An interference spectrum identification method and device based on a two-channel spectrum, an electronic device, and a storage medium

Abstract

The application discloses a kind of interference spectrum identification method, device, electronic equipment and storage medium based on two-channel atlas, the method and device are applied to electronic equipment, specifically, the original PRPD atlas to be identified is carried out binary processing, obtains two binary PRPD atlas;The phase difference between two binary PRPD atlas is calculated;One binary PRPD atlas is compensated based on phase difference and phase compensation processing;Uncompensated binary PRPD atlas and compensated binary PRPD atlas are extracted features, obtain two morphological contour features;Based on the relationship between the difference between two morphological contour features and the preset similarity decision threshold, and phase difference determine whether original PRPD atlas is interference spectrum.This scheme can realize the identification of interference spectrum without human intervention.In the basis of identification, it can greatly reduce the atlas data amount of subsequent artificial expert analysis, so as to save manpower cost.

Description

Technical Field

[0001] This application relates to the field of ultra-high frequency partial discharge monitoring technology, and more specifically, to a method, apparatus, electronic device, and storage medium for interference spectrum identification based on dual-channel spectrum. Background Technology

[0002] Ultra-high frequency (UHF) partial discharge monitoring technology is currently the main means of identifying the types of partial discharge defects inside GIS (Gas Insulated Switcher) equipment. In the UHF detection process, the accumulated PRPD (Phase-Resolved Partial Discharge) spectrum can obtain statistical information on the phase, discharge amplitude, and discharge frequency of the partial discharge signal, and is widely used in the process of identifying partial discharge types in GIS.

[0003] In actual partial discharge detection, the original PRPD (partial discharge potential) spectra detected fall into two categories: interference spectra acquired when no partial discharge occurs within the GIS (Gas Insulation System), and partial discharge spectra acquired by UHF (Ultra-High Frequency) sensors when partial discharge is detected within the GIS. In an AC GIS, the electrical signals of phases A, B, and C differ by 120°. If, at the same moment, PRPD spectra acquired by UHF sensors of different phases all exhibit similar spectra characteristics and are 120° out of phase, then the event corresponding to the PRPD spectra at that moment is likely a partial discharge within the GIS. However, if the PRPD spectra acquired by two UHF sensors of different phases have similar characteristics and completely identical phase distributions, this does not conform to the pattern of partial discharge within the GIS. Therefore, the PRPD spectra at that moment must be generated by external interference signals and can be directly eliminated in subsequent analysis.

[0004] However, the inventors of this application have discovered that currently, determining whether a raw PRPD spectrum is an interference spectrum or a partial discharge spectrum requires experts to visually compare and analyze the spectra from multiple sensor channels before a conclusion can be reached. This is not only labor-intensive but also relies heavily on the expert's understanding of the distinguishing features of the spectra and their personal experience, resulting in poor accuracy of the analysis results. Summary of the Invention

[0005] In view of this, this application provides a method, apparatus, electronic device and storage medium for identifying interference maps based on dual-channel maps, used to identify whether the acquired PRPD map is an interference map, so as to improve the accuracy of PRPD map identification.

[0006] To achieve the above objectives, the following solution is proposed:

[0007] An interference spectrum identification method based on dual-channel spectra, applied to electronic devices, includes the following steps:

[0008] The original PRPD map to be identified is binarized to obtain a binarized PRPD map. The original PRPD map includes a first PRPD map and at least one second PRPD map. The binarized PRPD map includes a first binarized PRPD map corresponding to the first PRPD map and a second binarized PRPD map corresponding to the second PRPD map.

[0009] Calculate the phase difference between the first binarized PRPD map and the second binarized PRPD map;

[0010] Based on the phase difference, the second binarized PRPD map is subjected to phase compensation processing to obtain a corrected second binarized PRPD map;

[0011] Feature extraction is performed on the first binarized PRPD map and the modified second binarized PRPD map to obtain a first morphological contour feature corresponding to the first binarized PRPD map and a second morphological contour feature corresponding to the modified second binarized PRPD map;

[0012] Based on the relationship between the difference between the first morphological contour feature and the second morphological contour feature and the preset similarity decision threshold, and the phase difference, it is determined whether the original PRPD map is an interference map.

[0013] Optionally, the step of binarizing the original PRPD map to be identified to obtain a binarized PRPD map includes the following steps:

[0014] The original PRPD map is converted into a two-dimensional grayscale image;

[0015] Each pixel of the two-dimensional grayscale value is calibrated based on the background grayscale value to obtain the binarized PRPD map.

