Grounding grid topological structure detection method and detection device based on adaptive differential

Abstract

The invention provides a grounding grid topological structure detection method and device based on adaptive differential. The method comprises the following steps: obtaining a transverse magnetic field component and a longitudinal magnetic field component of an area to be detected; performing peak extraction on the transverse magnetic field component and the longitudinal magnetic field component, and performing peak-dividing fitting to obtain parameters of each peak; and constructing a differential threshold, carrying out adaptive differential based on the parameter of each wave crest to obtain a differential result, and carrying out superposition imaging on all the differential results to obtain a grounding grid topological structure of the to-be-detected area. According to the scheme, different types of magnetic field wave crests of each magnetic field observation line are identified through the KNN algorithm, and adaptive differentiation is carried out on different wave crests through a threshold value judgment mode, so that the grounding topology magnetic field characteristics of a weak peak area are remarkably enhanced.

Description

Technical Field

[0001] The present application relates to the field of grounding grid topology structure detection, and in particular to a grounding grid topology structure detection method and detection device based on adaptive differentiation. Background Art

[0002] As an important part of the substation, the grounding grid plays the role of discharging fault current and improving the surface potential distribution. The grounding grid is usually composed of galvanized flat steel or copper and is buried underground for a long time as a concealed project. Therefore, the corrosion or breakpoints of the grounding grid underground are the main reasons for the deterioration of the grounding grid performance and the reduction of reliability. In addition, the corrosion or breakpoints of the grounding grid will also threaten the stable movement of the power system and the safety of equipment and personnel. Therefore, it is very important to diagnose the corrosion degree of the grounding grid. At present, the more mature grounding grid corrosion degree detection mostly needs to be carried out under the condition that the buried position and topological structure of the grounding grid conductor are known. However, in actual projects, the loss of the substation drawings that have been in operation for a long time, or the difference between the topological structure of the substation after transformation and the drawings will bring great difficulties to the subsequent grounding grid corrosion diagnosis and maintenance work.

[0003] CN103135137B proposes a grounding network topology detection method based on differential method, which injects current from one point and extracts current from another point through a grounding lead in a measurement area, measures the magnetic induction intensity of the ground surface perpendicular to the ground surface or the magnetic induction intensity parallel to the ground surface, and then obtains the modulus of the first-order derivative, the modulus of the third-order derivative, the modulus of the fifth-order derivative of the magnetic induction intensity perpendicular to the ground surface or the modulus of the second-order derivative and the modulus of the fourth-order derivative of the magnetic induction intensity parallel to the ground surface by differential method, and then obtains the coordinate position of the main lobe peak, that is, the position of the grounding network branch in the measurement area, and finally draws the grounding network topology according to the position of the grounding network branch. Although the whole detection process is simple and the calculation amount is small, CN103135137B does not take into account that discrete data differentiation is sensitive to abnormal data, so it will significantly increase the positioning error. At the same time, due to the scattered flow effect of the grounding network, the weak magnetic field area does not have a good detection capability.

[0004] CN109061379B proposes a grounding network topology structure and breakpoint identification method based on the induced voltage differential method. It first uses a transient electromagnetic central loop device to measure the electromagnetic response of the grounding network, and performs even-order differential processing along the x-direction and y-direction measurement lines for the induced voltage response of the grounding network measured at the same time. The positions of the y-direction conductor and the x-direction conductor of the grounding network are obtained by drawing the induced voltage x-direction and y-direction even-order differential slice diagrams. Finally, the x-direction and y-direction measurement line data after the even-order differential are merged, and the induced voltage even-order differential synthetic slice diagram is drawn to obtain the grid topology structure of the complete grounding network. However, under normal circumstances, due to the relatively weak magnetic field signal and the strong electromagnetic interference of the substation, the measurement data obtained by CN109061379B will have errors.

[0005] In summary, how to reduce the electromagnetic interference of the substation to detect the weak grounding network topology magnetic field characteristics is a problem that needs to be solved urgently in the existing technology. Summary of the invention

[0006] The embodiment of the present application provides a grounding grid topology structure detection method and detection device based on adaptive differentiation. This scheme uses the KNN algorithm to identify different types of magnetic field peaks for each magnetic field observation line, and then adaptively differentiates different peaks through threshold judgment, thereby significantly enhancing the grounding topology magnetic field characteristics in the weak peak area.

