A device and method for filtering out interference values in a plant radiation monitoring

Abstract

The present application relates to the technical field of signal analysis, and particularly relates to a device and method for filtering out interference values of plant radiation monitoring, wherein after the monitoring radiation signal is divided into each local waveform signal according to fluctuation and change, the non-Gaussian noise interference generated after the start and stop operation of the power equipment causes the characteristics of the sudden change of the monitoring radiation signal, the electromagnetic interference index of each local waveform signal is calculated, and then the characteristic local signal not interfered by the non-Gaussian noise interference and the interference local signal possibly interfered by the non-Gaussian noise interference are screened out based on the electromagnetic interference index; thus, the signal abnormality degree of each interference local signal is determined based on the deviation between the interference local signal and the characteristic local signal in the time domain and the frequency domain, so that the interference local signal and the characteristic local signal are screened out according to the signal abnormality degree, and the filtering effect of the radiation signal filtering through the order statistics filtering according to the normal radiation signal is better.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal analysis, and in particular to a device and method for filtering out interference values in plant radiation monitoring. BACKGROUND

[0002] In view of the interference that may exist in the radiation monitoring process, the existing method filters the radiation signal based on order statistics filtering. However, there are many power equipment in the plant for work and life in the plant, and such equipment generates non-Gaussian noise during operation, which interferes with the monitoring radiation signal monitored by the radiation monitor, resulting in poor filtering effect of the monitoring radiation signal by the order statistics filtering method. SUMMARY

[0003] In order to solve the technical problem of poor filtering effect of the monitoring radiation signal by the order statistics filtering method, the purpose of the present application is to provide a device and method for filtering out interference values in plant radiation monitoring, and the technical solution adopted is as follows:

[0004] The first aspect of the present application provides a method for filtering out interference values in plant radiation monitoring, comprising:

[0005] Collecting the monitoring radiation signal of each monitoring position after the start-stop operation of the power equipment in the plant;

[0006] According to the signal value fluctuation of the monitoring radiation signal, all local waveform signals are divided. According to the signal value mutation of each local waveform signal, the corresponding local fluctuation index is determined. According to the instantaneous change of the local fluctuation index of the local waveform signal in time sequence and the signal width change, the waveform mutation index of each local waveform signal is determined. According to the waveform mutation index and the local fluctuation index, the electromagnetic interference index of each local waveform signal is determined;

[0007] According to the time sequence distribution of the electromagnetic interference index, the monitoring radiation signal is divided into characteristic local signals and all interference local signals. According to the signal time domain deviation and signal frequency domain deviation between each interference local signal and the characteristic local signal, the signal abnormality degree of each interference local signal is determined.

[0008] According to the signal abnormality degree, the normal radiation signal is screened out. According to all normal radiation signals, the radiation signal is denoised by order statistics filtering.

[0009] Further, the acquisition process of the local waveform signal comprises:

[0010] All peak points and valley points of the monitoring radiation signal are determined by an automatic multi-scale peak detection algorithm; the monitoring radiation signal is divided into at least two local waveform signals with the valley points as intervals.

[0011] Further, the acquisition process of the local fluctuation index comprises:

[0012] According to a time interval between a sampling time corresponding to a peak point of each local waveform signal and a sampling time corresponding to a previous valley point, a corresponding fluctuation rising time length is determined; a difference between a signal value of the peak point of each local waveform signal and a signal value of the previous valley point is determined as a waveform rising amplitude value; a ratio between the waveform rising amplitude value and the fluctuation rising time length is normalized to determine a waveform rising steepness degree;

[0013] According to a difference between a signal value of each sampling time in the monitoring radiation signal and a signal value of a previous sampling time, a signal change value of each sampling time is determined; a mean value of signal change values of all sampling times between a peak point and a subsequent valley point of each local waveform signal is normalized to determine a corresponding waveform falling steepness degree;

[0014] According to a mean value between the waveform rising steepness degree and the waveform falling steepness degree, a local fluctuation index of each local waveform signal is determined.

[0015] Further, the acquisition process of the waveform mutation index comprises:

[0016] According to a product between a time length corresponding to each local waveform signal and a waveform rising amplitude value, a corresponding waveform pulse integral index is determined;

[0017] A difference between the waveform pulse integral index of each local waveform signal and the waveform pulse integral index of a previous local waveform signal is normalized to determine a trend change difference of each local waveform signal;

[0018] A difference between the local fluctuation index of each local waveform signal and the local fluctuation index of a previous local waveform signal is normalized to determine a fluctuation feature difference of each local waveform signal;

[0019] According to a mean value between the trend change difference and the fluctuation feature difference, a corresponding waveform mutation index is determined.

