Signal processing and calibration method, device and medium of long rod type gamma dose rate instrument

Abstract

The invention relates to the related technical field of measurement and calibration, in particular to a signal processing and calibration method and device for a long-rod type gamma dose rate instrument and a medium. The method comprises the following steps of: acquiring a real-time electric signal generated by the long-rod type gamma dose rate instrument by adopting a multi-sampling method, and converting the electric signal into a pulse signal; carrying out noise removal processing and abnormal value elimination processing on the pulse signals, and carrying out summary analysis to obtain a target pulse number; environment information and information of the long-rod type gamma dose rate instrument are collected, and a preset coefficient is corrected based on the environment information and attitude information of the long-rod type gamma dose rate instrument; calculating a ratio between the preset coefficient and the target pulse number; the ratio is gamma dosage rate. Through the arrangement, the obtained measurement result is more accurate.

Description

Technical Field

[0001] The present application relates to the technical field related to measurement and calibration, and in particular to a signal processing and calibration method device and medium for a long-rod gamma dose rate meter. Background Art

[0002] In the field of radiation measurement, gamma dose rate meters are essential devices for monitoring gamma ray radiation levels. Due to their unique structural design, long-rod gamma dose rate meters are often used in measurement scenarios where direct access is difficult, such as industrial flaw detection, nuclear facility inspection, and environmental monitoring. However, practical applications of long-rod gamma dose rate meters face numerous technical challenges.

[0003] The long rod structure affects gamma-ray detection efficiency, leading to deviations in measurement results. Furthermore, the detector and circuit system are susceptible to interference from external environmental factors, introducing noise signals and reducing measurement accuracy. Traditional gamma dose rate meters typically use fixed calibration coefficients, which are unable to adapt to changes in detector performance and the influence of environmental factors, resulting in large measurement errors.

[0004] Furthermore, in complex industrial environments or field monitoring, environmental factors such as temperature, humidity, and electromagnetic interference can affect detector performance and measurement results. Therefore, an algorithm is needed that can accurately and efficiently measure gamma-ray dose rates while overcoming the effects of long rod structures and environmental factors. Summary of the Invention

[0005] In view of this, the embodiments of the present application are directed to providing a signal processing and calibration method, device, and medium for a long-rod gamma dose rate meter.

[0006] The present application provides a signal processing and calibration method for a long-rod gamma dose rate meter, comprising: using a multiple sampling method to collect a real-time electrical signal generated by the long-rod gamma dose rate meter, and converting the electrical signal into a pulse signal;

[0007] The pulse signal is processed to remove noise and outliers, and then summarized and analyzed to obtain the target pulse number;

[0008] collecting environmental information and information of the long-rod gamma dose rate meter, and correcting a preset coefficient based on the environmental information and the posture information of the long-rod gamma dose rate meter;

[0009] The ratio between the preset coefficient and the target number of pulses is calculated; the ratio is the gamma dose rate.

[0010] In some embodiments, the noise removal process includes:

[0011] The moving average filtering algorithm is used to select the pulse count values ​​of n consecutive sampling points, calculate their average value, and use the average value as the processed pulse count value to remove high-frequency noise components and achieve data smoothing.

[0012] In some embodiments, the outlier removal process includes:

[0013] Determine the threshold value;

[0014] When the pulse count value of a certain sampling point exceeds the threshold, the data of the sampling point is determined to be an abnormal value and is eliminated.

[0015] In some embodiments, the environmental information includes: temperature data, humidity data, and background radiation.

[0016] In some embodiments, the information of the long rod type gamma dose rate meter includes: long rod material, long rod length, and tilt angle.

[0017] In some embodiments, collecting environmental information and information of a long-rod gamma dose rate meter, and correcting a preset coefficient based on the environmental information and attitude information of the long-rod gamma dose rate meter, includes:

[0018] Based on a pre-built coefficient correction model, a preset coefficient corresponding to the acquisition environment information and the information of the long-rod type gamma dose rate meter is determined.

[0019] In some embodiments, the pre-built model includes: a first dynamic compensation model and a second neural network compensation model;

[0020] The first dynamic compensation model is used to determine the corresponding preset coefficient based on information of the long-rod gamma dose rate meter;

[0021] The second neural network compensation model is used to correct the preset coefficient based on the environmental information.