[0007] In a first aspect, an embodiment of the present application provides a grounding grid topology detection method based on adaptive differentiation, the method comprising:

[0008] Deploy multiple transverse magnetic field observation lines and multiple longitudinal magnetic field observation lines in the area to be detected, each transverse magnetic field observation line obtains a set of transverse magnetic field components, and each longitudinal magnetic field observation line obtains a set of longitudinal magnetic field components;

[0009] The peak segmentation algorithm is used to segment the peaks in each group of transverse magnetic field components and the peaks in each group of longitudinal magnetic field components, and the peak features of each peak are extracted and input into the constructed peak type judgment model to obtain the peak type of each peak;

[0010] Only the peaks with bell-shaped and bell-shaped tailed peaks in each transverse magnetic field component and each longitudinal magnetic field component are retained as characteristic peaks, the peak information of each characteristic peak in each transverse magnetic field observation line is fitted as a function representation, and then the peak fitting is performed to obtain the peak parameters of each characteristic peak in each transverse magnetic field observation line, and the peak information of each characteristic peak in each longitudinal magnetic field observation line is fitted as a function representation, and then the peak fitting is performed to obtain the peak parameters of each characteristic peak in each longitudinal magnetic field observation line;

[0011] Constructing a differential threshold, performing second-order differentiation on the peak parameters of characteristic peaks greater than the differential threshold in each transverse magnetic field observation line to obtain a first transverse differential component, performing fourth-order differentiation on the peak parameters of characteristic peaks less than or equal to the differential threshold in each transverse magnetic field observation line to obtain a second transverse differential component, integrating the first transverse differential component and the second transverse differential component of each transverse magnetic field observation line to obtain a differential result of each transverse magnetic field observation line, performing second-order differentiation on the peak parameters of characteristic peaks greater than the differential threshold in each longitudinal magnetic field observation line to obtain a first longitudinal differential component, performing fourth-order differentiation on the peak parameters of characteristic peaks less than or equal to the differential threshold in each longitudinal magnetic field observation line to obtain a second longitudinal differential component, integrating the first longitudinal differential component and the second longitudinal differential component of each longitudinal magnetic field observation line to obtain a differential result of each longitudinal magnetic field observation line;

[0012] The differential result of each transverse magnetic field observation line is superimposed with the differential result of each longitudinal magnetic field observation line to obtain the grounding grid topology structure of the area to be detected.

[0013] In a second aspect, an embodiment of the present application provides a grounding grid topology detection device based on adaptive differentiation, comprising:

[0014] An acquisition module is used to deploy multiple transverse magnetic field observation lines and multiple longitudinal magnetic field observation lines in the area to be detected, each transverse magnetic field observation line acquires a set of transverse magnetic field components, and each longitudinal magnetic field observation line acquires a set of longitudinal magnetic field components;

[0015] The peak type extraction module uses a peak segmentation algorithm to segment the peaks in each group of transverse magnetic field components and the peaks in each group of longitudinal magnetic field components, extracts the peak features of each peak, and then inputs them into the constructed peak type judgment model to obtain the peak type of each peak;

[0016] A peak fitting module is used to retain the peaks of bell-shaped and bell-shaped tailed peaks in each transverse magnetic field component and each longitudinal magnetic field component as characteristic peaks, fit the peak information of each characteristic peak in each transverse magnetic field observation line into a function representation, and then perform peak fitting to obtain the peak parameters of each characteristic peak in each transverse magnetic field observation line, and fit the peak information of each characteristic peak in each longitudinal magnetic field observation line into a function representation, and then perform peak fitting to obtain the peak parameters of each characteristic peak in each longitudinal magnetic field observation line;

[0017] An adaptive differential module is used to perform second-order differentials on the peak parameters of characteristic peaks greater than the differential threshold in each transverse magnetic field observation line to obtain a first transverse differential component, perform fourth-order differentials on the peak parameters of characteristic peaks less than or equal to the differential threshold in each transverse magnetic field observation line to obtain a second transverse differential component, integrate the first transverse differential component and the second transverse differential component of each transverse magnetic field observation line to obtain a differential result of each transverse magnetic field observation line, perform second-order differentials on the peak parameters of characteristic peaks greater than the differential threshold in each longitudinal magnetic field observation line to obtain a first longitudinal differential component, perform fourth-order differentials on the peak parameters of characteristic peaks less than or equal to the differential threshold in each longitudinal magnetic field observation line to obtain a second longitudinal differential component, and fuse the first longitudinal differential component and the second longitudinal differential component of each longitudinal magnetic field observation line to obtain a differential result of each longitudinal magnetic field observation line.

[0018] The imaging module is used to superimpose the differential result of each transverse magnetic field observation line with the differential result of each longitudinal magnetic field observation line to obtain the grounding grid topology structure of the area to be detected.

[0019] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a grounding grid topology structure detection method based on adaptive differentiation.

[0020] In a fourth aspect, an embodiment of the present application provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process, and the process includes a grounding grid topology structure detection method based on adaptive differentiation.