[0020] Further, the acquisition process of the electromagnetic interference index comprises:

[0021] According to a product between the local fluctuation index and the waveform mutation index, an electromagnetic interference index of each local waveform signal is determined.

[0022] Further, the process of dividing the monitoring radiation signal into interference local signals and characteristic local signals according to the time sequence distribution of the electromagnetic interference index comprises:

[0023] Taking the first sampling moment of the local waveform signal corresponding to the maximum electromagnetic interference index as the interference mutation moment, and taking the local signal segment between the first sampling moment of the monitoring radiation signal and the interference mutation moment as the characteristic local signal.

[0024] According to the time length of the characteristic local signal, a reference traversal length is determined, and the monitoring radiation signal is traversed from the interference mutation moment as the starting point with the reference traversal length until the monitoring radiation signal traversal is completed, thereby obtaining all interference local signals.

[0025] Further, the process of obtaining the signal abnormality degree comprises:

[0026] The DTW distance between the characteristic local signal and each interference local signal is normalized to determine the time domain deviation feature index of each interference local signal.

[0027] The characteristic local signal and each interference local signal are sequentially taken as target signals, and the frequency domain feature parameter of the target signal is determined based on the product between the frequency spectrum width and the amplitude maximum value of the frequency spectrum signal corresponding to the target signal obtained by Fourier transform.

[0028] The difference between the frequency domain feature parameter of the characteristic local signal and the frequency domain feature parameter of each interference local signal is normalized to determine the frequency domain deviation feature index of each interference local signal.

[0029] The signal abnormality degree of each interference local signal is determined according to the mean value between the time domain deviation feature index and the frequency domain deviation feature index.

[0030] Further, the process of obtaining the normal radiation signal comprises:

[0031] The interference local signal with a signal abnormality degree less than a preset abnormality threshold is taken as the normal radiation signal.

[0032] Further, the process of performing radiation signal filtering on all normal radiation signals by sequential statistical filtering comprises:

[0033] All normal radiation signals are arranged in time sequence and combined to determine a combined radiation signal, and the monitoring radiation signal is determined by performing radiation signal filtering on the combined radiation signal by sequential statistical filtering, and the plant radiation monitoring is performed according to the monitoring radiation signal.

[0034] In a second aspect, the present application provides a device for filtering out interference values of plant radiation monitoring, the device comprising:

[0035] a data acquisition module configured to acquire a monitoring radiation signal of each monitoring position after start-stop operation of power equipment in a plant;

[0036] a first determination module configured to divide all local waveform signals according to signal value fluctuation of the monitoring radiation signal, determine a corresponding local fluctuation index according to signal value mutation of each local waveform signal, determine a waveform mutation index of each local waveform signal according to instantaneous change of the local fluctuation index and signal width change of the local waveform signal in time sequence, and determine an electromagnetic interference index of each local waveform signal according to the waveform mutation index and the local fluctuation index;

[0037] a second determination module configured to divide the monitoring radiation signal into a characteristic local signal and all interference local signals according to time sequence distribution of the electromagnetic interference index, and determine a signal abnormality degree of each interference local signal according to signal time domain deviation and signal frequency domain deviation between each interference local signal and the characteristic local signal;

[0038] a signal denoising module configured to screen out normal radiation signals according to the signal abnormality degree, and perform radiation signal denoising through order statistics filtering according to all normal radiation signals.

[0039] In a third aspect, the present application provides a computer device comprising a memory and a processor. The memory is configured to store computer program code, and the processor is configured to call and run the computer program code from the memory to execute the method of the first aspect or any embodiment of the first aspect of the present application.

[0040] In a fourth aspect, the present application provides a computer program product comprising computer program code, which, when executed, performs the method of the first aspect or any embodiment of the first aspect of the present application.

[0041] In a fifth aspect, the present application provides a computer readable storage medium storing computer program code, which, when executed, performs the method of the first aspect or any embodiment of the first aspect of the present application.

[0042] The present application has the following beneficial effects:

[0043] The application divides the monitoring radiation signal into each local waveform signal according to the fluctuation of the monitoring radiation signal, and calculates the electromagnetic interference index of each local waveform signal based on the characteristics that the non-Gaussian noise interference caused by the start-stop operation of the power equipment will lead to the mutation of the monitoring radiation signal, and then screens out the characteristic local signal not interfered by the non-Gaussian noise and the interference local signal possibly interfered by the non-Gaussian noise based on the electromagnetic interference index; thereby, the signal abnormality degree of each interference local signal corresponding to the non-Gaussian noise interference is determined based on the deviation between the interference local signal and the characteristic local signal in the time domain and the frequency domain, so that the interference local signal and the characteristic local signal are screened out according to the signal abnormality degree, and the filtering effect of the radiation signal filtering through the order statistics filtering based on the normal radiation signal is better, and the monitoring radiation signal after denoising is less interfered by the non-Gaussian noise. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0045] Figure 1 A flow chart of a method for filtering out interference values of plant radiation monitoring according to an embodiment of the present application;

