufficient accuracy are prone to occur, limiting the system's adaptability and measurement reliability in high-performance detection and real-time diagnosis.

[0034] This embodiment discloses a method for processing an adaptive count rate, referring to Figure 1 , including the following steps S110-S140: S110, obtaining the current cumulative time and cumulative number of particles of continuous monitoring.

[0035] The adaptive count rate processing method disclosed in the embodiments of the present application is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, and personal computers (PCs), and may also be a background server that runs the adaptive count rate processing method. The server may be implemented as a standalone server or a server cluster consisting of multiple servers.

[0036] Before starting the measurement task, the time recording unit and particle counting unit are initialized, and the starting timestamp and initial count value are synchronously set to zero. During the measurement cycle, the time recording unit continuously accumulates time intervals using a high-precision real-time clock and updates the current accumulated time in real time, ensuring the stability and linear traceability of the time record. At the same time, the particle counting unit receives the electrical signal pulses output by the radiation detector through a signal interface connected to the detector. It uses a preamplifier, shaping circuit, and selection logic to identify valid pulses and increments the current accumulated particle count by one each time a valid pulse arrives, forming real-time incremental particle count data. The time recording unit and particle counting unit are synchronized and scheduled by the master control unit to ensure that the current accumulated time and current accumulated particle count can be accurately read at any time. These are used for subsequent threshold judgment, counting strategy selection, and average count rate calculation, thus forming the basic input for the entire measurement process.

[0037] S120: Set multiple time thresholds of different sizes and multiple counting thresholds of different sizes.

[0038] In one possible implementation, multiple time thresholds of different sizes and multiple counting thresholds of different sizes are set, specifically including: setting a first time threshold and a second time threshold, wherein the value of the first time threshold is different from the value of the second time threshold, the first time threshold is greater than the second time threshold or the second time threshold is greater than the first time threshold; setting a first counting threshold and a second counting threshold, wherein the value of the first counting threshold is different from the value of the second counting threshold, the first counting threshold is greater than the second counting threshold or the second counting threshold is greater than the first counting threshold.

[0039] Specifically, two time parameters with different values ​​are received as user input or loaded through the configuration module, serving as the first and second time thresholds, respectively, to establish a multi-level time judgment mechanism. The relationship between the first and second time thresholds can be flexibly configured based on measurement requirements. The first time threshold can be set smaller than the second time threshold to implement a gradual delay judgment, or it can be set larger than the second time threshold to accommodate a reverse triggering strategy. These two time thresholds are written into the time judgment module and dynamically compared with the real-time accumulated time, providing a basis for switching counting strategies.

[0040] Accepts user input or predefined two different particle count parameters as the first and second counting thresholds to establish multi-level particle count trigger logic. The numerical difference between the first and second counting thresholds ensures a graded response at different flux levels. Depending on the usage scenario, the first counting threshold can be lower than the second counting threshold to prioritize low-flux responses, or vice versa to ensure statistical integrity at high flux levels. The first and second counting thresholds are input into the counting judgment module and compared with the current cumulative particle count in real time, providing conditional triggering for measurement path selection and average count rate calculation.

[0041] In one possible embodiment, multiple time thresholds of varying sizes and multiple counting thresholds of varying sizes are set, and the method further includes: obtaining a preset relative limit of a counting rate error and a desired statistical confidence level; determining a counting error based on a historical cumulative particle count under a Poisson distribution, where the counting error is the square root of the historical cumulative particle count; determining a counting rate error based on the counting error and the historical cumulative particle count; determining the historical cumulative particle count as a first counting threshold when the counting rate error is less than or equal to the relative limit of the counting rate error; calculating a particle growth rate based on the multiple historical cumulative particle counts; and calculating, based on the particle growth rate, an expected particle count that satisfies the desired statistical confidence level within a preset time period to obtain a second counting threshold.

[0042] Specifically, the system first receives two key user-defined statistical control parameters: the relative limit for the count rate error and the desired statistical confidence level. The relative limit for the count rate error represents the user's maximum allowable relative error in the average count rate calculation, for example, 5% or 3%. The desired statistical confidence level represents the required probability of statistical interval coverage, such as 95% or 99%. These two parameters are written into the accuracy control module and serve as target constraints for subsequent dynamic threshold generation, driving the dynamic derivation of the first and second count thresholds.