[0021] The main contributions and innovations of the present invention are as follows:

[0022] This scheme extracts the peak information from the detected magnetic field components by constructing a peak extraction algorithm, and then performs peak fitting after expressing each peak information as a function to separate the overlapping peaks in the same magnetic field observation line, while effectively removing noise interference and obtaining the buried depth of the conductor; this scheme constructs a differential threshold to perform second-order or fourth-order differentials on the peaks corresponding to different peak parameter values, which can enhance the details of the weak magnetic field area at the edge of the grounding grid, reduce the impact of uneven current distribution, and suppress the problem of the differential method being sensitive to noise, thereby obtaining a more complete and clear grounding grid topology.

[0023] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a flow chart of a grounding grid topology detection method based on adaptive differentiation according to an embodiment of the present application; Figure 2 is a logic flow chart of a grounding grid topology structure detection method based on adaptive differentiation according to an embodiment of the present application; Figure 3 is a schematic diagram of constructing a transverse magnetic field observation line and a longitudinal magnetic field observation line in a region to be detected according to an embodiment of the present application; Figure 4 is a schematic diagram of classification error of a peak extraction algorithm under different K values ​​according to an embodiment of the present application; Figure 5 is a schematic diagram of a peak overlap situation according to an embodiment of the present application; Figure 6 is a peak attenuation schematic diagram according to an embodiment of the present application; Figure 7 is a schematic diagram of a differential result after adaptive differential compensation according to an embodiment of the present application; Figure 8 is a schematic diagram of a grounding grid topology structure to a region to be detected according to an embodiment of the present application; Fig. 9 is a structural block diagram of a grounding grid topology detection device based on adaptive differentiation according to an embodiment of the present application; Fig.10 It is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0035] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0036] Embodiment 1

[0037] The embodiment of the present application provides a grounding grid topology structure detection method based on adaptive differentiation, which uses the KNN algorithm to identify different types of magnetic field peaks of each magnetic field observation line, and then adaptively differentiates different peaks by threshold judgment, thereby significantly enhancing the grounding topology magnetic field characteristics of the weak peak area. Specifically, reference Figure 1 as well as Figure 2 , the method comprising:

[0038] Deploy multiple transverse magnetic field observation lines and multiple longitudinal magnetic field observation lines in the area to be detected, each transverse magnetic field observation line obtains a set of transverse magnetic field components, and each longitudinal magnetic field observation line obtains a set of longitudinal magnetic field components;

[0039] The peak segmentation algorithm is used to segment the peaks in each group of transverse magnetic field components and the peaks in each group of longitudinal magnetic field components, and the peak features of each peak are extracted and input into the constructed peak type judgment model to obtain the peak type of each peak;

[0040] Only the peaks with bell-shaped and bell-shaped tailed peaks in each transverse magnetic field component and each longitudinal magnetic field component are retained as characteristic peaks, the peak information of each characteristic peak in each transverse magnetic field observation line is fitted as a function representation, and then the peak fitting is performed to obtain the peak parameters of each characteristic peak in each transverse magnetic field observation line, and the peak information of each characteristic peak in each longitudinal magnetic field observation line is fitted as a function representation, and then the peak fitting is performed to obtain the peak parameters of each characteristic peak in each longitudinal magnetic field observation line;

[0041] Constructing a differential threshold, performing second-order differentiation on the peak parameters of characteristic peaks greater than the differential threshold in each transverse magnetic field observation line to obtain a first transverse differential component, performing fourth-order differentiation on the peak parameters of characteristic peaks less than or equal to the differential threshold in each transverse magnetic field observation line to obtain a second transverse differential component, integrating the first transverse differential component and the second transverse differential component of each transverse magnetic field observation line to obtain a differential result of each transverse magnetic field observation line, performing second-order differentiation on the peak parameters of characteristic peaks greater than the differential threshold in each longitudinal magnetic field observation line to obtain a first longitudinal differential component, performing fourth-order differentiation on the peak parameters of characteristic peaks less than or equal to the differential threshold in each longitudinal magnetic field observation line to obtain a second longitudinal differential component, integrating the first longitudinal differential component and the second longitudinal differential component of each longitudinal magnetic field observation line to obtain a differential result of each longitudinal magnetic field observation line;

[0042] The differential result of each transverse magnetic field observation line is superimposed with the differential result of each longitudinal magnetic field observation line to obtain the grounding grid topology structure of the area to be detected.

[0043] In this solution, the intervals between the transverse magnetic field observation lines and the intervals between the longitudinal magnetic field observation lines are equal, and the area formed by the plurality of transverse magnetic field observation lines and the longitudinal magnetic field observation lines completely covers the area to be detected.