[0046] Figure 2 A structural diagram of a system for filtering out interference values of plant radiation monitoring according to an embodiment of the present application;

[0047] Figure 3 A structural diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object, the specific embodiments, structures, features and effects of the device and method for filtering out interference values of plant radiation monitoring according to the present application are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment, and the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form. In addition, the terms "first", "second" are used for description purposes only, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Therefore, the features with "first", "second" can be explicitly or implicitly included one or more features.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0050] The following will specifically describe a specific scheme of the device and method for filtering out interference values of plant radiation monitoring in conjunction with the accompanying drawings.

[0051] The embodiment of the present application provides a method for filtering out interference values of plant radiation monitoring, please refer to Figure 1 Fig. 1 shows a flowchart of the method for filtering out interference values of plant radiation monitoring provided by the embodiment of the present application, and the method comprises the following steps:

[0052] Step S101: Collecting monitoring radiation signals of each monitoring position after start-stop operation of power equipment in the plant.

[0053] The positions needing monitoring radiation in the plant are set as monitoring positions, and the radiation detector is placed at the monitoring positions to monitor the monitoring radiation signals after the start-stop operation of the power equipment in real time. It should be noted that the time interval for analyzing the monitoring radiation signals in the embodiment of the present application is set as 0.01 seconds, that is, the time interval between the adjacent two times or two sampling times on the monitoring radiation signals is 0.01 seconds, which is used for subsequent signal division calibration operation, and the time interval of the signals generated in the analysis process is the same as that of the monitoring radiation signals. The implementer can also set the time interval according to the specific sampling frequency of the radiation detector. The time length of the monitoring radiation signals in the embodiment of the present application is set as 30 minutes, which can be adjusted according to the specific implementation environment. In addition, it should be noted that there is only one start-stop operation in the analysis process of the embodiment of the present application, and no further description is made subsequently.

[0054] Step S102: Dividing all local waveform signals according to the signal value fluctuation of the monitoring radiation signals; determining the corresponding local fluctuation index according to the signal value mutation of each local waveform signal; determining the waveform mutation index of each local waveform signal according to the local fluctuation instantaneous change and signal width change of the local waveform signals in time sequence; and determining the electromagnetic interference index of each local waveform signal according to the waveform mutation index and the local fluctuation index.

[0055] Considering that the monitoring radiation signals usually appear as pulse signals, and the pulse will mutate under non-Gaussian noise interference, in order to analyze the pulse signals, all local waveform signals are first divided according to the signal value fluctuation of the monitoring radiation signals. Preferably, in some possible implementation manners of the embodiment of the present application, the process of obtaining the local waveform signals comprises:

[0056] All peak points and valley points of the monitoring radiation signal are determined by an automatic multiscale-based peak detection (AMPD) algorithm; the monitoring radiation signal is divided into at least two local waveform signals with the valley points as intervals. That is, the monitoring radiation signal is divided into local waveform signals with each peak as a unit, so that each local waveform signal behaves as a peak, corresponding to two valley points and one peak point, and the two valley points are located on both sides of the peak point. Thus, further analysis is performed according to the characteristics that the peak will mutate when the non-Gaussian noise interference occurs. It should be noted that the automatic multiscale-based peak detection algorithm for determining the peak points and valley points of the signal or curve is a technical means known to those skilled in the art, which will not be further limited and described here.

[0057] When the non-Gaussian noise interference occurs, the monitoring radiation signal will have abnormal fluctuations, so the corresponding local fluctuation index is determined according to the signal value mutation of each local waveform signal, so that the larger the local fluctuation index is, the more the corresponding local waveform signal conforms to the characteristics of the non-Gaussian noise interference.

[0058] Preferably, in some possible implementation manners of the embodiments of the present application, the process of obtaining the local fluctuation index comprises:

[0059] According to the time interval between the sampling time corresponding to the peak point of each local waveform signal and the sampling time corresponding to the previous valley point, the fluctuation rise time length is determined; the difference between the signal value of the peak point of each local waveform signal and the signal value of the previous valley point is determined as the waveform rise amplitude; and the ratio between the waveform rise amplitude and the fluctuation rise time length is normalized to determine the waveform rise steepness.

[0060] According to the analysis of the objective facts, the greater the drop of the peak and the shorter the time from the valley to the peak, the steeper the corresponding peak, so the smaller the fluctuation rise time length and the greater the waveform rise amplitude, the steeper the waveform rise process, that is, the more likely the corresponding local waveform signal is interfered by the non-Gaussian noise.