[0016] Optionally, calculating the phase difference between the first binarized PRPD map and the second binarized PRPD map includes the following steps:

[0017] Count the number of signal pixels at each coordinate value in the first binarized PRPD map and the second binarized PRPD map;

[0018] The phase difference is obtained by calculating the difference between the coordinate value corresponding to the maximum number of signal pixels in the first binarized PRPD map and the coordinate value corresponding to the maximum number of signal pixels in the second binarized PRPD map.

[0019] Optionally, the step of performing phase compensation processing on the second binarized PRPD map based on the phase difference to obtain a corrected second binarized PRPD map includes the following steps:

[0020] Determine the sign of the phase difference;

[0021] The second binarized PRPD map is shifted according to the sign of the phase difference to obtain the modified second binarized PRPD map.

[0022] Optionally, the step of shifting the second binarized PRPD map according to the sign of the phase difference to obtain the corrected second binarized PRPD map includes the following steps:

[0023] If the phase difference is positive, the second binarized PRPD map is shifted to the right to obtain the corrected second binarized PRPD map;

[0024] If the phase difference is negative, the second binarized PRPD map is shifted to the left to obtain the modified second binarized PRPD map.

[0025] Optionally, the step of extracting features from the first binarized PRPD map and the modified second binarized PRPD map to obtain a first morphological contour feature corresponding to the first binarized PRPD map and a second morphological contour feature corresponding to the modified second binarized PRPD map includes the following steps:

[0026] Calculate the first-order normalized central distance and the second-order normalized central distance of the first binarized PRPD map and the modified second binarized PRPD map, respectively;

[0027] The first morphological contour feature is obtained by calculating the first normalized center distance and the second normalized center distance of the first binarized PRPD map. The second morphological contour feature is obtained by calculating the first normalized center distance and the second normalized center distance of the modified second binarized PRPD map.

[0028] Optionally, the step of determining whether the original PRPD map is an interference map based on the relationship between the difference between the first morphological contour features and the second morphological contour features and a preset similarity decision threshold, and the phase difference, includes the following steps:

[0029] Based on the difference and the similarity judgment threshold, it is determined whether the first PRPD map and the second PRPD map are similar maps;

[0030] If the first PRPD map and the second PRPD map are similar maps and the phase difference is 0, then the original PRPD map is determined to be an interference map.

[0031] An interference spectrum identification device based on dual-channel spectra, applied to electronic devices, the interference spectrum identification device comprising:

[0032] The binarization processing module is configured to binarize the original PRPD map to be identified to obtain a binarized PRPD map. The original PRPD map includes a first PRPD map and at least one second PRPD map. The binarized PRPD map includes a first binarized PRPD map corresponding to the first PRPD map and a second binarized PRPD map corresponding to the second PRPD map.

[0033] The phase difference calculation module is configured to calculate the phase difference between the first binarized PRPD map and the second binarized PRPD map;

[0034] The compensation processing module is configured to perform phase compensation processing on the second binarized PRPD map based on the phase difference to obtain a corrected second binarized PRPD map;

[0035] The feature extraction module is configured to extract features from the first binarized PRPD map and the modified second binarized PRPD map to obtain a first morphological contour feature corresponding to the first binarized PRPD map and a second morphological contour feature corresponding to the modified second binarized PRPD map.

[0036] The recognition execution module is configured to determine whether the original PRPD map is an interference map based on the relationship between the difference between the first morphological contour feature and the second morphological contour feature and a preset similarity decision threshold, as well as the phase difference.

[0037] An electronic device includes at least one processor and a memory connected to the processor, wherein:

[0038] The memory is used to store computer programs or instructions;

[0039] The processor is used to execute the computer program or instructions to enable the electronic device to implement the interference pattern recognition method as described above.

[0040] A computer-readable storage medium is applied to an electronic device, the storage medium carrying one or more computer programs that can be executed by the electronic device, thereby enabling the electronic device to implement the interference pattern identification method as described above.

[0041] As can be seen from the above technical solution, this application discloses a method, apparatus, electronic device, and storage medium for identifying interference maps based on dual-channel maps. The method and apparatus are applied to electronic devices, specifically involving binarizing the original PRPD map to be identified to obtain two binarized PRPD maps; calculating the phase difference between the two binarized PRPD maps; performing phase compensation processing on one of the binarized PRPD maps based on the phase difference; extracting features from both the uncompensated and compensated binarized PRPD maps to obtain two morphological contour features; and determining whether the original PRPD map is an interference map based on the relationship between the difference between the two morphological contour features and a preset similarity decision threshold, as well as the phase difference. This solution can identify interference maps without human intervention. Eliminating interference maps based on identification significantly reduces the amount of map data required for subsequent manual expert analysis, thereby saving labor costs.