[0043] During the measurement cycle, the historical cumulative particle count is read in real time. Based on Poisson statistics, the square root rule is used to calculate the current counting error. This is done by taking the square root of the historical cumulative particle count to obtain the counting error corresponding to that period. This counting error, as the statistical standard deviation, reflects the degree of random fluctuation in particle events. It serves as the fundamental quantitative basis for assessing whether the current data meets accuracy constraints and serves as an input variable in the error determination module.

[0044] The counting error and the historical cumulative particle count are then used to calculate the corresponding counting rate error. The counting rate error is defined as the ratio of the counting error to the particle count. This is the square root of the result divided by the current cumulative particle count, forming a relative error expression. This value represents the statistical uncertainty of the average counting rate and is compared with the preset relative limit for the counting rate error. This is the key criterion for determining whether the first counting threshold meets the required accuracy.

[0045] If the currently calculated count rate error is less than or equal to the preset count rate error relative limit, the current historical cumulative particle count is determined to have met the confidence accuracy requirement. This historical cumulative particle count is then locked as the first count threshold and transmitted to the count judgment module as the basis for subsequent path determination. If this threshold is not met, particle count accumulation continues until the relative error falls within the target range, thereby ensuring the statistical validity of the first count threshold.

[0046] To obtain the second counting threshold, a sliding window is constructed based on the historical particle counts and historical time series. The particle growth rate (i.e., the growth rate of the number of particles per unit time) is calculated over multiple time periods. This particle growth rate is used to characterize the flux trend, serving as a reference for predicting future particle growth potential. This growth rate is then written into the flux estimation module for subsequent deduction.

[0047] Based on the aforementioned particle growth rate and the expected statistical confidence level, we further deduce the expected number of particles to be observed within a predetermined time period. This expected particle count must meet statistical stability requirements at a high confidence level and be separated from the first counting threshold to ensure a multi-level particle count triggering structure. This predicted particle count serves as the second counting threshold and is written into the threshold judgment module. Ultimately, the first counting threshold controls the minimum accuracy, while the second counting threshold controls the maximum accuracy. Together, they form an adaptive particle event judgment system driven by statistical constraints.

[0048] In one possible implementation, multiple time thresholds of different sizes and multiple count thresholds of different sizes are set, and specifically the following steps are included: obtaining multiple historical cumulative particle counts; calculating an average flux intensity based on a change rate calculated from the multiple historical cumulative particle counts and a collection time of the multiple historical cumulative particle counts; calculating a minimum observation time based on a quotient of a preset expected minimum count number and the average flux intensity to obtain a first time threshold; and calculating a second time threshold based on the first time threshold based on a preset redundant time compensation mechanism.

[0049] Specifically, multiple historical cumulative particle count samples are first retrieved from the historical data cache module. Each sample corresponds to the cumulative particle count within a separate time period. This historical cumulative particle count is generated by periodically sampling and archiving the detector's particle response data over different time intervals, forming a basic data sequence that reflects the dynamic characteristics of the particle flux. These data samples are arranged in a time series and passed as input variables to the flux inference module for flux intensity estimation.

[0050] The difference between each two adjacent historical cumulative particle counts is then taken, and combined with the corresponding acquisition duration, the particle growth rate for each time period is calculated. The particle growth rate is the change in particle count per unit time, reflecting the macroscopic growth trend of the current particle flux. A weighted average or sliding mean of all particle growth rates is then taken to obtain a stable average flux intensity estimate. This value is considered to be the representative particle flux response level under the current measurement environment and serves as the physical basis for calculating the time threshold.

[0051] Based on the set expected minimum number of counts, this minimum number of counts serves as the minimum statistical confidence requirement to ensure that the average count rate estimate has sufficient error constraints. Dividing this minimum number of counts by the previously calculated average flux intensity yields the theoretical minimum observation time required to meet the accuracy requirements, i.e., the first time threshold. This first time threshold, output by the accuracy-driven model, serves as the fundamental duration indicator in the dynamic adjustment mechanism, ensuring that sufficient particles can be accumulated to meet the error constraints given the given flux conditions.

[0052] After obtaining the first time threshold, the redundant time compensation mechanism is further invoked to derive the second time threshold based on the first time threshold. This mechanism multiplies the first time threshold by a configured redundancy factor, such as 1.5 or 2, to obtain the second time threshold. This ensures that in low-flux conditions, if the minimum statistical requirement is not met within the first time threshold, the measurement period can still be automatically extended to compensate for the insufficient error accumulation caused by particle sparsity. Ultimately, the first time threshold controls the minimum response period, and the second time threshold controls the maximum sampling tolerance time, together forming a dynamic duration judgment structure that meets error constraints and statistical confidence requirements.