[0061] Based on the difference between the signal value at each sampling moment and the signal value at the previous sampling moment in the monitored radiation signal, the signal change value at each sampling moment is determined. The mean of the signal change values ​​at all sampling moments corresponding to the peak and trough of each local waveform signal is normalized to determine the steepness of the waveform descent. The signal change value characterizes the degree of signal decline within a unit sampling time interval. For each local waveform signal, the larger the mean of the signal change values ​​at all sampling moments between its peak and trough, the faster the waveform declines, the steeper the waveform descent, and the more likely the corresponding local waveform signal is to be affected by non-Gaussian noise. It should be noted that the difference mentioned in this embodiment represents the absolute value of the difference, and will not be further elaborated upon later. It should also be noted that the signal change value at the first sampling moment in the monitored radiation signal is defaulted to 0.

[0062] Therefore, based on the correlation, the local fluctuation index of each local waveform signal is determined according to the mean between the steepness of the waveform rise and the steepness of the waveform fall, so that the larger the local fluctuation index, the more it conforms to the characteristics of being interfered with by non-Gaussian noise.

[0063] In one specific implementation of this invention, the process of obtaining the local fluctuation index is expressed by the following formula: ;in, Local waveform signal The local fluctuation index; Local waveform signal The signal value at the peak point; Local waveform signal The signal value of the trough point preceding the peak point; Local waveform signal The amplitude of the waveform rise; It is the absolute value symbol; Local waveform signal The time interval between the sampling time corresponding to the peak point and the sampling time corresponding to the previous trough point, which is the rise time of the fluctuation. This is a minimum-maximum normalization function; other normalization methods can be adopted depending on the specific implementation environment. Local waveform signal The steepness of the waveform rise; Local waveform signal The mean of the signal changes at all sampling times between the peak and the next trough; Local waveform signal The steepness of the waveform drop.

[0064] The non-Gaussian noise interference generated by the power equipment start-stop operation usually has a certain delay in the performance on the monitoring radiation signal. When the Gaussian noise interference occurs, the normal monitoring radiation signal will suddenly appear the characteristics of the non-Gaussian noise interference, so that the local waveform signal suddenly changes and the local fluctuation index suddenly increases. Therefore, the waveform mutation index of each local waveform signal is determined according to the instantaneous change of the local fluctuation index of the local waveform signal in the time sequence and the change of the signal width, so that the greater the waveform mutation index, the more the corresponding local waveform signal conforms to the characteristics of the non-Gaussian noise interference.

[0065] Preferably, in some possible implementation manners of the embodiment of the present application, the waveform mutation index acquisition process comprises:

[0066] The waveform pulse integral index of each local waveform signal is determined according to the product of the corresponding time length and the waveform rising amplitude. The waveform pulse integral index represents the waveform characteristics of the local waveform signal through two dimensions of the signal time length and the waveform rising amplitude, so that the occurrence of the non-Gaussian noise can be more clearly reflected in the change of the waveform pulse integral index, that is, the greater the deviation between the waveform pulse integral index of each local waveform signal and the waveform pulse integral index of the next local waveform signal, the higher the degree of waveform change, and the more likely the non-Gaussian noise interference occurs.

[0067] Therefore, the trend change difference of each local waveform signal is determined by normalizing the difference between the waveform pulse integral index of each local waveform signal and the waveform pulse integral index of the previous local waveform signal. The greater the trend change difference, the more the corresponding local waveform signal conforms to the characteristics of the non-Gaussian noise interference.

[0068] Further, considering that the local fluctuation index of the local waveform signal under the non-Gaussian noise interference is larger than that of the normal local waveform signal, the fluctuation feature difference of each local waveform signal is further determined by normalizing the difference between the local fluctuation index of each local waveform signal and the local fluctuation index of the next local waveform signal. The greater the fluctuation feature difference, the more the corresponding local waveform signal conforms to the characteristics of the non-Gaussian noise interference.

[0069] Finally, the waveform mutation index is determined based on the correlation between the mean value of the trend change difference and the fluctuation feature difference. In one specific implementation manner of the embodiment of the present application, the waveform mutation index acquisition process is represented by a formula as follows: ; wherein, is the waveform mutation index of the local waveform signal . is the local waveform signal The difference between the waveform pulse integral exponent and the waveform pulse integral exponent of the previous local waveform signal; Local waveform signal Differences in trend changes; Local waveform signal The difference between the local fluctuation index of the current local waveform signal and the local fluctuation index of the previous local waveform signal; Local waveform signal Differences in fluctuation characteristics.