[0042] Secondly, the solution proposed in this application is entirely based on expert knowledge, requires no data resources, is computationally simple, and requires very little hardware cost, which further improves the efficiency of partial discharge signal decision. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of an interference pattern recognition method according to an embodiment of this application;

[0045] Figure 2 This is a schematic diagram of the first PRPD map and the second PRPD map according to an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of the first and second binarized PRPD maps according to an embodiment of this application.

[0047] Figure 4 This is a schematic diagram showing the density of signal pixels at each coordinate in two binarized PRPD maps of the application embodiment;

[0048] Figure 5 This is a schematic diagram of the first binarized PRPD map and the modified second binarized PRPD map according to an embodiment of this application;

[0049] Figure 6 This is a block diagram of an interference pattern recognition device according to an embodiment of this application;

[0050] Figure 7 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0052] Based on the problems addressed in this application, and to achieve similarity comparison and phase difference estimation of signal feature morphology in dual-channel PRPD maps, this application proposes a PRPD map similarity measurement method based on phase difference compensation. This method includes PRPD phase feature extraction, estimating the phase difference between the two maps through phase distribution features; secondly, synchronizing the phases within the PRPD maps based on the estimated phase difference, extracting the geometric morphological distribution features of the PRPD maps, and finally setting a measurement threshold to assess whether the dual-channel PRPD maps have the same phase distribution. This scheme can autonomously determine the signal phase distribution difference of the maps. For maps acquired by non-in-phase sensors, it can directly determine whether the dual-channel PRPD maps have similar features and a ±120° phase difference. If this condition is met, the map may be generated by partial discharge signals within the GIS. If the PRPD map features are similar and the phase difference is 0°, the dual-channel PRPD map can be identified as an interference map, greatly reducing the workload of subsequent expert analysis of partial discharge conditions based on PRPD maps. Based on the above principles, the following specific implementation method is proposed.

[0053] Figure 1 This is a flowchart of an interference pattern identification method according to an embodiment of this application.

[0054] like Figure 1 As shown, the interference pattern identification method provided in this embodiment is applied to an electronic device, which can be understood as a computer, server, cloud platform, or embedded device with data computing and information processing capabilities. The interference pattern identification method specifically includes the following steps:

[0055] S1. The original PRPD map to be identified is binarized to obtain a binarized PRPD map.

[0056] The original PRPD map includes a first PRPD map and at least one second PRPD map, denoted as PRPD1 and PRPD2, respectively, as follows: Figure 2As shown, the two PRPD maps have similar features, but their phases differ. The binarized PRPD maps include a first binarized PRPD map B_PRPD1 corresponding to the first PRPD map and a second binarized PRPD map B_PRPD2 corresponding to the second PRPD map.

[0057] The original PRPD maps generated by any two UHF sensors arranged in a GIS device within the same time period are the first PRPD map PRPD1 and at least one second PRPD map PRPD2. The original PRPD maps can be represented as a three-dimensional matrix h×w×3, where h is the total number of pixels corresponding to the amplitude of the map, w is the total number of pixels corresponding to the phase within the map, and 3 represents the three RGB color channels. The PRPD map is converted from the RGB three-channel to a two-dimensional grayscale image G_PRPD, represented as a two-dimensional matrix h×w. Based on the consistent grayscale distribution of the background pixels, the background and signal pixels within the PRPD map are binarized, with background pixels marked as 0 and signal pixels marked as 1, resulting in B_PRPD1 and B_PRPD2 respectively. Figure 3 As shown. The specific process is as follows:

[0058] First, input the 3D RGB image PRPD1, where the image matrices of the R, G, and B channels can be represented as PRPD1_R(i,j), PRPD1_G(i,j), and PRPD1_B(i,j), respectively. Here, (i,j) represents the specific spatial coordinates within the image, with i ranging from [1, w] and j ranging from [1, h]. The grayscale image can be represented as PRPD1_gr(i,j), as shown in the following equation:

[0059] PRPD1_gr(i,j)=αPRPD1_R(i,j)+βPRPD1_G(i,j)+γPRPD1_B(i,j)

[0060] Wherein, α, β, and γ are grayscale weighting coefficients, which can be defined independently. In this application, the mean coefficient is used, that is, the mean value is 1 / 3. The first PRPD image is processed into a two-dimensional grayscale image using the above method, and the second PRPD image is processed into a two-dimensional grayscale image using the same method.

