Real-time measurement method for double-window dynamic switching of activity meter

Abstract

The invention discloses a real-time measurement method for double-window dynamic switching of an activity meter, which comprises the following steps of: 1) acquiring original activity data of a sample from a signal processing module of a radiation detector by the activity meter according to a fixed sampling period to obtain a sequence D; (2) a long window is maintained and used for obtaining N recent sampling points in the sequence D, the arithmetic mean value of sampling data in the current long window is calculated, and a long window value L is obtained; a short window is maintained in parallel and used for obtaining M recent sampling points in the current sequence D, the arithmetic mean value of sampling data in the current short window is calculated, and a short window value S is obtained; and 3) calculating an absolute difference value Diff between the long window value L and the short window value S, then judging whether Diffgt, an absolute threshold ThrA or Diffgt and a percentage threshold ThrB * L are satisfied in continuous K sampling periods, if so, switching the output to the short window value S, and if not, switching the output to the long window value L.

Description

Technical Field

[0001] This invention belongs to the field of nuclear instrumentation, radiation measurement and signal processing technology, and relates to a real-time data processing algorithm for activity meters. Specifically, it relates to a real-time measurement method for dynamic switching of dual windows for activity meters, which is used to solve the performance contradiction between steady-state measurement and dynamic response of activity meters, improve the measurement accuracy and operation response speed of activity meters, and is applicable to radioactivity measurement scenarios in fields such as nuclear medicine, radiochemistry, and nuclear power plants. Background Technology

[0002] As the core equipment for measuring radioactivity, the algorithm design of the data processing stage of the activity meter is crucial to ensuring measurement performance. The well-known data processing flow of the activity meter in this field is as follows: the radiation detector converts the nuclear radiation signal into an electrical signal, which is then quantized into a digital signal by an analog-to-digital converter (ADC) after signal conditioning circuitry (amplification, filtering, etc.), and sent to the microprocessor for data processing, ultimately outputting the radioactivity measurement value.

[0003] In terms of data processing algorithms, well-known techniques include moving average algorithms and low-pass filtering algorithms. Moving average algorithms smooth data by averaging multiple consecutive sampling points; low-pass filtering algorithms suppress high-frequency noise and improve data stability through frequency domain filtering. These algorithms are fundamental techniques for activity meter data processing.

[0004] The closest existing technical solution to this invention is the "activity meter measurement method with fixed single-window filtering", which is specifically divided into the following two categories.

[0005] (I) Implementation process of the long window fixed filtering method: During data processing, the microprocessor maintains a fixed window containing the most recent N sampling points (N is relatively large, for example, corresponding to a sampling duration of 10-30 seconds). The average value of the sampled data within the window is calculated, and this average value is output as the final activity measurement value. This method is suitable for steady-state activity measurement scenarios, such as when the sample is stably placed in the measurement chamber, it can obtain stable measurement results with small fluctuations.

[0006] (II) Implementation process of the short-window fixed filtering method: The microprocessor maintains a fixed window containing the most recent M sampling points (M is small, for example, corresponding to a sampling duration of 1-5 seconds). The average value of the sampling data within the window is calculated, and this average value is output as the final activity measurement value. This method is suitable for scenarios with dynamic changes in activity, such as when placing or removing samples, and can quickly track drastic changes in activity.

[0007] The existing technology has the following technical drawbacks.

[0008] 1. Incompatible performance: Although the long-window fixed filtering method can ensure high stability and accuracy of steady-state measurement, it is extremely slow to respond when the activity changes drastically (such as when placing / removing samples), and the reading "climbing" or "falling" process is lengthy, which seriously affects the operating efficiency; although the short-window fixed filtering method responds quickly, the measured value fluctuates greatly in steady state and the accuracy is insufficient, which cannot meet the requirements of precise measurement.

[0009] 2. Lack of adaptability: Existing technologies use a fixed window strategy, which cannot adaptively adjust the data processing method according to the actual changes in activity. Users can only manually switch based on experience or scenario (if the device supports it), which is cumbersome and prone to errors.

[0010] 3. Insufficient anti-interference capability: When faced with electromagnetic interference, signal glitches, etc., single-window filtering is prone to misjudgment. Long windows will smooth the interference but delay the response, while short windows will amplify the interference, causing the reading to fluctuate drastically. Summary of the Invention

[0011] In view of the problems existing in the prior art, the purpose of this invention is to provide a real-time measurement method for dynamic switching of dual windows in an activity meter.

[0012] This invention provides a method for parallel computation and dynamic switching of dual windows, enabling the activity meter to output a stable long window value in steady state and a fast-responding short window value when activity changes drastically. A reliable adaptive switching condition is designed, based on the absolute difference, relative ratio, and number of consecutive points between the long and short window values, to achieve accurate state identification and avoid erroneous switching. This invention ensures that the algorithm meets both performance requirements while possessing good anti-interference capabilities, guaranteeing the reliability of the measurement results.