[0070] Since a larger local fluctuation index more closely matches the characteristics of non-Gaussian noise interference, in order to more accurately determine the local waveform signal corresponding to the occurrence of non-Gaussian noise, the electromagnetic interference index of each local waveform signal is further determined based on the waveform abrupt change index and the local fluctuation index, so that a larger electromagnetic interference index more closely matches the characteristics of non-Gaussian noise. Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the electromagnetic interference index includes: determining the electromagnetic interference index of each local waveform signal based on the product between the local fluctuation index and the waveform abrupt change index.

[0071] Step S103: Based on the temporal distribution of the electromagnetic interference index, the monitored radiation signal is divided into characteristic local signals and all interfering local signals; based on the signal time-domain deviation and signal frequency-domain deviation between each interfering local signal and the characteristic local signal, the signal abnormality degree of each interfering local signal is determined.

[0072] Non-Gaussian noise is generated after the power equipment starts and stops. When non-Gaussian noise interference occurs, the electromagnetic interference index of the corresponding local waveform signal will show a large value. Therefore, based on the time distribution of the electromagnetic interference index, the monitored radiation signal is further divided into characteristic local signals that are not affected by noise interference and all interference local signals affected by noise interference (non-Gaussian noise).

[0073] In one specific implementation of this invention, the first sampling moment of the local waveform signal corresponding to the largest electromagnetic interference index is taken as the moment of interference mutation.

[0074] Since the electromagnetic interference index of the local waveform signal is obtained by comparing with the previous local waveform signal, the signal of the local waveform signal with the maximum electromagnetic interference index has been affected by the non-Gaussian noise interference, so the corresponding first sampling time is taken as the interference mutation time, that is, the non-Gaussian noise caused by the start-stop operation of the power equipment starts to interfere with the corresponding time of the monitoring radiation signal; then the local signal segment between the sampling time corresponding to the start-stop operation of the power equipment and the interference mutation time is not affected by the non-Gaussian noise signal segment, so this signal segment can be taken as a standard to gradually analyze the degree of non-Gaussian noise interference of each subsequent signal segment, thereby screening out the signal segment with less non-Gaussian noise interference for filtering processing, so that the denoised radiation signal is more accurate.

[0075] Further, the local signal segment between the first sampling time of the monitoring radiation signal and the interference mutation time is taken as a characteristic local signal; the reference traversal length is determined according to the time length of the characteristic local signal; the reference traversal length is traversed on the monitoring radiation signal starting from the interference mutation time until the monitoring radiation signal is traversed, and all interference local signals are obtained. That is, a local signal segment is taken as an interference local signal every reference traversal length starting from the interference mutation time until the monitoring radiation signal is completely divided; it should be noted that when the signal length of the monitoring radiation signal is insufficient for the reference traversal length during the traversal process, the remaining signal segment is taken as an interference local signal, which will not be further described here.

[0076] The characteristic local signal represents a signal segment that is not affected by non-Gaussian noise interference, and different interference local signals are affected by different degrees of non-Gaussian noise interference, so the characteristic local signal and the interference local signal can be further compared in terms of characteristic deviation to determine the signal anomaly degree of each interference local signal representing the degree of non-Gaussian noise interference; thereby further screening out the interference local signal with less non-Gaussian noise interference to make the subsequent signal filtering result more accurate. Preferably, in some possible implementation manners of the embodiments of the present application, the signal anomaly degree acquisition process comprises:

[0077] The DTW distance between the characteristic local signal and each interference local signal is normalized to determine the time domain deviation feature index of each interference local signal. According to the principle of dynamic time warping algorithm, the greater the DTW distance between the corresponding sequences of two signals, the smaller the similarity of the two signals in the time domain; therefore, the greater the time domain deviation feature index, the greater the characteristic deviation of the corresponding interference local signal from the characteristic local signal in the time domain, that is, the greater the influence of the corresponding characteristic local signal on the non-Gaussian noise interference, the more abnormal the corresponding signal, that is, the greater the signal anomaly degree.

[0078] After the corresponding feature deviation is calculated in the time domain, further quantization of the feature deviation is performed in the frequency domain, so that the accuracy of the subsequent signal abnormality degree is higher;In turn, the feature local signal and each interference local signal are taken as the target signal;Based on the product of the frequency spectrum width and the maximum value of the frequency spectrum signal corresponding to the target signal obtained by the Fourier transform, the frequency domain feature parameter of the target signal is determined;The frequency domain feature parameter is the integral of the frequency domain waveform pulse, and there is a large deviation between the interference local signal under non-Gaussian noise interference and the feature local signal under normal conditions in the frequency domain signal, so for each interference local signal, the greater the deviation between the corresponding frequency domain feature parameter and the frequency domain feature parameter of the feature local signal, the greater the influence of the corresponding interference local signal under non-Gaussian noise interference in the frequency domain, and the greater the corresponding signal abnormality degree;Therefore, the difference between the frequency domain feature parameter of the feature local signal and the frequency domain feature parameter of each interference local signal is further normalized to determine the frequency domain deviation feature index of each interference local signal;So that the greater the frequency domain deviation feature index, the greater the signal abnormality degree of the corresponding interference local signal.