[0053] S130 , determining a counting strategy based on a sequential relationship between the cumulative time reaching the time threshold and the cumulative number of particles reaching the counting threshold.

[0054] In one possible embodiment, determining a counting strategy based on a sequential relationship between the cumulative time reaching a time threshold and the cumulative number of particles reaching a counting threshold specifically includes: in an initial stage, setting a first time threshold and a second time threshold among a plurality of time thresholds, and setting a first counting threshold and a second counting threshold among a plurality of counting thresholds; setting the first time threshold to be less than the second time threshold, and simultaneously setting the first counting threshold to be less than the second counting threshold; determining a magnitude relationship between the cumulative time and the first time threshold, and simultaneously determining a magnitude relationship between the cumulative number of particles and the first counting threshold; if it is determined that the cumulative number of particles reaches the first counting threshold and the cumulative time is less than the first time threshold, determining to adopt a fixed-number timing strategy; continuing to determine a magnitude relationship between the cumulative time and the first time threshold, and simultaneously determining a magnitude relationship between the cumulative number of particles and the second counting threshold; if it is determined that the cumulative time reaches the first time threshold and the cumulative number of particles is less than the second counting threshold, determining to adopt the first counting strategy; or if it is determined that the cumulative number of particles reaches the second counting threshold and the cumulative time is less than the first time threshold, determining to adopt the second counting strategy.

[0055] Specifically, during the initialization phase, the measurement control unit loads two sets of decision thresholds for triggering the counting strategy: the first and second time thresholds, and the first and second count thresholds. These four parameters are set by the user or a higher-level authority and written into the time judgment module and the count judgment module, forming two independent but interconnected decision domains. The first time threshold is set lower than the second time threshold, and the first count threshold is set lower than the second count threshold, forming a low-to-high decision structure that supports flexible transitions under different particle fluxes.

[0056] During the measurement process, the control unit obtains the current cumulative time and the current cumulative particle count in real time, compares the current cumulative time with the first time threshold, and simultaneously compares the current cumulative particle count with the first counting threshold. If the current cumulative particle count reaches the first counting threshold but the cumulative time has not yet reached the first time threshold, the current environment is determined to be in a medium-to-high flux state, and the fixed-count timing strategy is prioritized. Measurements continue to track subsequent trigger conditions and determine the final average count rate calculation method.

[0057] After the above-mentioned fixed-count timing strategy is confirmed, the current accumulated time is continuously compared with the first time threshold, and the current accumulated particle count is continuously compared with the second counting threshold. If the accumulated time subsequently reaches the first time threshold but the accumulated particle count has not yet reached the second counting threshold, the accumulated particle count at that time is locked and divided by the first time threshold. The first counting strategy is then executed to calculate the average count rate. At this point, the response time is the first time threshold, which is suitable for scenarios with medium throughput and reasonable statistical confidence.

[0058] If, during continued measurement, the current cumulative particle count reaches the second counting threshold before the cumulative time, while the cumulative time has not yet reached the first time threshold, the second counting threshold is locked as the valid particle count, and the second counting strategy is executed to calculate the average count rate by dividing the second counting threshold by the cumulative time at that time. At this point, the response time is less than the first time threshold, making it suitable for quickly obtaining high-confidence results under high-throughput conditions. Through this conditional judgment path and strategy response mechanism, high adaptability to dynamic changes in particle flux is achieved, and optimal strategy selection and a trade-off between accuracy and timeliness are achieved.

[0059] In one possible embodiment, after determining the relationship between the accumulated time and the first time threshold and simultaneously determining the relationship between the accumulated particle count and the first counting threshold, the method further includes: if it is determined that the accumulated time has reached the first time threshold and the accumulated particle count is less than the first counting threshold, adopting a fixed timing strategy; continuing to determine the relationship between the accumulated time and the second time threshold and simultaneously determining the relationship between the accumulated particle count and the first counting threshold; if it is determined that the accumulated time has reached the second time threshold and the accumulated particle count is less than the first counting threshold, determining to adopt a third counting strategy; or if it is determined that the accumulated particle count has reached the first counting threshold and the accumulated time is less than the second time threshold, determining to adopt a fourth counting strategy.