[0013] This invention proposes a dual-window parallel computing architecture that maintains both a long window and a short window to obtain steady-state smooth values ​​and dynamic response values, respectively, providing a data foundation for dynamic switching.

[0014] This invention designs a triple switching condition based on absolute difference, relative ratio, and number of consecutive points to achieve accurate identification of transient and steady-state activity states, ensuring the reliability of switching.

[0015] This invention establishes a dynamic output decision mechanism that adaptively selects a long window value or a short window value for output based on the state recognition result, taking into account both steady-state accuracy and dynamic response.

[0016] The real-time measurement method for dynamic switching of dual windows in the activity meter of the present invention includes a three-step core process: data acquisition and parallel computing of dual windows, adaptive switching condition judgment, and dynamic output decision.

[0017] The dual-window parallel computing refers to simultaneously maintaining a long window containing the most recent N sampling points and a short window containing the most recent M sampling points, and calculating the long window value and the short window value respectively, where N corresponds to a duration greater than M.

[0018] The adaptive switching condition judgment refers to calculating the absolute difference between the long window value and the short window value, and judging whether the difference satisfies the absolute threshold condition or the relative ratio condition for K consecutive periods.

[0019] The dynamic output decision refers to outputting a short window value when the switching conditions are met, and outputting a long window value otherwise.

[0020] The technical solution of this invention is as follows: A real-time measurement method for dynamic switching of dual windows in an activity meter, comprising the following steps: 1) The activity meter obtains the raw activity data of the sample from the signal processing module of the radiation detector at a fixed sampling period, and obtains the sequence D = {d1, d2, ……, dn}; where di is the raw activity value at the i-th sampling time, i=1~n, and n is the total number of sampling points in the sampling period. 2) Maintain a long window to obtain the N most recent sampling points in the current sequence D, calculate the arithmetic mean of the sampled data within the current long window, and obtain the long window value L; maintain a short window in parallel to obtain the M most recent sampling points in the current sequence D, calculate the arithmetic mean of the sampled data within the current short window, and obtain the short window value S. 3) Calculate the absolute difference between the long window value L and the short window value S, Diff = |L - S|; then determine whether the absolute difference Diff in K consecutive sampling periods satisfies Diff > Thr_A or whether the absolute difference Diff in K consecutive sampling periods satisfies Diff > Thr_B × |L|; Thr_A is a preset absolute threshold, and Thr_B is a preset percentage threshold; if satisfied, the sample is currently in a state of transient activity change, and the short window value S of each sampling period is output as the activity measurement value of the activity meter in that sampling period to achieve rapid tracking of drastic activity changes; if not satisfied, the sample is currently in a steady state, and the long window value L of each sampling period is output as the activity measurement value of the activity meter in that sampling period to ensure the stability and accuracy of the activity measurement value.

[0021] Preferably, the absolute threshold Thr_A is set according to the accuracy requirements of the activity meter.

[0022] Preferably, the absolute threshold Thr_A is 37000Bq.

[0023] Preferably, the percentage threshold Thr_B is 10%.

[0024] Preferably, N is greater than M.

[0025] The advantages of this invention are as follows: 1. Simultaneously satisfying steady-state accuracy and dynamic response: This proposal employs parallel computation with dual windows and dynamic switching. In steady-state conditions, it outputs a long window value to ensure high measurement stability and accuracy; during periods of drastic activity change, it outputs a short window value for rapid response, completely resolving the contradiction in existing technologies where a single window cannot simultaneously achieve both performance goals. This advantage stems from the parallel architecture of the dual windows and the intelligent switching logic based on the difference.

[0026] 2. High handover reliability: Employing a triple judgment mechanism of "absolute difference + relative ratio + consecutive points," it effectively avoids erroneous handovers caused by momentary interference or signal glitches, ensuring the accuracy of handover timing. Compared to existing technologies with fixed windows or simple threshold judgments, its anti-interference capability is significantly improved. This advantage stems from the multi-dimensional design of handover conditions and continuous judgment logic.

[0027] 3. Low algorithm complexity and easy implementation: The algorithm logic for dual-window calculation and switching judgment is simple, with low computational load, and can be easily implemented on existing microprocessor platforms for activity meters without additional hardware costs. Compared to complex adaptive filtering or machine learning algorithms, it has higher engineering practicality. This advantage stems from the algorithm's lightweight design and hardware compatibility. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0029] The present invention will now be described in further detail with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0030] like Figure 1 As shown, this invention achieves an adaptive balance between steady-state accuracy and dynamic response in activity meters through a process of "data acquisition and dual-window parallel computing → adaptive switching condition judgment → dynamic output decision". This method can be implemented based on existing microprocessor platforms for activity meters, and the specific implementation scheme is as follows.