[0022] In some embodiments, further comprising:

[0023] The gamma dose rate is displayed, stored and transmitted.

[0024] The present application also provides an electronic device, comprising:

[0025] A processor, and a memory for storing a program executable by the processor;

[0026] The processor is used to implement the signal processing and calibration method of the long-rod gamma dose rate meter by running the program in the memory.

[0027] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor is enabled to execute the signal processing and calibration method of the long-rod type gamma dose rate meter.

[0028] The present application provides a signal processing and calibration method for a long-rod gamma dose rate meter. The method first utilizes a multiple sampling method to collect real-time electrical signals generated by the long-rod gamma dose rate meter and converts them into pulse signals. The pulse signals are then subjected to noise removal and outlier elimination, and summarized and analyzed to obtain a target pulse count. Furthermore, environmental information and information about the long-rod gamma dose rate meter are collected, and a preset coefficient is corrected based on the environmental information and the meter's posture information. Finally, the method calculates the ratio between the preset coefficient and the target pulse count; this ratio represents the gamma dose rate. With this configuration, the signal processing and calibration method for a long-rod gamma dose rate meter has the following beneficial effects: Improved measurement accuracy: Multiple sampling yields more stable data, resulting in a more accurate pulse count after processing, improving the accuracy of gamma dose rate calculations and reliably reflecting gamma ray radiation levels. Enhanced data reliability: Noise and outlier elimination are performed, eliminating interfering signals and unreasonable data, ensuring the reliability of the pulse counts used for calculations and improving the data quality of the measurement system. Enhanced Environmental Adaptability: The device collects environmental and posture information to calibrate preset coefficients, compensating for measurement deviations caused by environmental changes. This ensures the meter maintains optimal performance in complex environments, broadening its applicability. Accurate γ-dose rate output: After processing and calibration, the target pulse count is reliable, and the calculated γ-dose rate truly reflects radiation levels, providing a precise measurement method for radiation monitoring. Real-time Monitoring and Dynamic Adjustment: Preset coefficients are corrected in real time, and the γ-dose rate is dynamically calculated, enabling real-time and accurate monitoring of radiation levels and providing strong support for emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0030] Figure 1 It is a flowchart of a signal processing and calibration method of a long-rod gamma dose rate meter provided in one embodiment of the present application.

[0031] Figure 2 This is a partial flow chart of a method provided in one embodiment of the present application.

[0032] Figure 3This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0034] Figure 1 FIG. 1 is a flow chart of a signal processing and calibration method for a long-rod gamma dose rate meter provided in one embodiment of the present application. Figure 1 As shown, the method includes the following contents.

[0035] Step S110, using a multiple sampling method to collect real-time electrical signals generated by the long-rod gamma dose rate meter, and converting the electrical signals into pulse signals;

[0036] First, a multiple sampling method is used to collect the real-time electrical signals generated by the long-rod gamma dose rate meter. The purpose of multiple sampling is to obtain multiple samples of data within a certain time interval, thereby increasing data stability and reliability and reducing measurement deviations caused by random errors or short-term fluctuations. These collected electrical signals are then converted into pulse signals. Each pulse represents the electrical signal generated by a single interaction between gamma rays and the detector. By counting these pulse signals, the basic data for subsequent gamma dose rate calculations is provided.

[0037] Step S120, performing noise removal and outlier elimination processing on the pulse signal, and performing summary analysis to obtain the target pulse number;

[0038] The converted pulse signal is subjected to noise removal processing. A digital filtering algorithm, such as a moving average filter, is used to average the pulse counts of n consecutive sampling points to remove high-frequency noise. This is because the detector and circuit system may introduce some irrelevant noise signals, which may interfere with the measurement of the true gamma-ray signal. Filtering can make the data smoother, more stable, and closer to the actual situation.

[0039] Next, we remove outliers. Using a statistically based approach, we set a reasonable threshold. When the pulse count value at a sampling point exceeds this threshold, it is considered an outlier and removed. In actual measurements, abnormal pulse count data may occur due to accidental factors (such as sudden electromagnetic interference or transient detector failure). These outliers can seriously affect the accuracy of the measurement results. Removing them ensures higher reliability of the data used in subsequent analysis.