[0060] Specifically, when the current cumulative time reaches the first time threshold but the cumulative particle count has not yet reached the first counting threshold, the system identifies a low particle flux state and prioritizes the use of a timed timing strategy to ensure the accumulation of basic statistics. At this point, the first time threshold is fixed as the time reference, and the cumulative particle count at that time is read. The current average count rate is calculated by dividing this cumulative particle count by the first time threshold. This path provides a baseline, effective measurement value under extremely low flux conditions and serves as a starting point for subsequent strategy transitions.

[0061] After executing the timing strategy, the current accumulated time continues to be updated in real time and compared with the second time threshold. Simultaneously, the current accumulated particle count continues to be monitored and compared with the first counting threshold. If the accumulated time reaches the second time threshold but the accumulated particle count has not yet reached the first counting threshold, it indicates that the flux level remains low and the statistical confidence level remains insufficient. At this point, the third counting strategy is executed, calculating the average count rate by dividing the current accumulated particle count by the second time threshold. The second time threshold is then output as the response time, thereby completing a quantitative assessment of low flux within the maximum waiting period.

[0062] If the current cumulative particle count reaches the first counting threshold before the accumulated time reaches the second time threshold, it is determined that although the particle flux is not high, it has reached the minimum statistically valid standard before the delayed response. At this time, the fourth counting strategy is executed, using the first counting threshold as the particle count benchmark and dividing this particle count by the current accumulated time to complete the average counting rate calculation. This strategy has a response time earlier than the second time threshold and is suitable for achieving reliable measurement in slowly rising flux environments, thereby optimizing the measurement response speed while ensuring statistical validity. This branching mechanism ensures that idle waiting time is minimized and counting data validity is maintained within the low-flux dynamic range.

[0063] S140: Calculate the current average counting rate based on the counting strategy.

[0064] In one possible implementation, calculating the current average counting rate based on the counting strategy specifically further includes: using a first counting strategy to calculate the current average counting rate, specifically dividing the cumulative number of particles by a first time threshold to calculate the average counting rate; and using a second counting strategy to calculate the current average counting rate, specifically dividing the second counting threshold by the accumulated time to calculate the average counting rate.

[0065] Specifically, after adopting the first counting strategy, the control unit first strictly compares the current cumulative time with the first time threshold and locks the cumulative particle count at that time as the completed particle count. The measurement and calculation module uses this cumulative particle count as the numerator and the first time threshold as the denominator, performs a standard division operation, and generates the current average count rate data. This average count rate data represents the density of particle events per unit time within the set time period, and is used to describe the instantaneous intensity level of the current radiation field or particle flux. It is then transmitted to the display module, alarm module, or higher-level device for subsequent processing.

[0066] After determining to adopt the second counting strategy, the control unit locks the second counting threshold as a fixed statistical particle number, and reads the current accumulated time as the actual time length of the measurement cycle. The measurement calculation module uses the second counting threshold as the numerator and the accumulated time as the denominator, and performs a division calculation to obtain the current average counting rate. This strategy is suitable for situations with high particle flux or rapid statistical stability in a short period of time. It can achieve fast response and output a highly reliable particle density assessment value. The obtained average counting rate is used to drive the dynamic adjustment mechanism, such as adjusting the integration period, controlling the threshold discrimination sensitivity, or performing dose estimation, to ensure accuracy and efficiency in different particle field environments.

[0067] In one possible implementation, a counting strategy is determined based on the order in which the cumulative time reaches the time threshold and the cumulative number of particles reaches the counting threshold. Specifically, the strategy also includes: using a third counting strategy to calculate the current average counting rate, specifically dividing the cumulative number of particles by the second time threshold to calculate the average counting rate; and using a fourth counting strategy to calculate the current average counting rate, specifically dividing the first counting threshold by the cumulative time to calculate the average counting rate.

[0068] Specifically, when the third counting strategy is determined to be necessary, the control unit uses the second time threshold as a fixed time reference, simultaneously reads the current cumulative particle count, and locks it as the number of particles at the time the count is completed. Upon receiving this cumulative particle count, the measurement and calculation module uses it as the numerator and the second time threshold as the denominator to calculate the average count rate. This count rate represents the actual detected particle flux intensity within the set maximum response time. It is suitable for ensuring measurement integrity in extremely low flux conditions and provides basic particle flux estimation data for subsequent evaluation, display, and data recording modules.