[0079] Finally, combining the two dimensions of time domain and frequency domain, based on the correlation between the mean value of the time domain deviation feature index and the frequency domain deviation feature index, the signal abnormality degree of each interference local signal is determined;So that the greater the signal abnormality degree, the more abnormal the corresponding interference local signal, and the greater the degree of non-Gaussian noise interference.

[0080] In one specific implementation of the embodiment of the application, the signal abnormality degree acquisition process is represented by the formula: ; Wherein, is the signal abnormality degree of the first interference local signal; is the DTW distance between the first interference local signal and the feature local signal; is the time domain deviation feature index of the first interference local signal; is the frequency domain feature parameter of the first interference local signal; is the frequency domain feature parameter of the feature local signal; is the frequency domain deviation feature index of the first interference local signal.

[0081] Step S104: screening out normal radiation signals according to the signal abnormality degree;According to all normal radiation signals, the radiation signal is denoised by sequential statistical filtering.

[0082] Further, based on the signal abnormality degree of each interference partial signal, the normal radiation signal interfered by non-Gaussian noise is screened out, so that the filtering processing is performed based on the normal radiation signal, the interference of non-Gaussian noise on radiation monitoring is reduced, and the accuracy of radiation monitoring is improved.

[0083] Preferably, in some possible implementation manners of the embodiment of the present application, the obtaining process of the normal radiation signal includes:

[0084] The interference partial signal with a signal abnormality degree less than a preset abnormality threshold is taken as the normal radiation signal. In one specific implementation manner of the embodiment of the present application, the preset abnormality threshold is set to 0.3, which can be adjusted according to the specific implementation environment, and will not be further described here. In another specific implementation manner of the embodiment of the present application, the signal abnormality degrees of all interference partial signals are subjected to cluster analysis to obtain all signal clusters; the cluster analysis method adopts a k-means clustering algorithm, and the K value of the k-means clustering algorithm is obtained by an elbow method; the mean value of the signal abnormality degrees of all interference partial signals in each signal cluster is calculated to determine the corresponding reference abnormality degree; and all interference partial signals in the signal cluster with the minimum reference abnormality degree are taken as the normal radiation signal.

[0085] After the normal radiation signal is determined, the filtering method in the prior art is used for filtering processing to reduce the influence of noise on radiation monitoring to the greatest extent. Preferably, in some possible implementation manners of the embodiment of the present application, the process of radiation signal filtering by sequential statistical filtering according to all normal radiation signals includes:

[0086] After all normal radiation signals are arranged in time sequence and combined, the combined radiation signal is determined; the radiation signal filtering is performed on the combined radiation signal by sequential statistical filtering to determine the monitoring radiation signal; so that the influence of noise on the monitoring radiation signal is reduced as much as possible, and finally the plant radiation monitoring is performed according to the monitoring radiation signal, so that the radiation monitoring precision and accuracy are higher. It should be noted that the sequential statistical filtering is a technical means known to those skilled in the art, and the implementer can use other filtering methods according to the specific implementation environment, which will not be further limited and described here.

[0087] In summary, the method for filtering out the interference values of the plant radiation monitoring divides the monitoring radiation signal into each local waveform signal according to the fluctuation of the monitoring radiation signal, calculates the electromagnetic interference index of each local waveform signal based on the characteristics that the non-Gaussian noise interference caused by the start-stop operation of the power equipment leads to the mutation of the monitoring radiation signal, and then screens out the characteristic local signal not interfered by the non-Gaussian noise and the interference local signal possibly interfered by the non-Gaussian noise based on the electromagnetic interference index. Thus, the signal abnormality degree of each interference local signal corresponding to the signal interfered by the non-Gaussian noise is determined based on the deviation between the interference local signal and the characteristic local signal in the time domain and the frequency domain, so that the interference local signal and the characteristic local signal are screened out according to the signal abnormality degree, the filtering effect of the radiation signal filtering through the order statistics filtering based on the normal radiation signal is better, and the monitoring radiation signal after denoising is less interfered by the non-Gaussian noise.

[0088] The application also provides a system for filtering out the interference values of the plant radiation monitoring. Figure 2 The system is shown in the structural diagram of the system for filtering out the interference values of the plant radiation monitoring provided by one embodiment of the application, and the system comprises a data acquisition module 201, a first determination module 202, a second determination module 203 and a signal denoising module 204.