[0040] Finally, the pulse signals, after noise removal and outlier elimination, are aggregated and analyzed to obtain the target pulse number. This aggregate analysis integrates multiple valid pulse counts to calculate the total number of pulses within a specific timeframe. This target pulse number serves as the key basis for calculating the gamma dose rate.

[0041] Step S130, collecting environmental information and information of the long-rod type gamma dose rate meter, and calibrating a preset coefficient based on the environmental information and the posture information of the long-rod type gamma dose rate meter;

[0042] Collect environmental information, including factors that may affect gamma dose rate measurement, such as temperature, humidity, air pressure, background radiation, and the attitude information of the long-rod gamma dose rate meter itself (such as the tilt angle and orientation of the detector). This is because changes in environmental conditions will affect the performance of the detector and the detection efficiency of gamma rays. For example, temperature changes may affect the performance of the detector material, humidity changes may affect the stability of the circuit system, and changes in attitude may affect the path and intensity of gamma rays entering the detector.

[0043] The preset coefficients are then corrected based on the collected environmental and posture information. These coefficients are typically determined experimentally under standard environmental conditions. However, in practice, due to changes in the environment and posture, these coefficients need to be dynamically adjusted to ensure they accurately reflect the relationship between the gamma dose rate and the number of pulses under the current measurement conditions, thereby improving the accuracy of the measurement results.

[0044] Step S140 , calculating a ratio between a preset coefficient and a target number of pulses; the ratio is a gamma dose rate.

[0045] After the above processing, the ratio between the calibrated preset coefficient and the target pulse number is calculated, which is the gamma dose rate. Specifically, the target pulse number is substituted into the calibrated dose rate calculation formula to calculate the current gamma dose rate. This process converts the raw electrical signal into a gamma dose rate that can be directly used to assess radiation levels, providing users with more intuitive and accurate radiation monitoring data, helping them understand the gamma ray exposure in their environment and take appropriate protective measures or make relevant decisions.

[0046] Specifically, the detector of a long-rod gamma dose rate meter collects the electrical signals generated by gamma rays in real time. The detector converts these signals into pulses and uses a counter to record the number of pulses per unit time, N. To improve data reliability, a multi-sampling method is used, continuously collecting multiple pulse counts at a certain time interval.

[0047] In some embodiments, the noise removal process includes: using a moving average filtering algorithm to select pulse count values ​​of n consecutive sampling points, calculating their average value, and using the average value as the processed pulse count value to remove high-frequency noise components and achieve data smoothing. The specific process is as follows:

[0048] Determine the number of sampling points n: Select an appropriate number of continuous sampling points n based on the actual measurement requirements and signal characteristics. For example, when measuring gamma dose rate, if the signal fluctuates frequently, increase the value of n to better smooth the data; if the signal is relatively stable, select a smaller value of n to reduce the amount of calculation.

[0049] Calculate the average value: Select n consecutive sampling points from the collected pulse count value sequence and calculate the average value of the pulse count values ​​of these n sampling points. The formula for calculating the average value is: Average value = (Pulse count value of sampling point 1 + Pulse count value of sampling point 2 + ... + Pulse count value of sampling point n) / n.

[0050] Smoothing: The calculated average value is used as the processed pulse count value for the corresponding period, replacing the original pulse count values ​​of n sampling points. In this way, the processed pulse count value sequence is smoother than the original sequence, effectively removing high-frequency noise components, making the data more stable and reliable, and facilitating subsequent analysis and processing.

[0051] In some embodiments, the outlier removal process includes: determining a threshold; when the pulse count value of a certain sampling point exceeds the threshold, determining the data of the sampling point as an outlier and removing it.

[0052] Determine the threshold: Based on statistical analysis of a large amount of normal measurement data and an understanding of the measurement environment and measurement object, set a reasonable threshold. This threshold is usually a value greater than the upper limit of the normal pulse count value to distinguish normal signals from abnormal signals.