[0069] When it is determined that the fourth counting strategy needs to be implemented, the control unit uses the first counting threshold as a fixed particle number standard, simultaneously reads the current accumulated time, and uses this accumulated time as the actual measurement period. The measurement calculation module uses the first counting threshold as the numerator and the accumulated time as the denominator to perform a division operation to obtain the current average counting rate. This strategy is used to achieve the minimum statistical confidence requirement in advance in environments with gradually increasing flux or edge fluctuations, while also shortening the response cycle and improving real-time performance and data update frequency. The final calculated average counting rate result is used to drive the subsequent discrimination logic or generate dynamic control instructions to ensure an adaptive operating state between stability and speed.

[0070] This embodiment also discloses an adaptive counting rate processing device, referring to Figure 2 , comprising an acquisition module 201, a processing module 202 and an output module 203, the device is used to perform any of the above-mentioned adaptive count rate processing methods, wherein: The acquisition module 201 is used to obtain the current cumulative time and cumulative number of particles of continuous monitoring.

[0071] The processing module 202 is configured to set a plurality of time thresholds of different sizes and a plurality of counting thresholds of different sizes.

[0072] The processing module 202 is configured to determine a counting strategy based on a sequential relationship between the cumulative time reaching the time threshold and the cumulative number of particles reaching the counting threshold.

[0073] The output module 203 is configured to calculate the current average counting rate based on the counting strategy.

[0074] In a possible implementation, the acquisition module 201 is used to set a first time threshold and a second time threshold, wherein the value of the first time threshold is different from the value of the second time threshold, the first time threshold is greater than the second time threshold, or the second time threshold is greater than the first time threshold.

[0075] The acquisition module 201 is configured to set a first counting threshold and a second counting threshold, wherein the value of the first counting threshold is different from the value of the second counting threshold, the first counting threshold is greater than the second counting threshold or the second counting threshold is greater than the first counting threshold.

[0076] In a possible implementation, the acquisition module 201 is configured to set a first time threshold and a second time threshold among a plurality of time thresholds, and set a first counting threshold and a second counting threshold among a plurality of counting thresholds in an initial stage.

[0077] The acquisition module 201 is configured to set the first time threshold to be smaller than the second time threshold, and simultaneously set the first counting threshold to be smaller than the second counting threshold.

[0078] The processing module 202 is used to determine the relationship between the accumulated time and the first time threshold, and to determine the relationship between the accumulated number of particles and the first counting threshold.

[0079] The processing module 202 is configured to determine to adopt a fixed number timing strategy if it is determined that the cumulative number of particles reaches a first counting threshold and the cumulative time is less than a first time threshold.

[0080] The processing module 202 is used to continue to determine the relationship between the accumulated time and the first time threshold, and at the same time determine the relationship between the accumulated particle count and the second counting threshold.

[0081] Processing module 202 is configured to determine to adopt a first counting strategy if it is determined that the accumulated time has reached a first time threshold and the accumulated particle count is less than a second counting threshold. Alternatively, if it is determined that the accumulated particle count has reached a second counting threshold and the accumulated time is less than the first time threshold, determine to adopt a second counting strategy.

[0082] In a possible implementation, the output module 203 is configured to adopt a timing strategy if it is determined that the accumulated time reaches a first time threshold and the accumulated number of particles is less than a first counting threshold.

[0083] The processing module 202 is used to continue to determine the relationship between the accumulated time and the second time threshold, and at the same time determine the relationship between the accumulated number of particles and the first counting threshold.

[0084] Processing module 202 is configured to determine whether to adopt a third counting strategy if it is determined that the cumulative time has reached the second time threshold and the cumulative number of particles is less than the first counting threshold. Alternatively, if it is determined that the cumulative number of particles has reached the first counting threshold and the cumulative time is less than the second time threshold, determine whether to adopt a fourth counting strategy.

[0085] In a possible implementation, the processing module 202 is configured to calculate a current average counting rate using a first counting strategy, specifically by dividing the cumulative number of particles by a first time threshold to calculate the average counting rate.

[0086] The processing module 202 is configured to calculate the current average counting rate using a second counting strategy, specifically by dividing the second counting threshold by the accumulated time to calculate the average counting rate.