[0089] The data acquisition module 201 is used to acquire the monitoring radiation signal of each monitoring position after the start-stop operation of the power equipment in the plant.

[0090] The first determination module 202 is used to divide all the local waveform signals according to the signal value fluctuation of the monitoring radiation signal, determine the local fluctuation index of each local waveform signal according to the signal value mutation of the local waveform signal, determine the waveform mutation index of each local waveform signal according to the instantaneous change of the local fluctuation index of the local waveform signal and the signal width change in the time sequence, and determine the electromagnetic interference index of each local waveform signal according to the waveform mutation index and the local fluctuation index.

[0091] The second determination module 203 is used to divide the monitoring radiation signal into the characteristic local signal and all the interference local signals according to the time sequence distribution of the electromagnetic interference index, and determine the signal abnormality degree of each interference local signal according to the signal time domain deviation and the signal frequency domain deviation between each interference local signal and the characteristic local signal.

[0092] The signal denoising module 204 is used to screen out the normal radiation signal according to the signal abnormality degree, and perform the radiation signal denoising through the order statistics filtering based on all the normal radiation signals.

[0093] It should be noted that the system provided by the above embodiment is only used for example by dividing the above functional modules. In actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the system for filtering out the interference value of the plant radiation monitoring and the method for filtering out the interference value of the plant radiation monitoring provided by the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be described here.

[0094] The embodiment of the present application also provides a computer device, please refer to Figure 3 which shows a computer device structure schematic diagram provided by an embodiment of the present application, the computer device includes memory 301, processor 302 and computer program 303 stored in the memory 301 and running on the processor 302, wherein the processor 302 executes the computer program 303, so that the computer device can execute any one of the above-mentioned methods for filtering out the interference value of the plant radiation monitoring.

[0095] The embodiment of the present application also provides a computer program product, when the computer program product runs on the computer device, so that the computer device can execute any one of the above-mentioned methods for filtering out the interference value of the plant radiation monitoring.

[0096] The embodiment of the present application also provides a computer readable storage medium, the computer readable storage medium stores computer program code, when the computer program code runs on the computer device, so that the computer device can execute any one of the above-mentioned methods for filtering out the interference value of the plant radiation monitoring.

[0097] In the embodiments provided in the present application, it should be understood that the computer device, computer program product and computer readable storage medium provided are used to execute the corresponding method provided above, so the beneficial effects achieved can refer to the beneficial effects of the method provided above, which will not be described here.

[0098] It should be noted that the above-mentioned embodiment of the present application is only for description, and does not represent the advantages and disadvantages of the embodiment. The process depicted in the drawing does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0099] Each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. A method of filtering out plant site radiation monitoring interference values, characterized by, The method comprises: Collecting monitoring radiation signals of each monitoring position after power equipment start-stop operation in a plant area; According to signal value fluctuation of the monitoring radiation signals, all local waveform signals are divided; according to signal value mutation of each local waveform signal, corresponding local fluctuation index is determined; according to instantaneous change of the local fluctuation index of the local waveform signals in time sequence and signal width change, waveform mutation index of each local waveform signal is determined; according to the waveform mutation index and the local fluctuation index, electromagnetic interference index of each local waveform signal is determined; According to time sequence distribution of the electromagnetic interference index, the monitoring radiation signals are divided into characteristic local signals and all interference local signals; according to signal time domain deviation and signal frequency domain deviation between each interference local signal and the characteristic local signal, signal abnormality degree of each interference local signal is determined; According to the signal abnormality degree, normal radiation signals are screened out; all normal radiation signals are subjected to radiation signal denoising through order statistics filtering. The process of dividing the monitoring radiation signals into characteristic local signals and all interference local signals according to time sequence distribution of the electromagnetic interference index comprises: The first sampling time of the local waveform signal corresponding to the maximum electromagnetic interference index is taken as an interference mutation time; a local signal segment between the first sampling time of the monitoring radiation signals and the interference mutation time is taken as a characteristic local signal; According to time length of the characteristic local signal, reference traversal length is determined; the reference traversal length is used to traverse the monitoring radiation signals from the interference mutation time as a starting point until the monitoring radiation signals are traversed, so that all interference local signals are obtained.

2. The method of claim 1, wherein, The process of obtaining the local waveform signals comprises: All peak points and valley points of the monitoring radiation signals are determined through an automatic multi-scale peak detection algorithm; the monitoring radiation signals are divided into at least two local waveform signals at intervals of the valley points.