[0061] Then, the background grayscale value of each PRPD map is input as G_ground. Based on the following formula, each pixel of the two-dimensional grayscale image of the first PRPD map is processed to be 1 or 0, thereby obtaining the first binary grayscale PRPD map B_PRPD1. In this application, the background grayscale value G_ground corresponding to the input PRPD map is 255.

[0062]

[0063] Using the same principles and methods, the two-dimensional grayscale image of the second PRPD map can be processed to obtain the second binarized grayscale PRPD map B_PRPD2.

[0064] S2. Calculate the phase difference between the first binarized PRPD map and the second binarized PRPD map.

[0065] This method estimates the phase difference of a dual-channel PRPD image based on two binarized PRPD images, B_PRPD1 and B_PRPD2. Specifically, it counts the number of signal pixels corresponding to each phase coordinate in the two binarized PRPD images, denoted as N1(p) and N2(p), where p represents the phase coordinate. The maximum value between N1(p) and N2(p) is found, and the phase coordinates corresponding to the maximum value are labeled as p1 and p2, respectively. The phase coordinate difference is Ep, thus estimating the phase difference of the dual-channel PRPD image as Phase_E. p The specific process is as follows:

[0066] First, calculate N1(p) using the first binarized PRPD map B_PRPD1 and the second binarized PRPD map B_PRPD2 according to the following formula.

[0067]

[0068] The calculation method for N2(p) is the same as that for N1(p).

[0069] Then, based on the PRPD spectrum phase coordinates p1 and p2 calculated from N1(p) and N2(p), the coordinate difference Ep and phase difference Phase_E are calculated. p The specific formula process is as follows:

[0070] E p =p1-p2,

[0071] Phase_E p =E p ×360 / w,

[0072] like Figure 4 As shown, 125 is the phase coordinate value corresponding to the maximum value of N1(p), and 92 is the phase coordinate value corresponding to the maximum value of N2(p). The phase coordinate difference between the two spectra is calculated to be 33.

[0073] S3. Perform phase compensation processing on the second binarized PRPD map based on the phase difference to obtain the corrected second binarized PRPD map.

[0074] That is, the phase difference is compensated by applying phase difference compensation to the second binarized PRPD map based on the calculated phase difference, resulting in a new corrected second binarized PRPD map B_PRPD2_new. This is achieved through the following steps:

[0075] First, determine the phase difference Phase_E. p The positive and negative.

[0076] When Phase_E p When Phase_E is positive, the entire B_PRPD2 map is shifted to the right; when Phase_E is positive, the entire B_PRPD2 map is shifted to the right. p When the value is negative, the entire B_PRPD2 map is shifted to the left, and the compensated binary map can be represented as B_PRPD2_new, as shown in the following example. Figure 5 As shown.

[0077] When Phase_E p When B_PRPD2_new is a positive value, its specific representation is as follows.

[0078] B_PRPD2_new(i,j)=B_PRPD2(i-|Phase_E p |,j)when(i-|Phase_E p |)>0

[0079] B_PRPD2_new(i,j)=B_PRPD2(i+w-|Phase_E p |,j)when(i-|Phase_E p |)≤0

[0080] When Phase_E p When the value is negative, B_PRPD2_new is represented as follows.

[0081] B_PRPD2_new(i,j)=B_PRPD2(i+|Phase_E p |,j)when(i+|Phase_E p |)≤w

[0082] B_PRPD2_new(i,j)=B_PRPD2(i-w+|Phase_E p |,j)when(i+|Phase_E p |)>w

[0083] S4. Extract features from the first binarized PRPD map and the modified second binarized PRPD map to obtain the first morphological contour features and the second morphological contour features.

[0084] A similarity threshold is set based on the extracted B_PRPD1 and B_PRPD2_new. Then, processing is performed using the following methods:

[0085] First, calculate the first-order normalized central moments and second-order normalized central moments of the two binarized maps, namely the first binarized PRPD map and the modified second binarized PRPD map: η 20 η 02 η 11 .

[0086] Then, the translation, scaling, and rotation invariant moments, h0 and h1, of the two binarized maps are calculated based on the center distance. The morphological features F of the binarized maps are defined. i [h0, h1]. Includes the first morphological contour feature F1 and the second morphological contour feature F2. See the table below for details:

[0087] <![CDATA[h0=0.63837]]> <![CDATA[h0=0.63306]]> <![CDATA[h1=1.60891]]> <![CDATA[h1=1.59299]]>

[0088] The following steps, using the first binarized PRPD map B_PRPD1 as an example, specifically include:

[0089] Given the input value g(i,j) of B_PRPD1, the formulas for calculating the first-order normalized central moments and the second-order normalized central moments are as follows.