[0044] For example, the area to be detected in this solution is a square, and the schematic diagram of constructing the transverse magnetic field observation line and the longitudinal magnetic field observation line in the area to be detected is as follows: Figure 3 As shown, in order to verify the accuracy of this scheme, the area to be detected is divided into 3 sub-areas: Figure 3 Area ① is the area where conductors are arranged at non-uniform spacing. The intervals of the conductors from left to right are set to 2.5m, 2.5m, 7.5, 2.5m, and 5m. Area ② is the inclined conductor area. When the conductor is not perpendicular to the observation line, the amplitude of the magnetic field peak will attenuate. When θ=45°, the attenuation is maximum. Therefore, the conductor inclination angle is set to 45°. Area ③ is regularly arranged at equal spacing. The spacing between conductors in area ③ is 5m. The diameter of the conductor in the area to be detected is set to 40mm, the conductor resistivity is 1.58×10-7Ω·m, the soil resistivity is 80Ω·m, and the burial depth is 0.8m.

[0045] In some specific embodiments, the transverse magnetic field component and the longitudinal magnetic field component are obtained based on the transverse magnetic field observation line and the longitudinal magnetic field observation line by injecting a different frequency current into the conductor in the detection area. Specifically, Figure 3 In the figure, a rectangular coordinate system is established with the lower left corner of the grounding grid 2.5m away from the boundary as the origin. A current of 1A is injected along A(2.5,22.5) and drawn along B(22.5,2.5). The horizontal and vertical observation line spacing is 0.1m, the number of observation lines is 251, and the length of a single observation line is 25m.

[0046] Specifically, the transverse magnetic field component and the longitudinal magnetic field component are horizontal magnetic components of the area to be detected, and are represented by Bx and By.

[0047] In this scheme, the peaks in the magnetic field component are first segmented and the characteristic vector of each peak is extracted and then input into the constructed peak extraction algorithm to obtain the peak information.

[0048] Furthermore, when segmenting the peaks in the magnetic field component, a local minimum point marking method is used, that is, the curve between the points corresponding to two local minima is selected as a peak. Specifically, the local minimum x0 satisfies the following formula:

[0049]

[0050] In this scheme, the peak type judgment model is based on the KNN algorithm. By constructing a peak feature set including multiple peak differences, and comparing the peak features in the peak category feature set with the peak features input into the peak type judgment model one by one, K peak features that are closest to the input peak features are selected, and the peak category with the highest frequency in the K peak features is counted as the category of the input peak type judgment model. Specifically:

[0051] First, a peak feature set marked with each peak category is constructed, and the data in the peak feature set is normalized. The marked peak categories include bell-shaped, bell-shaped tailing, sine-shaped and oblique-shaped peaks. The bell-shaped and bell-shaped tailing are characteristic peaks, and the sine-shaped and oblique-shaped are non-characteristic peaks. The curve mean, curve variance, curve skewness coefficient, curve kurtosis coefficient and curve waveform length of each peak are selected as peak features. The calculation method of the curve mean, variance, skewness coefficient, kurtosis coefficient and waveform length of the peak is shown in the following formula:

[0052]

[0053] Wherein, t1 is the mean of the peak curve, t2 is the variance of the curve, t3 is the skewness coefficient of the curve, t4 is the kurtosis coefficient of the curve, t5 is the waveform length of the curve, x(n), n = 1, 2, ..., N is the peak sequence obtained from a magnetic field observation line, x L and x R are the left and right endpoints of the peak sequence respectively.

[0054] Then, a suitable K value is set according to the number of peaks in the constructed peak feature set. The K features closest to the input peak feature are selected from the peak feature set by calculating the Euclidean distance between the input peak feature and each peak in the peak feature set. Finally, sample statistics are performed on the selected K features, and the number of each peak category in the K feature vectors is counted. The peak category with the highest frequency is the category of the sample feature. The calculation formula for the Euclidean distance between the sample feature and each peak in the peak feature set is as follows:

[0055]

[0056] Among them, T i represents the sample features of the i-th output, T j represents the jth peak feature in the peak feature set, l represents the Tth i The lth characteristic attribute value in , that is, the curve mean, curve variance, curve skewness coefficient, curve kurtosis coefficient and curve waveform length, so the maximum value of l is 5, and the sample characteristics mentioned above are the characteristic representations of the transverse magnetic field component or the longitudinal magnetic field component.

[0057] In some specific embodiments, in order to ensure the accuracy of the peak type judgment model, 1000 peak samples with marked peak categories are selected from the peak feature set as a training set to train the classifier. The number of peak sample data of each type in the training set is 250. Then, 100 peak samples are additionally selected from the peak feature set as a test set to test the trained classifier. The classification errors under different K values ​​are shown in FIG. Figure 4 shown.