3. The method of filtering out plant-site radiation monitoring interference values of claim 2, wherein, The process of obtaining the local fluctuation index comprises: According to time interval between a sampling time corresponding to a peak point of each local waveform signal and a sampling time corresponding to a previous valley point, corresponding fluctuation rising time length is determined; difference between a signal value of the peak point of each local waveform signal and a signal value of the previous valley point is determined as waveform rising amplitude value; a ratio between the waveform rising amplitude value and the fluctuation rising time length is normalized to determine waveform rising steepness degree; According to difference between a signal value of each sampling time and a signal value of a previous sampling time in the monitoring radiation signals, signal change value of each sampling time is determined; mean value of signal change values of all sampling times corresponding to a peak point and a subsequent valley point of each local waveform signal is normalized to determine corresponding waveform falling steepness degree; According to mean value between the waveform rising steepness degree and the waveform falling steepness degree, local fluctuation index of each local waveform signal is determined.

4. The method of filtering out plant-site radiation monitoring interference values of claim 3, wherein, The process of obtaining the waveform mutation index comprises: determine a corresponding waveform pulse integral index according to a product between a time length corresponding to each local waveform signal and a waveform rising amplitude; determine a trend change difference of each local waveform signal by normalizing a difference between the waveform pulse integral index of each local waveform signal and the waveform pulse integral index of a previous local waveform signal; determine a fluctuation feature difference of each local waveform signal by normalizing a difference between the local fluctuation index of each local waveform signal and the local fluctuation index of a previous local waveform signal; determine a corresponding waveform mutation index according to a mean value between the trend change difference and the fluctuation feature difference.

5. The method of filtering out plant site radiation monitoring interference values of claim 1, wherein, The acquisition process of the electromagnetic interference index comprises: determine an electromagnetic interference index of each local waveform signal according to a product between the local fluctuation index and the waveform mutation index.

6. The method of filtering out plant site radiation monitoring interference values of claim 1, wherein, The acquisition process of the signal abnormality degree comprises: determine a time domain deviation feature index of each interference local signal by normalizing a DTW distance between the feature local signal and each interference local signal; determine a frequency domain feature parameter of the target signal based on a product between a frequency spectrum width and a maximum amplitude of a frequency spectrum signal corresponding to the target signal obtained through Fourier transform, by taking the feature local signal and each interference local signal as the target signal in turn; determine a frequency domain deviation feature index of each interference local signal by normalizing a difference between the frequency domain feature parameter of the feature local signal and the frequency domain feature parameter of each interference local signal; determine a signal abnormality degree of each interference local signal according to a mean value between the time domain deviation feature index and the frequency domain deviation feature index.

7. The method of filtering out plant site radiation monitoring interference values of claim 1, wherein, The acquisition process of the normal radiation signal comprises: take the interference local signal with a signal abnormality degree less than a preset abnormal threshold as the normal radiation signal.

8. The method of filtering out plant site radiation monitoring interference values of claim 1, wherein, The process of performing radiation signal denoising on all normal radiation signals through sequential statistical filtering comprises: combine the normal radiation signals arranged in time sequence to determine a combined radiation signal, perform radiation signal filtering on the combined radiation signal through sequential statistical filtering to determine a monitoring radiation signal, and perform plant radiation monitoring according to the monitoring radiation signal.

9. An apparatus for filtering out plant site radiation monitoring interference values, comprising: The device comprises: a data acquisition module configured to acquire monitoring radiation signals of each monitoring position after power equipment start-stop operation in a plant; a first determination module configured to divide all local waveform signals according to signal value fluctuation of the monitoring radiation signals, determine a local fluctuation index of each local waveform signal according to signal value mutation of the local waveform signal, determine a waveform mutation index of each local waveform signal according to instantaneous change of the local fluctuation index of the local waveform signal in time sequence and signal width change, and determine an electromagnetic interference index of each local waveform signal according to the waveform mutation index and the local fluctuation index. The second determining module is configured to divide the monitoring radiation signal into a characteristic local signal and all interference local signals according to the time sequence distribution of the electromagnetic interference index; and determine the signal abnormality degree of each interference local signal according to the signal time domain deviation and signal frequency domain deviation between each interference local signal and the characteristic local signal. The process of dividing the monitoring radiation signal into a characteristic local signal and all interference local signals according to the time sequence distribution of the electromagnetic interference index comprises: taking the first sampling time of the local waveform signal corresponding to the maximum electromagnetic interference index as the interference mutation time; and taking the local signal segment between the first sampling time of the monitoring radiation signal and the interference mutation time as the characteristic local signal; determining a reference traversal length according to the time length of the characteristic local signal; and traversing the monitoring radiation signal with the reference traversal length starting from the interference mutation time until the traversal of the monitoring radiation signal is completed, to obtain all interference local signals; The signal denoising module is configured to screen normal radiation signals according to the signal abnormality degree; and perform radiation signal denoising through sequential statistical filtering according to all normal radiation signals.

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