[0090]

[0091] Then, based on the two central moments, the translation, scaling, and rotation invariant moments of the two binarized maps, h0 and h1, are calculated. The specific calculation process includes the following steps:

[0092] h0=η 20 +η 02 ,

[0093] h1=(η 20 -η 02 ) 2 +4η1 2 1,

[0094] Specifically obtained

[0095] S5. Interference map identification is performed based on the relationship between the difference between the first and second morphological contour features and the similarity decision threshold, as well as the phase difference.

[0096] Specifically, the threshold F is first determined based on the difference and similarity between two morphological contour features. th =[e_h o [e_h1] Determines whether the first PRPD map and the second PRPD map are similar maps. This threshold is determined empirically or based on specific experiments; that is, when the difference between F1 and F2 is less than F... t If the first PRPD map and the second PRPD map are similar maps, then they are similar maps.

[0097] Then, if the first PRPD map and the second PRPD map are similar maps, and the phase difference Phase_E obtained above is... p If the value is 0 or approximately 0, the original PRPD map is determined to be an interference map. Here, approximately 0 means less than a certain preset value, which is determined based on experience or experiments.

[0098] As can be seen from the above technical solution, this embodiment provides a method for identifying interference maps based on dual-channel maps. This method is applied to electronic devices. Specifically, it involves binarizing the original PRPD map to be identified, obtaining two binarized PRPD maps; calculating the phase difference between the two binarized PRPD maps; performing phase compensation processing on one of the binarized PRPD maps based on the phase difference; extracting features from both the uncompensated and compensated binarized PRPD maps to obtain two morphological contour features; and determining whether the original PRPD map is an interference map based on the relationship between the difference between the two morphological contour features and a preset similarity decision threshold, as well as the phase difference. This solution can identify interference maps without human intervention. Eliminating interference maps based on identification significantly reduces the amount of map data required for subsequent manual expert analysis, thereby saving labor costs.

[0099] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0100] Although the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous.

[0101] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0102] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer.

[0103] Figure 6 This is a block diagram of an interference pattern recognition device according to an embodiment of this application.

[0104] like Figure 1 As shown, the interference pattern recognition device provided in this embodiment is applied to an electronic device, which can be understood as a computer, server, cloud platform or embedded device with data calculation function and information processing capability. The interference pattern recognition device specifically includes a binarization processing module 10, a phase difference calculation module 20, a compensation processing module 30, a feature extraction module 40 and a recognition execution module 50.

[0105] The binarization module is used to binarize the original PRPD map to be identified, resulting in a binarized PRPD map.

[0106] The original PRPD map includes a first PRPD map and at least one second PRPD map, denoted as PRPD1 and PRPD2, respectively, as follows: Figure 2 As shown, the two PRPD maps have similar features, but their phases differ. The binarized PRPD maps include a first binarized PRPD map B_PRPD1 corresponding to the first PRPD map and a second binarized PRPD map B_PRPD2 corresponding to the second PRPD map.

[0107] The original PRPD maps generated by any two UHF sensors arranged in a GIS device within the same time period are the first PRPD map PRPD1 and at least one second PRPD map PRPD2. The original PRPD maps can be represented as a three-dimensional matrix h×w×3, where h is the total number of pixels corresponding to the amplitude of the map, w is the total number of pixels corresponding to the phase within the map, and 3 represents the three RGB color channels. The PRPD map is converted from the RGB three-channel to a two-dimensional grayscale image G_PRPD, represented as a two-dimensional matrix h×w. Based on the consistent grayscale distribution of the background pixels, the background and signal pixels within the PRPD map are binarized, with background pixels marked as 0 and signal pixels marked as 1, resulting in B_PRPD1 and B_PRPD2 respectively. Figure 3 As shown. The specific process is as follows:

[0108] First, input the 3D RGB image PRPD1, where the image matrices of the R, G, and B channels can be represented as PRPD1_R(i,j), PRPD1_G(i,j), and PRPD1_B(i,j), respectively. Here, (i,j) represents the specific spatial coordinates within the image, with i ranging from [1, w] and j ranging from [1, h]. The grayscale image can be represented as PRPD1_gr(i,j), as shown in the following equation:

[0109] PRPD1_gr(i,j)=αPRPD1_R(i,j)+βPRPD1_G(i,j)+γPRPD1_B(i,j)

[0110] Wherein, α, β, and γ are grayscale weighting coefficients, which can be defined independently. In this application, the mean coefficient is used, that is, the mean value is 1 / 3. The first PRPD image is processed into a two-dimensional grayscale image using the above method, and the second PRPD image is processed into a two-dimensional grayscale image using the same method.