[0058] In this scheme, through The function is used to fit the peak information of each transverse magnetic field observation line or the peak information of each longitudinal magnetic field observation line into a function representation, wherein the peak information includes x, c, A c and h c , x represents the peak position in the transverse magnetic field observation line or the longitudinal magnetic field observation line, c represents the center position of the corresponding peak, A c h is a characteristic parameter reflecting the magnetic permeability of the spatial magnetic field and the magnitude of the excitation current. c Represents the buried depth of the conductor.

[0059] Specifically, the peak position x and the center position c of each peak in the transverse magnetic field observation line or the longitudinal magnetic field observation line can be directly observed, so A can be obtained by fitting based on x and c. c and h c .

[0060] In this scheme, the peak fitting uses the function representation of each peak in a single magnetic field observation line and the function representation of the actual magnetic field curve to construct the sum of square errors, and iteratively adjusts the function representation of each peak in a single magnetic field observation line so that the sum of square errors meets the set threshold.

[0061] Specifically, peak fitting mainly decomposes the peaks formed by the superposition of single magnetic field observation lines, so that the grounding grid topology of the area to be detected can be better represented by the peaks. In addition, since the original discrete data is affected by the measurement environment, direct differentiation will amplify the error and cause inaccurate positioning results. After fitting, no error will be introduced.

[0062] Specifically, the construction method of the sum of squared errors is expressed by the following formula:

[0063]

[0064] Where M is the sum of squared errors, g(j) is the function representation of each peak in a single magnetic field observation line, and g * (j) represents the discrete data of the actual magnetic field curve, and m is the number of peaks in a single magnetic field observation line, so

[0065] Furthermore, in the step of “adjusting the function representation of each peak in a single magnetic field observation line in an iterative manner”, by changing the characteristic parameter A reflecting the magnetic permeability of the spatial magnetic field and the magnitude of the excitation current c and the conductor burial depth h c To adjust the function representation of each characteristic peak, specifically, by defining the partial derivative, adjustment amount, step size and adjustment factor for A c Perform iterative adjustment by defining h c The partial derivative, adjustment amount, step size and adjustment factor of h c Make iterative adjustments.

[0066] Specifically, the derivative method is used to solve A c The formula for the partial derivative of is as follows:

[0067]

[0068] Among them, h ci Indicates the buried depth of the conductor of the i-th characteristic peak in the j observation curve.

[0069] Specifically, the derivative method is used to solve h c The formula for the partial derivative of is as follows:

[0070]

[0071] Among them, A ciIt represents the characteristic parameter of the spatial magnetic field permeability and excitation current of the i-th characteristic peak in the j observation curve.

[0072] In this scheme, A ci The formula for iterative adjustment is as follows:

[0073]

[0074] Where ΔA ci (n) is A ci The iterative adjustment amount, p a A ci The step size, β a A ci The adjustment factor.

[0075] In this scheme, h ci The formula for iterative adjustment is as follows:

[0076]

[0077] Where Δh ci (n) is h c The iterative adjustment amount, p h h c The step size, β h h c The adjustment factor.

[0078] In each round of iterative adjustment, judge ||ΔA ci (n+1)||≤ε,||Δh ci (n+1)‖≤ε is true, if true, then the peak parameters of each characteristic peak on the magnetic field observation line are output, if not true, then according to the iterative adjustment formula, let ΔA ci (n) = ΔA ci (n+1), Δh ci (n) = Δh ci Iteration continues after (n+1), where ε is the set threshold, the peak parameter is the same as the content of the peak information, and the peak parameter is the adjusted peak information.

[0079] Specifically, when fitting the function of the magnetic field observation line, there may be overlapping peaks, such as Figure 5 As shown in the X1 and X2 curves in FIG, these overlapping curves can be separated and noise interference can be effectively removed by peak fitting. At the same time, the buried depth of the conductor can also be obtained by fitting the peak parameters.

[0080] In this solution, the differential threshold construction formula is as follows:

[0081]

[0082] Among them, σ is the differential threshold, Bx(i, j), By(i, j) are the sets of point data measured in the area to be detected.

[0083] Specifically, the formula for the fourth-order differential of the peak parameters is expressed as follows:

[0084]

[0085] The formula for the second-order differential of the peak parameters is as follows:

[0086]

[0087] Among them, c and c' represent the center position of the crest curve, A and A' are characteristic parameters reflecting the magnetic permeability of the spatial magnetic field and the magnitude of the excitation current, and h and h' are the buried depths of the conductor.