[0053] Determining and eliminating outliers: When processing pulse counts, the pulse counts at each sampling point are checked one by one. If the pulse count at a sampling point exceeds a previously determined threshold, the data at that sampling point is considered an outlier and is eliminated from subsequent analysis and calculations.

[0054] In some embodiments, the environmental information includes: temperature data, humidity data, and background radiation.

[0055] Temperature Data: Temperature affects the performance of detectors and circuits. For example, high temperatures can alter the characteristics of electronic components, affecting signal generation and transmission. By collecting temperature data, you can apply temperature compensation to your measurements, improving accuracy.

[0056] Humidity data: Humidity affects the insulation performance and signal transmission of electrical equipment. High humidity can cause equipment leakage or signal attenuation. Collecting humidity data helps assess and compensate for humidity's effects on measurements.

[0057] Background radiation: Background radiation refers to the level of radiation naturally present in the environment. When measuring a target radiation source, the effect of background radiation must be subtracted to obtain an accurate target radiation dose rate.

[0058] The information of the long-rod type gamma dose rate meter includes: long-rod material, long-rod length, and tilt angle.

[0059] Rod material: Rod material properties such as density and atomic number affect gamma-ray attenuation. Different materials absorb and scatter gamma rays differently, so knowing the rod material is crucial to accurately assess its impact on gamma radiation.

[0060] Rod length: The rod length affects the attenuation of gamma rays before they reach the detector. Generally speaking, the longer the rod, the greater the probability that gamma rays will be absorbed and scattered as they pass through it, so compensation for rod length is necessary.

[0061] Tilt angle: Long-rod gamma dose rate meters may tilt during use. This tilt angle affects the path and angle of gamma rays entering the detector. By measuring and compensating for the tilt angle, its impact on measurement results can be reduced, improving measurement accuracy.

[0062] These environmental information and instrument information are crucial for accurate measurement of gamma dose rate. By comprehensively considering and compensating for these factors, the measurement accuracy and reliability of the long-rod gamma dose rate meter can be improved.

[0063] Furthermore, the collecting of environmental information and information of the long-rod type gamma dose rate meter, and correcting a preset coefficient based on the environmental information and the posture information of the long-rod type gamma dose rate meter, includes:

[0064] Based on a pre-built coefficient correction model, a preset coefficient corresponding to the acquisition environment information and the information of the long-rod type gamma dose rate meter is determined.

[0065] The pre-built model includes: a first dynamic compensation model and a second neural network compensation model; the first dynamic compensation model is used to determine the corresponding preset coefficient based on the information of the long-rod gamma dose rate meter; the second neural network compensation model is used to correct the preset coefficient based on the environmental information.

[0066] The first dynamic compensation model is used to determine the corresponding preset coefficient based on the information of the long-rod gamma dose rate meter. The information of the long-rod gamma dose rate meter includes the long-rod material, long-rod length, tilt angle, etc. Different long-rod materials have different absorption and scattering characteristics of gamma rays. The longer the long rod is, the greater the possibility that the gamma rays will be attenuated in the process of passing through the long rod. The tilt angle will change the effective path and geometric efficiency of the gamma rays entering the detector. Through methods such as Monte Carlo simulation, the detection efficiency of gamma rays under different long-rod materials, lengths and tilt angles is pre-simulated to establish a first dynamic compensation model. The model inputs information such as the long-rod material, length and tilt angle, and outputs the corresponding compensation coefficient, which is used to preliminarily correct the gamma dose rate measurement deviation caused by the characteristics of the long rod itself.

[0067] The second neural network compensation model is used to correct the preset coefficient based on the environmental information. Environmental information includes temperature data, humidity data, background radiation, etc. Temperature can affect the performance of detectors and circuits. For example, increased temperature may cause changes in detector sensitivity and increased noise in electronic components; humidity may affect the insulation performance of the equipment and the stability of signal transmission; and the presence of background radiation may interfere with the measurement of target gamma rays. In order to accurately compensate for the impact of these environmental factors, the second neural network compensation model is trained using a large amount of experimental data. Environmental information such as temperature, humidity, and background radiation are input, and after calculation by the model, a further correction factor for the preset coefficient is output. The model can learn the complex nonlinear relationship between environmental factors and gamma dose rate measurement errors, and realize adaptive and high-precision correction of the preset coefficient, thereby improving the accuracy and reliability of gamma dose rate measurement.