[0111] Then, the background grayscale value of each PRPD map is input as G_ground. Based on the following formula, each pixel of the two-dimensional grayscale image of the first PRPD map is processed to be 1 or 0, thereby obtaining the first binary grayscale PRPD map B_PRPD1. In this application, the background grayscale value G_ground corresponding to the input PRPD map is 255.

[0112]

[0113] Using the same principles and methods, the two-dimensional grayscale image of the second PRPD map can be processed to obtain the second binarized grayscale PRPD map B_PRPD2.

[0114] The phase difference calculation module is used to calculate the phase difference between the first binarized PRPD map and the second binarized PRPD map.

[0115] This method estimates the phase difference of a dual-channel PRPD image based on two binarized PRPD images, B_PRPD1 and B_PRPD2. Specifically, it counts the number of signal pixels corresponding to each phase coordinate in the two binarized PRPD images, denoted as N1(p) and N2(p), where p represents the phase coordinate. The maximum value between N1(p) and N2(p) is found, and the phase coordinates corresponding to the maximum value are labeled as p1 and p2, respectively. The phase coordinate difference is Ep, thus estimating the phase difference of the dual-channel PRPD image as Phase_E. p The specific process is as follows:

[0116] First, calculate N1(p) using the first binarized PRPD map B_PRPD1 and the second binarized PRPD map B_PRPD2 according to the following formula.

[0117]

[0118] The calculation method for N2(p) is the same as that for N1(p).

[0119] Then, based on the PRPD spectrum phase coordinates p1 and p2 calculated from N1(p) and N2(p), the coordinate difference Ep and phase difference Phase_E are calculated. p The specific formula process is as follows:

[0120] E p =p1-p2,

[0121] Phase_E p =E p ×360 / w,

[0122] like Figure 4 As shown, 125 is the phase coordinate value corresponding to the maximum value of N1(p), and 92 is the phase coordinate value corresponding to the maximum value of N2(p). The phase coordinate difference between the two spectra is calculated to be 33.

[0123] The compensation processing module is used to perform phase compensation processing on the second binarized PRPD map based on the phase difference to obtain the corrected second binarized PRPD map.

[0124] That is, the phase difference is compensated by applying phase difference compensation to the second binarized PRPD map based on the calculated phase difference, resulting in a new corrected second binarized PRPD map B_PRPD2_new. This is achieved through the following steps:

[0125] First, determine the phase difference Phase_E. p The positive and negative.

[0126] When Phase_E pWhen Phase_E is positive, the entire B_PRPD2 map is shifted to the right; when Phase_E is positive, the entire B_PRPD2 map is shifted to the right. p When the value is negative, the entire B_PRPD2 map is shifted to the left, and the compensated binary map can be represented as B_PRPD2_new, as shown in the following example. Figure 5 As shown.

[0127] When Phase_E p When B_PRPD2_new is a positive value, its specific representation is as follows.

[0128] B_PRPD2_new(i,j)=B_PRPD2(i-|Phase_E p |,j)when(i-|Phase_E p |)>0

[0129] B_PRPD2_new(i,j)=B_PRPD2(i+w-|Phase_E p |,j)when(i-|Phase_E p |)≤0

[0130] When Phase_E p When the value is negative, B_PRPD2_new is represented as follows.

[0131] B_PRPD2_new(i,j)=B_PRPD2(i+|Phase_E p |,j)when(i+|Phase_E p |)≤w

[0132] B_PRPD2_new(i,j)=B_PRPD2(i-w+|Phase_E p |,j)when(i+|Phase_E p |)>w

[0133] The feature extraction module is used to extract features from the first binarized PRPD map and the modified second binarized PRPD map to obtain the first morphological contour features and the second morphological contour features.

[0134] A similarity threshold is set based on the extracted B_PRPD1 and B_PRPD2_new. Then, processing is performed using the following methods:

[0135] First, calculate the first-order normalized central moments and second-order normalized central moments of the two binarized maps, namely the first binarized PRPD map and the modified second binarized PRPD map: η 20 η 02 η 11 .