[0031] (I) Data Acquisition and Dual-Window Parallel Computing 1. Data Acquisition: The activity meter acquires the raw activity data of the sample (radioactive source) from the signal processing module of the radiation detector at a fixed sampling period (e.g., 100ms, which can be adjusted according to actual needs), denoted as the sequence D = {d1, d2, ……,dn}, where di is the raw activity value at the i-th sampling time, i=1~n, and n is the total number of sampling points within the sampling period.

[0032] 2. Long Window Calculation: Maintain a long window containing the N most recent sampling points in sequence D, where N corresponds to a sampling duration of 10-30 seconds (e.g., when the sampling period is 100ms, N = 100, corresponding to 10 seconds). Calculate the arithmetic mean of the sampled data within the long window to obtain the long window value L.

[0033] 3. Short Window Calculation: Maintain a short window containing the M most recent sampling points in sequence D in parallel. The value of M corresponds to a sampling duration of 1-5 seconds (e.g., when the sampling period is 100ms, M = 10, corresponding to 1 second). Calculate the arithmetic mean of the sampled data within the short window to obtain the short window value S.

[0034] (ii) Adaptive switching condition judgment: In each sampling period, the following switching condition judgment steps are performed: 1. Difference Calculation: Calculate the absolute difference between the long window value L and the short window value S, Diff = |L - S|.

[0035] 2. Conditional Judgment: Determine whether the absolute difference Diff satisfies any of the following conditions for the subsequent K consecutive sampling periods (K can be set to 10, adjusted according to the sampling period and actual needs): Condition A (Absolute threshold judgment): Diff > Thr_A, where Thr_A is a preset absolute threshold (for example, Thr_A can be set to 37000Bq according to the accuracy requirements of the activity meter).

[0036] Condition B (relative threshold judgment): Diff > Thr_B × |L|, where Thr_B is a preset percentage threshold (e.g., 0.1 represents 10%).

[0037] (III) Dynamic Output Decisions 1. Transient state determination and output: When an absolute difference Diff is calculated for each of the above K consecutive cycles, and the K absolute differences Diff calculated within the K consecutive sampling cycles always satisfy condition A or condition B, the system determines that it is currently in a transient activity state (such as the process of putting / taking samples), and then switches the output to a short window value S to achieve rapid tracking of drastic activity changes.

[0038] 2. Steady-state determination and output: When conditions A and B are no longer met for K consecutive cycles, the system determines that it has entered a steady state and then switches the output back to the long window value L to ensure the stability and accuracy of the activity measurement value.

[0039] Although specific embodiments of the invention have been disclosed for illustrative purposes to aid in understanding and implementing the invention, those skilled in the art will understand that various substitutions, variations, and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, the invention should not be limited to the content disclosed in the preferred embodiments, and the scope of protection claimed by the invention is defined by the claims.

Claims

1. A real-time measurement method for dual-window dynamic switching of an activity meter, comprising the steps of: 1) The activity meter obtains raw activity data of a sample from a signal processing module of a radiation detector at a fixed sampling period to obtain a sequence D = {d1, d2, ……, dn}; wherein di is the raw activity value at the i-th sampling time, i = 1 ~ n, and n is the total number of sampling points in the sampling period; 2) A long window is maintained to obtain the last N sampling points in the current sequence D; the arithmetic mean of the sampling data in the current long window is calculated to obtain a long window value L; a short window is maintained in parallel to obtain the last M sampling points in the current sequence D; the arithmetic mean of the sampling data in the current short window is calculated to obtain a short window value S; 3) The absolute difference Diff = |L - S| between the long window value L and the short window value S is calculated; then it is determined whether the absolute difference Diff in the consecutive K sampling periods satisfies Diff > Thr_A or whether the absolute difference Diff in the consecutive K sampling periods satisfies Diff > Thr_B x |L|; Thr_A is a preset absolute threshold value, and Thr_B is a preset percentage threshold value; if yes, it is determined that the sample is currently in an activity transient state, the short window value S of each sampling period is output as the activity measurement value of the activity meter in the sampling period to realize rapid tracking of the activity change; if not, it is determined that the sample is currently in a steady state, and the long window value L of each sampling period is output as the activity measurement value of the activity meter in the sampling period to ensure the stability and accuracy of the activity measurement value.

2. The method of claim 1, wherein, The absolute threshold value Thr_A is set according to the accuracy requirement of the activity meter.

3. The method of claim 2, wherein, The absolute threshold value Thr_A is 37000 Bq.

4. The method according to claim 1 or 2 or 3, characterized in that, The percentage threshold value Thr_B is 10%.

5. The method according to claim 1 or 2 or 3, characterized in that, N is greater than M.

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