[0068] In some embodiments, the signal processing and calibration method of the long-rod gamma dose rate meter further includes: displaying, storing and transmitting the gamma dose rate.

[0069] Specifically, the calculated gamma dose rate is displayed in real time on the dose rate meter's display screen, allowing the user to intuitively understand the current gamma ray radiation level. The display screen can be a liquid crystal display (LCD) or light-emitting diode (LED) display, presenting measurement results in a clear and easy-to-read format. For example, it can display the dose rate value in digital form (unit: μSv / h) and can set alarm thresholds. When the dose rate exceeds the set value, an audible and visual alarm is triggered, reminding the user to pay attention to radiation safety.

[0070] The measured gamma dose rate data is stored in the dose rate meter's internal memory for subsequent data analysis and recording. This memory can be built-in flash memory, a hard drive, or other non-volatile storage media, capable of storing large amounts of measurement data, including dose rate values, measurement time, measurement location, and other related information. Users can access and manage the stored data as needed through the instrument's user interface or by connecting an external device (such as a computer) for further analysis, statistics, and archiving.

[0071] Gamma dose rate data is transmitted to external devices such as computers, mobile phones, and remote monitoring centers via a communication interface. This communication interface can be a wireless communication module (such as Wi-Fi, Bluetooth, 4G / 5G, etc.) or a wired communication interface (such as USB, Ethernet, etc.), enabling real-time data transmission and remote monitoring. For example, in nuclear facility monitoring, a dose rate meter can transmit data in real time to a control center, allowing staff to promptly understand radiation conditions and take appropriate measures. In environmental monitoring, data can be sent wirelessly to a cloud platform for researchers and the public to query and analyze.

[0072] These steps ensure the timeliness, integrity and availability of gamma dose rate measurement data, providing strong support for radiation monitoring and management.

[0073] The following combination Figure 2 , further explain the solution provided in this application:

[0074] Step 1: Data Acquisition: The detector of the long-rod gamma dose rate meter collects the electrical signals generated by gamma rays in real time. The detector converts these signals into pulses and uses a counter to record the number of pulses per unit time, N. To improve data reliability, a multi-sampling method is used, continuously collecting multiple pulse counts at a certain time interval.

[0075] Step 2: Signal preprocessing and noise separation:

[0076] Wavelet threshold denoising: The original pulse signal is decomposed into 5 layers using the Daubechies wavelet basis, and the high-frequency coefficients are dynamically adjusted using the improved SURE threshold formula (Equation 1):

[0077] λ = σ√(2ln(N)) / (1 + log2(j+1)) (Formula 1)

[0078] Where σ is the noise estimate, N is the signal length, and j is the number of decomposition layers.

[0079] Pulse pile-up correction: A sliding time window (typical value 10μs) is designed to identify overlapping pulses and reconstruct the true count based on the amplitude-time correlation.

[0080] Step 3: Long rod attenuation dynamic compensation

[0081] Establish an attenuation coefficient matrix: Use Monte Carlo simulation to obtain the attenuation curves of gamma rays of different energies in long rod materials (such as carbon fiber) and fit them into a piecewise function:

[0082] μ(E)=a·ln(E)+b·E^c (Formula 2, a, b, c are material parameters)

[0083] Real-time compensation calculation:

[0084] According to the current detection energy E, the dose rate D is reversely corrected:

[0085] D_corrected = D_raw × exp(μ(E)·L) (Equation 3, L is the effective length of the long rod)

[0086] Step 4: Multi-dimensional environmental calibration

[0087] Temperature drift compensation module: Build a BP neural network model, input temperature T, humidity H, background count rate B, and output compensation factor α:

[0088] α=f(T,H,B)=Sigmoid(w1·T+w2·H+w3·B+b)

[0089] Attitude error correction:

[0090] Calculate the long rod inclination angle θ through gyroscope data and correct the geometric detection efficiency:

[0091] η_corrected = η0 × cosθ × (1 + 0.02|θ|) (nonlinear term activated when |θ| > 15°)

[0092] Step 4: Dose Rate Synthesis Output

[0093] Use weighted fusion strategy to integrate data from each channel:

[0094] D_final=Σ(k_i·D_i) / √(Σk_i^2)

[0095] The weight k_i is dynamically assigned by the confidence level (signal-to-noise ratio / stability index) of each channel.