[0136] Then, the translation, scaling, and rotation invariant moments, h0 and h1, of the two binarized maps are calculated based on the center distance. The morphological features F of the binarized maps are defined. i [h0, h1]. Includes the first morphological contour feature F1 and the second morphological contour feature F2. See the table below for details:

[0137] <![CDATA[h0=0.63837]]> <![CDATA[h0=0.63306]]> <![CDATA[h1=1.60891]]> <![CDATA[h1=1.59299]]>

[0138] The following steps, using the first binarized PRPD map B_PRPD1 as an example, specifically include:

[0139] Given the input value g(i,j) of B_PRPD1, the formulas for calculating the first-order normalized central moments and the second-order normalized central moments are as follows.

[0140]

[0141] Then, based on the two central moments, the translation, scaling, and rotation invariant moments of the two binarized maps, h0 and h1, are calculated. The specific calculation process includes the following steps:

[0142] h0=η 20 +η 02 ,

[0143] h1=(η 20 -η 02 ) 2 +4η1 2 1,

[0144] Specifically obtained

[0145] The recognition execution module is used to identify interference maps based on the relationship between the difference between the first morphological contour features and the second morphological contour features and the similarity decision threshold, as well as the phase difference.

[0146] Specifically, the threshold F is first determined based on the difference and similarity between two morphological contour features. th =[e_h o [e_h1] Determines whether the first PRPD map and the second PRPD map are similar maps. This threshold is determined empirically or based on specific experiments; that is, when the difference between F1 and F2 is less than F... t If the first PRPD map and the second PRPD map are similar maps, then they are similar maps.

[0147] Then, if the first PRPD map and the second PRPD map are similar maps, and the phase difference Phase_E obtained above is... p If the value is 0 or approximately 0, the original PRPD map is determined to be an interference map. Here, approximately 0 means less than a certain preset value, which is determined based on experience or experiments.

[0148] As can be seen from the above technical solution, this embodiment provides an interference map identification device based on dual-channel maps. This device is applied to electronic devices. Specifically, it binarizes the original PRPD map to be identified, obtaining two binarized PRPD maps; calculates the phase difference between the two binarized PRPD maps; performs phase compensation processing on one of the binarized PRPD maps based on the phase difference; extracts features from both the uncompensated and compensated binarized PRPD maps, obtaining two morphological contour features; and determines whether the original PRPD map is an interference map based on the relationship between the difference between the two morphological contour features and a preset similarity decision threshold, as well as the phase difference. This solution can identify interference maps without human intervention. Eliminating interference maps based on identification significantly reduces the amount of map data required for subsequent manual expert analysis, thereby saving labor costs.

[0149] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0150] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0151] Figure 7 This is a block diagram of an electronic device according to an embodiment of this application.

[0152] The following is for reference. Figure 7 This document illustrates a structural diagram suitable for implementing the electronic device in the embodiments of this disclosure. The terminal device in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this disclosure.

[0153] The electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from an input device 706 into a random access memory (RAM) 703. The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0154] Typically, the following devices can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although electronic devices with various devices are shown in the figures, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0155] This application also provides an embodiment of a computer-readable storage medium.

[0156] This computer-readable storage medium is used in an electronic device and carries one or more computer programs. When these programs are executed by the electronic device, the device binarizes the original PRPD image to be identified, resulting in two binarized PRPD images. It then calculates the phase difference between the two binarized PRPD images, performs phase compensation on one of the binarized PRPD images based on the phase difference, extracts features from both the uncompensated and compensated binarized PRPD images to obtain two morphological contour features, and determines whether the original PRPD image is an interfering image based on the relationship between the difference between the two morphological contour features and a preset similarity threshold, as well as the phase difference. This solution can identify interfering images without human intervention. Eliminating interfering images after identification significantly reduces the amount of image data requiring subsequent human expert analysis, thus saving labor costs.

[0157] It should be noted that the computer-readable medium described above in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0158] In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0159] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0160] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0161] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0162] The technical solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for interference pattern recognition based on dual-channel spectra, applied to electronic devices, characterized in that, The interference pattern identification method includes the following steps: The original PRPD map to be identified is binarized to obtain a binarized PRPD map. The original PRPD map includes a first PRPD map and at least one second PRPD map. The binarized PRPD map includes a first binarized PRPD map corresponding to the first PRPD map and a second binarized PRPD map corresponding to the second PRPD map. The phase difference between the first binarized PRPD map and the second binarized PRPD map is calculated by counting the number of signal pixels at each coordinate value in the first and second binarized PRPD maps, calculating the difference between the coordinate value corresponding to the maximum number of signal pixels in the first binarized PRPD map and the coordinate value corresponding to the maximum number of signal pixels in the second binarized PRPD map, and obtaining the phase difference. Based on the phase difference, the second binarized PRPD map is subjected to phase compensation processing to obtain a corrected second binarized PRPD map. Specifically, the sign of the phase difference is determined, and the second binarized PRPD map is shifted according to the sign of the phase difference to obtain the corrected second binarized PRPD map. Feature extraction is performed on the first binarized PRPD map and the modified second binarized PRPD map to obtain a first morphological contour feature corresponding to the first binarized PRPD map and a second morphological contour feature corresponding to the modified second binarized PRPD map; Based on the relationship between the difference between the first morphological contour feature and the second morphological contour feature and a preset similarity judgment threshold, and the phase difference, it is determined whether the original PRPD map is an interference map. Specifically, it is determined whether the first PRPD map and the second PRPD map are similar maps based on the difference and the similarity judgment threshold. If the first PRPD map and the second PRPD map are similar maps and the phase difference is 0, then the original PRPD map is determined to be an interference map.