[0088] Specifically, the wave peaks far away from the current injection point and the inclined conductor will show obvious attenuation, such as Figure 6 As shown, the differential result after adaptive differential compensation is as follows Figure 7 As shown by Figure 7 It can be seen that the peak characteristics of the weak peak area are significantly enhanced through adaptive differentiation.

[0089] In this scheme, the differential result of each transverse magnetic field observation line is superimposed with the differential result of each longitudinal magnetic field observation line to obtain the grounding grid topology of the area to be detected. Figure 8 As shown, it can be seen that not only the details of the weak magnetic field area at the edge of the grounding grid are strengthened, but also the impact of uneven current distribution is reduced. At the same time, the problem of the differential method being sensitive to noise is suppressed, and a more complete and clear grounding grid topology is obtained.

[0090] Embodiment 2

[0091] Based on the same idea, refer to Figure 8 , the present application also proposes a grounding grid topology structure detection device based on adaptive differentiation, comprising:

[0092] An acquisition module is used to deploy multiple transverse magnetic field observation lines and multiple longitudinal magnetic field observation lines in the area to be detected, each transverse magnetic field observation line acquires a set of transverse magnetic field components, and each longitudinal magnetic field observation line acquires a set of longitudinal magnetic field components;

[0093] The peak type extraction module uses a peak segmentation algorithm to segment the peaks in each group of transverse magnetic field components and the peaks in each group of longitudinal magnetic field components, extracts the peak features of each peak, and then inputs them into the constructed peak type judgment model to obtain the peak type of each peak;

[0094] A peak fitting module is used to retain the peaks of bell-shaped and bell-shaped tailed peaks in each transverse magnetic field component and each longitudinal magnetic field component as characteristic peaks, fit the peak information of each characteristic peak in each transverse magnetic field observation line into a function representation, and then perform peak fitting to obtain the peak parameters of each characteristic peak in each transverse magnetic field observation line, and fit the peak information of each characteristic peak in each longitudinal magnetic field observation line into a function representation, and then perform peak fitting to obtain the peak parameters of each characteristic peak in each longitudinal magnetic field observation line;

[0095] An adaptive differential module is used to perform second-order differentials on the peak parameters of characteristic peaks greater than the differential threshold in each transverse magnetic field observation line to obtain a first transverse differential component, perform fourth-order differentials on the peak parameters of characteristic peaks less than or equal to the differential threshold in each transverse magnetic field observation line to obtain a second transverse differential component, integrate the first transverse differential component and the second transverse differential component of each transverse magnetic field observation line to obtain a differential result of each transverse magnetic field observation line, perform second-order differentials on the peak parameters of characteristic peaks greater than the differential threshold in each longitudinal magnetic field observation line to obtain a first longitudinal differential component, perform fourth-order differentials on the peak parameters of characteristic peaks less than or equal to the differential threshold in each longitudinal magnetic field observation line to obtain a second longitudinal differential component, and fuse the first longitudinal differential component and the second longitudinal differential component of each longitudinal magnetic field observation line to obtain a differential result of each longitudinal magnetic field observation line.

[0096] The imaging module is used to superimpose the differential result of each transverse magnetic field observation line with the differential result of each longitudinal magnetic field observation line to obtain the grounding grid topology structure of the area to be detected.

[0097] Embodiment 3

[0098] This embodiment also provides an electronic device, referring to Fig. 9 , comprises a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any of the above method embodiments.

[0099] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0100] Among them, the memory 404 may include a large capacity memory 404 for data or instructions. By way of example and not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 404 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 404 may be inside or outside the data processing device. In a specific embodiment, the memory 404 is a non-volatile memory. In a specific embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (Static Random-Access Memory, abbreviated as SRAM) or a dynamic random access memory (Dynamic Random Access Memory, abbreviated as DRAM), wherein the DRAM can be a fast page mode dynamic random access memory 404 (Fast Page Mode Dynamic Random Access Memory, abbreviated as FPMDRAM), an extended data output dynamic random access memory (Extended Date Out Dynamic Random Access Memory, abbreviated as EDODRAM), a synchronous dynamic random access memory (Synchronous Dynamic Random-Access Memory, abbreviated as SDRAM), etc.

[0101] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0102] The processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any one of the grounding grid topology detection methods based on adaptive differentiation in the above embodiments.

[0103] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0104] The transmission device 406 can be used to receive or send data via a network. The specific examples of the above-mentioned network may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0105] The input / output device 408 is used to input or output information. In this embodiment, the input information may be a transverse magnetic field component, a longitudinal magnetic field component, etc., and the output information may be a differential result, a grounding grid topology structure, etc.