[0087] The processing module 202 is configured to calculate the current average counting rate using a third counting strategy, specifically by dividing the accumulated number of particles by the second time threshold to calculate the average counting rate.

[0088] The processing module 202 is configured to calculate the current average counting rate using a fourth counting strategy, specifically by dividing the first counting threshold by the accumulated time to calculate the average counting rate.

[0089] In a possible implementation, the acquisition module 201 is configured to acquire a preset count rate error relative limit and an expected statistical confidence level.

[0090] The processing module 202 is configured to determine a counting error based on the historical cumulative number of particles under a Poisson distribution, where the counting error is the square root of the historical cumulative number of particles.

[0091] The processing module 202 is configured to determine a counting rate error based on the counting error and the historical accumulated particle count.

[0092] The processing module 202 is configured to determine that the historical cumulative particle count is a first counting threshold when the counting rate error is less than or equal to the counting rate error relative limit.

[0093] The processing module 202 is configured to calculate a particle growth rate based on a plurality of historical accumulated particle numbers.

[0094] The processing module 202 is configured to calculate an expected number of particles that meets an expected statistical confidence level within a preset time period according to the particle growth rate, and obtain a second counting threshold.

[0095] In a possible implementation, the acquisition module 201 is configured to acquire multiple historical cumulative particle counts.

[0096] The processing module 202 is configured to calculate an average flux intensity based on a change rate calculated from a plurality of historical cumulative particle counts and a collection time of the plurality of historical cumulative particle counts.

[0097] The processing module 202 is configured to calculate a minimum observation time according to a quotient of a preset expected minimum count number and an average flux intensity to obtain a first time threshold.

[0098] The processing module 202 is configured to calculate a second time threshold according to the first time threshold based on a preset redundant time compensation mechanism.

[0099] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0100] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0101] The communication bus 302 is used to implement the connection and communication between these components.

[0102] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0103] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0104] The processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and circuits to connect various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and application programs. The GPU is responsible for rendering and drawing content displayed on the display screen. The modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0105] Memory 305 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory may include non-transitory computer-readable storage medium. Memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. Memory 305 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, sound playback function, image playback function, etc.), instructions for implementing the aforementioned method embodiments, and the like. The data storage area may store data related to the aforementioned method embodiments, and the like. Memory 305 may also optionally be at least one storage device located remotely from the aforementioned processor 301. Memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface 303 module, and an application program for an adaptive count rate processing method.

[0106] exist Figure 3In the electronic device shown, user interface 303 is primarily used to provide an input interface for the user and to obtain user input data. Processor 301 can be used to invoke an application stored in memory 305 that includes an adaptive count rate processing method. When executed by one or more processors 301, the electronic device executes one or more of the methods described in the aforementioned embodiments.

[0107] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but 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 the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0108] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0110] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0111] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory 305 includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a magnetic disk, or an optical disk.

[0113] The present application also discloses a computer-readable storage medium storing instructions, which, when executed by one or more processors 301 , enable an electronic device to execute one or more methods in the above embodiments.

[0114] The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for processing an adaptive count rate, characterized in that: The method comprises: Get the current cumulative time and cumulative number of particles of continuous monitoring; Set multiple time thresholds of different sizes and multiple count thresholds of different sizes; determining a counting strategy based on a sequential relationship between the cumulative time reaching the time threshold and the cumulative number of particles reaching the counting threshold; Based on the counting strategy, the current average counting rate is calculated.

2. The method for processing an adaptive count rate according to claim 1, wherein: The setting of multiple time thresholds of different sizes and multiple counting thresholds of different sizes specifically includes: Setting a first time threshold and a second time threshold, wherein the value of the first time threshold is different from the value of the second time threshold, and the first time threshold is greater than the second time threshold or the second time threshold is greater than the first time threshold; A first counting threshold and a second counting threshold are set, wherein the value of the first counting threshold is different from the value of the second counting threshold, and the first counting threshold is greater than the second counting threshold or the second counting threshold is greater than the first counting threshold.