2. The interference pattern identification method as described in claim 1, characterized in that, The step of binarizing the original PRPD map to be identified to obtain a binarized PRPD map includes the following steps: The original PRPD map is converted into a two-dimensional grayscale image; Each pixel of the two-dimensional grayscale image is calibrated based on the background grayscale value to obtain the binarized PRPD map.

3. The interference pattern identification method as described in claim 1, characterized in that, The step of shifting the second binarized PRPD map according to the sign of the phase difference to obtain the corrected second binarized PRPD map includes the following steps: If the phase difference is positive, the second binarized PRPD map is shifted to the right to obtain the corrected second binarized PRPD map; If the phase difference is negative, the second binarized PRPD map is shifted to the left to obtain the modified second binarized PRPD map.

4. The interference pattern identification method as described in claim 1, characterized in that, The step of extracting features from the first binarized PRPD map and the modified second binarized PRPD map to obtain a first morphological contour feature corresponding to the first binarized PRPD map and a second morphological contour feature corresponding to the modified second binarized PRPD map includes the following steps: Calculate the first-order normalized central distance and the second-order normalized central distance of the first binarized PRPD map and the modified second binarized PRPD map, respectively; The first morphological contour feature is obtained by calculating the first normalized center distance and the second normalized center distance of the first binarized PRPD map. The second morphological contour feature is obtained by calculating the first normalized center distance and the second normalized center distance of the modified second binarized PRPD map.

5. An interference spectrum identification device based on dual-channel spectrum, applied to electronic equipment, characterized in that, The interference pattern identification device includes: The binarization processing module is configured to binarize the original PRPD map to be identified to obtain a binarized PRPD map. The original PRPD map includes a first PRPD map and at least one second PRPD map. The binarized PRPD map includes a first binarized PRPD map corresponding to the first PRPD map and a second binarized PRPD map corresponding to the second PRPD map. The phase difference calculation module is configured to calculate the phase difference between the first binarized PRPD map and the second binarized PRPD map. Specifically, it counts the number of signal pixels at each coordinate value in the first and second binarized PRPD maps, calculates the difference between the coordinate value corresponding to the maximum number of signal pixels in the first binarized PRPD map and the coordinate value corresponding to the maximum number of signal pixels in the second binarized PRPD map, and obtains the phase difference. The compensation processing module is configured to perform phase compensation processing on the second binarized PRPD map based on the phase difference to obtain a corrected second binarized PRPD map. Specifically, it determines the sign of the phase difference and shifts the second binarized PRPD map according to the sign of the phase difference to obtain the corrected second binarized PRPD map. The feature extraction module is configured to extract features from the first binarized PRPD map and the modified second binarized PRPD map to obtain a first morphological contour feature corresponding to the first binarized PRPD map and a second morphological contour feature corresponding to the modified second binarized PRPD map. The recognition execution module is configured to determine whether the original PRPD map is an interference map based on the relationship between the difference between the first morphological contour feature and the second morphological contour feature and a preset similarity judgment threshold, and the phase difference. Specifically, it determines whether the first PRPD map and the second PRPD map are similar maps based on the difference and the similarity judgment threshold. If the first PRPD map and the second PRPD map are similar maps and the phase difference is 0, then the original PRPD map is determined to be an interference map.

6. An electronic device, characterized in that, The electronic device includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instructions to enable the electronic device to implement the interference pattern recognition method as described in any one of claims 1 to 4.

7. A computer-readable storage medium for use in electronic devices, characterized in that, The storage medium carries one or more computer programs that can be executed by the electronic device, thereby enabling the electronic device to implement the interference pattern recognition method as described in any one of claims 1 to 4.

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