[0106] Optionally, in this embodiment, the processor 402 may be configured to perform the following steps through a computer program:

[0107] Deploy multiple transverse magnetic field observation lines and multiple longitudinal magnetic field observation lines in the area to be detected, each transverse magnetic field observation line obtains a set of transverse magnetic field components, and each longitudinal magnetic field observation line obtains a set of longitudinal magnetic field components;

[0108] The peak segmentation algorithm is used to segment the peaks in each group of transverse magnetic field components and the peaks in each group of longitudinal magnetic field components, and the peak features of each peak are extracted and input into the constructed peak type judgment model to obtain the peak type of each peak;

[0109] Only the peaks with bell-shaped and bell-shaped tailed peaks in each transverse magnetic field component and each longitudinal magnetic field component are retained as characteristic peaks, the peak information of each characteristic peak in each transverse magnetic field observation line is fitted as a function representation, and then the peak fitting is performed to obtain the peak parameters of each characteristic peak in each transverse magnetic field observation line, and the peak information of each characteristic peak in each longitudinal magnetic field observation line is fitted as a function representation, and then the peak fitting is performed to obtain the peak parameters of each characteristic peak in each longitudinal magnetic field observation line;

[0110] Constructing a differential threshold, performing second-order differentiation on the peak parameters of characteristic peaks greater than the differential threshold in each transverse magnetic field observation line to obtain a first transverse differential component, performing fourth-order differentiation on the peak parameters of characteristic peaks less than or equal to the differential threshold in each transverse magnetic field observation line to obtain a second transverse differential component, integrating the first transverse differential component and the second transverse differential component of each transverse magnetic field observation line to obtain a differential result of each transverse magnetic field observation line, performing second-order differentiation on the peak parameters of characteristic peaks greater than the differential threshold in each longitudinal magnetic field observation line to obtain a first longitudinal differential component, performing fourth-order differentiation on the peak parameters of characteristic peaks less than or equal to the differential threshold in each longitudinal magnetic field observation line to obtain a second longitudinal differential component, integrating the first longitudinal differential component and the second longitudinal differential component of each longitudinal magnetic field observation line to obtain a differential result of each longitudinal magnetic field observation line;

[0111] The differential result of each transverse magnetic field observation line is superimposed with the differential result of each longitudinal magnetic field observation line to obtain the grounding grid topology structure of the area to be detected.

[0112] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0113] In general, various embodiments may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0114] Embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, at this point, it should be noted that, for example, Fig. 9 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or storage blocks implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0115] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A grounding grid topology detection method based on adaptive differentiation, characterized in that: The following steps are involved: Deploy multiple transverse magnetic field observation lines and multiple longitudinal magnetic field observation lines in the area to be detected, each transverse magnetic field observation line obtains a set of transverse magnetic field components, and each longitudinal magnetic field observation line obtains a set of longitudinal magnetic field components; The peak segmentation algorithm is used to segment the peaks in each group of transverse magnetic field components and the peaks in each group of longitudinal magnetic field components, and the peak features of each peak are extracted and input into the constructed peak type judgment model to obtain the peak type of each peak; Only the peaks with bell-shaped and bell-shaped tailed peaks in each transverse magnetic field component and each longitudinal magnetic field component are retained as characteristic peaks, the peak information of each characteristic peak in each transverse magnetic field observation line is fitted as a function representation, and then the peak fitting is performed to obtain the peak parameters of each characteristic peak in each transverse magnetic field observation line, and the peak information of each characteristic peak in each longitudinal magnetic field observation line is fitted as a function representation, and then the peak fitting is performed to obtain the peak parameters of each characteristic peak in each longitudinal magnetic field observation line; Constructing a differential threshold, performing second-order differentiation on the peak parameters of characteristic peaks greater than the differential threshold in each transverse magnetic field observation line to obtain a first transverse differential component, performing fourth-order differentiation on the peak parameters of characteristic peaks less than or equal to the differential threshold in each transverse magnetic field observation line to obtain a second transverse differential component, integrating the first transverse differential component and the second transverse differential component of each transverse magnetic field observation line to obtain a differential result of each transverse magnetic field observation line, performing second-order differentiation on the peak parameters of characteristic peaks greater than the differential threshold in each longitudinal magnetic field observation line to obtain a first longitudinal differential component, performing fourth-order differentiation on the peak parameters of characteristic peaks less than or equal to the differential threshold in each longitudinal magnetic field observation line to obtain a second longitudinal differential component, integrating the first longitudinal differential component and the second longitudinal differential component of each longitudinal magnetic field observation line to obtain a differential result of each longitudinal magnetic field observation line; The differential result of each transverse magnetic field observation line is superimposed with the differential result of each longitudinal magnetic field observation line to obtain the grounding grid topology structure of the area to be detected.