[0096] Below, reference Figure 3 To describe the electronic device according to the embodiment of the present application. Figure 3 The figure shows a block diagram of an electronic device according to an embodiment of the present application.

[0097] like Figure 3 As shown, electronic device 300 includes one or more processors 310 and memory 320 .

[0098] The processor 310 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0099] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may execute the program instructions to implement the signal processing and calibration methods of the long-rod gamma dose rate meter of each embodiment of the present application described above and / or other desired functions. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0100] In one example, the electronic device 300 may further include an input device 330 and an output device 340 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0101] In addition, the input device 330 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 340 may output various information to the outside, including analysis results, etc. The output device 340 may include, for example, a display, a speaker, a printer, a communication network and its connected remote output device, etc.

[0102] Of course, to simplify, Figure 3 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0103] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the signal processing and calibration method of the long-rod gamma dose rate meter according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0104] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0105] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, causes the processor to execute the steps of the signal processing and calibration method of the long-rod type γ dose rate meter according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0106] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0107] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A signal processing and calibration method for a long-rod gamma dose rate meter, characterized in that: include: A multiple sampling method is used to collect the real-time electrical signal generated by the long-rod gamma dose rate meter and convert the electrical signal into a pulse signal; The pulse signal is processed to remove noise and outliers, and then summarized and analyzed to obtain the target pulse number; collecting environmental information and information of the long-rod gamma dose rate meter, and correcting a preset coefficient based on the environmental information and the posture information of the long-rod gamma dose rate meter; The ratio between the preset coefficient and the target number of pulses is calculated; the ratio is the gamma dose rate.

2. The signal processing and calibration method of the long-rod gamma dose rate meter according to claim 1, characterized in that: The noise removal process includes: The moving average filtering algorithm is used to select the pulse count values ​​of n consecutive sampling points, calculate their average value, and use the average value as the processed pulse count value to remove high-frequency noise components and achieve data smoothing.

3. The signal processing and calibration method of the long-rod gamma dose rate meter according to claim 1, characterized in that: The outlier removal process includes: Determine the threshold value; When the pulse count value of a certain sampling point exceeds the threshold, the data of the sampling point is determined to be an abnormal value and is eliminated.

4. The signal processing and calibration method of the long-rod gamma dose rate meter according to claim 1, characterized in that: The environmental information includes: temperature data, humidity data, and background radiation.

5. The signal processing and calibration method of the long-rod gamma dose rate meter according to claim 4, characterized in that: The information of the long-rod type gamma dose rate meter includes: long-rod material, long-rod length, and tilt angle.

6. The signal processing and calibration method of the long-rod gamma dose rate meter according to claim 5, characterized in that: The collecting of environmental information and information of the long-rod type gamma dose rate meter, and correcting a preset coefficient based on the environmental information and the posture information of the long-rod type gamma dose rate meter, includes: Based on a pre-built coefficient correction model, a preset coefficient corresponding to the acquisition environment information and the information of the long-rod type gamma dose rate meter is determined.

7. The signal processing and calibration method of the long-rod gamma dose rate meter according to claim 6, characterized in that: The pre-built model includes: a first dynamic compensation model and a second neural network compensation model; The first dynamic compensation model is used to determine the corresponding preset coefficient based on information of the long-rod gamma dose rate meter; The second neural network compensation model is used to correct the preset coefficient based on the environmental information.

8. The signal processing and calibration method of the long-rod gamma dose rate meter according to claim 6, characterized in that: Also includes: The gamma dose rate is displayed, stored and transmitted.

9. An electronic device, characterized in that: include: A processor, and a memory for storing a program executable by the processor; The processor is configured to implement the signal processing and calibration method of the long-rod gamma dose rate meter according to any one of claims 1 to 8 by running the program in the memory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the signal processing and calibration method of the long-rod gamma dose rate meter according to any one of claims 1 to 8.

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