3. The method for processing an adaptive count rate according to claim 1, wherein: The determining of the counting strategy based on the order of the cumulative time reaching the time threshold and the cumulative number of particles reaching the counting threshold specifically includes: In an initial stage, a first time threshold and a second time threshold are set among the plurality of time thresholds, and a first counting threshold and a second counting threshold are set among the plurality of counting thresholds; Setting the first time threshold to be smaller than the second time threshold, and setting the first counting threshold to be smaller than the second counting threshold; Determining a magnitude relationship between the accumulated time and the first time threshold, and simultaneously determining a magnitude relationship between the accumulated particle count and the first counting threshold; If it is determined that the cumulative number of particles reaches the first counting threshold and the cumulative time is less than the first time threshold, determining to adopt a fixed number timing strategy; Continue to determine the relationship between the accumulated time and the first time threshold, and simultaneously determine the relationship between the accumulated particle count and the second counting threshold; If it is determined that the accumulated time reaches the first time threshold and the accumulated number of particles is less than the second counting threshold, it is determined to adopt the first counting strategy; or if it is determined that the accumulated number of particles reaches the second counting threshold and the accumulated time is less than the first time threshold, it is determined to adopt the second counting strategy.

4. The method for processing an adaptive count rate according to claim 3, wherein: After determining the relationship between the accumulated time and the first time threshold, and determining the relationship between the accumulated number of particles and the first counting threshold, the method further includes: If it is determined that the accumulated time reaches the first time threshold and the accumulated particle number is less than the first counting threshold, a timing strategy is adopted; Continue to determine the relationship between the accumulated time and the second time threshold, and simultaneously determine the relationship between the accumulated particle count and the first counting threshold; If it is determined that the accumulated time reaches the second time threshold and the accumulated number of particles is less than the first counting threshold, it is determined to adopt the third counting strategy; or if it is determined that the accumulated number of particles reaches the first counting threshold and the accumulated time is less than the second time threshold, it is determined to adopt the fourth counting strategy.

5. The method for processing an adaptive count rate according to claim 4, wherein: The calculating of the current average counting rate based on the counting strategy specifically further includes: Calculating a current average counting rate using the first counting strategy, specifically by dividing the cumulative number of particles by the first time threshold to calculate the average counting rate; Calculating a current average count rate using the second counting strategy, specifically dividing the second counting threshold by the accumulated time to calculate the average count rate; Calculating a current average counting rate using the third counting strategy, specifically dividing the cumulative number of particles by the second time threshold to calculate the average counting rate; The fourth counting strategy is used to calculate the current average counting rate, specifically by dividing the first counting threshold by the accumulated time to calculate the average counting rate.

6. The method for processing an adaptive count rate according to claim 2, wherein: The step of setting multiple time thresholds of different sizes and multiple counting thresholds of different sizes specifically includes: Obtaining the preset relative limit of counting rate error and the expected statistical confidence level; Under Poisson distribution, a counting error is determined according to the historical cumulative number of particles, where the counting error is the square root of the historical cumulative number of particles; determining a counting rate error based on the counting error and the historical cumulative particle count; When the counting rate error is less than or equal to the counting rate error relative limit, determining the historical cumulative particle count as the first counting threshold; Calculating a particle growth rate based on a plurality of the historical accumulated particle numbers; According to the particle growth rate, the expected number of particles that meets the expected statistical confidence level within a preset time period is calculated to obtain the second counting threshold.

7. The method for processing an adaptive count rate according to claim 2, wherein: The step of setting multiple time thresholds of different sizes and multiple counting thresholds of different sizes specifically includes: Get multiple historical cumulative particle counts; Calculating an average flux intensity based on a change rate calculated from the plurality of historical cumulative particle counts and a collection time of the plurality of historical cumulative particle counts; Calculating a minimum observation time according to a quotient of a preset expected minimum count number and the average flux intensity to obtain the first time threshold; Based on a preset redundant time compensation mechanism, the second time threshold is calculated according to the first time threshold.

8. An adaptive count rate processing device, characterized in that The device is used to execute an adaptive count rate processing method according to any one of claims 1 to 7, and the device comprises an acquisition module (201), a processing module (202), and an output module (203), wherein: The acquisition module (201) is used to acquire the current cumulative time and cumulative number of particles under continuous monitoring; The processing module (202) is used to set a plurality of time thresholds of different sizes and a plurality of counting thresholds of different sizes; The processing module (202) is configured to determine a counting strategy based on a sequential relationship between the cumulative time reaching the time threshold and the cumulative number of particles reaching the counting threshold; The output module (203) is used to calculate the current average counting rate based on the counting strategy.

9. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a user interface (303), a network interface (304) and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are both used to communicate with other devices, the communication bus (302) is used to realize connection and communication between components in the electronic device, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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