2. The method for detecting the grounding grid topology structure based on adaptive differentiation according to claim 1, characterized in that: The intervals between the transverse magnetic field observation lines and the longitudinal magnetic field observation lines are equal, and the area formed by the plurality of transverse magnetic field observation lines and the longitudinal magnetic field observation lines completely covers the area to be detected.

3. The method for detecting the grounding grid topology structure based on adaptive differentiation according to claim 1, characterized in that: The local minimum point marking method is used as the peak segmentation algorithm. The curve between the points corresponding to two local minima is selected as a peak. The curve mean, curve variance, curve skewness coefficient, curve kurtosis coefficient and curve waveform length of each peak are obtained as peak features.

4. The method for detecting the grounding grid topology structure based on adaptive differentiation according to claim 1, characterized in that: The peak types obtained by the peak type judgment model include bell-shaped, bell-shaped tail, sine-shaped and oblique-line types, and interval zeroing operations are performed on the sinusoidal and oblique-line peaks.

5. The method for detecting the grounding grid topology structure based on adaptive differentiation according to claim 1, characterized in that: pass The peak information of each characteristic peak in each transverse magnetic field observation line or the peak information of each characteristic peak in each longitudinal magnetic field observation line is fitted into a function representation, wherein the peak information includes x, c, A c and h c , x represents the peak position in the transverse magnetic field observation line or the longitudinal magnetic field observation line, c represents the center position of the corresponding peak, A c h is a characteristic parameter reflecting the magnetic permeability of the spatial magnetic field and the magnitude of the excitation current. c Represents the buried depth of the conductor.

6. The method for detecting the grounding grid topology structure based on adaptive differentiation according to claim 1, characterized in that: The peak fitting is to construct the sum of square errors by using the function representation of each peak in a single magnetic field observation line and the function representation of the actual magnetic field curve, and iteratively adjust the function representation of each peak in a single magnetic field observation line so that the sum of square errors meets the set threshold.

7. The method for detecting the grounding grid topology structure based on adaptive differentiation according to claim 1, characterized in that: The construction formula of the differential threshold is as follows: Among them, σ is the differential threshold, Bx(i, j), By(i, j) are the sets of point data measured in the area to be detected.

8. A grounding grid topology detection device based on adaptive differentiation, characterized in that: include: An acquisition module is used to deploy multiple transverse magnetic field observation lines and multiple longitudinal magnetic field observation lines in the area to be detected, each transverse magnetic field observation line acquires a set of transverse magnetic field components, and each longitudinal magnetic field observation line acquires a set of longitudinal magnetic field components; The peak type extraction module uses a peak segmentation algorithm to segment the peaks in each group of transverse magnetic field components and the peaks in each group of longitudinal magnetic field components, extracts the peak features of each peak, and then inputs them into the constructed peak type judgment model to obtain the peak type of each peak; A peak fitting module is used to retain the peaks of bell-shaped and bell-shaped tailed peaks in each transverse magnetic field component and each longitudinal magnetic field component as characteristic peaks, fit the peak information of each characteristic peak in each transverse magnetic field observation line into a function representation, and then perform peak fitting to obtain the peak parameters of each characteristic peak in each transverse magnetic field observation line, and fit the peak information of each characteristic peak in each longitudinal magnetic field observation line into a function representation, and then perform peak fitting to obtain the peak parameters of each characteristic peak in each longitudinal magnetic field observation line; An adaptive differential module is used to perform second-order differentials on the peak parameters of characteristic peaks greater than the differential threshold in each transverse magnetic field observation line to obtain a first transverse differential component, perform fourth-order differentials on the peak parameters of characteristic peaks less than or equal to the differential threshold in each transverse magnetic field observation line to obtain a second transverse differential component, integrate the first transverse differential component and the second transverse differential component of each transverse magnetic field observation line to obtain a differential result of each transverse magnetic field observation line, perform second-order differentials on the peak parameters of characteristic peaks greater than the differential threshold in each longitudinal magnetic field observation line to obtain a first longitudinal differential component, perform fourth-order differentials on the peak parameters of characteristic peaks less than or equal to the differential threshold in each longitudinal magnetic field observation line to obtain a second longitudinal differential component, and fuse the first longitudinal differential component and the second longitudinal differential component of each longitudinal magnetic field observation line to obtain a differential result of each longitudinal magnetic field observation line. The imaging module is used to superimpose the differential result of each transverse magnetic field observation line with the differential result of each longitudinal magnetic field observation line to obtain the grounding grid topology structure of the area to be detected.

9. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the grounding grid topology structure detection method based on adaptive differentiation according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes a grounding grid topology detection method based on adaptive differentiation according to any one of claims 1 to 7.

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